System

Abstract

An object of a system according to an embodiment is to propose an individual action plan on the basis of a health condition of a user.SOLUTION: A system includes a meal record analysis unit, an exercise record analysis unit, an activity record analysis unit, and an action plan suggestion unit. The meal record analyzer analyzes a meal record of the user. The exercise record analyzer analyzes an exercise record of a user. The activity record analysis unit analyzes an activity record of a user. The action plan proposing section proposes an action plan on the basis of analysis results of the meal record analyzing section, the exercise record analyzing section, and the activity record analyzing section.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 had the problem that it is difficult to efficiently propose an individual action plan based on a user's health condition.

[0005] The system according to the embodiment aims to propose an individual action plan based on the user's health condition. [Means for solving the problem]

[0006] The system according to the embodiment includes a meal record analysis unit, an exercise record analysis unit, an activity record analysis unit, and an action plan proposal unit. The meal record analysis unit analyzes the user's meal record. The exercise record analysis unit analyzes the user's exercise record. The activity record analysis unit analyzes the user's activity record. The action plan proposal unit proposes an action plan based on the analysis results of the meal record analysis unit, exercise record analysis unit, and activity record analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an individual action plan based on the user's health status. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 coaching system according to an embodiment of the present invention is a system in which a generative AI analyzes and provides advice on a user's health status and goals, allowing the health coaching system to grasp the user's health status in detail and take specific actions to achieve the goals.

[0029] A health coaching system according to an embodiment includes a food record analysis unit, an exercise record analysis unit, an activity record analysis unit, and an action plan proposal unit. The food record analysis unit analyzes a user's food record. For example, it calculates nutrient intake based on the types and amounts of food entered by the user and determines whether the diet is balanced. The food record analysis unit also uses a generation AI to analyze meal details and provide nutritional balance advice. For example, the generation AI analyzes input data such as "I ate bread and eggs for breakfast" and provides advice such as "You're lacking in protein, so you should add yogurt next time." The exercise record analysis unit analyzes the user's exercise record. For example, it evaluates the effectiveness of the user's exercise based on the type and duration of the exercise performed and provides advice for the next training session. The exercise record analysis unit also uses the generation AI to analyze exercise details and optimize training. For example, the generation AI analyzes input data such as "I jogged for 30 minutes today" and provides advice such as "It would be effective to incorporate interval training next time." The activity record analysis unit analyzes the user's activity record. For example, the system evaluates a user's overall health status based on their daily activity level and sleep duration, and provides an action plan for achieving their goals. Furthermore, the activity record analysis unit uses the generation AI to analyze the details of their activities and propose specific action plans. For example, the generation AI analyzes input data such as "Today, I got eight hours of sleep and ate three balanced meals" and proposes an action plan such as "Tomorrow, you should exercise a little more." The action plan proposal unit proposes an action plan based on the analysis results of the food record analysis unit, exercise record analysis unit, and activity record analysis unit. For example, the generation AI comprehensively analyzes the user's diet, exercise, and activity records and proposes a specific action plan for achieving their goals. This allows the health coaching system according to the embodiment to comprehensively analyze the user's health status and propose a specific action plan. For example, a user can maintain their health by maintaining a nutritionally balanced diet and improve their physical strength by engaging in optimal training. Furthermore, following the comprehensive action plan allows users to efficiently achieve their health goals.

[0030] The meal record analysis unit learns the user's past eating patterns and can predict future meal plans. For example, when a user inputs a meal record for the past month, the generation AI learns the eating patterns based on that data and predicts the meal plan for the next week. For example, it suggests a balanced meal menu based on the foods the user frequently ate in the past. This makes it possible to learn the user's past eating patterns and predict future meal plans.

[0031] The food record analysis unit analyzes the origin and quality information of ingredients and can suggest healthier choices. For example, when a user inputs a food record, the generation AI analyzes the origin information of ingredients based on that data and suggests using fresh, locally grown ingredients. For example, it provides recipes using local agricultural products. This allows the analysis of the origin and quality information of ingredients to suggest healthier choices.

[0032] The food record analysis unit can automatically generate recipes based on the user's food records and suggest them to the user. For example, when the user inputs a food record, the generation AI automatically generates nutritionally balanced recipes based on that data. For example, it can suggest menus that take into account the nutrients the user wants to consume. This allows recipes to be automatically generated and suggested based on the user's food records.

[0033] The meal record analysis unit can add a function that allows a user to share their meal record with other users and receive feedback within the community. The meal record analysis unit can add a function that allows a user to share their meal record with other users and receive feedback within the community. For example, the user can receive suggestions for improving their meal or new recipes. This can add a function that allows a user to share their meal record with other users and receive feedback within the community.

[0034] The exercise record analysis unit can analyze muscle fatigue and recovery status based on the user's exercise record and suggest optimal rest times. For example, when a user inputs an exercise record, the exercise record analysis unit uses the data to analyze muscle fatigue and suggest optimal rest times. For example, it calculates the time required for muscle recovery and advises on the amount of rest time before the next training session. This allows the system to analyze muscle fatigue and recovery status based on the user's exercise record and suggest optimal rest times.

[0035] The exercise record analysis unit can analyze heart rate and calorie consumption during exercise and provide detailed training advice. For example, when a user inputs an exercise record, the generation AI analyzes heart rate and calorie consumption based on that data and provides detailed training advice. For example, if an exercise session results in a high heart rate, the generation AI can suggest ways to control the heart rate during the next session. This allows the system to analyze heart rate and calorie consumption during exercise and provide detailed training advice.

[0036] The exercise record analysis unit can suggest sports and activities suitable for the user. For example, when a user inputs an exercise record, the generation AI suggests sports and activities suitable for the user based on that data. For example, it suggests activities such as running or yoga depending on the user's physical strength and interests. This makes it possible to suggest sports and activities suitable for the user.

[0037] The exercise record analysis unit can add a function that allows a user to share their exercise records with other users and increase their motivation through competition and cooperation. The exercise record analysis unit can add a function that allows a user to share their exercise records within a community and increase their motivation through competition and cooperation with other users. For example, a challenge can be set to compete against each other in terms of running distance or time. This can add a function that allows a user to share their exercise records with other users and increase their motivation through competition and cooperation.

[0038] The activity record analysis unit can comprehensively analyze the user's activity, meal, and sleep records and make suggestions to optimize their lifestyle rhythm. For example, when the user inputs their activity, meal, and sleep records, the activity record analysis unit can comprehensively analyze the data and make suggestions to optimize their lifestyle rhythm. For example, it can provide advice on adjusting meal timing and sleep duration. This allows the user's activity, meal, and sleep records to be comprehensively analyzed and suggestions to optimize their lifestyle rhythm.

[0039] The activity record analysis unit can predict the user's health risks based on the recorded data and suggest preventive measures. For example, when the user inputs records of their activities, meals, and sleep, the generation AI can predict health risks based on that data and suggest preventive measures. For example, it can identify health risks caused by an unbalanced diet or lack of exercise and advise on improvement measures. This makes it possible to predict the user's health risks based on the recorded data and suggest preventive measures.

[0040] The activity record analysis unit can suggest relaxation methods and stress relief methods that are suitable for the user. For example, when the user inputs records of their activities, meals, and sleep, the activity record analysis unit uses that data to suggest relaxation methods and stress relief methods that are suitable for the user. For example, relaxation methods such as yoga and meditation are suggested. This makes it possible to suggest relaxation methods and stress relief methods that are suitable for the user.

[0041] The activity record analysis unit can add a community function that allows users to share recorded data with other users and support the achievement of health goals. The activity record analysis unit adds a function that allows users to share activity, meal, and sleep records within a community and support the achievement of health goals with other users. For example, users with the same goals can encourage each other. This allows users to share recorded data with other users and add a community function that supports the achievement of health goals.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The health coaching system can comprehensively analyze a user's health status and propose specific action plans. For example, a user can maintain health by maintaining a nutritionally balanced diet and improve physical fitness by engaging in optimal training. Following the comprehensive action plan also allows users to efficiently achieve their health goals. Furthermore, the system can collect and anonymize users' health data and analyze statistical health trends. This allows users to compare their health status with others and take more effective health management measures. For example, users can objectively evaluate their health status by referring to the average exercise and dietary habits of users of the same age. The system can also provide health advice tailored to the season and weather. For example, it can recommend vitamin D intake in winter and emphasize the importance of hydration in summer. This allows users to take appropriate actions to address seasonal health risks.

[0044] The food record analysis unit can learn a user's past eating patterns and predict future meal plans. For example, if a user enters a meal record for the past month, the generation AI can learn their eating patterns based on that data and predict their meal plan for the next week. For example, it can suggest balanced meal menus based on the foods the user frequently ate in the past. This allows the system to learn the user's past eating patterns and predict future meal plans. Furthermore, the system can also create meal plans taking into account the user's allergy information. For example, if a user is allergic to a specific food, the system can suggest menus that avoid that food. The system can also learn the user's food preferences and provide recipes that suit their tastes. For example, if a user likes spicy food, the system can suggest spicy dishes. This allows users to maintain their health while enjoying meals that suit their preferences.

[0045] The food record analysis unit can analyze the origin and quality of ingredients and suggest healthier choices. For example, when a user enters a food record, the generation AI uses that data to analyze the origin and quality of ingredients and suggests using fresh, locally grown ingredients. For example, it provides recipes using local agricultural products. This allows the system to analyze the origin and quality of ingredients and suggest healthier choices. Furthermore, the system can analyze the cultivation method and pesticide use information of ingredients and recommend organic and pesticide-free foods. For example, if the user is health-conscious, it can suggest recipes using organic foods. The system can also provide advice on how to store and cook ingredients. For example, it recommends eating ingredients rich in vitamin C raw and suggests ways to maximize the use of nutrients. This allows users to achieve a comprehensively healthy diet, from how to select ingredients to how to cook them.

[0046] The food record analysis unit can automatically generate recipes based on the user's food records and suggest them to the user. For example, when a user inputs a food record, the generation AI automatically generates nutritionally balanced recipes based on that data. For example, it can suggest menus that take into account the nutrients the user wants to consume. This allows recipes to be automatically generated and suggested based on the user's food records. Furthermore, the system can also generate recipes taking into account the user's food inventory information. For example, it can suggest recipes that use up all the ingredients in the refrigerator without waste. The system can also provide recipes that suit the user's cooking skills and time. For example, it can suggest simple and quick recipes for busy users, and provide challenging recipes for users who are good at cooking. This allows users to enjoy meals that suit their lifestyle.

[0047] The food record analysis unit can add a function that allows a user to share a food record with other users and receive feedback within the community. For example, a function can be added that allows a user to share a food record with other users within the community and receive feedback from other users. For example, the user can receive suggestions for improving their diet or new recipes. This allows a function to be added that allows a user to share a food record with other users and receive feedback within the community. Furthermore, the system can also compare food records between users and promote competition and cooperation in leading a healthy diet. For example, a challenge can be set in which users with the same goal compete to improve their diet. The system can also display a ranking within the community based on the user's food record to increase motivation. This allows users to achieve a healthy diet while cooperating with other users.

[0048] The exercise record analysis unit can analyze the user's muscle fatigue level and recovery state based on their exercise record and suggest optimal rest times. For example, when a user inputs their exercise record, the generation AI analyzes their muscle fatigue level based on that data and suggests optimal rest times. For example, it calculates the time required for muscle recovery and advises on the amount of rest time before the next workout. This allows the system to analyze the user's muscle fatigue level and recovery state based on their exercise record and suggest optimal rest times. Furthermore, the system can evaluate their recovery state by taking into account the user's sleep data. For example, it can provide advice that getting enough sleep will speed up recovery. The system can also consider the user's dietary data and emphasize the importance of nutritional supplementation. For example, it can provide advice that consuming appropriate nutrients after exercise will promote recovery. This allows users to continue effective training while managing their overall health.

[0049] The exercise record analysis unit can analyze heart rate and calorie consumption during exercise and provide detailed training advice. For example, when a user inputs an exercise record, the generation AI analyzes heart rate and calorie consumption based on that data and provides detailed training advice. For example, if a user performs an exercise that results in a high heart rate, the system can suggest ways to control their heart rate during the next workout. This allows the system to analyze heart rate and calorie consumption during exercise and provide detailed training advice. Furthermore, the system can analyze the user's exercise data over a long period of time and evaluate their training progress. For example, the system can analyze the user's physical fitness improvement trends based on data from the past few months. The system can also provide training plans tailored to the user's goals. For example, the system can suggest a long-distance running plan for a user aiming to run a marathon, and a weight training plan for a user who prioritizes strength training. This allows users to perform effective training tailored to their goals.

[0050] The exercise record analysis unit can suggest sports and activities suitable for the user. For example, when a user inputs their exercise record, the generation AI uses that data to suggest sports and activities suitable for the user. For example, it can suggest activities such as running or yoga based on the user's physical strength and interests. This allows it to suggest sports and activities suitable for the user. Furthermore, the system can change the type of sport based on the user's health condition and goals. For example, it can suggest swimming to avoid exercise that puts strain on the joints. The system can also suggest activities that suit the user's lifestyle. For example, it can suggest short, effective exercises for busy users and provide longer activities for users with more time. This allows users to enjoy sports and activities that suit their lifestyle.

[0051] The exercise record analysis unit can add a function that allows a user to share their exercise records with other users and increase their motivation through competition and cooperation. For example, a function can be added that allows a user to share their exercise records within a community and increase their motivation through competition and cooperation with other users. For example, a challenge can be set to compete in running distance or time. This allows a user to share their exercise records with other users and increase their motivation through competition and cooperation. Furthermore, the system can compare users' exercise records and provide incentives for living a healthy lifestyle. For example, users who achieve certain goals can be awarded badges or points to maintain motivation. The system can also display a ranking within the community based on the user's exercise records to stimulate a competitive spirit. This allows users to achieve a healthy lifestyle while cooperating with other users.

[0052] The activity record analysis unit can comprehensively analyze a user's activity, meal, and sleep records and make suggestions for optimizing their lifestyle. For example, when a user inputs their activity, meal, and sleep records, the generation AI comprehensively analyzes the data and makes suggestions for optimizing their lifestyle. For example, it provides advice on adjusting meal timing and sleep duration. This allows the system to comprehensively analyze the user's activity, meal, and sleep records and make suggestions for optimizing their lifestyle. Furthermore, the system can adjust the user's lifestyle based on their stress and energy levels. For example, if stress is high, the system recommends increasing relaxation time, and if energy levels are low, the system recommends rest. The system can also customize the lifestyle based on the user's goals. For example, the system can adjust meal timing for a user aiming to lose weight and suggest appropriate rest times for a user doing strength training. This allows users to achieve a lifestyle that suits their goals and improve their health.

[0053] The activity record analysis unit can predict a user's health risks and suggest preventive measures based on the recorded data. For example, when a user inputs activity, diet, and sleep records, the generation AI uses that data to predict health risks and suggest preventive measures. For example, it can identify health risks caused by poor diet or lack of exercise and recommend improvement measures. This allows the system to predict a user's health risks and suggest preventive measures based on the recorded data. Furthermore, the system can evaluate health risks by taking into account the user's family history and genetic information. For example, if a family member has a high risk of heart disease, it can suggest heart-healthy diets and exercise. The system can also provide preventive measures tailored to the user's lifestyle. For example, it can recommend regular stretching and exercise for users who do a lot of desk work, and advise appropriate nutrition for users who are often out and about. This allows users to understand their health risks and take appropriate preventive measures.

[0054] The activity record analysis unit can suggest relaxation and stress relief methods suitable for the user. For example, when a user inputs activity, meal, and sleep records, the generation AI uses that data to suggest relaxation and stress relief methods suitable for the user. For example, it suggests relaxation methods such as yoga and meditation. This allows it to suggest relaxation and stress relief methods suitable for the user. Furthermore, the system can customize relaxation methods taking into account the user's stress level and emotional state. For example, if stress is high, it might suggest deep breathing and mindfulness, and if relaxed, it might recommend light stretching or listening to music. The system can also provide relaxation methods tailored to the user's lifestyle. For example, it might suggest short, effective relaxation methods for busy users, and provide longer relaxation sessions for users with more time. This allows users to practice relaxation methods that suit their lifestyle and effectively relieve stress.

[0055] The activity record analysis unit can add a community function that allows users to share their recorded data with other users and support them in achieving their health goals. For example, a function can be added that allows users to share their activity, food, and sleep records within a community and support other users in achieving their health goals. For example, users with the same goals can encourage each other. This allows users to share their recorded data with other users and support them in achieving their health goals. Furthermore, the system can compare users' recorded data and provide incentives for living a healthy lifestyle. For example, users who achieve certain goals can be awarded badges or points to maintain motivation. The system can also display a ranking within the community based on the user's recorded data to stimulate a competitive spirit. This allows users to work together with other users to achieve a healthy lifestyle.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The food record analysis unit analyzes the user's food record. For example, it calculates nutrient intake based on the types and amounts of food entered by the user and determines whether it is balanced. The generative AI also analyzes the details of the meal and provides advice on nutritional balance. For example, it analyzes data entered such as "I ate bread and eggs for breakfast" and provides advice such as "You're not getting enough protein, so next time you should add yogurt." Step 2: The exercise record analysis unit analyzes the user's exercise record. For example, it evaluates the effectiveness of the exercise based on the type and duration of the exercise performed by the user and provides advice for the next training session. The generative AI also analyzes the details of the exercise and optimizes the training. For example, it analyzes data such as "I jogged for 30 minutes today" and provides advice such as "It would be effective to incorporate interval training next time." Step 3: The activity record analysis unit analyzes the user's activity record. For example, the unit evaluates the user's overall health status based on their daily activity level and sleep duration, and provides an action plan for achieving their goals. The generation AI also analyzes the details of the activity and suggests specific action plans. For example, if the user inputs data such as "I got eight hours of sleep today and had three balanced meals," it will analyze the data and provide an action plan such as "You should exercise a little more tomorrow." Step 4: The action plan suggestion unit proposes an action plan based on the analysis results of the food record analysis unit, exercise record analysis unit, and activity record analysis unit. For example, the generation AI comprehensively analyzes the user's food, exercise, and activity records and provides a specific action plan for achieving goals. This allows the user to maintain their health by continuing to eat a nutritionally balanced diet and improve their physical strength by performing optimal training. In addition, by following the comprehensive action plan, they can efficiently achieve their health goals.

[0058] (Example 2) A health coaching system according to an embodiment of the present invention is a system in which a generative AI analyzes and provides advice on a user's health status and goals, allowing the health coaching system to grasp the user's health status in detail and take specific actions to achieve the goals.

[0059] A health coaching system according to an embodiment includes a food record analysis unit, an exercise record analysis unit, an activity record analysis unit, and an action plan proposal unit. The food record analysis unit analyzes a user's food record. For example, it calculates nutrient intake based on the types and amounts of food entered by the user and determines whether the diet is balanced. The food record analysis unit also uses a generation AI to analyze meal details and provide nutritional balance advice. For example, the generation AI analyzes input data such as "I ate bread and eggs for breakfast" and provides advice such as "You're lacking in protein, so you should add yogurt next time." The exercise record analysis unit analyzes the user's exercise record. For example, it evaluates the effectiveness of the user's exercise based on the type and duration of the exercise performed and provides advice for the next training session. The exercise record analysis unit also uses the generation AI to analyze exercise details and optimize training. For example, the generation AI analyzes input data such as "I jogged for 30 minutes today" and provides advice such as "It would be effective to incorporate interval training next time." The activity record analysis unit analyzes the user's activity record. For example, the system evaluates a user's overall health status based on their daily activity level and sleep duration, and provides an action plan for achieving their goals. Furthermore, the activity record analysis unit uses the generation AI to analyze the details of their activities and propose specific action plans. For example, the generation AI analyzes input data such as "Today, I got eight hours of sleep and ate three balanced meals" and proposes an action plan such as "Tomorrow, you should exercise a little more." The action plan proposal unit proposes an action plan based on the analysis results of the food record analysis unit, exercise record analysis unit, and activity record analysis unit. For example, the generation AI comprehensively analyzes the user's diet, exercise, and activity records and proposes a specific action plan for achieving their goals. This allows the health coaching system according to the embodiment to comprehensively analyze the user's health status and propose a specific action plan. For example, a user can maintain their health by maintaining a nutritionally balanced diet and improve their physical strength by engaging in optimal training. Furthermore, following the comprehensive action plan allows users to efficiently achieve their health goals.

[0060] The meal record analysis unit learns the user's past eating patterns and can predict future meal plans. For example, when a user inputs a meal record for the past month, the generation AI learns the eating patterns based on that data and predicts the meal plan for the next week. For example, it suggests a balanced meal menu based on the foods the user frequently ate in the past. This makes it possible to learn the user's past eating patterns and predict future meal plans.

[0061] The food record analysis unit analyzes the origin and quality information of ingredients and can suggest healthier choices. For example, when a user inputs a food record, the generation AI analyzes the origin information of ingredients based on that data and suggests using fresh, locally grown ingredients. For example, it provides recipes using local agricultural products. This allows the analysis of the origin and quality information of ingredients to suggest healthier choices.

[0062] The meal record analysis unit uses the emotion estimation function to analyze the user's emotions regarding meals and can provide meal advice based on stress and satisfaction. For example, when a user inputs a meal record, the generation AI uses the emotion estimation function to analyze the user's emotions. For example, if the stress level during a meal is high, the generation AI can suggest ingredients and recipes that will help you relax. This allows the system to analyze the user's emotions regarding meals and provide meal advice based on stress and satisfaction.

[0063] The food record analysis unit can automatically generate recipes based on the user's food records and suggest them to the user. For example, when the user inputs a food record, the generation AI automatically generates nutritionally balanced recipes based on that data. For example, it can suggest menus that take into account the nutrients the user wants to consume. This allows recipes to be automatically generated and suggested based on the user's food records.

[0064] The meal record analysis unit can add a function that allows a user to share their meal record with other users and receive feedback within the community. The meal record analysis unit can add a function that allows a user to share their meal record with other users and receive feedback within the community. For example, the user can receive suggestions for improving their meal or new recipes. This can add a function that allows a user to share their meal record with other users and receive feedback within the community.

[0065] The exercise record analysis unit can analyze muscle fatigue and recovery status based on the user's exercise record and suggest optimal rest times. For example, when a user inputs an exercise record, the exercise record analysis unit uses the data to analyze muscle fatigue and suggest optimal rest times. For example, it calculates the time required for muscle recovery and advises on the amount of rest time before the next training session. This allows the system to analyze muscle fatigue and recovery status based on the user's exercise record and suggest optimal rest times.

[0066] The exercise record analysis unit can analyze heart rate and calorie consumption during exercise and provide detailed training advice. For example, when a user inputs an exercise record, the generation AI analyzes heart rate and calorie consumption based on that data and provides detailed training advice. For example, if an exercise session results in a high heart rate, the generation AI can suggest ways to control the heart rate during the next session. This allows the system to analyze heart rate and calorie consumption during exercise and provide detailed training advice.

[0067] The exercise record analysis unit can use the emotion estimation function to analyze the user's emotions after exercise and suggest a training plan to maintain motivation. For example, when a user inputs an exercise record, the exercise record analysis unit uses the emotion estimation function to analyze the user's emotions after exercise and suggest a training plan to maintain motivation. For example, if the user feels strong positive emotions after exercise, the generation AI can advise the user to continue similar training. This makes it possible to analyze the user's emotions after exercise and suggest a training plan to maintain motivation.

[0068] The exercise record analysis unit can suggest sports and activities suitable for the user. For example, when a user inputs an exercise record, the generation AI suggests sports and activities suitable for the user based on that data. For example, it suggests activities such as running or yoga depending on the user's physical strength and interests. This makes it possible to suggest sports and activities suitable for the user.

[0069] The exercise record analysis unit can add a function that allows a user to share their exercise records with other users and increase their motivation through competition and cooperation. The exercise record analysis unit can add a function that allows a user to share their exercise records within a community and increase their motivation through competition and cooperation with other users. For example, a challenge can be set to compete against each other in terms of running distance or time. This can add a function that allows a user to share their exercise records with other users and increase their motivation through competition and cooperation.

[0070] The exercise record analysis unit can use the emotion estimation function to analyze the user's emotions while exercising in real time and suggest exercises that will elicit positive emotions. For example, the exercise record analysis unit can use the emotion estimation function to analyze the user's emotions in real time while exercising and suggest exercises that will elicit positive emotions. For example, if the user is feeling tired, the exercise record analysis unit can suggest exercises that have a refreshing effect. This allows the user's emotions while exercising to be analyzed in real time and suggest exercises that will elicit positive emotions.

[0071] The activity record analysis unit can comprehensively analyze the user's activity, meal, and sleep records and make suggestions to optimize their lifestyle rhythm. For example, when the user inputs their activity, meal, and sleep records, the activity record analysis unit can comprehensively analyze the data and make suggestions to optimize their lifestyle rhythm. For example, it can provide advice on adjusting meal timing and sleep duration. This allows the user's activity, meal, and sleep records to be comprehensively analyzed and suggestions to optimize their lifestyle rhythm.

[0072] The activity record analysis unit can predict the user's health risks based on the recorded data and suggest preventive measures. For example, when the user inputs records of their activities, meals, and sleep, the generation AI can predict health risks based on that data and suggest preventive measures. For example, it can identify health risks caused by an unbalanced diet or lack of exercise and advise on improvement measures. This makes it possible to predict the user's health risks based on the recorded data and suggest preventive measures.

[0073] The activity record analysis unit uses the emotion estimation function to analyze the user's daily emotional fluctuations and provide an action plan based on the emotions. For example, when the user inputs a record of their activities, meals, and sleep, the activity record analysis unit uses the emotion estimation function to analyze the user's daily emotional fluctuations and provide an action plan based on the emotions. For example, it may suggest relaxation methods during times of high stress. This makes it possible to analyze the user's daily emotional fluctuations using the emotion estimation function and provide an action plan based on the emotions.

[0074] The activity record analysis unit can suggest relaxation methods and stress relief methods that are suitable for the user. For example, when the user inputs records of their activities, meals, and sleep, the activity record analysis unit uses that data to suggest relaxation methods and stress relief methods that are suitable for the user. For example, relaxation methods such as yoga and meditation are suggested. This makes it possible to suggest relaxation methods and stress relief methods that are suitable for the user.

[0075] The activity record analysis unit can add a community function that allows users to share recorded data with other users and support the achievement of health goals. The activity record analysis unit adds a function that allows users to share activity, meal, and sleep records within a community and support the achievement of health goals with other users. For example, users with the same goals can encourage each other. This allows users to share recorded data with other users and add a community function that supports the achievement of health goals.

[0076] The activity record analysis unit uses the emotion estimation function to analyze the user's emotions throughout the day in real time and propose an action plan to elicit positive emotions. For example, when the user inputs a record of their activities, meals, and sleep, the activity record analysis unit uses the emotion estimation function to analyze the user's emotions throughout the day in real time and propose an action plan to elicit positive emotions. For example, it may suggest relaxation methods during times of high stress. This allows the emotion estimation function to analyze the user's emotions throughout the day in real time and propose an action plan to elicit positive emotions.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The health coaching system can comprehensively analyze a user's health status and propose specific action plans. For example, a user can maintain health by maintaining a nutritionally balanced diet and improve physical fitness by engaging in optimal training. Following the comprehensive action plan also allows users to efficiently achieve their health goals. Furthermore, the system can collect and anonymize users' health data and analyze statistical health trends. This allows users to compare their health status with others and take more effective health management measures. For example, users can objectively evaluate their health status by referring to the average exercise and dietary habits of users of the same age. The system can also provide health advice tailored to the season and weather. For example, it can recommend vitamin D intake in winter and emphasize the importance of hydration in summer. This allows users to take appropriate actions to address seasonal health risks.

[0079] The food record analysis unit can learn a user's past eating patterns and predict future meal plans. For example, if a user enters a meal record for the past month, the generation AI can learn their eating patterns based on that data and predict their meal plan for the next week. For example, it can suggest balanced meal menus based on the foods the user frequently ate in the past. This allows the system to learn the user's past eating patterns and predict future meal plans. Furthermore, the system can also create meal plans taking into account the user's allergy information. For example, if a user is allergic to a specific food, the system can suggest menus that avoid that food. The system can also learn the user's food preferences and provide recipes that suit their tastes. For example, if a user likes spicy food, the system can suggest spicy dishes. This allows users to maintain their health while enjoying meals that suit their preferences.

[0080] The food record analysis unit can analyze the origin and quality of ingredients and suggest healthier choices. For example, when a user enters a food record, the generation AI uses that data to analyze the origin and quality of ingredients and suggests using fresh, locally grown ingredients. For example, it provides recipes using local agricultural products. This allows the system to analyze the origin and quality of ingredients and suggest healthier choices. Furthermore, the system can analyze the cultivation method and pesticide use information of ingredients and recommend organic and pesticide-free foods. For example, if the user is health-conscious, it can suggest recipes using organic foods. The system can also provide advice on how to store and cook ingredients. For example, it recommends eating ingredients rich in vitamin C raw and suggests ways to maximize the use of nutrients. This allows users to achieve a comprehensively healthy diet, from how to select ingredients to how to cook them.

[0081] The food record analysis unit can use the emotion estimation function to analyze the user's emotions regarding food and provide dietary advice based on stress and satisfaction. For example, when a user enters a food record, the generation AI uses the emotion estimation function to analyze the user's emotions. For example, if the stress level during a meal is high, the system can suggest relaxing ingredients and recipes. This allows the system to analyze the user's emotions regarding food and provide dietary advice based on stress and satisfaction. Furthermore, the system can adjust the timing of meals based on the user's emotions. For example, it can suggest light meals when the user is feeling stressed and recommend a full meal when the user is relaxing. The system can also adjust the amount of food eaten according to the user's emotions. For example, it can suggest small meals when satisfaction is low and provide an appropriate amount of food when satisfaction is high. This allows the user to enjoy meals that match their emotions.

[0082] The food record analysis unit can automatically generate recipes based on the user's food records and suggest them to the user. For example, when a user inputs a food record, the generation AI automatically generates nutritionally balanced recipes based on that data. For example, it can suggest menus that take into account the nutrients the user wants to consume. This allows recipes to be automatically generated and suggested based on the user's food records. Furthermore, the system can also generate recipes taking into account the user's food inventory information. For example, it can suggest recipes that use up all the ingredients in the refrigerator without waste. The system can also provide recipes that suit the user's cooking skills and time. For example, it can suggest simple and quick recipes for busy users, and provide challenging recipes for users who are good at cooking. This allows users to enjoy meals that suit their lifestyle.

[0083] The food record analysis unit can add a function that allows a user to share a food record with other users and receive feedback within the community. For example, a function can be added that allows a user to share a food record with other users within the community and receive feedback from other users. For example, the user can receive suggestions for improving their diet or new recipes. This allows a function to be added that allows a user to share a food record with other users and receive feedback within the community. Furthermore, the system can also compare food records between users and promote competition and cooperation in leading a healthy diet. For example, a challenge can be set in which users with the same goal compete to improve their diet. The system can also display a ranking within the community based on the user's food record to increase motivation. This allows users to achieve a healthy diet while cooperating with other users.

[0084] The exercise record analysis unit can analyze the user's muscle fatigue level and recovery state based on their exercise record and suggest optimal rest times. For example, when a user inputs their exercise record, the generation AI analyzes their muscle fatigue level based on that data and suggests optimal rest times. For example, it calculates the time required for muscle recovery and advises on the amount of rest time before the next workout. This allows the system to analyze the user's muscle fatigue level and recovery state based on their exercise record and suggest optimal rest times. Furthermore, the system can evaluate their recovery state by taking into account the user's sleep data. For example, it can provide advice that getting enough sleep will speed up recovery. The system can also consider the user's dietary data and emphasize the importance of nutritional supplementation. For example, it can provide advice that consuming appropriate nutrients after exercise will promote recovery. This allows users to continue effective training while managing their overall health.

[0085] The exercise record analysis unit can analyze heart rate and calorie consumption during exercise and provide detailed training advice. For example, when a user inputs an exercise record, the generation AI analyzes heart rate and calorie consumption based on that data and provides detailed training advice. For example, if a user performs an exercise that results in a high heart rate, the system can suggest ways to control their heart rate during the next workout. This allows the system to analyze heart rate and calorie consumption during exercise and provide detailed training advice. Furthermore, the system can analyze the user's exercise data over a long period of time and evaluate their training progress. For example, the system can analyze the user's physical fitness improvement trends based on data from the past few months. The system can also provide training plans tailored to the user's goals. For example, the system can suggest a long-distance running plan for a user aiming to run a marathon, and a weight training plan for a user who prioritizes strength training. This allows users to perform effective training tailored to their goals.

[0086] The exercise record analysis unit can use the emotion estimation function to analyze the user's emotions after exercise and suggest a training plan to maintain motivation. For example, when a user enters an exercise record, the generation AI can use the emotion estimation function to analyze the user's emotions after exercise and suggest a training plan to maintain motivation. For example, if the user feels strong positive emotions after exercise, the system can advise the user to continue the same training. This allows the system to analyze the user's emotions after exercise and suggest a training plan to maintain motivation. Furthermore, the system can adjust the difficulty of the training based on the user's emotions. For example, if the user feels very tired after exercise, the system can set the next training session to be easier. The system can also change the type of training depending on the user's emotions. For example, if the user is highly stressed, the system can suggest a relaxation exercise, and if the user feels strong positive emotions, the system can recommend a challenging exercise. This allows the user to train in a way that suits their emotions and improve their health while maintaining their motivation.

[0087] The exercise record analysis unit can suggest sports and activities suitable for the user. For example, when a user inputs their exercise record, the generation AI uses that data to suggest sports and activities suitable for the user. For example, it can suggest activities such as running or yoga based on the user's physical strength and interests. This allows it to suggest sports and activities suitable for the user. Furthermore, the system can change the type of sport based on the user's health condition and goals. For example, it can suggest swimming to avoid exercise that puts strain on the joints. The system can also suggest activities that suit the user's lifestyle. For example, it can suggest short, effective exercises for busy users and provide longer activities for users with more time. This allows users to enjoy sports and activities that suit their lifestyle.

[0088] The exercise record analysis unit can add a function that allows a user to share their exercise records with other users and increase their motivation through competition and cooperation. For example, a function can be added that allows a user to share their exercise records within a community and increase their motivation through competition and cooperation with other users. For example, a challenge can be set to compete in running distance or time. This allows a user to share their exercise records with other users and increase their motivation through competition and cooperation. Furthermore, the system can compare users' exercise records and provide incentives for living a healthy lifestyle. For example, users who achieve certain goals can be awarded badges or points to maintain motivation. The system can also display a ranking within the community based on the user's exercise records to stimulate a competitive spirit. This allows users to achieve a healthy lifestyle while cooperating with other users.

[0089] The exercise record analysis unit can use the emotion estimation function to analyze the user's emotions during exercise in real time and suggest exercises that will elicit positive emotions. For example, the emotion estimation function can be used to analyze the user's emotions in real time while they are exercising and suggest exercises that will elicit positive emotions. For example, if the user is feeling tired, the system can suggest exercises that have a refreshing effect. This allows the system to analyze the user's emotions during exercise in real time and suggest exercises that will elicit positive emotions. Furthermore, the system can adjust the intensity and type of exercise based on the user's emotions. For example, if the user is feeling stressed, the system can suggest yoga or stretching, which have a relaxing effect, and if the user is feeling strongly positive, the system can recommend high-intensity training. The system can also adjust the timing of exercise according to the user's emotions. For example, exercising during times when the user is relaxed can achieve more effective training. This allows the user to exercise in accordance with their emotions and improve their health while maintaining positive emotions.

[0090] The activity record analysis unit can comprehensively analyze a user's activity, meal, and sleep records and make suggestions for optimizing their lifestyle. For example, when a user inputs their activity, meal, and sleep records, the generation AI comprehensively analyzes the data and makes suggestions for optimizing their lifestyle. For example, it provides advice on adjusting meal timing and sleep duration. This allows the system to comprehensively analyze the user's activity, meal, and sleep records and make suggestions for optimizing their lifestyle. Furthermore, the system can adjust the user's lifestyle based on their stress and energy levels. For example, if stress is high, the system recommends increasing relaxation time, and if energy levels are low, the system recommends rest. The system can also customize the lifestyle based on the user's goals. For example, the system can adjust meal timing for a user aiming to lose weight and suggest appropriate rest times for a user doing strength training. This allows users to achieve a lifestyle that suits their goals and improve their health.

[0091] The activity record analysis unit can predict a user's health risks and suggest preventive measures based on the recorded data. For example, when a user inputs activity, diet, and sleep records, the generation AI uses that data to predict health risks and suggest preventive measures. For example, it can identify health risks caused by poor diet or lack of exercise and recommend improvement measures. This allows the system to predict a user's health risks and suggest preventive measures based on the recorded data. Furthermore, the system can evaluate health risks by taking into account the user's family history and genetic information. For example, if a family member has a high risk of heart disease, it can suggest heart-healthy diets and exercise. The system can also provide preventive measures tailored to the user's lifestyle. For example, it can recommend regular stretching and exercise for users who do a lot of desk work, and advise appropriate nutrition for users who are often out and about. This allows users to understand their health risks and take appropriate preventive measures.

[0092] The activity record analysis unit can use the emotion estimation function to analyze the user's daily emotional fluctuations and provide an action plan based on their emotions. For example, when a user enters activity, meal, and sleep records, the generation AI can use the emotion estimation function to analyze the user's daily emotional fluctuations and provide an action plan based on their emotions. For example, it can suggest relaxation methods during times of high stress. This allows the system to analyze the user's daily emotional fluctuations using the emotion estimation function and provide an action plan based on their emotions. Furthermore, the system can adjust the user's daily schedule based on their emotions. For example, it can recommend rest during times of high stress and productive activities during times of strong positive emotions. The system can also adjust the timing of meals and exercise based on the user's emotions. For example, eating during times of relaxation promotes digestion, and exercising during times of high energy levels ensures effective training. This allows users to achieve a lifestyle rhythm that suits their emotions and improve their health.

[0093] The activity record analysis unit can suggest relaxation and stress relief methods suitable for the user. For example, when a user inputs activity, meal, and sleep records, the generation AI uses that data to suggest relaxation and stress relief methods suitable for the user. For example, it suggests relaxation methods such as yoga and meditation. This allows it to suggest relaxation and stress relief methods suitable for the user. Furthermore, the system can customize relaxation methods taking into account the user's stress level and emotional state. For example, if stress is high, it might suggest deep breathing and mindfulness, and if relaxed, it might recommend light stretching or listening to music. The system can also provide relaxation methods tailored to the user's lifestyle. For example, it might suggest short, effective relaxation methods for busy users, and provide longer relaxation sessions for users with more time. This allows users to practice relaxation methods that suit their lifestyle and effectively relieve stress.

[0094] The activity record analysis unit can add a community function that allows users to share their recorded data with other users and support them in achieving their health goals. For example, a function can be added that allows users to share their activity, food, and sleep records within a community and support other users in achieving their health goals. For example, users with the same goals can encourage each other. This allows users to share their recorded data with other users and support them in achieving their health goals. Furthermore, the system can compare users' recorded data and provide incentives for living a healthy lifestyle. For example, users who achieve certain goals can be awarded badges or points to maintain motivation. The system can also display a ranking within the community based on the user's recorded data to stimulate a competitive spirit. This allows users to work together with other users to achieve a healthy lifestyle.

[0095] The activity record analysis unit can use the emotion estimation function to analyze the user's daily emotions in real time and suggest action plans to elicit positive emotions. For example, when a user enters activity, meal, and sleep records, the generation AI can use the emotion estimation function to analyze the user's daily emotions in real time and suggest action plans to elicit positive emotions. For example, it can suggest relaxation methods during times of high stress. This allows the system to analyze the user's daily emotions in real time and suggest action plans to elicit positive emotions using the emotion estimation function. Furthermore, the system can adjust the user's daily schedule based on the user's emotions. For example, it can recommend rest during times of high stress and productive activities during times of strong positive emotions. The system can also adjust the timing of meals and exercise according to the user's emotions. For example, eating during times of relaxation promotes digestion, and exercising during times of high energy levels ensures effective training. This allows users to achieve a lifestyle rhythm that suits their emotions and improve their health.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The food record analysis unit analyzes the user's food record. For example, it calculates nutrient intake based on the types and amounts of food entered by the user and determines whether it is balanced. The generative AI also analyzes the details of the meal and provides advice on nutritional balance. For example, it analyzes data entered such as "I ate bread and eggs for breakfast" and provides advice such as "You're not getting enough protein, so next time you should add yogurt." Step 2: The exercise record analysis unit analyzes the user's exercise record. For example, it evaluates the effectiveness of the exercise based on the type and duration of the exercise performed by the user and provides advice for the next training session. The generative AI also analyzes the details of the exercise and optimizes the training. For example, it analyzes data such as "I jogged for 30 minutes today" and provides advice such as "It would be effective to incorporate interval training next time." Step 3: The activity record analysis unit analyzes the user's activity record. For example, the unit evaluates the user's overall health status based on their daily activity level and sleep duration, and provides an action plan for achieving their goals. The generation AI also analyzes the details of the activity and suggests specific action plans. For example, if the user inputs data such as "I got eight hours of sleep today and had three balanced meals," it will analyze the data and provide an action plan such as "You should exercise a little more tomorrow." Step 4: The action plan suggestion unit proposes an action plan based on the analysis results of the food record analysis unit, exercise record analysis unit, and activity record analysis unit. For example, the generation AI comprehensively analyzes the user's food, exercise, and activity records and provides a specific action plan for achieving goals. This allows the user to maintain their health by continuing to eat a nutritionally balanced diet and improve their physical strength by performing optimal training. In addition, by following the comprehensive action plan, they can efficiently achieve their health goals.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, a 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0142] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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]

[0165] 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 record analysis unit that analyzes a user's meal record; an exercise record analysis unit that analyzes the user's exercise record; an activity record analysis unit that analyzes the user's activity record; an action plan proposal unit that proposes an action plan based on the analysis results of the diet record analysis unit, the exercise record analysis unit, and the activity record analysis unit. A system characterized by:

2. The diet record analysis unit Analyzing the origin and quality of ingredients to suggest healthier choices 2. The system of claim 1.

3. The exercise record analysis unit Analyzing muscle fatigue and recovery state based on the exercise record of the user and proposing optimal rest time 2. The system of claim 1.

4. The activity record analysis unit Comprehensively analyze the user's activity, diet, and sleep records and make suggestions to optimize their lifestyle.

2. The system of claim 1.

5. The diet record analysis unit Analyze the user's feelings about food and provide dietary advice based on stress and satisfaction 2. The system of claim 1.

6. The exercise record analysis unit Analyze the user's post-exercise emotions and suggest training plans to maintain motivation 2. The system of claim 1.

7. The activity record analysis unit Analyzing the user's daily emotional fluctuations and providing the action plan based on the emotional fluctuations.

2. The system of claim 1.

8. The activity record analysis unit Analyzing the user's daily emotions in real time and proposing an action plan that elicits positive emotions 2. The system of claim 1.

Images