system

Abstract

We provide the system. [Solution] A means of collecting individual health checkup results data and daily life data, A method for predicting health risks from data collected using a generated artificial intelligence model, A means for generating individually adapted health plans based on predicted health risks, A means of notifying the user of the generated health plan, A means of providing incentives when health conditions improve compared to the previous year, A system that includes this.

Description

Technical Field

[0001] The technology of this disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, there is a problem that even if many individuals receive the results of a health check, they cannot take specific actions. This may increase future health risks. Furthermore, the failure to protect the health of employees is also a factor that increases the burden of medical expenses for enterprises. Therefore, there is a need for a method to specifically predict individual health risks and provide an experience that encourages more people to undergo health checks by giving action proposals and incentives.

Means for Solving the Problems

[0005] To address this challenge, the present invention provides a system that predicts health risks by collecting individual health checkup results and daily life data and analyzing them with a generative AI model. This system has the function of generating a health plan tailored to the user and notifying the user. Furthermore, it enhances motivation for maintaining health by providing incentives when improvements in health status are confirmed. In addition, it is possible to continuously improve the health plan by adjusting the generative AI model based on user feedback.

[0006] "Health checkup results data" refers to information that indicates an individual's health status, and includes data such as physiological measurements like blood pressure, blood sugar levels, and cholesterol levels.

[0007] "Daily life data" refers to information about an individual's daily activities and habits, including data on diet, exercise, and sleep duration.

[0008] A "generative AI model" is an artificial intelligence algorithm used to analyze collected data and predict future health risks.

[0009] "Health risk" is an indicator that shows the likelihood of an individual developing illness or health problems in the future.

[0010] A "health plan" is a set of specific action plans, dietary guidelines, and exercise programs proposed to maintain or improve a user's health based on predicted health risks.

[0011] An "incentive" is a reward or perk offered to encourage an individual to engage in healthy behaviors.

[0012] "Feedback" refers to information based on user reactions, opinions, and results of using a system provided by the user. [Brief explanation of the drawing]

[0013] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0017] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is a system for supporting individual health management, which collects individual health checkup results and daily life data, predicts health risks based on that data, and provides an individually optimized health plan. The main components of this system are means for collecting and storing data, a generative AI model that analyzes the data and predicts health risks, a function to generate and provide a health plan based on the prediction to the user, a function to provide incentives, and a function to receive and utilize user feedback.

[0035] Data collection and storage

[0036] Users input the results obtained during their health checkups into their smartphones or PCs. These devices can also input or automatically synchronize data on daily life, such as diet, exercise, and sleep. The devices format this data appropriately and send it to the server in a secure manner. The server stores this data in a database and maintains data consistency through standardization.

[0037] Predicting health risks

[0038] The server inputs the collected data into a generating AI model to predict the user's future health risks. This model is trained on historical data and medical research, and can assess the user's risk of developing conditions such as hypertension or diabetes.

[0039] Health plan creation and provision

[0040] The server creates an optimal health plan for each individual user based on predicted health risks. This includes meal menu suggestions and exercise programs. The generated plan is notified to the user via their device. The user can view the plan details on their device and adjust their lifestyle according to the instructions.

[0041] Provision of incentives

[0042] The server determines whether the user's health has improved as a result of implementing the health plan. For example, if blood pressure has improved compared to the previous year's health checkup, the server provides the user with an incentive such as a free meal voucher at the company cafeteria. This feature serves as an incentive to encourage healthy behaviors in users.

[0043] Collecting and using feedback

[0044] Users can provide feedback on proposed health plans and incentives via the app or PC. The server analyzes this feedback and adjusts the algorithm of the generating AI model. As a result, the quality of the generated health plans and user satisfaction improve.

[0045] As a concrete example, if a user enters a health checkup result showing high blood pressure into the system, the system will suggest a low-sodium diet plan and a daily walking habit to the user. If the user follows this plan for a month and their blood pressure decreases in a follow-up checkup, they will be offered incentives such as free company meal vouchers. This entire process makes it easier for users to maintain their health, and allows companies to effectively manage their healthcare costs.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] Users input data obtained from health checkups (e.g., blood pressure, blood sugar levels) into their smartphone or PC. They can also manually input daily life data (e.g., diet, exercise history, sleep duration) or have it automatically synchronized from a wearable device.

[0049] Step 2:

[0050] The device handles the communication required to convert collected health checkup results and daily life data into an appropriate format and send it to the server. The data is encrypted, and measures are taken to protect privacy.

[0051] Step 3:

[0052] The server saves the received data to the database. After saving, it checks for missing or abnormal values ​​and cleanses the data as needed. It also formats the data to make it usable in the generative AI model.

[0053] Step 4:

[0054] The server uses the formatted data to run a generative AI model that predicts the user's future health risks. For example, the model calculates the risk of diseases such as diabetes and hypertension from past health checkup data and current lifestyle habits.

[0055] Step 5:

[0056] The server generates a health plan tailored to the user based on predicted health risks. This plan includes specific dietary guidelines and exercise plans. For example, if the model indicates a risk of high blood pressure, a diet plan to reduce sodium intake and aerobic exercise recommendations will be provided.

[0057] Step 6:

[0058] The device notifies the user of the generated health plan. The user can check the contents of the health plan on the device's display screen and learn specific ways to implement it in their daily life. The notification includes a reminder function to support the user in improving their lifestyle habits.

[0059] Step 7:

[0060] The server tracks health check results to determine if the user's health has improved. If improvement is confirmed compared to the previous year, an incentive (e.g., a free meal voucher at the company cafeteria) is provided. This process increases user motivation and encourages the continuation of healthy behaviors.

[0061] Step 8:

[0062] Users can provide feedback via their devices on how they felt about the health plans and incentives offered. This feedback information is sent to the server and used to further adjust the plans.

[0063] Step 9:

[0064] The server analyzes feedback collected from users and optimizes the generated AI model based on that analysis. This model improvement allows for more personalized health plan suggestions in the future, enabling a more specific response to user needs and preferences.

[0065] (Example 1)

[0066] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0067] In modern society, people's lifestyles are diversifying, and it is important to continuously manage individual health conditions. However, traditional methods have made it difficult to predict individual health risks and provide appropriate health plans. Furthermore, there is a lack of mechanisms to effectively utilize user feedback to continuously improve health plans.

[0068] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0069] In this invention, the server includes means for collecting personal health information and daily life information, means for predicting health risks from the collected information using a generative AI model, and means for generating individually tailored health programs based on the predicted health risks. This makes it possible to provide each user with an automatically and continuously optimized health program, thereby promoting improvement in the user's health condition.

[0070] "Personal health information" refers to health checkup results and other health-related data obtained by the user.

[0071] "Daily life information" refers to data related to the user's daily activities, including information on diet, exercise, sleep, etc.

[0072] A "generative AI model" refers to an artificial intelligence-powered model used to predict a user's health risks based on collected data.

[0073] "Health risk" refers to factors or conditions that may have a potential impact on health in the future.

[0074] An "individually tailored health program" refers to a personalized plan for maintaining or improving health, taking into account the user's specific health condition and risks.

[0075] The "evaluation period" refers to a specific time frame in which health status is measured and compared.

[0076] "Rewards" refer to incentives or benefits offered to users for improving their health.

[0077] "Response information" refers to users' impressions, opinions, and feedback regarding health programs and rewards.

[0078] This system collects users' health information and provides health programs based on analysis using a generative AI model. The system primarily utilizes the following hardware and software.

[0079] Users input their health and daily life information using devices such as smartphones and PCs. This includes dietary information, exercise habits, sleep patterns, and results of regular health checkups. The device converts the entered information into a predetermined format and transmits the data to a server via the internet.

[0080] The server stores received data using a secure database system. The stored data is standardized to maintain consistency in the collected information. Next, the server leverages a generative AI model to analyze the stored data and predict the user's health risks. This model utilizes machine learning algorithms and is trained on large historical datasets.

[0081] Based on the predicted health risks, the server creates a personalized health program for each user. This program includes meal plans and exercise suggestions, providing specific actionable guidelines tailored to individual user needs. The created program is notified to the user's device, allowing them to review its details and incorporate it into their daily life. Rewards may also be offered if improvements are observed in the user's health.

[0082] For example, if a user inputs into the system that their blood pressure is high based on their health checkup results, this information is sent to the server, and a generating AI model assesses the risk of hypertension. Based on the results, the server can provide the user with a low-salt diet program and an exercise plan including walking. If the user follows the program and their blood pressure improves in a follow-up examination, the server will provide rewards such as free services or coupons.

[0083] An example of a prompt message is: "Based on the health checkup results entered by the user, use a generative AI model to predict the risk of hypertension and propose an appropriate health plan."

[0084] Through this system, users can receive personalized health support, increasing their chances of maintaining and improving their health.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] Users input their health and daily life information using a device. Specifically, they input health checkup results and information about their diet, exercise, and sleep via a dedicated application on the device. The entered data is converted to an appropriate format and prepared for transmission. The input data is temporarily stored in the device's memory and then sent to the server using encryption technology.

[0088] Step 2:

[0089] The server receives data from terminals and stores it in a database. Health checkup information and daily life information are extracted from the received data, and standardization processing is performed so that this data is stored in a unified format. For example, processing such as unifying date formats and converting numerical units is carried out. The stored data is then converted into information necessary for subsequent analysis.

[0090] Step 3:

[0091] The server feeds data stored in the database into a generative AI model to predict the user's health risk. The input data is stored in a unified format. The generative AI model uses a machine learning algorithm based on historical data to output a risk score and classification result regarding the user's health status. This process involves massive data processing and real-time analysis.

[0092] Step 4:

[0093] The server generates personalized health programs based on prediction results obtained from a generated AI model. Inputs include predicted health risks and user lifestyle information. In conjunction with a rule-based system supervised by health experts, a program is created that includes appropriate dietary suggestions and exercise menus. The resulting health program is sent to the user's device.

[0094] Step 5:

[0095] The user checks the health program notified on their device. Using the device application, they check the details of the program they received and incorporate the suggested actions into their daily life. The user's progress is recorded on the device and sent to the server as feedback data.

[0096] Step 6:

[0097] The server analyzes user feedback data and evaluates changes in health status resulting from program implementation. Inputs include re-results of health checkups and user-reported information. If the health status meets predetermined evaluation criteria, the user receives a reward. Specifically, this may involve issuing electronic coupons or sending notifications via in-app messages.

[0098] (Application Example 1)

[0099] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0100] In personal health management, the challenge lies in efficiently and effectively providing a system that collects various health data, accurately predicts individual health risks, ensures data security, and allows users to provide information with confidence. Furthermore, it is necessary to appropriately evaluate improvements in health status and promote healthy behaviors through rewards.

[0101] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0102] In this invention, the server includes means for collecting personal health checkup results and daily life information, means for predicting health risks from the collected information using a generated artificial intelligence model, means for generating individually adapted health plans based on the predicted health risks, means for providing a security mechanism to encrypt the user's health information and prevent unauthorized access from external sources, and means for providing rewards when the user's health status improves compared to the previous year. This makes it possible to safely and effectively manage personal health data, promote individually optimized health management, and build a reward system that supports users' healthy behaviors.

[0103] "Health checkup results information" refers to data such as numerical values ​​and evaluations obtained as a result of tests that users have received at medical institutions, etc.

[0104] "Daily life information" refers to data on various activities and behaviors related to the user's daily life, such as records of meals, exercise, and sleep.

[0105] An "artificial intelligence model" is a computational program that utilizes machine learning and statistical methods to predict health risks from collected data.

[0106] "Health risk" is an indicator that assesses the degree to which a user is likely to develop a specific health problem in the future.

[0107] A "health plan" is a plan that provides specific action instructions, including guidance on diet and exercise, with the aim of maintaining or improving the health of the user.

[0108] A "reward" is an incentive given when a user's health condition improves, and may be provided in the form of discount coupons or points, for example.

[0109] A "security mechanism" is a system that uses technologies such as information encryption and access restrictions to prevent unauthorized access from external sources in order to protect users' health information.

[0110] The system of the present invention is designed to efficiently collect health checkup results and daily life information, predict health risks using a generated AI model, and provide individually adapted health plans based on the results. The implementation of the system mainly consists of the following steps.

[0111] Users can input their health checkup results using devices such as smartphones and personal computers. They can also manually input information about their daily lives, such as diet, exercise, and sleep, or automatically acquire this information by syncing with their smart devices. This data is securely transmitted from the device to a server where it is stored in a standardized format.

[0112] The server uses stored information to predict future health risks using generative AI models such as TENSORFLOW®. AI models trained on historical data and medical research analyze the collected information to calculate, for example, the risk of developing diabetes or hypertension.

[0113] Based on the user's health risks, the server generates a personalized health plan. This plan includes meal menus and exercise plans, and is communicated to the user through a frontend built with React Native. Based on this information, users can adjust their daily lifestyle and strive to maintain their health.

[0114] The system incorporates security mechanisms to ensure the safety of data provided by users. For example, data is encrypted using AES encryption technology to protect it from external access. Furthermore, a two-factor authentication system is employed in the authentication process to enhance the security of user information.

[0115] Furthermore, the server has a function to reward users if an improvement in their health is observed. For example, if an improvement is seen compared to the previous year's blood pressure readings, the server will offer a reward to encourage healthier behaviors in the user.

[0116] For example, if a user records their morning weight and steps using the app, the app can detect the improvement in weight management at the following month's check-up and offer a gym discount coupon as a reward for achieving their step goal.

[0117] Furthermore, an example of a prompt message for utilizing a generative AI model is: "Please provide a health plan generated by the AI ​​from user data. Please also suggest what to include in the incentives." Instructions can be sent to the server in this format.

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] Users input health checkup results and daily life information using their smartphones or computers. This data may include weight, blood pressure, diet, exercise levels, and sleep duration. This data is converted to digital format and encrypted as needed through the input interface. The output is stored on the device as encrypted health data.

[0121] Step 2:

[0122] The terminal transmits encrypted data to the server using a secure communication protocol. AES encryption technology is used to protect the data from unauthorized access. The server decrypts the received data and stores it in a database in a standardized format. The output is standardized health data.

[0123] Step 3:

[0124] The server inputs stored health data into a generating AI model to predict the user's health risks. The AI ​​model, built using TensorFlow, outputs numerical values ​​for the risk of developing conditions such as diabetes or hypertension based on the data. This process references historical medical data and training sets to predict risks. The output is predicted health risk data.

[0125] Step 4:

[0126] The server generates a personalized health plan based on predicted health risk data. This plan includes meal menus, exercise schedules, and lifestyle adjustment suggestions. The server then adjusts the generated health plan based on the user's preferences and feedback. The output is the individualized health plan.

[0127] Step 5:

[0128] The server notifies the user of the generated health plan through an application built with React Native. The user receives the notification, reviews the specific health plan, and can incorporate it into their daily life. The output is the health plan notification provided to the user.

[0129] Step 6:

[0130] Based on the user's implementation of the health plan instructed by the application in their daily life, the server evaluates the improvement in their health status. It compares and verifies this against past data, and if the criteria are met, it initiates a process to provide rewards to the user. The output is a reward proposal based on the evaluation results.

[0131] Step 7:

[0132] The server offers discounts or benefits as rewards to users whose health has improved. This information is notified to the user through the application, acting as an incentive. The output is a reward notification to the user.

[0133] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0134] This invention is a system that supports individual health management and provides a method for providing an individually optimized health plan by combining and analyzing an individual's health checkup results, daily life data, and emotional status. The system includes functions for collecting and storing health data, recognizing the user's emotions using an emotion engine, predicting health risks using a generative AI model, generating individually adapted health plans, notifying the user of the health plan, and providing incentives.

[0135] Data collection and storage

[0136] Users input health checkup data and daily life data into a smartphone or PC terminal. The terminal is also equipped with an emotion engine that recognizes emotions by matching the user's speech and entered text, and acquires emotional data such as whether the user is feeling stressed or motivated. This data is formatted on the terminal and sent to the server. The server stores the data and performs preprocessing to check its consistency and accuracy.

[0137] Predicting and planning for health risks

[0138] The server inputs all data, including emotional data provided by the emotion engine, into a generating AI model. The AI ​​model considers both health data and emotional states to predict health risks with greater accuracy. For example, a high stress level can be predicted to increase the risk of cardiovascular disease. Based on this prediction, the server generates a health plan optimized for the user. The generated plan suggests dietary guidelines and stress reduction programs that also take the user's emotional state into consideration.

[0139] Health plan notifications and incentives

[0140] The device notifies the user of the generated health plan. The notification method is customized based on the user's emotional state; for example, users who are relaxed receive rich text that can be read slowly, while users who are stressed receive concise visuals. The server also provides incentives to users, such as free company meal vouchers, if improvements in their health are confirmed based on the health check results and feedback.

[0141] Feedback and model improvement

[0142] Users provide feedback on their emotional state and health plan through their device. This feedback is sent to a server, which uses it to further improve the generated AI model. In particular, insights gained from emotional status can be used to adjust the model to enhance the applicability of the health plan.

[0143] For example, when a user says "I'm irritated," the emotion engine detects this and sends that state to the server. As a result, the server generates a plan aimed at stress reduction and provides the user with a push notification containing a video recommending relaxing breathing techniques. This feature allows users to receive support for comprehensively improving their emotions and health.

[0144] The following describes the processing flow.

[0145] Step 1:

[0146] Users input health checkup results and data related to their daily lives (e.g., diet, exercise, sleep patterns, etc.) into a smartphone or PC. Furthermore, devices equipped with an emotion engine collect emotional data through the user's voice and text input and recognize their emotional state.

[0147] Step 2:

[0148] The device converts the collected health and emotional data into an appropriate format and sends it to the server. The data is encrypted using security protocols, ensuring it reaches the server safely.

[0149] Step 3:

[0150] The server records the received data in a database and performs data formatting and preprocessing. Preprocessing ensures data consistency and accuracy by detecting outliers and imputing missing values.

[0151] Step 4:

[0152] The server inputs pre-processed data into a generating AI model to predict the user's health risks. The model also takes collected emotional data into account, providing more accurate predictions, for example, by predicting that prolonged high stress levels increase cardiovascular risk.

[0153] Step 5:

[0154] The server generates a health plan that reflects your emotional state based on your predicted health risks. This plan may include specific dietary restrictions, recommended exercises, and relaxation techniques. If your emotions indicate stress, stress reduction guidelines will be emphasized.

[0155] Step 6:

[0156] The device notifies the user of the generated health plan. The notification is customized according to the user's emotional state; for example, a user feeling stressed will receive a message in a gentle tone and a link to relaxation music.

[0157] Step 7:

[0158] The server tracks changes in the user's health status and analyzes the results compared to the previous year's health data. If improvements are recognized, the user is given an incentive. This measure aims to increase user motivation and maintain their desire to improve their health.

[0159] Step 8:

[0160] Users submit feedback on the provided health plans and incentives via their devices. This feedback data also includes reflections on their emotional state.

[0161] Step 9:

[0162] The server optimizes the AI ​​model based on user feedback. This allows for more user-friendly suggestions in future plan generation. Furthermore, by deepening the analysis of emotional data, it becomes possible to provide plans that respond quickly to changes in the user's emotions.

[0163] (Example 2)

[0164] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0165] In modern society, personal health management is becoming increasingly important. However, traditional health management systems have only provided uniform health plans without considering individual emotional states, and have failed to adequately meet individual needs. Furthermore, a lack of motivation for health improvement has made continuous use and improvement difficult.

[0166] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0167] In this invention, the server includes means for collecting personal health checkup results and daily life information, means for analyzing voice and text to recognize personal emotions, and means for transmitting health information and emotional information from the terminal to the server. This makes it possible to provide a more precise and personalized health plan based on individual data, including emotional states. Furthermore, it is possible to improve user motivation by providing incentives for health improvement.

[0168] "Personal health checkup results information" refers to various data obtained during regular health checkups, including information such as blood test results and physical measurement results.

[0169] "Daily life information" refers to data about an individual's activities in their daily life, including information such as the content of their meals, the amount of exercise they do, and the amount of sleep they do.

[0170] "Methods for analyzing voice and text to recognize individual emotions" refers to functions for analyzing emotions from voice input or written text, and is a technology for determining the user's stress level and psychological tendencies.

[0171] "Means of transmitting health and emotional information from a terminal to a server" refers to communication technology for transferring collected data to a server via a network, and includes the process of transmitting data over the internet.

[0172] A "generated artificial intelligence model" is a digital model built using machine learning techniques and is a program used for predictive analytics.

[0173] A "means of predicting health risks" is a method for estimating potential risk factors that may affect an individual's health status, based on collected data.

[0174] "A means of automatically generating a health plan optimized for the user" refers to a function that utilizes the results of health risk predictions to plan health improvement measures tailored to each individual user.

[0175] "A means of customizing and notifying users of their health plans based on their emotional state" refers to a mechanism that displays or communicates health plans in a format that suits the user's emotions.

[0176] "Means of providing incentives" refers to features that offer rewards or benefits to encourage users to take steps to improve their health.

[0177] "Means for collecting user feedback information and improving artificial intelligence models" refers to methods for gathering evaluations and opinions from users, updating artificial intelligence models based on these, and improving their accuracy and usefulness.

[0178] This invention is a system that supports individual health management, and is a method for providing an individually optimized health plan by combining and analyzing the user's health checkup results, daily life information, and emotional status.

[0179] Users can manually input their health checkup results and daily life information using a smartphone or PC. For example, they can input numerical data from their health checkup, their daily diet, exercise habits, and sleep duration. These devices are equipped with an emotion engine that analyzes voice and text to recognize emotions, allowing the system to identify the user's emotional state from the voice and text they input. For example, if a user says, "I'm tired today," the system will capture this and process it as emotional information.

[0180] The terminal formats the entered health and emotional information and sends it to the server via the internet. The server stores the received data in a database and performs preprocessing to maintain its consistency. Preprocessing involves using software such as Python or R to impute missing data values ​​and adjust the data scale.

[0181] The server inputs pre-processed data into a generative AI model. This model, built on machine learning platforms such as TensorFlow or PyTorch, predicts health risks by considering both health and emotional data. For example, it can predict an increased risk of cardiovascular disease if stress levels are high.

[0182] Based on this prediction, the server automatically generates a health plan optimized for the user. This plan may include suggestions for dietary improvements and activities to reduce stress. The generated plan is communicated according to the user's emotional state. For example, a relaxed user will receive a detailed explanation in text, while a stressed user will receive a simple visual.

[0183] Furthermore, the server continuously updates the generated AI model using information obtained from improved health status and user feedback. This allows for improved plan accuracy based on user feedback.

[0184] For example, if a user enters a request stating, "I've been feeling stressed a lot lately. I'd like some advice on how to improve my mental and physical health," this information will be analyzed, and a plan will be generated suggesting breathing exercises and relaxing activities to reduce stress.

[0185] In this way, this system integrates emotional and health data, enabling it to provide users with effective health management.

[0186] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0187] Step 1:

[0188] Users manually input health checkup results and daily life information into their smartphones or PCs. This input includes details such as diet, exercise levels, and sleep duration. Users also express their emotions through voice or text, and the device's emotion engine analyzes this information to generate emotion data. The input data is sent to the device in text or numerical format.

[0189] Step 2:

[0190] The terminal formats the collected health and sentiment information into a format suitable for the database. This formatting process includes checking the format of the original data and correcting outliers. The terminal then sends the formatted data to the server using the HTTP protocol. The output is the normalized health and sentiment information transferred to the server.

[0191] Step 3:

[0192] The server stores the received data in a database and then performs preprocessing to maintain data consistency. Preprocessing includes missing value imputation and data scaling. A Python script is used for this process, transforming the input data into an analyzable state. The output is a preprocessed, integrated dataset.

[0193] Step 4:

[0194] The server provides a pre-processed dataset as input to a generating AI model. This AI model, built on TensorFlow or PyTorch, predicts health risks based on the user's historical data. Specific data calculations include classification and regression analysis using neural networks. The output is a prediction of the user's health risks.

[0195] Step 5:

[0196] The server uses prediction results to generate a health plan optimized for the user. Natural language processing is used for plan generation, enabling flexible plan creation that takes emotions into account. For example, it may include suggestions for dietary improvements and activities to reduce stress. The output is a customized health plan.

[0197] Step 6:

[0198] The device notifies the user of the generated health plan. The notification method is adjusted based on the user's emotional state; a detailed text is displayed for relaxed users, while a concise visual is used for stressed users. The output is the health plan notification provided to the user.

[0199] Step 7:

[0200] Users input feedback on their health plan into their device. This feedback is sent to a server, which uses it to improve the artificial intelligence model. The feedback data is analyzed to help improve the model's accuracy, resulting in a higher quality health plan for the next session. The output is feedback information that helps in the next model adjustment.

[0201] (Application Example 2)

[0202] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0203] Maintaining good employee health is a crucial challenge in today's work environment. However, it is difficult to accurately understand the health and emotional state of individual employees and provide specific health guidance accordingly. Furthermore, there is a lack of mechanisms to appropriately evaluate and reward efforts to improve health, making it difficult to maintain employee motivation.

[0204] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0205] In this invention, the server includes means for collecting personal health checkup results data, daily life data, and emotional data; means for predicting health risks from the collected data using a generated artificial intelligence model; and means for generating individually adapted health plans based on the predicted health risks and emotional state. This makes it possible to provide optimal health guidance tailored to the user's health and emotional state in real time, and to further reward them according to their improvement.

[0206] "Personal health checkup data" refers to information about an individual's health status collected by medical institutions or specialists.

[0207] "Daily life data" refers to information about various actions and habits that individuals acquire in their daily lives.

[0208] "Emotional data" refers to data that indicates the emotional state of a user, obtained from their speech or entered text.

[0209] A "generated artificial intelligence model" is a model used to predict health risks based on a user's health and emotional state using machine learning algorithms.

[0210] "Methods for predicting health risks" refer to technologies that process collected data to infer potential health problems that may be predicted in the future.

[0211] A "health plan" is a plan that includes specific guidance and measures tailored to each individual user to promote health and reduce risks.

[0212] "Means of customizing notification formats based on emotional state" refers to a technology that selects the format that can deliver information most effectively according to the user's emotional state.

[0213] "Means of providing rewards based on progress" refers to a system that provides appropriate incentives for the degree of improvement in a user's health and their efforts.

[0214] The system of this invention provides advanced support for personal health management. The server collects and stores health checkup results, daily life data, and emotional data from individuals. This data is input via smartphones and PCs, and the system uses speech recognition technology and text analysis to estimate the user's emotional state. The terminals are equipped with an emotion engine, for example, which implements the machine learning library TensorFlow.

[0215] The server preprocesses the collected data and inputs the integrated data into a generated artificial intelligence model. This AI model analyzes the user's health check results and daily life data, and further considers emotional data to predict health risks more accurately. Based on this, the server generates an appropriate health plan and notifies the user in a customized format. The notification is optimized according to the emotional state and is sent to end-user devices such as smartphones and PCs in one of the following formats: text, visual, or audio.

[0216] When a user's health status shows significant improvement, the server rewards them according to their progress. This reward takes the form of workplace coupons or similar items to maintain motivation. Users can continuously provide feedback through this process, and emotion-based insights help improve the performance of the AI ​​model on the server side.

[0217] For example, if a factory worker reports via a robot that they have been experiencing severe palpitations recently, the server can analyze the data and send a notification recommending stress-reducing breathing exercises or short stretches. Another example of a health management prompt would be, "Generate an optimal health plan based on this employee's recent emotional data and health check results." This is expected to improve employee health and work efficiency.

[0218] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0219] Step 1:

[0220] The device collects health checkup data, daily life data, and emotional data from the user using voice and text input. This input data is temporarily formatted and corrected on the device. As a result, the formatted, integrated data is sent to the server.

[0221] Step 2:

[0222] The server preprocesses the received data. It verifies the consistency and accuracy of the input data, and, if necessary, imputes missing data and corrects outliers. This prepares a clean dataset as input for the generated AI model.

[0223] Step 3:

[0224] The server supplies pre-processed data to a generating AI model, which then predicts health risks based on the data. The AI ​​model uses machine learning algorithms to analyze the relationship between health status and emotional state. As an output of this process, an individual user's health risk assessment is generated.

[0225] Step 4:

[0226] The server generates a health plan based on a health risk assessment, taking into account the user's emotional state. Using the prompt "Generate the optimal health plan based on this user's emotional data and health checkup results," a personalized recommendation plan is created. The output of this process is a health plan suitable for the user.

[0227] Step 5:

[0228] The server generates a health plan and notifies the user. The emotion engine analyzes the user's current emotional state and, based on that, notifies the user of the plan in the most appropriate format, such as text or visuals. The system ensures that this notification is delivered to the user.

[0229] Step 6:

[0230] The server collects feedback after the health plan is implemented and provides rewards if health improves. Specifically, the reward content is determined using feedback data, and coupons usable at the workplace, etc., are issued to the user. This entire process aims to motivate users to continue managing their health.

[0231] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0232] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0233] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0234] [Second Embodiment]

[0235] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0236] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0237] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0238] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0239] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0240] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0241] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0242] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0243] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0244] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0245] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0246] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0247] This invention is a system for supporting individual health management, which collects individual health checkup results and daily life data, predicts health risks based on that data, and provides an individually optimized health plan. The main components of this system are means for collecting and storing data, a generative AI model that analyzes the data and predicts health risks, a function to generate and provide a health plan based on the prediction to the user, a function to provide incentives, and a function to receive and utilize user feedback.

[0248] Data collection and storage

[0249] Users input the results obtained during their health checkups into their smartphones or PCs. These devices can also input or automatically synchronize data on daily life, such as diet, exercise, and sleep. The devices format this data appropriately and send it to the server in a secure manner. The server stores this data in a database and maintains data consistency through standardization.

[0250] Predicting health risks

[0251] The server inputs the collected data into a generating AI model to predict the user's future health risks. This model is trained on historical data and medical research, and can assess the user's risk of developing conditions such as hypertension or diabetes.

[0252] Health plan creation and provision

[0253] The server creates an optimal health plan for each individual user based on predicted health risks. This includes meal menu suggestions and exercise programs. The generated plan is notified to the user via their device. The user can view the plan details on their device and adjust their lifestyle according to the instructions.

[0254] Provision of incentives

[0255] The server determines whether the user's health has improved as a result of implementing the health plan. For example, if blood pressure has improved compared to the previous year's health checkup, the server provides the user with an incentive such as a free meal voucher at the company cafeteria. This feature serves as an incentive to encourage healthy behaviors in users.

[0256] Collecting and using feedback

[0257] Users can provide feedback on proposed health plans and incentives via the app or PC. The server analyzes this feedback and adjusts the algorithm of the generating AI model. As a result, the quality of the generated health plans and user satisfaction improve.

[0258] As a concrete example, if a user enters a health checkup result showing high blood pressure into the system, the system will suggest a low-sodium diet plan and a daily walking habit to the user. If the user follows this plan for a month and their blood pressure decreases in a follow-up checkup, they will be offered incentives such as free company meal vouchers. This entire process makes it easier for users to maintain their health, and allows companies to effectively manage their healthcare costs.

[0259] The following describes the processing flow.

[0260] Step 1:

[0261] Users input data obtained from health checkups (e.g., blood pressure, blood sugar levels) into their smartphone or PC. They can also manually input daily life data (e.g., diet, exercise history, sleep duration) or have it automatically synchronized from a wearable device.

[0262] Step 2:

[0263] The device handles the communication required to convert collected health checkup results and daily life data into an appropriate format and send it to the server. The data is encrypted, and measures are taken to protect privacy.

[0264] Step 3:

[0265] The server saves the received data to the database. After saving, it checks for missing or abnormal values ​​and cleanses the data as needed. It also formats the data to make it usable in the generative AI model.

[0266] Step 4:

[0267] The server uses the formatted data to run a generative AI model that predicts the user's future health risks. For example, the model calculates the risk of diseases such as diabetes and hypertension from past health checkup data and current lifestyle habits.

[0268] Step 5:

[0269] The server generates a health plan tailored to the user based on predicted health risks. This plan includes specific dietary guidelines and exercise plans. For example, if the model indicates a risk of high blood pressure, a diet plan to reduce sodium intake and aerobic exercise recommendations will be provided.

[0270] Step 6:

[0271] The device notifies the user of the generated health plan. The user can check the contents of the health plan on the device's display screen and learn specific ways to implement it in their daily life. The notification includes a reminder function to support the user in improving their lifestyle habits.

[0272] Step 7:

[0273] The server tracks health check results to determine if the user's health has improved. If improvement is confirmed compared to the previous year, an incentive (e.g., a free meal voucher at the company cafeteria) is provided. This process increases user motivation and encourages the continuation of healthy behaviors.

[0274] Step 8:

[0275] Users can provide feedback via their devices on how they felt about the health plans and incentives offered. This feedback information is sent to the server and used to further adjust the plans.

[0276] Step 9:

[0277] The server analyzes the feedback collected from users and optimizes the generated AI model based on it. With the improvement of the model, the health plan proposed in subsequent times will be more personalized. This enables more specifically meeting the needs and preferences of users.

[0278] (Example 1)

[0279] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0280] In modern society, people's lifestyles are diverse, and it is important to continuously manage individual health conditions. However, it has been difficult to predict individual health risks and provide appropriate health plans with conventional methods. Also, there is a lack of a mechanism to continuously improve health plans by effectively utilizing user feedback.

[0281] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0282] In this invention, the server includes means for collecting personal health information and daily life information of an individual, means for predicting health risks from the collected information using a generated AI model, and means for generating a health program individually adapted based on the predicted health risks. Thereby, it becomes possible to automatically and continuously provide an optimized health program for each user and promote the improvement of the user's health condition.

[0283] "Personal health information" refers to the health examination results obtained by the user and health-related data that can be obtained.

[0284] "Daily life information" is data related to the daily activities of the user and includes information related to, for example, diet, exercise, and sleep.

[0285] The "generative AI model" refers to a model that utilizes artificial intelligence and is used to predict the health risks of users based on the collected data.

[0286] "Health risk" refers to factors or conditions that may potentially affect health in the future.

[0287] The "individually tailored health program" refers to a plan for maintaining and improving health that is individualized considering the specific health conditions and risks of the user.

[0288] The "evaluation period" refers to a specific time frame during which health status is measured and compared.

[0289] "Reward" refers to incentives or benefits provided to the user for the improvement of health status.

[0290] "Reaction information" refers to the feelings, opinions, and feedback of the user regarding the health program and rewards.

[0291] This system is configured to collect the health information of users and provide a health program based on analysis using a generative AI model. The system is mainly implemented by utilizing the following hardware and software.

[0292] The user inputs their health information and daily life information using a terminal such as a smartphone or PC. This includes dietary content, exercise status, sleep patterns, and the results of regular health check-ups. The terminal converts the input information into a predetermined format and transmits the data to the server via the Internet.

[0293] The server stores received data using a secure database system. The stored data is standardized to maintain consistency in the collected information. Next, the server leverages a generative AI model to analyze the stored data and predict the user's health risks. This model utilizes machine learning algorithms and is trained on large historical datasets.

[0294] Based on the predicted health risks, the server creates a personalized health program for each user. This program includes meal plans and exercise suggestions, providing specific actionable guidelines tailored to individual user needs. The created program is notified to the user's device, allowing them to review its details and incorporate it into their daily life. Rewards may also be offered if improvements are observed in the user's health.

[0295] For example, if a user inputs into the system that their blood pressure is high based on their health checkup results, this information is sent to the server, and a generating AI model assesses the risk of hypertension. Based on the results, the server can provide the user with a low-salt diet program and an exercise plan including walking. If the user follows the program and their blood pressure improves in a follow-up examination, the server will provide rewards such as free services or coupons.

[0296] An example of a prompt message is: "Based on the health checkup results entered by the user, use a generative AI model to predict the risk of hypertension and propose an appropriate health plan."

[0297] Through this system, users can receive personalized health support, increasing their chances of maintaining and improving their health.

[0298] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0299] Step 1:

[0300] The user inputs their health information and daily life information using a terminal. Specifically, the user inputs information related to medical examination results, diet, exercise, and sleep via a dedicated application on the terminal. The input data is converted into an appropriate format and prepared for transmission. The input data is temporarily stored on the memory of the terminal and then transmitted to the server using encryption technology.

[0301] Step 2:

[0302] The server receives the data sent from the terminal and stores it in the database. The server extracts health examination information and daily life information from the received data and performs standardization processing so that these are stored in a unified format. For example, processing such as unifying the date format and converting numerical units is performed. The stored data is converted into information required for subsequent analysis.

[0303] Step 3:

[0304] The server inputs the data stored in the database into a generative AI model to predict the user's health risk. The data in the stored unified format is used as the input. The generative AI model uses an algorithm learned based on past data through machine learning to output a risk score and classification result regarding the user's health status. This process includes massive data processing and real-time analysis.

[0305] Step 4:

[0306] Based on the prediction result obtained from the generative AI model, the server generates an individual health program. The input includes the predicted value of the health risk and the user's lifestyle information. In cooperation with a rule-based system supervised by health experts, a program including appropriate diet suggestions and exercise menus is created. The health program as the output is transmitted to the user's terminal.

[0307] Step 5:

[0308] The user checks the health program notified on their device. Using the device application, they check the details of the program they received and incorporate the suggested actions into their daily life. The user's progress is recorded on the device and sent to the server as feedback data.

[0309] Step 6:

[0310] The server analyzes user feedback data and evaluates changes in health status resulting from program implementation. Inputs include re-results of health checkups and user-reported information. If the health status meets predetermined evaluation criteria, the user receives a reward. Specifically, this may involve issuing electronic coupons or sending notifications via in-app messages.

[0311] (Application Example 1)

[0312] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0313] In personal health management, the challenge lies in efficiently and effectively providing a system that collects various health data, accurately predicts individual health risks, ensures data security, and allows users to provide information with confidence. Furthermore, it is necessary to appropriately evaluate improvements in health status and promote healthy behaviors through rewards.

[0314] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0315] In this invention, the server includes means for collecting personal health checkup results and daily life information, means for predicting health risks from the collected information using a generated artificial intelligence model, means for generating individually adapted health plans based on the predicted health risks, means for providing a security mechanism to encrypt the user's health information and prevent unauthorized access from external sources, and means for providing rewards when the user's health status improves compared to the previous year. This makes it possible to safely and effectively manage personal health data, promote individually optimized health management, and build a reward system that supports users' healthy behaviors.

[0316] "Health checkup results information" refers to data such as numerical values ​​and evaluations obtained as a result of tests that users have received at medical institutions, etc.

[0317] "Daily life information" refers to data on various activities and behaviors related to the user's daily life, such as records of meals, exercise, and sleep.

[0318] An "artificial intelligence model" is a computational program that utilizes machine learning and statistical methods to predict health risks from collected data.

[0319] "Health risk" is an indicator that assesses the degree to which a user is likely to develop a specific health problem in the future.

[0320] A "health plan" is a plan that provides specific action instructions, including guidance on diet and exercise, with the aim of maintaining or improving the health of the user.

[0321] A "reward" is an incentive given when a user's health condition improves, and may be provided in the form of discount coupons or points, for example.

[0322] A "security mechanism" is a system that uses technologies such as information encryption and access restrictions to prevent unauthorized access from external sources in order to protect users' health information.

[0323] The system of the present invention is designed to efficiently collect health checkup results and daily life information, predict health risks using a generated AI model, and provide individually adapted health plans based on the results. The implementation of the system mainly consists of the following steps.

[0324] Users can input their health checkup results using devices such as smartphones and personal computers. They can also manually input information about their daily lives, such as diet, exercise, and sleep, or automatically acquire this information by syncing with their smart devices. This data is securely transmitted from the device to a server where it is stored in a standardized format.

[0325] The server uses stored information to predict future health risks using generative AI models based on TensorFlow and other technologies. AI models trained on historical data and medical research analyze the collected information to calculate, for example, the risk of developing diabetes or hypertension.

[0326] Based on the user's health risks, the server generates a personalized health plan. This plan includes meal menus and exercise plans, and is communicated to the user through a frontend built with React Native. Based on this information, users can adjust their daily lifestyle and strive to maintain their health.

[0327] The system incorporates security mechanisms to ensure the safety of data provided by users. For example, data is encrypted using AES encryption technology to protect it from external access. Furthermore, a two-factor authentication system is employed in the authentication process to enhance the security of user information.

[0328] Furthermore, the server has a function to reward users if an improvement in their health is observed. For example, if an improvement is seen compared to the previous year's blood pressure readings, the server will offer a reward to encourage healthier behaviors in the user.

[0329] For example, if a user records their morning weight and steps using the app, the app can detect the improvement in weight management at the following month's check-up and offer a gym discount coupon as a reward for achieving their step goal.

[0330] Furthermore, an example of a prompt message for utilizing a generative AI model is: "Please provide a health plan generated by the AI ​​from user data. Please also suggest what to include in the incentives." Instructions can be sent to the server in this format.

[0331] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0332] Step 1:

[0333] Users input health checkup results and daily life information using their smartphones or computers. This data may include weight, blood pressure, diet, exercise levels, and sleep duration. This data is converted to digital format and encrypted as needed through the input interface. The output is stored on the device as encrypted health data.

[0334] Step 2:

[0335] The terminal transmits encrypted data to the server using a secure communication protocol. AES encryption technology is used to protect the data from unauthorized access. The server decrypts the received data and stores it in a database in a standardized format. The output is standardized health data.

[0336] Step 3:

[0337] The server inputs stored health data into a generating AI model to predict the user's health risks. The AI ​​model, built using TensorFlow, outputs numerical values ​​for the risk of developing conditions such as diabetes or hypertension based on the data. This process references historical medical data and training sets to predict risks. The output is predicted health risk data.

[0338] Step 4:

[0339] The server generates a personalized health plan based on predicted health risk data. This plan includes meal menus, exercise schedules, and lifestyle adjustment suggestions. The server then adjusts the generated health plan based on the user's preferences and feedback. The output is the individualized health plan.

[0340] Step 5:

[0341] The server notifies the user of the generated health plan through an application built with React Native. The user receives the notification, reviews the specific health plan, and can incorporate it into their daily life. The output is the health plan notification provided to the user.

[0342] Step 6:

[0343] Based on the user's implementation of the health plan instructed by the application in their daily life, the server evaluates the improvement in their health status. It compares and verifies this against past data, and if the criteria are met, it initiates a process to provide rewards to the user. The output is a reward proposal based on the evaluation results.

[0344] Step 7:

[0345] The server offers discounts or benefits as rewards to users whose health has improved. This information is notified to the user through the application, acting as an incentive. The output is a reward notification to the user.

[0346] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0347] This invention is a system that supports individual health management and provides a method for providing an individually optimized health plan by combining and analyzing an individual's health checkup results, daily life data, and emotional status. The system includes functions for collecting and storing health data, recognizing the user's emotions using an emotion engine, predicting health risks using a generative AI model, generating individually adapted health plans, notifying the user of the health plan, and providing incentives.

[0348] Data collection and storage

[0349] Users input health checkup data and daily life data into a smartphone or PC terminal. The terminal is also equipped with an emotion engine that recognizes emotions by matching the user's speech and entered text, and acquires emotional data such as whether the user is feeling stressed or motivated. This data is formatted on the terminal and sent to the server. The server stores the data and performs preprocessing to check its consistency and accuracy.

[0350] Predicting and planning for health risks

[0351] The server inputs all data, including emotional data provided by the emotion engine, into a generating AI model. The AI ​​model considers both health data and emotional states to predict health risks with greater accuracy. For example, a high stress level can be predicted to increase the risk of cardiovascular disease. Based on this prediction, the server generates a health plan optimized for the user. The generated plan suggests dietary guidelines and stress reduction programs that also take the user's emotional state into consideration.

[0352] Health plan notifications and incentives

[0353] The device notifies the user of the generated health plan. The notification method is customized based on the user's emotional state; for example, users who are relaxed receive rich text that can be read slowly, while users who are stressed receive concise visuals. The server also provides incentives to users, such as free company meal vouchers, if improvements in their health are confirmed based on the health check results and feedback.

[0354] Feedback and model improvement

[0355] Users provide feedback on their emotional state and health plan through their device. This feedback is sent to a server, which uses it to further improve the generated AI model. In particular, insights gained from emotional status can be used to adjust the model to enhance the applicability of the health plan.

[0356] For example, when a user says "I'm irritated," the emotion engine detects this and sends that state to the server. As a result, the server generates a plan aimed at stress reduction and provides the user with a push notification containing a video recommending relaxing breathing techniques. This feature allows users to receive support for comprehensively improving their emotions and health.

[0357] The following describes the processing flow.

[0358] Step 1:

[0359] Users input health checkup results and data related to their daily lives (e.g., diet, exercise, sleep patterns, etc.) into a smartphone or PC. Furthermore, devices equipped with an emotion engine collect emotional data through the user's voice and text input and recognize their emotional state.

[0360] Step 2:

[0361] The device converts the collected health and emotional data into an appropriate format and sends it to the server. The data is encrypted using security protocols, ensuring it reaches the server safely.

[0362] Step 3:

[0363] The server records the received data in a database and performs data formatting and preprocessing. Preprocessing ensures data consistency and accuracy by detecting outliers and imputing missing values.

[0364] Step 4:

[0365] The server inputs pre-processed data into a generating AI model to predict the user's health risks. The model also takes collected emotional data into account, providing more accurate predictions, for example, by predicting that prolonged high stress levels increase cardiovascular risk.

[0366] Step 5:

[0367] The server generates a health plan that reflects your emotional state based on your predicted health risks. This plan may include specific dietary restrictions, recommended exercises, and relaxation techniques. If your emotions indicate stress, stress reduction guidelines will be emphasized.

[0368] Step 6:

[0369] The device notifies the user of the generated health plan. The notification is customized according to the user's emotional state; for example, a user feeling stressed will receive a message in a gentle tone and a link to relaxation music.

[0370] Step 7:

[0371] The server tracks changes in the user's health status and analyzes the results compared to the previous year's health data. If improvements are recognized, the user is given an incentive. This measure aims to increase user motivation and maintain their desire to improve their health.

[0372] Step 8:

[0373] Users submit feedback on the provided health plans and incentives via their devices. This feedback data also includes reflections on their emotional state.

[0374] Step 9:

[0375] The server optimizes the AI ​​model based on user feedback. This allows for more user-friendly suggestions in future plan generation. Furthermore, by deepening the analysis of emotional data, it becomes possible to provide plans that respond quickly to changes in the user's emotions.

[0376] (Example 2)

[0377] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0378] In modern society, personal health management is becoming increasingly important. However, traditional health management systems have only provided uniform health plans without considering individual emotional states, and have failed to adequately meet individual needs. Furthermore, a lack of motivation for health improvement has made continuous use and improvement difficult.

[0379] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0380] In this invention, the server includes means for collecting personal health checkup results and daily life information, means for analyzing voice and text to recognize personal emotions, and means for transmitting health information and emotional information from the terminal to the server. This makes it possible to provide a more precise and personalized health plan based on individual data, including emotional states. Furthermore, it is possible to improve user motivation by providing incentives for health improvement.

[0381] "Personal health checkup results information" refers to various data obtained during regular health checkups, including information such as blood test results and physical measurement results.

[0382] "Daily life information" refers to data about an individual's activities in their daily life, including information such as the content of their meals, the amount of exercise they do, and the amount of sleep they do.

[0383] "Methods for analyzing voice and text to recognize individual emotions" refers to functions for analyzing emotions from voice input or written text, and is a technology for determining the user's stress level and psychological tendencies.

[0384] "Means of transmitting health and emotional information from a terminal to a server" refers to communication technology for transferring collected data to a server via a network, and includes the process of transmitting data over the internet.

[0385] A "generated artificial intelligence model" is a digital model built using machine learning techniques and is a program used for predictive analytics.

[0386] A "means of predicting health risks" is a method for estimating potential risk factors that may affect an individual's health status, based on collected data.

[0387] "A means of automatically generating a health plan optimized for the user" refers to a function that utilizes the results of health risk predictions to plan health improvement measures tailored to each individual user.

[0388] "A means of customizing and notifying users of their health plans based on their emotional state" refers to a mechanism that displays or communicates health plans in a format that suits the user's emotions.

[0389] "Means of providing incentives" refers to features that offer rewards or benefits to encourage users to take steps to improve their health.

[0390] "Means for collecting user feedback information and improving artificial intelligence models" refers to methods for gathering evaluations and opinions from users, updating artificial intelligence models based on these, and improving their accuracy and usefulness.

[0391] This invention is a system that supports individual health management, and is a method for providing an individually optimized health plan by combining and analyzing the user's health checkup results, daily life information, and emotional status.

[0392] Users can manually input their health checkup results and daily life information using a smartphone or PC. For example, they can input numerical data from their health checkup, their daily diet, exercise habits, and sleep duration. These devices are equipped with an emotion engine that analyzes voice and text to recognize emotions, allowing the system to identify the user's emotional state from the voice and text they input. For example, if a user says, "I'm tired today," the system will capture this and process it as emotional information.

[0393] The terminal formats the entered health and emotional information and sends it to the server via the internet. The server stores the received data in a database and performs preprocessing to maintain its consistency. Preprocessing involves using software such as Python or R to impute missing data values ​​and adjust the data scale.

[0394] The server inputs pre-processed data into a generative AI model. This model, built on machine learning platforms such as TensorFlow or PyTorch, predicts health risks by considering both health and emotional data. For example, it can predict an increased risk of cardiovascular disease if stress levels are high.

[0395] Based on this prediction, the server automatically generates a health plan optimized for the user. This plan may include suggestions for dietary improvements and activities to reduce stress. The generated plan is communicated according to the user's emotional state. For example, a relaxed user will receive a detailed explanation in text, while a stressed user will receive a simple visual.

[0396] Furthermore, the server continuously updates the generated AI model using information obtained from improved health status and user feedback. This allows for improved plan accuracy based on user feedback.

[0397] For example, if a user enters a request stating, "I've been feeling stressed a lot lately. I'd like some advice on how to improve my mental and physical health," this information will be analyzed, and a plan will be generated suggesting breathing exercises and relaxing activities to reduce stress.

[0398] In this way, this system integrates emotional and health data, enabling it to provide users with effective health management.

[0399] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0400] Step 1:

[0401] Users manually input health checkup results and daily life information into their smartphones or PCs. This input includes details such as diet, exercise levels, and sleep duration. Users also express their emotions through voice or text, and the device's emotion engine analyzes this information to generate emotion data. The input data is sent to the device in text or numerical format.

[0402] Step 2:

[0403] The terminal formats the collected health and sentiment information into a format suitable for the database. This formatting process includes checking the format of the original data and correcting outliers. The terminal then sends the formatted data to the server using the HTTP protocol. The output is the normalized health and sentiment information transferred to the server.

[0404] Step 3:

[0405] The server stores the received data in a database and then performs preprocessing to maintain data consistency. Preprocessing includes missing value imputation and data scaling. A Python script is used for this process, transforming the input data into an analyzable state. The output is a preprocessed, integrated dataset.

[0406] Step 4:

[0407] The server provides a pre-processed dataset as input to a generating AI model. This AI model, built on TensorFlow or PyTorch, predicts health risks based on the user's historical data. Specific data calculations include classification and regression analysis using neural networks. The output is a prediction of the user's health risks.

[0408] Step 5:

[0409] The server uses prediction results to generate a health plan optimized for the user. Natural language processing is used for plan generation, enabling flexible plan creation that takes emotions into account. For example, it may include suggestions for dietary improvements and activities to reduce stress. The output is a customized health plan.

[0410] Step 6:

[0411] The device notifies the user of the generated health plan. The notification method is adjusted based on the user's emotional state; a detailed text is displayed for relaxed users, while a concise visual is used for stressed users. The output is the health plan notification provided to the user.

[0412] Step 7:

[0413] Users input feedback on their health plan into their device. This feedback is sent to a server, which uses it to improve the artificial intelligence model. The feedback data is analyzed to help improve the model's accuracy, resulting in a higher quality health plan for the next session. The output is feedback information that helps in the next model adjustment.

[0414] (Application Example 2)

[0415] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0416] Maintaining good employee health is a crucial challenge in today's work environment. However, it is difficult to accurately understand the health and emotional state of individual employees and provide specific health guidance accordingly. Furthermore, there is a lack of mechanisms to appropriately evaluate and reward efforts to improve health, making it difficult to maintain employee motivation.

[0417] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0418] In this invention, the server includes means for collecting personal health checkup results data, daily life data, and emotional data; means for predicting health risks from the collected data using a generated artificial intelligence model; and means for generating individually adapted health plans based on the predicted health risks and emotional state. This makes it possible to provide optimal health guidance tailored to the user's health and emotional state in real time, and to further reward them according to their improvement.

[0419] "Personal health checkup data" refers to information about an individual's health status collected by medical institutions or specialists.

[0420] "Daily life data" refers to information about various actions and habits that individuals acquire in their daily lives.

[0421] "Emotional data" refers to data that indicates the emotional state of a user, obtained from their speech or entered text.

[0422] A "generated artificial intelligence model" is a model used to predict health risks based on a user's health and emotional state using machine learning algorithms.

[0423] "Methods for predicting health risks" refer to technologies that process collected data to infer potential health problems that may be predicted in the future.

[0424] A "health plan" is a plan that includes specific guidance and measures tailored to each individual user to promote health and reduce risks.

[0425] "Means of customizing notification formats based on emotional state" refers to a technology that selects the format that can deliver information most effectively according to the user's emotional state.

[0426] "Means of providing rewards based on progress" refers to a system that provides appropriate incentives for the degree of improvement in a user's health and their efforts.

[0427] The system of this invention provides advanced support for personal health management. The server collects and stores health checkup results, daily life data, and emotional data from individuals. This data is input via smartphones and PCs, and the system uses speech recognition technology and text analysis to estimate the user's emotional state. The terminals are equipped with an emotion engine, for example, which implements the machine learning library TensorFlow.

[0428] The server preprocesses the collected data and inputs the integrated data into a generated artificial intelligence model. This AI model analyzes the user's health check results and daily life data, and further considers emotional data to predict health risks more accurately. Based on this, the server generates an appropriate health plan and notifies the user in a customized format. The notification is optimized according to the emotional state and is sent to end-user devices such as smartphones and PCs in one of the following formats: text, visual, or audio.

[0429] When a user's health status shows significant improvement, the server rewards them according to their progress. This reward takes the form of workplace coupons or similar items to maintain motivation. Users can continuously provide feedback through this process, and emotion-based insights help improve the performance of the AI ​​model on the server side.

[0430] For example, if a factory worker reports via a robot that they have been experiencing severe palpitations recently, the server can analyze the data and send a notification recommending stress-reducing breathing exercises or short stretches. Another example of a health management prompt would be, "Generate an optimal health plan based on this employee's recent emotional data and health check results." This is expected to improve employee health and work efficiency.

[0431] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0432] Step 1:

[0433] The device collects health checkup data, daily life data, and emotional data from the user using voice and text input. This input data is temporarily formatted and corrected on the device. As a result, the formatted, integrated data is sent to the server.

[0434] Step 2:

[0435] The server preprocesses the received data. It verifies the consistency and accuracy of the input data, and, if necessary, imputes missing data and corrects outliers. This prepares a clean dataset as input for the generated AI model.

[0436] Step 3:

[0437] The server supplies pre-processed data to a generating AI model, which then predicts health risks based on the data. The AI ​​model uses machine learning algorithms to analyze the relationship between health status and emotional state. As an output of this process, an individual user's health risk assessment is generated.

[0438] Step 4:

[0439] The server generates a health plan based on a health risk assessment, taking into account the user's emotional state. Using the prompt "Generate the optimal health plan based on this user's emotional data and health checkup results," a personalized recommendation plan is created. The output of this process is a health plan suitable for the user.

[0440] Step 5:

[0441] The server generates a health plan and notifies the user. The emotion engine analyzes the user's current emotional state and, based on that, notifies the user of the plan in the most appropriate format, such as text or visuals. The system ensures that this notification is delivered to the user.

[0442] Step 6:

[0443] The server collects feedback after the health plan is implemented and provides rewards if health improves. Specifically, the reward content is determined using feedback data, and coupons usable at the workplace, etc., are issued to the user. This entire process aims to motivate users to continue managing their health.

[0444] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0445] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0446] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0447] [Third Embodiment]

[0448] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0449] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0450] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0451] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0452] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0453] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0454] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0455] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0456] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0457] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0458] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0459] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0460] This invention is a system for supporting individual health management, which collects individual health checkup results and daily life data, predicts health risks based on that data, and provides an individually optimized health plan. The main components of this system are means for collecting and storing data, a generative AI model that analyzes the data and predicts health risks, a function to generate and provide a health plan based on the prediction to the user, a function to provide incentives, and a function to receive and utilize user feedback.

[0461] Data collection and storage

[0462] Users input the results obtained during their health checkups into their smartphones or PCs. These devices can also input or automatically synchronize data on daily life, such as diet, exercise, and sleep. The devices format this data appropriately and send it to the server in a secure manner. The server stores this data in a database and maintains data consistency through standardization.

[0463] Predicting health risks

[0464] The server inputs the collected data into a generating AI model to predict the user's future health risks. This model is trained on historical data and medical research, and can assess the user's risk of developing conditions such as hypertension or diabetes.

[0465] Health plan creation and provision

[0466] The server creates an optimal health plan for each individual user based on predicted health risks. This includes meal menu suggestions and exercise programs. The generated plan is notified to the user via their device. The user can view the plan details on their device and adjust their lifestyle according to the instructions.

[0467] Provision of incentives

[0468] The server determines whether the user's health has improved as a result of implementing the health plan. For example, if blood pressure has improved compared to the previous year's health checkup, the server provides the user with an incentive such as a free meal voucher at the company cafeteria. This feature serves as an incentive to encourage healthy behaviors in users.

[0469] Collecting and using feedback

[0470] Users can provide feedback on proposed health plans and incentives via the app or PC. The server analyzes this feedback and adjusts the algorithm of the generating AI model. As a result, the quality of the generated health plans and user satisfaction improve.

[0471] As a concrete example, if a user enters a health checkup result showing high blood pressure into the system, the system will suggest a low-sodium diet plan and a daily walking habit to the user. If the user follows this plan for a month and their blood pressure decreases in a follow-up checkup, they will be offered incentives such as free company meal vouchers. This entire process makes it easier for users to maintain their health, and allows companies to effectively manage their healthcare costs.

[0472] The following describes the processing flow.

[0473] Step 1:

[0474] Users input data obtained from health checkups (e.g., blood pressure, blood sugar levels) into their smartphone or PC. They can also manually input daily life data (e.g., diet, exercise history, sleep duration) or have it automatically synchronized from a wearable device.

[0475] Step 2:

[0476] The device handles the communication required to convert collected health checkup results and daily life data into an appropriate format and send it to the server. The data is encrypted, and measures are taken to protect privacy.

[0477] Step 3:

[0478] The server saves the received data to the database. After saving, it checks for missing or abnormal values ​​and cleanses the data as needed. It also formats the data to make it usable in the generative AI model.

[0479] Step 4:

[0480] The server uses the formatted data to run a generative AI model that predicts the user's future health risks. For example, the model calculates the risk of diseases such as diabetes and hypertension from past health checkup data and current lifestyle habits.

[0481] Step 5:

[0482] The server generates a health plan tailored to the user based on predicted health risks. This plan includes specific dietary guidelines and exercise plans. For example, if the model indicates a risk of high blood pressure, a diet plan to reduce sodium intake and aerobic exercise recommendations will be provided.

[0483] Step 6:

[0484] The device notifies the user of the generated health plan. The user can check the contents of the health plan on the device's display screen and learn specific ways to implement it in their daily life. The notification includes a reminder function to support the user in improving their lifestyle habits.

[0485] Step 7:

[0486] The server tracks health check results to determine if the user's health has improved. If improvement is confirmed compared to the previous year, an incentive (e.g., a free meal voucher at the company cafeteria) is provided. This process increases user motivation and encourages the continuation of healthy behaviors.

[0487] Step 8:

[0488] Users can provide feedback via their devices on how they felt about the health plans and incentives offered. This feedback information is sent to the server and used to further adjust the plans.

[0489] Step 9:

[0490] The server analyzes feedback collected from users and optimizes the generated AI model based on that analysis. This model improvement allows for more personalized health plan suggestions in the future, enabling a more specific response to user needs and preferences.

[0491] (Example 1)

[0492] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0493] In modern society, people's lifestyles are diversifying, and it is important to continuously manage individual health conditions. However, traditional methods have made it difficult to predict individual health risks and provide appropriate health plans. Furthermore, there is a lack of mechanisms to effectively utilize user feedback to continuously improve health plans.

[0494] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0495] In this invention, the server includes means for collecting personal health information and daily life information, means for predicting health risks from the collected information using a generative AI model, and means for generating individually tailored health programs based on the predicted health risks. This makes it possible to provide each user with an automatically and continuously optimized health program, thereby promoting improvement in the user's health condition.

[0496] "Personal health information" refers to health checkup results and other health-related data obtained by the user.

[0497] "Daily life information" refers to data related to the user's daily activities, including information on diet, exercise, sleep, etc.

[0498] A "generative AI model" refers to an artificial intelligence-powered model used to predict a user's health risks based on collected data.

[0499] "Health risk" refers to factors or conditions that may have a potential impact on health in the future.

[0500] An "individually tailored health program" refers to a personalized plan for maintaining or improving health, taking into account the user's specific health condition and risks.

[0501] The "evaluation period" refers to a specific time frame in which health status is measured and compared.

[0502] "Rewards" refer to incentives or benefits offered to users for improving their health.

[0503] "Response information" refers to users' impressions, opinions, and feedback regarding health programs and rewards.

[0504] This system collects users' health information and provides health programs based on analysis using a generative AI model. The system primarily utilizes the following hardware and software.

[0505] Users input their health and daily life information using devices such as smartphones and PCs. This includes dietary information, exercise habits, sleep patterns, and results of regular health checkups. The device converts the entered information into a predetermined format and transmits the data to a server via the internet.

[0506] The server stores received data using a secure database system. The stored data is standardized to maintain consistency in the collected information. Next, the server leverages a generative AI model to analyze the stored data and predict the user's health risks. This model utilizes machine learning algorithms and is trained on large historical datasets.

[0507] Based on the predicted health risks, the server creates a personalized health program for each user. This program includes meal plans and exercise suggestions, providing specific actionable guidelines tailored to individual user needs. The created program is notified to the user's device, allowing them to review its details and incorporate it into their daily life. Rewards may also be offered if improvements are observed in the user's health.

[0508] For example, if a user inputs into the system that their blood pressure is high based on their health checkup results, this information is sent to the server, and a generating AI model assesses the risk of hypertension. Based on the results, the server can provide the user with a low-salt diet program and an exercise plan including walking. If the user follows the program and their blood pressure improves in a follow-up examination, the server will provide rewards such as free services or coupons.

[0509] An example of a prompt message is: "Based on the health checkup results entered by the user, use a generative AI model to predict the risk of hypertension and propose an appropriate health plan."

[0510] Through this system, users can receive personalized health support, increasing their chances of maintaining and improving their health.

[0511] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0512] Step 1:

[0513] Users input their health and daily life information using a device. Specifically, they input health checkup results and information about their diet, exercise, and sleep via a dedicated application on the device. The entered data is converted to an appropriate format and prepared for transmission. The input data is temporarily stored in the device's memory and then sent to the server using encryption technology.

[0514] Step 2:

[0515] The server receives data from terminals and stores it in a database. Health checkup information and daily life information are extracted from the received data, and standardization processing is performed so that this data is stored in a unified format. For example, processing such as unifying date formats and converting numerical units is carried out. The stored data is then converted into information necessary for subsequent analysis.

[0516] Step 3:

[0517] The server feeds data stored in the database into a generative AI model to predict the user's health risk. The input data is stored in a unified format. The generative AI model uses a machine learning algorithm based on historical data to output a risk score and classification result regarding the user's health status. This process involves massive data processing and real-time analysis.

[0518] Step 4:

[0519] The server generates personalized health programs based on prediction results obtained from a generated AI model. Inputs include predicted health risks and user lifestyle information. In conjunction with a rule-based system supervised by health experts, a program is created that includes appropriate dietary suggestions and exercise menus. The resulting health program is sent to the user's device.

[0520] Step 5:

[0521] The user checks the health program notified on their device. Using the device application, they check the details of the program they received and incorporate the suggested actions into their daily life. The user's progress is recorded on the device and sent to the server as feedback data.

[0522] Step 6:

[0523] The server analyzes user feedback data and evaluates changes in health status resulting from program implementation. Inputs include re-results of health checkups and user-reported information. If the health status meets predetermined evaluation criteria, the user receives a reward. Specifically, this may involve issuing electronic coupons or sending notifications via in-app messages.

[0524] (Application Example 1)

[0525] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0526] In personal health management, the challenge lies in efficiently and effectively providing a system that collects various health data, accurately predicts individual health risks, ensures data security, and allows users to provide information with confidence. Furthermore, it is necessary to appropriately evaluate improvements in health status and promote healthy behaviors through rewards.

[0527] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0528] In this invention, the server includes means for collecting personal health checkup results and daily life information, means for predicting health risks from the collected information using a generated artificial intelligence model, means for generating individually adapted health plans based on the predicted health risks, means for providing a security mechanism to encrypt the user's health information and prevent unauthorized access from external sources, and means for providing rewards when the user's health status improves compared to the previous year. This makes it possible to safely and effectively manage personal health data, promote individually optimized health management, and build a reward system that supports users' healthy behaviors.

[0529] "Health checkup results information" refers to data such as numerical values ​​and evaluations obtained as a result of tests that users have received at medical institutions, etc.

[0530] "Daily life information" refers to data on various activities and behaviors related to the user's daily life, such as records of meals, exercise, and sleep.

[0531] An "artificial intelligence model" is a computational program that utilizes machine learning and statistical methods to predict health risks from collected data.

[0532] "Health risk" is an indicator that assesses the degree to which a user is likely to develop a specific health problem in the future.

[0533] A "health plan" is a plan that provides specific action instructions, including guidance on diet and exercise, with the aim of maintaining or improving the health of the user.

[0534] A "reward" is an incentive given when a user's health condition improves, and may be provided in the form of discount coupons or points, for example.

[0535] A "security mechanism" is a system that uses technologies such as information encryption and access restrictions to prevent unauthorized access from external sources in order to protect users' health information.

[0536] The system of the present invention is designed to efficiently collect health checkup results and daily life information, predict health risks using a generated AI model, and provide individually adapted health plans based on the results. The implementation of the system mainly consists of the following steps.

[0537] Users can input their health checkup results using devices such as smartphones and personal computers. They can also manually input information about their daily lives, such as diet, exercise, and sleep, or automatically acquire this information by syncing with their smart devices. This data is securely transmitted from the device to a server where it is stored in a standardized format.

[0538] The server uses stored information to predict future health risks using generative AI models based on TensorFlow and other technologies. AI models trained on historical data and medical research analyze the collected information to calculate, for example, the risk of developing diabetes or hypertension.

[0539] Based on the user's health risks, the server generates a personalized health plan. This plan includes meal menus and exercise plans, and is communicated to the user through a frontend built with React Native. Based on this information, users can adjust their daily lifestyle and strive to maintain their health.

[0540] The system incorporates security mechanisms to ensure the safety of data provided by users. For example, data is encrypted using AES encryption technology to protect it from external access. Furthermore, a two-factor authentication system is employed in the authentication process to enhance the security of user information.

[0541] Furthermore, the server has a function to reward users if an improvement in their health is observed. For example, if an improvement is seen compared to the previous year's blood pressure readings, the server will offer a reward to encourage healthier behaviors in the user.

[0542] For example, if a user records their morning weight and steps using the app, the app can detect the improvement in weight management at the following month's check-up and offer a gym discount coupon as a reward for achieving their step goal.

[0543] Furthermore, an example of a prompt message for utilizing a generative AI model is: "Please provide a health plan generated by the AI ​​from user data. Please also suggest what to include in the incentives." Instructions can be sent to the server in this format.

[0544] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0545] Step 1:

[0546] Users input health checkup results and daily life information using their smartphones or computers. This data may include weight, blood pressure, diet, exercise levels, and sleep duration. This data is converted to digital format and encrypted as needed through the input interface. The output is stored on the device as encrypted health data.

[0547] Step 2:

[0548] The terminal transmits encrypted data to the server using a secure communication protocol. AES encryption technology is used to protect the data from unauthorized access. The server decrypts the received data and stores it in a database in a standardized format. The output is standardized health data.

[0549] Step 3:

[0550] The server inputs stored health data into a generating AI model to predict the user's health risks. The AI ​​model, built using TensorFlow, outputs numerical values ​​for the risk of developing conditions such as diabetes or hypertension based on the data. This process references historical medical data and training sets to predict risks. The output is predicted health risk data.

[0551] Step 4:

[0552] The server generates a personalized health plan based on predicted health risk data. This plan includes meal menus, exercise schedules, and lifestyle adjustment suggestions. The server then adjusts the generated health plan based on the user's preferences and feedback. The output is the individualized health plan.

[0553] Step 5:

[0554] The server notifies the user of the generated health plan through an application built with React Native. The user receives the notification, reviews the specific health plan, and can incorporate it into their daily life. The output is the health plan notification provided to the user.

[0555] Step 6:

[0556] Based on the user's implementation of the health plan instructed by the application in their daily life, the server evaluates the improvement in their health status. It compares and verifies this against past data, and if the criteria are met, it initiates a process to provide rewards to the user. The output is a reward proposal based on the evaluation results.

[0557] Step 7:

[0558] The server offers discounts or benefits as rewards to users whose health has improved. This information is notified to the user through the application, acting as an incentive. The output is a reward notification to the user.

[0559] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0560] This invention is a system that supports individual health management and provides a method for providing an individually optimized health plan by combining and analyzing an individual's health checkup results, daily life data, and emotional status. The system includes functions for collecting and storing health data, recognizing the user's emotions using an emotion engine, predicting health risks using a generative AI model, generating individually adapted health plans, notifying the user of the health plan, and providing incentives.

[0561] Data collection and storage

[0562] Users input health checkup data and daily life data into a smartphone or PC terminal. The terminal is also equipped with an emotion engine that recognizes emotions by matching the user's speech and entered text, and acquires emotional data such as whether the user is feeling stressed or motivated. This data is formatted on the terminal and sent to the server. The server stores the data and performs preprocessing to check its consistency and accuracy.

[0563] Predicting and planning for health risks

[0564] The server inputs all data, including emotional data provided by the emotion engine, into a generating AI model. The AI ​​model considers both health data and emotional states to predict health risks with greater accuracy. For example, a high stress level can be predicted to increase the risk of cardiovascular disease. Based on this prediction, the server generates a health plan optimized for the user. The generated plan suggests dietary guidelines and stress reduction programs that also take the user's emotional state into consideration.

[0565] Health plan notifications and incentives

[0566] The device notifies the user of the generated health plan. The notification method is customized based on the user's emotional state; for example, users who are relaxed receive rich text that can be read slowly, while users who are stressed receive concise visuals. The server also provides incentives to users, such as free company meal vouchers, if improvements in their health are confirmed based on the health check results and feedback.

[0567] Feedback and model improvement

[0568] Users provide feedback on their emotional state and health plan through their device. This feedback is sent to a server, which uses it to further improve the generated AI model. In particular, insights gained from emotional status can be used to adjust the model to enhance the applicability of the health plan.

[0569] For example, when a user says "I'm irritated," the emotion engine detects this and sends that state to the server. As a result, the server generates a plan aimed at stress reduction and provides the user with a push notification containing a video recommending relaxing breathing techniques. This feature allows users to receive support for comprehensively improving their emotions and health.

[0570] The following describes the processing flow.

[0571] Step 1:

[0572] Users input health checkup results and data related to their daily lives (e.g., diet, exercise, sleep patterns, etc.) into a smartphone or PC. Furthermore, devices equipped with an emotion engine collect emotional data through the user's voice and text input and recognize their emotional state.

[0573] Step 2:

[0574] The device converts the collected health and emotional data into an appropriate format and sends it to the server. The data is encrypted using security protocols, ensuring it reaches the server safely.

[0575] Step 3:

[0576] The server records the received data in a database and performs data formatting and preprocessing. Preprocessing ensures data consistency and accuracy by detecting outliers and imputing missing values.

[0577] Step 4:

[0578] The server inputs pre-processed data into a generating AI model to predict the user's health risks. The model also takes collected emotional data into account, providing more accurate predictions, for example, by predicting that prolonged high stress levels increase cardiovascular risk.

[0579] Step 5:

[0580] The server generates a health plan that reflects your emotional state based on your predicted health risks. This plan may include specific dietary restrictions, recommended exercises, and relaxation techniques. If your emotions indicate stress, stress reduction guidelines will be emphasized.

[0581] Step 6:

[0582] The device notifies the user of the generated health plan. The notification is customized according to the user's emotional state; for example, a user feeling stressed will receive a message in a gentle tone and a link to relaxation music.

[0583] Step 7:

[0584] The server tracks changes in the user's health status and analyzes the results compared to the previous year's health data. If improvements are recognized, the user is given an incentive. This measure aims to increase user motivation and maintain their desire to improve their health.

[0585] Step 8:

[0586] Users submit feedback on the provided health plans and incentives via their devices. This feedback data also includes reflections on their emotional state.

[0587] Step 9:

[0588] The server optimizes the AI ​​model based on user feedback. This allows for more user-friendly suggestions in future plan generation. Furthermore, by deepening the analysis of emotional data, it becomes possible to provide plans that respond quickly to changes in the user's emotions.

[0589] (Example 2)

[0590] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0591] In modern society, personal health management is becoming increasingly important. However, traditional health management systems have only provided uniform health plans without considering individual emotional states, and have failed to adequately meet individual needs. Furthermore, a lack of motivation for health improvement has made continuous use and improvement difficult.

[0592] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0593] In this invention, the server includes means for collecting personal health checkup results and daily life information, means for analyzing voice and text to recognize personal emotions, and means for transmitting health information and emotional information from the terminal to the server. This makes it possible to provide a more precise and personalized health plan based on individual data, including emotional states. Furthermore, it is possible to improve user motivation by providing incentives for health improvement.

[0594] "Personal health checkup results information" refers to various data obtained during regular health checkups, including information such as blood test results and physical measurement results.

[0595] "Daily life information" refers to data about an individual's activities in their daily life, including information such as the content of their meals, the amount of exercise they do, and the amount of sleep they do.

[0596] "Methods for analyzing voice and text to recognize individual emotions" refers to functions for analyzing emotions from voice input or written text, and is a technology for determining the user's stress level and psychological tendencies.

[0597] "Means of transmitting health and emotional information from a terminal to a server" refers to communication technology for transferring collected data to a server via a network, and includes the process of transmitting data over the internet.

[0598] A "generated artificial intelligence model" is a digital model built using machine learning techniques and is a program used for predictive analytics.

[0599] A "means of predicting health risks" is a method for estimating potential risk factors that may affect an individual's health status, based on collected data.

[0600] "A means of automatically generating a health plan optimized for the user" refers to a function that utilizes the results of health risk predictions to plan health improvement measures tailored to each individual user.

[0601] "A means of customizing and notifying users of their health plans based on their emotional state" refers to a mechanism that displays or communicates health plans in a format that suits the user's emotions.

[0602] "Means of providing incentives" refers to features that offer rewards or benefits to encourage users to take steps to improve their health.

[0603] "Means for collecting user feedback information and improving artificial intelligence models" refers to methods for gathering evaluations and opinions from users, updating artificial intelligence models based on these, and improving their accuracy and usefulness.

[0604] This invention is a system that supports individual health management, and is a method for providing an individually optimized health plan by combining and analyzing the user's health checkup results, daily life information, and emotional status.

[0605] Users can manually input their health checkup results and daily life information using a smartphone or PC. For example, they can input numerical data from their health checkup, their daily diet, exercise habits, and sleep duration. These devices are equipped with an emotion engine that analyzes voice and text to recognize emotions, allowing the system to identify the user's emotional state from the voice and text they input. For example, if a user says, "I'm tired today," the system will capture this and process it as emotional information.

[0606] The terminal formats the entered health and emotional information and sends it to the server via the internet. The server stores the received data in a database and performs preprocessing to maintain its consistency. Preprocessing involves using software such as Python or R to impute missing data values ​​and adjust the data scale.

[0607] The server inputs pre-processed data into a generative AI model. This model, built on machine learning platforms such as TensorFlow or PyTorch, predicts health risks by considering both health and emotional data. For example, it can predict an increased risk of cardiovascular disease if stress levels are high.

[0608] Based on this prediction, the server automatically generates a health plan optimized for the user. This plan may include suggestions for dietary improvements and activities to reduce stress. The generated plan is communicated according to the user's emotional state. For example, a relaxed user will receive a detailed explanation in text, while a stressed user will receive a simple visual.

[0609] Furthermore, the server continuously updates the generated AI model using information obtained from improved health status and user feedback. This allows for improved plan accuracy based on user feedback.

[0610] For example, if a user enters a request stating, "I've been feeling stressed a lot lately. I'd like some advice on how to improve my mental and physical health," this information will be analyzed, and a plan will be generated suggesting breathing exercises and relaxing activities to reduce stress.

[0611] In this way, this system integrates emotional and health data, enabling it to provide users with effective health management.

[0612] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0613] Step 1:

[0614] Users manually input health checkup results and daily life information into their smartphones or PCs. This input includes details such as diet, exercise levels, and sleep duration. Users also express their emotions through voice or text, and the device's emotion engine analyzes this information to generate emotion data. The input data is sent to the device in text or numerical format.

[0615] Step 2:

[0616] The terminal formats the collected health and sentiment information into a format suitable for the database. This formatting process includes checking the format of the original data and correcting outliers. The terminal then sends the formatted data to the server using the HTTP protocol. The output is the normalized health and sentiment information transferred to the server.

[0617] Step 3:

[0618] The server stores the received data in a database and then performs preprocessing to maintain data consistency. Preprocessing includes missing value imputation and data scaling. A Python script is used for this process, transforming the input data into an analyzable state. The output is a preprocessed, integrated dataset.

[0619] Step 4:

[0620] The server provides a pre-processed dataset as input to a generating AI model. This AI model, built on TensorFlow or PyTorch, predicts health risks based on the user's historical data. Specific data calculations include classification and regression analysis using neural networks. The output is a prediction of the user's health risks.

[0621] Step 5:

[0622] The server uses prediction results to generate a health plan optimized for the user. Natural language processing is used for plan generation, enabling flexible plan creation that takes emotions into account. For example, it may include suggestions for dietary improvements and activities to reduce stress. The output is a customized health plan.

[0623] Step 6:

[0624] The device notifies the user of the generated health plan. The notification method is adjusted based on the user's emotional state; a detailed text is displayed for relaxed users, while a concise visual is used for stressed users. The output is the health plan notification provided to the user.

[0625] Step 7:

[0626] Users input feedback on their health plan into their device. This feedback is sent to a server, which uses it to improve the artificial intelligence model. The feedback data is analyzed to help improve the model's accuracy, resulting in a higher quality health plan for the next session. The output is feedback information that helps in the next model adjustment.

[0627] (Application Example 2)

[0628] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0629] Maintaining good employee health is a crucial challenge in today's work environment. However, it is difficult to accurately understand the health and emotional state of individual employees and provide specific health guidance accordingly. Furthermore, there is a lack of mechanisms to appropriately evaluate and reward efforts to improve health, making it difficult to maintain employee motivation.

[0630] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0631] In this invention, the server includes means for collecting personal health checkup results data, daily life data, and emotional data; means for predicting health risks from the collected data using a generated artificial intelligence model; and means for generating individually adapted health plans based on the predicted health risks and emotional state. This makes it possible to provide optimal health guidance tailored to the user's health and emotional state in real time, and to further reward them according to their improvement.

[0632] "Personal health checkup data" refers to information about an individual's health status collected by medical institutions or specialists.

[0633] "Daily life data" refers to information about various actions and habits that individuals acquire in their daily lives.

[0634] "Emotional data" refers to data that indicates the emotional state of a user, obtained from their speech or entered text.

[0635] A "generated artificial intelligence model" is a model used to predict health risks based on a user's health and emotional state using machine learning algorithms.

[0636] "Methods for predicting health risks" refer to technologies that process collected data to infer potential health problems that may be predicted in the future.

[0637] A "health plan" is a plan that includes specific guidance and measures tailored to each individual user to promote health and reduce risks.

[0638] "Means of customizing notification formats based on emotional state" refers to a technology that selects the format that can deliver information most effectively according to the user's emotional state.

[0639] "Means of providing rewards based on progress" refers to a system that provides appropriate incentives for the degree of improvement in a user's health and their efforts.

[0640] The system of this invention provides advanced support for personal health management. The server collects and stores health checkup results, daily life data, and emotional data from individuals. This data is input via smartphones and PCs, and the system uses speech recognition technology and text analysis to estimate the user's emotional state. The terminals are equipped with an emotion engine, for example, which implements the machine learning library TensorFlow.

[0641] The server preprocesses the collected data and inputs the integrated data into a generated artificial intelligence model. This AI model analyzes the user's health check results and daily life data, and further considers emotional data to predict health risks more accurately. Based on this, the server generates an appropriate health plan and notifies the user in a customized format. The notification is optimized according to the emotional state and is sent to end-user devices such as smartphones and PCs in one of the following formats: text, visual, or audio.

[0642] When a user's health status shows significant improvement, the server rewards them according to their progress. This reward takes the form of workplace coupons or similar items to maintain motivation. Users can continuously provide feedback through this process, and emotion-based insights help improve the performance of the AI ​​model on the server side.

[0643] For example, if a factory worker reports via a robot that they have been experiencing severe palpitations recently, the server can analyze the data and send a notification recommending stress-reducing breathing exercises or short stretches. Another example of a health management prompt would be, "Generate an optimal health plan based on this employee's recent emotional data and health check results." This is expected to improve employee health and work efficiency.

[0644] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0645] Step 1:

[0646] The device collects health checkup data, daily life data, and emotional data from the user using voice and text input. This input data is temporarily formatted and corrected on the device. As a result, the formatted, integrated data is sent to the server.

[0647] Step 2:

[0648] The server preprocesses the received data. It verifies the consistency and accuracy of the input data, and, if necessary, imputes missing data and corrects outliers. This prepares a clean dataset as input for the generated AI model.

[0649] Step 3:

[0650] The server supplies pre-processed data to a generating AI model, which then predicts health risks based on the data. The AI ​​model uses machine learning algorithms to analyze the relationship between health status and emotional state. As an output of this process, an individual user's health risk assessment is generated.

[0651] Step 4:

[0652] The server generates a health plan based on a health risk assessment, taking into account the user's emotional state. Using the prompt "Generate the optimal health plan based on this user's emotional data and health checkup results," a personalized recommendation plan is created. The output of this process is a health plan suitable for the user.

[0653] Step 5:

[0654] The server generates a health plan and notifies the user. The emotion engine analyzes the user's current emotional state and, based on that, notifies the user of the plan in the most appropriate format, such as text or visuals. The system ensures that this notification is delivered to the user.

[0655] Step 6:

[0656] The server collects feedback after the health plan is implemented and provides rewards if health improves. Specifically, the reward content is determined using feedback data, and coupons usable at the workplace, etc., are issued to the user. This entire process aims to motivate users to continue managing their health.

[0657] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0658] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0659] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0660] [Fourth Embodiment]

[0661] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0662] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0663] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0664] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0665] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0666] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0667] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0668] The controlled 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 in the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0669] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0670] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0671] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0672] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0673] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0674] This invention is a system for supporting individual health management, which collects individual health checkup results and daily life data, predicts health risks based on that data, and provides an individually optimized health plan. The main components of this system are means for collecting and storing data, a generative AI model that analyzes the data and predicts health risks, a function to generate and provide a health plan based on the prediction to the user, a function to provide incentives, and a function to receive and utilize user feedback.

[0675] Data collection and storage

[0676] Users input the results obtained during their health checkups into their smartphones or PCs. These devices can also input or automatically synchronize data on daily life, such as diet, exercise, and sleep. The devices format this data appropriately and send it to the server in a secure manner. The server stores this data in a database and maintains data consistency through standardization.

[0677] Predicting health risks

[0678] The server inputs the collected data into a generating AI model to predict the user's future health risks. This model is trained on historical data and medical research, and can assess the user's risk of developing conditions such as hypertension or diabetes.

[0679] Health plan creation and provision

[0680] The server creates an optimal health plan for each individual user based on predicted health risks. This includes meal menu suggestions and exercise programs. The generated plan is notified to the user via their device. The user can view the plan details on their device and adjust their lifestyle according to the instructions.

[0681] Provision of incentives

[0682] The server determines whether the user's health has improved as a result of implementing the health plan. For example, if blood pressure has improved compared to the previous year's health checkup, the server provides the user with an incentive such as a free meal voucher at the company cafeteria. This feature serves as an incentive to encourage healthy behaviors in users.

[0683] Collecting and using feedback

[0684] Users can provide feedback on proposed health plans and incentives via the app or PC. The server analyzes this feedback and adjusts the algorithm of the generating AI model. As a result, the quality of the generated health plans and user satisfaction improve.

[0685] As a concrete example, if a user enters a health checkup result showing high blood pressure into the system, the system will suggest a low-sodium diet plan and a daily walking habit to the user. If the user follows this plan for a month and their blood pressure decreases in a follow-up checkup, they will be offered incentives such as free company meal vouchers. This entire process makes it easier for users to maintain their health, and allows companies to effectively manage their healthcare costs.

[0686] The following describes the processing flow.

[0687] Step 1:

[0688] Users input data obtained from health checkups (e.g., blood pressure, blood sugar levels) into their smartphone or PC. They can also manually input daily life data (e.g., diet, exercise history, sleep duration) or have it automatically synchronized from a wearable device.

[0689] Step 2:

[0690] The device handles the communication required to convert collected health checkup results and daily life data into an appropriate format and send it to the server. The data is encrypted, and measures are taken to protect privacy.

[0691] Step 3:

[0692] The server saves the received data to the database. After saving, it checks for missing or abnormal values ​​and cleanses the data as needed. It also formats the data to make it usable in the generative AI model.

[0693] Step 4:

[0694] The server uses the formatted data to run a generative AI model that predicts the user's future health risks. For example, the model calculates the risk of diseases such as diabetes and hypertension from past health checkup data and current lifestyle habits.

[0695] Step 5:

[0696] The server generates a health plan tailored to the user based on predicted health risks. This plan includes specific dietary guidelines and exercise plans. For example, if the model indicates a risk of high blood pressure, a diet plan to reduce sodium intake and aerobic exercise recommendations will be provided.

[0697] Step 6:

[0698] The device notifies the user of the generated health plan. The user can check the contents of the health plan on the device's display screen and learn specific ways to implement it in their daily life. The notification includes a reminder function to support the user in improving their lifestyle habits.

[0699] Step 7:

[0700] The server tracks health check results to determine if the user's health has improved. If improvement is confirmed compared to the previous year, an incentive (e.g., a free meal voucher at the company cafeteria) is provided. This process increases user motivation and encourages the continuation of healthy behaviors.

[0701] Step 8:

[0702] Users can provide feedback via their devices on how they felt about the health plans and incentives offered. This feedback information is sent to the server and used to further adjust the plans.

[0703] Step 9:

[0704] The server analyzes feedback collected from users and optimizes the generated AI model based on that analysis. This model improvement allows for more personalized health plan suggestions in the future, enabling a more specific response to user needs and preferences.

[0705] (Example 1)

[0706] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0707] In modern society, people's lifestyles are diversifying, and it is important to continuously manage individual health conditions. However, traditional methods have made it difficult to predict individual health risks and provide appropriate health plans. Furthermore, there is a lack of mechanisms to effectively utilize user feedback to continuously improve health plans.

[0708] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0709] In this invention, the server includes means for collecting personal health information and daily life information, means for predicting health risks from the collected information using a generative AI model, and means for generating individually tailored health programs based on the predicted health risks. This makes it possible to provide each user with an automatically and continuously optimized health program, thereby promoting improvement in the user's health condition.

[0710] "Personal health information" refers to health checkup results and other health-related data obtained by the user.

[0711] "Daily life information" refers to data related to the user's daily activities, including information on diet, exercise, sleep, etc.

[0712] A "generative AI model" refers to an artificial intelligence-powered model used to predict a user's health risks based on collected data.

[0713] "Health risk" refers to factors or conditions that may have a potential impact on health in the future.

[0714] An "individually tailored health program" refers to a personalized plan for maintaining or improving health, taking into account the user's specific health condition and risks.

[0715] The "evaluation period" refers to a specific time frame in which health status is measured and compared.

[0716] "Rewards" refer to incentives or benefits offered to users for improving their health.

[0717] "Response information" refers to users' impressions, opinions, and feedback regarding health programs and rewards.

[0718] This system collects users' health information and provides health programs based on analysis using a generative AI model. The system primarily utilizes the following hardware and software.

[0719] Users input their health and daily life information using devices such as smartphones and PCs. This includes dietary information, exercise habits, sleep patterns, and results of regular health checkups. The device converts the entered information into a predetermined format and transmits the data to a server via the internet.

[0720] The server stores received data using a secure database system. The stored data is standardized to maintain consistency in the collected information. Next, the server leverages a generative AI model to analyze the stored data and predict the user's health risks. This model utilizes machine learning algorithms and is trained on large historical datasets.

[0721] Based on the predicted health risks, the server creates a personalized health program for each user. This program includes meal plans and exercise suggestions, providing specific actionable guidelines tailored to individual user needs. The created program is notified to the user's device, allowing them to review its details and incorporate it into their daily life. Rewards may also be offered if improvements are observed in the user's health.

[0722] For example, if a user inputs into the system that their blood pressure is high based on their health checkup results, this information is sent to the server, and a generating AI model assesses the risk of hypertension. Based on the results, the server can provide the user with a low-salt diet program and an exercise plan including walking. If the user follows the program and their blood pressure improves in a follow-up examination, the server will provide rewards such as free services or coupons.

[0723] An example of a prompt message is: "Based on the health checkup results entered by the user, use a generative AI model to predict the risk of hypertension and propose an appropriate health plan."

[0724] Through this system, users can receive personalized health support, increasing their chances of maintaining and improving their health.

[0725] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0726] Step 1:

[0727] Users input their health and daily life information using a device. Specifically, they input health checkup results and information about their diet, exercise, and sleep via a dedicated application on the device. The entered data is converted to an appropriate format and prepared for transmission. The input data is temporarily stored in the device's memory and then sent to the server using encryption technology.

[0728] Step 2:

[0729] The server receives data from terminals and stores it in a database. Health checkup information and daily life information are extracted from the received data, and standardization processing is performed so that this data is stored in a unified format. For example, processing such as unifying date formats and converting numerical units is carried out. The stored data is then converted into information necessary for subsequent analysis.

[0730] Step 3:

[0731] The server feeds data stored in the database into a generative AI model to predict the user's health risk. The input data is stored in a unified format. The generative AI model uses a machine learning algorithm based on historical data to output a risk score and classification result regarding the user's health status. This process involves massive data processing and real-time analysis.

[0732] Step 4:

[0733] The server generates personalized health programs based on prediction results obtained from a generated AI model. Inputs include predicted health risks and user lifestyle information. In conjunction with a rule-based system supervised by health experts, a program is created that includes appropriate dietary suggestions and exercise menus. The resulting health program is sent to the user's device.

[0734] Step 5:

[0735] The user checks the health program notified on their device. Using the device application, they check the details of the program they received and incorporate the suggested actions into their daily life. The user's progress is recorded on the device and sent to the server as feedback data.

[0736] Step 6:

[0737] The server analyzes user feedback data and evaluates changes in health status resulting from program implementation. Inputs include re-results of health checkups and user-reported information. If the health status meets predetermined evaluation criteria, the user receives a reward. Specifically, this may involve issuing electronic coupons or sending notifications via in-app messages.

[0738] (Application Example 1)

[0739] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0740] In personal health management, the challenge lies in efficiently and effectively providing a system that collects various health data, accurately predicts individual health risks, ensures data security, and allows users to provide information with confidence. Furthermore, it is necessary to appropriately evaluate improvements in health status and promote healthy behaviors through rewards.

[0741] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0742] In this invention, the server includes means for collecting personal health checkup results and daily life information, means for predicting health risks from the collected information using a generated artificial intelligence model, means for generating individually adapted health plans based on the predicted health risks, means for providing a security mechanism to encrypt the user's health information and prevent unauthorized access from external sources, and means for providing rewards when the user's health status improves compared to the previous year. This makes it possible to safely and effectively manage personal health data, promote individually optimized health management, and build a reward system that supports users' healthy behaviors.

[0743] "Health checkup results information" refers to data such as numerical values ​​and evaluations obtained as a result of tests that users have received at medical institutions, etc.

[0744] "Daily life information" refers to data on various activities and behaviors related to the user's daily life, such as records of meals, exercise, and sleep.

[0745] An "artificial intelligence model" is a computational program that utilizes machine learning and statistical methods to predict health risks from collected data.

[0746] "Health risk" is an indicator that assesses the degree to which a user is likely to develop a specific health problem in the future.

[0747] A "health plan" is a plan that provides specific action instructions, including guidance on diet and exercise, with the aim of maintaining or improving the health of the user.

[0748] A "reward" is an incentive given when a user's health condition improves, and may be provided in the form of discount coupons or points, for example.

[0749] A "security mechanism" is a system that uses technologies such as information encryption and access restrictions to prevent unauthorized access from external sources in order to protect users' health information.

[0750] The system of the present invention is designed to efficiently collect health checkup results and daily life information, predict health risks using a generated AI model, and provide individually adapted health plans based on the results. The implementation of the system mainly consists of the following steps.

[0751] Users can input their health checkup results using devices such as smartphones and personal computers. They can also manually input information about their daily lives, such as diet, exercise, and sleep, or automatically acquire this information by syncing with their smart devices. This data is securely transmitted from the device to a server where it is stored in a standardized format.

[0752] The server uses stored information to predict future health risks using generative AI models based on TensorFlow and other technologies. AI models trained on historical data and medical research analyze the collected information to calculate, for example, the risk of developing diabetes or hypertension.

[0753] Based on the user's health risks, the server generates a personalized health plan. This plan includes meal menus and exercise plans, and is communicated to the user through a frontend built with React Native. Based on this information, users can adjust their daily lifestyle and strive to maintain their health.

[0754] The system incorporates security mechanisms to ensure the safety of data provided by users. For example, data is encrypted using AES encryption technology to protect it from external access. Furthermore, a two-factor authentication system is employed in the authentication process to enhance the security of user information.

[0755] Furthermore, the server has a function to reward users if an improvement in their health is observed. For example, if an improvement is seen compared to the previous year's blood pressure readings, the server will offer a reward to encourage healthier behaviors in the user.

[0756] For example, if a user records their morning weight and steps using the app, the app can detect the improvement in weight management at the following month's check-up and offer a gym discount coupon as a reward for achieving their step goal.

[0757] Furthermore, an example of a prompt message for utilizing a generative AI model is: "Please provide a health plan generated by the AI ​​from user data. Please also suggest what to include in the incentives." Instructions can be sent to the server in this format.

[0758] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0759] Step 1:

[0760] Users input health checkup results and daily life information using their smartphones or computers. This data may include weight, blood pressure, diet, exercise levels, and sleep duration. This data is converted to digital format and encrypted as needed through the input interface. The output is stored on the device as encrypted health data.

[0761] Step 2:

[0762] The terminal transmits encrypted data to the server using a secure communication protocol. AES encryption technology is used to protect the data from unauthorized access. The server decrypts the received data and stores it in a database in a standardized format. The output is standardized health data.

[0763] Step 3:

[0764] The server inputs stored health data into a generating AI model to predict the user's health risks. The AI ​​model, built using TensorFlow, outputs numerical values ​​for the risk of developing conditions such as diabetes or hypertension based on the data. This process references historical medical data and training sets to predict risks. The output is predicted health risk data.

[0765] Step 4:

[0766] The server generates a personalized health plan based on predicted health risk data. This plan includes meal menus, exercise schedules, and lifestyle adjustment suggestions. The server then adjusts the generated health plan based on the user's preferences and feedback. The output is the individualized health plan.

[0767] Step 5:

[0768] The server notifies the user of the generated health plan through an application built with React Native. The user receives the notification, reviews the specific health plan, and can incorporate it into their daily life. The output is the health plan notification provided to the user.

[0769] Step 6:

[0770] Based on the user's implementation of the health plan instructed by the application in their daily life, the server evaluates the improvement in their health status. It compares and verifies this against past data, and if the criteria are met, it initiates a process to provide rewards to the user. The output is a reward proposal based on the evaluation results.

[0771] Step 7:

[0772] The server offers discounts or benefits as rewards to users whose health has improved. This information is notified to the user through the application, acting as an incentive. The output is a reward notification to the user.

[0773] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0774] This invention is a system that supports individual health management and provides a method for providing an individually optimized health plan by combining and analyzing an individual's health checkup results, daily life data, and emotional status. The system includes functions for collecting and storing health data, recognizing the user's emotions using an emotion engine, predicting health risks using a generative AI model, generating individually adapted health plans, notifying the user of the health plan, and providing incentives.

[0775] Data collection and storage

[0776] Users input health checkup data and daily life data into a smartphone or PC terminal. The terminal is also equipped with an emotion engine that recognizes emotions by matching the user's speech and entered text, and acquires emotional data such as whether the user is feeling stressed or motivated. This data is formatted on the terminal and sent to the server. The server stores the data and performs preprocessing to check its consistency and accuracy.

[0777] Predicting and planning for health risks

[0778] The server inputs all data, including emotional data provided by the emotion engine, into a generating AI model. The AI ​​model considers both health data and emotional states to predict health risks with greater accuracy. For example, a high stress level can be predicted to increase the risk of cardiovascular disease. Based on this prediction, the server generates a health plan optimized for the user. The generated plan suggests dietary guidelines and stress reduction programs that also take the user's emotional state into consideration.

[0779] Health plan notifications and incentives

[0780] The device notifies the user of the generated health plan. The notification method is customized based on the user's emotional state; for example, users who are relaxed receive rich text that can be read slowly, while users who are stressed receive concise visuals. The server also provides incentives to users, such as free company meal vouchers, if improvements in their health are confirmed based on the health check results and feedback.

[0781] Feedback and model improvement

[0782] Users provide feedback on their emotional state and health plan through their device. This feedback is sent to a server, which uses it to further improve the generated AI model. In particular, insights gained from emotional status can be used to adjust the model to enhance the applicability of the health plan.

[0783] For example, when a user says "I'm irritated," the emotion engine detects this and sends that state to the server. As a result, the server generates a plan aimed at stress reduction and provides the user with a push notification containing a video recommending relaxing breathing techniques. This feature allows users to receive support for comprehensively improving their emotions and health.

[0784] The following describes the processing flow.

[0785] Step 1:

[0786] Users input health checkup results and data related to their daily lives (e.g., diet, exercise, sleep patterns, etc.) into a smartphone or PC. Furthermore, devices equipped with an emotion engine collect emotional data through the user's voice and text input and recognize their emotional state.

[0787] Step 2:

[0788] The device converts the collected health and emotional data into an appropriate format and sends it to the server. The data is encrypted using security protocols, ensuring it reaches the server safely.

[0789] Step 3:

[0790] The server records the received data in a database and performs data formatting and preprocessing. Preprocessing ensures data consistency and accuracy by detecting outliers and imputing missing values.

[0791] Step 4:

[0792] The server inputs pre-processed data into a generating AI model to predict the user's health risks. The model also takes collected emotional data into account, providing more accurate predictions, for example, by predicting that prolonged high stress levels increase cardiovascular risk.

[0793] Step 5:

[0794] The server generates a health plan that reflects your emotional state based on your predicted health risks. This plan may include specific dietary restrictions, recommended exercises, and relaxation techniques. If your emotions indicate stress, stress reduction guidelines will be emphasized.

[0795] Step 6:

[0796] The device notifies the user of the generated health plan. The notification is customized according to the user's emotional state; for example, a user feeling stressed will receive a message in a gentle tone and a link to relaxation music.

[0797] Step 7:

[0798] The server tracks changes in the user's health status and analyzes the results compared to the previous year's health data. If improvements are recognized, the user is given an incentive. This measure aims to increase user motivation and maintain their desire to improve their health.

[0799] Step 8:

[0800] Users submit feedback on the provided health plans and incentives via their devices. This feedback data also includes reflections on their emotional state.

[0801] Step 9:

[0802] The server optimizes the AI ​​model based on user feedback. This allows for more user-friendly suggestions in future plan generation. Furthermore, by deepening the analysis of emotional data, it becomes possible to provide plans that respond quickly to changes in the user's emotions.

[0803] (Example 2)

[0804] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0805] In modern society, personal health management is becoming increasingly important. However, traditional health management systems have only provided uniform health plans without considering individual emotional states, and have failed to adequately meet individual needs. Furthermore, a lack of motivation for health improvement has made continuous use and improvement difficult.

[0806] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0807] In this invention, the server includes means for collecting personal health checkup results and daily life information, means for analyzing voice and text to recognize personal emotions, and means for transmitting health information and emotional information from the terminal to the server. This makes it possible to provide a more precise and personalized health plan based on individual data, including emotional states. Furthermore, it is possible to improve user motivation by providing incentives for health improvement.

[0808] "Personal health checkup results information" refers to various data obtained during regular health checkups, including information such as blood test results and physical measurement results.

[0809] "Daily life information" refers to data about an individual's activities in their daily life, including information such as the content of their meals, the amount of exercise they do, and the amount of sleep they do.

[0810] "Methods for analyzing voice and text to recognize individual emotions" refers to functions for analyzing emotions from voice input or written text, and is a technology for determining the user's stress level and psychological tendencies.

[0811] "Means of transmitting health and emotional information from a terminal to a server" refers to communication technology for transferring collected data to a server via a network, and includes the process of transmitting data over the internet.

[0812] A "generated artificial intelligence model" is a digital model built using machine learning techniques and is a program used for predictive analytics.

[0813] A "means of predicting health risks" is a method for estimating potential risk factors that may affect an individual's health status, based on collected data.

[0814] "A means of automatically generating a health plan optimized for the user" refers to a function that utilizes the results of health risk predictions to plan health improvement measures tailored to each individual user.

[0815] "A means of customizing and notifying users of their health plans based on their emotional state" refers to a mechanism that displays or communicates health plans in a format that suits the user's emotions.

[0816] "Means of providing incentives" refers to features that offer rewards or benefits to encourage users to take steps to improve their health.

[0817] "Means for collecting user feedback information and improving artificial intelligence models" refers to methods for gathering evaluations and opinions from users, updating artificial intelligence models based on these, and improving their accuracy and usefulness.

[0818] This invention is a system that supports individual health management, and is a method for providing an individually optimized health plan by combining and analyzing the user's health checkup results, daily life information, and emotional status.

[0819] Users can manually input their health checkup results and daily life information using a smartphone or PC. For example, they can input numerical data from their health checkup, their daily diet, exercise habits, and sleep duration. These devices are equipped with an emotion engine that analyzes voice and text to recognize emotions, allowing the system to identify the user's emotional state from the voice and text they input. For example, if a user says, "I'm tired today," the system will capture this and process it as emotional information.

[0820] The terminal formats the entered health and emotional information and sends it to the server via the internet. The server stores the received data in a database and performs preprocessing to maintain its consistency. Preprocessing involves using software such as Python or R to impute missing data values ​​and adjust the data scale.

[0821] The server inputs pre-processed data into a generative AI model. This model, built on machine learning platforms such as TensorFlow or PyTorch, predicts health risks by considering both health and emotional data. For example, it can predict an increased risk of cardiovascular disease if stress levels are high.

[0822] Based on this prediction, the server automatically generates a health plan optimized for the user. This plan may include suggestions for dietary improvements and activities to reduce stress. The generated plan is communicated according to the user's emotional state. For example, a relaxed user will receive a detailed explanation in text, while a stressed user will receive a simple visual.

[0823] Furthermore, the server continuously updates the generated AI model using information obtained from improved health status and user feedback. This allows for improved plan accuracy based on user feedback.

[0824] For example, if a user enters a request stating, "I've been feeling stressed a lot lately. I'd like some advice on how to improve my mental and physical health," this information will be analyzed, and a plan will be generated suggesting breathing exercises and relaxing activities to reduce stress.

[0825] In this way, this system integrates emotional and health data, enabling it to provide users with effective health management.

[0826] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0827] Step 1:

[0828] Users manually input health checkup results and daily life information into their smartphones or PCs. This input includes details such as diet, exercise levels, and sleep duration. Users also express their emotions through voice or text, and the device's emotion engine analyzes this information to generate emotion data. The input data is sent to the device in text or numerical format.

[0829] Step 2:

[0830] The terminal formats the collected health and sentiment information into a format suitable for the database. This formatting process includes checking the format of the original data and correcting outliers. The terminal then sends the formatted data to the server using the HTTP protocol. The output is the normalized health and sentiment information transferred to the server.

[0831] Step 3:

[0832] The server stores the received data in a database and then performs preprocessing to maintain data consistency. Preprocessing includes missing value imputation and data scaling. A Python script is used for this process, transforming the input data into an analyzable state. The output is a preprocessed, integrated dataset.

[0833] Step 4:

[0834] The server provides a pre-processed dataset as input to a generating AI model. This AI model, built on TensorFlow or PyTorch, predicts health risks based on the user's historical data. Specific data calculations include classification and regression analysis using neural networks. The output is a prediction of the user's health risks.

[0835] Step 5:

[0836] The server uses prediction results to generate a health plan optimized for the user. Natural language processing is used for plan generation, enabling flexible plan creation that takes emotions into account. For example, it may include suggestions for dietary improvements and activities to reduce stress. The output is a customized health plan.

[0837] Step 6:

[0838] The device notifies the user of the generated health plan. The notification method is adjusted based on the user's emotional state; a detailed text is displayed for relaxed users, while a concise visual is used for stressed users. The output is the health plan notification provided to the user.

[0839] Step 7:

[0840] Users input feedback on their health plan into their device. This feedback is sent to a server, which uses it to improve the artificial intelligence model. The feedback data is analyzed to help improve the model's accuracy, resulting in a higher quality health plan for the next session. The output is feedback information that helps in the next model adjustment.

[0841] (Application Example 2)

[0842] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0843] Maintaining good employee health is a crucial challenge in today's work environment. However, it is difficult to accurately understand the health and emotional state of individual employees and provide specific health guidance accordingly. Furthermore, there is a lack of mechanisms to appropriately evaluate and reward efforts to improve health, making it difficult to maintain employee motivation.

[0844] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0845] In this invention, the server includes means for collecting personal health checkup results data, daily life data, and emotional data; means for predicting health risks from the collected data using a generated artificial intelligence model; and means for generating individually adapted health plans based on the predicted health risks and emotional state. This makes it possible to provide optimal health guidance tailored to the user's health and emotional state in real time, and to further reward them according to their improvement.

[0846] "Personal health checkup data" refers to information about an individual's health status collected by medical institutions or specialists.

[0847] "Daily life data" refers to information about various actions and habits that individuals acquire in their daily lives.

[0848] "Emotional data" refers to data that indicates the emotional state of a user, obtained from their speech or entered text.

[0849] A "generated artificial intelligence model" is a model used to predict health risks based on a user's health and emotional state using machine learning algorithms.

[0850] "Methods for predicting health risks" refer to technologies that process collected data to infer potential health problems that may be predicted in the future.

[0851] A "health plan" is a plan that includes specific guidance and measures tailored to each individual user to promote health and reduce risks.

[0852] "Means of customizing notification formats based on emotional state" refers to a technology that selects the format that can deliver information most effectively according to the user's emotional state.

[0853] "Means of providing rewards based on progress" refers to a system that provides appropriate incentives for the degree of improvement in a user's health and their efforts.

[0854] The system of this invention provides advanced support for personal health management. The server collects and stores health checkup results, daily life data, and emotional data from individuals. This data is input via smartphones and PCs, and the system uses speech recognition technology and text analysis to estimate the user's emotional state. The terminals are equipped with an emotion engine, for example, which implements the machine learning library TensorFlow.

[0855] The server preprocesses the collected data and inputs the integrated data into a generated artificial intelligence model. This AI model analyzes the user's health check results and daily life data, and further considers emotional data to predict health risks more accurately. Based on this, the server generates an appropriate health plan and notifies the user in a customized format. The notification is optimized according to the emotional state and is sent to end-user devices such as smartphones and PCs in one of the following formats: text, visual, or audio.

[0856] When a user's health status shows significant improvement, the server rewards them according to their progress. This reward takes the form of workplace coupons or similar items to maintain motivation. Users can continuously provide feedback through this process, and emotion-based insights help improve the performance of the AI ​​model on the server side.

[0857] For example, if a factory worker reports via a robot that they have been experiencing severe palpitations recently, the server can analyze the data and send a notification recommending stress-reducing breathing exercises or short stretches. Another example of a health management prompt would be, "Generate an optimal health plan based on this employee's recent emotional data and health check results." This is expected to improve employee health and work efficiency.

[0858] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0859] Step 1:

[0860] The device collects health checkup data, daily life data, and emotional data from the user using voice and text input. This input data is temporarily formatted and corrected on the device. As a result, the formatted, integrated data is sent to the server.

[0861] Step 2:

[0862] The server preprocesses the received data. It verifies the consistency and accuracy of the input data, and, if necessary, imputes missing data and corrects outliers. This prepares a clean dataset as input for the generated AI model.

[0863] Step 3:

[0864] The server supplies pre-processed data to a generating AI model, which then predicts health risks based on the data. The AI ​​model uses machine learning algorithms to analyze the relationship between health status and emotional state. As an output of this process, an individual user's health risk assessment is generated.

[0865] Step 4:

[0866] The server generates a health plan based on a health risk assessment, taking into account the user's emotional state. Using the prompt "Generate the optimal health plan based on this user's emotional data and health checkup results," a personalized recommendation plan is created. The output of this process is a health plan suitable for the user.

[0867] Step 5:

[0868] The server generates a health plan and notifies the user. The emotion engine analyzes the user's current emotional state and, based on that, notifies the user of the plan in the most appropriate format, such as text or visuals. The system ensures that this notification is delivered to the user.

[0869] Step 6:

[0870] The server collects feedback after the health plan is implemented and provides rewards if health improves. Specifically, the reward content is determined using feedback data, and coupons usable at the workplace, etc., are issued to the user. This entire process aims to motivate users to continue managing their health.

[0871] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0872] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0873] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0874] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0875] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0876] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0877] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0878] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0879] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0880] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0881] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0882] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0883] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0884] 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.

[0885] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0886] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0887] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0888] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0889] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0890] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0891] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0892] The following is further disclosed regarding the embodiments described above.

[0893] (Claim 1)

[0894] A means of collecting individual health checkup results data and daily life data,

[0895] A method for predicting health risks from data collected using a generated artificial intelligence model,

[0896] A means for generating individually adapted health plans based on predicted health risks,

[0897] A means of notifying the user of the generated health plan,

[0898] A means of providing incentives when health conditions improve compared to the previous year,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, further comprising an interface for inputting the aforementioned health checkup result data and daily life data.

[0902] (Claim 3)

[0903] The system according to claim 1, further comprising means for adjusting the generated artificial intelligence model and improving the health plan based on user feedback data.

[0904] "Example 1"

[0905] (Claim 1)

[0906] Means for collecting personal health information and daily life information,

[0907] A method for predicting health risks from information collected using a generative AI model,

[0908] A means for generating individually tailored health programs based on predicted health risks,

[0909] Means for providing the generated health program to the user,

[0910] A means of providing rewards if health status improves compared to the evaluation period,

[0911] A means of collecting user feedback information and adjusting the generated AI model,

[0912] A system that includes this.

[0913] (Claim 2)

[0914] The system according to claim 1, comprising an interface for inputting the aforementioned health information and daily life information.

[0915] (Claim 3)

[0916] The system according to claim 1, further comprising means for dynamically generating individually tailored health programs using the aforementioned generation AI model and optimizing the programs based on user response information.

[0917] "Application Example 1"

[0918] (Claim 1)

[0919] A means of collecting personal health checkup results and daily life information,

[0920] A method for predicting health risks from information collected using a generated artificial intelligence model,

[0921] A means for generating individually adapted health plans based on predicted health risks,

[0922] A means of notifying the user of the generated health plan,

[0923] A means to encrypt users' health information and incorporate a security mechanism to prevent unauthorized access from external sources,

[0924] A means of providing rewards when health conditions improve compared to the previous year,

[0925] A system that includes this.

[0926] (Claim 2)

[0927] The system according to claim 1, further comprising an operation screen for inputting the aforementioned health checkup results and daily life information.

[0928] (Claim 3)

[0929] The system according to claim 1, further comprising means for adjusting the generated artificial intelligence model and improving the health plan based on user response data.

[0930] "Example 2 of combining an emotion engine"

[0931] (Claim 1)

[0932] A means of collecting personal health checkup results and daily life information,

[0933] Methods for analyzing voice and text to recognize individual emotions,

[0934] A means of transmitting health information and emotional information from a terminal to a server,

[0935] Means for preprocessing data to maintain data consistency,

[0936] A method for predicting health risks from pre-processed information using a generated artificial intelligence model,

[0937] A means of automatically generating a health plan optimized for the user,

[0938] A means of customizing and notifying users of plans based on their emotional state,

[0939] A means of providing incentives when health conditions improve,

[0940] A means of collecting user feedback information and improving artificial intelligence models,

[0941] A system that includes this.

[0942] (Claim 2)

[0943] The system according to claim 1, comprising an interface for inputting the aforementioned health checkup results and daily life information.

[0944] (Claim 3)

[0945] The system according to claim 1, further comprising means for adjusting the generated artificial intelligence model and improving the health plan based on user feedback information.

[0946] "Application example 2 when combining with an emotional engine"

[0947] (Claim 1)

[0948] A means of collecting individual health checkup results data, daily life data, and emotional data,

[0949] A method for predicting health risks from data collected using a generated artificial intelligence model,

[0950] A means for generating individually adapted health plans based on predicted health risks and emotional states,

[0951] A means of notifying the user of the generated health plan and customizing the notification format based on their emotional state,

[0952] A means of providing rewards based on progress when health conditions improve,

[0953] A system that includes this.

[0954] (Claim 2)

[0955] The system according to claim 1, further comprising a method for use among users to input the aforementioned health checkup result data, daily life data, and emotional data.

[0956] (Claim 3)

[0957] The system according to claim 1, further comprising means for adjusting the generated artificial intelligence model and improving the health plan based on user feedback data. [Explanation of Symbols]

[0958] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting individual health checkup results data and daily life data, A method for predicting health risks from data collected using a generated artificial intelligence model, A means for generating individually adapted health plans based on predicted health risks, A means of notifying the user of the generated health plan, A means of providing incentives when health conditions improve compared to the previous year, A system that includes this.

2. The system according to claim 1, further comprising an interface for inputting the aforementioned health checkup result data and daily life data.

3. The system according to claim 1, further comprising means for adjusting the generated artificial intelligence model and improving the health plan based on user feedback data.

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