System

Abstract

An object of a system according to an embodiment is to provide a health improvement program customized based on a medical checkup result.SOLUTION: A system includes a medical examination result analysis unit and a program generation unit. The medical examination result analysis unit analyzes the medical examination result. The program generation unit generates a health improvement program customized on the basis of the health check result analyzed by the health check result analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide individual health improvement programs based on health checkup results, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a health improvement program customized based on the results of a health checkup. [Means for solving the problem]

[0006] The system according to the embodiment includes a health checkup result analysis unit and a program generation unit. The health checkup result analysis unit analyzes the health checkup results. The program generation unit generates a customized health improvement program based on the health checkup results analyzed by the health checkup result analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a health improvement program customized based on the results of the health checkup. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A health improvement system according to an embodiment of the present invention provides an individually customized health improvement program based on health checkup results. This promotes a shift to preventive medicine and contributes to improving the well-being of individuals and companies. It is also expected to contribute to reducing medical expenses for the elderly.

[0029] A health improvement system according to an embodiment includes a health checkup result analysis unit and a program generation unit. The health checkup result analysis unit analyzes health checkup results. For example, data such as blood test results, electrocardiogram results, weight, height, and blood pressure are provided as input information to the generation AI. The generation AI analyzes this data and identifies health risks and areas for improvement. The program generation unit generates a customized health improvement program based on the health checkup results analyzed by the health checkup result analysis unit. For example, the program generation unit proposes a meal plan to increase the intake of specific nutrients or an exercise program to address lack of exercise. As a result, the health improvement system according to an embodiment can promote a shift to preventive medicine and contribute to improving the well-being of individuals and companies by providing individually customized health improvement programs.

[0030] The health checkup result analysis unit can provide the generation AI with data such as blood test results, electrocardiogram results, weight, height, and blood pressure as input information. The health checkup result analysis unit provides the generation AI with data such as blood test results, electrocardiogram results, weight, height, and blood pressure as input information. The generation AI analyzes this data and identifies health risks and areas for improvement. This enables more accurate health assessment by conducting analysis based on detailed health data.

[0031] The program generation unit can propose a meal plan to increase the intake of specific nutrients or an exercise program to overcome a lack of exercise. The program generation unit, for example, proposes a meal plan to increase the intake of specific nutrients. For example, it provides a meal menu to increase the intake of vitamins and minerals. The program generation unit also proposes an exercise program to overcome a lack of exercise. For example, it provides a program for aerobic exercise or strength training. This allows for the provision of specific improvement measures according to individual health conditions, thereby improving health.

[0032] The health improvement program can be provided through a dedicated app or website. For example, the health improvement program can be provided through a dedicated app. For example, the health improvement program can be provided to users using a mobile app. Alternatively, the health improvement program can be provided through a dedicated website. For example, details of the health improvement program can be confirmed and the program can be implemented on the website. This allows the health improvement program to be provided through a platform that is easily accessible to users.

[0033] Health improvement programs can provide customized programs for each employee based on the results of workplace health checkups. Health improvement programs can provide customized programs for each employee based on the results of workplace health checkups. For example, the results of workplace health checkups can be analyzed to identify employees' health risks and areas for improvement, and a customized health improvement program can be provided based on these. This allows for the provision of specific improvement measures according to the health status of employees, thereby improving the health of the entire workplace.

[0034] The health improvement program can provide a program to reduce health risks for the elderly. The health improvement program provides, for example, a program to reduce health risks for the elderly. For example, it proposes an exercise program to prevent a decrease in bone density and a meal plan that takes nutritional balance into consideration. This allows the elderly to maintain their health by providing specific improvement measures to reduce health risks.

[0035] The health checkup result analysis unit can perform comparative analysis with past health checkup data to identify long-term health trends. For example, the health checkup result analysis unit imports past health checkup data and identifies long-term health trends. For example, it analyzes fluctuations in blood pressure and weight over the past five years to predict future risks. In this way, by identifying long-term health trends based on past data, future risks can be predicted and preventative measures can be taken.

[0036] The health checkup result analysis unit can incorporate genetic information as additional data and perform health assessments that take genetic risk factors into account. The health checkup result analysis unit, for example, incorporates genetic information as additional data and performs health assessments that take genetic risk factors into account. For example, it evaluates the risk of heart disease based on family history and suggests preventive measures. By taking genetic information into account, more accurate health assessments are possible and preventive measures can be provided that are tailored to individual risks.

[0037] The health checkup result analysis unit can combine environmental data with the analysis of health checkup results to evaluate the impact of environmental factors on health. For example, the health checkup result analysis unit can integrate health checkup results with air quality data from the residential area to evaluate the impact of environmental factors on health. For example, it can analyze the relationship between PM2.5 concentrations in the air and the risk of respiratory diseases. By taking environmental factors into account, this enables a more comprehensive health assessment and provides appropriate improvement measures.

[0038] The health checkup result analysis unit can analyze the health checkup results of your pet and provide a health improvement program for your pet. For example, the health checkup result analysis unit uses a generative AI to analyze your pet's health checkup results and provide an individually customized health improvement program. For example, it can suggest a meal plan or exercise program that includes specific nutrients. This helps maintain your pet's health by providing specific improvement measures according to your pet's health condition.

[0039] The program generation unit can incorporate the user's sleep data and provide specific advice to improve sleep quality. For example, the program generation unit uses a generation AI to analyze the user's sleep data and provide specific advice to improve sleep quality. For example, it can suggest ways to relax before bed or an appropriate sleeping environment. In this way, specific advice based on the user's sleep data can be provided to improve sleep quality and support health improvement.

[0040] The program generation unit can consider the user's occupation and daily activity level and suggest health improvement measures that can be implemented at work or at home. For example, the generation AI analyzes the user's occupation and daily activity level and suggests health improvement measures that can be implemented at work or at home. For example, if a user does a lot of desk work, regular stretching and light exercise can be recommended. This allows the system to suggest easy-to-implement improvement measures by providing specific health improvement measures tailored to the user's occupation and activity level.

[0041] The program generation unit can provide a health improvement program that reflects the user's hobbies and interests and can be practiced while having fun. For example, the program generation unit uses a generation AI to analyze the user's hobbies and interests and provide a health improvement program based on them. For example, it can suggest dance exercises to a user who likes dancing. In this way, by providing a program based on the user's hobbies and interests, the user can improve their health while having fun.

[0042] The program generation unit can generate a health maintenance program for the travel destination, allowing the user to maintain health even while traveling. For example, the program generation unit uses a generation AI to analyze the environment and facility information of the travel destination and provide a program that allows the user to maintain health even while traveling. For example, the program generation unit can suggest an exercise plan for the hotel gym or a nearby park. This allows the user to maintain health even while traveling by providing a specific program for maintaining health even while traveling.

[0043] The health improvement program can reflect user feedback in real time and make adjustments to maximize the program's effectiveness. For example, the generative AI collects user feedback in real time and reflects it in the health improvement program. For example, if a user sends a request to adjust the difficulty of an exercise program, the program is updated immediately. This maximizes the program's effectiveness by reflecting user feedback in real time and supports health improvement.

[0044] Health improvement programs can incorporate a user's social network data and suggest programs to work on together with friends and family. For example, a generative AI could analyze a user's social network data and suggest health improvement programs to work on together with friends and family. For example, it could provide an exercise program that the whole family can participate in. By working on it together with friends and family, this increases motivation to improve health and enables effective health maintenance.

[0045] The health improvement program can take into account the user's cultural background and propose culturally appropriate health improvement measures. For example, the generative AI analyzes the user's cultural background and provides a health improvement program based on that. For example, it proposes a meal plan tailored to a specific food culture. By providing a program based on the user's cultural background, it proposes health improvement measures that are easy to put into practice.

[0046] The health improvement program can provide a health improvement program for the user's pet and suggest ways to maintain health together with the pet. For example, the generative AI analyzes the health data of the user's pet and provides a health improvement program to work on together with the pet. For example, it suggests an exercise program to do together with the pet. This increases motivation to improve health by working on it together with the pet, and enables effective health maintenance.

[0047] Corporate health improvement programs can propose health improvement measures that can be implemented in the workplace, taking into account employees' job content and working hours. For example, a corporate health improvement program uses generative AI to analyze employees' job content and working hours and propose health improvement measures that can be implemented in the workplace. For example, employees who do a lot of desk work could be recommended to do regular stretching and light exercise. This provides specific health improvement measures tailored to employees' job content and working hours, thereby improving the health of the entire workplace.

[0048] Health improvement programs for companies can analyze the health data of the entire company and provide health improvement programs specialized for specific departments or teams. For example, generative AI can analyze the health data of the entire company and provide health improvement programs specialized for specific departments or teams. For example, a stress management program could be proposed to the sales department. This allows for effective health improvement by providing programs specialized for specific departments or teams based on the health data of the entire company.

[0049] Corporate health improvement programs can incorporate health improvement measures into a company's employee benefit program to raise employee health awareness. For example, generative AI can incorporate health improvement measures into a company's employee benefit program to raise employee health awareness. For example, it can provide a customized health program based on health checkup results. In this way, by incorporating health improvement measures into a company's employee benefit program, employee health awareness can be raised and improved health can be achieved.

[0050] A corporate health improvement program can analyze a company's health data, understand industry-wide health trends, and provide programs that address industry-specific health risks. For example, a corporate health improvement program uses generative AI to analyze a company's health data and understand industry-wide health trends. For example, common health risks in a specific industry can be identified and programs that address them can be provided. This allows for an understanding of industry-wide health trends and the provision of programs that address specific health risks, thereby achieving effective health improvement.

[0051] Health improvement programs for the elderly can incorporate movement data from daily life and provide specific advice for preventing falls and maintaining muscle strength. For example, generative AI analyzes movement data from the elderly's daily life and provides specific advice for preventing falls. For example, it suggests balance training and muscle strength training. In this way, specific advice based on the movement data from the elderly's daily life can be provided to help prevent falls and maintain muscle strength.

[0052] Health improvement programs for the elderly can analyze the health data of the elderly and provide programs aimed at maintaining and improving cognitive function. For example, generative AI analyzes the health data of the elderly and provides programs aimed at maintaining and improving cognitive function. For example, brain training and exercises for improving memory are suggested. This prevents cognitive decline by providing programs aimed at maintaining and improving cognitive function based on the elderly's health data.

[0053] Health improvement programs for the elderly can incorporate collaboration with the local community and provide a system to support health throughout the community. For example, generative AI can analyze the health data of elderly people and provide a health improvement program that incorporates collaboration with the local community. For example, it can propose local health events and workshops. By incorporating collaboration with the local community, this will support the health of the elderly throughout the community and achieve health improvement.

[0054] Health improvement programs for the elderly can analyze the health data of the elderly and provide support programs for caregivers, thereby reducing the burden on caregivers. For example, generative AI analyzes the health data of the elderly and provides support programs for caregivers. For example, it suggests stress management and relaxation methods for caregivers. By providing support programs for caregivers, it reduces the burden on caregivers and helps improve the health of the elderly.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The health improvement system can also incorporate the user's sleep data and provide specific advice to improve sleep quality. For example, the generative AI can analyze the user's sleep data and suggest ways to relax before bed and an appropriate sleeping environment. It can also provide advice on diet and exercise to improve sleep quality. This allows for specific advice based on the user's sleep data to improve sleep quality and support improved health.

[0057] The health improvement system can also consider the user's occupation and daily activity level to suggest health improvement measures that can be put into practice at work or at home. For example, the generative AI can analyze the user's occupation and daily activity level and recommend regular stretching and light exercise if they do a lot of desk work. It can also suggest exercises to reduce leg fatigue if their work involves a lot of standing. This allows the system to suggest easy-to-implement improvement measures by providing specific health improvement measures tailored to the user's occupation and activity level.

[0058] The health improvement system can also provide health improvement programs that reflect the user's hobbies and interests and allow them to practice them while having fun. For example, the generative AI can analyze a user's hobbies and interests and suggest dance exercises for a user who likes dancing. It can also provide hiking and walking plans for a user who likes the outdoors. In this way, by providing a program based on the user's hobbies and interests, they can improve their health while having fun.

[0059] The health improvement system can also generate a health maintenance program for your travel destination, allowing you to stay healthy even while traveling. For example, the generating AI can analyze the environment and facility information of your destination and suggest exercise plans for the hotel gym or a nearby park. It can also provide advice on what to eat and how to relax while traveling. This allows you to stay healthy even while traveling by providing a specific program for maintaining your health.

[0060] The health improvement system can also incorporate the user's social network data to suggest health improvement programs to be undertaken with friends and family. For example, the generative AI can analyze the user's social network data to provide an exercise program that the whole family can participate in. It can also suggest health events and activities that can be done with friends. This increases motivation to improve health by working together with friends and family, and enables effective health maintenance.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The health checkup result analysis unit analyzes the health checkup results. For example, data such as blood test results, electrocardiogram results, weight, height, and blood pressure are provided as input information to the generation AI. The generation AI analyzes this data and identifies health risks and areas for improvement. Step 2: The program generation unit generates a customized health improvement program based on the health checkup results analyzed by the health checkup result analysis unit. For example, it may propose a meal plan to increase the intake of specific nutrients or an exercise program to address lack of exercise.

[0063] (Example 2) A health improvement system according to an embodiment of the present invention provides an individually customized health improvement program based on health checkup results. This promotes a shift to preventive medicine and contributes to improving the well-being of individuals and companies. It is also expected to contribute to reducing medical expenses for the elderly.

[0064] A health improvement system according to an embodiment includes a health checkup result analysis unit and a program generation unit. The health checkup result analysis unit analyzes health checkup results. For example, data such as blood test results, electrocardiogram results, weight, height, and blood pressure are provided as input information to the generation AI. The generation AI analyzes this data and identifies health risks and areas for improvement. The program generation unit generates a customized health improvement program based on the health checkup results analyzed by the health checkup result analysis unit. For example, the program generation unit proposes a meal plan to increase the intake of specific nutrients or an exercise program to address lack of exercise. As a result, the health improvement system according to an embodiment can promote a shift to preventive medicine and contribute to improving the well-being of individuals and companies by providing individually customized health improvement programs.

[0065] The health checkup result analysis unit can provide the generation AI with data such as blood test results, electrocardiogram results, weight, height, and blood pressure as input information. The health checkup result analysis unit provides the generation AI with data such as blood test results, electrocardiogram results, weight, height, and blood pressure as input information. The generation AI analyzes this data and identifies health risks and areas for improvement. This enables more accurate health assessment by conducting analysis based on detailed health data.

[0066] The program generation unit can propose a meal plan to increase the intake of specific nutrients or an exercise program to overcome a lack of exercise. The program generation unit, for example, proposes a meal plan to increase the intake of specific nutrients. For example, it provides a meal menu to increase the intake of vitamins and minerals. The program generation unit also proposes an exercise program to overcome a lack of exercise. For example, it provides a program for aerobic exercise or strength training. This allows for the provision of specific improvement measures according to individual health conditions, thereby improving health.

[0067] The health improvement program can be provided through a dedicated app or website. For example, the health improvement program can be provided through a dedicated app. For example, the health improvement program can be provided to users using a mobile app. Alternatively, the health improvement program can be provided through a dedicated website. For example, details of the health improvement program can be confirmed and the program can be implemented on the website. This allows the health improvement program to be provided through a platform that is easily accessible to users.

[0068] Health improvement programs can provide customized programs for each employee based on the results of workplace health checkups. Health improvement programs can provide customized programs for each employee based on the results of workplace health checkups. For example, the results of workplace health checkups can be analyzed to identify employees' health risks and areas for improvement, and a customized health improvement program can be provided based on these. This allows for the provision of specific improvement measures according to the health status of employees, thereby improving the health of the entire workplace.

[0069] The health improvement program can provide a program to reduce health risks for the elderly. The health improvement program provides, for example, a program to reduce health risks for the elderly. For example, it proposes an exercise program to prevent a decrease in bone density and a meal plan that takes nutritional balance into consideration. This allows the elderly to maintain their health by providing specific improvement measures to reduce health risks.

[0070] The health checkup result analysis unit can perform comparative analysis with past health checkup data to identify long-term health trends. For example, the health checkup result analysis unit imports past health checkup data and identifies long-term health trends. For example, it analyzes fluctuations in blood pressure and weight over the past five years to predict future risks. In this way, by identifying long-term health trends based on past data, future risks can be predicted and preventative measures can be taken.

[0071] The health checkup result analysis unit can incorporate genetic information as additional data and perform health assessments that take genetic risk factors into account. The health checkup result analysis unit, for example, incorporates genetic information as additional data and performs health assessments that take genetic risk factors into account. For example, it evaluates the risk of heart disease based on family history and suggests preventive measures. By taking genetic information into account, more accurate health assessments are possible and preventive measures can be provided that are tailored to individual risks.

[0072] The health checkup result analysis unit can use the emotion estimation function to analyze the user's emotions upon receiving the health checkup results and make counseling suggestions to reduce stress or anxiety. The health checkup result analysis unit, for example, analyzes the user's facial expressions and voice upon receiving the health checkup results to estimate the user's emotions. For example, if the user is highly stressed or anxious, the unit can suggest relaxation techniques or counseling. This provides support according to the user's emotional state, thereby reducing stress and anxiety and helping to improve health.

[0073] The health checkup result analysis unit can combine environmental data with the analysis of health checkup results to evaluate the impact of environmental factors on health. For example, the health checkup result analysis unit can integrate health checkup results with air quality data from the residential area to evaluate the impact of environmental factors on health. For example, it can analyze the relationship between PM2.5 concentrations in the air and the risk of respiratory diseases. By taking environmental factors into account, this enables a more comprehensive health assessment and provides appropriate improvement measures.

[0074] The health checkup result analysis unit can analyze the health checkup results of your pet and provide a health improvement program for your pet. For example, the health checkup result analysis unit uses a generative AI to analyze your pet's health checkup results and provide an individually customized health improvement program. For example, it can suggest a meal plan or exercise program that includes specific nutrients. This helps maintain your pet's health by providing specific improvement measures according to your pet's health condition.

[0075] The health checkup result analysis unit can use the emotion estimation function to analyze the emotions of the entire family when they receive the health checkup results and suggest a health improvement program for the entire family. For example, the health checkup result analysis unit can analyze the emotions of the entire family when they receive the health checkup results and suggest a health improvement program for the entire family. For example, it can provide an exercise program that the entire family can participate in. By doing this, the entire family can work together to increase motivation to improve their health and achieve effective health maintenance.

[0076] The program generation unit can incorporate the user's sleep data and provide specific advice to improve sleep quality. For example, the program generation unit uses a generation AI to analyze the user's sleep data and provide specific advice to improve sleep quality. For example, it can suggest ways to relax before bed or an appropriate sleeping environment. In this way, specific advice based on the user's sleep data can be provided to improve sleep quality and support health improvement.

[0077] The program generation unit can consider the user's occupation and daily activity level and suggest health improvement measures that can be implemented at work or at home. For example, the generation AI analyzes the user's occupation and daily activity level and suggests health improvement measures that can be implemented at work or at home. For example, if a user does a lot of desk work, regular stretching and light exercise can be recommended. This allows the system to suggest easy-to-implement improvement measures by providing specific health improvement measures tailored to the user's occupation and activity level.

[0078] The program generation unit can use the emotion estimation function to generate a message or reminder for maintaining motivation according to the user's emotional state. For example, the program generation unit uses the emotion estimation function to generate a message for maintaining motivation according to the user's emotional state. For example, if the user has a strong positive emotion, an encouraging message is sent. This provides support according to the user's emotional state, thereby maintaining motivation and supporting the user in continuing the health improvement program.

[0079] The program generation unit can provide a health improvement program that reflects the user's hobbies and interests and can be practiced while having fun. For example, the program generation unit uses a generation AI to analyze the user's hobbies and interests and provide a health improvement program based on them. For example, it can suggest dance exercises to a user who likes dancing. In this way, by providing a program based on the user's hobbies and interests, the user can improve their health while having fun.

[0080] The program generation unit can generate a health maintenance program for the travel destination, allowing the user to maintain health even while traveling. For example, the program generation unit uses a generation AI to analyze the environment and facility information of the travel destination and provide a program that allows the user to maintain health even while traveling. For example, the program generation unit can suggest an exercise plan for the hotel gym or a nearby park. This allows the user to maintain health even while traveling by providing a specific program for maintaining health even while traveling.

[0081] The program generation unit can use the emotion estimation function to suggest a health improvement program incorporating music or art therapy based on the user's emotions. For example, the program generation unit uses the emotion estimation function to suggest music therapy based on the user's emotional state. For example, calm music is recommended when the user wants to relax. The program generation unit also uses the emotion estimation function to suggest art therapy based on the user's emotional state. For example, painting or handicrafts is recommended when stress is high. In this way, providing music or art therapy according to the user's emotional state supports relaxation and stress relief.

[0082] The health improvement program can reflect user feedback in real time and make adjustments to maximize the program's effectiveness. For example, the generative AI collects user feedback in real time and reflects it in the health improvement program. For example, if a user sends a request to adjust the difficulty of an exercise program, the program is updated immediately. This maximizes the program's effectiveness by reflecting user feedback in real time and supports health improvement.

[0083] Health improvement programs can incorporate a user's social network data and suggest programs to work on together with friends and family. For example, a generative AI could analyze a user's social network data and suggest health improvement programs to work on together with friends and family. For example, it could provide an exercise program that the whole family can participate in. By working on it together with friends and family, this increases motivation to improve health and enables effective health maintenance.

[0084] The health improvement program can take into account the user's cultural background and propose culturally appropriate health improvement measures. For example, the generative AI analyzes the user's cultural background and provides a health improvement program based on that. For example, it proposes a meal plan tailored to a specific food culture. By providing a program based on the user's cultural background, it proposes health improvement measures that are easy to put into practice.

[0085] The health improvement program can provide a health improvement program for the user's pet and suggest ways to maintain health together with the pet. For example, the generative AI analyzes the health data of the user's pet and provides a health improvement program to work on together with the pet. For example, it suggests an exercise program to do together with the pet. This increases motivation to improve health by working on it together with the pet, and enables effective health maintenance.

[0086] The health improvement program can use the emotion estimation function to suggest relaxation or stress relief activities based on the user's emotions. For example, the health improvement program uses the emotion estimation function to suggest relaxation activities based on the user's emotional state. For example, the program can recommend meditation or yoga when stress levels are high. This helps improve the user's health by providing activities for relaxation or stress relief that correspond to the user's emotional state.

[0087] Corporate health improvement programs can propose health improvement measures that can be implemented in the workplace, taking into account employees' job content and working hours. For example, a corporate health improvement program uses generative AI to analyze employees' job content and working hours and propose health improvement measures that can be implemented in the workplace. For example, employees who do a lot of desk work could be recommended to do regular stretching and light exercise. This provides specific health improvement measures tailored to employees' job content and working hours, thereby improving the health of the entire workplace.

[0088] Health improvement programs for companies can analyze the health data of the entire company and provide health improvement programs specialized for specific departments or teams. For example, generative AI can analyze the health data of the entire company and provide health improvement programs specialized for specific departments or teams. For example, a stress management program could be proposed to the sales department. This allows for effective health improvement by providing programs specialized for specific departments or teams based on the health data of the entire company.

[0089] A corporate health improvement program can use the emotion estimation function to monitor employees' emotional states and propose programs for managing workplace stress and improving mental health. A corporate health improvement program can, for example, use the emotion estimation function to monitor employees' emotional states and propose programs for managing stress. For example, relaxation methods can be recommended to employees with high levels of stress. This allows for workplace stress management and improved mental health by providing support according to the employee's emotional state.

[0090] Corporate health improvement programs can incorporate health improvement measures into a company's employee benefit program to raise employee health awareness. For example, generative AI can incorporate health improvement measures into a company's employee benefit program to raise employee health awareness. For example, it can provide a customized health program based on health checkup results. In this way, by incorporating health improvement measures into a company's employee benefit program, employee health awareness can be raised and improved health can be achieved.

[0091] A corporate health improvement program can analyze a company's health data, understand industry-wide health trends, and provide programs that address industry-specific health risks. For example, a corporate health improvement program uses generative AI to analyze a company's health data and understand industry-wide health trends. For example, common health risks in a specific industry can be identified and programs that address them can be provided. This allows for an understanding of industry-wide health trends and the provision of programs that address specific health risks, thereby achieving effective health improvement.

[0092] A corporate wellness program can use the emotion estimation function to suggest team building or communication improvement activities based on employees' emotions. A corporate wellness program can, for example, use the emotion estimation function to suggest team building activities based on employees' emotional states. For example, it can provide activities that help employees relax when they are feeling emotional. In this way, team building and communication improvement can be achieved by providing activities that correspond to employees' emotional states.

[0093] Health improvement programs for the elderly can incorporate movement data from daily life and provide specific advice for preventing falls and maintaining muscle strength. For example, generative AI analyzes movement data from the elderly's daily life and provides specific advice for preventing falls. For example, it suggests balance training and muscle strength training. In this way, specific advice based on the movement data from the elderly's daily life can be provided to help prevent falls and maintain muscle strength.

[0094] Health improvement programs for the elderly can analyze the health data of the elderly and provide programs aimed at maintaining and improving cognitive function. For example, generative AI analyzes the health data of the elderly and provides programs aimed at maintaining and improving cognitive function. For example, brain training and exercises for improving memory are suggested. This prevents cognitive decline by providing programs aimed at maintaining and improving cognitive function based on the elderly's health data.

[0095] A health improvement program for the elderly can use the emotion estimation function to suggest a program to reduce loneliness or promote social participation according to the elderly's emotional state. A health improvement program for the elderly, for example, uses the emotion estimation function to suggest a program to reduce loneliness according to the elderly's emotional state. For example, community activities can be recommended when emotions are low. In this way, by providing a program according to the elderly's emotional state, loneliness can be reduced, social participation can be promoted, and health improvement can be supported.

[0096] Health improvement programs for the elderly can incorporate collaboration with the local community and provide a system to support health throughout the community. For example, generative AI can analyze the health data of elderly people and provide a health improvement program that incorporates collaboration with the local community. For example, it can propose local health events and workshops. By incorporating collaboration with the local community, this will support the health of the elderly throughout the community and achieve health improvement.

[0097] Health improvement programs for the elderly can analyze the health data of the elderly and provide support programs for caregivers, thereby reducing the burden on caregivers. For example, generative AI analyzes the health data of the elderly and provides support programs for caregivers. For example, it suggests stress management and relaxation methods for caregivers. By providing support programs for caregivers, it reduces the burden on caregivers and helps improve the health of the elderly.

[0098] A health improvement program for the elderly can use the emotion estimation function to suggest a health improvement program that incorporates hobbies or recreational activities based on the elderly's emotions. For example, a health improvement program for the elderly can use the emotion estimation function to suggest hobbies or recreational activities based on the elderly's emotional state. For example, art or music activities can be recommended when emotions are low. This helps improve the elderly's health by providing hobbies and recreational activities that correspond to their emotional state.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The health improvement system can also incorporate the user's sleep data and provide specific advice to improve sleep quality. For example, the generative AI can analyze the user's sleep data and suggest ways to relax before bed and an appropriate sleeping environment. It can also provide advice on diet and exercise to improve sleep quality. This allows for specific advice based on the user's sleep data to improve sleep quality and support improved health.

[0101] The health improvement system can also consider the user's occupation and daily activity level to suggest health improvement measures that can be put into practice at work or at home. For example, the generative AI can analyze the user's occupation and daily activity level and recommend regular stretching and light exercise if they do a lot of desk work. It can also suggest exercises to reduce leg fatigue if their work involves a lot of standing. This allows the system to suggest easy-to-implement improvement measures by providing specific health improvement measures tailored to the user's occupation and activity level.

[0102] The health improvement system can also provide health improvement programs that reflect the user's hobbies and interests and allow them to practice them while having fun. For example, the generative AI can analyze a user's hobbies and interests and suggest dance exercises for a user who likes dancing. It can also provide hiking and walking plans for a user who likes the outdoors. In this way, by providing a program based on the user's hobbies and interests, they can improve their health while having fun.

[0103] The health improvement system can also generate a health maintenance program for your travel destination, allowing you to stay healthy even while traveling. For example, the generating AI can analyze the environment and facility information of your destination and suggest exercise plans for the hotel gym or a nearby park. It can also provide advice on what to eat and how to relax while traveling. This allows you to stay healthy even while traveling by providing a specific program for maintaining your health.

[0104] The health improvement system can also incorporate the user's social network data to suggest health improvement programs to be undertaken with friends and family. For example, the generative AI can analyze the user's social network data to provide an exercise program that the whole family can participate in. It can also suggest health events and activities that can be done with friends. This increases motivation to improve health by working together with friends and family, and enables effective health maintenance.

[0105] The health improvement system can also generate messages or reminders to maintain motivation based on the user's emotional state. For example, it can use the emotion estimation function to send encouraging messages according to the user's emotional state. If the user's emotions are strong, a message that enhances a sense of accomplishment can be provided, while if the user's emotions are strong, relaxation techniques or words of encouragement can be provided. This allows the system to provide support according to the user's emotional state, thereby maintaining motivation and helping the user continue with the health improvement program.

[0106] The health improvement system can further suggest health improvement programs incorporating music or art therapy based on the user's emotions. For example, the emotion estimation function can be used to suggest music therapy based on the user's emotional state. Calm music can be recommended when the user wants to relax, and lively music can be provided when the user wants to increase their energy. The emotion estimation function can also be used to suggest art therapy based on the user's emotional state. Painting or handicrafts can be recommended when stress is high. In this way, music or art therapy tailored to the user's emotional state can be provided to support relaxation and stress relief.

[0107] The health improvement system can also suggest relaxation or stress relief activities based on the user's emotions. For example, using the emotion estimation function, it can suggest relaxation activities based on the user's emotional state. When stress is high, it can recommend meditation or yoga, and when the user wants to relax, it can offer deep breathing or light stretching. This helps improve the user's health by providing activities for relaxation or stress relief according to the user's emotional state.

[0108] The health improvement system can also suggest programs to reduce loneliness or promote social participation according to the user's emotional state. For example, using the emotion estimation function, it can suggest a program to reduce loneliness based on the elderly person's emotional state. When emotions are low, it can recommend community activities or volunteer activities, and when positive emotions are strong, it can offer activities to develop new hobbies or interests. By providing programs according to the elderly person's emotional state, it can reduce loneliness, promote social participation, and support health improvement.

[0109] The health improvement system can also suggest health improvement programs that incorporate hobbies or recreational activities based on the user's emotions. For example, the emotion estimation function can be used to suggest hobbies and recreational activities based on the elderly person's emotional state. When emotions are low, art or music activities can be recommended, and when positive emotions are strong, outdoor activities or sports can be offered. This supports health improvement by providing hobbies and recreational activities that suit the elderly person's emotional state.

[0110] The processing flow of the second embodiment will be briefly explained below.

[0111] Step 1: The health checkup result analysis unit analyzes the health checkup results. For example, data such as blood test results, electrocardiogram results, weight, height, and blood pressure are provided as input information to the generation AI. The generation AI analyzes this data and identifies health risks and areas for improvement. Step 2: The program generation unit generates a customized health improvement program based on the health checkup results analyzed by the health checkup result analysis unit. For example, it may propose a meal plan to increase the intake of specific nutrients or an exercise program to address lack of exercise.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[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 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.

[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 (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).

[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] 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.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0140] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0156] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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]

[0179] 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 health checkup result analysis unit that analyzes the health checkup results; a program generation unit that generates a customized health improvement program based on the health checkup results analyzed by the health checkup result analysis unit. A system characterized by:

2. The health checkup result analysis unit Blood test, electrocardiogram, weight, height, and blood pressure data are provided as input information to the generation AI.

2. The system of claim 1.

3. The health improvement program comprises: Provide customized programs for each employee based on the results of workplace health checks 2. The system of claim 1.

4. The program generation unit Incorporates user sleep data and provides specific advice to improve sleep quality 2. The system of claim 1.

5. The health improvement program comprises: Incorporate user feedback in real time and make adjustments to maximize program effectiveness 2. The system of claim 1.

6. Corporate wellness programs include: Propose health improvement measures that can be implemented in the workplace, taking into consideration employees' job duties and working hours.

2. The system of claim 1.

7. Health improvement programs for seniors include: Incorporating data on daily life movements to provide specific advice on preventing falls and maintaining muscle strength 2. The system of claim 1.

8. The health checkup result analysis unit Analyze the user's emotions upon receiving the health checkup results and provide counseling suggestions to reduce stress or anxiety 2. The system of claim 1.

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