System

Abstract

An object of a system according to an embodiment is to propose an optimal exercise plan or diet plan to a user.SOLUTION: A system includes a data collection part, an analysis part, an exercise plan suggestion part, and a meal plan suggestion part. The data collection unit collects data from a wearable device, medical examination results, and photographs of meals. The analysis unit analyzes the data collected by the data collection unit. The exercise plan suggestion unit suggests an optimal exercise plan to the user based on the result analyzed by the analysis unit. The meal plan suggestion unit suggests an optimal meal plan to the user on the basis of the result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately proposed optimal exercise and meal plans for individual users, and there is room for improvement.

[0005] The system according to the embodiment aims to propose optimal exercise and meal plans to a user. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, an exercise plan proposal unit, and a meal plan proposal unit. The data collection unit collects data from wearable devices, health checkup results, and meal photos. The analysis unit analyzes the data collected by the data collection unit. The exercise plan proposal unit proposes an optimal exercise plan to the user based on the results of the analysis by the analysis unit. The meal plan proposal unit proposes an optimal meal plan to the user based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal exercise and meal plans to the user. [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) A health management system according to an embodiment of the present invention is a system in which a generative AI suggests optimal exercise and diet for a user based on data acquired from wearable devices, health checkup results, meal photos, etc. This enables the health management system to provide specific advice tailored to the user's health condition, contributing to maintaining health and improving performance.

[0029] A health management system according to an embodiment includes a data collection unit, an analysis unit, an exercise plan proposal unit, and a meal plan proposal unit. The data collection unit collects data from wearable devices, health checkup results, and meal photos. For example, the data collection unit collects heart rate and step count data from a smartwatch or fitness tracker. It can also collect blood test results and electrocardiogram data as health checkup results. It can also analyze meal photos using image recognition technology to identify ingredients and nutrients. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can use data mining technology to comprehensively evaluate the user's health status. It can also extract trends and patterns in the data using statistical analysis techniques. The exercise plan proposal unit proposes an optimal exercise plan for the user based on the results of the analysis by the analysis unit. For example, the exercise plan proposal unit proposes jogging distances and routes. The exercise plan proposal unit can also introduce and reserve gyms and gymnasiums. The exercise plan proposal unit can also propose exercise intensity and frequency based on the user's physical fitness level and goals. The meal plan proposal unit proposes an optimal meal plan for the user based on the results of the analysis by the analysis unit. For example, the meal plan proposal unit proposes home-cooked meal recipes and ingredient arrangements. The meal plan proposal unit can also provide dining out and takeout options. Furthermore, the meal plan proposal unit can propose a meal plan based on the user's nutritional balance and calorie restrictions. This allows the health management system according to the embodiment to provide specific advice tailored to the user's health condition, contributing to maintaining health and improving performance.

[0030] The data collection unit collects heart rate, step count, sleep data, blood pressure, and dietary details to comprehensively evaluate the user's health. For example, the data collection unit analyzes the user's social media posts and blog posts in addition to the heart rate and step count data obtained from the wearable device to evaluate their lifestyle habits and stress level. For example, it detects signs of stress from the frequency and content of social media posts and makes suggestions for lifestyle improvement. The data collection unit also analyzes the user's social media posts and blog posts using natural language processing technology to evaluate their lifestyle habits and stress level. For example, it performs sentiment analysis on the content of posts to detect increased stress. The data collection unit also integrates data from the wearable device and social media posts to comprehensively evaluate the user's lifestyle habits and stress level. For example, it correlates and analyzes heart rate fluctuations with the content of social media posts. This allows for a comprehensive evaluation of the user's health, enabling more precise exercise and meal plans to be provided.

[0031] The exercise plan suggestion unit can suggest jogging distances and routes, and introduce and reserve gyms and gymnasiums. The exercise plan suggestion unit, for example, collects the user's past medical records, and the generation AI incorporates them into the analysis to more precisely assess the user's health condition. For example, it may evaluate health risks taking into account past medical history and treatment history. The exercise plan suggestion unit also incorporates genetic information into the analysis to more precisely assess the user's health condition. For example, it may identify genetic risk factors and propose a health management plan based on them. The exercise plan suggestion unit also integrates medical records and genetic information, and the generation AI performs a comprehensive health assessment. For example, it may combine past diagnostic results with genetic risk to predict future health risks. This provides the user with an optimal exercise plan and maximizes the benefits of exercise.

[0032] The meal plan proposal unit can provide home-cooked recipes, ingredient arrangements, and dining out and takeout options. For example, the meal plan proposal unit uses an emotion estimation function to analyze the user's emotional state in real time and perform a health assessment that takes into account fluctuations in stress and motivation. For example, it calculates an emotion score by analyzing facial expressions and voice tone. The meal plan proposal unit also analyzes the user's emotional state and reflects fluctuations in stress level and motivation in the health assessment. For example, if the emotion score is low, it suggests relaxation methods. The meal plan proposal unit also builds a system that performs a health assessment that takes into account fluctuations in stress and motivation based on the emotion estimation data. For example, it adjusts a health management plan according to fluctuations in the emotion score. This provides the user with an optimal meal plan and supports a healthy diet.

[0033] The data collection unit can analyze SNS posts and blog articles to evaluate lifestyle habits and stress levels. For example, the data collection unit analyzes the user's SNS posts and blog articles in addition to heart rate and step count data obtained from the wearable device to evaluate lifestyle habits and stress levels. For example, it detects signs of stress from the frequency and content of SNS posts and makes suggestions for improving lifestyle habits. The data collection unit also analyzes the user's SNS posts and blog articles using natural language processing technology to evaluate lifestyle habits and stress levels. For example, it performs sentiment analysis on the content of posts to detect increased stress. The data collection unit also integrates data from the wearable device and SNS posts to comprehensively evaluate lifestyle habits and stress levels. For example, it correlates and analyzes heart rate fluctuations with the content of SNS posts. This allows for a more precise health assessment by evaluating the user's lifestyle habits and stress levels.

[0034] The data collection unit incorporates medical records and genetic information into the analysis to enable a more precise assessment of health status. For example, the data collection unit collects the user's past medical records, and the generation AI incorporates them into the analysis to enable a more precise assessment of health status. For example, health risks are assessed taking into account past medical history and treatment history. The data collection unit also incorporates genetic information into the analysis to enable a more precise assessment of the user's health status. For example, genetic risk factors are identified and a health management plan is proposed based on these. The data collection unit also integrates medical records and genetic information, and the generation AI performs a comprehensive health assessment. For example, past diagnostic results are combined with genetic risk to predict future health risks. This allows a more precise health assessment to be performed by taking into account the user's past medical records and genetic information.

[0035] The data collection unit can use a drone to monitor the exercise environment and living environment in real time. The data collection unit, for example, uses a drone to monitor the user's exercise environment and living environment in real time. For example, the drone can be used to check the congestion status of a jogging route and the weather. The data collection unit also uses a drone to monitor the user's living environment and use this information to help with health management. For example, the drone can measure the air quality and noise level of the living environment. The data collection unit also uses a drone to monitor the user's exercise environment in real time and propose an optimal exercise plan. For example, the drone can analyze footage captured by the drone to propose a safe jogging route. In this way, real-time monitoring of the user's exercise environment and living environment allows for a more precise health assessment.

[0036] The data collection unit can collect health data of the pet and make suggestions for maintaining the health of the pet together. The data collection unit, for example, collects health data of the user's pet and makes suggestions for maintaining the health of the pet together. For example, it analyzes the pet's exercise amount and dietary content and provides advice for living a healthy life together. The data collection unit also analyzes the pet's health data and proposes a plan for the user and pet to maintain their health together. For example, it proposes an exercise plan to do together based on the pet's health condition. The data collection unit also integrates the health data of the user and the pet and makes comprehensive suggestions for maintaining the health of both. For example, it provides a meal plan and exercise plan according to the pet's health condition. This supports a healthier lifestyle by comprehensively managing the health of the user and the pet.

[0037] The exercise plan suggestion unit can predict exercise effects and suggest exercise intensity and frequency based on the exercise history. The exercise plan suggestion unit, for example, analyzes the user's exercise history and builds a system that predicts exercise effects. For example, it suggests optimal exercise intensity and frequency based on past exercise data. The exercise plan suggestion unit also uses a generation AI to predict exercise effects based on the exercise history and suggest an optimal exercise plan. For example, it analyzes past exercise data to provide an effective training plan. The exercise plan suggestion unit also develops a system that predicts exercise effects based on the user's exercise history and suggests optimal exercise intensity and frequency. For example, it analyzes the exercise history and generates an effective exercise plan. This maximizes the exercise effect by providing an optimal exercise plan based on the user's exercise history.

[0038] The exercise plan proposal unit can analyze geographic information and propose an exercise plan according to the season and weather. The exercise plan proposal unit, for example, analyzes the user's geographic information and builds a system that proposes an exercise plan according to the season and weather. For example, it proposes the optimal exercise location and time based on weather data. The exercise plan proposal unit also generates an exercise plan according to the season and weather based on the geographic information. For example, it proposes an appropriate exercise plan taking into account the temperature and humidity of each season. The exercise plan proposal unit also analyzes the user's geographic information and proposes an exercise plan according to the season and weather. For example, it selects indoor and outdoor exercise locations based on weather data. In this way, by taking the user's geographic information into consideration, it provides an optimal exercise plan according to the season and weather.

[0039] The exercise plan proposal unit can work with friends and family to propose joint exercise plans. The exercise plan proposal unit, for example, builds a system that works with the user's friends and family to propose joint exercise plans. For example, it generates an exercise plan for a group. The exercise plan proposal unit also proposes exercise plans to be done together with friends and family. For example, it provides a fitness plan that the whole family can participate in. The exercise plan proposal unit also works with the user's friends and family to propose joint exercise plans. For example, it proposes a jogging plan to be done together with friends. In this way, by working with the user's friends and family, a joint exercise plan can be provided, improving the enjoyment of exercise.

[0040] The exercise plan proposal unit can propose an exercise plan that can be done in the office, taking into account the work environment. The exercise plan proposal unit, for example, builds a system that proposes an exercise plan that can be done in the office, taking into account the user's work environment. For example, it provides a stretching plan that can be done while doing desk work. The exercise plan proposal unit also proposes an exercise plan that suits the work environment. For example, it proposes simple exercises that can be done in the office. The exercise plan proposal unit also proposes an exercise plan that can be done in the office, taking into account the user's work environment. For example, it proposes light exercise that can be done during a meeting. In this way, the optimal exercise plan that can be done in the office is provided by taking into account the user's work environment.

[0041] The meal plan proposal unit can propose a meal plan for optimizing nutritional balance based on the meal history. The meal plan proposal unit, for example, analyzes the user's meal history and builds a system that proposes a meal plan for optimizing nutritional balance. For example, a balanced meal plan is provided based on past meal data. The meal plan proposal unit also proposes a meal plan for optimizing nutritional balance using a generation AI based on the meal history. For example, past meal data is analyzed to provide a meal plan that compensates for nutrient deficiencies. The meal plan proposal unit also develops a system that proposes a meal plan for optimizing nutritional balance based on the user's meal history. For example, the meal history is analyzed to generate a balanced meal plan. This provides a meal plan for optimizing nutritional balance based on the user's meal history.

[0042] The meal plan proposal unit can propose an individually customized meal plan by taking into account allergy information and food preferences. The meal plan proposal unit, for example, builds a system that proposes an individually customized meal plan by taking into account the user's allergy information and food preferences. For example, it provides recipes that do not contain allergenic ingredients. Furthermore, the meal plan proposal unit proposes an individually customized meal plan using a generation AI based on the allergy information and food preferences. For example, it provides healthy recipes using favorite ingredients. Furthermore, the meal plan proposal unit proposes an individually customized meal plan by taking into account the user's allergy information and food preferences. For example, it provides a balanced meal plan that avoids allergenic ingredients. In this way, an individually customized meal plan is provided by taking into account the user's allergy information and food preferences.

[0043] The meal plan proposal unit collects dietary data of family members and housemates and can propose meal plans that will help everyone stay healthy. The meal plan proposal unit, for example, collects dietary data of the user's family members and housemates and builds a system that proposes meal plans that will help everyone stay healthy. For example, it provides a meal plan that takes into account the nutritional balance of all family members. The meal plan proposal unit also uses a generation AI to propose a meal plan that will help everyone stay healthy based on the dietary data of the family members and housemates. For example, it analyzes the dietary history of all family members to provide a balanced meal plan. The meal plan proposal unit also collects dietary data of the user's family members and housemates and proposes a meal plan that will help everyone stay healthy. For example, it generates a meal plan that takes into account the nutritional balance of all family members. In this way, by collecting dietary data of the user's family members and housemates, a meal plan that will help everyone stay healthy is provided.

[0044] The meal plan proposal unit can propose meal plans according to travel destinations and business trip destinations. The meal plan proposal unit, for example, builds a system that proposes meal plans according to a user's travel destinations and business trip destinations. For example, it provides meal plans that take into account the food culture and ingredients of the travel destination. The meal plan proposal unit also generates meal plans according to travel destinations and business trip destinations. For example, it proposes healthy meal plans based on restaurant information at the business trip destination. The meal plan proposal unit also proposes meal plans according to the user's travel destinations and business trip destinations. For example, it provides balanced meal plans using ingredients at the travel destination. In this way, a healthy diet is supported by providing meal plans according to the user's travel destinations and business trip destinations.

[0045] The BtoC service department can provide special health plans according to a user's life stage. For example, the BtoC service department builds a system that provides special health plans according to a user's life stage. For example, a pregnant user is provided with a meal plan that takes nutritional balance into consideration. The BtoC service department also generates special health plans according to a user's life stage. For example, an exercise plan with adjusted exercise intensity is proposed for elderly users. The BtoC service department also provides special health plans according to a user's life stage. For example, a relaxation plan to reduce stress is proposed for a pregnant user. In this way, by providing special health plans according to a user's life stage, individual health needs can be met.

[0046] The BtoC service department can collect feedback and continuously improve the level of personalization of the service. For example, the BtoC service department collects user feedback and builds a system that continuously improves the level of personalization of the service. For example, it adjusts exercise plans and meal plans based on the feedback. The BtoC service department also uses generative AI to improve the level of personalization of the service based on the feedback. For example, it provides more individualized plans that reflect user opinions. The BtoC service department also collects user feedback and continuously improves the level of personalization of the service. For example, it improves the content of the service based on the feedback. In this way, the level of personalization of the service is continuously improved by collecting user feedback.

[0047] The BtoC service department can also support health management of pets and provide services for maintaining the health of both the user and the pet. For example, the BtoC service department builds a system that supports the health management of users' pets and provides services for maintaining the health of both the user and the pet. For example, it provides health plans based on the pet's health data. The BtoC service department also supports health management of pets and provides services for maintaining the health of both the user and the pet. For example, it provides health plans by analyzing the amount of exercise and diet of the pet. The BtoC service department also supports the health management of users' pets and provides services for maintaining the health of both the user and the pet. For example, it provides meal plans and exercise plans according to the pet's health condition. In this way, by supporting the health management of users' pets, it provides services for maintaining the health of both the user and the pet.

[0048] The BtoC service unit can propose health plans based on hobbies and interests. The BtoC service unit, for example, builds a system that proposes health plans based on a user's hobbies and interests. For example, it provides exercise plans and meal plans related to hobbies. The BtoC service unit also generates health plans based on hobbies and interests. For example, it proposes a hiking plan to a user who likes the outdoors. The BtoC service unit also proposes health plans based on a user's hobbies and interests. For example, it proposes new recipes to a user who likes cooking. In this way, individual needs can be met by providing health plans based on the user's hobbies and interests.

[0049] The BtoB service department can propose data-based marketing strategies to fitness businesses. For example, the BtoB service department builds a system that proposes marketing strategies based on user data to fitness businesses. For example, it conducts targeted marketing based on users' exercise history and health data. The BtoB service department also analyzes user data and proposes marketing strategies to fitness businesses. For example, it proposes campaigns targeting users who are interested in a specific exercise plan. The BtoB service department also proposes marketing strategies based on user data to fitness businesses. For example, it sends personalized marketing messages based on the user's health data. In this way, by proposing marketing strategies based on user data, the marketing effectiveness of fitness businesses is improved.

[0050] The BtoB service department can analyze player performance data and optimize training plans for professional sports management companies. The BtoB service department, for example, builds a system that analyzes player performance data and optimizes training plans for professional sports management companies. For example, it provides optimal training plans based on the player's exercise data. The BtoB service department also analyzes player performance data and optimizes training plans for professional sports management companies. For example, it proposes effective training plans based on the player's exercise history. The BtoB service department also analyzes player performance data and optimizes training plans for professional sports management companies. For example, it provides training plans to improve performance based on the player's health data. In this way, by analyzing player performance data, training plans can be optimized and support the improvement of player performance.

[0051] The BtoB service department can develop new fitness programs based on data for fitness businesses. For example, the BtoB service department builds a system for developing new fitness programs based on user data for fitness businesses. For example, it proposes new programs based on the user's exercise history and health data. The BtoB service department also analyzes user data and develops new fitness programs for fitness businesses. For example, it proposes programs targeted at users who are interested in a specific exercise plan. The BtoB service department also develops new fitness programs based on user data for fitness businesses. For example, it provides personalized fitness programs based on the user's health data. In this way, developing new fitness programs based on user data improves the services of fitness businesses.

[0052] The BtoB service department can propose new training equipment to professional sports management companies to improve performance based on athlete data. For example, the BtoB service department builds a system to propose new training equipment to professional sports management companies to improve performance based on athlete data. For example, it proposes optimal training equipment based on athlete exercise data. The BtoB service department also analyzes athlete data and proposes new training equipment to professional sports management companies. For example, it proposes effective training equipment based on athlete exercise history. The BtoB service department also proposes new training equipment to professional sports management companies to improve performance based on athlete data. For example, it provides training equipment to improve performance based on athlete health data. In this way, proposing new training equipment based on athlete data supports the improvement of athlete performance.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The health management system may further include a voice recognition unit. The voice recognition unit analyzes the user's voice and collects information on the user's health condition and lifestyle habits. For example, the voice recognition unit may record the user's daily exercise and dietary habits. The voice recognition unit may also evaluate the user's stress level from the user's tone of voice and speaking style. The voice recognition unit may also analyze the user's voice data and provide feedback to the health management system. This allows for more precise health assessments to be performed using the user's voice data.

[0055] The health management system may further include an environmental monitoring unit. The environmental monitoring unit monitors the user's living environment in real time and identifies factors that affect health. For example, it measures indoor air quality, temperature, and humidity and provides advice on maintaining a healthy environment. The environmental monitoring unit may also collect external environmental data and evaluate factors that affect the user's exercise and lifestyle habits. Furthermore, the environmental monitoring unit may analyze the user's living environment data and provide feedback to the health management system. This allows for health assessment that takes the user's living environment into account.

[0056] The health management system can further include a virtual reality (VR) unit. The VR unit provides the user with an exercise experience in a virtual environment. For example, when a user jogs or does yoga at home, VR can be used to recreate a realistic exercise environment. The VR unit can also analyze the user's exercise data in real time and provide feedback to maximize the effectiveness of the exercise. Furthermore, the VR unit can propose an exercise plan incorporating game elements to increase the user's motivation. This allows the user to continue exercising in a fun way.

[0057] The health management system may further include a pet management unit. The pet management unit collects health data of the user's pet and makes suggestions for maintaining the health of both the user and the pet. For example, it may analyze the pet's exercise and dietary habits and provide advice for living a healthy life together. The pet management unit may also analyze the pet's health data and propose a plan for both the user and the pet to maintain their health. Furthermore, the pet management unit may integrate the health data of the user and the pet and make comprehensive suggestions for maintaining the health of both. This allows for comprehensive management of the health of the user and the pet.

[0058] The health management system can also use drones to monitor the exercise environment and living environment in real time. For example, a drone can be used to check the congestion status of a jogging route and the weather. Drones can also be used to monitor the user's living environment and use this information to help with health management. For example, a drone can measure the air quality and noise level in the living environment. Drones can also be used to monitor the user's exercise environment in real time and suggest an optimal exercise plan. This allows for more precise health assessment by monitoring the user's exercise environment and living environment in real time.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The data collection unit collects data from wearable devices, health checkup results, and meal photos. For example, the data collection unit collects heart rate and step count data from smartwatches and fitness trackers. It can also collect blood test results and electrocardiogram data as health checkup results. It can also analyze meal photos using image recognition technology to identify ingredients and nutrients. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit may use data mining techniques to comprehensively evaluate the user's health status. The analysis unit may also use statistical analysis techniques to extract trends and patterns from the data. Step 3: The exercise plan suggestion unit suggests an optimal exercise plan for the user based on the results of the analysis by the analysis unit. For example, the exercise plan suggestion unit suggests jogging distances and routes. The exercise plan suggestion unit can also introduce and reserve gyms and gymnasiums. Furthermore, the exercise plan suggestion unit can suggest exercise intensity and frequency according to the user's physical fitness level and goals. Step 4: The meal plan suggestion unit suggests an optimal meal plan for the user based on the results of the analysis by the analysis unit. For example, the meal plan suggestion unit suggests home-cooked meals and ingredient arrangements. The meal plan suggestion unit can also provide dining out or takeout options. Furthermore, the meal plan suggestion unit can suggest a meal plan based on the user's nutritional balance and calorie restrictions.

[0061] (Example 2) A health management system according to an embodiment of the present invention is a system in which a generative AI suggests optimal exercise and diet for a user based on data acquired from wearable devices, health checkup results, meal photos, etc. This enables the health management system to provide specific advice tailored to the user's health condition, contributing to maintaining health and improving performance.

[0062] A health management system according to an embodiment includes a data collection unit, an analysis unit, an exercise plan proposal unit, and a meal plan proposal unit. The data collection unit collects data from wearable devices, health checkup results, and meal photos. For example, the data collection unit collects heart rate and step count data from a smartwatch or fitness tracker. It can also collect blood test results and electrocardiogram data as health checkup results. It can also analyze meal photos using image recognition technology to identify ingredients and nutrients. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can use data mining technology to comprehensively evaluate the user's health status. It can also extract trends and patterns in the data using statistical analysis techniques. The exercise plan proposal unit proposes an optimal exercise plan for the user based on the results of the analysis by the analysis unit. For example, the exercise plan proposal unit proposes jogging distances and routes. The exercise plan proposal unit can also introduce and reserve gyms and gymnasiums. The exercise plan proposal unit can also propose exercise intensity and frequency based on the user's physical fitness level and goals. The meal plan proposal unit proposes an optimal meal plan for the user based on the results of the analysis by the analysis unit. For example, the meal plan proposal unit proposes home-cooked meal recipes and ingredient arrangements. The meal plan proposal unit can also provide dining out and takeout options. Furthermore, the meal plan proposal unit can propose a meal plan based on the user's nutritional balance and calorie restrictions. This allows the health management system according to the embodiment to provide specific advice tailored to the user's health condition, contributing to maintaining health and improving performance.

[0063] The data collection unit collects heart rate, step count, sleep data, blood pressure, and dietary details to comprehensively evaluate the user's health. For example, the data collection unit analyzes the user's social media posts and blog posts in addition to the heart rate and step count data obtained from the wearable device to evaluate their lifestyle habits and stress level. For example, it detects signs of stress from the frequency and content of social media posts and makes suggestions for lifestyle improvement. The data collection unit also analyzes the user's social media posts and blog posts using natural language processing technology to evaluate their lifestyle habits and stress level. For example, it performs sentiment analysis on the content of posts to detect increased stress. The data collection unit also integrates data from the wearable device and social media posts to comprehensively evaluate the user's lifestyle habits and stress level. For example, it correlates and analyzes heart rate fluctuations with the content of social media posts. This allows for a comprehensive evaluation of the user's health, enabling more precise exercise and meal plans to be provided.

[0064] The exercise plan suggestion unit can suggest jogging distances and routes, and introduce and reserve gyms and gymnasiums. The exercise plan suggestion unit, for example, collects the user's past medical records, and the generation AI incorporates them into the analysis to more precisely assess the user's health condition. For example, it may evaluate health risks taking into account past medical history and treatment history. The exercise plan suggestion unit also incorporates genetic information into the analysis to more precisely assess the user's health condition. For example, it may identify genetic risk factors and propose a health management plan based on them. The exercise plan suggestion unit also integrates medical records and genetic information, and the generation AI performs a comprehensive health assessment. For example, it may combine past diagnostic results with genetic risk to predict future health risks. This provides the user with an optimal exercise plan and maximizes the benefits of exercise.

[0065] The meal plan proposal unit can provide home-cooked recipes, ingredient arrangements, and dining out and takeout options. For example, the meal plan proposal unit uses an emotion estimation function to analyze the user's emotional state in real time and perform a health assessment that takes into account fluctuations in stress and motivation. For example, it calculates an emotion score by analyzing facial expressions and voice tone. The meal plan proposal unit also analyzes the user's emotional state and reflects fluctuations in stress level and motivation in the health assessment. For example, if the emotion score is low, it suggests relaxation methods. The meal plan proposal unit also builds a system that performs a health assessment that takes into account fluctuations in stress and motivation based on the emotion estimation data. For example, it adjusts a health management plan according to fluctuations in the emotion score. This provides the user with an optimal meal plan and supports a healthy diet.

[0066] The data collection unit can analyze SNS posts and blog articles to evaluate lifestyle habits and stress levels. For example, the data collection unit analyzes the user's SNS posts and blog articles in addition to heart rate and step count data obtained from the wearable device to evaluate lifestyle habits and stress levels. For example, it detects signs of stress from the frequency and content of SNS posts and makes suggestions for improving lifestyle habits. The data collection unit also analyzes the user's SNS posts and blog articles using natural language processing technology to evaluate lifestyle habits and stress levels. For example, it performs sentiment analysis on the content of posts to detect increased stress. The data collection unit also integrates data from the wearable device and SNS posts to comprehensively evaluate lifestyle habits and stress levels. For example, it correlates and analyzes heart rate fluctuations with the content of SNS posts. This allows for a more precise health assessment by evaluating the user's lifestyle habits and stress levels.

[0067] The data collection unit incorporates medical records and genetic information into the analysis to enable a more precise assessment of health status. For example, the data collection unit collects the user's past medical records, and the generation AI incorporates them into the analysis to enable a more precise assessment of health status. For example, health risks are assessed taking into account past medical history and treatment history. The data collection unit also incorporates genetic information into the analysis to enable a more precise assessment of the user's health status. For example, genetic risk factors are identified and a health management plan is proposed based on these. The data collection unit also integrates medical records and genetic information, and the generation AI performs a comprehensive health assessment. For example, past diagnostic results are combined with genetic risk to predict future health risks. This allows a more precise health assessment to be performed by taking into account the user's past medical records and genetic information.

[0068] The data collection unit can use the emotion estimation function to analyze the user's emotional state and perform a health assessment that takes into account fluctuations in stress and motivation. The data collection unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and perform a health assessment that takes into account fluctuations in stress and motivation. For example, the data collection unit calculates an emotion score by analyzing facial expressions and voice tone. The data collection unit also analyzes the user's emotional state and reflects fluctuations in stress level and motivation in the health assessment. For example, if the emotion score is low, the data collection unit suggests relaxation methods. The data collection unit also builds a system that performs a health assessment that takes into account fluctuations in stress and motivation based on the emotion estimation data. For example, the health management plan is adjusted according to fluctuations in the emotion score. This allows for a more precise health assessment by taking the user's emotional state into account.

[0069] The data collection unit can use a drone to monitor the exercise environment and living environment in real time. The data collection unit, for example, uses a drone to monitor the user's exercise environment and living environment in real time. For example, the drone can be used to check the congestion status of a jogging route and the weather. The data collection unit also uses a drone to monitor the user's living environment and use this information to help with health management. For example, the drone can measure the air quality and noise level of the living environment. The data collection unit also uses a drone to monitor the user's exercise environment in real time and propose an optimal exercise plan. For example, the drone can analyze footage captured by the drone to propose a safe jogging route. In this way, real-time monitoring of the user's exercise environment and living environment allows for a more precise health assessment.

[0070] The data collection unit can collect health data of the pet and make suggestions for maintaining the health of the pet together. The data collection unit, for example, collects health data of the user's pet and makes suggestions for maintaining the health of the pet together. For example, it analyzes the pet's exercise amount and dietary content and provides advice for living a healthy life together. The data collection unit also analyzes the pet's health data and proposes a plan for the user and pet to maintain their health together. For example, it proposes an exercise plan to do together based on the pet's health condition. The data collection unit also integrates the health data of the user and the pet and makes comprehensive suggestions for maintaining the health of both. For example, it provides a meal plan and exercise plan according to the pet's health condition. This supports a healthier lifestyle by comprehensively managing the health of the user and the pet.

[0071] The data collection unit can use the emotion estimation function to analyze emotions in real time and provide positive feedback. For example, the data collection unit uses the emotion estimation function to analyze the emotions of a user when entering data in real time and provide positive feedback. For example, the data collection unit calculates an emotion score by analyzing facial expressions and voice at the time of entry. The data collection unit also builds a system that analyzes the user's emotional state in real time and provides positive feedback. For example, if the emotion score is low, it displays an encouraging message. The data collection unit also provides positive feedback to the user when entering data based on the emotion estimation data. For example, it displays appropriate encouragement or praise according to the input content. In this way, providing positive feedback to the user when entering data maintains the user's motivation.

[0072] The exercise plan suggestion unit can predict exercise effects and suggest exercise intensity and frequency based on the exercise history. The exercise plan suggestion unit, for example, analyzes the user's exercise history and builds a system that predicts exercise effects. For example, it suggests optimal exercise intensity and frequency based on past exercise data. The exercise plan suggestion unit also uses a generation AI to predict exercise effects based on the exercise history and suggest an optimal exercise plan. For example, it analyzes past exercise data to provide an effective training plan. The exercise plan suggestion unit also develops a system that predicts exercise effects based on the user's exercise history and suggests optimal exercise intensity and frequency. For example, it analyzes the exercise history and generates an effective exercise plan. This maximizes the exercise effect by providing an optimal exercise plan based on the user's exercise history.

[0073] The exercise plan proposal unit can analyze geographic information and propose an exercise plan according to the season and weather. The exercise plan proposal unit, for example, analyzes the user's geographic information and builds a system that proposes an exercise plan according to the season and weather. For example, it proposes the optimal exercise location and time based on weather data. The exercise plan proposal unit also generates an exercise plan according to the season and weather based on the geographic information. For example, it proposes an appropriate exercise plan taking into account the temperature and humidity of each season. The exercise plan proposal unit also analyzes the user's geographic information and proposes an exercise plan according to the season and weather. For example, it selects indoor and outdoor exercise locations based on weather data. In this way, by taking the user's geographic information into consideration, it provides an optimal exercise plan according to the season and weather.

[0074] The exercise plan suggestion unit can use the emotion estimation function to suggest an exercise plan to increase motivation. The exercise plan suggestion unit, for example, uses the emotion estimation function to build a system that suggests an exercise plan to increase a user's motivation. For example, it provides an exercise plan that increases motivation based on an emotion score. The exercise plan suggestion unit also analyzes the user's emotional state and suggests an exercise plan to increase motivation. For example, if the emotion score is low, it suggests a fun exercise plan. The exercise plan suggestion unit also suggests an exercise plan to increase the user's motivation based on the emotion estimation data. For example, it adjusts the exercise plan according to fluctuations in the emotion score. In this way, by providing an exercise plan that increases the user's motivation, it improves exercise continuity.

[0075] The exercise plan proposal unit can work with friends and family to propose joint exercise plans. The exercise plan proposal unit, for example, builds a system that works with the user's friends and family to propose joint exercise plans. For example, it generates an exercise plan for a group. The exercise plan proposal unit also proposes exercise plans to be done together with friends and family. For example, it provides a fitness plan that the whole family can participate in. The exercise plan proposal unit also works with the user's friends and family to propose joint exercise plans. For example, it proposes a jogging plan to be done together with friends. In this way, by working with the user's friends and family, a joint exercise plan can be provided, improving the enjoyment of exercise.

[0076] The exercise plan proposal unit can propose an exercise plan that can be done in the office, taking into account the work environment. The exercise plan proposal unit, for example, builds a system that proposes an exercise plan that can be done in the office, taking into account the user's work environment. For example, it provides a stretching plan that can be done while doing desk work. The exercise plan proposal unit also proposes an exercise plan that suits the work environment. For example, it proposes simple exercises that can be done in the office. The exercise plan proposal unit also proposes an exercise plan that can be done in the office, taking into account the user's work environment. For example, it proposes light exercise that can be done during a meeting. In this way, the optimal exercise plan that can be done in the office is provided by taking into account the user's work environment.

[0077] The exercise plan proposal unit can use the emotion estimation function to monitor emotions in real time and provide positive feedback. The exercise plan proposal unit, for example, uses the emotion estimation function to build a system that monitors the user's emotions in real time when exercising and provides positive feedback. For example, it displays an encouraging message based on an emotion score during exercise. The exercise plan proposal unit also analyzes the user's emotional state in real time and provides positive feedback during exercise. For example, it displays an encouraging message if the emotion score is low. The exercise plan proposal unit also provides positive feedback to the user when exercising based on the emotion estimation data. For example, it displays appropriate encouragement or praise based on the emotion score during exercise. In this way, providing positive feedback when the user exercises improves exercise continuity.

[0078] The meal plan proposal unit can propose a meal plan for optimizing nutritional balance based on the meal history. The meal plan proposal unit, for example, analyzes the user's meal history and builds a system that proposes a meal plan for optimizing nutritional balance. For example, a balanced meal plan is provided based on past meal data. The meal plan proposal unit also proposes a meal plan for optimizing nutritional balance using a generation AI based on the meal history. For example, past meal data is analyzed to provide a meal plan that compensates for nutrient deficiencies. The meal plan proposal unit also develops a system that proposes a meal plan for optimizing nutritional balance based on the user's meal history. For example, the meal history is analyzed to generate a balanced meal plan. This provides a meal plan for optimizing nutritional balance based on the user's meal history.

[0079] The meal plan proposal unit can propose an individually customized meal plan by taking into account allergy information and food preferences. The meal plan proposal unit, for example, builds a system that proposes an individually customized meal plan by taking into account the user's allergy information and food preferences. For example, it provides recipes that do not contain allergenic ingredients. Furthermore, the meal plan proposal unit proposes an individually customized meal plan using a generation AI based on the allergy information and food preferences. For example, it provides healthy recipes using favorite ingredients. Furthermore, the meal plan proposal unit proposes an individually customized meal plan by taking into account the user's allergy information and food preferences. For example, it provides a balanced meal plan that avoids allergenic ingredients. In this way, an individually customized meal plan is provided by taking into account the user's allergy information and food preferences.

[0080] The meal plan proposal unit can use the emotion estimation function to analyze emotions and make suggestions to increase meal satisfaction. The meal plan proposal unit, for example, uses the emotion estimation function to analyze a user's emotions toward a meal and build a system that makes suggestions to increase meal satisfaction. For example, the satisfaction level is evaluated based on an emotion score during the meal. The meal plan proposal unit also analyzes the user's emotions toward a meal and makes suggestions to increase satisfaction. For example, if the emotion score is low, it suggests improvements to the meal. The meal plan proposal unit also makes suggestions to increase the user's meal satisfaction based on the emotion estimation data. For example, it makes appropriate improvement suggestions based on the emotion score during the meal. In this way, the user's emotions toward a meal are analyzed and suggestions to increase meal satisfaction are made.

[0081] The meal plan proposal unit collects dietary data of family members and housemates and can propose meal plans that will help everyone stay healthy. The meal plan proposal unit, for example, collects dietary data of the user's family members and housemates and builds a system that proposes meal plans that will help everyone stay healthy. For example, it provides a meal plan that takes into account the nutritional balance of all family members. The meal plan proposal unit also uses a generation AI to propose a meal plan that will help everyone stay healthy based on the dietary data of the family members and housemates. For example, it analyzes the dietary history of all family members to provide a balanced meal plan. The meal plan proposal unit also collects dietary data of the user's family members and housemates and proposes a meal plan that will help everyone stay healthy. For example, it generates a meal plan that takes into account the nutritional balance of all family members. In this way, by collecting dietary data of the user's family members and housemates, a meal plan that will help everyone stay healthy is provided.

[0082] The meal plan proposal unit can propose meal plans according to travel destinations and business trip destinations. The meal plan proposal unit, for example, builds a system that proposes meal plans according to a user's travel destinations and business trip destinations. For example, it provides meal plans that take into account the food culture and ingredients of the travel destination. The meal plan proposal unit also generates meal plans according to travel destinations and business trip destinations. For example, it proposes healthy meal plans based on restaurant information at the business trip destination. The meal plan proposal unit also proposes meal plans according to the user's travel destinations and business trip destinations. For example, it provides balanced meal plans using ingredients at the travel destination. In this way, a healthy diet is supported by providing meal plans according to the user's travel destinations and business trip destinations.

[0083] The meal plan proposal unit can use the emotion estimation function to analyze emotions in real time and provide positive feedback. The meal plan proposal unit, for example, uses the emotion estimation function to analyze the emotions of a user when selecting a meal in real time and build a system that provides positive feedback. For example, an encouraging message is displayed based on the emotion score at the time of meal selection. The meal plan proposal unit also analyzes the user's emotional state in real time and provides positive feedback when selecting a meal. For example, an encouraging message is displayed if the emotion score is low. The meal plan proposal unit also provides positive feedback to the user when selecting a meal based on the emotion estimation data. For example, appropriate encouragement or praise is displayed based on the emotion score at the time of meal selection. This provides positive feedback when the user is selecting a meal, thereby supporting meal selection.

[0084] The BtoC service department can provide special health plans according to a user's life stage. For example, the BtoC service department builds a system that provides special health plans according to a user's life stage. For example, a pregnant user is provided with a meal plan that takes nutritional balance into consideration. The BtoC service department also generates special health plans according to a user's life stage. For example, an exercise plan with adjusted exercise intensity is proposed for elderly users. The BtoC service department also provides special health plans according to a user's life stage. For example, a relaxation plan to reduce stress is proposed for a pregnant user. In this way, by providing special health plans according to a user's life stage, individual health needs can be met.

[0085] The BtoC service department can collect feedback and continuously improve the level of personalization of the service. For example, the BtoC service department collects user feedback and builds a system that continuously improves the level of personalization of the service. For example, it adjusts exercise plans and meal plans based on the feedback. The BtoC service department also uses generative AI to improve the level of personalization of the service based on the feedback. For example, it provides more individualized plans that reflect user opinions. The BtoC service department also collects user feedback and continuously improves the level of personalization of the service. For example, it improves the content of the service based on the feedback. In this way, the level of personalization of the service is continuously improved by collecting user feedback.

[0086] The BtoC service unit can use the emotion estimation function to provide customized advice according to the user's emotional state. For example, the BtoC service unit uses the emotion estimation function to build a system that provides customized advice according to the user's emotional state. For example, it provides advice for reducing stress based on the emotion score. The BtoC service unit also analyzes the user's emotional state and provides customized advice. For example, if the emotion score is low, it suggests relaxation methods. The BtoC service unit also provides customized advice according to the user's emotional state based on the emotion estimation data. For example, it adjusts the content of the advice according to fluctuations in the emotion score. In this way, customized advice according to the user's emotional state can be provided to meet individual needs.

[0087] The BtoC service department can also support health management of pets and provide services for maintaining the health of both the user and the pet. For example, the BtoC service department builds a system that supports the health management of users' pets and provides services for maintaining the health of both the user and the pet. For example, it provides health plans based on the pet's health data. The BtoC service department also supports health management of pets and provides services for maintaining the health of both the user and the pet. For example, it provides health plans by analyzing the amount of exercise and diet of the pet. The BtoC service department also supports the health management of users' pets and provides services for maintaining the health of both the user and the pet. For example, it provides meal plans and exercise plans according to the pet's health condition. In this way, by supporting the health management of users' pets, it provides services for maintaining the health of both the user and the pet.

[0088] The BtoC service unit can propose health plans based on hobbies and interests. The BtoC service unit, for example, builds a system that proposes health plans based on a user's hobbies and interests. For example, it provides exercise plans and meal plans related to hobbies. The BtoC service unit also generates health plans based on hobbies and interests. For example, it proposes a hiking plan to a user who likes the outdoors. The BtoC service unit also proposes health plans based on a user's hobbies and interests. For example, it proposes new recipes to a user who likes cooking. In this way, individual needs can be met by providing health plans based on the user's hobbies and interests.

[0089] The BtoC service unit can use the emotion estimation function to analyze emotions in real time and provide positive feedback. The BtoC service unit, for example, uses the emotion estimation function to build a system that analyzes emotions in real time when a user uses a service and provides positive feedback. For example, it displays an encouraging message based on the emotion score while the service is being used. The BtoC service unit also analyzes the user's emotional state in real time and provides positive feedback while the service is being used. For example, it displays an encouraging message if the emotion score is low. The BtoC service unit also provides positive feedback when the user uses the service based on the emotion estimation data. For example, it displays appropriate encouragement or praise based on the emotion score while the service is being used. In this way, positive feedback is provided when the user uses the service, improving the service usage experience.

[0090] The BtoB service department can propose data-based marketing strategies to fitness businesses. For example, the BtoB service department builds a system that proposes marketing strategies based on user data to fitness businesses. For example, it conducts targeted marketing based on users' exercise history and health data. The BtoB service department also analyzes user data and proposes marketing strategies to fitness businesses. For example, it proposes campaigns targeting users who are interested in a specific exercise plan. The BtoB service department also proposes marketing strategies based on user data to fitness businesses. For example, it sends personalized marketing messages based on the user's health data. In this way, by proposing marketing strategies based on user data, the marketing effectiveness of fitness businesses is improved.

[0091] The BtoB service department can analyze player performance data and optimize training plans for professional sports management companies. The BtoB service department, for example, builds a system that analyzes player performance data and optimizes training plans for professional sports management companies. For example, it provides optimal training plans based on the player's exercise data. The BtoB service department also analyzes player performance data and optimizes training plans for professional sports management companies. For example, it proposes effective training plans based on the player's exercise history. The BtoB service department also analyzes player performance data and optimizes training plans for professional sports management companies. For example, it provides training plans to improve performance based on the player's health data. In this way, by analyzing player performance data, training plans can be optimized and support the improvement of player performance.

[0092] The BtoB service unit can use the emotion estimation function to analyze the emotional state of a player and provide advice for mental care. The BtoB service unit, for example, uses the emotion estimation function to build a system that analyzes the emotional state of a player and provides advice for mental care. For example, advice for stress reduction is provided based on the emotion score. The BtoB service unit also analyzes the emotional state of a player and provides advice for mental care. For example, if the emotion score is low, relaxation methods are suggested. The BtoB service unit also analyzes the emotional state of a player based on the emotion estimation data and provides advice for mental care. For example, the content of the advice is adjusted according to fluctuations in the emotion score. In this way, by analyzing the emotional state of a player, advice for mental care is provided and the mental health of the player is supported.

[0093] The BtoB service department can develop new fitness programs based on data for fitness businesses. For example, the BtoB service department builds a system for developing new fitness programs based on user data for fitness businesses. For example, it proposes new programs based on the user's exercise history and health data. The BtoB service department also analyzes user data and develops new fitness programs for fitness businesses. For example, it proposes programs targeted at users who are interested in a specific exercise plan. The BtoB service department also develops new fitness programs based on user data for fitness businesses. For example, it provides personalized fitness programs based on the user's health data. In this way, developing new fitness programs based on user data improves the services of fitness businesses.

[0094] The BtoB service department can propose new training equipment to professional sports management companies to improve performance based on athlete data. For example, the BtoB service department builds a system to propose new training equipment to professional sports management companies to improve performance based on athlete data. For example, it proposes optimal training equipment based on athlete exercise data. The BtoB service department also analyzes athlete data and proposes new training equipment to professional sports management companies. For example, it proposes effective training equipment based on athlete exercise history. The BtoB service department also proposes new training equipment to professional sports management companies to improve performance based on athlete data. For example, it provides training equipment to improve performance based on athlete health data. In this way, proposing new training equipment based on athlete data supports the improvement of athlete performance.

[0095] The BtoB service unit can use the emotion estimation function to monitor the emotional state of players in real time and provide positive feedback. The BtoB service unit, for example, uses the emotion estimation function to build a system that monitors the emotional state of players in real time and provides positive feedback. For example, it displays an encouraging message based on the emotion score. The BtoB service unit also analyzes the emotional state of players in real time and provides positive feedback. For example, it displays an encouraging message if the emotion score is low. The BtoB service unit also monitors the emotional state of players in real time based on the emotion estimation data and provides positive feedback. For example, it displays appropriate encouragement or praise depending on fluctuations in the emotion score. In this way, by monitoring the emotional state of players in real time, positive feedback can be provided and the mental health of players can be supported.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The health management system may further include a voice recognition unit. The voice recognition unit analyzes the user's voice and collects information on the user's health condition and lifestyle habits. For example, the voice recognition unit may record the user's daily exercise and dietary habits. The voice recognition unit may also evaluate the user's stress level from the user's tone of voice and speaking style. The voice recognition unit may also analyze the user's voice data and provide feedback to the health management system. This allows for more precise health assessments to be performed using the user's voice data.

[0098] The health management system may further include an environmental monitoring unit. The environmental monitoring unit monitors the user's living environment in real time and identifies factors that affect health. For example, it measures indoor air quality, temperature, and humidity and provides advice on maintaining a healthy environment. The environmental monitoring unit may also collect external environmental data and evaluate factors that affect the user's exercise and lifestyle habits. Furthermore, the environmental monitoring unit may analyze the user's living environment data and provide feedback to the health management system. This allows for health assessment that takes the user's living environment into account.

[0099] The health management system can further include a virtual reality (VR) unit. The VR unit provides the user with an exercise experience in a virtual environment. For example, when a user jogs or does yoga at home, VR can be used to recreate a realistic exercise environment. The VR unit can also analyze the user's exercise data in real time and provide feedback to maximize the effectiveness of the exercise. Furthermore, the VR unit can propose an exercise plan incorporating game elements to increase the user's motivation. This allows the user to continue exercising in a fun way.

[0100] The health management system may further include a pet management unit. The pet management unit collects health data of the user's pet and makes suggestions for maintaining the health of both the user and the pet. For example, it may analyze the pet's exercise and dietary habits and provide advice for living a healthy life together. The pet management unit may also analyze the pet's health data and propose a plan for both the user and the pet to maintain their health. Furthermore, the pet management unit may integrate the health data of the user and the pet and make comprehensive suggestions for maintaining the health of both. This allows for comprehensive management of the health of the user and the pet.

[0101] The health management system can further use an emotion estimation function to analyze the user's emotional state in real time and perform a health assessment that takes into account fluctuations in stress and motivation. For example, an emotion score can be calculated by analyzing facial expressions and voice tone. The emotion estimation function can also be used to analyze the user's emotional state and reflect fluctuations in stress level and motivation in the health assessment. Furthermore, a system can be constructed that performs a health assessment that takes into account fluctuations in stress and motivation based on the emotion estimation data. This allows for a more precise health assessment by taking the user's emotional state into account.

[0102] The health management system can further use the emotion estimation function to analyze the user's emotional state in real time and provide positive feedback. For example, an encouraging message can be displayed based on the emotion score. Also, a system can be constructed that uses the emotion estimation function to analyze the user's emotional state in real time and provide positive feedback. Furthermore, based on the emotion estimation data, positive feedback can be provided when the user enters data. This makes it possible to maintain the user's motivation by providing positive feedback when the user enters data.

[0103] The health management system can further use the emotion estimation function to monitor the user's emotional state in real time and provide positive feedback. For example, an encouraging message can be displayed based on the emotion score during exercise. The emotion estimation function can also be used to build a system that analyzes the user's emotional state in real time and provides positive feedback during exercise. Furthermore, based on the emotion estimation data, positive feedback can be provided to the user when exercising. This can improve the user's ability to continue exercising by providing positive feedback when exercising.

[0104] The health management system can further use the emotion estimation function to provide customized advice according to the user's emotional state. For example, advice for stress reduction can be provided based on the emotion score. It is also possible to build a system that uses the emotion estimation function to analyze the user's emotional state and provide customized advice. Furthermore, it is also possible to provide customized advice according to the user's emotional state based on the emotion estimation data. This allows for customized advice according to the user's emotional state to meet individual needs.

[0105] The health management system can further use an emotion estimation function to analyze the user's emotions and make suggestions to increase meal satisfaction. For example, satisfaction can be evaluated based on an emotion score during a meal. Also, a system can be constructed that uses the emotion estimation function to analyze the user's emotions regarding a meal and make suggestions to increase satisfaction. Furthermore, suggestions to increase the user's meal satisfaction can be made based on the emotion estimation data. In this way, by analyzing the user's emotions regarding a meal, suggestions to increase meal satisfaction can be made.

[0106] The health management system can also use drones to monitor the exercise environment and living environment in real time. For example, a drone can be used to check the congestion status of a jogging route and the weather. Drones can also be used to monitor the user's living environment and use this information to help with health management. For example, a drone can measure the air quality and noise level in the living environment. Drones can also be used to monitor the user's exercise environment in real time and suggest an optimal exercise plan. This allows for more precise health assessment by monitoring the user's exercise environment and living environment in real time.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The data collection unit collects data from wearable devices, health checkup results, and meal photos. For example, the data collection unit collects heart rate and step count data from smartwatches and fitness trackers. It can also collect blood test results and electrocardiogram data as health checkup results. It can also analyze meal photos using image recognition technology to identify ingredients and nutrients. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit may use data mining techniques to comprehensively evaluate the user's health status. The analysis unit may also use statistical analysis techniques to extract trends and patterns from the data. Step 3: The exercise plan suggestion unit suggests an optimal exercise plan for the user based on the results of the analysis by the analysis unit. For example, the exercise plan suggestion unit suggests jogging distances and routes. The exercise plan suggestion unit can also introduce and reserve gyms and gymnasiums. Furthermore, the exercise plan suggestion unit can suggest exercise intensity and frequency according to the user's physical fitness level and goals. Step 4: The meal plan suggestion unit suggests an optimal meal plan for the user based on the results of the analysis by the analysis unit. For example, the meal plan suggestion unit suggests home-cooked meals and ingredient arrangements. The meal plan suggestion unit can also provide dining out or takeout options. Furthermore, the meal plan suggestion unit can suggest a meal plan based on the user's nutritional balance and calorie restrictions.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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]

[0176] 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 data from wearable devices, health checkup results, and meal photos; an analysis unit that analyzes the data collected by the data collection unit; an exercise plan suggestion unit that suggests an optimal exercise plan to the user based on the results of the analysis by the analysis unit; a meal plan proposal unit that proposes an optimal meal plan to the user based on the results of the analysis by the analysis unit. A system characterized by:

2. The data collection unit Collects heart rate, steps, sleep data, blood pressure, and dietary information to provide a comprehensive assessment of the user's health.

2. The system of claim 1.

3. The exercise plan suggestion unit Suggest jogging distances and routes, and introduce and book gyms and sports halls 2. The system of claim 1.

4. The meal plan proposal unit Providing homemade recipes, ingredient ordering, dining out and takeaway options 2. The system of claim 1.

5. The data collection unit Analyze the social media posts and blog articles to assess lifestyle habits and stress levels 2. The system of claim 1.

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