System

Abstract

An object of a system according to an embodiment is to analyze a viewing history and evaluation data of a user and recommend optimal content.SOLUTION: A system includes a viewing history collection unit, a viewing pattern analysis unit, and a content recommendation unit. The viewing history collection unit collects a viewing history and evaluation data of a user. The viewing pattern analysis unit analyzes the data collected by the viewing history collection unit, and specifies the user's preference. The content recommendation unit recommends an optimum content based on the preference specified by the viewing pattern analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not fully optimize content recommendations based on users' viewing history and rating data, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the viewing history and evaluation data of users and recommend optimal content. [Means for solving the problem]

[0006] The system according to the embodiment includes a viewing history collection unit, a viewing pattern analysis unit, and a content recommendation unit. The viewing history collection unit collects user viewing histories and rating data. The viewing pattern analysis unit analyzes the data collected by the viewing history collection unit and identifies user preferences. The content recommendation unit recommends optimal content based on the preferences identified by the viewing pattern analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the viewing history and evaluation data of the user and recommend the most suitable content. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A recommendation system according to an embodiment of the present invention is a system that learns an individual's viewing patterns and makes cross-sectional recommendations for the individual, such as manga that the individual should read or movies that the individual should watch, etc. This allows the recommendation system to efficiently suggest content that matches the user's preferences.

[0029] A recommendation system according to an embodiment includes a viewing history collection unit, a viewing pattern analysis unit, and a content recommendation unit. The viewing history collection unit collects a user's viewing history and rating data. For example, the viewing history collection unit collects a list of movies the user has watched or manga the user has read in the past. The viewing history collection unit can also collect ratings (star ratings and comments) for each piece of content. The viewing pattern analysis unit analyzes the data collected by the viewing history collection unit to identify the user's preferences. For example, the viewing pattern analysis unit analyzes whether the user prefers action movies or romance movies. The viewing pattern analysis unit can also analyze whether the user prefers works by a particular manga artist. The content recommendation unit recommends optimal content based on the preferences identified by the viewing pattern analysis unit. For example, if the user prefers action movies, the content recommendation unit recommends the latest action movies or highly rated action movies. If the user prefers the works of a particular manga artist, the content recommendation unit recommends new works by that manga artist or similar works. As a result, the recommendation system according to the embodiment can recommend optimal content based on the user's viewing history and evaluation data.

[0030] The viewing history collection unit can collect real-time viewing behavior in addition to the user's viewing history. The viewing history collection unit, for example, records the number and timing of times a user pauses or rewinds while watching a movie, and analyzes viewing patterns based on that data. For example, it can identify a user's preference for frequently pausing at specific scenes. The viewing history collection unit can also record the frequency with which a user rewinds or fast-forwards while watching, and analyze viewing patterns based on that data. In this way, detailed viewing patterns can be analyzed by collecting viewing behavior in real time.

[0031] The viewing history collection unit can collect not only a user's viewing history, but also comments and shared content on social media. For example, the viewing history collection unit collects links to movies and manga that a user has shared on social media, and analyzes viewing patterns based on that data. For example, it can identify genres and works that a user frequently shares. The viewing history collection unit can also collect comments and comments made by a user on social media, and analyze viewing patterns based on that data. In this way, by collecting comments and shared content on social media, it is possible to obtain a wider range of preference data.

[0032] The viewing history collection unit can collect information on events and travel destinations attended by the user in addition to the user's viewing history. The viewing history collection unit collects, for example, information on film festivals and comic book events attended by the user and analyzes viewing patterns based on that data. For example, it identifies the genres preferred by users who attended a particular film festival. The viewing history collection unit can also collect information on travel destinations visited by the user and analyze viewing patterns based on that data. For example, it identifies the content preferred by users who visited a particular tourist spot. In this way, by collecting information on events and travel destinations, it is possible to analyze external factors related to viewing patterns.

[0033] When collecting viewing histories, the viewing history collection unit can also collect metadata about the content viewed by the user. The viewing history collection unit, for example, collects information about the directors and actors of movies viewed by the user and analyzes viewing patterns based on that data. For example, it can identify the preferences of users who prefer works by specific directors or actors. The viewing history collection unit can also collect information about the production year and genre of the content viewed by the user and analyze viewing patterns based on that data. In this way, by collecting metadata about the content viewed, detailed preference analysis can be performed.

[0034] In analyzing viewing patterns, the viewing pattern analysis unit predicts trends based on the user's viewing history and can predict future changes in preferences. The viewing pattern analysis unit, for example, develops a trend prediction algorithm for predicting future changes in preferences based on the user's viewing history. For example, it predicts the genre that the user is likely to like next based on the past viewing history. The viewing pattern analysis unit can also predict trends based on the user's viewing history and build a system for predicting future changes in preferences. This makes it possible to recommend more appropriate content by predicting future changes in preferences.

[0035] The viewing pattern analysis unit can also take into account the viewing time period and the viewing environment when analyzing a user's viewing pattern. For example, the viewing pattern analysis unit analyzes the time period when a user watches movies and identifies the viewing pattern based on that data. For example, it identifies the preferences of a user who often watches movies at night. The viewing pattern analysis unit can also analyze the type of device a user uses to watch movies and the viewing location and identify the viewing pattern based on that data. For example, it identifies the preferences of a user who often watches movies on a smartphone. This allows for detailed preference analysis by taking into account the viewing time period and the viewing environment.

[0036] The viewing pattern analysis unit can also take into account the user's purchase history and search history when analyzing viewing patterns. The viewing pattern analysis unit, for example, analyzes the user's purchase history and identifies viewing patterns based on that data. For example, it identifies the preferences of a user who often purchases movies or manga of a particular genre. The viewing pattern analysis unit can also analyze the user's search history and identify viewing patterns based on that data. For example, it identifies the preferences of a user who frequently searches for a particular keyword. In this way, by taking into account the purchase history and search history, it is possible to analyze the association between viewing preferences and other behavioral patterns.

[0037] The viewing pattern analysis unit can combine the analysis results of the viewing patterns with the user's health data. For example, the viewing pattern analysis unit incorporates the user's heart rate data into the analysis of the viewing patterns to suggest content that suits the user's health condition. For example, if the user's heart rate is high, it can suggest a relaxing movie. The viewing pattern analysis unit can also incorporate the user's stress level data into the analysis of the viewing patterns to suggest content that suits the user's health condition. In this way, by combining the health data, it is possible to suggest content that suits the user's health condition.

[0038] The content recommendation unit can also take into account the viewing history of the user's friends and family when recommending content. For example, the content recommendation unit collects the viewing history of the user's friends and family and suggests content based on shared preferences based on that data. For example, it can suggest movies that the whole family can enjoy. The content recommendation unit can also build a system for suggesting content based on shared preferences based on the viewing history of the user's friends and family. This makes it possible to suggest content based on shared preferences by taking into account the viewing history of friends and family.

[0039] When making recommendations, the content recommendation unit can also suggest related goods and event information based on the user's viewing history. The content recommendation unit, for example, suggests related goods and event information based on the user's viewing history. For example, it suggests related goods and screening events for movies that the user has watched. The content recommendation unit can also build a system for suggesting related goods and event information based on the user's viewing history. This can expand the entertainment experience by suggesting related goods and event information.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The viewing history collection unit can collect information on newsletters and e-mail magazines to which the user subscribes, in addition to the user's viewing history. For example, the unit collects the genres and contents of the newsletters to which the user subscribes, and analyzes viewing patterns based on that data. For example, it can identify the preferences of users who subscribe to newsletters of a particular genre. The viewing history collection unit can also collect the contents of the e-mail magazines to which the user subscribes, and analyze viewing patterns based on that data. In this way, by collecting information on newsletters and e-mail magazines, it is possible to analyze external factors related to viewing patterns.

[0042] When analyzing a user's viewing patterns, the viewing pattern analysis unit can also take into account the user's lifestyle and daily activity data. For example, the viewing pattern analysis unit can analyze the user's exercise habits and eating patterns and identify the viewing pattern based on that data. For example, the viewing pattern analysis unit can identify the preferences of a user who prefers content that helps them relax after exercise. The viewing pattern analysis unit can also analyze the user's daily activity data and identify the viewing pattern based on that data. This allows for detailed preference analysis by taking into account the lifestyle and daily activity data.

[0043] The content recommendation unit can consider not only the user's past viewing history but also the user's hobbies and interests when recommending content. For example, content related to the user's sports or art hobbies can be suggested. For example, if the user's hobby is soccer, movies and documentaries related to soccer can be suggested. The content recommendation unit can also suggest related content based on the user's interests. This allows more appropriate content to be recommended by taking the user's hobbies and interests into consideration.

[0044] The viewing history collection unit can collect information on online communities and forums in which the user participates, in addition to the user's viewing history. For example, it collects topics and posts in online communities in which the user participates, and analyzes viewing patterns based on that data. For example, it identifies the preferences of users who frequently post to specific topics. The viewing history collection unit can also collect information on forums in which the user participates, and analyze viewing patterns based on that data. In this way, by collecting information on online communities and forums, it is possible to analyze external factors related to viewing patterns.

[0045] When analyzing a user's viewing patterns, the viewing pattern analysis unit can also take into account the user's learning history and educational background. For example, it can analyze information on online courses taken by the user and qualifications obtained, and identify the viewing pattern based on that data. For example, it can identify the preferences of users who are taking online courses in a specific field. The viewing pattern analysis unit can also analyze the user's educational background and identify the viewing pattern based on that data. This allows for detailed preference analysis by taking into account the user's learning history and educational background.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The viewing history collection unit collects the user's viewing history and rating data. For example, it collects a list of movies the user has watched or manga they have read in the past, and also collects ratings (star ratings and comments) for each piece of content. Step 2: The viewing pattern analysis unit analyzes the data collected by the viewing history collection unit and identifies the user's preferences, such as whether the user prefers action movies, romance movies, or works by a particular manga artist. Step 3: The content recommendation unit recommends optimal content based on the preferences identified by the viewing pattern analysis unit. For example, if a user likes action movies, the unit will recommend the latest action movies or highly rated action movies. If a user likes the works of a particular manga artist, the unit will recommend new works by that manga artist or similar works.

[0048] (Example 2) A recommendation system according to an embodiment of the present invention is a system that learns an individual's viewing patterns and makes cross-sectional recommendations for the individual, such as manga that the individual should read or movies that the individual should watch, etc. This allows the recommendation system to efficiently suggest content that matches the user's preferences.

[0049] A recommendation system according to an embodiment includes a viewing history collection unit, a viewing pattern analysis unit, and a content recommendation unit. The viewing history collection unit collects a user's viewing history and rating data. For example, the viewing history collection unit collects a list of movies the user has watched or manga the user has read in the past. The viewing history collection unit can also collect ratings (star ratings and comments) for each piece of content. The viewing pattern analysis unit analyzes the data collected by the viewing history collection unit to identify the user's preferences. For example, the viewing pattern analysis unit analyzes whether the user prefers action movies or romance movies. The viewing pattern analysis unit can also analyze whether the user prefers works by a particular manga artist. The content recommendation unit recommends optimal content based on the preferences identified by the viewing pattern analysis unit. For example, if the user prefers action movies, the content recommendation unit recommends the latest action movies or highly rated action movies. If the user prefers the works of a particular manga artist, the content recommendation unit recommends new works by that manga artist or similar works. As a result, the recommendation system according to the embodiment can recommend optimal content based on the user's viewing history and evaluation data.

[0050] The viewing history collection unit can collect real-time viewing behavior in addition to the user's viewing history. The viewing history collection unit, for example, records the number and timing of times a user pauses or rewinds while watching a movie, and analyzes viewing patterns based on that data. For example, it can identify a user's preference for frequently pausing at specific scenes. The viewing history collection unit can also record the frequency with which a user rewinds or fast-forwards while watching, and analyze viewing patterns based on that data. In this way, detailed viewing patterns can be analyzed by collecting viewing behavior in real time.

[0051] The viewing history collection unit can collect not only a user's viewing history, but also comments and shared content on social media. For example, the viewing history collection unit collects links to movies and manga that a user has shared on social media, and analyzes viewing patterns based on that data. For example, it can identify genres and works that a user frequently shares. The viewing history collection unit can also collect comments and comments made by a user on social media, and analyze viewing patterns based on that data. In this way, by collecting comments and shared content on social media, it is possible to obtain a wider range of preference data.

[0052] The viewing history collection unit can use the emotion estimation function to collect emotions felt by the user while watching in real time. The viewing history collection unit uses, for example, facial expression recognition technology to collect emotions felt by the user while watching a movie in real time. For example, it records scenes in which the user smiles and analyzes viewing patterns based on that data. The viewing history collection unit can also collect emotions felt by the user while watching using voice analysis technology. For example, it analyzes the tone and speed of the user's voice to calculate an emotion score. In this way, detailed viewing patterns can be analyzed by collecting emotions felt while watching in real time using the emotion estimation function.

[0053] The viewing history collection unit can collect information on events and travel destinations attended by the user in addition to the user's viewing history. The viewing history collection unit collects, for example, information on film festivals and comic book events attended by the user and analyzes viewing patterns based on that data. For example, it identifies the genres preferred by users who attended a particular film festival. The viewing history collection unit can also collect information on travel destinations visited by the user and analyze viewing patterns based on that data. For example, it identifies the content preferred by users who visited a particular tourist spot. In this way, by collecting information on events and travel destinations, it is possible to analyze external factors related to viewing patterns.

[0054] When collecting viewing histories, the viewing history collection unit can also collect metadata about the content viewed by the user. The viewing history collection unit, for example, collects information about the directors and actors of movies viewed by the user and analyzes viewing patterns based on that data. For example, it can identify the preferences of users who prefer works by specific directors or actors. The viewing history collection unit can also collect information about the production year and genre of the content viewed by the user and analyze viewing patterns based on that data. In this way, by collecting metadata about the content viewed, detailed preference analysis can be performed.

[0055] In analyzing viewing patterns, the viewing pattern analysis unit predicts trends based on the user's viewing history and can predict future changes in preferences. The viewing pattern analysis unit, for example, develops a trend prediction algorithm for predicting future changes in preferences based on the user's viewing history. For example, it predicts the genre that the user is likely to like next based on the past viewing history. The viewing pattern analysis unit can also predict trends based on the user's viewing history and build a system for predicting future changes in preferences. This makes it possible to recommend more appropriate content by predicting future changes in preferences.

[0056] The viewing pattern analysis unit can also take into account the viewing time period and the viewing environment when analyzing a user's viewing pattern. For example, the viewing pattern analysis unit analyzes the time period when a user watches movies and identifies the viewing pattern based on that data. For example, it identifies the preferences of a user who often watches movies at night. The viewing pattern analysis unit can also analyze the type of device a user uses to watch movies and the viewing location and identify the viewing pattern based on that data. For example, it identifies the preferences of a user who often watches movies on a smartphone. This allows for detailed preference analysis by taking into account the viewing time period and the viewing environment.

[0057] The viewing pattern analysis unit can use the emotion estimation function to analyze the emotions felt by the user while watching. The viewing pattern analysis unit, for example, uses the emotion estimation function to analyze the emotions felt by the user while watching. For example, it identifies a scene that made the user laugh while watching and determines that the user likes that genre. The viewing pattern analysis unit can also use the emotion estimation function to analyze the emotions felt by the user while watching and identify preferences based on that data. In this way, detailed preferences can be identified by analyzing the emotions felt while watching.

[0058] The viewing pattern analysis unit can use the emotion estimation function to analyze the emotions felt by the user after viewing. The viewing pattern analysis unit, for example, uses the emotion estimation function to analyze the emotions felt by the user after viewing. For example, it identifies movies that moved the user after viewing and determines that the user prefers that genre. The viewing pattern analysis unit can also use the emotion estimation function to analyze the emotions felt by the user after viewing and identify preferences based on that data. In this way, detailed preferences can be identified by analyzing the emotions felt after viewing.

[0059] The viewing pattern analysis unit can also take into account the user's purchase history and search history when analyzing viewing patterns. The viewing pattern analysis unit, for example, analyzes the user's purchase history and identifies viewing patterns based on that data. For example, it identifies the preferences of a user who often purchases movies or manga of a particular genre. The viewing pattern analysis unit can also analyze the user's search history and identify viewing patterns based on that data. For example, it identifies the preferences of a user who frequently searches for a particular keyword. In this way, by taking into account the purchase history and search history, it is possible to analyze the association between viewing preferences and other behavioral patterns.

[0060] The viewing pattern analysis unit can combine the analysis results of the viewing patterns with the user's health data. For example, the viewing pattern analysis unit incorporates the user's heart rate data into the analysis of the viewing patterns to suggest content that suits the user's health condition. For example, if the user's heart rate is high, it can suggest a relaxing movie. The viewing pattern analysis unit can also incorporate the user's stress level data into the analysis of the viewing patterns to suggest content that suits the user's health condition. In this way, by combining the health data, it is possible to suggest content that suits the user's health condition.

[0061] The content recommendation unit can consider not only the user's past viewing history but also the user's current mood and emotional state when recommending content. For example, the content recommendation unit collects the user's current mood and emotional state in real time and recommends content based on that data. For example, if the user feels like relaxing, the content recommendation unit can suggest a relaxing movie. The content recommendation unit can also combine the user's past viewing history with the user's current mood and emotional state to suggest optimal content. This allows more appropriate content to be recommended by taking the user's current mood and emotional state into consideration.

[0062] The content recommendation unit can use the emotion estimation function to suggest content that the user can easily empathize with emotionally based on the emotions felt while watching. For example, the content recommendation unit uses the emotion estimation function to analyze the emotions felt by the user while watching and suggests content that the user can easily empathize with emotionally based on that data. For example, the content recommendation unit can suggest moving movies based on movies that moved the user. The content recommendation unit can also build a system for suggesting content that the user can easily empathize with emotionally based on the emotions felt while watching. This can increase user satisfaction by suggesting content that the user can easily empathize with emotionally.

[0063] The content recommendation unit can also take into account the viewing history of the user's friends and family when recommending content. For example, the content recommendation unit collects the viewing history of the user's friends and family and suggests content based on shared preferences based on that data. For example, it can suggest movies that the whole family can enjoy. The content recommendation unit can also build a system for suggesting content based on shared preferences based on the viewing history of the user's friends and family. This makes it possible to suggest content based on shared preferences by taking into account the viewing history of friends and family.

[0064] When making recommendations, the content recommendation unit can also suggest related goods and event information based on the user's viewing history. The content recommendation unit, for example, suggests related goods and event information based on the user's viewing history. For example, it suggests related goods and screening events for movies that the user has watched. The content recommendation unit can also build a system for suggesting related goods and event information based on the user's viewing history. This can expand the entertainment experience by suggesting related goods and event information.

[0065] The content recommendation unit can use the emotion estimation function to suggest the next content to watch based on the emotions felt by the user after viewing. For example, the content recommendation unit uses the emotion estimation function to analyze the emotions felt by the user after viewing and suggests the next content to watch based on that data. For example, the content recommendation unit can suggest the next most moving movie based on the emotions felt by the user after viewing. The content recommendation unit can also build a system for suggesting the next content to watch based on the emotions felt by the user after viewing. This can increase user satisfaction by suggesting the next content to watch based on the emotions felt after viewing.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The viewing history collection unit can collect information on newsletters and e-mail magazines to which the user subscribes, in addition to the user's viewing history. For example, the unit collects the genres and contents of the newsletters to which the user subscribes, and analyzes viewing patterns based on that data. For example, it can identify the preferences of users who subscribe to newsletters of a particular genre. The viewing history collection unit can also collect the contents of the e-mail magazines to which the user subscribes, and analyze viewing patterns based on that data. In this way, by collecting information on newsletters and e-mail magazines, it is possible to analyze external factors related to viewing patterns.

[0068] When analyzing a user's viewing patterns, the viewing pattern analysis unit can also take into account the user's lifestyle and daily activity data. For example, the viewing pattern analysis unit can analyze the user's exercise habits and eating patterns and identify the viewing pattern based on that data. For example, the viewing pattern analysis unit can identify the preferences of a user who prefers content that helps them relax after exercise. The viewing pattern analysis unit can also analyze the user's daily activity data and identify the viewing pattern based on that data. This allows for detailed preference analysis by taking into account the lifestyle and daily activity data.

[0069] The content recommendation unit can consider not only the user's past viewing history but also the user's hobbies and interests when recommending content. For example, content related to the user's sports or art hobbies can be suggested. For example, if the user's hobby is soccer, movies and documentaries related to soccer can be suggested. The content recommendation unit can also suggest related content based on the user's interests. This allows more appropriate content to be recommended by taking the user's hobbies and interests into consideration.

[0070] The viewing history collection unit can collect information on online communities and forums in which the user participates, in addition to the user's viewing history. For example, it collects topics and posts in online communities in which the user participates, and analyzes viewing patterns based on that data. For example, it identifies the preferences of users who frequently post to specific topics. The viewing history collection unit can also collect information on forums in which the user participates, and analyze viewing patterns based on that data. In this way, by collecting information on online communities and forums, it is possible to analyze external factors related to viewing patterns.

[0071] When analyzing a user's viewing patterns, the viewing pattern analysis unit can also take into account the user's learning history and educational background. For example, it can analyze information on online courses taken by the user and qualifications obtained, and identify the viewing pattern based on that data. For example, it can identify the preferences of users who are taking online courses in a specific field. The viewing pattern analysis unit can also analyze the user's educational background and identify the viewing pattern based on that data. This allows for detailed preference analysis by taking into account the user's learning history and educational background.

[0072] The viewing pattern analysis unit can use the emotion estimation function to analyze the emotions felt by the user while watching and estimate the user's stress level based on that data. For example, the viewing pattern analysis unit can analyze the emotions felt by the user while watching and identify scenes that increase stress. The viewing pattern analysis unit can also use the emotion estimation function to analyze the emotions felt by the user while watching and estimate the stress level based on that data. In this way, by analyzing the emotions felt while watching, the user's stress level can be estimated and appropriate content can be suggested.

[0073] The content recommendation unit can use the emotion estimation function to suggest relaxing content based on the emotions felt by the user after viewing. For example, if the user feels stressed after viewing, it can suggest relaxing movies or music. The content recommendation unit can also use the emotion estimation function to analyze the emotions felt by the user after viewing and suggest relaxing content based on that data. This can increase user satisfaction by suggesting relaxing content based on the emotions felt after viewing.

[0074] The viewing history collection unit can use the emotion estimation function to collect emotions felt by the user while watching in real time and analyze viewing patterns based on that data. For example, the viewing history collection unit can collect emotions felt by the user while watching in real time and analyze changes in emotions at specific scenes. The viewing history collection unit can also use the emotion estimation function to collect emotions felt by the user while watching in real time and analyze viewing patterns based on that data. In this way, detailed viewing patterns can be analyzed by collecting emotions felt by the user while watching in real time using the emotion estimation function.

[0075] The content recommendation unit can use the emotion estimation function to suggest content that can refresh the user emotionally based on the emotions felt while watching. For example, if the user feels tired while watching, it can suggest refreshing movies or music. The content recommendation unit can also use the emotion estimation function to analyze the emotions felt by the user while watching and suggest refreshing content based on that data. This can increase user satisfaction by suggesting refreshing content based on the emotions felt while watching.

[0076] The viewing pattern analysis unit can use the emotion estimation function to analyze the emotions felt by the user after viewing and estimate the user's happiness level based on that data. For example, the viewing pattern analysis unit can analyze the emotions felt by the user after viewing and identify scenes that increase the happiness level. The viewing pattern analysis unit can also use the emotion estimation function to analyze the emotions felt by the user after viewing and estimate the happiness level based on that data. In this way, by analyzing the emotions felt after viewing, the user's happiness level can be estimated and appropriate content can be suggested.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: The viewing history collection unit collects the user's viewing history and rating data. For example, it collects a list of movies the user has watched or manga they have read in the past, and also collects ratings (star ratings and comments) for each piece of content. Step 2: The viewing pattern analysis unit analyzes the data collected by the viewing history collection unit and identifies the user's preferences, such as whether the user prefers action movies, romance movies, or works by a particular manga artist. Step 3: The content recommendation unit recommends optimal content based on the preferences identified by the viewing pattern analysis unit. For example, if a user likes action movies, the unit will recommend the latest action movies or highly rated action movies. If a user likes the works of a particular manga artist, the unit will recommend new works by that manga artist or similar works.

[0079] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0080] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0081] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0084] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0085] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0086] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0087] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0088] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0089] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0090] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0091] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0092] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0093] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0120] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0129] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0130] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0131] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0132] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0133] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0134] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0135] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0136] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0137] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0138] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0139] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0140] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0141] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0142] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0143] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0144] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0145] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0146] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a viewing history collection unit that collects user viewing history and evaluation data; a viewing pattern analysis unit that analyzes the data collected by the viewing history collection unit and identifies user preferences; a content recommendation unit that recommends optimal content based on the preferences identified by the viewing pattern analysis unit. A system characterized by:

2. The viewing history collection unit Collect users' viewing history as well as their real-time viewing behavior 2. The system of claim 1.

3. The viewing history collection unit In addition to the user's viewing history, information about events the user has attended and travel destinations is also collected.

2. The system of claim 1.

4. The viewing pattern analysis unit When analyzing user viewing patterns, consider viewing time and viewing environment.

2. The system of claim 1.

5. The content recommendation unit The content recommendations take into account not only the user's past viewing history but also their current mood and emotional state.

2. The system of claim 1.

6. The viewing history collection unit Using emotion estimation function, we collect the emotions felt by users while watching in real time.

2. The system of claim 1.

7. The viewing pattern analysis unit Using emotion estimation function, analyze the emotions felt by users while watching 2. The system of claim 1.

8. The content recommendation unit Using emotion estimation, the system suggests the next content to watch based on the emotions felt by the user after watching.

2. The system of claim 1.

Images