System

Abstract

An object of the system according to the embodiment is to find alumni and friends in the junior high age and share memories.SOLUTION: A system includes a profile creation unit, a search unit, and a sharing unit. The profile creation unit registers information of the middle school age of the user. The search unit searches for an alumni or a friend based on the information registered by the profile creation unit. The sharing unit shares memories with the alumni and friends found by the search 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 technology has had the problem of making it difficult to find junior high school classmates and friends and share memories.

[0005] The system according to the embodiment aims to find classmates and friends from junior high school and share memories. [Means for solving the problem]

[0006] The system according to the embodiment includes a profile creation unit, a search unit, and a sharing unit. The profile creation unit registers information about the user's junior high school days. The search unit searches for classmates and friends based on the information registered by the profile creation unit. The sharing unit shares memories with classmates and friends found by the search unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to find classmates and friends from junior high school and share memories. [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 SNS platform according to an embodiment of the present invention is a system that enables users to easily find their junior high school classmates and friends, share fond memories, and promote reunions. Thus, the SNS platform enables users to easily find their junior high school classmates and friends, share fond memories, and promote reunions.

[0029] An SNS platform according to an embodiment includes a profile creation unit, a search unit, and a sharing unit. The profile creation unit registers information about a user's junior high school days. For example, the user can enter the name, location, and graduation year of the junior high school from which the user graduated. The profile creation unit can also register information about club activities and classes the user participated in during junior high school. The search unit searches for alumni and friends based on the information registered by the profile creation unit. For example, the user can search by specifying criteria such as graduation year, class, and club activities. The search unit also displays profile photos and self-introductions in the search results, allowing the user to confirm whether the person found is someone the user knows. The sharing unit shares fond memories with alumni and friends found by the search unit. For example, the user can post photos, videos, text, and the like to share memories from junior high school. The sharing unit also provides a comment and "like" function for posts, allowing the user to deepen interactions with other users. As a result, the SNS platform according to an embodiment makes it easy for users to find junior high school alumni and friends and share fond memories. For example, users can reconnect with classmates they've lost touch with after graduation and reminisce about old memories, and increase opportunities for reunions through reunions and local events.

[0030] The profile creation unit can use the generation AI to complement photos from the user's junior high school days. For example, when a user creates a profile, the profile creation unit automatically complements photos from their junior high school days using the generation AI. For example, based on some photos provided by the user, the generation AI generates other photos and adds them to the profile. The generation AI can complement missing parts using an image completion algorithm or a deep learning model. This creates a more detailed user profile.

[0031] The profile creation unit can use the generation AI to generate memories of the user's junior high school days. For example, the profile creation unit uses the generation AI to automatically generate memories of the user's junior high school days based on the profile information entered by the user. For example, the generation AI can generate episodes of club activities and school events in which the user participated and add them to the profile. The generation AI can generate the user's memories using text generation algorithms and natural language processing technology. This allows the user's profile to be created in more detail.

[0032] The profile creation unit can provide a function that allows the user to register a nickname or nickname used in junior high school. For example, when creating a profile, the profile creation unit adds a field in which the user can enter a nickname or nickname used in junior high school. For example, the user enters their own nickname in the "nickname" field. This allows the user to register a nickname or nickname used in junior high school.

[0033] The profile creation unit can estimate the user's friendships from junior high school based on the profile information and provide a friend list. The profile creation unit, for example, analyzes the profile information and develops an algorithm to automatically estimate the user's friendships from junior high school. For example, it can list friends who participated in the same class or club activities. The friendships are estimated based on criteria such as the same class, the same club activities, and mutual friends. The friend list is provided in the form of names, contact information, profile information, and the like. This makes it easier for users to find their friends from junior high school.

[0034] The search unit can use generation AI to provide complementary information based on the user's memory, thereby improving search accuracy. For example, the search unit can use generation AI to provide complementary information based on the user's memory for search results. For example, the search results can be complemented based on the characteristics and episodes of friends that the user remembers. The generation AI can use text generation algorithms and natural language processing technology to generate information that complements the user's memory. This provides complementary information based on the user's memory, improving search accuracy.

[0035] The search unit can remind the user of friends they have forgotten based on the search criteria. The search unit adds a function to automatically remind the user of friends they have forgotten based on the search criteria, for example. For example, the search unit suggests forgotten friends based on the graduation year and class information entered by the user. The search criteria are set based on criteria such as name, school name, and graduation year. Reminders are sent by notification, email, pop-up, or other methods. By reminding the user of friends they have forgotten, the chances of reuniting are increased.

[0036] The search unit can display episodes from the user's junior high school days in response to search results, thereby improving the relevance of the search results. For example, the search unit adds a function to automatically display episodes from the user's junior high school days in response to search results. For example, episodes related to the search results are generated and displayed. The episodes are displayed in the form of school events, club activity memories, etc. This increases the relevance of the search results, allowing the user to obtain more appropriate results.

[0037] The search unit can display members of clubs or clubs in which the user participated based on search conditions. The search unit adds a function to automatically display members of clubs or clubs in which the user participated based on search conditions, for example. For example, members are displayed based on club information entered by the user. Search conditions are set based on criteria such as club name, club name, and member names. Clubs or clubs are displayed in the form of soccer club, drama club, etc. This makes it easier for the user to find members of clubs or clubs in which the user participated.

[0038] The sharing unit can use a generation AI to generate captions for posted photos and videos. For example, the sharing unit can automatically generate captions for posted photos using a generation AI. For example, it can analyze the content of the photo and generate and display an appropriate caption. The generation AI can use a text generation algorithm or natural language processing technology to generate captions that explain the content of the photo or video. This provides a clearer explanation of the posted photo or video.

[0039] The sharing unit can suggest related episodes from junior high school based on the content of the post. For example, the sharing unit analyzes the content of the post and the generation AI automatically suggests related episodes from junior high school. For example, it generates and displays episodes related to photos posted by the user. Episodes are suggested in the form of school events, club activity memories, etc. This makes it easy for users to find related episodes.

[0040] The sharing section can suggest episodes that other users can relate to for posted memories. For example, the sharing section adds a function in which the generation AI automatically suggests episodes that other users can relate to for posted memories. For example, it displays relatable episodes related to the content posted by the user. Episodes are suggested in the form of memories of school events, club activities, etc. This makes it easier for users to share their empathy with other users.

[0041] The sharing unit can reconstruct the user's friendships from junior high school based on the posted content and provide a friend list. For example, the sharing unit analyzes the posted content, and the generation AI automatically reconstructs the user's friendships from junior high school. For example, the friend list is updated based on photos and stories posted by the user. Friendships are reconstructed based on criteria such as being in the same class, the same club activities, and mutual friends. The friend list is provided in the form of names, contact information, profile information, etc. This keeps the user's friend list up to date.

[0042] The reunion promotion function can use generation AI to generate a customized invitation based on the user's interests for event information. The reunion promotion function, for example, uses generation AI to automatically generate a customized invitation based on the user's interests for event information. For example, an invitation that reflects the content of an event in which the user is interested is generated. The generation AI can generate an invitation based on the user's interests using a text generation algorithm or natural language processing technology. In this way, a customized invitation based on the user's interests is generated.

[0043] The reunion promotion function can suggest episodes that can be talked about when reuniting based on the profile information of event participants. For example, the reunion promotion function uses a generation AI to automatically suggest episodes that can be talked about when reuniting based on the profile information of event participants. For example, it can suggest episodes from participants' junior high school days. Episodes are suggested in the form of memories of school events, club activities, etc. This automatically suggests episodes that can be talked about when reuniting.

[0044] The reunion promotion function can suggest a reunion matching function based on the user's friendships from junior high school for event information. The reunion promotion function adds a reunion matching function based on the user's friendships from junior high school for event information, for example. For example, it matches friends who participated in the same class or club activity. Friendships are matched based on criteria such as the same class, the same club activity, or common friends. The matching function is performed using methods such as algorithms or manual settings. This makes it possible to match reunions based on the user's friendships from junior high school.

[0045] The reunion promotion function can suggest people with common hobbies or interests when reuniting based on the profile information of event participants. For example, the reunion promotion function uses the generation AI to automatically suggest people with common hobbies or interests when reuniting based on the profile information of event participants. For example, it suggests people related to the participants' hobbies and interests. Profile information is used in the form of name, hobbies, interests, etc. Common hobbies or interests are suggested in the form of sports, music, reading, etc. This allows people with common hobbies and interests to be automatically suggested when reuniting.

[0046] The privacy protection function can use generative AI to suggest optimal settings based on the user's usage. For example, the privacy protection function uses generative AI to automatically suggest optimal settings for privacy settings based on the user's usage. For example, it can suggest appropriate privacy settings based on the functions the user frequently uses. The generative AI can analyze the user's usage using machine learning algorithms and data analysis techniques and suggest optimal settings. This allows the optimal privacy settings to be suggested based on the user's usage.

[0047] The privacy protection function can record the change history of privacy settings and provide a function that allows users to easily check past settings. For example, the privacy protection function automatically records the change history of privacy settings and adds a function that allows users to easily check past settings. For example, it displays a list of setting changes made by the user in the past. Privacy settings are recorded in the form of the scope of disclosure, data storage period, access permissions, etc. The change history is recorded in the form of the date and time when the settings were changed, the content of the change, the reason for the change, etc. This allows users to easily check past privacy settings.

[0048] The privacy protection function can provide customized settings for privacy settings based on the user's friendships from junior high school. For example, the privacy protection function proposes customized settings for privacy settings based on the user's friendships from junior high school. For example, it proposes settings that allow only specific friends to view. Friendships are customized based on criteria such as the same class, the same club activities, and mutual friends. The customized settings are proposed in the form of disclosure range, notification settings, data sharing range, etc. This allows customized settings to be proposed based on the user's friendships from junior high school.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The profile creation unit may provide a function for registering the user's hobbies and special skills from junior high school. For example, a field may be added for inputting hobbies such as sports, music, and art that the user was interested in during junior high school. This allows the user to register the hobbies and special skills from junior high school, making it easier to find friends who share common interests with other users. The profile creation unit may also provide a function for registering awards and commendations the user received during junior high school. For example, the user may enter medals received in school sports competitions or academic commendations. This creates a more detailed user profile. Furthermore, the profile creation unit may provide a function for registering the user's favorite books and movies from junior high school. For example, a field may be added for inputting the titles of the user's favorite books and movies. This allows the user to share their favorite books and movies from junior high school and make it easier to find common topics with other users.

[0051] The search unit can suggest related events and activities based on the user's memories from junior high school. For example, similar events and activities currently being held can be suggested based on information about clubs and extracurricular activities the user participated in during junior high school. This makes it easier for the user to relive their junior high school memories. The search unit can also suggest related tourist spots and travel plans based on places the user visited or travel destinations during junior high school. For example, current tourist information can be provided based on places the user visited on a school trip during junior high school. This gives the user an opportunity to revisit nostalgic places. Furthermore, the search unit can suggest related learning resources based on the teaching materials and reference books the user used during junior high school. For example, current learning resources can be provided based on the mathematics textbook the user used during junior high school. This makes it easier for the user to relive their junior high school learning experiences.

[0052] The reunion promotion function can suggest people who share common hobbies and interests when reuniting, based on the user's friendships from junior high school. For example, it can suggest friends who share current hobbies and interests based on information about the clubs and extracurricular activities the user participated in during junior high school. This makes it easier to meet up with friends who have common topics to talk about when reuniting. The reunion promotion function can also suggest stories that can be talked about when reuniting, based on the user's friendships from junior high school. For example, it can suggest stories about school events and activities the user participated in during junior high school. This makes it easier to meet up with friends who have common topics to talk about when reuniting. The reunion promotion function can also suggest people who share common goals and dreams when reuniting, based on the user's friendships from junior high school. For example, it can suggest friends who currently have the same goals based on the dreams and goals the user had in junior high school. This makes it easier to meet up with friends who share common goals when reuniting.

[0053] The search unit can suggest people who share common hobbies and interests when reuniting, based on the user's friendships from junior high school. For example, it can suggest friends who share current hobbies and interests based on information about the clubs and extracurricular activities the user participated in during junior high school. This makes it easier to meet up with friends who have common topics to talk about when reuniting. The search unit can also suggest episodes that can be talked about when reuniting, based on the user's friendships from junior high school. For example, it can suggest episodes about school events and activities the user participated in during junior high school. This makes it easier to meet up with friends who have common topics to talk about when reuniting. The search unit can also suggest people who share common goals and dreams when reuniting, based on the user's friendships from junior high school. For example, it can suggest friends who currently have the same goals based on the dreams and goals the user had in junior high school. This makes it easier to meet up with friends who share common goals when reuniting.

[0054] The privacy protection function can use generative AI to suggest optimal settings based on a user's usage. For example, generative AI can automatically suggest optimal privacy settings based on a user's usage. For example, it can suggest appropriate privacy settings based on the functions the user uses frequently. Generative AI can analyze a user's usage and suggest optimal settings using machine learning algorithms and data analysis techniques. This allows it to suggest optimal privacy settings based on the user's usage. Generative AI can also analyze the user's privacy setting change history and suggest optimal settings based on past settings. For example, it can suggest settings appropriate for the current usage based on past setting changes. This allows the user to set privacy settings while referring to past settings. Furthermore, generative AI can collect feedback on the user's privacy settings and suggest optimal settings. For example, it can suggest appropriate settings based on the user's sense of security or anxiety after changing the settings. This allows the user to set privacy settings with peace of mind.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The profile creation unit registers information about the user's junior high school days. For example, the user can enter the name, location, and year of graduation of the junior high school they graduated from. They can also register information about club activities and classes they participated in during junior high school. Step 2: The search unit searches for alumni and friends based on the information registered by the profile creation unit. For example, users can search by specifying criteria such as graduation year, class, club activities, etc. In addition, profile photos and self-introductions are displayed in the search results, allowing users to confirm whether the person they find is actually someone they know. Step 3: The sharing section allows users to share their fond memories with classmates and friends found through the search section. For example, users can post photos, videos, text, etc. to share their memories from junior high school. The sharing section also provides a comment and "like" function for posts, allowing users to deepen their interactions with other users.

[0057] (Example 2) The SNS platform according to an embodiment of the present invention is a system that enables users to easily find their junior high school classmates and friends, share fond memories, and promote reunions. Thus, the SNS platform enables users to easily find their junior high school classmates and friends, share fond memories, and promote reunions.

[0058] An SNS platform according to an embodiment includes a profile creation unit, a search unit, and a sharing unit. The profile creation unit registers information about a user's junior high school days. For example, the user can enter the name, location, and graduation year of the junior high school from which the user graduated. The profile creation unit can also register information about club activities and classes the user participated in during junior high school. The search unit searches for alumni and friends based on the information registered by the profile creation unit. For example, the user can search by specifying criteria such as graduation year, class, and club activities. The search unit also displays profile photos and self-introductions in the search results, allowing the user to confirm whether the person found is someone the user knows. The sharing unit shares fond memories with alumni and friends found by the search unit. For example, the user can post photos, videos, text, and the like to share memories from junior high school. The sharing unit also provides a comment and "like" function for posts, allowing the user to deepen interactions with other users. As a result, the SNS platform according to an embodiment makes it easy for users to find junior high school alumni and friends and share fond memories. For example, users can reconnect with classmates they've lost touch with after graduation and reminisce about old memories, and increase opportunities for reunions through reunions and local events.

[0059] The profile creation unit can use the generation AI to complement photos from the user's junior high school days. For example, when a user creates a profile, the profile creation unit automatically complements photos from their junior high school days using the generation AI. For example, based on some photos provided by the user, the generation AI generates other photos and adds them to the profile. The generation AI can complement missing parts using an image completion algorithm or a deep learning model. This creates a more detailed user profile.

[0060] The profile creation unit can use the generation AI to generate memories of the user's junior high school days. For example, the profile creation unit uses the generation AI to automatically generate memories of the user's junior high school days based on the profile information entered by the user. For example, the generation AI can generate episodes of club activities and school events in which the user participated and add them to the profile. The generation AI can generate the user's memories using text generation algorithms and natural language processing technology. This allows the user's profile to be created in more detail.

[0061] The profile creation unit can use the emotion estimation function to analyze the emotions felt by the user when creating a profile and provide advice to bring out those emotions. For example, the profile creation unit can analyze the user's emotions in real time when creating a profile and provide advice to bring out positive emotions. For example, it can display a message that makes the user feel nostalgic. The emotion estimation function can estimate the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. This allows the user to create a profile while feeling positive emotions.

[0062] The profile creation unit can provide a function that allows the user to register a nickname or nickname used in junior high school. For example, when creating a profile, the profile creation unit adds a field in which the user can enter a nickname or nickname used in junior high school. For example, the user enters their own nickname in the "nickname" field. This allows the user to register a nickname or nickname used in junior high school.

[0063] The profile creation unit can estimate the user's friendships from junior high school based on the profile information and provide a friend list. The profile creation unit, for example, analyzes the profile information and develops an algorithm to automatically estimate the user's friendships from junior high school. For example, it can list friends who participated in the same class or club activities. The friendships are estimated based on criteria such as the same class, the same club activities, and mutual friends. The friend list is provided in the form of names, contact information, profile information, and the like. This makes it easier for users to find their friends from junior high school.

[0064] The profile creation unit can use the emotion estimation function to suggest music or images that emphasize the nostalgia felt by the user when creating a profile. For example, when creating a profile, the profile creation unit uses the emotion estimation function to analyze the nostalgia felt by the user and suggests nostalgic music based on the analysis. For example, the emotion estimation function can play songs that were popular when the user was in junior high school. The emotion estimation function can estimate the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. This allows the user to create a profile while feeling nostalgic.

[0065] The search unit can use generation AI to provide complementary information based on the user's memory, thereby improving search accuracy. For example, the search unit can use generation AI to provide complementary information based on the user's memory for search results. For example, the search results can be complemented based on the characteristics and episodes of friends that the user remembers. The generation AI can use text generation algorithms and natural language processing technology to generate information that complements the user's memory. This provides complementary information based on the user's memory, improving search accuracy.

[0066] The search unit can remind the user of friends they have forgotten based on the search criteria. The search unit adds a function to automatically remind the user of friends they have forgotten based on the search criteria, for example. For example, the search unit suggests forgotten friends based on the graduation year and class information entered by the user. The search criteria are set based on criteria such as name, school name, and graduation year. Reminders are sent by notification, email, pop-up, or other methods. By reminding the user of friends they have forgotten, the chances of reuniting are increased.

[0067] The search unit can use the emotion estimation function to analyze the user's emotional response to search results and prioritize displaying search results that evoke emotions. For example, the search unit can analyze the user's emotional response to search results in real time and prioritize displaying search results that evoke positive emotions. For example, search results for friends that make the user feel happy are displayed at the top. The emotion estimation function can estimate the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. This allows the user to view search results with positive emotions.

[0068] The search unit can display episodes from the user's junior high school days in response to search results, thereby improving the relevance of the search results. For example, the search unit adds a function to automatically display episodes from the user's junior high school days in response to search results. For example, episodes related to the search results are generated and displayed. The episodes are displayed in the form of school events, club activity memories, etc. This increases the relevance of the search results, allowing the user to obtain more appropriate results.

[0069] The search unit can display members of clubs or clubs in which the user participated based on search conditions. The search unit adds a function to automatically display members of clubs or clubs in which the user participated based on search conditions, for example. For example, members are displayed based on club information entered by the user. Search conditions are set based on criteria such as club name, club name, and member names. Clubs or clubs are displayed in the form of soccer club, drama club, etc. This makes it easier for the user to find members of clubs or clubs in which the user participated.

[0070] The search unit can use the emotion estimation function to display the user's emotional reaction to search results in real time and support the selection of search results. For example, the search unit adds a function to display the user's emotional reaction to search results in real time and support the selection of search results. For example, it highlights search results that make the user feel happy. The emotion estimation function can estimate the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. This makes it easier for the user to select search results based on their emotional reaction.

[0071] The sharing unit can use a generation AI to generate captions for posted photos and videos. For example, the sharing unit can automatically generate captions for posted photos using a generation AI. For example, it can analyze the content of the photo and generate and display an appropriate caption. The generation AI can use a text generation algorithm or natural language processing technology to generate captions that explain the content of the photo or video. This provides a clearer explanation of the posted photo or video.

[0072] The sharing unit can suggest related episodes from junior high school based on the content of the post. For example, the sharing unit analyzes the content of the post and the generation AI automatically suggests related episodes from junior high school. For example, it generates and displays episodes related to photos posted by the user. Episodes are suggested in the form of school events, club activity memories, etc. This makes it easy for users to find related episodes.

[0073] The sharing section can suggest episodes that other users can relate to for posted memories. For example, the sharing section adds a function in which the generation AI automatically suggests episodes that other users can relate to for posted memories. For example, it displays relatable episodes related to the content posted by the user. Episodes are suggested in the form of memories of school events, club activities, etc. This makes it easier for users to share their empathy with other users.

[0074] The sharing unit can reconstruct the user's friendships from junior high school based on the posted content and provide a friend list. For example, the sharing unit analyzes the posted content, and the generation AI automatically reconstructs the user's friendships from junior high school. For example, the friend list is updated based on photos and stories posted by the user. Friendships are reconstructed based on criteria such as being in the same class, the same club activities, and mutual friends. The friend list is provided in the form of names, contact information, profile information, etc. This keeps the user's friend list up to date.

[0075] The sharing unit can use the emotion estimation function to display other users' emotional reactions to posted content in real time, thereby improving the quality of sharing. The sharing unit adds a function to, for example, display other users' emotional reactions to posted content in real time, thereby improving the quality of sharing. For example, the sharing unit displays a graph of the emotional reactions to content posted by a user. The emotion estimation function can estimate the emotions of other users using technologies such as facial expression recognition, voice analysis, and text analysis. This allows users to check the emotional reactions of other users in real time.

[0076] The reunion promotion function can use generation AI to generate a customized invitation based on the user's interests for event information. The reunion promotion function, for example, uses generation AI to automatically generate a customized invitation based on the user's interests for event information. For example, an invitation that reflects the content of an event in which the user is interested is generated. The generation AI can generate an invitation based on the user's interests using a text generation algorithm or natural language processing technology. In this way, a customized invitation based on the user's interests is generated.

[0077] The reunion promotion function can suggest episodes that can be talked about when reuniting based on the profile information of event participants. For example, the reunion promotion function uses a generation AI to automatically suggest episodes that can be talked about when reuniting based on the profile information of event participants. For example, it can suggest episodes from participants' junior high school days. Episodes are suggested in the form of memories of school events, club activities, etc. This automatically suggests episodes that can be talked about when reuniting.

[0078] The reunion promotion function can use the emotion estimation function to analyze the emotional reactions of event participants and provide advice to promote reunions. The reunion promotion function, for example, analyzes the emotional reactions of event participants in real time and provides advice to promote positive reunions. For example, it displays a message that makes participants feel happy. The emotion estimation function can estimate the emotions of participants using technologies such as facial expression recognition, voice analysis, and text analysis. This allows for the provision of advice to promote positive reunions.

[0079] The reunion promotion function can suggest a reunion matching function based on the user's friendships from junior high school for event information. The reunion promotion function adds a reunion matching function based on the user's friendships from junior high school for event information, for example. For example, it matches friends who participated in the same class or club activity. Friendships are matched based on criteria such as the same class, the same club activity, or common friends. The matching function is performed using methods such as algorithms or manual settings. This makes it possible to match reunions based on the user's friendships from junior high school.

[0080] The reunion promotion function can suggest people with common hobbies or interests when reuniting based on the profile information of event participants. For example, the reunion promotion function uses the generation AI to automatically suggest people with common hobbies or interests when reuniting based on the profile information of event participants. For example, it suggests people related to the participants' hobbies and interests. Profile information is used in the form of name, hobbies, interests, etc. Common hobbies or interests are suggested in the form of sports, music, reading, etc. This allows people with common hobbies and interests to be automatically suggested when reuniting.

[0081] The reunion promotion function uses the emotion estimation function to display the emotional reactions of event participants in real time, thereby improving the quality of the reunion. The reunion promotion function adds a function to display the emotional reactions of event participants in real time, thereby improving the quality of the reunion. For example, it displays a message that makes participants feel happy. The emotion estimation function can estimate the emotions of participants using technologies such as facial expression recognition, voice analysis, and text analysis. This improves the quality of the reunion.

[0082] The privacy protection function can use generative AI to suggest optimal settings based on the user's usage. For example, the privacy protection function uses generative AI to automatically suggest optimal settings for privacy settings based on the user's usage. For example, it can suggest appropriate privacy settings based on the functions the user frequently uses. The generative AI can analyze the user's usage using machine learning algorithms and data analysis techniques and suggest optimal settings. This allows the optimal privacy settings to be suggested based on the user's usage.

[0083] The privacy protection function can record the change history of privacy settings and provide a function that allows users to easily check past settings. For example, the privacy protection function automatically records the change history of privacy settings and adds a function that allows users to easily check past settings. For example, it displays a list of setting changes made by the user in the past. Privacy settings are recorded in the form of the scope of disclosure, data storage period, access permissions, etc. The change history is recorded in the form of the date and time when the settings were changed, the content of the change, the reason for the change, etc. This allows users to easily check past privacy settings.

[0084] The privacy protection function can use the emotion estimation function to analyze the user's emotional response to privacy settings and provide advice to provide a sense of security. The privacy protection function, for example, analyzes the user's emotional response to privacy settings in real time and provides advice to increase the sense of security. For example, it displays a message that makes the user feel secure. The emotion estimation function can estimate the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. This allows the user to configure privacy settings with a sense of security.

[0085] The privacy protection function can provide customized settings for privacy settings based on the user's friendships from junior high school. For example, the privacy protection function proposes customized settings for privacy settings based on the user's friendships from junior high school. For example, it proposes settings that allow only specific friends to view. Friendships are customized based on criteria such as the same class, the same club activities, and mutual friends. The customized settings are proposed in the form of disclosure range, notification settings, data sharing range, etc. This allows customized settings to be proposed based on the user's friendships from junior high school.

[0086] The privacy protection function displays the user's emotional reaction to changes in privacy settings in real time, thereby improving the quality of the settings. The privacy protection function adds a function that displays the user's emotional reaction to changes in privacy settings in real time, thereby improving the quality of the settings. For example, it suggests settings that make the user feel more secure. The emotion estimation function can estimate the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. This makes it easier for users to adjust their privacy settings based on their emotional reaction.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The profile creation unit may provide a function for registering the user's hobbies and special skills from junior high school. For example, a field may be added for inputting hobbies such as sports, music, and art that the user was interested in during junior high school. This allows the user to register the hobbies and special skills from junior high school, making it easier to find friends who share common interests with other users. The profile creation unit may also provide a function for registering awards and commendations the user received during junior high school. For example, the user may enter medals received in school sports competitions or academic commendations. This creates a more detailed user profile. Furthermore, the profile creation unit may provide a function for registering the user's favorite books and movies from junior high school. For example, a field may be added for inputting the titles of the user's favorite books and movies. This allows the user to share their favorite books and movies from junior high school and make it easier to find common topics with other users.

[0089] The search unit can suggest related events and activities based on the user's memories from junior high school. For example, similar events and activities currently being held can be suggested based on information about clubs and extracurricular activities the user participated in during junior high school. This makes it easier for the user to relive their junior high school memories. The search unit can also suggest related tourist spots and travel plans based on places the user visited or travel destinations during junior high school. For example, current tourist information can be provided based on places the user visited on a school trip during junior high school. This gives the user an opportunity to revisit nostalgic places. Furthermore, the search unit can suggest related learning resources based on the teaching materials and reference books the user used during junior high school. For example, current learning resources can be provided based on the mathematics textbook the user used during junior high school. This makes it easier for the user to relive their junior high school learning experiences.

[0090] The sharing unit can suggest episodes that other users can relate to for memories posted by a user. For example, if a user posts a photo from their junior high school days and another user participated in the same event or activity, the sharing unit will suggest that episode. This makes it easier for users to share their empathy with each other. The sharing unit can also suggest music or videos related to memories posted by a user. For example, for a photo posted by a user of club activities from their junior high school days, the sharing unit will suggest music or videos that were popular at the time. This allows users to more vividly relive their memories. Furthermore, the sharing unit can provide templates that make it easier for other users to comment or "like" memories posted by a user. For example, it can display templates for comments that show empathy for a photo posted by a user. This deepens interactions between users.

[0091] The reunion promotion function can suggest people who share common hobbies and interests when reuniting, based on the user's friendships from junior high school. For example, it can suggest friends who share current hobbies and interests based on information about the clubs and extracurricular activities the user participated in during junior high school. This makes it easier to meet up with friends who have common topics to talk about when reuniting. The reunion promotion function can also suggest stories that can be talked about when reuniting, based on the user's friendships from junior high school. For example, it can suggest stories about school events and activities the user participated in during junior high school. This makes it easier to meet up with friends who have common topics to talk about when reuniting. The reunion promotion function can also suggest people who share common goals and dreams when reuniting, based on the user's friendships from junior high school. For example, it can suggest friends who currently have the same goals based on the dreams and goals the user had in junior high school. This makes it easier to meet up with friends who share common goals when reuniting.

[0092] The profile creation unit can use the emotion estimation function to suggest music or images that emphasize the nostalgia the user feels when creating a profile. For example, when creating a profile, the emotion estimation function can be used to analyze the nostalgia the user feels and suggest nostalgic music based on that analysis. For example, songs that were popular when the user was in junior high school can be played. The emotion estimation function can estimate the user's emotions using techniques such as facial expression recognition, voice analysis, and text analysis. This allows the user to create a profile while feeling nostalgic. The emotion estimation function can also be used to suggest images that the user feels nostalgic about. For example, photos taken by the user in junior high school or school scenes can be displayed. This allows the user to create a profile while feeling nostalgic. Furthermore, the emotion estimation function can also be used to suggest episodes that the user feels nostalgic about. For example, episodes of school events or club activities that the user participated in during junior high school can be displayed. This allows the user to create a profile while feeling nostalgic.

[0093] The search unit can suggest people who share common hobbies and interests when reuniting, based on the user's friendships from junior high school. For example, it can suggest friends who share current hobbies and interests based on information about the clubs and extracurricular activities the user participated in during junior high school. This makes it easier to meet up with friends who have common topics to talk about when reuniting. The search unit can also suggest episodes that can be talked about when reuniting, based on the user's friendships from junior high school. For example, it can suggest episodes about school events and activities the user participated in during junior high school. This makes it easier to meet up with friends who have common topics to talk about when reuniting. The search unit can also suggest people who share common goals and dreams when reuniting, based on the user's friendships from junior high school. For example, it can suggest friends who currently have the same goals based on the dreams and goals the user had in junior high school. This makes it easier to meet up with friends who share common goals when reuniting.

[0094] The sharing unit can use the emotion estimation function to display other users' emotional reactions to posted content in real time, thereby improving the quality of sharing. For example, a function is added to display other users' emotional reactions to posted content in real time, thereby improving the quality of sharing. For example, a graph is displayed showing the emotional reactions to content posted by a user. The emotion estimation function can estimate the emotions of other users using technologies such as facial expression recognition, voice analysis, and text analysis. This allows a user to check the emotional reactions of other users in real time. The emotion estimation function can also be used to analyze other users' emotional reactions to posted content and provide advice on how to elicit positive emotions. For example, the emotion estimation function can suggest comments that will make other users feel happy about content posted by a user. This allows a user to share the posted content with positive emotions. Furthermore, the emotion estimation function can also be used to suggest related episodes and content based on other users' emotional reactions to the posted content. For example, the emotion estimation function can suggest episodes that other users can relate to in response to content posted by a user. This deepens interactions between users.

[0095] The reunion promotion function can improve the quality of reunions by using the emotion estimation function to display the emotional reactions of event participants in real time. For example, a function to improve the quality of reunions by displaying the emotional reactions of event participants in real time is added. For example, a message that makes participants feel happy can be displayed. The emotion estimation function can estimate the emotions of participants using technologies such as facial recognition, voice analysis, and text analysis. This improves the quality of reunions. The emotion estimation function can also be used to analyze the emotional reactions of event participants and provide advice to promote a positive reunion. For example, a message that makes participants feel happy can be displayed. This provides advice to promote a positive reunion. Furthermore, the emotion estimation function can also be used to suggest common topics and episodes during reunions based on the emotional reactions of event participants. For example, it can suggest episodes that participants can relate to. This allows participants to have common topics to talk about during reunions.

[0096] The privacy protection function can use generative AI to suggest optimal settings based on a user's usage. For example, generative AI can automatically suggest optimal privacy settings based on a user's usage. For example, it can suggest appropriate privacy settings based on the functions the user uses frequently. Generative AI can analyze a user's usage and suggest optimal settings using machine learning algorithms and data analysis techniques. This allows it to suggest optimal privacy settings based on the user's usage. Generative AI can also analyze the user's privacy setting change history and suggest optimal settings based on past settings. For example, it can suggest settings appropriate for the current usage based on past setting changes. This allows the user to set privacy settings while referring to past settings. Furthermore, generative AI can collect feedback on the user's privacy settings and suggest optimal settings. For example, it can suggest appropriate settings based on the user's sense of security or anxiety after changing the settings. This allows the user to set privacy settings with peace of mind.

[0097] The privacy protection function can use the emotion estimation function to analyze a user's emotional response to privacy settings and provide advice to provide a sense of security. For example, the function can analyze a user's emotional response to privacy settings in real time and provide advice to increase the sense of security. For example, the function can display a message that makes the user feel secure. The emotion estimation function can estimate a user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. This allows the user to configure privacy settings with a sense of security. The emotion estimation function can also be used to provide appropriate advice for privacy settings that make the user feel uneasy. For example, for settings that make the user feel uneasy, the function can suggest setting changes that increase the sense of security. This allows the user to configure privacy settings without feeling uneasy. Furthermore, the emotion estimation function can also be used to provide advice to reduce the stress the user feels when changing privacy settings. For example, for settings that make the user feel stressed, the function can display a message that helps the user relax. This allows the user to configure privacy settings without feeling stressed.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The profile creation unit registers information about the user's junior high school days. For example, the user can enter the name, location, and year of graduation of the junior high school they graduated from. They can also register information about club activities and classes they participated in during junior high school. Step 2: The search unit searches for alumni and friends based on the information registered by the profile creation unit. For example, users can search by specifying criteria such as graduation year, class, club activities, etc. In addition, profile photos and self-introductions are displayed in the search results, allowing users to confirm whether the person they find is actually someone they know. Step 3: The sharing section allows users to share their fond memories with classmates and friends found through the search section. For example, users can post photos, videos, text, etc. to share their memories from junior high school. The sharing section also provides a comment and "like" function for posts, allowing users to deepen their interactions with other users.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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]

[0167] 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 profile creation section for registering information about the user's junior high school days; a search unit that searches for alumni and friends based on the information registered by the profile creation unit; a sharing section for sharing memories with classmates and friends found by the search section; A system characterized by:

2. The profile creation unit Using emotion estimation function, we analyze the emotions felt by users when creating their profile and provide advice on how to bring out their emotions.

2. The system of claim 1.

3. The search unit Using generative AI to provide complementary information based on the user's memory, improving search accuracy 2. The system of claim 1.

4. The common part is Generate captions for posted photos and videos using generative AI 2. The system of claim 1.

5. The reunion promotion function is Use emotion estimation to analyze the emotional responses of event attendees and provide advice to promote reunions 2. The system of claim 1.

6. Privacy protection features include: Using emotion estimation, we analyze users' emotional reactions to privacy settings and provide advice to provide peace of mind.

2. The system of claim 1.

7. The search unit Using emotion estimation, the system analyzes users' emotional responses to search results and prioritizes search results that evoke emotions.

2. The system of claim 1.

8. The common part is Emotion estimation function displays other users' emotional reactions to posts in real time, improving the quality of sharing 2. The system of claim 1.

Images