System

Abstract

An object of a system according to an embodiment is to provide optimal custom guidance by utilizing personal data of an individual.SOLUTION: A system includes a data collection part, an analysis part, an instruction part, and a cooperation part. The data collection part collects personal data. The analysis unit analyzes the personal data collected by the data collection unit. The guidance unit provides custom guidance based on the result analyzed by the analysis unit. The cooperation unit cooperates with the IoT and the wearable terminal to execute the custom guidance provided by the guidance 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 yet effectively utilized individuals' personal data to provide customized instruction, and there is room for improvement.

[0005] The system according to the embodiment aims to provide optimal customized instruction by utilizing personal data of individuals. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a guidance unit, and a collaboration unit. The data collection unit collects personal data. The analysis unit analyzes the personal data collected by the data collection unit. The guidance unit provides customized guidance based on the results of the analysis by the analysis unit. The collaboration unit collaborates with IoT and wearable devices to execute the customized guidance provided by the guidance unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal customized instruction by utilizing personal data of an individual. [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 AI ​​personal trainer system according to an embodiment of the present invention collects and analyzes personal data and provides customized training tailored to individual goals and needs, thereby promoting self-improvement, managing health conditions, and improving quality of life.

[0029] The AI ​​personal trainer system according to the embodiment includes a data collection unit, an analysis unit, a training unit, and a collaboration unit. The data collection unit collects personal data, such as a user's daily exercise, food records, and sleep patterns. The data collection unit can also automatically collect data through IoT devices and wearable devices. The data collection unit can also collect data from cloud services. For example, the data collection unit collects data from smartwatches and fitness trackers to monitor the user's health status in real time. The analysis unit analyzes the personal data collected by the data collection unit. For example, the generation AI analyzes the data using data mining, statistical analysis, and machine learning algorithms. The generation AI can also analyze the data to understand the user's behavioral patterns, habits, and lifestyle. The generation AI can also estimate the user's emotional state in real time and dynamically adjust the frequency and content of data collection according to emotional fluctuations. The training unit provides customized training based on the results of the analysis by the analysis unit. For example, the generation AI can propose appropriate meal plans and exercise plans for a user aiming to lose weight. The generation AI can also provide advice on stress management and sleep improvement. Furthermore, the generation AI can provide customized guidance based on the user's emotional state. The linking unit links with IoT devices and wearable devices to execute the customized guidance provided by the guidance unit. For example, the generation AI collects data from a smartwatch or fitness tracker and monitors the user's health status in real time. The generation AI can also integrate and analyze data through API linking with cloud services. Furthermore, the generation AI can add smart devices in the home (e.g., smart refrigerators and smart lighting) and integrate and analyze them. This allows the AI ​​personal trainer system according to the embodiment to promote the user's personal growth, manage their health, and improve their quality of life. For example, the generation AI collects data such as the user's daily exercise, food records, and sleep patterns, and analyzes them to provide customized guidance.The generative AI can also estimate the user's emotional state in real time and dynamically adjust the frequency and content of data collection according to the emotion. Furthermore, the generative AI can also integrate and analyze data through API integration with cloud services.

[0030] The data collection unit can collect at least one of the following data: the user's daily exercise, food records, and sleep patterns. The data collection unit, for example, collects the user's daily exercise data. For example, the number of steps and the amount of exercise are recorded using a pedometer or fitness tracker. The data collection unit also collects the user's food records. For example, the contents of meals can be recorded using a food log app. The data collection unit also collects the user's sleep patterns. For example, the sleep duration and sleep quality can be recorded using a smartwatch or sleep tracker. In this way, collecting the user's daily data enables more precise analysis and customized guidance.

[0031] The instruction unit can propose an appropriate meal plan or exercise plan if the user is trying to lose weight. For example, the instruction unit proposes an appropriate meal plan if the user is trying to lose weight. For example, it provides a meal plan that takes calorie restriction and nutritional balance into consideration. The instruction unit also proposes an appropriate exercise plan if the user is trying to lose weight. For example, it provides an exercise plan that combines aerobic exercise and strength training. The instruction unit can also perform more precise data analysis by analyzing the user's behavioral patterns and modeling the impact of specific behaviors on other behaviors. For example, it can analyze the impact of exercise volume on food intake and model the correlation. This makes it possible to provide a specific plan according to the user's goals and support goal achievement.

[0032] The linking unit can collect data from a smartwatch or fitness tracker and monitor the user's health condition in real time. The linking unit, for example, collects data from a smartwatch and monitors the user's health condition in real time. For example, it collects data on heart rate and activity level and evaluates the health condition. The linking unit also collects data from a fitness tracker and monitors the user's health condition in real time. For example, it collects data on the number of steps and exercise level and evaluates the health condition. The linking unit can also collect data that takes into account the user's living environment (e.g., weather and geographical conditions) and analyze the impact of environmental factors on the health condition. For example, it analyzes the impact of temperature and humidity on athletic performance. This allows the user's health condition to be monitored in real time, enabling rapid response.

[0033] The analysis unit can integrate data stored in cloud storage and data from other health management apps to manage comprehensive health conditions. The analysis unit, for example, integrates data stored in cloud storage to manage comprehensive health conditions. For example, it integrates exercise data and dietary data stored in cloud storage to evaluate health conditions. The analysis unit can also integrate data from other health management apps to manage comprehensive health conditions. For example, it can integrate sleep data and stress data from other health management apps to evaluate health conditions. The analysis unit can also add audio and image data to the personal data collection, and the generation AI can analyze this data to evaluate the user's health conditions from multiple angles. For example, it can collect audio data and have the generation AI analyze the audio to evaluate the user's stress level and emotional state. This enables more accurate health management by integrating multiple data sources.

[0034] The analysis unit can analyze a user's behavioral patterns and model the impact of a specific behavior on other behaviors. For example, the analysis unit analyzes a user's behavioral patterns and models the impact of a specific behavior on other behaviors. For example, the analysis unit analyzes the impact of exercise volume on food intake and models the correlation. The analysis unit also builds a model to predict the user's health condition and lifestyle habits based on the analysis results of the behavioral patterns. For example, the analysis unit analyzes the impact of sleep patterns on daytime activity volume and creates a prediction model. The analysis unit also improves the accuracy of advice to the user by modeling the impact of a specific behavior on other behaviors. For example, the analysis unit analyzes the impact of stress levels on exercise habits and provides appropriate advice. This enables more precise data analysis by modeling the correlation of behavioral patterns.

[0035] The data collection unit collects data taking into account the user's living environment (weather or geographical conditions) and can analyze the impact of environmental factors on health status. The data collection unit, for example, collects data on the user's living environment (weather, geographical conditions, etc.) and analyzes the impact on health status. For example, it analyzes the impact of temperature and humidity on exercise performance. The data collection unit also collects data taking into account environmental factors and comprehensively evaluates the user's health status. For example, it analyzes the impact of differences in living environments between urban and suburban areas on health. The data collection unit also builds a health status prediction model based on the user's living environment data. For example, it analyzes the impact of changes in weather on sleep patterns and creates a prediction model. This enables more accurate health management through data collection and analysis that takes living environment into account.

[0036] The data collection unit adds audio or image data, and the generation AI analyzes this data to enable a multifaceted assessment of the user's health condition. For example, the data collection unit collects audio data, and the generation AI performs audio analysis to assess the user's stress level and emotional state. For example, it analyzes changes in voice tone and speaking style. The data collection unit also collects image data, and the generation AI performs image analysis to assess the user's health condition. For example, it analyzes changes in facial expressions and posture to detect signs of stress or fatigue. The data collection unit also analyzes a combination of audio and image data to assess the user's health condition from multiple angles. For example, it analyzes changes in audio and facial expressions simultaneously to provide a more accurate health assessment. This allows the user's health condition to be assessed from multiple angles by analyzing audio and image data.

[0037] The data collection unit collects data from users of different age groups and occupations, and the generation AI can perform analysis tailored to each individual's characteristics. For example, the data collection unit collects data from users of different age groups, and the generation AI analyzes their health conditions and lifestyle habits according to age. For example, it analyzes the differences in exercise habits between young people and the elderly. The data collection unit also collects data from users of different occupations, and the generation AI analyzes their health conditions and stress levels according to their occupational characteristics. For example, it analyzes the differences between desk work and physical labor. The data collection unit also collects data taking into account the characteristics of each age group and occupation, and the generation AI provides advice tailored to each individual's characteristics. For example, it suggests stress management methods specific to each occupation. This makes it possible to perform analysis tailored to the characteristics of different age groups and occupations.

[0038] The coaching unit can predict future behavior and health status based on the user's past data and provide long-term customized guidance based on that. The coaching unit, for example, analyzes the user's past data and builds a model to predict future behavior and health status. For example, it predicts future exercise performance based on past exercise data. The coaching unit also provides long-term customized guidance based on the predictive model. For example, it predicts future health risks and suggests preventive measures based on those risks. The coaching unit also provides long-term goal setting and customized guidance for achieving those goals based on the user's past data. For example, it creates a long-term diet plan and suggests meal plans and exercise plans based on that plan. This makes it possible to provide long-term customized guidance based on past data.

[0039] The guidance unit can introduce a scheduling function to provide advice at the optimal timing according to the user's lifestyle rhythm. The guidance unit introduces a scheduling function that analyzes the user's lifestyle rhythm and provides advice at the optimal timing. For example, it may suggest morning exercise or evening relaxation. The guidance unit also uses the scheduling function to provide customized guidance that matches the user's lifestyle rhythm. For example, it may provide advice on stretching and relaxation in between work. The guidance unit also develops a scheduling function that dynamically adjusts the timing of advice based on the user's lifestyle rhythm. For example, it may provide advice that can be done in a short amount of time when the user is busy. This makes it possible to provide advice that matches the user's lifestyle rhythm.

[0040] The coaching unit can incorporate advice from nutritionists or fitness experts into the content of the customized instruction, and the generation AI can integrate and provide it. For example, the coaching unit registers advice from nutritionists and fitness experts in a database, and the generation AI integrates it to provide customized instruction. For example, it proposes meal plans and exercise plans. Furthermore, the coaching unit uses the generation AI to provide comprehensive customized instruction to the user based on the expert advice. For example, it combines a nutritionally balanced meal plan with an effective exercise plan. Furthermore, the coaching unit incorporates advice from nutritionists and fitness experts in real time, and the generation AI provides the user with the latest customized instruction. For example, it proposes new diet and training methods. In this way, by integrating expert advice, more effective customized instruction is possible.

[0041] The instruction unit can provide customized instruction that takes into account regional health habits and food cultures to accommodate users from different cultural areas or regions. For example, the instruction unit registers the health habits and food cultures of different cultural areas or regions in a database, and the generation AI provides customized instruction that takes these into account. For example, it proposes a meal plan using ingredients unique to the region. The instruction unit also provides appropriate customized instruction to users based on the health habits and food cultures of each region. For example, it proposes an exercise plan that incorporates regional exercise habits. The instruction unit also provides customized instruction that takes into account regional health habits and food cultures to accommodate users from different cultural areas or regions. For example, it provides advice tailored to the region's climate and living environment. This makes it possible to provide customized instruction that takes into account regional health habits and food cultures.

[0042] The collaboration unit can predict future resource demands based on the user's resource management data and propose an optimal management method based on that. The collaboration unit, for example, analyzes the user's resource management data and builds a model to predict future resource demands. For example, it predicts future time management and financial management demands based on past data. The collaboration unit also proposes an optimal resource management method based on the prediction model. For example, it predicts future resource demands and proposes budget plans and schedules based on that. The collaboration unit also creates a long-term resource management plan based on the user's resource management data and provides advice based on that. For example, it predicts future resource demands and proposes resource allocation based on that. This makes it possible to predict future resource demands and propose an optimal management method based on that.

[0043] The collaboration unit can introduce a scheduling function to provide resource management advice at the optimal timing in accordance with the user's lifestyle rhythm. The collaboration unit, for example, introduces a scheduling function that analyzes the user's lifestyle rhythm and provides resource management advice at the optimal timing. For example, it suggests time management in the morning and relaxation in the evening. The collaboration unit also uses the scheduling function to provide resource management advice that matches the user's lifestyle rhythm. For example, it suggests reallocating resources or taking rest during breaks from work. The collaboration unit also develops a scheduling function that dynamically adjusts the timing of resource management advice based on the user's lifestyle rhythm. For example, it provides resource management advice that can be done in a short amount of time when the user is busy. This makes it possible to provide resource management advice that matches the user's lifestyle rhythm.

[0044] The collaboration unit can include management of household resources (housework or childcare), and the generation AI can integrate and provide them. For example, the collaboration unit collects household resource management data (housework, childcare, etc.), and the generation AI integrates it to provide resource management advice. For example, it proposes the division of household chores and childcare schedules. The collaboration unit also provides comprehensive resource management advice, including household resource management. For example, it integrates household resources with work resources and proposes a balanced resource allocation. The collaboration unit also provides resource management advice for both home and work, based on the household resource management data. For example, it adjusts the housework or childcare schedule to improve work efficiency. In this way, comprehensive resource management is possible by integrating household resource management.

[0045] The collaboration unit can provide advice that takes into account occupation-specific resource management methods to accommodate users with different occupations and lifestyles. For example, the collaboration unit collects resource management data from users with different occupations, and the generation AI provides advice that takes into account occupation-specific resource management methods. For example, it proposes resource management that takes into account the differences between desk work and physical labor. Furthermore, the collaboration unit provides advice that takes into account lifestyle-specific resource management methods to accommodate users with different lifestyles. For example, it proposes resource management that takes into account the differences between freelancers and company employees. Furthermore, the collaboration unit provides resource management advice that takes into account the characteristics of each occupation and lifestyle. For example, it proposes resource management that is suitable for occupations that include night shifts or for people with irregular lifestyles. This makes it possible to provide advice that takes into account occupation-specific resource management methods.

[0046] The linking unit can predict a user's health condition and provide preventive advice based on data collected from IoT devices and wearable devices. For example, the linking unit analyzes data collected from IoT devices and wearable devices to build a model that predicts a user's health condition. For example, it predicts health risks based on heart rate and activity data. The linking unit also provides preventive advice to the user based on the predictive model. For example, it predicts future health risks and suggests exercise and meal plans based on the predicted health risks. The linking unit also comprehensively evaluates the user's health condition based on data collected from IoT devices and wearable devices and provides preventive advice. For example, it analyzes sleep data and stress levels and provides health management advice. This makes it possible to predict a user's health condition and provide preventive advice based on data from IoT devices and wearable devices.

[0047] The linking unit can introduce a scheduling function to optimize the timing of data collection from the device in accordance with the user's lifestyle. The linking unit, for example, introduces a scheduling function that analyzes the user's lifestyle and collects data from the device at the optimal timing. For example, it may focus on collecting data about morning exercise and nighttime sleep. The linking unit also uses the scheduling function to collect data in accordance with the user's lifestyle. For example, it may collect activity data in between work and collect heart rate data when relaxing. The linking unit also develops a scheduling function that dynamically adjusts the timing of data collection from the device based on the user's lifestyle. For example, it may collect data that can be collected in a short amount of time when the user is busy. This makes it possible to optimize the timing of data collection from the device in accordance with the user's lifestyle.

[0048] The linking unit adds smart devices in the home (such as smart refrigerators or smart lighting), and the generation AI can integrate and analyze them. For example, the linking unit collects data from smart devices in the home (such as smart refrigerators and smart lighting), and the generation AI integrates and analyzes it. For example, it combines food ingredient data from the refrigerator with exercise data to propose a meal plan. The linking unit also comprehensively evaluates the user's lifestyle and health status based on the data from the smart devices. For example, it analyzes lighting brightness and temperature data to propose a comfortable living environment. The linking unit also integrates data collected from smart devices in the home, and the generation AI provides comprehensive health management advice to the user. For example, it proposes a nutritionally balanced meal plan based on food ingredient data from the refrigerator. This makes it possible to integrate and analyze smart devices in the home for more comprehensive health management.

[0049] The linking unit ensures data compatibility between devices from different manufacturers, allowing the generation AI to integrate and analyze the data. For example, the linking unit develops protocols to ensure data compatibility between devices from different manufacturers, and the generation AI integrates and analyzes the data. For example, it integrates data from smartwatches and fitness trackers. The linking unit also develops APIs to ensure data compatibility and integrates data collected from devices from different manufacturers. For example, it centrally manages data from smart devices from different brands. The linking unit also ensures data compatibility between devices from different manufacturers, allowing the generation AI to integrate the data and provide comprehensive health management advice. For example, it combines data from different devices to evaluate health status. This enables more comprehensive health management by ensuring data compatibility between devices from different manufacturers and integrating and analyzing it.

[0050] The linking unit can predict the user's health condition and provide preventive advice based on data collected from the cloud service. The linking unit, for example, analyzes data collected from the cloud service and builds a model to predict the user's health condition. For example, it predicts health risks based on heart rate and activity data. The linking unit also provides preventive advice to the user based on the prediction model. For example, it predicts future health risks and suggests exercise plans and meal plans based on the predicted health risks. The linking unit also comprehensively evaluates the user's health condition based on the data collected from the cloud service and provides preventive advice. For example, it analyzes sleep data and stress levels and provides health management advice. This makes it possible to predict the user's health condition and provide preventive advice based on data from the cloud service.

[0051] The linking unit can introduce a scheduling function to optimize the timing of data synchronization with the cloud service in accordance with the user's lifestyle. The linking unit introduces a scheduling function that analyzes the user's lifestyle and synchronizes data with the cloud service at the optimal timing. For example, it may synchronize data focusing on morning exercise and nighttime sleep. The linking unit also uses the scheduling function to synchronize data in accordance with the user's lifestyle. For example, it may synchronize activity data in between work and synchronize heart rate data when relaxing. The linking unit also develops a scheduling function that dynamically adjusts the timing of data synchronization with the cloud service based on the user's lifestyle. For example, it may synchronize data that can be synchronized in a short amount of time when the user is busy. This makes it possible to optimize the timing of data synchronization with the cloud service in accordance with the user's lifestyle.

[0052] The linking unit adds smart devices in the home (such as smart refrigerators and smart lighting), and the generation AI can integrate and analyze them. For example, the linking unit collects data from smart devices in the home (such as smart refrigerators and smart lighting), and the generation AI integrates and analyzes it. For example, it combines food ingredient data from the refrigerator with exercise data to propose a meal plan. The linking unit also comprehensively evaluates the user's lifestyle and health status based on the data from the smart devices. For example, it analyzes lighting brightness and temperature data to propose a comfortable living environment. The linking unit also integrates data collected from smart devices in the home, and the generation AI provides the user with comprehensive health management advice. For example, it proposes a nutritionally balanced meal plan based on food ingredient data from the refrigerator. This makes it possible to integrate and analyze smart devices in the home for more comprehensive health management.

[0053] The collaboration unit ensures data compatibility between different cloud services, allowing the generation AI to integrate and analyze the data. For example, the collaboration unit develops a protocol to ensure data compatibility between different cloud services, and the generation AI integrates and analyzes the data. For example, the collaboration unit integrates data from different health management apps. The collaboration unit also develops an API to ensure data compatibility and integrates data collected from different cloud services. For example, data from cloud services of different brands is centrally managed. The collaboration unit also ensures data compatibility between different cloud services, allowing the generation AI to integrate the data and provide comprehensive health management advice. For example, data from different cloud services can be combined to evaluate health status. This enables more comprehensive health management by ensuring data compatibility between different cloud services and integrating and analyzing it.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The data collection unit can also collect data to predict health status based on the user's living environment. For example, it can collect noise levels and air quality data in the user's living environment and analyze the impact of these environmental factors on the user's sleep and stress levels. It can also collect data on the user's commuting time and means of commuting and evaluate the impact of these factors on exercise intensity and stress. It can also collect temperature and humidity data in the user's home and analyze the impact of these factors on health status. This enables comprehensive health management that takes the user's living environment into account.

[0056] The linking unit can also incorporate a scheduling function to provide health management advice at optimal times in line with the user's lifestyle. For example, if a user has a morning rhythm, the system can provide advice on morning exercise and diet. Similarly, a user with a nocturnal rhythm can receive advice on evening relaxation and sleep improvement. Furthermore, based on the user's lifestyle, the system can dynamically adjust the timing of advice so that the user can most effectively accept the advice. This allows for health management tailored to the user's lifestyle.

[0057] The data collection unit can also collect the user's voice data, which the generation AI can then analyze to assess the user's health condition. For example, it can analyze changes in the user's voice tone and speaking style to assess their stress level and emotional state. The generation AI can also evaluate the user's sleep quality and fatigue level based on the user's voice data. Furthermore, it can analyze the voice data in combination with other personal data to assess the user's overall health condition. This makes it possible to utilize voice data to perform a multifaceted health assessment.

[0058] The data collection unit can also collect data to predict health status based on the user's living environment. For example, it can collect noise levels and air quality data in the user's living environment and analyze the impact of these environmental factors on the user's sleep and stress levels. It can also collect data on the user's commuting time and means of commuting and evaluate the impact of these factors on exercise intensity and stress. It can also collect temperature and humidity data in the user's home and analyze the impact of these factors on health status. This enables comprehensive health management that takes the user's living environment into account.

[0059] The analysis unit can also predict future behavior and health status based on the user's past data and provide long-term customized guidance based on that prediction. For example, it can predict future exercise performance based on past exercise data. It can also assess long-term health risks based on the predictive model and suggest preventive measures based on those risks. It can also set long-term goals based on the user's past data and provide customized guidance for achieving those goals. This enables long-term health management based on past data.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The data collection unit collects personal data. For example, it collects data such as the user's daily exercise, food records, and sleep patterns. The data collection unit can also collect data automatically through IoT devices and wearable devices. Furthermore, the data collection unit can collect data from cloud services. For example, it can collect data from smart watches and fitness trackers to monitor the user's health status in real time. Step 2: The analysis unit analyzes the personal data collected by the data collection unit. For example, the generation AI analyzes the data using data mining, statistical analysis, and machine learning algorithms. The generation AI can also analyze the data to understand the user's behavioral patterns, habits, and living situation. Furthermore, the generation AI can estimate the user's emotional state in real time and dynamically adjust the frequency and content of data collection according to emotional fluctuations. Step 3: The guidance unit provides customized guidance based on the results of the analysis by the analysis unit. For example, if the user is trying to lose weight, the generation AI can suggest appropriate meal plans and exercise plans. The generation AI can also provide advice on stress management and sleep improvement. Furthermore, the generation AI can provide customized guidance based on the user's emotional state. Step 4: The Collaboration Unit interacts with IoT and wearable devices to execute the custom guidance provided by the Guidance Unit. For example, the Generative AI collects data from smartwatches and fitness trackers to monitor the user's health status in real time. The Generative AI can also integrate and analyze data through API connections with cloud services. Furthermore, the Generative AI can add smart devices in the home (e.g., smart refrigerators and smart lighting) and integrate and analyze them.

[0062] (Example 2) The AI ​​personal trainer system according to an embodiment of the present invention collects and analyzes personal data and provides customized training tailored to individual goals and needs, thereby promoting self-improvement, managing health conditions, and improving quality of life.

[0063] The AI ​​personal trainer system according to the embodiment includes a data collection unit, an analysis unit, a training unit, and a collaboration unit. The data collection unit collects personal data, such as a user's daily exercise, food records, and sleep patterns. The data collection unit can also automatically collect data through IoT devices and wearable devices. The data collection unit can also collect data from cloud services. For example, the data collection unit collects data from smartwatches and fitness trackers to monitor the user's health status in real time. The analysis unit analyzes the personal data collected by the data collection unit. For example, the generation AI analyzes the data using data mining, statistical analysis, and machine learning algorithms. The generation AI can also analyze the data to understand the user's behavioral patterns, habits, and lifestyle. The generation AI can also estimate the user's emotional state in real time and dynamically adjust the frequency and content of data collection according to emotional fluctuations. The training unit provides customized training based on the results of the analysis by the analysis unit. For example, the generation AI can propose appropriate meal plans and exercise plans for a user aiming to lose weight. The generation AI can also provide advice on stress management and sleep improvement. Furthermore, the generation AI can provide customized guidance based on the user's emotional state. The linking unit links with IoT devices and wearable devices to execute the customized guidance provided by the guidance unit. For example, the generation AI collects data from a smartwatch or fitness tracker and monitors the user's health status in real time. The generation AI can also integrate and analyze data through API linking with cloud services. Furthermore, the generation AI can add smart devices in the home (e.g., smart refrigerators and smart lighting) and integrate and analyze them. This allows the AI ​​personal trainer system according to the embodiment to promote the user's personal growth, manage their health, and improve their quality of life. For example, the generation AI collects data such as the user's daily exercise, food records, and sleep patterns, and analyzes them to provide customized guidance.The generative AI can also estimate the user's emotional state in real time and dynamically adjust the frequency and content of data collection according to the emotion. Furthermore, the generative AI can also integrate and analyze data through API integration with cloud services.

[0064] The data collection unit can collect at least one of the following data: the user's daily exercise, food records, and sleep patterns. The data collection unit, for example, collects the user's daily exercise data. For example, the number of steps and the amount of exercise are recorded using a pedometer or fitness tracker. The data collection unit also collects the user's food records. For example, the contents of meals can be recorded using a food log app. The data collection unit also collects the user's sleep patterns. For example, the sleep duration and sleep quality can be recorded using a smartwatch or sleep tracker. In this way, collecting the user's daily data enables more precise analysis and customized guidance.

[0065] The instruction unit can propose an appropriate meal plan or exercise plan if the user is trying to lose weight. For example, the instruction unit proposes an appropriate meal plan if the user is trying to lose weight. For example, it provides a meal plan that takes calorie restriction and nutritional balance into consideration. The instruction unit also proposes an appropriate exercise plan if the user is trying to lose weight. For example, it provides an exercise plan that combines aerobic exercise and strength training. The instruction unit can also perform more precise data analysis by analyzing the user's behavioral patterns and modeling the impact of specific behaviors on other behaviors. For example, it can analyze the impact of exercise volume on food intake and model the correlation. This makes it possible to provide a specific plan according to the user's goals and support goal achievement.

[0066] The linking unit can collect data from a smartwatch or fitness tracker and monitor the user's health condition in real time. The linking unit, for example, collects data from a smartwatch and monitors the user's health condition in real time. For example, it collects data on heart rate and activity level and evaluates the health condition. The linking unit also collects data from a fitness tracker and monitors the user's health condition in real time. For example, it collects data on the number of steps and exercise level and evaluates the health condition. The linking unit can also collect data that takes into account the user's living environment (e.g., weather and geographical conditions) and analyze the impact of environmental factors on the health condition. For example, it analyzes the impact of temperature and humidity on athletic performance. This allows the user's health condition to be monitored in real time, enabling rapid response.

[0067] The analysis unit can integrate data stored in cloud storage and data from other health management apps to manage comprehensive health conditions. The analysis unit, for example, integrates data stored in cloud storage to manage comprehensive health conditions. For example, it integrates exercise data and dietary data stored in cloud storage to evaluate health conditions. The analysis unit can also integrate data from other health management apps to manage comprehensive health conditions. For example, it can integrate sleep data and stress data from other health management apps to evaluate health conditions. The analysis unit can also add audio and image data to the personal data collection, and the generation AI can analyze this data to evaluate the user's health conditions from multiple angles. For example, it can collect audio data and have the generation AI analyze the audio to evaluate the user's stress level and emotional state. This enables more accurate health management by integrating multiple data sources.

[0068] The data collection unit can estimate the user's emotional state in real time and dynamically adjust the frequency or content of data collection according to emotional fluctuations. For example, to estimate the user's emotional state in real time, the data collection unit uses a generation AI to analyze facial expressions and vocal tone and calculate an emotional score. If the emotional score is high, the frequency of data collection is increased, and if it is low, the frequency of data collection is decreased. The data collection unit also dynamically adjusts the type of data to be collected according to emotional fluctuations. For example, when stress is high, it focuses on collecting heart rate and breathing rate data, and when relaxed, it collects activity level and sleep data. The data collection unit also optimizes the timing of data collection based on the user's emotional state. For example, it collects data when the user is feeling positive and refrains from collecting data when the user is feeling negative. This enables data collection according to the user's emotional state, resulting in more accurate analysis.

[0069] The analysis unit can analyze a user's behavioral patterns and model the impact of a specific behavior on other behaviors. For example, the analysis unit analyzes a user's behavioral patterns and models the impact of a specific behavior on other behaviors. For example, the analysis unit analyzes the impact of exercise volume on food intake and models the correlation. The analysis unit also builds a model to predict the user's health condition and lifestyle habits based on the analysis results of the behavioral patterns. For example, the analysis unit analyzes the impact of sleep patterns on daytime activity volume and creates a prediction model. The analysis unit also improves the accuracy of advice to the user by modeling the impact of a specific behavior on other behaviors. For example, the analysis unit analyzes the impact of stress levels on exercise habits and provides appropriate advice. This enables more precise data analysis by modeling the correlation of behavioral patterns.

[0070] The data collection unit collects data taking into account the user's living environment (weather or geographical conditions) and can analyze the impact of environmental factors on health status. The data collection unit, for example, collects data on the user's living environment (weather, geographical conditions, etc.) and analyzes the impact on health status. For example, it analyzes the impact of temperature and humidity on exercise performance. The data collection unit also collects data taking into account environmental factors and comprehensively evaluates the user's health status. For example, it analyzes the impact of differences in living environments between urban and suburban areas on health. The data collection unit also builds a health status prediction model based on the user's living environment data. For example, it analyzes the impact of changes in weather on sleep patterns and creates a prediction model. This enables more accurate health management through data collection and analysis that takes living environment into account.

[0071] The data collection unit adds audio or image data, and the generation AI analyzes this data to enable a multifaceted assessment of the user's health condition. For example, the data collection unit collects audio data, and the generation AI performs audio analysis to assess the user's stress level and emotional state. For example, it analyzes changes in voice tone and speaking style. The data collection unit also collects image data, and the generation AI performs image analysis to assess the user's health condition. For example, it analyzes changes in facial expressions and posture to detect signs of stress or fatigue. The data collection unit also analyzes a combination of audio and image data to assess the user's health condition from multiple angles. For example, it analyzes changes in audio and facial expressions simultaneously to provide a more accurate health assessment. This allows the user's health condition to be assessed from multiple angles by analyzing audio and image data.

[0072] The data collection unit collects data from users of different age groups and occupations, and the generation AI can perform analysis tailored to each individual's characteristics. For example, the data collection unit collects data from users of different age groups, and the generation AI analyzes their health conditions and lifestyle habits according to age. For example, it analyzes the differences in exercise habits between young people and the elderly. The data collection unit also collects data from users of different occupations, and the generation AI analyzes their health conditions and stress levels according to their occupational characteristics. For example, it analyzes the differences between desk work and physical labor. The data collection unit also collects data taking into account the characteristics of each age group and occupation, and the generation AI provides advice tailored to each individual's characteristics. For example, it suggests stress management methods specific to each occupation. This makes it possible to perform analysis tailored to the characteristics of different age groups and occupations.

[0073] The data collection unit can use the emotion estimation function to collect data based on the user's emotional state and suggest a data collection method for eliciting positive emotions. The data collection unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and suggest a data collection method for eliciting positive emotions. For example, data is collected when the user is relaxed. The data collection unit also adjusts the timing and frequency of data collection based on the user's emotional state. For example, data is collected when the user is feeling strongly positive and data collection is refrained when the user is feeling negative. The data collection unit also suggests a data collection method for improving the user's emotional state based on the emotion estimation data. For example, relaxation data is collected when stress is high and advice is provided. This makes it possible to collect data based on the user's emotional state and suggest a method for eliciting positive emotions.

[0074] The coaching unit can estimate the user's emotional state in real time and provide customized guidance according to the emotion. For example, the coaching unit uses a generative AI to analyze the user's emotional state in real time and provide relaxation advice when stress levels are high. For example, it can suggest deep breathing or meditation techniques. The coaching unit also dynamically adjusts the content of the customized guidance according to the user's emotional state. For example, it can suggest an exercise plan when positive emotions are strong, and suggest relaxation when negative emotions are strong. The coaching unit also provides customized guidance to improve the user's emotional state based on the emotion estimation data. For example, it can provide relaxation advice when stress levels are high, eliciting positive emotions. This makes it possible to provide customized guidance according to the user's emotional state.

[0075] The coaching unit can predict future behavior and health status based on the user's past data and provide long-term customized guidance based on that. The coaching unit, for example, analyzes the user's past data and builds a model to predict future behavior and health status. For example, it predicts future exercise performance based on past exercise data. The coaching unit also provides long-term customized guidance based on the predictive model. For example, it predicts future health risks and suggests preventive measures based on those risks. The coaching unit also provides long-term goal setting and customized guidance for achieving those goals based on the user's past data. For example, it creates a long-term diet plan and suggests meal plans and exercise plans based on that plan. This makes it possible to provide long-term customized guidance based on past data.

[0076] The guidance unit can introduce a scheduling function to provide advice at the optimal timing according to the user's lifestyle rhythm. The guidance unit introduces a scheduling function that analyzes the user's lifestyle rhythm and provides advice at the optimal timing. For example, it may suggest morning exercise or evening relaxation. The guidance unit also uses the scheduling function to provide customized guidance that matches the user's lifestyle rhythm. For example, it may provide advice on stretching and relaxation in between work. The guidance unit also develops a scheduling function that dynamically adjusts the timing of advice based on the user's lifestyle rhythm. For example, it may provide advice that can be done in a short amount of time when the user is busy. This makes it possible to provide advice that matches the user's lifestyle rhythm.

[0077] The coaching unit can incorporate advice from nutritionists or fitness experts into the content of the customized instruction, and the generation AI can integrate and provide it. For example, the coaching unit registers advice from nutritionists and fitness experts in a database, and the generation AI integrates it to provide customized instruction. For example, it proposes meal plans and exercise plans. Furthermore, the coaching unit uses the generation AI to provide comprehensive customized instruction to the user based on the expert advice. For example, it combines a nutritionally balanced meal plan with an effective exercise plan. Furthermore, the coaching unit incorporates advice from nutritionists and fitness experts in real time, and the generation AI provides the user with the latest customized instruction. For example, it proposes new diet and training methods. In this way, by integrating expert advice, more effective customized instruction is possible.

[0078] The instruction unit can provide customized instruction that takes into account regional health habits and food cultures to accommodate users from different cultural areas or regions. For example, the instruction unit registers the health habits and food cultures of different cultural areas or regions in a database, and the generation AI provides customized instruction that takes these into account. For example, it proposes a meal plan using ingredients unique to the region. The instruction unit also provides appropriate customized instruction to users based on the health habits and food cultures of each region. For example, it proposes an exercise plan that incorporates regional exercise habits. The instruction unit also provides customized instruction that takes into account regional health habits and food cultures to accommodate users from different cultural areas or regions. For example, it provides advice tailored to the region's climate and living environment. This makes it possible to provide customized instruction that takes into account regional health habits and food cultures.

[0079] The instructor can use the emotion estimation function to provide customized instruction based on the user's emotional state and advice to elicit positive emotions. The instructor, for example, uses the emotion estimation function to analyze the user's emotional state in real time and provide customized instruction to elicit positive emotions. For example, the instructor can provide advice to help the user relax. The instructor can also dynamically adjust the content of the customized instruction based on the user's emotional state. For example, when the user is feeling strongly positive, the instructor can suggest a challenging exercise plan, and when the user is feeling negative, the instructor can suggest relaxation. The instructor can also provide customized instruction to improve the user's emotional state based on the emotion estimation data. For example, when stress is high, the instructor can provide relaxation advice to elicit positive emotions. This makes it possible to provide customized instruction based on the user's emotional state and advice to elicit positive emotions.

[0080] The collaboration unit can estimate the user's emotional state in real time and provide resource management advice according to the emotion. For example, the collaboration unit uses a generation AI to analyze the user's emotional state in real time and suggest resource reallocation when stress is high. For example, it may change work priorities to reduce stress. The collaboration unit also dynamically adjusts resource management advice according to the user's emotional state. For example, it may suggest tackling a new project when positive emotions are strong, and suggest rest when negative emotions are strong. The collaboration unit also provides resource management advice to improve the user's emotional state based on the emotion estimation data. For example, it may reallocate resources when stress is high to elicit positive emotions. This makes it possible to provide resource management advice according to the user's emotional state.

[0081] The collaboration unit can predict future resource demands based on the user's resource management data and propose an optimal management method based on that. The collaboration unit, for example, analyzes the user's resource management data and builds a model to predict future resource demands. For example, it predicts future time management and financial management demands based on past data. The collaboration unit also proposes an optimal resource management method based on the prediction model. For example, it predicts future resource demands and proposes budget plans and schedules based on that. The collaboration unit also creates a long-term resource management plan based on the user's resource management data and provides advice based on that. For example, it predicts future resource demands and proposes resource allocation based on that. This makes it possible to predict future resource demands and propose an optimal management method based on that.

[0082] The collaboration unit can introduce a scheduling function to provide resource management advice at the optimal timing in accordance with the user's lifestyle rhythm. The collaboration unit, for example, introduces a scheduling function that analyzes the user's lifestyle rhythm and provides resource management advice at the optimal timing. For example, it suggests time management in the morning and relaxation in the evening. The collaboration unit also uses the scheduling function to provide resource management advice that matches the user's lifestyle rhythm. For example, it suggests reallocating resources or taking rest during breaks from work. The collaboration unit also develops a scheduling function that dynamically adjusts the timing of resource management advice based on the user's lifestyle rhythm. For example, it provides resource management advice that can be done in a short amount of time when the user is busy. This makes it possible to provide resource management advice that matches the user's lifestyle rhythm.

[0083] The collaboration unit can include management of household resources (housework or childcare), and the generation AI can integrate and provide them. For example, the collaboration unit collects household resource management data (housework, childcare, etc.), and the generation AI integrates it to provide resource management advice. For example, it proposes the division of household chores and childcare schedules. The collaboration unit also provides comprehensive resource management advice, including household resource management. For example, it integrates household resources with work resources and proposes a balanced resource allocation. The collaboration unit also provides resource management advice for both home and work, based on the household resource management data. For example, it adjusts the housework or childcare schedule to improve work efficiency. In this way, comprehensive resource management is possible by integrating household resource management.

[0084] The collaboration unit can provide advice that takes into account occupation-specific resource management methods to accommodate users with different occupations and lifestyles. For example, the collaboration unit collects resource management data from users with different occupations, and the generation AI provides advice that takes into account occupation-specific resource management methods. For example, it proposes resource management that takes into account the differences between desk work and physical labor. Furthermore, the collaboration unit provides advice that takes into account lifestyle-specific resource management methods to accommodate users with different lifestyles. For example, it proposes resource management that takes into account the differences between freelancers and company employees. Furthermore, the collaboration unit provides resource management advice that takes into account the characteristics of each occupation and lifestyle. For example, it proposes resource management that is suitable for occupations that include night shifts or for people with irregular lifestyles. This makes it possible to provide advice that takes into account occupation-specific resource management methods.

[0085] The collaboration unit can use the emotion estimation function to provide resource management advice based on the user's emotional state and make suggestions to bring out positive emotions. The collaboration unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and provide resource management advice to bring out positive emotions. For example, it suggests resource allocation that will allow the user to relax. The collaboration unit also dynamically adjusts the content of resource management based on the user's emotional state. For example, it suggests taking on a new project when the user is feeling strongly positive, and suggests taking a rest when the user is feeling negative. The collaboration unit also provides resource management advice to improve the user's emotional state based on the emotion estimation data. For example, it reallocates resources when stress is high to bring out positive emotions. This makes it possible to provide resource management advice based on the user's emotional state and make suggestions to bring out positive emotions.

[0086] The collaboration unit can estimate the user's emotional state in real time and dynamically adjust device settings and data collection methods according to the emotion. For example, the collaboration unit uses a generative AI to analyze the user's emotional state in real time and dynamically adjust device settings according to the emotion. For example, it adjusts the brightness of smart lighting when stress is high. The collaboration unit also dynamically adjusts the device's data collection method based on the user's emotional state. For example, it focuses on collecting exercise data when positive emotions are strong, and collects relaxation data when negative emotions are strong. The collaboration unit also optimizes device settings and data collection methods based on the emotion estimation data. For example, it adjusts the smart device settings to create a relaxing environment for the user. This makes it possible to dynamically adjust device settings and data collection methods according to the user's emotional state.

[0087] The linking unit can predict a user's health condition and provide preventive advice based on data collected from IoT devices and wearable devices. For example, the linking unit analyzes data collected from IoT devices and wearable devices to build a model that predicts a user's health condition. For example, it predicts health risks based on heart rate and activity data. The linking unit also provides preventive advice to the user based on the predictive model. For example, it predicts future health risks and suggests exercise and meal plans based on the predicted health risks. The linking unit also comprehensively evaluates the user's health condition based on data collected from IoT devices and wearable devices and provides preventive advice. For example, it analyzes sleep data and stress levels and provides health management advice. This makes it possible to predict a user's health condition and provide preventive advice based on data from IoT devices and wearable devices.

[0088] The linking unit can introduce a scheduling function to optimize the timing of data collection from the device in accordance with the user's lifestyle. The linking unit, for example, introduces a scheduling function that analyzes the user's lifestyle and collects data from the device at the optimal timing. For example, it may focus on collecting data about morning exercise and nighttime sleep. The linking unit also uses the scheduling function to collect data in accordance with the user's lifestyle. For example, it may collect activity data in between work and collect heart rate data when relaxing. The linking unit also develops a scheduling function that dynamically adjusts the timing of data collection from the device based on the user's lifestyle. For example, it may collect data that can be collected in a short amount of time when the user is busy. This makes it possible to optimize the timing of data collection from the device in accordance with the user's lifestyle.

[0089] The linking unit adds smart devices in the home (such as smart refrigerators or smart lighting), and the generation AI can integrate and analyze them. For example, the linking unit collects data from smart devices in the home (such as smart refrigerators and smart lighting), and the generation AI integrates and analyzes it. For example, it combines food ingredient data from the refrigerator with exercise data to propose a meal plan. The linking unit also comprehensively evaluates the user's lifestyle and health status based on the data from the smart devices. For example, it analyzes lighting brightness and temperature data to propose a comfortable living environment. The linking unit also integrates data collected from smart devices in the home, and the generation AI provides comprehensive health management advice to the user. For example, it proposes a nutritionally balanced meal plan based on food ingredient data from the refrigerator. This makes it possible to integrate and analyze smart devices in the home for more comprehensive health management.

[0090] The linking unit ensures data compatibility between devices from different manufacturers, allowing the generation AI to integrate and analyze the data. For example, the linking unit develops protocols to ensure data compatibility between devices from different manufacturers, and the generation AI integrates and analyzes the data. For example, it integrates data from smartwatches and fitness trackers. The linking unit also develops APIs to ensure data compatibility and integrates data collected from devices from different manufacturers. For example, it centrally manages data from smart devices from different brands. The linking unit also ensures data compatibility between devices from different manufacturers, allowing the generation AI to integrate the data and provide comprehensive health management advice. For example, it combines data from different devices to evaluate health status. This enables more comprehensive health management by ensuring data compatibility between devices from different manufacturers and integrating and analyzing it.

[0091] The collaboration unit can use the emotion estimation function to suggest device settings and data collection methods based on the user's emotional state and make adjustments to elicit positive emotions. For example, the collaboration unit uses the emotion estimation function to analyze the user's emotional state in real time and suggest device settings to elicit positive emotions. For example, the collaboration unit adjusts the brightness of smart lighting when stress is high. The collaboration unit also dynamically adjusts the device's data collection method based on the user's emotional state. For example, the collaboration unit focuses on collecting exercise data when positive emotions are strong and collects relaxation data when negative emotions are strong. The collaboration unit also optimizes the device settings and data collection method based on the emotion estimation data. For example, the collaboration unit adjusts the smart device settings to create an environment where the user can relax. This makes it possible to suggest and adjust device settings and data collection methods based on the user's emotional state.

[0092] The linking unit can predict the user's health condition and provide preventive advice based on data collected from the cloud service. The linking unit, for example, analyzes data collected from the cloud service and builds a model to predict the user's health condition. For example, it predicts health risks based on heart rate and activity data. The linking unit also provides preventive advice to the user based on the prediction model. For example, it predicts future health risks and suggests exercise plans and meal plans based on the predicted health risks. The linking unit also comprehensively evaluates the user's health condition based on the data collected from the cloud service and provides preventive advice. For example, it analyzes sleep data and stress levels and provides health management advice. This makes it possible to predict the user's health condition and provide preventive advice based on data from the cloud service.

[0093] The linking unit can introduce a scheduling function to optimize the timing of data synchronization with the cloud service in accordance with the user's lifestyle. The linking unit introduces a scheduling function that analyzes the user's lifestyle and synchronizes data with the cloud service at the optimal timing. For example, it may synchronize data focusing on morning exercise and nighttime sleep. The linking unit also uses the scheduling function to synchronize data in accordance with the user's lifestyle. For example, it may synchronize activity data in between work and synchronize heart rate data when relaxing. The linking unit also develops a scheduling function that dynamically adjusts the timing of data synchronization with the cloud service based on the user's lifestyle. For example, it may synchronize data that can be synchronized in a short amount of time when the user is busy. This makes it possible to optimize the timing of data synchronization with the cloud service in accordance with the user's lifestyle.

[0094] The linking unit adds smart devices in the home (such as smart refrigerators and smart lighting), and the generation AI can integrate and analyze them. For example, the linking unit collects data from smart devices in the home (such as smart refrigerators and smart lighting), and the generation AI integrates and analyzes it. For example, it combines food ingredient data from the refrigerator with exercise data to propose a meal plan. The linking unit also comprehensively evaluates the user's lifestyle and health status based on the data from the smart devices. For example, it analyzes lighting brightness and temperature data to propose a comfortable living environment. The linking unit also integrates data collected from smart devices in the home, and the generation AI provides the user with comprehensive health management advice. For example, it proposes a nutritionally balanced meal plan based on food ingredient data from the refrigerator. This makes it possible to integrate and analyze smart devices in the home for more comprehensive health management.

[0095] The collaboration unit ensures data compatibility between different cloud services, allowing the generation AI to integrate and analyze the data. For example, the collaboration unit develops a protocol to ensure data compatibility between different cloud services, and the generation AI integrates and analyzes the data. For example, the collaboration unit integrates data from different health management apps. The collaboration unit also develops an API to ensure data compatibility and integrates data collected from different cloud services. For example, data from cloud services of different brands is centrally managed. The collaboration unit also ensures data compatibility between different cloud services, allowing the generation AI to integrate the data and provide comprehensive health management advice. For example, data from different cloud services can be combined to evaluate health status. This enables more comprehensive health management by ensuring data compatibility between different cloud services and integrating and analyzing it.

[0096] The collaboration unit can use the emotion estimation function to propose a data integration method and an analysis method based on the user's emotional state and make adjustments to elicit positive emotions. The collaboration unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and propose a data integration method to elicit positive emotions. For example, when stress is high, relaxation data is prioritized for integration. The collaboration unit also dynamically adjusts the data analysis method based on the user's emotional state. For example, when positive emotions are strong, exercise data is prioritized for analysis, and when negative emotions are strong, stress data is analyzed. The collaboration unit also optimizes the data integration method and analysis method based on the emotion estimation data. For example, the collaboration unit adjusts data integration and analysis to create an environment where the user can relax. This makes it possible to propose and adjust data integration and analysis methods based on the user's emotional state.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The AI ​​personal trainer system can also estimate the user's emotional state and provide incentives to motivate them based on the estimated emotions. For example, if the user's motivation to continue exercising is declining, the system can send them an encouraging message. When the user achieves their goal, the system can award badges and points to enhance their sense of accomplishment. Furthermore, depending on the user's emotional state, the system can suggest appropriate music or podcasts to improve their mood during training. This makes it possible to provide incentives based on the user's emotional state, which is expected to maintain and improve motivation.

[0099] The data collection unit can also collect data to predict health status based on the user's living environment. For example, it can collect noise levels and air quality data in the user's living environment and analyze the impact of these environmental factors on the user's sleep and stress levels. It can also collect data on the user's commuting time and means of commuting and evaluate the impact of these factors on exercise intensity and stress. It can also collect temperature and humidity data in the user's home and analyze the impact of these factors on health status. This enables comprehensive health management that takes the user's living environment into account.

[0100] The coaching unit can also estimate the user's emotional state in real time and provide customized guidance to support the user's stress management based on the estimated emotions. For example, if the user is in a high stress state, the system can suggest breathing exercises or meditation techniques for relaxation. If the user is relaxed, the system can suggest light exercise or stretching to maintain mental and physical balance. Furthermore, depending on the user's emotional state, the system can suggest appropriate rest periods and ways to refresh oneself, thereby reducing stress. This enables stress management based on the user's emotional state and helps maintain physical and mental health.

[0101] The linking unit can also incorporate a scheduling function to provide health management advice at optimal times in line with the user's lifestyle. For example, if a user has a morning rhythm, the system can provide advice on morning exercise and diet. Similarly, a user with a nocturnal rhythm can receive advice on evening relaxation and sleep improvement. Furthermore, based on the user's lifestyle, the system can dynamically adjust the timing of advice so that the user can most effectively accept the advice. This allows for health management tailored to the user's lifestyle.

[0102] The analysis unit can also estimate the user's emotional state and analyze the user's behavioral patterns based on the estimated emotions. For example, when the user has positive emotions, it can analyze whether the amount of exercise tends to increase. It can also evaluate whether there are changes in the amount and quality of food intake when the user has strong negative emotions. It can also predict fluctuations in behavioral patterns based on the user's emotional state and provide appropriate advice. This makes it possible to analyze behavioral patterns taking the user's emotional state into account, allowing for more accurate, customized guidance.

[0103] The data collection unit can also collect the user's voice data, which the generation AI can then analyze to assess the user's health condition. For example, it can analyze changes in the user's voice tone and speaking style to assess their stress level and emotional state. The generation AI can also evaluate the user's sleep quality and fatigue level based on the user's voice data. Furthermore, it can analyze the voice data in combination with other personal data to assess the user's overall health condition. This makes it possible to utilize voice data to perform a multifaceted health assessment.

[0104] The coaching unit can also estimate the user's emotional state and provide customized guidance to maintain the user's motivation based on the estimated emotions. For example, if the user's motivation to continue exercising is low, the system can send the user an encouraging message. When the user achieves a goal, the system can award badges and points to enhance the user's sense of accomplishment. Furthermore, depending on the user's emotional state, the system can suggest appropriate music or podcasts to improve the user's mood during training. This allows the user to maintain motivation based on their emotional state and support them in achieving their goals.

[0105] The data collection unit can also collect data to predict health status based on the user's living environment. For example, it can collect noise levels and air quality data in the user's living environment and analyze the impact of these environmental factors on the user's sleep and stress levels. It can also collect data on the user's commuting time and means of commuting and evaluate the impact of these factors on exercise intensity and stress. It can also collect temperature and humidity data in the user's home and analyze the impact of these factors on health status. This enables comprehensive health management that takes the user's living environment into account.

[0106] The collaboration unit can also estimate the user's emotional state and provide resource management advice to help the user manage stress based on the estimated emotion. For example, if the user is in a high stress state, the system can suggest reallocating resources to reduce stress. If the user is relaxed, the system can suggest tackling a new project, eliciting positive emotions. Furthermore, it can dynamically adjust the content of resource management according to the user's emotional state and suggest optimal resource allocation. This allows for resource management based on the user's emotional state.

[0107] The analysis unit can also predict future behavior and health status based on the user's past data and provide long-term customized guidance based on that prediction. For example, it can predict future exercise performance based on past exercise data. It can also assess long-term health risks based on the predictive model and suggest preventive measures based on those risks. It can also set long-term goals based on the user's past data and provide customized guidance for achieving those goals. This enables long-term health management based on past data.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The data collection unit collects personal data. For example, it collects data such as the user's daily exercise, food records, and sleep patterns. The data collection unit can also collect data automatically through IoT devices and wearable devices. Furthermore, the data collection unit can collect data from cloud services. For example, it can collect data from smart watches and fitness trackers to monitor the user's health status in real time. Step 2: The analysis unit analyzes the personal data collected by the data collection unit. For example, the generation AI analyzes the data using data mining, statistical analysis, and machine learning algorithms. The generation AI can also analyze the data to understand the user's behavioral patterns, habits, and living situation. Furthermore, the generation AI can estimate the user's emotional state in real time and dynamically adjust the frequency and content of data collection according to emotional fluctuations. Step 3: The guidance unit provides customized guidance based on the results of the analysis by the analysis unit. For example, if the user is trying to lose weight, the generation AI can suggest appropriate meal plans and exercise plans. The generation AI can also provide advice on stress management and sleep improvement. Furthermore, the generation AI can provide customized guidance based on the user's emotional state. Step 4: The Collaboration Unit interacts with IoT and wearable devices to execute the custom guidance provided by the Guidance Unit. For example, the Generative AI collects data from smartwatches and fitness trackers to monitor the user's health status in real time. The Generative AI can also integrate and analyze data through API connections with cloud services. Furthermore, the Generative AI can add smart devices in the home (e.g., smart refrigerators and smart lighting) and integrate and analyze them.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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]

[0177] 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 data collection unit that collects personal data; an analysis unit that analyzes the personal data collected by the data collection unit; a coaching unit that provides custom coaching based on the results analyzed by the analysis unit; and a linking unit that links with an IoT or wearable device to execute the custom instruction provided by the instruction unit. A system characterized by:

2. The data collection unit Collecting at least one of the following data: daily exercise, food records, and sleep patterns of the user 2. The system of claim 1.

3. The leadership team: Suggesting appropriate diet or exercise plans if the user is trying to lose weight 2. The system of claim 1.

4. The linking unit is Collect data from smartwatches or fitness trackers to monitor users' health in real time 2. The system of claim 1.

5. The analysis unit Integrate data stored in cloud storage or from other health management apps for comprehensive health management 2. The system of claim 1.

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