System

Abstract

An object of a system according to an embodiment is to provide an optimal meal menu and health advice based on a daily meal and an exercise amount of a user.SOLUTION: A system according to an embodiment includes a meal recording unit, an exercise recording unit, a menu suggestion unit, and an advice providing unit. The meal recording unit records daily meals of the user. The exercise recording unit records an exercise amount of the user. The menu suggestion unit suggests an optimal meal menu to the user on the basis of the data recorded by the meal recording unit and the exercise recording unit. The advice providing unit provides advice for preventing lifestyle-related diseases or leading a healthy life based on the data recorded by the meal recording unit and the exercise recording unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately provided optimal meal menus and health advice to users based on their daily diet and exercise, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal meal menu and health advice based on the user's daily diet and exercise amount. [Means for solving the problem]

[0006] The system according to the embodiment includes a meal recording unit, an exercise recording unit, a menu suggestion unit, and an advice providing unit. The meal recording unit records the user's daily meals. The exercise recording unit records the user's exercise amount. The menu suggestion unit suggests an optimal meal menu for the user based on the data recorded by the meal recording unit and the exercise recording unit. The advice providing unit provides advice for preventing lifestyle-related diseases and living a healthy life based on the data recorded by the meal recording unit and the exercise recording unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal meal menus and health advice based on the user's daily diet and exercise amount. [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 records a user's daily diet and exercise volume using a smartphone or smartwatch, and uses a generation AI to propose optimal diet and exercise menus to the user based on that data. This allows the health management system to propose optimal diet and exercise menus to the user and provide advice on preventing lifestyle-related diseases and living a healthy life.

[0029] A health management system according to an embodiment includes a food recording unit, an exercise recording unit, a menu suggestion unit, and an advice providing unit. The food recording unit records a user's daily meals. For example, the user takes photos of meals with a smartphone and sends the images to the generation AI, which then records the meal contents. The food recording unit can also record the time and amount of meals. The exercise recording unit records the user's exercise amount. For example, the exercise recording unit records the amount of exercise based on step count and heart rate data obtained from a smartwatch. The exercise recording unit can also record the type and duration of exercise. The menu suggestion unit suggests an optimal meal menu for the user based on the data recorded by the food recording unit and exercise recording unit. For example, the generation AI analyzes the user's calorie intake and nutrient balance and suggests low-calorie meal menus or menus that supplement specific nutrients. The advice providing unit provides advice for preventing lifestyle-related diseases and living a healthy life based on the data recorded by the food recording unit and exercise recording unit. For example, the generation AI analyzes the user's data and advises the user to limit their salt intake if they are at risk of high blood pressure. As a result, the health management system according to the embodiment can propose optimal meal and exercise menus based on the user's diet and exercise data, and provide advice on preventing lifestyle-related diseases and living a healthy life.

[0030] The meal recording unit can scan the barcodes of ingredients in addition to images of meals to obtain detailed nutritional information. For example, when a user takes an image of a meal, the meal recording unit can also scan the barcodes of ingredients, allowing the generation AI to obtain detailed nutritional information. For example, the barcode of a packaged food product can be scanned to automatically read the ingredient list. By scanning the barcodes of ingredients, the generation AI can also obtain information about the origin and production method of the ingredients and perform an analysis that takes into account variations in nutritional value. For example, this can reflect the difference in nutritional value between organically grown vegetables and conventionally grown vegetables. Furthermore, by scanning the barcodes of ingredients, the generation AI can check allergy information and the presence or absence of specific ingredients and suggest a meal menu suitable for the user. For example, it can select gluten-free ingredients. This allows detailed nutritional information to be obtained by scanning the barcodes of ingredients.

[0031] The meal recording unit can calculate variations in nutritional value by taking into account the origin and production method of ingredients when analyzing images of meals. For example, when analyzing images of meals, the meal recording unit calculates variations in nutritional value by taking into account information about the origin of ingredients. For example, it reflects the difference in nutritional value between locally grown vegetables and imported vegetables. It also calculates variations in nutritional value by taking into account the production method of ingredients (organic cultivation, pesticide-free cultivation, etc.). For example, it highly values ​​the vitamin content of organically grown vegetables. It also calculates variations in nutritional value by taking into account the processing method of ingredients (frozen, canned, fresh, etc.). For example, it reflects the difference in nutritional value between frozen vegetables and fresh vegetables. This makes it possible to calculate variations in nutritional value by taking into account the origin and production method of ingredients.

[0032] The food recording unit can improve analysis accuracy by adding voice input to the food record and allowing the user to verbally describe the meal contents. For example, when the user takes a picture of the meal, the food recording unit allows the generation AI to obtain detailed information by describing the meal contents through voice input. For example, the user might explain, "This is chicken salad, and the dressing is olive oil-based." Analysis accuracy can also be improved by adding voice input to the food record and allowing the user to verbally explain the types of ingredients and cooking method. For example, the user might explain, "This soup contains carrots, onions, and celery." Furthermore, the generation AI can obtain more accurate nutritional information by using voice input to describe the amount and portion size of the meal. For example, the user might explain, "This steak is 200 grams." In this way, adding voice input improves the analysis accuracy of food contents.

[0033] The food recording unit can share the results of food image analysis with other users and receive feedback within the community. The food recording unit, for example, builds a system that shares the results of food image analysis within the community and receives feedback from other users. For example, advice on meal content and recipe suggestions can be received. Furthermore, by sharing the analysis results, meals can be compared with those of other users and healthy eating ideas can be exchanged. For example, different recipes using the same ingredients can be shared. Furthermore, based on feedback within the community, suggestions for meal improvements and new menu items can be received. For example, advice such as "Adding nuts to this salad will improve the nutritional balance" can be received. In this way, the results of food image analysis can be shared and feedback can be received within the community.

[0034] The exercise recording unit can analyze the user's sleep data in addition to the exercise data to clarify the correlation between exercise and sleep. The exercise recording unit, for example, analyzes the exercise data and sleep data obtained from a smartwatch to clarify the correlation between exercise and sleep. For example, it evaluates the quality of sleep on days when the amount of exercise is high. The exercise data and sleep data of the user are also integrated, and the generative AI makes suggestions to optimize the balance between exercise and sleep. For example, it suggests the appropriate amount of sleep for post-exercise recovery. The exercise data and sleep data are also analyzed to comprehensively evaluate the user's health condition. For example, it analyzes the impact of lack of exercise on sleep quality. This makes it possible to analyze the exercise data and sleep data and clarify the correlation between exercise and sleep.

[0035] The exercise recording unit can record environmental data during exercise and analyze the impact on athletic performance. The exercise recording unit can use sensors in a smartwatch or smartphone, for example, to record temperature, humidity, and air quality during exercise and analyze the impact on athletic performance. For example, it can evaluate athletic performance in hot and humid environments. Furthermore, based on the environmental data, the generative AI can suggest optimal environmental conditions for athletic performance. For example, it can suggest appropriate exercise times and locations. Furthermore, it can analyze environmental data during exercise and evaluate the user's health risks. For example, it can analyze the impact on health of exercising in an environment with poor air quality. This makes it possible to record environmental data during exercise and analyze the impact on athletic performance.

[0036] The exercise recording unit can integrate the exercise data with other health data to evaluate the overall health status. For example, the exercise recording unit integrates exercise data and dietary data, and the generation AI evaluates the overall health status. For example, it evaluates the balance between exercise amount and calorie intake. The exercise data and stress level data are also integrated, and the generation AI comprehensively evaluates the user's health status. For example, it analyzes the impact of exercise on stress levels. The exercise data and other health data are also integrated, and the generation AI evaluates the user's health risks. For example, it comprehensively evaluates the impact of lack of exercise on health. This allows the exercise data to be integrated with other health data to evaluate the overall health status.

[0037] The exercise recording unit can compare the user's exercise performance with other users based on the exercise data, stimulating a competitive spirit. For example, the exercise recording unit compares the user's exercise data with other users, and the generation AI creates a ranking to stimulate a competitive spirit. For example, it displays rankings for the number of steps taken and calories burned. Furthermore, based on the exercise data, the exercise performance of users is compared, and the generation AI proposes challenges to enhance the competitive spirit. For example, it proposes exercise challenges to do with friends. Furthermore, by comparing the user's exercise data with other users, the generation AI introduces a reward system to stimulate a competitive spirit. For example, it awards badges or points to users who achieve certain exercise goals. This allows the user's exercise performance to be compared with other users based on the exercise data, stimulating a competitive spirit.

[0038] When proposing a meal menu, the menu suggestion unit takes into account the user's past meal history and can suggest a menu that the user will not get tired of. For example, the menu suggestion unit analyzes the user's past meal history and the generation AI suggests a menu that the user will not get tired of. For example, different recipes using the same ingredients are suggested. Furthermore, based on the past meal history, the generation AI suggests a menu that takes into account the user's tastes and preferences. For example, new recipes using ingredients that the user likes are suggested. Furthermore, the generation AI analyzes the user's meal history and suggests menus that match the season or events. For example, recipes using seasonal ingredients or menus for special occasions are suggested. In this way, it is possible to suggest menus that the user will not get tired of, taking into account the user's past meal history.

[0039] The menu suggestion unit can take into account the user's allergy information and ingredient preferences when suggesting a meal menu. For example, the menu suggestion unit uses the generation AI to suggest meal menus that avoid allergies based on the user's allergy information. For example, it suggests nut-free recipes for a user with a nut allergy. The generation AI also takes into account the user's ingredient preferences and suggests meal menus that suit the preferences. For example, it suggests recipes that use the user's favorite ingredients. The generation AI also integrates the user's allergy information and ingredient preferences to suggest safe and delicious meal menus. For example, it suggests recipes that avoid allergies but suit the preferences. This makes it possible to suggest meal menus that take into account the user's allergy information and ingredient preferences.

[0040] The menu suggestion unit can propose meal menus that incorporate seasonal and regional specialties. The menu suggestion unit, for example, proposes menus that incorporate seasonal ingredients. For example, it proposes salads using fresh vegetables in spring and hot soups in winter. It also proposes menus that incorporate regional specialties. For example, it proposes recipes using fresh local fish and vegetables. The generation AI also considers the season and regional specialties to propose the optimal meal menu for the user. For example, it proposes special menus that coincide with seasonal events and festivals. This makes it possible to propose menus that incorporate seasonal and regional specialties.

[0041] When proposing a meal menu, the menu suggestion unit also takes into account the dietary data of the user's family and housemates, and can propose a menu that will satisfy everyone. For example, the menu suggestion unit considers the dietary data of the user's family and housemates, and the generation AI proposes a menu that will satisfy everyone. For example, it proposes recipes that take into account the preferences and allergy information of all family members. The generation AI also proposes a balanced meal menu based on the dietary data of the family and housemates. For example, it adjusts the menu so that everyone can consume the nutrients they need. The generation AI also integrates the dietary data of the user's family and housemates and proposes a meal menu that everyone can enjoy. For example, it proposes recipes that use ingredients that everyone in the family likes. In this way, it is possible to consider the dietary data of the user's family and housemates and propose a menu that will satisfy everyone.

[0042] The menu suggestion unit can propose an effective training plan by taking into account the user's past exercise history when proposing an exercise menu. For example, the menu suggestion unit analyzes the user's past exercise history, and the generation AI proposes an effective training plan. For example, it proposes optimal exercise intensity and frequency based on past exercise data. The generation AI also proposes a training plan tailored to the user's goals based on the past exercise history. For example, it proposes a plan aimed at increasing muscle strength or endurance. The generation AI also analyzes the user's exercise history and proposes a training plan that the user will not get bored of. For example, it proposes a plan that combines different types of exercise. In this way, it is possible to propose an effective training plan by taking into account the user's past exercise history.

[0043] When proposing an exercise menu, the menu suggestion unit can monitor the user's physical condition and fatigue level in real time and suggest an appropriate exercise intensity. The menu suggestion unit monitors the user's physical condition and fatigue level in real time, for example, using sensors in a smartwatch or smartphone, and the generation AI suggests an appropriate exercise intensity. For example, the exercise intensity is adjusted based on heart rate and body temperature. The generation AI also analyzes the user's physical condition and fatigue level in real time and suggests a reasonable exercise menu. For example, if fatigue is accumulated, light exercise is suggested. The generation AI also monitors the user's physical condition and fatigue level and suggests an exercise menu suitable for recovery. For example, stretching and light aerobic exercise are suggested. In this way, the user's physical condition and fatigue level can be monitored in real time and appropriate exercise intensity can be suggested.

[0044] The menu suggestion unit can take into account the user's lifestyle and work schedule when suggesting an exercise menu. For example, the menu suggestion unit considers the user's lifestyle and work schedule, and the generation AI suggests the optimal exercise menu. For example, it suggests exercises that can be done in a short amount of time on busy days. The generation AI also makes suggestions to optimize the user's exercise time based on the lifestyle and schedule. For example, it suggests an exercise menu to be done in the morning. The generation AI also analyzes the user's schedule and suggests an exercise menu that can be continued without strain. For example, it suggests stretches and light exercises that can be done in between work. This makes it possible to suggest an exercise menu that takes into account the user's lifestyle and work schedule.

[0045] The menu suggestion unit can suggest group exercises that can be done together with the user's friends and family when proposing an exercise menu. For example, the menu suggestion unit considers the exercise data of the user's friends and family, and the generation AI suggests group exercises that can be done together. For example, the unit suggests walking or jogging for the whole family. The generation AI also suggests group exercises that can be done together with friends and family to increase motivation. For example, the unit suggests fitness challenges to do with friends. The generation AI also integrates the exercise data of the user's friends and family, and suggests group exercises that everyone can enjoy. For example, the unit suggests dance exercises for the whole family. This makes it possible to suggest group exercises that can be done together with the user's friends and family.

[0046] The advice providing unit can take the user's genetic information into consideration when providing preventive advice for lifestyle-related diseases and suggest specific measures for high-risk diseases. For example, the advice providing unit has the generation AI suggest specific measures for high-risk diseases based on the user's genetic information. For example, a user who is genetically at high risk of high blood pressure is advised to limit their salt intake. The generation AI also takes the genetic information into consideration and suggests preventive measures suitable for the user. For example, a low-carbohydrate meal menu is suggested for a user who is at high risk of diabetes. The generation AI also analyzes the user's genetic information and suggests regular health checks for high-risk diseases. For example, regular electrocardiogram tests are recommended for users who are at high risk of heart disease. In this way, specific measures for high-risk diseases can be suggested taking the user's genetic information into consideration.

[0047] The advice providing unit can monitor the user's stress level and mental health when providing advice on healthy living, and suggest appropriate measures. The advice providing unit can monitor the user's stress level and mental health, for example, using sensors in a smartwatch or smartphone, and the generating AI can suggest appropriate measures. For example, if stress is high, it can suggest relaxation methods. The generating AI can also analyze the user's stress level in real time and provide advice for stress management. For example, it can suggest deep breathing or meditation methods. The generating AI can also suggest mental health care methods appropriate for the user based on the mental health data. For example, it can recommend counseling or the use of support groups. This makes it possible to monitor the user's stress level and mental health, and suggest appropriate measures.

[0048] The advice providing unit can take into account the user's work environment and living environment when providing advice on preventing lifestyle-related diseases. For example, the advice providing unit considers the user's work environment and the generation AI provides advice on preventing lifestyle-related diseases. For example, for a user who does a lot of desk work, regular stretching and walking is recommended. The generation AI also suggests preventive measures suitable for the user based on their living environment. For example, for a user who lives in an urban area, it suggests methods to improve air quality and manage stress. The generation AI also analyzes the user's work environment and living environment and suggests specific measures to prevent high-risk lifestyle-related diseases. For example, for a user who often works night shifts, it recommends appropriate sleep management. This makes it possible to provide advice on preventing lifestyle-related diseases that takes into account the user's work environment and living environment.

[0049] The advice providing unit can suggest activities that incorporate the user's hobbies and interests when providing advice on healthy living. In the advice providing unit, for example, the generation AI suggests activities for a healthy lifestyle based on the user's hobbies and interests. For example, hiking or camping is suggested for a user who likes the outdoors. The generation AI also takes hobbies and interests into consideration and suggests activities that are suitable for the user. For example, dancing or music sessions are suggested for a user who likes music. The generation AI also analyzes the user's hobbies and interests and suggests specific activities for living a healthy lifestyle. For example, gardening or cooking classes are suggested. This makes it possible to suggest activities that incorporate the user's hobbies and interests.

[0050] When proposing an exercise menu, the menu suggestion unit can monitor the user's physical condition and fatigue level in real time and suggest an appropriate exercise intensity. The menu suggestion unit monitors the user's physical condition and fatigue level in real time, for example, using sensors in a smartwatch or smartphone, and the generation AI suggests an appropriate exercise intensity. For example, the exercise intensity is adjusted based on heart rate and body temperature. The generation AI also analyzes the user's physical condition and fatigue level in real time and suggests a reasonable exercise menu. For example, if fatigue is accumulated, light exercise is suggested. The generation AI also monitors the user's physical condition and fatigue level and suggests an exercise menu suitable for recovery. For example, stretching and light aerobic exercise are suggested. In this way, the user's physical condition and fatigue level can be monitored in real time and appropriate exercise intensity can be suggested.

[0051] The menu suggestion unit can take into account the user's lifestyle and work schedule when suggesting an exercise menu. For example, the menu suggestion unit considers the user's lifestyle and work schedule, and the generation AI suggests the optimal exercise menu. For example, it suggests exercises that can be done in a short amount of time on busy days. The generation AI also makes suggestions to optimize the user's exercise time based on the lifestyle and schedule. For example, it suggests an exercise menu to be done in the morning. The generation AI also analyzes the user's schedule and suggests an exercise menu that can be continued without strain. For example, it suggests stretches and light exercises that can be done in between work. This makes it possible to suggest an exercise menu that takes into account the user's lifestyle and work schedule.

[0052] The menu suggestion unit can suggest group exercises that can be done together with the user's friends and family when proposing an exercise menu. For example, the menu suggestion unit considers the exercise data of the user's friends and family, and the generation AI suggests group exercises that can be done together. For example, the unit suggests walking or jogging for the whole family. The generation AI also suggests group exercises that can be done together with friends and family to increase motivation. For example, the unit suggests fitness challenges to do with friends. The generation AI also integrates the exercise data of the user's friends and family, and suggests group exercises that everyone can enjoy. For example, the unit suggests dance exercises for the whole family. This makes it possible to suggest group exercises that can be done together with the user's friends and family.

[0053] The advice providing unit can take the user's genetic information into consideration when providing preventive advice for lifestyle-related diseases and suggest specific measures for high-risk diseases. For example, the advice providing unit has the generation AI suggest specific measures for high-risk diseases based on the user's genetic information. For example, a user who is genetically at high risk of high blood pressure is advised to limit their salt intake. The generation AI also takes the genetic information into consideration and suggests preventive measures suitable for the user. For example, a low-carbohydrate meal menu is suggested for a user who is at high risk of diabetes. The generation AI also analyzes the user's genetic information and suggests regular health checks for high-risk diseases. For example, regular electrocardiogram tests are recommended for users who are at high risk of heart disease. In this way, specific measures for high-risk diseases can be suggested taking the user's genetic information into consideration.

[0054] The advice providing unit can monitor the user's stress level and mental health when providing advice on healthy living, and suggest appropriate measures. The advice providing unit can monitor the user's stress level and mental health, for example, using sensors in a smartwatch or smartphone, and the generating AI can suggest appropriate measures. For example, if stress is high, it can suggest relaxation methods. The generating AI can also analyze the user's stress level in real time and provide advice for stress management. For example, it can suggest deep breathing or meditation methods. The generating AI can also suggest mental health care methods appropriate for the user based on the mental health data. For example, it can recommend counseling or the use of support groups. This makes it possible to monitor the user's stress level and mental health, and suggest appropriate measures.

[0055] The advice providing unit can take into account the user's work environment and living environment when providing advice on preventing lifestyle-related diseases. For example, the advice providing unit considers the user's work environment and the generation AI provides advice on preventing lifestyle-related diseases. For example, for a user who does a lot of desk work, regular stretching and walking is recommended. The generation AI also suggests preventive measures suitable for the user based on their living environment. For example, for a user who lives in an urban area, it suggests methods to improve air quality and manage stress. The generation AI also analyzes the user's work environment and living environment and suggests specific measures to prevent high-risk lifestyle-related diseases. For example, for a user who often works night shifts, it recommends appropriate sleep management. This makes it possible to provide advice on preventing lifestyle-related diseases that takes into account the user's work environment and living environment.

[0056] The advice providing unit can suggest activities that incorporate the user's hobbies and interests when providing advice on healthy living. In the advice providing unit, for example, the generation AI suggests activities for a healthy lifestyle based on the user's hobbies and interests. For example, hiking or camping is suggested for a user who likes the outdoors. The generation AI also takes hobbies and interests into consideration and suggests activities that are suitable for the user. For example, dancing or music sessions are suggested for a user who likes music. The generation AI also analyzes the user's hobbies and interests and suggests specific activities for living a healthy lifestyle. For example, gardening or cooking classes are suggested. This makes it possible to suggest activities that incorporate the user's hobbies and interests.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The health management system can further include a fluid recording unit that records the user's fluid intake. The fluid recording unit, for example, records the amount of water or beverages the user drinks, and the generation AI suggests appropriate fluid intake amounts. For example, it suggests the amount of fluid to replenish after exercise. The fluid recording unit can also calculate the optimal fluid intake amount for each individual user based on their weight and exercise level. For example, a user weighing 60 kg might be recommended to drink 2 liters of fluid per day. The fluid recording unit can also record the types of beverages the user drinks, and the generation AI can manage their caffeine and sugar intake. For example, it might suggest limiting coffee intake to avoid excessive caffeine intake. This allows the system to manage the user's fluid intake and support a healthy lifestyle.

[0059] The health management system may further include a sleep recording unit that records the user's sleep patterns. The sleep recording unit records the user's sleep time and sleep quality based on data obtained from, for example, a smartwatch. For example, it analyzes the ratio of deep sleep to light sleep. The sleep recording unit also records the user's sleep environment (temperature, humidity, noise, etc.), allowing the generation AI to suggest an optimal sleep environment. For example, it may advise the user to maintain an appropriate bedroom temperature. The sleep recording unit also analyzes the user's sleep patterns, allowing the generation AI to provide advice to improve sleep quality. For example, it may suggest ways to relax before bed. This allows the system to manage the user's sleep patterns and support a healthy lifestyle.

[0060] The health management system may further include a stress recording unit that records the user's stress level. The stress recording unit records the user's stress level, for example, based on heart rate and electrodermal activity obtained from the smartwatch. For example, the stress level may be evaluated based on heart rate fluctuations. The stress recording unit may also record the user's stress factors (work, home, environment, etc.), and the generating AI may provide advice for stress management. For example, the generating AI may suggest relaxation methods during periods of high stress. The stress recording unit may also analyze the user's stress level, and the generating AI may suggest specific measures to reduce stress. For example, the system may suggest meditation or deep breathing techniques. This allows the user to manage their stress level and support a healthy lifestyle.

[0061] The health management system may further include a training recording unit for improving the user's exercise performance. The training recording unit, for example, records the user's exercise data, and the generating AI proposes an optimal training plan. For example, it may propose a plan for strength training or aerobic exercise. The training recording unit may also analyze the user's exercise performance, and the generating AI may provide advice to maximize the effectiveness of the exercise. For example, it may adjust the frequency and intensity of exercise. The training recording unit may also allow the generating AI to propose specific measures for recovery based on the user's exercise data. For example, it may suggest stretching or massage techniques. This can improve the user's exercise performance and support a healthy lifestyle.

[0062] The health management system can further include an ingredient selection unit for customizing the user's meal menu. In the ingredient selection unit, the generation AI selects optimal ingredients based on the user's preferences and allergy information, for example. For example, gluten-free or vegan ingredients are suggested. The ingredient selection unit can also consider the user's nutritional balance and allow the generation AI to suggest healthy ingredients. For example, ingredients rich in vitamins and minerals are selected. The ingredient selection unit can also analyze the user's dietary history and allow the generation AI to suggest ingredients that the user will not tire of. For example, different recipes using the same ingredients are suggested. This allows the user's meal menu to be customized and supports a healthy lifestyle.

[0063] The health management system can further include a nutritional analysis unit for optimizing the user's meal menu. The nutritional analysis unit, for example, analyzes the user's dietary data, and the generation AI provides advice to optimize nutritional balance. For example, it suggests ingredients that supplement vitamin and mineral deficiencies. The nutritional analysis unit can also consider the user's health condition and allow the generation AI to suggest meal menus to enhance specific nutrients. For example, if there is an iron deficiency, it will suggest ingredients that are high in iron. The nutritional analysis unit can also analyze the user's dietary history, and allow the generation AI to suggest nutritionally balanced menus that will not become boring. For example, it can suggest recipes using different ingredients that contain the same nutrients. This can optimize the user's meal menu and support a healthy lifestyle.

[0064] The health management system may further include a feedback providing unit for improving the user's exercise performance. The feedback providing unit, for example, analyzes the user's exercise data, and the generation AI provides feedback to maximize the effects of the exercise. For example, the feedback providing unit may provide advice on improving form or adjusting exercise intensity. The feedback providing unit may also enable the generation AI to evaluate the user's exercise progress based on the user's exercise history and suggest specific improvements. For example, the feedback providing unit may provide step-by-step advice for achieving goals. The feedback providing unit may also monitor the user's exercise data in real time, and the generation AI may provide immediate feedback. For example, the feedback providing unit may provide advice on adjusting exercise form or pace in real time. This may improve the user's exercise performance and support a healthy lifestyle.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The food recording unit records the user's daily meals. For example, the user can take a photo of their meal with their smartphone and send it to the generation AI, which then records the meal contents. The food recording unit can also record the time and amount of meals. Step 2: The exercise recording unit records the amount of exercise performed by the user. For example, the exercise recording unit records the amount of exercise performed based on the number of steps and heart rate data obtained from the smartwatch. The exercise recording unit can also record the type and duration of exercise. Step 3: The menu suggestion unit suggests optimal meal menus to the user based on the data recorded by the food recording unit and exercise recording unit. For example, the generation AI analyzes the user's calorie intake and nutrient balance and suggests low-calorie meal menus or menus that supplement specific nutrients. Step 4: The advice provider provides advice on preventing lifestyle-related diseases and living a healthy life based on the data recorded by the food and exercise recorders. For example, the AI ​​analyzes the user's data and advises them to reduce their salt intake if they are at risk of high blood pressure.

[0067] (Example 2) A health management system according to an embodiment of the present invention records a user's daily diet and exercise volume using a smartphone or smartwatch, and uses a generation AI to propose optimal diet and exercise menus to the user based on that data. This allows the health management system to propose optimal diet and exercise menus to the user and provide advice on preventing lifestyle-related diseases and living a healthy life.

[0068] A health management system according to an embodiment includes a food recording unit, an exercise recording unit, a menu suggestion unit, and an advice providing unit. The food recording unit records a user's daily meals. For example, the user takes photos of meals with a smartphone and sends the images to the generation AI, which then records the meal contents. The food recording unit can also record the time and amount of meals. The exercise recording unit records the user's exercise amount. For example, the exercise recording unit records the amount of exercise based on step count and heart rate data obtained from a smartwatch. The exercise recording unit can also record the type and duration of exercise. The menu suggestion unit suggests an optimal meal menu for the user based on the data recorded by the food recording unit and exercise recording unit. For example, the generation AI analyzes the user's calorie intake and nutrient balance and suggests low-calorie meal menus or menus that supplement specific nutrients. The advice providing unit provides advice for preventing lifestyle-related diseases and living a healthy life based on the data recorded by the food recording unit and exercise recording unit. For example, the generation AI analyzes the user's data and advises the user to limit their salt intake if they are at risk of high blood pressure. As a result, the health management system according to the embodiment can propose optimal meal and exercise menus based on the user's diet and exercise data, and provide advice on preventing lifestyle-related diseases and living a healthy life.

[0069] The meal recording unit can scan the barcodes of ingredients in addition to images of meals to obtain detailed nutritional information. For example, when a user takes an image of a meal, the meal recording unit can also scan the barcodes of ingredients, allowing the generation AI to obtain detailed nutritional information. For example, the barcode of a packaged food product can be scanned to automatically read the ingredient list. By scanning the barcodes of ingredients, the generation AI can also obtain information about the origin and production method of the ingredients and perform an analysis that takes into account variations in nutritional value. For example, this can reflect the difference in nutritional value between organically grown vegetables and conventionally grown vegetables. Furthermore, by scanning the barcodes of ingredients, the generation AI can check allergy information and the presence or absence of specific ingredients and suggest a meal menu suitable for the user. For example, it can select gluten-free ingredients. This allows detailed nutritional information to be obtained by scanning the barcodes of ingredients.

[0070] The meal recording unit can calculate variations in nutritional value by taking into account the origin and production method of ingredients when analyzing images of meals. For example, when analyzing images of meals, the meal recording unit calculates variations in nutritional value by taking into account information about the origin of ingredients. For example, it reflects the difference in nutritional value between locally grown vegetables and imported vegetables. It also calculates variations in nutritional value by taking into account the production method of ingredients (organic cultivation, pesticide-free cultivation, etc.). For example, it highly values ​​the vitamin content of organically grown vegetables. It also calculates variations in nutritional value by taking into account the processing method of ingredients (frozen, canned, fresh, etc.). For example, it reflects the difference in nutritional value between frozen vegetables and fresh vegetables. This makes it possible to calculate variations in nutritional value by taking into account the origin and production method of ingredients.

[0071] The meal recording unit uses the emotion estimation function to analyze the user's emotions during meals and identify tendencies toward stress eating and overeating. For example, the meal recording unit analyzes the user's facial expressions when capturing images of the meal and uses the emotion estimation function to identify tendencies toward stress eating and overeating. For example, the stress level is evaluated from the facial expressions while eating. The unit also analyzes audio data during meals and uses the emotion estimation function to identify the user's emotions. For example, emotions are estimated from the tone and content of conversations during meals. The unit also records the user's emotional state before and after meals and uses the emotion estimation function to analyze the effects of meals. For example, tendencies toward stress eating are identified based on changes in emotions before and after meals. This makes it possible to analyze the user's emotions during meals and identify tendencies toward stress eating and overeating.

[0072] The food recording unit can improve analysis accuracy by adding voice input to the food record and allowing the user to verbally describe the meal contents. For example, when the user takes a picture of the meal, the food recording unit allows the generation AI to obtain detailed information by describing the meal contents through voice input. For example, the user might explain, "This is chicken salad, and the dressing is olive oil-based." Analysis accuracy can also be improved by adding voice input to the food record and allowing the user to verbally explain the types of ingredients and cooking method. For example, the user might explain, "This soup contains carrots, onions, and celery." Furthermore, the generation AI can obtain more accurate nutritional information by using voice input to describe the amount and portion size of the meal. For example, the user might explain, "This steak is 200 grams." In this way, adding voice input improves the analysis accuracy of food contents.

[0073] The food recording unit can share the results of food image analysis with other users and receive feedback within the community. The food recording unit, for example, builds a system that shares the results of food image analysis within the community and receives feedback from other users. For example, advice on meal content and recipe suggestions can be received. Furthermore, by sharing the analysis results, meals can be compared with those of other users and healthy eating ideas can be exchanged. For example, different recipes using the same ingredients can be shared. Furthermore, based on feedback within the community, suggestions for meal improvements and new menu items can be received. For example, advice such as "Adding nuts to this salad will improve the nutritional balance" can be received. In this way, the results of food image analysis can be shared and feedback can be received within the community.

[0074] The meal recording unit can use the emotion estimation function to monitor the user's emotions during meals in real time and make meal suggestions that will elicit positive emotions. The meal recording unit, for example, monitors the user's facial expressions in real time during meals and uses the emotion estimation function to make meal suggestions that will elicit positive emotions. For example, it can suggest meal plans that will produce many smiles. It can also analyze audio data during meals in real time and monitor the user's emotions using the emotion estimation function. For example, it can suggest meal plans that produce many happy conversations. It can also record the user's emotional state before and after meals in real time and make meal suggestions that will elicit positive emotions. For example, it can suggest menus that will help the user relax before eating. In this way, it can monitor the user's emotions during meals in real time and make meal suggestions that will elicit positive emotions.

[0075] The exercise recording unit can analyze the user's sleep data in addition to the exercise data to clarify the correlation between exercise and sleep. The exercise recording unit, for example, analyzes the exercise data and sleep data obtained from a smartwatch to clarify the correlation between exercise and sleep. For example, it evaluates the quality of sleep on days when the amount of exercise is high. The exercise data and sleep data of the user are also integrated, and the generative AI makes suggestions to optimize the balance between exercise and sleep. For example, it suggests the appropriate amount of sleep for post-exercise recovery. The exercise data and sleep data are also analyzed to comprehensively evaluate the user's health condition. For example, it analyzes the impact of lack of exercise on sleep quality. This makes it possible to analyze the exercise data and sleep data and clarify the correlation between exercise and sleep.

[0076] The exercise recording unit can record environmental data during exercise and analyze the impact on athletic performance. The exercise recording unit can use sensors in a smartwatch or smartphone, for example, to record temperature, humidity, and air quality during exercise and analyze the impact on athletic performance. For example, it can evaluate athletic performance in hot and humid environments. Furthermore, based on the environmental data, the generative AI can suggest optimal environmental conditions for athletic performance. For example, it can suggest appropriate exercise times and locations. Furthermore, it can analyze environmental data during exercise and evaluate the user's health risks. For example, it can analyze the impact on health of exercising in an environment with poor air quality. This makes it possible to record environmental data during exercise and analyze the impact on athletic performance.

[0077] The exercise recording unit can use the emotion estimation function to analyze the user's emotions while exercising and suggest an exercise menu that helps maintain motivation. The exercise recording unit, for example, analyzes the user's facial expressions while exercising and uses the emotion estimation function to suggest an exercise menu that helps maintain motivation. For example, an exercise menu that includes a lot of smiles is suggested. The exercise recording unit can also analyze audio data during exercise and use the emotion estimation function to identify the user's emotions and suggest an exercise menu that will increase motivation. For example, an exercise menu that includes a lot of encouraging words is suggested. The exercise recording unit can also record the user's emotional state before and after exercise and use the emotion estimation function to suggest an exercise menu that helps maintain motivation. For example, a menu that allows the user to relax after exercising is suggested. In this way, the exercise recording unit can analyze the user's emotions while exercising and suggest an exercise menu that helps maintain motivation.

[0078] The exercise recording unit can integrate the exercise data with other health data to evaluate the overall health status. For example, the exercise recording unit integrates exercise data and dietary data, and the generation AI evaluates the overall health status. For example, it evaluates the balance between exercise amount and calorie intake. The exercise data and stress level data are also integrated, and the generation AI comprehensively evaluates the user's health status. For example, it analyzes the impact of exercise on stress levels. The exercise data and other health data are also integrated, and the generation AI evaluates the user's health risks. For example, it comprehensively evaluates the impact of lack of exercise on health. This allows the exercise data to be integrated with other health data to evaluate the overall health status.

[0079] The exercise recording unit can compare the user's exercise performance with other users based on the exercise data, stimulating a competitive spirit. For example, the exercise recording unit compares the user's exercise data with other users, and the generation AI creates a ranking to stimulate a competitive spirit. For example, it displays rankings for the number of steps taken and calories burned. Furthermore, based on the exercise data, the exercise performance of users is compared, and the generation AI proposes challenges to enhance the competitive spirit. For example, it proposes exercise challenges to do with friends. Furthermore, by comparing the user's exercise data with other users, the generation AI introduces a reward system to stimulate a competitive spirit. For example, it awards badges or points to users who achieve certain exercise goals. This allows the user's exercise performance to be compared with other users based on the exercise data, stimulating a competitive spirit.

[0080] The exercise recording unit can use the emotion estimation function to monitor the user's emotions while exercising in real time and suggest exercises that will elicit positive emotions. The exercise recording unit, for example, monitors the user's facial expressions while exercising in real time and uses the emotion estimation function to suggest exercises that will elicit positive emotions. For example, it can suggest exercise menus that include a lot of smiles. It can also analyze audio data during exercise in real time and monitor the user's emotions using the emotion estimation function. For example, it can suggest exercise menus that include a lot of pleasant conversations. It can also record the user's emotional state before and after exercise in real time and suggest exercises that will elicit positive emotions. For example, it can suggest exercises that will allow the user to relax before exercising. In this way, it can monitor the user's emotions while exercising in real time and suggest exercises that will elicit positive emotions.

[0081] When proposing a meal menu, the menu suggestion unit takes into account the user's past meal history and can suggest a menu that the user will not get tired of. For example, the menu suggestion unit analyzes the user's past meal history and the generation AI suggests a menu that the user will not get tired of. For example, different recipes using the same ingredients are suggested. Furthermore, based on the past meal history, the generation AI suggests a menu that takes into account the user's tastes and preferences. For example, new recipes using ingredients that the user likes are suggested. Furthermore, the generation AI analyzes the user's meal history and suggests menus that match the season or events. For example, recipes using seasonal ingredients or menus for special occasions are suggested. In this way, it is possible to suggest menus that the user will not get tired of, taking into account the user's past meal history.

[0082] The menu suggestion unit can take into account the user's allergy information and ingredient preferences when suggesting a meal menu. For example, the menu suggestion unit uses the generation AI to suggest meal menus that avoid allergies based on the user's allergy information. For example, it suggests nut-free recipes for a user with a nut allergy. The generation AI also takes into account the user's ingredient preferences and suggests meal menus that suit the preferences. For example, it suggests recipes that use the user's favorite ingredients. The generation AI also integrates the user's allergy information and ingredient preferences to suggest safe and delicious meal menus. For example, it suggests recipes that avoid allergies but suit the preferences. This makes it possible to suggest meal menus that take into account the user's allergy information and ingredient preferences.

[0083] The menu suggestion unit can use the emotion estimation function to analyze the user's emotions regarding meals and suggest menus that elicit positive emotions. The menu suggestion unit, for example, analyzes the user's facial expressions while eating and suggests menus that elicit positive emotions using the emotion estimation function. For example, it suggests meal plans that involve a lot of smiles. It can also analyze audio data during meals and use the emotion estimation function to identify the user's emotions and suggest menus that elicit positive emotions. For example, it can suggest meal plans that involve a lot of pleasant conversation. It can also record the user's emotional state before and after meals and use the emotion estimation function to suggest menus that elicit positive emotions. For example, it can suggest menus that allow the user to relax before eating. In this way, it is possible to analyze the user's emotions regarding meals and suggest menus that elicit positive emotions.

[0084] The menu suggestion unit can propose meal menus that incorporate seasonal and regional specialties. The menu suggestion unit, for example, proposes menus that incorporate seasonal ingredients. For example, it proposes salads using fresh vegetables in spring and hot soups in winter. It also proposes menus that incorporate regional specialties. For example, it proposes recipes using fresh local fish and vegetables. The generation AI also considers the season and regional specialties to propose the optimal meal menu for the user. For example, it proposes special menus that coincide with seasonal events and festivals. This makes it possible to propose menus that incorporate seasonal and regional specialties.

[0085] When proposing a meal menu, the menu suggestion unit also takes into account the dietary data of the user's family and housemates, and can propose a menu that will satisfy everyone. For example, the menu suggestion unit considers the dietary data of the user's family and housemates, and the generation AI proposes a menu that will satisfy everyone. For example, it proposes recipes that take into account the preferences and allergy information of all family members. The generation AI also proposes a balanced meal menu based on the dietary data of the family and housemates. For example, it adjusts the menu so that everyone can consume the nutrients they need. The generation AI also integrates the dietary data of the user's family and housemates and proposes a meal menu that everyone can enjoy. For example, it proposes recipes that use ingredients that everyone in the family likes. In this way, it is possible to consider the dietary data of the user's family and housemates and propose a menu that will satisfy everyone.

[0086] The menu suggestion unit can use the emotion estimation function to monitor the user's emotions regarding meals in real time and suggest menus that elicit positive emotions. For example, the menu suggestion unit monitors the user's facial expressions in real time while eating and suggests menus that elicit positive emotions using the emotion estimation function. For example, it suggests meal plans that include a lot of smiles. It also analyzes audio data during meals in real time and monitors the user's emotions using the emotion estimation function. For example, it suggests meal plans that include a lot of happy conversations. It also records the user's emotional state before and after meals in real time and suggests menus that elicit positive emotions. For example, it suggests menus that allow the user to relax before eating. In this way, it is possible to monitor the user's emotions regarding meals in real time and suggest menus that elicit positive emotions.

[0087] The menu suggestion unit can propose an effective training plan by taking into account the user's past exercise history when proposing an exercise menu. For example, the menu suggestion unit analyzes the user's past exercise history, and the generation AI proposes an effective training plan. For example, it proposes optimal exercise intensity and frequency based on past exercise data. The generation AI also proposes a training plan tailored to the user's goals based on the past exercise history. For example, it proposes a plan aimed at increasing muscle strength or endurance. The generation AI also analyzes the user's exercise history and proposes a training plan that the user will not get bored of. For example, it proposes a plan that combines different types of exercise. In this way, it is possible to propose an effective training plan by taking into account the user's past exercise history.

[0088] When proposing an exercise menu, the menu suggestion unit can monitor the user's physical condition and fatigue level in real time and suggest an appropriate exercise intensity. The menu suggestion unit monitors the user's physical condition and fatigue level in real time, for example, using sensors in a smartwatch or smartphone, and the generation AI suggests an appropriate exercise intensity. For example, the exercise intensity is adjusted based on heart rate and body temperature. The generation AI also analyzes the user's physical condition and fatigue level in real time and suggests a reasonable exercise menu. For example, if fatigue is accumulated, light exercise is suggested. The generation AI also monitors the user's physical condition and fatigue level and suggests an exercise menu suitable for recovery. For example, stretching and light aerobic exercise are suggested. In this way, the user's physical condition and fatigue level can be monitored in real time and appropriate exercise intensity can be suggested.

[0089] The menu suggestion unit can use the emotion estimation function to analyze the user's emotions while exercising and suggest an exercise menu to maintain motivation. The menu suggestion unit, for example, analyzes the user's facial expressions while exercising and suggests an exercise menu to maintain motivation using the emotion estimation function. For example, it suggests an exercise menu with many smiling faces. It can also analyze audio data during exercise and use the emotion estimation function to identify the user's emotions and suggest an exercise menu to increase motivation. For example, it can suggest an exercise menu with many encouraging words. It can also record the user's emotional state before and after exercise and use the emotion estimation function to suggest an exercise menu to maintain motivation. For example, it can suggest a menu that allows the user to relax after exercising. In this way, it is possible to analyze the user's emotions while exercising and suggest an exercise menu to maintain motivation.

[0090] The menu suggestion unit can take into account the user's lifestyle and work schedule when suggesting an exercise menu. For example, the menu suggestion unit considers the user's lifestyle and work schedule, and the generation AI suggests the optimal exercise menu. For example, it suggests exercises that can be done in a short amount of time on busy days. The generation AI also makes suggestions to optimize the user's exercise time based on the lifestyle and schedule. For example, it suggests an exercise menu to be done in the morning. The generation AI also analyzes the user's schedule and suggests an exercise menu that can be continued without strain. For example, it suggests stretches and light exercises that can be done in between work. This makes it possible to suggest an exercise menu that takes into account the user's lifestyle and work schedule.

[0091] The menu suggestion unit can suggest group exercises that can be done together with the user's friends and family when proposing an exercise menu. For example, the menu suggestion unit considers the exercise data of the user's friends and family, and the generation AI suggests group exercises that can be done together. For example, the unit suggests walking or jogging for the whole family. The generation AI also suggests group exercises that can be done together with friends and family to increase motivation. For example, the unit suggests fitness challenges to do with friends. The generation AI also integrates the exercise data of the user's friends and family, and suggests group exercises that everyone can enjoy. For example, the unit suggests dance exercises for the whole family. This makes it possible to suggest group exercises that can be done together with the user's friends and family.

[0092] The menu suggestion unit can use the emotion estimation function to monitor the user's emotions while exercising in real time and suggest an exercise menu that elicits positive emotions. The menu suggestion unit, for example, monitors the user's facial expressions while exercising in real time and suggests an exercise menu that elicits positive emotions using the emotion estimation function. For example, it suggests an exercise menu that includes a lot of smiling faces. It also analyzes audio data during exercise in real time and monitors the user's emotions using the emotion estimation function. For example, it suggests an exercise menu that includes a lot of pleasant conversations. It also records the user's emotional state before and after exercise in real time and suggests an exercise menu that elicits positive emotions. For example, it suggests a menu that allows the user to relax before exercising. In this way, it is possible to monitor the user's emotions while exercising in real time and suggest an exercise menu that elicits positive emotions.

[0093] The advice providing unit can take the user's genetic information into consideration when providing preventive advice for lifestyle-related diseases and suggest specific measures for high-risk diseases. For example, the advice providing unit has the generation AI suggest specific measures for high-risk diseases based on the user's genetic information. For example, a user who is genetically at high risk of high blood pressure is advised to limit their salt intake. The generation AI also takes the genetic information into consideration and suggests preventive measures suitable for the user. For example, a low-carbohydrate meal menu is suggested for a user who is at high risk of diabetes. The generation AI also analyzes the user's genetic information and suggests regular health checks for high-risk diseases. For example, regular electrocardiogram tests are recommended for users who are at high risk of heart disease. In this way, specific measures for high-risk diseases can be suggested taking the user's genetic information into consideration.

[0094] The advice providing unit can monitor the user's stress level and mental health when providing advice on healthy living, and suggest appropriate measures. The advice providing unit can monitor the user's stress level and mental health, for example, using sensors in a smartwatch or smartphone, and the generating AI can suggest appropriate measures. For example, if stress is high, it can suggest relaxation methods. The generating AI can also analyze the user's stress level in real time and provide advice for stress management. For example, it can suggest deep breathing or meditation methods. The generating AI can also suggest mental health care methods appropriate for the user based on the mental health data. For example, it can recommend counseling or the use of support groups. This makes it possible to monitor the user's stress level and mental health, and suggest appropriate measures.

[0095] The advice providing unit can use the emotion estimation function to analyze the user's emotional state and provide advice that elicits positive emotions. The advice providing unit, for example, analyzes the user's facial expression and uses the emotion estimation function to provide advice that elicits positive emotions. For example, it suggests activities or hobbies that result in more smiles. It can also analyze voice data and use the emotion estimation function to identify the user's emotional state and provide advice that elicits positive emotions. For example, it recommends enjoyable conversations or music. It can also record the user's emotional state and use the emotion estimation function to provide advice that elicits positive emotions. For example, it suggests a relaxing environment or activity. In this way, it is possible to analyze the user's emotional state and provide advice that elicits positive emotions.

[0096] The advice providing unit can take into account the user's work environment and living environment when providing advice on preventing lifestyle-related diseases. For example, the advice providing unit considers the user's work environment and the generation AI provides advice on preventing lifestyle-related diseases. For example, for a user who does a lot of desk work, regular stretching and walking is recommended. The generation AI also suggests preventive measures suitable for the user based on their living environment. For example, for a user who lives in an urban area, it suggests methods to improve air quality and manage stress. The generation AI also analyzes the user's work environment and living environment and suggests specific measures to prevent high-risk lifestyle-related diseases. For example, for a user who often works night shifts, it recommends appropriate sleep management. This makes it possible to provide advice on preventing lifestyle-related diseases that takes into account the user's work environment and living environment.

[0097] The advice providing unit can suggest activities that incorporate the user's hobbies and interests when providing advice on healthy living. In the advice providing unit, for example, the generation AI suggests activities for a healthy lifestyle based on the user's hobbies and interests. For example, hiking or camping is suggested for a user who likes the outdoors. The generation AI also takes hobbies and interests into consideration and suggests activities that are suitable for the user. For example, dancing or music sessions are suggested for a user who likes music. The generation AI also analyzes the user's hobbies and interests and suggests specific activities for living a healthy lifestyle. For example, gardening or cooking classes are suggested. This makes it possible to suggest activities that incorporate the user's hobbies and interests.

[0098] The advice providing unit can use the emotion estimation function to monitor the user's emotional state in real time and provide advice that elicits positive emotions. The advice providing unit, for example, monitors the user's facial expressions in real time and uses the emotion estimation function to provide advice that elicits positive emotions. For example, it suggests activities and hobbies that result in more smiles. It also analyzes voice data in real time and uses the emotion estimation function to monitor the user's emotional state. For example, it recommends enjoyable conversations and music. It also records the user's emotional state in real time and uses the emotion estimation function to provide advice that elicits positive emotions. For example, it suggests a relaxing environment or activity. In this way, it is possible to monitor the user's emotional state in real time and provide advice that elicits positive emotions.

[0099] When proposing an exercise menu, the menu suggestion unit can monitor the user's physical condition and fatigue level in real time and suggest an appropriate exercise intensity. The menu suggestion unit monitors the user's physical condition and fatigue level in real time, for example, using sensors in a smartwatch or smartphone, and the generation AI suggests an appropriate exercise intensity. For example, the exercise intensity is adjusted based on heart rate and body temperature. The generation AI also analyzes the user's physical condition and fatigue level in real time and suggests a reasonable exercise menu. For example, if fatigue is accumulated, light exercise is suggested. The generation AI also monitors the user's physical condition and fatigue level and suggests an exercise menu suitable for recovery. For example, stretching and light aerobic exercise are suggested. In this way, the user's physical condition and fatigue level can be monitored in real time and appropriate exercise intensity can be suggested.

[0100] The menu suggestion unit can use the emotion estimation function to analyze the user's emotions while exercising and suggest an exercise menu to maintain motivation. The menu suggestion unit, for example, analyzes the user's facial expressions while exercising and suggests an exercise menu to maintain motivation using the emotion estimation function. For example, it suggests an exercise menu with many smiling faces. It can also analyze audio data during exercise and use the emotion estimation function to identify the user's emotions and suggest an exercise menu to increase motivation. For example, it can suggest an exercise menu with many encouraging words. It can also record the user's emotional state before and after exercise and use the emotion estimation function to suggest an exercise menu to maintain motivation. For example, it can suggest a menu that allows the user to relax after exercising. In this way, it is possible to analyze the user's emotions while exercising and suggest an exercise menu to maintain motivation.

[0101] The menu suggestion unit can take into account the user's lifestyle and work schedule when suggesting an exercise menu. For example, the menu suggestion unit considers the user's lifestyle and work schedule, and the generation AI suggests the optimal exercise menu. For example, it suggests exercises that can be done in a short amount of time on busy days. The generation AI also makes suggestions to optimize the user's exercise time based on the lifestyle and schedule. For example, it suggests an exercise menu to be done in the morning. The generation AI also analyzes the user's schedule and suggests an exercise menu that can be continued without strain. For example, it suggests stretches and light exercises that can be done in between work. This makes it possible to suggest an exercise menu that takes into account the user's lifestyle and work schedule.

[0102] The menu suggestion unit can suggest group exercises that can be done together with the user's friends and family when proposing an exercise menu. For example, the menu suggestion unit considers the exercise data of the user's friends and family, and the generation AI suggests group exercises that can be done together. For example, the unit suggests walking or jogging for the whole family. The generation AI also suggests group exercises that can be done together with friends and family to increase motivation. For example, the unit suggests fitness challenges to do with friends. The generation AI also integrates the exercise data of the user's friends and family, and suggests group exercises that everyone can enjoy. For example, the unit suggests dance exercises for the whole family. This makes it possible to suggest group exercises that can be done together with the user's friends and family.

[0103] The menu suggestion unit can use the emotion estimation function to monitor the user's emotions while exercising in real time and suggest an exercise menu that elicits positive emotions. The menu suggestion unit, for example, monitors the user's facial expressions while exercising in real time and suggests an exercise menu that elicits positive emotions using the emotion estimation function. For example, it suggests an exercise menu that includes a lot of smiling faces. It also analyzes audio data during exercise in real time and monitors the user's emotions using the emotion estimation function. For example, it suggests an exercise menu that includes a lot of pleasant conversations. It also records the user's emotional state before and after exercise in real time and suggests an exercise menu that elicits positive emotions. For example, it suggests a menu that allows the user to relax before exercising. In this way, it is possible to monitor the user's emotions while exercising in real time and suggest an exercise menu that elicits positive emotions.

[0104] The advice providing unit can take the user's genetic information into consideration when providing preventive advice for lifestyle-related diseases and suggest specific measures for high-risk diseases. For example, the advice providing unit has the generation AI suggest specific measures for high-risk diseases based on the user's genetic information. For example, a user who is genetically at high risk of high blood pressure is advised to limit their salt intake. The generation AI also takes the genetic information into consideration and suggests preventive measures suitable for the user. For example, a low-carbohydrate meal menu is suggested for a user who is at high risk of diabetes. The generation AI also analyzes the user's genetic information and suggests regular health checks for high-risk diseases. For example, regular electrocardiogram tests are recommended for users who are at high risk of heart disease. In this way, specific measures for high-risk diseases can be suggested taking the user's genetic information into consideration.

[0105] The advice providing unit can monitor the user's stress level and mental health when providing advice on healthy living, and suggest appropriate measures. The advice providing unit can monitor the user's stress level and mental health, for example, using sensors in a smartwatch or smartphone, and the generating AI can suggest appropriate measures. For example, if stress is high, it can suggest relaxation methods. The generating AI can also analyze the user's stress level in real time and provide advice for stress management. For example, it can suggest deep breathing or meditation methods. The generating AI can also suggest mental health care methods appropriate for the user based on the mental health data. For example, it can recommend counseling or the use of support groups. This makes it possible to monitor the user's stress level and mental health, and suggest appropriate measures.

[0106] The advice providing unit can use the emotion estimation function to analyze the user's emotional state and provide advice that elicits positive emotions. The advice providing unit, for example, analyzes the user's facial expression and uses the emotion estimation function to provide advice that elicits positive emotions. For example, it suggests activities or hobbies that result in more smiles. It can also analyze voice data and use the emotion estimation function to identify the user's emotional state and provide advice that elicits positive emotions. For example, it recommends enjoyable conversations or music. It can also record the user's emotional state and use the emotion estimation function to provide advice that elicits positive emotions. For example, it suggests a relaxing environment or activity. In this way, it is possible to analyze the user's emotional state and provide advice that elicits positive emotions.

[0107] The advice providing unit can take into account the user's work environment and living environment when providing advice on preventing lifestyle-related diseases. For example, the advice providing unit considers the user's work environment and the generation AI provides advice on preventing lifestyle-related diseases. For example, for a user who does a lot of desk work, regular stretching and walking is recommended. The generation AI also suggests preventive measures suitable for the user based on their living environment. For example, for a user who lives in an urban area, it suggests methods to improve air quality and manage stress. The generation AI also analyzes the user's work environment and living environment and suggests specific measures to prevent high-risk lifestyle-related diseases. For example, for a user who often works night shifts, it recommends appropriate sleep management. This makes it possible to provide advice on preventing lifestyle-related diseases that takes into account the user's work environment and living environment.

[0108] The advice providing unit can suggest activities that incorporate the user's hobbies and interests when providing advice on healthy living. In the advice providing unit, for example, the generation AI suggests activities for a healthy lifestyle based on the user's hobbies and interests. For example, hiking or camping is suggested for a user who likes the outdoors. The generation AI also takes hobbies and interests into consideration and suggests activities that are suitable for the user. For example, dancing or music sessions are suggested for a user who likes music. The generation AI also analyzes the user's hobbies and interests and suggests specific activities for living a healthy lifestyle. For example, gardening or cooking classes are suggested. This makes it possible to suggest activities that incorporate the user's hobbies and interests.

[0109] The advice providing unit can use the emotion estimation function to monitor the user's emotional state in real time and provide advice that elicits positive emotions. The advice providing unit, for example, monitors the user's facial expressions in real time and uses the emotion estimation function to provide advice that elicits positive emotions. For example, it suggests activities and hobbies that result in more smiles. It also analyzes voice data in real time and uses the emotion estimation function to monitor the user's emotional state. For example, it recommends enjoyable conversations and music. It also records the user's emotional state in real time and uses the emotion estimation function to provide advice that elicits positive emotions. For example, it suggests a relaxing environment or activity. In this way, it is possible to monitor the user's emotional state in real time and provide advice that elicits positive emotions.

[0110] The advice providing unit can use the emotion estimation function to monitor the user's emotional state in real time and provide advice that elicits positive emotions. The advice providing unit, for example, monitors the user's facial expressions in real time and uses the emotion estimation function to provide advice that elicits positive emotions. For example, it suggests activities and hobbies that result in more smiles. It also analyzes voice data in real time and uses the emotion estimation function to monitor the user's emotional state. For example, it recommends enjoyable conversations and music. It also records the user's emotional state in real time and uses the emotion estimation function to provide advice that elicits positive emotions. For example, it suggests a relaxing environment or activity. In this way, it is possible to monitor the user's emotional state in real time and provide advice that elicits positive emotions.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The health management system can further include a fluid recording unit that records the user's fluid intake. The fluid recording unit, for example, records the amount of water or beverages the user drinks, and the generation AI suggests appropriate fluid intake amounts. For example, it suggests the amount of fluid to replenish after exercise. The fluid recording unit can also calculate the optimal fluid intake amount for each individual user based on their weight and exercise level. For example, a user weighing 60 kg might be recommended to drink 2 liters of fluid per day. The fluid recording unit can also record the types of beverages the user drinks, and the generation AI can manage their caffeine and sugar intake. For example, it might suggest limiting coffee intake to avoid excessive caffeine intake. This allows the system to manage the user's fluid intake and support a healthy lifestyle.

[0113] The health management system may further include a sleep recording unit that records the user's sleep patterns. The sleep recording unit records the user's sleep time and sleep quality based on data obtained from, for example, a smartwatch. For example, it analyzes the ratio of deep sleep to light sleep. The sleep recording unit also records the user's sleep environment (temperature, humidity, noise, etc.), allowing the generation AI to suggest an optimal sleep environment. For example, it may advise the user to maintain an appropriate bedroom temperature. The sleep recording unit also analyzes the user's sleep patterns, allowing the generation AI to provide advice to improve sleep quality. For example, it may suggest ways to relax before bed. This allows the system to manage the user's sleep patterns and support a healthy lifestyle.

[0114] The health management system may further include a stress recording unit that records the user's stress level. The stress recording unit records the user's stress level, for example, based on heart rate and electrodermal activity obtained from the smartwatch. For example, the stress level may be evaluated based on heart rate fluctuations. The stress recording unit may also record the user's stress factors (work, home, environment, etc.), and the generating AI may provide advice for stress management. For example, the generating AI may suggest relaxation methods during periods of high stress. The stress recording unit may also analyze the user's stress level, and the generating AI may suggest specific measures to reduce stress. For example, the system may suggest meditation or deep breathing techniques. This allows the user to manage their stress level and support a healthy lifestyle.

[0115] The health management system may further include an emotion recording unit that records the user's emotional state. The emotion recording unit, for example, analyzes the user's facial expressions and voice data and records the user's emotional state using an emotion estimation function. For example, it analyzes smiling and angry expressions. The emotion recording unit can also provide advice to help the generation AI elicit positive emotions based on the user's emotional state. For example, it can suggest fun activities or hobbies. The emotion recording unit can also analyze the user's emotional state and allow the generation AI to suggest specific measures for stress management and mental health care. For example, it can recommend relaxation methods or counseling. This allows the system to manage the user's emotional state and support a healthy lifestyle.

[0116] The health management system may further include a training recording unit for improving the user's exercise performance. The training recording unit, for example, records the user's exercise data, and the generating AI proposes an optimal training plan. For example, it may propose a plan for strength training or aerobic exercise. The training recording unit may also analyze the user's exercise performance, and the generating AI may provide advice to maximize the effectiveness of the exercise. For example, it may adjust the frequency and intensity of exercise. The training recording unit may also allow the generating AI to propose specific measures for recovery based on the user's exercise data. For example, it may suggest stretching or massage techniques. This can improve the user's exercise performance and support a healthy lifestyle.

[0117] The health management system can further include an ingredient selection unit for customizing the user's meal menu. In the ingredient selection unit, the generation AI selects optimal ingredients based on the user's preferences and allergy information, for example. For example, gluten-free or vegan ingredients are suggested. The ingredient selection unit can also consider the user's nutritional balance and allow the generation AI to suggest healthy ingredients. For example, ingredients rich in vitamins and minerals are selected. The ingredient selection unit can also analyze the user's dietary history and allow the generation AI to suggest ingredients that the user will not tire of. For example, different recipes using the same ingredients are suggested. This allows the user's meal menu to be customized and supports a healthy lifestyle.

[0118] The health management system may further include a motivation recording unit to maintain the user's exercise motivation. The motivation recording unit, for example, analyzes the user's emotional state during exercise and provides advice to maintain motivation using an emotion estimation function. For example, it analyzes smiles and encouraging words during exercise. The motivation recording unit can also suggest specific measures to increase motivation for the generation AI based on the user's exercise history. For example, it can set exercise goals or introduce a reward system. The motivation recording unit can also monitor the user's emotional state in real time and suggest exercise menus that the generation AI can use to elicit positive emotions. For example, it can suggest fun exercises or group exercises. This helps maintain the user's exercise motivation and support a healthy lifestyle.

[0119] The health management system can further include a nutritional analysis unit for optimizing the user's meal menu. The nutritional analysis unit, for example, analyzes the user's dietary data, and the generation AI provides advice to optimize nutritional balance. For example, it suggests ingredients that supplement vitamin and mineral deficiencies. The nutritional analysis unit can also consider the user's health condition and allow the generation AI to suggest meal menus to enhance specific nutrients. For example, if there is an iron deficiency, it will suggest ingredients that are high in iron. The nutritional analysis unit can also analyze the user's dietary history, and allow the generation AI to suggest nutritionally balanced menus that will not become boring. For example, it can suggest recipes using different ingredients that contain the same nutrients. This can optimize the user's meal menu and support a healthy lifestyle.

[0120] The health management system may further include a feedback providing unit for improving the user's exercise performance. The feedback providing unit, for example, analyzes the user's exercise data, and the generation AI provides feedback to maximize the effects of the exercise. For example, the feedback providing unit may provide advice on improving form or adjusting exercise intensity. The feedback providing unit may also enable the generation AI to evaluate the user's exercise progress based on the user's exercise history and suggest specific improvements. For example, the feedback providing unit may provide step-by-step advice for achieving goals. The feedback providing unit may also monitor the user's exercise data in real time, and the generation AI may provide immediate feedback. For example, the feedback providing unit may provide advice on adjusting exercise form or pace in real time. This may improve the user's exercise performance and support a healthy lifestyle.

[0121] The health management system may further include a mental health recording unit to support the user's mental health. The mental health recording unit may, for example, record the user's emotional state and stress level and use an emotion estimation function to provide mental health care advice. For example, it may suggest relaxation techniques or counseling. The mental health recording unit may also use a generating AI to suggest specific measures for stress management based on the user's mental health data. For example, it may suggest meditation or deep breathing techniques. The mental health recording unit may also monitor the user's emotional state in real time, and the generating AI may suggest activities to elicit positive emotions. For example, it may suggest enjoyable activities or hobbies. This may support the user's mental health and encourage a healthy lifestyle.

[0122] The processing flow of the second embodiment will be briefly explained below.

[0123] Step 1: The food recording unit records the user's daily meals. For example, the user can take a photo of their meal with their smartphone and send it to the generation AI, which then records the meal contents. The food recording unit can also record the time and amount of meals. Step 2: The exercise recording unit records the amount of exercise performed by the user. For example, the exercise recording unit records the amount of exercise performed based on the number of steps and heart rate data obtained from the smartwatch. The exercise recording unit can also record the type and duration of exercise. Step 3: The menu suggestion unit suggests optimal meal menus to the user based on the data recorded by the food recording unit and exercise recording unit. For example, the generation AI analyzes the user's calorie intake and nutrient balance and suggests low-calorie meal menus or menus that supplement specific nutrients. Step 4: The advice provider provides advice on preventing lifestyle-related diseases and living a healthy life based on the data recorded by the food and exercise recorders. For example, the AI ​​analyzes the user's data and advises them to reduce their salt intake if they are at risk of high blood pressure.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 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.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The 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.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 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.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the 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.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 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.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 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.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The 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.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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]

[0191] 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 meal recording unit that records the user's daily meals; an exercise recording unit that records the amount of exercise of the user; a menu suggestion unit that suggests an optimal meal menu to a user based on the data recorded by the meal recording unit and the exercise recording unit; an advice providing unit that provides advice on preventing lifestyle-related diseases and living a healthy life based on the data recorded by the diet recording unit and the exercise recording unit. A system characterized by:

2. The meal recording unit In addition to meal images, scan ingredient barcodes to get detailed nutritional information 2. The system of claim 1.

3. The meal recording unit In food image analysis, the origin and production method of ingredients are taken into account to calculate the variation in nutritional value 2. The system of claim 1.

4. The meal recording unit Analyzing the user's emotions while eating and identifying tendencies toward stress eating and overeating 2. The system of claim 1.

5. The meal recording unit Adding voice input to food records and having the user verbally describe what they ate improves the accuracy of analysis.

2. The system of claim 1.

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