System

Abstract

An object of a system according to an embodiment is to provide an appropriate training plan and meal plan based on individual body composition information.SOLUTION: A system includes a body composition information acquisition unit, a data analysis unit, a training plan generation unit, and a meal plan generation unit. The body composition information acquisition unit acquires body composition information. The data analysis unit analyzes the body composition information acquired by the body composition information acquisition unit. The training plan generator generates a training plan based on the information analyzed by the data analyzer. The meal plan generation unit generates a meal plan based on the information analyzed by the data 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 do not adequately provide appropriate training and meal plans based on individual body composition, and there is room for improvement.

[0005] The system according to the embodiment aims to provide appropriate training and meal plans based on individual body composition information. [Means for solving the problem]

[0006] The system according to the embodiment includes a body composition information acquisition unit, a data analysis unit, a training plan generation unit, and a meal plan generation unit. The body composition information acquisition unit acquires body composition information. The data analysis unit analyzes the body composition information acquired by the body composition information acquisition unit. The training plan generation unit generates a training plan based on the information analyzed by the data analysis unit. The meal plan generation unit generates a meal plan based on the information analyzed by the data analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate training and meal plans based on individual body composition information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A health management system according to an embodiment of the present invention acquires body composition information, analyzes it using a generation AI, and proposes appropriate training and meal plans. This allows the health management system to propose individually customized training and meal plans based on the user's body composition information.

[0029] A health management system according to an embodiment includes a body composition information acquisition unit, a data analysis unit, a training plan generation unit, and a meal plan generation unit. The body composition information acquisition unit acquires body composition information. For example, it uses a device such as a weighing scale, a body fat scale, or a smartwatch to collect data such as weight, body fat percentage, muscle mass, bone mass, and water content. Furthermore, the body composition information acquisition unit can continuously acquire body composition information through daily use by the user. The data analysis unit analyzes the acquired body composition information. For example, the data analysis unit analyzes the data based on the user's health status and goals using statistical analysis or machine learning algorithms. The training plan generation unit generates a training plan based on the information analyzed by the data analysis unit. For example, the training plan generation unit specifically indicates how many times per week exercise is needed and what types of exercise are effective. It also provides detailed suggestions for exercise intensity, duration, number of sets, and repetitions. The meal plan generation unit generates a meal plan based on the information analyzed by the data analysis unit. For example, the meal plan generation unit specifically indicates a menu that takes into account daily calorie intake and nutritional balance. The system also provides detailed suggestions on how to select ingredients, cooking methods, meal timing, etc. This allows the health management system according to the embodiment to propose individually customized training and meal plans based on the user's body composition information.

[0030] The body composition information acquisition unit simultaneously records the user's lifestyle data and collects more detailed data. For example, the body composition information acquisition unit adds a function to record the user's lifestyle to a weighing scale or body fat scale, allowing the user to input sleep time and stress level. For example, the user inputs the previous day's sleep time when using the device. The body composition information acquisition unit also adds a function to automatically record the user's lifestyle to a smartwatch and collects data along with the body composition information. For example, it utilizes a sleep tracking function and a stress level measurement function. The body composition information acquisition unit also links with an app that records the user's lifestyle and centrally manages data. For example, the user's lifestyle data can be input and managed through a smartphone app. This makes it possible to propose training and meal plans that take the user's lifestyle into consideration.

[0031] The body composition information acquisition unit has a function for analyzing the user's movements and posture in real time to evaluate the quality of the exercise. The body composition information acquisition unit, for example, adds a sensor for analyzing the user's movements and posture to a weighing scale or body fat scale to evaluate the quality of the exercise. For example, an acceleration sensor or a gyro sensor is used to analyze the user's movements. The body composition information acquisition unit also adds a function for analyzing the user's movements and posture in real time to a smartwatch to evaluate the quality of the exercise. For example, it analyzes walking and running form and suggests areas for improvement. The body composition information acquisition unit also has a camera that analyzes the user's movements and posture to evaluate the quality of the exercise. For example, it uses video analysis technology to analyze the user's exercise form in real time. This makes it possible to evaluate the quality of the user's exercise and propose a more effective training plan.

[0032] The data analysis unit can refer to the user's past health data and medical history to propose more accurate training and meal plans. Data analysis and proposals by generative AI (generative AI processing) - When generative AI analyzes, it also refers to the user's past health data and medical history to make more accurate proposals. For example, generative AI analyzes the user's past health data and compares it with current body composition information to propose a training plan. For example, it suggests the optimal amount of exercise based on past weight fluctuations. Generative AI also refers to the user's medical history to propose a meal plan based on the user's health condition. For example, it suggests appropriate ingredients for users with a specific medical history. Generative AI also comprehensively analyzes the user's past health data and medical history to propose more accurate training and meal plans. For example, it predicts risks based on past data and proposes preventative plans. This allows it to propose more accurate training and meal plans that take the user's past health data and medical history into account.

[0033] The data analysis unit takes into account the user's living environment and can propose training and meal plans suitable for the region. Data analysis and proposal by generative AI (processing by generative AI) - When generative AI analyzes, it also takes into account the user's living environment (for example, the climate and food culture of the area where they live) and proposes a plan suitable for the region. For example, generative AI takes into account the climate of the area where the user lives and proposes an appropriate training plan. For example, it may propose indoor training for users living in cold regions. Generative AI may also consider the food culture of the area where the user lives and propose an appropriate meal plan. For example, it may propose a menu using local ingredients. Generative AI may also comprehensively analyze the user's living environment and propose training and meal plans suitable for the region. For example, it may propose a balanced plan tailored to the local climate and food culture. This makes it possible to propose training and meal plans suitable for the user's living environment.

[0034] The data analysis unit can compare data with other users' data and use it as a benchmark. Data analysis and proposals by generative AI (generative AI processing) - When generative AI analyzes data, it compares it with other users' data and uses it as a benchmark. For example, generative AI analyzes other users' data and proposes a training plan for the user as a benchmark. For example, it proposes the optimal amount of exercise based on the data of users of the same age group and body type. Generative AI also analyzes other users' dietary data and proposes a meal plan for the user as a benchmark. For example, it proposes the optimal menu based on the data of users with the same health goals. Generative AI also comprehensively analyzes other users' data and proposes training and meal plans for the user as a benchmark. For example, it proposes the optimal plan based on success stories. This allows other users' data to be used as a benchmark to propose more effective training and meal plans.

[0035] The data analysis unit can analyze social media posts and suggest training and meal plans based on interests and hobbies. Data analysis and suggestions by generative AI (generative AI processing) - When generative AI analyzes, it analyzes the user's social media posts and suggests plans based on their interests and hobbies. For example, generative AI analyzes the user's social media posts and suggests training plans based on their interests and hobbies. For example, it suggests the optimal plan based on the exercise content posted by the user. Generative AI also analyzes the user's social media posts and suggests meal plans based on their interests and hobbies. For example, it suggests the optimal menu based on the food content posted by the user. Generative AI also comprehensively analyzes the user's social media posts and suggests training and meal plans based on their interests and hobbies. For example, it suggests a balanced plan based on the user's posts. This makes it possible to suggest training and meal plans based on the user's interests and hobbies.

[0036] The training plan generation unit can refer to the training history and propose a training plan according to the user's progress. Proposing a training plan (processing by the generation AI) - When the generation AI proposes a training plan, it refers to the user's past training history and proposes a plan according to the user's progress. For example, the generation AI analyzes the user's past training history and proposes a training plan according to the user's progress. For example, it sets the next step based on the amount of exercise done in the past. The generation AI also analyzes the user's past training history and proposes a training plan according to the user's progress. For example, it sets a new goal based on past training results. The generation AI also analyzes the user's past training history and proposes a training plan according to the user's progress. For example, it proposes the optimal exercise intensity based on past training data. This makes it possible to propose a training plan according to the user's progress.

[0037] The training plan generation unit can propose a reasonable training plan taking into account the user's physical fitness level and exercise experience. Proposing a training plan (processing by the generation AI) - When the generation AI proposes a training plan, it considers the user's physical fitness level and exercise experience and proposes a reasonable plan. For example, the generation AI analyzes the user's physical fitness level and proposes a reasonable training plan. For example, it proposes a plan that starts with exercises for beginners. The generation AI also analyzes the user's exercise experience and proposes a reasonable training plan. For example, it sets an appropriate exercise intensity based on past exercise experience. The generation AI also comprehensively analyzes the user's physical fitness level and exercise experience and proposes a reasonable training plan. For example, it proposes a plan that gradually increases exercise intensity. This makes it possible to propose a reasonable training plan that suits the user's physical fitness level and exercise experience.

[0038] The training plan generation unit can suggest group training plans that can be done together with friends and family. Training plan suggestion (processing by generation AI) - When the generation AI suggests a training plan, it suggests a group training plan that can be done together with the user's friends and family. For example, the generation AI analyzes data of the user's friends and family to suggest a group training plan that can be done together. For example, it suggests an exercise program that the whole family can participate in. The generation AI also analyzes data of the user's friends and family to suggest a group training plan that can be done together. For example, it suggests pair training to do with friends. The generation AI also analyzes data of the user's friends and family to suggest a group training plan that can be done together. For example, it suggests a group fitness challenge. This makes it possible to suggest a group training plan that can be done together with the user's friends and family.

[0039] The training plan generation unit can incorporate activities based on hobbies and interests. Training plan proposal (processing by generation AI) - When the generation AI proposes a training plan, it incorporates activities based on the user's hobbies and interests. For example, the generation AI analyzes the user's hobbies and interests and proposes a training plan that incorporates activities based on them. For example, for a user who likes dancing, it would propose dance exercises. The generation AI also analyzes the user's hobbies and interests and proposes a training plan that incorporates activities based on them. For example, for a user who likes the outdoors, it would propose hiking. The generation AI also analyzes the user's hobbies and interests and proposes a training plan that incorporates activities based on them. For example, for a user who likes music, it would propose exercises that match music. This makes it possible to propose training plans that incorporate activities based on the user's hobbies and interests.

[0040] The meal plan generation unit can refer to the user's meal history and suggest menus that take into account preferences and allergies. Meal plan suggestion (processing by generation AI) - When the generation AI suggests a meal plan, it refers to the user's past meal history and suggests menus that take into account preferences and allergies. For example, the generation AI may analyze the user's past meal history and suggest menus that take into account preferences and allergies. For example, it may suggest menus that use ingredients that the user has enjoyed eating in the past. The generation AI may also analyze the user's past meal history and suggest menus that take into account allergies. For example, it may suggest menus that avoid ingredients that the user is allergic to. The generation AI may also comprehensively analyze the user's past meal history and suggest menus that take into account preferences and allergies. For example, it may select the most suitable ingredients based on past data. This makes it possible to suggest menus that take into account the user's preferences and allergies.

[0041] The meal plan generation unit can propose realistic menus by taking into account the availability of ingredients and the user's budget. Meal plan proposal (processing by the generation AI) - When the generation AI proposes a meal plan, it proposes realistic menus by taking into account the user's availability of ingredients and budget. For example, the generation AI may analyze the availability of ingredients in the user's area and propose realistic menus. For example, it may propose menus using locally available ingredients. The generation AI may also analyze the user's budget and propose cost-effective menus. For example, it may propose menus using inexpensive, nutritious ingredients. The generation AI may also comprehensively analyze the user's availability of ingredients and budget and propose realistic menus. For example, it may propose cost-effective menus using seasonal ingredients. This makes it possible to propose realistic menus that take into account the user's availability of ingredients and budget.

[0042] The meal plan generation unit can also consider the meal plans of family members and housemates to propose a common menu. Meal plan proposal (processing by generation AI) - When the generation AI proposes a meal plan, it also considers the meal plans of the user's family members and housemates to propose a common menu. For example, the generation AI analyzes the data of the user's family members and housemates to propose a common menu. For example, it proposes a balanced menu that the whole family can enjoy. The generation AI can also analyze the data of the user's family members and housemates to propose a common menu. For example, it proposes a menu that takes into account the allergies and preferences of housemates. The generation AI can also comprehensively analyze the data of the user's family members and housemates to propose a common menu. For example, it proposes a menu that takes into account the nutritional balance of the whole family. This makes it possible to propose a common menu that takes into account the meal plans of the user's family members and housemates.

[0043] The meal plan generation unit can propose appropriate menus taking into account cultural and religious backgrounds. Meal plan proposal (processing by the generation AI) - When the generation AI proposes a meal plan, it proposes an appropriate menu taking into account the user's cultural and religious backgrounds. For example, the generation AI may analyze the user's cultural background and propose an appropriate menu. For example, it may propose traditional dishes rooted in a particular culture. The generation AI may also analyze the user's religious background and propose an appropriate menu. For example, it may propose a menu using ingredients permitted by a particular religion. The generation AI may also comprehensively analyze the user's cultural and religious background and propose an appropriate menu. For example, it may propose a balanced meal plan that takes culture and religion into consideration. This makes it possible to propose an appropriate menu taking into account the user's cultural and religious backgrounds.

[0044] The data analysis unit continuously monitors changes in lifestyle and environment and updates the plan as appropriate. Continuous monitoring and feedback (processing by the generation AI) - When the generation AI continuously monitors, it also takes changes in the user's lifestyle and environment into account and updates the plan as appropriate. For example, the generation AI continuously monitors the user's lifestyle and updates the training plan accordingly. For example, if the user's sleep pattern changes, it adjusts exercise time. The generation AI also continuously monitors the user's living environment and updates the meal plan as changes occur. For example, if the user moves, it will suggest menus using ingredients from the new area. The generation AI also comprehensively monitors changes in the user's lifestyle and environment and updates the plan as appropriate. For example, if the user's work schedule changes, it will adjust the balance between training and meals. This allows the plan to be updated as appropriate to changes in the user's lifestyle and environment.

[0045] The data analysis unit can analyze the content of social media posts and provide feedback to maintain motivation. Continuous monitoring and feedback (processing by the generation AI) - When the generation AI continuously monitors, it analyzes the content of the user's social media posts and provides feedback to maintain motivation. For example, the generation AI continuously monitors the content of the user's social media posts and provides training feedback to maintain motivation. For example, it may send encouraging messages based on the exercise content posted by the user. The generation AI also continuously monitors the content of the user's social media posts and provides dietary feedback to maintain motivation. For example, it may suggest healthy recipes based on the diet content posted by the user. The generation AI also continuously monitors the content of the user's social media posts and provides comprehensive feedback to maintain motivation. For example, it may adjust the balance between training and diet based on the content posted by the user. This makes it possible to provide feedback to maintain the user's motivation.

[0046] The data analysis unit can refer to the data of friends and family and suggest joint health management options. Continuous monitoring and feedback (processing by the generation AI) - When the generation AI continuously monitors, it also refers to the data of the user's friends and family and suggests joint health management options. For example, the generation AI continuously monitors the data of the user's friends and family and suggests joint training plans. For example, it could suggest an exercise program that the whole family can participate in. The generation AI also continuously monitors the data of the user's friends and family and suggests joint meal plans. For example, it could suggest a balanced menu that the whole family can enjoy. The generation AI also comprehensively monitors the data of the user's friends and family and suggests joint health management plans. For example, it could suggest a balance of training and meals that takes into account the health status of all family members. This allows it to suggest joint health management options with the user's friends and family.

[0047] The data analysis unit can provide appropriate feedback by taking into account the workplace or school environment. Continuous monitoring and feedback (processing by the generation AI) - When the generation AI continuously monitors, it takes into account the user's workplace or school environment and provides appropriate feedback. For example, the generation AI continuously monitors the user's workplace environment and provides appropriate training feedback. For example, it suggests stretches that can be done in the office for users who do a lot of desk work. The generation AI also continuously monitors the user's school environment and provides appropriate dietary feedback. For example, it suggests easy-to-prepare, nutritionally balanced menus for student users. The generation AI also comprehensively monitors the user's workplace or school environment and provides appropriate feedback. For example, it suggests a balance of training and meals that matches the workplace or school schedule. This makes it possible to provide appropriate feedback according to the user's workplace or school environment.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The health management system may further include a sleep data acquisition unit that acquires the user's sleep data. For example, a smartwatch or dedicated sleep tracker may be used to collect data such as the user's sleep time, sleep quality, and the ratio of deep sleep to light sleep. The sleep data acquisition unit may also include a sensor that monitors the user's sleep environment (e.g., room temperature, humidity, and noise level). This allows the system to propose training and meal plans that take the user's sleep data into consideration.

[0050] The health management system may further include an activity measurement unit that measures the user's activity level. For example, a pedometer or an acceleration sensor may be used to measure the user's daily activity level. The activity measurement unit may also analyze the user's activity patterns (e.g., commuting time and exercise time) and propose an optimal training plan. This allows the system to propose training and meal plans that take the user's activity level into consideration.

[0051] The health management system may further include a water intake monitoring unit that monitors the user's water intake. For example, a smartwatch or a dedicated water intake tracker may be used to record the user's daily water intake. The water intake monitoring unit may also include a reminder function to encourage the user to drink adequate amounts of water. This allows the system to propose training and meal plans that take the user's water intake into account.

[0052] The health management system may further include a food intake recording unit that records the user's food intake. For example, the content and calories of the food the user has eaten may be recorded via a smartphone app. The food intake recording unit may also analyze the user's eating patterns (e.g., meal timing and frequency) and propose an optimal meal plan. This allows the system to propose training and meal plans that take the user's food intake into consideration.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The body composition information acquisition unit acquires body composition information. For example, data such as weight, body fat percentage, muscle mass, bone mass, and water content is collected using a device such as a weighing scale, body fat scale, or smartwatch. Furthermore, the body composition information acquisition unit can continuously acquire body composition information through daily use by the user. Step 2: The data analysis unit analyzes the acquired body composition information. For example, the data analysis unit analyzes the data based on the user's health status and goals using statistical analysis or machine learning algorithms. Step 3: The training plan generator generates a training plan based on the information analyzed by the data analyzer. For example, the training plan generator specifically indicates how many times per week exercise is needed and what types of exercise are effective. It also provides detailed recommendations on exercise intensity, duration, number of sets, and repetitions. Step 4: The meal plan generator generates a meal plan based on the information analyzed by the data analyzer. For example, the meal plan generator provides specific menu suggestions that take into account daily calorie intake and nutritional balance. It also provides detailed suggestions on how to select ingredients, cooking methods, and meal timing.

[0055] (Example 2) A health management system according to an embodiment of the present invention acquires body composition information, analyzes it using a generation AI, and proposes appropriate training and meal plans. This allows the health management system to propose individually customized training and meal plans based on the user's body composition information.

[0056] A health management system according to an embodiment includes a body composition information acquisition unit, a data analysis unit, a training plan generation unit, and a meal plan generation unit. The body composition information acquisition unit acquires body composition information. For example, it uses a device such as a weighing scale, a body fat scale, or a smartwatch to collect data such as weight, body fat percentage, muscle mass, bone mass, and water content. Furthermore, the body composition information acquisition unit can continuously acquire body composition information through daily use by the user. The data analysis unit analyzes the acquired body composition information. For example, the data analysis unit analyzes the data based on the user's health status and goals using statistical analysis or machine learning algorithms. The training plan generation unit generates a training plan based on the information analyzed by the data analysis unit. For example, the training plan generation unit specifically indicates how many times per week exercise is needed and what types of exercise are effective. It also provides detailed suggestions for exercise intensity, duration, number of sets, and repetitions. The meal plan generation unit generates a meal plan based on the information analyzed by the data analysis unit. For example, the meal plan generation unit specifically indicates a menu that takes into account daily calorie intake and nutritional balance. The system also provides detailed suggestions on how to select ingredients, cooking methods, meal timing, etc. This allows the health management system according to the embodiment to propose individually customized training and meal plans based on the user's body composition information.

[0057] The body composition information acquisition unit is equipped with a sensor that measures the user's emotional state and simultaneously collects emotional data. For example, the body composition information acquisition unit incorporates a sensor that measures emotional state into a weighing scale or body fat scale, and simultaneously collects emotional data when the user uses the device. For example, a sensor that measures electrodermal activity and heart rate variability is used. The body composition information acquisition unit also adds an emotional state measurement function to the smartwatch and collects emotional data during daily activities. For example, the emotional state is estimated by analyzing fluctuations in heart rate and skin temperature. The body composition information acquisition unit is also equipped with a camera that analyzes the user's facial expressions and collects emotional data. For example, facial expression recognition technology is used to analyze the user's emotional state in real time. This makes it possible to propose training and meal plans that take the user's emotional state into consideration.

[0058] The body composition information acquisition unit simultaneously records the user's lifestyle data and collects more detailed data. For example, the body composition information acquisition unit adds a function to record the user's lifestyle to a weighing scale or body fat scale, allowing the user to input sleep time and stress level. For example, the user inputs the previous day's sleep time when using the device. The body composition information acquisition unit also adds a function to automatically record the user's lifestyle to a smartwatch and collects data along with the body composition information. For example, it utilizes a sleep tracking function and a stress level measurement function. The body composition information acquisition unit also links with an app that records the user's lifestyle and centrally manages data. For example, the user's lifestyle data can be input and managed through a smartphone app. This makes it possible to propose training and meal plans that take the user's lifestyle into consideration.

[0059] The body composition information acquisition unit has a function for analyzing the user's movements and posture in real time to evaluate the quality of the exercise. The body composition information acquisition unit, for example, adds a sensor for analyzing the user's movements and posture to a weighing scale or body fat scale to evaluate the quality of the exercise. For example, an acceleration sensor or a gyro sensor is used to analyze the user's movements. The body composition information acquisition unit also adds a function for analyzing the user's movements and posture in real time to a smartwatch to evaluate the quality of the exercise. For example, it analyzes walking and running form and suggests areas for improvement. The body composition information acquisition unit also has a camera that analyzes the user's movements and posture to evaluate the quality of the exercise. For example, it uses video analysis technology to analyze the user's exercise form in real time. This makes it possible to evaluate the quality of the user's exercise and propose a more effective training plan.

[0060] The data analysis unit takes emotional data into consideration and can propose training and meal plans according to the emotional state. Data analysis and proposal by generative AI (processing by generative AI) - When generative AI analyzes, it also takes the user's emotional data into consideration and proposes training and meal plans according to the emotional state. For example, generative AI analyzes the user's emotional data and proposes a training plan according to the emotional state. For example, when stress is high, it proposes yoga, which has a relaxing effect. Generative AI also analyzes the user's emotional data and proposes a meal plan according to the emotional state. For example, when feeling depressed, it proposes a menu using ingredients that will lift your spirits. Generative AI also analyzes the user's emotional data and proposes a comprehensive training and meal plan according to the emotional state. For example, it adjusts the balance between training and meals according to the emotional state. This makes it possible to propose training and meal plans according to the user's emotional state.

[0061] The data analysis unit can refer to the user's past health data and medical history to propose more accurate training and meal plans. Data analysis and proposals by generative AI (generative AI processing) - When generative AI analyzes, it also refers to the user's past health data and medical history to make more accurate proposals. For example, generative AI analyzes the user's past health data and compares it with current body composition information to propose a training plan. For example, it suggests the optimal amount of exercise based on past weight fluctuations. Generative AI also refers to the user's medical history to propose a meal plan based on the user's health condition. For example, it suggests appropriate ingredients for users with a specific medical history. Generative AI also comprehensively analyzes the user's past health data and medical history to propose more accurate training and meal plans. For example, it predicts risks based on past data and proposes preventative plans. This allows it to propose more accurate training and meal plans that take the user's past health data and medical history into account.

[0062] The data analysis unit takes into account the user's living environment and can propose training and meal plans suitable for the region. Data analysis and proposal by generative AI (processing by generative AI) - When generative AI analyzes, it also takes into account the user's living environment (for example, the climate and food culture of the area where they live) and proposes a plan suitable for the region. For example, generative AI takes into account the climate of the area where the user lives and proposes an appropriate training plan. For example, it may propose indoor training for users living in cold regions. Generative AI may also consider the food culture of the area where the user lives and propose an appropriate meal plan. For example, it may propose a menu using local ingredients. Generative AI may also comprehensively analyze the user's living environment and propose training and meal plans suitable for the region. For example, it may propose a balanced plan tailored to the local climate and food culture. This makes it possible to propose training and meal plans suitable for the user's living environment.

[0063] The data analysis unit can compare data with other users' data and use it as a benchmark. Data analysis and proposals by generative AI (generative AI processing) - When generative AI analyzes data, it compares it with other users' data and uses it as a benchmark. For example, generative AI analyzes other users' data and proposes a training plan for the user as a benchmark. For example, it proposes the optimal amount of exercise based on the data of users of the same age group and body type. Generative AI also analyzes other users' dietary data and proposes a meal plan for the user as a benchmark. For example, it proposes the optimal menu based on the data of users with the same health goals. Generative AI also comprehensively analyzes other users' data and proposes training and meal plans for the user as a benchmark. For example, it proposes the optimal plan based on success stories. This allows other users' data to be used as a benchmark to propose more effective training and meal plans.

[0064] The data analysis unit can analyze social media posts and suggest training and meal plans based on interests and hobbies. Data analysis and suggestions by generative AI (generative AI processing) - When generative AI analyzes, it analyzes the user's social media posts and suggests plans based on their interests and hobbies. For example, generative AI analyzes the user's social media posts and suggests training plans based on their interests and hobbies. For example, it suggests the optimal plan based on the exercise content posted by the user. Generative AI also analyzes the user's social media posts and suggests meal plans based on their interests and hobbies. For example, it suggests the optimal menu based on the food content posted by the user. Generative AI also comprehensively analyzes the user's social media posts and suggests training and meal plans based on their interests and hobbies. For example, it suggests a balanced plan based on the user's posts. This makes it possible to suggest training and meal plans based on the user's interests and hobbies.

[0065] The data analysis unit uses the emotion estimation function to monitor the user's emotional state in real time and provide feedback according to the emotion. Continuous monitoring and feedback (processing by the generation AI) - Using the emotion estimation function, the generation AI continuously monitors the user's emotional state in real time and provides feedback according to the emotion. For example, using the emotion estimation function, the generation AI monitors the user's emotional state in real time and provides training feedback. For example, if the user is tired, it may suggest light exercise. Also, using the emotion estimation function, the generation AI monitors the user's emotional state in real time and provides dietary feedback. For example, if the user is feeling stressed, it may suggest foods that have a relaxing effect. Also, using the emotion estimation function, the generation AI monitors the user's emotional state in real time and provides comprehensive feedback. For example, it may adjust the balance between training and diet according to the user's emotional state. This makes it possible to provide feedback according to the user's emotional state.

[0066] The training plan generation unit can take emotional data into consideration and propose a training plan to maintain motivation. Proposing a training plan (processing by the generation AI) - When the generation AI proposes a training plan, it takes the user's emotional data into consideration and proposes a plan to maintain motivation. For example, the generation AI analyzes the user's emotional data and proposes a training plan to maintain motivation. For example, it may incorporate activities that the user enjoys. The generation AI may also analyze the user's emotional data and propose a training plan to maintain motivation. For example, it may set goals that make the user feel a sense of accomplishment. The generation AI may also analyze the user's emotional data and propose a training plan to maintain motivation. For example, it may propose an environment where the user can exercise while listening to their favorite music. This makes it possible to propose a training plan to maintain the user's motivation.

[0067] The training plan generation unit can refer to the training history and propose a training plan according to the user's progress. Proposing a training plan (processing by the generation AI) - When the generation AI proposes a training plan, it refers to the user's past training history and proposes a plan according to the user's progress. For example, the generation AI analyzes the user's past training history and proposes a training plan according to the user's progress. For example, it sets the next step based on the amount of exercise done in the past. The generation AI also analyzes the user's past training history and proposes a training plan according to the user's progress. For example, it sets a new goal based on past training results. The generation AI also analyzes the user's past training history and proposes a training plan according to the user's progress. For example, it proposes the optimal exercise intensity based on past training data. This makes it possible to propose a training plan according to the user's progress.

[0068] The training plan generation unit can propose a reasonable training plan taking into account the user's physical fitness level and exercise experience. Proposing a training plan (processing by the generation AI) - When the generation AI proposes a training plan, it considers the user's physical fitness level and exercise experience and proposes a reasonable plan. For example, the generation AI analyzes the user's physical fitness level and proposes a reasonable training plan. For example, it proposes a plan that starts with exercises for beginners. The generation AI also analyzes the user's exercise experience and proposes a reasonable training plan. For example, it sets an appropriate exercise intensity based on past exercise experience. The generation AI also comprehensively analyzes the user's physical fitness level and exercise experience and proposes a reasonable training plan. For example, it proposes a plan that gradually increases exercise intensity. This makes it possible to propose a reasonable training plan that suits the user's physical fitness level and exercise experience.

[0069] The training plan generation unit can suggest group training plans that can be done together with friends and family. Training plan suggestion (processing by generation AI) - When the generation AI suggests a training plan, it suggests a group training plan that can be done together with the user's friends and family. For example, the generation AI analyzes data of the user's friends and family to suggest a group training plan that can be done together. For example, it suggests an exercise program that the whole family can participate in. The generation AI also analyzes data of the user's friends and family to suggest a group training plan that can be done together. For example, it suggests pair training to do with friends. The generation AI also analyzes data of the user's friends and family to suggest a group training plan that can be done together. For example, it suggests a group fitness challenge. This makes it possible to suggest a group training plan that can be done together with the user's friends and family.

[0070] The training plan generation unit can incorporate activities based on hobbies and interests. Training plan proposal (processing by generation AI) - When the generation AI proposes a training plan, it incorporates activities based on the user's hobbies and interests. For example, the generation AI analyzes the user's hobbies and interests and proposes a training plan that incorporates activities based on them. For example, for a user who likes dancing, it would propose dance exercises. The generation AI also analyzes the user's hobbies and interests and proposes a training plan that incorporates activities based on them. For example, for a user who likes the outdoors, it would propose hiking. The generation AI also analyzes the user's hobbies and interests and proposes a training plan that incorporates activities based on them. For example, for a user who likes music, it would propose exercises that match music. This makes it possible to propose training plans that incorporate activities based on the user's hobbies and interests.

[0071] The training plan generation unit uses the emotion estimation function to monitor the user's emotional state in real time and adjust the training content according to the emotion. Training plan proposal (processing by the generation AI) - Using the emotion estimation function, the generation AI monitors the user's emotional state in real time when proposing a training plan and adjusts the training content according to the emotion. For example, using the emotion estimation function, the generation AI monitors the user's emotional state in real time and adjusts the training content. For example, when the user is tired, it may suggest light exercise. Also, using the emotion estimation function, the generation AI monitors the user's emotional state in real time and adjusts the training content. For example, when the user is feeling stressed, it may suggest exercise that has a relaxing effect. Also, using the emotion estimation function, the generation AI monitors the user's emotional state in real time and adjusts the training content. For example, when the user is feeling positive, it may suggest challenging exercise. This makes it possible to adjust the training content according to the user's emotional state.

[0072] The meal plan generation unit can take emotional data into consideration and suggest menus aimed at reducing stress and improving mood. Meal plan suggestion (processing by the generation AI) - When the generation AI suggests a meal plan, it takes the user's emotional data into consideration and suggests menus aimed at reducing stress and improving mood. For example, the generation AI may analyze the user's emotional data and suggest menus aimed at reducing stress. For example, it may suggest menus using herbal teas and ingredients that have a relaxing effect. The generation AI may also analyze the user's emotional data and suggest menus aimed at improving mood. For example, it may suggest menus using ingredients that have a mood-boosting effect. The generation AI may also analyze the user's emotional data and suggest a comprehensive meal plan aimed at reducing stress and improving mood. For example, it may adjust ingredients and cooking methods according to the user's emotional state. This makes it possible to suggest menus aimed at reducing stress and improving mood according to the user's emotional state.

[0073] The meal plan generation unit can refer to the user's meal history and suggest menus that take into account preferences and allergies. Meal plan suggestion (processing by generation AI) - When the generation AI suggests a meal plan, it refers to the user's past meal history and suggests menus that take into account preferences and allergies. For example, the generation AI may analyze the user's past meal history and suggest menus that take into account preferences and allergies. For example, it may suggest menus that use ingredients that the user has enjoyed eating in the past. The generation AI may also analyze the user's past meal history and suggest menus that take into account allergies. For example, it may suggest menus that avoid ingredients that the user is allergic to. The generation AI may also comprehensively analyze the user's past meal history and suggest menus that take into account preferences and allergies. For example, it may select the most suitable ingredients based on past data. This makes it possible to suggest menus that take into account the user's preferences and allergies.

[0074] The meal plan generation unit can propose realistic menus by taking into account the availability of ingredients and the user's budget. Meal plan proposal (processing by the generation AI) - When the generation AI proposes a meal plan, it proposes realistic menus by taking into account the user's availability of ingredients and budget. For example, the generation AI may analyze the availability of ingredients in the user's area and propose realistic menus. For example, it may propose menus using locally available ingredients. The generation AI may also analyze the user's budget and propose cost-effective menus. For example, it may propose menus using inexpensive, nutritious ingredients. The generation AI may also comprehensively analyze the user's availability of ingredients and budget and propose realistic menus. For example, it may propose cost-effective menus using seasonal ingredients. This makes it possible to propose realistic menus that take into account the user's availability of ingredients and budget.

[0075] The meal plan generation unit can also consider the meal plans of family members and housemates to propose a common menu. Meal plan proposal (processing by generation AI) - When the generation AI proposes a meal plan, it also considers the meal plans of the user's family members and housemates to propose a common menu. For example, the generation AI analyzes the data of the user's family members and housemates to propose a common menu. For example, it proposes a balanced menu that the whole family can enjoy. The generation AI can also analyze the data of the user's family members and housemates to propose a common menu. For example, it proposes a menu that takes into account the allergies and preferences of housemates. The generation AI can also comprehensively analyze the data of the user's family members and housemates to propose a common menu. For example, it proposes a menu that takes into account the nutritional balance of the whole family. This makes it possible to propose a common menu that takes into account the meal plans of the user's family members and housemates.

[0076] The meal plan generation unit can propose appropriate menus taking into account cultural and religious backgrounds. Meal plan proposal (processing by the generation AI) - When the generation AI proposes a meal plan, it proposes an appropriate menu taking into account the user's cultural and religious backgrounds. For example, the generation AI may analyze the user's cultural background and propose an appropriate menu. For example, it may propose traditional dishes rooted in a particular culture. The generation AI may also analyze the user's religious background and propose an appropriate menu. For example, it may propose a menu using ingredients permitted by a particular religion. The generation AI may also comprehensively analyze the user's cultural and religious background and propose an appropriate menu. For example, it may propose a balanced meal plan that takes culture and religion into consideration. This makes it possible to propose an appropriate menu taking into account the user's cultural and religious backgrounds.

[0077] The meal plan generation unit uses the emotion estimation function to monitor the user's emotional state in real time and suggest ingredients and cooking methods according to the emotion. Meal plan suggestion (processing by the generation AI) - Using the emotion estimation function, the generation AI monitors the user's emotional state in real time when proposing a meal plan and suggests ingredients and cooking methods according to the emotion. For example, using the emotion estimation function, the generation AI monitors the user's emotional state in real time and suggests ingredients. For example, when the user is feeling stressed, it suggests ingredients that have a relaxing effect. Also, using the emotion estimation function, the generation AI monitors the user's emotional state in real time and suggests cooking methods. For example, when the user is tired, it suggests easy cooking methods. Also, using the emotion estimation function, the generation AI monitors the user's emotional state in real time and makes comprehensive suggestions about ingredients and cooking methods. For example, it suggests menus that combine ingredients and cooking methods according to the user's emotional state. This makes it possible to suggest ingredients and cooking methods according to the user's emotional state.

[0078] The data analysis unit can continuously monitor emotional data and provide feedback according to changes in emotions. Continuous monitoring and feedback (processing by the generation AI) - When the generation AI continuously monitors, it also takes the user's emotional data into consideration and provides feedback according to changes in emotions. For example, the generation AI can continuously monitor the user's emotional data and provide training feedback according to changes in emotions. For example, when the user is feeling stressed, it can suggest exercises that have a relaxing effect. The generation AI can also continuously monitor the user's emotional data and provide dietary feedback according to changes in emotions. For example, when the user is feeling depressed, it can suggest ingredients that will lift their mood. The generation AI can also continuously monitor the user's emotional data and provide comprehensive feedback according to changes in emotions. For example, it can adjust the balance between training and diet according to the user's emotional state. This makes it possible to provide feedback according to changes in the user's emotions.

[0079] The data analysis unit continuously monitors changes in lifestyle and environment and updates the plan as appropriate. Continuous monitoring and feedback (processing by the generation AI) - When the generation AI continuously monitors, it also takes changes in the user's lifestyle and environment into account and updates the plan as appropriate. For example, the generation AI continuously monitors the user's lifestyle and updates the training plan accordingly. For example, if the user's sleep pattern changes, it adjusts exercise time. The generation AI also continuously monitors the user's living environment and updates the meal plan as changes occur. For example, if the user moves, it will suggest menus using ingredients from the new area. The generation AI also comprehensively monitors changes in the user's lifestyle and environment and updates the plan as appropriate. For example, if the user's work schedule changes, it will adjust the balance between training and meals. This allows the plan to be updated as appropriate to changes in the user's lifestyle and environment.

[0080] The data analysis unit can analyze the content of social media posts and provide feedback to maintain motivation. Continuous monitoring and feedback (processing by the generation AI) - When the generation AI continuously monitors, it analyzes the content of the user's social media posts and provides feedback to maintain motivation. For example, the generation AI continuously monitors the content of the user's social media posts and provides training feedback to maintain motivation. For example, it may send encouraging messages based on the exercise content posted by the user. The generation AI also continuously monitors the content of the user's social media posts and provides dietary feedback to maintain motivation. For example, it may suggest healthy recipes based on the diet content posted by the user. The generation AI also continuously monitors the content of the user's social media posts and provides comprehensive feedback to maintain motivation. For example, it may adjust the balance between training and diet based on the content posted by the user. This makes it possible to provide feedback to maintain the user's motivation.

[0081] The data analysis unit can refer to the data of friends and family and suggest joint health management options. Continuous monitoring and feedback (processing by the generation AI) - When the generation AI continuously monitors, it also refers to the data of the user's friends and family and suggests joint health management options. For example, the generation AI continuously monitors the data of the user's friends and family and suggests joint training plans. For example, it could suggest an exercise program that the whole family can participate in. The generation AI also continuously monitors the data of the user's friends and family and suggests joint meal plans. For example, it could suggest a balanced menu that the whole family can enjoy. The generation AI also comprehensively monitors the data of the user's friends and family and suggests joint health management plans. For example, it could suggest a balance of training and meals that takes into account the health status of all family members. This allows it to suggest joint health management options with the user's friends and family.

[0082] The data analysis unit can provide appropriate feedback by taking into account the workplace or school environment. Continuous monitoring and feedback (processing by the generation AI) - When the generation AI continuously monitors, it takes into account the user's workplace or school environment and provides appropriate feedback. For example, the generation AI continuously monitors the user's workplace environment and provides appropriate training feedback. For example, it suggests stretches that can be done in the office for users who do a lot of desk work. The generation AI also continuously monitors the user's school environment and provides appropriate dietary feedback. For example, it suggests easy-to-prepare, nutritionally balanced menus for student users. The generation AI also comprehensively monitors the user's workplace or school environment and provides appropriate feedback. For example, it suggests a balance of training and meals that matches the workplace or school schedule. This makes it possible to provide appropriate feedback according to the user's workplace or school environment.

[0083] The data analysis unit uses the emotion estimation function to monitor the user's emotional state in real time and provide feedback according to the emotion. Continuous monitoring and feedback (processing by the generation AI) - Using the emotion estimation function, the generation AI continuously monitors the user's emotional state in real time and provides feedback according to the emotion. For example, using the emotion estimation function, the generation AI monitors the user's emotional state in real time and provides training feedback. For example, if the user is tired, it may suggest light exercise. Also, using the emotion estimation function, the generation AI monitors the user's emotional state in real time and provides dietary feedback. For example, if the user is feeling stressed, it may suggest foods that have a relaxing effect. Also, using the emotion estimation function, the generation AI monitors the user's emotional state in real time and provides comprehensive feedback. For example, it may adjust the balance between training and diet according to the user's emotional state. This makes it possible to provide feedback according to the user's emotional state.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The health management system may further include a sleep data acquisition unit that acquires the user's sleep data. For example, a smartwatch or dedicated sleep tracker may be used to collect data such as the user's sleep time, sleep quality, and the ratio of deep sleep to light sleep. The sleep data acquisition unit may also include a sensor that monitors the user's sleep environment (e.g., room temperature, humidity, and noise level). This allows the system to propose training and meal plans that take the user's sleep data into consideration.

[0086] The health management system may further include a stress measurement unit that measures the user's stress level. For example, the stress level of the user may be monitored in real time using sensors that measure electrodermal activity or heart rate variability. The stress measurement unit may also suggest relaxation techniques (e.g., deep breathing or meditation) to reduce the user's stress level. This may allow for the suggestion of training and meal plans that take the user's stress level into account.

[0087] The health management system may further include an activity measurement unit that measures the user's activity level. For example, a pedometer or an acceleration sensor may be used to measure the user's daily activity level. The activity measurement unit may also analyze the user's activity patterns (e.g., commuting time and exercise time) and propose an optimal training plan. This allows the system to propose training and meal plans that take the user's activity level into consideration.

[0088] The health management system may further include a water intake monitoring unit that monitors the user's water intake. For example, a smartwatch or a dedicated water intake tracker may be used to record the user's daily water intake. The water intake monitoring unit may also include a reminder function to encourage the user to drink adequate amounts of water. This allows the system to propose training and meal plans that take the user's water intake into account.

[0089] The health management system may further include a food intake recording unit that records the user's food intake. For example, the content and calories of the food the user has eaten may be recorded via a smartphone app. The food intake recording unit may also analyze the user's eating patterns (e.g., meal timing and frequency) and propose an optimal meal plan. This allows the system to propose training and meal plans that take the user's food intake into consideration.

[0090] The health management system can also estimate the user's emotional state and adjust the difficulty of the training based on the estimated emotion. For example, if the user is feeling stressed, it can suggest a light exercise that has a relaxing effect. On the other hand, if the user is feeling positive, it can suggest a challenging exercise. This makes it possible to provide a training plan that suits the user's emotional state.

[0091] The health management system can also estimate the user's emotional state and adjust the contents of the meal based on the estimated emotion. For example, if the user is feeling depressed, it can suggest a menu using ingredients that have a mood-boosting effect. If the user is feeling stressed, it can suggest a menu using ingredients that have a relaxing effect. This makes it possible to provide a meal plan that matches the user's emotional state.

[0092] The health management system can further estimate the user's emotional state and provide feedback based on the estimated emotion. For example, if the user is tired, the system can provide feedback recommending that the user take a rest. If the user has a positive emotion, the system can provide feedback encouraging the user to train more. In this way, the system can provide feedback according to the user's emotional state.

[0093] The health management system can also estimate the user's emotional state and provide support to maintain motivation based on the estimated emotions. For example, if the user is losing motivation, it can send an encouraging message. If the user is feeling positive, it can set a goal that gives the user a sense of accomplishment. This makes it possible to provide motivation support that corresponds to the user's emotional state.

[0094] The health management system can also estimate the user's emotional state and suggest relaxation techniques based on the estimated emotions. For example, if the user is feeling stressed, it can suggest relaxation techniques such as deep breathing or meditation. If the user is tired, it can suggest listening to relaxing music. This makes it possible to provide relaxation techniques according to the user's emotional state.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The body composition information acquisition unit acquires body composition information. For example, data such as weight, body fat percentage, muscle mass, bone mass, and water content is collected using a device such as a weighing scale, body fat scale, or smartwatch. Furthermore, the body composition information acquisition unit can continuously acquire body composition information through daily use by the user. Step 2: The data analysis unit analyzes the acquired body composition information. For example, the data analysis unit analyzes the data based on the user's health status and goals using statistical analysis or machine learning algorithms. Step 3: The training plan generator generates a training plan based on the information analyzed by the data analyzer. For example, the training plan generator specifically indicates how many times per week exercise is needed and what types of exercise are effective. It also provides detailed recommendations on exercise intensity, duration, number of sets, and repetitions. Step 4: The meal plan generator generates a meal plan based on the information analyzed by the data analyzer. For example, the meal plan generator provides specific menu suggestions that take into account daily calorie intake and nutritional balance. It also provides detailed suggestions on how to select ingredients, cooking methods, and meal timing.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0142] 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.

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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]

[0164] 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 body composition information acquisition unit that acquires body composition information; a data analysis unit that analyzes the body composition information acquired by the body composition information acquisition unit; a training plan generation unit that generates a training plan based on the information analyzed by the data analysis unit; a meal plan generation unit that generates a meal plan based on the information analyzed by the data analysis unit. A system characterized by:

2. The body composition information acquisition unit Equipped with sensors that measure the user's emotional state, emotional data is also collected simultaneously.

2. The system of claim 1.

3. The body composition information acquisition unit Simultaneously record user lifestyle data and collect more detailed data 2. The system of claim 1.

4. The body composition information acquisition unit Equipped with a function to analyze the user's movements and posture in real time to evaluate the quality of exercise 2. The system of claim 1.

5. The data analysis unit Considers emotional data and suggests training and meal plans based on emotional state 2. The system of claim 1.

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