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Shovel Weather Driven Demand
Time Time Time Time Time Time Period 1 Period 2 Period 3 Period 4 Period 5 Period 6
SYSTEMS AND METHODS FOR
RECOMMENDING BUSINESS DECISIONS
INFLUENCED BY WEATHER ELEMENTS
BACKGROUND OF THE INVENTION  1. Field of the Invention
 The invention relates to business decision systems, and more particularly, to business decision systems and methods for recommending business decisions driven by weather elements.
 2. Background of the Invention
 The impact of weather is direct and dramatic on many facets of business and social life. As a result, many complex tools have been developed to forecast weather conditions. The Farmer's Almanac and the National Weather Service forecasts are two of the best known sources of weather forecasts. Business decisions are often influenced by these forecasts. These forecasts generally provide useful information that can help businesses and others make informed decisions regarding events or activities that are weather driven. Unfortunately, assessing weather forecast data, and generating a specific business action based on weather forecasts presents a daunting and complex challenge that prevents businesses from effectively using relationships between business activities and weather elements, and weather element forecasts to develop business actions.
 Furthermore, while forecasts, such as those provided by Farmer's Almanac and the National Weather Service generally provide accurate forecasts, existing forecasting techniques are not perfect—nor will they likely ever be. Thus, business decisions based on weather forecasts are subject to the uncertainties associated with weather forecasts.
 What is needed are cost effective systems and methods to generate business recommendations for specific business actions based on forecasted weather elements and relationships between a business activity and weather elements.
SUMMARY OF THE INVENTION
 The invention is directed to systems and methods to generate business recommendations for specific business actions based on weather element forecasts and relationships between a business activity and weather elements. The system includes a confidence level filter, an opportunity matrix filter, a weather decision point generator, a business rule recommendation engine and a business rules knowledge database. In a further feature a graphical user interface and an interface to external databases is provided. The interfaces allow the system to be used across a network, such as the Internet.
 Methods of generating business recommendations for business activities based on one or more weather elements are also provided. The methods include receiving a weather element relationship for a business activity and weather driven demand data for a set of time periods (e.g., a weeks, months, or seasons). The weather driven demand data provide an indication how a business activity will be influenced by one or more weather elements.
 The method proceeds by assigning opportunity measures to each of the data points within the weather driven demand data, and identifying weather decision points based on opportunity measures associated with a weather driven demand data point. The assignment of opportunity measures includes assigning tags, such as high opportunity, low opportunity, high risk, and low risk to each weather driven demand data based on a set of opportunity matrix rules. The opportunity matrix rules contain a knowledge base generated from the study of historical business activity results that were influenced by weather elements.
 The method then applies business weather rules to the weather decision points to generate business recommendations. The business weather rules provide specific actions, such as adding inventory or increasing markdowns.
 In a further feature, a weather element relationship confidence level is assigned to each data point within the weather driven demand data. The strength of the confidence level is based on how strongly correlated a product's business activity results are related to weather elements. This confidence level is then factored in to determine the weather decision points. In another further feature, a weather element forecast confidence level is assigned to each data point within the weather driven demand data. This confidence level can also then be factored in to determine weather decision points.
 The invention provides a cost effective system and method to generate business recommendations based on weather elements and relationships between a business activity and weather elements. The invention also provides an efficient approach to assessing the likelihood that a weather element forecast will be accurate.
 Further embodiments, features, and advantages of the invention, as well as the structure and operation of the various embodiments of the invention are described in detail below with reference to accompanying drawings.
BRIEF DESCRIPTION OF THE FIGURES
 The invention is described with reference to the accompanying drawings. In the drawings, like reference numbers indicate identical, or functionally or structurally similar elements. The drawing in which an element first appears is indicated by the left-most digit in the corresponding reference number.
 FIG. 1 is a diagram of a weather-based decision system, according to an embodiment of the invention.
 FIG. 2 is a flowchart of a method to generate business recommendations based on weather elements, according to an embodiment of the invention.
 FIG. 3 is a flowchart of a method that uses trends in weather elements to assign a confidence level to a weather element forecast, according to an embodiment of the invention.
 FIG. 4 is a chart that illustrates example weather driven demand data, according to an embodiment of the invention.
 FIG. 5 is a chart that illustrates an example output showing a business recommendation related to price promotions, according to an embodiment of the invention.
 FIG. 6 is a chart that illustrates an example output showing a business recommendation related to inventory allocation, according to an embodiment of the invention.
DETAILED DESCRIPTION OF THE
 While the invention is described herein with reference to illustrative embodiments for particular applications, it should be understood that the invention is not limited thereto. Specifically, the invention is described herein primarily in the context of a retail environment. However, it should be understood that the invention can be adapted and envisioned for use in many other applications, including but not limited to, retail products and services; manufacturing/production (e.g., construction, utilities, movie production companies, advertising agencies, forestry, mining, and the like); transportation; the entertainment industry; the restaurant industry; consumer activities and/or events (e.g., golfing, skiing, fishing, boating, vacations, family reunions, weddings, honeymoons, and the like); and processing, valuating, and trading of financial instruments (e.g., options, futures, swaps, and the like). Those skilled in the art with access to the teachings provided herein will recognize additional modifications, applications, and embodiments within the scope thereof and additional fields in which the invention would be of significant utility.
 FIG. 1 illustrates a weather-based decision system 100, according to an embodiment of the invention. Weatherbased decision system 100 can be used to provide business recommendations. These recommendations are based on known relationships between weather elements and a business activity, weather forecasts, historical weather data, and business rules. In some cases, historical business activity data will be used to generate recommendations, while in others historical business activity data will not be used. As used herein, weather element can include any type of weather element, such as temperature, low temperature, high temperature, or level of precipitation.
 The following are two high level examples of the types of business recommendations that weather-based decision system 100 can generate. Weather-based decision system 100 can be used to provide a business recommendation that advises a business to increase its inventory of boots for the coming fall. Alternatively, weather-based decision system 100 can be used to provide a recommendation that advises the planners of a series of outdoor concerts as to the potential number of attendees at the concerts.
 In other cases weather-based decision system 100 can be used to generate very specific and extensive recommendations. For example, a national department store may desire to receive recommendations regarding inventory levels for the coming fall for their entire stock of outdoor clothing (e.g., women's boots, men's boots, men's sweaters, women's sweaters, men's outerwear, etc.) for hundreds of stores in locations throughout the United States. Ordinarily, providing such a recommendation would be a daunting task. While still complex, weather-based decision system 100 simplifies this task, organizes and prioritizes recommendations, and improves business efficiency. In particular, weather-based decision system 100 automates this process, leveraging known weather element relationships for the merchandise and an extensive knowledge base of business
rules to generate a set of recommendations by product, date, and location with the ability to aggregate results within a geographic, time-based, product-based or combined geographic, time-based, and product-based hierarchy.
 Throughout the discussions herein, the invention is primarily described in the context of business rules and recommendations. However, the invention is not limited to these examples and can be widely used to make business recommendations regarding a broad range of activities, including but not limited to, commercial sales, retail sales, manufacturing, and event planning. The invention can be used to support recommendations for any type of activity, provided that a weather element relationship for the activity is known. Furthermore, the invention can be used to provide business recommendations for any future time period. A time period can be a day, week, weekend, month, season, or any other time period for which weather element measurements or business activity data are available.
 In the example of FIG. 1, weather-based decision system 100 includes data interface 105, confidence level filter 110, opportunity matrix filter 115, weather decision point generator 120, business rule recommendation engine 125, business rules knowledge database 130, and graphical user interface 135. Data interface 105 receives data regarding a known weather element relationship for a business activity. For example, the received data could include weather driven demand predictions for the sale of lawnmowers in Atlanta, Ga. for a future time period. Weather driven demand predictions can include a predicted expected sales increase or decrease in sales from last year for each day, or other time period, in the coming year.
 In addition the received data can include measures of the strength of the weather element relationship for lawnmower sales. The weather element relationship for a business activity, such as the sale of lawnmowers, can be quite complex. In particular, sales of lawnmowers can be a function of the temperature and level of precipitation. The sales of lawnmowers can also be a function of many other factors, such as state of the economy, housing market, sales promotions, etc. which must be filtered out of the model to specifically identify weather element impacts. The function will vary over time, such that, high temperatures and high levels of precipitation in the Spring may stimulate lawnmower sales. Whereas, high temperatures in mid-Summer may diminish lawnmower sales. Examples of the measures of the strength of the weather element relationship can include the model error (e.g., % standard deviation, R2, and Sig-F). Other received data can include the weather forecast for the weather elements used in the weather element relationship. Additional data needed by weather-based decision system 100 can be accessed through external database interface 140.
 In one embodiment of weather-based decision system 100, data interface 105 will also be used to receive measures of confidence in the weather forecast for the weather elements. One type of confidence measure that can be received is a confidence level that the weather element forecast is correct, based on a comparison between the weather element forecast and a weather element prediction using trends in weather factor measurements. A weather element forecast is based on a weather forecast, such as Farmer's Almanac, the National Weather Service forecast or