System

Abstract

An object of the system according to the embodiment is to allow users to effectively share health-related information and experiences and to obtain advice from experts and opinions of other users.SOLUTION: A system includes an information sharing part, an opinion exchange part, an expert advice part, a communication providing part, and a network construction support part. The information sharing unit shares information and experience related to health of the user. The opinion exchange unit obtains opinions of other users based on the information shared by the information sharing unit. The expert advice unit provides expert advice based on the opinion obtained by the opinion exchanging unit. The communication providing unit provides communication based on the interest and compatibility of the user on the basis of the advice provided by the expert advice unit. The network construction support unit supports construction of a network between users and construction of a support system on the basis of the communication provided by the communication providing 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 had the drawback of making it difficult for users to effectively share health-related information and experiences and obtain expert advice and the opinions of other users.

[0005] The system according to the embodiment aims to enable users to effectively share health-related information and experiences and obtain expert advice and opinions from other users. [Means for solving the problem]

[0006] The system according to the embodiment includes an information sharing unit, an opinion exchange unit, an expert advice unit, a communication providing unit, and a network building support unit. The information sharing unit shares information and experiences related to the user's health. The opinion exchange unit obtains opinions from other users based on the information shared by the information sharing unit. The expert advice unit provides expert advice based on the opinions obtained by the opinion exchange unit. The communication providing unit provides communication based on the user's interests and compatibility based on the advice provided by the expert advice unit. The network building support unit supports the building of a network between users and the establishment of a support system based on the communication provided by the communication providing unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to effectively share health information and experiences and obtain expert advice and opinions from other users. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A health community platform according to an embodiment of the present invention is a system that allows users to share health-related information and experiences and obtain expert advice and opinions from other users. This system uses AI to provide appropriate communication based on users' interests and compatibility, and supports the creation of networks and support systems among users. As a result, the health community platform stimulates information sharing and opinion exchange among users, and can create a support system for achieving health goals with the help of expert advice.

[0029] A health community platform according to an embodiment includes an information sharing unit, an opinion exchange unit, an expert advice unit, a communication providing unit, and a network building support unit. The information sharing unit allows users to share health-related information and experiences. For example, users can post information about successful diets, treatments for illnesses, healthy recipes, and the like. The opinion exchange unit obtains opinions from other users based on the information shared by the information sharing unit. For example, other users can comment on or ask questions about these posts, promoting active opinion exchange. The expert advice unit provides expert advice based on the opinions obtained by the opinion exchange unit. For example, questions about nutrition may be directed to a nutritionist, and questions about exercise may be directed to a fitness trainer. The communication providing unit provides communication based on the user's interests and compatibility based on the advice provided by the expert advice unit. For example, it may match users with the same hobbies or interests, or recommend users who are compatible. The network building support unit supports the construction of a network and a support system between users based on the communication provided by the communication providing unit. For example, it may create a group of users with the same health goals, or a group for sharing information about a specific illness. This will enable the health community platform to stimulate information sharing and exchange of opinions among users, and to build a support system for achieving health goals with the advice of experts.

[0030] The information sharing unit uses AI to automatically suggest related academic papers and research results for posts, improving the reliability of the information. For example, the information sharing unit uses AI to analyze the content of a user's post and automatically suggest related academic papers and research results. For example, the latest research results are displayed for posts about dieting. This improves the reliability of the information by suggesting related academic papers and research results.

[0031] The information sharing unit enables users to input voice and post images when posting, thereby realizing multimodal information sharing. The information sharing unit, for example, enables users to use voice input when posting. For example, speech recognition technology is used to convert speech into text and display it as the post content. The information sharing unit also enables users to post images. For example, images can be uploaded in formats such as JPEG, PNG, and GIF and shared with other users. This enables voice input and image posting, thereby realizing multimodal information sharing.

[0032] The information sharing unit can provide an automatic translation function using AI so that users of different languages ​​can share information with each other. The information sharing unit can provide an automatic translation function using AI so that users of different languages ​​can share information with each other. For example, it can automatically translate a post in Japanese into English. This allows users of different languages ​​to share information with each other, thereby realizing global communication.

[0033] The expert advice unit can use AI to analyze past answers from experts, evaluate the quality of the answers and the user's satisfaction, and recommend the most suitable expert. For example, the expert advice unit uses AI to analyze past answers from experts and evaluate the quality of the answers. For example, it calculates an evaluation score based on the accuracy and detail of the answer content. The expert advice unit also evaluates user satisfaction. For example, it calculates a satisfaction score based on user feedback. In this way, by analyzing past answers from experts and recommending the most suitable expert, user satisfaction is improved.

[0034] The expert advice unit uses AI to analyze the content of users' questions and can prioritize assigning them to experts according to the urgency and importance of the question. For example, the expert advice unit uses AI to analyze the content of users' questions and evaluate the urgency of the question. For example, it recommends experts who can respond immediately to questions with high urgency. The expert advice unit also evaluates the importance of the question. For example, it recommends experienced experts for questions with high importance. This allows for prompt and appropriate advice to be provided by prioritizing assignment to experts according to the urgency and importance of the question.

[0035] The expert advice unit can provide expert advice in video format to make it easier to understand visually. The expert advice unit, for example, builds a system that provides expert advice in video format. For example, a doctor explains health advice in video format. By providing expert advice in video format, it becomes easier for the user to understand visually.

[0036] The expert advice unit can use AI to summarize expert advice and provide concise advice to the user. The expert advice unit, for example, builds a system in which AI summarizes expert advice and provides concise advice to the user. For example, it displays a short summary of long advice. In this way, by summarizing expert advice, it is possible to provide concise advice that is easy for the user to understand.

[0037] The communication providing unit can use AI to analyze users' interests and compatibility, and automatically match users with common hobbies and interests. For example, the communication providing unit builds a system in which AI analyzes users' interests and compatibility, and automatically matches users with common hobbies and interests. For example, it recommends users with the same hobbies. This promotes communication by automatically matching users with common hobbies and interests.

[0038] The communication providing unit can use AI to analyze a user's past communication history and send messages that promote communication at optimal timing. The communication providing unit, for example, builds a system in which AI analyzes a user's past communication history and sends messages that promote communication at optimal timing. For example, it sends a message encouraging re-participation to a user who has been inactive for a long period of time. In this way, sending messages that promote communication at optimal timing stimulates user activity.

[0039] The communication providing unit can suggest offline events and workshops based on the user's interests and compatibility. The communication providing unit, for example, analyzes the user's interests and compatibility and builds a system that suggests offline events and workshops. For example, it suggests an event that brings together users with the same hobby. In this way, by suggesting offline events and workshops based on the user's interests and compatibility, interaction between users is promoted.

[0040] The communication providing unit can use AI to analyze users' interests and compatibility and match users from different regions and cultural spheres. For example, the communication providing unit can build a system in which AI analyzes users' interests and compatibility and matches users from different regions and cultural spheres. For example, it can match users from different countries. This promotes global communication by matching users from different regions and cultural spheres.

[0041] The network construction support unit can use AI to analyze users' interests and compatibility, and automatically group users who share common goals. For example, the network construction support unit builds a system in which AI analyzes users' interests and compatibility, and automatically groups users who share common goals. For example, it groups users who have the same health goals. This automatically groups users who share common goals, thereby supporting network construction.

[0042] The network building support unit can use AI to analyze communication within a group and suggest topics to promote active communication. For example, the network building support unit builds a system in which AI analyzes communication within a group and suggests topics to promote active communication. For example, it suggests topics that promote discussion within the group. In this way, by analyzing communication within a group and suggesting topics that promote active communication, it aims to revitalize the group.

[0043] The network construction support unit allows the AI ​​to automatically suggest related information and resources to promote information sharing within a group. The network construction support unit builds a system in which the AI ​​automatically suggests related information and resources to promote information sharing within a group. For example, the AI ​​suggests information related to discussions within the group. This increases the efficiency of information sharing by automatically suggesting related information and resources to promote information sharing within the group.

[0044] The network construction support unit can recommend related groups using AI to promote information sharing between different groups. The network construction support unit, for example, builds a system in which AI recommends related groups to promote information sharing between different groups. For example, it recommends different groups that share the same theme. By recommending related groups to promote information sharing between different groups, information sharing and interaction are stimulated.

[0045] The support system construction unit can use AI to analyze the user's health condition and activity status and automatically provide the necessary support. For example, the support system construction unit constructs a system in which AI analyzes the user's health condition and activity status and automatically provides the necessary support. For example, expert advice is provided to users whose health condition is deteriorating. In this way, the system supports the user's health management by analyzing the user's health condition and activity status and automatically providing the necessary support.

[0046] The support system construction unit can monitor progress and provide appropriate feedback to help users achieve their goals using AI. The support system construction unit, for example, builds a system in which AI monitors progress and provides appropriate feedback to help users achieve their goals. For example, a user who has set a diet goal is encouraged to regularly record their weight. This allows the progress to be monitored and appropriate feedback to be provided to help users achieve their goals, thereby maintaining the user's motivation.

[0047] The support system construction unit can analyze the user's health condition and activity status and provide support that combines different experts and resources. For example, the support system construction unit analyzes the user's health condition and activity status and constructs a system that provides support that combines different experts and resources. For example, it combines advice from a nutritionist and a fitness trainer. In this way, comprehensive support is realized by analyzing the user's health condition and activity status and providing support that combines different experts and resources.

[0048] The support system construction unit can use AI to analyze the user's health condition and activity status, and match users from different regions and cultural spheres to provide support. For example, the support system construction unit constructs a system in which AI analyzes the user's health condition and activity status, and matches users from different regions and cultural spheres to provide support. For example, it matches users from different countries. This allows global support to be provided by matching users from different regions and cultural spheres to provide support.

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

[0050] The health community platform may also include a data analysis unit that collects anonymized health data from users and analyzes statistical health trends. For example, the data analysis unit may collect users' food records and exercise data and analyze health trends by region. The data analysis unit may also provide statistical data on specific health issues, allowing users to compare their health status with that of other users. This allows users to objectively evaluate their health status and set specific goals for improvement.

[0051] When a user posts, the information sharing section can automatically summarize the post using AI, allowing other users to quickly understand the content. For example, it can summarize a long post in a short form, extracting and displaying the main points. It can also automatically link related past posts and information based on the summarized content. This allows users to efficiently collect information and quickly access the information they need.

[0052] When a user posts information, the information sharing section uses AI to automatically evaluate the reliability of the post and prioritize displaying highly reliable information. For example, it can analyze the sources and citations included in the post and calculate a reliability score. It can also display a warning for information with low reliability to alert the user. This allows users to manage their health based on highly reliable information.

[0053] When a user posts, the information sharing section can automatically categorize the content using AI and classify it into related categories. For example, it can automatically categorize posts into categories such as diet, exercise, and mental health, making it easier for users to access information in categories that interest them. It can also display popular and latest posts for each category. This allows users to efficiently collect information that matches their interests.

[0054] When a user posts, the information sharing unit's AI automatically analyzes the content and can link it to related health apps and devices. For example, if a user posts an exercise record, it will link with a fitness app to automatically update the exercise data. Similarly, if a user posts a meal record, it will link with a nutrition management app to automatically record calorie and nutrient data. This allows users to efficiently utilize multiple health apps and devices and manage their health comprehensively.

[0055] The expert advice section can also use AI to analyze the content of a user's question and automatically suggest similar questions and their answers from the past. For example, if a user posts a question about dieting, expert answers to similar questions from the past will be displayed. In addition, by linking related questions and answers, users can quickly access the information they need. This allows users to efficiently gather information and take advantage of expert advice.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: In the information sharing section, users can share health information and experiences. For example, users can post their success stories about dieting, treatments for illnesses, healthy recipes, etc. Step 2: The opinion exchange unit obtains opinions from other users based on the information shared by the information sharing unit. For example, other users can comment or ask questions about these posts, leading to an active exchange of opinions. Step 3: The Expert Advice Department provides expert advice based on the opinions obtained by the Opinion Exchange Department. For example, questions about nutrition are directed to a nutritionist, questions about exercise to a fitness trainer, etc. Step 4: The communication providing unit provides communication based on the user's interests and compatibility based on the advice provided by the expert advice unit. For example, it matches users with the same hobbies or interests, or recommends users who are compatible with the user. Step 5: The network construction support unit supports the construction of networks and support systems among users based on the communications provided by the communication provision unit. For example, it creates groups of users who share the same health goals or groups for sharing information about specific diseases.

[0058] (Example 2) A health community platform according to an embodiment of the present invention is a system that allows users to share health-related information and experiences and obtain expert advice and opinions from other users. This system uses AI to provide appropriate communication based on users' interests and compatibility, and supports the creation of networks and support systems among users. As a result, the health community platform stimulates information sharing and opinion exchange among users, and can create a support system for achieving health goals with the help of expert advice.

[0059] A health community platform according to an embodiment includes an information sharing unit, an opinion exchange unit, an expert advice unit, a communication providing unit, and a network building support unit. The information sharing unit allows users to share health-related information and experiences. For example, users can post information about successful diets, treatments for illnesses, healthy recipes, and the like. The opinion exchange unit obtains opinions from other users based on the information shared by the information sharing unit. For example, other users can comment on or ask questions about these posts, promoting active opinion exchange. The expert advice unit provides expert advice based on the opinions obtained by the opinion exchange unit. For example, questions about nutrition may be directed to a nutritionist, and questions about exercise may be directed to a fitness trainer. The communication providing unit provides communication based on the user's interests and compatibility based on the advice provided by the expert advice unit. For example, it may match users with the same hobbies or interests, or recommend users who are compatible. The network building support unit supports the construction of a network and a support system between users based on the communication provided by the communication providing unit. For example, it may create a group of users with the same health goals, or a group for sharing information about a specific illness. This will enable the health community platform to stimulate information sharing and exchange of opinions among users, and to build a support system for achieving health goals with the advice of experts.

[0060] The information sharing unit can analyze the content of posts, perform sentiment analysis, identify emotional tones, and prioritize displaying posts with positive sentiment. For example, the information sharing unit uses AI to analyze the content of users' posts and perform sentiment analysis. For example, it can detect keywords and phrases that indicate positive sentiment and prioritize displaying those posts. This improves user motivation by prioritize displaying posts with positive sentiment.

[0061] The information sharing unit uses AI to automatically suggest related academic papers and research results for posts, improving the reliability of the information. For example, the information sharing unit uses AI to analyze the content of a user's post and automatically suggest related academic papers and research results. For example, the latest research results are displayed for posts about dieting. This improves the reliability of the information by suggesting related academic papers and research results.

[0062] The information sharing unit can analyze the content of posts, infer the user's emotions using an emotion estimation function, and automatically generate encouragement and advice according to the emotions. For example, the information sharing unit uses AI to analyze the content of a user's posts and infer the user's emotions using the emotion estimation function. For example, if it infers that the user is feeling depressed based on the content of the post, it automatically generates an encouraging message. This automatically generates encouragement and advice according to the user's emotions, thereby improving the user's motivation.

[0063] The information sharing unit enables users to input voice and post images when posting, thereby realizing multimodal information sharing. The information sharing unit, for example, enables users to use voice input when posting. For example, speech recognition technology is used to convert speech into text and display it as the post content. The information sharing unit also enables users to post images. For example, images can be uploaded in formats such as JPEG, PNG, and GIF and shared with other users. This enables voice input and image posting, thereby realizing multimodal information sharing.

[0064] The information sharing unit can provide an automatic translation function using AI so that users of different languages ​​can share information with each other. The information sharing unit can provide an automatic translation function using AI so that users of different languages ​​can share information with each other. For example, it can automatically translate a post in Japanese into English. This allows users of different languages ​​to share information with each other, thereby realizing global communication.

[0065] The information sharing unit can use the emotion estimation function to analyze the emotion a user feels when posting in real time and make suggestions to elicit positive emotions. The information sharing unit, for example, uses the emotion estimation function to analyze the emotion a user feels when posting in real time. For example, the information sharing unit estimates the user's emotion from the content of the post and makes suggestions to elicit positive emotions. In this way, the user's emotion is analyzed in real time and suggestions to elicit positive emotions are made, thereby improving the user's motivation.

[0066] The expert advice unit can use AI to analyze past answers from experts, evaluate the quality of the answers and the user's satisfaction, and recommend the most suitable expert. For example, the expert advice unit uses AI to analyze past answers from experts and evaluate the quality of the answers. For example, it calculates an evaluation score based on the accuracy and detail of the answer content. The expert advice unit also evaluates user satisfaction. For example, it calculates a satisfaction score based on user feedback. In this way, by analyzing past answers from experts and recommending the most suitable expert, user satisfaction is improved.

[0067] The expert advice unit uses AI to analyze the content of users' questions and can prioritize assigning them to experts according to the urgency and importance of the question. For example, the expert advice unit uses AI to analyze the content of users' questions and evaluate the urgency of the question. For example, it recommends experts who can respond immediately to questions with high urgency. The expert advice unit also evaluates the importance of the question. For example, it recommends experienced experts for questions with high importance. This allows for prompt and appropriate advice to be provided by prioritizing assignment to experts according to the urgency and importance of the question.

[0068] The expert advice unit can use the emotion estimation function to analyze the user's emotion regarding the question and automatically generate expert advice corresponding to the emotion. The expert advice unit, for example, uses the emotion estimation function to analyze the user's emotion regarding the question. For example, the expert advice unit estimates the user's emotion from the content of the question and automatically generates advice corresponding to the emotion. In this way, the expert advice corresponding to the user's emotion is automatically generated, thereby improving user satisfaction.

[0069] The expert advice unit can provide expert advice in video format to make it easier to understand visually. The expert advice unit, for example, builds a system that provides expert advice in video format. For example, a doctor explains health advice in video format. By providing expert advice in video format, it becomes easier for the user to understand visually.

[0070] The expert advice unit can use AI to summarize expert advice and provide concise advice to the user. The expert advice unit, for example, builds a system in which AI summarizes expert advice and provides concise advice to the user. For example, it displays a short summary of long advice. In this way, by summarizing expert advice, it is possible to provide concise advice that is easy for the user to understand.

[0071] The expert advice unit can use the emotion estimation function to collect the user's emotional reactions to the expert advice and improve the quality of the advice. The expert advice unit, for example, uses the emotion estimation function to build a system that collects the user's emotional reactions to the expert advice. For example, the expert advice unit analyzes the user's facial expressions and voice and calculates an emotion score. In this way, the quality of the expert advice is improved by collecting the user's emotional reactions.

[0072] The communication providing unit can use AI to analyze users' interests and compatibility, and automatically match users with common hobbies and interests. For example, the communication providing unit builds a system in which AI analyzes users' interests and compatibility, and automatically matches users with common hobbies and interests. For example, it recommends users with the same hobbies. This promotes communication by automatically matching users with common hobbies and interests.

[0073] The communication providing unit can use AI to analyze a user's past communication history and send messages that promote communication at optimal timing. The communication providing unit, for example, builds a system in which AI analyzes a user's past communication history and sends messages that promote communication at optimal timing. For example, it sends a message encouraging re-participation to a user who has been inactive for a long period of time. In this way, sending messages that promote communication at optimal timing stimulates user activity.

[0074] The communication providing unit can use the emotion estimation function to provide communication based on the user's emotions and strengthen the emotional connection. The communication providing unit, for example, uses the emotion estimation function to build a system that provides communication based on the user's emotions. For example, an encouraging message is sent when the user has positive emotions. In this way, the emotional connection is strengthened by providing communication based on the user's emotions.

[0075] The communication providing unit can suggest offline events and workshops based on the user's interests and compatibility. The communication providing unit, for example, analyzes the user's interests and compatibility and builds a system that suggests offline events and workshops. For example, it suggests an event that brings together users with the same hobby. In this way, by suggesting offline events and workshops based on the user's interests and compatibility, interaction between users is promoted.

[0076] The communication providing unit can use AI to analyze users' interests and compatibility and match users from different regions and cultural spheres. For example, the communication providing unit can build a system in which AI analyzes users' interests and compatibility and matches users from different regions and cultural spheres. For example, it can match users from different countries. This promotes global communication by matching users from different regions and cultural spheres.

[0077] The communication providing unit uses the emotion estimation function to provide communication based on the user's emotions in real time, thereby eliciting positive emotions. The communication providing unit, for example, uses the emotion estimation function to build a system that provides communication based on the user's emotions in real time. For example, an encouraging message is sent when the user has positive emotions. In this way, positive emotions are elicited by providing communication based on the user's emotions in real time.

[0078] The network construction support unit can use AI to analyze users' interests and compatibility, and automatically group users who share common goals. For example, the network construction support unit builds a system in which AI analyzes users' interests and compatibility, and automatically groups users who share common goals. For example, it groups users who have the same health goals. This automatically groups users who share common goals, thereby supporting network construction.

[0079] The network building support unit can use AI to analyze communication within a group and suggest topics to promote active communication. For example, the network building support unit builds a system in which AI analyzes communication within a group and suggests topics to promote active communication. For example, it suggests topics that promote discussion within the group. In this way, by analyzing communication within a group and suggesting topics that promote active communication, it aims to revitalize the group.

[0080] The network construction support unit can use the emotion estimation function to analyze the emotions of users in a group and promote communication according to their emotions. For example, the network construction support unit uses the emotion estimation function to analyze the emotions of users in a group and build a system that promotes communication according to their emotions. For example, encouraging messages are sent to users who have positive emotions. In this way, the emotions of users in a group are analyzed and communication according to their emotions is promoted, thereby increasing the cohesion of the group.

[0081] The network construction support unit allows the AI ​​to automatically suggest related information and resources to promote information sharing within a group. The network construction support unit builds a system in which the AI ​​automatically suggests related information and resources to promote information sharing within a group. For example, the AI ​​suggests information related to discussions within the group. This increases the efficiency of information sharing by automatically suggesting related information and resources to promote information sharing within the group.

[0082] The network construction support unit can recommend related groups using AI to promote information sharing between different groups. The network construction support unit, for example, builds a system in which AI recommends related groups to promote information sharing between different groups. For example, it recommends different groups that share the same theme. By recommending related groups to promote information sharing between different groups, information sharing and interaction are stimulated.

[0083] The network construction support unit can use the emotion estimation function to analyze the emotions of users in a group in real time and make suggestions to bring out positive emotions. The network construction support unit, for example, uses the emotion estimation function to build a system that analyzes the emotions of users in a group in real time and makes suggestions to bring out positive emotions. For example, encouraging messages are sent to users who have positive emotions. In this way, the emotions of users in a group are analyzed in real time and suggestions to bring out positive emotions are made, thereby increasing the cohesion of the group.

[0084] The support system construction unit can use AI to analyze the user's health condition and activity status and automatically provide the necessary support. For example, the support system construction unit constructs a system in which AI analyzes the user's health condition and activity status and automatically provides the necessary support. For example, expert advice is provided to users whose health condition is deteriorating. In this way, the system supports the user's health management by analyzing the user's health condition and activity status and automatically providing the necessary support.

[0085] The support system construction unit can monitor progress and provide appropriate feedback to help users achieve their goals using AI. The support system construction unit, for example, builds a system in which AI monitors progress and provides appropriate feedback to help users achieve their goals. For example, a user who has set a diet goal is encouraged to regularly record their weight. This allows the progress to be monitored and appropriate feedback to be provided to help users achieve their goals, thereby maintaining the user's motivation.

[0086] The support system construction unit can use the emotion estimation function to provide support based on the user's emotions and strengthen emotional support. The support system construction unit, for example, uses the emotion estimation function to construct a system that provides support based on the user's emotions. For example, an encouraging message is sent when the user has negative emotions. In this way, by providing support based on the user's emotions, emotional support is strengthened and user satisfaction is improved.

[0087] The support system construction unit can analyze the user's health condition and activity status and provide support that combines different experts and resources. For example, the support system construction unit analyzes the user's health condition and activity status and constructs a system that provides support that combines different experts and resources. For example, it combines advice from a nutritionist and a fitness trainer. In this way, comprehensive support is realized by analyzing the user's health condition and activity status and providing support that combines different experts and resources.

[0088] The support system construction unit can use AI to analyze the user's health condition and activity status, and match users from different regions and cultural spheres to provide support. For example, the support system construction unit constructs a system in which AI analyzes the user's health condition and activity status, and matches users from different regions and cultural spheres to provide support. For example, it matches users from different countries. This allows global support to be provided by matching users from different regions and cultural spheres to provide support.

[0089] The support system construction unit can use the emotion estimation function to provide support based on the user's emotions in real time and make suggestions to elicit positive emotions. The support system construction unit, for example, uses the emotion estimation function to construct a system that provides support based on the user's emotions in real time. For example, an encouraging message is sent when the user has positive emotions. In this way, by providing support based on the user's emotions in real time, positive emotions are elicited and user satisfaction is improved.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The health community platform may also include a data analysis unit that collects anonymized health data from users and analyzes statistical health trends. For example, the data analysis unit may collect users' food records and exercise data and analyze health trends by region. The data analysis unit may also provide statistical data on specific health issues, allowing users to compare their health status with that of other users. This allows users to objectively evaluate their health status and set specific goals for improvement.

[0092] When a user posts, the information sharing section can automatically summarize the post using AI, allowing other users to quickly understand the content. For example, it can summarize a long post in a short form, extracting and displaying the main points. It can also automatically link related past posts and information based on the summarized content. This allows users to efficiently collect information and quickly access the information they need.

[0093] When a user posts information, the information sharing section uses AI to automatically evaluate the reliability of the post and prioritize displaying highly reliable information. For example, it can analyze the sources and citations included in the post and calculate a reliability score. It can also display a warning for information with low reliability to alert the user. This allows users to manage their health based on highly reliable information.

[0094] The information sharing unit can also use its emotion estimation function to provide appropriate feedback based on the content posted by the user. For example, if it estimates that the user is feeling stressed, it will automatically generate relaxation methods and stress management advice. Also, if the user is feeling positive, it will send encouraging messages to further motivate them. This provides appropriate feedback based on the user's emotions and supports health management.

[0095] When a user posts, the information sharing section can automatically categorize the content using AI and classify it into related categories. For example, it can automatically categorize posts into categories such as diet, exercise, and mental health, making it easier for users to access information in categories that interest them. It can also display popular and latest posts for each category. This allows users to efficiently collect information that matches their interests.

[0096] The information sharing unit can also use its emotion estimation function to provide emotional support based on the user's posts. For example, if it estimates that the user is feeling sad or anxious, it can send an encouraging message or suggest that the user join a support group. Also, if the user is feeling joy or a sense of accomplishment, it can send a congratulatory message. This provides support that is tailored to the user's emotions and strengthens emotional connections within the community.

[0097] When a user posts, the information sharing unit's AI automatically analyzes the content and can link it to related health apps and devices. For example, if a user posts an exercise record, it will link with a fitness app to automatically update the exercise data. Similarly, if a user posts a meal record, it will link with a nutrition management app to automatically record calorie and nutrient data. This allows users to efficiently utilize multiple health apps and devices and manage their health comprehensively.

[0098] The expert advice unit can also use the emotion estimation function to analyze the user's emotion in response to the question and provide expert advice according to the emotion. For example, if it is estimated that the user is feeling anxious, it provides advice that gives a sense of security. Also, if the user is excited, it provides advice that encourages calm judgment. In this way, expert advice according to the user's emotion is provided, improving user satisfaction.

[0099] The expert advice section can also use AI to analyze the content of a user's question and automatically suggest similar questions and their answers from the past. For example, if a user posts a question about dieting, expert answers to similar questions from the past will be displayed. In addition, by linking related questions and answers, users can quickly access the information they need. This allows users to efficiently gather information and take advantage of expert advice.

[0100] The expert advice unit can also use the emotion estimation function to analyze the user's emotion in response to the question and automatically generate expert advice according to the emotion. For example, if it is estimated that the user is feeling anxious, it provides advice that gives a sense of security. Also, if the user is excited, it provides advice that encourages calm judgment. In this way, expert advice according to the user's emotion is provided, improving user satisfaction.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: In the information sharing section, users can share health information and experiences. For example, users can post their success stories about dieting, treatments for illnesses, healthy recipes, etc. Step 2: The opinion exchange unit obtains opinions from other users based on the information shared by the information sharing unit. For example, other users can comment or ask questions about these posts, leading to an active exchange of opinions. Step 3: The Expert Advice Department provides expert advice based on the opinions obtained by the Opinion Exchange Department. For example, questions about nutrition are directed to a nutritionist, questions about exercise to a fitness trainer, etc. Step 4: The communication providing unit provides communication based on the user's interests and compatibility based on the advice provided by the expert advice unit. For example, it matches users with the same hobbies or interests, or recommends users who are compatible with the user. Step 5: The network construction support unit supports the construction of networks and support systems among users based on the communications provided by the communication provision unit. For example, it creates groups of users who share the same health goals or groups for sharing information about specific diseases.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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, in order to avoid confusion and to 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.

[0169] 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]

[0170] 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. an information sharing section that shares information and experiences about the user's health; an opinion exchange unit that obtains opinions of other users based on the information shared by the information sharing unit; an expert advice unit that provides expert advice based on the opinions obtained by the opinion exchange unit; a communication providing unit that provides communication based on the user's interests and compatibility based on the advice provided by the expert advice unit; a network construction support unit that supports the construction of a network between users and the construction of a support system based on the communication provided by the communication providing unit. A system characterized by:

2. The information sharing unit When users post, they can also input voice and post images, enabling multimodal information sharing.

2. The system of claim 1.

3. The expert advice department The AI ​​is used to analyze the past answers of the experts, evaluate the quality of the answers and the satisfaction of the user, and recommend the most suitable expert.

2. The system of claim 1.

4. The communication providing unit The AI ​​is used to analyze the interests and compatibility of the users, and automatically match users who share common hobbies and interests with each other.

2. The system of claim 1.

5. The network construction support unit The AI ​​is used to analyze the interests and compatibility of the users, and automatically group the users who have a common goal.

2. The system of claim 1.

6. The Support System Construction Department The AI ​​is used to analyze the user's health condition and activity status, and automatically provide necessary support.

2. The system of claim 1.

7. The information sharing unit Estimating the user's emotions and automatically generating the encouragement or advice according to the emotions.

2. The system of claim 1.

Images