System

Abstract

An object of the system according to the embodiment is to provide individualized health advice and comprehensive health management based on a health check result.SOLUTION: A system includes a health check result acquisition unit, an analysis unit, an advice providing unit, a nutrition management unit, a training support unit, and a mental health support unit. The medical examination result acquisition unit acquires a medical examination result. The analysis unit analyzes the medical examination result acquired by the medical examination result acquisition unit. The advice providing unit provides individualized health advice based on a result of the analysis by the analysis unit. The nutrition management unit performs nutrition management on the basis of a result analyzed by the analysis unit. The training support unit provides a training plan based on the result analyzed by the analysis unit. The mental health support unit supports mental health on the basis of a 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 provided personalized health advice or comprehensive health management based on health checkup results, and there is room for improvement.

[0005] The system according to the embodiment aims to provide personalized health advice and comprehensive health management based on the results of health checkups. [Means for solving the problem]

[0006] The system according to the embodiment includes a health checkup result acquisition unit, an analysis unit, an advice provision unit, a nutrition management unit, a training support unit, and a mental health support unit. The health checkup result acquisition unit acquires health checkup results. The analysis unit analyzes the health checkup results acquired by the health checkup result acquisition unit. The advice provision unit provides personalized health advice based on the results of the analysis by the analysis unit. The nutrition management unit performs nutritional management based on the results of the analysis by the analysis unit. The training support unit provides a training plan based on the results of the analysis by the analysis unit. The mental health support unit supports mental health based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide personalized health advice and comprehensive health management based on the results of health checkups. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The health management platform according to an embodiment of the present invention is an innovative system incorporating generative AI. This system provides personalized health advice and improvement plans through collaboration with health checkup results. This enables the health management platform to comprehensively support the user's health, preventing illness and maintaining and improving health.

[0029] A health management platform according to an embodiment includes a health checkup result acquisition unit, an analysis unit, an advice provision unit, a nutrition management unit, a training support unit, and a mental health support unit. The health checkup result acquisition unit acquires health checkup results, such as blood test results, electrocardiogram data, and physical measurements. The analysis unit analyzes the acquired health checkup results, such as by using statistical analysis or a machine learning algorithm. The advice provision unit provides personalized health advice based on the analysis results, such as advice based on the user's age, gender, and medical history. The nutrition management unit manages nutrition based on the analysis results, such as by balancing meals and calculating calories. The training support unit provides a training plan based on the analysis results, such as by suggesting the type, frequency, and intensity of exercise. The mental health support unit supports mental health based on the analysis results, such as by providing stress management and mental health care. This allows the health management platform to comprehensively support the user's health, preventing illness and maintaining and improving health.

[0030] The health check result acquisition unit can analyze the user's genetic information and provide health advice that takes genetic risk into account. For example, the health check result acquisition unit integrates the user's health check results with genetic information, and the generation AI analyzes the genetic risk. For example, if there is a specific genetic mutation, health advice based on that risk is provided. The generation AI also combines the health check results with genetic information and provides diet and exercise advice that takes genetic risk into account. For example, if there is a genetically high risk of diabetes, a low-carbohydrate diet is recommended. The generation AI also analyzes the user's genetic information and suggests preventive measures based on genetic risk. For example, if there is a genetically high risk of heart disease, regular heart checkups are recommended. This makes it possible to provide health advice that takes genetic risk into account.

[0031] The health checkup result acquisition unit updates the health checkup results in real time, allowing the generation AI to perform analysis based on the latest data each time. The health checkup result acquisition unit, for example, updates the health checkup results in real time, allowing the generation AI to evaluate the health condition based on the latest data. For example, it analyzes daily blood pressure measurement results in real time. Also, every time a user enters health checkup results, the generation AI instantly analyzes the data and provides the latest health advice. For example, it suggests dietary improvements based on the latest blood test results. Also, the health checkup results are stored in the cloud, and the generation AI accesses and analyzes them in real time. For example, it automatically updates regular health checkup results to keep track of the latest health condition. This allows the health condition to be evaluated based on the latest data.

[0032] The health check result acquisition unit can provide health advice that takes into account the user's living environment. For example, the health check result acquisition unit integrates health check results with climate data for the residential area, and the generation AI provides health advice according to the climate. For example, in areas with high humidity, it may recommend staying hydrated. The generation AI also combines health check results with air quality data and provides health advice according to the air quality. For example, it may recommend wearing a mask in areas with high air pollution. The generation AI also analyzes the user's living environment data and provides health advice according to the environment. For example, it may recommend taking measures to protect against the cold in cold regions. This makes it possible to provide health advice that takes into account the user's living environment.

[0033] The health checkup result acquisition unit can also analyze the health status of the user's family and support health management for the entire family. For example, the health checkup result acquisition unit uses the generation AI to analyze the health status of the user's family based on the health checkup results and support health management for the entire family. For example, it can propose a meal plan for the entire family. The generation AI can also integrate the user's health checkup results with the family's health data and provide health advice for the entire family. For example, it can propose an exercise plan for the entire family. The generation AI can also analyze the family's health risks based on the health checkup results and propose preventive measures. For example, it can recommend regular health checkups for the entire family. This can support health management for the entire family.

[0034] The generating AI can analyze a user's past health data and provide advice based on long-term health trends. For example, the generating AI can analyze a user's past health data and provide advice based on long-term health trends. For example, it can predict future risks based on past blood pressure data. The generating AI can also analyze long-term trends in health data and suggest preventive measures to the user. For example, it can suggest dietary improvements based on past weight fluctuations. The generating AI can also set long-term health goals based on the user's past health data and provide advice towards those goals. For example, it can suggest a training plan based on past exercise history. This makes it possible to provide advice based on long-term health trends.

[0035] The generating AI can analyze a user's lifestyle habits and provide an improvement plan based on that. For example, the generating AI can analyze the user's lifestyle data and provide an improvement plan based on their sleep patterns and meal timings. For example, if they are sleep deprived, it can recommend going to bed earlier. The generating AI can also suggest specific improvement plans to the user based on the lifestyle data. For example, adjusting meal timing can help stabilize blood sugar levels. The generating AI can also analyze the user's lifestyle habits and provide an optimal improvement plan. For example, it can give advice on changing a nocturnal lifestyle to a morning one. This makes it possible to provide an improvement plan based on lifestyle habits.

[0036] The generating AI can take into account the user's occupation and daily activity level and provide health advice that suits them. For example, the generating AI can analyze the user's occupational data and provide health advice that suits their occupation. For example, if they do a lot of desk work, regular stretching can be recommended. The generating AI can also provide specific health advice to the user based on their daily activity level. For example, if they are not getting enough exercise, walking can be recommended. The generating AI can also analyze the user's occupation and activity level and provide optimal health advice. For example, if they do a lot of standing work, it can suggest foot care methods. This makes it possible to provide health advice that suits their occupation and activity level.

[0037] The generating AI can take into account the user's hobbies and interests and provide a health improvement plan based on them. For example, the generating AI can analyze the user's hobby data and provide a health improvement plan based on the hobbies. For example, if the user likes the outdoors, hiking can be recommended. The generating AI can also suggest specific health improvement plans to the user based on the hobbies and interests. For example, if the user likes music, dancing can be recommended. The generating AI can also analyze the user's hobbies and interests and provide an optimal health improvement plan. For example, if the user likes reading, it can suggest improving posture while reading. This makes it possible to provide a health improvement plan based on the user's hobbies and interests.

[0038] The generation AI can analyze the user's dietary history and make suggestions to optimize the balance of nutrients. The generation AI can, for example, analyze the user's dietary history and make suggestions to optimize the balance of nutrients. For example, it can suggest ingredients that will make up for vitamin and mineral deficiencies. Furthermore, based on the dietary history data, the generation AI can make specific nutritional management suggestions to the user. For example, it can suggest a meal plan to increase protein intake. Furthermore, the generation AI can analyze the user's dietary history and suggest a meal menu to optimize the balance of nutrients. For example, it can provide a balanced one-week meal plan. This makes it possible to make suggestions to optimize the balance of nutrients.

[0039] The generating AI can take into account the user's allergy information and provide nutritional management that avoids allergies. For example, the generating AI can analyze the user's allergy information and provide nutritional management that avoids allergies. For example, it can suggest ingredients that do not contain specific allergens. Furthermore, based on the allergy information, the generating AI can make specific nutritional management suggestions to the user. For example, it can provide a balanced meal plan that avoids allergens. Furthermore, the generating AI can analyze the user's allergy information and suggest a method of ingesting nutrients that avoids allergies. For example, it can recommend supplements that do not contain allergens. This makes it possible to provide nutritional management that avoids allergies.

[0040] The generating AI can take into account ingredients in the user's area and provide nutritional management that utilizes local ingredients. For example, the generating AI can analyze ingredient data in the user's area and provide nutritional management that utilizes local ingredients. For example, it can suggest fresh vegetables that are locally grown. Furthermore, based on the local ingredient data, the generating AI can make specific nutritional management suggestions to the user. For example, it can provide a balanced meal plan using local specialties. Furthermore, by analyzing ingredients in the user's area, the generating AI can suggest methods for consuming nutrients that utilize local ingredients. For example, it can suggest a high-protein meal menu using local fish. This makes it possible to provide nutritional management that utilizes local ingredients.

[0041] The generating AI can take into account the user's dietary preferences and provide nutritional management tailored to their preferences. For example, the generating AI can analyze the user's dietary preferences and provide nutritional management tailored to their preferences. For example, it can suggest a balanced meal menu using their favorite ingredients. Furthermore, based on the dietary preference data, the generating AI can make specific nutritional management suggestions to the user. For example, it can provide recipes that are healthy variations of their favorite dishes. Furthermore, the generating AI can analyze the user's dietary preferences and suggest methods for ingesting nutrients tailored to their preferences. For example, it can suggest supplements using their favorite ingredients. This makes it possible to provide nutritional management tailored to their dietary preferences.

[0042] The generation AI can analyze the user's muscle mass and body fat percentage and provide a training plan based on that. For example, the generation AI can analyze the user's muscle mass and body fat percentage data and provide an optimal training plan. For example, it can suggest strength training to increase muscle mass. The generation AI can also suggest a specific training plan to the user based on muscle mass and body fat percentage. For example, it can recommend aerobic exercise to reduce body fat. The generation AI can also analyze the user's muscle mass and body fat percentage and adjust the plan while monitoring the training progress. For example, it can adjust the training intensity according to the increase in muscle mass. This makes it possible to provide a training plan based on muscle mass and body fat percentage.

[0043] The generation AI can analyze the user's exercise history and provide an optimal training plan based on past training data. For example, the generation AI can analyze the user's exercise history data and provide an optimal training plan based on past training data. For example, it can suggest a training menu based on past exercise volume. The generation AI can also suggest a specific training plan to the user based on the exercise history. For example, it can provide a new exercise plan that takes past training results into consideration. The generation AI can also analyze the user's exercise history and adjust the plan while monitoring the training progress. For example, it can adjust the training intensity based on past exercise data. This makes it possible to provide an optimal training plan based on past training data.

[0044] The generating AI can take the user's lifestyle rhythm into consideration and suggest the optimal training time. For example, the generating AI can analyze the user's lifestyle rhythm data and suggest the optimal training time. For example, it can suggest an exercise time that suits a morning-type lifestyle. The generating AI can also suggest specific training times to the user based on their lifestyle rhythm. For example, it can suggest an exercise time that suits a night-type lifestyle. The generating AI can also analyze the user's lifestyle rhythm and adjust the optimal exercise time while monitoring the training progress. For example, it can adjust the exercise time in response to changes in the lifestyle rhythm. This makes it possible to suggest the optimal training time based on the user's lifestyle.

[0045] The generation AI can take into account the user's exercise environment and provide an appropriate training plan. For example, the generation AI can analyze the user's exercise environment data and provide the optimal training plan. For example, it can suggest a training menu that can be done at home. The generation AI can also suggest a specific training plan to the user based on the exercise environment. For example, it can suggest a training menu for the gym. The generation AI can also analyze the user's exercise environment and adjust the plan while monitoring the training progress. For example, it can suggest an exercise menu that suits the home training environment. This makes it possible to provide a training plan that suits the exercise environment.

[0046] The generating AI can analyze the user's psychological state, identify the cause of stress, and provide stress management methods based on that. The generating AI, for example, analyzes the user's psychological state data, identifies the cause of stress, and provides stress management methods based on that. For example, if work stress is the cause, it will suggest relaxation methods. The generating AI will also suggest specific stress management methods to the user based on their psychological state. For example, if family problems are the cause, it will suggest ways to communicate with family. The generating AI will also analyze the user's psychological state and provide stress management methods based on the cause of stress. For example, if financial problems are the cause, it will provide advice on budget management. This makes it possible to identify the cause of stress and provide stress management methods based on that.

[0047] The generating AI can analyze the user's sleep data and make suggestions to improve sleep quality. The generating AI can, for example, analyze the user's sleep data and make suggestions to improve sleep quality. For example, it can suggest improvements to sleep time or sleep environment. Furthermore, based on the sleep data, the generating AI can make specific suggestions to the user to improve their sleep. For example, it can suggest ways to relax before bed or how to choose appropriate bedding. The generating AI can also analyze the user's sleep data and make suggestions to improve lifestyle habits to improve sleep quality. For example, it can suggest limiting caffeine intake and maintaining a regular lifestyle. This allows it to make suggestions to improve sleep quality.

[0048] The generative AI can take into account the user's hobbies and relaxation methods and provide mental health support based on them. For example, the generative AI can analyze the user's hobby data and provide mental health support based on their hobbies. For example, if they like music, it can suggest relaxing music. The generative AI can also suggest specific mental health support to the user based on their hobbies and relaxation methods. For example, if they like drawing, it can suggest art therapy. The generative AI can also analyze the user's hobbies and relaxation methods and provide the optimal mental health support. For example, if they like gardening, it can suggest growing plants. This makes it possible to provide mental health support based on their hobbies and relaxation methods.

[0049] The generative AI can take into account the user's social connections and provide mental health support based on that. For example, the generative AI can analyze the user's social connection data and provide mental health support based on that. For example, it can make suggestions to increase interactions with friends. The generative AI can also suggest specific mental health support to the user based on social connections. For example, it can suggest activities to increase communication with family. The generative AI can also analyze the user's social connections and provide optimal mental health support. For example, it can suggest participating in local community activities. This makes it possible to provide mental health support based on social connections.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The health management platform can also acquire the user's sleep data and have the analysis unit analyze that data. For example, the platform can analyze the user's sleep patterns and provide advice to improve the quality of their sleep. Specifically, the platform can suggest ways to optimize sleep time, how to choose bedding, and how to relax before bed. The analysis unit can also estimate the user's stress level based on the user's sleep data and provide advice on stress management. For example, if stress is high, the analysis unit can suggest relaxation methods and activities to relieve stress. This can improve the user's sleep quality and overall health.

[0052] The health management platform can also acquire the user's exercise data, and the analysis unit can analyze that data. For example, it can analyze the user's exercise history and provide an optimal exercise plan. Specifically, it can suggest the type, frequency, and intensity of exercise. The analysis unit can also evaluate the effectiveness of exercise based on the user's exercise data and adjust the exercise plan as necessary. For example, if the exercise is not effective, it can suggest changing the type or intensity of exercise. This can optimize the user's exercise habits and improve their health.

[0053] The health management platform can also acquire the user's dietary data, and the analysis unit can analyze that data. For example, the platform can analyze the user's dietary history and provide advice to optimize nutritional balance. Specifically, it can suggest ingredients and recipes to adjust vitamin and mineral intake. The analysis unit can also identify areas for improvement in the user's diet based on the user's dietary data and suggest specific measures to improve it. For example, it can provide a meal plan to avoid excessive calorie intake. This can improve the user's eating habits and improve their health.

[0054] The health management platform can also acquire data on the user's living environment, which the analysis unit can then analyze. For example, it can analyze the climate data of the user's residential area and provide health advice tailored to the climate. Specifically, it can recommend hydration in humid areas and suggest cold weather protection measures in cold regions. The analysis unit can also identify health risks based on the user's living environment data and suggest preventive measures. For example, it can recommend wearing a mask in areas with high air pollution. This makes it possible to provide health support that takes the user's living environment into consideration.

[0055] The health management platform can also acquire health data of the user's family, and the analysis unit can analyze that data. For example, it can analyze the health checkup results of all family members and support the health management of the entire family. Specifically, it can propose meal plans and exercise plans for the entire family. Furthermore, based on the health data of the user's family, the analysis unit can identify health risks for the entire family and propose preventive measures. For example, it can recommend regular health checkups for all family members. This can support the health management of the entire family.

[0056] The health management platform can also acquire the user's occupational data, and the analysis unit can analyze that data. For example, it can provide health advice tailored to the user's occupation. Specifically, if the user does a lot of desk work, it can recommend regular stretching, and if the user does a lot of standing work, it can suggest foot care methods. Furthermore, based on the user's occupational data, the analysis unit can identify health risks according to the user's occupation and suggest preventive measures. For example, it can provide advice on reducing the health risks associated with long periods of sitting at work. This makes it possible to provide health support tailored to the user's occupation.

[0057] The health management platform can also acquire user hobby data, and the analysis unit can analyze that data. For example, it can provide a health improvement plan based on the user's hobbies. Specifically, if the user likes the outdoors, hiking can be recommended, and if the user likes music, dancing can be suggested. Furthermore, based on the user's hobby data, the analysis unit can identify health risks associated with the hobby and suggest preventive measures. For example, it can suggest improving posture after reading for long periods of time. This makes it possible to provide health support based on the user's hobbies.

[0058] The health management platform can also acquire data on the user's social connections, and the analysis unit can analyze that data. For example, it can provide mental health support based on the user's social connections. Specifically, it can suggest ways to increase interactions with friends and activities to increase communication with family. Furthermore, based on the user's social connection data, the analysis unit can identify health risks associated with social connections and suggest preventive measures. For example, it can recommend participation in community activities to reduce feelings of loneliness. This makes it possible to provide mental health support that takes the user's social connections into account.

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

[0060] Step 1: The medical examination result acquisition unit acquires medical examination results, such as blood test results, electrocardiogram data, and physical measurements. Step 2: The analysis unit analyzes the acquired health checkup results, for example, by using statistical analysis or machine learning algorithms. Step 3: The advice provider provides personalized health advice based on the analyzed results, for example, advice based on the user's age, gender, and medical history. Step 4: The nutrition management department performs nutrition management based on the analyzed results, such as balancing meals and calculating calories. Step 5: The training support section provides a training plan based on the analyzed results, for example, suggesting the type, frequency, and intensity of exercise. Step 6: The mental health support department provides mental health support based on the analyzed results, such as stress management and mental health care.

[0061] (Example 2) The health management platform according to an embodiment of the present invention is an innovative system incorporating generative AI. This system provides personalized health advice and improvement plans through collaboration with health checkup results. This enables the health management platform to comprehensively support the user's health, preventing illness and maintaining and improving health.

[0062] A health management platform according to an embodiment includes a health checkup result acquisition unit, an analysis unit, an advice provision unit, a nutrition management unit, a training support unit, and a mental health support unit. The health checkup result acquisition unit acquires health checkup results, such as blood test results, electrocardiogram data, and physical measurements. The analysis unit analyzes the acquired health checkup results, such as by using statistical analysis or a machine learning algorithm. The advice provision unit provides personalized health advice based on the analysis results, such as advice based on the user's age, gender, and medical history. The nutrition management unit manages nutrition based on the analysis results, such as by balancing meals and calculating calories. The training support unit provides a training plan based on the analysis results, such as by suggesting the type, frequency, and intensity of exercise. The mental health support unit supports mental health based on the analysis results, such as by providing stress management and mental health care. This allows the health management platform to comprehensively support the user's health, preventing illness and maintaining and improving health.

[0063] The health check result acquisition unit can analyze the user's genetic information and provide health advice that takes genetic risk into account. For example, the health check result acquisition unit integrates the user's health check results with genetic information, and the generation AI analyzes the genetic risk. For example, if there is a specific genetic mutation, health advice based on that risk is provided. The generation AI also combines the health check results with genetic information and provides diet and exercise advice that takes genetic risk into account. For example, if there is a genetically high risk of diabetes, a low-carbohydrate diet is recommended. The generation AI also analyzes the user's genetic information and suggests preventive measures based on genetic risk. For example, if there is a genetically high risk of heart disease, regular heart checkups are recommended. This makes it possible to provide health advice that takes genetic risk into account.

[0064] The health checkup result acquisition unit updates the health checkup results in real time, allowing the generation AI to perform analysis based on the latest data each time. The health checkup result acquisition unit, for example, updates the health checkup results in real time, allowing the generation AI to evaluate the health condition based on the latest data. For example, it analyzes daily blood pressure measurement results in real time. Also, every time a user enters health checkup results, the generation AI instantly analyzes the data and provides the latest health advice. For example, it suggests dietary improvements based on the latest blood test results. Also, the health checkup results are stored in the cloud, and the generation AI accesses and analyzes them in real time. For example, it automatically updates regular health checkup results to keep track of the latest health condition. This allows the health condition to be evaluated based on the latest data.

[0065] The health checkup result acquisition unit can use the emotion estimation function to provide health checkup result feedback that takes into account the user's emotional state. For example, when analyzing health checkup results, the health checkup result acquisition unit takes the user's emotional state into consideration, and the generation AI provides appropriate feedback. For example, if stress is high, it can suggest relaxation methods. The emotion estimation function can also be used to provide health checkup result feedback that corresponds to the user's emotional state. For example, it can display encouraging messages that elicit positive emotions. The system can also monitor the user's emotional state in real time and adjust the health checkup result feedback. For example, if negative emotions are strong, it can provide feedback in kind words. This makes it possible to provide feedback that takes into account the user's emotional state.

[0066] The health check result acquisition unit can provide health advice that takes into account the user's living environment. For example, the health check result acquisition unit integrates health check results with climate data for the residential area, and the generation AI provides health advice according to the climate. For example, in areas with high humidity, it may recommend staying hydrated. The generation AI also combines health check results with air quality data and provides health advice according to the air quality. For example, it may recommend wearing a mask in areas with high air pollution. The generation AI also analyzes the user's living environment data and provides health advice according to the environment. For example, it may recommend taking measures to protect against the cold in cold regions. This makes it possible to provide health advice that takes into account the user's living environment.

[0067] The health checkup result acquisition unit can also analyze the health status of the user's family and support health management for the entire family. For example, the health checkup result acquisition unit uses the generation AI to analyze the health status of the user's family based on the health checkup results and support health management for the entire family. For example, it can propose a meal plan for the entire family. The generation AI can also integrate the user's health checkup results with the family's health data and provide health advice for the entire family. For example, it can propose an exercise plan for the entire family. The generation AI can also analyze the family's health risks based on the health checkup results and propose preventive measures. For example, it can recommend regular health checkups for the entire family. This can support health management for the entire family.

[0068] The health check result acquisition unit can monitor the user's emotional state in real time and provide health advice according to the emotion. For example, the health check result acquisition unit uses the generation AI to monitor the user's emotional state in real time based on the health check results and provide health advice according to the emotion. For example, if stress is high, relaxation methods are suggested. In addition, the emotion estimation function is used to provide health advice according to the user's emotional state in real time. For example, an exercise plan that elicits positive emotions is suggested. In addition, the user's emotional state is analyzed in real time, and the generation AI provides health advice according to the emotion. For example, if negative emotions are strong, feedback is given in kind words. This makes it possible to provide health advice according to the user's emotional state in real time.

[0069] The generating AI can analyze a user's past health data and provide advice based on long-term health trends. For example, the generating AI can analyze a user's past health data and provide advice based on long-term health trends. For example, it can predict future risks based on past blood pressure data. The generating AI can also analyze long-term trends in health data and suggest preventive measures to the user. For example, it can suggest dietary improvements based on past weight fluctuations. The generating AI can also set long-term health goals based on the user's past health data and provide advice towards those goals. For example, it can suggest a training plan based on past exercise history. This makes it possible to provide advice based on long-term health trends.

[0070] The generating AI can analyze a user's lifestyle habits and provide an improvement plan based on that. For example, the generating AI can analyze the user's lifestyle data and provide an improvement plan based on their sleep patterns and meal timings. For example, if they are sleep deprived, it can recommend going to bed earlier. The generating AI can also suggest specific improvement plans to the user based on the lifestyle data. For example, adjusting meal timing can help stabilize blood sugar levels. The generating AI can also analyze the user's lifestyle habits and provide an optimal improvement plan. For example, it can give advice on changing a nocturnal lifestyle to a morning one. This makes it possible to provide an improvement plan based on lifestyle habits.

[0071] The generation AI can use the emotion estimation function to provide personalized health advice that takes into account the user's emotional state. For example, the generation AI can use the emotion estimation function to provide personalized health advice that takes into account the user's emotional state. For example, if stress is high, it can suggest relaxation methods. The generation AI can also analyze the user's emotional state in real time and provide health advice that corresponds to the emotion. For example, it can suggest an exercise plan that elicits positive emotions. The generation AI can also provide health advice that corresponds to the user's emotional state based on the emotion estimation data. For example, if negative emotions are strong, it can provide feedback in kind words. This makes it possible to provide personalized health advice that takes into account the emotional state.

[0072] The generating AI can take into account the user's occupation and daily activity level and provide health advice that suits them. For example, the generating AI can analyze the user's occupational data and provide health advice that suits their occupation. For example, if they do a lot of desk work, regular stretching can be recommended. The generating AI can also provide specific health advice to the user based on their daily activity level. For example, if they are not getting enough exercise, walking can be recommended. The generating AI can also analyze the user's occupation and activity level and provide optimal health advice. For example, if they do a lot of standing work, it can suggest foot care methods. This makes it possible to provide health advice that suits their occupation and activity level.

[0073] The generating AI can take into account the user's hobbies and interests and provide a health improvement plan based on them. For example, the generating AI can analyze the user's hobby data and provide a health improvement plan based on the hobbies. For example, if the user likes the outdoors, hiking can be recommended. The generating AI can also suggest specific health improvement plans to the user based on the hobbies and interests. For example, if the user likes music, dancing can be recommended. The generating AI can also analyze the user's hobbies and interests and provide an optimal health improvement plan. For example, if the user likes reading, it can suggest improving posture while reading. This makes it possible to provide a health improvement plan based on the user's hobbies and interests.

[0074] The generation AI can use the emotion estimation function to provide advice to improve motivation according to the user's emotional state. For example, the generation AI uses the emotion estimation function to provide advice to improve motivation according to the user's emotional state. For example, it can display encouraging messages that elicit positive emotions. The generation AI can also analyze the user's emotional state in real time and provide advice to improve motivation according to the emotion. For example, if negative emotions are strong, it can provide feedback in kind words. The generation AI can also provide advice to improve motivation according to the user's emotional state based on the emotion estimation data. For example, if stress is high, it can suggest relaxation methods. This makes it possible to provide advice to improve motivation according to the emotional state.

[0075] The generation AI can analyze the user's dietary history and make suggestions to optimize the balance of nutrients. The generation AI can, for example, analyze the user's dietary history and make suggestions to optimize the balance of nutrients. For example, it can suggest ingredients that will make up for vitamin and mineral deficiencies. Furthermore, based on the dietary history data, the generation AI can make specific nutritional management suggestions to the user. For example, it can suggest a meal plan to increase protein intake. Furthermore, the generation AI can analyze the user's dietary history and suggest a meal menu to optimize the balance of nutrients. For example, it can provide a balanced one-week meal plan. This makes it possible to make suggestions to optimize the balance of nutrients.

[0076] The generating AI can take into account the user's allergy information and provide nutritional management that avoids allergies. For example, the generating AI can analyze the user's allergy information and provide nutritional management that avoids allergies. For example, it can suggest ingredients that do not contain specific allergens. Furthermore, based on the allergy information, the generating AI can make specific nutritional management suggestions to the user. For example, it can provide a balanced meal plan that avoids allergens. Furthermore, the generating AI can analyze the user's allergy information and suggest a method of ingesting nutrients that avoids allergies. For example, it can recommend supplements that do not contain allergens. This makes it possible to provide nutritional management that avoids allergies.

[0077] The generation AI can use the emotion estimation function to make nutritional management suggestions that take into account the user's emotional state. For example, the generation AI can use the emotion estimation function to make nutritional management suggestions that take into account the user's emotional state. For example, if stress is high, it can suggest ingredients that have a relaxing effect. The generation AI can also analyze the user's emotional state in real time and make nutritional management suggestions that correspond to the emotion. For example, it can suggest meal menus that elicit positive emotions. The generation AI can also make nutritional management suggestions that correspond to the user's emotional state based on the emotion estimation data. For example, if negative emotions are strong, it can suggest ingredients that will improve mood. This makes it possible to make nutritional management suggestions that take into account the emotional state.

[0078] The generating AI can take into account ingredients in the user's area and provide nutritional management that utilizes local ingredients. For example, the generating AI can analyze ingredient data in the user's area and provide nutritional management that utilizes local ingredients. For example, it can suggest fresh vegetables that are locally grown. Furthermore, based on the local ingredient data, the generating AI can make specific nutritional management suggestions to the user. For example, it can provide a balanced meal plan using local specialties. Furthermore, by analyzing ingredients in the user's area, the generating AI can suggest methods for consuming nutrients that utilize local ingredients. For example, it can suggest a high-protein meal menu using local fish. This makes it possible to provide nutritional management that utilizes local ingredients.

[0079] The generating AI can take into account the user's dietary preferences and provide nutritional management tailored to their preferences. For example, the generating AI can analyze the user's dietary preferences and provide nutritional management tailored to their preferences. For example, it can suggest a balanced meal menu using their favorite ingredients. Furthermore, based on the dietary preference data, the generating AI can make specific nutritional management suggestions to the user. For example, it can provide recipes that are healthy variations of their favorite dishes. Furthermore, the generating AI can analyze the user's dietary preferences and suggest methods for ingesting nutrients tailored to their preferences. For example, it can suggest supplements using their favorite ingredients. This makes it possible to provide nutritional management tailored to their dietary preferences.

[0080] The generation AI can use the emotion estimation function to suggest meals that correspond to the user's emotional state. For example, the generation AI uses the emotion estimation function to suggest meals that correspond to the user's emotional state. For example, if stress is high, ingredients that have a relaxing effect will be suggested. The generation AI also analyzes the user's emotional state in real time and suggests meal menus that correspond to the emotion. For example, meal menus that elicit positive emotions will be suggested. The generation AI also suggests meals that correspond to the user's emotional state based on the emotion estimation data. For example, if negative emotions are strong, ingredients that will improve mood will be suggested. This makes it possible to suggest meals that correspond to the emotional state.

[0081] The generation AI can analyze the user's muscle mass and body fat percentage and provide a training plan based on that. For example, the generation AI can analyze the user's muscle mass and body fat percentage data and provide an optimal training plan. For example, it can suggest strength training to increase muscle mass. The generation AI can also suggest a specific training plan to the user based on muscle mass and body fat percentage. For example, it can recommend aerobic exercise to reduce body fat. The generation AI can also analyze the user's muscle mass and body fat percentage and adjust the plan while monitoring the training progress. For example, it can adjust the training intensity according to the increase in muscle mass. This makes it possible to provide a training plan based on muscle mass and body fat percentage.

[0082] The generation AI can analyze the user's exercise history and provide an optimal training plan based on past training data. For example, the generation AI can analyze the user's exercise history data and provide an optimal training plan based on past training data. For example, it can suggest a training menu based on past exercise volume. The generation AI can also suggest a specific training plan to the user based on the exercise history. For example, it can provide a new exercise plan that takes past training results into consideration. The generation AI can also analyze the user's exercise history and adjust the plan while monitoring the training progress. For example, it can adjust the training intensity based on past exercise data. This makes it possible to provide an optimal training plan based on past training data.

[0083] The generation AI can use the emotion estimation function to provide a training plan that takes the user's emotional state into consideration. For example, the generation AI can use the emotion estimation function to provide a training plan that takes the user's emotional state into consideration. For example, if stress is high, it can suggest exercises that have a relaxation effect. The generation AI can also analyze the user's emotional state in real time and provide a training plan that suits the emotion. For example, it can suggest an exercise menu that brings out positive emotions. The generation AI can also provide a training plan that suits the user's emotional state based on the emotion estimation data. For example, if negative emotions are strong, it can provide feedback in kind words. This makes it possible to provide a training plan that takes the emotional state into consideration.

[0084] The generating AI can take the user's lifestyle rhythm into consideration and suggest the optimal training time. For example, the generating AI can analyze the user's lifestyle rhythm data and suggest the optimal training time. For example, it can suggest an exercise time that suits a morning-type lifestyle. The generating AI can also suggest specific training times to the user based on their lifestyle rhythm. For example, it can suggest an exercise time that suits a night-type lifestyle. The generating AI can also analyze the user's lifestyle rhythm and adjust the optimal exercise time while monitoring the training progress. For example, it can adjust the exercise time in response to changes in the lifestyle rhythm. This makes it possible to suggest the optimal training time based on the user's lifestyle.

[0085] The generation AI can take into account the user's exercise environment and provide an appropriate training plan. For example, the generation AI can analyze the user's exercise environment data and provide the optimal training plan. For example, it can suggest a training menu that can be done at home. The generation AI can also suggest a specific training plan to the user based on the exercise environment. For example, it can suggest a training menu for the gym. The generation AI can also analyze the user's exercise environment and adjust the plan while monitoring the training progress. For example, it can suggest an exercise menu that suits the home training environment. This makes it possible to provide a training plan that suits the exercise environment.

[0086] The generation AI can use the emotion estimation function to provide a training plan to improve motivation according to the user's emotional state. For example, the generation AI can use the emotion estimation function to provide a training plan to improve motivation according to the user's emotional state. For example, it can suggest an exercise menu that elicits positive emotions. The generation AI can also analyze the user's emotional state in real time and provide a training plan according to the emotion. For example, if negative emotions are strong, it can provide feedback in kind words. The generation AI can also provide a training plan according to the user's emotional state based on the emotion estimation data. For example, if stress is high, it can suggest exercises that have a relaxation effect. This makes it possible to provide a training plan to improve motivation according to the emotional state.

[0087] The generating AI can analyze the user's psychological state, identify the cause of stress, and provide stress management methods based on that. The generating AI, for example, analyzes the user's psychological state data, identifies the cause of stress, and provides stress management methods based on that. For example, if work stress is the cause, it will suggest relaxation methods. The generating AI will also suggest specific stress management methods to the user based on their psychological state. For example, if family problems are the cause, it will suggest ways to communicate with family. The generating AI will also analyze the user's psychological state and provide stress management methods based on the cause of stress. For example, if financial problems are the cause, it will provide advice on budget management. This makes it possible to identify the cause of stress and provide stress management methods based on that.

[0088] The generating AI can analyze the user's sleep data and make suggestions to improve sleep quality. The generating AI can, for example, analyze the user's sleep data and make suggestions to improve sleep quality. For example, it can suggest improvements to sleep time or sleep environment. Furthermore, based on the sleep data, the generating AI can make specific suggestions to the user to improve their sleep. For example, it can suggest ways to relax before bed or how to choose appropriate bedding. The generating AI can also analyze the user's sleep data and make suggestions to improve lifestyle habits to improve sleep quality. For example, it can suggest limiting caffeine intake and maintaining a regular lifestyle. This allows it to make suggestions to improve sleep quality.

[0089] The generative AI can use the emotion estimation function to provide mental health support that takes into account the user's emotional state. For example, the generative AI can use the emotion estimation function to provide mental health support that takes into account the user's emotional state. For example, if stress is high, it can suggest relaxation methods. The generative AI can also analyze the user's emotional state in real time and provide mental health support that suits the emotion. For example, it can suggest activities that bring out positive emotions. The generative AI can also provide mental health support that suits the user's emotional state based on the emotion estimation data. For example, if negative emotions are strong, it can provide feedback in kind words. This makes it possible to provide mental health support that takes into account the user's emotional state.

[0090] The generative AI can take into account the user's hobbies and relaxation methods and provide mental health support based on them. For example, the generative AI can analyze the user's hobby data and provide mental health support based on their hobbies. For example, if they like music, it can suggest relaxing music. The generative AI can also suggest specific mental health support to the user based on their hobbies and relaxation methods. For example, if they like drawing, it can suggest art therapy. The generative AI can also analyze the user's hobbies and relaxation methods and provide the optimal mental health support. For example, if they like gardening, it can suggest growing plants. This makes it possible to provide mental health support based on their hobbies and relaxation methods.

[0091] The generative AI can take into account the user's social connections and provide mental health support based on that. For example, the generative AI can analyze the user's social connection data and provide mental health support based on that. For example, it can make suggestions to increase interactions with friends. The generative AI can also suggest specific mental health support to the user based on social connections. For example, it can suggest activities to increase communication with family. The generative AI can also analyze the user's social connections and provide optimal mental health support. For example, it can suggest participating in local community activities. This makes it possible to provide mental health support based on social connections.

[0092] The generation AI can use the emotion estimation function to suggest relaxation methods that correspond to the user's emotional state. For example, the generation AI uses the emotion estimation function to suggest relaxation methods that correspond to the user's emotional state. For example, if stress is high, it may suggest meditation or deep breathing. The generation AI can also analyze the user's emotional state in real time and suggest relaxation methods that correspond to the emotion. For example, it may suggest relaxation music that elicits positive emotions. The generation AI can also suggest relaxation methods that correspond to the user's emotional state based on the emotion estimation data. For example, if negative emotions are strong, it may suggest aromatherapy. This makes it possible to suggest relaxation methods that correspond to the emotional state.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The health management platform can also acquire the user's sleep data and have the analysis unit analyze that data. For example, the platform can analyze the user's sleep patterns and provide advice to improve the quality of their sleep. Specifically, the platform can suggest ways to optimize sleep time, how to choose bedding, and how to relax before bed. The analysis unit can also estimate the user's stress level based on the user's sleep data and provide advice on stress management. For example, if stress is high, the analysis unit can suggest relaxation methods and activities to relieve stress. This can improve the user's sleep quality and overall health.

[0095] The health management platform can also acquire the user's exercise data, and the analysis unit can analyze that data. For example, it can analyze the user's exercise history and provide an optimal exercise plan. Specifically, it can suggest the type, frequency, and intensity of exercise. The analysis unit can also evaluate the effectiveness of exercise based on the user's exercise data and adjust the exercise plan as necessary. For example, if the exercise is not effective, it can suggest changing the type or intensity of exercise. This can optimize the user's exercise habits and improve their health.

[0096] The health management platform can also acquire the user's dietary data, and the analysis unit can analyze that data. For example, the platform can analyze the user's dietary history and provide advice to optimize nutritional balance. Specifically, it can suggest ingredients and recipes to adjust vitamin and mineral intake. The analysis unit can also identify areas for improvement in the user's diet based on the user's dietary data and suggest specific measures to improve it. For example, it can provide a meal plan to avoid excessive calorie intake. This can improve the user's eating habits and improve their health.

[0097] The health management platform can also monitor the user's emotional state in real time, with the analysis unit analyzing the data. For example, it can analyze the user's emotional state and provide health advice based on that emotion. Specifically, if stress levels are high, it can suggest relaxation methods and activities to elicit positive emotions. The analysis unit can also provide feedback based on the user's emotional data according to their emotional state. For example, if negative emotions are strong, it can provide gentle feedback. This makes it possible to provide health support that takes the user's emotional state into consideration.

[0098] The health management platform can also acquire data on the user's living environment, which the analysis unit can then analyze. For example, it can analyze the climate data of the user's residential area and provide health advice tailored to the climate. Specifically, it can recommend hydration in humid areas and suggest cold weather protection measures in cold regions. The analysis unit can also identify health risks based on the user's living environment data and suggest preventive measures. For example, it can recommend wearing a mask in areas with high air pollution. This makes it possible to provide health support that takes the user's living environment into consideration.

[0099] The health management platform can also acquire health data of the user's family, and the analysis unit can analyze that data. For example, it can analyze the health checkup results of all family members and support the health management of the entire family. Specifically, it can propose meal plans and exercise plans for the entire family. Furthermore, based on the health data of the user's family, the analysis unit can identify health risks for the entire family and propose preventive measures. For example, it can recommend regular health checkups for all family members. This can support the health management of the entire family.

[0100] The health management platform can also acquire the user's occupational data, and the analysis unit can analyze that data. For example, it can provide health advice tailored to the user's occupation. Specifically, if the user does a lot of desk work, it can recommend regular stretching, and if the user does a lot of standing work, it can suggest foot care methods. Furthermore, based on the user's occupational data, the analysis unit can identify health risks according to the user's occupation and suggest preventive measures. For example, it can provide advice on reducing the health risks associated with long periods of sitting at work. This makes it possible to provide health support tailored to the user's occupation.

[0101] The health management platform can also acquire user hobby data, and the analysis unit can analyze that data. For example, it can provide a health improvement plan based on the user's hobbies. Specifically, if the user likes the outdoors, hiking can be recommended, and if the user likes music, dancing can be suggested. Furthermore, based on the user's hobby data, the analysis unit can identify health risks associated with the hobby and suggest preventive measures. For example, it can suggest improving posture after reading for long periods of time. This makes it possible to provide health support based on the user's hobbies.

[0102] The health management platform can also acquire data on the user's social connections, and the analysis unit can analyze that data. For example, it can provide mental health support based on the user's social connections. Specifically, it can suggest ways to increase interactions with friends and activities to increase communication with family. Furthermore, based on the user's social connection data, the analysis unit can identify health risks associated with social connections and suggest preventive measures. For example, it can recommend participation in community activities to reduce feelings of loneliness. This makes it possible to provide mental health support that takes the user's social connections into account.

[0103] The health management platform can also monitor the user's emotional state in real time, with the analysis unit analyzing the data. For example, it can analyze the user's emotional state and provide advice to improve motivation based on the emotion. Specifically, it can display encouraging messages that elicit positive emotions, and provide gentle feedback if negative emotions are strong. The analysis unit can also suggest relaxation methods based on the user's emotional data, based on the user's emotional state. For example, it can suggest meditation or deep breathing if stress levels are high. This makes it possible to provide support for improving motivation that takes the user's emotional state into consideration.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The medical examination result acquisition unit acquires medical examination results, such as blood test results, electrocardiogram data, and physical measurements. Step 2: The analysis unit analyzes the acquired health checkup results, for example, by using statistical analysis or machine learning algorithms. Step 3: The advice provider provides personalized health advice based on the analyzed results, for example, advice based on the user's age, gender, and medical history. Step 4: The nutrition management department performs nutrition management based on the analyzed results, such as balancing meals and calculating calories. Step 5: The training support section provides a training plan based on the analyzed results, for example, suggesting the type, frequency, and intensity of exercise. Step 6: The mental health support department provides mental health support based on the analyzed results, such as stress management and mental health care.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the 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.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0138] 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.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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]

[0173] 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 health checkup result acquisition unit that acquires health checkup results; an analysis unit that analyzes the medical examination results acquired by the medical examination result acquisition unit; an advice providing unit that provides personalized health advice based on the results of the analysis by the analysis unit; a nutritional management unit that performs nutritional management based on the results of the analysis by the analysis unit; a training support unit that provides a training plan based on the results of the analysis by the analysis unit; a mental health support unit that supports mental health based on the results of the analysis by the analysis unit. A system characterized by:

2. The health checkup result acquisition unit Analyzing the user's genetic information and providing the health advice taking into account genetic risks 2. The system of claim 1.

3. The health checkup result acquisition unit The health checkup results are updated in real time, and the generation AI performs the analysis based on the latest data each time.

2. The system of claim 1.

4. The health checkup result acquisition unit Providing feedback on the health checkup results that takes into account the user's emotional state 2. The system of claim 1.

5. The health checkup result acquisition unit Providing health advice that takes into account the user's living environment 2. The system of claim 1.

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