System

Abstract

An object of the system according to the embodiment is to analyze various data of a user and make a personalized proposal.SOLUTION: A system includes a user data acquisition unit, an analysis unit, and a proposal unit. The user data acquisition unit acquires data related to the lifestyle, health condition, hobbies, learning, and business goals of the user. The analysis unit analyzes the data acquired by the user data acquisition unit. The suggestion unit makes a personalized suggestion on the basis of the result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been able to comprehensively analyze a variety of user data and provide personalized suggestions, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze various data of a user and make personalized suggestions. [Means for solving the problem]

[0006] The system according to the embodiment includes a user data acquisition unit, an analysis unit, and a suggestion unit. The user data acquisition unit acquires data related to a user's lifestyle, health status, hobbies, and learning and business goals. The analysis unit analyzes the data acquired by the user data acquisition unit. The suggestion unit makes personalized suggestions based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze various data of a user and make personalized suggestions. [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 innovative platform according to the embodiment of the present invention is a system that provides a personalized companion to users' lifestyles, health conditions, hobbies, learning and business goals, thereby enriching and streamlining their daily lives.

[0029] An innovative platform according to an embodiment includes a user data acquisition unit, an analysis unit, and a suggestion unit. The user data acquisition unit acquires data related to a user's lifestyle, health status, hobbies, and learning and business goals. For example, the user data acquisition unit collects user activity data using a sensor. The user data acquisition unit can also acquire the user's hobbies and interests through a questionnaire. The user data acquisition unit can also analyze application logs to understand the user's learning progress. The analysis unit analyzes the data acquired by the user data acquisition unit. For example, the analysis unit can evaluate the user's health status using statistical analysis. The analysis unit can also suggest new activities based on the user's hobbies using machine learning algorithms. The analysis unit can also analyze the user's business data using data mining technology and suggest optimal business strategies. The suggestion unit makes personalized suggestions based on the results of the analysis by the analysis unit. For example, the suggestion unit can suggest optimal exercise plans and meal plans based on the user's health data. The suggestion unit can also suggest efficient learning methods based on the user's learning progress. The suggestion unit can also suggest optimal business strategies based on the user's business data. This allows the innovative platform according to embodiments to provide personalized recommendations based on a user's lifestyle, health status, hobbies, learning and business goals.

[0030] The analysis unit can analyze the user's health data and propose optimal exercise and meal plans. For example, the analysis unit collects the user's health data, and the generation AI analyzes that data to propose optimal exercise and meal plans. For example, the generation AI creates an individually customized exercise plan based on data such as the user's weight, blood pressure, and heart rate. The analysis unit can also analyze the user's dietary data and propose nutritionally balanced meal plans. For example, it can analyze the user's diet and recommend calorie restriction or the intake of specific nutrients. This makes it possible to propose optimal exercise and meal plans based on the user's health data.

[0031] The analysis unit can analyze the user's learning progress and suggest efficient learning methods. For example, the analysis unit collects the user's learning progress, and the generation AI analyzes the data to suggest efficient learning methods. For example, the generation AI creates an individually customized learning schedule based on the user's study time and test results. The analysis unit can also analyze the user's learning style and suggest optimal learning materials and techniques. For example, if the user prefers visual learning, visual learning materials will be recommended. This makes it possible to suggest efficient learning methods based on the user's learning progress.

[0032] The analysis unit can analyze the user's business data and propose optimal business strategies. For example, the analysis unit collects the user's business data, and the generation AI analyzes that data to propose optimal business strategies. For example, the generation AI creates an individually customized marketing strategy based on the user's sales data and marketing data. The analysis unit can also analyze the user's customer data and propose optimal sales strategies. For example, it can analyze customer purchasing history and propose effective approaches to target customers. This makes it possible to propose optimal business strategies based on the user's business data.

[0033] The analysis unit can analyze data related to the user's hobbies and suggest new hobbies and activities. For example, the analysis unit collects data related to the user's hobbies, and the generation AI analyzes the data to suggest new hobbies and activities. For example, the generation AI suggests individually customized hobbies based on the results of a survey about the user's hobbies and their behavioral logs. The analysis unit can also analyze the user's purchase history and suggest related new hobbies and activities. For example, if the user has purchased sports equipment, new sports and activities can be suggested. This makes it possible to suggest new hobbies and activities based on the data related to the user's hobbies.

[0034] The analysis unit analyzes the user's past behavioral history, predicts future behavior, and makes proactive suggestions. For example, the analysis unit collects the user's past behavioral history, and the generation AI analyzes that data to predict future behavior. For example, if the user has the habit of running every weekend, the generation AI can suggest the optimal running course based on the weather forecast. The analysis unit can also allow the generation AI to predict future behavior based on the user's past behavioral history and make suggestions to encourage necessary preparations. For example, if the user has a business trip on a specific day each month, the generation AI can provide a preparation list for the business trip in advance. This makes it possible to predict future behavior based on the user's past behavioral history and make proactive suggestions.

[0035] The analysis unit can incorporate data on the user's family and friends and make personalized suggestions that take social networks into account. For example, the analysis unit collects data on the user's family and friends, and the generation AI analyzes the data to make suggestions that take social networks into account. For example, based on the health data of all family members, the analysis unit can propose a health management plan that the entire family can participate in. The analysis unit can also incorporate data on the user's friends and the generation AI can make suggestions that take social networks into account. For example, based on the events and activities that friends are participating in, the analysis unit can make suggestions that encourage the user to participate. This makes it possible to make personalized suggestions that take into account the data on the user's family and friends.

[0036] The analysis unit can utilize the user's geographical location information to suggest services and events specific to the region. For example, the analysis unit collects the user's geographical location information, and the generation AI analyzes that data to suggest services and events specific to the region. For example, if the user is traveling, the generation AI can suggest tourist spots and restaurants in that area. The analysis unit can also allow the generation AI to suggest services and events specific to the region based on the user's geographical location information. For example, it can suggest events and festivals in the area where the user lives. This makes it possible to suggest services and events specific to the region based on the user's geographical location information.

[0037] Image recognition technology can analyze a user's clothing and belongings and make fashion and lifestyle suggestions. For example, image recognition technology analyzes a user's clothing, and a generation AI makes fashion suggestions. For example, it can suggest trendy outfits based on photos taken by the user. Image recognition technology can also analyze a user's belongings and a generation AI can make lifestyle suggestions. For example, it can suggest activities that match the user's hobbies and interests based on photos taken by the user. Image recognition technology can also analyze a user's clothing and belongings and a generation AI can make fashion and lifestyle suggestions. For example, it can suggest fashion items that are appropriate for the season based on photos taken by the user. This makes it possible to analyze a user's clothing and belongings and make fashion and lifestyle suggestions.

[0038] Image recognition technology can analyze a user's living environment and make suggestions for interior design and lifestyle improvements. Image recognition technology, for example, analyzes a user's living environment, and a generation AI can make suggestions for interior design. For example, optimal furniture arrangement and decorations can be suggested based on photos of a room taken by the user. Image recognition technology can also analyze a user's living environment, and a generation AI can make suggestions for lifestyle improvements. For example, efficient storage methods and organization can be suggested based on photos of a kitchen taken by the user. Image recognition technology can also analyze a user's living environment, and a generation AI can make suggestions for interior design and lifestyle improvements. For example, a comfortable relaxation space can be suggested based on photos of a living room taken by the user. This allows the user's living environment to be analyzed, and suggestions for interior design and lifestyle improvements can be made.

[0039] Image recognition technology can analyze images of places a user has visited and make suggestions for travel and leisure activities. For example, image recognition technology analyzes images of places a user has visited, and a generation AI makes travel suggestions. For example, a system can suggest recommended tourist spots to visit next based on photos of tourist spots taken by the user. Image recognition technology can also analyze images of places a user has visited, and a generation AI can make suggestions for leisure activities. For example, a system can suggest outdoor activities and picnics based on photos of a park taken by the user. Image recognition technology can also analyze images of places a user has visited, and a generation AI can make suggestions for travel and leisure activities. For example, a system can suggest the next vacation destination or marine sports based on photos of a beach taken by the user. This allows image recognition technology to analyze images of places a user has visited and make suggestions for travel and leisure activities.

[0040] The analysis unit can analyze the user's lifestyle rhythm and provide the service at the optimal timing. For example, the analysis unit collects the user's lifestyle rhythm, and the generation AI analyzes that data to provide the service at the optimal timing. For example, if the user has a habit of exercising in the morning, the generation AI can suggest an exercise plan that is optimal for the morning. The analysis unit can also allow the generation AI to provide the service at the optimal timing based on the user's lifestyle rhythm. For example, if the user has a habit of relaxing in the evening, the generation AI can suggest relaxing music or meditation in the evening. The analysis unit can also analyze the user's lifestyle rhythm and allow the generation AI to provide the service at the optimal timing. For example, if the user has a habit of engaging in hobby activities on weekends, the generation AI can suggest new hobbies or activities for the weekend. This allows the service to be provided at the optimal timing based on the user's lifestyle rhythm.

[0041] The analysis unit can incorporate data on the user's family and friends and provide a service that takes social networks into consideration. For example, the analysis unit collects data on the user's family and friends, and the generation AI analyzes the data to provide a service that takes social networks into consideration. For example, based on the health data of all family members, it can propose a health management plan that the entire family can participate in. The analysis unit can also incorporate data on the user's friends, and the generation AI can provide a service that takes social networks into consideration. For example, based on events and activities in which friends are participating, it can make suggestions to encourage the user to participate. The analysis unit can also incorporate data on the user's family and friends, and the generation AI can provide a service that takes social networks into consideration. For example, it can suggest hobbies and activities that can be enjoyed together with family and friends. This makes it possible to provide a service that takes into consideration the data on the user's family and friends.

[0042] The analysis unit can utilize the user's geographical location information to suggest services and events specific to the region. For example, the analysis unit collects the user's geographical location information, and the generation AI analyzes the data to suggest services and events specific to the region. For example, if the user is traveling, the analysis unit can suggest tourist spots and restaurants in the region. The analysis unit can also enable the generation AI to suggest services and events specific to the region based on the user's geographical location information. For example, the analysis unit can suggest events and festivals in the region where the user lives. The analysis unit can also enable the generation AI to suggest services and events specific to the region based on the user's geographical location information. For example, if the user is on a business trip, the analysis unit can suggest business-related events and networking opportunities in the region. This makes it possible to suggest services and events specific to the region based on the user's geographical location information.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The analysis unit can analyze the user's lifestyle rhythm and provide the service at the optimal timing. For example, if the user has a habit of exercising in the morning, the analysis unit can suggest an optimal exercise plan for the morning. The analysis unit can also allow the generation AI to provide the service at the optimal timing based on the user's lifestyle rhythm. For example, if the user has a habit of relaxing in the evening, the analysis unit can suggest relaxing music or meditation in the evening. The analysis unit can also analyze the user's lifestyle rhythm and allow the generation AI to provide the service at the optimal timing. For example, if the user has a habit of engaging in hobby activities on weekends, the analysis unit can suggest new hobbies or activities for the weekend. This allows the service to be provided at the optimal timing based on the user's lifestyle rhythm.

[0045] The analysis unit can incorporate data on the user's family and friends to provide services that take social networks into account. For example, based on the health data of all family members, it can propose a health management plan that the entire family can participate in. The analysis unit can also incorporate data on the user's friends, and the generation AI can provide services that take social networks into account. For example, based on events and activities in which friends are participating, it can make suggestions to encourage the user to participate. The analysis unit can also incorporate data on the user's family and friends, and the generation AI can provide services that take social networks into account. For example, it can suggest hobbies and activities that can be enjoyed together with family and friends. This makes it possible to provide services that take into account the data on the user's family and friends.

[0046] The analysis unit can utilize the user's geographical location information to suggest services and events specific to the region. For example, if the user is traveling, the analysis unit can suggest tourist spots and restaurants in that region. The analysis unit can also allow the generation AI to suggest services and events specific to the region based on the user's geographical location information. For example, the analysis unit can suggest events and festivals in the region where the user lives. The analysis unit can also allow the generation AI to suggest services and events specific to the region based on the user's geographical location information. For example, if the user is on a business trip, the analysis unit can suggest business-related events and networking opportunities in that region. This makes it possible to suggest services and events specific to the region based on the user's geographical location information.

[0047] The analysis unit analyzes the user's past behavioral history, predicts future behavior, and makes proactive suggestions. For example, the user's past behavioral history is collected, and the generation AI analyzes that data to predict future behavior. For example, if the user has the habit of running every weekend, the generation AI can suggest the optimal running course based on the weather forecast. The analysis unit can also allow the generation AI to predict future behavior based on the user's past behavioral history and make suggestions to encourage necessary preparations. For example, if the user has a business trip on a specific day each month, the generation AI can provide a preparation list for the trip in advance. This makes it possible to predict future behavior based on the user's past behavioral history and make proactive suggestions.

[0048] The analysis unit can incorporate data on the user's family and friends and make personalized suggestions that take social networks into account. For example, data on the user's family and friends is collected, and the generation AI analyzes that data to make suggestions that take social networks into account. For example, a health management plan that the entire family can participate in is proposed based on the health data of all family members. The analysis unit can also incorporate data on the user's friends, and the generation AI can make suggestions that take social networks into account. For example, suggestions are made to encourage the user to participate in events and activities that their friends are participating in. This makes it possible to make personalized suggestions that take into account the data on the user's family and friends.

[0049] The analysis unit can analyze the user's lifestyle rhythm and provide the service at the optimal timing. For example, if the user has a habit of exercising in the morning, the analysis unit can suggest an optimal exercise plan for the morning. The analysis unit can also allow the generation AI to provide the service at the optimal timing based on the user's lifestyle rhythm. For example, if the user has a habit of relaxing in the evening, the analysis unit can suggest relaxing music or meditation in the evening. The analysis unit can also analyze the user's lifestyle rhythm and allow the generation AI to provide the service at the optimal timing. For example, if the user has a habit of engaging in hobby activities on weekends, the analysis unit can suggest new hobbies or activities for the weekend. This allows the service to be provided at the optimal timing based on the user's lifestyle rhythm.

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

[0051] Step 1: The user data acquisition unit acquires data on the user's lifestyle, health status, hobbies, and learning and business goals. For example, it uses sensors to collect user activity data, obtains information about the user's hobbies and interests through questionnaires, and analyzes application logs to understand the user's learning progress. Step 2: The analysis unit analyzes the data acquired by the user data acquisition unit. For example, it evaluates the user's health status using statistical analysis, suggests new activities based on the user's hobbies using machine learning algorithms, and analyzes the user's business data using data mining techniques to propose optimal business strategies. Step 3: The suggestion unit makes personalized suggestions based on the results of the analysis by the analysis unit. For example, it suggests optimal exercise and meal plans based on the user's health data, efficient study methods based on the user's study progress, and optimal business strategies based on the user's business data.

[0052] (Example 2) The innovative platform according to the embodiment of the present invention is a system that provides a personalized companion to users' lifestyles, health conditions, hobbies, learning and business goals, thereby enriching and streamlining their daily lives.

[0053] An innovative platform according to an embodiment includes a user data acquisition unit, an analysis unit, and a suggestion unit. The user data acquisition unit acquires data related to a user's lifestyle, health status, hobbies, and learning and business goals. For example, the user data acquisition unit collects user activity data using a sensor. The user data acquisition unit can also acquire the user's hobbies and interests through a questionnaire. The user data acquisition unit can also analyze application logs to understand the user's learning progress. The analysis unit analyzes the data acquired by the user data acquisition unit. For example, the analysis unit can evaluate the user's health status using statistical analysis. The analysis unit can also suggest new activities based on the user's hobbies using machine learning algorithms. The analysis unit can also analyze the user's business data using data mining technology and suggest optimal business strategies. The suggestion unit makes personalized suggestions based on the results of the analysis by the analysis unit. For example, the suggestion unit can suggest optimal exercise plans and meal plans based on the user's health data. The suggestion unit can also suggest efficient learning methods based on the user's learning progress. The suggestion unit can also suggest optimal business strategies based on the user's business data. This allows the innovative platform according to embodiments to provide personalized recommendations based on a user's lifestyle, health status, hobbies, learning and business goals.

[0054] The analysis unit can analyze the user's health data and propose optimal exercise and meal plans. For example, the analysis unit collects the user's health data, and the generation AI analyzes that data to propose optimal exercise and meal plans. For example, the generation AI creates an individually customized exercise plan based on data such as the user's weight, blood pressure, and heart rate. The analysis unit can also analyze the user's dietary data and propose nutritionally balanced meal plans. For example, it can analyze the user's diet and recommend calorie restriction or the intake of specific nutrients. This makes it possible to propose optimal exercise and meal plans based on the user's health data.

[0055] The analysis unit can analyze the user's learning progress and suggest efficient learning methods. For example, the analysis unit collects the user's learning progress, and the generation AI analyzes the data to suggest efficient learning methods. For example, the generation AI creates an individually customized learning schedule based on the user's study time and test results. The analysis unit can also analyze the user's learning style and suggest optimal learning materials and techniques. For example, if the user prefers visual learning, visual learning materials will be recommended. This makes it possible to suggest efficient learning methods based on the user's learning progress.

[0056] The analysis unit can analyze the user's business data and propose optimal business strategies. For example, the analysis unit collects the user's business data, and the generation AI analyzes that data to propose optimal business strategies. For example, the generation AI creates an individually customized marketing strategy based on the user's sales data and marketing data. The analysis unit can also analyze the user's customer data and propose optimal sales strategies. For example, it can analyze customer purchasing history and propose effective approaches to target customers. This makes it possible to propose optimal business strategies based on the user's business data.

[0057] The analysis unit can analyze data related to the user's hobbies and suggest new hobbies and activities. For example, the analysis unit collects data related to the user's hobbies, and the generation AI analyzes the data to suggest new hobbies and activities. For example, the generation AI suggests individually customized hobbies based on the results of a survey about the user's hobbies and their behavioral logs. The analysis unit can also analyze the user's purchase history and suggest related new hobbies and activities. For example, if the user has purchased sports equipment, new sports and activities can be suggested. This makes it possible to suggest new hobbies and activities based on the data related to the user's hobbies.

[0058] The analysis unit can analyze the user's emotional data and make personalized suggestions based on the emotions. For example, the analysis unit collects the user's emotional data, and the generation AI analyzes the data to make suggestions based on the emotions. For example, if the user is feeling stressed, the analysis unit can suggest relaxation methods or activities for relieving stress. The analysis unit can also collect the user's emotional data in real time, and the generation AI can provide services based on the emotions based on the data. For example, if the user is tired, the analysis unit can suggest relaxing music or meditation. This makes it possible to make personalized suggestions based on the user's emotional data.

[0059] The analysis unit analyzes the user's past behavioral history, predicts future behavior, and makes proactive suggestions. For example, the analysis unit collects the user's past behavioral history, and the generation AI analyzes that data to predict future behavior. For example, if the user has the habit of running every weekend, the generation AI can suggest the optimal running course based on the weather forecast. The analysis unit can also allow the generation AI to predict future behavior based on the user's past behavioral history and make suggestions to encourage necessary preparations. For example, if the user has a business trip on a specific day each month, the generation AI can provide a preparation list for the business trip in advance. This makes it possible to predict future behavior based on the user's past behavioral history and make proactive suggestions.

[0060] The analysis unit can analyze the user's voice data, infer their emotions and health state from their tone of voice and speaking style, and provide services based on that. For example, the analysis unit collects the user's voice data, and the generation AI analyzes that data to infer their emotions and health state. For example, if the user sounds tired, the analysis unit can suggest relaxing music or a break. The analysis unit can also analyze the user's voice data in real time, and the generation AI can provide services based on that data according to their emotions and health state. For example, if the user sounds tense, the analysis unit can suggest relaxation techniques or deep breathing. This makes it possible to infer the user's emotions and health state based on their voice data, and provide services based on that.

[0061] The analysis unit can incorporate data on the user's family and friends and make personalized suggestions that take social networks into account. For example, the analysis unit collects data on the user's family and friends, and the generation AI analyzes the data to make suggestions that take social networks into account. For example, based on the health data of all family members, the analysis unit can propose a health management plan that the entire family can participate in. The analysis unit can also incorporate data on the user's friends and the generation AI can make suggestions that take social networks into account. For example, based on the events and activities that friends are participating in, the analysis unit can make suggestions that encourage the user to participate. This makes it possible to make personalized suggestions that take into account the data on the user's family and friends.

[0062] The analysis unit can utilize the user's geographical location information to suggest services and events specific to the region. For example, the analysis unit collects the user's geographical location information, and the generation AI analyzes that data to suggest services and events specific to the region. For example, if the user is traveling, the generation AI can suggest tourist spots and restaurants in that area. The analysis unit can also allow the generation AI to suggest services and events specific to the region based on the user's geographical location information. For example, it can suggest events and festivals in the area where the user lives. This makes it possible to suggest services and events specific to the region based on the user's geographical location information.

[0063] The analysis unit uses the emotion estimation function to monitor in real time how a user feels about a specific service, and can use this information to improve the service. The analysis unit, for example, uses the emotion estimation function to monitor in real time how a user feels about a specific service. For example, if a user is dissatisfied with a provided service, the service can be improved based on that feedback. The analysis unit can also use the emotion estimation function to monitor in real time how a user feels about a specific service, and identify areas in the service that need improvement based on that data. For example, if a user feels positive about a specific function, the function can be enhanced. This makes it possible to monitor user emotions in real time and use this information to improve the service.

[0064] Image recognition technology can infer emotions from images taken by a user and make suggestions based on those emotions. For example, image recognition technology analyzes images taken by a user, and a generation AI infers emotions from those images. For example, if a user has taken many photos of themselves smiling, the generation AI can suggest positive activities and events. Image recognition technology can also infer emotions from images taken by a user and make suggestions based on those emotions. For example, if a user has taken many landscape photos, the generation AI can suggest nature walks and outdoor activities. This makes it possible to infer emotions from images taken by a user and make suggestions based on those emotions.

[0065] Image recognition technology can infer emotions from images taken by a user and make suggestions based on those emotions. For example, image recognition technology analyzes images taken by a user, and a generation AI infers emotions from those images. For example, if a user takes many photos of smiling faces, positive activities and events can be suggested. Image recognition technology can also infer emotions from images taken by a user, and a generation AI can make suggestions based on those emotions. For example, if a user takes many landscape photos, nature walks and outdoor activities can be suggested. Image recognition technology can also analyze images taken by a user, and a generation AI can infer emotions from those images and make suggestions based on those emotions. For example, if a user takes many photos of their pets, pet-related events and services can be suggested. This makes it possible to infer emotions from images taken by a user and make suggestions based on those emotions.

[0066] Image recognition technology can analyze a user's clothing and belongings and make fashion and lifestyle suggestions. For example, image recognition technology analyzes a user's clothing, and a generation AI makes fashion suggestions. For example, it can suggest trendy outfits based on photos taken by the user. Image recognition technology can also analyze a user's belongings and a generation AI can make lifestyle suggestions. For example, it can suggest activities that match the user's hobbies and interests based on photos taken by the user. Image recognition technology can also analyze a user's clothing and belongings and a generation AI can make fashion and lifestyle suggestions. For example, it can suggest fashion items that are appropriate for the season based on photos taken by the user. This makes it possible to analyze a user's clothing and belongings and make fashion and lifestyle suggestions.

[0067] Image recognition technology can estimate a user's health condition from their facial color and facial expression and suggest necessary health management. Image recognition technology, for example, analyzes a user's facial color and facial expression, and the generation AI estimates their health condition. For example, if the user looks pale, it can suggest rest and nutritional supplements. Image recognition technology can also analyze a user's facial expression, and the generation AI can estimate their health condition and suggest necessary health management. For example, if the user looks tired, it can suggest relaxation methods and rest. Image recognition technology can also analyze a user's facial color and facial expression, and the generation AI can estimate their health condition and suggest necessary health management. For example, if the user looks stressed, it can suggest activities to relieve stress. This makes it possible to estimate a user's health condition from their facial color and facial expression and suggest necessary health management.

[0068] Image recognition technology can analyze a user's living environment and make suggestions for interior design and lifestyle improvements. Image recognition technology, for example, analyzes a user's living environment, and a generation AI can make suggestions for interior design. For example, optimal furniture arrangement and decorations can be suggested based on photos of a room taken by the user. Image recognition technology can also analyze a user's living environment, and a generation AI can make suggestions for lifestyle improvements. For example, efficient storage methods and organization can be suggested based on photos of a kitchen taken by the user. Image recognition technology can also analyze a user's living environment, and a generation AI can make suggestions for interior design and lifestyle improvements. For example, a comfortable relaxation space can be suggested based on photos of a living room taken by the user. This allows the user's living environment to be analyzed, and suggestions for interior design and lifestyle improvements can be made.

[0069] Image recognition technology can analyze images of places a user has visited and make suggestions for travel and leisure activities. For example, image recognition technology analyzes images of places a user has visited, and a generation AI makes travel suggestions. For example, a system can suggest recommended tourist spots to visit next based on photos of tourist spots taken by the user. Image recognition technology can also analyze images of places a user has visited, and a generation AI can make suggestions for leisure activities. For example, a system can suggest outdoor activities and picnics based on photos of a park taken by the user. Image recognition technology can also analyze images of places a user has visited, and a generation AI can make suggestions for travel and leisure activities. For example, a system can suggest the next vacation destination or marine sports based on photos of a beach taken by the user. This allows image recognition technology to analyze images of places a user has visited and make suggestions for travel and leisure activities.

[0070] The emotion estimation function can analyze a user's emotional response to an image they have taken and suggest new hobbies or activities based on their emotions. For example, the emotion estimation function analyzes a user's emotional response to an image they have taken, and the generation AI can suggest new hobbies or activities based on their emotions. For example, if a user has positive emotions toward a landscape photo they have taken, the generation AI can suggest hiking or nature walks. The emotion estimation function can also analyze a user's emotional response to an image they have taken, and the generation AI can suggest new hobbies or activities based on their emotions. For example, if a user has positive emotions toward a photo of food they have taken, the generation AI can suggest cooking classes or new recipes. The emotion estimation function can also analyze a user's emotional response to an image they have taken, and the generation AI can suggest new hobbies or activities based on their emotions. For example, if a user has positive emotions toward a photo of an artwork they have taken, the generation AI can suggest art classes or visiting a museum. This allows the generation AI to analyze a user's emotional response to an image they have taken and suggest new hobbies or activities based on their emotions.

[0071] The analysis unit can analyze the user's emotional data and prioritize services based on the user's emotions. For example, the analysis unit collects the user's emotional data, and the generation AI analyzes the data to prioritize services based on the user's emotions. For example, if the user is feeling stressed, relaxation services can be prioritized. The analysis unit can also analyze the user's emotional data in real time, and the generation AI can prioritize services based on the user's emotions based on the data. For example, if the user is tired, relaxing music or meditation can be prioritized. The analysis unit can also collect the user's emotional data over the long term, and the generation AI can analyze the user's emotional patterns based on the data and prioritize services based on the predicted emotions. For example, if the user is prone to stress at certain times of the year, relaxation services appropriate for that time can be prioritized. This makes it possible to prioritize services based on the user's emotional data.

[0072] The analysis unit can analyze the user's lifestyle rhythm and provide the service at the optimal timing. For example, the analysis unit collects the user's lifestyle rhythm, and the generation AI analyzes that data to provide the service at the optimal timing. For example, if the user has a habit of exercising in the morning, the generation AI can suggest an exercise plan that is optimal for the morning. The analysis unit can also allow the generation AI to provide the service at the optimal timing based on the user's lifestyle rhythm. For example, if the user has a habit of relaxing in the evening, the generation AI can suggest relaxing music or meditation in the evening. The analysis unit can also analyze the user's lifestyle rhythm and allow the generation AI to provide the service at the optimal timing. For example, if the user has a habit of engaging in hobby activities on weekends, the generation AI can suggest new hobbies or activities for the weekend. This allows the service to be provided at the optimal timing based on the user's lifestyle rhythm.

[0073] The analysis unit can analyze the user's voice data, infer their emotions and health status from their tone of voice and speaking style, and provide services based on that. For example, the analysis unit collects the user's voice data, and the generation AI analyzes that data to infer their emotions and health status. For example, if the user sounds tired, the generation AI can suggest relaxing music or rest. The analysis unit can also analyze the user's voice data in real time, and the generation AI can use that data to provide services tailored to their emotions and health status. For example, if the user sounds tense, the generation AI can suggest relaxation techniques or deep breathing exercises. The analysis unit can also collect the user's voice data over the long term, and the generation AI can use that data to analyze patterns of their emotions and health status and provide services based on the predicted status. For example, if the user tends to get tired at certain times of the year, the generation AI can suggest health management tailored to that time. This makes it possible to infer their emotions and health status based on the user's voice data and provide services based on that.

[0074] The analysis unit can incorporate data on the user's family and friends and provide a service that takes social networks into consideration. For example, the analysis unit collects data on the user's family and friends, and the generation AI analyzes the data to provide a service that takes social networks into consideration. For example, based on the health data of all family members, it can propose a health management plan that the entire family can participate in. The analysis unit can also incorporate data on the user's friends, and the generation AI can provide a service that takes social networks into consideration. For example, based on events and activities in which friends are participating, it can make suggestions to encourage the user to participate. The analysis unit can also incorporate data on the user's family and friends, and the generation AI can provide a service that takes social networks into consideration. For example, it can suggest hobbies and activities that can be enjoyed together with family and friends. This makes it possible to provide a service that takes into consideration the data on the user's family and friends.

[0075] The analysis unit can utilize the user's geographical location information to suggest services and events specific to the region. For example, the analysis unit collects the user's geographical location information, and the generation AI analyzes the data to suggest services and events specific to the region. For example, if the user is traveling, the analysis unit can suggest tourist spots and restaurants in the region. The analysis unit can also enable the generation AI to suggest services and events specific to the region based on the user's geographical location information. For example, the analysis unit can suggest events and festivals in the region where the user lives. The analysis unit can also enable the generation AI to suggest services and events specific to the region based on the user's geographical location information. For example, if the user is on a business trip, the analysis unit can suggest business-related events and networking opportunities in the region. This makes it possible to suggest services and events specific to the region based on the user's geographical location information.

[0076] The emotion estimation function can monitor in real time how a user feels about a specific service and use the data to improve the service. For example, the emotion estimation function monitors in real time how a user feels about a specific service. For example, if a user is dissatisfied with a service provided, the service can be improved based on that feedback. The emotion estimation function can also monitor in real time how a user feels about a specific service and identify areas for improvement in the service based on the data. For example, if a user feels positive about a specific function, the function can be enhanced. The emotion estimation function can also monitor in real time how a user feels about a specific service and use the data to improve the service. For example, if a user feels negatively about a specific service, the cause can be identified and improvements can be proposed. This makes it possible to monitor user emotions in real time and use the data to improve the service.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The analysis unit can analyze the user's lifestyle rhythm and provide the service at the optimal timing. For example, if the user has a habit of exercising in the morning, the analysis unit can suggest an optimal exercise plan for the morning. The analysis unit can also allow the generation AI to provide the service at the optimal timing based on the user's lifestyle rhythm. For example, if the user has a habit of relaxing in the evening, the analysis unit can suggest relaxing music or meditation in the evening. The analysis unit can also analyze the user's lifestyle rhythm and allow the generation AI to provide the service at the optimal timing. For example, if the user has a habit of engaging in hobby activities on weekends, the analysis unit can suggest new hobbies or activities for the weekend. This allows the service to be provided at the optimal timing based on the user's lifestyle rhythm.

[0079] The analysis unit can incorporate data on the user's family and friends to provide services that take social networks into account. For example, based on the health data of all family members, it can propose a health management plan that the entire family can participate in. The analysis unit can also incorporate data on the user's friends, and the generation AI can provide services that take social networks into account. For example, based on events and activities in which friends are participating, it can make suggestions to encourage the user to participate. The analysis unit can also incorporate data on the user's family and friends, and the generation AI can provide services that take social networks into account. For example, it can suggest hobbies and activities that can be enjoyed together with family and friends. This makes it possible to provide services that take into account the data on the user's family and friends.

[0080] The analysis unit can utilize the user's geographical location information to suggest services and events specific to the region. For example, if the user is traveling, the analysis unit can suggest tourist spots and restaurants in that region. The analysis unit can also allow the generation AI to suggest services and events specific to the region based on the user's geographical location information. For example, the analysis unit can suggest events and festivals in the region where the user lives. The analysis unit can also allow the generation AI to suggest services and events specific to the region based on the user's geographical location information. For example, if the user is on a business trip, the analysis unit can suggest business-related events and networking opportunities in that region. This makes it possible to suggest services and events specific to the region based on the user's geographical location information.

[0081] The analysis unit can analyze the user's voice data, infer their emotions and health state from their tone of voice and speaking style, and provide services based on that. For example, if the user sounds tired, it can suggest relaxing music or rest. The analysis unit can also analyze the user's voice data in real time, and the generation AI can use that data to provide services tailored to their emotions and health state. For example, if the user sounds tense, it can suggest relaxation techniques or deep breathing. The analysis unit can also collect the user's voice data over the long term, and the generation AI can use that data to analyze patterns of emotions and health state and provide services based on the predicted state. For example, if the user tends to get tired at certain times of the year, it can suggest health management tailored to that time. This makes it possible to infer the user's emotions and health state based on their voice data and provide services based on that.

[0082] The analysis unit can analyze the user's emotional data and prioritize services based on the user's emotions. For example, if the user is feeling stressed, relaxation services can be provided preferentially. The analysis unit can also analyze the user's emotional data in real time, and the generation AI can prioritize services based on the user's emotions based on that data. For example, if the user is tired, it can prioritize suggestions of relaxing music or meditation. The analysis unit can also collect the user's emotional data over the long term, and the generation AI can analyze the user's emotional patterns based on that data and prioritize services based on the predicted emotions. For example, if the user is prone to feeling stressed at certain times of the year, it can prioritize relaxation services that are appropriate for that time of year. This makes it possible to prioritize services based on the user's emotional data.

[0083] The analysis unit analyzes the user's past behavioral history, predicts future behavior, and makes proactive suggestions. For example, the user's past behavioral history is collected, and the generation AI analyzes that data to predict future behavior. For example, if the user has the habit of running every weekend, the generation AI can suggest the optimal running course based on the weather forecast. The analysis unit can also allow the generation AI to predict future behavior based on the user's past behavioral history and make suggestions to encourage necessary preparations. For example, if the user has a business trip on a specific day each month, the generation AI can provide a preparation list for the trip in advance. This makes it possible to predict future behavior based on the user's past behavioral history and make proactive suggestions.

[0084] The analysis unit can incorporate data on the user's family and friends and make personalized suggestions that take social networks into account. For example, data on the user's family and friends is collected, and the generation AI analyzes that data to make suggestions that take social networks into account. For example, a health management plan that the entire family can participate in is proposed based on the health data of all family members. The analysis unit can also incorporate data on the user's friends, and the generation AI can make suggestions that take social networks into account. For example, suggestions are made to encourage the user to participate in events and activities that their friends are participating in. This makes it possible to make personalized suggestions that take into account the data on the user's family and friends.

[0085] The analysis unit can analyze the user's emotional data and make personalized suggestions based on the emotions. For example, the user's emotional data is collected, and the generation AI analyzes that data to make suggestions based on the emotions. For example, if the user is feeling stressed, the analysis unit can suggest relaxation methods or activities to relieve stress. The analysis unit can also collect the user's emotional data in real time, and the generation AI can provide services based on the emotions based on that data. For example, if the user is tired, the analysis unit can suggest relaxing music or meditation. This makes it possible to make personalized suggestions based on the user's emotional data.

[0086] The analysis unit can analyze the user's emotional data and prioritize services based on the user's emotions. For example, if the user is feeling stressed, relaxation services can be provided preferentially. The analysis unit can also analyze the user's emotional data in real time, and the generation AI can prioritize services based on the user's emotions based on that data. For example, if the user is tired, it can prioritize suggestions of relaxing music or meditation. The analysis unit can also collect the user's emotional data over the long term, and the generation AI can analyze the user's emotional patterns based on that data and prioritize services based on the predicted emotions. For example, if the user is prone to feeling stressed at certain times of the year, it can prioritize relaxation services that are appropriate for that time of year. This makes it possible to prioritize services based on the user's emotional data.

[0087] The analysis unit can analyze the user's lifestyle rhythm and provide the service at the optimal timing. For example, if the user has a habit of exercising in the morning, the analysis unit can suggest an optimal exercise plan for the morning. The analysis unit can also allow the generation AI to provide the service at the optimal timing based on the user's lifestyle rhythm. For example, if the user has a habit of relaxing in the evening, the analysis unit can suggest relaxing music or meditation in the evening. The analysis unit can also analyze the user's lifestyle rhythm and allow the generation AI to provide the service at the optimal timing. For example, if the user has a habit of engaging in hobby activities on weekends, the analysis unit can suggest new hobbies or activities for the weekend. This allows the service to be provided at the optimal timing based on the user's lifestyle rhythm.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The user data acquisition unit acquires data on the user's lifestyle, health status, hobbies, and learning and business goals. For example, it uses sensors to collect user activity data, obtains information about the user's hobbies and interests through questionnaires, and analyzes application logs to understand the user's learning progress. Step 2: The analysis unit analyzes the data acquired by the user data acquisition unit. For example, it evaluates the user's health status using statistical analysis, suggests new activities based on the user's hobbies using machine learning algorithms, and analyzes the user's business data using data mining techniques to propose optimal business strategies. Step 3: The suggestion unit makes personalized suggestions based on the results of the analysis by the analysis unit. For example, it suggests optimal exercise and meal plans based on the user's health data, efficient study methods based on the user's study progress, and optimal business strategies based on the user's business data.

[0090] 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.

[0091] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0092] 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.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 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.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0144] 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."

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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]

[0157] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a user data acquisition unit that acquires data related to the user's lifestyle, health condition, hobbies, and learning and business goals; an analysis unit that analyzes the data acquired by the user data acquisition unit; a proposal unit that makes personalized proposals based on the results of the analysis by the analysis unit. A system characterized by:

2. The analysis unit Analyze the user's health data and propose optimal exercise and meal plans 2. The system of claim 1.

3. The analysis unit Analyzing the user's learning progress and proposing efficient learning methods 2. The system of claim 1.

4. The analysis unit Analyze the user's business data and propose optimal business strategies 2. The system of claim 1.

5. The analysis unit Analyzing data related to the user's hobbies and suggesting new hobbies and activities 2. The system of claim 1.

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