System

Abstract

An object of a system according to an embodiment is to provide an individual walking plan on the basis of a health condition and a lifestyle of a user.SOLUTION: A system according to an embodiment includes a walking plan generation unit, a monitoring unit, and a data analysis unit. The walking plan generator generates a walking plan based on the health condition and the lifestyle of the user. The monitoring unit collects data during walking based on the walking plan generated by the walking plan generating unit. The data analyzer analyzes the data collected by the monitor and provides feedback to the user.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 makes it difficult to provide individual walking plans based on a user's health condition and lifestyle habits, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a personalized walking plan based on the user's health condition and lifestyle habits. [Means for solving the problem]

[0006] The system according to the embodiment includes a walking plan generation unit, a monitoring unit, and a data analysis unit. The walking plan generation unit generates a walking plan based on the user's health condition and lifestyle habits. The monitoring unit collects data during walking based on the walking plan generated by the walking plan generation unit. The data analysis unit analyzes the data collected by the monitoring unit and provides feedback to the user. [Effects of the Invention]

[0007] The system according to the embodiment can provide a personalized walking plan based on the user's health condition and lifestyle habits. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The health management system according to an embodiment of the present invention aims to extend healthy lifespan through walking, and uses a generation AI to generate walking plans, monitor them, and analyze data to provide optimal walking plans for individual users. This allows the health management system to improve the health of users and extend healthy lifespan for society as a whole.

[0029] A health management system according to an embodiment includes a walking plan generation unit, a monitoring unit, and a data analysis unit. The walking plan generation unit generates a walking plan based on the user's health condition and lifestyle habits. For example, the walking plan generation unit proposes a walking plan based on health indicators such as the user's heart rate, blood pressure, and weight. The walking plan generation unit can also generate a plan taking into account lifestyle data such as the user's meal frequency, exercise habits, and sleep patterns. The walking plan generation unit also customizes the walking distance, time, and route according to the user's walking goals. For example, a walking plan with appropriate exercise intensity and frequency is proposed for a user who wants to extend their healthy lifespan. The monitoring unit collects data during walking. For example, the monitoring unit collects data such as the number of steps, heart rate, and calories burned via a smartphone or wearable device. The monitoring unit can also record walking routes using GPS data. The monitoring unit also monitors the user's exercise intensity and pace in real time and provides appropriate feedback. For example, the monitoring unit may advise the user, such as, "Your current pace is appropriate" or "Try walking a little faster." The data analysis unit analyzes the collected data and provides feedback to the user. For example, the data analysis unit evaluates the user's health condition based on the walking data and suggests areas for improvement. The data analysis unit can also analyze the health condition of society as a whole based on the collected data and support policy recommendations and health promotion activities in the local community. For example, the data analysis unit analyzes trends in walking habits and health conditions by region and makes policy recommendations for health promotion. In this way, the health management system according to the embodiment can improve the health condition of users and extend the healthy life expectancy of society as a whole.

[0030] The walking plan generation unit learns the user's past walking data and can propose optimal walking plans according to the season and weather. For example, the generation AI of the walking plan generation unit analyzes the user's past walking data and learns walking patterns for each season. For example, it selects cooler times of the day in summer and suggests warmer daytime times in winter. The walking plan generation unit also obtains weather data in real time, and the generation AI adjusts the walking plan based on that information. For example, it suggests indoor walking or alternative exercise on rainy days. Furthermore, in order to propose walking routes according to the season and weather, the generation AI analyzes local weather data and selects the optimal route. For example, it suggests routes with a lot of shade in summer. This makes it possible to provide optimal walking plans according to the season and weather.

[0031] The walking plan generation unit can generate a comprehensive health management plan by taking into account the user's dietary and sleep data. In the walking plan generation unit, for example, the generation AI analyzes the user's dietary data and proposes a walking plan based on nutritional balance. For example, it adjusts the walking time and exercise intensity after meals. The walking plan generation unit also proposes the optimal walking time based on sleep data. For example, it recommends walking in the morning after getting enough sleep. Furthermore, the walking plan generation unit integrates dietary and sleep data, and the generation AI generates a comprehensive health management plan. For example, it suggests light walking to aid digestion after meals. This makes it possible to provide a comprehensive health management plan that takes dietary and sleep data into account.

[0032] The walking plan generation unit can generate a walking plan for walking with the pet, taking into account the health condition of the user's pet. For example, the generation AI in the walking plan generation unit analyzes the health data of the user's pet and proposes a walking plan according to the pet's amount of exercise. For example, the exercise intensity is adjusted based on the pet's age and weight. The walking plan generation unit also considers the pet's health condition and proposes a route for walking with the pet. For example, it selects a route that includes a park or dog run where the pet can enjoy themselves. Furthermore, the walking plan generation unit generates a walking plan that matches the pet's physical condition based on the pet's health data. For example, if the pet gets tired easily, it proposes a short walk. This makes it possible to provide a plan for walking with the pet.

[0033] The walking plan generation unit can share walking data of the user's friends and family and propose walking plans for the group. For example, the generation AI of the walking plan generation unit shares walking data of the user's friends and family and proposes walking plans based on the health status of the entire group. For example, it adjusts the route and exercise intensity so that everyone can enjoy. Furthermore, to encourage group walking, the generation AI sets a common walking goal and provides real-time feedback on the progress of achievement. For example, it sets a goal for everyone to achieve a certain number of steps. Furthermore, the generation AI of the walking plan generation unit analyzes the walking data of the entire group and proposes the optimal walking time and route. For example, it generates a plan that matches everyone's schedule. This makes it possible to provide walking plans for the group.

[0034] The walking plan generation unit can analyze the user's genetic information and provide a walking plan based on genetic risk. In the walking plan generation unit, for example, the generation AI analyzes the user's genetic information and provides a walking plan based on genetic risk. For example, if there is a high risk of heart disease, light exercise is recommended. In addition, the walking plan generation unit uses the generation AI to propose a walking plan to prevent specific health risks based on the user's genetic information. For example, if there is a risk of diabetes, exercise to manage blood sugar levels is recommended. Furthermore, the walking plan generation unit analyzes the genetic information and the generation AI provides a walking plan that is optimal for the user's health condition. For example, if bone density is low, walking aimed at strengthening bones is suggested. This makes it possible to provide a walking plan based on genetic risk.

[0035] The walking plan generation unit can propose optimal walking times and routes, taking into account the user's occupation and daily activity level. For example, the generation AI in the walking plan generation unit analyzes the user's occupation data and proposes optimal walking times based on the user's daily activity level. For example, if the user does a lot of desk work, walking after work is recommended. The walking plan generation unit also considers the user's daily activity level, and the generation AI proposes optimal walking routes. For example, if the user's activity level is low, a route that increases the amount of exercise is selected. Furthermore, the walking plan generation unit provides the user with an optimal walking plan based on the user's occupation and activity level. For example, if the user does a lot of standing work, a route that reduces foot fatigue is proposed. This makes it possible to provide an optimal walking plan that takes into account the user's occupation and daily activity level.

[0036] The walking plan generation unit can propose walking plans for touring tourist spots and natural parks, taking into account the user's hobbies and interests. For example, the generation AI of the walking plan generation unit analyzes the user's hobbies and interests and proposes walking plans for touring tourist spots. For example, it selects a route that visits historical sites. The walking plan generation unit also considers the user's interests and provides walking plans for touring natural parks. For example, it selects parks with many flowers and plants. Furthermore, the generation AI of the walking plan generation unit proposes the optimal walking plan for the user based on the hobbies and interests. For example, it proposes a scenic route for a user whose hobby is photography. This makes it possible to provide walking plans that match the hobbies and interests.

[0037] The walking plan generation unit can provide a plan that combines walking with other exercises according to the user's fitness goals. For example, the generation AI of the walking plan generation unit analyzes the user's fitness goals and provides a plan that combines walking and yoga. For example, it suggests yoga that has a relaxing effect after walking. Furthermore, the walking plan generation unit proposes a plan that combines walking and strength training based on the user's exercise goals. For example, it recommends strength training that can be done at home after walking. Furthermore, the walking plan generation unit provides a comprehensive plan that combines walking with other exercises according to the fitness goals. For example, it suggests walking several times a week and yoga classes on the weekends. This makes it possible to provide a comprehensive exercise plan that matches the fitness goals.

[0038] The data analysis unit analyzes the walking data for each region, identifies health risks specific to the region, and can propose countermeasures. In the data analysis unit, for example, the generation AI analyzes the walking data for each region and identifies health risks in a specific region. For example, it identifies regions where people are lacking exercise and proposes measures to promote exercise in those regions. The data analysis unit also uses the generation AI to propose countermeasures to prevent health risks based on the walking data for each region. For example, it recommends holding a walking event in a specific region. Furthermore, the data analysis unit uses the generation AI to analyze the health data for each region and proposes countermeasures for health risks specific to the region. For example, it proposes the development of walking routes in a specific region. This makes it possible to identify health risks specific to the region and propose countermeasures.

[0039] The data analysis unit integrates walking data with other health data to obtain a more precise understanding of the user's health condition. For example, the generation AI in the data analysis unit integrates walking data with hospital medical data to obtain a more precise understanding of the user's health condition. For example, it compares walking frequency with medical data and analyzes the effects of health improvement. The data analysis unit also integrates walking data with other health data to allow the generation AI to obtain a comprehensive understanding of the user's health condition. For example, it combines blood pressure and blood sugar data with walking data for analysis. Furthermore, the data analysis unit allows the generation AI to integrate walking data with medical data to detect health risks early. For example, it detects abnormal patterns from walking data and compares them with medical data. This allows the integration of walking data with other health data to obtain a more precise understanding of the user's health condition.

[0040] The data analysis unit can plan health events for each region based on the walking data and encourage participation. For example, the generation AI in the data analysis unit analyzes the walking data and plans health events for each region. For example, a walking competition can be held to encourage participation from local residents. Furthermore, the generation AI in the data analysis unit can suggest the optimal time and location for the health event based on the walking data for each region. For example, the event can be held at a time when there are many participants. Furthermore, the generation AI in the data analysis unit analyzes the walking data and suggests the content of the health event for each region. For example, it can select a walking route and provide feedback to participants. This makes it possible to plan health events for each region and encourage participation.

[0041] The data analysis unit can make suggestions for improving local infrastructure based on the walking data. For example, the generation AI analyzes the walking data and makes suggestions for improving local infrastructure. For example, it may propose the development of a park based on the usage of walking routes. The data analysis unit also makes suggestions for improving sidewalks based on the walking data for each region. For example, it may propose the expansion of sidewalks to increase pedestrian safety. Furthermore, the data analysis unit uses the generation AI to analyze the walking data and provide information useful for improving local infrastructure. For example, it may propose the installation of a new sidewalk based on the frequency of use of walking routes. This makes it possible to make suggestions for improving local infrastructure.

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

[0043] The walking plan generation unit can propose walking plans for touring tourist spots and natural parks, taking into account the user's hobbies and interests. For example, the generation AI analyzes the user's hobbies and interests and proposes walking plans for touring tourist spots. For example, it selects a route that visits historical sites. The walking plan generation unit also considers the user's interests and provides walking plans for touring natural parks. For example, it selects parks with many flowers and plants. Furthermore, the walking plan generation unit proposes the optimal walking plan for the user based on the hobbies and interests. For example, it proposes a scenic route for a user whose hobby is photography. This makes it possible to provide walking plans that match the user's hobbies and interests.

[0044] The walking plan generation unit can provide a plan that combines walking with other exercises according to the user's fitness goals. For example, the generation AI analyzes the user's fitness goals and provides a plan that combines walking and yoga. For example, it may suggest yoga that has a relaxing effect after walking. The walking plan generation unit also proposes a plan that combines walking and strength training based on the user's exercise goals. For example, it may recommend strength training that can be done at home after walking. Furthermore, the walking plan generation unit provides a comprehensive plan that combines walking with other exercises according to the fitness goals. For example, it may suggest walking several times a week and yoga classes on the weekends. This makes it possible to provide a comprehensive exercise plan that matches the user's fitness goals.

[0045] The data analysis unit analyzes the walking data for each region, identifies health risks specific to the region, and can propose countermeasures. For example, the generation AI analyzes the walking data for each region and identifies health risks in a specific region. For example, it identifies regions where people are lacking exercise and proposes measures to promote exercise in those regions. The data analysis unit also uses the generation AI to propose countermeasures to prevent health risks based on the walking data for each region. For example, it recommends holding walking events in specific regions. Furthermore, the data analysis unit uses the generation AI to analyze the health data for each region and proposes countermeasures for health risks specific to the region. For example, it proposes developing walking routes in specific regions. This makes it possible to identify health risks specific to the region and propose countermeasures.

[0046] The data analysis unit integrates walking data with other health data to obtain a more precise understanding of the user's health condition. For example, the generation AI integrates walking data with hospital medical data to obtain a more precise understanding of the user's health condition. For example, it compares walking frequency with medical data and analyzes the effects of health improvements. The data analysis unit also integrates walking data with other health data to allow the generation AI to obtain a comprehensive understanding of the user's health condition. For example, it combines blood pressure and blood sugar data with walking data for analysis. Furthermore, the data analysis unit allows the generation AI to integrate walking data with medical data to detect health risks early. For example, it detects abnormal patterns from walking data and compares them with medical data. This allows the integration of walking data with other health data to obtain a more precise understanding of the user's health condition.

[0047] The walking plan generation unit can generate a walking plan for walking with the pet, taking into account the health condition of the user's pet. For example, the generation AI analyzes the health data of the user's pet and proposes a walking plan that suits the pet's exercise volume. For example, the exercise intensity is adjusted based on the pet's age and weight. The walking plan generation unit also considers the pet's health condition and proposes a route for walking with the pet. For example, it selects a route that includes a park or dog run where the pet can enjoy themselves. Furthermore, the walking plan generation unit generates a walking plan that suits the pet's physical condition based on the pet's health data. For example, if the pet gets tired easily, it will propose a short walk. This makes it possible to provide a plan for walking with the pet.

[0048] The walking plan generation unit can share walking data with the user's friends and family and propose walking plans for the group. For example, the generation AI can share walking data with the user's friends and family and propose walking plans based on the health status of the entire group. For example, it can adjust the route and exercise intensity so that everyone can enjoy. Furthermore, to encourage group walking, the generation AI can set a common walking goal and provide real-time feedback on the progress of the goal. For example, it can set a goal for everyone to achieve a certain number of steps. Furthermore, the walking plan generation unit can analyze the walking data of the entire group and propose optimal walking times and routes. For example, it can generate a plan that matches everyone's schedule. This makes it possible to provide walking plans for the group.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The walking plan generator generates a walking plan based on the user's health condition and lifestyle habits. For example, it proposes a walking plan based on the user's health indicators, such as heart rate, blood pressure, and weight. It can also generate a plan taking into account lifestyle data, such as the user's eating frequency, exercise habits, and sleep patterns. Furthermore, it customizes the walking distance, time, route, etc., depending on the user's walking purpose. Step 2: The monitoring unit collects data while walking. For example, it collects data such as the number of steps, heart rate, and calories burned via a smartphone or wearable device. It can also record the walking route using GPS data. It also monitors the user's exercise intensity and pace in real time and provides appropriate feedback. For example, it gives advice such as "Your current pace is appropriate" or "Try walking a little faster." Step 3: The data analysis unit analyzes the collected data and provides feedback to the user. For example, it evaluates the user's health status based on walking data and suggests areas for improvement. It can also analyze the health status of society as a whole based on the collected data and support policy recommendations and health promotion activities in local communities. For example, it can analyze trends in walking habits and health status by region and make policy recommendations for health promotion.

[0051] (Example 2) The health management system according to an embodiment of the present invention aims to extend healthy lifespan through walking, and uses a generation AI to generate walking plans, monitor them, and analyze data to provide optimal walking plans for individual users. This allows the health management system to improve the health of users and extend healthy lifespan for society as a whole.

[0052] A health management system according to an embodiment includes a walking plan generation unit, a monitoring unit, and a data analysis unit. The walking plan generation unit generates a walking plan based on the user's health condition and lifestyle habits. For example, the walking plan generation unit proposes a walking plan based on health indicators such as the user's heart rate, blood pressure, and weight. The walking plan generation unit can also generate a plan taking into account lifestyle data such as the user's meal frequency, exercise habits, and sleep patterns. The walking plan generation unit also customizes the walking distance, time, and route according to the user's walking goals. For example, a walking plan with appropriate exercise intensity and frequency is proposed for a user who wants to extend their healthy lifespan. The monitoring unit collects data during walking. For example, the monitoring unit collects data such as the number of steps, heart rate, and calories burned via a smartphone or wearable device. The monitoring unit can also record walking routes using GPS data. The monitoring unit also monitors the user's exercise intensity and pace in real time and provides appropriate feedback. For example, the monitoring unit may advise the user, such as, "Your current pace is appropriate" or "Try walking a little faster." The data analysis unit analyzes the collected data and provides feedback to the user. For example, the data analysis unit evaluates the user's health condition based on the walking data and suggests areas for improvement. The data analysis unit can also analyze the health condition of society as a whole based on the collected data and support policy recommendations and health promotion activities in the local community. For example, the data analysis unit analyzes trends in walking habits and health conditions by region and makes policy recommendations for health promotion. In this way, the health management system according to the embodiment can improve the health condition of users and extend the healthy life expectancy of society as a whole.

[0053] The walking plan generation unit learns the user's past walking data and can propose optimal walking plans according to the season and weather. For example, the generation AI of the walking plan generation unit analyzes the user's past walking data and learns walking patterns for each season. For example, it selects cooler times of the day in summer and suggests warmer daytime times in winter. The walking plan generation unit also obtains weather data in real time, and the generation AI adjusts the walking plan based on that information. For example, it suggests indoor walking or alternative exercise on rainy days. Furthermore, in order to propose walking routes according to the season and weather, the generation AI analyzes local weather data and selects the optimal route. For example, it suggests routes with a lot of shade in summer. This makes it possible to provide optimal walking plans according to the season and weather.

[0054] The walking plan generation unit can generate a comprehensive health management plan by taking into account the user's dietary and sleep data. In the walking plan generation unit, for example, the generation AI analyzes the user's dietary data and proposes a walking plan based on nutritional balance. For example, it adjusts the walking time and exercise intensity after meals. The walking plan generation unit also proposes the optimal walking time based on sleep data. For example, it recommends walking in the morning after getting enough sleep. Furthermore, the walking plan generation unit integrates dietary and sleep data, and the generation AI generates a comprehensive health management plan. For example, it suggests light walking to aid digestion after meals. This makes it possible to provide a comprehensive health management plan that takes dietary and sleep data into account.

[0055] The walking plan generation unit uses the emotion estimation function to propose a walking plan that matches the user's emotional state, thereby maintaining motivation. For example, the walking plan generation unit uses the emotion estimation function to analyze the user's emotional state in real time, and the generation AI adjusts the walking plan based on the results. For example, if the user is highly stressed, the generation AI proposes a route that has a relaxing effect. The walking plan generation unit also proposes a walking plan based on the user's emotional data to maintain motivation. For example, if the user is feeling depressed, the generation AI recommends a short walk. Furthermore, the walking plan generation unit uses the emotion estimation function to suggest music or podcasts that match the user's emotional state, thereby increasing motivation while walking. For example, it plays uplifting music. This allows the user to maintain motivation by providing a walking plan that matches the user's emotional state.

[0056] The walking plan generation unit can generate a walking plan for walking with the pet, taking into account the health condition of the user's pet. For example, the generation AI in the walking plan generation unit analyzes the health data of the user's pet and proposes a walking plan according to the pet's amount of exercise. For example, the exercise intensity is adjusted based on the pet's age and weight. The walking plan generation unit also considers the pet's health condition and proposes a route for walking with the pet. For example, it selects a route that includes a park or dog run where the pet can enjoy themselves. Furthermore, the walking plan generation unit generates a walking plan that matches the pet's physical condition based on the pet's health data. For example, if the pet gets tired easily, it proposes a short walk. This makes it possible to provide a plan for walking with the pet.

[0057] The walking plan generation unit can share walking data of the user's friends and family and propose walking plans for the group. For example, the generation AI of the walking plan generation unit shares walking data of the user's friends and family and proposes walking plans based on the health status of the entire group. For example, it adjusts the route and exercise intensity so that everyone can enjoy. Furthermore, to encourage group walking, the generation AI sets a common walking goal and provides real-time feedback on the progress of achievement. For example, it sets a goal for everyone to achieve a certain number of steps. Furthermore, the generation AI of the walking plan generation unit analyzes the walking data of the entire group and proposes the optimal walking time and route. For example, it generates a plan that matches everyone's schedule. This makes it possible to provide walking plans for the group.

[0058] The walking plan generation unit uses the emotion estimation function to detect the stress and fatigue the user feels while walking in real time and suggest appropriate rest points. The walking plan generation unit, for example, uses the emotion estimation function to detect the stress and fatigue the user feels while walking in real time, and the generation AI suggests appropriate rest points. For example, it may suggest a nearby bench when fatigue increases. The walking plan generation unit also analyzes the stress level while walking based on the user's emotion data and suggests rest points where the user can relax. For example, it may recommend taking a break in a place with lots of nature. Furthermore, the walking plan generation unit uses the emotion estimation function to monitor the user's fatigue level in real time, and the generation AI suggests appropriate times to take a break. For example, it may encourage a break when a certain level of fatigue is reached. This makes it possible to detect stress and fatigue while walking in real time and provide appropriate rest points.

[0059] The walking plan generation unit can analyze the user's genetic information and provide a walking plan based on genetic risk. In the walking plan generation unit, for example, the generation AI analyzes the user's genetic information and provides a walking plan based on genetic risk. For example, if there is a high risk of heart disease, light exercise is recommended. In addition, the walking plan generation unit uses the generation AI to propose a walking plan to prevent specific health risks based on the user's genetic information. For example, if there is a risk of diabetes, exercise to manage blood sugar levels is recommended. Furthermore, the walking plan generation unit analyzes the genetic information and the generation AI provides a walking plan that is optimal for the user's health condition. For example, if bone density is low, walking aimed at strengthening bones is suggested. This makes it possible to provide a walking plan based on genetic risk.

[0060] The walking plan generation unit can propose optimal walking times and routes, taking into account the user's occupation and daily activity level. For example, the generation AI in the walking plan generation unit analyzes the user's occupation data and proposes optimal walking times based on the user's daily activity level. For example, if the user does a lot of desk work, walking after work is recommended. The walking plan generation unit also considers the user's daily activity level, and the generation AI proposes optimal walking routes. For example, if the user's activity level is low, a route that increases the amount of exercise is selected. Furthermore, the walking plan generation unit provides the user with an optimal walking plan based on the user's occupation and activity level. For example, if the user does a lot of standing work, a route that reduces foot fatigue is proposed. This makes it possible to provide an optimal walking plan that takes into account the user's occupation and daily activity level.

[0061] The walking plan generation unit can propose walking plans for touring tourist spots and natural parks, taking into account the user's hobbies and interests. For example, the generation AI of the walking plan generation unit analyzes the user's hobbies and interests and proposes walking plans for touring tourist spots. For example, it selects a route that visits historical sites. The walking plan generation unit also considers the user's interests and provides walking plans for touring natural parks. For example, it selects parks with many flowers and plants. Furthermore, the generation AI of the walking plan generation unit proposes the optimal walking plan for the user based on the hobbies and interests. For example, it proposes a scenic route for a user whose hobby is photography. This makes it possible to provide walking plans that match the hobbies and interests.

[0062] The walking plan generation unit can provide a plan that combines walking with other exercises according to the user's fitness goals. For example, the generation AI of the walking plan generation unit analyzes the user's fitness goals and provides a plan that combines walking and yoga. For example, it suggests yoga that has a relaxing effect after walking. Furthermore, the walking plan generation unit proposes a plan that combines walking and strength training based on the user's exercise goals. For example, it recommends strength training that can be done at home after walking. Furthermore, the walking plan generation unit provides a comprehensive plan that combines walking with other exercises according to the fitness goals. For example, it suggests walking several times a week and yoga classes on the weekends. This makes it possible to provide a comprehensive exercise plan that matches the fitness goals.

[0063] The data analysis unit analyzes the walking data for each region, identifies health risks specific to the region, and can propose countermeasures. In the data analysis unit, for example, the generation AI analyzes the walking data for each region and identifies health risks in a specific region. For example, it identifies regions where people are lacking exercise and proposes measures to promote exercise in those regions. The data analysis unit also uses the generation AI to propose countermeasures to prevent health risks based on the walking data for each region. For example, it recommends holding a walking event in a specific region. Furthermore, the data analysis unit uses the generation AI to analyze the health data for each region and proposes countermeasures for health risks specific to the region. For example, it proposes the development of walking routes in a specific region. This makes it possible to identify health risks specific to the region and propose countermeasures.

[0064] The data analysis unit integrates walking data with other health data to obtain a more precise understanding of the user's health condition. For example, the generation AI in the data analysis unit integrates walking data with hospital medical data to obtain a more precise understanding of the user's health condition. For example, it compares walking frequency with medical data and analyzes the effects of health improvement. The data analysis unit also integrates walking data with other health data to allow the generation AI to obtain a comprehensive understanding of the user's health condition. For example, it combines blood pressure and blood sugar data with walking data for analysis. Furthermore, the data analysis unit allows the generation AI to integrate walking data with medical data to detect health risks early. For example, it detects abnormal patterns from walking data and compares them with medical data. This allows the integration of walking data with other health data to obtain a more precise understanding of the user's health condition.

[0065] The data analysis unit can use the emotion estimation function to analyze the emotional state of each region and suggest measures to improve mental health. For example, the data analysis unit can use the emotion estimation function to analyze the emotional state of each region, and the generation AI can suggest measures to improve mental health. For example, the data analysis unit can suggest walking routes that have a relaxing effect in areas with high stress. The data analysis unit can also use the emotion estimation function to analyze the emotional state of each region, and the generation AI can provide measures to improve mental health. For example, the data analysis unit can recommend walking events in areas where emotional states are deteriorating. Furthermore, the data analysis unit can use the emotion estimation function to analyze the emotional state of each region, and the generation AI can provide information that is useful for improving mental health. For example, the data analysis unit can suggest relaxation methods for areas where emotional states are deteriorating. This makes it possible to analyze the emotional state of each region and suggest measures to improve mental health.

[0066] The data analysis unit can plan health events for each region based on the walking data and encourage participation. For example, the generation AI in the data analysis unit analyzes the walking data and plans health events for each region. For example, a walking competition can be held to encourage participation from local residents. Furthermore, the generation AI in the data analysis unit can suggest the optimal time and location for the health event based on the walking data for each region. For example, the event can be held at a time when there are many participants. Furthermore, the generation AI in the data analysis unit analyzes the walking data and suggests the content of the health event for each region. For example, it can select a walking route and provide feedback to participants. This makes it possible to plan health events for each region and encourage participation.

[0067] The data analysis unit can make suggestions for improving local infrastructure based on the walking data. For example, the generation AI analyzes the walking data and makes suggestions for improving local infrastructure. For example, it may propose the development of a park based on the usage of walking routes. The data analysis unit also makes suggestions for improving sidewalks based on the walking data for each region. For example, it may propose the expansion of sidewalks to increase pedestrian safety. Furthermore, the data analysis unit uses the generation AI to analyze the walking data and provide information useful for improving local infrastructure. For example, it may propose the installation of a new sidewalk based on the frequency of use of walking routes. This makes it possible to make suggestions for improving local infrastructure.

[0068] The data analysis unit can use the emotion estimation function to analyze the emotional state of each region and suggest emotionally positive local activities. For example, the data analysis unit can use the emotion estimation function to analyze the emotional state of each region and have the generation AI suggest emotionally positive local activities. For example, the data analysis unit can recommend community events in regions with high emotion scores. Furthermore, the data analysis unit can use the emotion estimation function to analyze the emotional state of each region and have the generation AI suggest emotionally positive local activities. For example, the data analysis unit can recommend walking events in regions with good emotional states. Furthermore, the data analysis unit can use the emotion estimation function to analyze the emotional state of each region and have the generation AI suggest emotionally positive local activities. For example, the data analysis unit can recommend relaxation events in regions with high emotion scores. In this way, the emotional state of each region can be analyzed and emotionally positive local activities can be suggested.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The walking plan generation unit can propose walking plans for touring tourist spots and natural parks, taking into account the user's hobbies and interests. For example, the generation AI analyzes the user's hobbies and interests and proposes walking plans for touring tourist spots. For example, it selects a route that visits historical sites. The walking plan generation unit also considers the user's interests and provides walking plans for touring natural parks. For example, it selects parks with many flowers and plants. Furthermore, the walking plan generation unit proposes the optimal walking plan for the user based on the hobbies and interests. For example, it proposes a scenic route for a user whose hobby is photography. This makes it possible to provide walking plans that match the user's hobbies and interests.

[0071] The walking plan generation unit can provide a plan that combines walking with other exercises according to the user's fitness goals. For example, the generation AI analyzes the user's fitness goals and provides a plan that combines walking and yoga. For example, it may suggest yoga that has a relaxing effect after walking. The walking plan generation unit also proposes a plan that combines walking and strength training based on the user's exercise goals. For example, it may recommend strength training that can be done at home after walking. Furthermore, the walking plan generation unit provides a comprehensive plan that combines walking with other exercises according to the fitness goals. For example, it may suggest walking several times a week and yoga classes on the weekends. This makes it possible to provide a comprehensive exercise plan that matches the user's fitness goals.

[0072] The data analysis unit analyzes the walking data for each region, identifies health risks specific to the region, and can propose countermeasures. For example, the generation AI analyzes the walking data for each region and identifies health risks in a specific region. For example, it identifies regions where people are lacking exercise and proposes measures to promote exercise in those regions. The data analysis unit also uses the generation AI to propose countermeasures to prevent health risks based on the walking data for each region. For example, it recommends holding walking events in specific regions. Furthermore, the data analysis unit uses the generation AI to analyze the health data for each region and proposes countermeasures for health risks specific to the region. For example, it proposes developing walking routes in specific regions. This makes it possible to identify health risks specific to the region and propose countermeasures.

[0073] The data analysis unit integrates walking data with other health data to obtain a more precise understanding of the user's health condition. For example, the generation AI integrates walking data with hospital medical data to obtain a more precise understanding of the user's health condition. For example, it compares walking frequency with medical data and analyzes the effects of health improvements. The data analysis unit also integrates walking data with other health data to allow the generation AI to obtain a comprehensive understanding of the user's health condition. For example, it combines blood pressure and blood sugar data with walking data for analysis. Furthermore, the data analysis unit allows the generation AI to integrate walking data with medical data to detect health risks early. For example, it detects abnormal patterns from walking data and compares them with medical data. This allows the integration of walking data with other health data to obtain a more precise understanding of the user's health condition.

[0074] The data analysis unit can use the emotion estimation function to analyze the emotional state of each region and suggest measures to improve mental health. For example, the emotion estimation function can be used to analyze the emotional state of each region, and the generation AI can suggest measures to improve mental health. For example, the generation AI can suggest walking routes that have a relaxing effect in areas with high stress. The data analysis unit also uses the emotion data for each region to allow the generation AI to provide measures to improve mental health. For example, the generation AI can recommend walking events in areas where emotional states are deteriorating. The data analysis unit also uses the emotion estimation function to analyze the emotional state of each region, and the generation AI can provide information that is useful for improving mental health. For example, the generation AI can suggest relaxation methods for areas where emotional states are deteriorating. This makes it possible to analyze the emotional state of each region and suggest measures to improve mental health.

[0075] The walking plan generation unit uses the emotion estimation function to detect the stress and fatigue the user feels while walking in real time and suggest appropriate rest points. For example, the emotion estimation function can be used to detect the stress and fatigue the user feels while walking in real time, and the generation AI can suggest appropriate rest points. For example, it can suggest a nearby bench when fatigue increases. The walking plan generation unit also uses the emotion estimation function to analyze the stress level while walking based on the user's emotion data and suggest rest points where the user can relax. For example, it can recommend taking a break in a place with lots of nature. Furthermore, the walking plan generation unit uses the emotion estimation function to monitor the user's fatigue level in real time, and the generation AI can suggest appropriate times to take a break. For example, it can encourage the user to take a break when a certain level of fatigue is reached. This makes it possible to detect stress and fatigue while walking in real time and provide appropriate rest points.

[0076] The walking plan generation unit can generate a walking plan for walking with the pet, taking into account the health condition of the user's pet. For example, the generation AI analyzes the health data of the user's pet and proposes a walking plan that suits the pet's exercise volume. For example, the exercise intensity is adjusted based on the pet's age and weight. The walking plan generation unit also considers the pet's health condition and proposes a route for walking with the pet. For example, it selects a route that includes a park or dog run where the pet can enjoy themselves. Furthermore, the walking plan generation unit generates a walking plan that suits the pet's physical condition based on the pet's health data. For example, if the pet gets tired easily, it will propose a short walk. This makes it possible to provide a plan for walking with the pet.

[0077] The walking plan generation unit can share walking data with the user's friends and family and propose walking plans for the group. For example, the generation AI can share walking data with the user's friends and family and propose walking plans based on the health status of the entire group. For example, it can adjust the route and exercise intensity so that everyone can enjoy. Furthermore, to encourage group walking, the generation AI can set a common walking goal and provide real-time feedback on the progress of the goal. For example, it can set a goal for everyone to achieve a certain number of steps. Furthermore, the walking plan generation unit can analyze the walking data of the entire group and propose optimal walking times and routes. For example, it can generate a plan that matches everyone's schedule. This makes it possible to provide walking plans for the group.

[0078] The walking plan generation unit uses the emotion estimation function to propose a walking plan that matches the user's emotional state, helping to maintain motivation. For example, the emotion estimation function is used to analyze the user's emotional state in real time, and the generation AI adjusts the walking plan based on the results. For example, if the user is highly stressed, the generation AI proposes a route that has a relaxing effect. The walking plan generation unit also proposes a walking plan based on the user's emotional data to help maintain motivation. For example, if the user is feeling depressed, the generation AI recommends a short walk. Furthermore, the walking plan generation unit uses the emotion estimation function to suggest music or podcasts that match the user's emotional state, increasing motivation while walking. For example, it plays uplifting music. This allows the generation AI to provide a walking plan that matches the user's emotional state, helping to maintain motivation.

[0079] The data analysis unit uses the emotion estimation function to analyze the emotional state of each region and suggest emotionally positive local activities. For example, the emotion estimation function is used to analyze the emotional state of each region, and the generation AI suggests emotionally positive local activities. For example, community events in regions with high emotion scores are recommended. Furthermore, the data analysis unit uses the emotion data for each region to suggest local activities that elicit positive emotions. For example, walking events are recommended in regions with good emotional states. Furthermore, the data analysis unit uses the emotion estimation function to analyze the emotional state of each region, and the generation AI suggests emotionally positive local activities. For example, relaxation events are recommended in regions with high emotion scores. In this way, the emotional state of each region can be analyzed and emotionally positive local activities can be suggested.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The walking plan generator generates a walking plan based on the user's health condition and lifestyle habits. For example, it proposes a walking plan based on the user's health indicators, such as heart rate, blood pressure, and weight. It can also generate a plan taking into account lifestyle data, such as the user's eating frequency, exercise habits, and sleep patterns. Furthermore, it customizes the walking distance, time, route, etc., depending on the user's walking purpose. Step 2: The monitoring unit collects data while walking. For example, it collects data such as the number of steps, heart rate, and calories burned via a smartphone or wearable device. It can also record the walking route using GPS data. It also monitors the user's exercise intensity and pace in real time and provides appropriate feedback. For example, it gives advice such as "Your current pace is appropriate" or "Try walking a little faster." Step 3: The data analysis unit analyzes the collected data and provides feedback to the user. For example, it evaluates the user's health status based on walking data and suggests areas for improvement. It can also analyze the health status of society as a whole based on the collected data and support policy recommendations and health promotion activities in local communities. For example, it can analyze trends in walking habits and health status by region and make policy recommendations for health promotion.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0110] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 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 walking plan generation unit that generates a walking plan based on the user's health condition and lifestyle habits; a monitoring unit that collects data during walking based on the walking plan generated by the walking plan generation unit; a data analysis unit that analyzes the data collected by the monitoring unit and provides feedback to the user. A system characterized by:

2. The walking plan generation unit The system learns the user's past walking data and proposes optimal walking plans according to the season and weather.

2. The system of claim 1.

3. The walking plan generation unit Generate a comprehensive health management plan that takes into account the user's dietary and sleep data 2. The system of claim 1.

4. The walking plan generation unit To propose a walking plan according to the emotional state of the user and maintain motivation.

2. The system of claim 1.

5. The walking plan generation unit Creating a walking plan for the user with their pet, taking into consideration the health condition of the pet.

2. The system of claim 1.

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