System

Abstract

An object of the system according to the embodiment is to efficiently collect the latest and past information on a specific person or character and acquire information while avoiding spoiler.SOLUTION: A system includes a media information collection unit, an information disclosure setting unit, and an old information acquisition unit. The media information collecting unit collects media information related to a specific person or character. The information disclosure setting part sets to what extent the user discloses information. The old information acquisition unit acquires old information from a cache or the like of a website that has already been released.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 technology has faced the challenge of making it difficult to efficiently collect the latest and past information about specific people or characters while avoiding spoilers.

[0005] The system according to the embodiment aims to efficiently collect the latest and past information about a specific person or character, and to obtain the information while avoiding spoilers. [Means for solving the problem]

[0006] The system according to the embodiment includes a media information collection unit, an information disclosure setting unit, and an old information acquisition unit. The media information collection unit collects media information related to a specific person or character. The information disclosure setting unit sets the extent of information a user will disclose. The old information acquisition unit acquires old information from the cache of a website that is no longer publicly available. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect the latest and past information about a specific person or character, and can obtain the information while avoiding spoilers. [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) The information collection system according to an embodiment of the present invention is a system that collects information about a person's "oshi" without missing anything, and the generation AI provides the necessary information while avoiding spoilers. This allows the information collection system to collect information about a person's "oshi" without missing anything, allowing you to enjoy the information while avoiding spoilers.

[0029] The information collection system according to the embodiment includes a media information collection unit, an information disclosure setting unit, and an old information acquisition unit. The media information collection unit collects appearance information about the "oshi" (favorite person). For example, the generation AI automatically collects media information about the "oshi" (favorite person)'s appearances and appearances from various online sources. The generation AI analyzes information such as television programs, movies, magazines, and social media posts to select content in which the "oshi" (favorite person) appears. The generation AI receives input from prompts containing the "oshi"'s name and related keywords, and the generation AI collects information based on the prompts. The information disclosure setting unit sets the user's information disclosure scope. For example, the user can interact with the generation AI to fine-tune the "level of information disclosure allowed." The generation AI filters information based on the user's settings and provides information within an appropriate scope. The old information acquisition unit acquires past information. For example, the generation AI automatically acquires caches of websites that have already been closed, ensuring that past information is not missed. The generation AI acquires caches of, for example, past blog posts, news articles, and social media posts, and displays them in chronological order. As a result, the information collection system according to the embodiment allows users to collect information about their favorite idols without missing anything and enjoy the experience while avoiding spoilers. For example, users can get a comprehensive view of their favorite idols' past activities, and the filtering function to avoid spoilers allows users to collect information with peace of mind.

[0030] The media information collection unit can simultaneously collect viewer ratings or reviews and provide them to the user. For example, when the generation AI collects information on the appearance of a "favorite" person, the media information collection unit analyzes viewer ratings and reviews for each media and provides highly rated content to the user preferentially. For example, it collects rating data from movie and drama review sites and displays it as recommended content to the user. This allows it to provide highly rated content to the user.

[0031] The media information collection unit can automatically generate highlights of scenes in which the "favorite" appears and provide them to the user. For example, when the generation AI collects information on the appearance of the "favorite," the media information collection unit automatically generates highlights of scenes in which the "favorite" appears using video analysis technology and provides them to the user. For example, it extracts scenes in which the "favorite" appears from movies or dramas and displays them as short clips. This allows important scenes to be provided to the user efficiently.

[0032] The media information collection unit can also simultaneously collect merchandise or event information and provide it to the user. For example, when the generation AI collects appearance information for an "oshi" (favorite), the media information collection unit simultaneously collects related merchandise information and provides it to the user. For example, it automatically searches for merchandise and limited edition products related to movies and dramas and displays them to the user. This allows the user to be provided with relevant merchandise and event information.

[0033] The media information collection unit can collect comments or impressions from other fans and provide them to the user. For example, when the generation AI collects information on the appearance of an "oshi" (favorite), the media information collection unit analyzes the comments and impressions of other fans on social media and provides them to the user. For example, it analyzes hashtags on Twitter and Instagram and displays positive comments. This allows the user to be provided with the opinions of other fans.

[0034] The information disclosure setting unit can refer to past setting history and make suggestions that match the user's preferences. For example, when the generation AI sets the user's information disclosure range, the information disclosure setting unit analyzes the past setting history and makes suggestions that match the user's preferences. For example, it can suggest optimal settings based on the information disclosure range that was set in the past. This makes it possible to suggest the optimal information disclosure range based on the user's preferences.

[0035] The information disclosure setting unit can make optimal suggestions by referring to the setting trends of other users. For example, when the generation AI sets the user's information disclosure range, the information disclosure setting unit analyzes the setting trends of other users and proposes optimal settings. For example, it refers to the setting trends of users who have the same "favorite." This makes it possible to propose the optimal information disclosure range by referring to the setting trends of other users.

[0036] The information disclosure setting unit can enable detailed settings for each different media format. For example, when the generation AI sets the user's information disclosure range, the information disclosure setting unit enables detailed settings for each different media format, such as text, images, and videos. For example, text can be displayed in detail, and images and videos can be displayed only in outline. This allows detailed settings for each different media format.

[0037] The information disclosure setting unit can visually display the setting contents so that the user can intuitively understand them. For example, when the generation AI sets the user's information disclosure range, the information disclosure setting unit provides an interface that visually displays the setting contents. For example, the setting contents are displayed using a slider or checkbox. This allows the user to intuitively understand the setting contents.

[0038] The old information acquisition unit can also refer to archive databases to provide more detailed information. For example, when the generation AI automatically acquires old information, the old information acquisition unit can refer to archive databases of related media to provide more detailed information. For example, it can acquire past news articles and program information from archive databases of newspapers and television stations. This allows it to provide more detailed past information.

[0039] The old information acquisition unit can evaluate the reliability of information and display highly reliable information preferentially. For example, when the generation AI automatically acquires old information, the old information acquisition unit uses an algorithm to evaluate the reliability of the information and display highly reliable information preferentially. For example, the display order is determined based on the reliability score of the information source. This allows highly reliable information to be provided preferentially.

[0040] The old information acquisition unit also references backup data to ensure the integrity of the information. For example, when the generation AI automatically acquires old information, the old information acquisition unit references backup data from related media to ensure the integrity of the information. For example, it acquires past news articles and program information from backup data from newspapers and television stations. This ensures the integrity of the information.

[0041] The old information acquisition unit can evaluate the importance of information and display important information preferentially. For example, when the generation AI automatically acquires old information, the old information acquisition unit uses an algorithm to evaluate the importance of the information and display important information preferentially. For example, it evaluates the importance based on the content and impact of the information. This allows important information to be provided preferentially.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The information gathering system can further include a suggestion unit that suggests new content that will interest the user. The suggestion unit, for example, analyzes the user's viewing history and rating data to identify content that the user has not yet seen but may be interested in. For example, if the user is interested in a particular genre or theme, the suggestion unit can suggest new movies or dramas related to that genre or theme. It can also suggest works similar to content that the user has previously given a high rating. This makes it easier for users to discover new content, improving the usefulness of the information gathering system.

[0044] The information collection system may further include a viewing time optimization unit that optimizes viewing time based on the user's viewing history. The viewing time optimization unit, for example, analyzes the viewing time of content the user has viewed in the past and identifies the time period during which the user can view content most efficiently. For example, if the user often views content at night, new content may be suggested that matches that time period. Also, if the user prefers content that can be viewed in a short amount of time, short films and episodes may be preferentially displayed. This allows the user to enjoy a viewing experience that suits their lifestyle.

[0045] The information collection system may further include an event suggestion unit that provides event information that may interest the user. The event suggestion unit may analyze, for example, the user's viewing history and rating data to identify events that the user may be interested in. For example, if the user is interested in a particular actor or artist, the event suggestion unit may provide information on events and live performances in which that actor or artist will appear. Furthermore, if the user is interested in a particular genre, the event suggestion unit may also provide information on events related to that genre. This allows the user to obtain event information that matches their interests.

[0046] The information collection system may further include a customization unit that customizes the viewing experience based on the user's viewing history. The customization unit may, for example, analyze the genres and themes of content the user has previously viewed and identify content that the user is most likely to be interested in. For example, if the user likes action movies, new action movies may be preferentially suggested. Also, if the user likes the works of a particular actor or director, new works by that actor or director may be suggested. This allows the user to have a viewing experience that suits their preferences.

[0047] The information collection system may further include a pattern analysis unit that analyzes viewing patterns based on the user's viewing history. The pattern analysis unit, for example, analyzes the viewing patterns of content the user has viewed in the past and identifies the most efficient viewing method for the user. For example, if the user often views content during a specific time period, the system may suggest new content tailored to that time period. Also, if the user often views content continuously, the system may suggest content suitable for continuous viewing. This allows the user to enjoy a viewing experience that matches their viewing pattern.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The media information gathering unit collects information about the "oshi"'s appearances. The generation AI automatically collects media information about the "oshi"'s appearances and appearances from various sources on the internet. For example, it analyzes information from television programs, movies, magazines, social media posts, etc., and selects content in which the "oshi" appears. The input to the generation AI is a prompt containing the "oshi"'s name and related keywords, and the generation AI collects information based on that prompt. Step 2: The information disclosure setting unit sets the user's information disclosure scope. The user can interact with the generating AI to fine-tune the "extent of information disclosure they are willing to allow." The generating AI filters information based on the user's settings and provides information within an appropriate range. Step 3: The old information acquisition unit acquires past information. The generation AI automatically acquires the cache of websites that are no longer publicly available, collecting past information without missing anything. For example, it acquires caches of past blog posts, news articles, social media posts, etc., and organizes and displays them in chronological order.

[0050] (Example 2) The information collection system according to an embodiment of the present invention is a system that collects information about a person's "oshi" without missing anything, and the generation AI provides the necessary information while avoiding spoilers. This allows the information collection system to collect information about a person's "oshi" without missing anything, allowing you to enjoy the information while avoiding spoilers.

[0051] The information collection system according to the embodiment includes a media information collection unit, an information disclosure setting unit, and an old information acquisition unit. The media information collection unit collects appearance information about the "oshi" (favorite person). For example, the generation AI automatically collects media information about the "oshi" (favorite person)'s appearances and appearances from various online sources. The generation AI analyzes information such as television programs, movies, magazines, and social media posts to select content in which the "oshi" (favorite person) appears. The generation AI receives input from prompts containing the "oshi"'s name and related keywords, and the generation AI collects information based on the prompts. The information disclosure setting unit sets the user's information disclosure scope. For example, the user can interact with the generation AI to fine-tune the "level of information disclosure allowed." The generation AI filters information based on the user's settings and provides information within an appropriate scope. The old information acquisition unit acquires past information. For example, the generation AI automatically acquires caches of websites that have already been closed, ensuring that past information is not missed. The generation AI acquires caches of, for example, past blog posts, news articles, and social media posts, and displays them in chronological order. As a result, the information collection system according to the embodiment allows users to collect information about their favorite idols without missing anything and enjoy the experience while avoiding spoilers. For example, users can get a comprehensive view of their favorite idols' past activities, and the filtering function to avoid spoilers allows users to collect information with peace of mind.

[0052] The media information collection unit can simultaneously collect viewer ratings or reviews and provide them to the user. For example, when the generation AI collects information on the appearance of a "favorite" person, the media information collection unit analyzes viewer ratings and reviews for each media and provides highly rated content to the user preferentially. For example, it collects rating data from movie and drama review sites and displays it as recommended content to the user. This allows it to provide highly rated content to the user.

[0053] The media information collection unit can automatically generate highlights of scenes in which the "favorite" appears and provide them to the user. For example, when the generation AI collects information on the appearance of the "favorite," the media information collection unit automatically generates highlights of scenes in which the "favorite" appears using video analysis technology and provides them to the user. For example, it extracts scenes in which the "favorite" appears from movies or dramas and displays them as short clips. This allows important scenes to be provided to the user efficiently.

[0054] The media information collection unit can use the emotion estimation function to identify the scenes in which the user is most interested and display those scenes preferentially. For example, the media information collection unit uses the emotion estimation function to analyze the user's viewing history and rating data to identify the scenes in which the user is most interested in their "favorite" appearing. For example, scenes that have been highly rated in the past or scenes that have been frequently played are displayed preferentially. This allows the most suitable scenes to be provided based on the user's interests.

[0055] The media information collection unit can also simultaneously collect merchandise or event information and provide it to the user. For example, when the generation AI collects appearance information for an "oshi" (favorite), the media information collection unit simultaneously collects related merchandise information and provides it to the user. For example, it automatically searches for merchandise and limited edition products related to movies and dramas and displays them to the user. This allows the user to be provided with relevant merchandise and event information.

[0056] The media information collection unit can collect comments or impressions from other fans and provide them to the user. For example, when the generation AI collects information on the appearance of an "oshi" (favorite), the media information collection unit analyzes the comments and impressions of other fans on social media and provides them to the user. For example, it analyzes hashtags on Twitter and Instagram and displays positive comments. This allows the user to be provided with the opinions of other fans.

[0057] The media information collection unit can use the emotion estimation function to preferentially display appearance information that evokes the most positive emotions in the user. For example, the media information collection unit uses the emotion estimation function to analyze the user's viewing history and rating data to identify appearance information about the "favorite" that evokes the most positive emotions in the user. For example, content that has been highly rated in the past can be preferentially displayed. This allows the user to be provided with information that evokes the most positive emotions in the user.

[0058] The information disclosure setting unit can refer to past setting history and make suggestions that match the user's preferences. For example, when the generation AI sets the user's information disclosure range, the information disclosure setting unit analyzes the past setting history and makes suggestions that match the user's preferences. For example, it can suggest optimal settings based on the information disclosure range that was set in the past. This makes it possible to suggest the optimal information disclosure range based on the user's preferences.

[0059] The information disclosure setting unit can make optimal suggestions by referring to the setting trends of other users. For example, when the generation AI sets the user's information disclosure range, the information disclosure setting unit analyzes the setting trends of other users and proposes optimal settings. For example, it refers to the setting trends of users who have the same "favorite." This makes it possible to propose the optimal information disclosure range by referring to the setting trends of other users.

[0060] The information disclosure setting unit can use the emotion estimation function to identify the information disclosure range that the user feels most comfortable with and provide information within that range. For example, the information disclosure setting unit can use the emotion estimation function to analyze the user's viewing history and rating data to identify the information disclosure range that the user feels most comfortable with. For example, the information disclosure setting unit can make a suggestion based on the information disclosure range of content that the user has given high ratings in the past. This makes it possible to provide the information disclosure range that the user feels most comfortable with.

[0061] The information disclosure setting unit can enable detailed settings for each different media format. For example, when the generation AI sets the user's information disclosure range, the information disclosure setting unit enables detailed settings for each different media format, such as text, images, and videos. For example, text can be displayed in detail, and images and videos can be displayed only in outline. This allows detailed settings for each different media format.

[0062] The information disclosure setting unit can visually display the setting contents so that the user can intuitively understand them. For example, when the generation AI sets the user's information disclosure range, the information disclosure setting unit provides an interface that visually displays the setting contents. For example, the setting contents are displayed using a slider or checkbox. This allows the user to intuitively understand the setting contents.

[0063] The information disclosure setting unit can use the emotion estimation function to identify the information disclosure range that will evoke the most positive emotion for the user and provide information within that range. For example, the information disclosure setting unit can use the emotion estimation function to analyze the user's viewing history and rating data to identify the information disclosure range that will evoke the most positive emotion for the user. For example, the information disclosure setting unit can make a suggestion based on the information disclosure range for content that has been highly rated in the past. This makes it possible to provide the information disclosure range that will evoke the most positive emotion for the user.

[0064] The old information acquisition unit can also refer to archive databases to provide more detailed information. For example, when the generation AI automatically acquires old information, the old information acquisition unit can refer to archive databases of related media to provide more detailed information. For example, it can acquire past news articles and program information from archive databases of newspapers and television stations. This allows it to provide more detailed past information.

[0065] The old information acquisition unit can evaluate the reliability of information and display highly reliable information preferentially. For example, when the generation AI automatically acquires old information, the old information acquisition unit uses an algorithm to evaluate the reliability of the information and display highly reliable information preferentially. For example, the display order is determined based on the reliability score of the information source. This allows highly reliable information to be provided preferentially.

[0066] The old information acquisition unit can use the emotion estimation function to identify past information that the user is most interested in and display that information preferentially. The old information acquisition unit, for example, uses the emotion estimation function to analyze the user's viewing history and rating data in order to identify past information that the user is most interested in. For example, it preferentially displays information about content that the user has given high ratings in the past. This makes it possible to provide past information that the user is most interested in.

[0067] The old information acquisition unit also references backup data to ensure the integrity of the information. For example, when the generation AI automatically acquires old information, the old information acquisition unit references backup data from related media to ensure the integrity of the information. For example, it acquires past news articles and program information from backup data from newspapers and television stations. This ensures the integrity of the information.

[0068] The old information acquisition unit can evaluate the importance of information and display important information preferentially. For example, when the generation AI automatically acquires old information, the old information acquisition unit uses an algorithm to evaluate the importance of the information and display important information preferentially. For example, it evaluates the importance based on the content and impact of the information. This allows important information to be provided preferentially.

[0069] The old information acquisition unit can use the emotion estimation function to preferentially display past information that the user feels most positive about. The old information acquisition unit, for example, uses the emotion estimation function to analyze the user's viewing history and rating data to identify past information that the user feels most positive about. For example, the old information acquisition unit preferentially displays information about content that the user has given a high rating to in the past. This makes it possible to provide past information that the user feels most positive about.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The information gathering system can further include a suggestion unit that suggests new content that will interest the user. The suggestion unit, for example, analyzes the user's viewing history and rating data to identify content that the user has not yet seen but may be interested in. For example, if the user is interested in a particular genre or theme, the suggestion unit can suggest new movies or dramas related to that genre or theme. It can also suggest works similar to content that the user has previously given a high rating. This makes it easier for users to discover new content, improving the usefulness of the information gathering system.

[0072] The information collection system may further include an emotion adjustment unit that estimates the user's emotions and adjusts the display order of content based on the estimated emotions. The emotion adjustment unit, for example, analyzes the user's emotions toward content previously viewed and prioritizes displaying content that evokes the user's most positive emotions. For example, if the user has positive emotions toward a particular actor or genre, movies starring that actor or dramas in that genre may be prioritized for display. Furthermore, if the user is feeling stressed, the system may suggest relaxing content. This allows the user to enjoy content that matches their emotions.

[0073] The information collection system may further include a viewing time optimization unit that optimizes viewing time based on the user's viewing history. The viewing time optimization unit, for example, analyzes the viewing time of content the user has viewed in the past and identifies the time period during which the user can view content most efficiently. For example, if the user often views content at night, new content may be suggested that matches that time period. Also, if the user prefers content that can be viewed in a short amount of time, short films and episodes may be preferentially displayed. This allows the user to enjoy a viewing experience that suits their lifestyle.

[0074] The information collection system may further include a notification adjustment unit that estimates the user's emotions and adjusts the timing of notifications based on the estimated emotions. The notification adjustment unit, for example, analyzes the emotions the user felt when receiving notifications in the past and sends notifications at the timing when the user feels the most positive emotions. For example, sending notifications during times when the user is relaxed can elicit a positive reaction to the notifications. It may also be possible to refrain from sending notifications during times when the user is busy. This allows the user to receive information at the appropriate time without feeling stressed.

[0075] The information collection system may further include an event suggestion unit that provides event information that may interest the user. The event suggestion unit may analyze, for example, the user's viewing history and rating data to identify events that the user may be interested in. For example, if the user is interested in a particular actor or artist, the event suggestion unit may provide information on events and live performances in which that actor or artist will appear. Furthermore, if the user is interested in a particular genre, the event suggestion unit may also provide information on events related to that genre. This allows the user to obtain event information that matches their interests.

[0076] The information collection system may further include an advertisement adjustment unit that estimates the user's emotions and adjusts the content of advertisements to be displayed based on the estimated emotions. The advertisement adjustment unit, for example, analyzes the user's emotions toward content that the user has viewed in the past and preferentially displays advertisements that evoke the user's most positive emotions. For example, if the user has positive emotions toward a particular brand or product, advertisements for that brand or product may be preferentially displayed. Also, if the user is feeling stressed, advertisements that help the user relax may be displayed. This allows the user to receive advertisements that match their emotions.

[0077] The information collection system may further include a customization unit that customizes the viewing experience based on the user's viewing history. The customization unit may, for example, analyze the genres and themes of content the user has previously viewed and identify content that the user is most likely to be interested in. For example, if the user likes action movies, new action movies may be preferentially suggested. Also, if the user likes the works of a particular actor or director, new works by that actor or director may be suggested. This allows the user to have a viewing experience that suits their preferences.

[0078] The information collection system may further include a recommendation unit that estimates the user's emotions and recommends content based on the estimated emotions. The recommendation unit, for example, analyzes the user's emotions toward content that the user has viewed in the past and preferentially suggests content that evokes the user's most positive emotions. For example, if the user has positive emotions toward a particular genre or theme, the recommendation unit may suggest new movies or dramas related to that genre or theme. In addition, if the user is feeling stressed, the recommendation unit may suggest content that will help the user relax. This allows the user to enjoy content that matches their emotions.

[0079] The information collection system may further include a pattern analysis unit that analyzes viewing patterns based on the user's viewing history. The pattern analysis unit, for example, analyzes the viewing patterns of content the user has viewed in the past and identifies the most efficient viewing method for the user. For example, if the user often views content during a specific time period, the system may suggest new content tailored to that time period. Also, if the user often views content continuously, the system may suggest content suitable for continuous viewing. This allows the user to enjoy a viewing experience that matches their viewing pattern.

[0080] The information collection system may further include a filtering unit that estimates the user's emotions and filters content based on the estimated emotions. The filtering unit, for example, analyzes the user's emotions toward content that the user has viewed in the past and preferentially displays content that evokes the user's most positive emotions. For example, if the user has positive emotions toward a particular genre or theme, content related to that genre or theme may be preferentially displayed. Furthermore, if the user is feeling stressed, content that helps the user relax may be displayed. This allows the user to enjoy content that matches their emotions.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The media information gathering unit collects information about the "oshi"'s appearances. The generation AI automatically collects media information about the "oshi"'s appearances and appearances from various sources on the internet. For example, it analyzes information from television programs, movies, magazines, social media posts, etc., and selects content in which the "oshi" appears. The input to the generation AI is a prompt containing the "oshi"'s name and related keywords, and the generation AI collects information based on that prompt. Step 2: The information disclosure setting unit sets the user's information disclosure scope. The user can interact with the generating AI to fine-tune the "extent of information disclosure they are willing to allow." The generating AI filters information based on the user's settings and provides information within an appropriate range. Step 3: The old information acquisition unit acquires past information. The generation AI automatically acquires the cache of websites that are no longer publicly available, collecting past information without missing anything. For example, it acquires caches of past blog posts, news articles, social media posts, etc., and organizes and displays them in chronological order.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 media information gathering department; an information disclosure setting unit; The old information acquisition section and Equipped with A system characterized by:

2. The media information collection unit Automatically generate highlights of scenes in which the character appears and provide them to users 2. The system of claim 1.

3. The media information collection unit Collect merchandise or event information at the same time and provide it to users 2. The system of claim 1.

4. The information disclosure setting unit Refer to past settings history and make suggestions tailored to the user's preferences 2. The system of claim 1.

5. The old information acquisition unit Also refer to related archive databases to provide more detailed information 2. The system of claim 1.

6. The media information collection unit Identify the scenes in which the user is most interested and display those scenes preferentially 2. The system of claim 1.

7. The information disclosure setting unit Identify the information disclosure range that users feel most comfortable with and provide information within that range 2. The system of claim 1.

8. The old information acquisition unit Identifying past information that is of most interest to the user and displaying that information preferentially 2. The system of claim 1.

Images