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Publication numberUS20080133417 A1
Publication typeApplication
Application numberUS 10/117,356
Publication dateJun 5, 2008
Filing dateApr 5, 2002
Priority dateOct 18, 1999
Also published asUS8315931, US20100306124
Publication number10117356, 117356, US 2008/0133417 A1, US 2008/133417 A1, US 20080133417 A1, US 20080133417A1, US 2008133417 A1, US 2008133417A1, US-A1-20080133417, US-A1-2008133417, US2008/0133417A1, US2008/133417A1, US20080133417 A1, US20080133417A1, US2008133417 A1, US2008133417A1
InventorsGary Robinson
Original AssigneeEmergent Music Llc
Export CitationBiBTeX, EndNote, RefMan
External Links: USPTO, USPTO Assignment, Espacenet
System to determine quality through reselling of items
US 20080133417 A1
Abstract
A system for determining a market value of items includes establishment of a sequence of items to be sold or licensed and a sale of income rights to the sale or licensure. A table of income rights corresponding to the sale to consumers is established, and speculators are able to make determinations of the potential for future sales of the items. After sale or licensure to consumers, the transactions to consumers are considered finalized, and speculators receive income based on the transfer.
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Claims(12)
1. A system for valuation and financing of items, the system comprising:
determining a sales unit of an item to be financed;
establishing financial rights for the sales units, by which transfer of the sales unit is conditional on at least a partial transfer of the financial rights for that sales unit;
segmenting groupings of the financial rights according to a substantially sequential production of the sales units;
selling the financial rights.
2. The system of claim 1, wherein the sales unit is a copyright license.
3. The system of claim 1, wherein the financial right accrues to the buyer, so that upon transfer of at least a portion of the sales unit, the buyer of the financial rights becomes entitled to a return from the financial rights.
4. The system of claim 1, wherein:
the sales unit is a copyright license and the financial rights are rights to royalties from the copyright license;
the rights to royalties accrue to the buyer, so that upon transfer of at least a portion of the copyright license, the buyer of the rights to royalties becomes entitled to a return from the rights to royalties.
5. The system of claim 1, further comprising:
determining an initial production run;
determining an incremental production run; and
establishing the financial rights based on the incremental production run.
6. The system of claim 1, further comprising any two or more of an Input Unit, a Combining Unit, an Input Valuation Unit, and a Reward Unit.
7. A method for financing of items, the method comprising:
determining a sales unit of an item to be financed;
establishing financial rights for the sales units, by which transfer of the sales unit is conditional on at least a partial transfer of the financial rights for that sales unit;
segmenting groupings of the financial rights according to a substantially sequential production of the sales units;
selling the financial rights.
8. The method of claim 7, wherein the sales unit is a copyright license.
9. The method of claim 7, wherein the financial right accrues to the buyer, so that upon transfer of at least a portion of the sales unit, the buyer of the financial rights becomes entitled to a return from the financial rights.
10. The method of claim 7, wherein:
the sales unit is a copyright license and the financial rights are rights to royalties from the copyright license;
the rights to royalties accrue to the buyer, so that upon transfer of at least a portion of the copyright license, the buyer of the rights to royalties becomes entitled to a return from the rights to royalties.
11. The method of claim 7, further comprising:
determining an initial production run;
determining an incremental production run; and
establishing the financial rights based on the incremental production run.
12. The method of claim 7, further comprising any two or more of an Input Unit, a Combining Unit, an Input Valuation Unit, and a Reward Unit.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority from U.S. Provisional Patent Application No. 60/281,673, filed Apr. 5, 2001. The entire contents of the aforementioned patent application are incorporated herein by reference.

This application claims priority as a continuation-in-part application from U.S. patent application Ser. No. 09/691,316, filed, Oct. 18, 2000. The entire contents of the aforementioned patent application are reproduced below:

IMPROVED ONLINE COMMUNITY THROUGH RATINGS Cross References to Related Applications

United States Provisional Patent Applications

    • Community-Based Market Movement Prediction: No. 60/160,044; filed Oct. 18, 1999
    • Portfolio Management By Community; No. 60/166,430; filed Nov. 19, 1999
    • Clusters for Rapid Artist-Audience Matching: No. 60/165,794; filed Nov. 16, 1999
    • A Mechanism for Quickly Identifying High-Quality Items: No. 60/176,154; filed Jan. 14, 2000
    • A Mechanism for Quickly Identifying High-Quality Items Version 000118: No. 60/176,953, filed Jan. 18, 2000
    • A Mechanism for Quickly Identifying High-Quality Items Version 000216; No. 60/182,836, filed Feb. 16, 2000
    • A Mechanism for Quickly Identifying High-Quality Items Version 000405; No. 60/194,988, filed Apr. 5, 2000
    • A Mechanism for Quickly Identifying High-Quality Items: No. 60/200,204; filed Apr. 28, 2000
    • A Mechanism for Quickly Identifying High-Quality Items: No. 60/209,930; filed Jun. 7, 2000
    • A Mechanism for Quickly Identifying High-Quality Items: No. 60/218,866; filed Jul. 18, 2000
    • A Mechanism for Quickly Identifying High-Quality Items: No. 60/232,742 filed Sep. 15, 2000

The entire disclosures thereof of the above-enumerated United States Provisional patent applications, including the specifications, drawings, claims and abstracts, are considered as being part of the disclosure of this application and are hereby incorporated by reference herein.

BRIEF SUMMARY OF THE INVENTION

The invention involves some or all of an Input Unit, a Combining Unit, an Input Valuation Unit, and a Reward Unit (this list is not meant to be inclusive).

The Input Unit is a facility or medium in which users can register opinions. A non-inclusive, list of workable Input Units is: an electronic listing of musical recordings with attached means for the input of ratings of those recordings, an electronic listing of securities with input means for predictions of price movements of those securities, and “message board” or “discussion group” software in which messages can be rated by the readers of those messages.

The Combining Unit is a calculating mechanism accepting input received from a plurality users and combining it into a single value. In various embodiments, arithmetic averaging, geometric averaging, meta-analytical, or Bayesian calculations, among other possibilities, are used. Bayesian expectations based upon the beta or Direchlet distributions are among the applicable Bayesian calculations. Meta-analytical calculations include the inverse normal method and Fisher's inverse chi-square method. The Combining Unit is used to detect an overall group opinion, such as stock market prediction or rating of a message, in the input given by users regarding an item.

When using the inverse normal meta-analytical method, a correlation matrix between the various users' ratings can be calculated and used in calculating the combined p-value.

The Input Valuation Unit has responsibility for determining the value of each user's input to the system.

The key values involved in this calculation are the accuracy, timeliness, and volume of the user's ratings. However various embodiments may also incorporate to other values, or leave one or more of the mentioned values out of the calculations.

Some embodiments do this by assigning a value to every rating individually, then summarizing this information to create an assessment of the user who generated those ratings. Some embodiments never create an individual value for each rating, but rather use the ratings supplied by each user in to calculate an overall valuation of the individual's contributions.

The key attributes which are considered by the calculations for the value of individual ratings are their timeliness and accuracy.

In various embodiments, accuracy measurements for individual ratings are based on the difference between the individual's rating and the population average, the absolute value of that difference, the square of that difference, and other calculations that are representative of what is intuitively understood as the general degree of nonsimilarity between two values. In some such embodiments the individual's rating is ignored in counting the population average. In some others all ratings supplied earlier than the one in question are ignored due to their possible influence on the mindset of the rater.

Accuracy measurements for the individual's overall contribution may be created by first calculating accuracy for the individual ratings and then calculating their (arithmetic or geometric) average or a Bayesian expectation or a z-score of statistical significance calculation for any tendency toward accurate or inaccurate ratings, or, in still further embodiments, other approaches are used. Alternatively, no measure of the individual value of each rating is calculated, and instead, an overall measure such as statistical correlation is used. When correlation is used, the correlation is measured between the user's ratings and the population averages, which, in various embodiments, may exclude the user's ratings and/or all previous ratings. Some other embodiments use such techniques as normalizing the difference between the individual rating and the population to be a real number between 0 and 1 which is uniformly distributed under the null hypothesis of randomness; such normalization techniques enable further statistical calculations to be carried out, such as combining these differences (which can now be considered to be p-values) using meta-analytical calculations including the inverse normal technique and Fisher's inverse chi-square technique. Some other embodiments calculate a Bayesian expectation of these differences, where the difference are or are not normalized, according to the particular embodiment. This list, like other such lists in the summary, however, is for purposes of example only and is not meant to be inclusive.

Where population averages are mentioned in the above paragraphs, some implementations use a Bayesian expectation of the next rating generated by the population rather than use the average itself.

When Bayesian expectations are mentioned, various embodiments base such calculations on the beta and Direchlet distributions, and other distributions.

In some embodiments which work in conjunction with external markets such as stock prices on the New York Stock Exchange, accuracy is determined by comparing the predictions of users with the valuations that eventually emerge in the market. A prediction that a stock will have a certain price at a future date and time can be considered a rating of the stock. If that rating differs from the current price, the rater is saying that he disagrees with the overall ratings currently provided by the community as a whole, but thinks that the community will agree with him at the future date and time. Thus some of accuracy calculations described above, such as the correlation between the user's rating and the rating the community as a whole, are calculated in various such embodiments.

Similarly, in embodiments where the items being rated are Web pages (identified by URL's), the ultimate opinion of the community of a page is reflected in the relative number of links, taken from across the Web, that ultimately point to that page. Calculations in this and other cases similar in their basic natures (although not necessarily involving URL's or stock prices) are carried out similarly.

In some embodiments users don't predict specific prices. Instead they make simpler predictions such as the direction of movement. In one such embodiment, the user can predict an upward or downward movement, or make no prediction at all if he thinks the situation will stay the same or if he doesn't feel he has cause to predict one way or the other. On this basis, a 0 or 1 is assigned as the rating, depending on whether the prediction is for a downward or upward movement, in that order. Then, at the time the prediction is supposed to have taken place, the system determines the actual direction of movement. If the stock has gone down, a 0 is assigned as the rating marketplace's rating. If it stayed the same, 0.5 is assigned. If it went up, 1 is assigned. Then, the correlation is calculated as described elsewhere between the individual's rating and the overall rating. In other similar embodiments, other calculations are used to determine the accuracy.

Timeliness is another key value used by the Input Valuation Unit in many embodiments. It refers to how early a rating is inputted by the user, since the earlier a rating is inputted, the less likely it is to be influenced by other people's ratings of the same item, and therefore the more likely it is to reflect the user's actual prescience in predicting the ultimate opinion of the population. Timeliness is not always included in the calculations, however; for instance, in embodiments where there is no way for users to “cheat” by only giving their own ratings after they have seen the ratings of others, timeliness becomes less important or even irrelevant. An example of this is cases where ratings are not made public until a certain point in time, when the overall opinion of the population is calculated and displayed.

Various embodiments use various techniques for calculating the timeliness value.

The most basic calculation simply counts the number of ratings of the item which are previous to the rating in question; the “best” timeliness value, then, is 0.

In some embodiments it is considered to be mathematically convenient to have a number between 0 and 1. For instance, in many such embodiments, 1/n, or 1-1/n, where n is the position of the rating in time (1st, 2nd, and so on), is used. In some others, n/N, where N is the total number of ratings for the item, is used, which we consider to be a ranking normalized to the unit interval (and the normalized rank of a randomly chosen user is uniformly distributed on that interval). One key difference between these calculations is the fact that the calculations based on 1/n are scaled such that small differences in the lower values of n have more import than small differences between large of n. That is 1/1 is quite different from 1/2 (remembering that the range is 0 to 1), but 1/999 is not very different from 1/1000. This is consistent with the intuitive fact that the user inputting the first rating for an item has no chance of “freeloading” by copying the rating of anyone else, while the second user to rate can copy the first rating; that first rating, if the first user is skilled at rating, may accurately reflect the eventual average rating of the entire population; this would enable the second user to appear to be an accurate rater when in fact he is only an accurate copier. Similarly, the 3rd rater can use the average of the first two ratings, but the value of being able to use 2 prior ratings rather than 1 is less than the value of being able to use 1 rather than 0.

The cumulative exponential distribution provides another technique for calculating a timeliness value that gives more importance to differences in small n's, and has certain theoretical advantages as well.

However, timeliness values based on a uniform distribution have the advantage of being p-values under the null hypothesis of randomness, and therefore are amenable to meta-analytical combining techniques for combining the timeliness with the accuracy and/or volume of the user's ratings to calculate a statistical significance, in embodiments where those values, too, can be considered to be p-values.

Some values combine the timeliness of the rating under consideration (for instance, the number of earlier ratings) with the total number of ratings, due to the fact that when there are more ratings, the population average is more meaningful. Ranking (comparing the number of ratings for the current item to the number of ratings for other items), exponential and other calculations similar to those already described are used to derive a value representing the magnitude of the collection of ratings for the item in question are used in various embodiments; then these two timeliness-related values are combined by multiplication or other means.

When an embodiment involves long-term items such as stocks which have values that change over time, and for which an individual rating can be made, at any time or at many times, regarding the future community rating of the item, some of the timeliness calculations mentioned above, which assume a first rating, are not applicable. In such embodiments other mechanisms are used. One example is a set of embodiments that allow individuals to make predictive ratings regarding the question of what the community rating will be at some time in the future, also specified by the individual. One such embodiment calculates the difference between the time a rating is made, and the time the rater expects the community to agree with him (an example being a prediction that a stock will reach a certain price at a certain date and time in the future). The average such difference is calculated for each individual, and than the differences are ranked. This ranking is made into a unit interval rank and is used similarly to the other timeliness calculations already described.

Thus a user can obtain a better reputation for adding value to the system by making longer-range stock market predictions, assuming such predictions tend to be as accurate as those of someone else who tends to make shorter term predictions.

The volume value, representing the number of ratings inputted by a given user, in various embodiments, is also massaged by means similar to those already described. A rank on the unit interval, is, in some embodiments, created by comparing the current user's volume to the volume of ratings generated by other users; in others the number of ratings generated by the current user is used to divide one; in others a cumulative exponential distribution is computed; in others other massaging techniques are used.

One or more of the values of accuracy, timeliness, and volume are then combined with each other and/or with other values to compute an overall valuation of an individual's ratings.

Some preferred embodiments do this by multiplying values which have all been normalized to the unit interval and such that the best value in each case is 1. The product will only be near 1 if all of the factors are near 1, meaning that the user's ratings tend to be accurate and timely, and that he generates a good number of ratings. In one preferred embodiment, accuracy is calculated by the correlation between the user's ratings and the population average, timeliness is calculated by 1/n, and volume is calculated by ranking the users according to each user's volume of ratings and generating a unit interval rank such that, if M is the number of users, the best rank is M/M (that is, 1) and the worst is 1/M. These values are multiplied together; the users who add the most value with their ratings will have a product near 1. In some variants of these embodiments, weights are applied by taking one or more of the values to be multiplied to a power. For instance, if it wants to tune the system to motivate users to generate more ratings, and embodiment can square the volume value before multiplying it with the other values.

Some other embodiments using ranking techniques to generate unit interval ranks for all values to be considered as p-values which, under the null hypothesis that all rankings are random, can be combined via meta-analytical techniques to generate a combined p-value representing the confidence with which we can reject that null hypothesis (i.e., accept the conclusion that the combined p-value is near an extreme because all the values are good). In some meta-analytical techniques, weights can be applied to the p-values being combined, analogously to applying weights before multiplying the values.

When using the inverse normal meta-analytical method, a correlation matrix between the three variables to be combined can be calculated and used in calculating the combined p-value. (This example should not be construed to exclude other ways of taking advantage of the correlation matrix.)

Other embodiments use such techniques as simple arithmetic averaging and computing a Bayesian expectation.

The Reward Unit's purpose is to provide incentive for users to do the filtering and discovery work necessary to unearth the valuable items, be they stock picks, songs, messages on message boards, or other kinds of items.

One set of embodiments involves Reward Units that are based on providing earlier access to timely information to the people that add the most value to the system. This is the Early Access Unit.

For instance, some embodiments look for cases where several people are making similar predictions for stock market movements, where those people have a record for accuracy in past predictions, and when the agreement of so many people is unlikely to be due to chance alone. Thus, such a combined prediction may be of value. Since the earlier an investor knows what is going to happen in the stock market, the more likely he can make money from that knowledge, it can be expected that an investor who is also a rater in a system built on this invention would be motivated to provide a greater number of earlier, more accurate predictions so that he will get, in a more timely fashion, the benefit of the system's searches for unusual agreement among predictions.

Other embodiments of the Early Access Unit are applicable to online message boards. Sites such as The Motley Fool's fool.com allow users to enter ratings for messages in their message boards.

Users who subsequently come to the board then have the benefit of this screening and can avoid messages that are less highly-rated. This is considered to be a valuable service. Therefore embodiments which stagger the time elapsed before a user gets to see a rating provide a way to reward certain individuals.

In one such embodiment, the rating summarization to be displayed on the board are calculated once per minute, in two different versions. The “elite” version summarizes the ratings based on all ratings that have been received up to that point in time. (Summarization takes various forms in different embodiments; for instance, in some embodiments it simply consists of counting the number of “thumbs up” ratings received by an item.) Thus, this elite version is the same as most current presentations. The “normal” version ignores all ratings submitted within some time period, for instance, the most recent 15 minutes. Thus, such an embodiment can present the elite display to the few people who contribute the most to the system through timely, accurate, and voluminous ratings, whereas most people would see the normal version. In other embodiments, many different levels of display are used rather just two, up to the extreme of having a slightly different delay period for each user.

The Early Access Unit is only worthwhile as a motivator in domains where timeliness is important. A discussion group focusing on news relevant to the state of the stock market, populated by serious investors (particularly day-traders), is one such domain. A discussion group populated by general news professionals, trying to get the latest scoop before the population in general, would be another example. Note that in domains such as the one in this paragraph, a way for a user to improve his timeliness ratings is to submit his own message, which would be a valuable message, and to rate it highly. That way he is assured a perfect timeliness value for that item. (Note that in most such embodiments, the populations ultimate rating of items submitted by a user is part of the overall calculation for the value added by the user to the system.)

Another set of embodiments use a Reward Unit we will refer to as the Public Reputation Unit. The purpose of this unit is to reward people who contribute timely, accurate, and/or voluminous ratings to the system through publicly enhancing their reputation.

Various embodiments do this by such means as presenting a ranked list of the top contributors (akin to the lists of top scorers seen in many video games) and providing a summary of the user's contributions when a page dedicated to the user is brought up.

Some embodiments provide the opportunity for users with the highest reputations to be paid for making further contributions in the form of ratings. The preferred subsets of such embodiments do this in such a way that it is not possible for a user o motivate the to “cheat” by doing things like always giving positive ratings in order to motivate the people who create items to pay him to rate their new items.

For example, consider an embodiment in the field of music. Musicians will want their songs to be gain more attention by getting a high rating on a listing of new music. But, there is so much new music emerging every day, just getting enough attention for users to discover the music and rate it is a major challenge in itself. Thus, it would behoove musicians to pay raters to rate their music (that is, as long as the believed that their music was good enough to be rated highly!) This embodiment of the current invention presents a list of users, identifying them by their online ID's and not by their real identities, and listing the degree to which they have contributed to the system. It does not show the average of the ratings they have inputted or any other indication of how high the ratings have been. Instead it presents the ranking of each user as a contributor to the system, where the best contributors have the most accurate ratings, the most timely ratings, and have a high volume of ratings (the calculations for combining these values in order to rank by the combined value are described elsewhere in the present document). When a musician is choosing who to pay, and the musician believes his music will be rated highly (otherwise he should give up and go home), he will be motivated to pay the highest-ranked raters, since part of the rating is accuracy, and this musician believes that an accurate rating will be a high one. At the same time, the musician is also rewarding raters who give a high number of timely ratings, even though the musician may not care about rewarding those things. This embodiment also provides mechanisms by which raters can post suggested fees and musicians can enter their credit card numbers in order to cause money to be transferred to the raters. Various embodiments in other domains use similar techniques.

In some embodiments prizes are awarded to the most contributing user or users; the service displays a notice about a or prizes that will be awarded to the top user(s) on a particular date, and this motivates the users to contribute accurate and timely ratings in order to increase their chances of winning a prize.

Another set of embodiments is particularly beneficial for companies which market products to consumers. A notice is given that monetary prizes or free products will be given to the best contributors. The subject of the ratings is messages on a message board which is devoted to product improvement suggestions. When the accuracy of ratings is calculated, it is compared not to the community's combined rating, but to an the value that is assigned to the item by the sponsoring company. One embodiment assigns a 1 if management decides that a suggestion will be implemented and 0 if it is not, whereas ratings from users have multiple levels (“poor”, “below average”, “average”, “good”, “excellent”, translated in the software to 0, 0.25. 0.5. 0.75. and 1, for example). This mismatch does not impede the calculations, such as correlation, described elsewhere, although such correlations would rarely be perfect. Another embodiment allows management to assign a multi-level numerical rating to each item; management has the ability to take an item out of consideration when it chooses to, at which point management cannot change its rating of an item. Up until then, management can update its opinion based on continued user input in the discussion group, which would cause accuracy calculations to be re-executed.

This methodology for getting suggestions is very attractive for businesses because it rewards, not only the person who made a suggestion, but also those who helped to bring it to management's attention and argue for its importance by rating the suggestion highly and having an ongoing discussion about it. Note some embodiments enact this methodology in the message board context where any message can contain a suggestion tagged a suggestion and thus enter the process of being judged by management, whereas other messages, which may be discussions about suggestions rather than suggestions themselves, are rated and the accuracy of those ratings is determined as for regular message boards, discussed elsewhere in this document.

In preferred embodiments based upon this methodology, a notice is presented via the user interface to the effect that only the most early item containing a particular suggestion will be rated by management as good. That is, if two people present different messages each containing, in effect, the same suggestion, only the first one can be rated as good by management. This means that the second person who likes a particular idea is motivated to try to improve his ranking by rating the original message highly rather than by submitting his own item and rating it highly.

Most such embodiments give extra credit to the originator of a suggestion, based on the management-supplied rating of the item which contains the suggestion. For instance, in one such embodiment, the average management rating for the suggestions made by each user is calculated, and through ranking against other users a number between 0 and 1 is generated, then this is multiplied by the accuracy, timeliness and volume numbers generated as described elsewhere to determine the overall value of the contributions of the user. This overall value is then used to determine whether the user is among the top few who receive a monetary or product reward. In additional benefit to this methodology is that people who make suggestions will be motivated to do their own filtering, since they will be penalized for making poor suggestions.

In preferred embodiments of the methodology discussed in the previous few paragraphs, the sponsoring company's record is displayed. This information includes the amount of monetary award and/or number and models of products awarded; the time period in which the awards were granted, and the promises being made about future awards. This enables consumers to decide if it is worth there while to contribute their effort to the system.

It should be noted that various ways of interacting with the user are implemented in various embodiments. In today's technology, a particularly convenient set of embodiments is based on the user interface and networking elements of the World Wide Web. Other embodiments are based on such technologies as interactive TV, kiosks, and input and output over the telephone by voice and touch tones.

The techniques mentioned in this summary are not intended to be exhaustive, but rather to point to various examples of ways of implementing the overall concepts. Other examples are found in the details section. Other examples are not mentioned, but equally come within the scope of the invention.

DETAILED DESCRIPTION OF THE INVENTION

Improving Online Community Through Ratings

There is a lot of “noise” in most Internet discussions in open forums. Many, people tend to simply state their prejudices with varying levels of vociferousness. There are also a large number of very thoughtful, valuable messages. As someone who has used online facilities both to get information and (in an earlier lifetime when I had time!) for conversation, I would really like some way of distinguishing the messages that are likely to be meaningful (“signal”, in the jargon) from the messages that are likely to be “noise”. I would like to increase the signal-to-noise ratio for my online discussion time. I want to read thoughtful messages that will really engage me—whether I agree with the ultimate conclusions of the author or not. Either way, I benefit, as long as the thoughtfulness is there. (In fact, I am likely to learn more by reading messages I disagree with, since the author is less likely to simply be stating things I already know!)

Several of attempts have been made to accomplish this through asking users to rate the messages they read, for instance, on a 5-point scale from “poor” to “excellent”. For instance, the original business model behind Athenium, L.L.C. was to create NewsVillage, a Web-based overlay on the Usenet, that allowed people to rate Usenet messages and then read only the ones with the highest average rating. Other attempts include GroupLens, the Usenet-rating project begun at the University Minnesota that ultimately led to the company Net Perceptions, which supplies collaborative filtering technology for recommending music, books, etc and is now the recommendation engine at Amazon.com.

These experiments in message-rating have never really panned out. They have never “caught on” to allow a service to grow to critical mass. I think the main reason for this is very simple: it takes effort to provide meaningful ratings.

Especially if you read many messages a day, the added effort involved in rating each one is prohibitive to the point that people just don't do it because they don't get anything in return for doing so—except perhaps by being told that they are “helping the community”. If a system forces people to rate a message before going on to the next one, they will, for the most part, enter random ratings or enter the same rating every time.

Anything that adds value to something usually requires work (there are exceptions, of course, but this is not one of them). In the “real world”, if you do work to add value to a project, you are paid for that work. If you help to build a house, you are normally paid in a respected common currency for that effort. Sure, some people get their friends together to help them build a log cabin in the woods without any form of compensation other than the pleasure of being together, but it is unlikely, to say the least, that that is ever going to be the dominant model for home-building.

Clearly, knowing what people tend to write the most rewarding messages, and knowing what messages have been the most rewarding for the people who have read them so far, would be very valuable as a way of separating the signal from the noise and making one's limited time online more valuable. Ratings provides a means to acquire that information, but the raters have to be rewarded for their effort.

One company tried to do this by awarding a tiny fraction of a frequent-flier mile for each rating a user made for Usenet articles. I can no longer find that company on the Web, so I assume that effort didn't go anywhere. I believe the problem with that effort was that, for enough value to be added in cash dollars or advertising exposure that frequent-flyer miles could be bartered or purchased from the airlines, many, many ratings had to be given. Abstractly but, I believe, realistically, this is because of a “conversion exchange” in taking items of value in the “economy” of the Usenet and trying to convert them into items of value in the “real-world” economy. The conversion rate is extremely steep.

So we need a reward system that stays within the economy of the Usenet.

This economy recognizes several forms of value, for instance:

a) Respect. People want to be respected for their contributions.

b) Self-esteem. People want to feel good about their contributions, which is linked but not identical to winning the respect of others.

c) Receiving help. People want to be able to ask questions when they need some help or advice and receive generous and helpful answers.

d) Interaction. Some people don't care whether they get respect or not, they just want to interact. Some people actively create arguments online just for the interaction; hostile interaction can feel better than none.

e) The pleasure of self-expression.

All the above are driving values in the economy of online discussions. Collectively, they are the “currency” of online discussions. To efficiently pay people back for their efforts, they should be repaid in that same currency.

We want to motivate people to provide meaningful ratings. To provide an efficient reward for their efforts, we need to pay them back in respect, self-esteem, help, and interaction.

My idea on how to do this is to calculate the value of each person's ratings, and to make a very public display of the value of people's contributions in this sphere.

People can obviously contribute to the system by providing meaningful messages

They are repaid for their efforts by the respect they receive, the fun of interacting with people's responses, etc. I have also learned in my years of participating online that people who are respected members of an online community tend to receive help, when they need it, much more reliably and quickly than people with no reputations in those communities.

For those reasons, people do actively contribute messages to online communities.

Now, suppose they received those same rewards for posting meaningful ratings. Then people would be motivated for creating meaningful ratings similarly to how they are presently motivated to create messages—and this implies that meaningful ratings would be happily created.

Assume, for now, that we know how to compute the value of ratings (a technique will be specified using a Bayesian approach later in this write-up).

Suppose we post a list of users in order of the value of their ratings. In our online system, we make this list a very visible aspect of the system. We use all the marketing know-how we can muster to communicate the truthful and accurate idea that ratings are as valuable to the system as messages are,

because if one has limited time and doesn't know which messages are the most worth reading, and therefore misses out on those best messages, they might as well not have been written at all. Ratings plus messages add up to a highly rewarding experience; one without the other is less valuable. Meaningful ratings are therefore to be respected to a comparable (if not identical) degree to meaningful messages.

Let's look at how this can be used to reward ratings contributors in the currencies of online communities.

a) Respect. The top ratings contributors can win real respect on the system through the public display of their names (logon ID's, or whatever identifier makes sense). This is similar to electronic arcade games, where, although you may not see the top players actually playing, their initials are stored in the system's memory, and the high scores and the player's initials are displayed between games.

b) Self-esteem. Obviously, people who rank above-average in contributing to the system through their ratings have earned the right feel good about that.

c) Receiving help. In a system incorporating this idea, each person's postings to the discussion boards would include their level of contribution to the system in a highly visible placement. So even people who have not contributed at all in the form of useful messages, but who have provided meaningful value to the system in the form of ratings, will be the recipient of appreciation and therefore help from others. This “level of contribution” does not need to distinguish between the value they have added by means of ratings vs. the value they have added by means of highly-regarded messages (which should also be a factor in computing the level). This reinforces the idea that the value created by ratings or messages are fungible. As an extreme example of the importance of being a valued member of an online community, I once told a friend of mine who needed help with his computer to log onto a forum on Compuserve in which I happened to be well-known and to ask his questions there. Over time, I had reached the point that just about any question I asked received a quick and helpful response. But my friend was unknown to that community. When he logged on, he got no response at all to several questions. He came to the conclusion that online communities didn't work for him, gave up, and never went back.

d) Interaction. People who are known as contributors to the community are more likely to receive rewarding interaction with regard to messages they post.

e) The pleasure of self-expression. People love to give their opinions on things. This pleasure has been present in previous attempts to motivate people to give ratings to messages in online discussions; now it is supplemented and perhaps overshadowed by the other forms of currency mentioned here.

Because the system motivates ratings in the above ways, it will receive the ratings that will enable people to avoid reading worthless messages, and to focus their attention on the valuable ones, thus making their time spent in online discussions considerably more valuable.

NOTE 1.

If the intent is to produce recommendations for the community as a whole it is important that ratings not be based primarily on agreement or disagreement with the ideas expressed, but on their thoughtfulness and/or entertainment value. Of course, agreement will never disappear as a criterion for ratings, but appropriate wording in explanations and instructions in the system can stress the idea that thoughtfulness and entertainment are the criteria.

NOTE 2. Not addressed in this write-up is the fact that ratings can also be used to derive a profile of the rater's interests, which can in turn be used to compute the expectation of the value he, as an individual with his own tastes and interests, would give in rating messages he hasn't read yet. These expectations can be presented as the priority with which he should read the various messages in the discussion, which would be different to the priorities presented to someone with different interests and tastes.

NOTE 3. Ratings Input.

We want to minimize the degree of effort needed to create ratings. So, the most common case should take no effort at all.

One solution to this involves a ratings mechanism based on a pair of “radio buttons”. (Radio buttons are user-interface features, usually a collection of small, circular, clickable buttons, which allow none of the buttons to be in the “on” state or one of them to be in the on state, but not more than that.) One radio button is labeled, “Less thoughtful and entertaining than average”. The other is labeled, “More thoughtful or entertaining than average”. Picking neither button means abstaining.

NOTE 4. Computing the value of ratings.

First, we have to decide on what we mean when we say a rating is valuable to a community.

We will say a rating is “valuable” to a community if it is representative of the opinion of that community. That is, if most people in a community will particularly value a given message, a representative rating is one that indicates that the message has above-average value.

Note that within a particular discussion area, the overall community can be broken into sub-communities of people who have similar tastes and interests which might be different from those of the overall community. All the analysis discussed here can occur at either the level of the community as a whole or any of sub-community of like-minded people (see Note 2, above), so this system can be used to give either customized recommendations of messages or general recommendations of articles to the whole community.

Representativeness is a powerful measure of the value of a rating simply because the purpose of the ratings is to be able to predict the community opinion from a small number of votes, so that people can prioritize the order in which they read messages for maximum expected benefit. A rating must be representative of a community's opinion in order to usefully guide that community.

There are varying degrees of statistical sophistication that can be used to compute the value of a user's ratings. I will now describe one very simple approach which is rooted in Bayesian statistics.

This takes two steps. First, we calculated the “expected rating” of each message. We calculate an “expected value” of each user's ratings.

Expected rating of messages:

Some users who won't care about giving ratings. Using the input mechanism of Note 3, such users will abstain. We will only consider the actual votes.

Using the method of Note 3, votes are divided into “Less thoughtful and entertaining than average” and “More thoughtful and entertaining than average”. We will assign a value of 1 to “more” votes and a value of 0 to “less” votes. Let p be the ratio of “more” votes to the total number of votes in the system as a whole, considering all messages. Using a Bayesian analysis based on the beta distribution, the expected rating for a message which has accumulated N actual ratings from users is (pw+m)/(w+N) where m is the number of people who voted that the messages was more thoughtful than average and w is a parameter that is adjusted for best performance as more data comes in from the field (it can start at 1).

This means that when nobody has voted on a particular message, its expected rating is p (for instance, if the system is equally divided between “less” and “more” votes, the expected rating for a particular message which has no votes yet is 0.5). But as votes come in, the expected rating moves in one direction or the other according to the proportion of “less” to “more” votes. For instance, if every vote regarding a particular message is a “more”, the expected rating asymptotically approaches 1 (ever-increasing belief that the next vote will be 1).

Expected Value of a User's Ratings

We will take a very simple approach here for illustrative purposes. More accurate approaches can be built, but as the amount of data grows, their advantages over this very simple approach becomes less and less.

We have already noted that a rating is “representative” if it agrees with the majority of people who have made the effort to vote on the message in question

We will assign a value of 1 to a representative vote, and a value of 0 to a non-representative vote.

Let p be the proportion of votes that are representative in the system as a whole. Then, again using a Bayesian analysis based on the beta distribution, we compute the expected representativeness of a user's ratings as (pw+r)/(w+N) where N is the number of votes, r is the number of the user's representative votes, w is chosen by the system to optimize performance but is initialized at 1.

(Note: by optimizing performance, we mean picking a value that most maximizes the accuracy of the formula in predicting behavior; this can be easily done using known optimization techniques once there is a meaningful amount of data.)

This expected representativeness can be thought of as the expected “value to the system” of the user's ratings.

Then, to measure the user's overall contribution to the system, we can multiply the expected value of his ratings by the number of his ratings. Users who contribute a large number of valuable (representative) ratings are rewarded with a high profile on the contributor's list and impressive contribution rankings on all their messages.

This analysis of measuring the representativeness of a user's ratings has one major limitation, however. It doesn't take into account the fact that a rating has much more value if it is the first rating on an item than if it is the 100th. The first rating will provide real guidance to all who see the message after that; the 100th rating will not change people's actions in a major way. Also, later raters might choose to simply copy earlier raters.

Therefore, we want to weight earlier ratings more than later ones. The question is, how much more valuable is the 1st rating than the second one, and the 2nd one more than the 3rd, etc.?

A reasonable methodology when a small amount of data is available is to use a cumulative exponential distribution to model this weight. Taking this approach, we calculate the expected value of a user's ratings as follows:
Let f ( r ) = { 0 , rating r is a hit 1 , rating r is a miss ,
let R be the collection of all the user's ratings, let c(r) be a natural number representing the count of ratings other users had assigned to r before the user in question assigned his, and let
g(r)=e -λc(r)
where λ is a chosen parameter of the exponential distribution. A reasonable value for λ would be chosen such that g(r)=0.5 when c(r)=1; in other words, the value of a message's first rating is twice as much as the value of its second rating.

Then the expected value v of a user's ratings is v = pw + r R g ( r ) f ( r ) w + r R g ( r ) .

Then the performance of the system can be tuned, once real-world data is obtained, by using standard computer optimization techniques such as simulated annealing or genetic algorithms to find optimal values for w and λ.

The exponential distribution is not necessarily the best possible basis for calculating g(r). It is not an aim of the present invention to find the optimal calculation technique for g(r). Koza's genetic programming technique can be used, another optimization method may be used, or a genetic algorithm technique such as the one discussed in U.S. Pat. No. 5,884,282 for evolving a monotonic function based on a 49-bit chromosome.

A Knowledge Generation System (Kgen)

Introduction

This write-up describes a technical and business model with the potential to generate reusable knowledge and organize it in an accessible manner. The knowledge is thus available for retrieval on a when-needed basis. The system has some properties that may enable it to attain exponential growth if used in a public setting, leading to a high valuation for the company.

For the purpose of this write-up I will be referring to it by the brief handle “Kgen”.

Ratings and Value-Proportionate Compensation

Knowledge donation, knowledge valuation, knowledge organization, and full coverage are all similarly important.

The Usenet, for example, receives many thousands of units of knowledge donation per day (in the form of individual articles). These are of varying value. DejaNews allows the user to retrieve these articles based on keywords. In other words, DejaNews allows the user to retrieve relevant knowledge. But it is very inefficient at enabling useful research to be done, because most of the knowledge on the Usenet has little or no value, and DejaNews provides no way to tell which is which.

It is important, in a truly effective knowledge system, to have a way to valuate the knowledge items. Knowledge can be valuated by means of ratings. This important point will be discussed in much depth later in this write-up.

Organization is also important. Knowledge is primarily organized on the Web in two ways:

    • Hierarchically. It is no accident that just about every product for which information is provided on the Web has a hierarchically organized FAQ associated with it for easy access to information. Hierarchical (or “outline”) organization works well for this purpose, and has evolved to be a dominant form of knowledge organization on the Web.

Indexed. Search engines have the ability to automatically organize knowledge through enabling retrieval based on concepts (or, more primitively, keywords). However, it is a thesis of this write-up that such indexed retrieval could be facilitated by means of human input of appropriate keywords and weights. Indeed, historically, knowledge collections such as research articles have always had author-generated keywords associated with them. It is a thesis of this write-up that the fact that keywords for Internet-hosted articles tend to be automatically extracted does not mean that it is preferable to do so, but only that no motivational system has yet been created to motivate humans to supply keywords as an additional aid to indexing.

Organization in each of these ways (or ideally, both together), makes the knowledge easier to find and thus of more practical utility.

Another factor is full coverage of the subject area. This is important because if the goal is to generate a knowledge base that people will return to time and time again, they need to consistently be successful in finding the information they need there. If they fail to do so too often, they will be frustrated and spend too much time in fruitless searches, lowering the utility of the system as a whole.

Up to now, services on the Internet have focused on easy knowledge donation (for instance, the Usenet) combined with automatically indexed retrieval.

Without a powerful motivational system in place, this is probably the best that can be expected. Knowledge donation today occurs in the context of individuals having the pleasure of communicating with (and sometimes helping) each other. The knowledge is to a large degree a by-product and thus the “signal-to-noise” ratio is poor. Automatic indexing, which cannot distinguish between signal and noise, can retrieve the relevant knowledge, but not the valuable knowledge.

There is clearly much room for improvement to this state of affairs. What is needed is a motivational system that causes people to contribute low-noise knowledge in such a way that it is stored in a highly organized way for ease of retrieval. Further, a system that encourages full coverage would have a further advantage.

Valuation is the Key to Providing a Motivational System

In order to create a motivational system governed by something other than social feelings and altruism, people needed to be compensated for their work.

Contributing content (knowledge) is work.

Participating in organizing it is work.

But, in order for compensation to be used effectively enough to be useful, it must go where due. Content that is pure noise cannot be compensated equally to content that is very valuable. First of all, in that case, there won't be enough compensation to go around. The system could give every item the same extremely low compensation as a way of conserving compensation-resources, but such low compensation won't be enough to motivate the creation of highly valuable individual items. Moreover, people can get the same reward for less work by creating worthless content. In fact, an equal rewards system motivates the lowest level of quality that the system will award compensation for; anything else would be a waste of time and energy on the part of the person doing the work.

(Note that in the above paragraph we are ignoring such factors as the feeling of doing a job well and peer pressure. However, these factors are not enough to generally obviate the need for proportionate compensation in the real world, and there is no evidence to lead us to believe that the knowledge-generation world will be significantly different in this respect.)

So, compensation should be proportional to value.

Any currency-based economy is an example of this. One doesn't pay the same amount of money for any object no matter what it is. A marble costs less than a Ford Escort, and a Ford Escort costs less than a battleship. In fact, the graduations are very fine; an “average” marble costs less than a “fancy” marble. All economies have evolved this way because it enables them to work more efficiently and effectively. It is a universal principle.

Kgen needs to embody this same universal principle: compensation proportionate to value.

In order to compensate proportionally to value, value must be measured. Ratings provide a means to do this.

Ratings are a simple way for value to be determined. From movie ratings provided by professional reviewers to product ratings provided by Consumer Reports, ratings are ubiquitous and a simple way to communicate valuations.

However, ratings are another form of work. To motivate non-random, meaningful ratings, value-proportionate compensation must be provided.

One attempt to provide compensation for ratings is an apparently defunct Web site that requested ratings of Usenet postings in exchange for frequent-flyer miles.

This is an example of a violation of the “compensation proportionate to value” principle. The site apparently did not work well enough to stay around. One possible reason for this is this compensation principle—the site gave users no motivation to donate meaningful ratings—it only provided users motivation to randomly click one of the ratings buttons.

Without meaningful ratings, we don't know the value of the donated knowledge items or effort in creating organization. Without valuations, we can't compensate properly, and can't expect to have an efficient, effective motivational system.

(Note: There are other potential sources of valuation, such as observing the amount of time spent reading a given item. Indeed, it may turn out that such passive valuation turns out to be a good adjunct to ratings. However, there is no known example where passive valuation has been an effective engine for large-scale valuation of knowledge. Some of its limitations are obvious. For instance, consider the case of assuming value is proportionate to the time spent reading an article. The user may go to the bathroom while reading an article. Or, it may be that one article very concisely communicates valuable information and so can be read in a short time, while an alternative article on the same subject is confusing and rambling and takes much longer to acquire the same information—in which case the first article would be more time-efficient and hence have more utility. There may turn out to be a use for such passive valuation sources, but at present there is no reason to assume that they can be the primary source of valuations in an effective knowledge generation system.)

We need meaningful ratings. Meaningful ratings are work. Work won't be done without compensation. Compensation must be proportionate to value.

So we need a way of measuring the value of ratings

Rating the ratings themselves is obviously an endless conundrum. Another solution is needed.

Such a solution is available. The general principle is that people who are providing meaningful ratings tend to agree with each other and to vary their ratings from item to item, whereas people who don't tend to provide ratings that are random (or, equivalently, constant). This will be described in more depth later in this write-up. We will refer to this solution as “expert convergence”.

By providing a means to estimate the value of ratings, we can compensate users for providing meaningful ones, thus providing the necessary foundation for effective knowledge generation.

The Forms of Compensation

We have established that the heart of the knowledge-generation system is value-proportionate compensation. The value of the contributed knowledge and organizational effort is determined through ratings. The value of the ratings is determined through expert convergence. So we know how everything is valued.

The remaining question is: how do we provide compensation?

There is more than one form of compensation available to us:

Revenue Sharing

The Mining Company has pioneered the concept of sharing advertising revenues with the people who are community managers for various subject areas. There are hundreds of such managers. However their compensation is typically low (in the hundreds of dollars per month) as there is not at this time a huge audience to for any one community that can give a real income. However, revenue sharing is nevertheless one viable component of a compensation package.

Public Reputation: Respect of Peers

Public reputation has always been a major motivator in many areas. People who play video games try to get their initials onto the lists of the highest-ever scorers that are typically stored in each machine and displayed at the end of each game.

Authors and musicians are partly motivated in their efforts by their public reputations. The same is true for businesspeople within the community of their businesses.

Another manifestation of this, and one more to-the-point in some ways, is the valuable work being done in the open-source software community on large and successful projects, best exemplified by Linux. Linux is universally reputed to be more solid and stable than Windows NT, despite the fact that no money is paid to Linux developers and NT developers and quite highly paid, including stock options that have made millionaires of many Microsoft employees. Open-source developers become known for the quality of their work within the open-source community. Since there is no financial compensation, one can reasonably assume that this reputation is one significant factor in motivating this high-quality work.

We can provide visible public reputations to our users. These will take two forms. One is a top-scorer screen, similar to those used in video games. The other is a database from which the score of any user can be seen. Scores will be based upon the value they add to the system.

Typically, two scores will be presented: the amount of value the user has added over time, and the average value per effort donated (whether a rating, article, organizational effort, etc.)

It is expected that these scores will be presented in terms of percentile ranks within the overall community as such ranks will be more readily understood than other methods.

Such examples as Linux seem indicative of the outcome that a large-scale knowledge base can be largely motivated through enabling people to earn the respect of their peers through contributing to the system.

Public Reputation Earning the Informal Right to Receive Assistance.

As a general principle, I have often observed that “newbies” an online community typically get slower and fewer responses to questions, if they receive any at all, than people who contribute often and therefore have reputations for being a valuable member. People want to help people who are themselves helpers.

One problem with this way of deciding who to help and who not to help is that a person may be a major contributor in other subject areas, and now be in need of help in an unfamiliar one. Since he hasn't been in that area often, he is unknown to that community, and therefore thought of as a freeloader—even though he may be very valuable to the larger community.

Our scoring system solves that problem by providing overall percentile rankings for each person which persist as they move from subject area to subject area. The value a user adds in his area of expertise will mark him as a valuable contributor.

The community aspects of Kgen will be described below. The point being made here is that by earning a reputation for contributing to the system, one will earn a higher probability of receiving human assistance when needed in the context of those communities.

Public Reputation Support for Independent Income.

All over the word, there are experts that can help with a given problem you or I may have at a given moment. It may be a business management question, a programming question, a recommendation for a home appliance, etc. It could be any of thousands of things.

The problem is that we don't know where to go to find someone who is immediately available to help us and who we can trust to do a good job. If we had a way of finding someone with the appropriate knowledge, and they had a reputation that enabled us to trust that they could help us, in many cases we would be happy to pay an appropriate fee for their services.

Kgen's public reputations provide a basis for this trust. Someone who is highly-ranked within the knowledge area he contributes to is someone who is likely to be superior at supplying assistance within that subject area.

So, by supplying contact information for the contributors, we help empower them to earn money in exchange for their proven skill. They are enabled to do so because of the quality of their work in adding value to the system through one or more of the possible forms of contribution.

Public Reputation Personal Pride

Regardless of practical uses for public reputation, people are also highly motivated by factors of pride and self-esteem. Many people are highly motivated by finding ways to experience themselves as good at something. In this case, the additional factor of being good at something that is contributing to a community makes scoring highly within Kgen even more meaningful as a source of pride.

There are people who spend hours a week as unpaid co-moderators at AOL, who are given an “official,” public status at AOL—so much so that the question has recently come up as to whether they should be considered to be actual employees of AOL. One can guess that a major factor in motivating this work is the pride of being a leader in the community.

It is expected that similar factors of pride will motivate many people to want to earn their stripes as contributors to what can become the greatest single accumulation of immediately useful knowledge in the world.

Again, there can be no doubt that similar motivations can be found among the contributors to Linux. They also want to contribute to something large that will be a real force in the world. That project has succeeded, and it seems quite reasonable to postulate that this one will too.

Public Reputation: Summary

It is a thesis of this write-up that these various forms of public reputation will be a meaningful factor in motivating people to contribute knowledge, organization, and ratings to the system. The main benefit of these forms of compensation are that they cost us nothing.

Other Compensation

There is no reason that we can't look towards other compensation mechanisms such as frequent flyer miles as well. These compensation mechanisms would all be tied to the quality of the donated work.

Structure of a Public Service

Some embodiments are primarily structured as an FAQ. FAQ's have evolved over time to be a dominant form of easily-accessible knowledge on the Web. As noted above, virtually every manufacturer with a Web presence has an FAQ describing their product.

Large FAQ's are hierarchical in nature. They are broken into various subject areas. To find the answer to a given question, the user works his way through the hierarchy to find the answer he wants.

At the same time, most sites index everything with their search engines, so the FAQ entries are available through keyword and concept searches, too. Recent technology such as Apple's Sherlock and Ask Jeeves enable natural language searches; over time we should be able to add such technology, either through strategic partnerships or technology purchases.

Forms of Contribution

    • Knowledge items (FAQ entries). Users can write questions and answers that they think will be useful to the community. These Q&A's will be considered to be “knowledge items.” Users can also write alternative answers to existing questions. Perhaps they can additionally write questions without answers (this needs more thought) in the expectation that somebody will write a question.
    • Suggestions. If a user believes an item is in the wrong place in the hierarchy, he can make a suggestion that it be moved to another location (each location will be marked through a numbering system). Easy access will be provided so a user can visit the suggested location to see whether they think it is suitable. Similarly, a user can make a suggestion that a Q&A be dropped due to being redundant. When a suggestion has received enough positive ratings that it is statistically measured to be highly likely to be worthwhile, the system will automatically carry it out. This can involve moving or deleting an item, or perhaps other acts as the system evolves.
    • In addition suggestions for improving an item will inevitably turn up in the discussion forums associated with that item.
    • Ratings. Users can rate knowledge items and suggestions. Ratings of each type of contribution are considered to be of value.
      Public Reputation

Each user's ID will be displayed on each of the knowledge items he contributes. The ID will be clickable and take the user to a page displaying the accumulated reputation of the user. His percentile rank for quality and frequency of contributions will be displayed.

A reason not to break these items out is that everyone realizes that writing knowledge items is a valuable form of contribution. To date, there is no precedent for the public valuing of ratings. In order to stress the fact that all forms of contribution are valuable and worthy of esteem, it may be advisable to not separate them in the display.

There will also be means to access a user's scores through an interface that allows an ID to be typed in and the score retrieved. Finally, as we add lists of people who want to help others to each subject area, the scores will be available through that list.

A Learning-Oriented FAQ

We will redefine the FAQ, for the purposes of this section, as Learning Items (LI's) and start to define a different way of looking at things.

When students study from a textbook, they frequently have questions about specific things appearing in the book.

LI's could work in conjunction with existing course materials such as a textbook. An LI would explain something that was perceived to be not as clear as possible. The reference section on an

Enter LI screen would contain the page and/or section and/or paragraph number that was being explicated.

The idea would be to generate a database that would make it easier to understand the course material.

During the Enter LI phase, the team would work together. Students would each be required to create one LI. However, they are also in the process of studying, and will have questions. So, on the team discussion page and/or by chat or other means, they can ask questions of their teammates when they don't understand something.

These questions can then become fodder for someone to write an LI.

So, the process of give-and-take during the Author LI phase is providing a forum where students can help each other understand the material at the same time as they are working towards creating their individual LI's, and helping each other with that goal.

As in the case of FAQ Q&A's, each LI will eventually be rated by the other team according to how much value it adds:

    • Does it add value by explaining something that was harder to understand in the original text?
    • Does it do so better than other LI's on the same question?

Overtime, the database of LI's for a particular set of learning materials will become so complete that it would be hard to create a valuable new LI.

In that case, as was suggested above, we can randomly delete some of them. However, in this context, there is no problem associated with it, because LI's are not general-purpose just-in-time problem-solving FAQ entries that really should be available to the company as a whole. They are just for the purpose of learning, and may only be available to the students taking the course (or who already took the course). So there is no problem in removing some of them—there is no outside database to compare to see what's missing.

Of course, all LI's for previous rounds can be available to students or the course through View Results.

Also, if the instructor chose to use other basic course materials at another time, there is no reason why all previous LI's could not be displayed.

Also, people who already took a course could log on to the system at any time to refresh their memory about something. So we would be creating a useful knowledge repository, just as in the FAQ model, but a user would have to take the course to get access to it.

There is still a problem of cooperative cheating, where, for example, students taking a course at one time copy the LI's and store them somewhere where they are available to students taking the course later. A similar problem exists with the SAT's, but security measures make it very difficult to copy an SAT. These are issues to discuss.

One final point is that it can be imagined that through more elaborate structuring tools, an entire course materials set could possibly be evolved in this way, which could ultimately replace the need for outside course materials.

Structure of the Best-Practice Mining System

Use of the Kgen process for brainstorming and the mining of best-practices is similar to the learning model, except that study materials may be unnecessary.

The ratings processing allows the system to determine which practices are likely to actually be “best” since rating quality ultimately depends on the expertise of the rater. Rather than giving everyone an equal vote about which practices are of value, including those people who will simply pick the most obviously correct practices, or who vote randomly, Kgen gives the most votes to the ratings most likely to be meaningful. Thus Kgen's discernment is greater than that of a one-vote-per-person system.

Technical Issues

Measuring the Quality of Ratings

I will present a technique for doing this here. I call this technique “expert convergence.” However, I want to stress that it is very likely that more efficient techniques will be found once we start serious work in this.

The calculated quality of a rating is really a statistically-calculated expectation of the quality. There is no computational way to know the quality for certain.

Imagine that we have a knowledge item and a collection of n ratings for that rating, r1, r2, . . . , m, on a 5-point rating scale.

Suppose we make guesses, g, about the real value of the item, and see how well that guess matches up to the actual ratings we have in hand.

First we guess ½ point, then 1 point, then 2 points, etc.

At each point we calculate the following number: a number between 0 and 1 representing the probabilistic unlikeliness that the as many of the ratings would have been as close as they are to g under the assumption that the ratings were simply randomly chosen by the users.

In the patents that have been issued in my name by the United States Patent Office, I have described detailed algorithms for accomplishing this. There are other ways, as well.

One of the points will be calculated to be more unlikely than any other.

We will use that point as our estimate of the “real” value of the item on the 5 point scale.

Some ratings will be close to this point and some will be far away. The ones closest to the point are the ones we assume have the most value, because they are the closest to the estimated real value of the item.

A useful measure of the value of the rating would be the distance from g calculated above (its p-value, in statistical language; “better” p-values are near 0; p-values are always between 0 and 1).

Measuring the Effectiveness of Raters

Effective raters tend to consistently give meaningful ratings.

Assume, for a given user, we have a collection of p-values calculated from his ratings of various knowledge items: p1, p2, . . . , pn.

Then a meaningful measure, m, of his effectiveness at consistently contributing meaningful ratings would be given as (1−((1−p1)(1−p2) . . . (1−pn))ˆ(1/n))), where ˆ is the power operator, for reasons that are beyond the scope of this write-up.

Also, under the assumption that his ratings are random, the likelihood of their being as consistently good as they are by chance alone can be computed based on the above calculation, resulting in another p-value. Thus we have a possible statistical measure of goodness when we want to find the users we are most confident will produce good ratings.

Another consideration is the fact that some ineffective raters will tend to always choose the same rating for each question. Thus, the variance of ratings provided by a user can be used as another way of refining our effectiveness measure. Variances can be converted to p-values, and combined through multiplication with the p-value determined above; using other techniques a new “combined” p-value can be calculated that incorporates both factors.

Measuring the Quality of Knowledge Items

In order to calculate the likely quality of a rating, described above, we made guesses about the actual value of the item, and chose one to be our estimate of the real value of the item. However, that was done in the context of trying to judge the worth of a rating, in order to judge the effectiveness of the rater.

Now that we have calculated the effectiveness of each rater, we can do a better job of guessing the actual value of the item. We can do this be using our knowledge about the rater to weight each rating.

For example, we can use (1-m), calculated above, as the weight of a rating generated by a user with measured effectiveness m. Alternatively, we can rank the raters according to m, and use the rank as the weight.

These weights can be used in calculating a weighted average of the ratings, which can be calculated arithmetically or geometrically.

My collaborative filtering experimentation has shown that all these approaches will “work”, but some will work better than others. We can deploy or system using the simplest possible approach, and then try other approaches on the data we collect, and eventually deploy the calculation that works best for predicting the ratings of effective raters for given test items.

Finally it should be noted that iterative techniques are possible. When we originally calculated an item's best guess of the real value, in order to measure “expert convergence” for one item, we did so without incorporating any knowledge of the raters. After doing that, it would be possible to go back to the beginning of the calculations are recalculate the expert convergence by taking the weights into account. That would ultimately change (and improve) our measure of the effectiveness of each rater; we can repeat until convergence.

Portfolio Management by Community

The purpose of this invention is to maximize portfolio selection by means of creating a open environment for receiving, rewarding, and integrating trade suggestions from a broad community.

A key to the invention is that individuals compete with each other for being the best performers, which automatically causes the system to reward them with the best compensation.

The environment is a network such as the Internet, client/server systems, etc. Input and output occurs by means of known techniques of network-based software, such as HTML-based World-Wide-Web browsers, client/server software on a packet-switching network, etc.

One key element of the invention is means for anyone to register as a “manager” of a portfolio. A portfolio can have any number of managers. In the preferred embodiment, the registration process involves obtaining a login ID and password (as is done in many World Wide Web sites and other online services today) and specifying the portfolio(s) the user would like to manage. Portfolios are chosen, in various embodiments, by pull-down menus, scrolling lists, etc.

In preferred embodiments, managers can also create portfolios by inputting the stocks and amounts of the initial state; this is done by standard input means such as dialog boxes, HTML pages, etc., as decided upon by the user interface designer in each particular embodiment. In creating a portfolio, preferred embodiments give the manager the ability to input his decision regarding whether he alone will manage it, or whether other people can manage it as well. In some further embodiments, he can input a list of users who can serve as managers for the new fund. In must such embodiments, means are provided to change these decisions and alter any lists of managers at later times.

Means are provided for managers to make their trade suggestions or other input relevant to portfolio management. These means involve the traditional means for data input at use in most Web sites. In the case of inputting trade suggestions, a good example is the user interface of http://www.schwabb.com/.

The performance of each manager is tracked over time. In preferred embodiments, this includes tracking the direction and amount in change in a portfolio's value that would have occurred if that manager's input, and no other trades, had been acted upon. These results are measured on a regular basis (in preferred embodiments, daily, on those days when the manager gave input).

At regular intervals (in preferred embodiments, daily), before the market opens on each day, trade recommendations and other information made by the various managers of a fund are combined into an overall set of trade recommendations. This is done by combining the suggestions of the various managers in such a way as to optimize the gain in portfolio value. Various embodiments perform this step in various ways.

In one embodiment, the following steps are carried out for each portfolio. First, each manager's performance expectation is computed according to the formula p = t c + n
where p is the estimated performance, t is the total change in valuation of the portfolio if only the current manager's input were used, n is the number of days in which the manager made changes, and c is a constant.

For convenience, t is given as the change proportionate to the starting valuation; that is, if the starting value of the portfolio was $100,000 and at the end of the period under consideration, the manager's suggestions would have led to a valuation of $120,000, then t is +0.2.

c serves to compensate for the fact that we have different amounts of data for different users. For instance, say c is 1. Then, before a manager makes any suggestions the estimate of change due to his suggestions is 0. If he has made one suggestion, and that has resulted in an increase of 0.01, then his estimator is 0.005. As he makes more suggestions, c becomes less important, and the value of the estimator asymptotically approaches his average change.

For the next step, we ignore all the managers for whom p is 0 or negative. In this step, we sort all managers in order of p. Then we calculate weights for each manager as follows. If there are N such managers, the one with the best performance gets a weight of 2 N N 2 + N ,
the next best gets a weight of 2 ( N - 1 ) N 2 + N ,
etc. down to a weight of 2 N 2 + N .
Finally, we take the trade volumes recommended by each manager for each stock, multiply each such volume by the recommending manager's weight, and total the weighted recommended trade volumes for each stock. Depending on the embodiment, the system outputs the combined recommendation (rounding to the nearest whole share price for each stock), or automatically causes those trades to be performed.

To assign a value for c, preferred embodiments try different values to see which results in the maximum “yield” in simulations using the historical database, once there is one. In various embodiments this can be done by such techniques as genetic algorithms, simulated annealing, etc. When there is no historical data or little such data, some embodiments initialize c to 1.

In another embodiment the following combining method is used.

A paper by Singer, et. al. (1998) describes an algorithm for completely automated portfolio selection. They describe a technique for using “side information” within their algorithm. Refer to that document for details. This side information improves the performance of the algorithm. In the embodiment under discussion, the advice given by the managers is not concerned with the details of specific trades, but is rather concerned with providing the side information. In this embodiment, the side information consists of characterizing the market for the next market day as “bullish” or “bearish.”

On each day, prior to market opening (or on the evening before), each manager has the opportunity to give his guess regarding which of the two categories the stocks will be in. The system tracks this information uses it to make a combined guess, incorporating the guesses of various managers into one.

A paper by Cesa-Bianchi, et. al. (1997), Warmuth describes an algorithm for doing this, which they term Algorithm P*. Refer to that document for details. The input is a series of predictions from each manager regarding whether the market will be bullish (1) or bearish (0) on the next day; the output is a real number between 0 and 1. Rounding to the nearer of 1 or 0, the system sets the side information to bullish or bearish. Details of how to use the information maybe found in Singer. The output is a set of trades to make, which is displayed as the output in some embodiments, or is fed into a module for causing the actual trades to be executed in another embodiment.

It should not be construed that this invention is dependent on any particular combining technology. In fact, a preferred embodiment provides an application programming interface (API), using standard industry practices, such that any combining methodology can be programmed and “plugged in.” In once such embodiment, a C++ base class is provided which can be subclassed to provide the combining functionality. In another embodiment, a Java interface is defined that is programmed to provide the functionality.

In another a C header file is provided which defines the signature of a set of functions to provide the functionality. In preferred API-based embodiments, means are provided to test different algorithms against historical data to determine their performance.

It must not be construed that this invention is dependent upon any particular combining technique.

Another key to this invention is compensation of the managers. In various embodiments, different techniques are used to perform compensation.

In one embodiment, a simulations are run with and without the input of each particular manager, and the amount of growth that is attributable to his individual input is computed by means of subtraction. This number is computed for the best manager first, then the next best manager, etc. A fixed percentage of the difference is paid to each of them as a fee. This percentage was determined and input by the manager who originally created the portfolio in question. Investors are billed on a monthly basis for these fees.

In another embodiment, compensation is not directly financial but instead is time-based. Just as immediate quotes are considered more valuable than 15-minute delayed quotes, it is more advantageous to get the combined trade recommendations from the system sooner rather than later. At the time the market opens for the day, the best manager receives the recommendation first (in one embodiment, this is done by online “instant messager”); the next best manager gets the recommendation second, etc. The intervals are evenly spaced. The total time allocated to this process is adjusted so that enough people are motivated to be managers that the portfolio performs well, but also so that the higher-performing managers are rewarded for their efforts.

It should not be construed that this invention is dependent upon any particular form on compensation.

Notes

In some embodiments, certain qualifications are required before a manager can actually make recommendations, such as lack of a criminal record; in some such embodiments, a manual check is carried out before newly registered managers are allowed to make recommendations; in other such embodiments, checking is done automatically by access to online systems such as credit database.

In some embodiments, the system simply receives input from users and makes trade suggestions. In other embodiments, the system has built-in software which can manage investor money and order trades, consistent with other existing systems that currently perform those functions.

Means are supplied in most embodiments for funds to be added to a portfolio at any time for purchase of new stock, as new investors get involved or as existing investors change the level of their investments or pull out. Computations regarding the performance of various managers are adjusted to account for this.

In some embodiments, some or all of the managers are not individual people, but are organizations or even software programs. The word “manager” in this document should therefore be read accordingly.

BIBLIOGRAPHY

  • Singer, Helmbold, Schapire, and Warmuth (1998). On-Line Portfolio Selection Using Multiplicative Updates. Mathematical Finance, 8(4): 325-347.
  • Cesa-Bianchi, Freund, Haussler, Helmbold, Schapire, Warmuth (1997). How to Use Expert Advice. Journal of the Association for Computing Machinery, 44(3): 427-485.
    Community-Based Market Movement Prediction

If one assumes that some people are better at predicting market and stock price movements (referred to below as “market predictions” than others, certain opportunities become available. This invention capitalizes on one of those opportunities.)

The purpose of this invention is to enable communities to make more accurate market predictions than individuals can make on their own.

The first step is to set up an input means, for instance a site on the World Wide Web (although any other electronic communications medium will do just as well), whereby individuals can enter their market predictions. This is easily within the state of art of Internet, client/server, and other communications-based programming disciplines.

We will use the example of predicting S&P 500 index movement, although other predictions are possible including individual stocks. In fact, this invention is not limited to security markets but is applicable to other markets as well, such as the price of tulips.

The system, in preferred embodiments, allows individual users to input their predictions at any time. At certain points in time, these individual inputs are examined and group predictions are made.

In some embodiments, this prediction is only directional: users input their guesses regarding the question of whether the stock will go up or down. In other embodiments, guesses are more specific as to the magnitude of the movement.

A key to this invention is measuring the accuracy of each individual contributor. Different embodiments carry this operation out in different ways. In one embodiment, in which the user only inputs directional predictions, a 1 is awarded if the prediction is correct, and a 0 is awarded if the direction is incorrect; the average of these numbers is then computed. (In such binary embodiments, a prediction of no movement is ignored.)

In another embodiment, a calculation based on Bayesian statistics is used, We assume a beta distribution, and calculate the probability of correctness in the N+1th guess, where h is the number of correct guesses so far, N is the number of guesses so far, and ah, and ai are hyperparameters of the beta distribution: α h + h α h + α t + N

In the preferred embodiment based on the beta distribution, the beta hyperparameters are chosen so that: α h α h + α t = the population average of correct guesses
and ah is initially set to 1, and refined as real-world data comes in to optimize the overall accuracy of the computed probability of correctness. That is, the mean distance between the Bayesian estimates of user correctness rates based on a small samples of data, compared to average correctness rates computed with the complete, large-scale data set should be as small as possible.

Another estimator of accuracy would be the statistical correlation between the real index movements and the guesses. In this case, the guesses can be bi-valued directional guesses, or take other forms such as real or floating-point values.

It should not be construed that this invention is dependent on any particular estimator of the accuracy of individual contributors.

In the next step, the system generates a group prediction based on the predictions of the individual contributors, combined with their accuracy levels.

In one embodiment this is accomplished as follows. Let M be the number of users who have inputted their guesses on the index movement. The guesses of users 1 . . . M are then given as g1 . . . gM. Also, in preferred embodiments, there is a “weight” associated with each user, given as w1 . . . WM.

In some embodiments, the weights are simply set to be equal to the estimators of accuracy. In other embodiments other values are used. In some such embodiments, we sort the users in order of the embodiment's accuracy estimator, rank them from 1 to M, and use the ranks as the weights.

It should not be construed that this invention is dependent upon any particular weighting scheme.

The guesses may be purely directional, multi-leveled or real-valued.

In one embodiment, the group estimator is simply the weighted average of the guesses:
w1g1+w2g2+ . . . +wMgM.

In a preferred embodiment, a statistical approach is used wherein we start with a null hypothesis stating that “Guesses are random” and calculate a p-value representing the certainty with which we can reject that hypothesis.

In some such embodiments, the guesses are massaged so that, if the null hypothesis were true, they would taken on a uniform distribution on the unit interval. In one embodiment, this is accomplished as described in U.S. Pat. No. 5,884,282, authored by Gary Robinson, hereby incorporated by reference, where we replace the “ratings” of that invention with discrete levels of predicted movement; the levels may be labeled, for example “large negative movement,” “medium negative movement,” “slight negative movement,” “no movement,” “slight positive movement,” etc.; input means are supplied for users to specify those levels for their guesses. Note that this introduces a small random element, the effects of which can be eliminated through integration, as is also described in that patent. These uniformly distributed (relative to the null hypothesis) guesses can be converted to z-scores by use of a standard cumulative normal distribution table (as is also described in that patent). Let z1 . . . zN represent the z-scores of the guesses (for instance, a negative z-score might indicate a guess of downward movement and a positive one a guess of upward movement; the more extreme the z-score the more extreme the guess). Then z C = w 1 z 1 + w 2 z 2 + + w M z M w 1 2 + w 2 2 + + w M 2
is also standard normally distributed [Hedges & Olkin]. Thus, a p-value, pc, can be computed relative to the z-score zc. P-values such that either pc or (1-pc) are very near 0 mean that we can confidently reject the null hypothesis. This means we can safely assume a non-random pattern of guessing in one direction or the other. If there is such a non-random pattern of guessing, the most likely cause is that there is some degree of consensus among our guessers that the index will be moving in a particular direction, which if random chance is not responsible, is probably due to a common response to available information that the users feel may influence the market. The closer pc or (1-pc) is to 0, the more confident we can be that some such consensus exists.

One of the preferred embodiments is similar to the z-score technique but uses a combining method known as

It is useful for the system to be able to measure this confidence. In some embodiments, pc is displayed unmodified. Users can then use this number in making their investment decisions. In preferred embodiments, it is used to generate a measure that is more meaningful to end-users. In one such embodiment, data is collected over time regarding the actual percentage of correct group guesses which have historically corresponded to various ranges of pc; for instance, this percentage is computed for intervals of 0 to 0.0001, 0.0001 to 0.001, 0.001 to 0.01, 0.01 to 0.1, 0.1 to 0.5, etc. (and the same is done for (1-pc)). Then, an estimator regarding the probability that the group guess is correct can be presented to the user. In other embodiments, techniques such as neural nets or genetic programming are used to approximate a function that takes pc as input and outputs an estimator of historic probability of correctness.

Other embodiments use other means of combining guesses. In particular, Hedges & Olkin, pp. 27-46, give a number of ways of arriving at pc from a set of uniformly distributed (under the null hypothesis) random variables. This invention should not be construed to be limited to the combining techniques mentioned above.

A key aspect of the present invention is its technique for motivating users to contribute guesses, and, more importantly, “best guesses”, to the system.

If everyone could have immediate access to group predictions, there would be little reason for individuals to contribute their guesses. To deal with this problem, immediate access is not given to all users. In some embodiments, immediate access to predictions is restricted to those users who regularly help the system by providing their own guesses. In other embodiments, a user who wants the group prediction with respect to a specific question (such as, “Will the S&P 500 index be up or down 10 minutes after opening tomorrow?”) must input his own guess with respect to that particular question.

Preferred embodiments also provide motivation for users to give their “best guesses” rather than simply save effort by choosing random guesses. In some such embodiments, the access to group predictions is staggered in the sense that the “most valuable” users get access to the group predictions first, and the “least valuable” users get them last. In most such embodiments, “most valuable” and “least valuable” are determined by some combination of the accuracy of a user's guesses and the frequency of those guesses.

In one embodiment this is accomplished by ranking the users for both accuracy and frequency of guesses, and taking the average. Other embodiments use a weighted average; others use a geometric mean or weighted geometric mean; other techniques are possible; this invention must not be construed to be limited to particular methods for combining these attributes.

One embodiment does not combine the accuracy and frequency; instead the timing of when a user receives a group prediction is determined only by his accuracy; however, he only receives group predictions for questions he has inputted his own guess for.

In some embodiments, users can receive multiple group predictions per question. This is because the system can refine its prediction relative to a particular question as, over time, more users input their guesses, and, as the event in question draws closer, users revise their guesses.

In some embodiments, users receive one prediction per guess or revision.

Various embodiments use different techniques for staggering users. A preferred embodiment makes group predictions available at time intervals based on their overall rank on the scale of “most valuable” to “least valuable” such that there are equal intervals between the releases of the information. Another embodiment creates intervals as follows: if the interval between the release of the group prediction to the most valuable user and the release of the same prediction to the second most valuable user is x, then the further interval before the release to the third most valuable is (½)x, the further interval before the release to the fourth most valuable is (¼)x, and so on.

There is some amount of time between the first release and the last. This can be shorter or longer depending on the needs of the system. If it is too long, the predictions won't be useful to the users who are less than the most valuable. If it is too short, the staggering technique will not produce the necessary motivation for inputting best guesses. Preferred embodiments allow this time period to be tuned in real-world use for best results. The quality of predictions is measured as the staggering is spread out over varying amounts of time; the normal amount of time is chosen to maximize the quality (and/or other criteria, according to the embodiment).

In preferred embodiments, the total staggering time adjusts itself according to the nearness of the event being predicted. In one such embodiment, the staggering time is always a fixed percentage of the time remaining to the event in question; other embodiments use other scaling techniques.

Users can be given access to group prediction by displaying them on a Web page, emailing them, using an “instant message” service, etc, In some embodiments, special devices are created for wireless access to the system.

In some embodiments, users are only notified about the strongest group predictions. For instance, in the embodiment described above using the standard normal distribution, users are alerted only when pc or (1−pc) is less than a certain value.

The staggering technique has another purpose. Assume that a system using this technique turned out to be highly accurate relative to other predictive means. Large investment houses would then want to make investments based on those predictions. It is possible that large enough investments would actually change the market situation, making the predictions invalid. Some such large investors may profit as they change conditions, but other investors, especially smaller investors, whose guesses might be greatly contributing to the accuracy of the system, would then be receiving incorrect predictions. This would lessen or eliminate their motivation for participating, which would make their guesses unavailable to the system. Since the system is based on the concept of having a large number of users making guesses, this could be very harmful to the system's value and functionality.

Note that “users” of the system can be institutions and even other computer programs.

REFERENCES

  • Hedges, L. & Olkin, I. (1985). Statistical Methods for Meta-Analysis. San Diego, Calif.: Academic Press, Inc., pp. 370-381.
    Rating-By-Rating User Valuation

This appendix discusses aspects of the invention that relate to certain mathematical calculations

One problem being addressed is the fact that people can supply ratings that are essentially random (due to not making the effort to provide truly meaningful ratings), or which are consciously destructive or manipulative. For instance, it has been commented that on Amazon.com, every time a new book comes out, the first ratings and reviews are from the author's friends, which are then counteracted with contradictory reviews from his enemies.

The key to solving this problem is to weight each user's ratings according to their reliability. For instance, if the author's friends and enemies are providing ratings simply to satisfy personal needs to help or hurt the author, it would be helpful if those ratings carried a lower weight than those of other users who have a past reputation for responsible, accurate ratings.

A problem solved by this invention is to provide a way to calculate that past reputation.

This reputation can be thought of as the expected “value to the system” of the user's ratings. This is bound up with the degree to which the user's ratings are representative of the real opinions of the population, particularly the population of clusters which are more appreciative of the genre into which the particular artist's work fits.

(To measure the user's overall contribution to the system, we can multiply the expected value of his ratings by the number of his ratings. Users who contribute a large number of valuable [representative] ratings are, in some embodiments, rewarded with a high profile such as presence on a list of people who are especially reliable raters.)

One can measure the representativeness of a user's ratings by calculating the correlation between those ratings and the average ratings of the larger population.

This analysis of measuring the representativeness of a user's ratings has s major limitation, however. It doesn't take into account the fact that a rating has much more value if it is the first rating on an item than if it is the 100th. The first rating will provide real guidance to those who are wondering whether to download or buy a recording before other ratings have been entered; the 100th rating will not change people's actions in a major way. So early ratings add much more actual value to the community. Also, later raters might choose to simply copy earlier raters, so they can mislead any correlation calculations that way.

Therefore, we want to weight earlier ratings more than later ones. The question is, how much more valuable is the 1st rating than the second one, and the 2nd one more than the 3rd, etc.?

Let S be the set of all items; let N be the number of all items; for s εS and 0<i≦N, si is the ith item. Let u be the user whose rating representativeness we wish to compute.

Let gi,u be the number of ratings received by si previous to u's rating. (i.e., if u gives the first rating for item si, gi,u is 0.) Let ti be the total number or ratings for the ith item.

Let ri,u be u's rating of the ith item, normalized to the unit interval. Let ai be the average of the ratings for the ith item other than u's, also normalized to the unit interval.

Let λ1 and λ2 be constants.

Let qu be the representativeness of u's ratings, calculated as follows: q u = i = 1 N - λ 1 g i , u ( 1 - - λ 2 t i ) a i - r i , u i = 1 N - λ 1 g i , u ( 1 - - λ 2 t i ) .

Then qu is a number on the unit interval which is close to 1 if the

's ratings have tended to be predictive of those of the community as a whole, and 0 if not.

λ1 and λ2 are tuned for performance. λ1 is a parameter of the cumulative exponential distribution determining the rate of “drop-off” associated with the importance of a rating as more ratings for a given item precede

's rating. λ2 is a parameter of the cumulative exponential distribution determining the rate at which the drop-off is associated with the number of total ratings. For instance, if there are no ratings for an item other than 's, the rating has no importance in calculating representativeness and is therefore given weight 0. These parameters can be set manually by intuitive understanding of the effect they have on the calculation. In some embodiments they are set by setting up a training situation in which a number of users rate the items without the means to see other people's ratings; furthermore, these users are selected and given financial or other motivation for putting the effort in to input the most accurate ratings they can generate. These controlled ratings are averaged. Then standard computer optimization techniques such as simulated annealing or genetic algorithms are used to determine values for λ1 and λ2 that optimize the correlation between these averages and qu, qu is calculated using the entire population of users in usual viewing mode (such that they could see the ratings of other users). In preferred embodiments, tuning activities are carried out within the memberships of individual clusters. That is, the controlled ratings given by members of a cluster are used to tune the parameters relative to the general ratings given by other members of the same cluster. This is carried out for each cluster. If it is deemed that there aren't enough members of some clusters to effectively tune the parameters separately for each cluster, then in such cases the values for λ1 and λ2 are averaged across all clusters, and clusters without enough members can use those averaged values. In addition, if a given user has created ratings in multiple clusters, some embodiments simply use the average of his representativeness numbers for all clusters as his single viewable representativeness and some clusters display separate representativeness numbers depending on the cluster in which the numbers are being viewed.

The representativeness of a user is then used for various purposes in various embodiments. In some embodiments, it is presented to artists as a reason to pay a particular user to providing ratings and reviews for new items. In further embodiments, it is used as a weight for the user's ratings when calculating overall average ratings for an item. In some embodiments, listings are provided showing the users' rankings as trustworthy raters, giving “ego gratification”; in must such embodiments these numbers are also available when viewing the user's profile, along with other information presented about the user.

It should not be construed that this invention is dependent upon the particular calculation method for representativeness which is described above.

For example, another embodiment uses the following algorithm for computing the representativeness qu of user u:

Calculate the average rating for each item, not counting u's rating. For each item, rank the population of ratings in order of their distance from the average rating. In embodiments where discrete ratings are used (that is, some small number of rating levels such as “Excellent” to “Poor” rather than a continuous scale), there will be ties. Simply give each rating a random rank to eliminate ties. For instance, if the average rating is 3, and the ratings in order of their distance from the average are, 3, 3, 4, 2, 5, 5, 1, then after randomization one of the 3's, randomly chosen, will have the top rank, the other will have the next highest rank, the 4 will have the third highest rank, etc.

Call the distance from the average, based on these ranks, the “discrete closeness.” Label the ranks such that the closest rating has rank 0, the next closest 1, etc., up to N−1, where N is the total number of ratings of the item. Now pick a random number on the interval (0,1]. Add it to the discrete closeness. Call this quantity the “real closeness” of user u to the average for the ith item and label it pi,u. If user u's ratings are randomly distributed with respect to the average rating for each item, then the population of pi,u's has a uniform distribution on the unit interval. It can be shown that, due to this, the quantity x u = - 2 i = 1 N log ( 1 - p i , u )
has chi-square distribution with 2N degrees of freedom. A chi-square table can then be used to lookup a p-value, pu′, relative to a given value of xu. The quantity pu=1−pu′ is also a p-value and has a very useful meaning. It approaches 0 when the distance between u's ratings and the averages are consistently close to 0, “consistently” being the key word. Also, as N increases, pu becomes still closer to 0. It represents the confidence with which we can reject the “null hypothesis” that u's ratings do not have an unusual tendency to agree with the average of the community. So pu is an excellent indicator of the confidence we should have that user u consistently agrees with the ultimate judgement of the community (in most embodiments, this is the community within a taste cluster).

Preferred embodiments using the chi-square approach also include weights relative to how early u was in rating each item and to take into account the number of ratings for each item. Let wi,u=e 1 g i,u (1−e 2 t i ), where gi,u and ti are defined as before. Let y u = i = 1 N p i , j w i , u . Then p u = Prob { y u b } = i = 1 N b 1 / w i , u d i . where d i = ( w i , u - w 1 ) ( w i , u - w 2 ) ( w i , u - w i - 1 ) ( w i , u - w i + 1 ) ( w i , u - w N ) w i , u N - 1
We use pu=1−pu′ as the measure of representativeness, with numbers closer to 0 being better, as before.

Finally further embodiments provide weights for one or both of the terms in the expression for wi,u. Proper weights can be found using the same procedures as are used for finding λ1 and λ2; using genetic algorithms and other optimization techniques, in some embodiments all these weights are found at the same time.

In general, in various preferred embodiments of the invention, various algorithms that allow a representativeness number to be calculated which includes the predictive nature of the user's ratings are used, so the invention as a whole has no dependency on any particular method.

When displaying the quantities calculated as the representativeness numbers, preferred embodiments calculate rankings of the various users with respect to those numbers, or percentile rankings, or some other simplifying number, since the representativeness numbers themselves are not intuitively comprehensible to most users.

Another useful feature emerges if we take gi,u to be a measure of elapsed time in days between the public release of an item and the time the user rated it (which can be 0 if the review preceded or coincided with the public release), and A2=∞. Then the approaches mentioned above for calculating representativeness can be extended to such situations as measuring the value of a user in predicting the overall long-term sales of particular items (or even to predicting stock market prices and movements and other similar applications).

For instance, in some embodiments, a correspondence is made between ratings and ultimate sales volumes. In one such embodiment, the following algorithm is executed. For each rating level, all items with that average rating (when rounded) are located which have been on sale for a year or longer. Then, within each cluster, average sales volumes for each rating level's items are calculated. Then this correspondence is used to assign “sales ratings” to each item based on the total sales of that particular item; the actual sales are matched to the closest of the rating-associated levels of average sales, and the corresponding rating is used as the sales rating. (If there hasn't yet been enough activity in a particular cluster to conduct this exercise meaningfully, system-wide averages are used.)

In this embodiment pi,u is computed using rankings of distances from the sales rating rather than from the average rating. Then λ2 is set to ∞ (in other words, the (1−e 2 t i ) term is set to 1). Then we calculate the representativeness, pu, as before.

As with the case of calculating representativeness with respect to general ratings, it should not be construed that this invention is dependent upon the specific calculations given here for calculating a user's ratings' representativeness with respect to sales; other calculations which accept equivalent information, including the user's ratings, the sales volumes, and time data for ratings and sales (or, equivalently, elapsed time data), outputting a representativeness which involves a predictive component, will also serve the purpose of providing equivalent means for use by the invention overall.

For instance, in some embodiments, a rank-based technique is used for calculating representativeness. In one such embodiment, time data is used to determine the items that the user rated soon after their release (or at or before their release) and that have now been on the market long enough to meaningfully measure sales volumes. These items are used to perform Spearman rank correlation between the user's ratings and general ratings or sales volume; other items are ignored. Other embodiments perform rank correlation based on this restricted sample and separately perform rank correlation upon all items rated by the user, and perform a weighted average on the results.

Note 1: In some embodiments, it is possible for a user to change his review and rating of an item over time, since he may come to feel differently about it with more experience. But for purposes of calculating, his earlier ratings are stored. In preferred such iterations, the last rating of an item entered on the first day that he rated that item is used.

Note 2: In cases where the cluster has too few ratings or sales to do meaningful calculations, “virtual” clusters can be created by combining clusters with similar taste signatures into one larger clusters for purpose of computing representativeness. In preferred such embodiments, clusters are successively added to the original cluster, and the representativeness recalculated as long as the representativeness number continues to rise with each iteration. When it declines, this process ends. The maximum representativeness number obtained in this way is the one assigned to the user.

Note 3: In various embodiments the discussed calculations are conducted at either the “artist level” or “item level”. That is, in some embodiments the artists are rated and calculations done from those ratings and in others item ratings are used.

A Mechanism for Quickly Identifying High-Quality Items

The purpose of the invention is to use market mechanisms to enable new items that are unusually valuable to become known to the population as quickly as possible.

The technology domain is computer networks such as the Internet.

World Wide Web sites such as HSX (www.hsx.com) currently allow users to use credits (in the case of HSX, called Hollywood Dollars) to buy the equivalent of stocks and bonds (in the case of HSX, called MovieStocks, MusicStocks and StarBonds). These are traded as in the traditional stock and bond markets. Users compete to earn the most credits through trading; in some cases, such credits can be traded for merchandise.

Because these concepts are fully explained and exemplified on such Web sites as HSX and Sports Futures (www.sportsdaq.com), no further explanation while be given here.

In the rest of this specification, terms such as “investment,” “stock,” “shares,” “securities,” etc. will generally refer to those terms as used in a system exemplifying the present invention. However, their basic meanings are taken from analogy to the stock market and to the other already existing system such as Sports Futures and HSX.

The prices of these securities serve as a indicators of the popularity of the items represented by the security, whether an album, an actor, or a movie.

However, these prices are indicators regarding how the item is viewed by the community as a whole. This is not useful if the goal is to find an earlier indicator of new items that most people haven't heard of and that are likely to be popular in the future when more people become aware of them.

The first innovation in the present invention is to enable individual investors to use credits earned through trading to publicize the items they believe have the potential to be more popular in the future.

In preferred embodiments, only credits earned through investing may be used to publicize the items. In particular, systems such as HSX give users free credits when they start using the system in order to invest; however, in preferred embodiments of the present invention, those free credits cannot be used for publicizing items.

The reason for this is to give the option to publicize items to those individuals who have historically shown that they are able to determine which items will be more popular in the future. Those users who have earned a significant number of credits, which are earned only through prescient investments, have shown themselves able to make such predictions. It is therefore only fitting that they should have a greater means to publicize items than users who have not demonstrated such ability.

In one embodiment, publicity is achieved by means of a list of items sorted by the number of credits being spent to publicize them. (mp3.com currently has a feature where fans of artists can bid money to list their items on a similar list.) Investors are thus, in effect, “bidding” for the top spots. Bids from more than one investor for the same item are added together to produce a great total bid for the item. In various embodiments, this list can be of fixed length, with only a predefined number of spots available (in which case only bids that result in winning a spot would actually be charged to the user's credits), or of unlimited internal length, with each user choosing how many spots they want to see. That is, a user might set his system profile to show only the top ten spots on each list. Other embodiments allow users to specify a minimum number of credits such that only items with at least that many credits bid on them will show up on the list. The list is available for all users to view, in a prominent and easily-reached spot on the service.

In another embodiment, publicity is achieved by such means as emailing information about the current top item (the one for which the most credits have been bid to publicize the item) to users. In some such embodiments this occurs on a daily basis; in others, the emailings are more frequent; in other embodiments, “instant messager” or “pager” software is used.

In another embodiment, publicity is achieved by enabling investors to bid to have the regular listings for items to be highlighted in some way, for instance, by showing them in a different color. In some such embodiments, only a fixed percentage of items can be highlighted in this way.

In embodiments where a publicity list is included, the preferred such embodiments include means to make the list available outside of the system itself. These means are accomplished by such means as XML, XML-RPC (XML Remote Procedure Call), SOAP (Simple Object Access Protocol), JRI (Java Remote Invocation), CORBA, and other techniques which allow separate software systems to communicate information.

For instance, in cases where the items are musical recordings, the publicity list might be made available to various Web sites which focus on MP3 and other music-related issues. This would enable them to help their users find new high-quality recordings more efficiently.

In most embodiments, spots are purchased on the publicity list for a fixed amount of time, for instance, a day or a week.

In preferred embodiments, means are provided to filter the publicity information (whether it is represented through a publicity list or through other means such as color) according to the tastes and interests of the viewer.

For instance, in one embodiment, there are separate publicity lists for each category of item. For example, in a music-related system, each musical style has its own publicity list. In some such embodiments, the categories aren't rigidly associated with the items themselves, but rather, users are given the means to specify what category they want their credits to go toward listing an item in.

In preferred embodiments which include categories, users can use some of their credits to publicize an item in one category, and use further credits to publicize the same item in one or more other categories. This is useful when there is no single clear category for an item; for instance some recordings could be categorized equally well as rock or jazz.

In some embodiments, other means than categories are used to filter the publicity information for different users. For instance, the techniques described in U.S. Pat. No. 5,884,282, incorporated herein by reference, assign an appropriateness to each item, which is between 0 and 1, and which is relative to the tastes of the individual user viewing the output of the system. In that invention these numbers are used to order recommendations of items such as movies from the ones the user is expected like least to the ones he is expected to like most.

These appropriateness numbers, which are computed relative to the individual viewer's tastes, can be multiplied by the number of publicity credits achieved by each item, resulting an recomputed publicity credits which are skewed to account for the individual viewer's tastes. Since in the present invention we expect the publicity credits to have a strong correlation to the quality of an item, the term “goodness” is In various embodiments, various other means of combining these two numbers—the publicity credits and the individualized goodness—can be used, such as computing an average.

In some embodiments, the following reasoning is taken into account. To get a high position on the publicity list, the placer should not use points (points are discussed here for convenience but the reasoning regarding using money is similar), because once he gets a lot of points, the number of points has more to do with his past success and his risk comfort zones than it does with his actual confidence in the particular item. Instead, he should risk his reputation when he puts an item high on the list. For instance, in a preferred embodiment, his position on the list is due to the confidence level he expresses for that item. Standard means are provided for him to specify this confidence level; on a Web-based interface, it may take the form of a pull-down list describing various confidence levels from high to low, or a text input area where a number may be typed. Then the weighted average of how people rate items he put on the, where the weight is based on his confidence level. (These ratings may be collected independently from the list itself, for instance, in another portion of a Web site where people rate items they have heard.) This weighted average becomes his reputation. If he's highly confident, he can get a high list placement, but if other people disagree, it will hurt his reputation. For instance, there can be 5 confidence levels, and if he gives it a 5, he will probably get the top spot on the publicity list, but the weight on ratings given for that item may be 5 times normal. In preferred embodiments, the reputation is displayed next to each entry on the publicity list. In one such embodiment reputation consists of the weighted average of ratings described earlier in this paragraph, and is displayed next to the entry in the publicity list. In other embodiments, the average ratings are translated into a reliability measure; in one simple form, this measure is displayed as “mostly reliable” or “mostly unreliable”, depending on whether the weighted average of ratings is less than average or greater than average.

In other embodiments, input means are provided, such as a button, in the publicity list next to each entry, where people can rate the appropriateness of that entry.

In some other embodiments, the maximum sales ranking (computed using similar means to old “top-40 lists”, where the sales over a particular period of time such as a week are ranked) attained by an item over some (longer) period of time is computed and used to judge whether the item should have been recommended and thus to construct a reputation for the person who placed the item in the publicity list. For instance, for 6 months, week-by-week sales are measured, and the week of greatest sales is considered. A recommended item may be required to reach some particular level, for instance, to achieve sales in the top half of weekly sales ranks when the best week of every item is considered. If it does not achieve the required level, the lister's reputation goes down, otherwise it goes up. For example, in some embodiments, 1 times the weight is used if the required level is achieved, and −1 times the weight is used if the required level is not achieved. Then these numbers are summed and divided by the sum of the weights to compute the weighted average. This number is then translated into some easy-to-interpret form, such as “mostly reliable” or “mostly unreliable”, as described earlier, or some other form.

Another way of looking at it, however, is this: the top spot on the list gets the most looks and therefore the most ratings, and therefore can affect the user's reputation the most . . . so no weight needs to be used; we simply average all the ratings given to any of the items the user has placed in the list; we average them all together to come up with a reputation.

In some embodiments, an item may only have one listing on the publicity list, even though more than one person wants it listed. This leads to the question of how to combine the reputation-related risk undertaken by multiple listers into one listing position, and how to calculate the changes to the reputation of each lister.

One solution, used in preferred embodiments, is as follows. Users are enabled to assign a special kind of point—we'll call them “repupoints”—to each item they want to list. Users can assign some fixed range of repupoints to any item, for instance, between 1 and 5. Users can assign repupoints to as many items as they want, without limitation. These are summed for each item; items are placed in order of the total number of repupoints for each item. The number of repupoints assigned by each user is that user's weight. In such embodiments, the combined reputations of the listers is displayed with the entry in the publicity list, for instance, by averaging the reputations.

If their reputation drops below a particular level, they can no longer assign the maximum number of repupoints. Below another lower level, they can not assign more than a particular (lower) number of repupoints, etc.; in some embodiments this is a continuous scale, while in others there are fixed steps. This technique limits the damage to the system that can be done by people who don't care about their reputations, while still enabling people whose reputations are poor to improve them by placing listings in lower slots. This way they can improve their reputations without risking the time of as many other users, who don't necessarily look below the top tier of listings.

In preferred embodiments, the lister's reputation calculation system is not based on how good the item is, but instead is based on how much the popularity of an item improves in the period after the listing is placed. In such embodiments there is a “popularity improvement measurement mechanism.” This invention is not limited to any particular way to calculate this, but an example will be given for convenience. Listings generally only have a limited life, for instance, one week. In one simple popularity improvement measurement system where listings last 1 week, it is required that the average number of downloads of the item per day in the period beginning 3 days before the listing began and ending on the 4th day of the listing is less then the average number of downloads per day in the period beginning on the 4th day of listing and ending on the 3rd day after the listing. (Other embodiments may use other time periods, according to the particular application and judgement of the designers.) In this way, the listing was shown to have value, since downloads increased correspondingly to the presence of the listing. (In order to increase the accuracy of this mechanism, it is helpful to have information such as reviews, “chat” and discussion-group discussions, and ratings of the item easily accessible from the publicity list entry. For instance, in some embodiments the average of user ratings for the item is presented in the publicity list. If the ratings aren't good, the presence of the item in the list can actually have a deleterious effect on the number of downloads, since more people may see the negative ratings.) Following one mechanism described above, if the popularity increases, we give 1*the weight, and if it decreases, we give −1*the weight; we add these numbers for each week and item the user lists, and divide by the sum of the weights, giving the reputation.

Some embodiments take a slightly different approach at the cost of some additional complexity. The lister inputs a percentage of people who will rate the item “worth having been downloaded” (if the rating is collected later) or who will simply download the item (for embodiments where passive rating collection is desired). Placement is based on this percentage; the higher the percentage, the higher in the list. If multiple people want to list an item, the percentage can be averaged between the listers; the estimates supplied by each lister can be weighted according to the reputation of each lister. If the listers are too conservative in their estimates, the entry will have a low placement on the list and maximum benefit will not be attained. If they are too high in their estimates, the system then punishes them by lowering their reputations. In preferred embodiments the system punishes those who overshoot their estimates by a great amount more than those who only slightly undershoot it. In one of the simplest embodiments, there are no weights, and the system assigns I if the estimate is equal to or less than the reality, and −1 if it is less than the reality. In another embodiment, the system converts all percentages to the equivalent number between 0 and 1 (0.75 for 75%, for example), and calculates the reality minus the lister's estimate. If for instance, the lister estimates 75% and the reality is 90%, the “score” will be 0.15. If he estimates 90% and the reality is 10%, the score will be −0.8. These scores are averaged for each user to determine the reputation, which, as described elsewhere, is usually subsequently converted into an easier-to-understand form for display purposes.

It must be noted that publicity lists involving user reputation calculations as described here can be used in many contexts other than the ones described in this invention disclosure, and therefore may be considered to constitute an independent invention.

Another innovation of the invention lies in providing means for manufacturers to publicize their new products while simultaneous rewarding the participants who add the most value to the service.

In one embodiment this is accomplished by enabling users to buy new items with credits instead of using money. The users who have the most credits to spend are the ones who have added the most value by helping to publicize items that eventually become popular, and who the item producers would therefore assume would be most likely to be able do the same for their new items. Thus, both parties benefit.

In further embodiments, only credits earned through investing can be used to buy items. The software keeps track of how many credits each user has earned through investing and uses that tabulation to restrict purchases to credits earned in that way. This further ensures that only users who are skilled at discerning and publicizing high-quality new items will purchase the items.

In some embodiments, purchases are not made using credits, but rather, item producers simply give new items to those who have earned the most credits. The system makes a display available which enables item producers to see who has earned the most credits (for instance, in some such embodiments the listing of users is sorted by total amount of credits earned through investing); in some such embodiments the display includes such information as email address or name and address where items, such as musical CD's can be mailed. In preferred further embodiments, only qualified item suppliers are allowed to access such listings for privacy reasons; this is accomplished using such means as supplier logon using digital certificates. In some further embodiments, sending items to users is accomplished automatically through interaction with the item supplier's computers by XML-RPC, or other means.

In some embodiments, similar means to those mentioned above are used to discern the most valuable users, and those users are paid money in return for carefully considering particular items.

By motivating the most valuable users to consider particular items, it can be expected that those users will choose to invest credits in the more valuable such items, thus making a credit profit if the item eventually becomes more popular; they can also be expected to use some of their credits to publicize such items.

In some embodiments, items consist of information generated by the users themselves. For example, in one embodiment, the invention is used as a brainstorming tool. Users have a problem to solve, and generate ideas that might be used to solve the problem with the goal of deciding on a few “best” solutions. One problem with traditional brainstorming methods is that they don't scale well to a large number of people—which is unfortunate because the more people there are, and the more different backgrounds and abilities they have, the higher the likelihood that someone will generate an optimal solution, particularly through active interaction with the input of other people. A problem with traditional methods as they scale to include a large number of people is that it is hard for particular ideas, even if extraordinarily good, to emerge from the noise. The person whose ideas are most likely to emerge is not necessarily the person with the best ideas, but rather the person with the most social skill and convincing manner. The present invention serves to create a very dynamic market-based solution, where the velocity at which a good idea can spread is greatly accelerated by the market process, giving more people the chance to interact with that idea more quickly, thus generating further enhancing ideas more quickly, greatly increasing the rate at which the entire process occurs.

In some embodiments, such as the brainstorming one just mentioned, an input form is provided, for instance, by means of an HTML form, for the user to input an information item. It may be an idea, it may be an article, or even a humorous piece.

The item is initially assumed to be worth a certain number of credits, for instance in some embodiments every new item is assumed to be worth 1,000 credits.

In some embodiments, the initial valuation is based on the history of the item creator. For instance, suppose the average current item valuation is X, the average valuation for items created by the creator of the item in question is Y, the number of items created by that user is N, and W is a weighting factor. Then one embodiment uses the formula (WX+NY)/W to calculate the initial value for new items. W may be chosen using various means in various embodiments; in one embodiment, standard computer optimization methods are used to determine the value of W such that, using historical data, at the end of 24 hours of a new item's availability, the output of the formula most closely equals the valuation as determined by the marketplace. In other embodiments, other formulas are used. For instance, in one embodiment, a Bayesian calculation using a multinomial distribution based on the Direchlet distribution is used. This involves breaking up the possible valuations into discrete ranges. Any competent practitioner of Bayesian statistics has the know-how to generate an appropriate algorithm for this.

In some embodiments, an initial valuation is not considered to be required. Instead, the originator of an item chooses to make a certain number of shares of the item available on the marketplace, which are auctioned. For instance, the creator of a new musical recording might make 10% of its shares available, which can only be purchased in fixed lots of 1% each. These lots are auctioned using standard auction mechanisms or market-making mechanisms. The creator receives the credits paid for the shares. This mechanism provides an initial, tentative valuation of the item: after this first round of stock sales, the item in this case can be considered to be worth 10 times whatever the shares sold for. However, the people who bought those shares will now be interested in helping to publicize the item, since they believe the item has worth and that when it is better known, shares will be sellable at a higher price in the future.

In some embodiments people desiring to sell shares can choose whether to put them up for auction or to sell them at a fixed price.

Market-making mechanisms for facilitating the trading of stock are already in use at HSX, Sports Futures, and elsewhere, and will therefore not be described in detail here.

However, it should be noted that use of a valuation device which exists outside of the marketplace itself can facilitate market-making and provide a standard by which shares of a given item can be translated into the external world (for instance, into cash).

In some embodiments which involve the online music industry, for example, the relative download rates of various items are used to provide the valuations of those items. The external valuation of an item is always proportional to its current download rate. This external valuation is used by the system to enable the system to buy and sell shares which are offered for sale or purchase commensurate with that valuation. This helps the system play the role of market maker.

In other embodiments, other mechanisms are used to create the external valuation.

In one such embodiment, a list is kept which represents the current population-wide popularity of an item. For instance, in a brainstorming-related embodiment, this list shows how important each item is thought to be by the population. Each user is given input means to enter a current importance number for each item, between 0 and 100. If a user does not enter a number, his entry is assumed to be 0 (it has no importance to that user). These numbers are averaged to compute the popularity of each item; they are listed in order of popularity, and this popularity measure is also used to form the external valuation.

In another embodiment, the external valuation is proportional to the number of hyperlinks going to an item. For instance, this is an appropriate technique when the items are Web pages or sites.

In another embodiment, the external valuation is proportional to the number of items on user hard disks. For instance, Napster is a process that runs on the computer of each user. It can be modified to count the number of MP3 files on the hard disk of each user and send that information (preferably anonymously) to a central server where the results can be tabulated (preferably, only one occurrence of each MP3 file on a given user's machine is counted). Thus, if the present invention were integrated with Napster, for instance, in order to quickly identify the best new MP3's, this external measure would be practical. Integration with Gnutella could proceed similarly, although in that case the range of types of items is much greater.

In another set of embodiments, limitations are placed on how much input users can give to external valuations.

In some such embodiments, users are given an ability to express their opinions regarding external valuations proportional to the amount of credits they have earned through their investments. For instance, one such embodiment allows users to allocate various percentages of their earned credits toward external valuations.

For example, let us assume a minimal system for ease of explanation. Suppose two users have earned credits in the system. User A has earned 100 credits, and user B has earned 200 credits. Suppose there are three items for which external valuations are desired. If A allocates 25% of his credits to item 1, 25% of his credits to item 2, and 50% of his credits to item 3; and B allocates 50% of his credits to item 1, 25% to item 2, and 25% to item 3, then item 1 will have 25+100=125 credits, item 2 will have 25+50=75 credits, and item 3 will have 50+50=100 credits. These credits, then, comprise the external valuation of the items. Note that using percentages for these allocations has the advantage that the amounts can be recomputed dynamically as the number of credits earned by various individuals rises and falls.

An alternative method is to give each individual a certain number of points to allocate for external valuations, which does not change over time. However, this method does not give more weight to people who have demonstrated their prescience. In some compromise embodiments, each user has a fixed number of points given to him, which are added to proportionate to his earned credits. Other variants also fall within the scope of the invention.

Note that these allocations, in preferred embodiments, are not time-limited. A user can continue to allocate a percentage to a particular item over time. This helps the external valuation be more constant and resistant to short-term trends.

Other embodiments use other means for dividing credits or points for external valuation purposes. For instance, in one such embodiment, the user simply lists his favorite items in order of how high he things they should be valued. (Various input means can be used to accomplish this, for instance, “dragging and dropping” items to place them higher or lower in a visual display where a higher position means an item is to be more highly valued; this could be accomplished, for instance, by means of a Java applet.) Then, an algorithm runs that allocates 1 unit to the lowest item on the list, 2 to the next highest, and n to the highest (assuming there are n items in total). Then the total number of allocated units is computed. Then for each item i where I=1 . . . n, where ui represents the number of units allocated to the ith item, U represents the total number of units allocated by the algorithm for the current user, and C represents the current user's current number of credits, and ci represents the number of credits allocated by the current user toward the external valuation of item I, ci=(C*ui)/U.

Users who have earned more are given more rights to buy space on the publicity lists for the items they favor, but, in some preferred embodiments, rather than giving such users the right to bid proportional to their earned points on individual items, each person has a maximum of one “vote” per item (or another relatively small number of votes); but the more points a person has earned, the more items he can vote on. For instance, it might cost a certain number of points to buy a vote, but only one vote can be bought for a particular item. In some embodiments, these votes are purchased with real money, but the right to purchase them can only be acquired by earning points through the investment process, which may be based on a non-money system.

As elsewhere in this document, the examples given are intended to be illustrative and not limiting.

In some other embodiments, usually one involving audio or video items which are available for the public to hear or view, and in which the items are played consecutively without user choice about the order the items will be played in, means is be provided for users to “skip over” the rest of an item after they have heard part of it. The valuations of the various items are inversely proportional to the number of such skips the item receives. An example would be a Web site that plays songs one after the other, as a radio station would, but which enables users to skip over ones they don't like by clicking an HTML button on the site.

In some embodiments, there is a minimum allowable initial value for new items.

When used as a brainstorming tool, some embodiments only provide an external valuation at two points: first, when an item is created at which time it is some minimum number (or a number that is a factor of the total number of items, for instance, one divided by this number), and when a trading session is over the idea is finally accepted or rejected as an actionable idea, at which time the valuation the sum of the initial values of all items divided by the number of accepted items; the rejected items each have a value of 0 credits. (This differs from the mechanism used by Sports Futures, where each “contract” is worth $0 or $100 at the end of the season in that it accounts for the fact that new items may be created at any time.)

In some embodiments, a mechanism is provided through which the user must spend credits to create a new item. This ensures that users are careful to create items which they believe have quality. In one such embodiment, a user who creates an item is required to buy all the shares of that item at the item's minimum allowable valuation. He may then make any number of those shares that he chooses available for sale. In some related embodiments, the item creator is given a special price he can buy the shares at which is not necessarily associated with the item's minimum allowable valuation. In some related embodiments, a fixed proportion of the shares, for instance, ½ of the shares, are always made available for sale and the item's creator keeps the rest.

In preferred embodiments, each user gets a regular “allowance” of credits, for instance, on a weekly basis, that he can use to buy shares and/or to publicize items. Additionally, in preferred embodiments, each user gets a certain number of credits when he first creates an account with the system. Some embodiments display prohibitions about the same person creating more than one account (thereby collecting the initial allocation of credits more than once), including stating the right to terminate any duplicate account. In some embodiments, digital certificates from 3rd-party companies, which try to guarantee uniqueness, are required as part of registration. In some embodiments, cookies are used to ensure that there is only one user per computer.

In some embodiments, there are different categories of credits. In some such embodiments, credits received in the initial allocation, and/or credits received from a regular allowance, are not usable for publicizing items; in such embodiments, credits earned through profitable trading can be used for that purpose. When a trade is made, the difference in credits between the purchase price and the sale price is allocated to the fund which can be used for publicizing items. This difference can be negative and will thus lower the credits available in that fund.

In some embodiments, a marketplace is created for a fixed amount of time, for instance, when the invention is used for brainstorming to solve a problem which needs to be solved by a particular time.

In some embodiments, mechanisms are provided by means of which users can receive cash payments in exchange for their credits. For instance, a check can be created by the computer system and subsequently mailed; in others, software and network systems can be used to cause the sum to be placed in the users credit card account. In some embodiments, the translation of credits to cash occurs at the user's discretion; in some embodiments with time limits, all credits are converted to cash at the end of the marketplace's lifetime. In some embodiments, only certain types of credits are exchangeable for cash, for instance, credits earned through trading (as described elsewhere in this application).

In some embodiments, the invention is framed as a competitive game in order to further motivate the users. For instance, a scoreboard is available where various players' current scores are listed; credits are considered to be points. In some such embodiments, only credits earned from trading are translatable into points.

In some embodiments, the creator of a session is given means to specify certain requirements for participation that are related to the users' past performance in other sessions. For instance, if a new brainstorming session is created to solve a particular problem, some embodiments might enable registration for the session to those who have previously earned a certain number of credits through trading, or users may be required to buy their way in with credits, or other restrictions may be applied.

In most embodiments, discussion software user interfaces are attached to the items. That is, when viewing an item, the user can see comments written by other users about the item, or add comments to those already written; alternatively, the comments are a click away from the display of the item.

In some embodiments, shares pay dividends, as shares in the regular stock market do.

In various further embodiments, shares and/or their associated dividends are purchased and paid in real money.

As an example, in one embodiment an item is a downloadable song which is paid for by the downloader, as can be done with Liquid Audio's software. Each song is divided into 10000 shares. Revenues derived from sales of the song, after overhead costs such as a fee to Liquid Audio, would be paid to shareholders as dividends. The entity which owns the actual song may choose to keep a certain number of shares for himself, and to make the rest available to the market. He may set an initial price for the shares. Users of this embodiment may then purchase shares. Since the real value of the shares would be based on expectation of future dividends, and this would be based on sales volumes, market forces would be expected cause the share price to generally be proportional to current sales. In this embodiment, a publicity list is also provided whereby interested parties can bid real money for high placement. Shareholders in a song by a new and unknown artist, who bought many shares cheaply, and who believe in the future of the artist, would be very motivated to raise sales by publicizing him in this manner. The sponsor of the trading system itself profits by taking a portion of all revenues, as well as by keeping the fees paid for the publicity list.

Other, related, embodiments are different in various aspects from the one described above. Various embodiments focus on other domains than music. In some embodiments the number of shares is determinable by owner of the item. In some the number kept by the creator of the item is fixed, for instance, 50%. In some no publicity means is provided; in some, other means than the publicity list are provided. In various embodiment, various procedures are used for the system owner to make a profit, including keeping publicity bids, keeping a transaction fee for trades, etc. It should not be construed that this invention is limited to such details of implementation.

In some embodiments, an information display is publicly available which states the amounts of current dividends. In typical examples of such embodiments, dividends are calculated on a daily basis based on the number of sales each day; the current day's per-share dividends are made available to all users. In other embodiments, other periods of time, such as weekly, monthly, or quarterly are used in the calculation of dividends. As is the case with real stocks, dividends are subject to change over time.

In some embodiments, the system is not strictly tied to the model of the stock market and securities, but uses other means for the same effect.

In particular, what is needed is a means to enable people who accurately predict which new items will be highly valued to profit by those predictions.

One such means is to enable people to buy the new item at a reduced price compared to the likely later price, so that those items can be resold later. This is the same principle that guides people who buy particular baseball cards today in the hopes of selling them in the future at a profit if those particular cards become more valuable in the future.

For example, in one embodiment relating to music, means are presented whereby the publisher of a new song can specify a price to sell the song, where such price may only be available for a limited amount of time. Subsequently, users are presented with the means to buy a number of copies of the song. This may occur by downloading multiple copies of the song. Alternatively and equivalently, only one digital recording may be downloaded, but with a contractual right specifying the number of “copies” it represents by giving the user the right to make that number of copies, including the right to resell those copies to other people. The contract can be presented online and signed with a digital signature. Alternatively and still equivalently, no copy needs to be downloaded at all. Instead, the number of “copies” purchased by the user is kept track of by the system. In many such embodiments, the system subsequently presents means for other users to purchase the copies from the original purchaser. The system is an agent for the selling of the copies, similar to a store which sells used CD's, except that in the embodiment being discussed the recordings are virtual: only one physical copy need be stored on the magnetic media of the server (or, it can even be stored on another machine), and the server keeps track of who owns how many “copies” in its database.

Of course, this aspect of the invention should not be construed as being limited to music; any digital item can be equivalently handled by an embodiment of the invention. Examples include video recordings, electronic books, articles, etc. Further examples include such items as concert tickets, where the tickets can be purchased for new artists at a reduced price for concerts that haven't been scheduled yet, such that the tickets are good for any concert and are sold by the system before other tickets. The tickets may be kept only in digital form for concerts broadcast over the Internet that may only be seen by ticketholders, or the system may provide means for tickets to be displayed on the screen and/or printed for concerts that the ticketholder can physically attend.

In some embodiments, the system resells any of the earlier-purchased item that the owner desires to resell before it sells “new” items. That is, if a user (Joe) likes a new artist and buys 1,000 copies of his recording in the hope of reselling them later for more money, the system is required to resell whatever copies Joe makes available for resale before it can sell other full-price copies of the recording. Otherwise, Joe may not be able to efficiently resell his copies, and the ability of the system to motivate Joe and others like him to identify high-quality new items will be compromised. To facilitate this requirement, means are provided whereby Joe can input the number of copies he wants to resell at the “official price” at the current time. In general embodiments of this invention generally provide an order in which items are resold by the system when a number of users are trying to resell items at the same time; the preferred ordering is that in which the most profit is made per sale, however other orderings are possible.

Other embodiments provide no such structure and instead rely on market processes to accomplish similar aims. For example, there is no requirement that any particular copies be sold ahead of other copies. However, it is understood that whoever bought copies for the lowest price is in the best position to slightly underbid the rest of the market while still making a profit. So, no formal mechanism need be included to enable him to sell his copies. In must such embodiments, any owner of one or more copies can offer to sell one or more of them at any time for any price. These offerings are posted (in most cases, the currently offered copies are listed together in an area, such as a Web page or set of Web pages, that is dedicated to the song in question. Such listings are useful when a user would like to buy a number of copies, so that he can eventually resell them himself. One exemplary user interface lists offerings in lots, where all copies in a lot have the same price. Input means is provided where the user can input the number of copies he wants to acquire, and an output is generated showing the total cost and the average cost per copy, where the copies are purchased from the least-expensive lots such that one lot may only be partially used, but all copies in less-expensive lots are completely sold out.

In some embodiments the user can specify a maximum average price he would like to pay, and the system will automatically reserve the least-expensive collection of copies available, compare the average price maximum average price, and: a) if the average price is less than or equal to the maximum, the purchase is completed; or b) if the average price is more than the maximum, the reserved copies are released and the user is informed that the desired number of copies could not be purchased at the desired average price.

However, in cases where a consumer simply wants to buy one copy, a simple interface may be provided in which only the lowest-priced offering is displayed. In cases where the items are completely digital, there is no difference between copies, and therefore no reason to show any but the lowest-priced offering.

In some embodiments a user who wishes to sell an item is presented with a screen showing (at least) the least expensive lots currently offered for sale, so that he can consider them in deciding how to price his offering. In further embodiments, for the sake of convenience, the user is enabled to input the price and number of his own items he wants to sell on the same screen as the one where he views the lots that other people are selling.

Other embodiments may create an auction marketplace modeled closely after eBay and other Web-based auction sites.

Some embodiments accomplish these aims by providing “Right To Download” (RTD) certificates, which may be completely electronic. In some such embodiments, there is a database, which may be distributed, which stores RTD objects. These objects contain an owner ID, a secure identifier such as a password (which may be encrypted), an identifier of the digital item represented by the object (for instance, a particular musical recording in LiquidAudio format), and the current asking price (and may have other information as well such as the entire purchase history of the object).

An owner of such an RTD can sell it at any time. In one embodiment, there is a Web site associated with the database. The owner of an RTD can use the user interface on the Web site to indicate he would like to sell one or more of his RTD's for a particular item. This causes that number of RTD's, owned by that user and associated with the item in question, to go into a “for sale” state. For instance, this may be accomplished by setting a flag in the RTD object in the database. In various further embodiments, these items can either go on sale at a price chosen by the owner, or be auctioned.

In other embodiments, RTD's can be bought and sold by other means. For instance, each RTD may be given a unique identifier. A transaction may be created in which credits or money is transferred from the buyer to the seller causing the owner information in the RTD object in the database to change from the seller's information to the buyer's information. One example of a way to take advantage of this type of transfer would be on an auction site such as eBay. A seller can auction off an RTD for a particular item by means of the auction site's standard mechanisms. When the auction is complete, the seller goes to the Web site set up for the purpose of completing such transactions and sets an attribute of the RTD object such that the object goes into a “sale in progress” state. The seller also enters the agreed-upon amount into another attribute of the object. He then sends the RTD object's unique identifier to the buyer.

The buyer can then log onto the same Web site, at which he enters the unique identifier of the RTD object and his credit card information. The site presents a confirmation that he wishes to buy the right to download at the agreed-upon price (stored in the object) and if he agrees to the confirmation, the RTD is now his.

When the owner of an RTD actually downloads the item associated with the RTD, an attribute is modified such that the RTD goes into a “download completed” state, indicating that no more downloads may be made using that RTD. This makes the RTD valueless.

In some embodiments, downloads may be made directly from the Web site whose purpose it is to complete such transactions. However, using interprocess communications mechanisms such as SOAP, a separate site can initiate the download by accepting the unique identifier of the RTD object, and checking with an RTD server process (which usually exists on another machine on the Internet) to see that an item exists with that identifier and that it is not in the “download completed” state.

When a person buys RTD's, in some embodiments he can immediately make them available for sale at another price. Of course, assuming the other price is higher, as will certainly be the usual case, it's unlikely that there will be any buyers, because RTD's for lower prices will normally be queued ahead of the RTD's in question. But when the time comes that there are no lower-priced RTD's, the ones in question will automatically be sold.

The concept behind the RTD also applies beyond the world of downloads. CD's may be purchased and held, or even pre-purchased before being made, and that ownership can be transferred using the same principles as described for RTD's. This can be considered to be a variant of the right-to-download concept appropriately called the right-to-buy (RTB). It is exactly the same idea, but instead of allowing a download to occur, the physical shipment of the item is allowed to occur. While the focus in this specification is on RTD's because of that concept's intrinsic appeal in an Internet environment, the invention should not be construed as being restricted to RTD's to the exclusion of RTB's.

Note that the “buying” aspect of an RTB can involve a transfer of no money, in which case the object can be seen more as a right-to-be-shipped an item rather than a right-to-buy. But in many cases a non-zero purchase amount for an item will make more sense. For instance, there are certain fixed manufacturing and shipping costs associated with an item. These can be represented in the buy-price encoded into the RTB. Thus, an RTB itself might cost $0.10 at first, and as the associated item attains real demand, subsequent resales of RTB might go up to $1.00 or even $10.00 or more. However the fixed purchase price may be $2.00, covering the fixed costs of manufacturing and shipping the item.

Thus the last person to purchase the RTB might pay $10.00 for it, and then another $2.00 to actually complete the transaction and cause the item to be shipped.

In other embodiments a “right-to-experience” is sold rather than an RTD or RTB. By “experience,” we might mean “view” for a visual work, “hear” for a musical work, etc. It is the act of experiencing a work rather than acquiring it. For instance, in some scenarios, works might be freely downloaded to a user's hard drive, but a fee is charged for experiencing them. This may be a per-experience fee (i.e., per-listen, per-view, etc.) or it may be a fixed fee, for instance, a monthly fee for unlimited listens.

One fixed fee scenario in the music realm would be a service that gave users the right to download and listen to any music they want to download and listen to, as frequently as they want (or perhaps limited by some maximum number of downloads or other constraint) for a monthly fee. For instance, one can imagine a service such as Napster charging $20 per month for use, enabling the user to have access to any music he can find in the Napster network. A player such as RealJukeBox could easily be modified to work with Napster to count the playings, and provide those counts to a central server.

In such cases, the organization providing the fixed-fee service may nevertheless be required to pay proportional to the number of experiences. For instance, radio stations provide music for a fixed fee (nothing) but are required to pay royalties based on audience size and the number of playings, or on music-related revenues. Then, one can imagine a system in which money is paid to the owners or producers of recordings based on the proportion of the overall hearings each recording attracts. For instance, user A and user B might each be playing $20 per month for an “unlimited” music service. However, user A might listen to 1000 songs (not necessarily unique) per month, whereas user B might listen to 100 songs per month. If, in each case, 5% of listening time goes to Rolling Stones songs, user A is playing Rolling Stones songs many more times per day than user B, while not paying any more money.

This is not incompatible with artists, song owners, or “right-to-experience” (RTE) owners getting a fixed fee for each hearing. It simply means that the organization providing the fixed-fee service will make more money from user B (it might even lose money by user A, but the amounts overall will be such that at least some fixed-fee service providers will be able to make money overall by providing these services).

Some embodiments of the present invention are thus geared toward providing a marketplace for RTE's, similar to the marketplaces for RTD's and RTB's. In this case, however, the buyers may be organizations providing fixed-fee services rather than the end-consumers themselves. Such organizations would be motivated to take advantage of these markets in order to buy bearings more cheaply by buying hearings associated with lesser-known artists, whose market prices for RTE's are probably less. These organizations would then be motivated to market such artists to their users in order to get users to listen to those artists rather than the more currently popular artists; in doing so, they lessen their costs while, ideally, maintaining their revenue stream. (Alternatively, fixed-fee services can be set up that do not keep track of the number of hearings but only of the number of downloads of each recording. In such a case, the RTD market is used similarly to save money.) In some embodiments, no traditional “download” takes place, as in the case of “Internet radio stations” which broadcast streaming audio such that if the user hears a song twice, it has been streamed twice. In such a case the RTE paradigm is a good match.

In preferred RTE embodiments, RTE's are frequently bought in lots, such as 10,000 RTE's. In some such embodiments an interface is presented to buyers allowing them to request a number of RTE's of their choosing, for instance, above a certain minimum.

In some embodiments, RTD's may be sold at various Web sites which communicate with the system which stores the actual RTD database objects by means of interprocess communication facilities such as SOAP, Java RMI, or other similar technologies. In some such embodiments, there a listing is made available by those means which lists identifiers of the items for which RTD's currently exist; the system can also preferably provide descriptive information about those items such as titles, performers or other kinds of information which pertains to the particular type of item. Another interface is provided by means of which an RTD for a particular kind of item is requested. When an RTD is made available in this way, it is “locked,” and other requests for RTD's will result in other RTD's being made available. The unlocked RTD with the lowest price is made available next. (In other embodiments, market-making mechanisms are used to provide a “current price” for the RTD's associated with an item; this enables all sites currently selling an RTD's associated with an item to sell them for the same current price.)

In some further embodiments, sites can choose to sell RTD's together with an automatic invocation of the RTD. In such cases, the user does not need to know anything about the concept of an RTD; all he knows is that the item is being made available at a low price; he pays the price by credit cards or other techniques and the item is immediately downloaded. In the case of an RTB, the price presented to the user may be the price of the RTB plus the associated fixed per-item cost, if any; so the user only sees the price he needs to pay in order to have the item shipped to him. Again, he may be completely unaware of the underlying RTB mechanism.

It should not be construed that the RTD (RTB) concept is limited to the implementation details spelled out here. The fundamental idea is that there is a database with records indicating the state of each RTD, and there are one or more identification fields by which a the data for a particular RTD can be located in the database along with permission granted to modify it. This identification fields typically but not exclusively involve a unique identifier for the RTD records, as well as a unique identifier for the owning user, and a password for that user. RTD's can be transferred between people at different prices at different times. One RTD can represent one right to download (or buy); in some embodiments, rights to download or buy multiple items may be incorporated into one set of RTD records, although such an approach might be more complex than the one-RTD-per-item approach.

In some embodiments, RTD's are used to determine the prescience of the owner of those RTD's. For instance, if we take the average of the last few sales of the RTD's associated with an item as an indicator of the current market value of such RTD's, then the price at which the owner originally purchased those RTD's can be compared with their market value, and an average change per RTD calculated. This average can also include the change in price between purchase and sale for RTD's he no longer owns. This average, when considered alongside with the number of RTD's the user has purchased, is a good indicator of his prescience.

For instance, one way of combining these numbers is a + S b + N ,
where S is the sum of the changes in value for every RTD the user has purchased, N is the number of such item's, and a and b are chosen such that a/b is the average change in RTD value across the entire population (excluding the user under consideration), and b is chosen to be consistent with the amount of evidence one RTD gives regarding a user's prescience. This can be determined using historical data once the system has such data using an optimization algorithm. As an example of how to do this, the system can “hide” one randomly chosen RTD owned by each user in the system. Then, for a particular “guess” regarding the optimal value of b, we calculate the user's prescience, ignoring the hidden RTD's. We then calculate the average difference between the calculated prescience and the actual change in value of the hidden RTD's. We then use an optimization algorithm to find the value of b that minimizes this average difference. For example, if the optimization strategy is genetic algorithms, and d is the average difference, then an appropriate fitness measure for a particular value of b would be 1/d; the genetic algorithm would find the value of b with the maximum fitness.

This prescience measure can then be used as the determinant of the user's ability to place items in a publicity list or external valuation list.

In preferred embodiments, there is no restriction against a user being able to buy copies at different prices at different times. For instance, when a high-quality recording from a new artist is first made available, it may be able to command only a very low price. As its popularity grows, it may be able to command incrementally higher prices. Users who are interested in profiting from this ongoing growth in value may wish to buy recordings at the current intermediate price in order to resell at an anticipated higher price later.

In some embodiments, the publisher of items may guarantee that he will never sell items for less than a particular price, thus increasing the security of potential early buyers. In some such cases, the guarantee may apply only to copies other than the ones being sold at the time; i.e. a user may buy 1,000 copies for price X while being guaranteed that other copies will not be sold for less than twice that price.

Taking this idea further, in most (preferred) embodiments appropriate constraints will be provided so that, for instance, the price of each download isn't forced to zero. (This would happen if the number of downloads being offered on the trading market was greater than the number of people who were interested in the recording, and there were no lower limits on pricing. Then, holders of downloads would all try to underprice each other, forcing the price toward zero.)

We therefore need a market control mechanism—a way to make sure that the market price of RTD's (or similar items—for convenience the language will discuss RTD's but also applies to the variants discussed above such as RTB's) purchased by speculators will increase over time, enabling the speculators to make a profit.

The first such mechanism to be discussed is to make a limited number of items available at a low price at the time of a song's release and to sell all other RTD's at a fixed, much higher price.

For instance, assume a new, unknown band wants to use the market to increase the speed with which its new song gets known. It may then make 10,000 RTD's available at $1.00 per RTD, and promise to charge $12.00 (in some embodiments, this would be a minimum promised price, not a fixed price) for all subsequent RTD's.

Then speculators may buy all 10,000 initial RTD's. If the song does indeed become very popular, those speculators who wait until then to sell their RTD's will be able to charge very near to $12.00 for each one. Such speculators therefore will make nearly 12 times their initial investment.

Setting a minimum price for RTD's sold to people other than the initial speculators is one constraint mechanism which can be used to cause the market price for the speculator's copies to rise. Another way is to limit the number of RTD's that are available at any given time. The approach to doing this that I will describe is from a verbal suggestion of Robin Hanson. However, it must be stressed that the invention is not limited to any particular market constraint mechanism; the examples being given here are examples only. Under this approach, an initial number of RTD's is made available by the owner of the item at a low price. Many or all of these are purchased by speculators. Once these have all been purchased, if it seems that there is a larger market for the item, the owners of RTD's for the song can vote on the question of whether more RTD's should be generated. If the decision is “Yes”, than, in some embodiments, each RTD becomes two RTD's. In others, means are provided for RTD owners to also vote on the number of new RTD's to be generated. For instance, different proposed numbers can be voted on, and the one with the most votes wins. By thus allowing the number of RTD's to be increased in a limited fashion, more are made available for people to buy (and speculators to profit from) while it is not true that so many are created that the market price is driven toward zero.

Of course, all this may be accomplished via a Web or other network computer interface where techniques in common use and obvious to any competent practitioner are used to present RTD purchasing, voting, and other necessary interfaces.

In some embodiments, only profits obtained by resale may be used for the various publicity mechanisms such as a publicity list, or those profits plus some limited amounts of other funds may be used.

In some embodiments, the number of copies made available at a given price may be limited.

In some markets, the value of an item may be very much related to personal tastes. For instance, at the time of this writing far fewer people in the United States are interested in instrumental dulcimer music than in mainstream pop. A publisher who wishes to maximize the value he will derive from his items needs to be very cognizant of the size of his market. For instance, if a particular genre has 10,000 people interested in it, and the publisher makes 10,000 copies of an item by a new artist available at a very low introductory price, there won't be anyone left to buy the item later at full price when the artist becomes better-known. Thus, if the artist eventually becomes highly respected within the genre, a significant sum of money will have been “left on the table.”

For the sake of such markets where these issues are a concern, means are provided for helping users understand the size of their potential markets.

For instance, allowing the user to specify a particular recording in the genre and see how many copies of have been sold. The utility of this may be increased by also showing this number as a percentage of the overall population.

In embodiments which break the market into various categories (such as musical genres), the average number of sales of songs in the genre may be shown; additionally it may be displayed as a percentage of the average number of sales in total.

Another method for determining the potential market size for a song is provided in some other embodiments (this general market size can be automatically and tentatively applied to all songs from a given artist). Means is provided for the publisher to specify artists who should appeal to similar tastes as the current artist or song. In preferred embodiments, they are listed in order of similarity; that is, the most similar artist is listed first, the next most similar artist second, etc Then the actual market sizes for these artists are calculated and averaged together, with the numbers for the most similar artists being given the most weight.

Various embodiments of the present invention use different means for paying any money due to users. In some, in order to minimize overhead, dividends are only paid on a monthly, quarterly, or annual basis, or only after they have accumulated to being greater than some figure. In some embodiments payments are made directly to checking accounts; in some checks are mailed; in some, the payments are credited to credit cards; in others, other means are used.

In some embodiments, purchases of stocks or items can be made automatically under certain rules. For instance, a user may want to buy 1,000 copies of each new work by a particular musician as long as he can get them for less than a particular price. Input means are provided whereby the user can specify such actions.

In some embodiments, means are provided for otherwise unrelated Web sites (or other entities facilitating networked computing) to participate in the market mechanism; in preferred such embodiments, they are enabled to profit from doing so. For instance, in one such embodiment, SOAP is used to communicate the necessary information to enable other Web sites to provide the inputs and outputs necessary to facilitate transactions in the market, such as display of items available to buy shares in, the price per share, current dividends, etc. In preferred such embodiments, an interface is provided whereby such an entity can tell the market service when an item sale is made, for instance, when a song is paid for via a Liquid Audio download; this interface uses similar means such as SOAP. In most embodiments, entities that help facilitate the market in this manner receive a share of the profits.

The “market price” of an item may be displayed for users to see. In embodiments which use market-making mechanisms, the current market price as determined by that mechanism may be used. In some embodiments, the current lowest offering price of an item is used as its “market price.” In others, the most recently completed sale price may be used. In some embodiments, the market-making mechanism may be willing to buy at one price and sell at a slightly higher price; thus, “bid” and “asked” prices can be presented. Other variations are possible and also fall within the scope of the present invention. In some embodiments, the market price information, however it may be determined according to the particular embodiment, is made available by an interprocess communications mechanism such as XML-RPC or SOAP. This information may be used as one measure of the likely quality of an item which may be usable by other services. For instance, Web search engines use many factors in deciding how high to rank various Web pages that may contain the key words the user is searching for. One such factor, in certain advanced search engines such as Google, is counting the number of links to each page. Another factor, to be used in a similar way, could be the current market valuation of each page.

One possibility that must be contended with when trying to use the market value of an item as an indicator of its quality is the potential for fraud; that is, users who make certain trades expressly for the purpose of making an item seem more valuable than it really is. For instance, the creator of a musical item could, under a set of pseudonyms, purchase all the RTD's made available at the beginning of the item's market availability, and then buy and sell the RTD's using those pseudonyms at any price; if, for instance the last sale of the item were used in some context as a measure of its quality, then he could make a sale between two of his pseudonyms at a high price, thus creating a spurious measure of high quality.

Thus, in embodiments where market value is used as an indicator of quality, certain mechanisms may be used to hide (or denote as unreliable) the market value for items where that market value is questionable. For instance, if the number of entities trading an item is smaller than some fixed number, the market value may be hidden or made unavailable through the interprocess communication mechanism; this is because the more entities involved in trading an item, the less likely it is that those entities are all pseudonyms or people in collaboration with the stakeholder in the item. Another mechanism has two key parts; first, there must be a transaction cost, possibly paid to the operator of the market, and second, there is a requirement, for a market value to be considered valid, of a certain number of daily trades. Under this mechanism, people who try to manipulate the market will find it costly, since they will be paying the transaction fees but not realizing any upside from their trades; whereas legitimate users who are prescient about which items will go up in value will be able to make money from their trades. This will encourage legitimate, prescient users to continue trading while discouraging fraud. Through real-world trial-and-error, an appropriate balance of the number of required trades and the cost per trade can be determined. As elsewhere in this specification, the details are for purposes of example only and should not be considered to limit the scope of the invention. Like other concepts described in this specification, the present one should not be considered to be useful only in the context of the other aspects of this specification.

One possible use for the invention is a system that stores product suggestions, and uses the described mechanism to bring the most attention to the most important suggestions, and to enable the people that make and publicize those suggestions to be highlighted to be rewarded. To that effect, an set of screens should be provided whereby manufacturers can set up accounts where they can exchange credits earned in the system for actual money. Alternatively, they can make their products available for credits. The interface could optionally enable them to only compensate credits earned for suggestions relating to their own products; the system would thus keep track of the source of all credits. This scheme may be appropriate in other situations as well; for instance, a site could be devoted to finding human interest stories for newspapers; the newspapers could pay people for credits earned in helping them find stories appropriate to their particular newspaper. In some embodiments, there are separate areas for discussing the products of different companies. In some such embodiments, users or management of the system can create areas for various companies without the involvement of those companies.

In cases where product improvement ideas are the items, the value, in some embodiments, is based on the proportion between the value of the feature and the amount of work it will take to make. For instance, an easy fix of a moderately-important bug might float to the top of the list, because this proportion would be high. To make this happen, text descriptions and help information says that that is what the value should be based on. This makes sense because it will form the basis of the company's decision whether or not to act on a suggestion. The external value comes from the question of whether a company acts on the suggestion; thus there must be a way for the company to input information on whether this has occurred. (This could involve a logon ID for each company, or preferably different logon ID's for various authorized representatives of such companies; their records would include their company identifiers. Certain individuals are authorized to indicate that a another person is authorized by the company in question.) In this scenario, some embodiments keep the external value undermined until official company action takes places: either the company fixes the bug or dismisses the item as something it will never address. (The same mechanism will work for feature suggestions as well.) If the item is dismissed, the external value is 0. If the item is accepted (bug fixed, or new suggestion integrated into the product) the value is some number which may be the same for all accepted items. Once the outcome is determined, all shares have the appropriate value given by the external measure.

Miscellaneous Notes

FAQ-Generating Application:

Investors share ad revenues for FAQ items. The external measure incorporates not only the number of access, but the percentage of times the answer was not satisfactory. Investors can email the answer owner to change its place in the hierarchy or to change its keywords or the weights on those keywords. If the owner isn't responsive, ownership switches to somebody else who gets equity, although the original owner also retains some equity.

Mix a certain number of publicity list entries with popularity list, invisibly.

For weblogs, make ultimate valuation tied to visits per day. Current should be based on expectation of that.

The invention may be used to facilitate a task force to solve a particular problem. In some embodiments users “buy” their way onto a particular task force by means of credits. This ensures that participants are more highly motivated, increasing the “signal-to-noise” ratio.

Have sign-off sheet with specific punishment if they use automated or manual means to sabotage the popularity counters for sales, downloads, weblogs, etc.

Publicity list also links to reviews. Reviews are qualified in that the reviewer only suggests you will agree with his review if you are within a certain distance of his taste, where that is given, for example, in percentage of the population. One possibility: look at number of MP3's on disk to determine your taste—download needs to be able to scan them.

Publicity list includes links to free samples.

Offer people a way to make suggestions to the author to improve a brainstorming idea after they've invested in it; deal with the question of whether it makes sense to do that or create a new item . . . they should want to help the author first, and if that fails, try on their own.

Enable any individual to create a fund.

For product improvement ideas, in some embodiments the company orders them in value or assigns a value after a certain amount of time or only for those reaching a certain point.

In embodiments powering a “mega FAQ,” enable people to suggest keywords for entries to make them more valuable—also have the value of an entry related not only to the number of times the user's query is satisfied, but also reduced by the number of wrong hits to an entry. This motivates people to make their queries dead on. They can actually associate questions such as “how many so and so in a so and so”.

Preferred embodiments enable people to give their permission (“opt-in”) for feeding their info into an engine which allows musicians to choose where their work will be sent to for initial publicity. People with great track records with regard to prescience, for instance, as determined by points earned, are the most appropriate recipients. But some people will not want their info made public due to privacy concerns.

In some embodiments, the number of credits earned by a person is considered to be an indicator of the degree to which he has contributed positively to the community. For instance, on a free-of-charge MP3 site which aims at connecting artists with their ideal audiences, people who earn credits through investing are people who were prescient in identifying the best new recordings and artists before the rest of the community. In many cases, such people helped to popularize the artists by posting to the publicity list or by other means (certainly, anyone who invests in a particular item is motivated to help popularize it, and therefore it can be assumed that this occurs a certain amount of the time). In embodiments where credits are not convertible to money, items, or other usual means of compensation, value may still be found in credits by publicizing the number of credits earned by each person (or the rankings of credits or other forms of recognition based on the number of credits). Such publicity should be made in the context of a description of the fact that such publicity is recognition for adding value to the community by identifying the best items early. For instance, one simple embodiment lists the Top Ten Contributors to the Community by choosing the 10 people with the greatest number of credits and listing them in order of credits (or alphabetically, or some other order). Thus, this recognition of being a real contributor to the community is the ultimate currency represented by credits.

Instead of buying “shares” in an item, the same effect can be achieved in many embodiments by selling “percentages” of the item or its related profits.

REFERENCES

  • Bugzilla: bugzilla.mozilla.org/
  • SOAP specification: http://static.userland.com/xmlRpcCom/soap/SOAPv11.htm
  • http://www.redherring.com/cod/2000/0125.html
  • Google: www.google.com
  • Napster: www.napster.com
  • Gnutella: gnutella.wego.com
  • Papers related to “idea futures” by Robin Hanson at http://hanson.gmu.edu/ifpubs.html#Hanson

The entire contents of U.S. patent application Ser. No. 09/714,789, filed, Nov. 16, 2000, are incorporated herein by reference, including the specifications, drawings, and abstracts, are hereby incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to business methods, and to techniques and systems for financing items through future sales rights. The invention has particular advantages when used to provide sales options for copyrightable material such as entertainment recordings. The invention further relates to a system to determine quality through reselling items. The context for this invention is network-connected computer systems which allow a number of individuals to interact with a central system for carrying out these sales.

2. Background

In the entertainment industry, manufacturers and distributors are faced with fixed costs of manufacture, and distribution, regardless of quantity or popularity of the entertainment product. This means that they must make an estimation of the future popularity and sales of the particular item. While in some instances the manufacturer and distributor have accurate predictive data, it is particularly difficult to predict the degree of acceptance and market success most entertainment items will have.

Regardless, there are generally people who have a significant degree of understanding of particular entertainment markets, and who can judge the potential success of a particular item. These people may be willing to provide financing for entertainment products in the particular entertainment markets. To the extent that these people can provide financing and can provide good predictions of the performance of entertainment products in the marketplace it would be desired to create a market structure which allows their knowledge and expertise to be used financing create an economic incentive to manufacture or distribute entertainment, copyrightable or other products.

This invention is intended to hasten the identification of high-quality items by enabling those who are particularly good at identifying them to make a profit from doing so. We do not distinguish between physical objects such as paintings and digital objects such as MP3 files except as noted below.

We assume the existence of objects such as musical recordings which may or may not have value to a particular community. In the digital world, an MP3 may be in a genre that only a small subset of the population is interested in. But, as a separate matter from the genre, it may or may not have quality—quality that would motivate people interested in that genre to want to hear it.

BRIEF SUMMARY OF THE INVENTION

In accordance with the present invention, a method of optimizing valuations of items in a market includes establishing values for the items and providing a sequence of the items for which speculators may invest. The items are then sold to consumers of the items, or rights to the items are transferred to consumers. The speculators who bought the income rights to the items are then provided with income in accordance with the income rights for the particular ones of the items sold to the consumers or for the rights transferred to consumers.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart showing the transfer of funds according to the present invention;

FIG. 2 is a diagram showing database tables in accordance with one embodiment of the present invention;

FIG. 3 is a flowchart showing original owner creating RT's in accordance with one embodiment of the present invention;

FIG. 4 is a flowchart showing a speculator buying RT's in accordance with one embodiment of the present invention; and

FIG. 5 is a flowchart showing a consumer finalizing an RT in accordance with one embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

For the sake of simplicity, we will focus on one particular example—MP3 files. But it must not be construed that this invention is limited to such files; it equally well applies to paintings, books, CD's and other objects which consumers may find appealing or unappealing.

A problem to be addressed is the fact that there are a huge number of MP3's, most of them of very low quality regardless of the genre they may happen to be in. It takes time and effort to listen to many of these recordings in order to find the gems. Those who perform that service—who we shall refer to as “scouts” (or speculators)—should therefore have the opportunity to be rewarded for it.

One aspect of this invention is that the entity owning the MP3 is given the ability to sell a certain number of “rights” at a low price with the expectation that these rights will be worth more later; the scouts would be rights to the MP3's they think will be popular in the future with the expectation of reselling them later for a profit.

The meaning of the term “rights” varies from embodiment to embodiment. Several variants that fall within the scope of the invention are listed here, although this list is not meant to be exhaustive:

A right to download (RTD): Applicable for MP3 files or other digital works. The scout buys the RTD and resells it later at a higher price to a consumer, who actually does the download. A database keeps track of who owns the RTD at each point in time; sales are registered in the database.

A right to experience (RTE): This is applicable in some circumstances to MP3 files or other digital works. For instance, in the context of a subscriber service, consumers do not necessarily own MP3's, but pay a fee for the right to play them. A user can play any available song at any time, and it may be streamed from a remote server or it may exist on the local audio or computer system. The subscriber may not pay for each play of a given song, but in the current scenario, some entity does—most likely, the organization providing the subscription service. Since each play therefore has value, scouts would be well-advised to buy RTE's at a low price before an artist is well-known, and resell them later—often directly to subscription services—at a higher price when a demand has been built. Thus, subscription services buy RTE's according to how many times users play various songs.

A right-to-buy (RTB): This is applicable to physical objects such as CD's. Scouts buy the right-to-buy a CD at a certain price (which may be $0; i.e., the purchase of an RTB may be the only necessary purchase. It may be non-zero to cover such aspects as shipping and handling, which would be fixed for the lifetime of an RTB). Scouts then resell the RTB to consumers at a higher price after a demand is established.

For short, we will call such variants RT's that is, “rights to . . . ”

In each case, there is a “finalization event.” For RTD's this occurs when the object is downloaded. For RTE's, it occurs when the object is experienced (for instance, a listener hears a streamed audio file once). For RTB's, it occurs when the physical object is bought (and/or shipped).

For purposes of example, we will focus on RTD's relative to songs, but except where noted the concepts apply equivalently to RTB's and RTE's. Also, they apply equally well to various formats in which digital property can be encoded; some formats in fact may be superior to MP3's for profitable transactions due to built-in copy protection features; Liquid Audio is an example of a company marketing such technology.

Suppose a scout buys 1,000 RT's for a particular song at $0.10 each with the expectation of selling them at a later time for $1 each. But suppose the owner of the song makes a large number of RT's available through a number of channels at $1 each. This could very significantly slow the rate of sales of the scout's RT's. In fact, if more RT's are made available than there is a market for RT's, many of the scout's RT's might never be sold. This means that after the scout buys RT's, his fortunes are tied to subsequent management decisions on the part of the original owners. So his profit is determined not only by his own prescience in determining which new recordings are likely to become popular later, but also by the unpredictable decisions of the owners.

One factor making it hard to avoid this kind of problem is that the market size for a particular song can not be exactly known in advance. This motivates the owner to make as many copies available as possible through as many channels as possible. Moreover, a sale made by a scout means the owner gets $0.10 for each copy; a direct sale means the owner gets $1 for each copy.

So the fortunes of the scouts and the fortunes of the owner are at odds.

This creates the possibility for trouble. Even if there is a contract stating that RTD's after the first 1,000 will be sold for $1 each, the fact is that if the owner violates the contract, the scouts could lose money and be faced with the expensive prospect of suing for damages.

So, ideally, there should be a technique for eliminating this problem. Such a technique is a key aspect of the invention.

The solution provided by the present invention is to finalize the RT's in sequence. For instance, if a scout buys the first 1,000 RTD's, he will supply the first 1,000 RTD's which are downloaded. The original owner may way to flood the market with direct-sale RTD's, but he cannot be finalized until the scout finalizes his RTD's.

This mechanism even protects against the extreme case of an owner selling RT's at $0.10 each and then subsequently direct-selling the RT's for an even lower price (which he might want to do, for instance, in order to use that song as a loss-leader, building popularity, so that he can sell other songs more quickly later).

If the owner then says he is going to flood the market with $0.05 RTD's, people will want to buy them at that price—but they won't be available. If the scout was right in his belief that demand would exist such that consumers would eventually want to pay a higher price, then the consumers who are most eager to obtain the RTD's will buy them at the higher price, since the alternative is not to have them at all, at least for a very long time. The scout, knowing this, can wait as long as necessary to sell his RTD's at the higher price.

This mechanism therefore protects the scouts against decisions by the original owners that could otherwise have a negative effect on the scouts. It applies equally well to RTE's, RTB's and other equivalent forms of RT's.

Any of a number of market mechanisms may be used to carry out the sales. For instance, blocks of some fixed number of RT's may be auctioned on eBay. (In fact, eBay has recently announced that it will make software mechanisms available for 3rd-party companies to set up auctions without direct human intervention.) Or, market-maker software may be provided emulating the market-making techniques used in various stock markets. In preferred embodiments, the original owner controls how many RT's are made available for sale to the scouts at any point in time.

In preferred embodiments, RT's may be sold in any order until the finalization event occurs, at which time they are removed from the marketplace. That is, for example, and RTD for the 10,000th download may be purchased before the RTD for the 5,000th download is purchased. In preferred embodiments, there is a free market for selling RT's. At any time, scouts may buy them or such organizations as retail stores can by them, without distinction.

(In some embodiments, consumers may buy them too, and subsequently trigger the finalization event for their own use. Interfaces are provided for such purposes. For instance, in the case of RTD's, in some such embodiments each RTD has a unique identification number. When a consumer purchases an RTD, the ID is presented to him by such means as a Web interface or email. For instance, if a consumer purchases and RTD using a Web site that operates according to standard Web retailing design principles, the ID can be presented after the purchase is paid for by means of a confirmation Web page or in an automatically-sent email. However, since consumers typically want immediate gratification, and the finalization event for a particular RTD might not be allowed for some time, many embodiments will not include features for consumers to purchase RTD's.)

In some simple embodiments, the original owner simply sells RT's to the scouts at a fixed low price. In such cases, a fixed number of RT's are usually made available for this purpose; later RT's are made available to consumers without first being made available to scouts.

Records representing the status of each RT are stored in a database (which may be a RAM-based data structure, a disk-based structure, or a structure in another storage medium). In various embodiments, there may be one record per RT, or RT's may be represented in blocks. In most block-based embodiments, a record will represent a block of RTD's purchased at one time by one scout. Other representations are equally workable and are equivalently included in the present invention.

In most embodiments, the database provides an indicator of availability of an RT. An RT is available if its current owner is willing to sell it. In some embodiments, the state of unavailability is indicated simply by deleting the RT's record from the database. In others, there is a flag indicating availability or unavailability.

In most embodiments, an RTD is automatically made unavailable when a download occurs. (Equivalently, in embodiments involving RTE's, the RTE is made unavailable when the experience occurs; in RTB's it happens when the object is bought.) In some cases, such as some embodiments involving RTB's, an object may be made available again at a later date by switching the flag. This is not the case for RT's which by virtue of their nature may only occur once.

In some embodiments, the original owner may choose, at any time, whether the next sequence of RT's is to be made available to scouts or to consumers. In some embodiments this is accomplished by means of a “resellable” indicator in the database. If reselling is not allowed for a particular RT, then it is of no use to speculators and they won't want to buy it.

In some embodiments, RT's for particular sequence numbers are not entered into the database until a commitment has been made to sell them.

However, preferred embodiments perform this function by means of “minimum price” data in the database, which may be stored with a separate record for each RT or for blocks of RT's (or as an indicator that applies to all future RT's, at least until the indicator is changed). If the minimum price is the maximum price the consumers are likely to pay, then the RT will be of no use to scouts.

A central server (or set of servers working in concert) keeps track of finalization events.

The database contains information regarding the price for finalization events. In some embodiments, scouts and retailers can set the finalization price for RT's they have purchased. In others, the finalization price is fixed at the outset by the original owner. In preferred embodiments, scouts and retailers can set the price so long as it is under a maximum price fixed by the original owner, which may have a system-wide default if the original owner does not specify such a price. This prevents one hostile scout or retailer from halting sales by setting the finalization price of a RT so high that no consumer will buy it; since the RT's are finalized sequentially, lower-priced, subsequent RT's would then never be sold.

In preferred embodiments RT's may be purchased out-of-sequence; that is, for example, a particular scout may believe that a song will sell 100,000 copies while most scouts think it will sell 50,000. Therefore in an auction setting, the scout or retailer may be able to buy the RT's associated with the 90,000th through 100,000th finalizations at a bargain price compared to the earlier finalizations; if he is right, he may make an exceptional profit. A user interface is provided whereby the scout or retailer can specify the range of finalization numbers he wants to buy at a certain price; in most embodiments other scouts or retailers may be given the chance to outbid him in an auction; i.e., it is made known via the user interface that someone has bid on a particular range of finalizations and the opportunity is presented to input counter-bids. Any standard auction mechanism such as Dutch auctions may be used for this.

In some embodiments RT's don't have to be purchased in sequential blocks according to finalization sequence; for instance, scouts and retailers can purchase every nth finalization between two numbers. This enables them to invest in a wider range of finalizations depending on how confident they are in the number of RT's they expect to be sold without buying a huge number of them.

In this preferred embodiment, song finalizations, that is, the actual downloads, are for a fixed price. That way, the value of the nth RT is simply dependent on the perceived probability that n or more of the RT's will be finalized. The owner of an RT cannot refuse a sale; when n−1th finalizations have been sold, the nth one will be sold next.

The preferred embodiment draws a clear distinction between RT's and finalizations (which may be a download or a purchase of a physical CD, or take other forms).

There is a speculator market for RT's.

In the preferred embodiment, there is not a speculator market for finalizations. There is no need for the price of finalizations to be variable. For consumer-friendliness—that is, for the sake of consumers who just want their music and don't want to hear about speculation—and for retailers that want to keep everything as simple as possible—finalizations are sold as the associated objects always have been. Finalizations for CD-related RT's for instance, are sold on the same fixed-price basis under which CD's can be purchased on Amazon.com In fact, they may be sold through Amazon.com.

These prices will not be out of line with the norms for CD prices.

Since the normal fixed price for a finalization means that it would be absurd for RT's to sell for more than that price, that creates a natural limit for the price of RT's.

In this preferred embodiment, original owners can put any number of RT's on the speculator market at any time. They go into an auction, and will therefore receive the highest price any speculator is willing to pay for each RT. Further trades of RT's take place in a market setting.

It is to be expected that RT's associated with finalizations that are far in the future will sell for less than RT's associated with immediately upcoming ones.

For example, say 100,000 copies of a CD have been sold, and the original owner now decides to sell more RT's to the speculators. It is extremely likely that at least 100,001 CD's will be sold, so the price for the next RT is likely to be very close to the regular price for the CD. However, if the speculator decides to sell 900,000 RT's, then the “IPO” price for the 1,000,000 one might be very, very low, because it may not be at all obvious that the CD will ever sell 1,000,000 copies. The speculator's skill—the area in which they make their profits—is in judging how many copies a particular work will sell. Our sequential approach, described in my most recent patent application, greatly enhances the ability of a speculator to profit from that skill; it removes many factors that could distort his profits.

Since RT's are finalized in sequence, if a consumer buys an RT from the speculators market, he may not be able to finalize it for some time.

But there is a queue where consumers can buy the next RT to be finalized, separate from the speculator's market—actually they are not really buying an RT at all, they are buying a finalization, which will be immediate because they are buying the next finalization.

EXAMPLE 1

An original owner wants to make 100,000 RT's available for sale for a particular item, for instance, a recording of a particular song that will be downloaded. The price will be the same for every download, $0.25. He wants to sell 10,000 downloads directly, but since he isn't sure that the song will sell more downloads than that, he makes subsequent download available to the speculator's market. See FIG. 2. The original owner causes 90 records to be entered into database table RTTable (1), each of which represents a sales unit of 1,000 RT's. Status for every record is set to Avail (meaning that the RT is available to be purchased). SpeculatorID is set to null. ItemID is an identifier of the particular song that will be downloaded. (We will assume it is 104). That is, this table may contain RT information for many different songs; the ItemID allows us to associate a particular record with a particular song. Each record has a unique (within the ItemID) SeqNo in the range of 11 to 100. That is, SeqNo's may not be unique in the overall table, but combined with the ItemID comprise a unique key into the table. They start at 11 in this case because the original owner has already committed the first 10,000 downloads to be sold by him directly. FinalizedCount is set to 0 because none of the RT's represented by this block have been finalized yet. See FIG. 3.

A set of auctions is arranged, through methods similar to those on eBay, whereby these blocks of 1,000 RT's are sold. In fact, the auctions could be conducted on eBay.

Now, at some point soon after the song has been released a speculator, represented in SpeculatorTable 2 by the record with SpeculatorID=452, comes to believe that the song is going to sell 100,000 copies. He places a bid on the block represented by the record with ItemID 104 and SeqNo 100.

Assume he wins the auction with a bid of $100. Now Status is set to Purchased, and RTTable.SpeculatorID is set to 452. See FIG. 4.

Before and after this purchase, other speculators will have been purchasing other blocks.

Separately, downloads are available to consumers. The first 10,000 have no effect on our database because they were not in the speculator's market. Subsequent downloads update our database. Continuing with our example of speculator 452, assume that the 90,001st download occurs. The RTTable record with ItemID 104 and SeqNo 100 is retrieved and FinalizedCount is changed to 1. The record is saved back into the database.

This change to the database represents the fact that one download has been conducted against speculator 452's block. Each time a download occurs after that, FinalizedCount is incremented for the same record until it reaches 1,000. (By that point the original owner may have entered some more RTTable records to represent succeeding downloads, or he may sell succeeding ones directly).

When FinalizedCount reaches 1,000, the SpeculatorTable record with SpeculatorID 452 is retrieved and payment for 1,000 downloads, is made into his bank account. This payment is $250 minus some processing fees, which in our example happen to be 5%. So $237.50 is deposited into the bank account, and he has made a net profit of $137.50 for correctly identifying that 100,000 downloads would occur. See FIG. 5.

Note that Example 1 is only to be considered as an example. To list a few of the many variations that could occur: Different numbers of downloads can be represented by a record other than 1,000, including, for example, 1. Instead of representing downloads, the records may represent listens to a song (RTE's), or RTB's. Instead of purchasing the RT's with money, the RT's could be purchased with some other valuable such as points earned by doing a useful service. (For example, on the Emergent Music web site, http://www.emergentmusic.com, points are earned by accurately rating music and by recommending music that others subsequently find to be worthwhile.) The subsequent payment, however, could be in money, or in points that have some other kind of value. As another variation, the original owner could share in the profits of each finalization; that is the consumer may pay $0.25 for a download, where $0.0125 went to transaction fees and $0.10 went to the original owner, leaving $0.1375 for the speculator. Many other variations are possible.

FIG. 1 shows the operation of an exemplary embodiment of the present invention. In order to establish a market, first a financial right is determined 13. The financial right is established as a right which can be sold. This becomes a right which is the subject of the transaction, referenced as RT 15. The RT can be a right to download (RTD) 21, a right to experience (RTE) 22 or a right-to-buy (RTB) 23.

A finalization event is defined and the finalization event takes place 25. The finalization event may be one or more of object downloaded 31 in the case of an RTD, object is experienced 32 in the case of an RTE or physical object is bought 33 in the case of an RTB. The finalization event is deemed a purchase 35 of the RT.

The finalization events are permitted to occur in a sequential order. That order is established as a sequence, so that the RT's are finalized in the sequence 37. The RT's are then sold 39, and records representing status of each RT are stored 41 in a database.

FIG. 2 is a diagram showing database tables in accordance with one embodiment of the present invention. An RT table 61 and a speculator table 62 are shown. The RT table 61 includes an item ID, a sequence numbers, a status indication, a speculator ID, and a count of finalized transactions. The speculator table 62 includes a speculator ID which should correspond to the speculator ID of the RT table. The speculator table 62 also includes a name, address and bank account for the speculator identified in the speculator ID.

FIG. 3 is a flowchart showing original owner creating RT's in accordance with one embodiment of the present invention. As can be seen, the original owner decides to make a particular number of RT's available. These appear as the item ID in the RT table 61. The RT's are provided in blocks as desired by the original owner. The corresponding rows are added to the RT table with sequence numbers. The items in each row are given the appropriate values in the RT table.

FIG. 4 is a flowchart showing a speculator buying RT's in accordance with one embodiment of the present invention. The speculator decides to buy a particular block of RT's. The record is then updated in the RT table.

FIG. 5 is a flowchart showing a consumer finalizing an RT in accordance with one embodiment of the present invention. The consumer causes the finalization of the RT. The RT table is then updated for that particular sequence number.

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Classifications
U.S. Classification705/52
International ClassificationG06Q30/00, H04K1/00, H04L9/00
Cooperative ClassificationG06Q30/00, G06Q30/0282
European ClassificationG06Q30/0282, G06Q30/00
Legal Events
DateCodeEventDescription
Mar 9, 2005ASAssignment
Owner name: TRANSPOSE, LLC, MAINE
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:VIRTUAL DEVELOPMENT CORPORATION;REEL/FRAME:015867/0541
Effective date: 20050308
Dec 16, 2004ASAssignment
Owner name: VIRTUAL DEVELOPMENT CORPORATION, MAINE
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:ROBINSON, GARY;REEL/FRAME:015470/0670
Effective date: 20041215