System

Abstract

An object of a system according to an embodiment is to provide individual preventive measures and risk management based on genetic information and lifestyle of a user.SOLUTION: A system includes a health information providing unit, an analysis unit, a prediction unit, an advice providing unit, a tracking unit, and a warning unit. The health information providing unit provides health information such as genetic information, lifestyle, and medical history of the user. The analysis unit analyzes the health information provided by the health information providing unit. The prediction unit predicts a genetic disease risk or a future influence on health on the basis of a result analyzed by the analysis unit. The advice providing unit provides advice on individual preventive measures and risk management based on the result predicted by the prediction unit. The tracking unit tracks the lifestyle and the health data based on the advice provided by the advice providing unit. The warning unit detects a change in the important health information based on the data tracked by the tracking unit and issues a warning.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately address individual preventive measures or risk management based on a user's genetic information or lifestyle habits, and there is room for improvement.

[0005] The system according to the embodiment aims to provide individual preventive measures and risk management based on the user's genetic information and lifestyle habits. [Means for solving the problem]

[0006] The system according to the embodiment includes a health information providing unit, an analysis unit, a prediction unit, an advice providing unit, a tracking unit, and a warning unit. The health information providing unit provides health information such as the user's genetic information, lifestyle habits, and medical history. The analysis unit analyzes the health information provided by the health information providing unit. The prediction unit predicts hereditary disease risks and future health impacts based on the results of the analysis by the analysis unit. The advice providing unit provides individualized preventive measures and risk management advice based on the results predicted by the prediction unit. The tracking unit tracks lifestyle habits and health data based on the advice provided by the advice providing unit. The warning unit detects changes in important health information based on the data tracked by the tracking unit and issues a warning. [Effects of the Invention]

[0007] The system according to the embodiment can provide individualized preventive measures and risk management based on the user's genetic information and lifestyle habits. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A preventive medicine promotion system according to an embodiment of the present invention provides a user with health information such as genetic information, lifestyle habits, and medical history, and a generative AI analyzes the information to predict hereditary disease risks and future health impacts, provides personalized preventive measures and risk management advice, tracks lifestyle habits and health data, and detects and alerts changes in important health information. This allows the preventive medicine promotion system to comprehensively manage the user's health information and provide specific action plans for achieving preventive medicine goals.

[0029] A preventive medicine promotion system according to an embodiment includes a health information providing unit, an analysis unit, a prediction unit, an advice providing unit, a tracking unit, and a warning unit. The health information providing unit provides health information such as a user's genetic information, lifestyle habits, and medical history. For example, the user inputs genetic test results, daily diet, exercise habits, past medical history, etc. The analysis unit uses a generation AI to analyze the health information provided by the health information providing unit. For example, the generation AI analyzes the health information using a text generation AI (e.g., LLM). The generation AI can also analyze the health information using a multimodal generation AI. The generation AI can also analyze the health information using statistical analysis or machine learning algorithms. The prediction unit predicts hereditary disease risk and future health impacts based on the results of the analysis by the analysis unit. For example, the generation AI predicts disease risk based on the presence or absence of gene mutations and a risk score. The generation AI can also predict future health impacts using a simulation model or a prediction algorithm. The advice providing unit provides individual preventive measures and risk management advice based on the results predicted by the prediction unit. For example, the generation AI may provide advice on dietary guidance, exercise plans, the importance of regular health checks, etc. The generation AI may also provide lifestyle improvement suggestions and risk management advice. The tracking unit tracks lifestyle and health data based on the advice provided by the advice providing unit. For example, when a user inputs a daily diet and exercise record, the generation AI analyzes the data and evaluates progress toward achieving preventive medical goals. The generation AI provides feedback on the degree to which the user has achieved the set goals and modifies the action plan as necessary. The warning unit detects changes in important health information based on the data tracked by the tracking unit and issues a warning. For example, if the generation AI detects abnormal fluctuations in blood pressure or blood sugar levels, it notifies the user of the fluctuations and recommends consulting a specialist or undergoing further evaluation. As a result, the preventive medical care promotion system according to the embodiment can comprehensively manage the user's health information and provide a specific action plan for achieving preventive medical goals.For example, early detection of genetic disease risks and appropriate preventive measures can reduce future health risks, and lifestyle changes can improve overall health.

[0030] The analysis unit combines genetic information and lifestyle data to analyze the interactions between specific environmental factors and genetic factors, enabling more precise risk prediction. For example, the analysis unit combines the user's genetic information and lifestyle data, and the generation AI analyzes the interactions between specific environmental factors and genetic factors. For example, the analysis unit analyzes the relationship between smoking habits and specific gene mutations to predict lung cancer risk. The analysis unit can also use the generation AI to analyze the relationship between dietary habits and gene mutations to predict diabetes risk. The analysis unit can also use the generation AI to analyze the relationship between exercise habits and gene mutations to predict heart disease risk. This enables more precise risk prediction by analyzing the interactions between environmental factors and genetic factors.

[0031] The analysis unit can collect health information in real time and provide analysis results on the spot, enabling immediate feedback. For example, the analysis unit collects data such as heart rate and step count in real time when a user uses a smartwatch or fitness tracker, and the generation AI provides analysis results on the spot. For example, the generation AI can evaluate the user's health condition based on the data collected in real time and provide immediate feedback. The analysis unit can also evaluate the user's exercise habits and eating habits based on the data collected in real time and provide immediate feedback. The analysis unit can also evaluate the user's sleep patterns based on the data collected in real time and provide immediate feedback. This allows immediate feedback by providing analysis results in real time.

[0032] By including family health histories in the health information provided by the user, the health information provision unit can analyze health risks for the entire family and suggest preventive measures for each family. For example, the user inputs family health histories, and the generation AI analyzes the risk of hereditary diseases for the entire family based on that data and suggests preventive measures. For example, the generation AI analyzes the cancer risk for the entire family based on family medical history data and suggests preventive measures. The health information provision unit can also analyze the genetic information of family members, analyze the risk of heart disease for the entire family, and suggest preventive measures. The health information provision unit can also analyze the lifestyle habit data of family members, analyze the diabetes risk for the entire family, and suggest preventive measures. This allows the health risks for the entire family to be analyzed and preventive measures to be suggested for each family.

[0033] The health information providing unit can provide health information using voice input or image analysis, making it easier for users to provide information. For example, the health information providing unit allows a user to provide health information using voice input, and the generation AI analyzes the voice data to predict health risks. For example, the user inputs their dietary details and exercise habits by voice, and the generation AI predicts health risks based on that data. The health information providing unit can also allow the generation AI to analyze the user's image data to predict health risks. For example, the user takes photos of their meals, and the generation AI analyzes the image data to evaluate the dietary details and predict health risks. The health information providing unit can also allow the generation AI to analyze videos of the user's exercise, evaluate their exercise habits, and predict health risks. This makes it easier for users to provide health information.

[0034] The advice providing unit uses the generation AI to learn the user's past health data, predict the effectiveness of individual preventive measures, and suggest the optimal preventive measures. For example, the advice providing unit uses the generation AI to learn the user's past health data and predict the effectiveness of specific preventive measures. For example, the generation AI predicts the effectiveness of specific dietary improvement measures based on past dietary data. The advice providing unit can also use the generation AI to learn the user's past exercise data and predict the effectiveness of a specific exercise program. For example, the generation AI predicts the effectiveness of a specific exercise program based on past exercise data. The advice providing unit can also use the generation AI to learn the user's past stress data and predict the effectiveness of specific stress management measures. For example, the generation AI predicts the effectiveness of specific stress management measures based on past stress data. In this way, past health data can be learned and optimal preventive measures can be suggested.

[0035] The tracking unit uses the generation AI to track the user's lifestyle data over a long period of time and analyze changes in health status over time. For example, the generation AI tracks the user's lifestyle data over several years and analyzes changes in health status over time. For example, the generation AI predicts changes in weight based on long-term dietary data. The tracking unit can also track the user's exercise data over a long period of time and analyze changes in health status over time. For example, the generation AI predicts changes in cardiopulmonary function based on long-term exercise data. The tracking unit can also track the user's sleep data over a long period of time and analyze changes in health status over time. For example, the generation AI predicts changes in sleep quality based on long-term sleep data. This makes it possible to track lifestyle data over a long period of time and analyze changes in health status.

[0036] The tracking unit can promote self-assessment by comparing the user's lifestyle habit data with other users and providing a benchmark. In the tracking unit, for example, the generation AI compares the user's lifestyle habit data with other users and provides a benchmark. For example, the generation AI evaluates the amount of exercise compared to users of the same age. In addition, the tracking unit can also compare the user's dietary data with other users and provide a benchmark. For example, the generation AI evaluates the balance of the user's diet compared to users of the same age. In addition, the tracking unit can also compare the user's sleep data with other users and provide a benchmark. For example, the generation AI evaluates the quality of the user's sleep compared to users of the same age. This can promote self-assessment by comparing with other users.

[0037] The warning unit can use the generation AI to monitor the user's health data in real time and issue an immediate warning when an abnormality is detected. For example, the generation AI can monitor the user's blood pressure data in real time and issue an immediate warning when an abnormal fluctuation is detected. For example, the generation AI can send an alert when blood pressure suddenly rises. The warning unit can also monitor the user's blood glucose level data in real time and issue an immediate warning when an abnormal fluctuation is detected. For example, the generation AI can send an alert when blood glucose levels suddenly rise. The warning unit can also monitor the user's heart rate data in real time and issue an immediate warning when an abnormal fluctuation is detected. For example, the generation AI can send an alert when the heart rate suddenly rises. This makes it possible to monitor health data in real time and issue an immediate warning when an abnormality is detected.

[0038] The warning unit can use the generation AI to analyze the user's health data, identify the cause of the abnormality, and propose specific countermeasures. For example, the generation AI can analyze the user's blood pressure data, identify the cause of abnormal fluctuations, and propose specific countermeasures. For example, the generation AI can suggest reviewing salt intake. The warning unit can also analyze the user's blood sugar level data, identify the cause of abnormal fluctuations, and propose specific countermeasures. For example, the generation AI can suggest reviewing dietary habits. The warning unit can also analyze the user's heart rate data, identify the cause of abnormal fluctuations, and propose specific countermeasures. For example, the generation AI can suggest reviewing exercise habits. This makes it possible to identify the cause of the abnormality and propose specific countermeasures.

[0039] The warning unit can analyze the user's health data using the generating AI and automatically notify family members or medical professionals when an abnormality is detected. For example, the generating AI analyzes the user's blood pressure data and automatically notifies family members when an abnormal fluctuation is detected. For example, the generating AI sends an alert to family members when blood pressure suddenly rises. The warning unit can also analyze the user's blood glucose level data and automatically notify medical professionals when an abnormal fluctuation is detected. For example, the generating AI sends an alert to medical professionals when blood glucose levels suddenly rise. The warning unit can also analyze the user's heart rate data and automatically notify family members or medical professionals when an abnormal fluctuation is detected. For example, the generating AI sends an alert to family members or medical professionals when heart rate suddenly rises. This makes it possible to automatically notify family members or medical professionals when an abnormality is detected.

[0040] The warning unit uses the generation AI to analyze the user's health data, and when an abnormality is detected, it can compare it with past data to identify the abnormal pattern. For example, the generation AI analyzes the user's blood pressure data, and when an abnormal fluctuation is detected, it compares it with past data to identify the abnormal pattern. For example, the generation AI identifies the abnormality based on past blood pressure fluctuation patterns. The warning unit can also analyze the user's blood glucose level data, and when an abnormal fluctuation is detected, it can compare it with past data to identify the abnormal pattern. For example, the generation AI identifies the abnormality based on past blood glucose fluctuation patterns. The warning unit can also analyze the user's heart rate data, and when an abnormal fluctuation is detected, it can compare it with past data to identify the abnormal pattern. For example, the generation AI identifies the abnormality based on past heart rate fluctuation patterns. In this way, when an abnormality is detected, it can compare it with past data to identify the abnormal pattern.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The preventive medicine promotion system can further analyze a user's social network data to evaluate the impact of social connections on health. For example, the analysis unit analyzes a user's social media data and evaluates the strength and frequency of social connections. The generative AI can predict mental health risks if the user has a high sense of loneliness. The analysis unit can also analyze the frequency of communication with the user's friends and family and predict stress risks if social support is lacking. Furthermore, the analysis unit can analyze data on interpersonal relationships at the user's workplace and predict heart disease risks if workplace stress is high. This makes it possible to make risk predictions that take social connections into account.

[0043] The preventive medicine promotion system can further analyze the user's sleep data and evaluate the impact of sleep quality on health. For example, the analysis unit analyzes the user's sleep patterns and predicts the risk of a weakened immune system if the user continues to sleep poorly. The generation AI can predict mental health risks if the user's sleep quality is poor. The analysis unit can also predict the risk of sleep apnea syndrome based on the user's sleep data. Furthermore, the analysis unit can analyze the user's sleep data and predict the risk of diabetes if the user continues to sleep for long periods of time. This makes it possible to make risk predictions that take sleep data into account.

[0044] The preventive medicine promotion system can further analyze the user's dietary data and evaluate the impact of nutritional balance on health. For example, the analysis unit analyzes the user's diet and predicts the risk of nutritional deficiency if the nutritional balance is unbalanced. The generative AI can predict the risk of osteoporosis if there is a deficiency of specific nutrients. The analysis unit can also predict the risk of obesity if excessive calorie intake continues based on the user's dietary data. Furthermore, the analysis unit can analyze the user's dietary data and predict the risk of allergies if there is a high intake of specific food groups. This makes it possible to make risk predictions that take dietary data into account.

[0045] The preventive medicine promotion system can further analyze the user's exercise data and evaluate the impact of exercise habits on health. For example, the analysis unit analyzes the user's exercise patterns and predicts the risk of heart disease if the user continues to exercise less. The generative AI can predict mental health risks if the user exercises less frequently. The analysis unit can also predict the risk of joint disorders if the user continues to exercise excessively based on the user's exercise data. Furthermore, the analysis unit can analyze the user's exercise data and predict the risk of muscle damage if the user does certain types of exercise frequently. This makes it possible to make risk predictions that take exercise data into account.

[0046] The preventive medicine promotion system can further analyze the user's environmental data and evaluate the impact of environmental factors on health. For example, the analysis unit analyzes air quality data in the user's residence and predicts the risk of respiratory disease if the air pollution is high. The generation AI can analyze noise data in the user's residence and predict the risk of stress if the noise level is high. The analysis unit can also predict the risk of digestive disease if the water quality is poor based on water quality data in the user's residence. Furthermore, the analysis unit can analyze temperature data in the user's residence and predict the risk of heatstroke if extreme temperatures persist. This makes it possible to make risk predictions that take environmental data into account.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The health information provider provides health information such as the user's genetic information, lifestyle habits, medical history, etc. For example, the user inputs the results of a genetic test, daily diet, exercise habits, past medical history, etc. Step 2: The analysis unit uses a generation AI to analyze the health information provided by the health information provision unit. For example, the generation AI may analyze the health information using a text generation AI (e.g., LLM). The generation AI may also analyze the health information using a multimodal generation AI. The generation AI may also analyze the health information using statistical analysis or machine learning algorithms. Step 3: The prediction unit predicts hereditary disease risk and future health impacts based on the results of the analysis by the analysis unit. For example, the generation AI predicts disease risk based on the presence or absence of genetic mutations and risk scores. The generation AI can also predict future health impacts using simulation models and prediction algorithms. Step 4: The advice provider provides individualized preventive measures and risk management advice based on the results predicted by the prediction provider. For example, the generation AI may provide advice on dietary guidance, exercise plans, and the importance of regular health checks. The generation AI may also provide suggestions for improving lifestyle habits and risk management advice. Step 5: The tracking unit tracks lifestyle and health data based on the advice provided by the advice provider. For example, the user inputs a record of their daily diet and exercise, and the generating AI analyzes the data and evaluates their progress toward achieving their preventive medical goals. The generating AI provides feedback on how well the user has achieved their set goals and modifies the action plan as necessary. Step 6: The alerting unit detects changes in important health information based on the data tracked by the tracking unit and issues a warning. For example, if the generating AI detects abnormal fluctuations in blood pressure or blood sugar levels, it will notify the user of the fluctuations and recommend consulting a specialist or undergoing further evaluation.

[0049] (Example 2) A preventive medicine promotion system according to an embodiment of the present invention provides a user with health information such as genetic information, lifestyle habits, and medical history, and a generative AI analyzes the information to predict hereditary disease risks and future health impacts, provides personalized preventive measures and risk management advice, tracks lifestyle habits and health data, and detects and alerts changes in important health information. This allows the preventive medicine promotion system to comprehensively manage the user's health information and provide specific action plans for achieving preventive medicine goals.

[0050] A preventive medicine promotion system according to an embodiment includes a health information providing unit, an analysis unit, a prediction unit, an advice providing unit, a tracking unit, and a warning unit. The health information providing unit provides health information such as a user's genetic information, lifestyle habits, and medical history. For example, the user inputs genetic test results, daily diet, exercise habits, past medical history, etc. The analysis unit uses a generation AI to analyze the health information provided by the health information providing unit. For example, the generation AI analyzes the health information using a text generation AI (e.g., LLM). The generation AI can also analyze the health information using a multimodal generation AI. The generation AI can also analyze the health information using statistical analysis or machine learning algorithms. The prediction unit predicts hereditary disease risk and future health impacts based on the results of the analysis by the analysis unit. For example, the generation AI predicts disease risk based on the presence or absence of gene mutations and a risk score. The generation AI can also predict future health impacts using a simulation model or a prediction algorithm. The advice providing unit provides individual preventive measures and risk management advice based on the results predicted by the prediction unit. For example, the generation AI may provide advice on dietary guidance, exercise plans, the importance of regular health checks, etc. The generation AI may also provide lifestyle improvement suggestions and risk management advice. The tracking unit tracks lifestyle and health data based on the advice provided by the advice providing unit. For example, when a user inputs a daily diet and exercise record, the generation AI analyzes the data and evaluates progress toward achieving preventive medical goals. The generation AI provides feedback on the degree to which the user has achieved the set goals and modifies the action plan as necessary. The warning unit detects changes in important health information based on the data tracked by the tracking unit and issues a warning. For example, if the generation AI detects abnormal fluctuations in blood pressure or blood sugar levels, it notifies the user of the fluctuations and recommends consulting a specialist or undergoing further evaluation. As a result, the preventive medical care promotion system according to the embodiment can comprehensively manage the user's health information and provide a specific action plan for achieving preventive medical goals.For example, early detection of genetic disease risks and appropriate preventive measures can reduce future health risks, and lifestyle changes can improve overall health.

[0051] The analysis unit combines genetic information and lifestyle data to analyze the interactions between specific environmental factors and genetic factors, enabling more precise risk prediction. For example, the analysis unit combines the user's genetic information and lifestyle data, and the generation AI analyzes the interactions between specific environmental factors and genetic factors. For example, the analysis unit analyzes the relationship between smoking habits and specific gene mutations to predict lung cancer risk. The analysis unit can also use the generation AI to analyze the relationship between dietary habits and gene mutations to predict diabetes risk. The analysis unit can also use the generation AI to analyze the relationship between exercise habits and gene mutations to predict heart disease risk. This enables more precise risk prediction by analyzing the interactions between environmental factors and genetic factors.

[0052] The analysis unit can collect health information in real time and provide analysis results on the spot, enabling immediate feedback. For example, the analysis unit collects data such as heart rate and step count in real time when a user uses a smartwatch or fitness tracker, and the generation AI provides analysis results on the spot. For example, the generation AI can evaluate the user's health condition based on the data collected in real time and provide immediate feedback. The analysis unit can also evaluate the user's exercise habits and eating habits based on the data collected in real time and provide immediate feedback. The analysis unit can also evaluate the user's sleep patterns based on the data collected in real time and provide immediate feedback. This allows immediate feedback by providing analysis results in real time.

[0053] The analysis unit can use the emotion estimation function to analyze the user's emotional state and make risk predictions that take into account the impact of stress and anxiety on health. For example, the analysis unit analyzes the user's voice data and evaluates the stress level using the emotion estimation function, and the generation AI predicts health risks based on that data. For example, the generation AI can evaluate the user's stress level using voice analysis technology and predict the risk of heart disease if the stress level is high. The analysis unit can also analyze the user's facial expression data and evaluate the anxiety level using the emotion estimation function, and predict the risk of depression if the anxiety level is high. The analysis unit can also analyze the user's biometric data (heart rate and electrodermal activity), evaluate the stress level using the emotion estimation function, and predict the risk of high blood pressure if the stress level is high. This enables risk predictions that take emotional states into account.

[0054] By including family health histories in the health information provided by the user, the health information provision unit can analyze health risks for the entire family and suggest preventive measures for each family. For example, the user inputs family health histories, and the generation AI analyzes the risk of hereditary diseases for the entire family based on that data and suggests preventive measures. For example, the generation AI analyzes the cancer risk for the entire family based on family medical history data and suggests preventive measures. The health information provision unit can also analyze the genetic information of family members, analyze the risk of heart disease for the entire family, and suggest preventive measures. The health information provision unit can also analyze the lifestyle habit data of family members, analyze the diabetes risk for the entire family, and suggest preventive measures. This allows the health risks for the entire family to be analyzed and preventive measures to be suggested for each family.

[0055] The health information providing unit can provide health information using voice input or image analysis, making it easier for users to provide information. For example, the health information providing unit allows a user to provide health information using voice input, and the generation AI analyzes the voice data to predict health risks. For example, the user inputs their dietary details and exercise habits by voice, and the generation AI predicts health risks based on that data. The health information providing unit can also allow the generation AI to analyze the user's image data to predict health risks. For example, the user takes photos of their meals, and the generation AI analyzes the image data to evaluate the dietary details and predict health risks. The health information providing unit can also allow the generation AI to analyze videos of the user's exercise, evaluate their exercise habits, and predict health risks. This makes it easier for users to provide health information.

[0056] The health information provision unit can use the emotion estimation function to analyze the emotional state of the user when providing health information and design an interface for eliciting positive emotions. For example, when the user inputs health information, the health information provision unit can use the emotion estimation function to analyze the emotional state and provide an interface for eliciting positive emotions. For example, the generation AI can display an encouraging message when the user inputs health information to elicit positive emotions. The health information provision unit can also provide an interface for the generation AI to analyze the user's emotional state and elicit positive emotions. For example, the generation AI can play relaxing music when the user inputs health information to elicit positive emotions. The health information provision unit can also provide an interface for the generation AI to analyze the user's emotional state and elicit positive emotions. For example, the generation AI can provide positive feedback when the user inputs health information to elicit positive emotions. This can elicit positive emotions when the user provides health information.

[0057] The advice providing unit uses the generation AI to learn the user's past health data, predict the effectiveness of individual preventive measures, and suggest the optimal preventive measures. For example, the advice providing unit uses the generation AI to learn the user's past health data and predict the effectiveness of specific preventive measures. For example, the generation AI predicts the effectiveness of specific dietary improvement measures based on past dietary data. The advice providing unit can also use the generation AI to learn the user's past exercise data and predict the effectiveness of a specific exercise program. For example, the generation AI predicts the effectiveness of a specific exercise program based on past exercise data. The advice providing unit can also use the generation AI to learn the user's past stress data and predict the effectiveness of specific stress management measures. For example, the generation AI predicts the effectiveness of specific stress management measures based on past stress data. In this way, past health data can be learned and optimal preventive measures can be suggested.

[0058] The advice providing unit can use the emotion estimation function to suggest preventive measures according to the user's emotional state and provide advice that is easy to implement. For example, the advice providing unit can use the emotion estimation function to analyze the user's emotional state and suggest relaxation preventive measures if stress is high. For example, the generation AI can suggest meditation or deep breathing techniques. The advice providing unit can also analyze the user's emotional state and suggest preventive measures to elicit positive emotions. For example, the generation AI can suggest activities that allow the user to relax. The advice providing unit can also analyze the user's emotional state and suggest preventive measures based on the emotions. For example, the generation AI can suggest an exercise program to help the user reduce stress. This makes it possible to suggest preventive measures according to the user's emotional state and provide advice that is easy to implement.

[0059] The tracking unit uses the generation AI to track the user's lifestyle data over a long period of time and analyze changes in health status over time. For example, the generation AI tracks the user's lifestyle data over several years and analyzes changes in health status over time. For example, the generation AI predicts changes in weight based on long-term dietary data. The tracking unit can also track the user's exercise data over a long period of time and analyze changes in health status over time. For example, the generation AI predicts changes in cardiopulmonary function based on long-term exercise data. The tracking unit can also track the user's sleep data over a long period of time and analyze changes in health status over time. For example, the generation AI predicts changes in sleep quality based on long-term sleep data. This makes it possible to track lifestyle data over a long period of time and analyze changes in health status.

[0060] The tracking unit can promote self-assessment by comparing the user's lifestyle habit data with other users and providing a benchmark. In the tracking unit, for example, the generation AI compares the user's lifestyle habit data with other users and provides a benchmark. For example, the generation AI evaluates the amount of exercise compared to users of the same age. In addition, the tracking unit can also compare the user's dietary data with other users and provide a benchmark. For example, the generation AI evaluates the balance of the user's diet compared to users of the same age. In addition, the tracking unit can also compare the user's sleep data with other users and provide a benchmark. For example, the generation AI evaluates the quality of the user's sleep compared to users of the same age. This can promote self-assessment by comparing with other users.

[0061] The tracking unit can use the emotion estimation function to analyze the relationship between the user's emotional state and lifestyle habits and suggest lifestyle improvement measures based on the emotions. The tracking unit can, for example, use the emotion estimation function to analyze the relationship between the user's emotional state and eating habits and suggest eating improvement measures based on the emotions. For example, the generation AI can suggest meals that will help you relax when you are under high stress. The tracking unit can also analyze the relationship between the user's emotional state and exercise habits and suggest exercise improvement measures based on the emotions. For example, the generation AI can suggest exercises that will help you relax when you are under high stress. The tracking unit can also analyze the relationship between the user's emotional state and sleep habits and suggest sleep improvement measures based on the emotions. For example, the generation AI can suggest a sleeping environment that will help you relax when you are under high stress. This makes it possible to suggest lifestyle improvement measures based on the emotional state.

[0062] The warning unit can use the generation AI to monitor the user's health data in real time and issue an immediate warning when an abnormality is detected. For example, the generation AI can monitor the user's blood pressure data in real time and issue an immediate warning when an abnormal fluctuation is detected. For example, the generation AI can send an alert when blood pressure suddenly rises. The warning unit can also monitor the user's blood glucose level data in real time and issue an immediate warning when an abnormal fluctuation is detected. For example, the generation AI can send an alert when blood glucose levels suddenly rise. The warning unit can also monitor the user's heart rate data in real time and issue an immediate warning when an abnormal fluctuation is detected. For example, the generation AI can send an alert when the heart rate suddenly rises. This makes it possible to monitor health data in real time and issue an immediate warning when an abnormality is detected.

[0063] The warning unit can use the generation AI to analyze the user's health data, identify the cause of the abnormality, and propose specific countermeasures. For example, the generation AI can analyze the user's blood pressure data, identify the cause of abnormal fluctuations, and propose specific countermeasures. For example, the generation AI can suggest reviewing salt intake. The warning unit can also analyze the user's blood sugar level data, identify the cause of abnormal fluctuations, and propose specific countermeasures. For example, the generation AI can suggest reviewing dietary habits. The warning unit can also analyze the user's heart rate data, identify the cause of abnormal fluctuations, and propose specific countermeasures. For example, the generation AI can suggest reviewing exercise habits. This makes it possible to identify the cause of the abnormality and propose specific countermeasures.

[0064] The warning unit can use the emotion estimation function to analyze the user's emotional state and issue a warning that takes into account the impact of stress and anxiety on health. For example, the warning unit can use the emotion estimation function to analyze the user's emotional state and issue a stress management warning if stress is high. For example, the generation AI can suggest a relaxation method. The warning unit can also analyze the user's emotional state and issue an anxiety management warning if anxiety is high. For example, the generation AI can suggest a relaxation method. The warning unit can also analyze the user's emotional state and issue a warning that takes into account the impact of stress and anxiety on health. For example, the generation AI can suggest a relaxation method if stress or anxiety is high. In this way, a warning that takes the emotional state into account can be issued.

[0065] The warning unit can analyze the user's health data using the generating AI and automatically notify family members or medical professionals when an abnormality is detected. For example, the generating AI analyzes the user's blood pressure data and automatically notifies family members when an abnormal fluctuation is detected. For example, the generating AI sends an alert to family members when blood pressure suddenly rises. The warning unit can also analyze the user's blood glucose level data and automatically notify medical professionals when an abnormal fluctuation is detected. For example, the generating AI sends an alert to medical professionals when blood glucose levels suddenly rise. The warning unit can also analyze the user's heart rate data and automatically notify family members or medical professionals when an abnormal fluctuation is detected. For example, the generating AI sends an alert to family members or medical professionals when heart rate suddenly rises. This makes it possible to automatically notify family members or medical professionals when an abnormality is detected.

[0066] The warning unit uses the generation AI to analyze the user's health data, and when an abnormality is detected, it can compare it with past data to identify the abnormal pattern. For example, the generation AI analyzes the user's blood pressure data, and when an abnormal fluctuation is detected, it compares it with past data to identify the abnormal pattern. For example, the generation AI identifies the abnormality based on past blood pressure fluctuation patterns. The warning unit can also analyze the user's blood glucose level data, and when an abnormal fluctuation is detected, it can compare it with past data to identify the abnormal pattern. For example, the generation AI identifies the abnormality based on past blood glucose fluctuation patterns. The warning unit can also analyze the user's heart rate data, and when an abnormal fluctuation is detected, it can compare it with past data to identify the abnormal pattern. For example, the generation AI identifies the abnormality based on past heart rate fluctuation patterns. In this way, when an abnormality is detected, it can compare it with past data to identify the abnormal pattern.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The preventive medicine promotion system can further analyze a user's social network data to evaluate the impact of social connections on health. For example, the analysis unit analyzes a user's social media data and evaluates the strength and frequency of social connections. The generative AI can predict mental health risks if the user has a high sense of loneliness. The analysis unit can also analyze the frequency of communication with the user's friends and family and predict stress risks if social support is lacking. Furthermore, the analysis unit can analyze data on interpersonal relationships at the user's workplace and predict heart disease risks if workplace stress is high. This makes it possible to make risk predictions that take social connections into account.

[0069] The preventive medicine promotion system can further analyze the user's sleep data and evaluate the impact of sleep quality on health. For example, the analysis unit analyzes the user's sleep patterns and predicts the risk of a weakened immune system if the user continues to sleep poorly. The generation AI can predict mental health risks if the user's sleep quality is poor. The analysis unit can also predict the risk of sleep apnea syndrome based on the user's sleep data. Furthermore, the analysis unit can analyze the user's sleep data and predict the risk of diabetes if the user continues to sleep for long periods of time. This makes it possible to make risk predictions that take sleep data into account.

[0070] The preventive medicine promotion system can further analyze the user's dietary data and evaluate the impact of nutritional balance on health. For example, the analysis unit analyzes the user's diet and predicts the risk of nutritional deficiency if the nutritional balance is unbalanced. The generative AI can predict the risk of osteoporosis if there is a deficiency of specific nutrients. The analysis unit can also predict the risk of obesity if excessive calorie intake continues based on the user's dietary data. Furthermore, the analysis unit can analyze the user's dietary data and predict the risk of allergies if there is a high intake of specific food groups. This makes it possible to make risk predictions that take dietary data into account.

[0071] The preventive medicine promotion system can further analyze the user's exercise data and evaluate the impact of exercise habits on health. For example, the analysis unit analyzes the user's exercise patterns and predicts the risk of heart disease if the user continues to exercise less. The generative AI can predict mental health risks if the user exercises less frequently. The analysis unit can also predict the risk of joint disorders if the user continues to exercise excessively based on the user's exercise data. Furthermore, the analysis unit can analyze the user's exercise data and predict the risk of muscle damage if the user does certain types of exercise frequently. This makes it possible to make risk predictions that take exercise data into account.

[0072] The preventive medicine promotion system can further analyze the user's environmental data and evaluate the impact of environmental factors on health. For example, the analysis unit analyzes air quality data in the user's residence and predicts the risk of respiratory disease if the air pollution is high. The generation AI can analyze noise data in the user's residence and predict the risk of stress if the noise level is high. The analysis unit can also predict the risk of digestive disease if the water quality is poor based on water quality data in the user's residence. Furthermore, the analysis unit can analyze temperature data in the user's residence and predict the risk of heatstroke if extreme temperatures persist. This makes it possible to make risk predictions that take environmental data into account.

[0073] The preventive medicine promotion system can further analyze the user's emotional state and evaluate the impact of emotions on health. For example, the analysis unit analyzes the user's voice data and evaluates stress levels using an emotion estimation function, and the generation AI predicts health risks based on that data. The generation AI can evaluate the user's stress level using voice analysis technology and predict heart disease risk if stress is high. The analysis unit can also analyze the user's facial expression data and evaluate anxiety levels using an emotion estimation function, and predict depression risk if anxiety is high. Furthermore, the analysis unit can analyze the user's biometric data (heart rate and electrodermal activity), evaluate stress levels using an emotion estimation function, and predict high blood pressure risk if stress is high. This makes it possible to make risk predictions that take emotional states into account.

[0074] The preventive medicine promotion system can further analyze the user's emotional state and provide health advice based on the emotion. For example, the advice providing unit can analyze the user's emotional state using the emotion estimation function and provide relaxation advice if the user is under high stress. The generation AI can suggest meditation or deep breathing techniques. The advice providing unit can also analyze the user's emotional state and provide advice to elicit positive emotions. For example, the generation AI can suggest activities that will help the user relax. The advice providing unit can also analyze the user's emotional state and provide health advice based on the emotion. For example, the generation AI can suggest an exercise program to help the user reduce stress. This makes it possible to provide health advice according to the user's emotional state.

[0075] The preventive medicine promotion system can further analyze the user's emotional state and issue a health risk warning based on the emotion. For example, the warning unit uses the emotion estimation function to analyze the user's emotional state, and if stress is high, issues a stress management warning. The generation AI can suggest relaxation methods. The warning unit can also analyze the user's emotional state and issue an anxiety management warning if anxiety is high. For example, the generation AI can suggest relaxation methods. The warning unit can also analyze the user's emotional state and issue a warning that takes into account the impact of stress and anxiety on health. For example, if stress or anxiety is high, the generation AI can suggest relaxation methods. This makes it possible to issue a warning that takes into account the emotional state.

[0076] The preventive medicine promotion system can further analyze the user's emotional state and track health data based on emotions. For example, the tracking unit can analyze the user's emotional state using an emotion estimation function and suggest lifestyle improvement measures based on emotions. The generation AI can suggest meals that will help the user relax when stress is high. The tracking unit can also analyze the relationship between the user's emotional state and exercise habits and suggest exercise improvement measures based on emotions. For example, the generation AI can suggest exercises that will help the user relax when stress is high. The tracking unit can also analyze the relationship between the user's emotional state and sleep habits and suggest sleep improvement measures based on emotions. For example, the generation AI can suggest a sleeping environment that will help the user relax when stress is high. This makes it possible to suggest lifestyle improvement measures based on emotional states.

[0077] The preventive medicine promotion system can further analyze the user's emotional state and provide health data based on the emotion. For example, the health information provision unit can analyze the user's emotional state using an emotion estimation function and provide an interface for eliciting positive emotions. The generation AI can display an encouraging message when the user enters health information, thereby eliciting positive emotions. The health information provision unit can also provide an interface for the generation AI to analyze the user's emotional state and elicit positive emotions. For example, the generation AI can play relaxing music when the user enters health information, thereby eliciting positive emotions. The health information provision unit can also provide an interface for the generation AI to analyze the user's emotional state and elicit positive emotions. For example, the generation AI can provide positive feedback when the user enters health information, thereby eliciting positive emotions. This can elicit positive emotions when the user provides health information.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The health information provider provides health information such as the user's genetic information, lifestyle habits, medical history, etc. For example, the user inputs the results of a genetic test, daily diet, exercise habits, past medical history, etc. Step 2: The analysis unit uses a generation AI to analyze the health information provided by the health information provision unit. For example, the generation AI may analyze the health information using a text generation AI (e.g., LLM). The generation AI may also analyze the health information using a multimodal generation AI. The generation AI may also analyze the health information using statistical analysis or machine learning algorithms. Step 3: The prediction unit predicts hereditary disease risk and future health impacts based on the results of the analysis by the analysis unit. For example, the generation AI predicts disease risk based on the presence or absence of genetic mutations and risk scores. The generation AI can also predict future health impacts using simulation models and prediction algorithms. Step 4: The advice provider provides individualized preventive measures and risk management advice based on the results predicted by the prediction provider. For example, the generation AI may provide advice on dietary guidance, exercise plans, and the importance of regular health checks. The generation AI may also provide suggestions for improving lifestyle habits and risk management advice. Step 5: The tracking unit tracks lifestyle and health data based on the advice provided by the advice provider. For example, the user inputs a record of their daily diet and exercise, and the generating AI analyzes the data and evaluates their progress toward achieving their preventive medical goals. The generating AI provides feedback on how well the user has achieved their set goals and modifies the action plan as necessary. Step 6: The alerting unit detects changes in important health information based on the data tracked by the tracking unit and issues a warning. For example, if the generating AI detects abnormal fluctuations in blood pressure or blood sugar levels, it will notify the user of the fluctuations and recommend consulting a specialist or undergoing further evaluation.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The 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.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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]

[0147] 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 information providing unit that provides health information such as the user's genetic information, lifestyle habits, and medical history; an analysis unit that analyzes the health information provided by the health information providing unit; a prediction unit that predicts the risk of hereditary diseases and future health effects based on the results of the analysis by the analysis unit; an advice providing unit that provides advice on individual preventive measures and risk management based on the results predicted by the prediction unit; a tracking unit that tracks lifestyle habits and health data based on the advice provided by the advice providing unit; and a warning unit that detects changes in important health information based on the data tracked by the tracking unit and issues a warning. A system characterized by:

2. The analysis unit Combining the genetic information with lifestyle data will allow for more precise risk prediction by analyzing the interactions between specific environmental and genetic factors.

2. The system of claim 1.

3. The health information providing unit: By including the health history of the family in the health information provided by the user, the health risks of the entire family are analyzed and the preventive measures are proposed for each family.

2. The system of claim 1.

4. The advice providing unit Using a generative AI to learn the user's past health data, predict the effectiveness of individual preventive measures, and propose the optimal preventive measures.

2. The system of claim 1.

5. The tracking unit Generative AI is used to track the user's lifestyle data over a long period of time and analyze changes in health status over time.

2. The system of claim 1.

6. The warning unit Using generative AI to monitor the user's health data in real time and issue the alert immediately when an abnormality is detected.

2. The system of claim 1.

7. The analysis unit Analyzing the emotional state of the user and making risk predictions that take into account the impact of stress and anxiety on health.

2. The system of claim 1.

8. The health information providing unit: Analyzing the emotional state of the user when providing the health information and designing an interface to elicit positive emotions.

2. The system of claim 1.

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