System

Abstract

An object of a system according to an embodiment is to generate a personal AI by utilizing individual certification information and provide a service to a user.SOLUTION: A system according to an embodiment includes a generation AI, an individual certification information acquiring unit, a personal AI generating unit, and a service providing unit. The generation AI acquires identity certification information of a user. The individual certification information acquisition unit acquires individual certification information of a user. The personal AI generation part generates a personal AI on the basis of the individual certification information acquired by the individual certification information acquiring part. The service providing unit provides a service of the personal AI generated by the personal AI generation unit to the user through the communication application.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately utilized individual certification information to generate personal AI and provide such services, and there is room for improvement.

[0005] The system according to the embodiment aims to generate a personal AI by utilizing individual certification information and provide services to users. [Means for solving the problem]

[0006] The system according to the embodiment comprises a generation AI, an individual certification information acquisition unit, a personal AI generation unit, and a service provision unit. The generation AI acquires the user's individual certification information. The individual certification information acquisition unit acquires the user's individual certification information. The personal AI generation unit generates a personal AI based on the individual certification information acquired by the individual certification information acquisition unit. The service provision unit provides the user with a service using the personal AI generated by the personal AI generation unit via a communication application. [Effects of the Invention]

[0007] The system according to the embodiment can generate a personal AI by utilizing individual certification information and provide services to 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The identity-certified personal AI platform according to an embodiment of the present invention is a system in which a generating AI provides personalized services based on a user's identity certification information, and the services can be accessed through a communication application. As a result, the identity-certified personal AI platform can provide personalized services to users, improving convenience and satisfaction.

[0029] An individual-certification-type personal AI platform according to an embodiment includes a generation AI, an individual certification information acquisition unit, a personal AI generation unit, and a service providing unit. The generation AI generates a personalized AI based on a user's individual certification information. For example, the generation AI analyzes information such as the user's name, age, gender, hobbies, and past behavioral history to generate an AI for providing optimal services to the user. The individual certification information acquisition unit acquires the user's individual certification information. For example, the individual certification information acquisition unit acquires basic information such as the user's name and age from an input form. The individual certification information acquisition unit can also acquire the user's past behavioral history from a database. The personal AI generation unit generates a personal AI based on the individual certification information acquired by the individual certification information acquisition unit. For example, the personal AI generation unit uses the generation AI to generate a personal AI based on the user's hobbies and interests. The personal AI generation unit can also analyze the user's past behavioral history to generate an AI for providing optimal services to the user. The service providing unit provides a service to the user using the personal AI generated by the personal AI generation unit through a communication application. For example, the service providing unit provides a service to the user using the chat function of LINE (registered trademark). The service providing unit can also send important information and reminders to users by using the notification function of LINE (registered trademark). This allows the identity-certified personal AI platform according to the embodiment to provide personalized services to users, thereby improving convenience and satisfaction.

[0030] The service providing unit can suggest products or services that the user may be interested in based on the user's past search history or purchase history. The service providing unit, for example, analyzes the user's past search history to suggest products that the user may be interested in. For example, it suggests related products based on keywords that the user has searched for in the past. The service providing unit can also analyze the user's past purchase history to suggest services that the user may be interested in. For example, it can suggest services related to products that the user has purchased in the past. The service providing unit can also combine the user's search history and purchase history to make more accurate suggestions. This makes it possible to suggest products and services based on the user's interests.

[0031] The service providing unit can suggest the most suitable restaurant to the user based on the user's preferences or past behavioral history. The service providing unit, for example, suggests the most suitable restaurant based on the user's preferences. For example, the service providing unit suggests a restaurant that suits the user based on the type of cuisine the user likes or information about restaurants the user has visited in the past. The service providing unit can also analyze the user's past behavioral history to suggest the most suitable restaurant to the user. For example, the service providing unit can suggest a restaurant that suits the user based on ratings and reviews of restaurants the user has visited in the past. The service providing unit can also combine the user's preferences and behavioral history to make more accurate restaurant suggestions. This makes it possible to suggest the most suitable restaurant to the user.

[0032] The service providing unit can suggest news articles or event information that may interest the user. The service providing unit, for example, suggests news articles based on the user's interests. For example, the service providing unit analyzes the content and keywords of articles that the user has read in the past and suggests related news articles. The service providing unit also suggests event information that may interest the user. For example, it can suggest related event information based on information about events that the user has attended in the past or topics in which the user is interested. The service providing unit can also combine news articles and event information to provide the user with the most suitable information. This makes it possible to suggest news articles and event information that may interest the user.

[0033] The service providing unit can provide advice on health management or lifestyle improvements based on the user's health condition or lifestyle habits. The service providing unit, for example, provides health management advice based on the user's health condition. For example, it analyzes data such as the user's heart rate and blood pressure to provide advice on appropriate exercise and diet. The service providing unit also provides advice on lifestyle improvements based on the user's lifestyle habits. For example, it can analyze the user's sleep patterns and dietary records to suggest improvements to lifestyle habits. The service providing unit can also combine the health condition and lifestyle habits to provide more comprehensive advice. This makes it possible to provide the user with advice on health management and lifestyle improvements.

[0034] The service providing unit can encrypt data or control access to prevent unauthorized access by third parties. The service providing unit, for example, encrypts data. For example, it encrypts data such as a user's personal identification information or behavioral history to prevent unauthorized access by third parties. The service providing unit also performs access control. For example, it can limit the authority to access user data to prevent unauthorized access by third parties. The service providing unit can also combine encryption and access control to provide higher security. This makes it possible to safely manage user data and prevent unauthorized access.

[0035] The service providing unit can improve the service based on user feedback and enhance the user experience. The service providing unit, for example, collects user feedback. For example, users can rate and comment on the provided service. The service providing unit also analyzes the collected feedback and identifies areas for improvement in the service. For example, it can extract problems and areas for improvement in the service based on user ratings and comments. The service providing unit also improves the service based on feedback and enhances the user experience. For example, it can add new functions that reflect user opinions. This makes it possible to improve the service based on user feedback and enhance the user experience.

[0036] The personal identification information acquisition unit collects the user's biometric information in real time, and the generation AI can provide health management services based on the biometric information. The personal identification information acquisition unit, for example, uses a wearable device to collect the user's biometric information. For example, a smartwatch or fitness tracker can be used to monitor heart rate and skin temperature in real time. The personal identification information acquisition unit also allows the generation AI to evaluate the user's health condition based on the collected biometric information and provide appropriate advice. For example, if the heart rate is high, the generation AI can suggest breathing techniques to help relax. The personal identification information acquisition unit can also collect biometric information over the long term, and the generation AI can learn the user's health patterns based on that data. For example, the generation AI can provide advice on regular exercise and diet to support health management. This allows the generation AI to provide health management services based on the user's biometric information.

[0037] The individual proof information acquisition unit analyzes the user's past dialogue history, and the generation AI can generate a response optimized for the user's communication style. For example, the individual proof information acquisition unit collects the user's past dialogue history, and the generation AI analyzes that data. For example, the individual proof information acquisition unit understands the user's communication style based on LINE (registered trademark) chat history. Furthermore, the individual proof information acquisition unit generates a response optimized for the user's communication style based on the analysis results. For example, the individual proof information acquisition unit can provide a message that reflects the user's preferred language and tone. The individual proof information acquisition unit can also analyze the past dialogue history over the long term, and the generation AI can learn the user's communication patterns. For example, the individual proof information acquisition unit predicts the user's reaction to a specific topic or question and prepares an appropriate response. This allows the generation of a response optimized for the user's communication style.

[0038] The individual proof information acquisition unit analyzes the user's voice data, and the generation AI can provide personalized services based on the user's voice tone or speaking style. For example, the individual proof information acquisition unit collects the user's voice data, and the generation AI analyzes the data. For example, the individual proof information acquisition unit identifies the user's speaking style and voice tone based on LINE (registered trademark) voice messages. Based on the analysis results, the generation AI can provide services optimized for the user's voice tone and speaking style. For example, if the user wants to relax, the generation AI can respond in a gentle tone. The individual proof information acquisition unit can also collect voice data over the long term, allowing the generation AI to learn the user's voice patterns. For example, the generation AI can predict the voice tone corresponding to a specific emotional state and provide services tailored to that tone. This allows personalized services to be provided based on the user's voice tone and speaking style.

[0039] The individual proof information acquisition unit uses the user's location information, and the generation AI can provide services appropriate to that location. The individual proof information acquisition unit, for example, collects the user's location information, and the generation AI provides services based on that data. For example, the location information sharing function of LINE (registered trademark) is used to determine the user's current location. The individual proof information acquisition unit also allows the generation AI to provide services appropriate to the user's current location based on the location information. For example, information about nearby events and traffic information can be provided in real time. The individual proof information acquisition unit can also collect location information over the long term, and the generation AI can learn the user's movement patterns. For example, the generation AI can provide optimal services based on the user's frequently visited places. This allows the generation AI to provide appropriate services based on the user's location information.

[0040] The service providing unit analyzes the chat history of the communication application, and the generation AI can provide information based on the user's interests. For example, the service providing unit collects the chat history of the communication application, and the generation AI analyzes the data. For example, it extracts topics and keywords that the user has previously discussed. The service providing unit then uses the analysis results to allow the generation AI to provide information based on the user's interests. For example, it can suggest news articles and event information that the user might find interesting. The service providing unit can also analyze the chat history over the long term, allowing the generation AI to learn changes in the user's interests. For example, it can identify topics that are gaining interest at specific times and provide information accordingly. This makes it possible to provide information based on the user's interests.

[0041] The service providing unit utilizes the group chat function of a communication application, and the generation AI can provide personalized services to multiple users simultaneously. For example, the service providing unit utilizes the group chat function of a communication application, and the generation AI collects data on multiple users. For example, it identifies the interests and concerns of members in the group. The service providing unit then uses the collected data to have the generation AI provide personalized services to each user in the group. For example, it can suggest event information that is of common interest to the entire group. The service providing unit can also analyze the group chat history over the long term, and the generation AI can learn the dynamics within the group. For example, it can identify topics and topics that are led by specific members and provide services accordingly. This allows personalized services to be provided to multiple users simultaneously.

[0042] The service providing unit uses the video call function of a communication application, and the generation AI can provide video assistance to the user in real time. For example, the service providing unit uses the video call function of a communication application, and the generation AI analyzes the user's facial expressions and voice in real time. For example, it uses facial expression recognition technology to understand the user's emotional state. The service providing unit also allows the generation AI to provide real-time assistance to the user during a video call. For example, if the user is in trouble, it can immediately suggest a solution. The service providing unit can also analyze video call history over the long term, and the generation AI can learn the user's communication patterns. For example, it can predict the user's reaction in a specific situation and provide assistance accordingly. This allows video assistance to be provided to the user in real time.

[0043] The service providing unit allows the generation AI to periodically deliver personalized newsletters to users through the official account of the communication application. For example, the service providing unit uses the official account of the communication application, and the generation AI creates a newsletter based on the user's interests. For example, it collects articles related to topics that interest the user. The service providing unit also periodically updates the content of the newsletter, and the generation AI provides information that matches the user's latest interests. For example, the newsletter can be customized based on the user's search history and chat history. The service providing unit can also analyze the newsletter distribution history over the long term, and the generation AI can learn the user's reactions. For example, it can understand trends in the articles that users frequently read and create newsletters accordingly. This allows personalized newsletters to be periodically delivered to users.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The service providing unit can improve the service based on user feedback and enhance the user experience. For example, users can rate and comment on the provided service. The collected feedback can also be analyzed to identify areas for improvement in the service. Furthermore, new functions can be added based on the feedback, and services that reflect user opinions can be provided. This makes it possible to improve the service based on user feedback and enhance the user experience.

[0046] The service providing unit can encrypt data and control access to prevent unauthorized access by third parties. For example, it can encrypt data such as a user's personal identification information and behavioral history to prevent unauthorized access by third parties. It can also perform access control to limit the authority to access user data. Furthermore, it can combine encryption and access control to provide even higher security. This allows for safe management of user data and prevention of unauthorized access.

[0047] The service providing unit can provide advice on health management and lifestyle improvements based on the user's health condition and lifestyle habits. For example, it can analyze data such as the user's heart rate and blood pressure to provide appropriate exercise and dietary advice. It can also analyze the user's sleep patterns and dietary records to suggest improvements to lifestyle habits. It can also combine the user's health condition and lifestyle habits to provide more comprehensive advice. This makes it possible to provide the user with advice on health management and lifestyle improvements.

[0048] The service providing unit can use the user's location information to provide services tailored to that location. For example, it can grasp the user's current location and provide nearby event information and traffic information in real time. It can also provide optimal services based on the user's frequently visited locations, based on the location information. Furthermore, by collecting location information over the long term and learning the user's movement patterns, it can provide more accurate services. This makes it possible to provide appropriate services based on the user's location information.

[0049] The service providing unit can periodically deliver personalized newsletters to users through the official account of the communication application. For example, the service providing unit can create a newsletter by collecting articles related to topics that interest the user. The content of the newsletter can also be periodically updated to provide information that matches the user's latest interests. Furthermore, by analyzing the newsletter distribution history over the long term and learning from the user's reactions, the service providing unit can create more accurate newsletters. This allows the service providing unit to periodically deliver personalized newsletters to users.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The individual identification information acquisition unit acquires the user's individual identification information. For example, basic information such as the user's name and age can be acquired from an input form, and the user's past behavior history can also be acquired from a database. Step 2: The personal AI generation unit generates a personal AI based on the individual certification information acquired by the individual certification information acquisition unit. For example, the generation AI is used to generate a personal AI based on the user's hobbies and interests, and an AI is generated to provide optimal services by analyzing the user's past behavioral history. Step 3: The service providing unit provides the personal AI generated by the personal AI generating unit to the user through a communication application. For example, the service can be provided to the user using the chat function and notification function of LINE (registered trademark) to send important information and reminders.

[0052] (Example 2) The identity-certified personal AI platform according to an embodiment of the present invention is a system in which a generating AI provides personalized services based on a user's identity certification information, and the services can be accessed through a communication application. As a result, the identity-certified personal AI platform can provide personalized services to users, improving convenience and satisfaction.

[0053] An individual-certification-type personal AI platform according to an embodiment includes a generation AI, an individual certification information acquisition unit, a personal AI generation unit, and a service providing unit. The generation AI generates a personalized AI based on a user's individual certification information. For example, the generation AI analyzes information such as the user's name, age, gender, hobbies, and past behavioral history to generate an AI for providing optimal services to the user. The individual certification information acquisition unit acquires the user's individual certification information. For example, the individual certification information acquisition unit acquires basic information such as the user's name and age from an input form. The individual certification information acquisition unit can also acquire the user's past behavioral history from a database. The personal AI generation unit generates a personal AI based on the individual certification information acquired by the individual certification information acquisition unit. For example, the personal AI generation unit uses the generation AI to generate a personal AI based on the user's hobbies and interests. The personal AI generation unit can also analyze the user's past behavioral history to generate an AI for providing optimal services to the user. The service providing unit provides a service to the user using the personal AI generated by the personal AI generation unit through a communication application. For example, the service providing unit provides a service to the user using the chat function of LINE (registered trademark). The service providing unit can also send important information and reminders to users by using the notification function of LINE (registered trademark). This allows the identity-certified personal AI platform according to the embodiment to provide personalized services to users, thereby improving convenience and satisfaction.

[0054] The service providing unit can suggest products or services that the user may be interested in based on the user's past search history or purchase history. The service providing unit, for example, analyzes the user's past search history to suggest products that the user may be interested in. For example, it suggests related products based on keywords that the user has searched for in the past. The service providing unit can also analyze the user's past purchase history to suggest services that the user may be interested in. For example, it can suggest services related to products that the user has purchased in the past. The service providing unit can also combine the user's search history and purchase history to make more accurate suggestions. This makes it possible to suggest products and services based on the user's interests.

[0055] The service providing unit can suggest the most suitable restaurant to the user based on the user's preferences or past behavioral history. The service providing unit, for example, suggests the most suitable restaurant based on the user's preferences. For example, the service providing unit suggests a restaurant that suits the user based on the type of cuisine the user likes or information about restaurants the user has visited in the past. The service providing unit can also analyze the user's past behavioral history to suggest the most suitable restaurant to the user. For example, the service providing unit can suggest a restaurant that suits the user based on ratings and reviews of restaurants the user has visited in the past. The service providing unit can also combine the user's preferences and behavioral history to make more accurate restaurant suggestions. This makes it possible to suggest the most suitable restaurant to the user.

[0056] The service providing unit can suggest news articles or event information that may interest the user. The service providing unit, for example, suggests news articles based on the user's interests. For example, the service providing unit analyzes the content and keywords of articles that the user has read in the past and suggests related news articles. The service providing unit also suggests event information that may interest the user. For example, it can suggest related event information based on information about events that the user has attended in the past or topics in which the user is interested. The service providing unit can also combine news articles and event information to provide the user with the most suitable information. This makes it possible to suggest news articles and event information that may interest the user.

[0057] The service providing unit can provide advice on health management or lifestyle improvements based on the user's health condition or lifestyle habits. The service providing unit, for example, provides health management advice based on the user's health condition. For example, it analyzes data such as the user's heart rate and blood pressure to provide advice on appropriate exercise and diet. The service providing unit also provides advice on lifestyle improvements based on the user's lifestyle habits. For example, it can analyze the user's sleep patterns and dietary records to suggest improvements to lifestyle habits. The service providing unit can also combine the health condition and lifestyle habits to provide more comprehensive advice. This makes it possible to provide the user with advice on health management and lifestyle improvements.

[0058] The service providing unit can encrypt data or control access to prevent unauthorized access by third parties. The service providing unit, for example, encrypts data. For example, it encrypts data such as a user's personal identification information or behavioral history to prevent unauthorized access by third parties. The service providing unit also performs access control. For example, it can limit the authority to access user data to prevent unauthorized access by third parties. The service providing unit can also combine encryption and access control to provide higher security. This makes it possible to safely manage user data and prevent unauthorized access.

[0059] The service providing unit can improve the service based on user feedback and enhance the user experience. The service providing unit, for example, collects user feedback. For example, users can rate and comment on the provided service. The service providing unit also analyzes the collected feedback and identifies areas for improvement in the service. For example, it can extract problems and areas for improvement in the service based on user ratings and comments. The service providing unit also improves the service based on feedback and enhances the user experience. For example, it can add new functions that reflect user opinions. This makes it possible to improve the service based on user feedback and enhance the user experience.

[0060] The individual proof information acquisition unit collects user emotional data, and the generation AI can provide personalized services based on the emotional data. The individual proof information acquisition unit, for example, collects user emotional data. For example, it analyzes the content and tone of messages sent by the user on LINE (registered trademark) and calculates an emotional score. The individual proof information acquisition unit can also build a system that analyzes emotions from the user's daily conversations and behavior. The generation AI provides services tailored to the user's mood based on the collected emotional data. For example, if the user is feeling stressed, it can suggest relaxation methods to elicit positive emotions. The generation AI can also collect user emotional data over the long term and learn the user's emotional patterns based on that data. For example, it can identify trends in emotional fluctuations on specific days of the week or at specific times of the day and provide services accordingly. This allows personalized services to be provided based on the user's emotions.

[0061] The personal identification information acquisition unit collects the user's biometric information in real time, and the generation AI can provide health management services based on the biometric information. The personal identification information acquisition unit, for example, uses a wearable device to collect the user's biometric information. For example, a smartwatch or fitness tracker can be used to monitor heart rate and skin temperature in real time. The personal identification information acquisition unit also allows the generation AI to evaluate the user's health condition based on the collected biometric information and provide appropriate advice. For example, if the heart rate is high, the generation AI can suggest breathing techniques to help relax. The personal identification information acquisition unit can also collect biometric information over the long term, and the generation AI can learn the user's health patterns based on that data. For example, the generation AI can provide advice on regular exercise and diet to support health management. This allows the generation AI to provide health management services based on the user's biometric information.

[0062] The individual proof information acquisition unit analyzes the user's past dialogue history, and the generation AI can generate a response optimized for the user's communication style. For example, the individual proof information acquisition unit collects the user's past dialogue history, and the generation AI analyzes that data. For example, the individual proof information acquisition unit understands the user's communication style based on LINE (registered trademark) chat history. Furthermore, the individual proof information acquisition unit generates a response optimized for the user's communication style based on the analysis results. For example, the individual proof information acquisition unit can provide a message that reflects the user's preferred language and tone. The individual proof information acquisition unit can also analyze the past dialogue history over the long term, and the generation AI can learn the user's communication patterns. For example, the individual proof information acquisition unit predicts the user's reaction to a specific topic or question and prepares an appropriate response. This allows the generation of a response optimized for the user's communication style.

[0063] The individual proof information acquisition unit analyzes the user's voice data, and the generation AI can provide personalized services based on the user's voice tone or speaking style. For example, the individual proof information acquisition unit collects the user's voice data, and the generation AI analyzes the data. For example, the individual proof information acquisition unit identifies the user's speaking style and voice tone based on LINE (registered trademark) voice messages. Based on the analysis results, the generation AI can provide services optimized for the user's voice tone and speaking style. For example, if the user wants to relax, the generation AI can respond in a gentle tone. The individual proof information acquisition unit can also collect voice data over the long term, allowing the generation AI to learn the user's voice patterns. For example, the generation AI can predict the voice tone corresponding to a specific emotional state and provide services tailored to that tone. This allows personalized services to be provided based on the user's voice tone and speaking style.

[0064] The individual proof information acquisition unit uses the user's location information, and the generation AI can provide services appropriate to that location. The individual proof information acquisition unit, for example, collects the user's location information, and the generation AI provides services based on that data. For example, the location information sharing function of LINE (registered trademark) is used to determine the user's current location. The individual proof information acquisition unit also allows the generation AI to provide services appropriate to the user's current location based on the location information. For example, information about nearby events and traffic information can be provided in real time. The individual proof information acquisition unit can also collect location information over the long term, and the generation AI can learn the user's movement patterns. For example, the generation AI can provide optimal services based on the user's frequently visited places. This allows the generation AI to provide appropriate services based on the user's location information.

[0065] The individual proof information acquisition unit uses the emotion estimation function to analyze the user's emotional state in real time, and the generation AI can suggest relaxation or stress relief methods according to the emotional state. The individual proof information acquisition unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time. For example, it calculates an emotion score based on the content of LINE (registered trademark) messages and voice tone. The individual proof information acquisition unit also allows the generation AI to suggest relaxation or stress relief methods according to the user's emotions based on the emotion estimation results. For example, if the user is feeling stressed, it can suggest meditation or deep breathing. The individual proof information acquisition unit can also collect emotion estimation data over the long term, allowing the generation AI to learn the user's emotional patterns. For example, for a user who is prone to stress in certain situations, it can suggest relaxation methods in advance. This allows it to suggest relaxation or stress relief methods according to the user's emotions.

[0066] The service providing unit analyzes the chat history of the communication application, and the generation AI can provide information based on the user's interests. For example, the service providing unit collects the chat history of the communication application, and the generation AI analyzes the data. For example, it extracts topics and keywords that the user has previously discussed. The service providing unit then uses the analysis results to allow the generation AI to provide information based on the user's interests. For example, it can suggest news articles and event information that the user might find interesting. The service providing unit can also analyze the chat history over the long term, allowing the generation AI to learn changes in the user's interests. For example, it can identify topics that are gaining interest at specific times and provide information accordingly. This makes it possible to provide information based on the user's interests.

[0067] The service providing unit utilizes the group chat function of a communication application, and the generation AI can provide personalized services to multiple users simultaneously. For example, the service providing unit utilizes the group chat function of a communication application, and the generation AI collects data on multiple users. For example, it identifies the interests and concerns of members in the group. The service providing unit then uses the collected data to have the generation AI provide personalized services to each user in the group. For example, it can suggest event information that is of common interest to the entire group. The service providing unit can also analyze the group chat history over the long term, and the generation AI can learn the dynamics within the group. For example, it can identify topics and topics that are led by specific members and provide services accordingly. This allows personalized services to be provided to multiple users simultaneously.

[0068] The service providing unit analyzes the frequency of use of stamps or emojis in communication applications, and the generation AI can infer the user's emotional state and provide appropriate services. The service providing unit, for example, collects the frequency of use of stamps and emojis in communication applications, and the generation AI analyzes the data. For example, it identifies the frequency with which specific stamps or emojis are used. The service providing unit then uses the analysis results to allow the generation AI to infer the user's emotional state and provide appropriate services. For example, it can infer emotions from the stamps a user frequently uses and suggest relaxation methods accordingly. The service providing unit can also collect data on stamp and emoji use over the long term, allowing the generation AI to learn the user's emotional patterns. For example, it can identify stamps that are frequently used in specific emotional states and provide services accordingly. This allows appropriate services to be provided based on the user's emotional state.

[0069] The service providing unit uses the video call function of a communication application, and the generation AI can provide video assistance to the user in real time. For example, the service providing unit uses the video call function of a communication application, and the generation AI analyzes the user's facial expressions and voice in real time. For example, it uses facial expression recognition technology to understand the user's emotional state. The service providing unit also allows the generation AI to provide real-time assistance to the user during a video call. For example, if the user is in trouble, it can immediately suggest a solution. The service providing unit can also analyze video call history over the long term, and the generation AI can learn the user's communication patterns. For example, it can predict the user's reaction in a specific situation and provide assistance accordingly. This allows video assistance to be provided to the user in real time.

[0070] The service providing unit allows the generation AI to periodically deliver personalized newsletters to users through the official account of the communication application. For example, the service providing unit uses the official account of the communication application, and the generation AI creates a newsletter based on the user's interests. For example, it collects articles related to topics that interest the user. The service providing unit also periodically updates the content of the newsletter, and the generation AI provides information that matches the user's latest interests. For example, the newsletter can be customized based on the user's search history and chat history. The service providing unit can also analyze the newsletter distribution history over the long term, and the generation AI can learn the user's reactions. For example, it can understand trends in the articles that users frequently read and create newsletters accordingly. This allows personalized newsletters to be periodically delivered to users.

[0071] The service providing unit uses the emotion estimation function to analyze the user's emotions in real time while chatting on a communication application, and the generation AI can provide appropriate advice or support to the user. The service providing unit, for example, uses the emotion estimation function to analyze the user's emotions in real time while chatting on a communication application. For example, it calculates an emotion score based on the content and tone of the message. The service providing unit also allows the generation AI to provide appropriate advice or support to the user based on the emotion estimation results. For example, if the user is feeling stressed, it can suggest relaxation methods. The service providing unit can also collect emotion data during chats over the long term, and the generation AI can learn the user's emotional patterns. For example, it can predict the user's emotional changes in specific situations and provide support accordingly. This makes it possible to provide appropriate advice and support in real time based on the user's emotions.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The identity-certified personal AI platform can further estimate the user's emotions and provide services based on the estimated emotions. For example, if the user is feeling stressed, it can suggest relaxation and stress relief methods. If the user is feeling positive, it can provide information about entertainment and hobbies. Furthermore, by collecting user emotional data over the long term and learning emotional patterns, it can provide more accurate services.

[0074] The service providing unit can suggest products and services that the user might be interested in based on the user's emotional data. For example, if the user is feeling down, entertainment products to lift their spirits can be suggested. If the user wants to relax, relaxation goods and services can be suggested. Furthermore, the service providing unit can analyze the user's emotional data and suggest products and services that correspond to changes in their emotions.

[0075] The service providing unit can suggest the most suitable restaurant to the user based on the user's emotional data. For example, if the user is feeling stressed, it can suggest a restaurant with a relaxing atmosphere. Also, if the user is feeling positive, it can suggest a restaurant with a fun atmosphere. Furthermore, by collecting the user's emotional data over the long term and learning their emotional patterns, it is possible to suggest restaurants with even greater accuracy.

[0076] The service providing unit can suggest news articles and event information that the user is likely to be interested in based on the user's emotional data. For example, if the user is feeling down, it can suggest positive news articles. If the user is feeling excited, it can suggest exciting event information. Furthermore, it can analyze the user's emotional data and suggest news articles and event information according to changes in emotions.

[0077] The service providing unit can provide advice on health management and lifestyle improvements based on the user's emotional data. For example, if the user is feeling stressed, it can suggest relaxation and stress relief methods. Also, if the user has positive emotions, it can provide advice on maintaining a healthy lifestyle. Furthermore, by collecting the user's emotional data over the long term and learning their emotional patterns, it can provide more comprehensive advice.

[0078] The service providing unit can improve the service based on user feedback and enhance the user experience. For example, users can rate and comment on the provided service. The collected feedback can also be analyzed to identify areas for improvement in the service. Furthermore, new functions can be added based on the feedback, and services that reflect user opinions can be provided. This makes it possible to improve the service based on user feedback and enhance the user experience.

[0079] The service providing unit can encrypt data and control access to prevent unauthorized access by third parties. For example, it can encrypt data such as a user's personal identification information and behavioral history to prevent unauthorized access by third parties. It can also perform access control to limit the authority to access user data. Furthermore, it can combine encryption and access control to provide even higher security. This allows for safe management of user data and prevention of unauthorized access.

[0080] The service providing unit can provide advice on health management and lifestyle improvements based on the user's health condition and lifestyle habits. For example, it can analyze data such as the user's heart rate and blood pressure to provide appropriate exercise and dietary advice. It can also analyze the user's sleep patterns and dietary records to suggest improvements to lifestyle habits. It can also combine the user's health condition and lifestyle habits to provide more comprehensive advice. This makes it possible to provide the user with advice on health management and lifestyle improvements.

[0081] The service providing unit can use the user's location information to provide services tailored to that location. For example, it can grasp the user's current location and provide nearby event information and traffic information in real time. It can also provide optimal services based on the user's frequently visited locations, based on the location information. Furthermore, by collecting location information over the long term and learning the user's movement patterns, it can provide more accurate services. This makes it possible to provide appropriate services based on the user's location information.

[0082] The service providing unit can periodically deliver personalized newsletters to users through the official account of the communication application. For example, the service providing unit can create a newsletter by collecting articles related to topics that interest the user. The content of the newsletter can also be periodically updated to provide information that matches the user's latest interests. Furthermore, by analyzing the newsletter distribution history over the long term and learning from the user's reactions, the service providing unit can create more accurate newsletters. This allows the service providing unit to periodically deliver personalized newsletters to users.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The individual identification information acquisition unit acquires the user's individual identification information. For example, basic information such as the user's name and age can be acquired from an input form, and the user's past behavior history can also be acquired from a database. Step 2: The personal AI generation unit generates a personal AI based on the individual certification information acquired by the individual certification information acquisition unit. For example, the generation AI is used to generate a personal AI based on the user's hobbies and interests, and an AI is generated to provide optimal services by analyzing the user's past behavioral history. Step 3: The service providing unit provides the personal AI generated by the personal AI generating unit to the user through a communication application. For example, the service can be provided to the user using the chat function and notification function of LINE (registered trademark) to send important information and reminders.

[0085] 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] 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.

[0100] 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.

[0101] 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 AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The 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.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 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.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, a 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The 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.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, 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. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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 AI 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] 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]

[0152] 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. Generative AI and an individual certification information acquisition unit that acquires individual certification information of a user; a personal AI generation unit that generates a personal AI based on the individual certification information acquired by the individual certification information acquisition unit; a service providing unit that provides a service to a user using the personal AI generated by the personal AI generating unit through a communication application; A system characterized by:

2. The service providing unit Suggesting products or services that may be of interest to the user based on the user's past search history or purchase history 2. The system of claim 1.

3. The service providing unit Suggesting the most suitable restaurant to the user based on the user's preferences or past behavior history 2. The system of claim 1.

4. The service providing unit Suggest news articles or event information that may be of interest to the user 2. The system of claim 1.

5. The service providing unit Providing advice on health management or lifestyle improvement based on the user's health condition or lifestyle habits 2. The system of claim 1.

Images