Evaluation method, evaluation device, evaluation program, and evaluation system

Abstract

To provide an evaluation method, an evaluation system and an evaluation device which calculate a satisfaction degree of a user who receives provision of various types of content.SOLUTION: In an evaluation system in which a provider side system and a viewer side system are connected through a network, a control unit 223 owned by an evaluation device 22 of the provider side system comprises an acquisition unit 2231, a score calculation unit 2232, a satisfaction degree calculation unit 2233, an update unit 2234, a decision unit 2235, a generation unit 2236 and a determination unit 2237. The acquisition unit 2231 acquires biometric information of a user who is receiving provision of content. The satisfaction degree calculation unit 2233 calculates the user's satisfaction degree for the content, by statistical processing for the biometric information.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an evaluation method, an evaluation Apparatus, evaluation program, and evaluation system Regarding. [Background technology]

[0002] 2. Description of the Related Art Conventionally, the degree of satisfaction of a user who has enjoyed entertainment such as watching sports or video content with the entertainment may be evaluated by a questionnaire or the like.

[0003] Furthermore, a technique for evaluating the degree of satisfaction of a user who uses an attraction at an amusement facility based on biometric information is known (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 11-39564 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the conventional techniques have a problem in that it is sometimes difficult to calculate the degree of satisfaction of a user who receives content such as an event or video content.

[0006] For example, the technology described in Patent Document 1 does not take into consideration the calculation of satisfaction levels in watching events such as sports games or video content.

[0007] The present invention has been made in consideration of the above, and aims to calculate the satisfaction level of users who receive various content (events (sports, games (direct viewing at venues, etc. and streamed video and audio), etc.), various video and audio content (movies, games, etc.)). [Means for solving the problem]

[0008] In order to solve the above-mentioned problems and achieve the objectives, the evaluation method of the present invention acquires biometric information of a user receiving content, and calculates the user's satisfaction with the content by statistically processing the biometric information, using a computer. [Effects of the Invention]

[0009] According to the present invention, it is possible to calculate the degree of satisfaction of a user who receives various types of content. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram illustrating an example of the configuration of an evaluation system according to an embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of the configuration of a viewer-side system according to the embodiment. [Figure 3] FIG. 3 is a diagram illustrating an example of the configuration of an evaluation device according to an embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of evaluation information. [Figure 5] FIG. 5 is a diagram illustrating an example of threshold information. [Figure 6] FIG. 6 is a diagram illustrating an example of user information. [Figure 7] FIG. 7 is a diagram showing an example of turning point information. [Figure 8] FIG. 8 is a diagram illustrating an example of scenario information. [Figure 9] FIG. 9 is a diagram illustrating the flow of the satisfaction degree calculation process. [Figure 10] FIG. 10 is a diagram illustrating the flow of the satisfaction rate calculation process. [Figure 11] FIG. 11 is a diagram illustrating the flow of the price correction process. [Figure 12] FIG. 12 is a diagram illustrating the flow of the satisfaction level improvement factor determination process. [Figure 13] FIG. 13 is a diagram showing an example of a score calculation method. [Figure 14] FIG. 14 is a diagram illustrating the flow of the match determination process. [Figure 15] FIG. 15 is a diagram showing an example of the management screen. [Figure 16] FIG. 16 is a flowchart showing the processing flow of the evaluation system. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments of the evaluation method, evaluation system, and evaluation device disclosed in the present application will be described in detail with reference to the accompanying drawings. Note that the present invention is not limited to the embodiments described below.

[0012] First, an evaluation system according to an embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing the configuration of the evaluation system according to an embodiment.

[0013] As shown in Fig. 1, the evaluation system 1 includes a provider system 2 and a viewer system 3. The provider system 2 and the viewer system 3 are connected via a network N. For example, the network N is the Internet or an intranet.

[0014] The provider system 2 provides content to users and evaluates the user's satisfaction with the content.

[0015] The viewer-side system 3 outputs content (images, sounds, etc.) to the user (viewer) and transmits to the provider-side system 2 information required for evaluating the degree of satisfaction.

[0016] 1, the provider-side system 2 includes a providing device 21 and an evaluation device 22. The viewer-side system 3 includes an output device 31 and a biosensor device 32.

[0017] The providing device 21 is a device for providing content, and is, for example, a server.

[0018] The providing device 21 transmits content to the output device 31 via the network N. For example, the content is a moving image, an audio, or a combination of a moving image and an audio.

[0019] The content may also be XR content that uses a virtual space, for example, VR content that outputs moving images and audio in a virtual space.

[0020] The output device 31 outputs the content provided by the providing device 21. The output device 31 is an image and audio output device configured with a display such as a liquid crystal display, a speaker, etc., and is capable of outputting images and audio.

[0021] In this embodiment, the output device 31 is a VR goggle (with integrated headphones). Note that the output device 31 may also be an information processing device such as a personal computer or a smartphone having image display and audio playback functions.

[0022] The biosensor device 32 measures biometric information of a user viewing content. For example, the biosensor device 32 is composed of a temperature sensor, a pulse sensor, an electroencephalogram sensor, etc., and measures biometric information such as the user's body temperature, heart rate, and electroencephalogram.

[0023] 2 is a diagram showing an example of how to use the viewer-side system according to the embodiment. As shown in FIG. 2, a user wears an output device 31 including VR goggles 311 and the like to view content.

[0024] The VR goggles 311 are connected to the network N via a communication interface 31a, and output video content received from the provider device 21 of the provider-side system 2 via the network N to the user.

[0025] The user also wears a headgear-type electroencephalogram (EEG) measuring device 321 and a wristband-type wearable device 322 having a temperature (body temperature) sensor and a pulse sensor. The EEG measuring device 321 and the wearable device 322 are examples of the biosensor device 32. The EEG measuring device 321 and the wearable device 322 are connected to a network N via a communication interface 32a.

[0026] The electroencephalogram measuring device 321 measures the user's electroencephalogram and transmits the measured electroencephalogram to the evaluation device 22 via the network N. In addition, the wearable device 322 measures the user's heart rate and body temperature and transmits the measured heart rate and body temperature from the communication interface 32a via the network N to the evaluation device 22 of the provider-side system 2.

[0027] The configuration of the evaluation device 22 will be described with reference to Fig. 3. Fig. 3 is a diagram showing an example of the configuration of the evaluation device according to the embodiment.

[0028] As shown in FIG. 3, the evaluation device 22 includes a communication unit 221, a storage unit 222, and a control unit 223.

[0029] The communication unit 221 is an interface for communicating data with other devices via the network N. The communication unit 221 is, for example, a network interface card (NIC).

[0030] The storage unit 222 and the control unit 223 of the evaluation device 22 are realized by a computer having, for example, a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), a flash memory, an input / output port, etc., or various circuits.

[0031] The computer's CPU functions as the acquisition unit 2231, score calculation unit 2232, satisfaction calculation unit 2233, update unit 2234, determination unit 2235, generation unit 2236 and judgment unit 2237 of the control unit 223, for example, by reading and executing a program stored in ROM.

[0032] The storage unit 222 is configured with a RAM or a flash memory. The storage unit 222 has an evaluation information table 2221, a threshold information table 2222, a user information table 2223, a turning point information table 2224, a scenario information table 2225, and the like, which store evaluation information, threshold information, user information, turning point information, and scenario information, respectively.

[0033] The evaluation device 22 may acquire the above-mentioned programs and various information via another computer or portable recording medium connected via a wired or wireless network.

[0034] The following describes each piece of information stored in the storage unit 222. How each piece of information is used will be described later along with the processing performed by the control unit 223.

[0035] Rating information J1 is information relating to ratings of satisfaction with content. As shown in rating information table 2221 in Fig. 4, the data items of rating information J1 include rating information ID, content ID, user ID, time, score, and element tag, and data for each data item is stored in a data record indicated by rating information ID data, which is a key code. In other words, rating information table 2221 stores content ID data, user ID data, time data, score data, and element tag data in association with rating information ID data.

[0036] The content ID is information for identifying the content. The user ID is information for identifying the user. The time is the time elapsed since the content started. The score is an evaluation value calculated from biometric information at each time. The element tag is information for identifying the elements related to the evaluation, that is, the objects that contributed to the evaluation.

[0037] For example, if the content is a video of a sports match, the element indicated by the element tag would be the players taking part in the match.

[0038] 4, for example, the rating information data for rating ID data "E001" includes content ID data "C01," user ID data "U01," time data "0:02:00," score data "0.1," and element tag data "E11," which are stored in the rating information table 2221. In other words, the rating information table 2221 stores data indicating that for the time "0:02:06" portion of the content with content ID "C01," the user with user ID "U01" has an rating score of "0.1" and its rating element is "E11."

[0039] While the user is viewing content, each piece of data based on the user's biosignal is stored at any time, for example, at 10-second intervals, in this evaluation information table 2221. In this case, for example, the time is the start time of the 10-second interval (or the middle, etc. is also acceptable), and the score is the average value over 5 seconds (a score calculated from the average value of the biosignal, or an average score calculated from the biosignal values ​​at each measurement timing, etc.).

[0040] The threshold information table 2222 is a data table that stores information about a threshold for determining whether a user is satisfied. The threshold information is information about a threshold for determining whether a user is in a satisfied state by comparing with score data calculated based on the user's biosignal, which will be described later.

[0041] 5, the data items of the threshold information table 2222 include a threshold information ID, a content type ID, a content type, a threshold, and a target content, and data for each data item is stored in a data record indicated by threshold information ID data, which is a key code. That is, the threshold information table 2222 stores content type data and threshold data in association with threshold information ID data.

[0042] 5, for example, the threshold information table 2222 stores the content type ID data "CK01," content type data "sports," threshold data "0.7," and target content data "C01, C02" as threshold information data for threshold information ID data "T001." In other words, the threshold information table 2222 stores data indicating that if score data calculated based on a user's biological signal for content (specified by type) whose type is "sports" (content type ID "CK01"), specifically for content "C01" and "C02" (specified individually), is equal to or greater than the threshold data "0.7," the user is presumed to be satisfied. This is based on the fact that the degree of stimulation, etc., that satisfies varies depending on the content type; for example, with video games, a user is not satisfied unless the user is very excited.

[0043] The data in the threshold information table 2222 is set appropriately prior to evaluation by a designer calculating and setting appropriate threshold data through experiments or the like when designing the device, or by setting according to user preferences or the like.

[0044] The user information table 2223 is a data table that stores information about users for analyzing factors that determine the user's satisfaction with content.

[0045] 6, the data items of the user information table 2223 include a user information ID, a user ID, a highly rated element (individual), and a highly rated element (attribute), and the data of each data item is stored in a data record indicated by user information ID data, which is a key code. That is, the user information table 2223 stores the user ID, the highly rated element (individual), and the highly rated element (attribute) in association with the user information ID data.

[0046] Elements are the components that make up content, and include not only people and other objects in a video, but also scenery, as well as sounds emitted from objects such as human speech, animal cries, and the sound of a car engine. Easy-to-understand examples include athletes competing in a sports game and characters appearing in a movie (including real actors and fictional characters).

[0047] The highly rated element (individual) is information for identifying an element that is highly rated by the user. The highly rated element (attribute) is information for identifying an attribute of an element that is highly rated by the user.

[0048] For example, if the content is footage of a sports game, the highly rated element (individual) would be a player that the user highly rates (favorite), and the highly rated element (attribute) would be a sports team that the user highly rates (favorite).

[0049] In the example of FIG. 6, the user information ID data "U01" in the user information table 2223 stores highly rated element (individual) data "E12", "E24", "E30", and highly rated element (attribute) data "T01".

[0050] These data can be obtained by conducting a questionnaire survey of users or by analyzing the past content usage history and evaluation history of the content used.

[0051] The turning point information table 2224 is a data table that stores information about important aspects of the content, so-called turning points.

[0052] 7, the data items of the turning point information table 2224 include a turning point ID, a content ID, a time, an element, and a contribution degree (the contribution degree is stored in association with each element), and the data of each data item is stored in a data record indicated by turning point ID data, which is a key code. In other words, the turning point information table 2224 stores content ID data, time data, element data, and contribution degree data (contribution degree data is further stored in association with each element data) in association with turning point ID data.

[0053] The content ID data is identification information of the content in which the turning point indicated by the turning point ID data exists. The time data is data on the position of the turning point in the content in which the turning point exists (playback time from the start point of content playback). The element data is identification data of the element that appears at the turning point, and the contribution data is the degree of contribution (influence) that the corresponding element at the turning point has on the satisfaction of the content user.

[0054] For example, in the example shown in Figure 7, the record for turning point data "TP01" in the turning point information table 2224 stores the time "0:02:06", element data "E11", "E24", and "E30", and contribution data corresponding to each element data "0.1", "0.2", and "0.4".

[0055] 8, the scenario information table 2225 contains data items such as a scenario ID, a content ID, a scenario name, a time period, an emotion type, and an intensity, and data for each data item is stored in a data record indicated by scenario ID data, which is a key code. In other words, the scenario information table 2225 stores content ID data, scenario name data, time period data, and emotion type and intensity data in association with scenario ID data.

[0056] The content ID is identification data for the content containing each scenario. The scenario name is the name of each scenario, and the time period data is the (playback) time period for each scenario's content. The emotion type and intensity are the type and intensity of the emotion set for each scenario.

[0057] For example, the recorder for scenario ID data "SC01" in scenario information table 2225 in Fig. 8 stores content ID data "C12," scenario name data "Prologue," playback time period "0:0:00-0:10:00," emotion data "Joy," and emotion intensity data "Medium." In other words, the scenario indicated by scenario ID data "SC01" has a scenario name of "Prologue," is a scene (to be played, etc.) in the time period "0:0:00-0:10:00" in the content with content ID "C12," and the emotion set as a target for that scenario is "Joy" and its intensity is "Medium."

[0058] The following describes the processing content of each unit of the control unit 223. In the following description, the subject of processing by the acquisition unit 2231, score calculation unit 2232, satisfaction level calculation unit 2233, update unit 2234, determination unit 2235, generation unit 2236, and judgment unit 2237 can be rephrased as the control unit 223.

[0059] The acquisition unit 2231 acquires biometric information of a user receiving an event and content. In this embodiment, the biometric information acquired includes heart rate, brain waves, voice, and facial expression. The heart rate and brain waves can be measured in the manner described in FIG. 2. Voice can be collected by a microphone installed in the space where the user watches the content. Furthermore, facial expression can be extracted by performing image recognition processing on an image captured by a camera installed in the space where the user watches the content.

[0060] The evaluation system 1 can evaluate the satisfaction of not only users viewing content but also users participating in an event. In this case, for example, if the event is a sports match, the user wears the biosensor device 32 and watches the match at the venue where the sports match is being held, and the acquisition unit 2231 acquires the bioinformation of the watching user from the sensor worn by the user. In this case, it is preferable to realize the biosensor device 32 by using a mobile terminal such as a smartphone owned by the user as a processing device, communication device, etc., and connecting a biosensor having an interface (e.g., Universal Serial Bus) for connecting to the mobile terminal.

[0061] The score calculation unit 2232 analyzes the biometric information acquired by the acquisition unit 2231 and calculates a score indicating the user's satisfaction level at any time, for example, at predetermined intervals (10-second intervals). In this embodiment, the score calculation unit 2232 calculates a score from biometric information such as heart rate, brain waves, voice, and facial expression. Here, an example of a specific method for calculating a score from biometric information will be described with reference to Fig. 13. Fig. 13 is a diagram showing an example of a score calculation method.

[0062] Indicators of comfort and arousal obtained from biological information are known (reference: Yosuke Sakairi et al., "Development of a two-dimensional mood scale to measure psychological arousal and comfort" (https: / / ci.nii.ac.jp / naid / 40005807982 / )).

[0063] For example, it is known that the larger the beta waves in the brain are relative to the alpha waves, the higher the level of arousal (the smaller the beta waves in the brain are relative to the alpha waves, the lower the level of arousal). Research has also shown that the larger the average heartbeat period, the greater the level of comfort. It can be inferred that satisfaction is achieved when comfort is achieved, and that the magnitude of satisfaction increases with increasing comfort and arousal. Therefore, the level of arousal can be estimated based on the output signal of the brainwave sensor, and the level of comfort can be estimated based on the output signal of the heartbeat sensor, and satisfaction can be estimated based on this arousal level data and comfort level data.

[0064] Specifically, the score calculation unit 2232 calculates the level of alertness using a predetermined arithmetic expression or a data table showing the relationship between the electroencephalogram data and the level of alertness, based on the electroencephalogram data acquired by the acquisition unit 2231. Furthermore, the score calculation unit 2232 calculates the level of comfort, based on the heartbeat data acquired by the acquisition unit 2231, using a predetermined arithmetic expression or a data table showing the relationship between the heartbeat data and the level of comfort.

[0065] The score calculation unit 2232 then plots the calculated comfort level and arousal level data in a two-dimensional space with the X axis representing comfort level and the Y axis representing arousal level. The score calculation unit 2232 calculates a satisfaction level score from the plot position. As described above, satisfaction level is obtained if the level is comfortable, and it can be estimated that the magnitude of satisfaction level increases as the level of comfort level and the level of arousal level increase. Therefore, for example, when a plotted point exists in the first quadrant, the distance d from the origin can be calculated as the score (other quadrants indicate no satisfaction). Furthermore, since the first and fourth quadrants have positive comfort levels, the comfort level score may be calculated according to the plot position of these quadrants, and since the second and third quadrants have negative comfort levels, the discomfort level (negative comfort level) score may be calculated according to the plot position of these quadrants.

[0066] Specifically, a data table in which each region obtained by dividing the above-mentioned two-dimensional space is treated as a data cell and a comfort level score is stored in each data cell is created in advance by a designer or the like and stored in the storage unit 222. Then, the score calculation unit 2232 performs processing to calculate the comfort level score from the data stored in the cell of the data table corresponding to the region where the awakening level and comfort level are plotted.

[0067] The score may be calculated using an arithmetic formula (prepared in advance by a designer or the like) that uses the awakening level data and the comfort level data as parameters.

[0068] In the example shown in Figure 13, comfort and arousal levels are expressed as positive and negative values ​​with the normal state as the origin (a form in which the first through fourth quadrants exist), but it is also possible to determine the position of the origin depending on how the analysis results are used. For example, if the minimum comfort and arousal levels (maximum discomfort / non-arousal state) are used as the origin and expressed only in the first quadrant, it would be possible to process and express only positive values. Furthermore, by limiting the range of comfort and arousal levels (for example, values ​​outside the upper and lower limits are set to the upper and lower limit values ​​or are invalidated), it is possible to extract and analyze only specific states, for example, only states with a relatively high evaluation.

[0069] Alternatively, a method of calculating the score based on voice or facial expression is also possible. Specifically, a model that has learned voice or facial expression (or both) and evaluation, for example, a model that has been generated in advance using training data that combines the score calculated based on the brain waves and heart rate described above with voice or facial expression, is used to calculate the score based on the detected voice or facial expression.

[0070] The voice can be acquired by a microphone installed in the space where the user is viewing the content, and the facial expression can be acquired by image recognition from an image taken by a camera installed in the space where the user is viewing the content.

[0071] It is also possible to calculate (estimate) a score based on the psychological and physical conditions suggested by various biological signals known from various medical papers, etc.

[0072] Next, a method for acquiring time-series data during content playback in this embodiment, that is, a procedure for storing evaluation data in the evaluation information table 2221 in FIG. 4, will be described.

[0073] First, at the start of content playback, the providing device 21 (the user ID is based on the connection information of the viewer-side system 3 in the providing device 21) notifies the evaluation device 22 (score calculation unit 2232) of the content ID and the user ID. Furthermore, during content playback, the providing device 21 sequentially notifies the evaluation device 22 of playback position time data of the content (expressed as the elapsed time from the start of content playback in normal playback mode). Furthermore, during content playback, the providing device 21 sequentially notifies the evaluation device 22 of sub-information included in the content (data on features and characteristics, etc., of each playback scene (playback time) of the content, such as characters, objects present, background features, various scene situations, etc.). The evaluation device 22 itself can also detect various features and characteristics, etc., of each playback scene of the content by analyzing the video, audio, etc. of the content.

[0074] During content playback, the acquisition unit 2231 acquires a biometric signal, and the score calculation unit 2232 calculates an evaluation score at predetermined time intervals (for example, every 10 seconds). The score calculation unit 2232 then generates evaluation ID data, and sequentially stores the evaluation score data, content ID data, user ID data, content playback position time data, and element tag data based on sub-information data included in the content, for the evaluation target, in a record of the evaluation information table 2221 having the evaluation ID data as primary key data (corresponding to step S11 in FIG. 9 described later).

[0075] By this process, time-series data of the evaluation scores for content playback is stored in the evaluation information table 2221.

[0076] Next, a method for calculating the satisfaction level based on the time-series data of the evaluation scores stored in the evaluation information table 2221 will be described.

[0077] In this embodiment, the satisfaction level calculation unit 2233 calculates the user's satisfaction level with content by statistical processing of biometric information, but more specifically, the satisfaction level calculation unit 2233 calculates the satisfaction level based on the score calculated from the biometric information, and also taking into account the influence of the type of content.

[0078] FIG. 9 is a diagram for explaining the flow of the satisfaction degree calculation process, and hereinafter, a method for calculating the satisfaction degree performed by the satisfaction degree calculation unit 2233 will be specifically explained using FIG.

[0079] The process begins when a rating information user, such as a user who wants to understand the satisfaction level of a content user (viewer), selects an evaluation target. The rating information user performs various operations and checks rating information, etc., using a terminal device that can be connected to the evaluation device 22. Although the configuration of this terminal device is not shown in the figure, it can be realized by a computer, smartphone, etc. that has a function for communicating with the evaluation device 22, a function for notifying various information (display and audio output), and an operation function such as a touch panel.

[0080] Specifically, the terminal device accesses the evaluation information table 2221 of the evaluation device 22 and displays information on target content for which satisfaction can be calculated, i.e., information on content for which evaluation information is stored (for selection), to the evaluation information user. The content information for selection includes content identification information such as content name, user identification data such as content user name, and data such as content usage time, which can be obtained based on the data in the evaluation information table 2221 and the content providing device 21 (the content information and content usage user information held by the providing device 21 are searched and acquired based on the content ID data and user ID in the evaluation information table 2221). Then, when the evaluation information user selects an evaluation target based on the various displayed information, the satisfaction level calculation unit 2233 calculates the satisfaction level for the selected evaluation target.

[0081] The satisfaction level calculation unit 2233 searches the threshold information table 2222 based on the type of content to be evaluated (obtained by searching for the content information evaluation target held by the providing device 21 based on the content ID data of the content to be evaluated), and selects and obtains a threshold value according to the type of content to be evaluated (step S12).

[0082] Then, the satisfaction level calculation unit 2233 calculates the satisfaction level of the evaluation target by performing statistical processing based on the time-series data accumulated in the evaluation information table 2221 and the acquired threshold value (step S13). Then, the calculated satisfaction level data is stored in a satisfaction level information table (not shown) in the storage unit.

[0083] For example, the satisfaction level calculation unit 2233 calculates the percentage of time during which the score exceeds the threshold value during the period during which the content is provided as the satisfaction level.

[0084] Specifically, when calculating the satisfaction level of the evaluation target 2221a as shown in Fig. 4, the satisfaction level calculation unit 2233 searches for the content information evaluation target held by the providing device 21 based on the content ID data, and acquires "sports (content type ID: CK01)" as the type of content of the evaluation target. Then, the satisfaction level calculation unit 2233 searches the threshold information table 2222 shown in Fig. 5 based on the acquired content type "sports (content type ID: CK01)", and acquires and determines "0.7" as the threshold.

[0085] Next, the satisfaction level calculation unit 2233 sequentially reads out the time-series data of the evaluation target from the evaluation information table 2221, compares the score data of each record with the determined threshold value "0.7," and determines whether each record (time period of the corresponding content) is satisfied (score of 0.7 or more) or dissatisfied (score of less than 0.7). Then, the satisfaction level calculation unit 2233 calculates the temporal proportion of the determined satisfaction or dissatisfaction (proportion of the satisfied time to the total content time) as the satisfaction level. Specifically, in the case of the evaluation target 2221a shown in FIG. 4, the number of records determined as satisfied is 3 (30 seconds in terms of time), and the number of records determined as dissatisfied is 4 (40 seconds in terms of time), and the satisfaction level is calculated as 3 / (3+4)=0.43 (30 seconds / (30 seconds+40 seconds)=0.43).

[0086] In this way, the evaluation device 22 calculates the degree of satisfaction by comparing a score based on multiple pieces of emotional data calculated from biometric information with a threshold value set for each type of content.

[0087] The update unit 2234 estimates whether the calculated satisfaction level is appropriate based on the user's behavior after receiving the content to be evaluated, and updates the threshold set for the content type (step S14). That is, the update unit 2234 performs a learning process on the threshold to set a more appropriate threshold.

[0088] In this way, the evaluation device 22 updates the threshold value in accordance with the behavior of the user who has received the content after the content has been provided.

[0089] The estimation of whether the satisfaction level is appropriate can be realized by performing statistical processing on the content satisfaction data of the evaluation target calculated by the update unit 2234, or the evaluation score data of each record of the evaluation target content (for more detailed learning: the method shown in Figure 9), according to the user's behavior after the content to be evaluated is provided.

[0090] This is a satisfaction threshold learning process based on the phenomenon that content satisfaction affects the subsequent behavior of content users. Possible content user behaviors that are affected by content satisfaction include, for example, whether or not a user re-uses similar (same type) content after using the content, or whether or not a user purchases related products after using the content. Furthermore, for example, the timing and frequency of such re-use, the amount of money spent on related products, etc., can also be used as information suggesting the level of satisfaction.

[0091] For example, if a satisfaction level is defined as "satisfaction level 0.8 (or higher)" as "the amount of purchases of products related to the provided content exceeds a certain value within one month after the content is provided" (or if it is defined in this way based on the past performance of a given user regarding their satisfaction and behavior after the content is provided), the threshold is increased or decreased based on the satisfaction with a new similar content provision and the subsequent behavior. Specifically, if the satisfaction with a new sports content provision is "satisfaction level 0.8 (or higher)," but the user does not purchase any products related to the sports content within the following month, it is presumed that the rating of "satisfaction level 0.8 (or higher)" is too high, and the corresponding threshold data is increased (making it difficult to increase satisfaction). Conversely, if the satisfaction with a new sports content provision is "satisfaction level 0.7 (less than 0.8)," but the user purchases more than a certain amount of products related to the sports content within the following month, it is presumed that the rating of "satisfaction level 0.7" is too low, and the corresponding threshold data is decreased (making it easier to increase satisfaction).

[0092] The evaluation device 22 can obtain necessary information by linking with other systems, etc. For example, the purchase price of a product related to the content can be obtained by linking with an EC (electronic commerce) site or a credit card system, etc.

[0093] This allows for flexible and accurate evaluation of satisfaction levels depending on the type of content (sports, movies, video games, etc.).

[0094] Furthermore, the satisfaction level calculation unit 2233 can calculate a satisfaction evaluation value for the content, for example, a satisfaction rate, from the satisfaction levels of a plurality of users for the same content. Fig. 10 is a diagram illustrating the flow of the satisfaction rate calculation process.

[0095] In the example shown in FIG. 10, the satisfaction level calculation unit 2233 calculates the satisfaction rate as the ratio of the number of users whose satisfaction level with the content exceeds a certain value (for example, 0.8) to the number of users who viewed the same content (step S21).

[0096] It is also possible to use a value obtained by various statistical processes as the satisfaction evaluation value for the content, such as using the average value of the satisfaction levels of each user for the content as the average satisfaction value.

[0097] In addition, in the above example, the satisfaction evaluation value for the same content was calculated, but it is also possible to statistically process the satisfaction data for related content such as content from a series or content of the same type, and calculate the satisfaction evaluation value for the related content.

[0098] Furthermore, the satisfaction level calculation unit 2233 may calculate the satisfaction level as a price of the content in another form. That is, based on the idea that something with high satisfaction level has a high market value (price), the satisfaction level is treated as a market value (price).

[0099] Specifically, as shown in FIG. 11, the basic price of the content is determined separately based on the supply and demand situation, market conditions, weather, etc., just like a general product, and is stored in the providing device 21 (step S31).

[0100] Then, the satisfaction degree calculation unit 2233 calculates a correction value for the price of the content in accordance with the content user's satisfaction with the content provision (step S32).The satisfaction degree calculation unit 2233 then corrects the basic price of the content acquired from the providing device 21 by the calculated correction value, and presents the corrected price to the content user.

[0101] For example, if the satisfaction level for a content exceeds a certain value (e.g., 0.8), the content offering price will be the basic content price increased by 10%. Also, if the satisfaction level is within a certain range (e.g., 0.4 or more and less than 0.8), the basic content price will be the content offering price.

[0102] If the satisfaction level with the content does not exceed a certain value (for example, 0.4), the content offering price is proposed to be 10% less than the basic price of the content.

[0103] The price corresponds to the download price of the video content, the participation fee for the event, etc.

[0104] It is also possible to calculate a correction price for correcting the basic price of each content based on a satisfaction evaluation value obtained by various statistical processes of the satisfaction level of multiple users with the use of each content, and reflect the calculated correction price in the price of each content. In this case, for example, the providing device 21 acquires correction price information from the evaluation device 22, corrects the basic price, and provides the corrected content price information to the user.

[0105] The determination unit 2235 extracts scenes that contribute greatly to satisfaction from among multiple scenes in the content to be evaluated based on scores calculated from biometric information (read from the evaluation information table 2221), and determines them as important scenes. The method of determining these important scenes may, for example, determine as important scenes scenes whose scores have a predetermined contribution level equal to or greater than a threshold, or scenes with scores in the top predetermined number (a predetermined number) or in the top predetermined percentage (a predetermined number) of scenes in the content to be evaluated. The determination unit 2235 then stores information about these determined important scenes in the storage unit 222.

[0106] In this way, the evaluation device 22 determines the degree of contribution to satisfaction of the elements associated with each scene of the content based on the score calculated from the biological information.

[0107] Then, the user of the evaluation information can obtain the important scene information stored in the memory unit 222, connect the important scenes to create a digest version of the content, or analyze the important scenes (scenes that give high user satisfaction) and use them as reference information for creating new content.

[0108] For example, the evaluation device 22 determines important scenes according to scores based on a plurality of pieces of emotion data calculated from biometric information, and generates digest content configured according to the determined important scenes.

[0109] The determining unit 2235 can also extract the scene (important scene) that contributes most to satisfaction for each user attribute. Specifically, satisfaction data related to the use of the same content (or related content) by users with the same attribute is selected and collected, and the scene (important scene) that contributes most to satisfaction for users with the same attribute is determined by the above-mentioned process.

[0110] In this way, the evaluation device 22 determines the degree of contribution to satisfaction in each scene of the content based on the score calculated from the biometric information and the attributes of the user whose biometric information was acquired.

[0111] This method makes it possible to analyze scenes that contribute significantly to satisfaction (important scenes) for each user with the same attributes. This means that it is possible to create digest versions of content targeted at a specific user group, or to use this information as reference for creating new content.

[0112] The evaluation device 22 can determine important scenes according to scores based on multiple pieces of emotion data calculated from biometric information, and provide important scene information indicating the determined important scenes to the provision device 21. In this case, the provision device 21 generates digest content configured according to the important scenes identified by the important scene information (for example, by connecting the important scenes).

[0113] Furthermore, the determination unit 2235 determines the element that contributes most to satisfaction among the elements that appear in each scene of the content based on the score calculated from the biometric information of the content user in each of multiple scenes in the content to be evaluated.

[0114] Elements are components that make up content, and include not only people and other objects in a video, but also scenery, etc., as well as sounds emitted from objects such as human speech, animal cries, and the sound of a car engine. Easy-to-understand examples include athletes competing in a sports game and characters appearing in a movie (including real actors and fictional characters), and in this embodiment, the contribution of these elements to satisfaction is analyzed individually.

[0115] Here, the processing of the determining unit 2235 will be described using a specific example in which the content is video content of a sports match. In this case, the elements are players participating in the match. The team to which the player belongs corresponds to the attribute of the player.

[0116] 12 is a diagram illustrating the flow of the satisfaction level improvement element determination process. First, the determination unit 2235 refers to the evaluation information table 2221 and identifies records (evaluation information IDs) whose scores are greater than a predetermined value among the analysis target contents (selected by the analysis implementer through a selection operation) (step S41).

[0117] Next, the determination unit 2235 reads the element tag data of the identified record and ascertains the elements that appeared or occurred in high-scoring scenes, i.e., important scenes, in this case, the status of the players who appeared in important scenes.The determination unit 2235 then calculates the appearance and occurrence status of the elements that appeared or occurred in important scenes through statistical processing, in this case, the number of times each player appeared in all important scenes.The determination unit 2235 then uses statistical processing on this calculation result to determine the contribution of each element to satisfaction.For example, the top three players in terms of the number of appearances in all important scenes are identified as players with high contributions, and information ranked in order of most frequently appearing is generated and provided as contribution information.

[0118] In this way, the evaluation device 22 determines the degree of contribution to satisfaction of the elements associated with each scene of the content based on the score calculated from the biological information.

[0119] It is also possible to generate various types of contribution information according to the usage pattern, such as generating and providing the element that appears or occurs in the scene with the highest score as the highest contribution element.

[0120] It is also effective to add an element of the magnitude of the contribution degree or data indicating the magnitude of the contribution degree to the element tag when generating the element tag in the evaluation information table 2221 shown in FIG.

[0121] For example, when the content is a video, the score calculation unit 2232 stores only the player who appears largest among the players appearing in the frame corresponding to each time as an element tag, or adds data indicating the size of the player in the frame to the player (element identification data) data of the element tag. Then, statistical processing is performed using this element tag data to generate various types of contribution information. This allows for detailed analysis, and it is expected that more appropriate contribution information will be obtained.

[0122] The determining unit 2235 can also generate information on the degree of contribution to satisfaction by performing the above-mentioned statistical processing on score data of content usage (viewing) by multiple users for the same content or related content (such as content of games by the same team in the sports example above). In this case, it is possible to expect effects such as being able to grasp the conditions that satisfy a demographic formed by multiple target users (for example, a demographic of fans of a specific sports team).

[0123] Furthermore, the determination unit 2235 can generate information on the degree of contribution to satisfaction based on the user's gaze in addition to the score. In this case, the score calculation unit 2232 acquires (via the acquisition unit 2231) gaze data of the content user when using (viewing) the content, estimates the element on the image that the content user is looking at, that is, paying attention to (the player in the sports example above), and adds it as element tag data in the evaluation information table 2221. Alternatively, the score calculation unit 2232 stores only the element on the image that the gaze is directed at as element tag data in the evaluation information table 2221. Note that the gaze data can be detected by, for example, an eye tracker.

[0124] Then, the determination unit 2235 uses the element tag data in the evaluation information table 2221, which includes elements of the gaze data of the content user, to perform the statistical processing described above, thereby generating information regarding the degree of contribution to satisfaction, and provides the information to the user.

[0125] In this case, the accuracy of estimating the element on the image that is of interest is improved, and it can be expected that the contribution information will be more appropriate.

[0126] On the other hand, the determining unit 2235 can also generate and provide negative contribution information, that is, information on elements in the content that led to the dissatisfaction level.

[0127] In this case, the score calculation unit 2232 must use a scoring system that includes dissatisfaction levels. Specifically, the upper and lower limits of the comfort axis and the arousal axis shown in FIG. 13 are set to values ​​that include both satisfaction and dissatisfaction levels (the input heart rate and electroencephalogram data are not subject to upper and lower limit processing or are set to a moderate value). Various conditions (such as settings in a two-dimensional spatial data table) must be set so that the higher the satisfaction level, the larger the score, and the lower the dissatisfaction level (or vice versa). For ease of understanding, a state of neither satisfaction nor dissatisfaction is set to 0 (the origin). Then, using the score data calculated and stored under such a score calculation environment, negative contribution information is generated using a method similar to the method for generating information on the contribution to satisfaction level described above. However, when generating negative contribution information, data with a lower score in the evaluation information table 2221 is selected and statistical processing, etc., is performed, in contrast to the case of generating (positive) contribution information.

[0128] The factor that contributes most to satisfaction can be, for example, the player who most excited (fascinated) the user, while the factor that contributes most to dissatisfaction can be, for example, the player who most disappointed (disheartened) the user.

[0129] The determination unit 2235 can also generate satisfaction contribution information by further considering the user's preferences, etc. In other words, it is based on the idea that the user's satisfaction level is affected by the user's preference state for elements in content, and it is estimated that elements that the user likes will have a large influence on the user, and correction is made to increase the contribution to satisfaction.

[0130] Specifically, the determination unit 2235 performs a process of increasing the score data of the record for the scene in which the element preferred by the user appears in the evaluation information table 2221, and then generates the satisfaction contribution information in the same manner as described above.

[0131] More specifically, the determination unit 2235 acquires the highly rated element (individual) data and the highly rated element (attribute) data of the user to be rated (user who uses (views) the content to be rated) from the record of the user in the user information table 2223 shown in Fig. 6. The user to be rated and the content to be rated are set by a person (user) who has acquired the evaluation information (information on the degree of contribution to satisfaction) through manual operation or the like.

[0132] Then, the determination unit 2235 performs a process to increase the score data, for example, a process to increase the score data by 10%, for records in the target records (records of the user to be rated and the content to be rated) of the evaluation information table 2221 in which the acquired highly rated element (individual) data or highly rated element (attribute) data is included in the element tag data, and then generates satisfaction contribution information in the same manner as described above.

[0133] For example, if the content is a video of a sports game, the highly rated element (individual) in the user information table 2223 is the user's favorite player, and the highly rated element (attribute) is the user's favorite team. In this case, the determination unit 2235 generates satisfaction contribution information in the same manner as above, by increasing the score data of the target record in the evaluation information table 2221 for a scene in which the user's favorite player or the user's favorite team appears by 10% (the score data may be increased by 10% independently for a scene in which the user's favorite player appears and a scene in which the user's favorite team appears).

[0134] By performing such processing, satisfaction contribution information is generated taking into consideration the preferences of the user to be evaluated, and therefore, more appropriate contribution information can be expected.

[0135] The determination unit 2235 can also generate satisfaction contribution information taking into account important situations in the content, for example, so-called turning points such as a reversal scene in sports. In other words, this is based on the idea that the user's satisfaction with an element in the content is affected by the importance of the scene in which the element appears, and it is estimated that the influence on the user is particularly large at turning points with high scene importance, and correction is made to increase the contribution to satisfaction.

[0136] Specifically, the determining unit 2235 performs a process of increasing the score data of the record for the scene corresponding to the turning point in the evaluation information table 2221, and then generates the satisfaction level contribution information in the same manner as described above.

[0137] More specifically, the determining unit 2235 extracts, as target turning point records, records whose content (content ID) is the same as the content to be evaluated (content ID of the content to be evaluated) from the turning point information table 2224 shown in Fig. 7. Next, the determining unit 2235 extracts records whose time data in the content to be evaluated (with the user to be evaluated also specified) in the evaluation information table 2221 corresponds to (matches) the time data of the extracted target turning point record. Then, the determining unit 2235 performs a process of increasing the score data in the extracted record in the evaluation information table 2221, for example, a process of increasing the score data by 10%, and then generates satisfaction contribution information in the same manner as described above.

[0138] By performing such processing, satisfaction contribution information is generated taking into consideration important aspects of the content (turning points, etc.), and therefore, more appropriate contribution information can be expected.

[0139] 7 (elements appearing at the turning point of the record) and contributions, score data can be assigned to each appearing element at the corresponding scene (time) in the content to be rated (the user to be rated can also be specified) (for example, the product of the score data and contributions is assigned to each element), and satisfaction contribution information can be generated in the same manner as described above. In this case, satisfaction contribution information can be generated based on elements such as the role and performance of each element at the turning point.

[0140] In addition, the judgment unit 2237 judges whether the emotions set by the author or the like for the scenario of each scene in the content (the emotions that the content author intended the content user to have in that scene) match the emotions that the content user actually had in that scene indicated by the biometric information.

[0141] In this way, the evaluation device 22 determines whether or not the set emotion (e.g., the emotion set by the author, etc.) that is the emotion set for each scenario of the content matches the estimated emotion (e.g., the emotion actually felt by the user) that is the emotion of the content user for the same scenario as the set emotion estimated based on biometric information.

[0142] In other words, the judgment unit 2237 provides comparative information between the output expected by the content provider (the psychological response of the content-using user targeted by the content provider to the content) and the actual user output (the actual psychological response of the content-using user to the content), allowing users of the comparative information to evaluate the content.

[0143] Specifically, as shown in FIG. 14, the judgment unit 2237 judges whether the emotion type and intensity (score) calculated from the biometric information (step S51) match the emotion type and intensity stored in the scenario information table 2225 (step S52).

[0144] More specifically, the determination unit 2237 extracts (searches for the content ID data of the content to be evaluated) the evaluation information record of the content to be evaluated (for example, set by the evaluation information user through a selection operation) from the evaluation information table 2221. The determination unit 2237 also extracts the scenario information record of the content to be evaluated from the scenario information table 2225 shown in Fig. 8. The determination unit 2237 then compares the score data stored in the records of the same scenes of the content (same time: "time" in the evaluation information table 2221 and "time period" in the scenario information table 2225) for the extracted evaluation information record and scenario information record, and provides the comparison results, as well as information obtained by performing statistical processing on the comparison results, to the user of the comparison information.

[0145] In addition, the scenario information table 2225 stores in advance the target scores and the like for each scene in each content, which are input by the user of the comparison information and the like.

[0146] Furthermore, here, a video game in which the objective is to defeat enemy characters will be taken as an example of content provided based on a scenario.

[0147] The creator of the game (content) creates the game by taking into consideration the emotions and intensity of the game player's feelings during the scenario in the game and by setting the enemy characters that will appear in the scenario and the difficulty level (difficult, normal, easy).

[0148] For example, in the scenario with scenario ID data "SC01" in Figure 8, the game creator has created a scene that combines enemy character "A" and difficulty level "easy" to target the emotion "joy" and its intensity "medium."

[0149] 13, the score calculation unit 2232 calculates the score using the method described in Fig. 13, but the axes of the arousal axis and the comfort level are set so that the comfort level and the arousal level take both positive and negative values. The first, second, third, and fourth quadrants represent the emotion types "joy," "anger," "sadness," and "pleasure," respectively, and the distance from the origin represents the intensity of the emotion.

[0150] That is, for example, when the plotted point is located in the first quadrant, the determination unit 2237 determines the emotion type to be "joy." When the plotted point is located in the second quadrant, the determination unit 2237 determines the emotion type to be "anger." When the plotted point is located in the third quadrant, the determination unit 2237 determines the emotion type to be "sad." When the plotted point is located in the fourth quadrant, the determination unit 2237 determines the emotion type to be "happy."

[0151] Furthermore, the determination unit 2237 determines the intensity according to the absolute value of the score (a value according to the distance from the origin, with the upper limit normalized to 1). For example, if the absolute value of the score is less than 0.5, the determination unit 2237 considers the intensity to be "weak." If the absolute value of the score is 0.8 or greater, the determination unit 2237 considers the intensity to be "strong." If the absolute value of the score is 0.5 or greater and less than 0.8, the determination unit 2237 considers the intensity to be "medium."

[0152] The score calculation unit 2232 calculates these values ​​(data) and stores them in the evaluation information table 2221, but in this example, an "emotion type" item is added.

[0153] The determination unit 2237 then compares the intensity data based on the emotion type data and score data in the evaluation information table 2221 for the same timing (content playback time period) with the emotion type data and intensity data in the scenario information table 2225, and displays (reports) the comparison information. For example, the determination unit 2237 may display the emotion type data and intensity data side by side, display the difference between the emotion type data and intensity data, or display the actually measured emotion type data and intensity data for the time period of the set scenario in a time-series graph (the emotion type data and intensity data in the scenario information table 2225 are represented on an axis using axis information display), etc.

[0154] Although the "time" in the evaluation information table 2221 and the "time period" in the scenario information table 2225 may not match, a method can be adopted in which the above processing is performed by performing statistical processing such as averaging the emotion type data and intensity data in the evaluation information table 2221 at the "time" included in the "time period" in the scenario information table 2225 to convert the timing to match.

[0155] Furthermore, the determination unit 2237 can feed back the determination result to the providing device 21 (step S53). For example, the providing device 21 changes the content in accordance with the feedback.

[0156] For example, if the emotion type in the scenario of the above video game is "joy," but the user's emotion type obtained by the judgment unit 2237 is "sad," the providing device 21 makes changes such as changing the characters in the target scenario of the game or increasing the difficulty level of the game so that the user can feel joy in defeating the enemy character.

[0157] Furthermore, by combining parameters obtained from biometric information, it is possible to calculate complex emotion types such as fear or carelessness, and it is also possible to change the content based on the results of the determination using these.

[0158] For example, if the content is a horror movie and the user's emotion type is not "fear," or the intensity of the emotion type "fear" is "weak," the providing device 21 makes changes to the content to make it seem more frightening, such as narrowing the viewing angle of the content or increasing the volume.

[0159] Furthermore, the determination unit 2237 can determine the difference between the measured emotion type and intensity and the set emotion type and intensity, as well as the duration of the difference, and the providing device 21 can change the content based on the results. For example, if the content is a video game and the length of the period during which the user's emotion type is "careless" exceeds a certain length, the providing device 21 changes the content so that an all-out attack of enemy characters occurs at that timing, or advances the timing of enemy characters appearing in other scenes or the timing of intensifying attacks, thereby changing the content to strengthen the stimulus to make the user careless.

[0160] The generation unit 2236 determines, from among a plurality of images included in the content, an image whose score calculated from the biometric information is equal to or greater than a threshold, and edits images including the determined image to generate new content, for example, digest version content. Note that, although moving images are preferable as the content in this case, it is also applicable to creating a digest version using still images selected from a plurality of still images, creating a digest version using audio, etc.

[0161] This method makes it easy to create a digest version.

[0162] Specifically, the generation unit 2236 refers to the evaluation information table 2221 and identifies evaluation information records in which the score of the content for which a digest version is to be created (selected by a digest version creator or the like through a selection operation) exceeds a threshold value (for example, 0.8).

[0163] The generation unit 2236 then acquires from the providing device 21 frames of the video of the content corresponding to the time data in the identified rating information record and splices them together to generate a digest version of the video. The generated video may be stored in the providing device 21 as a digest version of the content, and may be provided from the providing device 21 based on a digest version viewing operation by a user. This method makes it possible to easily create a digest version of the content and provide the digest version of the content to content users as appropriate.

[0164] Furthermore, by storing the determination result by the determination unit 2237 in the storage unit 222, the output device 31 provides the content user with information based on the result when outputting content. For example, the output device 31 notifies the content user of the timing when the image with the highest determined score in the content being used will be output, thereby preventing the user from missing a scene of interest.

[0165] In this case, the evaluation device 22 determines an important scene according to a score based on a plurality of pieces of emotion data calculated from the biometric information, and provides important scene information indicating the important scene to the output device 31. Then, the output device 31 notifies the output timing of the content details of the important scene in the content based on the important scene information provided by the evaluation device 22.

[0166] In this way, the evaluation results of the user's satisfaction can be utilized to improve the satisfaction of users who will receive the same content later.

[0167] Furthermore, the output device 31 can search for viewing scenes in the used content according to the score in response to a user's instruction, for example, search for and display the scene of the image with the highest score, by using the determination result by the determination unit 2237 stored in the storage unit 222. This makes it possible to provide a search function for interesting scenes in the used content.

[0168] In addition, for users receiving the content for the first time, it is also possible to provide useful functions such as notifying them of noteworthy scenes using the above-mentioned method by utilizing evaluation information from other users or multiple users who have used the content to be used.

[0169] For example, while outputting a moving image content, the output device 31 notifies by text or voice that a noteworthy scene will be output immediately before the scene with the highest score (for example, 3 seconds before: determined by the time data and score data of the same content as the content currently being used in the evaluation information table 2221 and the playback time data of the content currently being used).

[0170] The evaluation device 22 displays a management screen on the management terminal used by the administrator. Fig. 15 is a diagram showing an example of the management screen. Here, the content is assumed to be a moving image. The management screen is generated based on the various data described above.

[0171] 15, the management screen displays the type and length of the content. This data is provided by the providing device 21.

[0172] In the example of FIG. 15, it is displayed that the type of content is "sports" and the length is "60 minutes."

[0173] The management screen also displays the score (displayed in a graph format showing time transition) and satisfaction level for each user calculated by the evaluation device 22.

[0174] 15, it is displayed that the satisfaction level of user U01 is "0.8," that of user U02 is "0.6," and that of user U03 is "0.4." The progress of each user's score is displayed as a graph with the horizontal axis representing the elapsed playback time of the content.

[0175] The management screen also displays the time of the most highly rated scene (the scene that contributed most to the satisfaction level) determined by the evaluation device 22.

[0176] Furthermore, the management screen displays information specifying the highest-rated element (element that contributes most to satisfaction) determined by the evaluation device 22 and information related to that element.

[0177] In the example of FIG. 15, it is displayed that the highest rated scene is "36:05", the highest rated element is "E20", and the element "E20" is "XX player".

[0178] By displaying such a management screen, various data related to content evaluation, such as how each content user felt about the content and when, can be easily grasped, which can be used as a reference for content creation and provision methods, etc.

[0179] The processing flow of the evaluation system 1 will be described using Figure 16. Figure 16 is a flowchart showing the processing flow of the evaluation system. This processing is executed when a user of the content performs an operation to start the content (start playback, etc.). This processing is also executed by one of the control units in the evaluation system 1, and each associated device performs corresponding processing and operation based on instructions from the control unit. For example, in this example, this processing is executed by the control unit 223 of the evaluation device 22, and each associated device performs corresponding processing and operation based on instructions from the control unit 223.

[0180] 16, the output device 31 starts outputting the content to be started (playback) (step S101). The content is a moving image selected by a content user and provided to the output device 31 from the providing device 21.

[0181] Next, the evaluation device 22 acquires biometric information of the user who is viewing the content (step S102). The biometric information is measured by the biometric sensor device 32 worn by the content user.

[0182] Then, the evaluation device 22 calculates a score from the biological information (step S103). For example, the evaluation device 22 calculates a score based on the comfort level and the awakening level obtained from the biological information. The evaluation device 22 then stores the calculated score in the evaluation information table 2221 together with the content ID data, playback time data, etc. of the content being used.

[0183] Next, if the use (playback) of the content has not ended (No at step S104), the evaluation device 22 returns to step S102 and repeats the process.

[0184] On the other hand, when the use of the content has ended (step S104, Yes), the evaluation device 22 calculates the satisfaction level from the percentage of time the score exceeded the threshold using statistical methods, etc., and stores it in the memory unit 222 (step S105), and then ends the processing.

[0185] Then, content evaluation information is created using the evaluation information stored in the evaluation information table 2221 and the satisfaction level stored in the storage unit 222, and this content evaluation information is provided to content users and content creators. Furthermore, this content evaluation information is used to update the content offering price, as reference information for creating new content, etc.

[0186] As described above, the control unit 223 of the evaluation device 22 according to the embodiment has an acquisition unit 2231 and a satisfaction level calculation unit 2233. The acquisition unit 2231 acquires biometric information of a user who is provided with content such as an event and video content. The satisfaction level calculation unit 2233 calculates the user's satisfaction with the content by statistically processing the biometric information.

[0187] As a result, according to this embodiment, it is possible to calculate the degree of satisfaction of a user who receives an event or content.

[0188] Furthermore, the evaluation device 22 acquires biometric information from the user in real time while the content is being provided, and performs the evaluation, thereby eliminating any room for the user's will and enabling accurate evaluation.

[0189] Furthermore, in this embodiment, unlike a questionnaire, the user does not need to perform any additional work, and the entire process, including obtaining the original data (biometric information), can be carried out electronically, thereby reducing the amount of work required.

[0190] Further advantages and modifications will readily occur to those skilled in the art. Therefore, the invention in its broader aspects is not limited to the specific details and representative embodiments shown and described above. Accordingly, various modifications may be made without departing from the spirit or scope of the general inventive concept as defined by the appended claims and their equivalents. [Explanation of symbols]

[0191] N Network 1. Rating System 2. Provider System 3. Viewer system 21 Providing device 22 Evaluation equipment 31 Output Device 32 Biosensor Device 221 Communications Department 222 Storage section 223 Control Unit 321 Electroencephalogram (EEG) measuring device 322 Wearable Devices 2221 Evaluation Information Table 2222 Threshold Information Table 2223 User Information Table 2224 Turning Point Information Table 2225 Scenario Information Table 2231 Acquisition Department 2232 Score calculation unit 2234 Update Department 2235 Decision Section 2236 Generation part 2237 Judgment Department

Claims

1. A method for evaluating a scene in a content that reproduces video or audio, comprising: acquiring biometric information of a user receiving the content; Calculating a score based on a plurality of indices calculated from the biological information; correcting the score in accordance with the user's evaluation of the elements present in the scene to be evaluated; The score is compared with a threshold value to calculate the user's satisfaction with the scene to be evaluated in the content. An evaluation method in which processing is performed by a computer.

2. A data table in which the elements and the user ratings are stored in association with each other, extracting from the data table the elements that are highly rated by the users who have received the content; correcting the score based on the extracted elements; The evaluation method according to claim 1 .

3. A control unit that performs processing to evaluate content, The control unit acquiring biometric information of a user receiving the content in an evaluation target scene in the content in which video or audio is played back; Calculating a score based on a plurality of indices calculated from the biological information; correcting the score in accordance with the user's evaluation of the elements present in the scene to be evaluated; The score is compared with a threshold value to calculate the user's satisfaction with the scene to be evaluated in the content. Evaluation equipment.

4. The control unit extracting the elements that are highly rated by the user receiving the content from a data table in which the elements and the user's ratings are associated and stored; correcting the score based on the extracted elements; The evaluation device according to claim 3 .

5. A control unit that performs processing to evaluate content, acquiring biometric information of a user receiving the content in an evaluation target scene in the content in which video or audio is played back; Calculating a score based on a plurality of indices calculated from the biological information; correcting the score in accordance with the user's evaluation of the elements present in the scene to be evaluated; The score is compared with a threshold value to calculate the user's satisfaction with the scene to be evaluated in the content. The evaluation program that causes the processing to be performed.

6. The control unit: extracting the elements that are highly rated by the user receiving the content from a data table in which the elements and the user's ratings are associated and stored; correcting the score based on the extracted elements; The evaluation program according to claim 5 .

7. An evaluation system including an evaluation device, an output device, and a biosensor device, the output device outputs the content to a user; the biosensor device measures bioinformation of the user; The evaluation device acquiring biometric information of the user receiving the content in an evaluation target scene in the content in which video or audio is played back; Calculating a score based on a plurality of indices calculated from the biological information; correcting the score in accordance with the user's evaluation of the elements present in the scene to be evaluated; The score is compared with a threshold value to calculate the user's satisfaction with the scene to be evaluated in the content. Rating system.

8. A data table in which the elements and the user ratings are stored in association with each other, extracting from the data table the elements that are highly rated by the users who are receiving the content; correcting the score based on the extracted elements; The evaluation system according to claim 7 .

Images