WO2004003802A2 - Measurement of content ratings through vision and speech recognition - Google Patents
Measurement of content ratings through vision and speech recognition Download PDFInfo
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- WO2004003802A2 WO2004003802A2 PCT/IB2003/002951 IB0302951W WO2004003802A2 WO 2004003802 A2 WO2004003802 A2 WO 2004003802A2 IB 0302951 W IB0302951 W IB 0302951W WO 2004003802 A2 WO2004003802 A2 WO 2004003802A2
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- customer
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04H—BROADCAST COMMUNICATION
- H04H60/00—Arrangements for broadcast applications with a direct linking to broadcast information or broadcast space-time; Broadcast-related systems
- H04H60/29—Arrangements for monitoring broadcast services or broadcast-related services
- H04H60/33—Arrangements for monitoring the users' behaviour or opinions
Definitions
- the present invention relates generally to vision and speech recognition, and more particularly, to methods and devices for measuring customer satisfaction through vision and/or speech recognition.
- manufacturers and vendors of the displayed products often want information that they'd rather not reveal to the participants, such as characteristics like gender and ethnicity. This type of information can be very useful to manufacturers and vendors in marketing their products. However, because the manufacturers perceive the participants as not wanting to supply such information or be offended by such questioning, the manufacturers and vendors do not ask such questions on their product questionnaires. Therefore it is an object of the present invention to provide methods and apparatus for automatically measuring a customer's satisfaction of a product, service, or content.
- a method for measuring customer satisfaction with at least one of a service, product, and content comprising: acquiring at least one of image and speech data for the customer; analyzing the acquired at least one of image and speech data for at least one of the following: (a) detection of a gaze of the customer; (b) detection of a facial expression of the customer; (c) detection of an emotion of the customer; (d) detection of a speech of the customer; and (e) detection of an interaction of the customer with at least one of the service, product, and content; and determining customer satisfaction based on at least one of (a) - (e).
- the method further comprises determining at least one of a gender, ethnicity, and age of the customer from the at least one of image and speech data.
- the acquiring preferably comprises identifying the customer in the image data.
- the identifying preferably comprises detecting a face in the image data.
- the identifying comprises classifying objects in the image data as people and non-people.
- the detection of a gaze of the customer preferably comprises at least one of determining if a direction of the detected gaze is towards at least one of the service, product, and content and the duration of the gaze towards at least one of the service, product, and content.
- the detection of a facial expression of the customer comprises determining whether the detected facial expression is one of satisfaction or dissatisfaction.
- the method preferably further comprises detecting whether the gaze of the customer is towards at least one of the service, product, and content at a time when the facial expression is detected and wherein the determining of the customer satisfaction is at least partly based thereon.
- the detection of an emotion of the customer is at least partly based on the detection of at least one of the speech and facial expression of the customer.
- the detection of an emotion of the customer preferably comprises detecting an intensity of the emotion of the customer.
- the detecting of an intensity of emotion is at least partly based on the detection of at least one of the speech and facial expression of the customer.
- the detecting of a speech of the customer preferably comprises detecting specific phrases of the recognized speech.
- the detecting of a speech of the customer comprises detecting emotion in the recognized speech.
- the detection of an interaction of the customer with at least one of the service, product, and content preferably comprises detecting a physical interaction with at least one of the product, service, and content. Also provided is an apparatus for measuring customer satisfaction with at least one of a service, product, and content.
- the apparatus comprising: at least one of a camera and microphone for acquiring at least one of image and speech data for the customer; and a processor having means for analyzing the acquired at least one of image and speech data for at least one of the following: (a) detection of a gaze of the customer; (b) detection of a facial expression of the customer; (c) detection of an emotion of the customer; (d) detection of a speech of the customer; and (e) detection of an interaction of the customer with at least one of the service, product, and content; wherein the processor further has means for determining customer satisfaction based on at least one of (a) - (e).
- the processor further has means for determining at least one of a gender, ethnicity, and age of the customer from the at least one of image and speech data.
- a computer program product for carrying out the methods of the present invention and a program storage device for the storage of the computer program product therein.
- Figure 1 illustrates schematic of a preferred implementation of an apparatus for carrying out the methods of the present invention.
- FIGS. 2a and 2b illustrate a flowchart showing a preferred implementation of a method of the present invention.
- Apparatus 100 includes at least one, and preferably several cameras 102 having a field of view sufficient to capture image data within a predetermined area of a displayed product, service, or content 104.
- the term camera is used in its generic sense to mean all image capturing devices.
- the cameras 102 are preferably digital video cameras, however, they also may be analog video cameras, digital still image cameras and the like. If an analog camera is used, its output must be appropriately converted to a digital format.
- the cameras 102 can be fixed or have a pan, tilt, and zoom capability.
- the apparatus also includes at least one microphone 106 for capturing speech data from the predetermined area.
- the microphone 106 is preferably a digital microphone, however, other types of microphones can also be utilized if the output signal thereof is appropriately converted to a digital format.
- the term microphone is used in its generic sense to mean all sound capturing devices.
- the cameras 102 and microphone 106 are useful in acquiring image and speech data for a customer 108a, 108b or other objects 109 within the predetermined area. Although, either a microphone 106 or at least one camera 102 is necessary for practicing the methods of the present invention, it is preferred that both are utilized.
- the term "customer" refers to any person detected in the image and/or speech data within the field of view/sound of the cameras 102 and microphone 106.
- Apparatus 100 also includes a processor 114, such as a personal computer.
- the image and speech recognition means 110, 112, although shown in Figure 1 as separate modules, are preferably implemented in the processor 114 to carry out a set of instructions which analyze the input image and speech data from the cameras 102 and microphone 106.
- the processor 114 further has means for determining at least one of a gender, ethnicity, and age of the customer 108a, 108b from the captured image and/or speech data.
- the apparatus 100 also includes an output means 116 for outputting a result of the analysis by the processor 114.
- the output means 116 can be a printer, monitor, or an electronic signal for use in a further method or apparatus.
- Figures 2a and 2b illustrate a flowchart showing a preferred implementation of a method to be preferably carried out by apparatus 100, the method being generally referred to by reference numeral 200.
- the method 200 measures customer satisfaction with at least one of a service, product, and content (collectively referred to herein as a "product").
- the product can be displayed in a public area, such as a shopping area in which the product (e.g., a consumer product) is displayed within the predetermined area or in a private area in which the product (e.g., content such as a television program) is being viewed within the predetermined area.
- a public area such as a shopping area in which the product (e.g., a consumer product) is displayed within the predetermined area or in a private area in which the product (e.g., content such as a television program) is being viewed within the predetermined area.
- image and speech data are acquired for the predetermined area by the cameras 102 and/or microphone 106.
- the customer(s) 108a, 108b are identified in the image and/or speech data at step 204.
- the image data is so utilized using any method known in the art for recognizing humans in image data.
- One such method is where faces are detected in the image data and each face is associated with a person. Once a face is found then it can be safely assumed that a human being exists.
- An example of the recognition of people in image data by the detection of faces is disclosed in Gutta et al., Mixture of Experts for Classification of Gender, ethnic Origin, and Pose of Human Faces, IEEE Transactions on Neural Networks, Vol. 11, No. 4, July 200.
- Another method is to classify objects in the image data as people and non-people.
- Examples of some of the features that can be determined by an analysis of the image and/or speech data are: detection of a gaze of the customer 108a, 108b; detection of a facial expression of the customer 108a, 108b; detection of an emotion of the customer 108a, 108b; detection of a speech of the customer 108a, 108b; and detection of an interaction of the customer 108a, 108b with the product, one or more of which may be utilized to measure a customer's interest/satisfaction in a product.
- detection of a gaze of the customer(s) 108a, 108b such is preferably carried out at step 206.
- customer 108a in Figure 1 would be classified as having a gaze towards the product 104, while customer 108b would be classified as having a gaze away from the product 104.
- the method 200 proceeds along path 208-NO and the customer 208b is not used in the analysis except for his or her apparent non-interest in the product 104 and the method loops back to step 204 where customers continue to be identified in the image data. If a customer 108a is found to have a gaze towards the product 104, the method continues along path 208-YES where other features are detected for that customer 108a.
- the duration of the gaze can also be detected from the image data. It can be assumed that duration of gaze towards the product is indicative of interest in the product.
- Methods for detecting gaze in image data are well known in the art, such as that disclosed in Rickert et al., Gaze Estimation using Morphable Models, Proceedings of the Third International Conference on Automatic Face and Gesture Recognition, Nara, Japan, April 14-16, 1998.
- the detection of a facial expression of the customer is preferably carried out at step 210 only for those customers 108a that are found to be gazing towards the product 104.
- the detection of a facial expression of the customer 108a comprises determining whether the detected facial expression is one of satisfaction or dissatisfaction. For instance, the detection of a smile or excited look would indicate satisfaction, while the detection of a frown or perplexed look would indicate dissatisfaction.
- Methods for detecting facial expressions are well known in the art, such as that disclosed in Colmenarez et al., Modeling the Dynamics of Facial Expressions, CUES Workshop held in conjunction with the International Conference on Computer Vision and Pattern Recognition, Hawaii, USA, December 10 - 15, 2001.
- the detection of speech is preferably carried out at step 212 and can be useful for not only identifying the customers 108a, 108b in the predetermined area but also in determining a measure of their satisfaction with the product.
- the detecting of a speech of the customer 108a, 108b can detect specific phrases in the recognized speech. For instance, the recognition of terms “that's great” or “cool” would indicate a measure of satisfaction while the terms “stinks” or “terrible” would indicate a measure of dissatisfaction.
- the emotion of a detected customer 108a, 108b can be detected. Since customer 108a is gazing at the product, only his or her emotion would be detected.
- the detection of an emotion of the customer 108a is preferably based on (at least in part) the detection of the speech and/or facial expression of the customer 108a. Furthermore, an intensity of a detected emotion can also be detected. For instance, certain facial expressions, such as an excited look, have a greater emotional intensity than a smile. Similarly, an intensity of emotion can also be detected in the detected speech of the customer 108a, such as where the customer changes his speech pattern (e.g., speaks faster or louder) or uses expletives.
- a determination that the customer 108a touched the product and possibly played with certain switches or other portions of the product can indicate a measure of satisfaction with the product, particularly when coupled with the detection of a favorable emotion, speech, and/or facial expression.
- a determination of physical interaction can be made by analyzing the image data from the cameras 102 and/or from feedback from tactile sensors (not shown). Such methods for determining a physical interaction with products are well known in the art. As discussed above, the detection of other features such as gender, gender origin, and age of the customer 108a, 108b may also be made, preferably at step 218.
- the method 200 can determine that most women are satisfied with a particular product, while most men are either dissatisfied or not interested with the product. Similar marketing strategies may be learned from an analysis of satisfaction and ethnic origin and/or age.
- customer satisfaction is determined based on at least one of the above- discussed features, and preferably a combination of such features. A simple algorithm for such a determination would be to assign weights to each of the features and calculate a score therefrom which indicates a measure of satisfaction/dissatisfaction.
- a score that is less than a predetermined number would indicate a dissatisfaction while a score above the predetermined number would indicate a satisfaction with the product 104.
- Another example would be to assign a point for each feature where a possible satisfaction is indicated, where a cumulative score of the points for all of the features detected over a predetermined number would indicate a satisfaction while a cumulative score below the predetermined number would indicate a dissatisfaction with the product 104.
- the algorithm may also be complicated and provide for a great number of scenarios and combinations of the detected features.
- a customer 108a who is detected to be gazing at the product 104 for a long duration of time and whom there is detected a high intensity of emotion in his or her speech and facial expressions would indicate a great satisfaction with the product while a customer 108a who looks at a product with a dissatisfied facial expression and a dissatisfied emotion in his or her speech would indicate little or no interest in the product.
- a customer 108a who only glances at a product 104 for a short tome and has little or no emotion in his or her speech and facial expression may indicate little or no interest in the product 104.
- the results of the analysis are output for review, statistical analysis, or use in another method or apparatus.
- the methods of the present invention are particularly suited to be carried out by a computer software program, such computer software program preferably containing modules corresponding to the individual steps of the methods.
- a computer software program such computer software program preferably containing modules corresponding to the individual steps of the methods.
- Such software can of course be embodied in a computer-readable medium, such as an integrated chip or a peripheral device.
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Priority Applications (3)
Application Number | Priority Date | Filing Date | Title |
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AU2003247000A AU2003247000A1 (en) | 2002-06-27 | 2003-06-13 | Measurement of content ratings through vision and speech recognition |
EP03761741A EP1520242A1 (en) | 2002-06-27 | 2003-06-13 | Measurement of content ratings through vision and speech recognition |
JP2004517151A JP2005531080A (en) | 2002-06-27 | 2003-06-13 | Content rating measurement via visual and speech recognition |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
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US10/183,759 US20040001616A1 (en) | 2002-06-27 | 2002-06-27 | Measurement of content ratings through vision and speech recognition |
US10/183,759 | 2002-06-27 |
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WO2004003802A2 true WO2004003802A2 (en) | 2004-01-08 |
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PCT/IB2003/002951 WO2004003802A2 (en) | 2002-06-27 | 2003-06-13 | Measurement of content ratings through vision and speech recognition |
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US (1) | US20040001616A1 (en) |
EP (1) | EP1520242A1 (en) |
JP (1) | JP2005531080A (en) |
CN (1) | CN1662922A (en) |
AU (1) | AU2003247000A1 (en) |
WO (1) | WO2004003802A2 (en) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11488181B2 (en) | 2016-11-01 | 2022-11-01 | International Business Machines Corporation | User satisfaction in a service based industry using internet of things (IoT) devices in an IoT network |
Families Citing this family (120)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7616233B2 (en) * | 2003-06-26 | 2009-11-10 | Fotonation Vision Limited | Perfecting of digital image capture parameters within acquisition devices using face detection |
US7269292B2 (en) * | 2003-06-26 | 2007-09-11 | Fotonation Vision Limited | Digital image adjustable compression and resolution using face detection information |
US7792970B2 (en) * | 2005-06-17 | 2010-09-07 | Fotonation Vision Limited | Method for establishing a paired connection between media devices |
US7792335B2 (en) * | 2006-02-24 | 2010-09-07 | Fotonation Vision Limited | Method and apparatus for selective disqualification of digital images |
US7574016B2 (en) * | 2003-06-26 | 2009-08-11 | Fotonation Vision Limited | Digital image processing using face detection information |
KR20070029794A (en) * | 2004-07-08 | 2007-03-14 | 코닌클리케 필립스 일렉트로닉스 엔.브이. | A method and a system for communication between a user and a system |
US8488023B2 (en) * | 2009-05-20 | 2013-07-16 | DigitalOptics Corporation Europe Limited | Identifying facial expressions in acquired digital images |
US8235725B1 (en) | 2005-02-20 | 2012-08-07 | Sensory Logic, Inc. | Computerized method of assessing consumer reaction to a business stimulus employing facial coding |
JP5015926B2 (en) * | 2005-08-04 | 2012-09-05 | コーニンクレッカ フィリップス エレクトロニクス エヌ ヴィ | Apparatus and method for monitoring individuals interested in property |
JP2007041988A (en) * | 2005-08-05 | 2007-02-15 | Sony Corp | Information processing device, method and program |
US8542928B2 (en) * | 2005-09-26 | 2013-09-24 | Canon Kabushiki Kaisha | Information processing apparatus and control method therefor |
US8326775B2 (en) * | 2005-10-26 | 2012-12-04 | Cortica Ltd. | Signature generation for multimedia deep-content-classification by a large-scale matching system and method thereof |
US7804983B2 (en) | 2006-02-24 | 2010-09-28 | Fotonation Vision Limited | Digital image acquisition control and correction method and apparatus |
DE602007012246D1 (en) * | 2006-06-12 | 2011-03-10 | Tessera Tech Ireland Ltd | PROGRESS IN EXTENDING THE AAM TECHNIQUES FROM GRAY CALENDAR TO COLOR PICTURES |
US20080065468A1 (en) * | 2006-09-07 | 2008-03-13 | Charles John Berg | Methods for Measuring Emotive Response and Selection Preference |
US9167305B2 (en) | 2007-01-03 | 2015-10-20 | Tivo Inc. | Authorable content rating system |
US8295542B2 (en) * | 2007-01-12 | 2012-10-23 | International Business Machines Corporation | Adjusting a consumer experience based on a 3D captured image stream of a consumer response |
US8588464B2 (en) * | 2007-01-12 | 2013-11-19 | International Business Machines Corporation | Assisting a vision-impaired user with navigation based on a 3D captured image stream |
US8269834B2 (en) | 2007-01-12 | 2012-09-18 | International Business Machines Corporation | Warning a user about adverse behaviors of others within an environment based on a 3D captured image stream |
US8055067B2 (en) | 2007-01-18 | 2011-11-08 | DigitalOptics Corporation Europe Limited | Color segmentation |
CN101711388B (en) | 2007-03-29 | 2016-04-27 | 神经焦点公司 | The effect analysis of marketing and amusement |
JP4904188B2 (en) * | 2007-03-30 | 2012-03-28 | 三菱電機インフォメーションシステムズ株式会社 | Distribution device, distribution program and distribution system |
WO2008137581A1 (en) * | 2007-05-01 | 2008-11-13 | Neurofocus, Inc. | Neuro-feedback based stimulus compression device |
US20090328089A1 (en) * | 2007-05-16 | 2009-12-31 | Neurofocus Inc. | Audience response measurement and tracking system |
US8392253B2 (en) | 2007-05-16 | 2013-03-05 | The Nielsen Company (Us), Llc | Neuro-physiology and neuro-behavioral based stimulus targeting system |
US20090033622A1 (en) * | 2007-05-30 | 2009-02-05 | 24/8 Llc | Smartscope/smartshelf |
KR20080110489A (en) * | 2007-06-14 | 2008-12-18 | 소니 가부시끼 가이샤 | Information processing apparatus and method and program |
US8533042B2 (en) | 2007-07-30 | 2013-09-10 | The Nielsen Company (Us), Llc | Neuro-response stimulus and stimulus attribute resonance estimator |
US8386313B2 (en) | 2007-08-28 | 2013-02-26 | The Nielsen Company (Us), Llc | Stimulus placement system using subject neuro-response measurements |
US8392255B2 (en) | 2007-08-29 | 2013-03-05 | The Nielsen Company (Us), Llc | Content based selection and meta tagging of advertisement breaks |
US20090083129A1 (en) | 2007-09-20 | 2009-03-26 | Neurofocus, Inc. | Personalized content delivery using neuro-response priming data |
US8327395B2 (en) | 2007-10-02 | 2012-12-04 | The Nielsen Company (Us), Llc | System providing actionable insights based on physiological responses from viewers of media |
US9513699B2 (en) * | 2007-10-24 | 2016-12-06 | Invention Science Fund I, LL | Method of selecting a second content based on a user's reaction to a first content |
US20090112694A1 (en) * | 2007-10-24 | 2009-04-30 | Searete Llc, A Limited Liability Corporation Of The State Of Delaware | Targeted-advertising based on a sensed physiological response by a person to a general advertisement |
US20090112696A1 (en) * | 2007-10-24 | 2009-04-30 | Jung Edward K Y | Method of space-available advertising in a mobile device |
US20090112693A1 (en) * | 2007-10-24 | 2009-04-30 | Jung Edward K Y | Providing personalized advertising |
US20090113297A1 (en) * | 2007-10-24 | 2009-04-30 | Searete Llc, A Limited Liability Corporation Of The State Of Delaware | Requesting a second content based on a user's reaction to a first content |
US9582805B2 (en) * | 2007-10-24 | 2017-02-28 | Invention Science Fund I, Llc | Returning a personalized advertisement |
JP2011505175A (en) | 2007-10-31 | 2011-02-24 | エムセンス コーポレイション | System and method for providing distributed collection and centralized processing of physiological responses from viewers |
US8750578B2 (en) | 2008-01-29 | 2014-06-10 | DigitalOptics Corporation Europe Limited | Detecting facial expressions in digital images |
US8171407B2 (en) * | 2008-02-21 | 2012-05-01 | International Business Machines Corporation | Rating virtual world merchandise by avatar visits |
JP5159375B2 (en) | 2008-03-07 | 2013-03-06 | インターナショナル・ビジネス・マシーンズ・コーポレーション | Object authenticity determination system and method in metaverse, and computer program thereof |
US9710816B2 (en) * | 2008-08-05 | 2017-07-18 | Ford Motor Company | Method and system of measuring customer satisfaction with purchased vehicle |
US20100060713A1 (en) * | 2008-09-10 | 2010-03-11 | Eastman Kodak Company | System and Method for Enhancing Noverbal Aspects of Communication |
US20100185564A1 (en) * | 2009-01-21 | 2010-07-22 | Mccormick & Company, Inc. | Method and questionnaire for measuring consumer emotions associated with products |
IT1392812B1 (en) * | 2009-02-06 | 2012-03-23 | Gfk Eurisko S R L | DEVICE FOR THE CONDUCT OF MARKET INVESTIGATIONS. |
US20100250325A1 (en) | 2009-03-24 | 2010-09-30 | Neurofocus, Inc. | Neurological profiles for market matching and stimulus presentation |
US10987015B2 (en) | 2009-08-24 | 2021-04-27 | Nielsen Consumer Llc | Dry electrodes for electroencephalography |
US9560984B2 (en) * | 2009-10-29 | 2017-02-07 | The Nielsen Company (Us), Llc | Analysis of controlled and automatic attention for introduction of stimulus material |
US20110106750A1 (en) | 2009-10-29 | 2011-05-05 | Neurofocus, Inc. | Generating ratings predictions using neuro-response data |
KR101708682B1 (en) * | 2010-03-03 | 2017-02-21 | 엘지전자 주식회사 | Apparatus for displaying image and and method for operationg the same |
KR20110066631A (en) * | 2009-12-11 | 2011-06-17 | 한국전자통신연구원 | Apparatus and method for game design evaluation |
US8684742B2 (en) | 2010-04-19 | 2014-04-01 | Innerscope Research, Inc. | Short imagery task (SIT) research method |
US8655428B2 (en) | 2010-05-12 | 2014-02-18 | The Nielsen Company (Us), Llc | Neuro-response data synchronization |
CA2815273A1 (en) * | 2010-10-21 | 2012-04-26 | Holybrain Bvba | Method and apparatus for neuropsychological modeling of human experience and purchasing behavior |
US20120143693A1 (en) * | 2010-12-02 | 2012-06-07 | Microsoft Corporation | Targeting Advertisements Based on Emotion |
US8836777B2 (en) | 2011-02-25 | 2014-09-16 | DigitalOptics Corporation Europe Limited | Automatic detection of vertical gaze using an embedded imaging device |
US8620113B2 (en) | 2011-04-25 | 2013-12-31 | Microsoft Corporation | Laser diode modes |
US8760395B2 (en) | 2011-05-31 | 2014-06-24 | Microsoft Corporation | Gesture recognition techniques |
CN102298694A (en) * | 2011-06-21 | 2011-12-28 | 广东爱科数字科技有限公司 | Man-machine interaction identification system applied to remote information service |
US8564684B2 (en) * | 2011-08-17 | 2013-10-22 | Digimarc Corporation | Emotional illumination, and related arrangements |
US8635637B2 (en) | 2011-12-02 | 2014-01-21 | Microsoft Corporation | User interface presenting an animated avatar performing a media reaction |
US9100685B2 (en) | 2011-12-09 | 2015-08-04 | Microsoft Technology Licensing, Llc | Determining audience state or interest using passive sensor data |
CN102541259A (en) * | 2011-12-26 | 2012-07-04 | 鸿富锦精密工业(深圳)有限公司 | Electronic equipment and method for same to provide mood service according to facial expression |
US9569986B2 (en) | 2012-02-27 | 2017-02-14 | The Nielsen Company (Us), Llc | System and method for gathering and analyzing biometric user feedback for use in social media and advertising applications |
US8898687B2 (en) | 2012-04-04 | 2014-11-25 | Microsoft Corporation | Controlling a media program based on a media reaction |
US9451087B2 (en) * | 2012-04-16 | 2016-09-20 | Avaya Inc. | Agent matching based on video analysis of customer presentation |
CA2775700C (en) | 2012-05-04 | 2013-07-23 | Microsoft Corporation | Determining a future portion of a currently presented media program |
CN102930298B (en) * | 2012-09-02 | 2015-04-29 | 北京理工大学 | Audio visual emotion recognition method based on multi-layer boosted HMM |
WO2014061015A1 (en) * | 2012-10-16 | 2014-04-24 | Sobol Shikler Tal | Speech affect analyzing and training |
US9299084B2 (en) * | 2012-11-28 | 2016-03-29 | Wal-Mart Stores, Inc. | Detecting customer dissatisfaction using biometric data |
JP2015111358A (en) * | 2013-12-06 | 2015-06-18 | 株式会社ニコン | Electronic apparatus |
JP2015111357A (en) * | 2013-12-06 | 2015-06-18 | 株式会社ニコン | Electronic apparatus |
JP2015130045A (en) * | 2014-01-07 | 2015-07-16 | 日本放送協会 | Charge presentation device and charge presentation system |
JP6708122B2 (en) * | 2014-06-30 | 2020-06-10 | 日本電気株式会社 | Guidance processing device and guidance method |
US9922350B2 (en) | 2014-07-16 | 2018-03-20 | Software Ag | Dynamically adaptable real-time customer experience manager and/or associated method |
US10380687B2 (en) | 2014-08-12 | 2019-08-13 | Software Ag | Trade surveillance and monitoring systems and/or methods |
EP3009979A1 (en) * | 2014-10-15 | 2016-04-20 | Wipro Limited | System and method for recommending content to a user based on facial image analysis |
US9449218B2 (en) * | 2014-10-16 | 2016-09-20 | Software Ag Usa, Inc. | Large venue surveillance and reaction systems and methods using dynamically analyzed emotional input |
US9269374B1 (en) * | 2014-10-27 | 2016-02-23 | Mattersight Corporation | Predictive video analytics system and methods |
US9576190B2 (en) * | 2015-03-18 | 2017-02-21 | Snap Inc. | Emotion recognition in video conferencing |
US9467718B1 (en) | 2015-05-06 | 2016-10-11 | Echostar Broadcasting Corporation | Apparatus, systems and methods for a content commentary community |
US9936250B2 (en) | 2015-05-19 | 2018-04-03 | The Nielsen Company (Us), Llc | Methods and apparatus to adjust content presented to an individual |
JP6561639B2 (en) * | 2015-07-09 | 2019-08-21 | 富士通株式会社 | Interest level determination device, interest level determination method, and interest level determination program |
US10255487B2 (en) * | 2015-12-24 | 2019-04-09 | Casio Computer Co., Ltd. | Emotion estimation apparatus using facial images of target individual, emotion estimation method, and non-transitory computer readable medium |
US10268689B2 (en) | 2016-01-28 | 2019-04-23 | DISH Technologies L.L.C. | Providing media content based on user state detection |
US10984036B2 (en) | 2016-05-03 | 2021-04-20 | DISH Technologies L.L.C. | Providing media content based on media element preferences |
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US11037550B2 (en) | 2018-11-30 | 2021-06-15 | Dish Network L.L.C. | Audio-based link generation |
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CN109784678A (en) * | 2018-12-26 | 2019-05-21 | 秒针信息技术有限公司 | A kind of customer satisfaction appraisal procedure and assessment system based on audio |
JP2019114293A (en) * | 2019-03-26 | 2019-07-11 | 株式会社ニコン | Electronic apparatus |
CN110569714A (en) * | 2019-07-23 | 2019-12-13 | 咪咕文化科技有限公司 | Method for obtaining user satisfaction, server and computer readable storage medium |
US11712627B2 (en) | 2019-11-08 | 2023-08-01 | Activision Publishing, Inc. | System and method for providing conditional access to virtual gaming items |
JP7354813B2 (en) * | 2019-12-05 | 2023-10-03 | 富士通株式会社 | Detection method, notification method, detection program and notification program |
CN111507774A (en) * | 2020-04-28 | 2020-08-07 | 上海依图网络科技有限公司 | Data processing method and device |
JP7063360B2 (en) * | 2020-09-11 | 2022-05-09 | 株式会社ニコン | Electronic device system and transmission method |
Family Cites Families (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH0546743A (en) * | 1991-08-09 | 1993-02-26 | Matsushita Electric Ind Co Ltd | Personal identification device |
IT1257073B (en) * | 1992-08-11 | 1996-01-05 | Ist Trentino Di Cultura | RECOGNITION SYSTEM, ESPECIALLY FOR THE RECOGNITION OF PEOPLE. |
US5619619A (en) * | 1993-03-11 | 1997-04-08 | Kabushiki Kaisha Toshiba | Information recognition system and control system using same |
US5774591A (en) * | 1995-12-15 | 1998-06-30 | Xerox Corporation | Apparatus and method for recognizing facial expressions and facial gestures in a sequence of images |
-
2002
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- 2003-06-13 EP EP03761741A patent/EP1520242A1/en not_active Withdrawn
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Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11488181B2 (en) | 2016-11-01 | 2022-11-01 | International Business Machines Corporation | User satisfaction in a service based industry using internet of things (IoT) devices in an IoT network |
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EP1520242A1 (en) | 2005-04-06 |
JP2005531080A (en) | 2005-10-13 |
US20040001616A1 (en) | 2004-01-01 |
AU2003247000A1 (en) | 2004-01-19 |
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