System

Abstract

An object of the system according to the embodiment is to display profile information or a post of a user using a face photograph as a key and to perform communication or matching.SOLUTION: A system according to an embodiment includes a face recognition unit, a profile display unit, a post display unit, a communication unit, a matching unit, and a community formation unit. The face recognition unit analyzes the face photograph. The profile display unit displays profile information on the basis of a result analyzed by the face recognition unit. The post display unit displays the latest post based on the profile information displayed by the profile display unit. The communication unit transmits a message based on the post displayed by the post display unit. The matching unit performs matching according to the preference of the face on the basis of the message transmitted by the communication unit. The community formation unit forms a community according to the preference based on a result of the matching by the matching unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately provided community services using facial photos as a key, and there is room for improvement.

[0005] The system according to the embodiment aims to display user profile information and posts using facial photos as a key, and to facilitate communication and matching. [Means for solving the problem]

[0006] The system according to the embodiment includes a face recognition unit, a profile display unit, a post display unit, a communication unit, a matching unit, and a community creation unit. The face recognition unit analyzes a face photo. The profile display unit displays profile information based on the results of the analysis by the face recognition unit. The post display unit displays recent posts based on the profile information displayed by the profile display unit. The communication unit sends messages based on the posts displayed by the post display unit. The matching unit performs matching according to facial preferences based on the messages sent by the communication unit. The community creation unit creates a community according to preferences based on the results of matching by the matching unit. [Effects of the Invention]

[0007] The system according to the embodiment displays user profile information and posts using facial photos as a key, and can facilitate communication and matching. [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 community service according to the embodiment of the present invention is a system that enables users to browse profile information and recent posts using facial photos as a key, and realizes communication, matching, and community building between users. As a result, the community service enables users to browse profile information and recent posts using facial photos as a key, and realizes communication, matching, and community building between users.

[0029] A community service according to an embodiment includes a face recognition unit, a profile display unit, a post display unit, a communication unit, a matching unit, and a community building unit. The face recognition unit analyzes a face photo. For example, the face recognition unit analyzes a face photo taken with a smartphone camera and extracts facial information about the registrant. The face recognition unit can also analyze facial feature points using a face recognition algorithm. The face recognition unit can also use a combination of face recognition algorithms to improve the accuracy of analyzing the face photo. The profile display unit displays profile information based on the results of the analysis by the face recognition unit. For example, the profile display unit displays information such as the registrant's name, age, and hobbies. The profile display unit can also customize the display content based on user settings. The profile display unit can also automatically update the registrant's latest profile information. The post display unit displays recent posts based on the profile information displayed by the profile display unit. For example, the post display unit displays photos, videos, and text messages recently posted by the registrant. The post display unit can also analyze the content of the posts and adjust the display order based on the user's interests. The post display unit can also use the generation AI to automatically summarize the post content, allowing the user to understand the content in a short amount of time. The communication unit sends messages based on the posts displayed by the post display unit. For example, the communication unit provides an interface for the user to send messages to other registered users. The communication unit can also analyze the content of the message using the generation AI and automatically generate appropriate reply candidates. The communication unit can also analyze the emotion of the message sent by the user using an emotion estimation function and adjust the reply content based on that emotion. The matching unit performs matching according to facial preferences based on the message sent by the communication unit. For example, the matching unit analyzes the user's facial preference and presents matching candidates based on that. The matching unit can also analyze the user's past matching history and customize matching candidates based on that history.The matching unit can also use the emotion estimation function to analyze the emotion a user feels when viewing the face of a matching candidate and adjust the matching candidate based on that emotion. The community formation unit can form a community according to preferences based on the matching results obtained by the matching unit. For example, the community formation unit can analyze a user's preferences and interests and form a community based on the analysis. The community formation unit can also analyze a user's past community participation history and propose a new community based on that history. The community formation unit can also use the emotion estimation function to analyze the emotion a user felt when joining a community and adjust the community activities based on that emotion. This allows the community service according to the embodiment to enable communication, matching, and community formation between users by viewing profile information and recent posts using facial photos as a key. For example, if a user takes a photo of another registered user's face while walking around town, the user can view the registered user's profile and recent posts and further communicate by sending messages. Matching and community formation according to user preferences are also possible, promoting interaction between users.

[0030] The face recognition unit can compare past and current photos of a user and notify the user if there are any changes in the profile information. For example, the face recognition unit uses face recognition technology to compare past and current photos of a user and detect changes in appearance. For example, it notifies the user of changes in hairstyle or whether the user wears glasses. The face recognition unit also uses face recognition technology to detect estimated changes in the user's age and notifies the user of the changes. For example, it notifies the user if the user has become younger or older. The face recognition unit also uses face recognition technology to detect changes in the user's facial expression and notifies the user of the changes. For example, it notifies the user if the user smiles more or less. In this way, the latest information can be provided by notifying the user of changes in the user's profile information.

[0031] The face recognition unit can estimate a user's hobbies and interests from a facial photograph taken by the user and display related profile information. The face recognition unit, for example, uses face recognition technology to estimate a user's hobbies from a facial photograph taken by the user and displays related profile information. For example, sports-related information is displayed for a user who looks like he or she likes sports. The face recognition unit also uses face recognition technology to estimate a user's interests from a facial photograph taken by the user and displays related profile information. For example, music-related information is displayed for a user who looks like he or she likes music. The face recognition unit also uses face recognition technology to comprehensively analyze a user's hobbies and interests from a facial photograph taken by the user and displays related profile information. For example, information on both sports and music is displayed for a user who is interested in both sports and music. This makes it possible to display profile information according to a user's hobbies and interests.

[0032] The face recognition unit can estimate a person's health condition from a facial photograph taken by the user and provide health advice. The face recognition unit, for example, uses face recognition technology to estimate a person's health condition from a facial photograph taken by the user and provide health advice. For example, the face recognition unit estimates a person's health condition from facial color and skin condition and provides health management advice. The face recognition unit also uses face recognition technology to analyze a person's facial expression from a facial photograph taken by the user, estimates a stress state, and provides stress management advice. For example, the face recognition unit estimates a stress level from changes in facial expression and suggests relaxation methods. The face recognition unit also uses face recognition technology to comprehensively analyze a person's health condition from a facial photograph taken by the user and provide health advice. For example, the face recognition unit comprehensively analyzes changes in facial color, skin condition, and facial expression and provides comprehensive health management advice. This makes it possible to provide advice according to the user's health condition.

[0033] The post display unit can use the generation AI to analyze the content of recent posts and select posts to be displayed preferentially based on the user's interests and concerns. The post display unit, for example, uses the generation AI to analyze the content of recent posts and select posts to be displayed preferentially based on the user's interests and concerns. For example, if the user is interested in sports, sports-related posts are displayed preferentially. The post display unit also uses the generation AI to analyze the content of recent posts and select posts to be displayed preferentially based on the user's past browsing history. For example, posts in categories that the user has viewed frequently in the past are displayed preferentially. The post display unit also uses the generation AI to analyze the content of recent posts and select posts to be displayed preferentially by comprehensively considering the user's interests and concerns and past browsing history. For example, if the user is interested in sports, sports-related posts that the user has viewed frequently in the past are displayed preferentially. This makes it possible to select posts to be displayed preferentially based on the user's interests and concerns.

[0034] The post display unit can use the generation AI to automatically summarize the post content, allowing the user to understand the content in a short amount of time. The post display unit, for example, uses the generation AI to automatically summarize the post content, allowing the user to understand the content in a short amount of time. For example, it converts a long post into a summary of a few lines. The post display unit also uses the generation AI to automatically summarize the post content and highlight important information. For example, it extracts the main points of the post and displays the important information with emphasis. The post display unit also uses the generation AI to automatically summarize the post content and customize the summary content based on the user's interests and concerns. For example, it prioritizes summarizing information that the user is interested in. In this way, the post content can be automatically summarized, allowing the user to understand the content in a short amount of time.

[0035] The post display unit uses a generation AI to automatically translate the post content into different languages, thereby supporting international users. The post display unit, for example, uses a generation AI to automatically translate the post content into different languages, thereby supporting international users. For example, it translates an English post into Japanese. The post display unit also uses a generation AI to automatically translate the post content into different languages, thereby supporting multiple languages. For example, it translates an English post into Japanese, Chinese, Spanish, etc. The post display unit also uses a generation AI to automatically translate the post content into different languages ​​and display it based on the user's language setting. For example, it automatically translates and displays the post into the language set by the user. This allows the post content to be automatically translated into different languages, thereby supporting international users.

[0036] The communication unit can use the generation AI to analyze the content of messages sent by users and automatically generate appropriate reply candidates. For example, the communication unit uses the generation AI to analyze the content of messages sent by users and automatically generate appropriate reply candidates. For example, it automatically generates answers to questions or messages of gratitude. The communication unit also uses the generation AI to analyze the content of messages sent by users and automatically generate reply candidates based on past reply history. For example, it generates reply candidates based on similar messages sent in the past. The communication unit also uses the generation AI to analyze the content of messages sent by users and automatically generate reply candidates based on the user's interests and concerns. For example, it generates replies related to topics that interest the user. In this way, by analyzing the content of messages sent by users and automatically generating appropriate reply candidates, communication can be carried out smoothly.

[0037] The communication unit can use the generation AI to analyze the communication history between users and customize the reply content based on past interactions. For example, the communication unit uses the generation AI to analyze the communication history between users and customize the reply content based on past interactions. For example, it generates a reply that reflects the topic of a previous conversation. The communication unit also uses the generation AI to analyze the communication history between users and customize the reply content based on the user's interests and concerns. For example, it generates a reply related to a topic that interests the user. The communication unit also uses the generation AI to analyze the communication history between users and customize the reply content based on the user's emotional state. For example, if the user is expressing positive emotions, it generates a positive reply. This enables more personalized communication by analyzing the communication history between users and customizing the reply content based on past interactions.

[0038] The communication unit can use the generation AI to automatically translate messages sent by users into different languages, thereby supporting international communication. For example, the communication unit can use the generation AI to automatically translate messages sent by users into different languages, thereby supporting international communication. For example, translating an English message into Japanese. The communication unit can also use the generation AI to automatically translate messages sent by users into different languages, thereby supporting multiple languages. For example, translating an English message into Japanese, Chinese, Spanish, etc. The communication unit can also use the generation AI to automatically translate messages sent by users into different languages ​​and display them based on the user's language settings. For example, the message can be automatically translated into the language set by the user and displayed. This can support international communication by automatically translating messages sent by users into different languages.

[0039] The communication unit can use the generation AI to analyze communication between users and automatically match users with common interests. For example, the communication unit can use the generation AI to analyze communication between users and automatically match users with common interests. For example, it can match users with the same hobbies. The communication unit can also use the generation AI to analyze communication between users and match users with common interests based on past interactions. For example, it can match users who have previously conversed on the same topic. The communication unit can also use the generation AI to analyze communication between users and comprehensively match users with common interests based on the users' interests. For example, it can prioritize matching users with the same hobbies. This enables more appropriate matching by analyzing communication between users and automatically matching users with common interests.

[0040] The matching unit can use the generation AI to analyze the user's facial preferences in detail and present more accurate matching candidates. The matching unit, for example, uses the generation AI to analyze the user's facial preferences in detail and present more accurate matching candidates. For example, it prioritizes presenting users with specific facial features. The matching unit also uses the generation AI to analyze the user's facial preferences in detail and presents matching candidates based on past matching history. For example, it prioritizes presenting users with facial features that have been preferred in the past. The matching unit also uses the generation AI to analyze the user's facial preferences in detail and presents matching candidates based on the user's interests and concerns. For example, it prioritizes presenting users with specific hobbies and interests. This enables more appropriate matching by analyzing the user's facial preferences in detail and presenting more accurate matching candidates.

[0041] The matching unit can use the generation AI to analyze the user's past matching history and customize matching candidates based on that history. For example, the matching unit uses the generation AI to analyze the user's past matching history and customize matching candidates based on that history. For example, it may preferentially present users with facial features that have been favored in the past. The matching unit also uses the generation AI to analyze the user's past matching history and customize matching candidates based on the user's interests and concerns. For example, it may preferentially present users with specific hobbies and concerns. The matching unit also uses the generation AI to analyze the user's past matching history and customize matching candidates based on the user's emotional responses. For example, it may preferentially present candidates that showed positive emotions. This enables more appropriate matching by analyzing the user's past matching history and customizing matching candidates based on that history.

[0042] The matching unit can use the generation AI to adapt the user's facial preferences to different cultural spheres and regions, thereby supporting international matching. The matching unit, for example, uses the generation AI to adapt the user's facial preferences to different cultural spheres and regions, thereby supporting international matching. For example, it performs matching that takes into account facial features of different cultural spheres. The matching unit also uses the generation AI to adapt the user's facial preferences to different cultural spheres and regions, thereby performing matching that is compatible with multiple regions. For example, it performs matching that is adapted for each region, such as Asia, Europe, and America. The matching unit also uses the generation AI to adapt the user's facial preferences to different cultural spheres and regions, thereby performing matching based on the user's language setting. For example, it performs international matching based on the language set by the user. This allows the user's facial preferences to be adapted to different cultural spheres and regions, thereby supporting international matching.

[0043] The matching unit can use the generation AI to analyze a user's facial preferences and match users with related hobbies and interests. For example, the matching unit uses the generation AI to analyze a user's facial preferences and match users with related hobbies and interests. For example, it prioritizes presenting users with the same hobbies. The matching unit also uses the generation AI to analyze a user's facial preferences and match users with related hobbies and interests based on past matching history. For example, it matches users who have had the same hobbies in the past. The matching unit also uses the generation AI to analyze a user's facial preferences and comprehensively match users with related hobbies and interests based on the user's interests. For example, it prioritizes matching users with the same hobbies and interests. This enables more appropriate matching by analyzing a user's facial preferences and matching users with related hobbies and interests.

[0044] The community formation unit uses the generation AI to analyze user preferences and interests in detail, allowing it to form more accurate communities. The community formation unit, for example, uses the generation AI to analyze user preferences and interests in detail and form more accurate communities. For example, it forms a community that brings together users with specific hobbies and interests. The community formation unit also uses the generation AI to analyze user preferences and interests in detail and form a community based on past community participation history. For example, it forms a community similar to a community that the user has previously participated in. The community formation unit also uses the generation AI to analyze user preferences and interests in detail and form a community based on the user's emotional response. For example, it forms a community that brings together users who have expressed positive emotions. This allows for more accurate analysis of user preferences and interests, enabling more appropriate communities.

[0045] The community formation unit can use the generation AI to analyze the user's past community participation history and propose new communities based on that history. For example, the community formation unit can use the generation AI to analyze the user's past community participation history and propose new communities based on that history. For example, it can propose communities similar to communities the user has previously participated in. The community formation unit can also use the generation AI to analyze the user's past community participation history and propose new communities based on the user's interests and concerns. For example, it can propose communities related to users with specific hobbies and concerns. The community formation unit can also use the generation AI to analyze the user's past community participation history and propose new communities based on the user's emotional responses. For example, it can propose communities that showed positive emotions. This enables more appropriate communities to be created by analyzing the user's past community participation history and proposing new communities based on that history.

[0046] The community formation unit can use the generation AI to adapt the user's preferences and interests to different cultural spheres and regions, thereby forming an international community. The community formation unit, for example, uses the generation AI to adapt the user's preferences and interests to different cultural spheres and regions, thereby forming an international community. For example, a community is formed that brings together users from different cultural spheres. The community formation unit also uses the generation AI to adapt the user's preferences and interests to different cultural spheres and regions, thereby forming communities that correspond to multiple regions. For example, communities adapted to each region, such as Asia, Europe, and America, are formed. The community formation unit also uses the generation AI to adapt the user's preferences and interests to different cultural spheres and regions, thereby forming a community based on the user's language setting. For example, an international community is formed based on the language set by the user. In this way, an international community can be formed by adapting the user's preferences and interests to different cultural spheres and regions.

[0047] The community formation unit can use the generation AI to analyze the user's preferences and interests and suggest related events and activities. For example, the community formation unit uses the generation AI to analyze the user's preferences and interests and suggest related events and activities. For example, it suggests events related to users with specific hobbies or interests. The community formation unit also uses the generation AI to analyze the user's preferences and interests and suggest related events and activities based on their past event participation history. For example, it suggests events similar to events that the user has previously attended. The community formation unit also uses the generation AI to analyze the user's preferences and interests and suggest related events and activities based on the user's emotional response. For example, it suggests events that showed positive emotions. This enables more appropriate community activities by analyzing the user's preferences and interests and suggesting related events and activities.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The community service may further include an activity analysis unit that analyzes the user's activity history. The activity analysis unit may analyze events and activities that the user has participated in in the past and suggest new events and activities based on that history. For example, the activity analysis unit may analyze sporting events that the user has participated in in the past and suggest the next sporting event. The activity analysis unit may also suggest new activities that the user may be interested in based on the user's activity history. For example, if the user has participated in outdoor activities in the past, the activity analysis unit may suggest a new hiking event. Furthermore, the activity analysis unit may analyze the user's activity history and suggest events that will provide greater satisfaction based on the user's evaluation of events that they have participated in in the past. This makes it possible to suggest more personalized events and activities based on the user's activity history.

[0050] The face recognition unit can infer a user's occupation from a facial photograph and display related profile information. For example, face recognition technology can be used to infer that a user is a businessman from a facial photograph and display business-related information. The face recognition unit can also infer that a user is a student from a facial photograph and display information related to school and academics. Furthermore, the face recognition unit can infer that a user is an artist from a facial photograph and display information related to art and creative activities. This makes it possible to display profile information according to a user's occupation.

[0051] The face recognition unit can infer a user's lifestyle from a facial photograph and display related profile information. For example, face recognition technology can be used to infer from a user's facial photograph that the user is health-conscious, and information related to health and fitness can be displayed. The face recognition unit can also infer from a user's facial photograph that the user likes to travel, and display information related to travel. Furthermore, the face recognition unit can infer from a user's facial photograph that the user is a homely person, and display information related to home and childcare. This makes it possible to display profile information tailored to the user's lifestyle.

[0052] The facial recognition unit can infer a user's cultural background from a facial photograph and display related profile information. For example, facial recognition technology can be used to infer that a user is of Asian descent from a facial photograph and display information related to Asian culture. The facial recognition unit can also infer that a user is of European descent from a facial photograph and display information related to European culture. Furthermore, the facial recognition unit can infer that a user is of African descent from a facial photograph and display information related to African culture. This makes it possible to display profile information according to the user's cultural background.

[0053] The face recognition unit can estimate a user's living environment from a facial photograph and display related profile information. For example, face recognition technology can be used to estimate from a user's facial photograph that the user lives in an urban area and display information related to urban life. The face recognition unit can also estimate from a user's facial photograph that the user lives in a suburban area and display information related to suburban life. Furthermore, the face recognition unit can estimate from a user's facial photograph that the user lives in a rural area and display information related to rural life. This makes it possible to display profile information according to the user's living environment.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The face recognition unit analyzes the face photo. For example, it analyzes a face photo taken with a smartphone camera and extracts the registrant's facial information. It can also analyze facial feature points using a face recognition algorithm, and combine multiple face recognition algorithms to improve analysis accuracy. Step 2: The profile display unit displays profile information based on the results of the analysis by the face recognition unit. For example, it can display information such as the registrant's name, age, and hobbies, and can customize the display content based on the user's settings. It can also automatically update the latest profile information. Step 3: The post display unit displays recent posts based on the profile information displayed by the profile display unit. For example, it can display photos, videos, and text messages recently posted by the subscriber, and can analyze the content of the posts to adjust the display order based on the user's interests. It can also use generation AI to automatically summarize the content of the posts, allowing the user to understand the content in a short amount of time. Step 4: The communication unit sends a message based on the post displayed by the post display unit. For example, it can provide an interface for users to send messages to other registered users, analyze the content of the message using a generation AI, and automatically generate appropriate reply candidates. It can also analyze the emotion of the message sent by the user using an emotion estimation function and adjust the reply content based on that emotion. Step 5: The matching unit performs matching according to facial preferences based on the message sent by the communication unit. For example, it analyzes the user's facial preferences and presents matching candidates based on them. It can also analyze the user's past matching history and customize matching candidates based on that history. Furthermore, it can use an emotion estimation function to analyze the user's emotions when looking at the faces of matching candidates and adjust matching candidates based on those emotions. Step 6: The community formation unit forms a community according to the preferences based on the matching results obtained by the matching unit. For example, it may analyze the user's preferences and interests and form a community based on them. It may also analyze the user's past community participation history and suggest new communities based on that history. Furthermore, it may use an emotion estimation function to analyze the emotions the user felt when joining a community and adjust the community activities based on those emotions.

[0056] (Example 2) The community service according to the embodiment of the present invention is a system that enables users to browse profile information and recent posts using facial photos as a key, and realizes communication, matching, and community building between users. As a result, the community service enables users to browse profile information and recent posts using facial photos as a key, and realizes communication, matching, and community building between users.

[0057] A community service according to an embodiment includes a face recognition unit, a profile display unit, a post display unit, a communication unit, a matching unit, and a community building unit. The face recognition unit analyzes a face photo. For example, the face recognition unit analyzes a face photo taken with a smartphone camera and extracts facial information about the registrant. The face recognition unit can also analyze facial feature points using a face recognition algorithm. The face recognition unit can also use a combination of face recognition algorithms to improve the accuracy of analyzing the face photo. The profile display unit displays profile information based on the results of the analysis by the face recognition unit. For example, the profile display unit displays information such as the registrant's name, age, and hobbies. The profile display unit can also customize the display content based on user settings. The profile display unit can also automatically update the registrant's latest profile information. The post display unit displays recent posts based on the profile information displayed by the profile display unit. For example, the post display unit displays photos, videos, and text messages recently posted by the registrant. The post display unit can also analyze the content of the posts and adjust the display order based on the user's interests. The post display unit can also use the generation AI to automatically summarize the post content, allowing the user to understand the content in a short amount of time. The communication unit sends messages based on the posts displayed by the post display unit. For example, the communication unit provides an interface for the user to send messages to other registered users. The communication unit can also analyze the content of the message using the generation AI and automatically generate appropriate reply candidates. The communication unit can also analyze the emotion of the message sent by the user using an emotion estimation function and adjust the reply content based on that emotion. The matching unit performs matching according to facial preferences based on the message sent by the communication unit. For example, the matching unit analyzes the user's facial preference and presents matching candidates based on that. The matching unit can also analyze the user's past matching history and customize matching candidates based on that history.The matching unit can also use the emotion estimation function to analyze the emotion a user feels when viewing the face of a matching candidate and adjust the matching candidate based on that emotion. The community formation unit can form a community according to preferences based on the matching results obtained by the matching unit. For example, the community formation unit can analyze a user's preferences and interests and form a community based on the analysis. The community formation unit can also analyze a user's past community participation history and propose a new community based on that history. The community formation unit can also use the emotion estimation function to analyze the emotion a user felt when joining a community and adjust the community activities based on that emotion. This allows the community service according to the embodiment to enable communication, matching, and community formation between users by viewing profile information and recent posts using facial photos as a key. For example, if a user takes a photo of another registered user's face while walking around town, the user can view the registered user's profile and recent posts and further communicate by sending messages. Matching and community formation according to user preferences are also possible, promoting interaction between users.

[0058] The face recognition unit can estimate the user's age, gender, and emotional state, and customize and display profile information based on that. The face recognition unit, for example, uses face recognition technology to estimate the user's age and display profile information appropriate for that age. For example, it emphasizes hobbies and school information for younger users, and work history and family information for older users. The face recognition unit also uses face recognition technology to estimate the user's gender and display profile information appropriate for that gender. For example, it emphasizes sports and work-related information for men, and fashion and beauty-related information for women. The face recognition unit also uses an emotion estimation function to analyze the user's emotional state and customize and display profile information based on that emotion. For example, if the user is expressing positive emotions, positive information is emphasized and displayed. This makes it possible to customize and display profile information according to the user's age, gender, and emotional state.

[0059] The face recognition unit can compare past and current photos of a user and notify the user if there are any changes in the profile information. For example, the face recognition unit uses face recognition technology to compare past and current photos of a user and detect changes in appearance. For example, it notifies the user of changes in hairstyle or whether the user wears glasses. The face recognition unit also uses face recognition technology to detect estimated changes in the user's age and notifies the user of the changes. For example, it notifies the user if the user has become younger or older. The face recognition unit also uses face recognition technology to detect changes in the user's facial expression and notifies the user of the changes. For example, it notifies the user if the user smiles more or less. In this way, the latest information can be provided by notifying the user of changes in the user's profile information.

[0060] The face recognition unit can estimate a user's hobbies and interests from a facial photograph taken by the user and display related profile information. The face recognition unit, for example, uses face recognition technology to estimate a user's hobbies from a facial photograph taken by the user and displays related profile information. For example, sports-related information is displayed for a user who looks like he or she likes sports. The face recognition unit also uses face recognition technology to estimate a user's interests from a facial photograph taken by the user and displays related profile information. For example, music-related information is displayed for a user who looks like he or she likes music. The face recognition unit also uses face recognition technology to comprehensively analyze a user's hobbies and interests from a facial photograph taken by the user and displays related profile information. For example, information on both sports and music is displayed for a user who is interested in both sports and music. This makes it possible to display profile information according to a user's hobbies and interests.

[0061] The face recognition unit can estimate a person's health condition from a facial photograph taken by the user and provide health advice. The face recognition unit, for example, uses face recognition technology to estimate a person's health condition from a facial photograph taken by the user and provide health advice. For example, the face recognition unit estimates a person's health condition from facial color and skin condition and provides health management advice. The face recognition unit also uses face recognition technology to analyze a person's facial expression from a facial photograph taken by the user, estimates a stress state, and provides stress management advice. For example, the face recognition unit estimates a stress level from changes in facial expression and suggests relaxation methods. The face recognition unit also uses face recognition technology to comprehensively analyze a person's health condition from a facial photograph taken by the user and provide health advice. For example, the face recognition unit comprehensively analyzes changes in facial color, skin condition, and facial expression and provides comprehensive health management advice. This makes it possible to provide advice according to the user's health condition.

[0062] The face recognition unit can estimate a user's emotions from a facial photograph taken by the user and customize the display content of the profile information based on the emotions. For example, the face recognition unit uses an emotion estimation function to analyze the emotions expressed by the user when photographing the faces of other registrants and customize the display content of the profile information based on the emotions. For example, positive information is displayed for a user with positive emotions. The face recognition unit also uses the emotion estimation function to analyze the emotions expressed by the user when photographing the faces of other registrants and adjust the display layout of the profile information based on the emotions. For example, a layout with bright colors is displayed for a user with positive emotions. The face recognition unit also uses the emotion estimation function to analyze the emotions expressed by the user when photographing the faces of other registrants and comprehensively customize the display content and layout of the profile information based on the emotions. For example, positive information and a layout with bright colors are displayed for a user with positive emotions. This makes it possible to customize the display of profile information according to the user's emotions.

[0063] The post display unit can use the generation AI to analyze the content of recent posts and select posts to be displayed preferentially based on the user's interests and concerns. The post display unit, for example, uses the generation AI to analyze the content of recent posts and select posts to be displayed preferentially based on the user's interests and concerns. For example, if the user is interested in sports, sports-related posts are displayed preferentially. The post display unit also uses the generation AI to analyze the content of recent posts and select posts to be displayed preferentially based on the user's past browsing history. For example, posts in categories that the user has viewed frequently in the past are displayed preferentially. The post display unit also uses the generation AI to analyze the content of recent posts and select posts to be displayed preferentially by comprehensively considering the user's interests and concerns and past browsing history. For example, if the user is interested in sports, sports-related posts that the user has viewed frequently in the past are displayed preferentially. This makes it possible to select posts to be displayed preferentially based on the user's interests and concerns.

[0064] The post display unit can use the emotion estimation function to analyze the emotional reaction of the user to a post viewed by the user and select the next post to display based on the reaction. For example, the post display unit uses the emotion estimation function to analyze the emotional reaction of the user to a post viewed by the user and select the next post to display based on the reaction. For example, the post display unit next displays a post similar to a post to which the user responded positively. The post display unit also uses the emotion estimation function to analyze the emotional reaction of the user to a post viewed by the user and does not next display a post to which the user responded negatively. For example, it does not next display a post to which the user responded annoyed. The post display unit also uses the emotion estimation function to analyze the emotional reaction of the user to a post viewed by the user and comprehensively selects the next post to display based on the reaction. For example, it preferentially displays posts similar to posts to which the user responded positively and does not display posts to which the user responded negatively. This allows the system to provide a more personalized browsing experience by selecting the next post to display based on the user's emotional reaction.

[0065] The post display unit can use the generation AI to automatically summarize the post content, allowing the user to understand the content in a short amount of time. The post display unit, for example, uses the generation AI to automatically summarize the post content, allowing the user to understand the content in a short amount of time. For example, it converts a long post into a summary of a few lines. The post display unit also uses the generation AI to automatically summarize the post content and highlight important information. For example, it extracts the main points of the post and displays the important information with emphasis. The post display unit also uses the generation AI to automatically summarize the post content and customize the summary content based on the user's interests and concerns. For example, it prioritizes summarizing information that the user is interested in. In this way, the post content can be automatically summarized, allowing the user to understand the content in a short amount of time.

[0066] The post display unit uses a generation AI to automatically translate the post content into different languages, thereby supporting international users. The post display unit, for example, uses a generation AI to automatically translate the post content into different languages, thereby supporting international users. For example, it translates an English post into Japanese. The post display unit also uses a generation AI to automatically translate the post content into different languages, thereby supporting multiple languages. For example, it translates an English post into Japanese, Chinese, Spanish, etc. The post display unit also uses a generation AI to automatically translate the post content into different languages ​​and display it based on the user's language setting. For example, it automatically translates and displays the post into the language set by the user. This allows the post content to be automatically translated into different languages, thereby supporting international users.

[0067] The post display unit can use the emotion estimation function to analyze the emotional reactions of users to posts viewed by the user and adjust the display order of the posts based on the reactions. For example, the post display unit uses the emotion estimation function to analyze the emotional reactions of users to posts viewed by the user and adjust the display order of the posts based on the reactions. For example, the post display unit preferentially displays posts that have generated positive reactions. The post display unit also uses the emotion estimation function to analyze the emotional reactions of users to posts viewed by the user and arranges posts that have generated negative reactions lower in the display order. For example, arranges posts that have generated discomfort for the user lower in the display order. The post display unit also uses the emotion estimation function to analyze the emotional reactions of users to posts viewed by the user and comprehensively adjusts the display order of the posts based on the reactions. For example, posts that have generated positive reactions are displayed higher and posts that have generated negative reactions are displayed lower. In this way, by adjusting the display order of posts based on the user's emotional reactions, a more personalized viewing experience can be provided.

[0068] The communication unit can use the generation AI to analyze the content of messages sent by users and automatically generate appropriate reply candidates. For example, the communication unit uses the generation AI to analyze the content of messages sent by users and automatically generate appropriate reply candidates. For example, it automatically generates answers to questions or messages of gratitude. The communication unit also uses the generation AI to analyze the content of messages sent by users and automatically generate reply candidates based on past reply history. For example, it generates reply candidates based on similar messages sent in the past. The communication unit also uses the generation AI to analyze the content of messages sent by users and automatically generate reply candidates based on the user's interests and concerns. For example, it generates replies related to topics that interest the user. In this way, by analyzing the content of messages sent by users and automatically generating appropriate reply candidates, communication can be carried out smoothly.

[0069] The communication unit can use the generation AI to analyze the communication history between users and customize the reply content based on past interactions. For example, the communication unit uses the generation AI to analyze the communication history between users and customize the reply content based on past interactions. For example, it generates a reply that reflects the topic of a previous conversation. The communication unit also uses the generation AI to analyze the communication history between users and customize the reply content based on the user's interests and concerns. For example, it generates a reply related to a topic that interests the user. The communication unit also uses the generation AI to analyze the communication history between users and customize the reply content based on the user's emotional state. For example, if the user is expressing positive emotions, it generates a positive reply. This enables more personalized communication by analyzing the communication history between users and customizing the reply content based on past interactions.

[0070] The communication unit can use the emotion estimation function to analyze the emotion of a message sent by a user and adjust the reply content based on that emotion. For example, the communication unit uses the emotion estimation function to analyze the emotion of a message sent by a user and adjust the reply content based on that emotion. For example, it generates a positive reply for a positive emotion and an encouraging reply for a negative emotion. The communication unit also uses the emotion estimation function to analyze the emotion of a message sent by a user and adjust the tone of the reply content based on that emotion. For example, it generates a reply with a calm tone if the emotion is high. The communication unit also uses the emotion estimation function to analyze the emotion of a message sent by a user and comprehensively adjust the reply content and tone based on that emotion. For example, it generates a positive reply with a bright tone for a positive emotion and an encouraging reply with a calm tone for a negative emotion. In this way, by analyzing the emotion of a message sent by a user and adjusting the reply content based on that emotion, more appropriate communication is possible.

[0071] The communication unit can use the generation AI to automatically translate messages sent by users into different languages, thereby supporting international communication. For example, the communication unit can use the generation AI to automatically translate messages sent by users into different languages, thereby supporting international communication. For example, translating an English message into Japanese. The communication unit can also use the generation AI to automatically translate messages sent by users into different languages, thereby supporting multiple languages. For example, translating an English message into Japanese, Chinese, Spanish, etc. The communication unit can also use the generation AI to automatically translate messages sent by users into different languages ​​and display them based on the user's language settings. For example, the message can be automatically translated into the language set by the user and displayed. This can support international communication by automatically translating messages sent by users into different languages.

[0072] The communication unit can use the generation AI to analyze communication between users and automatically match users with common interests. For example, the communication unit can use the generation AI to analyze communication between users and automatically match users with common interests. For example, it can match users with the same hobbies. The communication unit can also use the generation AI to analyze communication between users and match users with common interests based on past interactions. For example, it can match users who have previously conversed on the same topic. The communication unit can also use the generation AI to analyze communication between users and comprehensively match users with common interests based on the users' interests. For example, it can prioritize matching users with the same hobbies. This enables more appropriate matching by analyzing communication between users and automatically matching users with common interests.

[0073] The matching unit can use the generation AI to analyze the user's facial preferences in detail and present more accurate matching candidates. The matching unit, for example, uses the generation AI to analyze the user's facial preferences in detail and present more accurate matching candidates. For example, it prioritizes presenting users with specific facial features. The matching unit also uses the generation AI to analyze the user's facial preferences in detail and presents matching candidates based on past matching history. For example, it prioritizes presenting users with facial features that have been preferred in the past. The matching unit also uses the generation AI to analyze the user's facial preferences in detail and presents matching candidates based on the user's interests and concerns. For example, it prioritizes presenting users with specific hobbies and interests. This enables more appropriate matching by analyzing the user's facial preferences in detail and presenting more accurate matching candidates.

[0074] The matching unit can use the generation AI to analyze the user's past matching history and customize matching candidates based on that history. For example, the matching unit uses the generation AI to analyze the user's past matching history and customize matching candidates based on that history. For example, it may preferentially present users with facial features that have been favored in the past. The matching unit also uses the generation AI to analyze the user's past matching history and customize matching candidates based on the user's interests and concerns. For example, it may preferentially present users with specific hobbies and concerns. The matching unit also uses the generation AI to analyze the user's past matching history and customize matching candidates based on the user's emotional responses. For example, it may preferentially present candidates that showed positive emotions. This enables more appropriate matching by analyzing the user's past matching history and customizing matching candidates based on that history.

[0075] The matching unit can use the emotion estimation function to analyze the emotion the user felt when looking at the face of a matching candidate and adjust the matching candidates based on that emotion. For example, the matching unit uses the emotion estimation function to analyze the emotion the user felt when looking at the face of a matching candidate and adjust the matching candidates based on that emotion. For example, the matching unit preferentially presents candidates that show a positive emotion. The matching unit also uses the emotion estimation function to analyze the emotion the user felt when looking at the face of a matching candidate and does not display candidates that show a negative emotion. For example, the matching unit does not display candidates that show an unpleasant feeling. The matching unit also uses the emotion estimation function to analyze the emotion the user felt when looking at the face of a matching candidate and comprehensively adjust the matching candidates based on that emotion. For example, the matching unit preferentially presents candidates that show a positive emotion and does not display candidates that show a negative emotion. In this way, by analyzing the emotion the user felt when looking at the face of a matching candidate and adjusting the matching candidates based on that emotion, more appropriate matching is possible.

[0076] The matching unit can use the generation AI to adapt the user's facial preferences to different cultural spheres and regions, thereby supporting international matching. The matching unit, for example, uses the generation AI to adapt the user's facial preferences to different cultural spheres and regions, thereby supporting international matching. For example, it performs matching that takes into account facial features of different cultural spheres. The matching unit also uses the generation AI to adapt the user's facial preferences to different cultural spheres and regions, thereby performing matching that is compatible with multiple regions. For example, it performs matching that is adapted for each region, such as Asia, Europe, and America. The matching unit also uses the generation AI to adapt the user's facial preferences to different cultural spheres and regions, thereby performing matching based on the user's language setting. For example, it performs international matching based on the language set by the user. This allows the user's facial preferences to be adapted to different cultural spheres and regions, thereby supporting international matching.

[0077] The matching unit can use the generation AI to analyze a user's facial preferences and match users with related hobbies and interests. For example, the matching unit uses the generation AI to analyze a user's facial preferences and match users with related hobbies and interests. For example, it prioritizes presenting users with the same hobbies. The matching unit also uses the generation AI to analyze a user's facial preferences and match users with related hobbies and interests based on past matching history. For example, it matches users who have had the same hobbies in the past. The matching unit also uses the generation AI to analyze a user's facial preferences and comprehensively match users with related hobbies and interests based on the user's interests. For example, it prioritizes matching users with the same hobbies and interests. This enables more appropriate matching by analyzing a user's facial preferences and matching users with related hobbies and interests.

[0078] The matching unit can use the emotion estimation function to analyze the emotion the user felt when looking at the face of a matching candidate, and prioritize the matching candidates based on the emotion. For example, the matching unit uses the emotion estimation function to analyze the emotion the user felt when looking at the face of a matching candidate, and prioritize the matching candidates based on the emotion. For example, candidates that show a positive emotion are displayed at the top. The matching unit also uses the emotion estimation function to analyze the emotion the user felt when looking at the face of a matching candidate, and display candidates that show a negative emotion at the bottom. For example, candidates that show annoyance are displayed at the bottom. The matching unit also uses the emotion estimation function to analyze the emotion the user felt when looking at the face of a matching candidate, and comprehensively prioritize the matching candidates based on the emotion. For example, candidates that show a positive emotion are displayed at the top, and candidates that show a negative emotion are displayed at the bottom. In this way, by analyzing the emotion the user felt when looking at the face of a matching candidate and prioritizing the matching candidates based on the emotion, more appropriate matching is possible.

[0079] The community formation unit uses the generation AI to analyze user preferences and interests in detail, allowing it to form more accurate communities. The community formation unit, for example, uses the generation AI to analyze user preferences and interests in detail and form more accurate communities. For example, it forms a community that brings together users with specific hobbies and interests. The community formation unit also uses the generation AI to analyze user preferences and interests in detail and form a community based on past community participation history. For example, it forms a community similar to a community that the user has previously participated in. The community formation unit also uses the generation AI to analyze user preferences and interests in detail and form a community based on the user's emotional response. For example, it forms a community that brings together users who have expressed positive emotions. This allows for more accurate analysis of user preferences and interests, enabling more appropriate communities.

[0080] The community formation unit can use the generation AI to analyze the user's past community participation history and propose new communities based on that history. For example, the community formation unit can use the generation AI to analyze the user's past community participation history and propose new communities based on that history. For example, it can propose communities similar to communities the user has previously participated in. The community formation unit can also use the generation AI to analyze the user's past community participation history and propose new communities based on the user's interests and concerns. For example, it can propose communities related to users with specific hobbies and concerns. The community formation unit can also use the generation AI to analyze the user's past community participation history and propose new communities based on the user's emotional responses. For example, it can propose communities that showed positive emotions. This enables more appropriate communities to be created by analyzing the user's past community participation history and proposing new communities based on that history.

[0081] The community formation unit can use the generation AI to adapt the user's preferences and interests to different cultural spheres and regions, thereby forming an international community. The community formation unit, for example, uses the generation AI to adapt the user's preferences and interests to different cultural spheres and regions, thereby forming an international community. For example, a community is formed that brings together users from different cultural spheres. The community formation unit also uses the generation AI to adapt the user's preferences and interests to different cultural spheres and regions, thereby forming communities that correspond to multiple regions. For example, communities adapted to each region, such as Asia, Europe, and America, are formed. The community formation unit also uses the generation AI to adapt the user's preferences and interests to different cultural spheres and regions, thereby forming a community based on the user's language setting. For example, an international community is formed based on the language set by the user. In this way, an international community can be formed by adapting the user's preferences and interests to different cultural spheres and regions.

[0082] The community formation unit can use the generation AI to analyze the user's preferences and interests and suggest related events and activities. For example, the community formation unit uses the generation AI to analyze the user's preferences and interests and suggest related events and activities. For example, it suggests events related to users with specific hobbies or interests. The community formation unit also uses the generation AI to analyze the user's preferences and interests and suggest related events and activities based on their past event participation history. For example, it suggests events similar to events that the user has previously attended. The community formation unit also uses the generation AI to analyze the user's preferences and interests and suggest related events and activities based on the user's emotional response. For example, it suggests events that showed positive emotions. This enables more appropriate community activities by analyzing the user's preferences and interests and suggesting related events and activities.

[0083] The community formation unit can use the emotion estimation function to analyze the emotion a user had when joining a community and adjust the community members based on that emotion. For example, the community formation unit uses the emotion estimation function to analyze the emotion a user had when joining a community and adjust the community members based on that emotion. For example, it prioritizes members who express positive emotions to join. The community formation unit also uses the emotion estimation function to analyze the emotion a user had when joining a community and excludes members who express negative emotions. For example, it excludes members who express discomfort. The community formation unit also uses the emotion estimation function to analyze the emotion a user had when joining a community and comprehensively adjust the community members based on that emotion. For example, it prioritizes members who express positive emotions to join and excludes members who express negative emotions. In this way, a more appropriate community can be created by analyzing the emotion a user had when joining a community and adjusting the community members based on that emotion.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The community service may further include an activity analysis unit that analyzes the user's activity history. The activity analysis unit may analyze events and activities that the user has participated in in the past and suggest new events and activities based on that history. For example, the activity analysis unit may analyze sporting events that the user has participated in in the past and suggest the next sporting event. The activity analysis unit may also suggest new activities that the user may be interested in based on the user's activity history. For example, if the user has participated in outdoor activities in the past, the activity analysis unit may suggest a new hiking event. Furthermore, the activity analysis unit may analyze the user's activity history and suggest events that will provide greater satisfaction based on the user's evaluation of events that they have participated in in the past. This makes it possible to suggest more personalized events and activities based on the user's activity history.

[0086] The face recognition unit can infer a user's occupation from a facial photograph and display related profile information. For example, face recognition technology can be used to infer that a user is a businessman from a facial photograph and display business-related information. The face recognition unit can also infer that a user is a student from a facial photograph and display information related to school and academics. Furthermore, the face recognition unit can infer that a user is an artist from a facial photograph and display information related to art and creative activities. This makes it possible to display profile information according to a user's occupation.

[0087] The face recognition unit can infer a user's lifestyle from a facial photograph and display related profile information. For example, face recognition technology can be used to infer from a user's facial photograph that the user is health-conscious, and information related to health and fitness can be displayed. The face recognition unit can also infer from a user's facial photograph that the user likes to travel, and display information related to travel. Furthermore, the face recognition unit can infer from a user's facial photograph that the user is a homely person, and display information related to home and childcare. This makes it possible to display profile information tailored to the user's lifestyle.

[0088] The facial recognition unit can infer a user's cultural background from a facial photograph and display related profile information. For example, facial recognition technology can be used to infer that a user is of Asian descent from a facial photograph and display information related to Asian culture. The facial recognition unit can also infer that a user is of European descent from a facial photograph and display information related to European culture. Furthermore, the facial recognition unit can infer that a user is of African descent from a facial photograph and display information related to African culture. This makes it possible to display profile information according to the user's cultural background.

[0089] The face recognition unit can estimate a user's living environment from a facial photograph and display related profile information. For example, face recognition technology can be used to estimate from a user's facial photograph that the user lives in an urban area and display information related to urban life. The face recognition unit can also estimate from a user's facial photograph that the user lives in a suburban area and display information related to suburban life. Furthermore, the face recognition unit can estimate from a user's facial photograph that the user lives in a rural area and display information related to rural life. This makes it possible to display profile information according to the user's living environment.

[0090] The communication unit can estimate the user's emotions and adjust the timing of message transmission based on those emotions. For example, if the user is showing positive emotions, the communication unit can send a message immediately. On the other hand, if the user is showing negative emotions, the communication unit can send a message after a short delay. Furthermore, the communication unit can adjust the frequency of message transmission based on the user's emotions. For example, if the user is feeling stressed, the communication unit can reduce the frequency of message transmission. This makes it possible to send appropriate messages according to the user's emotions.

[0091] The communication unit can estimate the user's emotions and adjust the content of the message based on the emotions. For example, if the user is expressing positive emotions, it can send a message with upbeat content. On the other hand, if the user is expressing negative emotions, it can send a message with encouraging content. Furthermore, the communication unit can adjust the length of the message based on the user's emotions. For example, if the user is busy, it can send a short message. This makes it possible to provide appropriate message content according to the user's emotions.

[0092] The communication unit can estimate the user's emotions and adjust the tone of the message based on the emotions. For example, if the user is expressing positive emotions, the communication unit can send a message with a friendly tone. On the other hand, if the user is expressing negative emotions, the communication unit can send a message with a calm tone. Furthermore, the communication unit can adjust the format of the message based on the user's emotions. For example, if the user is emotional, the communication unit can send a message that makes heavy use of emojis and stamps. This makes it possible to adjust the tone of the message appropriately according to the user's emotions.

[0093] The communication unit can estimate the user's emotions and adjust the destination of messages based on those emotions. For example, if the user is expressing positive emotions, the message can be sent to friends and family. On the other hand, if the user is expressing negative emotions, the message can be sent to experts or counselors. Furthermore, the communication unit can prioritize the destinations of messages based on the user's emotions. For example, if the user is expressing urgent emotions, the message can be sent to someone who can respond immediately. This makes it possible to send messages to appropriate destinations according to the user's emotions.

[0094] The communication unit can estimate the user's emotions and adjust the message sending method based on the emotions. For example, if the user is expressing positive emotions, a text message can be sent. On the other hand, if the user is expressing negative emotions, a voice message can be sent. Furthermore, the communication unit can select a means of sending a message based on the user's emotions. For example, if the user is emotional, a video call can be suggested. This makes it possible to send a message in an appropriate manner according to the user's emotions.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The face recognition unit analyzes the face photo. For example, it analyzes a face photo taken with a smartphone camera and extracts the registrant's facial information. It can also analyze facial feature points using a face recognition algorithm, and combine multiple face recognition algorithms to improve analysis accuracy. Step 2: The profile display unit displays profile information based on the results of the analysis by the face recognition unit. For example, it can display information such as the registrant's name, age, and hobbies, and can customize the display content based on the user's settings. It can also automatically update the latest profile information. Step 3: The post display unit displays recent posts based on the profile information displayed by the profile display unit. For example, it can display photos, videos, and text messages recently posted by the subscriber, and can analyze the content of the posts to adjust the display order based on the user's interests. It can also use generation AI to automatically summarize the content of the posts, allowing the user to understand the content in a short amount of time. Step 4: The communication unit sends a message based on the post displayed by the post display unit. For example, it can provide an interface for users to send messages to other registered users, analyze the content of the message using a generation AI, and automatically generate appropriate reply candidates. It can also analyze the emotion of the message sent by the user using an emotion estimation function and adjust the reply content based on that emotion. Step 5: The matching unit performs matching according to facial preferences based on the message sent by the communication unit. For example, it analyzes the user's facial preferences and presents matching candidates based on them. It can also analyze the user's past matching history and customize matching candidates based on that history. Furthermore, it can use an emotion estimation function to analyze the user's emotions when looking at the faces of matching candidates and adjust matching candidates based on those emotions. Step 6: The community formation unit forms a community according to the preferences based on the matching results obtained by the matching unit. For example, it may analyze the user's preferences and interests and form a community based on them. It may also analyze the user's past community participation history and suggest new communities based on that history. Furthermore, it may use an emotion estimation function to analyze the emotions the user felt when joining a community and adjust the community activities based on those emotions.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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]

[0164] 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 face recognition unit that analyzes facial photos; a profile display unit that displays profile information based on the analysis result by the face recognition unit; a post display unit that displays recent posts based on the profile information displayed by the profile display unit; a communication unit that transmits a message based on the post displayed by the post display unit; a matching unit that performs matching according to facial preferences based on the message transmitted by the communication unit; a community forming unit that forms a community according to preferences based on the matching result obtained by the matching unit. A system characterized by:

2. The face recognition unit It estimates the user's age, gender, and emotional state, and displays customized profile information based on that information. The system of claim 1 .

3. The post display unit Analyze recent posts using generative AI and prioritize posts based on user interests. The system of claim 1 .

4. The communication unit Using generative AI, the content of messages sent by users is analyzed and appropriate reply candidates are automatically generated. The system of claim 1 .

5. The matching unit Using generative AI to analyze the user's facial preferences in detail and present more accurate matching candidates. The system of claim 1 .

6. The community formation unit Using generative AI to analyze user preferences and interests in detail and create a more accurate community The system of claim 1 .

7. The face recognition unit It estimates a person's emotions from a photo of their face and customizes the profile information displayed based on that emotion. The system of claim 1 .

8. The post display unit Generative AI is used to analyze the sentiment of posts and prioritize positive posts. The system of claim 1 .

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