System

Abstract

An object of the system according to the embodiment is to provide an individually customized health management menu and support a user to be able to continue to work well.SOLUTION: A system includes a result comparison part, a menu generation part, a trace part, a tuning part, and a support part. The result comparison unit compares the result of the human dock with the normal value. The menu generation unit generates a meal or exercise menu customized for each individual based on the result of comparison by the result comparison unit. The tracing unit traces daily results. The tuning unit tunes the menu on the basis of the result traced by the tracing unit. The support part supports the individual so that the individual can continue to work well.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] With conventional technology, it is difficult to provide an individually customized health management menu based on the results of a medical checkup, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an individually customized health management menu and support the patient in continuing the menu. [Means for solving the problem]

[0006] The system according to the embodiment includes a result comparison unit, a menu generation unit, a tracing unit, a tuning unit, and a support unit. The result comparison unit compares the results of a medical checkup with normal values. The menu generation unit generates a customized diet and exercise menu for each individual based on the results compared by the result comparison unit. The tracing unit traces daily results. The tuning unit tunes the menu based on the results traced by the tracing unit. The support unit supports each individual to persevere and continue the program. [Effects of the Invention]

[0007] The system according to the embodiment can provide an individually customized health management menu and support the patient in continuing the menu. [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) The health management system according to an embodiment of the present invention is a system in which AI generates and customizes for each individual a diet and exercise menu to bring each category closer to the normal values ​​by comparing the results of a medical checkup with normal values. As a result, the health management system can provide a menu customized according to each individual's health condition and provide ongoing support to help maintain health.

[0029] A health management system according to an embodiment includes a result comparison unit, a menu generation unit, a tracing unit, a tuning unit, and a support unit. The result comparison unit compares the results of a medical checkup with normal values. For example, the result comparison unit compares the values ​​of each category, such as blood pressure, blood sugar, and cholesterol, with normal values. The menu generation unit generates a customized diet and exercise menu for each individual based on the results compared by the result comparison unit. For example, the menu generation unit suggests a low-salt diet menu for high blood pressure. Also, the menu generation unit suggests an exercise menu focusing on aerobic exercise for high blood sugar. The tracing unit traces daily results. For example, the tracing unit collects and records daily health data. The tuning unit tunes the menu based on the results traced by the tracing unit. For example, the tuning unit readjusts the menu if the diet or exercise does not seem to be effective. The support unit supports each individual to persevere. For example, the support unit visualizes progress toward achieving goals and sends encouraging messages. As a result, the health management system according to the embodiment can provide a menu customized according to the health condition of each individual and provide continuous support to help maintain health.

[0030] The menu generation unit can suggest a low-salt meal menu when blood pressure is high. For example, the menu generation unit suggests a low-salt meal menu when blood pressure is high. For example, the menu generation unit suggests a menu centered on oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and vegetables for dinner. The menu generation unit also suggests ingredients and cooking methods with low salt. For example, the menu generation unit suggests recipes that use seasonings and ingredients with low salt. This makes it possible to support blood pressure management by suggesting an appropriate meal menu when blood pressure is high.

[0031] The menu generation unit can suggest an exercise menu centered on aerobic exercise when blood sugar levels are high. For example, the menu generation unit suggests an exercise menu centered on aerobic exercise when blood sugar levels are high. For example, the menu generation unit suggests walking for 30 minutes every day and adding strength training twice a week. The menu generation unit also suggests specific methods and frequencies of aerobic exercise. For example, the menu generation unit suggests specific examples of exercise, such as walking, jogging, and cycling, as well as exercise duration and frequency. This makes it possible to support blood sugar level management by suggesting an appropriate exercise menu when blood sugar levels are high.

[0032] The tuning unit can readjust the menu when the effects of diet or exercise are not apparent. For example, the tuning unit may suggest extending walking time to 45 minutes and adding another vegetable to meals. The tuning unit may also suggest specific adjustment methods when the effects are not apparent. For example, the tuning unit may suggest adjusting the intensity or frequency of exercise. This allows for support of effective health management by readjusting the menu when the effects of diet or exercise are not apparent.

[0033] The support unit can visualize progress toward achieving a goal and send encouraging messages. For example, the support unit can visualize progress toward achieving a goal and send encouraging messages. For example, the support unit can display exercise and diet records in graphs and charts so that the user can check their progress. The support unit can also periodically send encouraging messages. For example, the support unit can send a message such as, "You did a great job this week! Keep it up!" In this way, by visualizing progress toward achieving a goal and sending encouraging messages, the user's motivation can be maintained.

[0034] Generative AI can learn from past health data and predict future health conditions, making preventative suggestions. Generative AI can, for example, learn from past health data and predict future health conditions. For example, generative AI can predict blood pressure fluctuations over the next year based on blood pressure data from the past five years and suggest preventative measures. Generative AI can also predict future health risks based on past health data. For example, generative AI can predict future health risks based on past diet and exercise data and make preventative suggestions. This allows generative AI to learn from past health data and predict future health conditions, making preventative suggestions.

[0035] Generative AI can analyze an individual's genetic information and perform a health assessment based on the normal values ​​for each category. Generative AI can, for example, analyze an individual's genetic information and perform a health assessment based on the normal values ​​for each category. For example, if an individual has a high genetic risk of high blood pressure, generative AI can suggest stricter blood pressure management. Generative AI can also evaluate individual health risks based on genetic information. For example, if an individual has a high risk of diabetes based on genetic information, generative AI can suggest an appropriate diet and exercise menu. In this way, by analyzing an individual's genetic information and performing a health assessment based on the normal values ​​for each category, more precise health management can be supported.

[0036] Generative AI can integrate the results of a health checkup with other health data to perform a comprehensive health assessment. For example, generative AI can integrate the results of a health checkup with data from wearable devices to perform a comprehensive health assessment. For example, generative AI can combine heart rate data and blood pressure data to evaluate cardiovascular risk. Generative AI can also integrate daily health records with the results of a health checkup to perform a comprehensive health assessment. For example, generative AI can integrate diet and exercise data with the results of a health checkup to evaluate health risks. This allows the results of a health checkup to be integrated with other health data to perform a comprehensive health assessment, supporting more precise health management.

[0037] Generative AI can develop health assessment algorithms specialized for different age groups and genders to make more personalized suggestions. Generative AI can, for example, develop health assessment algorithms specialized for different age groups to make personalized suggestions. For example, generative AI can suggest low-impact exercise menus for the elderly. Generative AI can also develop health assessment algorithms specialized for gender to make personalized suggestions. For example, generative AI can suggest nutritionally balanced meal menus for women. This allows us to develop health assessment algorithms specialized for different age groups and genders to make more personalized suggestions, supporting individual health management.

[0038] The generation AI can learn the user's dietary history and suggest meal menus that take into consideration preferences and allergies. For example, the generation AI can learn the user's dietary history and suggest meal menus that take into consideration preferences and allergies. For example, if the user has a dairy allergy, the generation AI can suggest a menu that does not contain dairy products. The generation AI can also suggest meal menus that match the user's preferences. For example, the generation AI can suggest recipes that use the user's favorite ingredients. In this way, the generation AI can learn the user's dietary history and suggest meal menus that take into consideration preferences and allergies, thereby improving the user's meal satisfaction.

[0039] The generation AI can consider the season and local ingredients and suggest meal menus using seasonal ingredients. The generation AI, for example, considers the season and local ingredients and suggests meal menus using seasonal ingredients. For example, the generation AI suggests a menu using fresh asparagus in the spring. The generation AI also suggests recipes using ingredients unique to the region. For example, the generation AI suggests dishes using local specialties. In this way, by considering the season and local ingredients and suggesting meal menus using seasonal ingredients, it is possible to improve the user's meal satisfaction.

[0040] The generation AI can include cooking recipe videos and cooking methods when suggesting meal menus, making it easy for users to put the recipes into practice. For example, the generation AI can include cooking recipe videos when suggesting meal menus, making it easy for users to put the recipes into practice. For example, the generation AI can explain how to cook oatmeal using a video. The generation AI can also provide recipes that explain the cooking methods in detail. For example, the generation AI can provide recipes that explain the cooking procedures step by step. In this way, by including cooking recipe videos and cooking methods when suggesting meal menus, it is possible to make it easy for users to put the recipes into practice.

[0041] Generative AI can consider the health status of all family members and suggest meal menus that the whole family can enjoy. Generative AI, for example, considers the health status of all family members and suggests meal menus that everyone can enjoy. For example, generative AI suggests nutritionally balanced menus for children. Generative AI can also suggest meal menus that suit the preferences of all family members. For example, generative AI suggests recipes that use ingredients that everyone in the family likes. In this way, by considering the health status of all family members and suggesting meal menus that the whole family can enjoy, it is possible to support the health management of the whole family.

[0042] The generation AI can learn the user's exercise history and suggest an effective exercise menu. The generation AI, for example, learns the user's exercise history and suggests an effective exercise menu. For example, the generation AI suggests optimal exercise intensity and frequency based on past exercise data. The generation AI also suggests an individual exercise menu based on the user's exercise history. For example, the generation AI suggests specific menus for strength training and aerobic exercise based on the user's exercise history. In this way, by learning the user's exercise history and suggesting an effective exercise menu, the user's exercise effects can be improved.

[0043] The generation AI can take the user's lifestyle into consideration and suggest the optimal exercise time. The generation AI, for example, analyzes the user's lifestyle and suggests the optimal exercise time. For example, the generation AI sets the exercise time taking into consideration the user's work schedule and sleep patterns. The generation AI also suggests exercise timing based on the user's lifestyle. For example, if morning exercise is effective, the generation AI will suggest a morning exercise menu. In this way, the user's exercise effectiveness can be improved by taking the user's lifestyle into consideration and suggesting the optimal exercise time.

[0044] Generative AI can incorporate stretching and relaxation elements into exercise menus to support overall health. Generative AI can, for example, incorporate stretching and relaxation elements into exercise menus to support overall health. For example, generative AI might suggest yoga as a cool-down after exercise. Generative AI can also suggest specific methods for stretching and relaxation. For example, generative AI might provide detailed explanations of stretching procedures and relaxation techniques. This makes it possible to incorporate stretching and relaxation elements into exercise menus to support overall health.

[0045] The generative AI can suggest exercise menus that can be done together with the user's friends and family, thereby strengthening social support. The generative AI can suggest exercise menus that can be done together with the user's friends and family, thereby strengthening social support. For example, the generative AI can suggest walking or jogging for the whole family. The generative AI can also suggest specific methods for group exercise. For example, the generative AI can suggest exercise procedures and schedules for doing exercise together with friends and family. This can strengthen social support by suggesting exercise menus that can be done together with the user's friends and family.

[0046] Generative AI can analyze daily health data in real time and instantly tune menus. Generative AI can, for example, analyze daily health data in real time and instantly tune menus. For example, if blood pressure is high, generative AI can suggest a low-salt meal menu. Generative AI can also adjust exercise menus based on daily health data. For example, generative AI can adjust exercise intensity and frequency in real time. This allows for real-time analysis of daily health data and instantaneous menu tuning, supporting users in managing their health.

[0047] The generating AI can flexibly adjust the menu, taking into account the user's living environment. The generating AI can, for example, analyze the user's living environment and flexibly adjust the menu. For example, if the weather is bad, the generating AI can suggest an exercise menu that can be done indoors. The generating AI can also adjust exercise time to suit the work schedule. For example, the generating AI can suggest a short, effective exercise menu for busy days. In this way, the generating AI can support the user's health management by flexibly adjusting the menu, taking into account the user's living environment.

[0048] The generation AI can collect user feedback and reflect it in tuning the menu. The generation AI, for example, collects user feedback and reflects it in tuning the menu. For example, the generation AI adjusts the next menu by reflecting the user's preferences for meal menus. The generation AI also adjusts the intensity and frequency of exercise by reflecting the user's opinions on the exercise menu. In this way, by collecting user feedback and reflecting it in tuning the menu, more personalized suggestions can be made.

[0049] The generation AI can analyze the success stories of other users and suggest effective menus. The generation AI can, for example, analyze the success stories of other users and suggest effective menus. For example, the generation AI can suggest an exercise menu based on the success stories of users who have the same health goals. The generation AI can also suggest meal menus by referring to the success stories of other users. For example, the generation AI can suggest meal recipes and cooking methods based on success stories. In this way, the generation AI can support the user's health management by analyzing the success stories of other users and suggesting effective menus.

[0050] The generation AI can visualize the user's progress and enable them to feel a sense of accomplishment. For example, the generation AI can display exercise and meal records in graphs and charts so that the user can check their progress. The generation AI can also provide feedback when a goal is achieved. For example, the generation AI can send praise or encouraging messages when a goal is achieved. This makes the user's progress visible and enables them to feel a sense of accomplishment, thereby maintaining their motivation.

[0051] The generative AI can form a community of users and provide an environment where they can encourage each other. The generative AI can, for example, form a community of users and provide an environment where they can encourage each other. For example, the generative AI can create online forums or chat groups so that users can share their experiences and advice. The generative AI can also suggest methods of support within the community. For example, the generative AI can provide encouraging messages and advice. This allows users to form a community of users and provide an environment where they can encourage each other, thereby maintaining their motivation.

[0052] The generating AI can incorporate game elements into the continuous support, allowing users to enjoy health management. The generating AI, for example, can incorporate game elements into the continuous support, allowing users to enjoy health management. For example, the generating AI can award points or badges for exercise and meal records, allowing users to feel a sense of accomplishment. The generating AI can also provide a gamified health management program. For example, the generating AI can provide a program that incorporates game elements so that users can enjoy health management. In this way, by incorporating game elements into the continuous support, it is possible to enjoy health management.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] Health management systems can collect a user's sleep data and evaluate the quality of their sleep. For example, the system can record the user's sleep time, the ratio of deep sleep to light sleep, the number of times they woke up during the night, and other data to provide an overall sleep score. The system can also provide advice on how to improve sleep quality. For example, the system can suggest ways to relax before bed or how to create an appropriate bedroom environment. This allows the user to understand their own sleep quality and obtain specific measures to improve it.

[0055] Health management systems can monitor a user's fluid intake and encourage proper hydration. For example, the system can record the amount of fluid a user consumes throughout the day and send reminders if they are not reaching their target intake. The system can also adjust the amount of fluid needed to take into account exercise and environmental factors such as temperature. This helps users maintain proper hydration and support their health.

[0056] The health management system can learn the user's dietary history and suggest nutritionally balanced meal menus. For example, the system can evaluate nutrient deficiencies and excesses based on the data of the user's past meals and suggest balanced menus. The system can also provide individually customized meal menus taking into account the user's preferences and allergies. This makes it easier for the user to maintain a healthy diet.

[0057] The health management system can learn the user's exercise history and suggest effective exercise menus. For example, the system can suggest optimal exercise intensity and frequency based on past exercise data. The system can also suggest individual exercise menus based on the user's exercise history. This allows users to maximize the benefits of their exercise and maintain their health.

[0058] The health management system can take into account the user's lifestyle and suggest optimal exercise times. For example, the system can analyze the user's work schedule and sleep patterns to set exercise times. The system can also suggest exercise timing based on the user's lifestyle. This allows the user to exercise in accordance with their own lifestyle, making it easier to maintain their health.

[0059] The health management system can suggest exercise menus that can be done with the user's friends and family, strengthening social support. For example, the system can suggest walking or jogging for the whole family. The system can also suggest specific methods for group exercise. This allows users to enjoy exercise with friends and family and maintain their health while receiving social support.

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

[0061] Step 1: The result comparison unit compares the results of the medical checkup with normal values. For example, it compares the values ​​in each category, such as blood pressure, blood sugar, and cholesterol, with normal values. Step 2: The menu generation unit generates a customized diet and exercise menu for each individual based on the results compared by the result comparison unit. For example, if blood pressure is high, a low-salt diet menu is suggested, and if blood sugar is high, an exercise menu focusing on aerobic exercise is suggested. Step 3: The tracing section traces the daily results, for example, collecting and recording daily health data. Step 4: The tuning unit tunes the menu based on the results traced by the tracing unit. For example, the menu is readjusted if the effects of diet or exercise are not apparent. Step 5: The support team helps individuals persevere by, for example, visualizing progress toward achieving goals and sending encouraging messages.

[0062] (Example 2) The health management system according to an embodiment of the present invention is a system in which AI generates and customizes for each individual a diet and exercise menu to bring each category closer to the normal values ​​by comparing the results of a medical checkup with normal values. As a result, the health management system can provide a menu customized according to each individual's health condition and provide ongoing support to help maintain health.

[0063] A health management system according to an embodiment includes a result comparison unit, a menu generation unit, a tracing unit, a tuning unit, and a support unit. The result comparison unit compares the results of a medical checkup with normal values. For example, the result comparison unit compares the values ​​of each category, such as blood pressure, blood sugar, and cholesterol, with normal values. The menu generation unit generates a customized diet and exercise menu for each individual based on the results compared by the result comparison unit. For example, the menu generation unit suggests a low-salt diet menu for high blood pressure. Also, the menu generation unit suggests an exercise menu focusing on aerobic exercise for high blood sugar. The tracing unit traces daily results. For example, the tracing unit collects and records daily health data. The tuning unit tunes the menu based on the results traced by the tracing unit. For example, the tuning unit readjusts the menu if the diet or exercise does not seem to be effective. The support unit supports each individual to persevere. For example, the support unit visualizes progress toward achieving goals and sends encouraging messages. As a result, the health management system according to the embodiment can provide a menu customized according to the health condition of each individual and provide continuous support to help maintain health.

[0064] The menu generation unit can suggest a low-salt meal menu when blood pressure is high. For example, the menu generation unit suggests a low-salt meal menu when blood pressure is high. For example, the menu generation unit suggests a menu centered on oatmeal and fruit for breakfast, salad and chicken breast for lunch, and fish and vegetables for dinner. The menu generation unit also suggests ingredients and cooking methods with low salt. For example, the menu generation unit suggests recipes that use seasonings and ingredients with low salt. This makes it possible to support blood pressure management by suggesting an appropriate meal menu when blood pressure is high.

[0065] The menu generation unit can suggest an exercise menu centered on aerobic exercise when blood sugar levels are high. For example, the menu generation unit suggests an exercise menu centered on aerobic exercise when blood sugar levels are high. For example, the menu generation unit suggests walking for 30 minutes every day and adding strength training twice a week. The menu generation unit also suggests specific methods and frequencies of aerobic exercise. For example, the menu generation unit suggests specific examples of exercise, such as walking, jogging, and cycling, as well as exercise duration and frequency. This makes it possible to support blood sugar level management by suggesting an appropriate exercise menu when blood sugar levels are high.

[0066] The tuning unit can readjust the menu when the effects of diet or exercise are not apparent. For example, the tuning unit may suggest extending walking time to 45 minutes and adding another vegetable to meals. The tuning unit may also suggest specific adjustment methods when the effects are not apparent. For example, the tuning unit may suggest adjusting the intensity or frequency of exercise. This allows for support of effective health management by readjusting the menu when the effects of diet or exercise are not apparent.

[0067] The support unit can visualize progress toward achieving a goal and send encouraging messages. For example, the support unit can visualize progress toward achieving a goal and send encouraging messages. For example, the support unit can display exercise and diet records in graphs and charts so that the user can check their progress. The support unit can also periodically send encouraging messages. For example, the support unit can send a message such as, "You did a great job this week! Keep it up!" In this way, by visualizing progress toward achieving a goal and sending encouraging messages, the user's motivation can be maintained.

[0068] Generative AI can learn from past health data and predict future health conditions, making preventative suggestions. Generative AI can, for example, learn from past health data and predict future health conditions. For example, generative AI can predict blood pressure fluctuations over the next year based on blood pressure data from the past five years and suggest preventative measures. Generative AI can also predict future health risks based on past health data. For example, generative AI can predict future health risks based on past diet and exercise data and make preventative suggestions. This allows generative AI to learn from past health data and predict future health conditions, making preventative suggestions.

[0069] Generative AI can analyze an individual's genetic information and perform a health assessment based on the normal values ​​for each category. Generative AI can, for example, analyze an individual's genetic information and perform a health assessment based on the normal values ​​for each category. For example, if an individual has a high genetic risk of high blood pressure, generative AI can suggest stricter blood pressure management. Generative AI can also evaluate individual health risks based on genetic information. For example, if an individual has a high risk of diabetes based on genetic information, generative AI can suggest an appropriate diet and exercise menu. In this way, by analyzing an individual's genetic information and performing a health assessment based on the normal values ​​for each category, more precise health management can be supported.

[0070] The generative AI can use the emotion estimation function to analyze the user's emotional state in real time and provide health advice according to the stress level. The generative AI, for example, uses the emotion estimation function to analyze the user's emotional state in real time. For example, the generative AI uses facial expression recognition technology to analyze the user's emotional state from their facial expressions. The generative AI also uses voice analysis technology to analyze the user's emotional state from the tone and speed of their voice. Furthermore, the generative AI uses biometric data to analyze the user's emotional state from their heart rate and electrodermal activity. This allows the generative AI to analyze the user's emotional state in real time using the emotion estimation function and provide health advice according to their stress level, thereby supporting the user's stress management.

[0071] Generative AI can integrate the results of a health checkup with other health data to perform a comprehensive health assessment. For example, generative AI can integrate the results of a health checkup with data from wearable devices to perform a comprehensive health assessment. For example, generative AI can combine heart rate data and blood pressure data to evaluate cardiovascular risk. Generative AI can also integrate daily health records with the results of a health checkup to perform a comprehensive health assessment. For example, generative AI can integrate diet and exercise data with the results of a health checkup to evaluate health risks. This allows the results of a health checkup to be integrated with other health data to perform a comprehensive health assessment, supporting more precise health management.

[0072] Generative AI can develop health assessment algorithms specialized for different age groups and genders to make more personalized suggestions. Generative AI can, for example, develop health assessment algorithms specialized for different age groups to make personalized suggestions. For example, generative AI can suggest low-impact exercise menus for the elderly. Generative AI can also develop health assessment algorithms specialized for gender to make personalized suggestions. For example, generative AI can suggest nutritionally balanced meal menus for women. This allows us to develop health assessment algorithms specialized for different age groups and genders to make more personalized suggestions, supporting individual health management.

[0073] The generative AI can use its emotion estimation function to analyze the emotions of users when they enter health data in real time and provide positive feedback. The generative AI, for example, uses its emotion estimation function to analyze the emotions of users when they enter health data in real time. For example, the generative AI uses facial expression recognition technology to analyze the emotional state from the user's facial expressions. The generative AI also uses voice analysis technology to analyze the emotional state from the tone and speed of the user's voice. Furthermore, the generative AI uses biometric data to analyze the emotional state from the user's heart rate and electrodermal activity. This allows the generative AI to analyze the emotions of users when they enter health data in real time using the emotion estimation function and provide positive feedback, thereby maintaining the user's motivation.

[0074] The generation AI can learn the user's dietary history and suggest meal menus that take into consideration preferences and allergies. For example, the generation AI can learn the user's dietary history and suggest meal menus that take into consideration preferences and allergies. For example, if the user has a dairy allergy, the generation AI can suggest a menu that does not contain dairy products. The generation AI can also suggest meal menus that match the user's preferences. For example, the generation AI can suggest recipes that use the user's favorite ingredients. In this way, the generation AI can learn the user's dietary history and suggest meal menus that take into consideration preferences and allergies, thereby improving the user's meal satisfaction.

[0075] The generation AI can consider the season and local ingredients and suggest meal menus using seasonal ingredients. The generation AI, for example, considers the season and local ingredients and suggests meal menus using seasonal ingredients. For example, the generation AI suggests a menu using fresh asparagus in the spring. The generation AI also suggests recipes using ingredients unique to the region. For example, the generation AI suggests dishes using local specialties. In this way, by considering the season and local ingredients and suggesting meal menus using seasonal ingredients, it is possible to improve the user's meal satisfaction.

[0076] The generation AI can use the emotion estimation function to analyze the user's emotions about eating in real time and make suggestions to improve meal satisfaction. The generation AI, for example, uses the emotion estimation function to analyze the user's emotions about eating in real time. For example, the generation AI uses facial expression recognition technology to analyze the user's emotional state from their facial expressions. The generation AI also uses voice analysis technology to analyze the emotional state from the tone and speed of the user's voice. Furthermore, the generation AI uses biometric data to analyze the emotional state from the user's heart rate and electrodermal activity. This allows the emotion estimation function to analyze the user's emotions about eating in real time and make suggestions to improve meal satisfaction, thereby improving the user's dining experience.

[0077] The generation AI can include cooking recipe videos and cooking methods when suggesting meal menus, making it easy for users to put the recipes into practice. For example, the generation AI can include cooking recipe videos when suggesting meal menus, making it easy for users to put the recipes into practice. For example, the generation AI can explain how to cook oatmeal using a video. The generation AI can also provide recipes that explain the cooking methods in detail. For example, the generation AI can provide recipes that explain the cooking procedures step by step. In this way, by including cooking recipe videos and cooking methods when suggesting meal menus, it is possible to make it easy for users to put the recipes into practice.

[0078] Generative AI can consider the health status of all family members and suggest meal menus that the whole family can enjoy. Generative AI, for example, considers the health status of all family members and suggests meal menus that everyone can enjoy. For example, generative AI suggests nutritionally balanced menus for children. Generative AI can also suggest meal menus that suit the preferences of all family members. For example, generative AI suggests recipes that use ingredients that everyone in the family likes. In this way, by considering the health status of all family members and suggesting meal menus that the whole family can enjoy, it is possible to support the health management of the whole family.

[0079] The generative AI uses the emotion estimation function to analyze the emotions of the user while eating in real time and make suggestions to improve the enjoyment of the meal. The generative AI, for example, uses the emotion estimation function to analyze the emotions of the user while eating in real time. For example, the generative AI uses facial expression recognition technology to analyze the emotional state from the user's facial expressions. The generative AI also uses voice analysis technology to analyze the emotional state from the tone and speed of the user's voice. Furthermore, the generative AI uses biometric data to analyze the emotional state from the user's heart rate and electrodermal activity. This allows the generative AI to analyze the emotions of the user while eating in real time using the emotion estimation function and make suggestions to improve the enjoyment of the meal, thereby improving the user's dining experience.

[0080] The generation AI can learn the user's exercise history and suggest an effective exercise menu. The generation AI, for example, learns the user's exercise history and suggests an effective exercise menu. For example, the generation AI suggests optimal exercise intensity and frequency based on past exercise data. The generation AI also suggests an individual exercise menu based on the user's exercise history. For example, the generation AI suggests specific menus for strength training and aerobic exercise based on the user's exercise history. In this way, by learning the user's exercise history and suggesting an effective exercise menu, the user's exercise effects can be improved.

[0081] The generation AI can take the user's lifestyle into consideration and suggest the optimal exercise time. The generation AI, for example, analyzes the user's lifestyle and suggests the optimal exercise time. For example, the generation AI sets the exercise time taking into consideration the user's work schedule and sleep patterns. The generation AI also suggests exercise timing based on the user's lifestyle. For example, if morning exercise is effective, the generation AI will suggest a morning exercise menu. In this way, the user's exercise effectiveness can be improved by taking the user's lifestyle into consideration and suggesting the optimal exercise time.

[0082] The generation AI can use the emotion estimation function to analyze the user's emotions during exercise in real time and make suggestions to maintain motivation. The generation AI, for example, uses the emotion estimation function to analyze the user's emotions during exercise in real time. For example, the generation AI uses facial expression recognition technology to analyze the user's emotional state from their facial expressions. The generation AI also uses voice analysis technology to analyze the emotional state from the tone and speed of the user's voice. Furthermore, the generation AI uses biometric data to analyze the user's emotional state from their heart rate and electrodermal activity. This allows the emotion estimation function to analyze the user's emotions during exercise in real time and make suggestions to maintain motivation, thereby improving the effectiveness of the user's exercise.

[0083] Generative AI can incorporate stretching and relaxation elements into exercise menus to support overall health. Generative AI can, for example, incorporate stretching and relaxation elements into exercise menus to support overall health. For example, generative AI might suggest yoga as a cool-down after exercise. Generative AI can also suggest specific methods for stretching and relaxation. For example, generative AI might provide detailed explanations of stretching procedures and relaxation techniques. This makes it possible to incorporate stretching and relaxation elements into exercise menus to support overall health.

[0084] The generative AI can suggest exercise menus that can be done together with the user's friends and family, thereby strengthening social support. The generative AI can suggest exercise menus that can be done together with the user's friends and family, thereby strengthening social support. For example, the generative AI can suggest walking or jogging for the whole family. The generative AI can also suggest specific methods for group exercise. For example, the generative AI can suggest exercise procedures and schedules for doing exercise together with friends and family. This can strengthen social support by suggesting exercise menus that can be done together with the user's friends and family.

[0085] The generation AI can use the emotion estimation function to analyze the user's emotions after exercise in real time and provide positive feedback. The generation AI, for example, uses the emotion estimation function to analyze the user's emotions after exercise in real time. For example, the generation AI uses facial expression recognition technology to analyze the user's emotional state from their facial expressions. The generation AI also uses voice analysis technology to analyze the emotional state from the tone and speed of the user's voice. Furthermore, the generation AI uses biometric data to analyze the emotional state from the user's heart rate and electrodermal activity. This allows the emotion estimation function to analyze the user's emotions after exercise in real time and provide positive feedback, thereby maintaining the user's motivation.

[0086] Generative AI can analyze daily health data in real time and instantly tune menus. Generative AI can, for example, analyze daily health data in real time and instantly tune menus. For example, if blood pressure is high, generative AI can suggest a low-salt meal menu. Generative AI can also adjust exercise menus based on daily health data. For example, generative AI can adjust exercise intensity and frequency in real time. This allows for real-time analysis of daily health data and instantaneous menu tuning, supporting users in managing their health.

[0087] The generating AI can flexibly adjust the menu, taking into account the user's living environment. The generating AI can, for example, analyze the user's living environment and flexibly adjust the menu. For example, if the weather is bad, the generating AI can suggest an exercise menu that can be done indoors. The generating AI can also adjust exercise time to suit the work schedule. For example, the generating AI can suggest a short, effective exercise menu for busy days. In this way, the generating AI can support the user's health management by flexibly adjusting the menu, taking into account the user's living environment.

[0088] The generation AI can collect user feedback and reflect it in tuning the menu. The generation AI, for example, collects user feedback and reflects it in tuning the menu. For example, the generation AI adjusts the next menu by reflecting the user's preferences for meal menus. The generation AI also adjusts the intensity and frequency of exercise by reflecting the user's opinions on the exercise menu. In this way, by collecting user feedback and reflecting it in tuning the menu, more personalized suggestions can be made.

[0089] The generation AI can analyze the success stories of other users and suggest effective menus. The generation AI can, for example, analyze the success stories of other users and suggest effective menus. For example, the generation AI can suggest an exercise menu based on the success stories of users who have the same health goals. The generation AI can also suggest meal menus by referring to the success stories of other users. For example, the generation AI can suggest meal recipes and cooking methods based on success stories. In this way, the generation AI can support the user's health management by analyzing the success stories of other users and suggesting effective menus.

[0090] The generation AI uses the emotion estimation function to analyze the user's emotional response to menu changes in real time and make optimal changes. The generation AI, for example, uses the emotion estimation function to analyze the user's emotional response to menu changes in real time. For example, the generation AI uses facial expression recognition technology to analyze the user's emotional state from their facial expressions. The generation AI also uses voice analysis technology to analyze the emotional state from the tone and speed of the user's voice. Furthermore, the generation AI uses biometric data to analyze the user's emotional state from their heart rate and electrodermal activity. This allows the emotion estimation function to analyze the user's emotional response to menu changes in real time and make optimal changes, thereby supporting the user's health management.

[0091] The generation AI can periodically provide encouraging messages and rewards to maintain the user's motivation. For example, the generation AI can periodically send encouraging messages to maintain the user's motivation. For example, it can send a message such as, "You've done well this week! Keep it up!" The generation AI can also provide rewards to enhance the user's sense of accomplishment. For example, the generation AI can award points or badges when a goal is achieved. This can support the user's health management by periodically providing encouraging messages and rewards to maintain the user's motivation.

[0092] The generation AI can visualize the user's progress and enable them to feel a sense of accomplishment. For example, the generation AI can display exercise and meal records in graphs and charts so that the user can check their progress. The generation AI can also provide feedback when a goal is achieved. For example, the generation AI can send praise or encouraging messages when a goal is achieved. This makes the user's progress visible and enables them to feel a sense of accomplishment, thereby maintaining their motivation.

[0093] The generative AI can use the emotion estimation function to analyze the user's emotional state and provide personalized support to maintain motivation. The generative AI, for example, uses the emotion estimation function to analyze the user's emotional state in real time. For example, the generative AI uses facial expression recognition technology to analyze the user's emotional state from their facial expressions. The generative AI also uses voice analysis technology to analyze the user's emotional state from the tone and speed of their voice. Furthermore, the generative AI uses biometric data to analyze the user's emotional state from their heart rate and electrodermal activity. This allows the generative AI to analyze the user's emotional state in real time using the emotion estimation function and provide personalized support to maintain motivation, thereby supporting the user's health management.

[0094] The generative AI can form a community of users and provide an environment where they can encourage each other. The generative AI can, for example, form a community of users and provide an environment where they can encourage each other. For example, the generative AI can create online forums or chat groups so that users can share their experiences and advice. The generative AI can also suggest methods of support within the community. For example, the generative AI can provide encouraging messages and advice. This allows users to form a community of users and provide an environment where they can encourage each other, thereby maintaining their motivation.

[0095] The generating AI can incorporate game elements into the continuous support, allowing users to enjoy health management. The generating AI, for example, can incorporate game elements into the continuous support, allowing users to enjoy health management. For example, the generating AI can award points or badges for exercise and meal records, allowing users to feel a sense of accomplishment. The generating AI can also provide a gamified health management program. For example, the generating AI can provide a program that incorporates game elements so that users can enjoy health management. In this way, by incorporating game elements into the continuous support, it is possible to enjoy health management.

[0096] The generation AI can use the emotion estimation function to analyze the user's emotional state in real time and provide support messages at appropriate times. The generation AI, for example, uses the emotion estimation function to analyze the user's emotional state in real time. For example, the generation AI uses facial expression recognition technology to analyze the user's emotional state from their facial expressions. The generation AI also uses voice analysis technology to analyze the user's emotional state from the tone and speed of their voice. Furthermore, the generation AI uses biometric data to analyze the user's emotional state from their heart rate and electrodermal activity. This allows the generation AI to analyze the user's emotional state in real time using the emotion estimation function and provide support messages at appropriate times, thereby maintaining the user's motivation.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] Health management systems can collect a user's sleep data and evaluate the quality of their sleep. For example, the system can record the user's sleep time, the ratio of deep sleep to light sleep, the number of times they woke up during the night, and other data to provide an overall sleep score. The system can also provide advice on how to improve sleep quality. For example, the system can suggest ways to relax before bed or how to create an appropriate bedroom environment. This allows the user to understand their own sleep quality and obtain specific measures to improve it.

[0099] Health management systems can monitor a user's fluid intake and encourage proper hydration. For example, the system can record the amount of fluid a user consumes throughout the day and send reminders if they are not reaching their target intake. The system can also adjust the amount of fluid needed to take into account exercise and environmental factors such as temperature. This helps users maintain proper hydration and support their health.

[0100] The health management system can estimate a user's emotional state and suggest relaxation methods for stress management. For example, the system can analyze the user's emotional state from their facial expressions and voice, and if it determines that stress is high, it can suggest deep breathing or meditation. The system can also provide relaxing music or natural sounds depending on the user's emotional state. This allows users to effectively manage stress and maintain their physical and mental health.

[0101] The health management system can learn the user's dietary history and suggest nutritionally balanced meal menus. For example, the system can evaluate nutrient deficiencies and excesses based on the data of the user's past meals and suggest balanced menus. The system can also provide individually customized meal menus taking into account the user's preferences and allergies. This makes it easier for the user to maintain a healthy diet.

[0102] The health management system can learn the user's exercise history and suggest effective exercise menus. For example, the system can suggest optimal exercise intensity and frequency based on past exercise data. The system can also suggest individual exercise menus based on the user's exercise history. This allows users to maximize the benefits of their exercise and maintain their health.

[0103] The health management system can use its emotion estimation function to analyze the user's emotional state in real time and provide positive feedback. For example, the system can analyze the user's emotional state from their facial expressions and voice, and send an encouraging message if the user is feeling down. The system can also send words of praise when the user achieves a goal. This helps maintain the user's motivation and makes it easier for them to continue managing their health.

[0104] The health management system can take into account the user's lifestyle and suggest optimal exercise times. For example, the system can analyze the user's work schedule and sleep patterns to set exercise times. The system can also suggest exercise timing based on the user's lifestyle. This allows the user to exercise in accordance with their own lifestyle, making it easier to maintain their health.

[0105] The health management system uses its emotion estimation function to analyze the user's emotions in real time while eating a meal and make suggestions to improve meal satisfaction. For example, the system analyzes the user's emotional state from their facial expressions and voice, and if they are not satisfied with the meal, suggests a recipe that suits their preferences. The system can also provide advice on creating a relaxing environment while eating. This allows the user to enjoy their meal more and improve their meal satisfaction.

[0106] The health management system can suggest exercise menus that can be done with the user's friends and family, strengthening social support. For example, the system can suggest walking or jogging for the whole family. The system can also suggest specific methods for group exercise. This allows users to enjoy exercise with friends and family and maintain their health while receiving social support.

[0107] The health management system can use its emotion estimation function to analyze the user's emotions in real time while exercising and make suggestions to maintain motivation. For example, the system can analyze the user's emotional state from their facial expressions and voice and send encouraging messages if they feel tired during exercise. The system can also suggest more challenging exercise menus if the user is enjoying the exercise. This makes it easier for users to maintain motivation during exercise and maximize the benefits of exercise.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The result comparison unit compares the results of the medical checkup with normal values. For example, it compares the values ​​in each category, such as blood pressure, blood sugar, and cholesterol, with normal values. Step 2: The menu generation unit generates a customized diet and exercise menu for each individual based on the results compared by the result comparison unit. For example, if blood pressure is high, a low-salt diet menu is suggested, and if blood sugar is high, an exercise menu focusing on aerobic exercise is suggested. Step 3: The tracing section traces the daily results, for example, collecting and recording daily health data. Step 4: The tuning unit tunes the menu based on the results traced by the tracing unit. For example, the menu is readjusted if the effects of diet or exercise are not apparent. Step 5: The support team helps individuals persevere by, for example, visualizing progress toward achieving goals and sending encouraging messages.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] 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.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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]

[0177] 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 result comparison unit that compares the results of the medical checkup with normal values; a menu generation unit that generates a meal and exercise menu customized for each individual based on the results compared by the result comparison unit; A tracing section that tracks daily results, a tuning unit that tunes a menu based on the results of tracing by the tracing unit; A support department that supports each individual to persevere and continue. A system characterized by:

2. The generated AI is The results of the health checkup will be integrated with other health data to provide a comprehensive health assessment.

2. The system of claim 1.

3. The generated AI is Learns the user's eating history and suggests meal menus that take into account their preferences and allergies 2. The system of claim 1.

4. The generated AI is Learn the user's exercise history and suggest effective exercise menus 2. The system of claim 1.

5. The generated AI is Analyze daily health data in real time and instantly adjust the menu 2. The system of claim 1.

6. The generated AI is Providing regular encouraging messages and rewards to keep users motivated 2. The system of claim 1.

7. The generated AI is Analyzes users' emotional state in real time and provides health advice based on stress levels 2. The system of claim 1.

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