System

Abstract

An object of a system according to an embodiment is to comprehensively predict a health condition of a user and make a specific lifestyle improvement proposal.SOLUTION: A system according to an embodiment includes a basic information collection unit, a health information collection unit, a meal data collection unit, an exercise data collection unit, a health condition analysis unit, and a lifestyle improvement suggestion unit. The basic information collection unit collects the height, weight, and age of the user. The health information collecting unit collects the user's daily blood pressure and body fat percentage. The meal data collection unit collects pictures of meals of the user. The exercise data collection unit collects daily exercise data of a user. The health condition analysis unit analyzes the data collected by the basic information collection unit, the health information collection unit, the diet data collection unit, and the exercise data collection unit, and predicts a health condition when a current life is maintained. The lifestyle improvement suggestion unit presents a specific example of lifestyle improvement for setting the health condition predicted by the health condition analysis unit to a certain level or higher.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been able to comprehensively predict a user's health condition and provide specific lifestyle improvement suggestions, and there is room for improvement.

[0005] The system according to the embodiment aims to comprehensively predict the health condition of a user and provide specific suggestions for improving their lifestyle. [Means for solving the problem]

[0006] The system according to the embodiment includes a basic information collection unit, a health information collection unit, a dietary data collection unit, an exercise data collection unit, a health condition analysis unit, and a lifestyle improvement suggestion unit. The basic information collection unit collects the user's height, weight, and age. The health information collection unit collects the user's daily blood pressure and body fat percentage. The dietary data collection unit collects photos of the user's meals. The exercise data collection unit collects the user's daily exercise data. The health condition analysis unit analyzes the data collected by the basic information collection unit, health information collection unit, dietary data collection unit, and exercise data collection unit, and predicts the user's health condition if the current lifestyle is maintained. The lifestyle improvement suggestion unit presents specific examples of lifestyle improvements to raise the health condition predicted by the health condition analysis unit to a certain level or above. [Effects of the Invention]

[0007] The system according to the embodiment can comprehensively predict the health condition of the user and make specific suggestions for improving their lifestyle. [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 management system according to an embodiment of the present invention is a system that analyzes and evaluates a user's health condition if the user maintains their current lifestyle based on basic physical information, daily health information, dietary data, and daily exercise data, and presents specific examples of lifestyle improvements that will improve the user's health to a certain level or above. This allows the health management system to comprehensively evaluate the user's health condition and present specific lifestyle improvements.

[0029] A health management system according to an embodiment includes a basic information collection unit, a health information collection unit, a dietary data collection unit, an exercise data collection unit, a health condition analysis unit, and a lifestyle improvement suggestion unit. The basic information collection unit collects the user's height, weight, and age. For example, the user inputs their height, weight, and age into an application. The basic information collection unit can also collect the user's genetic information. The health information collection unit collects the user's daily blood pressure and body fat percentage. For example, the user inputs the data using a blood pressure monitor or a body fat scale. The health information collection unit can also capture the user's skin color and texture with a camera, allowing the generation AI to evaluate the skin's health condition. The dietary data collection unit collects photos of the user's meals. For example, the user takes photos of their meals and uploads them to the application. The dietary data collection unit can also scan barcodes of ingredients, allowing the generation AI to analyze detailed nutritional information. The exercise data collection unit collects the user's daily exercise data. For example, the user inputs the data using a pedometer or fitness tracker. The exercise data collection unit also collects heart rate and oxygen saturation during exercise, allowing the generation AI to evaluate the effects of the exercise in detail. The health condition analysis unit analyzes the data collected by the basic information collection unit, health information collection unit, dietary data collection unit, and exercise data collection unit, and predicts the health condition if the current lifestyle is maintained. For example, the generation AI analyzes time-series data and predicts changes in the health condition. The lifestyle improvement suggestion unit presents specific examples of lifestyle improvements to bring the health condition predicted by the health condition analysis unit to a certain level or above. For example, the generation AI analyzes the user's lifestyle habits in detail and proposes an individual lifestyle improvement plan. As a result, the health management system according to the embodiment can comprehensively evaluate the user's health condition and present specific lifestyle improvement measures.

[0030] The basic information collection unit can collect additional genetic information about the user, and the generation AI can present a health index that takes genetic risk into account based on the genetic information. The basic information collection unit, for example, collects the user's genetic information, and the generation AI evaluates genetic risk based on that information. For example, if there is a specific genetic mutation, the health index can be adjusted based on that risk. This allows for a more personalized health assessment by presenting a health index that takes genetic risk into account.

[0031] The basic information collection unit allows the user to input their lifestyle habits (smoking, drinking, sleep patterns), and the generation AI can perform a health assessment that takes these effects into account. For example, the basic information collection unit allows the user to input their smoking habits, and the generation AI can perform a health assessment that takes these effects into account. For example, smokers can be presented with a health index that takes into account the risk of declining lung function. This makes it possible to provide a more realistic health index by performing a health assessment that takes lifestyle habits into account.

[0032] The basic information collection unit uses voice input to collect basic information about the user, and the generation AI can analyze the voice data to calculate health indices. For example, the basic information collection unit allows the user to input basic information such as height, weight, and age by voice, and the generation AI analyzes the voice data to calculate health indices. For example, the data is converted into text using voice recognition technology, and the basal metabolic rate is calculated. In this way, using voice input reduces the input burden on the user and makes it possible to provide health indices more quickly.

[0033] The basic information collection unit collects health information about family members, allowing the generation AI to evaluate the health risk of the entire family. For example, the basic information collection unit collects health information about the user's family, and the generation AI evaluates the health risk of the entire family based on that data. For example, it presents health indicators taking into account genetic risks based on family history. This allows for more comprehensive health management by evaluating the health risk of the entire family.

[0034] The health information collection unit can capture the color and texture of the user's skin with a camera, and the generation AI can evaluate the health condition of the skin. For example, the health information collection unit can capture the color and texture of the user's skin with a camera, and the generation AI can analyze the data to evaluate the health condition of the skin. For example, it evaluates the presence or absence of dryness and blemishes and makes skin care suggestions. In this way, by evaluating the health condition of the skin, it is possible to provide information that is useful for the user's skin care.

[0035] The health information collection unit collects the user's heart rate and respiratory rate using a wearable device, and the generation AI analyzes this data to perform a health assessment. The health information collection unit, for example, collects the user's heart rate and respiratory rate using a wearable device, and the generation AI analyzes the data to perform a health assessment. For example, it analyzes heart rate fluctuations and evaluates stress levels. This allows for a more detailed assessment of the user's health condition by analyzing the heart rate and respiratory rate.

[0036] The health information collection unit can link the user's health information with smart home devices and perform health assessments that take environmental data (temperature, humidity, air quality) into account. For example, the health information collection unit links the user's health information with smart home devices and performs health assessments that take environmental data (temperature, humidity, air quality) into account. For example, if the indoor air quality is poor, the health information collection unit can evaluate the risk to the respiratory system. This allows for a more accurate assessment of the user's health condition by performing health assessments that take environmental data into account.

[0037] The health information collection unit can link the user's health information with other health apps and integrate data from the health apps to provide a comprehensive health assessment. For example, the health information collection unit links the user's health information with other health apps, and the generation AI provides a comprehensive health assessment. For example, it links with a fitness app to integrate exercise data. This allows the system to provide a more comprehensive health assessment by linking with other health apps.

[0038] The meal data collection unit scans the barcodes of ingredients in addition to photos of meals, and the generation AI can analyze detailed nutritional information based on the barcodes. The meal data collection unit, for example, scans the barcodes of ingredients in addition to photos of meals, and the generation AI can analyze detailed nutritional information based on that data. For example, ingredient information of ingredients is obtained from the barcodes, and calories and nutrients are evaluated. In this way, using barcodes can provide more detailed nutritional information.

[0039] The dietary data collection unit analyzes the user's dietary history over a long period of time, and the generation AI can evaluate changes in dietary patterns based on the dietary history. The dietary data collection unit, for example, collects the user's dietary history over a long period of time, and the generation AI evaluates changes in dietary patterns based on that data. For example, dietary data from the past year is analyzed to evaluate changes in nutritional balance. This makes it possible to evaluate changes in dietary patterns by analyzing a long-term dietary history.

[0040] The dietary data collection unit shares dietary data with other users, and the generation AI can provide a health assessment based on the population data. The dietary data collection unit, for example, shares a user's dietary data with other users, and the generation AI can provide a health assessment based on the population data. For example, the health indicators of a group of users with the same dietary pattern are compared. This allows the health assessment to be provided based on population data, making it possible to evaluate the health status from a broader perspective.

[0041] The dietary data collection unit links dietary data with a recipe app, and the generation AI can suggest healthy recipes based on the dietary data. The dietary data collection unit, for example, links a user's dietary data with a recipe app, and the generation AI suggests healthy recipes based on that data. For example, it suggests balanced recipes based on calorie intake. In this way, by linking with the recipe app, healthy dietary suggestions can be made to the user.

[0042] The exercise data collection unit collects the user's exercise data as well as their heart rate and oxygen saturation during exercise, allowing the generation AI to evaluate the effectiveness of the exercise in detail based on the heart rate and oxygen saturation. The exercise data collection unit, for example, collects the user's exercise data as well as their heart rate and oxygen saturation during exercise, allowing the generation AI to evaluate the effectiveness of the exercise in detail based on that data. For example, it analyzes heart rate fluctuations and evaluates the intensity of the exercise. In this way, by collecting heart rate and oxygen saturation during exercise, the effectiveness of the exercise can be evaluated in more detail.

[0043] The exercise data collection unit analyzes the user's exercise history over a long period of time, and the generation AI can evaluate changes in exercise patterns based on the exercise history. For example, the exercise data collection unit collects the user's exercise history over a long period of time, and the generation AI evaluates changes in exercise patterns based on that data. For example, the exercise data from the past year is analyzed to evaluate fluctuations in exercise habits. This makes it possible to evaluate changes in exercise patterns by analyzing long-term exercise history.

[0044] The exercise data collection unit can share the user's exercise data with other users, and the generation AI can provide an exercise evaluation based on the group data. For example, the exercise data collection unit can share the user's exercise data with other users, and the generation AI can provide an exercise evaluation based on the group data. For example, the exercise effects of a group of users with the same exercise pattern can be compared. This allows the exercise effects to be evaluated from a broader perspective by providing an exercise evaluation based on group data.

[0045] The exercise data collection unit links the exercise data with a fitness app, and the generation AI can propose an individual exercise plan based on the exercise data. The exercise data collection unit, for example, links the user's exercise data with a fitness app, and the generation AI proposes an individual exercise plan based on that data. For example, it proposes an optimal exercise plan based on exercise history. In this way, by linking with the fitness app, it is possible to propose an optimal exercise plan for the user.

[0046] The health condition analysis unit analyzes the user's health data in chronological order, and the generation AI can predict changes in health condition based on the health data. The health condition analysis unit, for example, collects the user's health data in chronological order, and the generation AI predicts changes in health condition based on that data. For example, it analyzes data from the past year and predicts future health risks. This makes it possible to predict changes in health condition by analyzing time-series data.

[0047] The health status analysis unit integrates the user's health data with other health indicators (e.g., sleep data), and the generation AI can perform a comprehensive health assessment based on the health data and health indicators. The health status analysis unit, for example, integrates the user's health data with other health indicators (e.g., sleep data), and the generation AI can perform a comprehensive health assessment. For example, the health assessment can be performed by integrating sleep data and exercise data. This allows for a more comprehensive health assessment to be provided by integrating with other health indicators.

[0048] The health status analysis unit aggregates the user's health data by region, and the generation AI can evaluate health trends by region based on the health data by region. The health status analysis unit, for example, aggregates the user's health data by region, and the generation AI can evaluate health trends by region based on that data. For example, it evaluates the risk of high blood pressure in a specific region. In this way, by evaluating health trends by region, it is possible to understand health risks specific to each region.

[0049] The health condition analysis unit links health data with insurance companies, and the generation AI can propose optimized insurance plans based on the health data. The health condition analysis unit, for example, links a user's health data with insurance companies, and the generation AI can propose optimized insurance plans based on that data. For example, it proposes insurance plans based on health risks. In this way, by collaborating with insurance companies, it is possible to propose the most suitable insurance plan for the user.

[0050] The lifestyle improvement suggestion unit analyzes the user's lifestyle habits in detail, and the generation AI can propose an individual lifestyle improvement plan based on the lifestyle habits. The lifestyle improvement suggestion unit, for example, analyzes the user's lifestyle habits in detail, and the generation AI can propose an individual lifestyle improvement plan based on that data. For example, it proposes an optimal improvement plan based on eating and exercise habits. In this way, by analyzing the lifestyle habits in detail, it is possible to propose an optimal lifestyle improvement plan for the user.

[0051] The lifestyle improvement suggestion unit allows the generation AI to set long-term lifestyle improvement goals based on the user's health data. For example, the generation AI sets long-term lifestyle improvement goals based on the user's health data. For example, it sets goals for weight or blood pressure and proposes a plan to achieve them. In this way, by setting long-term lifestyle improvement goals, the generation AI can support the user in maintaining their health.

[0052] The lifestyle improvement suggestion unit can share the lifestyle improvement plan with other users, and the generation AI can provide improvement suggestions based on the group data. For example, the lifestyle improvement suggestion unit can share the user's lifestyle improvement plan with other users, and the generation AI can provide improvement suggestions based on the group data. For example, it can compare the results of user groups with the same improvement plan. This makes it possible to support lifestyle improvements from a broader perspective by providing improvement suggestions based on group data.

[0053] The lifestyle improvement suggestion unit can link the lifestyle improvement plan with smart home devices and make improvement suggestions that take environmental data into consideration. For example, the lifestyle improvement suggestion unit links the user's lifestyle improvement plan with smart home devices, and the generation AI makes improvement suggestions that take environmental data into consideration based on that data. For example, if the indoor air quality is poor, ventilation is suggested. In this way, by linking with smart home devices, lifestyle improvement suggestions that take environmental data into consideration can be made.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The health management system may further include a sleep data collection unit that collects the user's sleep data. The sleep data collection unit acquires sleep data from, for example, a smartwatch or fitness tracker used by the user, and the generation AI analyzes the data to evaluate the quality of sleep. For example, it may analyze the depth of sleep and frequency of interruptions and provide suggestions for improving the user's sleep. This enables a comprehensive health assessment that takes sleep data into account.

[0056] The health management system can further include a social activity data collection unit that collects social activity data on users. The social activity data collection unit, for example, analyzes the frequency of posts and messages on the user's social media accounts, and the generation AI evaluates the level of social activity based on that data. For example, if social activity is low, a health index that takes into account feelings of isolation and depression risk can be presented. This enables health assessment that takes social activity into account.

[0057] The health management system can further include a hobby data collection unit that collects data on the user's hobbies and interests. The hobby data collection unit collects, for example, information about the events and hobbies the user participates in, and the generation AI evaluates the user's mental health based on that data. For example, if the user is not actively engaging in hobby activities, it can suggest a new hobby to relieve stress. This enables health evaluation that takes hobbies and interests into account.

[0058] The health management system can further include a workplace environment data collection unit that collects data on the user's workplace. The workplace environment data collection unit collects, for example, the temperature, humidity, and noise level of the user's workplace, and the generation AI evaluates the health risks of the workplace environment based on that data. For example, if the noise level is high, a health index that takes stress risk into account can be presented. This enables health evaluation that takes the workplace environment into account.

[0059] The health management system can further include a travel data collection unit that collects the user's travel data. The travel data collection unit, for example, collects the user's travel history and activity data during the trip, and the generation AI evaluates the health benefits of the trip based on that data. For example, it can analyze the number of steps taken and the amount of activity during the trip to evaluate the positive impact that the trip has on health. This enables health evaluation that takes travel data into account.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The basic information collection unit collects the user's height, weight, and age. For example, the user enters their height, weight, and age into the application. The basic information collection unit can also collect the user's genetic information. Step 2: The health information collection unit collects the user's daily blood pressure and body fat percentage. For example, the user inputs the data using a blood pressure monitor or body fat scale. The health information collection unit can also capture the user's skin color and texture with a camera, allowing the generation AI to evaluate the health of the skin. Step 3: The meal data collection unit collects photos of the user's meals. For example, the user can take photos of their meals and upload them to the application. The meal data collection unit can also scan the barcodes of ingredients, which the AI ​​can then analyze to obtain detailed nutritional information. Step 4: The exercise data collection unit collects the user's daily exercise data. For example, the user inputs data using a pedometer or fitness tracker. The exercise data collection unit also collects heart rate and oxygen saturation during exercise, allowing the generation AI to evaluate the effects of the exercise in detail. Step 5: The health status analysis unit analyzes the data collected by the basic information collection unit, health information collection unit, dietary data collection unit, and exercise data collection unit, and predicts the health status if the current lifestyle is maintained. For example, the generation AI analyzes time-series data and predicts changes in health status. Step 6: The lifestyle improvement suggestion unit presents specific examples of lifestyle improvements to improve the health condition predicted by the health condition analysis unit to a certain level or above. For example, the generation AI analyzes the user's lifestyle habits in detail and proposes an individual lifestyle improvement plan.

[0062] (Example 2) A health management system according to an embodiment of the present invention is a system that analyzes and evaluates a user's health condition if the user maintains their current lifestyle based on basic physical information, daily health information, dietary data, and daily exercise data, and presents specific examples of lifestyle improvements that will improve the user's health to a certain level or above. This allows the health management system to comprehensively evaluate the user's health condition and present specific lifestyle improvements.

[0063] A health management system according to an embodiment includes a basic information collection unit, a health information collection unit, a dietary data collection unit, an exercise data collection unit, a health condition analysis unit, and a lifestyle improvement suggestion unit. The basic information collection unit collects the user's height, weight, and age. For example, the user inputs their height, weight, and age into an application. The basic information collection unit can also collect the user's genetic information. The health information collection unit collects the user's daily blood pressure and body fat percentage. For example, the user inputs the data using a blood pressure monitor or a body fat scale. The health information collection unit can also capture the user's skin color and texture with a camera, allowing the generation AI to evaluate the skin's health condition. The dietary data collection unit collects photos of the user's meals. For example, the user takes photos of their meals and uploads them to the application. The dietary data collection unit can also scan barcodes of ingredients, allowing the generation AI to analyze detailed nutritional information. The exercise data collection unit collects the user's daily exercise data. For example, the user inputs the data using a pedometer or fitness tracker. The exercise data collection unit also collects heart rate and oxygen saturation during exercise, allowing the generation AI to evaluate the effects of the exercise in detail. The health condition analysis unit analyzes the data collected by the basic information collection unit, health information collection unit, dietary data collection unit, and exercise data collection unit, and predicts the health condition if the current lifestyle is maintained. For example, the generation AI analyzes time-series data and predicts changes in the health condition. The lifestyle improvement suggestion unit presents specific examples of lifestyle improvements to bring the health condition predicted by the health condition analysis unit to a certain level or above. For example, the generation AI analyzes the user's lifestyle habits in detail and proposes an individual lifestyle improvement plan. As a result, the health management system according to the embodiment can comprehensively evaluate the user's health condition and present specific lifestyle improvement measures.

[0064] The basic information collection unit can collect additional genetic information about the user, and the generation AI can present a health index that takes genetic risk into account based on the genetic information. The basic information collection unit, for example, collects the user's genetic information, and the generation AI evaluates genetic risk based on that information. For example, if there is a specific genetic mutation, the health index can be adjusted based on that risk. This allows for a more personalized health assessment by presenting a health index that takes genetic risk into account.

[0065] The basic information collection unit allows the user to input their lifestyle habits (smoking, drinking, sleep patterns), and the generation AI can perform a health assessment that takes these effects into account. For example, the basic information collection unit allows the user to input their smoking habits, and the generation AI can perform a health assessment that takes these effects into account. For example, smokers can be presented with a health index that takes into account the risk of declining lung function. This makes it possible to provide a more realistic health index by performing a health assessment that takes lifestyle habits into account.

[0066] The basic information collection unit can estimate the user's stress level using the emotion estimation function and analyze the impact of stress on health. For example, the basic information collection unit can use the emotion estimation function to estimate the user's stress level in real time and perform a health assessment based on that data. For example, if stress is high, a health index that takes cardiovascular risk into account is presented. This makes it possible to provide a more comprehensive health index by performing a health assessment that takes stress level into account.

[0067] The basic information collection unit uses voice input to collect basic information about the user, and the generation AI can analyze the voice data to calculate health indices. For example, the basic information collection unit allows the user to input basic information such as height, weight, and age by voice, and the generation AI analyzes the voice data to calculate health indices. For example, the data is converted into text using voice recognition technology, and the basal metabolic rate is calculated. In this way, using voice input reduces the input burden on the user and makes it possible to provide health indices more quickly.

[0068] The basic information collection unit collects health information about family members, allowing the generation AI to evaluate the health risk of the entire family. For example, the basic information collection unit collects health information about the user's family, and the generation AI evaluates the health risk of the entire family based on that data. For example, it presents health indicators taking into account genetic risks based on family history. This allows for more comprehensive health management by evaluating the health risk of the entire family.

[0069] The basic information collection unit can use the emotion estimation function to analyze the emotion of the user when entering information in real time and provide positive feedback based on the emotion. The basic information collection unit can, for example, use the emotion estimation function to analyze the emotion of the user when entering basic information in real time and provide positive feedback. For example, it can display an encouraging message when the entry is complete. In this way, the user's emotion when entering information is taken into consideration and positive feedback is provided, thereby improving the user's motivation.

[0070] The health information collection unit can capture the color and texture of the user's skin with a camera, and the generation AI can evaluate the health condition of the skin. For example, the health information collection unit can capture the color and texture of the user's skin with a camera, and the generation AI can analyze the data to evaluate the health condition of the skin. For example, it evaluates the presence or absence of dryness and blemishes and makes skin care suggestions. In this way, by evaluating the health condition of the skin, it is possible to provide information that is useful for the user's skin care.

[0071] The health information collection unit collects the user's heart rate and respiratory rate using a wearable device, and the generation AI analyzes this data to perform a health assessment. The health information collection unit, for example, collects the user's heart rate and respiratory rate using a wearable device, and the generation AI analyzes the data to perform a health assessment. For example, it analyzes heart rate fluctuations and evaluates stress levels. This allows for a more detailed assessment of the user's health condition by analyzing the heart rate and respiratory rate.

[0072] The health information collection unit can use the emotion estimation function to analyze the user's emotional state in association with daily health information. The health information collection unit, for example, uses the emotion estimation function to analyze the user's emotional state in association with daily health information. For example, the health information collection unit evaluates the relationship between stress level and blood pressure. This allows for a more comprehensive evaluation of the user's health state by analyzing the emotional state and health information in association with each other.

[0073] The health information collection unit can link the user's health information with smart home devices and perform health assessments that take environmental data (temperature, humidity, air quality) into account. For example, the health information collection unit links the user's health information with smart home devices and performs health assessments that take environmental data (temperature, humidity, air quality) into account. For example, if the indoor air quality is poor, the health information collection unit can evaluate the risk to the respiratory system. This allows for a more accurate assessment of the user's health condition by performing health assessments that take environmental data into account.

[0074] The health information collection unit can link the user's health information with other health apps and integrate data from the health apps to provide a comprehensive health assessment. For example, the health information collection unit links the user's health information with other health apps, and the generation AI provides a comprehensive health assessment. For example, it links with a fitness app to integrate exercise data. This allows the system to provide a more comprehensive health assessment by linking with other health apps.

[0075] The health information collection unit can use the emotion estimation function to analyze the user's emotions when entering health information and improve the accuracy of the entry based on the emotions. For example, the health information collection unit can use the emotion estimation function to analyze the user's emotions when entering health information and improve the accuracy of the entry based on the data. For example, if the user is under high stress, suggestions can be made to reduce entry errors. This allows the accuracy of health information entry to be improved by taking the user's emotional state into consideration.

[0076] The meal data collection unit scans the barcodes of ingredients in addition to photos of meals, and the generation AI can analyze detailed nutritional information based on the barcodes. The meal data collection unit, for example, scans the barcodes of ingredients in addition to photos of meals, and the generation AI can analyze detailed nutritional information based on that data. For example, ingredient information of ingredients is obtained from the barcodes, and calories and nutrients are evaluated. In this way, using barcodes can provide more detailed nutritional information.

[0077] The dietary data collection unit analyzes the user's dietary history over a long period of time, and the generation AI can evaluate changes in dietary patterns based on the dietary history. The dietary data collection unit, for example, collects the user's dietary history over a long period of time, and the generation AI evaluates changes in dietary patterns based on that data. For example, dietary data from the past year is analyzed to evaluate changes in nutritional balance. This makes it possible to evaluate changes in dietary patterns by analyzing a long-term dietary history.

[0078] The dietary data collection unit shares dietary data with other users, and the generation AI can provide a health assessment based on the population data. The dietary data collection unit, for example, shares a user's dietary data with other users, and the generation AI can provide a health assessment based on the population data. For example, the health indicators of a group of users with the same dietary pattern are compared. This allows the health assessment to be provided based on population data, making it possible to evaluate the health status from a broader perspective.

[0079] The dietary data collection unit links dietary data with a recipe app, and the generation AI can suggest healthy recipes based on the dietary data. The dietary data collection unit, for example, links a user's dietary data with a recipe app, and the generation AI suggests healthy recipes based on that data. For example, it suggests balanced recipes based on calorie intake. In this way, by linking with the recipe app, healthy dietary suggestions can be made to the user.

[0080] The dietary data collection unit can use the emotion estimation function to analyze the emotion of the user when inputting dietary data and promote positive dietary habits based on the emotion. For example, the dietary data collection unit can use the emotion estimation function to analyze the emotion of the user when inputting dietary data and promote positive dietary habits based on the data. For example, when positive emotion is strong, a healthy diet is recommended. In this way, by taking emotion into consideration, positive dietary habits of the user can be promoted.

[0081] The exercise data collection unit collects the user's exercise data as well as their heart rate and oxygen saturation during exercise, allowing the generation AI to evaluate the effectiveness of the exercise in detail based on the heart rate and oxygen saturation. The exercise data collection unit, for example, collects the user's exercise data as well as their heart rate and oxygen saturation during exercise, allowing the generation AI to evaluate the effectiveness of the exercise in detail based on that data. For example, it analyzes heart rate fluctuations and evaluates the intensity of the exercise. In this way, by collecting heart rate and oxygen saturation during exercise, the effectiveness of the exercise can be evaluated in more detail.

[0082] The exercise data collection unit analyzes the user's exercise history over a long period of time, and the generation AI can evaluate changes in exercise patterns based on the exercise history. For example, the exercise data collection unit collects the user's exercise history over a long period of time, and the generation AI evaluates changes in exercise patterns based on that data. For example, the exercise data from the past year is analyzed to evaluate fluctuations in exercise habits. This makes it possible to evaluate changes in exercise patterns by analyzing long-term exercise history.

[0083] The exercise data collection unit can use the emotion estimation function to analyze the user's emotions while exercising and evaluate the relationship between emotions and exercise based on the emotions. For example, the exercise data collection unit can use the emotion estimation function to analyze the user's emotions while exercising and evaluate the relationship between emotions and exercise based on the data. For example, it can evaluate the tendency for exercise to be more effective when positive emotions are strong. In this way, by evaluating the relationship between emotions and exercise, it is possible to gain a deeper understanding of the user's exercise habits.

[0084] The exercise data collection unit can share the user's exercise data with other users, and the generation AI can provide an exercise evaluation based on the group data. For example, the exercise data collection unit can share the user's exercise data with other users, and the generation AI can provide an exercise evaluation based on the group data. For example, the exercise effects of a group of users with the same exercise pattern can be compared. This allows the exercise effects to be evaluated from a broader perspective by providing an exercise evaluation based on group data.

[0085] The exercise data collection unit links the exercise data with a fitness app, and the generation AI can propose an individual exercise plan based on the exercise data. The exercise data collection unit, for example, links the user's exercise data with a fitness app, and the generation AI proposes an individual exercise plan based on that data. For example, it proposes an optimal exercise plan based on exercise history. In this way, by linking with the fitness app, it is possible to propose an optimal exercise plan for the user.

[0086] The exercise data collection unit can use the emotion estimation function to analyze the emotion a user has when inputting exercise data and promote positive exercise habits based on the emotion. For example, the exercise data collection unit can use the emotion estimation function to analyze the emotion a user has when inputting exercise data and promote positive exercise habits based on the data. For example, exercise can be recommended when positive emotions are strong. In this way, taking emotions into consideration can promote positive exercise habits for the user.

[0087] The health condition analysis unit analyzes the user's health data in chronological order, and the generation AI can predict changes in health condition based on the health data. The health condition analysis unit, for example, collects the user's health data in chronological order, and the generation AI predicts changes in health condition based on that data. For example, it analyzes data from the past year and predicts future health risks. This makes it possible to predict changes in health condition by analyzing time-series data.

[0088] The health status analysis unit integrates the user's health data with other health indicators (e.g., sleep data), and the generation AI can perform a comprehensive health assessment based on the health data and health indicators. The health status analysis unit, for example, integrates the user's health data with other health indicators (e.g., sleep data), and the generation AI can perform a comprehensive health assessment. For example, the health assessment can be performed by integrating sleep data and exercise data. This allows for a more comprehensive health assessment to be provided by integrating with other health indicators.

[0089] The health condition analysis unit can use the emotion estimation function to analyze the user's emotional state and perform a health assessment based on the emotional state. The health condition analysis unit, for example, uses the emotion estimation function to reflect the user's emotional state in the health assessment. For example, if the stress level is high, a health assessment that takes cardiovascular risk into account is performed. In this way, by reflecting the emotional state in the health assessment, a more accurate health assessment is possible.

[0090] The health status analysis unit aggregates the user's health data by region, and the generation AI can evaluate health trends by region based on the health data by region. The health status analysis unit, for example, aggregates the user's health data by region, and the generation AI can evaluate health trends by region based on that data. For example, it evaluates the risk of high blood pressure in a specific region. In this way, by evaluating health trends by region, it is possible to understand health risks specific to each region.

[0091] The health condition analysis unit links health data with insurance companies, and the generation AI can propose optimized insurance plans based on the health data. The health condition analysis unit, for example, links a user's health data with insurance companies, and the generation AI can propose optimized insurance plans based on that data. For example, it proposes insurance plans based on health risks. In this way, by collaborating with insurance companies, it is possible to propose the most suitable insurance plan for the user.

[0092] The health condition analysis unit uses the emotion estimation function to analyze the emotions of the user when undergoing a health assessment, and can improve the acceptability of the assessment results based on the emotions. For example, the health condition analysis unit uses the emotion estimation function to analyze the emotions of the user when undergoing a health assessment, and can improve the acceptability of the assessment results based on the data. For example, it provides positive feedback. In this way, by taking emotions into consideration, the acceptability of the assessment results can be improved.

[0093] The lifestyle improvement suggestion unit analyzes the user's lifestyle habits in detail, and the generation AI can propose an individual lifestyle improvement plan based on the lifestyle habits. The lifestyle improvement suggestion unit, for example, analyzes the user's lifestyle habits in detail, and the generation AI can propose an individual lifestyle improvement plan based on that data. For example, it proposes an optimal improvement plan based on eating and exercise habits. In this way, by analyzing the lifestyle habits in detail, it is possible to propose an optimal lifestyle improvement plan for the user.

[0094] The lifestyle improvement suggestion unit allows the generation AI to set long-term lifestyle improvement goals based on the user's health data. For example, the generation AI sets long-term lifestyle improvement goals based on the user's health data. For example, it sets goals for weight or blood pressure and proposes a plan to achieve them. In this way, by setting long-term lifestyle improvement goals, the generation AI can support the user in maintaining their health.

[0095] The lifestyle improvement suggestion unit can share the lifestyle improvement plan with other users, and the generation AI can provide improvement suggestions based on the group data. For example, the lifestyle improvement suggestion unit can share the user's lifestyle improvement plan with other users, and the generation AI can provide improvement suggestions based on the group data. For example, it can compare the results of user groups with the same improvement plan. This makes it possible to support lifestyle improvements from a broader perspective by providing improvement suggestions based on group data.

[0096] The lifestyle improvement suggestion unit can link the lifestyle improvement plan with smart home devices and make improvement suggestions that take environmental data into consideration. For example, the lifestyle improvement suggestion unit links the user's lifestyle improvement plan with smart home devices, and the generation AI makes improvement suggestions that take environmental data into consideration based on that data. For example, if the indoor air quality is poor, ventilation is suggested. In this way, by linking with smart home devices, lifestyle improvement suggestions that take environmental data into consideration can be made.

[0097] The lifestyle improvement suggestion unit can use the emotion estimation function to analyze the emotions of the user when carrying out the lifestyle improvement plan and provide positive feedback based on the emotions. For example, the lifestyle improvement suggestion unit can use the emotion estimation function to analyze the emotions of the user when carrying out the lifestyle improvement plan and provide positive feedback based on the data. For example, it can display compliments when the positive emotions are strong. In this way, by taking emotions into consideration, it is possible to improve the user's motivation and support the execution of the lifestyle improvement plan.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The health management system may further include a sleep data collection unit that collects the user's sleep data. The sleep data collection unit acquires sleep data from, for example, a smartwatch or fitness tracker used by the user, and the generation AI analyzes the data to evaluate the quality of sleep. For example, it may analyze the depth of sleep and frequency of interruptions and provide suggestions for improving the user's sleep. This enables a comprehensive health assessment that takes sleep data into account.

[0100] The health management system can further include a social activity data collection unit that collects social activity data on users. The social activity data collection unit, for example, analyzes the frequency of posts and messages on the user's social media accounts, and the generation AI evaluates the level of social activity based on that data. For example, if social activity is low, a health index that takes into account feelings of isolation and depression risk can be presented. This enables health assessment that takes social activity into account.

[0101] The health management system can further include a hobby data collection unit that collects data on the user's hobbies and interests. The hobby data collection unit collects, for example, information about the events and hobbies the user participates in, and the generation AI evaluates the user's mental health based on that data. For example, if the user is not actively engaging in hobby activities, it can suggest a new hobby to relieve stress. This enables health evaluation that takes hobbies and interests into account.

[0102] The health management system can further include a workplace environment data collection unit that collects data on the user's workplace. The workplace environment data collection unit collects, for example, the temperature, humidity, and noise level of the user's workplace, and the generation AI evaluates the health risks of the workplace environment based on that data. For example, if the noise level is high, a health index that takes stress risk into account can be presented. This enables health evaluation that takes the workplace environment into account.

[0103] The health management system can further include a travel data collection unit that collects the user's travel data. The travel data collection unit, for example, collects the user's travel history and activity data during the trip, and the generation AI evaluates the health benefits of the trip based on that data. For example, it can analyze the number of steps taken and the amount of activity during the trip to evaluate the positive impact that the trip has on health. This enables health evaluation that takes travel data into account.

[0104] The health management system may further include a stress management unit that estimates the user's emotions and provides a stress management plan based on the estimated emotions. The stress management unit may, for example, use an emotion estimation function to analyze the user's stress level in real time and suggest specific actions to reduce stress based on the data. For example, it may suggest deep breathing or meditation. This enables stress management that takes emotions into account.

[0105] The health management system can further include a meal support unit that estimates the user's emotions and supports meal selection based on the estimated emotions. The meal support unit, for example, uses an emotion estimation function to analyze the user's emotional state and suggests meals based on the emotions. For example, if the user is under high stress, it can suggest foods that have a relaxing effect. This makes it possible to select meals that take emotions into consideration.

[0106] The health management system may further include an exercise motivation unit that estimates the user's emotions and improves the user's motivation to exercise based on the estimated emotions. The exercise motivation unit may, for example, use an emotion estimation function to analyze the user's emotional state and provide feedback to improve the user's motivation to exercise based on the data. For example, a message recommending exercise may be displayed when the user is feeling a strong positive emotion. This makes it possible to improve the user's motivation to exercise while taking emotions into consideration.

[0107] The health management system may further include a sleep improvement unit that estimates the user's emotions and improves the quality of sleep based on the estimated emotions. The sleep improvement unit, for example, uses an emotion estimation function to analyze the user's emotional state and makes suggestions for improving the quality of sleep based on the data. For example, if the user is highly stressed, it may suggest music with a relaxing effect. This makes it possible to improve sleep while taking emotions into consideration.

[0108] The health management system may further include a feedback customization unit that estimates the user's emotions and customizes the health assessment feedback based on the estimated emotions. The feedback customization unit may, for example, use an emotion estimation function to analyze the user's emotional state and adjust the content and tone of the feedback based on that data. For example, if the user has a strong negative emotion, the feedback may be provided in a gentler tone. This makes it possible to customize the feedback taking emotions into consideration.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The basic information collection unit collects the user's height, weight, and age. For example, the user enters their height, weight, and age into the application. The basic information collection unit can also collect the user's genetic information. Step 2: The health information collection unit collects the user's daily blood pressure and body fat percentage. For example, the user inputs the data using a blood pressure monitor or body fat scale. The health information collection unit can also capture the user's skin color and texture with a camera, allowing the generation AI to evaluate the health of the skin. Step 3: The meal data collection unit collects photos of the user's meals. For example, the user can take photos of their meals and upload them to the application. The meal data collection unit can also scan the barcodes of ingredients, which the AI ​​can then analyze to obtain detailed nutritional information. Step 4: The exercise data collection unit collects the user's daily exercise data. For example, the user inputs data using a pedometer or fitness tracker. The exercise data collection unit also collects heart rate and oxygen saturation during exercise, allowing the generation AI to evaluate the effects of the exercise in detail. Step 5: The health status analysis unit analyzes the data collected by the basic information collection unit, health information collection unit, dietary data collection unit, and exercise data collection unit, and predicts the health status if the current lifestyle is maintained. For example, the generation AI analyzes time-series data and predicts changes in health status. Step 6: The lifestyle improvement suggestion unit presents specific examples of lifestyle improvements to improve the health condition predicted by the health condition analysis unit to a certain level or above. For example, the generation AI analyzes the user's lifestyle habits in detail and proposes an individual lifestyle improvement plan.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] 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.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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]

[0178] 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 basic information collection unit that collects the user's height, weight, and age; a health information collection unit that collects the user's daily blood pressure and body fat percentage; a meal data collection unit that collects photos of meals taken by the user; an exercise data collection unit that collects daily exercise data of a user; a health condition analysis unit that analyzes the data collected by the basic information collection unit, the health information collection unit, the dietary data collection unit, and the exercise data collection unit, and predicts the health condition if the user maintains their current lifestyle; a lifestyle improvement suggestion unit that suggests specific examples of lifestyle improvements to improve the health condition predicted by the health condition analysis unit to a certain level or higher. A system characterized by:

2. The basic information collection unit Estimate the user's stress level using emotion estimation function and analyze the impact of stress on health 2. The system of claim 1.

3. The health information collection unit: The camera captures the color and texture of the user's skin, and the generative AI evaluates the health of said skin.

2. The system of claim 1.

4. The dietary data collection unit In addition to a photo of the meal, the user scans the barcodes of the ingredients, and the AI ​​analyzes detailed nutritional information based on the barcodes.

2. The system of claim 1.

5. The exercise data collection unit In addition to the user's exercise data, the system collects heart rate and oxygen saturation during exercise, and the AI ​​generates a detailed evaluation of the exercise effect based on the heart rate and oxygen saturation.

2. The system of claim 1.

6. The health condition analysis unit Analyzes the user's health data over time, and the generation AI predicts changes in health status based on the health data.

2. The system of claim 1.

7. The lifestyle improvement proposal department A detailed analysis of the user's lifestyle habits is conducted, and the AI ​​generation system proposes an individual lifestyle improvement plan based on the lifestyle habits.

2. The system of claim 1.

8. The basic information collection unit Emotion estimation function analyzes the user's emotions in real time as they type, and provides positive feedback based on those emotions.

2. The system of claim 1.

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