System

Abstract

An object of a system according to an embodiment is to propose an optimal health improvement menu according to a user's preference and physical fitness level.SOLUTION: A system includes a user information collection unit, an analysis unit, and a proposal unit. The user information collection unit collects information on the user's preference and physical strength level. The analysis unit analyzes the information collected by the user information collection unit. The suggestion unit suggests an optimal health improvement menu based on the information analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to individually propose health improvement menus that match a user's preferences and physical fitness level.

[0005] The system according to the embodiment aims to propose an optimal health improvement menu according to the user's preferences and physical fitness level. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information collection unit, an analysis unit, and a suggestion unit. The user information collection unit collects information related to a user's preferences and physical fitness level. The analysis unit analyzes the information collected by the user information collection unit. The suggestion unit suggests an optimal health improvement menu based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal health improvement menu according to the user's preferences and physical fitness level. [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 improvement menu suggestion system according to an embodiment of the present invention is a system that uses a generation AI to suggest a health improvement menu tailored to the preferences and physical fitness level of each individual user. As a result, the health improvement menu suggestion system can suggest an optimal health improvement menu based on the user's preferences and physical fitness level.

[0029] A health improvement menu suggestion system according to an embodiment includes a user information collection unit, an analysis unit, and a suggestion unit. The user information collection unit collects information related to a user's preferences and physical fitness level. For example, prompts input by the user include age, gender, exercise experience, current physical fitness level, and preferred exercise type. The analysis unit analyzes the information collected by the user information collection unit. For example, the generation AI performs analysis to suggest an optimal health improvement menu for the user based on the user's age, gender, exercise experience, current physical fitness level, and preferred exercise type. The suggestion unit suggests an optimal health improvement menu based on the information analyzed by the analysis unit. For example, light stretching or yoga is suggested for a user with low physical fitness, and running or high-intensity training is suggested for a user with high physical fitness. This allows the health improvement menu suggestion system to suggest an optimal health improvement menu based on the user's preferences and physical fitness level.

[0030] The user information collection unit can collect the user's age, gender, exercise experience, current physical fitness level, and preferred type of exercise. For example, the user uses an app to record their daily diet and sends that data to the generation AI. The generation AI analyzes the nutritional balance and calorie intake of the diet and reflects this in a health improvement menu. The user information collection unit also collects the user's age, gender, exercise experience, current physical fitness level, and preferred type of exercise. For example, this information can be collected in the form of a questionnaire and sent to the generation AI. By collecting detailed information about the user, more accurate menu suggestions can be made.

[0031] The suggestion unit can suggest light stretching, yoga, running, or high-intensity training according to the user's physical fitness level. The suggestion unit, for example, analyzes the user's stress level and psychological state and suggests a menu that also takes mental health into consideration. For example, the user uses an app to record their daily stress level and sends that data to the generation AI. The generation AI analyzes the stress level and suggests yoga or stretching that has a relaxing effect. The suggestion unit also suggests light stretching, yoga, running, or high-intensity training according to the user's physical fitness level. For example, light stretching or yoga is suggested for a user with low physical fitness, and running or high-intensity training is suggested for a user with high physical fitness. This makes it possible to suggest an appropriate exercise menu according to the user's physical fitness level.

[0032] The suggestion unit can regenerate menus based on user feedback. For example, the suggestion unit uses an emotion estimation function to analyze the emotions the user feels when entering input and generate prompts that elicit positive emotions. For example, the suggestion unit uses a camera to analyze the user's facial expressions when entering input and calculates an emotion score. The generation AI uses this score to generate prompts that elicit positive emotions. The suggestion unit also regenerates menus based on user feedback. For example, if the user enters their thoughts on the difficulty and effectiveness of an exercise, the generation AI can reflect this in subsequent menu suggestions. This allows the system to provide more appropriate menus that reflect user feedback.

[0033] The analysis unit collects the user's dietary history and sleep patterns and can suggest a health improvement menu based on this data. For example, the analysis unit uses an app to record the user's daily diet and sends that data to the generation AI. The generation AI analyzes the nutritional balance and calorie intake of the meals and reflects this in the health improvement menu. The analysis unit also collects the user's sleep patterns and suggests a health improvement menu based on this data. For example, the user uses an app to record the amount of sleep and quality of sleep and sends that data to the generation AI. The generation AI analyzes the sleep patterns and reflects this in the health improvement menu. This makes it possible to suggest a health improvement menu that takes the user's dietary history and sleep patterns into account.

[0034] The analysis unit can analyze the user's stress level and psychological state and suggest a menu that also takes mental health into consideration. For example, the analysis unit uses an app that allows the user to record their daily stress level, and sends that data to the generation AI. The generation AI analyzes the stress level and suggests yoga or stretching exercises that have a relaxing effect. The analysis unit can also analyze the user's psychological state and suggest a menu that also takes mental health into consideration. For example, the user takes a psychological test and sends the results to the generation AI. The generation AI analyzes the psychological state and suggests a menu that also takes mental health into consideration. This makes it possible to suggest a health improvement menu that takes the user's mental health into consideration.

[0035] The analysis unit can take into consideration the user's home environment and work environment and propose a menu that is appropriate for the environment. For example, the analysis unit collects information about the user's home environment (e.g., family composition and size of the home) in the form of a questionnaire and sends that data to the generation AI. Based on that data, the generation AI proposes an exercise menu that is appropriate for the home environment. The analysis unit also collects information about the user's work environment (e.g., workplace stress level and working hours) in the form of a questionnaire and sends that data to the generation AI. Based on that data, the generation AI proposes an exercise menu that is appropriate for the work environment. This makes it possible to propose a health improvement menu that is appropriate for the user's home environment and work environment.

[0036] The analysis unit can analyze the user's past exercise history and suggest a menu based on their long-term exercise patterns. For example, the analysis unit collects the user's past exercise history from a fitness tracker or app and sends that data to the generation AI. The generation AI then analyzes the long-term exercise patterns based on that data. The analysis unit also analyzes the user's exercise history to understand the duration of exercise and trends in changes. For example, it can analyze the types and frequency of exercise the user has done in the past and suggest a menu based on their long-term exercise patterns. This makes it possible to suggest a health improvement menu that takes into account the user's past exercise history.

[0037] The suggestion unit can suggest the optimal menu for achieving the user's health goals based on the user's health goals. For example, the suggestion unit inputs the health goals set by the user into the generation AI and suggests the optimal exercise menu based on that data. For example, it suggests a menu centered on aerobic exercise for a user whose goal is weight loss, and suggests weight training for a user whose goal is to increase muscle strength. The suggestion unit also suggests the optimal menu for achieving the user's health goals based on the user's health goals. For example, if the user's goal is to reduce stress, it suggests yoga or stretching, which have a relaxing effect. In this way, it is possible to suggest the optimal exercise menu based on the user's health goals.

[0038] The suggestion unit can suggest exercise menus according to the season and weather, and suggest indoor exercise menus when outdoor exercise is difficult. The suggestion unit, for example, collects seasonal and weather data and adjusts the exercise menu based on that data. For example, it suggests exercises that can be done indoors on rainy days and suggests outdoor exercise on sunny days. The suggestion unit also suggests exercise menus according to the season and weather, and suggests indoor exercise menus when outdoor exercise is difficult. For example, it suggests yoga or stretching that can be done indoors on cold winter days and suggests running during cooler hours on hot summer days. In this way, it is possible to suggest an appropriate exercise menu according to the season and weather.

[0039] The suggestion unit can work with the user's friends and family to suggest group exercise menus. For example, the suggestion unit collects information about the user's friends and family and suggests group exercise menus based on that data. For example, it can suggest stretching or yoga that the whole family can do. The suggestion unit can also work with the user's friends and family to suggest group exercise menus. For example, it can suggest running or team sports to do with friends. This makes it possible to suggest group exercise menus in collaboration with the user's friends and family.

[0040] The suggestion unit can suggest a menu that incorporates exercises based on the user's hobbies and interests. For example, the suggestion unit collects the user's hobbies and interests in the form of a questionnaire and suggests an exercise menu based on that data. For example, the suggestion unit suggests dance exercises to a user who likes dancing. The suggestion unit also suggests a menu that incorporates exercises based on the user's hobbies and interests. For example, the suggestion unit suggests hiking or cycling to a user who likes outdoor activities. In this way, an exercise menu can be suggested that is based on the user's hobbies and interests.

[0041] The suggestion unit can analyze user feedback in real time and instantly adjust the menu. For example, the suggestion unit analyzes feedback entered by the user after exercising in real time and adjusts the menu based on that data. For example, the suggestion unit regenerates a menu based on the user's thoughts on the difficulty and effectiveness of the exercise. The suggestion unit also analyzes user feedback in real time and instantly adjusts the menu. For example, the suggestion unit adjusts the menu based on the discomfort or fatigue the user felt during exercise. This allows the user's feedback to be reflected in real time and the menu to be instantly adjusted.

[0042] The suggestion unit can suggest a low-stress menu by taking into account the user's physical condition (e.g., muscle pain or fatigue). The suggestion unit, for example, analyzes the physical condition (e.g., muscle pain or fatigue) input by the user and suggests a low-stress exercise menu based on that data. For example, if there is muscle pain, it suggests light stretching or yoga. The suggestion unit also suggests a low-stress menu by taking into account the user's physical condition. For example, if there is fatigue, it suggests low-intensity exercise. In this way, it is possible to suggest a low-stress exercise menu that takes into account the user's physical condition.

[0043] The suggestion unit can save the user's customization history and reflect it in suggestions from the next time onwards. The suggestion unit, for example, saves the customization details made by the user in a database and suggests exercise menus for the next time onwards based on that data. For example, it gives priority to suggesting exercises that have been customized in the past. The suggestion unit also saves the user's customization history and reflects it in suggestions from the next time onwards. For example, it adjusts the menu for the next time onwards based on the customization details made by the user in the past. This allows the user's customization history to be saved and reflected in suggestions from the next time onwards.

[0044] The suggestion unit allows users to share customization information with other users and promote community-based customization. The suggestion unit, for example, builds a platform where users can share customization details made by users with other users. For example, the user posts the customization details and receives feedback from other users. The suggestion unit also allows users to share customization information with other users and promote community-based customization. For example, users share customization information with each other through online forums or group chats. This allows users to share customization information with other users and promote community-based customization.

[0045] The suggestion unit can monitor the user's heart rate and calorie consumption during exercise in real time and provide feedback. For example, the suggestion unit monitors the user's heart rate during exercise using a smartwatch and sends the data to the generation AI. The generation AI provides feedback on the intensity and effectiveness of the exercise based on the data. The suggestion unit also monitors the user's calorie consumption during exercise and provides feedback based on the data. For example, it displays the calories burned during exercise in real time and provides feedback to the user. This makes it possible to monitor the user's heart rate and calorie consumption during exercise in real time and provide appropriate feedback.

[0046] The suggestion unit can analyze the user's exercise form and provide feedback that encourages them to exercise in the correct form. For example, the suggestion unit takes a picture of the user's exercise form with a camera and sends the video to the generation AI. The generation AI analyzes the video and provides feedback that encourages them to exercise in the correct form. The suggestion unit also analyzes the user's exercise form and provides feedback that encourages them to exercise in the correct form. For example, it analyzes the posture and movements of the user's exercise and provides advice that encourages them to exercise in the correct form. In this way, the user's exercise form can be analyzed and feedback that encourages them to exercise in the correct form can be provided.

[0047] The suggestion unit can suggest music or podcasts for the user to listen to while exercising, thereby improving the enjoyment of exercise. For example, the suggestion unit collects the user's music preferences in the form of a questionnaire and suggests music to listen to while exercising based on that data. For example, the suggestion unit suggests up-tempo music. The suggestion unit also suggests podcasts for the user to listen to while exercising. For example, the suggestion unit suggests podcasts based on the user's interests. This makes it possible to suggest music or podcasts for the user to listen to while exercising, thereby improving the enjoyment of exercise.

[0048] The analysis unit analyzes the user's long-term health data and can grasp trends in the health condition. The analysis unit, for example, collects the user's long-term health data (e.g., weight, body fat percentage, exercise history) and analyzes health condition trends based on that data. For example, it grasps weight gain / loss and exercise continuity. The analysis unit also analyzes the user's health data and grasps long-term health condition trends. For example, it analyzes changes and trends in the health condition based on the user's health data. In this way, the user's long-term health data can be analyzed and trends in the health condition can be grasped.

[0049] The analysis unit can predict future health risks based on the user's health data and suggest preventive measures. The analysis unit, for example, analyzes the user's health data and predicts future health risks based on that data. For example, it predicts risks due to weight gain or lack of exercise. The analysis unit also predicts future health risks based on the user's health data and suggests preventive measures. For example, it suggests preventive measures such as exercise programs, dietary advice, and lifestyle improvements based on the user's health data. This makes it possible to predict future health risks based on the user's health data and suggest appropriate preventive measures.

[0050] The analysis unit anonymizes the user's health data and compares it with other users to understand their relative health status. For example, the analysis unit anonymizes the user's health data and compares it with other users based on that data. For example, the analysis unit compares it with users of the same age or gender to understand their relative health status. The analysis unit also anonymizes the user's health data and compares it with other users to understand their relative health status. For example, the analysis unit compares it with people with the same exercise level based on the user's health data. In this way, the analysis unit anonymizes the user's health data and compares it with other users to understand their relative health status.

[0051] The analysis unit can periodically provide a personalized health report based on the user's health data. The analysis unit, for example, analyzes the user's health data and generates a personalized health report based on that data. For example, the analysis unit summarizes changes in weight and body fat percentage in the report. The analysis unit also periodically provides a personalized health report based on the user's health data. For example, the analysis unit provides individual health advice and visualizes data based on the user's health data. This makes it possible to periodically provide a personalized health report based on the user's health data.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The analysis unit can also collect data on the user's lifestyle habits and suggest health improvement menus based on this. For example, it can collect the user's commute method and time and suggest light exercises that can be done during the commute. It can also collect the user's hobbies and daily activities and customize an exercise menu based on this. For example, for a user whose hobby is gardening, it can suggest stretches and exercises that can be done while gardening. It can also analyze the user's sleep patterns and stress levels and suggest an exercise menu that has a relaxing effect based on this. This makes it possible to provide a health improvement menu that takes into account the user's overall lifestyle habits.

[0054] The suggestion unit can suggest an optimal menu for achieving the user's health goals based on the user's health goals. For example, it suggests a menu centered on aerobic exercise to a user whose goal is weight loss, and suggests weight training to a user whose goal is to increase muscle strength. The suggestion unit also suggests an optimal menu for achieving the user's health goals based on the user's health goals. For example, if the user's goal is to reduce stress, it suggests yoga or stretching, which have a relaxing effect. In this way, it is possible to suggest an optimal exercise menu based on the user's health goals.

[0055] The analysis unit can take into account the user's home and work environments and propose a menu that is appropriate for the environment. For example, the analysis unit collects information about the user's home environment (e.g., family composition and size of the home) in the form of a questionnaire and sends that data to the generation AI. The generation AI then proposes an exercise menu that is appropriate for the home environment based on that data. The analysis unit also collects information about the user's work environment (e.g., workplace stress level and working hours) in the form of a questionnaire and sends that data to the generation AI. The generation AI then proposes an exercise menu that is appropriate for the work environment based on that data. This makes it possible to propose a health improvement menu that is appropriate for the user's home and work environments.

[0056] The analysis unit can analyze the user's long-term health data and grasp trends in the health condition. For example, it collects the user's long-term health data (e.g., weight, body fat percentage, exercise history) and analyzes health condition trends based on that data. For example, it grasps weight gain / loss and exercise continuity. The analysis unit also analyzes the user's health data and grasps long-term health condition trends. For example, it analyzes changes and trends in the health condition based on the user's health data. In this way, it is possible to analyze the user's long-term health data and grasp trends in the health condition.

[0057] The suggestion unit can analyze user feedback in real time and instantly adjust the menu. For example, it can analyze feedback entered by the user after exercising in real time and adjust the menu based on that data. For example, it can regenerate a menu based on the user's thoughts on the difficulty and effectiveness of the exercise. The suggestion unit can also analyze user feedback in real time and instantly adjust the menu. For example, it can adjust the menu based on the discomfort or fatigue the user felt during exercise. This allows the user's feedback to be reflected in real time and the menu to be instantly adjusted.

[0058] The analysis unit can predict future health risks based on the user's health data and suggest preventive measures. For example, the analysis unit analyzes the user's health data and predicts future health risks based on that data. For example, it predicts risks due to weight gain or lack of exercise. The analysis unit also predicts future health risks based on the user's health data and suggests preventive measures. For example, it suggests preventive measures such as exercise programs, dietary advice, and lifestyle improvements based on the user's health data. This makes it possible to predict future health risks based on the user's health data and suggest appropriate preventive measures.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The user information collection unit collects information about the user's preferences and fitness level. For example, the prompts the user enters may include age, gender, exercise experience, current fitness level, and preferred exercise type. Step 2: The analysis unit analyzes the information collected by the user information collection unit. For example, the generation AI performs analysis to propose the optimal health improvement menu for the user based on the user's age, gender, exercise experience, current physical fitness level, and preferred exercise type. Step 3: The suggestion unit proposes an optimal health improvement menu based on the information analyzed by the analysis unit. For example, it suggests light stretching or yoga to a user with low physical strength, and running or high-intensity training to a user with high physical strength.

[0061] (Example 2) The health improvement menu suggestion system according to an embodiment of the present invention is a system that uses a generation AI to suggest a health improvement menu tailored to the preferences and physical fitness level of each individual user. As a result, the health improvement menu suggestion system can suggest an optimal health improvement menu based on the user's preferences and physical fitness level.

[0062] A health improvement menu suggestion system according to an embodiment includes a user information collection unit, an analysis unit, and a suggestion unit. The user information collection unit collects information related to a user's preferences and physical fitness level. For example, prompts input by the user include age, gender, exercise experience, current physical fitness level, and preferred exercise type. The analysis unit analyzes the information collected by the user information collection unit. For example, the generation AI performs analysis to suggest an optimal health improvement menu for the user based on the user's age, gender, exercise experience, current physical fitness level, and preferred exercise type. The suggestion unit suggests an optimal health improvement menu based on the information analyzed by the analysis unit. For example, light stretching or yoga is suggested for a user with low physical fitness, and running or high-intensity training is suggested for a user with high physical fitness. This allows the health improvement menu suggestion system to suggest an optimal health improvement menu based on the user's preferences and physical fitness level.

[0063] The user information collection unit can collect the user's age, gender, exercise experience, current physical fitness level, and preferred type of exercise. For example, the user uses an app to record their daily diet and sends that data to the generation AI. The generation AI analyzes the nutritional balance and calorie intake of the diet and reflects this in a health improvement menu. The user information collection unit also collects the user's age, gender, exercise experience, current physical fitness level, and preferred type of exercise. For example, this information can be collected in the form of a questionnaire and sent to the generation AI. By collecting detailed information about the user, more accurate menu suggestions can be made.

[0064] The suggestion unit can suggest light stretching, yoga, running, or high-intensity training according to the user's physical fitness level. The suggestion unit, for example, analyzes the user's stress level and psychological state and suggests a menu that also takes mental health into consideration. For example, the user uses an app to record their daily stress level and sends that data to the generation AI. The generation AI analyzes the stress level and suggests yoga or stretching that has a relaxing effect. The suggestion unit also suggests light stretching, yoga, running, or high-intensity training according to the user's physical fitness level. For example, light stretching or yoga is suggested for a user with low physical fitness, and running or high-intensity training is suggested for a user with high physical fitness. This makes it possible to suggest an appropriate exercise menu according to the user's physical fitness level.

[0065] The suggestion unit can regenerate menus based on user feedback. For example, the suggestion unit uses an emotion estimation function to analyze the emotions the user feels when entering input and generate prompts that elicit positive emotions. For example, the suggestion unit uses a camera to analyze the user's facial expressions when entering input and calculates an emotion score. The generation AI uses this score to generate prompts that elicit positive emotions. The suggestion unit also regenerates menus based on user feedback. For example, if the user enters their thoughts on the difficulty and effectiveness of an exercise, the generation AI can reflect this in subsequent menu suggestions. This allows the system to provide more appropriate menus that reflect user feedback.

[0066] The analysis unit collects the user's dietary history and sleep patterns and can suggest a health improvement menu based on this data. For example, the analysis unit uses an app to record the user's daily diet and sends that data to the generation AI. The generation AI analyzes the nutritional balance and calorie intake of the meals and reflects this in the health improvement menu. The analysis unit also collects the user's sleep patterns and suggests a health improvement menu based on this data. For example, the user uses an app to record the amount of sleep and quality of sleep and sends that data to the generation AI. The generation AI analyzes the sleep patterns and reflects this in the health improvement menu. This makes it possible to suggest a health improvement menu that takes the user's dietary history and sleep patterns into account.

[0067] The analysis unit can analyze the user's stress level and psychological state and suggest a menu that also takes mental health into consideration. For example, the analysis unit uses an app that allows the user to record their daily stress level, and sends that data to the generation AI. The generation AI analyzes the stress level and suggests yoga or stretching exercises that have a relaxing effect. The analysis unit can also analyze the user's psychological state and suggest a menu that also takes mental health into consideration. For example, the user takes a psychological test and sends the results to the generation AI. The generation AI analyzes the psychological state and suggests a menu that also takes mental health into consideration. This makes it possible to suggest a health improvement menu that takes the user's mental health into consideration.

[0068] The analysis unit uses the emotion estimation function to analyze the emotions of the user when typing and can generate prompts that elicit positive emotions. For example, the analysis unit uses a camera to analyze the user's facial expressions when typing and calculates an emotion score. The generation AI generates prompts that elicit positive emotions based on that score. The analysis unit also records the user's voice when typing and estimates the emotion using voice analysis technology. For example, it analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit also analyzes the text the user types and estimates the emotion. For example, it analyzes the content and style of the text and calculates an emotion score. This makes it possible to generate prompts that take the user's emotions into consideration and elicit positive emotions.

[0069] The analysis unit can take into consideration the user's home environment and work environment and propose a menu that is appropriate for the environment. For example, the analysis unit collects information about the user's home environment (e.g., family composition and size of the home) in the form of a questionnaire and sends that data to the generation AI. Based on that data, the generation AI proposes an exercise menu that is appropriate for the home environment. The analysis unit also collects information about the user's work environment (e.g., workplace stress level and working hours) in the form of a questionnaire and sends that data to the generation AI. Based on that data, the generation AI proposes an exercise menu that is appropriate for the work environment. This makes it possible to propose a health improvement menu that is appropriate for the user's home environment and work environment.

[0070] The analysis unit can analyze the user's past exercise history and suggest a menu based on their long-term exercise patterns. For example, the analysis unit collects the user's past exercise history from a fitness tracker or app and sends that data to the generation AI. The generation AI then analyzes the long-term exercise patterns based on that data. The analysis unit also analyzes the user's exercise history to understand the duration of exercise and trends in changes. For example, it can analyze the types and frequency of exercise the user has done in the past and suggest a menu based on their long-term exercise patterns. This makes it possible to suggest a health improvement menu that takes into account the user's past exercise history.

[0071] The analysis unit uses the emotion estimation function to analyze the user's exercise preferences based on their emotions and suggest a menu that matches their emotions. For example, the analysis unit analyzes the user's emotional state in real time and estimates their exercise preferences based on that data. The generation AI then suggests an exercise menu that matches their emotions based on that data. The analysis unit also analyzes the user's emotional state and understands their exercise preferences based on their emotions. For example, if the user is feeling stressed, it suggests yoga, which has a relaxing effect, and suggests an exercise menu that elicits positive emotions. This makes it possible to suggest an exercise menu based on the user's emotions.

[0072] The suggestion unit can suggest the optimal menu for achieving the user's health goals based on the user's health goals. For example, the suggestion unit inputs the health goals set by the user into the generation AI and suggests the optimal exercise menu based on that data. For example, it suggests a menu centered on aerobic exercise for a user whose goal is weight loss, and suggests weight training for a user whose goal is to increase muscle strength. The suggestion unit also suggests the optimal menu for achieving the user's health goals based on the user's health goals. For example, if the user's goal is to reduce stress, it suggests yoga or stretching, which have a relaxing effect. In this way, it is possible to suggest the optimal exercise menu based on the user's health goals.

[0073] The suggestion unit can suggest exercise menus according to the season and weather, and suggest indoor exercise menus when outdoor exercise is difficult. The suggestion unit, for example, collects seasonal and weather data and adjusts the exercise menu based on that data. For example, it suggests exercises that can be done indoors on rainy days and suggests outdoor exercise on sunny days. The suggestion unit also suggests exercise menus according to the season and weather, and suggests indoor exercise menus when outdoor exercise is difficult. For example, it suggests yoga or stretching that can be done indoors on cold winter days and suggests running during cooler hours on hot summer days. In this way, it is possible to suggest an appropriate exercise menu according to the season and weather.

[0074] The suggestion unit can work with the user's friends and family to suggest group exercise menus. For example, the suggestion unit collects information about the user's friends and family and suggests group exercise menus based on that data. For example, it can suggest stretching or yoga that the whole family can do. The suggestion unit can also work with the user's friends and family to suggest group exercise menus. For example, it can suggest running or team sports to do with friends. This makes it possible to suggest group exercise menus in collaboration with the user's friends and family.

[0075] The suggestion unit can suggest a menu that incorporates exercises based on the user's hobbies and interests. For example, the suggestion unit collects the user's hobbies and interests in the form of a questionnaire and suggests an exercise menu based on that data. For example, the suggestion unit suggests dance exercises to a user who likes dancing. The suggestion unit also suggests a menu that incorporates exercises based on the user's hobbies and interests. For example, the suggestion unit suggests hiking or cycling to a user who likes outdoor activities. In this way, an exercise menu can be suggested that is based on the user's hobbies and interests.

[0076] The suggestion unit can use the emotion estimation function to suggest a menu incorporating entertainment elements that the user can enjoy. For example, the suggestion unit analyzes the user's emotional state in real time and suggests an exercise menu incorporating entertainment elements based on the data. For example, it suggests exercises that use music. The suggestion unit also uses the emotion estimation function to suggest a menu incorporating entertainment elements that the user can enjoy. For example, it suggests an exercise menu incorporating game elements. In this way, it is possible to suggest an exercise menu incorporating entertainment elements that the user can enjoy.

[0077] The suggestion unit can analyze user feedback in real time and instantly adjust the menu. For example, the suggestion unit analyzes feedback entered by the user after exercising in real time and adjusts the menu based on that data. For example, the suggestion unit regenerates a menu based on the user's thoughts on the difficulty and effectiveness of the exercise. The suggestion unit also analyzes user feedback in real time and instantly adjusts the menu. For example, the suggestion unit adjusts the menu based on the discomfort or fatigue the user felt during exercise. This allows the user's feedback to be reflected in real time and the menu to be instantly adjusted.

[0078] The suggestion unit can suggest a low-stress menu by taking into account the user's physical condition (e.g., muscle pain or fatigue). The suggestion unit, for example, analyzes the physical condition (e.g., muscle pain or fatigue) input by the user and suggests a low-stress exercise menu based on that data. For example, if there is muscle pain, it suggests light stretching or yoga. The suggestion unit also suggests a low-stress menu by taking into account the user's physical condition. For example, if there is fatigue, it suggests low-intensity exercise. In this way, it is possible to suggest a low-stress exercise menu that takes into account the user's physical condition.

[0079] The suggestion unit can use the emotion estimation function to customize the menu according to the user's emotions and suggest a menu that will elicit positive emotions. The suggestion unit, for example, analyzes the user's emotional state in real time and customizes the exercise menu based on that data. For example, on a stressful day, the suggestion unit can suggest yoga, which has a relaxing effect. The suggestion unit can also use the emotion estimation function to customize the menu according to the user's emotions and suggest a menu that will elicit positive emotions. For example, if the user has negative emotions about exercise, the suggestion unit can suggest a menu that incorporates entertainment elements that will make the exercise fun. This makes it possible to customize the menu according to the user's emotions and suggest an exercise menu that will elicit positive emotions.

[0080] The suggestion unit can save the user's customization history and reflect it in suggestions from the next time onwards. The suggestion unit, for example, saves the customization details made by the user in a database and suggests exercise menus for the next time onwards based on that data. For example, it gives priority to suggesting exercises that have been customized in the past. The suggestion unit also saves the user's customization history and reflects it in suggestions from the next time onwards. For example, it adjusts the menu for the next time onwards based on the customization details made by the user in the past. This allows the user's customization history to be saved and reflected in suggestions from the next time onwards.

[0081] The suggestion unit allows users to share customization information with other users and promote community-based customization. The suggestion unit, for example, builds a platform where users can share customization details made by users with other users. For example, the user posts the customization details and receives feedback from other users. The suggestion unit also allows users to share customization information with other users and promote community-based customization. For example, users share customization information with each other through online forums or group chats. This allows users to share customization information with other users and promote community-based customization.

[0082] The suggestion unit uses the emotion estimation function to make customization suggestions based on the user's emotions, thereby increasing emotional satisfaction. The suggestion unit, for example, analyzes the user's emotional state in real time and makes customization suggestions based on that data. For example, on a stressful day, the suggestion unit suggests exercises that have a relaxing effect. The suggestion unit also uses the emotion estimation function to make customization suggestions based on the user's emotions, thereby increasing emotional satisfaction. For example, if the user has negative feelings about exercise, the suggestion unit suggests a menu that incorporates entertainment elements that make the exercise fun. In this way, customization suggestions based on the user's emotions can be made, thereby increasing emotional satisfaction.

[0083] The suggestion unit can monitor the user's heart rate and calorie consumption during exercise in real time and provide feedback. For example, the suggestion unit monitors the user's heart rate during exercise using a smartwatch and sends the data to the generation AI. The generation AI provides feedback on the intensity and effectiveness of the exercise based on the data. The suggestion unit also monitors the user's calorie consumption during exercise and provides feedback based on the data. For example, it displays the calories burned during exercise in real time and provides feedback to the user. This makes it possible to monitor the user's heart rate and calorie consumption during exercise in real time and provide appropriate feedback.

[0084] The suggestion unit can analyze the user's exercise form and provide feedback that encourages them to exercise in the correct form. For example, the suggestion unit takes a picture of the user's exercise form with a camera and sends the video to the generation AI. The generation AI analyzes the video and provides feedback that encourages them to exercise in the correct form. The suggestion unit also analyzes the user's exercise form and provides feedback that encourages them to exercise in the correct form. For example, it analyzes the posture and movements of the user's exercise and provides advice that encourages them to exercise in the correct form. In this way, the user's exercise form can be analyzed and feedback that encourages them to exercise in the correct form can be provided.

[0085] The suggestion unit can use the emotion estimation function to analyze the user's emotional state during exercise and provide feedback to maintain motivation. The suggestion unit, for example, analyzes the user's emotional state during exercise in real time and provides feedback to maintain motivation based on the data. For example, it sends an encouraging message that elicits positive emotions. The suggestion unit also uses the emotion estimation function to analyze the user's emotional state during exercise and provide feedback to maintain motivation. For example, it provides advice to maintain motivation based on the discomfort or fatigue the user feels during exercise. In this way, it is possible to analyze the user's emotional state during exercise and provide feedback to maintain motivation.

[0086] The suggestion unit can suggest music or podcasts for the user to listen to while exercising, thereby improving the enjoyment of exercise. For example, the suggestion unit collects the user's music preferences in the form of a questionnaire and suggests music to listen to while exercising based on that data. For example, the suggestion unit suggests up-tempo music. The suggestion unit also suggests podcasts for the user to listen to while exercising. For example, the suggestion unit suggests podcasts based on the user's interests. This makes it possible to suggest music or podcasts for the user to listen to while exercising, thereby improving the enjoyment of exercise.

[0087] The suggestion unit uses the emotion estimation function to provide feedback based on the user's emotions during exercise, thereby increasing emotional satisfaction. The suggestion unit, for example, analyzes the user's emotional state during exercise in real time and provides feedback based on that data. For example, it sends an encouraging message that elicits positive emotions. The suggestion unit also uses the emotion estimation function to provide feedback based on the user's emotions during exercise. For example, it provides advice to increase emotional satisfaction based on the discomfort or fatigue the user feels during exercise. In this way, it is possible to provide feedback based on the user's emotions during exercise and increase emotional satisfaction.

[0088] The analysis unit analyzes the user's long-term health data and can grasp trends in the health condition. The analysis unit, for example, collects the user's long-term health data (e.g., weight, body fat percentage, exercise history) and analyzes health condition trends based on that data. For example, it grasps weight gain / loss and exercise continuity. The analysis unit also analyzes the user's health data and grasps long-term health condition trends. For example, it analyzes changes and trends in the health condition based on the user's health data. In this way, the user's long-term health data can be analyzed and trends in the health condition can be grasped.

[0089] The analysis unit can predict future health risks based on the user's health data and suggest preventive measures. The analysis unit, for example, analyzes the user's health data and predicts future health risks based on that data. For example, it predicts risks due to weight gain or lack of exercise. The analysis unit also predicts future health risks based on the user's health data and suggests preventive measures. For example, it suggests preventive measures such as exercise programs, dietary advice, and lifestyle improvements based on the user's health data. This makes it possible to predict future health risks based on the user's health data and suggest appropriate preventive measures.

[0090] The analysis unit uses the emotion estimation function to analyze the relationship between the user's emotional state and health data and make emotion-based health improvement suggestions. The analysis unit, for example, analyzes the user's emotional state and health data in real time and understands the relationship. For example, it analyzes the relationship between high-stress days and exercise volume. The analysis unit also uses the emotion estimation function to analyze the relationship between the user's emotional state and health data and make emotion-based health improvement suggestions. For example, it analyzes the relationship between the user's emotional state and health data and suggests an exercise menu for stress reduction. This allows the analysis of the relationship between the user's emotional state and health data and emotion-based health improvement suggestions.

[0091] The analysis unit anonymizes the user's health data and compares it with other users to understand their relative health status. For example, the analysis unit anonymizes the user's health data and compares it with other users based on that data. For example, the analysis unit compares it with users of the same age or gender to understand their relative health status. The analysis unit also anonymizes the user's health data and compares it with other users to understand their relative health status. For example, the analysis unit compares it with people with the same exercise level based on the user's health data. In this way, the analysis unit anonymizes the user's health data and compares it with other users to understand their relative health status.

[0092] The analysis unit can periodically provide a personalized health report based on the user's health data. The analysis unit, for example, analyzes the user's health data and generates a personalized health report based on that data. For example, the analysis unit summarizes changes in weight and body fat percentage in the report. The analysis unit also periodically provides a personalized health report based on the user's health data. For example, the analysis unit provides individual health advice and visualizes data based on the user's health data. This makes it possible to periodically provide a personalized health report based on the user's health data.

[0093] The analysis unit uses the emotion estimation function to analyze health data based on the user's emotional state and make suggestions to increase emotional satisfaction. The analysis unit, for example, analyzes the user's emotional state in real time and analyzes health data based on that data. For example, it analyzes the relationship between days with high emotion scores and health data. The analysis unit also uses the emotion estimation function to analyze health data based on the user's emotional state and make suggestions to increase emotional satisfaction. For example, it suggests an exercise menu to increase emotional satisfaction based on the user's emotional state. This makes it possible to analyze health data based on the user's emotional state and make suggestions to increase emotional satisfaction.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The analysis unit can also collect data on the user's lifestyle habits and suggest health improvement menus based on this. For example, it can collect the user's commute method and time and suggest light exercises that can be done during the commute. It can also collect the user's hobbies and daily activities and customize an exercise menu based on this. For example, for a user whose hobby is gardening, it can suggest stretches and exercises that can be done while gardening. It can also analyze the user's sleep patterns and stress levels and suggest an exercise menu that has a relaxing effect based on this. This makes it possible to provide a health improvement menu that takes into account the user's overall lifestyle habits.

[0096] The suggestion unit can analyze the user's emotional state and suggest an exercise menu based on the emotion. For example, if the user is feeling stressed, it can suggest relaxing yoga or meditation. If the user is feeling positive, it can suggest energetic dance exercises or running. Furthermore, it can monitor the user's emotional state in real time and provide appropriate feedback if the emotion changes during exercise. For example, if the user feels tired during exercise, it can send an encouraging message to maintain motivation. This makes it possible to suggest an exercise menu according to the user's emotional state and increase emotional satisfaction.

[0097] The suggestion unit can suggest an optimal menu for achieving the user's health goals based on the user's health goals. For example, it suggests a menu centered on aerobic exercise to a user whose goal is weight loss, and suggests weight training to a user whose goal is to increase muscle strength. The suggestion unit also suggests an optimal menu for achieving the user's health goals based on the user's health goals. For example, if the user's goal is to reduce stress, it suggests yoga or stretching, which have a relaxing effect. In this way, it is possible to suggest an optimal exercise menu based on the user's health goals.

[0098] The analysis unit can take into account the user's home and work environments and propose a menu that is appropriate for the environment. For example, the analysis unit collects information about the user's home environment (e.g., family composition and size of the home) in the form of a questionnaire and sends that data to the generation AI. The generation AI then proposes an exercise menu that is appropriate for the home environment based on that data. The analysis unit also collects information about the user's work environment (e.g., workplace stress level and working hours) in the form of a questionnaire and sends that data to the generation AI. The generation AI then proposes an exercise menu that is appropriate for the work environment based on that data. This makes it possible to propose a health improvement menu that is appropriate for the user's home and work environments.

[0099] The suggestion unit can use the emotion estimation function to suggest a menu that incorporates entertainment elements that the user can enjoy. For example, the suggestion unit can analyze the user's emotional state in real time and suggest an exercise menu that incorporates entertainment elements based on that data. For example, it can suggest exercises that use music. The suggestion unit can also use the emotion estimation function to suggest a menu that incorporates entertainment elements that the user can enjoy. For example, it can suggest an exercise menu that incorporates game elements. This makes it possible to suggest an exercise menu that incorporates entertainment elements that the user can enjoy.

[0100] The analysis unit can analyze the user's long-term health data and grasp trends in the health condition. For example, it collects the user's long-term health data (e.g., weight, body fat percentage, exercise history) and analyzes health condition trends based on that data. For example, it grasps weight gain / loss and exercise continuity. The analysis unit also analyzes the user's health data and grasps long-term health condition trends. For example, it analyzes changes and trends in the health condition based on the user's health data. In this way, it is possible to analyze the user's long-term health data and grasp trends in the health condition.

[0101] The suggestion unit can analyze user feedback in real time and instantly adjust the menu. For example, it can analyze feedback entered by the user after exercising in real time and adjust the menu based on that data. For example, it can regenerate a menu based on the user's thoughts on the difficulty and effectiveness of the exercise. The suggestion unit can also analyze user feedback in real time and instantly adjust the menu. For example, it can adjust the menu based on the discomfort or fatigue the user felt during exercise. This allows the user's feedback to be reflected in real time and the menu to be instantly adjusted.

[0102] The suggestion unit can use the emotion estimation function to analyze the user's emotional state during exercise and provide feedback to maintain motivation. For example, the suggestion unit can analyze the user's emotional state during exercise in real time and provide feedback to maintain motivation based on that data. For example, it can send an encouraging message that elicits positive emotions. The suggestion unit can also use the emotion estimation function to analyze the user's emotional state during exercise and provide feedback to maintain motivation. For example, it can provide advice to maintain motivation based on the discomfort or fatigue the user feels during exercise. In this way, the suggestion unit can analyze the user's emotional state during exercise and provide feedback to maintain motivation.

[0103] The analysis unit can predict future health risks based on the user's health data and suggest preventive measures. For example, the analysis unit analyzes the user's health data and predicts future health risks based on that data. For example, it predicts risks due to weight gain or lack of exercise. The analysis unit also predicts future health risks based on the user's health data and suggests preventive measures. For example, it suggests preventive measures such as exercise programs, dietary advice, and lifestyle improvements based on the user's health data. This makes it possible to predict future health risks based on the user's health data and suggest appropriate preventive measures.

[0104] The suggestion unit can use the emotion estimation function to customize according to the user's emotions and suggest a menu that will elicit positive emotions. For example, it can analyze the user's emotional state in real time and customize an exercise menu based on that data. For example, on a stressful day, it can suggest yoga, which has a relaxing effect. The suggestion unit can also use the emotion estimation function to customize according to the user's emotions and suggest a menu that will elicit positive emotions. For example, if the user has negative emotions about exercise, it can suggest a menu that incorporates entertainment elements that will make the exercise fun. In this way, it can customize according to the user's emotions and suggest an exercise menu that will elicit positive emotions.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The user information collection unit collects information about the user's preferences and fitness level. For example, the prompts the user enters may include age, gender, exercise experience, current fitness level, and preferred exercise type. Step 2: The analysis unit analyzes the information collected by the user information collection unit. For example, the generation AI performs analysis to propose the optimal health improvement menu for the user based on the user's age, gender, exercise experience, current physical fitness level, and preferred exercise type. Step 3: The suggestion unit proposes an optimal health improvement menu based on the information analyzed by the analysis unit. For example, it suggests light stretching or yoga to a user with low physical strength, and running or high-intensity training to a user with high physical strength.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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]

[0174] 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 user information collection unit that collects information about the user's preferences and physical fitness level; an analysis unit that analyzes the information collected by the user information collection unit; a suggestion unit that suggests an optimal health improvement menu based on the information analyzed by the analysis unit. A system characterized by:

2. The proposal unit Depending on the user's physical fitness level, the system suggests light stretching, yoga, running, or high-intensity training.

2. The system of claim 1.

3. The analysis unit Considering the user's home and work environment, the system proposes menus that are suitable for the environment.

2. The system of claim 1.

4. The proposal unit Based on the user's health goals, the system proposes the optimal menu for achieving those goals.

2. The system of claim 1.

5. The proposal unit Analyze user feedback in real time and adjust menus instantly 2. The system of claim 1.

6. The proposal unit Monitor the user's heart rate and calorie consumption during exercise in real time and provide feedback 2. The system of claim 1.

7. The analysis unit Analyzing the relationship between the user's emotional state and health data, and making suggestions for improving health based on the user's emotions.

2. The system of claim 1.

8. The analysis unit Analyze the emotions of the user as they type and generate prompts that elicit positive emotions.

2. The system of claim 1.

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