Source: https://patents.google.com/patent/JP2014511620A/en
Timestamp: 2020-01-29 08:53:02
Document Index: 342370122

Matched Legal Cases: ['Application No. 61', 'Application No. 61', 'Application No. 61', 'Application No. 61', 'Application No. 61', 'Application No. 61', 'Application No. 61']

JP2014511620A - Emotion based video recommendation - Google Patents
Emotion based video recommendation Download PDF
JP2014511620A
JP2014511620A JP2013555636A JP2013555636A JP2014511620A JP 2014511620 A JP2014511620 A JP 2014511620A JP 2013555636 A JP2013555636 A JP 2013555636A JP 2013555636 A JP2013555636 A JP 2013555636A JP 2014511620 A JP2014511620 A JP 2014511620A
JP2013555636A
カリオビー，ラナ エル
スコット サドウスキー，リチャード
オリオン ワイルダー−スミス，オリヴァー
ライト ピカール，ロザリンド
バーガト，メイ
アフェクティヴァ，インコーポレイテッドＡｆｆｅｃｔｉｖａ，Ｉｎｃ．
2011-02-27 Priority to US201161447089P priority Critical
2011-02-27 Priority to US61/447,089 priority
2011-02-28 Priority to US61/447,464 priority
2011-03-24 Priority to US61/467,209 priority
2011-10-20 Priority to US61/549,560 priority
2011-12-07 Priority to US201161568130P priority
2011-12-07 Priority to US61/568,130 priority
2011-12-28 Priority to US61/580,880 priority
2011-12-28 Priority to US201161580880P priority
2011-12-30 Priority to US61/581,913 priority
2011-12-30 Priority to US201161581913P priority
2012-02-27 Application filed by アフェクティヴァ，インコーポレイテッドＡｆｆｅｃｔｉｖａ，Ｉｎｃ． filed Critical アフェクティヴァ，インコーポレイテッドＡｆｆｅｃｔｉｖａ，Ｉｎｃ．
2012-02-27 Priority to PCT/US2012/026805 priority patent/WO2012158234A2/en
2014-05-15 Publication of JP2014511620A publication Critical patent/JP2014511620A/en
Mental status analysis is provided that allows data analysis on emotion-based video recommendations. Video response can be evaluated based on viewing and sampling of various videos. Data regarding the viewer of the video is captured, and this data includes facial information and / or physiological data. Face and physiological information can be gathered for a group of viewers. In some embodiments, demographic information may be collected and used as a basis for visualizing emotional responses to the video. In some embodiments, the video is ranked using data captured from an individual viewer or a group of viewers.
CROSS REFERENCE TO RELATED APPLICATIONS This application is based on US Provisional Patent Application No. 61 / 447,089, “Recommendation and Visualization of Effect Responses to Videos”, filed February 27, 2011, on February 28, 2011. US Provisional Patent Application No. 61 / 447,464 “Video Ranking Based on Effect”, US Provisional Patent Application No. 61 / 467,209, filed March 24, 2011, “Baseline Face” "Analysis", US Provisional Patent Application No. 61 / 549,560, filed October 20, 2011, "Mental State Analysis of Voters", US Provisional Patent filed December 7, 2011 Application No. 61 / 568,130, “Mental State Evaluation Learning for Advertising”, US Provisional Patent Application No. 61 / 580,880, filed December 28, 2011, “Affect Based Concept Testing”, and This claim claims priority to US Provisional Patent Application No. 61 / 581,913 “Affected Based Evaluation of Advertisement Effects” filed on December 30, 2011. To the extent permitted, the entire contents of each of the above applications are incorporated herein by reference.
The present application relates generally to mental state analysis, and in particular to video recommendation based on emotion.
People spend a great deal of time watching and interacting with video. Video may be viewed in many situations, including education, entertainment, daily news acquisition, watching the latest movies, and many others. The video can be a movie, a television program, a web series, a webisode, a video, a music video, or a video clip. The video can be viewed as an independent element on the electronic display or can be part of a web page. Evaluation of these images and people's reaction to them is very important for measuring effectiveness of education, commerce, and entertainment. People can self-assess the video or answer a question list in a cumbersome way of entering the number of specific stars corresponding to the level of likes or dislikes. Assessing a portion of a video is even more cumbersome and difficult when a short time evaluation of the video can be useful. Such star-based recommendations are inaccurate, subjective, and often unreliable.
Playing the first media representation for the individual, capturing mental state data for the individual while the first media representation is being played, and the individual based on the mental state data for the captured individual A computer-implemented method for making emotion-based recommendations including the step of recommending a second media representation to the computer. The method may further include generating mental state information by analyzing the mental state data. The method may further include correlating mental state data captured for the individual with mental state data collected from other people who have experienced the first media representation. The step of recommending the second media representation to the individual may be further based on a correlation step between the individual and other people. The first media representation may include one of the group consisting of movies, TV shows, web series, webisodes, videos, video clips, computer games, electronic books, and electronic magazines. The second media representation may include one of the group consisting of movies, television programs, web series, webisodes, videos, video clips, computer games, electronic books, and electronic magazines. The first media representation can be played on a web-enabled interface. The first media representation may include one of YouTube (TM), Video (TM) video, and Netflix (TM) video. The second media representation may include one of YouTube (TM), Video (TM) video, and Netflix (TM) video. The method may further include ranking the first media representation relative to another media representation based on the captured mental state data. The ranking step may relate to individuals based on mental state data from individuals. The ranking step may be based on an expected preference for the individual.
The mental state data may further include capturing mental state data from multiple persons and aggregating mental state data from the multiple persons. The method may further include ranking the first media representation relative to another media representation based on mental state data aggregated from a plurality of persons. The mental state data may include one of the group consisting of physiological data, facial data, and actigraph data. The face data may include information about one or more of the group consisting of facial expressions, motion units, head gestures, smiles, wrinkles between eyebrows, glances, lowered eyebrows, raised eyebrows, grinning smiles, and attention. The physiological data may include one or more of skin potential, heart rate, heart rate variability, skin temperature, and respiration. The method may further include a mental state inference step based on the collected mental state data. Mental status is one of the group consisting of frustration, confusion, discouragement, jealousy, cognitive overload, concentration, immersion, interest, boredom, inquiry, confidence, trust, joy, incentive, doubt, and satisfaction. Can be included. The step of playing the first media representation is performed on the mobile device and may further include a step of recording a facial image using the mobile device as part of the mental state data capture step.
In embodiments, a computer program product embodied in a computer-readable medium relates to a code for playing a first media representation to the individual and the individual while the first media representation is being played. Code for capturing mental state data and code for recommending a second media representation to the individual based on the mental state data for the captured individual may be included. In some embodiments, a computer system for making emotion-based recommendations includes a memory for storing instructions and one or more processors attached to the memory, the one or more processors Based on the playing of the first media representation for the individual, capturing mental state data for the individual while the first media representation is being played, and the mental state data for the captured individual And recommending the second media representation to the individual.
In some embodiments, a computer-implemented method for ranking emotion-based includes displaying a plurality of media representations for a group of people and a group of media representations while the plurality of media representations are displayed. Capturing mental state data from people, correlating mental state data captured from a group of people who watched multiple media representations, and ranking media representations against each other based on mental state data Step. The method may further include tagging the plurality of media representations using mental state information based on the captured mental state data.
Various features, aspects and advantages of numerous embodiments will become more apparent from the following description.
The following detailed description of specific embodiments can be understood by referring to the following drawings.
FIG. 1 is a flowchart relating to video display. FIG. 2 is a system for capturing a facial response to an image. FIG. 3 is a diagram of a recommendation system. FIG. 4 is a flowchart relating to emotion display. FIG. 5 is a graph display for displaying emotions. FIG. 6 is a graph display that displays the aggregated emotions. FIG. 7 is a flowchart relating to recommendation based on emotion. FIG. 8 is a flowchart regarding video ranking based on emotion. FIG. 9 is a system diagram for analyzing mental state information.
This disclosure describes various methods and systems for analyzing people's mental state as they watch videos. The ability to properly assess people's reaction to a video allows for the accurate recommendation of other videos. These videos may be for any purpose including, but not limited to, entertainment, education, or general information. Evaluation of mental states in response to images provides unparalleled insight into people's true response to these images. A mental state can be an emotional state or a cognitive state. Examples of emotional states include happiness or sadness. Examples of recognition states include concentration or confusion. By observing, capturing and analyzing these mental states, it is possible to obtain important information about people's reactions to the images. Some of the terms commonly used in assessing mental status are arousal and / or incentive. Awakening is an indication of the amount of human activation or excitement. Incentive is an indication of whether a person has received favorably or negatively. Emotions can include arousal and incentive analysis. Emotions may also include facial analysis for facial expressions such as smiling or wrinkling between eyebrows. The analysis may be as simple as watching someone watch a video when someone smiles or someone frowns. In some embodiments, other video recommendations may be based on tracking when someone smiles while watching one or more videos, causing the individual to smile. Recommend videos that have similarities to the video.
FIG. 1 is a flowchart relating to video display. Flow 100 is for a computer-implemented method for rendering video. The flow 100 may begin with video selection (110). The video may be selected by a system that automates the collection of emotions related to multiple videos. In embodiments, the video may be selected by a person who wishes to collect emotions related to the video. The video may include one of YouTube (TM) and Video (TM) videos. In flow 100, video embedding (120) within the web-enabled interface may continue, and the web-enabled interface triggers the collection of mental state data. The web-enabled interface can include a web page, a web application, or the like. Embedding (120) may include inserting a video link to the URL of the web page that triggers emotion collection. Embedding (120) may include providing a link that allows the user to insert their video. Emotions can be collected by evaluating facial expressions. Evaluation of facial expressions can include evaluation of wrinkles between smiles or eyebrows. Emotions can include an evaluation of one of the group consisting of attention, immersion, interest, likes and dislikes. Emotions can be collected by assessing physiology.
In flow 100, web-enabled interface distribution (130) continues. Distribution of the web-enabled interface may include sending a URL. URL transmission is a group of emails, text messages, Facebook (trademark) posts, Twitter (trademark) messages, Google+ (trademark) posts, LinkedIn (trademark) posts, social network updates, and blog entries. Can be achieved using one of these. In some embodiments, this transmission may be accomplished by pressing or selecting a button on a web page associated with the video. By selecting a button, the video can be distributed. In some embodiments, mental state data or analysis of mental state data may also be distributed with the video by selecting a button. The flow 100 may further include playing the video (140), possibly in a web-enabled interface, and collecting mental state data (150) while the video is playing. Mental status data may be collected for a group of people viewing the video.
The mental state data collected may include one of the group consisting of physiological data, facial data, and actigraph data. Physiological data can include one or more of skin potential, heart rate, heart rate variability, skin temperature, and respiration. The face data may include information regarding one or more of the group consisting of facial expressions, motion units, head gestures, smiles, wrinkles between eyebrows, glances, lowered eyebrows, attention, and the like. The mental states that can be inferred are a group of frustration, confusion, discouragement, jealousy, cognitive overload, concentration, immersion, interest, boredom, inquiry, confidence, trust, joy, frustration, incentives, doubts, etc. One of these may be included. Mental status data may be collected for an individual. Similarly, mental state data may be captured from multiple people.
In flow 100, analysis of mental state data (160) may continue. By analyzing mental state data (160), mental state information may be generated. A mental state for one or more viewers can be inferred based on the collected mental state data.
In flow 100, the aggregation of mental state data (170) may continue. The mental state data may be collected from a plurality of people who view the video, or the mental state data from a plurality of people may be aggregated. Thus, mental state data is aggregated across a group of people. The result of aggregation (170) may be shown as part of a graphical display posting.
The flow 100 may further include a media representation recommendation (180). By using mental state data aggregation (170) as part of the input, media representations may be recommended to individuals based on the aggregated mental state data. The media representation may be one of the group consisting of movies, television programs, web series, webisodes, videos, video clips, computer games, advertisements, electronic books, and electronic magazines. The flow 100 may further include recommending a media representation to the second person based on the mental state data collected from the first person. The various steps in flow 100 can be reordered, repeated, deleted, etc. without departing from the disclosed inventive concepts. Various embodiments of the flow 100 may be included in a computer program product embodied in a computer-readable medium that includes code executable by one or more processors. Flow 100 may include tagging multiple media representations using mental state information based on captured mental state data.
FIG. 2 is a system for capturing a facial response to an image. System 200 includes an electronic display 220 and a webcam 230. System 200 captures a facial response to video 222 shown on electronic display 220. The face data can include a collection of information related to video and mental state. Face data includes information about one or more of the group consisting of facial expressions, motion units, head gestures, smiles, wrinkles between eyebrows, glances, lowered eyebrows, raised eyebrows, grinning smiles, and attention. obtain. In some embodiments, the webcam 230 can capture an image of the person 210. An image of person 210 can also be a computer (such as a laptop, netbook, or tablet) camera, video camera, still camera, mobile phone camera, mobile device camera (including but not limited to a front camera), Thermal imaging cameras, CCD devices, 3D cameras, depth cameras, and other types of webcams used to capture different views of viewers or other types that may allow the captured image data to be used by electronic systems It may be captured by an image capture device. Capturing the facial response of the person 210 to the video 222 displayed on the display 220 may include collecting mental state data. Capturing the facial response of the person 210 to the video 222 displayed on the display 220 may include capturing physiological data. Physiological data can include one or more of heart rate, heart rate variability, skin temperature, respiration, and the like.
The electronic display 220 can display an image. Video 222 may include, but is not limited to, a computer display, laptop screen, netbook screen, tablet computer screen, mobile phone display, mobile device display, remote computer with display, television or projector, etc. May be displayed on an electronic display. The electronic display 220 may include connections to keyboards, mice, joysticks, touch pads, pen-type scanners, motion sensors, and other input means. Video 222 may be displayed in a web page, website, web-enabled application, or the like. An image of person 210 may be captured 240 by a video capture device. In some embodiments, a video of person 210 is captured, while in other embodiments, a series of still images are captured.
Analysis of motion units, gestures, mental states, and physiological data can be accomplished using captured images of person 210. The motion unit can be used to identify smiles, astringent faces, and other facial indicators of mental state. Gestures, including head gestures, can indicate interest or curiosity. For example, a head gesture moving toward the video 222 may indicate a desire for increased interest or elucidation. Based on the captured image, the physiological function can be analyzed. An emotion analysis (250) can be performed based on the captured information and images. The analysis may include face analysis and head gesture analysis. The analysis may include an evaluation of physiology and may include an evaluation of one of the group consisting of heart rate, heart rate variability, breathing, sweating, body temperature, and other physical evaluations.
FIG. 3 is a diagram of a recommendation system. The person 310 can view the video. While the person 310 is watching the video, the emotion collector 320 can collect emotion data regarding the person 310. Emotion collector 320 may be a webcam or other camera device. Emotion collector 320 may be a biosensor attached to person 310 at one or more locations. Emotion data collected from the person 310 by the emotion collector 320 can be stored in the emotion repository 330. Emotion repository 330 may reside on a local computer or a remote server, or may be distributed, or may be part of a cloud computing system.
The emotion analyzer 350 can analyze emotion data collected from the person 310. The emotion analyzer 350 can recognize a mental state including information on concentration, likes, dislikes, and the like. The emotion analyzer 350 can recognize a smile or a face. Based on the analysis performed by emotion analyzer 350, recommendation engine 360 can recommend video or other media representation to person 310. Media representation recommendations for individuals may be based on aggregated mental state data. The aggregated data may relate to a plurality of videos by an individual, or may relate to a plurality of people. The recommendation may be based on elements common to one or more videos viewed by the person 310. For example, if a person 310 smiles for each of the videos of a particular actress that he or she has watched, the recommendation engine 360 may ask the person 310 for another Can recommend pictures. In another example, if a series of sports videos are preferred by person 310, another sports video may be recommended.
Other people 322 may watch the same video as the person 310. In some embodiments, multiple videos are viewed by people 310 and other people 322. In embodiments, different subsets of videos are viewed by each person. Emotion collector 320 may capture emotion data for each of people 322. Emotion collector 320 may be a single unit, such as a kiosk in a mall, or a device that collects the emotions of multiple people viewing video in a meeting room or movie theater location. Alternatively, emotion collector 320 may be a separate device, such as when each person owns his own computer, laptop, mobile phone, mobile device, or the like. The sentiment repository 330 can hold sentiment data from people from whom sentiment data is collected.
Emotion aggregator 340 can retrieve emotion data from the emotion repository and correlate emotion data for person 310 with other people 322. The emotion aggregator 340 can recognize a plurality of videos or trends of a person 310 who has watched a movie, for example. Emotion aggregator 340 can determine a correlation vector for person 310 and person 322, or a subset thereof. Correlation can be performed using a weighted Euclidean or Mahalanobis distance estimate between two vectors (one vector containing personal emotion data). There are many ways to calculate distance or similarity / dissimilarity. By using collaborative filtering or the like, matching of emotion data between two parties or between people can be supported. In some embodiments, the comparison is based on the same content viewed by person 310 and by each individual of other people 322. If one vector is sufficiently short distance from another person's vector, the emotion aggregator 340 looks for other content that is favored or smiled. This other content can be recommended to the person 310 by the recommendation engine 360 because there are hypothetical similarities based on the collected emotion data.
In some embodiments, the sentiment data collected for the new video is compared with the past database of sentiment data for the video by reviewing the sentiment data stored in the sentiment repository using the sentiment aggregator 340 and sentiment analyzer 350. can do. A new video can be evaluated to determine how this video is ranked relative to other videos. For example, by comparing this new video with the list of “Top 100” in the video, the relative number of smiles that this new video has compared to the list of “Top 100” in the video where people smiled. It is possible. In embodiments, a group of people can watch a new video and collect emotional data. It is possible to aggregate together emotion data collected about people. The emotion data aggregated for the new video may then be compared to the emotion data aggregated for the other video. This type of comparison may be used by video producers to rank and evaluate new videos produced. Similarly, for example, a purchaser of an advertising spot can evaluate a new video based on aggregate emotion data collected from a group of people. For specific purposes, emotional profiles can be generated and compared with a “best combination” video set by network studios, advertisers, or other people with similar commercial interests.
In some cases, there is a good correlation for one type of video, but not for another type of video. For example, a good correlation may be obtained for a drama video, but a correlation may be poor for a comedy video. Based on this information, another drama video can be recommended. Collaborative filtering can be done to identify the full potential of correlation and thus the areas where video can be recommended.
The recommendation engine 360 can make a recommendation to the person 310 whose emotions have been collected. The recommendation engine 360 may make these recommendations based on the correlation between the person 310 and other people 322. Similarly, the recommendation engine 360 may make a recommendation to one or more of the people 322 based on the video viewed by the person 310.
FIG. 4 is a flowchart relating to emotion display. Flow 400 describes a computer-implemented method for displaying emotions. Flow 400 may begin with displaying a first web-enabled interface (410). The first web-enabled interface may include a web page. In flow 400, playback (420) of the video on the first web-enabled interface may continue. The video may include a YouTube (trademark) or a Video (trademark) video. The video may be one of the group consisting of movies, TV shows, web series, webisodes, videos, video clips, or may be media such as computer games, advertisements, e-books, e-magazines, or movie trailers. . In flow 400, mental state data capture (430) may continue while the video is being played. The flow may further include mental state inference (432) based on the collected mental state data. Mental status is one of the group consisting of frustration, confusion, discouragement, cognitive overload, concentration, immersion, interest, boredom, inquiry, confidence, trust, joy, incentive, doubt, and satisfaction. May be included.
The acquisition of mental state data may further include a face image recording (434). The flow 400 may further include analyzing (436) a facial image for facial expressions. The face data includes information on one or more of the group consisting of facial expressions, motion units, head gestures, smiles, wrinkles between eyebrows, glances, lowered eyebrows, raised eyebrows, grinning, attention, etc. obtain. A face thumbnail can be generated (438) using the facial expression. In some embodiments, a representative low resolution image may be included in the thumbnail, rather than an image obtained directly from a webcam or other imaging device.
Flow 400 may be followed by generation (440) of a thumbnail set for the played video, where the thumbnail includes scenes from the video, and the thumbnail set may be generated automatically. The flow 400 may further include analyzing the thumbnail set and removing frames from the thumbnail set based on a dark color threshold. Instead of the removed frame, another frame can be used. In flow 400, the thumbnail set may be displayed 450 on a second web-enabled interface. The second web enabled interface may include a web page. In some embodiments, the thumbnail relates to the played video.
In embodiments, a personal thumbnail is a “scene” from a video, which is a still image of a specified size. Various items may be useful in generating thumbnails, and will be briefly described here. A thumbnail complex or zotrope is a horizontal array of images. By using the dark threshold, the average value of the color of the image is analyzed to determine if it is “dark”. The start offset is the number of seconds until the video starts the thumbnail generation process. The number of seconds between frames can be automatically generated or manually specified, and means the number of seconds between individual thumbnail images. The zoo rope width is the width of the final image and may be slightly different from the width of each thumbnail multiplied by the number of thumbnails. The size string may be of the form “width × height”, examples include 24 × 24, 32 × 32, 40 × 32, etc. The size string determines the dimensions of the individual thumbnails. By examining individual thumbnails, it may be determined whether the image is “too dark”. Some movie trailers often fade out to black. Black or very dark frames often produce poorly performed thumbnails. In order to find a better frame, it is possible to recursively search forward and backward. If a frame is too dark, the recursive algorithm searches a small amount before and after to see if it can find a better frame that can be found within a certain recursion limit. When a good image is found or when the recursion limit is reached, the video is advanced for the appropriate number of seconds between frames and the next thumbnail image is identified.
In some embodiments, the flow 400 may further include generating a thumbnail set for the recorded face image (438) and displaying the thumbnail set for the face image on the second web-enabled interface (450). . One thumbnail in the thumbnail set may be selected based on facial expressions. This one thumbnail may indicate the facial expression of the moving image. This one thumbnail may show a unique expression. This one thumbnail may show a typical expression.
In flow 400, the generation (460) of a graphical representation of captured mental state data may continue. The graph display may be a line graph indicating the amount of a specific mental state or the amount of a specific facial expression. Similarly, the graph display may be a more complex dashboard type display. Flow 400 may be followed by causing a graphical display to be displayed on the second web-enabled interface (470). The graphical display may include a score representing mental state data. The score may relate to a particular mental state, such as attention, frustration, discouragement, or other mental state. The score can provide a numerical representation of the mental state.
In some embodiments, video playback is performed on a mobile device and facial image recording is performed using the mobile device. In embodiments, mental state data is captured and aggregated from multiple people. The various steps in flow 400 can be reordered, repeated, deleted, etc. without departing from the disclosed inventive concepts. Various embodiments of flow 400 may be included in a computer program product embodied in a computer-readable medium that includes code executable by one or more processors.
FIG. 5 is a graph display for displaying emotions. The display or dashboard 500 is a graphical representation of a mental state analysis that is shown for video viewer analysis and may be presented on an electronic display. The display may be a television monitor, projector, computer monitor (including laptop screens, tablet screens, netbook screens, etc.), cell phone display, mobile device, or other electronic display. In embodiments, the display may be a web page. For example, an example of a window 500 that includes a rendering of a video 510 with associated mental state information is shown. Visualization may further include a rendering associated with video 510. The user may be able to select from multiple video renderings using various buttons and / or tabs. The user interface can display a plurality of parameters in synchronization with the video rendering 510 according to time. In various embodiments, any number of options available to the user may be provided, and some may include other types of rendering instead of video. The thumbnail image set for the selected rendering includes, in the illustrated example, thumbnail 1 (530), thumbnail 2 (532) through thumbnail N (536), which are displayed below the rendering along with the timeline 538. May be. The thumbnail may indicate a “storyboard” of the picture of the video rendering. This storyboard can assist the user in identifying a particular scene or location during video rendering. Some embodiments may not include thumbnails, or may have a single thumbnail related to rendering, and in various embodiments may have thumbnails of equal length, Other embodiments may have thumbnails of different lengths. In some embodiments, the start and / or end of the thumbnail may be determined based on a captured viewer's mental state change associated with the rendering, or based on a particular point of interest in the video rendering. May be. One or more viewer thumbnails may be shown along the timeline 538. Viewer thumbnails may include peak facial expressions, facial expressions at key points of video rendering 510, and the like.
Some embodiments may include the ability for the user to select a particular type of mental state information to display using various buttons or other selection methods. The mental state information may be based on one or more descriptors. The one or more descriptors may include, but are not limited to, one of action unit 4 (AU4), action unit 12 (AU12), and incentive. As an example, window 500 shows smile mental state information when the user has previously selected smile button 540. In various embodiments, other types of mental state information that may be selectable by the user include an eyebrow drop button 542, an eyebrow button 544, an attention button 546, and an incentive score button 548, depending on the embodiment. Or other types of mental state information may be included. The overview button 549 may be usable so that the user can simultaneously display graphs of a plurality of types of mental state information. The mental state information may include probability information for one or more descriptors, and the probability for one of the one or more descriptors may vary depending on the portion of the video rendering.
In the illustrated example, since the smile option 540 is selected, a smile graph 550 showing smile mental state information aggregated for a plurality of individuals whose mental state data regarding the video has been collected may be shown relative to the reference line 552. The male smile graph 554 and the female smile graph 556 may be shown so that the mental state information aggregated by the visual display can be displayed. These graphs are provided as examples only. The mental state information may be based on a demographic base such that a viewer who constitutes a certain demographic layer reacts to the video. Graphs based on various demographics may be shown using various types of lines as shown, or using color or other differentiation methods. Slider 558 may allow the user to select a particular time point on the timeline and indicate the value of the selected mental state at that particular time point. Video 510 may be linked to slider 558. In some embodiments, the slider 558 can be selected and moved using a mouse or other pointing device. Video 510 can jump to the point where slider 558 is moved. The mental state can be used to evaluate the value of the video.
In some embodiments, demographic button 560 may be used to select various types of demographic based mental state information. Such demographics include gender, age, race, income level, education, or other types of demographics, including classifying respondents as respondents who responded well and responders who responded poorly. Can be included. Display a graph legend 562 showing various demographic groups, the type or color of each group's line, the percentage of total respondents in each group and / or the absolute number of respondents, and / or other information about the demographic group May be. Depending on the type of demographic selected, mental state information may be aggregated. Thus, for some embodiments, the aggregation of mental state information is performed on a demographic basis such that the mental state information is grouped on the demographic basis. Thus, the video can be evaluated in response to various demographic groups.
FIG. 6 is a graph display for displaying emotions based on mental state analysis together with the aggregated results from a group of people. This rendering may be displayed on a web page, web-enabled application, dashboard, or other type of electronic display display. Graph 610 may be shown for an individual for whom emotion data has been collected. Another graph 612 may be shown for emotions collected for another individual or for emotions aggregated from multiple people. Mental state analysis may be based on collection of facial images or physiological data. In some embodiments, the graph 610 may indicate the amount or probability of a smile being observed for an individual. Higher values or points on the graph may indicate a stronger or larger smile. At some point, if the collection of images is lost, or if a person's face cannot be identified, the graph may disappear or decline. The probability or intensity of emotion may be given along the y-axis 616. A timeline may be provided along the x-axis 614. Aggregated information may be based on obtaining an average, median, or other statistical or calculated value based on information collected from a group of people. In some embodiments, a combination of aggregated mental state information is achieved using computational aggregation.
In some embodiments, pictorial smile mark icons 620, 622, and 624 may be shown that provide an indication of the amount of a smile or other facial expression. The first full smile mark icon 620 may indicate that a very large smile is being observed. A second ordinary smile mark icon 622 may indicate that a smile is being observed. A third face icon 624 may indicate that there is no smile. These icons may correspond to regions on the y-axis 616 that indicate the probability or intensity of smiles.
The face thumbnail image set associated with the selected graph or graphs includes face thumbnail 1 (650), face thumbnail 2 (652) through face thumbnail N (660) in the illustrated example, which are , May be shown above or below the graph, and may be displayed with a timeline or other parameters along the x-axis 614. The thumbnail may indicate a “storyboard” of the face rendering picture. This storyboard can assist the user in identifying a particular scene or location during face rendering. Some embodiments may not include thumbnails, or may have a single thumbnail associated with face rendering, and various embodiments may have thumbnails of equal length. In other embodiments, the thumbnails may have different lengths. In some embodiments, the start and / or end of the thumbnail may be determined based on a captured viewer's mental state change associated with the rendering, or based on a particular point of interest in the video rendering. May be. One or more viewer thumbnails may be shown along a timeline or other parameter 614. Viewer thumbnails may include peak facial expressions, facial expressions at video rendering key points, and the like.
In the illustrated example, the video thumbnail image set including scenes from the video relating to the selected graph or graphs includes video thumbnail 1 (630), video thumbnail 2 (632) to video thumbnail N (640). Including, these may be shown above or below the graph and may be displayed along with a timeline or other parameters along the x-axis 614. The thumbnail may indicate a “storyboard” of the picture of the video rendering. This storyboard can assist the user in identifying a particular scene or location during video rendering. Some embodiments may not include thumbnails, or may have a single thumbnail associated with rendering, and in various embodiments may have thumbnails of equal length, Other embodiments may have thumbnails of different lengths. In some embodiments, the start and / or end of the thumbnail may be determined based on a captured viewer's mental state change associated with the rendering, or based on a particular point of interest in the video rendering. May be.
FIG. 7 is a flowchart relating to recommendation based on emotion. Flow 700 describes a computer-implemented method of emotion-based recommendation. Flow 700 may begin by playing 710 a first media representation for the individual. The first media representation may be one of the group consisting of movies, television shows, web series, webisodes, videos, video clips, computer games, electronic books, and electronic magazines, or other media body. The first media representation may include a YouTube ™ video, a Video ™ video, or a Netflix ™ video. The first media representation may be played on a web-enabled interface or other electronic display interface. The web-enabled interface can include a web page. The playback of the first media representation can be performed on the mobile device. In flow 710, mental state data capture 720 for the individual while the first media representation is being played may continue. The collected mental state data may include physiological data, face data, actigraph data, and the like. The acquisition of mental state data may further include a face image recording (722). Facial image capture can be achieved with a webcam or other camera. The playback of the first media representation may be performed on the mobile device, and the recording of the facial image may also be performed using the mobile device. Recording a facial image using a mobile device (722) can be part of capturing mental state data. The flow 700 may further include analyzing (730) facial images for facial expressions. The facial expression can include a smile. The facial expression can include wrinkles between the eyebrows. The analysis of the face image may further include inferring a mental state using the face image (732). Mental states can include frustration, confusion, discouragement, jealousy, cognitive overload, concentration, immersion, interest, boredom, inquiry, confidence, trust, joy, satisfaction, incentives, suspicion, happiness, and so on.
In flow 700, the mental state data captured for the individual may be correlated 740 with mental state data collected from other people who have experienced the first media representation. This correlation may include identifying similar likes and dislikes and various other mental states that are similar. In some embodiments, the distribution of responses to various images can be correlated. In other embodiments, the differences can be correlated, for example, identifying the most different responses. In some embodiments, certain mental states may be identified as similar during some periods of the correlation while other mental states are identified as different. The flow 700 may include optimizing the media representation (742) based on the mental state data. Optimization (742) may include recommending content modifications or content changes, such as deleting scenes, reducing certain material, or highlighting certain actors. In embodiments, the media representation includes a mixture of advertisements and content. Optimization (742) may select one or more advertisements to be interspersed with the content. Optimization (742) may include the ordering of one or more advertisements that are interspersed with the content. Optimization (742) may include selecting the number of times to play one or more advertisements in the content. Optimization (742) may include identifying the portion of the advertisement that is removed to create a shortened advertisement.
The flow 700 may include recommending (750) a second media representation to the individual based on the mental state data captured for the individual. The recommendation of the second media representation to the individual may be based on the correlation between the individual and other people. The second media representation may be one of the group consisting of movies, television programs, web series, webisodes, videos, video clips, computer games, electronic books, electronic magazines, and the like. The second media representation may include YouTube (TM) video, Video (TM) video, or Netflix (TM) video.
The flow 700 displays a thumbnail set on a second web-enabled interface or digital display along with generating a thumbnail set for the played first media representation (760) and analyzing mental state data from the individual (764). (762) may further be included. The thumbnail set may include scenes from the first media representation. Selection of thumbnails from the thumbnail set can be based on facial expressions. The thumbnail set can be automatically generated and can include removing frames from the thumbnail set based on a dark color threshold. Instead of the removed frame, another frame can be used. The flow 700 may further include a display (766) of analysis of mental state data from other people. The various steps in flow 700 can be reordered, repeated, deleted, etc. without departing from the disclosed inventive concepts. Various embodiments of flow 700 may be included in a computer program product embodied in a computer-readable medium that includes code executable by one or more processors.
FIG. 8 is a flow diagram for emotion-based video ranking, including a flow 800 describing a computer-implemented method of emotion-based ranking. The flow 800 may begin with displaying 810 multiple media representations for a group of people. The plurality of media representations may include a video. The plurality of videos may include a YouTube ™ video, a Video ™ video, or a Netflix ™ video. Further, the plurality of media representations may include one of the group consisting of movies, movie trailers, television programs, web series, webisodes, videos, video clips, advertisements, music videos, computer games, electronic books, and electronic magazines. May be included. In flow 800, capture of mental state data from a group of people (820) may be followed while multiple media representations are being displayed. Thus, mental state data can be captured from multiple people. The emotion data may include a face image. In some embodiments, the media representation is played on the mobile device and the facial image is recorded using the mobile device. Flow 800 may include aggregating mental state data from multiple persons (822). The flow 800 may further include analyzing (830) a facial image for facial expressions. The facial expression can include a smile. The facial expression can include wrinkles between the eyebrows. The flow 800 may further include inferring a mental state using the facial image (832). Mental status is one of the group consisting of frustration, confusion, discouragement, jealousy, cognitive overload, concentration, immersion, interest, boredom, inquiry, self-confidence, trust, joy, incentiveness, doubt, and satisfaction. One can be included.
The flow 800 may include correlating 840 mental state data captured from a group of people who have viewed multiple media representations and whose mental state data has been captured. Multiple videos viewed by a group of people may include several common videos viewed by each person within the group. In some embodiments, multiple videos may not include the same set of videos. Flow 800 may continue to tag (842) multiple media representations using mental state information based on the captured mental state data. The emotion information may simply be emotion data. In other embodiments, the emotion information may be an inferred mental state. In still other embodiments, the emotion information may be a result of correlation. In flow 800, based on the collected mental state data, ranking the media representation relative to another media representation may be followed (844). The ranking may be based on mental state data captured from an individual for that individual. The ranking may be based on an expected preference for the individual. In some embodiments, the ranking of the first media representation compared to another media representation may be based on mental state data aggregated from multiple people. The ranking may be compared to previously stored media representations along with emotion information. Ranking can include ranking a video relative to another video based on captured mental state data. The flow 800 may further include a display (846) of a video that produces a particular emotion. This particular emotion may include one of the group consisting of smiles, immersion, attention, interest, sadness, likes, dislikes, and the like. The ranking may further include a display of the video that caused more smiles. As a result of the ranking, media representations can be saved based on which are the most interesting, sad, attract the most tears, or cause other reactions. The flow 800 may further include searching for the video based on certain specific emotion data (848). Search (848) can identify videos that are very attractive, funny, sad, or touching.
The flow 800 may include recommending (850) a second media representation to the individual based on the captured emotional data and based on the ranking. The second media representation may be one of the group consisting of movies, movie trailers, television programs, web series, webisodes, videos, video clips, advertisements, music videos, computer games, electronic books, and electronic magazines. .
Recommendations to or from the individual can be provided based on the mental state. One or more recommendations can be made to the individual based on the mental state, emotion, or facial expression. Correlation may be performed between one individual and other people who showed similar emotions between multiple videos. Correlations can include recordings of other videos, games, or other experiences along with their emotions. Similarly, recommendations for movies, videos, video clips, webisodes, or other activities may be made to individuals based on their emotions. The various steps in flow 800 can be reordered, repeated, deleted, etc. without departing from the disclosed inventive concepts. Various embodiments of the flow 800 may be included in a computer program product embodied in a computer-readable medium that includes code executable by one or more processors.
FIG. 9 is a system diagram for analyzing mental state information. System 900 may include the Internet 910, an intranet, or other computer network that may be used to communicate between two or more of the various computers of system 900. The video client machine or client computer 920 includes a memory 926 that stores instructions and one or more processors 924 attached to the memory 926, wherein the one or more processors 924 store instructions stored in the memory 926. Can be executed. The memory 926 may be used for instruction storage, mental state data storage, system support, and the like. The client computer 920 may also include an internet connection to convey viewer mental state information 930 and a display 922 that can present various videos to one or more viewers. Client computer 920 may allow mental state data to be collected from one or more viewers as they observe one or more videos. In some embodiments, there may be multiple client computers 920 that collect mental state data from viewers as they watch videos. The video client computer 920 may include a camera, such as a web camera 928, that captures viewer interaction with video that includes viewer video in some embodiments. The camera 928 includes a webcam, a computer (such as a laptop, netbook, or tablet) camera, a video camera, a still camera, a mobile phone camera, a mobile device camera (including but not limited to a front camera), Thermal imaging cameras, CCD devices, 3D cameras, depth cameras, and other types of webcams used to capture different views of viewers or other types that may allow the captured image data to be used by electronic systems It can mean an image capture device.
Once mental state data is collected, the client computer can upload information to a server or analysis computer 950 based on mental state data from multiple viewers viewing the video. Client computer 920 may communicate with server 950 over the Internet 910, an intranet, other computer networks, or using other methods suitable for communication between two computers. In some embodiments, the functionality of analysis computer 950 may be embodied in a client computer.
The analysis computer 950 may include a connection to the Internet 910 that allows the analysis computer 950 to receive mental state information 940. Furthermore, the analysis computer 950 may include a memory 956 that stores instructions, data, help information, and the like, and one or more processors 954 attached to the memory 956. Can execute instructions. Memory 956 can be used for instruction storage, mental state data storage, system support, and the like. Analysis computer 950 may use the Internet 910 or other computer communication method to obtain mental state information 940. The analysis computer 950 can receive mental state information collected from a plurality of viewers from one or more client computers 920, and can aggregate mental state information regarding the plurality of viewers observing the video. .
Analysis computer 950 can process mental state data collected from one or more viewers or aggregated mental state data to generate mental state information for the one or more viewers. In some embodiments, analysis server 950 can obtain mental state information 930 from video client 920. In this case, the mental state data captured by the video client 920 is analyzed by the video client 920, and mental state information for uploading is generated. Based on the generated mental state information, the analysis server 950 can present a value based on the mental state information regarding one or more videos. The analysis computer 950 can associate the aggregated mental state information with the rendering and can also associate with a collection of criteria for the context being measured.
In some embodiments, the analysis computer 950 can receive or provide aggregate mental state information based on mental state data from multiple viewers viewing the video and aggregate it into a rendering on the display 952. Mental state information can be shown. In some embodiments, the analysis computer may be set up to receive mental state data collected from multiple viewers when viewing the video in real-time or near real-time embodiments. In at least one embodiment, a single computer may incorporate client, server, and analysis functionality. Viewer mental state data may be collected from one or more client computers 920 to form mental state information for one or more viewers viewing the video. Using the mental state information obtained from analyzing the mental state date of one or more viewers, video values based on the mental state information can be presented. System 900 includes code for playing a first media representation to an individual, code for capturing mental state data about the individual while the first media representation is being played, and a captured individual And a computer program product embodied in a computer readable medium including code for recommending a second media representation to an individual based on mental state data for the computer. System 900 can include functionality for making emotion-based recommendations including a memory for storing instructions and one or more processors attached to the memory, wherein the one or more processors are Playing a first media representation for the individual, capturing mental state data for the individual while the first media representation is being played, and for the individual based on the mental state data for the captured individual It is configured to recommend the second media expression. The system 900 includes code for selecting video, code for embedding video in a web-enabled interface (the web-enabled interface activates the collection of mental state data), and code for distributing the web-enabled interface. A computer program product embodied in a computer readable medium. System 900 can include functionality for rendering a video that includes a memory for storing instructions and one or more processors attached to the memory, wherein the one or more processors are And the web-enabled interface (the web-enabled interface activates the collection of mental state data) is configured to embed video and distribute the web-enabled interface.
The method may be performed on one or more processors in one or more computer systems. Embodiments may include various forms of distributed computing, client / server computing, and cloud-based computing. Further, with respect to the flow diagrams of the present disclosure, it will be understood that the depicted steps or boxes are provided for purposes of illustration and explanation only. These steps may be changed, deleted, or rearranged, and other steps may be added without departing from the scope of the present disclosure. Further, each step may include one or more substeps. While the above drawings and description illustrate the functional aspects of the disclosed system, the specific configurations of software and / or hardware that embody these functional aspects are not explicitly stated or apparent from the context Therefore, it is not assumed from these descriptions. All such software and / or hardware configurations are intended to be within the scope of this disclosure.
The block and flow diagram illustrations illustrate methods, apparatus, systems, and computer program products. Each element of the block and flow diagram illustrations, and each combination of elements in the block and flow diagram illustrations, is a function, a step or group of methods, apparatus, system, computer program product and / or computer implementation. The method is shown. Any of these functions can be implemented by computer program instructions, special purpose hardware based computer systems, special purpose hardware and computer instruction combinations, general purpose hardware and computer instruction combinations, computer systems, and the like. Any of these may be generally referred to herein as a “circuit”, “module”, or “system”.
Programmable devices that implement any of the computer program products or computer-implemented methods described above include one or more processors, microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors, programmable devices, programmable gate arrays, programmable arrays. It may include logic, memory devices, or application specific integrated circuits. By appropriately using or configuring each of them, processing of computer program instructions, execution of computer logic, storage of computer data, and the like can be performed.
It will be appreciated that a computer may include a computer program product from a computer-readable storage medium, which may be internal or external, removable and replaceable, or fixed. In addition, the computer may include a basic input / output system (BIOS), firmware, operating system, database, or the like that may include, interface with, or support the software and hardware described herein. obtain.
Embodiments of the present invention are not limited to applications involving conventional computer programs or programmable devices that execute them. For example, it is conceivable that the embodiments of the present invention may include an optical computer, a quantum computer, an analog computer, or the like. A particular machine that can perform any of the described functions may be generated by loading a computer program into a computer. This particular machine provides a means to perform any of the described functions.
Any combination of one or more computer readable media may be utilized. The computer readable medium may be a computer readable medium for storage. The computer readable storage medium may be electronic, magnetic, optical, electromagnetic, infrared, semiconductor, or any suitable combination of the above. Further examples of computer readable storage media include electrical connections having one or more wires, portable computer floppy disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM). ), Flash, MRAM, FeRAM, phase change memory, optical fiber, portable compact disk read only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above. In the context of this document, a computer-readable storage medium may be any tangible medium that can contain or store a program used by or in connection with an instruction execution system, apparatus, or device.
It will be appreciated that the computer program instructions may include computer executable code. Various languages for representing computer program instructions include, but are not limited to, C, C ++, Java, JavaScript (trademark), ActionScript (trademark), assembly language, Lisp, Perl, Tel, Python, Ruby, hard Hardware description languages, database programming languages, functional programming languages, imperative programming languages, and the like. In embodiments, computer program instructions may be stored, compiled, or interpreted and executed on a computer, a programmable data processor, a processor, a heterogeneous combination of processor architectures, or the like. Without limitation, embodiments of the present invention may take the form of web-based computer software including client / server software, service-based software, peer-to-peer software, or the like.
In embodiments, a computer may allow execution of computer program instructions that include multiple programs or threads. Multiple programs or threads may be processed substantially simultaneously to improve processor utilization and facilitate substantially simultaneous functionality. For implementation purposes, any of the methods, program code, program instructions, etc. described herein may be implemented in one or more threads. Each thread may create other threads, and these other threads themselves may have priorities associated with them. In some embodiments, the computer may process these threads based on priority or other order.
Unless explicitly stated or apparent from the context, the verbs “execute” and “process” are used interchangeably to execute, process, interpret, compile, assemble, link , Load, or a combination of the above. Thus, embodiments for executing or processing computer program instructions, computer executable code, etc. may function in accordance with the instructions or code in any manner described. Furthermore, the described method steps include any suitable method that causes one or more entities or entities to perform these steps. An organization that performs a step or part of a step need not be located in a particular geographical location or border. For example, if an entity located in the United States causes a method step or part thereof to be performed outside the United States, the method is deemed to have been performed in the United States by the entity that performed the step.
While the invention has been disclosed in connection with preferred embodiments shown and described in detail, various changes and modifications thereto will become apparent to those skilled in the art. Accordingly, the spirit and scope of the present invention is not limited by the above examples, but is to be understood in the broadest sense permitted by law.
In a computer-implemented method for making emotion-based recommendations,
Playing the first media representation to the individual;
Capturing mental state data about the individual while the first media representation is being played;
Recommending a second media representation to the individual based on the mental state data for the captured individual;
The method of claim 1, further comprising generating mental state information by analyzing the mental state data.
2. The method of claim 1, further comprising correlating the mental state data captured for the individual with mental state data collected from other people who have experienced the first media representation. how to.
4. The method of claim 3, wherein the step of recommending the second media representation to the individual is further based on the correlation step between the individual and the other people.
The method of claim 1, wherein the first media representation is one of the group consisting of a movie, a television program, a web series, a webisode, a video, a video clip, a computer game, an electronic book, and an electronic magazine. A method characterized by comprising.
2. The method of claim 1, wherein the second media representation is one of the group consisting of a movie, a television program, a web series, a webisode, a video, a video clip, a computer game, an electronic book, and an electronic magazine. A method characterized by comprising.
The method of claim 1, wherein the first media representation is played on a web-enabled interface.
The method of claim 1, wherein the first media representation includes one of YouTube (TM), Video (TM) video, and Netflix (TM) video.
2. The method of claim 1, wherein the second media representation includes one of YouTube (TM), Video (TM) video, and Netflix (TM) video.
The method of claim 1, further comprising ranking the first media representation relative to another media representation based on the captured mental state data.
11. The method of claim 10, wherein the ranking step is for the individual based on the mental state data captured from the individual.
12. The method of claim 11, wherein the ranking step is based on an expected preference for the individual.
The method of claim 1, further comprising the step of collecting the mental state data from a plurality of persons and aggregating the mental state data from the plurality of persons.
14. The method of claim 13, further comprising ranking the first media representation relative to another media representation based on the mental state data aggregated from the plurality of persons. And how to.
2. The method of claim 1, wherein the mental state data includes one of the group consisting of physiological data, facial data, and actigraph data.
16. The method of claim 15, wherein the face data is selected from the group consisting of facial expressions, motion units, head gestures, smiles, wrinkles between eyebrows, glances, lowered eyebrows, raised eyebrows, grinning smiles, and attention. A method comprising information about one or more.
16. The method of claim 15, wherein the physiological data includes one or more of skin potential, heart rate, heart rate variability, skin temperature, and respiration.
The method of claim 1, further comprising a mental state inference step based on the collected mental state data.
19. The method of claim 18, wherein the mental state is frustration, confusion, discouragement, jealousy, cognitive overload, concentration, immersion, interest, boredom, inquiry, confidence, trust, joy, attraction, doubt, And one of the group consisting of satisfaction.
The method of claim 1, wherein the step of playing the first media representation is performed on a mobile device, and as part of the step of capturing the mental state data, The method further comprising the step of recording.
In a computer program product embodied in a computer-readable medium,
A code for playing the first media representation to the individual;
Code for capturing mental state data about the individual while the first media representation is being played;
A code for recommending a second media representation to the individual based on the mental state data for the captured individual;
23. The computer program product of claim 21, further comprising code for generating mental state information by analyzing the mental state data.
22. The computer program product of claim 21, further comprising code for correlating the mental state data captured for the individual with mental state data collected from other people who have experienced the first media representation. A computer program product characterized by that.
24. The computer program product of claim 23, wherein the recommendation of the second media representation to the individual is further based on the correlation between the individual and the other people.
23. The computer program product of claim 21, wherein the first media representation is one of the group consisting of a movie, a television program, a web series, a webisode, a video, a video clip, a computer game, an electronic book, and an electronic magazine. A computer program product characterized by including one.
23. The computer program product of claim 21, wherein the second media representation is one of the group consisting of a movie, a television program, a web series, a webisode, a video, a video clip, a computer game, an electronic book, and an electronic magazine. A computer program product characterized by including one.
The computer program product of claim 21, wherein the first media representation is played on a web-enabled interface.
23. The computer program product of claim 21, wherein the first media representation includes one of YouTube (TM), Video (TM) video, and Netflix (TM) video. Product.
24. The computer program product of claim 21, wherein the second media representation includes one of YouTube (TM), Video (TM) video, and Netflix (TM) video. Product.
23. The computer program product of claim 21, further comprising code for ranking the first media representation relative to another media representation based on the captured mental state data. Computer program product to do.
31. The computer program product of claim 30, wherein the ranking relates to the individual based on the mental state data captured from the individual.
32. The computer program product of claim 31, wherein the ranking is based on an expected preference for the individual.
24. The computer program product of claim 21, wherein the mental state data is captured from a plurality of people and further includes an aggregation of the mental state data from the plurality of people.
34. The computer program product of claim 33, further comprising code for ranking the first media representation relative to another media representation based on the mental state data aggregated from the plurality of persons. A computer program product characterized by comprising.
The computer program product of claim 21, wherein the mental state data includes one of the group consisting of physiological data, facial data, and actigraph data.
36. The computer program product according to claim 35, wherein the face data is a group consisting of facial expressions, motion units, head gestures, smiles, wrinkles between eyebrows, glances, lowered eyebrows, raised eyebrows, grinning smiles, and attention. A computer program product comprising information about one or more of the above.
36. The computer program product of claim 35, wherein the physiological data includes one or more of skin potential, heart rate, heart rate variability, skin temperature, and respiration.
The computer program product according to claim 21, further comprising code for inferring a mental state based on the collected mental state data.
39. The computer program product of claim 38, wherein the mental state is frustration, confusion, discouragement, jealousy, cognitive overload, concentration, immersion, interest, boredom, exploration, confidence, trust, joy, incentive, A computer program product comprising one of the group consisting of doubt and satisfaction.
24. The computer program product of claim 21, wherein the playback of the first media representation is performed on a mobile device, and as part of the capture of the mental state data, A computer program product further comprising a record.
One or more processors attached to the memory;
In the computer system for making an emotion-based recommendation including the one or more processors,
Capturing mental state data for the individual while the first media representation is being played;
A computer system configured to perform:
42. The computer system of claim 41, wherein the one or more processors are further configured to generate mental state information by analyzing the mental state data.
42. The computer system of claim 41, wherein the one or more processors include the mental state data captured for the individual and mental state data collected from other people who have experienced the first media representation. A computer system further configured to correlate.
44. The computer system of claim 43, wherein the recommendation of the second media representation to the individual is further based on the correlation between the individual and the other people.
42. The computer system of claim 41, wherein the first media representation is one of the group consisting of a movie, a television program, a web series, a webisode, a video, a video clip, a computer game, an electronic book, and an electronic magazine. A computer system comprising:
42. The computer system of claim 41, wherein the second media representation is one of the group consisting of a movie, a television program, a web series, a webisode, a video, a video clip, a computer game, an electronic book, and an electronic magazine. A computer system comprising:
42. The computer system of claim 41, wherein the first media representation is played on a web-enabled interface.
42. The computer system of claim 41, wherein the first media representation includes one of YouTube (TM), Vimeo (TM) video, and Netflix (TM) video.
42. The computer system of claim 41, wherein the second media representation includes one of Youtube (TM), Video (TM) video, and Netflix (TM) video.
42. The computer system of claim 41, wherein the one or more processors rank the first media representation relative to another media representation based on the captured mental state data. A computer system further configured.
51. The computer system of claim 50, wherein the ranking is for the individual based on the mental state data captured from the individual.
52. The computer system of claim 51, wherein the ranking is based on an expected preference for the individual.
42. The computer system of claim 41, wherein the mental state data is captured from a plurality of people and further includes an aggregation of the mental state data from the plurality of people.
54. The computer system of claim 53, wherein the one or more processors compare the first media representation to another media representation based on the mental state data aggregated from the plurality of persons. A computer system further configured to rank.
42. The computer system of claim 41, wherein the mental state data includes one of a group consisting of physiological data, facial data, and actigraph data.
56. The computer system according to claim 55, wherein the face data is selected from the group consisting of facial expressions, motion units, head gestures, smiles, wrinkles between eyebrows, glances, lowered eyebrows, raised eyebrows, grinning smiles, and attention. A computer system comprising information relating to one or more of:
56. The computer system of claim 55, wherein the physiological data includes one or more of skin potential, heart rate, heart rate variability, skin temperature, and respiration.
42. The computer system of claim 41, wherein the one or more processors are further configured to infer a mental state based on the collected mental state data.
59. The computer system of claim 58, wherein the mental state is frustration, confusion, discouragement, jealousy, cognitive overload, concentration, immersion, interest, boredom, inquiry, self-confidence, trust, joy, incentive, doubt. , And one of the group of satisfaction.
42. The computer system of claim 41, wherein the playback of the first media representation is performed on a mobile device, and recording a facial image using the mobile device as part of the capture of the mental state data. A computer system further comprising:
In a computer-implemented method for ranking based on emotion,
Displaying multiple media representations to a group of people;
Capturing mental state data from the group of people while the plurality of media representations are displayed;
Correlating the mental state data captured from the group of people who viewed the plurality of media representations;
Ranking the media representations relative to each other based on the mental state data;
62. The method of claim 61, further comprising tagging the plurality of media representations using mental state information based on the captured mental state data.
Code to display multiple media representations for a group of people;
Code for capturing mental state data from the group of people while the plurality of media representations are displayed;
Code for correlating the mental state data captured from the group of people who viewed the plurality of media representations;
A code for ranking the media representations against each other based on the mental state data;
64. The computer program product of claim 63, further comprising code for tagging the plurality of media representations using mental state information based on the captured mental state data. Product.
66. The computer system of claim 65, wherein the one or more processors are further configured to tag the plurality of media representations using mental state information based on the captured mental state data. A computer system characterized by that.
JP2013555636A 2011-02-27 2012-02-27 Emotion based video recommendation Pending JP2014511620A (en)
US61/447,089 2011-02-27
US61/447,464 2011-02-28
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US201161568130P true 2011-12-07 2011-12-07
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US201161581913P true 2011-12-30 2011-12-30
PCT/US2012/026805 WO2012158234A2 (en) 2011-02-27 2012-02-27 Video recommendation based on affect
JP2014511620A true JP2014511620A (en) 2014-05-15
JP2013555636A Pending JP2014511620A (en) 2011-02-27 2012-02-27 Emotion based video recommendation
US (2) US9106958B2 (en)
AU (1) AU2012256402A1 (en)
BR (1) BR112013021503A2 (en)
WO (1) WO2012158234A2 (en)
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2012-02-27 BR BR112013021503A patent/BR112013021503A2/en not_active IP Right Cessation
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WO2012158234A3 (en) 2013-03-21
EP2678820A4 (en) 2014-12-03
US9106958B2 (en) 2015-08-11
EP2678820A2 (en) 2014-01-01
KR20140004740A (en) 2014-01-13
US20120222057A1 (en) 2012-08-30
CN103339649A (en) 2013-10-02
AU2012256402A1 (en) 2013-07-11
BR112013021503A2 (en) 2018-06-12
WO2012158234A2 (en) 2012-11-22
WO2012158234A4 (en) 2013-05-10
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