System

Abstract

An object of the system according to the embodiment is to provide an optimal health method based on individual genetic factors and lifestyle, and to promote preventive life.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a provision unit, a monitoring unit, and an assignment unit. The collection unit collects genetic elements and lifestyle data. The analysis unit analyzes the data collected by the collection unit and specifies a disease to be prevented. The provision part teaches the health method specified by the analysis part to the subscriber. The monitoring unit monitors an execution status of the subscriber who takes the preventive life on the basis of the health method taught by the providing unit. The awarding unit awards bonus points based on the execution status monitored by the monitoring unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not provided sufficient preventive measures based on individual genetic factors and lifestyles, resulting in low efficiency of health management.

[0005] The system according to the embodiment aims to provide optimal health regimens based on individual genetic factors and lifestyles, and to promote preventive lifestyles. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a monitoring unit, and an awarding unit. The collection unit collects data on genetic factors and lifestyle. The analysis unit analyzes the data collected by the collection unit and identifies diseases that should be prevented. The provision unit instructs the subscriber on the health methods identified by the analysis unit. The monitoring unit monitors the subscriber's implementation status of a preventive lifestyle based on the health methods instructed by the provision unit. The awarding unit awards bonus points based on the implementation status monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal health regimens based on individual genetic factors and lifestyles, and promote preventative living. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A health management system according to an embodiment of the present invention provides personalized health practices based on genetic factors and lifestyle, promoting the health of subscribers and improving the financial health of health insurance associations. The health management system collects genetic and lifestyle data, analyzes it using AI, identifies diseases to be prevented, and health practices for those diseases, and then teaches them to subscribers. Subscribers follow the health practices and receive bonus points if they are healthy. For example, the health management system collects data on each subscriber's genetic factors and lifestyle. For example, genetic information includes family history and genetic test results, while lifestyle information includes dietary habits and exercise frequency. AI then analyzes the collected data to identify diseases to be prevented. For example, if a subscriber has a high genetic risk of diabetes or an unhealthy lifestyle, it identifies diabetes prevention practices. The identified health practices are then taught to subscribers. For example, specific health practices, such as dietary improvements and exercise recommendations, are taught to subscribers. Subscribers then follow the health practices and receive bonus points from the health insurance association if they are healthy and implement the preventive lifestyle practices. For example, if no abnormalities are found in a regular health checkup or if a member continues to live a preventive lifestyle, bonus points are awarded. This increases the member's motivation and encourages them to live a preventive lifestyle. On the other hand, if a member becomes ill without living a preventive lifestyle, the full amount of insurance premiums will not be paid. For example, if a member becomes ill as a result of ignoring the health methods they have been taught and continuing an unhealthy lifestyle, part of the insurance premium will not be paid. This provides an incentive for members and encourages them to live a preventive lifestyle. As a result, the health management system improves the financial health of the health insurance association and reduces overall medical expenses. As a result, the health management system can reduce the risk of developing disease and reduce medical expenses by encouraging each member to live an appropriate preventive lifestyle. For example, by practicing diabetes prevention methods, the onset of diabetes can be prevented and medical expenses can be reduced.

[0029] A health management system according to an embodiment includes a collection unit, an analysis unit, a provision unit, a monitoring unit, and an assignment unit. The collection unit collects data on genetic factors and lifestyles. The genetic factors include, for example, genetic markers and gene mutations. The lifestyle data includes, for example, dietary habits, exercise habits, and sleep patterns. The collection unit can collect data using, for example, a genetic testing kit or a smartphone app. For example, a genetic testing kit is used to collect saliva samples and analyze the genetic information. The collection unit can also collect dietary and exercise data through a smartphone app. The analysis unit analyzes the data collected by the collection unit and identifies diseases that should be prevented. The analysis unit can identify diseases that should be prevented from past data, for example, using a machine learning algorithm. For example, the analysis unit can analyze genetic factors and lifestyle data using deep learning to assess disease risk. The analysis unit can also classify disease risk using a decision tree. The provision unit instructs subscribers on the health practices identified by the analysis unit. The provision unit can provide specific health practices through, for example, a smartphone app. For example, health techniques such as dietary therapy and exercise programs are provided using a notification function. The providing unit can also provide the health techniques through video tutorials. The monitoring unit monitors the implementation status of the subscriber who practices a preventive lifestyle based on the health techniques taught by the providing unit. The monitoring unit, for example, periodically monitors the implementation status of the subscriber's health techniques. For example, the implementation status is grasped through weekly check-ins or self-reports. The monitoring unit can also monitor the implementation status through remote monitoring. The awarding unit awards bonus points based on the implementation status monitored by the monitoring unit. For example, the awarding unit awards bonus points if no abnormalities are found in regular health checkups or if the subscriber continues to practice a preventive lifestyle. For example, bonus points are awarded if no abnormalities are found in annual health checkups. The awarding unit can also award bonus points if no abnormalities are found in semi-annual blood tests.As a result, the health management system according to the embodiment can provide individualized health regimens based on genetic factors and lifestyle, promote the health of subscribers, and improve the financial health of health insurance associations.

[0030] The collection unit can collect data using a genetic testing kit or a smartphone app. Genetic testing kits include, for example, saliva samples and blood samples. The collection unit, for example, collects saliva samples using a genetic testing kit and analyzes genetic information. The collection unit can also collect data through a smartphone app. For example, the smartphone app can be used to input meal details and exercise frequency and transmit the data. This allows efficient data collection by using a genetic testing kit or a smartphone app. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data collected by the smartphone app into AI and have the AI ​​analyze the data.

[0031] The analysis unit can use a machine learning algorithm to identify diseases that should be prevented from past data. Examples of machine learning algorithms include deep learning and decision trees. The analysis unit can, for example, use deep learning to analyze genetic factors and lifestyle data and assess disease risk. The analysis unit can also classify disease risk using decision trees. For example, deep learning learns from large amounts of data and has advanced pattern recognition capabilities. Decision trees hierarchically classify data and improve prediction accuracy. As a result, diseases that should be prevented can be identified with high accuracy using machine learning algorithms. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the data collected by the collection unit into AI and have the AI ​​perform disease risk assessment.

[0032] The providing unit can provide specific health practices through a smartphone app. The smartphone app includes, for example, a notification function and an interface. The providing unit can provide, for example, health practices such as dietary therapy and exercise programs using the smartphone app. The providing unit can also provide health practices through video tutorials. For example, the notification function can be used to provide health practices periodically, allowing subscribers to easily access them. The interface is user-friendly and intuitive to operate. This allows subscribers to easily access health practices by providing them through the smartphone app. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can use AI to recommend the health practices that are best suited to the subscriber.

[0033] The monitoring unit can periodically monitor the implementation status of the health practice. Periodically includes, for example, daily, weekly, or monthly. The monitoring unit grasps the implementation status, for example, through weekly check-ins or self-reports. The monitoring unit can also monitor the implementation status through remote monitoring. For example, the monitoring unit can check the implementation status of the health practice through daily check-ins, evaluate the implementation status through weekly self-reports, and monitor the implementation status in detail through monthly remote monitoring. This allows the subscriber's implementation status of the health practice to be grasped through regular monitoring. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can use AI to analyze the subscriber's implementation status in real time and provide feedback.

[0034] The awarding unit can award bonus points if no abnormalities are found in a regular health checkup or if the subscriber continues a preventive lifestyle. Periodic health checkups include, for example, annual health checkups and semi-annual blood tests. Preventive lifestyles include, for example, dietary restrictions and exercise programs. The awarding unit awards bonus points, for example, if no abnormalities are found in a regular health checkup. The awarding unit can also award bonus points if no abnormalities are found in a semi-annual blood test. For example, bonus points are awarded if the subscriber continues a dietary restriction. Bonus points are awarded if the subscriber is currently performing an exercise program. As a result, the awarding of bonus points increases the subscriber's motivation to continue a preventive lifestyle. Some or all of the above-described processing in the awarding unit may be performed using, for example, AI, or may be performed without AI. For example, the awarding unit can use AI to evaluate the subscriber's performance and automatically award bonus points.

[0035] The collection unit can analyze the frequency of use of the genetic testing kit and select the optimal collection method. For example, the collection unit can send reminders to users who use the genetic testing kit frequently to collect data regularly. The collection unit can also provide easy-to-use guides to users who use the kit infrequently, lowering the hurdle of data collection. The collection unit can also customize the data collection method according to the frequency of use and suggest the optimal collection method to the user. For example, the collection unit can send regular reminders to users who use the genetic testing kit frequently to encourage data collection. For users who use the kit infrequently, the collection unit can provide easy-to-use guides to lower the hurdle of data collection. The collection unit can customize the data collection method according to the frequency of use and suggest the optimal collection method to the user. This improves the efficiency of data collection by selecting the optimal collection method according to the frequency of use. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without AI. For example, the collection unit can input data on the frequency of use of the genetic testing kit into AI and have the AI ​​select the optimal collection method.

[0036] The collection unit can filter data based on the user's current health condition and lifestyle habits when collecting data. For example, when the user inputs their current health condition, the collection unit selects data to collect based on that information. The collection unit can also collect only relevant data based on the user's lifestyle habits (e.g., diet, exercise). The collection unit can also dynamically adjust the data to be collected in response to changes in the user's health condition. For example, when the user inputs their current health condition, the collection unit selects data to be collected based on that information. Only relevant data is collected based on the user's lifestyle habits. In response to changes in the user's health condition, the collection unit dynamically adjusts the data to be collected in response to those changes. In this way, highly relevant data can be collected by filtering data based on the user's health condition and lifestyle habits. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's health condition data into AI and have the AI ​​perform the filtering.

[0037] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user selects voice input, the collection unit collects data using voice recognition technology. Furthermore, if the user selects text input, the collection unit can also collect data using text analysis technology. Furthermore, if the user selects image input, the collection unit can also collect data using image analysis technology. For example, if the user selects voice input, the collection unit collects data using voice recognition technology. If the user selects text input, the collection unit collects data using text analysis technology. If the user selects image input, the collection unit collects data using image analysis technology. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit inputs the user's input data into AI and has the AI ​​select the optimal collection means.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting data related to health risks in that area. Furthermore, if the user is traveling, the collection unit can also collect data related to health risks at the travel destination. Furthermore, the collection unit can collect data related to region-specific health risks based on the user's place of residence. For example, if the user is in a specific area, the collection unit prioritizes collecting data related to health risks in that area. If the user is traveling, the collection unit collects data related to health risks at the travel destination. Data related to region-specific health risks is collected based on the user's place of residence. This allows for efficient collection of highly relevant data by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location information into AI and have the AI ​​select highly relevant data.

[0039] The collection unit can analyze the user's social media activities and collect relevant data when collecting data. For example, the collection unit collects relevant data based on health information shared by the user on social media. The collection unit can also collect data related to health risks from the user's social media activities. The collection unit can also collect relevant data by referring to the activities of the user's friends on social media. For example, the collection unit collects relevant data based on health information shared by the user on social media. Data related to health risks is collected from the user's social media activities. Relevant data is collected by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into AI and have the AI ​​collect the relevant data.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit, for example, improves the collection method based on feedback provided by the user in the past. The collection unit can also select the optimal collection means from the user's past feedback. The collection unit can also customize the collection process by reflecting the user's feedback. For example, the collection method is improved based on feedback provided by the user in the past. The optimal collection means is selected from the user's past feedback. The collection process is customized by reflecting the user's feedback. In this way, the collection method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into AI and have the AI ​​customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the disease. For example, the analysis unit performs a detailed analysis for a disease with a high importance. The analysis unit can also perform a simplified analysis for a disease with a low importance. The analysis unit can also determine the priority of the analysis according to the importance of the disease. For example, a detailed analysis is performed for a disease with a high importance. A simplified analysis is performed for a disease with a low importance. The priority of the analysis is determined according to the importance of the disease. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the disease. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input disease importance data into AI and have the AI ​​adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the disease category. For example, the analysis unit applies a dedicated analysis algorithm to cardiovascular disease. The analysis unit can also apply a dedicated analysis algorithm to diabetes. The analysis unit can also apply a dedicated analysis algorithm to cancer. For example, a dedicated analysis algorithm is applied to cardiovascular disease. A dedicated analysis algorithm is applied to diabetes. A dedicated analysis algorithm is applied to cancer. In this way, highly accurate analysis is possible by applying an analysis algorithm according to the disease category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input disease category data into AI and have the AI ​​select an appropriate analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit improves the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also optimize the analysis algorithm based on the user's past analysis results. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. For example, the analysis unit improves the accuracy of the current analysis based on the user's past analysis results. The analysis unit optimizes the analysis algorithm based on the user's past analysis results. The analysis unit adjusts the level of detail of the analysis by referring to the user's past analysis results. In this way, the accuracy of the current analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into AI and have the AI ​​improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, if data is submitted early, the analysis unit prioritizes analysis. Furthermore, if data is submitted late, the analysis unit can postpone analysis. Furthermore, the analysis unit can adjust the analysis schedule according to the time of data submission. For example, if data is submitted early, the analysis is prioritized. If data is submitted late, the analysis is postponed. The analysis schedule is adjusted according to the time of data submission. In this way, efficient analysis is possible by determining the priority of analysis based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of data submission into AI and have the AI ​​determine the priority of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. For example, highly relevant data is analyzed prioritized. Less relevant data is analyzed later. The analysis order is dynamically adjusted according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of the data into AI and have the AI ​​adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit provides analysis results in simple language to a user with little expertise. The analysis unit can also provide analysis results using detailed technical terms to a user with extensive expertise. The analysis unit can also adjust the way in which the analysis results are expressed according to the user's level of expertise. For example, the analysis unit provides analysis results in simple language to a user with little expertise. The analysis unit provides analysis results using detailed technical terms to a user with extensive expertise. The way in which the analysis results are expressed is adjusted according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into AI and have the AI ​​adjust the use of technical terms in the analysis.

[0047] When providing a health regimen, the providing unit can adjust the level of detail of the provision based on the disease prevention effect. For example, the providing unit provides detailed explanations for health regimens with high preventive effect. The providing unit can also provide simplified explanations for health regimens with low preventive effect. The providing unit can also determine the priority of provision based on the preventive effect. For example, detailed explanations are provided for health regimens with high preventive effect. Simplified explanations are provided for health regimens with low preventive effect. The priority of provision is determined based on the preventive effect. This enables the provision of efficient health regimens by adjusting the level of detail of provision based on the disease prevention effect. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input preventive effect data into AI and have the AI ​​adjust the level of detail of provision.

[0048] When providing health regimens, the providing unit can apply different providing algorithms depending on the disease category. For example, the providing unit applies a dedicated providing algorithm to cardiovascular disease. The providing unit can also apply a dedicated providing algorithm to diabetes. The providing unit can also apply a dedicated providing algorithm to cancer. For example, a dedicated providing algorithm is applied to cardiovascular disease. A dedicated providing algorithm is applied to diabetes. A dedicated providing algorithm is applied to cancer. In this way, by applying a providing algorithm according to the disease category, it is possible to provide highly accurate health regimens. Some or all of the above-mentioned processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input disease category data into AI and have the AI ​​select an appropriate providing algorithm.

[0049] When providing a health regimen, the providing unit can improve the accuracy of the provision by referring to the user's past health regimen implementation results. For example, the providing unit improves the accuracy of the current provision based on the user's past health regimen implementation results. The providing unit can also optimize the provision algorithm based on the user's past health regimen implementation results. The providing unit can also adjust the level of detail of the provision by referring to the user's past health regimen implementation results. For example, the providing unit improves the accuracy of the current provision based on the user's past health regimen implementation results. The providing unit optimizes the provision algorithm based on the user's past health regimen implementation results. The providing unit adjusts the level of detail of the provision by referring to the user's past health regimen implementation results. This improves the accuracy of the current provision by referring to the past health regimen implementation results. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past health regimen implementation result data into AI and have the AI ​​improve the accuracy of the provision.

[0050] When providing health tips, the providing unit can adjust the timing of the provision based on the user's lifestyle rhythm. For example, if the user is a morning person, the providing unit can provide the health tips in the morning. Furthermore, if the user is a night owl, the providing unit can also provide the health tips in the evening. Furthermore, the providing unit can provide the health tips at the optimal timing according to the user's lifestyle rhythm. For example, if the user is a morning person, the providing unit can provide the health tips in the morning. If the user is a night owl, the providing unit can provide the health tips in the evening. The health tips are provided at the optimal timing according to the user's lifestyle rhythm. This allows for the provision of health tips efficiently by adjusting the timing of the provision based on the user's lifestyle rhythm. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's lifestyle rhythm data into AI and have the AI ​​adjust the timing of the provision.

[0051] When providing health tips, the providing unit can select the optimal providing method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide the health tips through an app. Furthermore, if the user is using a tablet, the providing unit can provide health tips optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can provide concise and highly visible health tips. For example, if the user is using a smartphone, the providing unit can provide the health tips through an app. If the user is using a tablet, the providing unit can provide health tips optimized for a large screen. If the user is using a smartwatch, the providing unit can provide concise and highly visible health tips. This enables the provision of efficient health tips by taking into account the user's device information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information into AI and have the AI ​​select the optimal providing method.

[0052] When providing health tips, the providing unit can make the provided content multilingual according to the user's language setting. The providing unit, for example, automatically sets the language of the health tips based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the health tips in a specific language when the user selects that language. For example, the providing unit automatically sets the language of the health tips based on the language setting of the user's device. The providing unit can provide a language switching function when the user uses multiple languages. When the user selects a specific language, the health tips are provided in that language. This enables efficient provision of health tips by making the provided content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's language setting data into AI and have the AI ​​select a method for making the provided content multilingual.

[0053] During monitoring, the monitoring unit can select the optimal monitoring method by referring to the user's past health practice history. The monitoring unit, for example, optimizes the current monitoring method based on the user's past health practice history. The monitoring unit can also adjust the monitoring frequency based on the user's past health practice history. The monitoring unit can also adjust the level of monitoring detail by referring to the user's past health practice history. For example, the current monitoring method can be optimized based on the user's past health practice history. The monitoring frequency can be adjusted based on the user's past health practice history. The level of monitoring detail can be adjusted by referring to the user's past health practice history. In this way, the optimal monitoring method can be selected by referring to the past health practice history. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past health practice history data into AI and have the AI ​​select the optimal monitoring method.

[0054] During monitoring, the monitoring unit can customize the monitoring means based on the user's current living situation. For example, if the user is busy, the monitoring unit provides simplified monitoring means. Furthermore, if the user is relaxed, the monitoring unit can also provide detailed monitoring means. Furthermore, the monitoring unit can select the optimal monitoring means according to the user's living situation. For example, if the user is busy, the monitoring unit provides simplified monitoring means. If the user is relaxed, the monitoring unit provides detailed monitoring means. The optimal monitoring means is selected according to the user's living situation. This enables efficient monitoring by customizing the monitoring means based on the user's living situation. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or without AI. For example, the monitoring unit can input the user's living situation data into AI and have the AI ​​customize the monitoring means.

[0055] The monitoring unit can improve the monitoring method by reflecting user feedback during monitoring. The monitoring unit improves the monitoring method, for example, based on feedback provided by the user. The monitoring unit can also select the optimal monitoring means based on the user feedback. The monitoring unit can also adjust the level of monitoring detail by reflecting the user feedback. For example, the monitoring unit improves the monitoring method based on feedback provided by the user. The optimal monitoring means is selected based on the user feedback. The level of monitoring detail is adjusted by reflecting the user feedback. In this way, the monitoring method can be optimized by reflecting the user feedback. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input user feedback data into AI and have the AI ​​improve the monitoring method.

[0056] During monitoring, the monitoring unit can select the optimal monitoring method by taking into account the user's geographical location information. For example, if the user is in a specific area, the monitoring unit prioritizes monitoring related to health risks in that area. Furthermore, if the user is traveling, the monitoring unit can also perform monitoring related to health risks at the travel destination. Furthermore, the monitoring unit can perform monitoring related to health risks specific to the area based on the user's place of residence. For example, if the user is in a specific area, the monitoring unit prioritizes monitoring related to health risks in that area. If the user is traveling, the monitoring unit performs monitoring related to health risks at the travel destination. Based on the user's place of residence, the monitoring unit performs monitoring related to health risks specific to the area. This enables efficient monitoring by taking the user's geographical location information into account. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's geographical location information into AI and have the AI ​​select the optimal monitoring method.

[0057] During monitoring, the monitoring unit can analyze the user's social media activities and suggest monitoring measures. The monitoring unit can, for example, suggest relevant monitoring measures based on health information shared by the user on social media. The monitoring unit can also suggest monitoring measures related to health risks based on the user's social media activities. The monitoring unit can also suggest relevant monitoring measures based on the activities of the user's friends on social media. For example, the monitoring unit can suggest relevant monitoring measures based on health information shared by the user on social media. The monitoring unit can suggest monitoring measures related to health risks based on the user's social media activities. The monitoring unit can suggest relevant monitoring measures based on the activities of the user's friends on social media. This enables efficient monitoring by analyzing social media activities. Some or all of the above-mentioned processing in the monitoring unit can be performed, for example, using AI, or can be performed without using AI. For example, the monitoring unit can input the user's social media data into AI and have the AI ​​suggest monitoring measures.

[0058] During monitoring, the monitoring unit can customize the monitoring method by reflecting the user's past feedback. The monitoring unit, for example, improves the monitoring method based on feedback provided by the user in the past. The monitoring unit can also select the optimal monitoring means based on the user's past feedback. The monitoring unit can also adjust the level of monitoring detail by reflecting the user's feedback. For example, the monitoring unit improves the monitoring method based on feedback provided by the user in the past. The optimal monitoring means is selected based on the user's past feedback. The level of monitoring detail is adjusted by reflecting the user's feedback. In this way, the monitoring method can be optimized by reflecting the user's feedback. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's feedback data into AI and have the AI ​​customize the monitoring method.

[0059] When awarding bonus points, the awarding unit can select the optimal awarding method by referring to the user's past health checkup results. For example, the awarding unit optimizes the current bonus point awarding method based on the user's past health checkup results. The awarding unit can also adjust the frequency of bonus point awarding based on the user's past health checkup results. The awarding unit can also adjust the level of detail of bonus points by referring to the user's past health checkup results. For example, the current bonus point awarding method can be optimized based on the user's past health checkup results. The frequency of bonus point awarding can be adjusted based on the user's past health checkup results. The level of detail of bonus points can be adjusted by referring to the user's past health checkup results. In this way, the optimal bonus point awarding method can be selected by referring to the past health checkup results. Some or all of the above-mentioned processing in the awarding unit may be performed using, for example, AI, or may be performed without using AI. For example, the awarding unit can input the user's past health checkup result data into AI and have the AI ​​select the optimal bonus point awarding method.

[0060] When awarding bonus points, the awarding unit can customize the awarding method based on the user's current living situation. For example, if the user is busy, the awarding unit provides a simplified bonus point awarding method. Furthermore, if the user is relaxed, the awarding unit can provide a detailed bonus point awarding method. Furthermore, the awarding unit can select an optimal bonus point awarding method according to the user's living situation. For example, if the user is busy, the awarding unit provides a simplified bonus point awarding method. If the user is relaxed, the awarding unit provides a detailed bonus point awarding method. The optimal bonus point awarding method is selected according to the user's living situation. This enables efficient awarding of bonus points by customizing the awarding method based on the user's living situation. Some or all of the above-described processing in the awarding unit may be performed, for example, using AI or without AI. For example, the awarding unit may input the user's living situation data into AI and have the AI ​​customize the awarding method.

[0061] The awarding unit can improve the method of awarding bonus points by reflecting user feedback when awarding bonus points. The awarding unit improves the method of awarding bonus points based on, for example, feedback provided by the user. The awarding unit can also select an optimal means of awarding bonus points based on the user feedback. The awarding unit can also adjust the level of detail of awarding bonus points by reflecting the user feedback. For example, the awarding unit improves the method of awarding bonus points based on feedback provided by the user. The optimal means of awarding bonus points is selected based on the user feedback. The level of detail of awarding bonus points is adjusted by reflecting the user feedback. In this way, the method of awarding bonus points can be optimized by reflecting the user feedback. Some or all of the above-described processing in the awarding unit may be performed using, for example, AI, or may be performed without using AI. For example, the awarding unit inputs user feedback data into AI and causes the AI ​​to improve the awarding method.

[0062] When awarding bonus points, the awarding unit can select the optimal awarding method by taking into account the user's geographical location information. For example, if the user is in a specific area, the awarding unit prioritizes awarding bonus points related to health risks in that area. Furthermore, if the user is traveling, the awarding unit can also award bonus points related to health risks at the travel destination. Furthermore, the awarding unit can also award bonus points related to health risks specific to the area based on the user's place of residence. For example, if the user is in a specific area, the awarding unit prioritizes awarding bonus points related to health risks in that area. If the user is traveling, the awarding unit awards bonus points related to health risks at the travel destination. Bonus points related to health risks specific to the area are awarded based on the user's place of residence. This enables efficient awarding of bonus points by taking into account the user's geographical location information. Some or all of the above-described processing by the awarding unit may be performed using, for example, AI, or may be performed without AI. For example, the awarding unit can input the user's geographical location information into AI and have the AI ​​select the optimal awarding method.

[0063] When awarding bonus points, the awarding unit can analyze the user's social media activity and suggest a means of awarding. For example, the awarding unit can suggest a relevant means of awarding bonus points based on health information shared by the user on social media. The awarding unit can also suggest a means of awarding bonus points related to health risks based on the user's social media activity. The awarding unit can also suggest a relevant means of awarding bonus points based on the activity of the user's friends on social media. For example, the awarding unit can suggest a relevant means of awarding bonus points based on health information shared by the user on social media. The awarding unit can suggest a means of awarding bonus points related to health risks based on the user's social media activity. The awarding unit can suggest a relevant means of awarding bonus points based on the activity of the user's friends on social media. This enables efficient awarding of bonus points by analyzing social media activity. Some or all of the above-described processing by the awarding unit may be performed using, for example, AI, or may be performed without using AI. For example, the awarding unit can input the user's social media data into AI and have the AI ​​suggest an awarding means.

[0064] When awarding bonus points, the awarding unit can customize the awarding method by reflecting the user's past feedback. The awarding unit can improve the bonus point awarding method based on, for example, feedback provided by the user in the past. The awarding unit can also select an optimal bonus point awarding means based on the user's past feedback. The awarding unit can also adjust the level of detail of bonus point awarding by reflecting the user's feedback. For example, the awarding unit can improve the bonus point awarding method based on feedback provided by the user in the past. The optimal bonus point awarding means can be selected based on the user's past feedback. The level of detail of bonus point awarding can be adjusted by reflecting the user's feedback. In this way, the method of awarding bonus points can be optimized by reflecting the user's feedback. Some or all of the above-mentioned processing in the awarding unit can be performed using, for example, AI, or can be performed without using AI. For example, the awarding unit can input user feedback data into AI and have the AI ​​customize the awarding method.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The health management system may further include a communication unit. The communication unit enables two-way communication between the subscriber and a medical professional. For example, if the subscriber has a question about a health regimen, the subscriber can directly ask the medical professional through the communication unit. The medical professional can also provide personalized advice based on the subscriber's health condition. Furthermore, the communication unit can send regular encouraging messages to the subscriber to maintain motivation when implementing the health regimen. This allows the subscriber to strengthen cooperation with the medical professional and implement the health regimen more effectively.

[0067] The health management system may further include a reporting unit. The reporting unit periodically generates reports on the subscriber's health status and the implementation status of health regimens, and provides these to the subscriber and medical professionals. For example, a monthly report may detail changes in the subscriber's health status and the implementation status of health regimens. The reporting unit may also issue alerts if an abnormality is detected. Furthermore, the reporting unit may visualize the subscriber's progress toward goals set by the subscriber and provide feedback to maintain motivation. This allows the subscriber to understand their own health status and take appropriate measures.

[0068] The health management system may further include a reminder unit. The reminder unit sets reminders for the subscriber to practice health practices and notifies them at appropriate times. For example, it may remind the subscriber to exercise or eat daily. The reminder unit may also adjust the timing of reminders based on the subscriber's schedule. Furthermore, the reminder unit may send a follow-up notification if the subscriber ignores a reminder. This makes it easier for the subscriber to continue practicing health practices.

[0069] The health management system may further include an incentive section. The incentive section provides rewards that can be earned by subscribers who practice health practices. For example, points may be awarded if subscribers continue practicing health practices for a certain period of time, and the points may be exchanged for products or services. The incentive section may also provide special rewards if subscribers achieve their set goals. Furthermore, the incentive section may introduce a system for subscribers to compete with each other to increase motivation. This may increase the subscribers' motivation to practice health practices.

[0070] The health management system may further include an education department. The education department provides participants with health-related knowledge and skills. For example, the education department may provide the latest health information through online courses or webinars. The education department may also provide specific advice and techniques to help participants implement health regimens. The education department may also provide participants with tools and resources to understand their health status and take appropriate measures. This may help participants deepen their health knowledge and implement health regimens more effectively.

[0071] The health management system may further include a feedback unit. The feedback unit provides feedback based on the results of the subscriber's health regimen. For example, after the subscriber exercises, the feedback unit may provide feedback on the effectiveness of the exercise and areas for improvement. The feedback unit may also provide feedback on nutritional balance and areas for improvement when the subscriber records their meals. Furthermore, the feedback unit may provide feedback on the subscriber's progress toward the goals they have set and offer advice to maintain their motivation. This allows the subscriber to understand the effectiveness of their health regimen and make continuous improvements.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The collection unit collects genetic and lifestyle data. Genetic factors include genetic markers and gene mutations, while lifestyle data includes dietary habits, exercise habits, and sleep patterns. The collection unit collects data using a genetic testing kit and a smartphone app. For example, a genetic testing kit is used to collect saliva samples and analyze the genetic information. Dietary and exercise data can also be collected through a smartphone app. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies diseases that should be prevented. The analysis unit uses machine learning algorithms to identify diseases that should be prevented from past data. For example, deep learning can be used to analyze genetic factors and lifestyle data to evaluate disease risk. It can also use decision trees to classify disease risk. Step 3: The provider teaches the subscriber the health techniques identified by the analysis unit. The provider provides specific health techniques through a smartphone app. For example, health techniques such as dietary therapy and exercise programs are provided using a notification function. Health techniques can also be provided through video tutorials. Step 4: The monitoring unit monitors the implementation status of the subscriber's preventive lifestyle based on the health regimen taught by the provider. The monitoring unit periodically monitors the implementation status of the subscriber's health regimen. For example, the implementation status can be grasped through weekly check-ins or self-reports. Implementation status can also be monitored through remote monitoring. Step 5: The awarding unit awards bonus points based on the execution status monitored by the monitoring unit. The awarding unit awards bonus points when no abnormalities are found in regular health checkups or when a preventive lifestyle is continued. For example, bonus points are awarded when no abnormalities are found in an annual health checkup. Bonus points can also be awarded when no abnormalities are found in a semi-annual blood test.

[0074] (Example 2) A health management system according to an embodiment of the present invention provides personalized health practices based on genetic factors and lifestyle, promoting the health of subscribers and improving the financial health of health insurance associations. The health management system collects genetic and lifestyle data, analyzes it using AI, identifies diseases to be prevented, and health practices for those diseases, and then teaches them to subscribers. Subscribers follow the health practices and receive bonus points if they are healthy. For example, the health management system collects data on each subscriber's genetic factors and lifestyle. For example, genetic information includes family history and genetic test results, while lifestyle information includes dietary habits and exercise frequency. AI then analyzes the collected data to identify diseases to be prevented. For example, if a subscriber has a high genetic risk of diabetes or an unhealthy lifestyle, it identifies diabetes prevention practices. The identified health practices are then taught to subscribers. For example, specific health practices, such as dietary improvements and exercise recommendations, are taught to subscribers. Subscribers then follow the health practices and receive bonus points from the health insurance association if they are healthy and implement the preventive lifestyle practices. For example, if no abnormalities are found in a regular health checkup or if a member continues to live a preventive lifestyle, bonus points are awarded. This increases the member's motivation and encourages them to live a preventive lifestyle. On the other hand, if a member becomes ill without living a preventive lifestyle, the full amount of insurance premiums will not be paid. For example, if a member becomes ill as a result of ignoring the health methods they have been taught and continuing an unhealthy lifestyle, part of the insurance premium will not be paid. This provides an incentive for members and encourages them to live a preventive lifestyle. As a result, the health management system improves the financial health of the health insurance association and reduces overall medical expenses. As a result, the health management system can reduce the risk of developing disease and reduce medical expenses by encouraging each member to live an appropriate preventive lifestyle. For example, by practicing diabetes prevention methods, the onset of diabetes can be prevented and medical expenses can be reduced.

[0075] A health management system according to an embodiment includes a collection unit, an analysis unit, a provision unit, a monitoring unit, and an assignment unit. The collection unit collects data on genetic factors and lifestyles. The genetic factors include, for example, genetic markers and gene mutations. The lifestyle data includes, for example, dietary habits, exercise habits, and sleep patterns. The collection unit can collect data using, for example, a genetic testing kit or a smartphone app. For example, a genetic testing kit is used to collect saliva samples and analyze the genetic information. The collection unit can also collect dietary and exercise data through a smartphone app. The analysis unit analyzes the data collected by the collection unit and identifies diseases that should be prevented. The analysis unit can identify diseases that should be prevented from past data, for example, using a machine learning algorithm. For example, the analysis unit can analyze genetic factors and lifestyle data using deep learning to assess disease risk. The analysis unit can also classify disease risk using a decision tree. The provision unit instructs subscribers on the health practices identified by the analysis unit. The provision unit can provide specific health practices through, for example, a smartphone app. For example, health techniques such as dietary therapy and exercise programs are provided using a notification function. The providing unit can also provide the health techniques through video tutorials. The monitoring unit monitors the implementation status of the subscriber who practices a preventive lifestyle based on the health techniques taught by the providing unit. The monitoring unit, for example, periodically monitors the implementation status of the subscriber's health techniques. For example, the implementation status is grasped through weekly check-ins or self-reports. The monitoring unit can also monitor the implementation status through remote monitoring. The awarding unit awards bonus points based on the implementation status monitored by the monitoring unit. For example, the awarding unit awards bonus points if no abnormalities are found in regular health checkups or if the subscriber continues to practice a preventive lifestyle. For example, bonus points are awarded if no abnormalities are found in annual health checkups. The awarding unit can also award bonus points if no abnormalities are found in semi-annual blood tests.As a result, the health management system according to the embodiment can provide individualized health regimens based on genetic factors and lifestyle, promote the health of subscribers, and improve the financial health of health insurance associations.

[0076] The collection unit can collect data using a genetic testing kit or a smartphone app. Genetic testing kits include, for example, saliva samples and blood samples. The collection unit, for example, collects saliva samples using a genetic testing kit and analyzes genetic information. The collection unit can also collect data through a smartphone app. For example, the smartphone app can be used to input meal details and exercise frequency and transmit the data. This allows efficient data collection by using a genetic testing kit or a smartphone app. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data collected by the smartphone app into AI and have the AI ​​analyze the data.

[0077] The analysis unit can use a machine learning algorithm to identify diseases that should be prevented from past data. Examples of machine learning algorithms include deep learning and decision trees. The analysis unit can, for example, use deep learning to analyze genetic factors and lifestyle data and assess disease risk. The analysis unit can also classify disease risk using decision trees. For example, deep learning learns from large amounts of data and has advanced pattern recognition capabilities. Decision trees hierarchically classify data and improve prediction accuracy. As a result, diseases that should be prevented can be identified with high accuracy using machine learning algorithms. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the data collected by the collection unit into AI and have the AI ​​perform disease risk assessment.

[0078] The providing unit can provide specific health practices through a smartphone app. The smartphone app includes, for example, a notification function and an interface. The providing unit can provide, for example, health practices such as dietary therapy and exercise programs using the smartphone app. The providing unit can also provide health practices through video tutorials. For example, the notification function can be used to provide health practices periodically, allowing subscribers to easily access them. The interface is user-friendly and intuitive to operate. This allows subscribers to easily access health practices by providing them through the smartphone app. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can use AI to recommend the health practices that are best suited to the subscriber.

[0079] The monitoring unit can periodically monitor the implementation status of the health practice. Periodically includes, for example, daily, weekly, or monthly. The monitoring unit grasps the implementation status, for example, through weekly check-ins or self-reports. The monitoring unit can also monitor the implementation status through remote monitoring. For example, the monitoring unit can check the implementation status of the health practice through daily check-ins, evaluate the implementation status through weekly self-reports, and monitor the implementation status in detail through monthly remote monitoring. This allows the subscriber's implementation status of the health practice to be grasped through regular monitoring. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can use AI to analyze the subscriber's implementation status in real time and provide feedback.

[0080] The awarding unit can award bonus points if no abnormalities are found in a regular health checkup or if the subscriber continues a preventive lifestyle. Periodic health checkups include, for example, annual health checkups and semi-annual blood tests. Preventive lifestyles include, for example, dietary restrictions and exercise programs. The awarding unit awards bonus points, for example, if no abnormalities are found in a regular health checkup. The awarding unit can also award bonus points if no abnormalities are found in a semi-annual blood test. For example, bonus points are awarded if the subscriber continues a dietary restriction. Bonus points are awarded if the subscriber is currently performing an exercise program. As a result, the awarding of bonus points increases the subscriber's motivation to continue a preventive lifestyle. Some or all of the above-described processing in the awarding unit may be performed using, for example, AI, or may be performed without AI. For example, the awarding unit can use AI to evaluate the subscriber's performance and automatically award bonus points.

[0081] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit delays the timing of data collection so that data collection can be performed in a relaxed state. Furthermore, if the user is relaxed, the collection unit can immediately start data collection and efficiently acquire information. Furthermore, if the user is in a hurry, the collection unit can simplify the collection process to collect necessary data in a short time. This enables efficient data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0082] The collection unit can analyze the frequency of use of the genetic testing kit and select the optimal collection method. For example, the collection unit can send reminders to users who use the genetic testing kit frequently to collect data regularly. The collection unit can also provide easy-to-use guides to users who use the kit infrequently, lowering the hurdle of data collection. The collection unit can also customize the data collection method according to the frequency of use and suggest the optimal collection method to the user. For example, the collection unit can send regular reminders to users who use the genetic testing kit frequently to encourage data collection. For users who use the kit infrequently, the collection unit can provide easy-to-use guides to lower the hurdle of data collection. The collection unit can customize the data collection method according to the frequency of use and suggest the optimal collection method to the user. This improves the efficiency of data collection by selecting the optimal collection method according to the frequency of use. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without AI. For example, the collection unit can input data on the frequency of use of the genetic testing kit into AI and have the AI ​​select the optimal collection method.

[0083] The collection unit can filter data based on the user's current health condition and lifestyle habits when collecting data. For example, when the user inputs their current health condition, the collection unit selects data to collect based on that information. The collection unit can also collect only relevant data based on the user's lifestyle habits (e.g., diet, exercise). The collection unit can also dynamically adjust the data to be collected in response to changes in the user's health condition. For example, when the user inputs their current health condition, the collection unit selects data to be collected based on that information. Only relevant data is collected based on the user's lifestyle habits. In response to changes in the user's health condition, the collection unit dynamically adjusts the data to be collected in response to those changes. In this way, highly relevant data can be collected by filtering data based on the user's health condition and lifestyle habits. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's health condition data into AI and have the AI ​​perform the filtering.

[0084] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user selects voice input, the collection unit collects data using voice recognition technology. Furthermore, if the user selects text input, the collection unit can also collect data using text analysis technology. Furthermore, if the user selects image input, the collection unit can also collect data using image analysis technology. For example, if the user selects voice input, the collection unit collects data using voice recognition technology. If the user selects text input, the collection unit collects data using text analysis technology. If the user selects image input, the collection unit collects data using image analysis technology. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit inputs the user's input data into AI and has the AI ​​select the optimal collection means.

[0085] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting stress-related data. Furthermore, if the user is relaxed, the collection unit can also collect overall health data in a balanced manner. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting only the most important data. For example, if the user is feeling stressed, the collection unit prioritizes collecting stress-related data. If the user is relaxed, the collection unit collects overall health data in a balanced manner. If the user is in a hurry, the collection unit prioritizes collecting only the most important data. Thus, by determining the priority of data according to the user's emotions, important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0086] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting data related to health risks in that area. Furthermore, if the user is traveling, the collection unit can also collect data related to health risks at the travel destination. Furthermore, the collection unit can collect data related to region-specific health risks based on the user's place of residence. For example, if the user is in a specific area, the collection unit prioritizes collecting data related to health risks in that area. If the user is traveling, the collection unit collects data related to health risks at the travel destination. Data related to region-specific health risks is collected based on the user's place of residence. This allows for efficient collection of highly relevant data by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location information into AI and have the AI ​​select highly relevant data.

[0087] The collection unit can analyze the user's social media activities and collect relevant data when collecting data. For example, the collection unit collects relevant data based on health information shared by the user on social media. The collection unit can also collect data related to health risks from the user's social media activities. The collection unit can also collect relevant data by referring to the activities of the user's friends on social media. For example, the collection unit collects relevant data based on health information shared by the user on social media. Data related to health risks is collected from the user's social media activities. Relevant data is collected by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into AI and have the AI ​​collect the relevant data.

[0088] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit, for example, improves the collection method based on feedback provided by the user in the past. The collection unit can also select the optimal collection means from the user's past feedback. The collection unit can also customize the collection process by reflecting the user's feedback. For example, the collection method is improved based on feedback provided by the user in the past. The optimal collection means is selected from the user's past feedback. The collection process is customized by reflecting the user's feedback. In this way, the collection method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into AI and have the AI ​​customize the collection method.

[0089] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. If the user is relaxed, the analysis unit provides a detailed analysis result. If the user is in a hurry, the analysis unit provides a summary analysis result. This allows the analysis presentation method to be adjusted according to the user's emotions, thereby providing an easy-to-understand analysis result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0090] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the disease. For example, the analysis unit performs a detailed analysis for a disease with a high importance. The analysis unit can also perform a simplified analysis for a disease with a low importance. The analysis unit can also determine the priority of the analysis according to the importance of the disease. For example, a detailed analysis is performed for a disease with a high importance. A simplified analysis is performed for a disease with a low importance. The priority of the analysis is determined according to the importance of the disease. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the disease. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input disease importance data into AI and have the AI ​​adjust the level of detail of the analysis.

[0091] During analysis, the analysis unit can apply different analysis algorithms depending on the disease category. For example, the analysis unit applies a dedicated analysis algorithm to cardiovascular disease. The analysis unit can also apply a dedicated analysis algorithm to diabetes. The analysis unit can also apply a dedicated analysis algorithm to cancer. For example, a dedicated analysis algorithm is applied to cardiovascular disease. A dedicated analysis algorithm is applied to diabetes. A dedicated analysis algorithm is applied to cancer. In this way, highly accurate analysis is possible by applying an analysis algorithm according to the disease category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input disease category data into AI and have the AI ​​select an appropriate analysis algorithm.

[0092] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit improves the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also optimize the analysis algorithm based on the user's past analysis results. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. For example, the analysis unit improves the accuracy of the current analysis based on the user's past analysis results. The analysis unit optimizes the analysis algorithm based on the user's past analysis results. The analysis unit adjusts the level of detail of the analysis by referring to the user's past analysis results. In this way, the accuracy of the current analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into AI and have the AI ​​improve the accuracy of the analysis.

[0093] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. This enables efficient analysis by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0094] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, if data is submitted early, the analysis unit prioritizes analysis. Furthermore, if data is submitted late, the analysis unit can postpone analysis. Furthermore, the analysis unit can adjust the analysis schedule according to the time of data submission. For example, if data is submitted early, the analysis is prioritized. If data is submitted late, the analysis is postponed. The analysis schedule is adjusted according to the time of data submission. In this way, efficient analysis is possible by determining the priority of analysis based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of data submission into AI and have the AI ​​determine the priority of analysis.

[0095] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. For example, highly relevant data is analyzed prioritized. Less relevant data is analyzed later. The analysis order is dynamically adjusted according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of the data into AI and have the AI ​​adjust the order of analysis.

[0096] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit provides analysis results in simple language to a user with little expertise. The analysis unit can also provide analysis results using detailed technical terms to a user with extensive expertise. The analysis unit can also adjust the way in which the analysis results are expressed according to the user's level of expertise. For example, the analysis unit provides analysis results in simple language to a user with little expertise. The analysis unit provides analysis results using detailed technical terms to a user with extensive expertise. The way in which the analysis results are expressed is adjusted according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into AI and have the AI ​​adjust the use of technical terms in the analysis.

[0097] The providing unit can estimate the user's emotions and adjust the method of providing health practices based on the estimated user emotions. For example, if the user is tense, the providing unit can provide a simple, highly visible health practice. Furthermore, if the user is relaxed, the providing unit can provide a detailed health practice. Furthermore, if the user is in a hurry, the providing unit can provide a health practice that focuses on the key points. For example, if the user is tense, the providing unit can provide a simple, highly visible health practice. If the user is relaxed, the providing unit can provide a detailed health practice. If the user is in a hurry, the providing unit can provide a health practice that focuses on the key points. This allows the method of providing health practices to be adjusted according to the user's emotions, thereby providing easy-to-understand health practices. The estimation of emotions is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the method of providing health practices.

[0098] When providing a health regimen, the providing unit can adjust the level of detail of the provision based on the disease prevention effect. For example, the providing unit provides detailed explanations for health regimens with high preventive effect. The providing unit can also provide simplified explanations for health regimens with low preventive effect. The providing unit can also determine the priority of provision based on the preventive effect. For example, detailed explanations are provided for health regimens with high preventive effect. Simplified explanations are provided for health regimens with low preventive effect. The priority of provision is determined based on the preventive effect. This enables the provision of efficient health regimens by adjusting the level of detail of provision based on the disease prevention effect. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input preventive effect data into AI and have the AI ​​adjust the level of detail of provision.

[0099] When providing health regimens, the providing unit can apply different providing algorithms depending on the disease category. For example, the providing unit applies a dedicated providing algorithm to cardiovascular disease. The providing unit can also apply a dedicated providing algorithm to diabetes. The providing unit can also apply a dedicated providing algorithm to cancer. For example, a dedicated providing algorithm is applied to cardiovascular disease. A dedicated providing algorithm is applied to diabetes. A dedicated providing algorithm is applied to cancer. In this way, by applying a providing algorithm according to the disease category, it is possible to provide highly accurate health regimens. Some or all of the above-mentioned processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input disease category data into AI and have the AI ​​select an appropriate providing algorithm.

[0100] When providing a health regimen, the providing unit can improve the accuracy of the provision by referring to the user's past health regimen implementation results. For example, the providing unit improves the accuracy of the current provision based on the user's past health regimen implementation results. The providing unit can also optimize the provision algorithm based on the user's past health regimen implementation results. The providing unit can also adjust the level of detail of the provision by referring to the user's past health regimen implementation results. For example, the providing unit improves the accuracy of the current provision based on the user's past health regimen implementation results. The providing unit optimizes the provision algorithm based on the user's past health regimen implementation results. The providing unit adjusts the level of detail of the provision by referring to the user's past health regimen implementation results. This improves the accuracy of the current provision by referring to the past health regimen implementation results. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past health regimen implementation result data into AI and have the AI ​​improve the accuracy of the provision.

[0101] The providing unit can estimate the user's emotions and adjust the frequency of providing health practices based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide the health practices more frequently. Furthermore, if the user is relaxed, the providing unit can provide the health practices more frequently. Furthermore, if the user is in a hurry, the providing unit can provide the health practices at the minimum necessary frequency. For example, if the user is feeling stressed, the providing unit can provide the health practices less frequently. If the user is relaxed, the providing unit can provide the health practices more frequently. If the user is in a hurry, the providing unit can provide the health practices at the minimum necessary frequency. This allows for efficient provision of health practices by adjusting the frequency of providing health practices according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the provision frequency.

[0102] When providing health tips, the providing unit can adjust the timing of the provision based on the user's lifestyle rhythm. For example, if the user is a morning person, the providing unit can provide the health tips in the morning. Furthermore, if the user is a night owl, the providing unit can also provide the health tips in the evening. Furthermore, the providing unit can provide the health tips at the optimal timing according to the user's lifestyle rhythm. For example, if the user is a morning person, the providing unit can provide the health tips in the morning. If the user is a night owl, the providing unit can provide the health tips in the evening. The health tips are provided at the optimal timing according to the user's lifestyle rhythm. This allows for the provision of health tips efficiently by adjusting the timing of the provision based on the user's lifestyle rhythm. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's lifestyle rhythm data into AI and have the AI ​​adjust the timing of the provision.

[0103] When providing health tips, the providing unit can select the optimal providing method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide the health tips through an app. Furthermore, if the user is using a tablet, the providing unit can provide health tips optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can provide concise and highly visible health tips. For example, if the user is using a smartphone, the providing unit can provide the health tips through an app. If the user is using a tablet, the providing unit can provide health tips optimized for a large screen. If the user is using a smartwatch, the providing unit can provide concise and highly visible health tips. This enables the provision of efficient health tips by taking into account the user's device information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information into AI and have the AI ​​select the optimal providing method.

[0104] When providing health tips, the providing unit can make the provided content multilingual according to the user's language setting. The providing unit, for example, automatically sets the language of the health tips based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the health tips in a specific language when the user selects that language. For example, the providing unit automatically sets the language of the health tips based on the language setting of the user's device. The providing unit can provide a language switching function when the user uses multiple languages. When the user selects a specific language, the health tips are provided in that language. This enables efficient provision of health tips by making the provided content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's language setting data into AI and have the AI ​​select a method for making the provided content multilingual.

[0105] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user's emotions. For example, if the user is nervous, the monitoring unit provides a simple, highly visible monitoring method. Furthermore, if the user is relaxed, the monitoring unit can also provide a detailed monitoring method. Furthermore, if the user is in a hurry, the monitoring unit can also provide a monitoring method that focuses on the key points. For example, if the user is nervous, the monitoring unit provides a simple, highly visible monitoring method. If the user is relaxed, the monitoring unit provides a detailed monitoring method. If the user is in a hurry, the monitoring unit provides a monitoring method that focuses on the key points. This enables efficient monitoring by adjusting the monitoring method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the monitoring unit may be performed using, for example, an AI, or without an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI adjust the monitoring method.

[0106] During monitoring, the monitoring unit can select the optimal monitoring method by referring to the user's past health practice history. The monitoring unit, for example, optimizes the current monitoring method based on the user's past health practice history. The monitoring unit can also adjust the monitoring frequency based on the user's past health practice history. The monitoring unit can also adjust the level of monitoring detail by referring to the user's past health practice history. For example, the current monitoring method can be optimized based on the user's past health practice history. The monitoring frequency can be adjusted based on the user's past health practice history. The level of monitoring detail can be adjusted by referring to the user's past health practice history. In this way, the optimal monitoring method can be selected by referring to the past health practice history. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past health practice history data into AI and have the AI ​​select the optimal monitoring method.

[0107] During monitoring, the monitoring unit can customize the monitoring means based on the user's current living situation. For example, if the user is busy, the monitoring unit provides simplified monitoring means. Furthermore, if the user is relaxed, the monitoring unit can also provide detailed monitoring means. Furthermore, the monitoring unit can select the optimal monitoring means according to the user's living situation. For example, if the user is busy, the monitoring unit provides simplified monitoring means. If the user is relaxed, the monitoring unit provides detailed monitoring means. The optimal monitoring means is selected according to the user's living situation. This enables efficient monitoring by customizing the monitoring means based on the user's living situation. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or without AI. For example, the monitoring unit can input the user's living situation data into AI and have the AI ​​customize the monitoring means.

[0108] The monitoring unit can improve the monitoring method by reflecting user feedback during monitoring. The monitoring unit improves the monitoring method, for example, based on feedback provided by the user. The monitoring unit can also select the optimal monitoring means based on the user feedback. The monitoring unit can also adjust the level of monitoring detail by reflecting the user feedback. For example, the monitoring unit improves the monitoring method based on feedback provided by the user. The optimal monitoring means is selected based on the user feedback. The level of monitoring detail is adjusted by reflecting the user feedback. In this way, the monitoring method can be optimized by reflecting the user feedback. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input user feedback data into AI and have the AI ​​improve the monitoring method.

[0109] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit prioritizes stress-related monitoring. Furthermore, if the user is relaxed, the monitoring unit can prioritize overall health monitoring. Furthermore, if the user is in a hurry, the monitoring unit can prioritize the most important monitoring items. For example, if the user is feeling stressed, the monitoring unit prioritizes stress-related monitoring. If the user is relaxed, the monitoring unit prioritizes overall health monitoring. If the user is in a hurry, the monitoring unit prioritizes the most important monitoring items. This enables efficient monitoring by determining monitoring priorities according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit can be performed using, for example, an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI determine the monitoring priorities.

[0110] During monitoring, the monitoring unit can select the optimal monitoring method by taking into account the user's geographical location information. For example, if the user is in a specific area, the monitoring unit prioritizes monitoring related to health risks in that area. Furthermore, if the user is traveling, the monitoring unit can also perform monitoring related to health risks at the travel destination. Furthermore, the monitoring unit can perform monitoring related to health risks specific to the area based on the user's place of residence. For example, if the user is in a specific area, the monitoring unit prioritizes monitoring related to health risks in that area. If the user is traveling, the monitoring unit performs monitoring related to health risks at the travel destination. Based on the user's place of residence, the monitoring unit performs monitoring related to health risks specific to the area. This enables efficient monitoring by taking into account the user's geographical location information. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's geographical location information into AI and have the AI ​​select the optimal monitoring method.

[0111] During monitoring, the monitoring unit can analyze the user's social media activities and suggest monitoring measures. The monitoring unit can, for example, suggest relevant monitoring measures based on health information shared by the user on social media. The monitoring unit can also suggest monitoring measures related to health risks based on the user's social media activities. The monitoring unit can also suggest relevant monitoring measures based on the activities of the user's friends on social media. For example, the monitoring unit can suggest relevant monitoring measures based on health information shared by the user on social media. The monitoring unit can suggest monitoring measures related to health risks based on the user's social media activities. The monitoring unit can suggest relevant monitoring measures based on the activities of the user's friends on social media. This enables efficient monitoring by analyzing social media activities. Some or all of the above-mentioned processing in the monitoring unit can be performed, for example, using AI, or can be performed without using AI. For example, the monitoring unit can input the user's social media data into AI and have the AI ​​suggest monitoring measures.

[0112] During monitoring, the monitoring unit can customize the monitoring method by reflecting the user's past feedback. The monitoring unit, for example, improves the monitoring method based on feedback provided by the user in the past. The monitoring unit can also select the optimal monitoring means based on the user's past feedback. The monitoring unit can also adjust the level of monitoring detail by reflecting the user's feedback. For example, the monitoring unit improves the monitoring method based on feedback provided by the user in the past. The optimal monitoring means is selected based on the user's past feedback. The level of monitoring detail is adjusted by reflecting the user's feedback. In this way, the monitoring method can be optimized by reflecting the user's feedback. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's feedback data into AI and have the AI ​​customize the monitoring method.

[0113] The awarding unit can estimate the user's emotion and adjust the bonus point awarding method based on the estimated user's emotion. For example, if the user is nervous, the awarding unit provides a simple and highly visible bonus point awarding method. Furthermore, if the user is relaxed, the awarding unit can also provide a detailed bonus point awarding method. Furthermore, if the user is in a hurry, the awarding unit can also provide a bonus point awarding method that focuses on the main points. For example, if the user is nervous, the awarding unit provides a simple and highly visible bonus point awarding method. If the user is relaxed, the awarding unit provides a detailed bonus point awarding method. If the user is in a hurry, the awarding unit provides a bonus point awarding method that focuses on the main points. This enables efficient awarding of bonus points by adjusting the bonus point awarding method according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the awarding unit may be performed using, for example, AI, or without AI. For example, the awarding unit can input the user's emotional data into the generation AI and have the generation AI adjust the method of awarding bonus points.

[0114] When awarding bonus points, the awarding unit can select the optimal awarding method by referring to the user's past health checkup results. For example, the awarding unit optimizes the current bonus point awarding method based on the user's past health checkup results. The awarding unit can also adjust the frequency of bonus point awarding based on the user's past health checkup results. The awarding unit can also adjust the level of detail of bonus points by referring to the user's past health checkup results. For example, the current bonus point awarding method can be optimized based on the user's past health checkup results. The frequency of bonus point awarding can be adjusted based on the user's past health checkup results. The level of detail of bonus points can be adjusted by referring to the user's past health checkup results. In this way, the optimal bonus point awarding method can be selected by referring to the past health checkup results. Some or all of the above-mentioned processing in the awarding unit may be performed using, for example, AI, or may be performed without using AI. For example, the awarding unit can input the user's past health checkup result data into AI and have the AI ​​select the optimal bonus point awarding method.

[0115] When awarding bonus points, the awarding unit can customize the awarding method based on the user's current living situation. For example, if the user is busy, the awarding unit provides a simplified bonus point awarding method. Furthermore, if the user is relaxed, the awarding unit can provide a detailed bonus point awarding method. Furthermore, the awarding unit can select an optimal bonus point awarding method according to the user's living situation. For example, if the user is busy, the awarding unit provides a simplified bonus point awarding method. If the user is relaxed, the awarding unit provides a detailed bonus point awarding method. The optimal bonus point awarding method is selected according to the user's living situation. This enables efficient awarding of bonus points by customizing the awarding method based on the user's living situation. Some or all of the above-described processing in the awarding unit may be performed, for example, using AI or without AI. For example, the awarding unit may input the user's living situation data into AI and have the AI ​​customize the awarding method.

[0116] The awarding unit can improve the method of awarding bonus points by reflecting user feedback when awarding bonus points. The awarding unit improves the method of awarding bonus points based on, for example, feedback provided by the user. The awarding unit can also select an optimal means of awarding bonus points based on the user feedback. The awarding unit can also adjust the level of detail of awarding bonus points by reflecting the user feedback. For example, the awarding unit improves the method of awarding bonus points based on feedback provided by the user. The optimal means of awarding bonus points is selected based on the user feedback. The level of detail of awarding bonus points is adjusted by reflecting the user feedback. In this way, the method of awarding bonus points can be optimized by reflecting the user feedback. Some or all of the above-described processing in the awarding unit may be performed using, for example, AI, or may be performed without using AI. For example, the awarding unit inputs user feedback data into AI and causes the AI ​​to improve the awarding method.

[0117] The assigning unit can estimate the user's emotions and determine the priority of bonus points based on the estimated user's emotions. For example, if the user is feeling stressed, the assigning unit can prioritize bonus points related to stress reduction. Furthermore, if the user is relaxed, the assigning unit can prioritize bonus points related to overall health. Furthermore, if the user is in a hurry, the assigning unit can prioritize the most important bonus points. For example, if the user is feeling stressed, the assigning unit can prioritize bonus points related to stress reduction. If the user is relaxed, the assigning unit can prioritize bonus points related to overall health. If the user is in a hurry, the assigning unit can prioritize the most important bonus points. This allows for efficient bonus point allocation by determining the priority of bonus points according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the assigning unit can be performed using, for example, AI, or without AI. For example, the granting unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of bonus points.

[0118] When awarding bonus points, the awarding unit can select the optimal awarding method by taking into account the user's geographical location information. For example, if the user is in a specific area, the awarding unit prioritizes awarding bonus points related to health risks in that area. Furthermore, if the user is traveling, the awarding unit can also award bonus points related to health risks at the travel destination. Furthermore, the awarding unit can also award bonus points related to health risks specific to the area based on the user's place of residence. For example, if the user is in a specific area, the awarding unit prioritizes awarding bonus points related to health risks in that area. If the user is traveling, the awarding unit awards bonus points related to health risks at the travel destination. Bonus points related to health risks specific to the area are awarded based on the user's place of residence. This enables efficient awarding of bonus points by taking into account the user's geographical location information. Some or all of the above-described processing by the awarding unit may be performed using, for example, AI, or may be performed without AI. For example, the awarding unit can input the user's geographical location information into AI and have the AI ​​select the optimal awarding method.

[0119] When awarding bonus points, the awarding unit can analyze the user's social media activity and suggest a means of awarding. For example, the awarding unit can suggest a relevant means of awarding bonus points based on health information shared by the user on social media. The awarding unit can also suggest a means of awarding bonus points related to health risks based on the user's social media activity. The awarding unit can also suggest a relevant means of awarding bonus points based on the activity of the user's friends on social media. For example, the awarding unit can suggest a relevant means of awarding bonus points based on health information shared by the user on social media. The awarding unit can suggest a means of awarding bonus points related to health risks based on the user's social media activity. The awarding unit can suggest a relevant means of awarding bonus points based on the activity of the user's friends on social media. This enables efficient awarding of bonus points by analyzing social media activity. Some or all of the above-described processing by the awarding unit may be performed using, for example, AI, or may be performed without using AI. For example, the awarding unit can input the user's social media data into AI and have the AI ​​suggest an awarding means.

[0120] When awarding bonus points, the awarding unit can customize the awarding method by reflecting the user's past feedback. The awarding unit can improve the bonus point awarding method based on, for example, feedback provided by the user in the past. The awarding unit can also select an optimal bonus point awarding means based on the user's past feedback. The awarding unit can also adjust the level of detail of bonus point awarding by reflecting the user's feedback. For example, the awarding unit can improve the bonus point awarding method based on feedback provided by the user in the past. The optimal bonus point awarding means can be selected based on the user's past feedback. The level of detail of bonus point awarding can be adjusted by reflecting the user's feedback. In this way, the method of awarding bonus points can be optimized by reflecting the user's feedback. Some or all of the above-mentioned processing in the awarding unit can be performed using, for example, AI, or can be performed without using AI. For example, the awarding unit can input user feedback data into AI and have the AI ​​customize the awarding method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, monitoring unit, and award unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect genetic and lifestyle data using the camera 42 and microphone 38B of the smart device 14. The analysis unit, realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data and identifies diseases that should be prevented. The provision unit instructs the subscriber on specific health methods through the control unit 46A of the smart device 14. The monitoring unit monitors the subscriber's performance status using the communication I / F 44 of the smart device 14. The award unit, realized by the specific processing unit 290 of the data processing device 12, awards bonus points based on the performance status monitored by the monitoring unit. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, monitoring unit, and awarding unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect genetic and lifestyle data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit, realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data and identifies diseases that should be prevented. The provision unit instructs the subscriber on specific health methods through the control unit 46A of the smart glasses 214. The monitoring unit monitors the subscriber's performance status using the communication I / F 44 of the smart glasses 214. The awarding unit, realized by the specific processing unit 290 of the data processing device 12, awards bonus points based on the performance status monitored by the monitoring unit. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, monitoring unit, and award unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect genetic element and lifestyle data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to identify diseases that should be prevented. The provision unit instructs the subscriber on specific health methods through the control unit 46A of the headset terminal 314. The monitoring unit monitors the subscriber's performance status using the communication I / F 44 of the headset terminal 314. The award unit is realized by the specific processing unit 290 of the data processing device 12 and awards bonus points based on the performance status monitored by the monitoring unit. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, monitoring unit, and awarding unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect data on genetic factors and lifestyle using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to identify diseases that should be prevented. The provision unit instructs the subscriber on specific health methods through the control unit 46A of the robot 414. The monitoring unit monitors the subscriber's performance status using the communication I / F 44 of the robot 414. The awarding unit is realized by the specific processing unit 290 of the data processing device 12 and awards bonus points based on the performance status monitored by the monitoring unit.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] The health management system may further include a communication unit. The communication unit enables two-way communication between the subscriber and a medical professional. For example, if the subscriber has a question about a health regimen, the subscriber can directly ask the medical professional through the communication unit. The medical professional can also provide personalized advice based on the subscriber's health condition. Furthermore, the communication unit can send regular encouraging messages to the subscriber to maintain motivation when implementing the health regimen. This allows the subscriber to strengthen cooperation with the medical professional and implement the health regimen more effectively.

[0123] The collection unit can estimate the user's emotions and adjust the data collection method based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can simplify the data collection process to reduce the burden on the user. Also, if the user is relaxed, detailed data collection can be performed. Furthermore, if the user is in a hurry, the collection unit can select the optimal method for collecting necessary data in a short amount of time. This allows for efficient data collection by adjusting the data collection method according to the user's emotions.

[0124] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is feeling stressed, stress-related data can be analyzed with priority. Also, if the user is relaxed, overall health data can be analyzed in a balanced manner. Furthermore, if the user is in a hurry, only the most important data can be analyzed with priority. In this way, by determining the priority of analysis according to the user's emotions, important data can be analyzed with priority.

[0125] The provision unit can estimate the user's emotions and adjust the method of providing health regimens based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible health regimen can be provided. If the user is relaxed, a detailed health regimen can be provided. Furthermore, if the user is in a hurry, a health regimen that focuses on the main points can be provided. In this way, by adjusting the method of providing health regimens according to the user's emotions, it is possible to provide health regimens that are easy to understand.

[0126] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible monitoring method can be provided. If the user is relaxed, a detailed monitoring method can be provided. Furthermore, if the user is in a hurry, a monitoring method that focuses on the main points can be provided. This allows for efficient monitoring by adjusting the monitoring method according to the user's emotions.

[0127] The health management system may further include a reporting unit. The reporting unit periodically generates reports on the subscriber's health status and the implementation status of health regimens, and provides these to the subscriber and medical professionals. For example, a monthly report may detail changes in the subscriber's health status and the implementation status of health regimens. The reporting unit may also issue alerts if an abnormality is detected. Furthermore, the reporting unit may visualize the subscriber's progress toward goals set by the subscriber and provide feedback to maintain motivation. This allows the subscriber to understand their own health status and take appropriate measures.

[0128] The health management system may further include a reminder unit. The reminder unit sets reminders for the subscriber to practice health practices and notifies them at appropriate times. For example, it may remind the subscriber to exercise or eat daily. The reminder unit may also adjust the timing of reminders based on the subscriber's schedule. Furthermore, the reminder unit may send a follow-up notification if the subscriber ignores a reminder. This makes it easier for the subscriber to continue practicing health practices.

[0129] The health management system may further include an incentive section. The incentive section provides rewards that can be earned by subscribers who practice health practices. For example, points may be awarded if subscribers continue practicing health practices for a certain period of time, and the points may be exchanged for products or services. The incentive section may also provide special rewards if subscribers achieve their set goals. Furthermore, the incentive section may introduce a system for subscribers to compete with each other to increase motivation. This may increase the subscribers' motivation to practice health practices.

[0130] The health management system may further include an education department. The education department provides participants with health-related knowledge and skills. For example, the education department may provide the latest health information through online courses or webinars. The education department may also provide specific advice and techniques to help participants implement health regimens. The education department may also provide participants with tools and resources to understand their health status and take appropriate measures. This may help participants deepen their health knowledge and implement health regimens more effectively.

[0131] The health management system may further include a feedback unit. The feedback unit provides feedback based on the results of the subscriber's health regimen. For example, after the subscriber exercises, the feedback unit may provide feedback on the effectiveness of the exercise and areas for improvement. The feedback unit may also provide feedback on nutritional balance and areas for improvement when the subscriber records their meals. Furthermore, the feedback unit may provide feedback on the subscriber's progress toward the goals they have set and offer advice to maintain their motivation. This allows the subscriber to understand the effectiveness of their health regimen and make continuous improvements.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The collection unit collects genetic and lifestyle data. Genetic factors include genetic markers and gene mutations, while lifestyle data includes dietary habits, exercise habits, and sleep patterns. The collection unit collects data using a genetic testing kit and a smartphone app. For example, a genetic testing kit is used to collect saliva samples and analyze the genetic information. Dietary and exercise data can also be collected through a smartphone app. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies diseases that should be prevented. The analysis unit uses machine learning algorithms to identify diseases that should be prevented from past data. For example, deep learning can be used to analyze genetic factors and lifestyle data to evaluate disease risk. It can also use decision trees to classify disease risk. Step 3: The provider teaches the subscriber the health techniques identified by the analysis unit. The provider provides specific health techniques through a smartphone app. For example, health techniques such as dietary therapy and exercise programs are provided using a notification function. Health techniques can also be provided through video tutorials. Step 4: The monitoring unit monitors the implementation status of the subscriber's preventive lifestyle based on the health regimen taught by the provider. The monitoring unit periodically monitors the implementation status of the subscriber's health regimen. For example, the implementation status can be grasped through weekly check-ins or self-reports. Implementation status can also be monitored through remote monitoring. Step 5: The awarding unit awards bonus points based on the execution status monitored by the monitoring unit. The awarding unit awards bonus points when no abnormalities are found in regular health checkups or when a preventive lifestyle is continued. For example, bonus points are awarded when no abnormalities are found in an annual health checkup. Bonus points can also be awarded when no abnormalities are found in a semi-annual blood test.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0139] 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.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The 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.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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 AI 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0155] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0165] 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.

[0166] 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.

[0167] 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 AI 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0181] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0182] 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.

[0183] 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.

[0184] 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 AI 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.

[0185] 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.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0192] 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."

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] [Explanation of symbols]

[0206] 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 collection department that collects genetic and lifestyle data; an analysis unit that analyzes the data collected by the collection unit and identifies diseases that should be prevented; a providing unit that teaches the health method identified by the analysis unit to the subscriber; a monitoring unit that monitors the implementation status of the subscriber who practices a preventive lifestyle based on the health method taught by the providing unit; and an awarding unit that awards bonus points based on the execution status monitored by the monitoring unit. A system characterized by:

2. The collecting unit Collect data using genetic testing kits or smartphone apps 2. The system of claim 1.

3. The analysis unit Using machine learning algorithms to identify diseases that should be prevented from past data 2. The system of claim 1.

4. The providing unit Providing specific health tips through a smartphone app 2. The system of claim 1.

5. The monitoring unit Regularly monitor the implementation of health laws 2. The system of claim 1.

6. The granting unit Bonus points will be awarded if no abnormalities are found in regular health checkups or if you continue to live a preventative lifestyle.

2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze the frequency of use of genetic testing kits and select the optimal collection method 2. The system of claim 1.

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