System

Abstract

An object of the system according to the embodiment is to analyze health data of a user and propose an individualized preventive measure.SOLUTION: A system includes a health data collection part, an analysis part, and a preventive measure proposal part. The health data collection unit collects health data of a user. The analysis unit analyzes the health data collected by the health data collection unit. A preventive measure proposal part proposes an individualized preventive measure on the basis of a result analyzed by the analysis part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to effectively utilize a user's health data to propose personalized preventive measures.

[0005] The system according to the embodiment aims to analyze a user's health data and propose personalized preventive measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a health data collection unit, an analysis unit, and a preventive measure suggestion unit. The health data collection unit collects health data of a user. The analysis unit analyzes the health data collected by the health data collection unit. The preventive measure suggestion unit suggests individualized preventive measures based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze a user's health data and suggest personalized preventative measures. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The preventive medical system according to an embodiment of the present invention is a system in which generative AI analyzes individual health data and proposes personalized preventive measures. This enables early detection and prevention of diseases, and is expected to extend healthy life expectancy.

[0029] A preventive medical system according to an embodiment includes a health data collection unit, an analysis unit, and a preventive measure suggestion unit. The health data collection unit collects a user's health data. For example, the health data collection unit collects data obtained from a smartwatch or fitness tracker. The health data collection unit can also collect data such as the user's diet, sleep patterns, and stress level. For example, a smartwatch records heart rate and step count, while a fitness tracker measures exercise volume. The analysis unit analyzes the health data collected by the health data collection unit. For example, the generation AI analyzes the data using a text generation AI (e.g., LLM). The generation AI can also integrate and analyze multiple data sources using a multimodal generation AI. The generation AI can also analyze the data using a machine learning algorithm. For example, the text generation AI builds a predictive model based on the user's health data and evaluates the user's health status. The multimodal generation AI integrates and analyzes text data and image data. The machine learning algorithm learns from past data and predicts future health risks. The preventive measure suggestion unit proposes personalized preventive measures based on the results of the analysis by the analysis unit. For example, the generating AI can provide specific advice to improve a user's diet and exercise habits. The generating AI can also suggest lifestyle changes to reduce the risk of certain diseases based on genetic information. The generating AI can also provide customized health education programs based on the user's health data. For example, the generating AI can analyze a user's diet and suggest a nutritionally balanced meal plan. The generating AI can analyze a user's exercise habits and suggest an effective exercise program. The generating AI can analyze a user's genetic information and suggest lifestyle changes to reduce the risk of certain diseases. This enables the preventive medical system according to the embodiment to detect and prevent diseases early, potentially extending healthy life expectancy. For example, by implementing preventive measures provided by the generating AI, a user can improve their health and reduce the risk of disease. The generating AI can continuously monitor the user's health data and issue an early warning if an abnormality is detected.For example, if an abnormality in heart rate or blood pressure is detected, the system will send a notification to the user urging them to visit a medical institution. Generative AI will also educate and raise awareness about health for users. For example, it will provide information on the latest health research results and effective preventative measures. Generative AI will also analyze health data from the entire local community to understand the health status of the entire region. This will enable it to propose measures to address health issues specific to the region. For example, it will propose preventative measures for diseases that are prevalent in a specific region and conduct awareness-raising activities for local residents.

[0030] The health data collection unit can collect data from a smartwatch or fitness tracker. The health data collection unit collects data from, for example, a smartwatch or fitness tracker. For example, the smartwatch records heart rate and step count, and the fitness tracker measures exercise volume. The health data collection unit can also collect data obtained from the smartwatch or fitness tracker in real time. For example, the smartwatch monitors heart rate in real time, and the fitness tracker measures exercise volume in real time. In this way, by collecting data from the smartwatch or fitness tracker, the user's health condition can be more accurately understood.

[0031] The analysis unit can collect health data in real time and provide analysis results on the spot. The analysis unit collects data in real time from, for example, a smartwatch or fitness tracker, and the generation AI immediately provides analysis results. For example, it analyzes heart rate and step count data in real time. The analysis unit can also allow the generation AI to instantly evaluate health status based on the data collected in real time. For example, if an abnormal heart rate is detected, a notification is sent to the user immediately. This allows for quick response by analyzing health data in real time and providing results on the spot.

[0032] When analyzing health data, the analysis unit can also perform analysis based on medical history and family health data. For example, the analysis unit registers the user's past medical history in a database, and the generation AI analyzes the health condition based on that data. For example, past medical history and treatment history are taken into consideration. The analysis unit can also collect health data of the user's family, and the generation AI can analyze the health condition based on that data. For example, family medical history and genetic information are taken into consideration. The analysis unit can also integrate the user's medical history and family health data for analysis. For example, future health risks are predicted based on the user's past medical history and family medical history. This enables more accurate analysis of the health condition by taking into account past medical history and family health data.

[0033] The health data collection unit collects voice data and image data, and the analysis unit can analyze the voice data and image data to understand the health condition. The health data collection unit, for example, collects a user's voice data and constructs a system in which a generation AI analyzes the voice to evaluate the health condition. For example, the stress level is estimated from the tone of voice and speaking style. The health data collection unit can also collect a user's image data and the generation AI analyzes the image to evaluate the health condition. For example, the health condition is evaluated from facial expressions and skin condition. The health data collection unit can also collect integrated voice data and image data. For example, the user's voice and facial expressions are analyzed simultaneously. The analysis unit, for example, analyzes voice data to evaluate the user's emotional state. For example, the stress level is estimated from the tone of voice and speaking style. The analysis unit can also analyze image data to evaluate the user's health condition. For example, the health condition is evaluated from facial expressions and skin condition. The analysis unit can also integrate and analyze voice data and image data. For example, the user's voice and facial expressions are analyzed simultaneously. This allows the user's health condition to be understood more comprehensively by analyzing the voice data and image data.

[0034] When analyzing health data, the analysis unit can compare data from different regions and cultural spheres and perform analysis from a global perspective. The analysis unit, for example, collects health data from different regions and builds a system in which the generation AI analyzes the user's health status based on that data. For example, it compares dietary and exercise habits between regions. The analysis unit can also collect health data from different cultural spheres and use the generation AI to analyze the user's health status based on that data. For example, it compares health and lifestyle habits between cultural spheres. The analysis unit can also integrate and analyze data from different regions and cultural spheres. For example, it can evaluate health status from a global perspective based on data from different regions and cultural spheres. This makes it possible to evaluate health status from a global perspective by comparing data from different regions and cultural spheres.

[0035] When analyzing health data, the preventive measure suggestion unit can suggest preventive measures based on environmental factors such as season and weather. For example, the preventive measure suggestion unit builds a system in which the generative AI suggests appropriate preventive measures by taking into account seasonal health risks. For example, it may recommend taking vitamins to prevent colds in winter. The preventive measure suggestion unit can also suggest preventive measures according to changes in weather. For example, it may suggest indoor exercises on rainy days. The preventive measure suggestion unit can also suggest preventive measures to improve the user's health by taking environmental factors into account. For example, it may provide advice on diet and exercise according to the season and weather. This allows more appropriate preventive measures to be suggested by taking into account environmental factors such as season and weather.

[0036] When analyzing health data, the preventive measure suggestion unit can suggest preventive measures based on individual factors such as lifestyle and occupation. The preventive measure suggestion unit, for example, collects lifestyle data from users and builds a system in which the generation AI suggests personalized preventive measures based on that data. For example, it suggests exercise for users who do a lot of desk work. The preventive measure suggestion unit can also collect occupational data from users and the generation AI can suggest preventive measures based on that data. For example, it suggests rest for users who do a lot of physical labor. The preventive measure suggestion unit can also suggest preventive measures to improve the user's health by taking into account individual factors such as lifestyle and occupation. For example, it suggests regular stretching and exercise for users who do a lot of desk work. This makes it possible to suggest more personalized preventive measures by taking into account individual factors such as lifestyle and occupation.

[0037] When analyzing health data, the preventive measure suggestion unit can suggest preventive measures according to age group and gender. For example, the preventive measure suggestion unit considers health risks for each age group and builds a system in which the generative AI suggests appropriate preventive measures. For example, it may recommend exercise to maintain bone density for elderly people. The preventive measure suggestion unit can also suggest preventive measures according to gender. For example, it may suggest a diet to balance hormones for women. The preventive measure suggestion unit can also provide specific advice to improve the user's health by suggesting preventive measures according to age group and gender. For example, it may suggest establishing an exercise habit for young people and recommend health checkups for middle-aged and elderly people. This allows for more appropriate health management by suggesting preventive measures according to age group and gender.

[0038] When analyzing health data, the preventive measure suggestion unit can suggest preventive measures by referring to health habits in different cultural areas and regions. For example, the preventive measure suggestion unit analyzes health habits in different cultural areas, and builds a system in which the generation AI suggests preventive measures according to the user's health condition. For example, the preventive measure suggestion unit may recommend a Mediterranean diet. The preventive measure suggestion unit may also suggest preventive measures by referring to local health habits. For example, it may suggest health methods that are popular in a particular region. The preventive measure suggestion unit may also suggest preventive measures to improve the user's health condition by taking into account health habits in different cultural areas and regions. For example, it may provide dietary and exercise advice by referring to health habits in East Asia. This allows preventive measures to be suggested from a more multifaceted perspective by referring to health habits in different cultural areas and regions.

[0039] The analysis unit can monitor health data in real time and send an immediate notification when an abnormality is detected. The analysis unit collects data in real time from, for example, a smartwatch or fitness tracker, and builds a system that sends an immediate notification when the generation AI detects an abnormality. For example, it detects abnormalities in heart rate or blood pressure. The analysis unit can also immediately send a notification to the user when the generation AI detects an abnormality based on the data collected in real time. For example, if an abnormality in heart rate is detected, a notification is sent to the user immediately. The analysis unit can also send a notification to the user urging them to visit a medical institution when an abnormality is detected. For example, if an abnormality in blood pressure is detected, a notification is sent to the user urging them to visit a medical institution. This makes it possible to monitor health data in real time and send an immediate notification when an abnormality is detected, enabling rapid response.

[0040] When monitoring health data, the analysis unit can compare it with past data to detect abnormalities. For example, the analysis unit registers the user's past health data in a database, and builds a system in which the generation AI detects abnormalities based on that data. For example, it compares it with past heart rate data. The analysis unit can also evaluate changes in the user's health condition based on past data. For example, it compares past data with current data to detect abnormalities. The analysis unit can also predict future health risks based on past data. For example, it learns from past data and predicts future health risks. This allows for more accurate detection of abnormalities by comparing it with past data.

[0041] When monitoring health data, the analysis unit can integrate and analyze data from different devices. The analysis unit integrates data from different devices, such as a smartwatch, fitness tracker, and smartphone, to build a system in which generative AI analyzes health status. For example, it integrates heart rate and step count data. The analysis unit can also evaluate a user's health status based on data from different devices. For example, it can integrate and analyze data from a smartwatch and a fitness tracker. The analysis unit can also predict a user's health risks based on data from different devices. For example, it can integrate and analyze data from a smartwatch and a smartphone. This allows for a more accurate assessment of health status by integrating and analyzing data from different devices.

[0042] When monitoring health data, the analysis unit can detect abnormalities based on data from different regions and cultural spheres. For example, the analysis unit collects health data from different regions and builds a system in which the generation AI analyzes the user's health status based on that data. For example, the eating habits and exercise habits between regions are compared. The analysis unit can also collect health data from different cultural spheres and the generation AI can analyze the user's health status based on that data. For example, the health habits and lifestyle habits between cultural spheres are compared. The analysis unit can also evaluate the user's health status based on the data from different regions and cultural spheres. For example, it can detect abnormalities based on data from different regions and cultural spheres. This allows for more accurate detection of abnormalities by referring to data from different regions and cultural spheres.

[0043] When analyzing health data, the preventive measure suggestion unit can provide health education that reflects the latest research results and trend information. For example, the preventive measure suggestion unit constructs a system in which a generation AI provides a health education program tailored to the user's health condition based on the latest research results. For example, new exercise methods and dietary methods are introduced. The preventive measure suggestion unit can also provide health education tailored to the user's health condition based on trend information. For example, a health education program is provided based on the latest health trends and industry news. The preventive measure suggestion unit can also provide an education program to improve the user's health condition by reflecting the latest research results and trend information. For example, a new exercise method or dietary method is introduced. In this way, by providing health education that reflects the latest research results and trend information, the user's health condition can be more effectively improved.

[0044] When analyzing the health data, the preventive measure suggestion unit can provide a customized educational program according to individual health goals. For example, the preventive measure suggestion unit constructs a system in which a generation AI provides a customized health educational program based on the user's health goals. For example, a program for weight loss or muscle building is provided. The preventive measure suggestion unit can also provide a customized educational program according to the user's health goals. For example, a program for preventing or managing a specific disease is provided. The preventive measure suggestion unit can also provide specific advice for improving the user's health condition by providing an educational program according to the individual health goals. For example, a program for weight loss or muscle building is provided. In this way, the user's health condition can be more effectively improved by providing a customized educational program according to the individual health goals.

[0045] When analyzing health data, the preventive measure suggestion unit can provide health education tailored to age group and gender. For example, the preventive measure suggestion unit considers health risks for each age group and builds a system in which the generative AI provides an appropriate health education program. For example, it may recommend exercise to maintain bone density for elderly people. The preventive measure suggestion unit can also provide health education tailored to gender. For example, it may suggest a diet to balance hormones for women. The preventive measure suggestion unit can also provide specific advice to improve the user's health by providing health education tailored to age group and gender. For example, it may suggest establishing an exercise habit for young people and recommend health checkups for middle-aged and older people. This enables more appropriate health management by providing health education tailored to age group and gender.

[0046] When analyzing health data, the preventive measure suggestion unit can provide health education by taking into account health habits in different cultural areas and regions. For example, the preventive measure suggestion unit analyzes health habits in different cultural areas, and constructs a system in which the generation AI provides a health education program tailored to the user's health condition. For example, the unit may recommend a Mediterranean diet. The preventive measure suggestion unit can also provide health education by taking into account local health habits. For example, it may suggest health methods that are popular in a particular region. The preventive measure suggestion unit can also provide health education to improve the user's health condition by taking into account health habits in different cultural areas and regions. For example, it may provide dietary and exercise advice by taking into account health habits in East Asia. This makes it possible to provide health education from a more multifaceted perspective by taking into account health habits in different cultural areas and regions.

[0047] The analysis unit analyzes health data from local communities in real time, enabling early detection of health problems specific to the region. For example, the analysis unit collects health data from local communities in real time, and the generation AI uses that data to build a system that detects health problems specific to the region early. For example, it detects the spread of specific diseases. The analysis unit can also detect health problems specific to the region early based on data collected in real time. For example, it can detect the spread of specific diseases. The analysis unit can also continuously monitor health data from local communities and issue early warnings if an abnormality is detected. For example, if it detects the spread of a specific disease, it can suggest preventive measures to local residents. This makes it possible to analyze health data from local communities in real time and detect health problems specific to the region early, enabling rapid response.

[0048] When analyzing health data for a local community, the analysis unit can compare it with past data to understand changes in the local health status. For example, the analysis unit registers the local community's past health data in a database, and builds a system in which the generation AI understands changes in the local health status based on that data. For example, it compares past disease epidemics with current data. The analysis unit can also evaluate changes in the local health status based on past data. For example, it compares past data with current data to understand changes in the local health status. The analysis unit can also predict local health risks based on past data. For example, it learns from past data and predicts future health risks. This makes it possible to accurately understand changes in the local health status by comparing with past data.

[0049] When analyzing health data from a local community, the analysis unit can compare data from different regions and cultural spheres to propose health management measures. For example, the analysis unit collects health data from different regions and builds a system in which the generation AI analyzes the health status of the region based on that data. For example, the eating habits and exercise habits of each region can be compared. The analysis unit can also collect health data from different cultural spheres and the generation AI can analyze the health status of the region based on that data. For example, the health habits and lifestyle habits of each cultural sphere can be compared. The analysis unit can also evaluate the health status of the region based on the data from the region and cultural sphere. For example, the analysis unit can propose health management measures based on data from different regions and cultural spheres. In this way, by comparing data from different regions and cultural spheres, health management measures can be proposed from a more multifaceted perspective.

[0050] When analyzing health data from local communities, the analysis unit can propose health management measures tailored to different age groups and genders. For example, the analysis unit may consider health risks for each age group and build a system in which generative AI proposes appropriate health management measures. For example, it may recommend exercise to maintain bone density for elderly people. The analysis unit can also propose health management measures tailored to gender. For example, it may suggest a diet to balance hormones for women. By proposing health management measures tailored to age groups and genders, the analysis unit can also provide specific advice to improve the health status of local communities. For example, it may suggest that young people establish exercise habits and recommend that middle-aged and elderly people undergo health checkups. This allows for more appropriate health management by proposing health management measures tailored to different age groups and genders.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The preventive medical system can also be equipped with an environmental data collection unit that collects data on the user's living environment. For example, data on the user's living environment and working environment can be collected, and the generation AI can analyze the user's health condition based on that data. For example, the air quality and noise level in the living environment can be monitored to assess health risks. Health risks can also be predicted by analyzing stress levels and workload in the working environment. This allows for a more comprehensive assessment of the user's health condition by taking living environment data into account.

[0053] The preventive medical system can further include a social data collection unit that collects the user's social activity data. For example, data on the user's social activities and hobbies is collected, and the generation AI analyzes the user's health status based on that data. For example, the frequency and quality of social activities can be evaluated to predict the risk of social isolation. It can also analyze data on hobbies and leisure activities to evaluate the user's mental health status. This allows for a more comprehensive assessment of the user's health status by taking social activity data into account.

[0054] The preventive medical system can further include an economic data collection unit that collects data on the user's financial situation. For example, the generation AI can collect data on the user's income and expenses, and analyze the user's health status based on that data. For example, it can evaluate the user's level of financial stress and predict health risks. It can also analyze the impact of changes in the user's financial situation on their health. This allows for a more comprehensive assessment of the user's health status by taking into account the user's financial situation data.

[0055] The preventive medical system can further include a genetic data collection unit that collects the user's genetic information. For example, the generation AI can collect the user's genetic information and analyze the health condition based on that data. For example, it can evaluate the risk of genetic diseases and suggest preventive measures. It can also provide an individualized health management plan based on the genetic information. This allows for more accurate assessment of the health condition by taking genetic information into account.

[0056] The preventive medical system can also be equipped with a life event data collection unit that collects data on the user's life events. For example, data on life events such as marriage, childbirth, and job changes can be collected, and the generation AI can analyze the user's health status based on that data. For example, it can evaluate the impact of life events on health and suggest appropriate preventive measures. It can also provide health management plans tailored to each life event. This allows for a more comprehensive assessment of the user's health status by taking life event data into account.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The health data collection unit collects the user's health data. For example, it collects data obtained from a smartwatch or fitness tracker. The health data collection unit can also collect data such as the user's diet, sleep patterns, and stress level. For example, a smartwatch records heart rate and number of steps, and a fitness tracker measures the amount of exercise. Step 2: The analysis unit analyzes the health data collected by the health data collection unit. For example, the generation AI analyzes the data using a text generation AI (e.g., LLM). The generation AI can also use a multimodal generation AI to integrate and analyze multiple data sources. The generation AI can also analyze the data using a machine learning algorithm. For example, the text generation AI builds a predictive model based on the user's health data and evaluates their health status. The multimodal generation AI integrates and analyzes text data and image data. The machine learning algorithm learns from past data and predicts future health risks. Step 3: The preventive measures suggestion unit suggests personalized preventive measures based on the results of the analysis by the analysis unit. For example, the generation AI provides specific advice for improving the user's diet and exercise habits. The generation AI can also suggest lifestyle changes to reduce the risk of specific diseases based on genetic information. The generation AI can also provide customized health education programs based on the user's health data. For example, the generation AI analyzes the user's diet and suggests a nutritionally balanced meal plan. The generation AI analyzes the user's exercise habits and suggests an effective exercise program. The generation AI analyzes the user's genetic information and suggests lifestyle changes to reduce the risk of specific diseases.

[0059] (Example 2) The preventive medical system according to an embodiment of the present invention is a system in which generative AI analyzes individual health data and proposes personalized preventive measures. This enables early detection and prevention of diseases, and is expected to extend healthy life expectancy.

[0060] A preventive medical system according to an embodiment includes a health data collection unit, an analysis unit, and a preventive measure suggestion unit. The health data collection unit collects a user's health data. For example, the health data collection unit collects data obtained from a smartwatch or fitness tracker. The health data collection unit can also collect data such as the user's diet, sleep patterns, and stress level. For example, a smartwatch records heart rate and step count, while a fitness tracker measures exercise volume. The analysis unit analyzes the health data collected by the health data collection unit. For example, the generation AI analyzes the data using a text generation AI (e.g., LLM). The generation AI can also integrate and analyze multiple data sources using a multimodal generation AI. The generation AI can also analyze the data using a machine learning algorithm. For example, the text generation AI builds a predictive model based on the user's health data and evaluates the user's health status. The multimodal generation AI integrates and analyzes text data and image data. The machine learning algorithm learns from past data and predicts future health risks. The preventive measure suggestion unit proposes personalized preventive measures based on the results of the analysis by the analysis unit. For example, the generating AI can provide specific advice to improve a user's diet and exercise habits. The generating AI can also suggest lifestyle changes to reduce the risk of certain diseases based on genetic information. The generating AI can also provide customized health education programs based on the user's health data. For example, the generating AI can analyze a user's diet and suggest a nutritionally balanced meal plan. The generating AI can analyze a user's exercise habits and suggest an effective exercise program. The generating AI can analyze a user's genetic information and suggest lifestyle changes to reduce the risk of certain diseases. This enables the preventive medical system according to the embodiment to detect and prevent diseases early, potentially extending healthy life expectancy. For example, by implementing preventive measures provided by the generating AI, a user can improve their health and reduce the risk of disease. The generating AI can continuously monitor the user's health data and issue an early warning if an abnormality is detected.For example, if an abnormality in heart rate or blood pressure is detected, the system will send a notification to the user urging them to visit a medical institution. Generative AI will also educate and raise awareness about health for users. For example, it will provide information on the latest health research results and effective preventative measures. Generative AI will also analyze health data from the entire local community to understand the health status of the entire region. This will enable it to propose measures to address health issues specific to the region. For example, it will propose preventative measures for diseases that are prevalent in a specific region and conduct awareness-raising activities for local residents.

[0061] The health data collection unit can collect data from a smartwatch or fitness tracker. The health data collection unit collects data from, for example, a smartwatch or fitness tracker. For example, the smartwatch records heart rate and step count, and the fitness tracker measures exercise volume. The health data collection unit can also collect data obtained from the smartwatch or fitness tracker in real time. For example, the smartwatch monitors heart rate in real time, and the fitness tracker measures exercise volume in real time. In this way, by collecting data from the smartwatch or fitness tracker, the user's health condition can be more accurately understood.

[0062] The analysis unit can analyze emotional data and integrate emotional states such as stress or happiness into health data for analysis. For example, the analysis unit uses a generative AI to analyze the user's emotional data and quantify stress levels and happiness. For example, it analyzes the content of the user's social media posts and diary entries to calculate an emotional score. The analysis unit can also analyze the user's emotional data using voice analysis technology. For example, it can analyze the user's voice to evaluate their emotional state. The analysis unit can also analyze the user's emotional data using facial expression analysis technology. For example, it can analyze the user's facial expressions to evaluate their emotional state. In this way, by integrating the emotional data into the health data, the user's health condition can be more comprehensively evaluated.

[0063] The analysis unit can collect health data in real time and provide analysis results on the spot. The analysis unit collects data in real time from, for example, a smartwatch or fitness tracker, and the generation AI immediately provides analysis results. For example, it analyzes heart rate and step count data in real time. The analysis unit can also allow the generation AI to instantly evaluate health status based on the data collected in real time. For example, if an abnormal heart rate is detected, a notification is sent to the user immediately. This allows for quick response by analyzing health data in real time and providing results on the spot.

[0064] When analyzing health data, the analysis unit can also perform analysis based on medical history and family health data. For example, the analysis unit registers the user's past medical history in a database, and the generation AI analyzes the health condition based on that data. For example, past medical history and treatment history are taken into consideration. The analysis unit can also collect health data of the user's family, and the generation AI can analyze the health condition based on that data. For example, family medical history and genetic information are taken into consideration. The analysis unit can also integrate the user's medical history and family health data for analysis. For example, future health risks are predicted based on the user's past medical history and family medical history. This enables more accurate analysis of the health condition by taking into account past medical history and family health data.

[0065] The health data collection unit can use the emotion estimation function to adjust the timing or method of data collection according to the user's emotional state. For example, the health data collection unit analyzes the user's emotional state in real time and builds a system that collects health data when the user's emotions are stable. For example, data collection is performed when stress is low. The health data collection unit can also use the emotion estimation function to adjust the timing of data collection according to the user's emotional state. For example, data collection is performed when the user is relaxed. The health data collection unit can also use the emotion estimation function to adjust the method of data collection according to the user's emotional state. For example, when the user is feeling stressed, the frequency of data collection is reduced. In this way, by adjusting the timing and method of data collection according to the user's emotional state, more accurate health data can be collected.

[0066] The health data collection unit collects voice data and image data, and the analysis unit can analyze the voice data and image data to understand the health condition. The health data collection unit, for example, collects a user's voice data and constructs a system in which a generation AI analyzes the voice to evaluate the health condition. For example, the stress level is estimated from the tone of voice and speaking style. The health data collection unit can also collect a user's image data and the generation AI analyzes the image to evaluate the health condition. For example, the health condition is evaluated from facial expressions and skin condition. The health data collection unit can also collect integrated voice data and image data. For example, the user's voice and facial expressions are analyzed simultaneously. The analysis unit, for example, analyzes voice data to evaluate the user's emotional state. For example, the stress level is estimated from the tone of voice and speaking style. The analysis unit can also analyze image data to evaluate the user's health condition. For example, the health condition is evaluated from facial expressions and skin condition. The analysis unit can also integrate and analyze voice data and image data. For example, the user's voice and facial expressions are analyzed simultaneously. This allows the user's health condition to be understood more comprehensively by analyzing the voice data and image data.

[0067] When analyzing health data, the analysis unit can compare data from different regions and cultural spheres and perform analysis from a global perspective. The analysis unit, for example, collects health data from different regions and builds a system in which the generation AI analyzes the user's health status based on that data. For example, it compares dietary and exercise habits between regions. The analysis unit can also collect health data from different cultural spheres and use the generation AI to analyze the user's health status based on that data. For example, it compares health and lifestyle habits between cultural spheres. The analysis unit can also integrate and analyze data from different regions and cultural spheres. For example, it can evaluate health status from a global perspective based on data from different regions and cultural spheres. This makes it possible to evaluate health status from a global perspective by comparing data from different regions and cultural spheres.

[0068] The preventive measure suggestion unit can analyze the emotional data and suggest preventive measures according to the emotional state. For example, the preventive measure suggestion unit uses a generation AI to analyze the user's emotional data and suggest relaxation and stress management preventive measures during times of high stress. For example, it can introduce meditation and deep breathing techniques. The preventive measure suggestion unit can also suggest preventive measures according to the user's emotional state. For example, during times of low happiness, it can suggest activities to elicit positive emotions. The preventive measure suggestion unit can also suggest preventive measures to improve the user's health condition based on the emotional data. For example, during times of high stress, it can suggest relaxation and stress management preventive measures. In this way, by suggesting preventive measures according to the emotional state, the user's health condition can be more effectively improved.

[0069] When analyzing health data, the preventive measure suggestion unit can suggest preventive measures based on environmental factors such as season and weather. For example, the preventive measure suggestion unit builds a system in which the generative AI suggests appropriate preventive measures by taking into account seasonal health risks. For example, it may recommend taking vitamins to prevent colds in winter. The preventive measure suggestion unit can also suggest preventive measures according to changes in weather. For example, it may suggest indoor exercises on rainy days. The preventive measure suggestion unit can also suggest preventive measures to improve the user's health by taking environmental factors into account. For example, it may provide advice on diet and exercise according to the season and weather. This allows more appropriate preventive measures to be suggested by taking into account environmental factors such as season and weather.

[0070] When analyzing health data, the preventive measure suggestion unit can suggest preventive measures based on individual factors such as lifestyle and occupation. The preventive measure suggestion unit, for example, collects lifestyle data from users and builds a system in which the generation AI suggests personalized preventive measures based on that data. For example, it suggests exercise for users who do a lot of desk work. The preventive measure suggestion unit can also collect occupational data from users and the generation AI can suggest preventive measures based on that data. For example, it suggests rest for users who do a lot of physical labor. The preventive measure suggestion unit can also suggest preventive measures to improve the user's health by taking into account individual factors such as lifestyle and occupation. For example, it suggests regular stretching and exercise for users who do a lot of desk work. This makes it possible to suggest more personalized preventive measures by taking into account individual factors such as lifestyle and occupation.

[0071] The preventive measure suggestion unit can use the emotion estimation function to suggest preventive measures according to the user's emotional state and elicit positive emotions. The preventive measure suggestion unit, for example, uses the emotion estimation function to build a system that suggests preventive measures according to the user's emotional state. For example, during times of high stress, the preventive measure suggestion unit can suggest relaxation activities. The preventive measure suggestion unit can also use the emotion estimation function to suggest preventive measures according to the user's emotional state and elicit positive emotions. For example, during times of low happiness, the preventive measure suggestion unit can suggest activities to elicit positive emotions. The preventive measure suggestion unit can also use the emotion estimation function to suggest preventive measures to improve the user's emotional state. For example, during times of high stress, the preventive measures suggest relaxation and stress management. In this way, the preventive measures according to the emotional state can be suggested and positive emotions can be elicited, thereby more effectively improving the user's health condition.

[0072] When analyzing health data, the preventive measure suggestion unit can suggest preventive measures according to age group and gender. For example, the preventive measure suggestion unit considers health risks for each age group and builds a system in which the generative AI suggests appropriate preventive measures. For example, it may recommend exercise to maintain bone density for elderly people. The preventive measure suggestion unit can also suggest preventive measures according to gender. For example, it may suggest a diet to balance hormones for women. The preventive measure suggestion unit can also provide specific advice to improve the user's health by suggesting preventive measures according to age group and gender. For example, it may suggest establishing an exercise habit for young people and recommend health checkups for middle-aged and elderly people. This allows for more appropriate health management by suggesting preventive measures according to age group and gender.

[0073] When analyzing health data, the preventive measure suggestion unit can suggest preventive measures by referring to health habits in different cultural areas and regions. For example, the preventive measure suggestion unit analyzes health habits in different cultural areas, and builds a system in which the generation AI suggests preventive measures according to the user's health condition. For example, the preventive measure suggestion unit may recommend a Mediterranean diet. The preventive measure suggestion unit may also suggest preventive measures by referring to local health habits. For example, it may suggest health methods that are popular in a particular region. The preventive measure suggestion unit may also suggest preventive measures to improve the user's health condition by taking into account health habits in different cultural areas and regions. For example, it may provide dietary and exercise advice by referring to health habits in East Asia. This allows preventive measures to be suggested from a more multifaceted perspective by referring to health habits in different cultural areas and regions.

[0074] The analysis unit can analyze the emotional data and use changes in the emotional state as an indicator for early detection. For example, the analysis unit constructs a system in which a generative AI analyzes the user's emotional data and uses changes in the emotional state as an indicator for early detection. For example, it suggests relaxation before stress increases. The analysis unit can also detect health risks in the user early based on the emotional data. For example, it can detect the risk of depression early based on changes in the emotional state. The analysis unit can also continuously monitor the emotional data and issue an early warning if an abnormality is detected. For example, if it detects a sudden change in the emotional state, it can send a notification to the user urging them to visit a medical institution. In this way, health risks can be detected early by using changes in the emotional state as an indicator for early detection.

[0075] The analysis unit can monitor health data in real time and send an immediate notification when an abnormality is detected. The analysis unit collects data in real time from, for example, a smartwatch or fitness tracker, and builds a system that sends an immediate notification when the generation AI detects an abnormality. For example, it detects abnormalities in heart rate or blood pressure. The analysis unit can also immediately send a notification to the user when the generation AI detects an abnormality based on the data collected in real time. For example, if an abnormality in heart rate is detected, a notification is sent to the user immediately. The analysis unit can also send a notification to the user urging them to visit a medical institution when an abnormality is detected. For example, if an abnormality in blood pressure is detected, a notification is sent to the user urging them to visit a medical institution. This makes it possible to monitor health data in real time and send an immediate notification when an abnormality is detected, enabling rapid response.

[0076] When monitoring health data, the analysis unit can compare it with past data to detect abnormalities. For example, the analysis unit registers the user's past health data in a database, and builds a system in which the generation AI detects abnormalities based on that data. For example, it compares it with past heart rate data. The analysis unit can also evaluate changes in the user's health condition based on past data. For example, it compares past data with current data to detect abnormalities. The analysis unit can also predict future health risks based on past data. For example, it learns from past data and predicts future health risks. This allows for more accurate detection of abnormalities by comparing it with past data.

[0077] The analysis unit can use the emotion estimation function to monitor changes in the user's emotional state and detect abnormalities. For example, the analysis unit uses the emotion estimation function to build a system that monitors changes in the user's emotional state. For example, the analysis unit can suggest relaxation before stress builds up. The analysis unit can also use the emotion estimation function to monitor changes in the user's emotional state and detect abnormalities. For example, the analysis unit can detect sudden changes in the emotional state. The analysis unit can also use the emotion estimation function to continuously monitor the user's emotional state and issue an early warning if an abnormality is detected. For example, if a sudden change in the emotional state is detected, the analysis unit can send a notification to the user urging them to visit a medical institution. In this way, health risks can be detected early by monitoring changes in the emotional state and detecting abnormalities.

[0078] When monitoring health data, the analysis unit can integrate and analyze data from different devices. The analysis unit integrates data from different devices, such as a smartwatch, fitness tracker, and smartphone, to build a system in which generative AI analyzes health status. For example, it integrates heart rate and step count data. The analysis unit can also evaluate a user's health status based on data from different devices. For example, it can integrate and analyze data from a smartwatch and a fitness tracker. The analysis unit can also predict a user's health risks based on data from different devices. For example, it can integrate and analyze data from a smartwatch and a smartphone. This allows for a more accurate assessment of health status by integrating and analyzing data from different devices.

[0079] When monitoring health data, the analysis unit can detect abnormalities based on data from different regions and cultural spheres. For example, the analysis unit collects health data from different regions and builds a system in which the generation AI analyzes the user's health status based on that data. For example, the eating habits and exercise habits between regions are compared. The analysis unit can also collect health data from different cultural spheres and the generation AI can analyze the user's health status based on that data. For example, the health habits and lifestyle habits between cultural spheres are compared. The analysis unit can also evaluate the user's health status based on the data from different regions and cultural spheres. For example, it can detect abnormalities based on data from different regions and cultural spheres. This allows for more accurate detection of abnormalities by referring to data from different regions and cultural spheres.

[0080] The preventive measure suggestion unit can analyze the emotional data and provide a health education program according to the emotional state. For example, the preventive measure suggestion unit uses the generation AI to analyze the user's emotional data and provide a health education program for relaxation and stress management during times of high stress. For example, it can introduce meditation and deep breathing techniques. The preventive measure suggestion unit can also provide a health education program according to the user's emotional state based on the emotional data. For example, during times of low happiness, it can provide an education program to elicit positive emotions. The preventive measure suggestion unit can also provide an education program to improve the user's health condition based on the emotional data. For example, during times of high stress, it can provide a health education program for relaxation and stress management. In this way, by providing a health education program according to the emotional state, the user's health condition can be more effectively improved.

[0081] When analyzing health data, the preventive measure suggestion unit can provide health education that reflects the latest research results and trend information. For example, the preventive measure suggestion unit constructs a system in which a generation AI provides a health education program tailored to the user's health condition based on the latest research results. For example, new exercise methods and dietary methods are introduced. The preventive measure suggestion unit can also provide health education tailored to the user's health condition based on trend information. For example, a health education program is provided based on the latest health trends and industry news. The preventive measure suggestion unit can also provide an education program to improve the user's health condition by reflecting the latest research results and trend information. For example, a new exercise method or dietary method is introduced. In this way, by providing health education that reflects the latest research results and trend information, the user's health condition can be more effectively improved.

[0082] When analyzing the health data, the preventive measure suggestion unit can provide a customized educational program according to individual health goals. For example, the preventive measure suggestion unit constructs a system in which a generation AI provides a customized health educational program based on the user's health goals. For example, a program for weight loss or muscle building is provided. The preventive measure suggestion unit can also provide a customized educational program according to the user's health goals. For example, a program for preventing or managing a specific disease is provided. The preventive measure suggestion unit can also provide specific advice for improving the user's health condition by providing an educational program according to the individual health goals. For example, a program for weight loss or muscle building is provided. In this way, the user's health condition can be more effectively improved by providing a customized educational program according to the individual health goals.

[0083] The preventive measure suggestion unit can use the emotion estimation function to provide health education according to the user's emotional state and elicit positive emotions. The preventive measure suggestion unit, for example, uses the emotion estimation function to build a system that provides a health education program according to the user's emotional state. For example, during times of high stress, the preventive measure suggestion unit can also use the emotion estimation function to provide health education according to the user's emotional state and elicit positive emotions. For example, during times of low happiness, the preventive measure suggestion unit can provide an education program to elicit positive emotions. The preventive measure suggestion unit can also use the emotion estimation function to provide health education to improve the user's emotional state. For example, during times of high stress, the preventive measure suggestion unit can provide a health education program for relaxation and stress management. In this way, the user's health condition can be more effectively improved by providing health education according to the user's emotional state and eliciting positive emotions.

[0084] When analyzing health data, the preventive measure suggestion unit can provide health education tailored to age group and gender. For example, the preventive measure suggestion unit considers health risks for each age group and builds a system in which the generative AI provides an appropriate health education program. For example, it may recommend exercise to maintain bone density for elderly people. The preventive measure suggestion unit can also provide health education tailored to gender. For example, it may suggest a diet to balance hormones for women. The preventive measure suggestion unit can also provide specific advice to improve the user's health by providing health education tailored to age group and gender. For example, it may suggest establishing an exercise habit for young people and recommend health checkups for middle-aged and older people. This enables more appropriate health management by providing health education tailored to age group and gender.

[0085] When analyzing health data, the preventive measure suggestion unit can provide health education by taking into account health habits in different cultural areas and regions. For example, the preventive measure suggestion unit analyzes health habits in different cultural areas, and constructs a system in which the generation AI provides a health education program tailored to the user's health condition. For example, the unit may recommend a Mediterranean diet. The preventive measure suggestion unit can also provide health education by taking into account local health habits. For example, it may suggest health methods that are popular in a particular region. The preventive measure suggestion unit can also provide health education to improve the user's health condition by taking into account health habits in different cultural areas and regions. For example, it may provide dietary and exercise advice by taking into account health habits in East Asia. This makes it possible to provide health education from a more multifaceted perspective by taking into account health habits in different cultural areas and regions.

[0086] The analysis unit can analyze the emotional data of the local community and propose health management measures according to the emotional state. For example, the analysis unit uses a generative AI to analyze the emotional data of the local community and propose health management measures such as relaxation and stress management in areas with high stress. For example, local events or workshops can be held. The analysis unit can also propose health management measures according to the emotional state based on the emotional data of the local community. For example, in areas with low happiness levels, it can propose activities to bring out positive emotions. The analysis unit can also propose health management measures to improve the health of the entire region based on the emotional data of the local community. For example, in areas with high stress, it can propose health management measures such as relaxation and stress management. In this way, by proposing health management measures according to the emotional state of the local community, it is possible to improve the health of the entire region.

[0087] The analysis unit analyzes health data from local communities in real time, enabling early detection of health problems specific to the region. For example, the analysis unit collects health data from local communities in real time, and the generation AI uses that data to build a system that detects health problems specific to the region early. For example, it detects the spread of specific diseases. The analysis unit can also detect health problems specific to the region early based on data collected in real time. For example, it can detect the spread of specific diseases. The analysis unit can also continuously monitor health data from local communities and issue early warnings if an abnormality is detected. For example, if it detects the spread of a specific disease, it can suggest preventive measures to local residents. This makes it possible to analyze health data from local communities in real time and detect health problems specific to the region early, enabling rapid response.

[0088] When analyzing health data for a local community, the analysis unit can compare it with past data to understand changes in the local health status. For example, the analysis unit registers the local community's past health data in a database, and builds a system in which the generation AI understands changes in the local health status based on that data. For example, it compares past disease epidemics with current data. The analysis unit can also evaluate changes in the local health status based on past data. For example, it compares past data with current data to understand changes in the local health status. The analysis unit can also predict local health risks based on past data. For example, it learns from past data and predicts future health risks. This makes it possible to accurately understand changes in the local health status by comparing with past data.

[0089] The analysis unit can use the emotion estimation function of the local community to suggest health management measures according to the emotional state and elicit positive emotions. The analysis unit, for example, uses the emotion estimation function to build a system that suggests health management measures according to the emotional state of the local community. For example, in areas with high stress, it suggests relaxation. The analysis unit can also use the emotion estimation function to suggest health management measures according to the emotional state of the local community and elicit positive emotions. For example, in areas with low happiness levels, it suggests activities to elicit positive emotions. The analysis unit can also use the emotion estimation function to suggest health management measures to improve the emotional state of the local community. For example, in areas with high stress, it suggests health management measures such as relaxation and stress management. In this way, it is possible to suggest health management measures according to the emotional state of the local community and elicit positive emotions, thereby improving the health of the entire region.

[0090] When analyzing health data from a local community, the analysis unit can compare data from different regions and cultural spheres to propose health management measures. For example, the analysis unit collects health data from different regions and builds a system in which the generation AI analyzes the health status of the region based on that data. For example, the eating habits and exercise habits of each region can be compared. The analysis unit can also collect health data from different cultural spheres and the generation AI can analyze the health status of the region based on that data. For example, the health habits and lifestyle habits of each cultural sphere can be compared. The analysis unit can also evaluate the health status of the region based on the data from the region and cultural sphere. For example, the analysis unit can propose health management measures based on data from different regions and cultural spheres. In this way, by comparing data from different regions and cultural spheres, health management measures can be proposed from a more multifaceted perspective.

[0091] When analyzing health data from local communities, the analysis unit can propose health management measures tailored to different age groups and genders. For example, the analysis unit may consider health risks for each age group and build a system in which generative AI proposes appropriate health management measures. For example, it may recommend exercise to maintain bone density for elderly people. The analysis unit can also propose health management measures tailored to gender. For example, it may suggest a diet to balance hormones for women. By proposing health management measures tailored to age groups and genders, the analysis unit can also provide specific advice to improve the health status of local communities. For example, it may suggest that young people establish exercise habits and recommend that middle-aged and elderly people undergo health checkups. This allows for more appropriate health management by proposing health management measures tailored to different age groups and genders.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The preventive medical system can also be equipped with an environmental data collection unit that collects data on the user's living environment. For example, data on the user's living environment and working environment can be collected, and the generation AI can analyze the user's health condition based on that data. For example, the air quality and noise level in the living environment can be monitored to assess health risks. Health risks can also be predicted by analyzing stress levels and workload in the working environment. This allows for a more comprehensive assessment of the user's health condition by taking living environment data into account.

[0094] The preventive medical system can further include a social data collection unit that collects the user's social activity data. For example, data on the user's social activities and hobbies is collected, and the generation AI analyzes the user's health status based on that data. For example, the frequency and quality of social activities can be evaluated to predict the risk of social isolation. It can also analyze data on hobbies and leisure activities to evaluate the user's mental health status. This allows for a more comprehensive assessment of the user's health status by taking social activity data into account.

[0095] The preventive medical system can further include an economic data collection unit that collects data on the user's financial situation. For example, the generation AI can collect data on the user's income and expenses, and analyze the user's health status based on that data. For example, it can evaluate the user's level of financial stress and predict health risks. It can also analyze the impact of changes in the user's financial situation on their health. This allows for a more comprehensive assessment of the user's health status by taking into account the user's financial situation data.

[0096] The preventive medical system can further include a genetic data collection unit that collects the user's genetic information. For example, the generation AI can collect the user's genetic information and analyze the health condition based on that data. For example, it can evaluate the risk of genetic diseases and suggest preventive measures. It can also provide an individualized health management plan based on the genetic information. This allows for more accurate assessment of the health condition by taking genetic information into account.

[0097] The preventive medical system can also be equipped with a life event data collection unit that collects data on the user's life events. For example, data on life events such as marriage, childbirth, and job changes can be collected, and the generation AI can analyze the user's health status based on that data. For example, it can evaluate the impact of life events on health and suggest appropriate preventive measures. It can also provide health management plans tailored to each life event. This allows for a more comprehensive assessment of the user's health status by taking life event data into account.

[0098] The preventive medical system can further analyze the user's emotional data and provide a health management plan tailored to their emotional state. For example, the generative AI could analyze the user's emotional data and provide a relaxation and stress management plan during times of high stress. For example, it could recommend meditation or deep breathing techniques. It can also provide a health management plan tailored to the user's emotional state based on the emotional data. For example, during times of low happiness, it could suggest activities to elicit positive emotions. This allows the system to provide a health management plan tailored to the user's emotional state, thereby more effectively improving the user's health.

[0099] The preventive medical system can further analyze the user's emotional data and provide a meal plan tailored to their emotional state. For example, the generative AI could analyze the user's emotional data and suggest meals that have a relaxing effect during times of high stress. For example, it could recommend herbal teas or foods containing antioxidants. The system can also provide a meal plan tailored to the user's emotional state based on the emotional data. For example, it could suggest foods that have a mood-boosting effect during times of low happiness. This allows the system to provide a meal plan tailored to the user's emotional state, thereby more effectively improving the user's health.

[0100] The preventive medical system can further analyze the user's emotional data and provide an exercise plan tailored to their emotional state. For example, the generative AI could analyze the user's emotional data and suggest relaxation exercises during times of high stress, such as yoga or stretching. The system can also provide an exercise plan tailored to the user's emotional state based on the emotional data. For example, during times of low happiness, it could suggest exercises that increase endorphins. This allows the system to provide an exercise plan tailored to the user's emotional state, thereby more effectively improving the user's health.

[0101] The preventive medical system can further analyze the user's emotional data and provide a sleep management plan tailored to their emotional state. For example, the generative AI could analyze the user's emotional data and provide a sleep management plan with a relaxation effect during times of high stress. For example, it could suggest ways to relax before bed or a comfortable sleeping environment. It can also provide a sleep management plan tailored to the user's emotional state based on the emotional data. For example, during times when happiness levels are low, it could provide advice to promote comfortable sleep. This allows the user's health to be improved more effectively by providing a sleep management plan tailored to their emotional state.

[0102] The preventive medical system can further analyze the user's emotional data and provide a mental health care plan tailored to their emotional state. For example, the generative AI could analyze the user's emotional data and provide a mental health care plan during periods of high stress. For example, it could suggest counseling or the use of a mental health app. It can also provide a mental health care plan tailored to the user's emotional state based on the emotional data. For example, during periods of low happiness, it could suggest mental health care activities to elicit positive emotions. This allows the system to provide a mental health care plan tailored to the user's emotional state, thereby more effectively improving the user's health.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The health data collection unit collects the user's health data. For example, it collects data obtained from a smartwatch or fitness tracker. The health data collection unit can also collect data such as the user's diet, sleep patterns, and stress level. For example, a smartwatch records heart rate and number of steps, and a fitness tracker measures the amount of exercise. Step 2: The analysis unit analyzes the health data collected by the health data collection unit. For example, the generation AI analyzes the data using a text generation AI (e.g., LLM). The generation AI can also use a multimodal generation AI to integrate and analyze multiple data sources. The generation AI can also analyze the data using a machine learning algorithm. For example, the text generation AI builds a predictive model based on the user's health data and evaluates their health status. The multimodal generation AI integrates and analyzes text data and image data. The machine learning algorithm learns from past data and predicts future health risks. Step 3: The preventive measures suggestion unit suggests personalized preventive measures based on the results of the analysis by the analysis unit. For example, the generation AI provides specific advice for improving the user's diet and exercise habits. The generation AI can also suggest lifestyle changes to reduce the risk of specific diseases based on genetic information. The generation AI can also provide customized health education programs based on the user's health data. For example, the generation AI analyzes the user's diet and suggests a nutritionally balanced meal plan. The generation AI analyzes the user's exercise habits and suggests an effective exercise program. The generation AI analyzes the user's genetic information and suggests lifestyle changes to reduce the risk of specific diseases.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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]

[0172] 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 data collection unit that collects health data of a user; an analysis unit that analyzes the health data collected by the health data collection unit; a preventive measure suggestion unit that suggests individualized preventive measures based on the results of the analysis by the analysis unit. A system characterized by:

2. The health data collection unit: Collect data from your smartwatch or fitness tracker 2. The system of claim 1.

3. The analysis unit Collecting the health data in real time and providing analysis results on the spot 2. The system of claim 1.

4. The preventive measure suggestion unit When analyzing the health data, the preventive measures are also suggested based on environmental factors such as season and weather.

2. The system of claim 1.

5. The analysis unit Analyzing emotional data and using changes in emotional state as an indicator for early detection 2. The system of claim 1.

6. The preventive measure suggestion unit Analyzing emotional data and providing health education programs tailored to emotional states 2. The system of claim 1.

7. The analysis unit Analyzing emotional data from local communities and proposing health management measures based on their emotional state 2. The system of claim 1.

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