System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting identification information of a user; means for centrally managing health information of the user; means for analyzing the health information of the user using an algorithm and generating advice according to a health condition; and means for transmitting the health information and the advice to a terminal of the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Currently, health information is managed in a distributed manner by medical institutions and health checkup data, making it difficult for users to manage it centrally. Although My Number and health insurance cards are linked, this does not improve user convenience. Furthermore, with the declining birthrate and aging population, efficient and effective health management methods are needed. The present invention aims to solve these problems and realize more efficient health management and an extension of users' healthy lifespans. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for inputting a user's identification information, a means for centrally managing the user's health information, a means for analyzing the user's health information using an algorithm and generating advice based on the user's health condition, and a means for transmitting the health information and advice to the user's device. The system also includes a means for using the user's Individual Number and health insurance card information as the user's identification information and collecting lifestyle data and health checkup results as health information. This allows the system to predict the user's health risks and suggest preventive measures and treatments. The system also provides a chatbot function, generates exercise programs, and transmits them to the user's device, thereby supporting the user's comprehensive health management.

[0006] "User identification information" refers to personal information used to identify a user, and specifically includes My Number and health insurance card information.

[0007] "Health information" is data that indicates the user's health condition, and specifically includes lifestyle data and health checkup results.

[0008] "Centralized management" refers to consolidating multiple pieces of information and data in one place and managing them in a unified manner.

[0009] An "algorithm" is a set of computational steps or formulas that accomplish a particular purpose.

[0010] "Analysis" is the process of examining data and revealing its structure and relationships.

[0011] "Health risks" refers to the risk of health problems or diseases that a user may face in the future.

[0012] "Advice" refers to providing guidance or suggestions, such as preventative measures or treatments, based on the user's health status.

[0013] "Terminal" refers to electronic devices such as smartphones used by users.

[0014] The "chatbot function" is a function that automatically responds to questions from users and provides health information and advice.

[0015] "Exercise program" refers to an exercise plan or menu designed based on the user's health condition and lifestyle habits. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The system of the present invention provides a mechanism for inputting a user's identification information, centrally managing health information, analyzing the user's health status using an algorithm, and generating appropriate advice and sending it to the user's device. The processing content of the system's program and specific examples are explained below.

[0038] Basic configuration

[0039] This system consists of a user device (e.g., a smartphone), a server, and an AI analysis module. Users enter health information through a smartphone app and send it to the server. The server analyzes the received data and provides advice and information to the user.

[0040] Enter user identification information

[0041] The user launches the smartphone app and scans their My Number and health insurance card. The smartphone encodes this data and sends it to the server, which receives it and authenticates the user by checking it against a database.

[0042] Collection of health information

[0043] The device periodically prompts the user to enter their health checkup results and lifestyle habits. The user enters this information into a smartphone app, and the device then sends it to a server.

[0044] Centralized management and analysis of health information

[0045] The server stores the received health information in a database. An algorithm analyzes the user's health status based on this data and assesses their potential health risks. Based on the analysis, the server generates appropriate advice, preventative measures, and treatments.

[0046] Generating and Sending Advice

[0047] The AI ​​analysis module on the server generates personalized advice based on the user's health information, including dietary improvements, exercise recommendations, stress management, etc. The generated advice is then sent to the device and notified to the user.

[0048] Chatbot functionality

[0049] The device accepts health-related questions from users. These questions are sent to a server, where an AI chatbot generates answers. The AI ​​consults relevant medical databases and historical data to provide appropriate answers. These answers are then sent back to the device and displayed to the user.

[0050] Specific examples

[0051] For example, if a user types a question such as "I can't seem to get rid of my fatigue lately," the device sends this question to the server, where the AI ​​chatbot analyzes the question. Based on the user's past health and lifestyle data, the AI ​​suggests advice such as "reducing screen time at night to improve sleep quality" or "taking appropriate vitamins as needed." This information is sent to the device and notified to the user.

[0052] Exercise program suggestions

[0053] If the user is judged to be lacking in exercise, the server's AI analysis module will generate an exercise program based on the user's health condition and lifestyle. For example, it may suggest walking three times a week or stretching every morning. This exercise program is then sent to the device and notified to the user.

[0054] As described above, this system centrally manages users' health information and analyzes it using algorithms to provide individually customized advice and treatments, thereby improving the efficiency of health management and extending users' healthy lifespans.

[0055] The processing flow will be explained below.

[0056] Step 1:

[0057] The user launches the smartphone app and uses the camera function to scan their My Number and health insurance card.

[0058] Step 2:

[0059] The device encodes the scanned data and sends it to the server as the user's identification information.

[0060] Step 3:

[0061] The server compares the received identification information with a database and authenticates the user. If authentication is successful, the server sends a notification of authentication completion to the terminal.

[0062] Step 4:

[0063] The device receives a notification that authentication is complete, displays a message on the app screen indicating successful authentication, and the user can proceed to the next step.

[0064] Step 5:

[0065] The device periodically sends notifications to the user prompting them to enter their health checkup results and lifestyle habits data.

[0066] Step 6:

[0067] Users receive a notification and enter information such as health check results, daily diet, exercise, and sleep time into a smartphone app.

[0068] Step 7:

[0069] The terminal formats the entered information and sends it to the server.

[0070] Step 8:

[0071] The server stores the received health information in a database, allowing users' health information to be managed centrally.

[0072] Step 9:

[0073] An AI analysis module on the server analyzes the stored data and compares it with the user's current health status and past data.

[0074] Step 10:

[0075] The AI ​​analysis module evaluates the user's health risks based on the analysis results. For example, it identifies the user's future risk of high blood pressure based on their blood pressure data and lifestyle habits.

[0076] Step 11:

[0077] The server generates appropriate advice, preventative measures, and treatments based on the evaluation results.

[0078] Step 12:

[0079] The server sends the generated advice and preventative measures to the device.

[0080] Step 13:

[0081] The device will notify the user of the advice and preventative measures it receives and display them on the app screen, allowing the user to use this information to improve their lifestyle habits.

[0082] Step 14:

[0083] The device accepts health-related questions from the user, who then enters and submits the question.

[0084] Step 15:

[0085] The terminal sends the entered question to the server.

[0086] Step 16:

[0087] The server's AI chatbot analyzes the question and references relevant medical databases and past user health information.

[0088] Step 17:

[0089] The AI ​​chatbot generates an appropriate response, which the server then sends to the device.

[0090] Step 18:

[0091] The device receives the response and displays it to the user.

[0092] Step 19:

[0093] When a user receives an alert indicating lack of exercise, an AI analysis module on the server generates an individually customized exercise program.

[0094] Step 20:

[0095] The server transmits the generated exercise program to the terminal.

[0096] Step 21:

[0097] The device notifies the user of the exercise program and displays details on the app screen, and the user follows the suggested exercise program to perform daily exercise.

[0098] Example 1

[0099] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0100] In modern society, personal health management has become an important issue. However, conventional health management systems not only centrally manage users' health information, but also lack the necessary systems to provide appropriate advice based on that information. Furthermore, they lack mechanisms to quickly and appropriately respond to users' questions about their health status, or to provide individually customized exercise programs. This makes it difficult for users to properly manage their health.

[0101] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0102] In this invention, the server includes a means for inputting user identification information, a means for centrally managing user health information, a means for analyzing the user's health information using an algorithm and generating advice according to the user's health condition, a means for generating answers to questions entered by the user using a generative AI model, and a means for generating an exercise program based on the user's health condition. This not only enables the user's own health information to be centrally managed, but also enables the user to receive appropriate advice and exercise programs, and to receive prompt and appropriate answers to health-related questions.

[0103] "User identification information" refers to information for identifying an individual user, and includes, for example, an identification number and authentication information.

[0104] "Centralized management" means concentrating and managing multiple pieces of data and information in one place.

[0105] An "algorithm" refers to a computational procedure or process for solving a specific problem, implemented as a program.

[0106] "Health information" refers to information about the user's health condition, and specifically includes weight, blood pressure, diet, exercise history, and the like.

[0107] "Advice" means any suggestions or advice provided to a User for the purpose of improving or maintaining their health.

[0108] "Terminal" refers to a hardware device that is directly operated by a user, and specifically includes smartphones and tablets.

[0109] A "generative AI model" refers to a model that uses artificial intelligence to generate a specific output from data, such as to generate an answer to a question.

[0110] An "exercise program" refers to an exercise plan designed based on the user's health condition and lifestyle, and includes weekly exercise content and stretching methods.

[0111] "Database" refers to a system for efficiently storing, retrieving, and managing data, such as those used to store health and identifying information.

[0112] This system inputs a user's identification information, centrally manages health information, analyzes the user's health status using an algorithm, generates appropriate advice, and sends it to the user's device. This system consists of a user device, a server, and an AI analysis module.

[0113] Basic configuration

[0114] The implementation of this system requires the following elements:

[0115] User device (e.g. smartphone)

[0116] Server (e.g., EC2 instance on Amazon Web Services)

[0117] Database (e.g. Amazon RDS)

[0118] AI analysis module (e.g., TENSORFLOW® or PyTorch)

[0119] Communication protocol (e.g. HTTPS)

[0120] Enter user identification information

[0121] The user launches the smartphone app and uses the app's scanning function to input their My Number and health insurance card information. The smartphone then uses its camera to capture this information and sends it to the server in an encoded format. The server then collates the received data with a database (e.g., MySQL (registered trademark)) for authentication. The OAuth 2.0 protocol is used for this authentication.

[0122] Collection of health information

[0123] The device periodically sends notifications to the user prompting them to enter their health checkup results and lifestyle habit data. For example, a notification saying "Please weigh yourself" is sent every morning at 8:00. The user enters this data (e.g., weight, blood pressure, dietary details) through the app. The device sends this data to the server in real time. This transmission uses the HTTPS protocol.

[0124] Centralized management and analysis of health information

[0125] The server stores the received health information in a database (e.g., Amazon RDS). An AI analysis module (e.g., using TensorFlow or PyTorch) performs analysis based on the stored health information. This evaluates the user's potential health risks. The evaluation results are stored in the database.

[0126] Generating and Sending Advice

[0127] The server's AI analysis module generates appropriate advice based on the user's health information. Specific advice includes dietary improvements, recommended exercise, and stress management. The generated advice is sent to the device using APNS or FCM. The device then notifies the user of the received advice.

[0128] Chatbot functionality

[0129] The terminal accepts health-related questions from the user, such as the following prompt:

[0130] "I've been feeling tired lately and it's bothering me. What should I do?"

[0131] The question is encrypted and sent to a server, where an AI chatbot (e.g., GPT-4 (registered trademark) model) analyzes the question and generates an appropriate answer by referencing relevant medical databases and past data. The answer is then sent to the device and displayed to the user.

[0132] Exercise program suggestions

[0133] If the user is judged to be lacking in exercise, the server's AI analysis module will generate an exercise program based on the user's health condition and lifestyle. For example, it may include specific suggestions such as walking three times a week or stretching every morning. These exercise programs are then sent to the device via the notification function, and the user is notified.

[0134] Through each of these processes, the system can efficiently manage the user's health and provide individually customized advice and treatments.

[0135] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0136] Step 1: Enter user identification information

[0137] The user launches the smartphone app and scans their My Number and health insurance card. The device uses the camera to read this information, converts it into an encoded format, encrypts it (e.g., AES256), and sends it to the server. The server then compares the received identification information with a database (e.g., MySQL) to authenticate the user. This authentication uses the OAuth 2.0 protocol.

[0138] Input: User identification information (My Number, health insurance card)

[0139] Output: Authentication result

[0140] Specific behavior:

[0141] User scans information with camera

[0142] The device encrypts the data and sends it to the server

[0143] The server authenticates the user against a database

[0144] Step 2: Gathering health information

[0145] The device periodically sends notifications to the user, prompting them to enter their health checkup results and lifestyle habits. For example, it sends a notification every morning at 8:00 to "weigh yourself." The user enters this data through the app, and the device sends the input data to the server in real time. Transmission is via the HTTPS protocol.

[0146] Input: User's health information (weight, blood pressure, dietary details)

[0147] Output: Collected health data

[0148] Specific behavior:

[0149] The device periodically sends notifications

[0150] User enters data in the app

[0151] The device sends the input data to the server

[0152] Step 3: Centralize and analyze health information

[0153] The server stores the submitted health information in a database (e.g., Amazon RDS). An AI analysis module (e.g., TensorFlow or PyTorch) analyzes the user's health status based on the received data. The algorithm compares the data with past data, evaluates the user's potential health risks, and stores the results in the database.

[0154] Input: Collected health data

[0155] Output: Analysis and evaluation results

[0156] Specific behavior:

[0157] The server stores the health data in a database

[0158] AI analysis module analyzes health data

[0159] Health risk assessment results are stored in a database

[0160] Step 4: Generate and send advice

[0161] The server's AI analysis module generates appropriate advice based on the health information and analysis results. This advice may include dietary improvements, exercise recommendations, stress management, etc. The generated advice is sent to the device using APNS or FCM. The device then notifies the user of the received advice.

[0162] Input: Analysis results and evaluation results

[0163] Output: personalized advice

[0164] Specific behavior:

[0165] Server generates advice

[0166] Send advice to your device

[0167] Notify users of advice

[0168] Step 5: Chatbot functionality

[0169] The device accepts questions from the user. An example question might be, "I've been feeling tired lately. What should I do?" This question is encrypted and sent to the server. The server uses an AI chatbot (e.g., a GPT-4 model) to analyze the question and generate an appropriate answer by referencing relevant medical databases and past data. The generated answer is then sent back to the device and displayed to the user.

[0170] Input: User question

[0171] Output: The generated answer

[0172] Specific behavior:

[0173] The device accepts questions

[0174] The server analyzes the question and generates an answer

[0175] Send the answer to the device and display it to the user

[0176] Step 6: Propose an exercise program

[0177] If the user is judged to be lacking in exercise, the server's AI analysis module will generate an exercise program based on their health condition and lifestyle. For example, it may suggest walking three times a week or stretching every morning. This exercise program is sent to the device using the notification function and displayed to the user.

[0178] Input: Health and lifestyle data

[0179] Output: Exercise program

[0180] Specific behavior:

[0181] The server generates the exercise program

[0182] Send exercise programs to your device

[0183] Notify users of exercise programs

[0184] Through these steps, the system can centralize the user's health information and provide appropriate advice and exercise programs. Furthermore, the chatbot function using the generative AI model can quickly respond to users' health-related questions.

[0185] (Application example 1)

[0186] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0187] Conventional health management systems only analyze a user's health information and provide advice based on the results, but lack the ability to automatically generate meal plans that are optimal for each user's individual health condition. This makes it difficult for users to select meals that are appropriate for their health condition, resulting in a decrease in the efficiency and effectiveness of health management. There is also a need for a system that allows users to easily obtain an appropriate meal plan and put it into practice.

[0188] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0189] In this invention, the server includes a means for inputting user identification information, a means for centrally managing user health information, a means for analyzing the user's health information using an algorithm and generating advice based on the user's health condition, and a means for generating a meal plan based on the health condition and transmitting the meal plan to the user's terminal. This allows the user to easily obtain an optimal meal plan based on their health condition, enabling efficient and effective health management.

[0190] "User identification information" refers to information for identifying individual users, such as their My Number or health insurance card information.

[0191] "Health information" is data related to the user's health condition, and includes, for example, health checkup results and lifestyle habit data.

[0192] An "algorithm" is a method or calculation method for analyzing data according to a set procedure.

[0193] "Health-based advice" refers to advice and recommendations that are individually customized based on the results of analyzing a user's health information.

[0194] "User's terminal" refers to an electronic device used by a user, such as a smartphone or tablet.

[0195] "Centralized management" means aggregating and managing multiple health information in a unified manner.

[0196] "Meal Plan" means a meal suggestion or plan that takes into account the user's health condition.

[0197] The system of the present invention inputs user identification information, centrally manages health information, analyzes health status using an algorithm, generates appropriate advice, and transmits it to the user's device. It also has the function of generating a meal plan based on the user's health status and providing it to the user's device.

[0198] Basic configuration

[0199] This system consists of a device used by the user (e.g., a smartphone), a server that processes the data, and an AI analysis module.

[0200] Enter user identification information

[0201] The user launches the smartphone app and scans their My Number and health insurance card. The device encodes this data and sends it to the server, which receives it and authenticates the user by checking it against a database.

[0202] Collection of health information

[0203] The device periodically prompts the user to enter their health checkup results and lifestyle habits. The user enters this information into a smartphone app and sends it to a server.

[0204] Centralized management and analysis of health information

[0205] The server stores the received health information in a database. The AI ​​analysis module uses this data to analyze the user's health status and assess potential health risks. Based on the analysis results, the server generates appropriate advice, preventive measures, and treatments.

[0206] Generating and Sending Advice

[0207] The AI ​​analysis module on the server generates personalized advice based on the user's health information, including dietary improvements, exercise recommendations, stress management, etc. The generated advice is then sent to the device and notified to the user.

[0208] Chatbot functionality

[0209] The device accepts health-related questions from users. These questions are sent to a server, where an AI chatbot generates answers. The AI ​​consults relevant medical databases and historical data to provide appropriate answers. These answers are then sent back to the device and displayed to the user.

[0210] Generate a meal plan

[0211] Based on the user's health condition, the server's AI analysis module generates a personalized meal plan. For example, if the user needs to lose weight, it will provide a low-calorie meal plan, and if the user needs to recover from fatigue, it will provide a nutritious meal plan. This meal plan is sent to the terminal, where the user can easily order.

[0212] Example of a system

[0213] For example, if a user types a question such as "I can't seem to get rid of my fatigue lately," the device sends this question to the server, where the AI ​​chatbot analyzes the question. Based on the user's past health and lifestyle data, the AI ​​suggests advice such as "reducing screen time at night to improve sleep quality" or "taking appropriate vitamins as needed." This information is sent to the device and notified to the user.

[0214] Hardware and software used

[0215] Hardware: Smartphones, servers

[0216] Software: Python, Flask, scikit-learn

[0217] Prompt Sentence Examples

[0218] "I've been feeling tired lately. Is there any good way to get more nutrition?"

[0219] The present invention allows users to easily obtain optimal meal plans and advice based on their own health condition, enabling them to manage their health more efficiently and effectively.

[0220] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0221] Step 1:

[0222] Enter user identification information

[0223] How it works: A user launches the smartphone app and scans their My Number and health insurance card.

[0224] Input: My Number, health insurance card information

[0225] Data processing and calculation: The device encodes and encrypts this identification information and sends it to the server.

[0226] Output: The encrypted identity is sent to the server.

[0227] Step 2:

[0228] User identity authentication

[0229] How it works: The server checks the received identification information against a database and authenticates the user.

[0230] Input: Encrypted identity

[0231] Data processing and calculation: The server performs a database search and checks for data matches.

[0232] Output: User authentication result is generated.

[0233] Step 3:

[0234] Collection of health information

[0235] How it works: The device periodically prompts the user to enter health checkup results and lifestyle data.

[0236] Input: Health checkup results and lifestyle data entered by the user

[0237] Data processing and calculation: The health information collected by the device is sent to the server.

[0238] Output: Health information is stored on the server.

[0239] Step 4:

[0240] Centralized management and analysis of health information

[0241] How it works: The server stores the received health information in a database.

[0242] Input: Health information submitted by the user

[0243] Data processing and calculation: Analyze health information using an AI analysis module and assess potential health risks.

[0244] Output: Health risk assessment and analysis results are generated.

[0245] Step 5:

[0246] Advice generation and delivery

[0247] How it works: An AI analysis module on the server generates personalized advice based on the user's health information.

[0248] Input: Health risk assessment results and analysis results

[0249] Data processing and calculation: Generates advice on improving diet, recommending exercise, and managing stress, and sends it to the user's device.

[0250] Output: The customized advice is sent to the user's device.

[0251] Step 6:

[0252] Using chatbot functionality

[0253] How it works: The device accepts health-related questions from users, which are then sent to a server where an AI chatbot consults relevant databases and historical data to generate appropriate answers.

[0254] Input: User question

[0255] Data processing and calculation: The AI ​​chatbot analyzes the question, collects relevant information, and generates an answer.

[0256] Output: The generated answer is sent to the user's device.

[0257] Step 7:

[0258] Meal plan generation and delivery

[0259] How it works: The server's AI analysis module generates an individually customized meal plan and sends it to the user's device.

[0260] Input: Health analysis results and individual health status

[0261] Data processing and calculation: AI generates optimal meal plans based on the user's health condition. For example, it will recommend low-calorie meals for users who need to lose weight, and nutritious meals for users who need to recover from fatigue.

[0262] Output: The customized meal plan is sent to the user's device.

[0263] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0264] The system of the present invention provides a mechanism for inputting a user's identification information, centrally managing health information, analyzing the user's health status using an algorithm, generating appropriate advice, and transmitting it to the user's device. Furthermore, it incorporates an emotion engine that analyzes the user's emotional state and adjusts the advice and suggestions. The system's program processing is explained below with specific examples.

[0265] Basic configuration

[0266] This system consists of a user device (e.g., a smartphone), a server, an AI analysis module, and an emotion engine. Users input health information and emotional state through a smartphone app and send it to the server. The server analyzes the received data and provides advice and information to the user.

[0267] Enter user identification information

[0268] The user launches the smartphone app and scans their My Number and health insurance card. The smartphone encodes this data and sends it to the server, which receives it and authenticates the user by checking it against a database.

[0269] Collection of health information

[0270] The device periodically prompts the user to enter their health checkup results and lifestyle habits. The user enters this information into a smartphone app, and the device then sends it to a server.

[0271] Collecting Emotional Data

[0272] The device provides an interface for inputting the user's emotional state. The user inputs the emotional state (e.g., stress, happiness, etc.), and the device transmits it to the server.

[0273] Centralized management and analysis of health information

[0274] The server stores the received health and emotional information in a database, and an algorithm uses this data to analyze the user's health status and generate advice based on the user's current health and emotional state.

[0275] Generating and Sending Advice

[0276] The AI ​​analysis module on the server generates personalized advice based on the user's health information and emotional state. For example, if the user is feeling stressed, the AI ​​will suggest ways to relieve stress. This advice is then sent to the device and notified to the user.

[0277] Chatbot functionality

[0278] The device accepts health-related questions from users. These questions are sent to a server, where an AI chatbot generates answers. The AI ​​consults relevant medical databases and historical data to provide appropriate answers. These answers are then sent back to the device and displayed to the user.

[0279] Specific examples

[0280] For example, if a user types a question such as "I've been feeling stressed lately," the device sends this question to the server, where the AI ​​chatbot analyzes the question. The emotion engine determines the stress level from the user's input and behavior, and the AI ​​provides advice such as "breathing techniques to relax." This information is sent to the device and notified to the user.

[0281] Exercise program suggestions

[0282] When a user receives an alert indicating insufficient exercise, the server's AI analysis module generates an exercise program based on the user's health and emotional state. For example, it may suggest light exercise for a depressed user, or a more vigorous exercise program for a healthy user. These exercise programs are then sent to the device and notified to the user.

[0283] As described above, this system centrally manages a user's health and emotional information and analyzes it using an algorithm to provide individually customized advice and treatments. This not only improves the efficiency of health management and extends the user's healthy lifespan, but also provides more personalized support that responds to the user's emotions.

[0284] The processing flow will be explained below.

[0285] Step 1:

[0286] The user launches the smartphone app and uses the camera function to scan their My Number and health insurance card.

[0287] Step 2:

[0288] The device encodes the scanned data and sends it to the server as the user's identification information.

[0289] Step 3:

[0290] The server compares the received identification information with a database and authenticates the user. If authentication is successful, the server sends a notification of authentication completion to the terminal.

[0291] Step 4:

[0292] The device receives a notification that authentication is complete, displays a message on the app screen indicating successful authentication, and the user can proceed to the next step.

[0293] Step 5:

[0294] The device periodically sends notifications to the user prompting them to enter their health checkup results and lifestyle habits data.

[0295] Step 6:

[0296] Users receive a notification and enter information such as health check results, daily diet, exercise, and sleep time into a smartphone app.

[0297] Step 7:

[0298] The terminal formats the entered information and sends it to the server.

[0299] Step 8:

[0300] The server stores the received health information in a database, allowing users' health information to be managed centrally.

[0301] Step 9:

[0302] The device provides an interface for inputting the user's emotional state. The user inputs the emotional state (e.g., stress, happiness, etc.), and the device transmits it to the server.

[0303] Step 10:

[0304] The server stores the received emotion information in a database.

[0305] Step 11:

[0306] An AI analysis module on the server analyzes the stored health and emotional information and compares it with the user's current health status and past data.

[0307] Step 12:

[0308] The AI ​​analysis module evaluates the user's health risks based on the analysis results. For example, it identifies the user's future risk of high blood pressure based on their blood pressure data and lifestyle habits.

[0309] Step 13:

[0310] The server generates appropriate advice, preventative measures, and treatments based on the evaluation results, and an emotion engine adjusts the advice content based on the user's emotional state.

[0311] Step 14:

[0312] The server sends the generated advice and preventative measures to the device.

[0313] Step 15:

[0314] The device will notify the user of the advice and preventative measures it receives and display them on the app screen, allowing the user to use this information to improve their lifestyle habits.

[0315] Step 16:

[0316] The device accepts health-related questions from the user, who then enters and submits the question.

[0317] Step 17:

[0318] The terminal sends the entered question to the server.

[0319] Step 18:

[0320] The server's AI chatbot analyzes the question and references relevant medical databases and past user health information.

[0321] Step 19:

[0322] The AI ​​chatbot generates an appropriate response, which the server then sends to the device.

[0323] Step 20:

[0324] The device receives the response and displays it to the user.

[0325] Step 21:

[0326] When a user receives an alert indicating insufficient exercise, the AI ​​analysis module in the server generates an individually customized exercise program, and the emotion engine adjusts the program appropriately based on the user's emotional state.

[0327] Step 22:

[0328] The server transmits the generated exercise program to the terminal.

[0329] Step 23:

[0330] The device notifies the user of the exercise program and displays details on the app screen, and the user follows the suggested exercise program to perform daily exercise.

[0331] Example 2

[0332] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0333] In modern society, it is becoming increasingly important to centrally manage each individual's health and emotional state and provide appropriate advice. However, conventional health management systems have struggled to provide personalized advice that fully takes into account the user's emotional state. Furthermore, they lacked technology to efficiently authenticate and securely manage user identification information. This made it difficult to provide individually customized health support.

[0334] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0335] In this invention, the server includes means for inputting user identification information, means for centrally managing health information, means for analyzing the user's health information using an algorithm and generating advice according to the user's health condition, means for analyzing the user's emotional state and adjusting the advice and suggestions, means for providing an interface for inputting data, and means for transmitting the health information and advice to the user's terminal. This makes it possible to manage the user's health information and emotional information in an integrated manner and provide advice optimized for each individual condition.

[0336] "User" refers to any individual or organization that uses this system.

[0337] "Identification information" is data for uniquely identifying a user, and includes, for example, information such as a My Number or health insurance card.

[0338] "Health information" is data that indicates the user's health condition, and includes information such as weight, blood pressure, pulse rate, dietary content, amount of exercise, and sleep time.

[0339] An "algorithm" refers to a specific procedure for performing processing according to a certain calculation procedure.

[0340] "Emotional state" is data that indicates the psychological state of the user, and includes, for example, stress level, happiness level, tiredness, and the like.

[0341] "Interface" refers to a device or means for inputting or outputting data.

[0342] "Terminal" means an electronic device that allows a user to input data or receive information, including, for example, a smartphone or tablet.

[0343] A "server" refers to a computer system that processes and stores data over a network.

[0344] "AI analysis module" refers to software or hardware that uses artificial intelligence to analyze data and generate processing results.

[0345] A "database" refers to a system that organizes and stores information according to certain standards and allows it to be retrieved as needed.

[0346] The system of the present invention mainly manages and analyzes a user's identification information, health information, and emotional state to provide individually customized health advice. This system is composed of a user terminal, a server, an AI analysis module, and an emotion engine.

[0347] Enter user identification information

[0348] The user launches the smartphone app and scans their My Number and health insurance card with the camera. The device encodes this data using OCR (Optical Character Recognition) technology and sends it to the server using a secure protocol (e.g., HTTPS). The server compares the received data with a database and authenticates the user. If authentication is successful, the server starts a user session and allows subsequent data transmission.

[0349] Collection of health information

[0350] The device periodically uses push notifications to prompt the user to enter their health checkup results and lifestyle data (e.g., diet, exercise, and sleep duration). After the user has entered this information, the device encrypts the input data and sends it back to the server. The server verifies the received data and stores it in a database.

[0351] Collecting Emotional Data

[0352] The device displays options and sliders for inputting emotional states. After the user inputs their current emotional state (e.g., stress level, happiness, fatigue), and confirms the data, the device sends it to the server, which then performs further analysis based on the received emotional data.

[0353] Storage and analysis of health and emotional information

[0354] The server stores the received health and emotional information in a database. The AI ​​analysis module processes this data in real time and uses predictive models to analyze the user's health and emotional state. The analysis results in the calculation of the user's health risks and emotional trends.

[0355] Generating and Sending Advice

[0356] The server uses a generative AI model to customize personalized advice based on the user's health and emotional information. For example, if a user inputs a high stress level, the AI ​​model generates specific suggestions such as "try deep breathing" or "take a short walk." This advice is sent to the device and displayed to the user as a notification.

[0357] Chatbot functionality

[0358] Users enter health-related questions into the app as text. For example, if they ask, "How can I improve the quality of my sleep?", the device sends the question to the server. The server uses an AI chatbot module to analyze the question and generate an appropriate answer by referencing relevant medical databases and past question data. This answer is then sent to the device and displayed to the user.

[0359] Exercise program suggestions

[0360] When a user receives an alert about lack of exercise, the server's AI analysis module generates an appropriate exercise program based on the user's health condition, emotional state, and past exercise history. For example, it might suggest a program such as "10 minutes of light stretching" for a user who tends to feel depressed, or "30 minutes of light jogging" for a user who appears healthy. The exercise program is sent to the device and notified to the user.

[0361] Specific examples

[0362] For example, if a user types a question such as "I've been feeling stressed lately," the device sends this question to the server, where the AI ​​chatbot analyzes the question. The emotion engine determines the stress level from the user's input and behavior, and the AI ​​provides advice such as "breathing techniques to relax" or "do some light exercise." This information is sent to the device and notified to the user.

[0363] Example prompt: "I've been feeling stressed lately. How can I relieve this?"

[0364] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0365] Step 1: Enter user identification information

[0366] The user launches the smartphone app and scans their My Number and health insurance card with the camera. The device uses OCR technology to encode this information and sends it to the server via a secure protocol (HTTPS). The server compares the received identification information with a database and authenticates the user. If authentication is successful, the server starts a user session and allows subsequent data transmission.

[0367] input:

[0368] Image data of the user's My Number and health insurance card

[0369] output:

[0370] Encoded identity information, authentication result (success or failure)

[0371] Specific behavior:

[0372] Extracts text data from images using OCR technology and encodes it. Sends data securely using HTTPS and authenticates it against a database.

[0373] Step 2: Gathering health information

[0374] The device periodically uses push notifications to prompt the user to enter health checkup results and lifestyle habit data. The user enters data such as dietary habits, exercise, and sleep duration into a smartphone app. The device encrypts the entered data and sends it to the server. The server verifies the received data and stores it in a database.

[0375] input:

[0376] Health checkup results and lifestyle data entered by the user

[0377] output:

[0378] Encrypted health information stored in a database

[0379] Specific behavior:

[0380] Push notifications prompt users to enter data, encrypt the data and send it in real time. Received data is verified and accurately stored in the database.

[0381] Step 3: Collecting emotion data

[0382] Using the interface displayed on the device, the user inputs their current emotional state (e.g., stress level, happiness, fatigue), and the device securely transmits this data to the server, which then analyzes the received emotional data and stores the necessary information in a database.

[0383] input:

[0384] Emotional state data entered by the user

[0385] output:

[0386] Encrypted emotion data, stored in a database

[0387] Specific behavior:

[0388] Emotional information is collected from the user via the interface, encrypted and transmitted, and then analyzed and stored after reception.

[0389] Step 4: Storing and analyzing health and emotional information

[0390] The server stores the received health and emotional information in a database. The AI ​​analysis module uses this data to analyze the user's health and emotional state in real time and calculates risk assessments and trends using predictive models.

[0391] input:

[0392] Encrypted health and emotional information

[0393] output:

[0394] Analysis results (health risks, emotional trends)

[0395] Specific behavior:

[0396] The AI ​​analysis module analyzes the received data and uses predictive models to calculate risk assessments and trends of the user's health and emotional state.

[0397] Step 5: Generate and send advice

[0398] Using a generative AI model installed on the server, the system generates personalized advice based on the user's health and emotional information. For example, a user with a high stress level will be given specific suggestions such as "try taking deep breaths." This advice is sent to the device and notified to the user.

[0399] input:

[0400] Analysis results

[0401] output:

[0402] Personalized advice

[0403] Specific behavior:

[0404] The generative AI model creates appropriate advice based on the analysis results and sends it to the user's device.

[0405] Step 6: Chatbot functionality

[0406] Users input health-related questions into the app, such as "How can I improve the quality of my sleep?" The device sends the question to the server, where the AI ​​chatbot analyzes the question, references relevant information, and generates an appropriate answer. The generated answer is then sent back to the device and displayed to the user.

[0407] input:

[0408] User Questions

[0409] output:

[0410] Chatbot Answers

[0411] Specific behavior:

[0412] The chatbot function analyzes the user's question and generates an answer by referring to a related medical database and a database of past questions.

[0413] Step 7: Propose an exercise program

[0414] When a user receives an alert indicating lack of exercise, the server's AI analysis module generates an appropriate exercise program based on the user's health condition, emotional state, and past exercise data. For example, a depressed user might be recommended a 10-minute light stretching program, while a lively user might be recommended a 30-minute light jog. These exercise programs are then sent to the device and notified to the user.

[0415] input:

[0416] Analysis results, past exercise data

[0417] output:

[0418] Exercise program suggestions

[0419] Specific behavior:

[0420] The AI ​​analysis module generates an exercise program based on the data and notifies the user of the optimal exercise suggestions via the device.

[0421] (Application example 2)

[0422] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0423] Conventional health management systems can centrally manage a user's health information and provide advice, but they are unable to provide appropriate advice or meal menus that take into account the user's emotional state.In addition, they do not integrate a function to suggest meal menus based on the user's health and emotional state and to easily order them, making it difficult to improve the convenience of health management for users, and this needs to be improved.

[0424] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0425] In this invention, the server includes means for inputting user identification information, means for centrally managing user health information, means for analyzing the user's health information using an algorithm and generating advice based on the user's health condition, means for analyzing the user's emotional state, means for generating advice and a meal menu individually customized based on the user's health condition and emotional state and providing this to the user, and means for ordering the generated meal menu through a food delivery platform. This enables more personalized health management and meal suggestions based on the user's health condition and emotional state.

[0426] "User identification information" is unique information that identifies a user.

[0427] "Health information" refers to data related to the user's health condition, including health checkup results and lifestyle data.

[0428] An "algorithm" is a procedure and calculation method for analyzing a user's health information and generating advice based on their health condition.

[0429] "Emotional state" refers to a user's psychological state, such as stress, happiness, or anger.

[0430] "Centralized management" means integrating multiple user data and managing it efficiently.

[0431] "Advice" refers to specific recommendations or suggestions for improvement based on the user's health or emotional state.

[0432] "Health Status" refers to the user's current physical health, assessed based on various health information.

[0433] "User's device" refers to a communication device that is directly used by the user, such as a smartphone or tablet.

[0434] A "food delivery platform" is a system that provides a series of services from when users order food online until the food is delivered.

[0435] "Customized advice" refers to advice that is individually tailored to each user based on their health and emotional state.

[0436] "Meal Menu" refers to the specific dishes and combinations of ingredients offered to Users.

[0437] The present invention provides a system that improves the efficiency of health management and dietary suggestions for users and provides individually customized advice. The specific configuration and operation of the system will be described below.

[0438] System configuration

[0439] The system consists of the following elements:

[0440] 1. User device: A device operated by a user, such as a smartphone or tablet.

[0441] 2. Server: The central system that processes and stores data.

[0442] 3. Database: Data storage for centralized management of user identity, health information, and emotional state.

[0443] 4. AI analysis module: Analyzes health information and emotional state using machine learning models such as TensorFlow.

[0444] 5. Emotion Analysis Engine: Analyzes the user's emotional state using the Emotion API.

[0445] 6. Food delivery platform: An online system that allows users to order customized meal menus.

[0446] Processing flow

[0447] Entering and authenticating user identity

[0448] Through a smartphone app, users enter identifying information, such as government identification and a scan of their health insurance card, which is encoded and sent to a server that authenticates the user against a database.

[0449] Health and emotional data collection

[0450] The user's device periodically requests input of health checkup results and lifestyle habits data. Emotional states are also input, and this data is sent to the server. Emotional states are analyzed using the Emotion API.

[0451] Unified management and analysis of health and emotional information

[0452] The server stores the received health and emotional information in a database, and uses an AI analysis module to analyze this data and generate advice based on the user's current health and emotional state.

[0453] Generate and provide advice and meal menus

[0454] The AI ​​analysis module on the server generates personalized advice and meal menus based on the user's health information and emotional state, and these advice and meal menus are sent to the user's device.

[0455] Ordering food from the menu

[0456] Users can easily order the provided meal menu through the food delivery platform, and the order information is sent to the food delivery platform via the server, and the food is delivered to the user.

[0457] Specific use cases

[0458] For example, if a user inputs "I've been feeling stressed lately" and records "lack of exercise" as health information, the system will suggest appropriate meal options based on this. Suggested options include "mint tea to relieve stress" and "vegetable soup that's easy to digest." Users can order these options with the touch of a button.

[0459] Prompt Sentence Examples

[0460] "The user has entered 'I've been feeling stressed lately.' Health information has been recorded as 'not getting enough exercise.' Based on this, please suggest an appropriate meal menu."

[0461] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0462] Step 1:

[0463] Entering and authenticating user identity

[0464] Users launch the smartphone app and scan their government-issued identification and health insurance card information.

[0465] Input: Identification information (official identification information, medical insurance card information)

[0466] Output: Authentication request data (encoded identity information)

[0467] The server receives this, compares it with the database, authenticates the user, and returns the authentication result to the user's device.

[0468] Behavior: If the user is successfully authenticated, the primary functionality of the application becomes available.

[0469] Step 2:

[0470] Health and emotional data collection

[0471] The user device periodically requests input of health checkup results and lifestyle habits. The user also inputs their emotional state. This data is sent from the user device to the server.

[0472] Input: Health information (health checkup results, lifestyle data), emotional data (stress level, happiness level)

[0473] Output: Health information and emotion data (data sent to the server)

[0474] The server stores the received data in a database.

[0475] How it works: Users enter health and emotional data through the app and verify that the data is sent accurately to the server.

[0476] Step 3:

[0477] Analysis of information stored in the database

[0478] The server inputs the stored health and emotional information into an AI analysis module (using TensorFlow).

[0479] Input: Stored health and emotional information (data from database)

[0480] Output: Analysis results (health status, emotional status)

[0481] The AI ​​analysis module analyzes the user's health and emotional state and generates results.

[0482] How it works: The AI ​​model processes the data to analyze the user's health and emotional state.

[0483] Step 4:

[0484] Advice and meal menu generation

[0485] The server generates appropriate advice and meal menus based on the analysis results.

[0486] Input: Analysis results (health status, emotional status)

[0487] Output: Customized advice and meal menu (data sent to user device)

[0488] The generated advice and meal menu are sent to the user's device.

[0489] How it works: The server uses the analysis results to provide the user with optimal advice and meal plans.

[0490] Step 5:

[0491] Ordering food from the menu

[0492] Users can view the offered meal menu and place their order through the food delivery platform.

[0493] Input: Customized meal menu (displayed on user's device)

[0494] Output: Order data (data sent to food delivery platform)

[0495] The user terminal sends the order to the server, which then forwards the order data to the food delivery platform.

[0496] How it works: When the user presses the bulk order button, the specified meal menu is sent to the delivery service.

[0497] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0498] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0499] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0500] [Second embodiment]

[0501] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0502] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0503] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0504] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0505] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0506] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0507] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0508] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0509] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0510] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0511] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0512] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0513] The system of the present invention provides a mechanism for inputting a user's identification information, centrally managing health information, analyzing the user's health status using an algorithm, and generating appropriate advice and sending it to the user's device. The processing content of the system's program and specific examples are explained below.

[0514] Basic configuration

[0515] This system consists of a user device (e.g., a smartphone), a server, and an AI analysis module. Users enter health information through a smartphone app and send it to the server. The server analyzes the received data and provides advice and information to the user.

[0516] Enter user identification information

[0517] The user launches the smartphone app and scans their My Number and health insurance card. The smartphone encodes this data and sends it to the server, which receives it and authenticates the user by checking it against a database.

[0518] Collection of health information

[0519] The device periodically prompts the user to enter their health checkup results and lifestyle habits. The user enters this information into a smartphone app, and the device then sends it to a server.

[0520] Centralized management and analysis of health information

[0521] The server stores the received health information in a database. An algorithm analyzes the user's health status based on this data and assesses their potential health risks. Based on the analysis, the server generates appropriate advice, preventative measures, and treatments.

[0522] Generating and Sending Advice

[0523] The AI ​​analysis module on the server generates personalized advice based on the user's health information, including dietary improvements, exercise recommendations, stress management, etc. The generated advice is then sent to the device and notified to the user.

[0524] Chatbot functionality

[0525] The device accepts health-related questions from users. These questions are sent to a server, where an AI chatbot generates answers. The AI ​​consults relevant medical databases and historical data to provide appropriate answers. These answers are then sent back to the device and displayed to the user.

[0526] Specific examples

[0527] For example, if a user types a question such as "I can't seem to get rid of my fatigue lately," the device sends this question to the server, where the AI ​​chatbot analyzes the question. Based on the user's past health and lifestyle data, the AI ​​suggests advice such as "reducing screen time at night to improve sleep quality" or "taking appropriate vitamins as needed." This information is sent to the device and notified to the user.

[0528] Exercise program suggestions

[0529] If the user is judged to be lacking in exercise, the server's AI analysis module will generate an exercise program based on the user's health condition and lifestyle. For example, it may suggest walking three times a week or stretching every morning. This exercise program is then sent to the device and notified to the user.

[0530] As described above, this system centrally manages users' health information and analyzes it using algorithms to provide individually customized advice and treatments, thereby improving the efficiency of health management and extending users' healthy lifespans.

[0531] The processing flow will be explained below.

[0532] Step 1:

[0533] The user launches the smartphone app and uses the camera function to scan their My Number and health insurance card.

[0534] Step 2:

[0535] The device encodes the scanned data and sends it to the server as the user's identification information.

[0536] Step 3:

[0537] The server compares the received identification information with a database and authenticates the user. If authentication is successful, the server sends a notification of authentication completion to the terminal.

[0538] Step 4:

[0539] The device receives a notification that authentication is complete, displays a message on the app screen indicating successful authentication, and the user can proceed to the next step.

[0540] Step 5:

[0541] The device periodically sends notifications to the user prompting them to enter their health checkup results and lifestyle habits data.

[0542] Step 6:

[0543] Users receive a notification and enter information such as health check results, daily diet, exercise, and sleep time into a smartphone app.

[0544] Step 7:

[0545] The terminal formats the entered information and sends it to the server.

[0546] Step 8:

[0547] The server stores the received health information in a database, allowing users' health information to be managed centrally.

[0548] Step 9:

[0549] An AI analysis module on the server analyzes the stored data and compares it with the user's current health status and past data.

[0550] Step 10:

[0551] The AI ​​analysis module evaluates the user's health risks based on the analysis results. For example, it identifies the user's future risk of high blood pressure based on their blood pressure data and lifestyle habits.

[0552] Step 11:

[0553] The server generates appropriate advice, preventative measures, and treatments based on the evaluation results.

[0554] Step 12:

[0555] The server sends the generated advice and preventative measures to the device.

[0556] Step 13:

[0557] The device will notify the user of the advice and preventative measures it receives and display them on the app screen, allowing the user to use this information to improve their lifestyle habits.

[0558] Step 14:

[0559] The device accepts health-related questions from the user, who then enters and submits the question.

[0560] Step 15:

[0561] The terminal sends the entered question to the server.

[0562] Step 16:

[0563] The server's AI chatbot analyzes the question and references relevant medical databases and past user health information.

[0564] Step 17:

[0565] The AI ​​chatbot generates an appropriate response, which the server then sends to the device.

[0566] Step 18:

[0567] The device receives the response and displays it to the user.

[0568] Step 19:

[0569] When a user receives an alert indicating lack of exercise, an AI analysis module on the server generates an individually customized exercise program.

[0570] Step 20:

[0571] The server transmits the generated exercise program to the terminal.

[0572] Step 21:

[0573] The device notifies the user of the exercise program and displays details on the app screen, and the user follows the suggested exercise program to perform daily exercise.

[0574] Example 1

[0575] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0576] In modern society, personal health management has become an important issue. However, conventional health management systems not only centrally manage users' health information, but also lack the necessary systems to provide appropriate advice based on that information. Furthermore, they lack mechanisms to quickly and appropriately respond to users' questions about their health status, or to provide individually customized exercise programs. This makes it difficult for users to properly manage their health.

[0577] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0578] In this invention, the server includes a means for inputting user identification information, a means for centrally managing user health information, a means for analyzing the user's health information using an algorithm and generating advice according to the user's health condition, a means for generating answers to questions entered by the user using a generative AI model, and a means for generating an exercise program based on the user's health condition. This not only enables the user's own health information to be centrally managed, but also enables the user to receive appropriate advice and exercise programs, and to receive prompt and appropriate answers to health-related questions.

[0579] "User identification information" refers to information for identifying an individual user, and includes, for example, an identification number and authentication information.

[0580] "Centralized management" means concentrating and managing multiple pieces of data and information in one place.

[0581] An "algorithm" refers to a computational procedure or process for solving a specific problem, implemented as a program.

[0582] "Health information" refers to information about the user's health condition, and specifically includes weight, blood pressure, diet, exercise history, and the like.

[0583] "Advice" means any suggestions or advice provided to a User for the purpose of improving or maintaining their health.

[0584] "Terminal" refers to a hardware device that is directly operated by a user, and specifically includes smartphones and tablets.

[0585] A "generative AI model" refers to a model that uses artificial intelligence to generate a specific output from data, such as to generate an answer to a question.

[0586] An "exercise program" refers to an exercise plan designed based on the user's health condition and lifestyle, and includes weekly exercise content and stretching methods.

[0587] "Database" refers to a system for efficiently storing, retrieving, and managing data, such as those used to store health and identifying information.

[0588] This system inputs a user's identification information, centrally manages health information, analyzes the user's health status using an algorithm, generates appropriate advice, and sends it to the user's device. This system consists of a user device, a server, and an AI analysis module.

[0589] Basic configuration

[0590] The implementation of this system requires the following elements:

[0591] User device (e.g. smartphone)

[0592] Server (e.g., EC2 instance on Amazon Web Services)

[0593] Database (e.g. Amazon RDS)

[0594] AI analysis modules (e.g. TensorFlow or PyTorch)

[0595] Communication protocol (e.g. HTTPS)

[0596] Enter user identification information

[0597] The user launches the smartphone app and uses the app's scanning function to input their My Number and health insurance card information. The smartphone uses its camera to capture this information and sends it to the server in an encoded format. The server then collates the received data with a database (e.g., MySQL) for authentication. This authentication uses the OAuth 2.0 protocol.

[0598] Collection of health information

[0599] The device periodically sends notifications to the user prompting them to enter their health checkup results and lifestyle habit data. For example, a notification saying "Please weigh yourself" is sent every morning at 8:00. The user enters this data (e.g., weight, blood pressure, dietary details) through the app. The device sends this data to the server in real time. This transmission uses the HTTPS protocol.

[0600] Centralized management and analysis of health information

[0601] The server stores the received health information in a database (e.g., Amazon RDS). An AI analysis module (e.g., using TensorFlow or PyTorch) performs analysis based on the stored health information. This evaluates the user's potential health risks. The evaluation results are stored in the database.

[0602] Generating and Sending Advice

[0603] The server's AI analysis module generates appropriate advice based on the user's health information. Specific advice includes dietary improvements, recommended exercise, and stress management. The generated advice is sent to the device using APNS or FCM. The device then notifies the user of the received advice.

[0604] Chatbot functionality

[0605] The terminal accepts health-related questions from the user, such as the following prompt:

[0606] "I've been feeling tired lately and it's bothering me. What should I do?"

[0607] The question is encrypted and sent to a server, where an AI chatbot (e.g., a GPT-4 model) analyzes the question and generates an appropriate answer by referencing relevant medical databases and past data. The answer is then sent to the device and displayed to the user.

[0608] Exercise program suggestions

[0609] If the user is judged to be lacking in exercise, the server's AI analysis module will generate an exercise program based on the user's health condition and lifestyle. For example, it may include specific suggestions such as walking three times a week or stretching every morning. These exercise programs are then sent to the device via the notification function, and the user is notified.

[0610] Through each of these processes, the system can efficiently manage the user's health and provide individually customized advice and treatments.

[0611] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0612] Step 1: Enter user identification information

[0613] The user launches the smartphone app and scans their My Number and health insurance card. The device uses the camera to read this information, converts it into an encoded format, encrypts it (e.g., AES256), and sends it to the server. The server then compares the received identification information with a database (e.g., MySQL) to authenticate the user. This authentication uses the OAuth 2.0 protocol.

[0614] Input: User identification information (My Number, health insurance card)

[0615] Output: Authentication result

[0616] Specific behavior:

[0617] User scans information with camera

[0618] The device encrypts the data and sends it to the server

[0619] The server authenticates the user against a database

[0620] Step 2: Gathering health information

[0621] The device periodically sends notifications to the user, prompting them to enter their health checkup results and lifestyle habits. For example, it sends a notification every morning at 8:00 to "weigh yourself." The user enters this data through the app, and the device sends the input data to the server in real time. Transmission is via the HTTPS protocol.

[0622] Input: User's health information (weight, blood pressure, dietary details)

[0623] Output: Collected health data

[0624] Specific behavior:

[0625] The device periodically sends notifications

[0626] User enters data in the app

[0627] The device sends the input data to the server

[0628] Step 3: Centralize and analyze health information

[0629] The server stores the submitted health information in a database (e.g., Amazon RDS). An AI analysis module (e.g., TensorFlow or PyTorch) analyzes the user's health status based on the received data. The algorithm compares the data with past data, evaluates the user's potential health risks, and stores the results in the database.

[0630] Input: Collected health data

[0631] Output: Analysis and evaluation results

[0632] Specific behavior:

[0633] The server stores the health data in a database

[0634] AI analysis module analyzes health data

[0635] Health risk assessment results are stored in a database

[0636] Step 4: Generate and send advice

[0637] The server's AI analysis module generates appropriate advice based on the health information and analysis results. This advice may include dietary improvements, exercise recommendations, stress management, etc. The generated advice is sent to the device using APNS or FCM. The device then notifies the user of the received advice.

[0638] Input: Analysis results and evaluation results

[0639] Output: personalized advice

[0640] Specific behavior:

[0641] Server generates advice

[0642] Send advice to your device

[0643] Notify users of advice

[0644] Step 5: Chatbot functionality

[0645] The device accepts questions from the user. An example question might be, "I've been feeling tired lately. What should I do?" This question is encrypted and sent to the server. The server uses an AI chatbot (e.g., a GPT-4 model) to analyze the question and generate an appropriate answer by referencing relevant medical databases and past data. The generated answer is then sent back to the device and displayed to the user.

[0646] Input: User question

[0647] Output: The generated answer

[0648] Specific behavior:

[0649] The device accepts questions

[0650] The server analyzes the question and generates an answer

[0651] Send the answer to the device and display it to the user

[0652] Step 6: Propose an exercise program

[0653] If the user is judged to be lacking in exercise, the server's AI analysis module will generate an exercise program based on their health condition and lifestyle. For example, it may suggest walking three times a week or stretching every morning. This exercise program is sent to the device using the notification function and displayed to the user.

[0654] Input: Health and lifestyle data

[0655] Output: Exercise program

[0656] Specific behavior:

[0657] The server generates the exercise program

[0658] Send exercise programs to your device

[0659] Notify users of exercise programs

[0660] Through these steps, the system can centralize the user's health information and provide appropriate advice and exercise programs. Furthermore, the chatbot function using the generative AI model can quickly respond to users' health-related questions.

[0661] (Application example 1)

[0662] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0663] Conventional health management systems only analyze a user's health information and provide advice based on the results, but lack the ability to automatically generate meal plans that are optimal for each user's individual health condition. This makes it difficult for users to select meals that are appropriate for their health condition, resulting in a decrease in the efficiency and effectiveness of health management. There is also a need for a system that allows users to easily obtain an appropriate meal plan and put it into practice.

[0664] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0665] In this invention, the server includes a means for inputting user identification information, a means for centrally managing user health information, a means for analyzing the user's health information using an algorithm and generating advice based on the user's health condition, and a means for generating a meal plan based on the health condition and transmitting the meal plan to the user's terminal. This allows the user to easily obtain an optimal meal plan based on their health condition, enabling efficient and effective health management.

[0666] "User identification information" refers to information for identifying individual users, such as their My Number or health insurance card information.

[0667] "Health information" is data related to the user's health condition, and includes, for example, health checkup results and lifestyle habit data.

[0668] An "algorithm" is a method or calculation method for analyzing data according to a set procedure.

[0669] "Health-based advice" refers to advice and recommendations that are individually customized based on the results of analyzing a user's health information.

[0670] "User's terminal" refers to an electronic device used by a user, such as a smartphone or tablet.

[0671] "Centralized management" means aggregating and managing multiple health information in a unified manner.

[0672] "Meal Plan" means a meal suggestion or plan that takes into account the user's health condition.

[0673] The system of the present invention inputs user identification information, centrally manages health information, analyzes health status using an algorithm, generates appropriate advice, and transmits it to the user's device. It also has the function of generating a meal plan based on the user's health status and providing it to the user's device.

[0674] Basic configuration

[0675] This system consists of a device used by the user (e.g., a smartphone), a server that processes the data, and an AI analysis module.

[0676] Enter user identification information

[0677] The user launches the smartphone app and scans their My Number and health insurance card. The device encodes this data and sends it to the server, which receives it and authenticates the user by checking it against a database.

[0678] Collection of health information

[0679] The device periodically prompts the user to enter their health checkup results and lifestyle habits. The user enters this information into a smartphone app and sends it to a server.

[0680] Centralized management and analysis of health information

[0681] The server stores the received health information in a database. The AI ​​analysis module uses this data to analyze the user's health status and assess potential health risks. Based on the analysis results, the server generates appropriate advice, preventive measures, and treatments.

[0682] Generating and Sending Advice

[0683] The AI ​​analysis module on the server generates personalized advice based on the user's health information, including dietary improvements, exercise recommendations, stress management, etc. The generated advice is then sent to the device and notified to the user.

[0684] Chatbot functionality

[0685] The device accepts health-related questions from users. These questions are sent to a server, where an AI chatbot generates answers. The AI ​​consults relevant medical databases and historical data to provide appropriate answers. These answers are then sent back to the device and displayed to the user.

[0686] Generate a meal plan

[0687] Based on the user's health condition, the server's AI analysis module generates a personalized meal plan. For example, if the user needs to lose weight, it will provide a low-calorie meal plan, and if the user needs to recover from fatigue, it will provide a nutritious meal plan. This meal plan is sent to the terminal, where the user can easily order.

[0688] Example of a system

[0689] For example, if a user types a question such as "I can't seem to get rid of my fatigue lately," the device sends this question to the server, where the AI ​​chatbot analyzes the question. Based on the user's past health and lifestyle data, the AI ​​suggests advice such as "reducing screen time at night to improve sleep quality" or "taking appropriate vitamins as needed." This information is sent to the device and notified to the user.

[0690] Hardware and software used

[0691] Hardware: Smartphones, servers

[0692] Software: Python, Flask, scikit-learn

[0693] Prompt Sentence Examples

[0694] "I've been feeling tired lately. Is there any good way to get more nutrition?"

[0695] The present invention allows users to easily obtain optimal meal plans and advice based on their own health condition, enabling them to manage their health more efficiently and effectively.

[0696] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0697] Step 1:

[0698] Enter user identification information

[0699] How it works: A user launches the smartphone app and scans their My Number and health insurance card.

[0700] Input: My Number, health insurance card information

[0701] Data processing and calculation: The device encodes and encrypts this identification information and sends it to the server.

[0702] Output: The encrypted identity is sent to the server.

[0703] Step 2:

[0704] User identity authentication

[0705] How it works: The server checks the received identification information against a database and authenticates the user.

[0706] Input: Encrypted identity

[0707] Data processing and calculation: The server performs a database search and checks for data matches.

[0708] Output: User authentication result is generated.

[0709] Step 3:

[0710] Collection of health information

[0711] How it works: The device periodically prompts the user to enter health checkup results and lifestyle data.

[0712] Input: Health checkup results and lifestyle data entered by the user

[0713] Data processing and calculation: The health information collected by the device is sent to the server.

[0714] Output: Health information is stored on the server.

[0715] Step 4:

[0716] Centralized management and analysis of health information

[0717] How it works: The server stores the received health information in a database.

[0718] Input: Health information submitted by the user

[0719] Data processing and calculation: Analyze health information using an AI analysis module and assess potential health risks.

[0720] Output: Health risk assessment and analysis results are generated.

[0721] Step 5:

[0722] Advice generation and delivery

[0723] How it works: An AI analysis module on the server generates personalized advice based on the user's health information.

[0724] Input: Health risk assessment results and analysis results

[0725] Data processing and calculation: Generates advice on improving diet, recommending exercise, and managing stress, and sends it to the user's device.

[0726] Output: The customized advice is sent to the user's device.

[0727] Step 6:

[0728] Using chatbot functionality

[0729] How it works: The device accepts health-related questions from users, which are then sent to a server where an AI chatbot consults relevant databases and historical data to generate appropriate answers.

[0730] Input: User question

[0731] Data processing and calculation: The AI ​​chatbot analyzes the question, collects relevant information, and generates an answer.

[0732] Output: The generated answer is sent to the user's device.

[0733] Step 7:

[0734] Meal plan generation and delivery

[0735] How it works: The server's AI analysis module generates an individually customized meal plan and sends it to the user's device.

[0736] Input: Health analysis results and individual health status

[0737] Data processing and calculation: AI generates optimal meal plans based on the user's health condition. For example, it will recommend low-calorie meals for users who need to lose weight, and nutritious meals for users who need to recover from fatigue.

[0738] Output: The customized meal plan is sent to the user's device.

[0739] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0740] The system of the present invention provides a mechanism for inputting a user's identification information, centrally managing health information, analyzing the user's health status using an algorithm, generating appropriate advice, and transmitting it to the user's device. Furthermore, it incorporates an emotion engine that analyzes the user's emotional state and adjusts the advice and suggestions. The system's program processing is explained below with specific examples.

[0741] Basic configuration

[0742] This system consists of a user device (e.g., a smartphone), a server, an AI analysis module, and an emotion engine. Users input health information and emotional state through a smartphone app and send it to the server. The server analyzes the received data and provides advice and information to the user.

[0743] Enter user identification information

[0744] The user launches the smartphone app and scans their My Number and health insurance card. The smartphone encodes this data and sends it to the server, which receives it and authenticates the user by checking it against a database.

[0745] Collection of health information

[0746] The device periodically prompts the user to enter their health checkup results and lifestyle habits. The user enters this information into a smartphone app, and the device then sends it to a server.

[0747] Collecting Emotional Data

[0748] The device provides an interface for inputting the user's emotional state. The user inputs the emotional state (e.g., stress, happiness, etc.), and the device transmits it to the server.

[0749] Centralized management and analysis of health information

[0750] The server stores the received health and emotional information in a database, and an algorithm uses this data to analyze the user's health status and generate advice based on the user's current health and emotional state.

[0751] Generating and Sending Advice

[0752] The AI ​​analysis module on the server generates personalized advice based on the user's health information and emotional state. For example, if the user is feeling stressed, the AI ​​will suggest ways to relieve stress. This advice is then sent to the device and notified to the user.

[0753] Chatbot functionality

[0754] The device accepts health-related questions from users. These questions are sent to a server, where an AI chatbot generates answers. The AI ​​consults relevant medical databases and historical data to provide appropriate answers. These answers are then sent back to the device and displayed to the user.

[0755] Specific examples

[0756] For example, if a user types a question such as "I've been feeling stressed lately," the device sends this question to the server, where the AI ​​chatbot analyzes the question. The emotion engine determines the stress level from the user's input and behavior, and the AI ​​provides advice such as "breathing techniques to relax." This information is sent to the device and notified to the user.

[0757] Exercise program suggestions

[0758] When a user receives an alert indicating insufficient exercise, the server's AI analysis module generates an exercise program based on the user's health and emotional state. For example, it may suggest light exercise for a depressed user, or a more vigorous exercise program for a healthy user. These exercise programs are then sent to the device and notified to the user.

[0759] As described above, this system centrally manages a user's health and emotional information and analyzes it using an algorithm to provide individually customized advice and treatments. This not only improves the efficiency of health management and extends the user's healthy lifespan, but also provides more personalized support that responds to the user's emotions.

[0760] The processing flow will be explained below.

[0761] Step 1:

[0762] The user launches the smartphone app and uses the camera function to scan their My Number and health insurance card.

[0763] Step 2:

[0764] The device encodes the scanned data and sends it to the server as the user's identification information.

[0765] Step 3:

[0766] The server compares the received identification information with a database and authenticates the user. If authentication is successful, the server sends a notification of authentication completion to the terminal.

[0767] Step 4:

[0768] The device receives a notification that authentication is complete, displays a message on the app screen indicating successful authentication, and the user can proceed to the next step.

[0769] Step 5:

[0770] The device periodically sends notifications to the user prompting them to enter their health checkup results and lifestyle habits data.

[0771] Step 6:

[0772] Users receive a notification and enter information such as health check results, daily diet, exercise, and sleep time into a smartphone app.

[0773] Step 7:

[0774] The terminal formats the entered information and sends it to the server.

[0775] Step 8:

[0776] The server stores the received health information in a database, allowing users' health information to be managed centrally.

[0777] Step 9:

[0778] The device provides an interface for inputting the user's emotional state. The user inputs the emotional state (e.g., stress, happiness, etc.), and the device transmits it to the server.

[0779] Step 10:

[0780] The server stores the received emotion information in a database.

[0781] Step 11:

[0782] An AI analysis module on the server analyzes the stored health and emotional information and compares it with the user's current health status and past data.

[0783] Step 12:

[0784] The AI ​​analysis module evaluates the user's health risks based on the analysis results. For example, it identifies the user's future risk of high blood pressure based on their blood pressure data and lifestyle habits.

[0785] Step 13:

[0786] The server generates appropriate advice, preventative measures, and treatments based on the evaluation results, and an emotion engine adjusts the advice content based on the user's emotional state.

[0787] Step 14:

[0788] The server sends the generated advice and preventative measures to the device.

[0789] Step 15:

[0790] The device will notify the user of the advice and preventative measures it receives and display them on the app screen, allowing the user to use this information to improve their lifestyle habits.

[0791] Step 16:

[0792] The device accepts health-related questions from the user, who then enters and submits the question.

[0793] Step 17:

[0794] The terminal sends the entered question to the server.

[0795] Step 18:

[0796] The server's AI chatbot analyzes the question and references relevant medical databases and past user health information.

[0797] Step 19:

[0798] The AI ​​chatbot generates an appropriate response, which the server then sends to the device.

[0799] Step 20:

[0800] The device receives the response and displays it to the user.

[0801] Step 21:

[0802] When a user receives an alert indicating insufficient exercise, the AI ​​analysis module in the server generates an individually customized exercise program, and the emotion engine adjusts the program appropriately based on the user's emotional state.

[0803] Step 22:

[0804] The server transmits the generated exercise program to the terminal.

[0805] Step 23:

[0806] The device notifies the user of the exercise program and displays details on the app screen, and the user follows the suggested exercise program to perform daily exercise.

[0807] Example 2

[0808] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0809] In modern society, it is becoming increasingly important to centrally manage each individual's health and emotional state and provide appropriate advice. However, conventional health management systems have struggled to provide personalized advice that fully takes into account the user's emotional state. Furthermore, they lacked technology to efficiently authenticate and securely manage user identification information. This made it difficult to provide individually customized health support.

[0810] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0811] In this invention, the server includes means for inputting user identification information, means for centrally managing health information, means for analyzing the user's health information using an algorithm and generating advice according to the user's health condition, means for analyzing the user's emotional state and adjusting the advice and suggestions, means for providing an interface for inputting data, and means for transmitting the health information and advice to the user's terminal. This makes it possible to manage the user's health information and emotional information in an integrated manner and provide advice optimized for each individual condition.

[0812] "User" refers to any individual or organization that uses this system.

[0813] "Identification information" is data for uniquely identifying a user, and includes, for example, information such as a My Number or health insurance card.

[0814] "Health information" is data that indicates the user's health condition, and includes information such as weight, blood pressure, pulse rate, dietary content, amount of exercise, and sleep time.

[0815] An "algorithm" refers to a specific procedure for performing processing according to a certain calculation procedure.

[0816] "Emotional state" is data that indicates the psychological state of the user, and includes, for example, stress level, happiness level, tiredness, and the like.

[0817] "Interface" refers to a device or means for inputting or outputting data.

[0818] "Terminal" means an electronic device that allows a user to input data or receive information, including, for example, a smartphone or tablet.

[0819] A "server" refers to a computer system that processes and stores data over a network.

[0820] "AI analysis module" refers to software or hardware that uses artificial intelligence to analyze data and generate processing results.

[0821] A "database" refers to a system that organizes and stores information according to certain standards and allows it to be retrieved as needed.

[0822] The system of the present invention mainly manages and analyzes a user's identification information, health information, and emotional state to provide individually customized health advice. This system is composed of a user terminal, a server, an AI analysis module, and an emotion engine.

[0823] Enter user identification information

[0824] The user launches the smartphone app and scans their My Number and health insurance card with the camera. The device encodes this data using OCR (Optical Character Recognition) technology and sends it to the server using a secure protocol (e.g., HTTPS). The server compares the received data with a database and authenticates the user. If authentication is successful, the server starts a user session and allows subsequent data transmission.

[0825] Collection of health information

[0826] The device periodically uses push notifications to prompt the user to enter their health checkup results and lifestyle data (e.g., diet, exercise, and sleep duration). After the user has entered this information, the device encrypts the input data and sends it back to the server. The server verifies the received data and stores it in a database.

[0827] Collecting Emotional Data

[0828] The device displays options and sliders for inputting emotional states. After the user inputs their current emotional state (e.g., stress level, happiness, fatigue), and confirms the data, the device sends it to the server, which then performs further analysis based on the received emotional data.

[0829] Storage and analysis of health and emotional information

[0830] The server stores the received health and emotional information in a database. The AI ​​analysis module processes this data in real time and uses predictive models to analyze the user's health and emotional state. The analysis results in the calculation of the user's health risks and emotional trends.

[0831] Generating and Sending Advice

[0832] The server uses a generative AI model to customize personalized advice based on the user's health and emotional information. For example, if a user inputs a high stress level, the AI ​​model generates specific suggestions such as "try deep breathing" or "take a short walk." This advice is sent to the device and displayed to the user as a notification.

[0833] Chatbot functionality

[0834] Users enter health-related questions into the app as text. For example, if they ask, "How can I improve the quality of my sleep?", the device sends the question to the server. The server uses an AI chatbot module to analyze the question and generate an appropriate answer by referencing relevant medical databases and past question data. This answer is then sent to the device and displayed to the user.

[0835] Exercise program suggestions

[0836] When a user receives an alert about lack of exercise, the server's AI analysis module generates an appropriate exercise program based on the user's health condition, emotional state, and past exercise history. For example, it might suggest a program such as "10 minutes of light stretching" for a user who tends to feel depressed, or "30 minutes of light jogging" for a user who appears healthy. The exercise program is sent to the device and notified to the user.

[0837] Specific examples

[0838] For example, if a user types a question such as "I've been feeling stressed lately," the device sends this question to the server, where the AI ​​chatbot analyzes the question. The emotion engine determines the stress level from the user's input and behavior, and the AI ​​provides advice such as "breathing techniques to relax" or "do some light exercise." This information is sent to the device and notified to the user.

[0839] Example prompt: "I've been feeling stressed lately. How can I relieve this?"

[0840] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0841] Step 1: Enter user identification information

[0842] The user launches the smartphone app and scans their My Number and health insurance card with the camera. The device uses OCR technology to encode this information and sends it to the server via a secure protocol (HTTPS). The server compares the received identification information with a database and authenticates the user. If authentication is successful, the server starts a user session and allows subsequent data transmission.

[0843] input:

[0844] Image data of the user's My Number and health insurance card

[0845] output:

[0846] Encoded identity information, authentication result (success or failure)

[0847] Specific behavior:

[0848] Extracts text data from images using OCR technology and encodes it. Sends data securely using HTTPS and authenticates it against a database.

[0849] Step 2: Gathering health information

[0850] The device periodically uses push notifications to prompt the user to enter health checkup results and lifestyle habit data. The user enters data such as dietary habits, exercise, and sleep duration into a smartphone app. The device encrypts the entered data and sends it to the server. The server verifies the received data and stores it in a database.

[0851] input:

[0852] Health checkup results and lifestyle data entered by the user

[0853] output:

[0854] Encrypted health information stored in a database

[0855] Specific behavior:

[0856] Push notifications prompt users to enter data, encrypt the data and send it in real time. Received data is verified and accurately stored in the database.

[0857] Step 3: Collecting emotion data

[0858] Using the interface displayed on the device, the user inputs their current emotional state (e.g., stress level, happiness, fatigue), and the device securely transmits this data to the server, which then analyzes the received emotional data and stores the necessary information in a database.

[0859] input:

[0860] Emotional state data entered by the user

[0861] output:

[0862] Encrypted emotion data, stored in a database

[0863] Specific behavior:

[0864] Emotional information is collected from the user via the interface, encrypted and transmitted, and then analyzed and stored after reception.

[0865] Step 4: Storing and analyzing health and emotional information

[0866] The server stores the received health and emotional information in a database. The AI ​​analysis module uses this data to analyze the user's health and emotional state in real time and calculates risk assessments and trends using predictive models.

[0867] input:

[0868] Encrypted health and emotional information

[0869] output:

[0870] Analysis results (health risks, emotional trends)

[0871] Specific behavior:

[0872] The AI ​​analysis module analyzes the received data and uses predictive models to calculate risk assessments and trends of the user's health and emotional state.

[0873] Step 5: Generate and send advice

[0874] Using a generative AI model installed on the server, the system generates personalized advice based on the user's health and emotional information. For example, a user with a high stress level will be given specific suggestions such as "try taking deep breaths." This advice is sent to the device and notified to the user.

[0875] input:

[0876] Analysis results

[0877] output:

[0878] Personalized advice

[0879] Specific behavior:

[0880] The generative AI model creates appropriate advice based on the analysis results and sends it to the user's device.

[0881] Step 6: Chatbot functionality

[0882] Users input health-related questions into the app, such as "How can I improve the quality of my sleep?" The device sends the question to the server, where the AI ​​chatbot analyzes the question, references relevant information, and generates an appropriate answer. The generated answer is then sent back to the device and displayed to the user.

[0883] input:

[0884] User Questions

[0885] output:

[0886] Chatbot Answers

[0887] Specific behavior:

[0888] The chatbot function analyzes the user's question and generates an answer by referring to a related medical database and a database of past questions.

[0889] Step 7: Propose an exercise program

[0890] When a user receives an alert indicating lack of exercise, the server's AI analysis module generates an appropriate exercise program based on the user's health condition, emotional state, and past exercise data. For example, a depressed user might be recommended a 10-minute light stretching program, while a lively user might be recommended a 30-minute light jog. These exercise programs are then sent to the device and notified to the user.

[0891] input:

[0892] Analysis results, past exercise data

[0893] output:

[0894] Exercise program suggestions

[0895] Specific behavior:

[0896] The AI ​​analysis module generates an exercise program based on the data and notifies the user of the optimal exercise suggestions via the device.

[0897] (Application example 2)

[0898] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0899] Conventional health management systems can centrally manage a user's health information and provide advice, but they are unable to provide appropriate advice or meal menus that take into account the user's emotional state.In addition, they do not integrate a function to suggest meal menus based on the user's health and emotional state and to easily order them, making it difficult to improve the convenience of health management for users, and this needs to be improved.

[0900] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0901] In this invention, the server includes means for inputting user identification information, means for centrally managing user health information, means for analyzing the user's health information using an algorithm and generating advice based on the user's health condition, means for analyzing the user's emotional state, means for generating advice and a meal menu individually customized based on the user's health condition and emotional state and providing this to the user, and means for ordering the generated meal menu through a food delivery platform. This enables more personalized health management and meal suggestions based on the user's health condition and emotional state.

[0902] "User identification information" is unique information that identifies a user.

[0903] "Health information" refers to data related to the user's health condition, including health checkup results and lifestyle data.

[0904] An "algorithm" is a procedure and calculation method for analyzing a user's health information and generating advice based on their health condition.

[0905] "Emotional state" refers to a user's psychological state, such as stress, happiness, or anger.

[0906] "Centralized management" means integrating multiple user data and managing it efficiently.

[0907] "Advice" refers to specific recommendations or suggestions for improvement based on the user's health or emotional state.

[0908] "Health Status" refers to the user's current physical health, assessed based on various health information.

[0909] "User's device" refers to a communication device that is directly used by the user, such as a smartphone or tablet.

[0910] A "food delivery platform" is a system that provides a series of services from when users order food online until the food is delivered.

[0911] "Customized advice" refers to advice that is individually tailored to each user based on their health and emotional state.

[0912] "Meal Menu" refers to the specific dishes and combinations of ingredients offered to Users.

[0913] The present invention provides a system that improves the efficiency of health management and dietary suggestions for users and provides individually customized advice. The specific configuration and operation of the system will be described below.

[0914] System configuration

[0915] The system consists of the following elements:

[0916] 1. User device: A device operated by a user, such as a smartphone or tablet.

[0917] 2. Server: The central system that processes and stores data.

[0918] 3. Database: Data storage for centralized management of user identity, health information, and emotional state.

[0919] 4. AI analysis module: Analyzes health information and emotional state using machine learning models such as TensorFlow.

[0920] 5. Emotion Analysis Engine: Analyzes the user's emotional state using the Emotion API.

[0921] 6. Food delivery platform: An online system that allows users to order customized meal menus.

[0922] Processing flow

[0923] Entering and authenticating user identity

[0924] Through a smartphone app, users enter identifying information, such as government identification and a scan of their health insurance card, which is encoded and sent to a server that authenticates the user against a database.

[0925] Health and emotional data collection

[0926] The user's device periodically requests input of health checkup results and lifestyle habits data. Emotional states are also input, and this data is sent to the server. Emotional states are analyzed using the Emotion API.

[0927] Unified management and analysis of health and emotional information

[0928] The server stores the received health and emotional information in a database, and uses an AI analysis module to analyze this data and generate advice based on the user's current health and emotional state.

[0929] Generate and provide advice and meal menus

[0930] The AI ​​analysis module on the server generates personalized advice and meal menus based on the user's health information and emotional state, and these advice and meal menus are sent to the user's device.

[0931] Ordering food from the menu

[0932] Users can easily order the provided meal menu through the food delivery platform, and the order information is sent to the food delivery platform via the server, and the food is delivered to the user.

[0933] Specific use cases

[0934] For example, if a user inputs "I've been feeling stressed lately" and records "lack of exercise" as health information, the system will suggest appropriate meal options based on this. Suggested options include "mint tea to relieve stress" and "vegetable soup that's easy to digest." Users can order these options with the touch of a button.

[0935] Prompt Sentence Examples

[0936] "The user has entered 'I've been feeling stressed lately.' Health information has been recorded as 'not getting enough exercise.' Based on this, please suggest an appropriate meal menu."

[0937] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0938] Step 1:

[0939] Entering and authenticating user identity

[0940] Users launch the smartphone app and scan their government-issued identification and health insurance card information.

[0941] Input: Identification information (official identification information, medical insurance card information)

[0942] Output: Authentication request data (encoded identity information)

[0943] The server receives this, compares it with the database, authenticates the user, and returns the authentication result to the user's device.

[0944] Behavior: If the user is successfully authenticated, the primary functionality of the application becomes available.

[0945] Step 2:

[0946] Health and emotional data collection

[0947] The user device periodically requests input of health checkup results and lifestyle habits. The user also inputs their emotional state. This data is sent from the user device to the server.

[0948] Input: Health information (health checkup results, lifestyle data), emotional data (stress level, happiness level)

[0949] Output: Health information and emotion data (data sent to the server)

[0950] The server stores the received data in a database.

[0951] How it works: Users enter health and emotional data through the app and verify that the data is sent accurately to the server.

[0952] Step 3:

[0953] Analysis of information stored in the database

[0954] The server inputs the stored health and emotional information into an AI analysis module (using TensorFlow).

[0955] Input: Stored health and emotional information (data from database)

[0956] Output: Analysis results (health status, emotional status)

[0957] The AI ​​analysis module analyzes the user's health and emotional state and generates results.

[0958] How it works: The AI ​​model processes the data to analyze the user's health and emotional state.

[0959] Step 4:

[0960] Advice and meal menu generation

[0961] The server generates appropriate advice and meal menus based on the analysis results.

[0962] Input: Analysis results (health status, emotional status)

[0963] Output: Customized advice and meal menu (data sent to user device)

[0964] The generated advice and meal menu are sent to the user's device.

[0965] How it works: The server uses the analysis results to provide the user with optimal advice and meal plans.

[0966] Step 5:

[0967] Ordering food from the menu

[0968] Users can view the offered meal menu and place their order through the food delivery platform.

[0969] Input: Customized meal menu (displayed on user's device)

[0970] Output: Order data (data sent to food delivery platform)

[0971] The user terminal sends the order to the server, which then forwards the order data to the food delivery platform.

[0972] How it works: When the user presses the bulk order button, the specified meal menu is sent to the delivery service.

[0973] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0974] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0975] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0976] [Third embodiment]

[0977] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0978] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0979] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0980] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0981] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0982] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0983] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0984] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0985] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0986] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0987] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0988] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0989] The system of the present invention provides a mechanism for inputting a user's identification information, centrally managing health information, analyzing the user's health status using an algorithm, and generating appropriate advice and sending it to the user's device. The processing content of the system's program and specific examples are explained below.

[0990] Basic configuration

[0991] This system consists of a user device (e.g., a smartphone), a server, and an AI analysis module. Users enter health information through a smartphone app and send it to the server. The server analyzes the received data and provides advice and information to the user.

[0992] Enter user identification information

[0993] The user launches the smartphone app and scans their My Number and health insurance card. The smartphone encodes this data and sends it to the server, which receives it and authenticates the user by checking it against a database.

[0994] Collection of health information

[0995] The device periodically prompts the user to enter their health checkup results and lifestyle habits. The user enters this information into a smartphone app, and the device then sends it to a server.

[0996] Centralized management and analysis of health information

[0997] The server stores the received health information in a database. An algorithm analyzes the user's health status based on this data and assesses their potential health risks. Based on the analysis, the server generates appropriate advice, preventative measures, and treatments.

[0998] Generating and Sending Advice

[0999] The AI ​​analysis module on the server generates personalized advice based on the user's health information, including dietary improvements, exercise recommendations, stress management, etc. The generated advice is then sent to the device and notified to the user.

[1000] Chatbot functionality

[1001] The device accepts health-related questions from users. These questions are sent to a server, where an AI chatbot generates answers. The AI ​​consults relevant medical databases and historical data to provide appropriate answers. These answers are then sent back to the device and displayed to the user.

[1002] Specific examples

[1003] For example, if a user types a question such as "I can't seem to get rid of my fatigue lately," the device sends this question to the server, where the AI ​​chatbot analyzes the question. Based on the user's past health and lifestyle data, the AI ​​suggests advice such as "reducing screen time at night to improve sleep quality" or "taking appropriate vitamins as needed." This information is sent to the device and notified to the user.

[1004] Exercise program suggestions

[1005] If the user is judged to be lacking in exercise, the server's AI analysis module will generate an exercise program based on the user's health condition and lifestyle. For example, it may suggest walking three times a week or stretching every morning. This exercise program is then sent to the device and notified to the user.

[1006] As described above, this system centrally manages users' health information and analyzes it using algorithms to provide individually customized advice and treatments, thereby improving the efficiency of health management and extending users' healthy lifespans.

[1007] The processing flow will be explained below.

[1008] Step 1:

[1009] The user launches the smartphone app and uses the camera function to scan their My Number and health insurance card.

[1010] Step 2:

[1011] The device encodes the scanned data and sends it to the server as the user's identification information.

[1012] Step 3:

[1013] The server compares the received identification information with a database and authenticates the user. If authentication is successful, the server sends a notification of authentication completion to the terminal.

[1014] Step 4:

[1015] The device receives a notification that authentication is complete, displays a message on the app screen indicating successful authentication, and the user can proceed to the next step.

[1016] Step 5:

[1017] The device periodically sends notifications to the user prompting them to enter their health checkup results and lifestyle habits data.

[1018] Step 6:

[1019] Users receive a notification and enter information such as health check results, daily diet, exercise, and sleep time into a smartphone app.

[1020] Step 7:

[1021] The terminal formats the entered information and sends it to the server.

[1022] Step 8:

[1023] The server stores the received health information in a database, allowing users' health information to be managed centrally.

[1024] Step 9:

[1025] An AI analysis module on the server analyzes the stored data and compares it with the user's current health status and past data.

[1026] Step 10:

[1027] The AI ​​analysis module evaluates the user's health risks based on the analysis results. For example, it identifies the user's future risk of high blood pressure based on their blood pressure data and lifestyle habits.

[1028] Step 11:

[1029] The server generates appropriate advice, preventative measures, and treatments based on the evaluation results.

[1030] Step 12:

[1031] The server sends the generated advice and preventative measures to the device.

[1032] Step 13:

[1033] The device will notify the user of the advice and preventative measures it receives and display them on the app screen, allowing the user to use this information to improve their lifestyle habits.

[1034] Step 14:

[1035] The device accepts health-related questions from the user, who then enters and submits the question.

[1036] Step 15:

[1037] The terminal sends the entered question to the server.

[1038] Step 16:

[1039] The server's AI chatbot analyzes the question and references relevant medical databases and past user health information.

[1040] Step 17:

[1041] The AI ​​chatbot generates an appropriate response, which the server then sends to the device.

[1042] Step 18:

[1043] The device receives the response and displays it to the user.

[1044] Step 19:

[1045] When a user receives an alert indicating lack of exercise, an AI analysis module on the server generates an individually customized exercise program.

[1046] Step 20:

[1047] The server transmits the generated exercise program to the terminal.

[1048] Step 21:

[1049] The device notifies the user of the exercise program and displays details on the app screen, and the user follows the suggested exercise program to perform daily exercise.

[1050] Example 1

[1051] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1052] In modern society, personal health management has become an important issue. However, conventional health management systems not only centrally manage users' health information, but also lack the necessary systems to provide appropriate advice based on that information. Furthermore, they lack mechanisms to quickly and appropriately respond to users' questions about their health status, or to provide individually customized exercise programs. This makes it difficult for users to properly manage their health.

[1053] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1054] In this invention, the server includes a means for inputting user identification information, a means for centrally managing user health information, a means for analyzing the user's health information using an algorithm and generating advice according to the user's health condition, a means for generating answers to questions entered by the user using a generative AI model, and a means for generating an exercise program based on the user's health condition. This not only enables the user's own health information to be centrally managed, but also enables the user to receive appropriate advice and exercise programs, and to receive prompt and appropriate answers to health-related questions.

[1055] "User identification information" refers to information for identifying an individual user, and includes, for example, an identification number and authentication information.

[1056] "Centralized management" means concentrating and managing multiple pieces of data and information in one place.

[1057] An "algorithm" refers to a computational procedure or process for solving a specific problem, implemented as a program.

[1058] "Health information" refers to information about the user's health condition, and specifically includes weight, blood pressure, diet, exercise history, and the like.

[1059] "Advice" means any suggestions or advice provided to a User for the purpose of improving or maintaining their health.

[1060] "Terminal" refers to a hardware device that is directly operated by a user, and specifically includes smartphones and tablets.

[1061] A "generative AI model" refers to a model that uses artificial intelligence to generate a specific output from data, such as to generate an answer to a question.

[1062] An "exercise program" refers to an exercise plan designed based on the user's health condition and lifestyle, and includes weekly exercise content and stretching methods.

[1063] "Database" refers to a system for efficiently storing, retrieving, and managing data, such as those used to store health and identifying information.

[1064] This system inputs a user's identification information, centrally manages health information, analyzes the user's health status using an algorithm, generates appropriate advice, and sends it to the user's device. This system consists of a user device, a server, and an AI analysis module.

[1065] Basic configuration

[1066] The implementation of this system requires the following elements:

[1067] User device (e.g. smartphone)

[1068] Server (e.g., EC2 instance on Amazon Web Services)

[1069] Database (e.g. Amazon RDS)

[1070] AI analysis modules (e.g. TensorFlow or PyTorch)

[1071] Communication protocol (e.g. HTTPS)

[1072] Enter user identification information

[1073] The user launches the smartphone app and uses the app's scanning function to input their My Number and health insurance card information. The smartphone uses its camera to capture this information and sends it to the server in an encoded format. The server then collates the received data with a database (e.g., MySQL) for authentication. This authentication uses the OAuth 2.0 protocol.

[1074] Collection of health information

[1075] The device periodically sends notifications to the user prompting them to enter their health checkup results and lifestyle habit data. For example, a notification saying "Please weigh yourself" is sent every morning at 8:00. The user enters this data (e.g., weight, blood pressure, dietary details) through the app. The device sends this data to the server in real time. This transmission uses the HTTPS protocol.

[1076] Centralized management and analysis of health information

[1077] The server stores the received health information in a database (e.g., Amazon RDS). An AI analysis module (e.g., using TensorFlow or PyTorch) performs analysis based on the stored health information. This evaluates the user's potential health risks. The evaluation results are stored in the database.

[1078] Generating and Sending Advice

[1079] The server's AI analysis module generates appropriate advice based on the user's health information. Specific advice includes dietary improvements, recommended exercise, and stress management. The generated advice is sent to the device using APNS or FCM. The device then notifies the user of the received advice.

[1080] Chatbot functionality

[1081] The terminal accepts health-related questions from the user, such as the following prompt:

[1082] "I've been feeling tired lately and it's bothering me. What should I do?"

[1083] The question is encrypted and sent to a server, where an AI chatbot (e.g., a GPT-4 model) analyzes the question and generates an appropriate answer by referencing relevant medical databases and past data. The answer is then sent to the device and displayed to the user.

[1084] Exercise program suggestions

[1085] If the user is judged to be lacking in exercise, the server's AI analysis module will generate an exercise program based on the user's health condition and lifestyle. For example, it may include specific suggestions such as walking three times a week or stretching every morning. These exercise programs are then sent to the device via the notification function, and the user is notified.

[1086] Through each of these processes, the system can efficiently manage the user's health and provide individually customized advice and treatments.

[1087] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1088] Step 1: Enter user identification information

[1089] The user launches the smartphone app and scans their My Number and health insurance card. The device uses the camera to read this information, converts it into an encoded format, encrypts it (e.g., AES256), and sends it to the server. The server then compares the received identification information with a database (e.g., MySQL) to authenticate the user. This authentication uses the OAuth 2.0 protocol.

[1090] Input: User identification information (My Number, health insurance card)

[1091] Output: Authentication result

[1092] Specific behavior:

[1093] User scans information with camera

[1094] The device encrypts the data and sends it to the server

[1095] The server authenticates the user against a database

[1096] Step 2: Gathering health information

[1097] The device periodically sends notifications to the user, prompting them to enter their health checkup results and lifestyle habits. For example, it sends a notification every morning at 8:00 to "weigh yourself." The user enters this data through the app, and the device sends the input data to the server in real time. Transmission is via the HTTPS protocol.

[1098] Input: User's health information (weight, blood pressure, dietary details)

[1099] Output: Collected health data

[1100] Specific behavior:

[1101] The device periodically sends notifications

[1102] User enters data in the app

[1103] The device sends the input data to the server

[1104] Step 3: Centralize and analyze health information

[1105] The server stores the submitted health information in a database (e.g., Amazon RDS). An AI analysis module (e.g., TensorFlow or PyTorch) analyzes the user's health status based on the received data. The algorithm compares the data with past data, evaluates the user's potential health risks, and stores the results in the database.

[1106] Input: Collected health data

[1107] Output: Analysis and evaluation results

[1108] Specific behavior:

[1109] The server stores the health data in a database

[1110] AI analysis module analyzes health data

[1111] Health risk assessment results are stored in a database

[1112] Step 4: Generate and send advice

[1113] The server's AI analysis module generates appropriate advice based on the health information and analysis results. This advice may include dietary improvements, exercise recommendations, stress management, etc. The generated advice is sent to the device using APNS or FCM. The device then notifies the user of the received advice.

[1114] Input: Analysis results and evaluation results

[1115] Output: personalized advice

[1116] Specific behavior:

[1117] Server generates advice

[1118] Send advice to your device

[1119] Notify users of advice

[1120] Step 5: Chatbot functionality

[1121] The device accepts questions from the user. An example question might be, "I've been feeling tired lately. What should I do?" This question is encrypted and sent to the server. The server uses an AI chatbot (e.g., a GPT-4 model) to analyze the question and generate an appropriate answer by referencing relevant medical databases and past data. The generated answer is then sent back to the device and displayed to the user.

[1122] Input: User question

[1123] Output: The generated answer

[1124] Specific behavior:

[1125] The device accepts questions

[1126] The server analyzes the question and generates an answer

[1127] Send the answer to the device and display it to the user

[1128] Step 6: Propose an exercise program

[1129] If the user is judged to be lacking in exercise, the server's AI analysis module will generate an exercise program based on their health condition and lifestyle. For example, it may suggest walking three times a week or stretching every morning. This exercise program is sent to the device using the notification function and displayed to the user.

[1130] Input: Health and lifestyle data

[1131] Output: Exercise program

[1132] Specific behavior:

[1133] The server generates the exercise program

[1134] Send exercise programs to your device

[1135] Notify users of exercise programs

[1136] Through these steps, the system can centralize the user's health information and provide appropriate advice and exercise programs. Furthermore, the chatbot function using the generative AI model can quickly respond to users' health-related questions.

[1137] (Application example 1)

[1138] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1139] Conventional health management systems only analyze a user's health information and provide advice based on the results, but lack the ability to automatically generate meal plans that are optimal for each user's individual health condition. This makes it difficult for users to select meals that are appropriate for their health condition, resulting in a decrease in the efficiency and effectiveness of health management. There is also a need for a system that allows users to easily obtain an appropriate meal plan and put it into practice.

[1140] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1141] In this invention, the server includes a means for inputting user identification information, a means for centrally managing user health information, a means for analyzing the user's health information using an algorithm and generating advice based on the user's health condition, and a means for generating a meal plan based on the health condition and transmitting the meal plan to the user's terminal. This allows the user to easily obtain an optimal meal plan based on their health condition, enabling efficient and effective health management.

[1142] "User identification information" refers to information for identifying individual users, such as their My Number or health insurance card information.

[1143] "Health information" is data related to the user's health condition, and includes, for example, health checkup results and lifestyle habit data.

[1144] An "algorithm" is a method or calculation method for analyzing data according to a set procedure.

[1145] "Health-based advice" refers to advice and recommendations that are individually customized based on the results of analyzing a user's health information.

[1146] "User's terminal" refers to an electronic device used by a user, such as a smartphone or tablet.

[1147] "Centralized management" means aggregating and managing multiple health information in a unified manner.

[1148] "Meal Plan" means a meal suggestion or plan that takes into account the user's health condition.

[1149] The system of the present invention inputs user identification information, centrally manages health information, analyzes health status using an algorithm, generates appropriate advice, and transmits it to the user's device. It also has the function of generating a meal plan based on the user's health status and providing it to the user's device.

[1150] Basic configuration

[1151] This system consists of a device used by the user (e.g., a smartphone), a server that processes the data, and an AI analysis module.

[1152] Enter user identification information

[1153] The user launches the smartphone app and scans their My Number and health insurance card. The device encodes this data and sends it to the server, which receives it and authenticates the user by checking it against a database.

[1154] Collection of health information

[1155] The device periodically prompts the user to enter their health checkup results and lifestyle habits. The user enters this information into a smartphone app and sends it to a server.

[1156] Centralized management and analysis of health information

[1157] The server stores the received health information in a database. The AI ​​analysis module uses this data to analyze the user's health status and assess potential health risks. Based on the analysis results, the server generates appropriate advice, preventive measures, and treatments.

[1158] Generating and Sending Advice

[1159] The AI ​​analysis module on the server generates personalized advice based on the user's health information, including dietary improvements, exercise recommendations, stress management, etc. The generated advice is then sent to the device and notified to the user.

[1160] Chatbot functionality

[1161] The device accepts health-related questions from users. These questions are sent to a server, where an AI chatbot generates answers. The AI ​​consults relevant medical databases and historical data to provide appropriate answers. These answers are then sent back to the device and displayed to the user.

[1162] Generate a meal plan

[1163] Based on the user's health condition, the server's AI analysis module generates a personalized meal plan. For example, if the user needs to lose weight, it will provide a low-calorie meal plan, and if the user needs to recover from fatigue, it will provide a nutritious meal plan. This meal plan is sent to the terminal, where the user can easily order.

[1164] Example of a system

[1165] For example, if a user types a question such as "I can't seem to get rid of my fatigue lately," the device sends this question to the server, where the AI ​​chatbot analyzes the question. Based on the user's past health and lifestyle data, the AI ​​suggests advice such as "reducing screen time at night to improve sleep quality" or "taking appropriate vitamins as needed." This information is sent to the device and notified to the user.

[1166] Hardware and software used

[1167] Hardware: Smartphones, servers

[1168] Software: Python, Flask, scikit-learn

[1169] Prompt Sentence Examples

[1170] "I've been feeling tired lately. Is there any good way to get more nutrition?"

[1171] The present invention allows users to easily obtain optimal meal plans and advice based on their own health condition, enabling them to manage their health more efficiently and effectively.

[1172] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1173] Step 1:

[1174] Enter user identification information

[1175] How it works: A user launches the smartphone app and scans their My Number and health insurance card.

[1176] Input: My Number, health insurance card information

[1177] Data processing and calculation: The device encodes and encrypts this identification information and sends it to the server.

[1178] Output: The encrypted identity is sent to the server.

[1179] Step 2:

[1180] User identity authentication

[1181] How it works: The server checks the received identification information against a database and authenticates the user.

[1182] Input: Encrypted identity

[1183] Data processing and calculation: The server performs a database search and checks for data matches.

[1184] Output: User authentication result is generated.

[1185] Step 3:

[1186] Collection of health information

[1187] How it works: The device periodically prompts the user to enter health checkup results and lifestyle data.

[1188] Input: Health checkup results and lifestyle data entered by the user

[1189] Data processing and calculation: The health information collected by the device is sent to the server.

[1190] Output: Health information is stored on the server.

[1191] Step 4:

[1192] Centralized management and analysis of health information

[1193] How it works: The server stores the received health information in a database.

[1194] Input: Health information submitted by the user

[1195] Data processing and calculation: Analyze health information using an AI analysis module and assess potential health risks.

[1196] Output: Health risk assessment and analysis results are generated.

[1197] Step 5:

[1198] Advice generation and delivery

[1199] How it works: An AI analysis module on the server generates personalized advice based on the user's health information.

[1200] Input: Health risk assessment results and analysis results

[1201] Data processing and calculation: Generates advice on improving diet, recommending exercise, and managing stress, and sends it to the user's device.

[1202] Output: The customized advice is sent to the user's device.

[1203] Step 6:

[1204] Using chatbot functionality

[1205] How it works: The device accepts health-related questions from users, which are then sent to a server where an AI chatbot consults relevant databases and historical data to generate appropriate answers.

[1206] Input: User question

[1207] Data processing and calculation: The AI ​​chatbot analyzes the question, collects relevant information, and generates an answer.

[1208] Output: The generated answer is sent to the user's device.

[1209] Step 7:

[1210] Meal plan generation and delivery

[1211] How it works: The server's AI analysis module generates an individually customized meal plan and sends it to the user's device.

[1212] Input: Health analysis results and individual health status

[1213] Data processing and calculation: AI generates optimal meal plans based on the user's health condition. For example, it will recommend low-calorie meals for users who need to lose weight, and nutritious meals for users who need to recover from fatigue.

[1214] Output: The customized meal plan is sent to the user's device.

[1215] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1216] The system of the present invention provides a mechanism for inputting a user's identification information, centrally managing health information, analyzing the user's health status using an algorithm, generating appropriate advice, and transmitting it to the user's device. Furthermore, it incorporates an emotion engine that analyzes the user's emotional state and adjusts the advice and suggestions. The system's program processing is explained below with specific examples.

[1217] Basic configuration

[1218] This system consists of a user device (e.g., a smartphone), a server, an AI analysis module, and an emotion engine. Users input health information and emotional state through a smartphone app and send it to the server. The server analyzes the received data and provides advice and information to the user.

[1219] Enter user identification information

[1220] The user launches the smartphone app and scans their My Number and health insurance card. The smartphone encodes this data and sends it to the server, which receives it and authenticates the user by checking it against a database.

[1221] Collection of health information

[1222] The device periodically prompts the user to enter their health checkup results and lifestyle habits. The user enters this information into a smartphone app, and the device then sends it to a server.

[1223] Collecting Emotional Data

[1224] The device provides an interface for inputting the user's emotional state. The user inputs the emotional state (e.g., stress, happiness, etc.), and the device transmits it to the server.

[1225] Centralized management and analysis of health information

[1226] The server stores the received health and emotional information in a database, and an algorithm uses this data to analyze the user's health status and generate advice based on the user's current health and emotional state.

[1227] Generating and Sending Advice

[1228] The AI ​​analysis module on the server generates personalized advice based on the user's health information and emotional state. For example, if the user is feeling stressed, the AI ​​will suggest ways to relieve stress. This advice is then sent to the device and notified to the user.

[1229] Chatbot functionality

[1230] The device accepts health-related questions from users. These questions are sent to a server, where an AI chatbot generates answers. The AI ​​consults relevant medical databases and historical data to provide appropriate answers. These answers are then sent back to the device and displayed to the user.

[1231] Specific examples

[1232] For example, if a user types a question such as "I've been feeling stressed lately," the device sends this question to the server, where the AI ​​chatbot analyzes the question. The emotion engine determines the stress level from the user's input and behavior, and the AI ​​provides advice such as "breathing techniques to relax." This information is sent to the device and notified to the user.

[1233] Exercise program suggestions

[1234] When a user receives an alert indicating insufficient exercise, the server's AI analysis module generates an exercise program based on the user's health and emotional state. For example, it may suggest light exercise for a depressed user, or a more vigorous exercise program for a healthy user. These exercise programs are then sent to the device and notified to the user.

[1235] As described above, this system centrally manages a user's health and emotional information and analyzes it using an algorithm to provide individually customized advice and treatments. This not only improves the efficiency of health management and extends the user's healthy lifespan, but also provides more personalized support that responds to the user's emotions.

[1236] The processing flow will be explained below.

[1237] Step 1:

[1238] The user launches the smartphone app and uses the camera function to scan their My Number and health insurance card.

[1239] Step 2:

[1240] The device encodes the scanned data and sends it to the server as the user's identification information.

[1241] Step 3:

[1242] The server compares the received identification information with a database and authenticates the user. If authentication is successful, the server sends a notification of authentication completion to the terminal.

[1243] Step 4:

[1244] The device receives a notification that authentication is complete, displays a message on the app screen indicating successful authentication, and the user can proceed to the next step.

[1245] Step 5:

[1246] The device periodically sends notifications to the user prompting them to enter their health checkup results and lifestyle habits data.

[1247] Step 6:

[1248] Users receive a notification and enter information such as health check results, daily diet, exercise, and sleep time into a smartphone app.

[1249] Step 7:

[1250] The terminal formats the entered information and sends it to the server.

[1251] Step 8:

[1252] The server stores the received health information in a database, allowing users' health information to be managed centrally.

[1253] Step 9:

[1254] The device provides an interface for inputting the user's emotional state. The user inputs the emotional state (e.g., stress, happiness, etc.), and the device transmits it to the server.

[1255] Step 10:

[1256] The server stores the received emotion information in a database.

[1257] Step 11:

[1258] An AI analysis module on the server analyzes the stored health and emotional information and compares it with the user's current health status and past data.

[1259] Step 12:

[1260] The AI ​​analysis module evaluates the user's health risks based on the analysis results. For example, it identifies the user's future risk of high blood pressure based on their blood pressure data and lifestyle habits.

[1261] Step 13:

[1262] The server generates appropriate advice, preventative measures, and treatments based on the evaluation results, and an emotion engine adjusts the advice content based on the user's emotional state.

[1263] Step 14:

[1264] The server sends the generated advice and preventative measures to the device.

[1265] Step 15:

[1266] The device will notify the user of the advice and preventative measures it receives and display them on the app screen, allowing the user to use this information to improve their lifestyle habits.

[1267] Step 16:

[1268] The device accepts health-related questions from the user, who then enters and submits the question.

[1269] Step 17:

[1270] The terminal sends the entered question to the server.

[1271] Step 18:

[1272] The server's AI chatbot analyzes the question and references relevant medical databases and past user health information.

[1273] Step 19:

[1274] The AI ​​chatbot generates an appropriate response, which the server then sends to the device.

[1275] Step 20:

[1276] The device receives the response and displays it to the user.

[1277] Step 21:

[1278] When a user receives an alert indicating insufficient exercise, the AI ​​analysis module in the server generates an individually customized exercise program, and the emotion engine adjusts the program appropriately based on the user's emotional state.

[1279] Step 22:

[1280] The server transmits the generated exercise program to the terminal.

[1281] Step 23:

[1282] The device notifies the user of the exercise program and displays details on the app screen, and the user follows the suggested exercise program to perform daily exercise.

[1283] Example 2

[1284] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1285] In modern society, it is becoming increasingly important to centrally manage each individual's health and emotional state and provide appropriate advice. However, conventional health management systems have struggled to provide personalized advice that fully takes into account the user's emotional state. Furthermore, they lacked technology to efficiently authenticate and securely manage user identification information. This made it difficult to provide individually customized health support.

[1286] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1287] In this invention, the server includes means for inputting user identification information, means for centrally managing health information, means for analyzing the user's health information using an algorithm and generating advice according to the user's health condition, means for analyzing the user's emotional state and adjusting the advice and suggestions, means for providing an interface for inputting data, and means for transmitting the health information and advice to the user's terminal. This makes it possible to manage the user's health information and emotional information in an integrated manner and provide advice optimized for each individual condition.

[1288] "User" refers to any individual or organization that uses this system.

[1289] "Identification information" is data for uniquely identifying a user, and includes, for example, information such as a My Number or health insurance card.

[1290] "Health information" is data that indicates the user's health condition, and includes information such as weight, blood pressure, pulse rate, dietary content, amount of exercise, and sleep time.

[1291] An "algorithm" refers to a specific procedure for performing processing according to a certain calculation procedure.

[1292] "Emotional state" is data that indicates the psychological state of the user, and includes, for example, stress level, happiness level, tiredness, and the like.

[1293] "Interface" refers to a device or means for inputting or outputting data.

[1294] "Terminal" means an electronic device that allows a user to input data or receive information, including, for example, a smartphone or tablet.

[1295] A "server" refers to a computer system that processes and stores data over a network.

[1296] "AI analysis module" refers to software or hardware that uses artificial intelligence to analyze data and generate processing results.

[1297] A "database" refers to a system that organizes and stores information according to certain standards and allows it to be retrieved as needed.

[1298] The system of the present invention mainly manages and analyzes a user's identification information, health information, and emotional state to provide individually customized health advice. This system is composed of a user terminal, a server, an AI analysis module, and an emotion engine.

[1299] Enter user identification information

[1300] The user launches the smartphone app and scans their My Number and health insurance card with the camera. The device encodes this data using OCR (Optical Character Recognition) technology and sends it to the server using a secure protocol (e.g., HTTPS). The server compares the received data with a database and authenticates the user. If authentication is successful, the server starts a user session and allows subsequent data transmission.

[1301] Collection of health information

[1302] The device periodically uses push notifications to prompt the user to enter their health checkup results and lifestyle data (e.g., diet, exercise, and sleep duration). After the user has entered this information, the device encrypts the input data and sends it back to the server. The server verifies the received data and stores it in a database.

[1303] Collecting Emotional Data

[1304] The device displays options and sliders for inputting emotional states. After the user inputs their current emotional state (e.g., stress level, happiness, fatigue), and confirms the data, the device sends it to the server, which then performs further analysis based on the received emotional data.

[1305] Storage and analysis of health and emotional information

[1306] The server stores the received health and emotional information in a database. The AI ​​analysis module processes this data in real time and uses predictive models to analyze the user's health and emotional state. The analysis results in the calculation of the user's health risks and emotional trends.

[1307] Generating and Sending Advice

[1308] The server uses a generative AI model to customize personalized advice based on the user's health and emotional information. For example, if a user inputs a high stress level, the AI ​​model generates specific suggestions such as "try deep breathing" or "take a short walk." This advice is sent to the device and displayed to the user as a notification.

[1309] Chatbot functionality

[1310] Users enter health-related questions into the app as text. For example, if they ask, "How can I improve the quality of my sleep?", the device sends the question to the server. The server uses an AI chatbot module to analyze the question and generate an appropriate answer by referencing relevant medical databases and past question data. This answer is then sent to the device and displayed to the user.

[1311] Exercise program suggestions

[1312] When a user receives an alert about lack of exercise, the server's AI analysis module generates an appropriate exercise program based on the user's health condition, emotional state, and past exercise history. For example, it might suggest a program such as "10 minutes of light stretching" for a user who tends to feel depressed, or "30 minutes of light jogging" for a user who appears healthy. The exercise program is sent to the device and notified to the user.

[1313] Specific examples

[1314] For example, if a user types a question such as "I've been feeling stressed lately," the device sends this question to the server, where the AI ​​chatbot analyzes the question. The emotion engine determines the stress level from the user's input and behavior, and the AI ​​provides advice such as "breathing techniques to relax" or "do some light exercise." This information is sent to the device and notified to the user.

[1315] Example prompt: "I've been feeling stressed lately. How can I relieve this?"

[1316] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1317] Step 1: Enter user identification information

[1318] The user launches the smartphone app and scans their My Number and health insurance card with the camera. The device uses OCR technology to encode this information and sends it to the server via a secure protocol (HTTPS). The server compares the received identification information with a database and authenticates the user. If authentication is successful, the server starts a user session and allows subsequent data transmission.

[1319] input:

[1320] Image data of the user's My Number and health insurance card

[1321] output:

[1322] Encoded identity information, authentication result (success or failure)

[1323] Specific behavior:

[1324] Extracts text data from images using OCR technology and encodes it. Sends data securely using HTTPS and authenticates it against a database.

[1325] Step 2: Gathering health information

[1326] The device periodically uses push notifications to prompt the user to enter health checkup results and lifestyle habit data. The user enters data such as dietary habits, exercise, and sleep duration into a smartphone app. The device encrypts the entered data and sends it to the server. The server verifies the received data and stores it in a database.

[1327] input:

[1328] Health checkup results and lifestyle data entered by the user

[1329] output:

[1330] Encrypted health information stored in a database

[1331] Specific behavior:

[1332] Push notifications prompt users to enter data, encrypt the data and send it in real time. Received data is verified and accurately stored in the database.

[1333] Step 3: Collecting emotion data

[1334] Using the interface displayed on the device, the user inputs their current emotional state (e.g., stress level, happiness, fatigue), and the device securely transmits this data to the server, which then analyzes the received emotional data and stores the necessary information in a database.

[1335] input:

[1336] Emotional state data entered by the user

[1337] output:

[1338] Encrypted emotion data, stored in a database

[1339] Specific behavior:

[1340] Emotional information is collected from the user via the interface, encrypted and transmitted, and then analyzed and stored after reception.

[1341] Step 4: Storing and analyzing health and emotional information

[1342] The server stores the received health and emotional information in a database. The AI ​​analysis module uses this data to analyze the user's health and emotional state in real time and calculates risk assessments and trends using predictive models.

[1343] input:

[1344] Encrypted health and emotional information

[1345] output:

[1346] Analysis results (health risks, emotional trends)

[1347] Specific behavior:

[1348] The AI ​​analysis module analyzes the received data and uses predictive models to calculate risk assessments and trends of the user's health and emotional state.

[1349] Step 5: Generate and send advice

[1350] Using a generative AI model installed on the server, the system generates personalized advice based on the user's health and emotional information. For example, a user with a high stress level will be given specific suggestions such as "try taking deep breaths." This advice is sent to the device and notified to the user.

[1351] input:

[1352] Analysis results

[1353] output:

[1354] Personalized advice

[1355] Specific behavior:

[1356] The generative AI model creates appropriate advice based on the analysis results and sends it to the user's device.

[1357] Step 6: Chatbot functionality

[1358] Users input health-related questions into the app, such as "How can I improve the quality of my sleep?" The device sends the question to the server, where the AI ​​chatbot analyzes the question, references relevant information, and generates an appropriate answer. The generated answer is then sent back to the device and displayed to the user.

[1359] input:

[1360] User Questions

[1361] output:

[1362] Chatbot Answers

[1363] Specific behavior:

[1364] The chatbot function analyzes the user's question and generates an answer by referring to a related medical database and a database of past questions.

[1365] Step 7: Propose an exercise program

[1366] When a user receives an alert indicating lack of exercise, the server's AI analysis module generates an appropriate exercise program based on the user's health condition, emotional state, and past exercise data. For example, a depressed user might be recommended a 10-minute light stretching program, while a lively user might be recommended a 30-minute light jog. These exercise programs are then sent to the device and notified to the user.

[1367] input:

[1368] Analysis results, past exercise data

[1369] output:

[1370] Exercise program suggestions

[1371] Specific behavior:

[1372] The AI ​​analysis module generates an exercise program based on the data and notifies the user of the optimal exercise suggestions via the device.

[1373] (Application example 2)

[1374] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1375] Conventional health management systems can centrally manage a user's health information and provide advice, but they are unable to provide appropriate advice or meal menus that take into account the user's emotional state.In addition, they do not integrate a function to suggest meal menus based on the user's health and emotional state and to easily order them, making it difficult to improve the convenience of health management for users, and this needs to be improved.

[1376] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1377] In this invention, the server includes means for inputting user identification information, means for centrally managing user health information, means for analyzing the user's health information using an algorithm and generating advice based on the user's health condition, means for analyzing the user's emotional state, means for generating advice and a meal menu individually customized based on the user's health condition and emotional state and providing this to the user, and means for ordering the generated meal menu through a food delivery platform. This enables more personalized health management and meal suggestions based on the user's health condition and emotional state.

[1378] "User identification information" is unique information that identifies a user.

[1379] "Health information" refers to data related to the user's health condition, including health checkup results and lifestyle data.

[1380] An "algorithm" is a procedure and calculation method for analyzing a user's health information and generating advice based on their health condition.

[1381] "Emotional state" refers to a user's psychological state, such as stress, happiness, or anger.

[1382] "Centralized management" means integrating multiple user data and managing it efficiently.

[1383] "Advice" refers to specific recommendations or suggestions for improvement based on the user's health or emotional state.

[1384] "Health Status" refers to the user's current physical health, assessed based on various health information.

[1385] "User's device" refers to a communication device that is directly used by the user, such as a smartphone or tablet.

[1386] A "food delivery platform" is a system that provides a series of services from when users order food online until the food is delivered.

[1387] "Customized advice" refers to advice that is individually tailored to each user based on their health and emotional state.

[1388] "Meal Menu" refers to the specific dishes and combinations of ingredients offered to Users.

[1389] The present invention provides a system that improves the efficiency of health management and dietary suggestions for users and provides individually customized advice. The specific configuration and operation of the system will be described below.

[1390] System configuration

[1391] The system consists of the following elements:

[1392] 1. User device: A device operated by a user, such as a smartphone or tablet.

[1393] 2. Server: The central system that processes and stores data.

[1394] 3. Database: Data storage for centralized management of user identity, health information, and emotional state.

[1395] 4. AI analysis module: Analyzes health information and emotional state using machine learning models such as TensorFlow.

[1396] 5. Emotion Analysis Engine: Analyzes the user's emotional state using the Emotion API.

[1397] 6. Food delivery platform: An online system that allows users to order customized meal menus.

[1398] Processing flow

[1399] Entering and authenticating user identity

[1400] Through a smartphone app, users enter identifying information, such as government identification and a scan of their health insurance card, which is encoded and sent to a server that authenticates the user against a database.

[1401] Health and emotional data collection

[1402] The user's device periodically requests input of health checkup results and lifestyle habits data. Emotional states are also input, and this data is sent to the server. Emotional states are analyzed using the Emotion API.

[1403] Unified management and analysis of health and emotional information

[1404] The server stores the received health and emotional information in a database, and uses an AI analysis module to analyze this data and generate advice based on the user's current health and emotional state.

[1405] Generate and provide advice and meal menus

[1406] The AI ​​analysis module on the server generates personalized advice and meal menus based on the user's health information and emotional state, and these advice and meal menus are sent to the user's device.

[1407] Ordering food from the menu

[1408] Users can easily order the provided meal menu through the food delivery platform, and the order information is sent to the food delivery platform via the server, and the food is delivered to the user.

[1409] Specific use cases

[1410] For example, if a user inputs "I've been feeling stressed lately" and records "lack of exercise" as health information, the system will suggest appropriate meal options based on this. Suggested options include "mint tea to relieve stress" and "vegetable soup that's easy to digest." Users can order these options with the touch of a button.

[1411] Prompt Sentence Examples

[1412] "The user has entered 'I've been feeling stressed lately.' Health information has been recorded as 'not getting enough exercise.' Based on this, please suggest an appropriate meal menu."

[1413] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1414] Step 1:

[1415] Entering and authenticating user identity

[1416] Users launch the smartphone app and scan their government-issued identification and health insurance card information.

[1417] Input: Identification information (official identification information, medical insurance card information)

[1418] Output: Authentication request data (encoded identity information)

[1419] The server receives this, compares it with the database, authenticates the user, and returns the authentication result to the user's device.

[1420] Behavior: If the user is successfully authenticated, the primary functionality of the application becomes available.

[1421] Step 2:

[1422] Health and emotional data collection

[1423] The user device periodically requests input of health checkup results and lifestyle habits. The user also inputs their emotional state. This data is sent from the user device to the server.

[1424] Input: Health information (health checkup results, lifestyle data), emotional data (stress level, happiness level)

[1425] Output: Health information and emotion data (data sent to the server)

[1426] The server stores the received data in a database.

[1427] How it works: Users enter health and emotional data through the app and verify that the data is sent accurately to the server.

[1428] Step 3:

[1429] Analysis of information stored in the database

[1430] The server inputs the stored health and emotional information into an AI analysis module (using TensorFlow).

[1431] Input: Stored health and emotional information (data from database)

[1432] Output: Analysis results (health status, emotional status)

[1433] The AI ​​analysis module analyzes the user's health and emotional state and generates results.

[1434] How it works: The AI ​​model processes the data to analyze the user's health and emotional state.

[1435] Step 4:

[1436] Advice and meal menu generation

[1437] The server generates appropriate advice and meal menus based on the analysis results.

[1438] Input: Analysis results (health status, emotional status)

[1439] Output: Customized advice and meal menu (data sent to user device)

[1440] The generated advice and meal menu are sent to the user's device.

[1441] How it works: The server uses the analysis results to provide the user with optimal advice and meal plans.

[1442] Step 5:

[1443] Ordering food from the menu

[1444] Users can view the offered meal menu and place their order through the food delivery platform.

[1445] Input: Customized meal menu (displayed on user's device)

[1446] Output: Order data (data sent to food delivery platform)

[1447] The user terminal sends the order to the server, which then forwards the order data to the food delivery platform.

[1448] How it works: When the user presses the bulk order button, the specified meal menu is sent to the delivery service.

[1449] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1450] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1451] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1452] [Fourth embodiment]

[1453] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1454] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1455] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1456] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1457] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1458] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1459] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1460] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1461] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1462] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1463] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1464] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1465] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1466] The system of the present invention provides a mechanism for inputting a user's identification information, centrally managing health information, analyzing the user's health status using an algorithm, and generating appropriate advice and sending it to the user's device. The processing content of the system's program and specific examples are explained below.

[1467] Basic configuration

[1468] This system consists of a user device (e.g., a smartphone), a server, and an AI analysis module. Users enter health information through a smartphone app and send it to the server. The server analyzes the received data and provides advice and information to the user.

[1469] Enter user identification information

[1470] The user launches the smartphone app and scans their My Number and health insurance card. The smartphone encodes this data and sends it to the server, which receives it and authenticates the user by checking it against a database.

[1471] Collection of health information

[1472] The device periodically prompts the user to enter their health checkup results and lifestyle habits. The user enters this information into a smartphone app, and the device then sends it to a server.

[1473] Centralized management and analysis of health information

[1474] The server stores the received health information in a database. An algorithm analyzes the user's health status based on this data and assesses their potential health risks. Based on the analysis, the server generates appropriate advice, preventative measures, and treatments.

[1475] Generating and Sending Advice

[1476] The AI ​​analysis module on the server generates personalized advice based on the user's health information, including dietary improvements, exercise recommendations, stress management, etc. The generated advice is then sent to the device and notified to the user.

[1477] Chatbot functionality

[1478] The device accepts health-related questions from users. These questions are sent to a server, where an AI chatbot generates answers. The AI ​​consults relevant medical databases and historical data to provide appropriate answers. These answers are then sent back to the device and displayed to the user.

[1479] Specific examples

[1480] For example, if a user types a question such as "I can't seem to get rid of my fatigue lately," the device sends this question to the server, where the AI ​​chatbot analyzes the question. Based on the user's past health and lifestyle data, the AI ​​suggests advice such as "reducing screen time at night to improve sleep quality" or "taking appropriate vitamins as needed." This information is sent to the device and notified to the user.

[1481] Exercise program suggestions

[1482] If the user is judged to be lacking in exercise, the server's AI analysis module will generate an exercise program based on the user's health condition and lifestyle. For example, it may suggest walking three times a week or stretching every morning. This exercise program is then sent to the device and notified to the user.

[1483] As described above, this system centrally manages users' health information and analyzes it using algorithms to provide individually customized advice and treatments, thereby improving the efficiency of health management and extending users' healthy lifespans.

[1484] The processing flow will be explained below.

[1485] Step 1:

[1486] The user launches the smartphone app and uses the camera function to scan their My Number and health insurance card.

[1487] Step 2:

[1488] The device encodes the scanned data and sends it to the server as the user's identification information.

[1489] Step 3:

[1490] The server compares the received identification information with a database and authenticates the user. If authentication is successful, the server sends a notification of authentication completion to the terminal.

[1491] Step 4:

[1492] The device receives a notification that authentication is complete, displays a message on the app screen indicating successful authentication, and the user can proceed to the next step.

[1493] Step 5:

[1494] The device periodically sends notifications to the user prompting them to enter their health checkup results and lifestyle habits data.

[1495] Step 6:

[1496] Users receive a notification and enter information such as health check results, daily diet, exercise, and sleep time into a smartphone app.

[1497] Step 7:

[1498] The terminal formats the entered information and sends it to the server.

[1499] Step 8:

[1500] The server stores the received health information in a database, allowing users' health information to be managed centrally.

[1501] Step 9:

[1502] An AI analysis module on the server analyzes the stored data and compares it with the user's current health status and past data.

[1503] Step 10:

[1504] The AI ​​analysis module evaluates the user's health risks based on the analysis results. For example, it identifies the user's future risk of high blood pressure based on their blood pressure data and lifestyle habits.

[1505] Step 11:

[1506] The server generates appropriate advice, preventative measures, and treatments based on the evaluation results.

[1507] Step 12:

[1508] The server sends the generated advice and preventative measures to the device.

[1509] Step 13:

[1510] The device will notify the user of the advice and preventative measures it receives and display them on the app screen, allowing the user to use this information to improve their lifestyle habits.

[1511] Step 14:

[1512] The device accepts health-related questions from the user, who then enters and submits the question.

[1513] Step 15:

[1514] The terminal sends the entered question to the server.

[1515] Step 16:

[1516] The server's AI chatbot analyzes the question and references relevant medical databases and past user health information.

[1517] Step 17:

[1518] The AI ​​chatbot generates an appropriate response, which the server then sends to the device.

[1519] Step 18:

[1520] The device receives the response and displays it to the user.

[1521] Step 19:

[1522] When a user receives an alert indicating lack of exercise, an AI analysis module on the server generates an individually customized exercise program.

[1523] Step 20:

[1524] The server transmits the generated exercise program to the terminal.

[1525] Step 21:

[1526] The device notifies the user of the exercise program and displays details on the app screen, and the user follows the suggested exercise program to perform daily exercise.

[1527] Example 1

[1528] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1529] In modern society, personal health management has become an important issue. However, conventional health management systems not only centrally manage users' health information, but also lack the necessary systems to provide appropriate advice based on that information. Furthermore, they lack mechanisms to quickly and appropriately respond to users' questions about their health status, or to provide individually customized exercise programs. This makes it difficult for users to properly manage their health.

[1530] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1531] In this invention, the server includes a means for inputting user identification information, a means for centrally managing user health information, a means for analyzing the user's health information using an algorithm and generating advice according to the user's health condition, a means for generating answers to questions entered by the user using a generative AI model, and a means for generating an exercise program based on the user's health condition. This not only enables the user's own health information to be centrally managed, but also enables the user to receive appropriate advice and exercise programs, and to receive prompt and appropriate answers to health-related questions.

[1532] "User identification information" refers to information for identifying an individual user, and includes, for example, an identification number and authentication information.

[1533] "Centralized management" means concentrating and managing multiple pieces of data and information in one place.

[1534] An "algorithm" refers to a computational procedure or process for solving a specific problem, implemented as a program.

[1535] "Health information" refers to information about the user's health condition, and specifically includes weight, blood pressure, diet, exercise history, and the like.

[1536] "Advice" means any suggestions or advice provided to a User for the purpose of improving or maintaining their health.

[1537] "Terminal" refers to a hardware device that is directly operated by a user, and specifically includes smartphones and tablets.

[1538] A "generative AI model" refers to a model that uses artificial intelligence to generate a specific output from data, such as to generate an answer to a question.

[1539] An "exercise program" refers to an exercise plan designed based on the user's health condition and lifestyle, and includes weekly exercise content and stretching methods.

[1540] "Database" refers to a system for efficiently storing, retrieving, and managing data, such as those used to store health and identifying information.

[1541] This system inputs a user's identification information, centrally manages health information, analyzes the user's health status using an algorithm, generates appropriate advice, and sends it to the user's device. This system consists of a user device, a server, and an AI analysis module.

[1542] Basic configuration

[1543] The implementation of this system requires the following elements:

[1544] User device (e.g. smartphone)

[1545] Server (e.g., EC2 instance on Amazon Web Services)

[1546] Database (e.g. Amazon RDS)

[1547] AI analysis modules (e.g. TensorFlow or PyTorch)

[1548] Communication protocol (e.g. HTTPS)

[1549] Enter user identification information

[1550] The user launches the smartphone app and uses the app's scanning function to input their My Number and health insurance card information. The smartphone uses its camera to capture this information and sends it to the server in an encoded format. The server then collates the received data with a database (e.g., MySQL) for authentication. This authentication uses the OAuth 2.0 protocol.

[1551] Collection of health information

[1552] The device periodically sends notifications to the user prompting them to enter their health checkup results and lifestyle habit data. For example, a notification saying "Please weigh yourself" is sent every morning at 8:00. The user enters this data (e.g., weight, blood pressure, dietary details) through the app. The device sends this data to the server in real time. This transmission uses the HTTPS protocol.

[1553] Centralized management and analysis of health information

[1554] The server stores the received health information in a database (e.g., Amazon RDS). An AI analysis module (e.g., using TensorFlow or PyTorch) performs analysis based on the stored health information. This evaluates the user's potential health risks. The evaluation results are stored in the database.

[1555] Generating and Sending Advice

[1556] The server's AI analysis module generates appropriate advice based on the user's health information. Specific advice includes dietary improvements, recommended exercise, and stress management. The generated advice is sent to the device using APNS or FCM. The device then notifies the user of the received advice.

[1557] Chatbot functionality

[1558] The terminal accepts health-related questions from the user, such as the following prompt:

[1559] "I've been feeling tired lately and it's bothering me. What should I do?"

[1560] The question is encrypted and sent to a server, where an AI chatbot (e.g., a GPT-4 model) analyzes the question and generates an appropriate answer by referencing relevant medical databases and past data. The answer is then sent to the device and displayed to the user.

[1561] Exercise program suggestions

[1562] If the user is judged to be lacking in exercise, the server's AI analysis module will generate an exercise program based on the user's health condition and lifestyle. For example, it may include specific suggestions such as walking three times a week or stretching every morning. These exercise programs are then sent to the device via the notification function, and the user is notified.

[1563] Through each of these processes, the system can efficiently manage the user's health and provide individually customized advice and treatments.

[1564] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1565] Step 1: Enter user identification information

[1566] The user launches the smartphone app and scans their My Number and health insurance card. The device uses the camera to read this information, converts it into an encoded format, encrypts it (e.g., AES256), and sends it to the server. The server then compares the received identification information with a database (e.g., MySQL) to authenticate the user. This authentication uses the OAuth 2.0 protocol.

[1567] Input: User identification information (My Number, health insurance card)

[1568] Output: Authentication result

[1569] Specific behavior:

[1570] User scans information with camera

[1571] The device encrypts the data and sends it to the server

[1572] The server authenticates the user against a database

[1573] Step 2: Gathering health information

[1574] The device periodically sends notifications to the user, prompting them to enter their health checkup results and lifestyle habits. For example, it sends a notification every morning at 8:00 to "weigh yourself." The user enters this data through the app, and the device sends the input data to the server in real time. Transmission is via the HTTPS protocol.

[1575] Input: User's health information (weight, blood pressure, dietary details)

[1576] Output: Collected health data

[1577] Specific behavior:

[1578] The device periodically sends notifications

[1579] User enters data in the app

[1580] The device sends the input data to the server

[1581] Step 3: Centralize and analyze health information

[1582] The server stores the submitted health information in a database (e.g., Amazon RDS). An AI analysis module (e.g., TensorFlow or PyTorch) analyzes the user's health status based on the received data. The algorithm compares the data with past data, evaluates the user's potential health risks, and stores the results in the database.

[1583] Input: Collected health data

[1584] Output: Analysis and evaluation results

[1585] Specific behavior:

[1586] The server stores the health data in a database

[1587] AI analysis module analyzes health data

[1588] Health risk assessment results are stored in a database

[1589] Step 4: Generate and send advice

[1590] The server's AI analysis module generates appropriate advice based on the health information and analysis results. This advice may include dietary improvements, exercise recommendations, stress management, etc. The generated advice is sent to the device using APNS or FCM. The device then notifies the user of the received advice.

[1591] Input: Analysis results and evaluation results

[1592] Output: personalized advice

[1593] Specific behavior:

[1594] Server generates advice

[1595] Send advice to your device

[1596] Notify users of advice

[1597] Step 5: Chatbot functionality

[1598] The device accepts questions from the user. An example question might be, "I've been feeling tired lately. What should I do?" This question is encrypted and sent to the server. The server uses an AI chatbot (e.g., a GPT-4 model) to analyze the question and generate an appropriate answer by referencing relevant medical databases and past data. The generated answer is then sent back to the device and displayed to the user.

[1599] Input: User question

[1600] Output: The generated answer

[1601] Specific behavior:

[1602] The device accepts questions

[1603] The server analyzes the question and generates an answer

[1604] Send the answer to the device and display it to the user

[1605] Step 6: Propose an exercise program

[1606] If the user is judged to be lacking in exercise, the server's AI analysis module will generate an exercise program based on their health condition and lifestyle. For example, it may suggest walking three times a week or stretching every morning. This exercise program is sent to the device using the notification function and displayed to the user.

[1607] Input: Health and lifestyle data

[1608] Output: Exercise program

[1609] Specific behavior:

[1610] The server generates the exercise program

[1611] Send exercise programs to your device

[1612] Notify users of exercise programs

[1613] Through these steps, the system can centralize the user's health information and provide appropriate advice and exercise programs. Furthermore, the chatbot function using the generative AI model can quickly respond to users' health-related questions.

[1614] (Application example 1)

[1615] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1616] Conventional health management systems only analyze a user's health information and provide advice based on the results, but lack the ability to automatically generate meal plans that are optimal for each user's individual health condition. This makes it difficult for users to select meals that are appropriate for their health condition, resulting in a decrease in the efficiency and effectiveness of health management. There is also a need for a system that allows users to easily obtain an appropriate meal plan and put it into practice.

[1617] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1618] In this invention, the server includes a means for inputting user identification information, a means for centrally managing user health information, a means for analyzing the user's health information using an algorithm and generating advice based on the user's health condition, and a means for generating a meal plan based on the health condition and transmitting the meal plan to the user's terminal. This allows the user to easily obtain an optimal meal plan based on their health condition, enabling efficient and effective health management.

[1619] "User identification information" refers to information for identifying individual users, such as their My Number or health insurance card information.

[1620] "Health information" is data related to the user's health condition, and includes, for example, health checkup results and lifestyle habit data.

[1621] An "algorithm" is a method or calculation method for analyzing data according to a set procedure.

[1622] "Health-based advice" refers to advice and recommendations that are individually customized based on the results of analyzing a user's health information.

[1623] "User's terminal" refers to an electronic device used by a user, such as a smartphone or tablet.

[1624] "Centralized management" means aggregating and managing multiple health information in a unified manner.

[1625] "Meal Plan" means a meal suggestion or plan that takes into account the user's health condition.

[1626] The system of the present invention inputs user identification information, centrally manages health information, analyzes health status using an algorithm, generates appropriate advice, and transmits it to the user's device. It also has the function of generating a meal plan based on the user's health status and providing it to the user's device.

[1627] Basic configuration

[1628] This system consists of a device used by the user (e.g., a smartphone), a server that processes the data, and an AI analysis module.

[1629] Enter user identification information

[1630] The user launches the smartphone app and scans their My Number and health insurance card. The device encodes this data and sends it to the server, which receives it and authenticates the user by checking it against a database.

[1631] Collection of health information

[1632] The device periodically prompts the user to enter their health checkup results and lifestyle habits. The user enters this information into a smartphone app and sends it to a server.

[1633] Centralized management and analysis of health information

[1634] The server stores the received health information in a database. The AI ​​analysis module uses this data to analyze the user's health status and assess potential health risks. Based on the analysis results, the server generates appropriate advice, preventive measures, and treatments.

[1635] Generating and Sending Advice

[1636] The AI ​​analysis module on the server generates personalized advice based on the user's health information, including dietary improvements, exercise recommendations, stress management, etc. The generated advice is then sent to the device and notified to the user.

[1637] Chatbot functionality

[1638] The device accepts health-related questions from users. These questions are sent to a server, where an AI chatbot generates answers. The AI ​​consults relevant medical databases and historical data to provide appropriate answers. These answers are then sent back to the device and displayed to the user.

[1639] Generate a meal plan

[1640] Based on the user's health condition, the server's AI analysis module generates a personalized meal plan. For example, if the user needs to lose weight, it will provide a low-calorie meal plan, and if the user needs to recover from fatigue, it will provide a nutritious meal plan. This meal plan is sent to the terminal, where the user can easily order.

[1641] Example of a system

[1642] For example, if a user types a question such as "I can't seem to get rid of my fatigue lately," the device sends this question to the server, where the AI ​​chatbot analyzes the question. Based on the user's past health and lifestyle data, the AI ​​suggests advice such as "reducing screen time at night to improve sleep quality" or "taking appropriate vitamins as needed." This information is sent to the device and notified to the user.

[1643] Hardware and software used

[1644] Hardware: Smartphones, servers

[1645] Software: Python, Flask, scikit-learn

[1646] Prompt Sentence Examples

[1647] "I've been feeling tired lately. Is there any good way to get more nutrition?"

[1648] The present invention allows users to easily obtain optimal meal plans and advice based on their own health condition, enabling them to manage their health more efficiently and effectively.

[1649] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1650] Step 1:

[1651] Enter user identification information

[1652] How it works: A user launches the smartphone app and scans their My Number and health insurance card.

[1653] Input: My Number, health insurance card information

[1654] Data processing and calculation: The device encodes and encrypts this identification information and sends it to the server.

[1655] Output: The encrypted identity is sent to the server.

[1656] Step 2:

[1657] User identity authentication

[1658] How it works: The server checks the received identification information against a database and authenticates the user.

[1659] Input: Encrypted identity

[1660] Data processing and calculation: The server performs a database search and checks for data matches.

[1661] Output: User authentication result is generated.

[1662] Step 3:

[1663] Collection of health information

[1664] How it works: The device periodically prompts the user to enter health checkup results and lifestyle data.

[1665] Input: Health checkup results and lifestyle data entered by the user

[1666] Data processing and calculation: The health information collected by the device is sent to the server.

[1667] Output: Health information is stored on the server.

[1668] Step 4:

[1669] Centralized management and analysis of health information

[1670] How it works: The server stores the received health information in a database.

[1671] Input: Health information submitted by the user

[1672] Data processing and calculation: Analyze health information using an AI analysis module and assess potential health risks.

[1673] Output: Health risk assessment and analysis results are generated.

[1674] Step 5:

[1675] Advice generation and delivery

[1676] How it works: An AI analysis module on the server generates personalized advice based on the user's health information.

[1677] Input: Health risk assessment results and analysis results

[1678] Data processing and calculation: Generates advice on improving diet, recommending exercise, and managing stress, and sends it to the user's device.

[1679] Output: The customized advice is sent to the user's device.

[1680] Step 6:

[1681] Using chatbot functionality

[1682] How it works: The device accepts health-related questions from users, which are then sent to a server where an AI chatbot consults relevant databases and historical data to generate appropriate answers.

[1683] Input: User question

[1684] Data processing and calculation: The AI ​​chatbot analyzes the question, collects relevant information, and generates an answer.

[1685] Output: The generated answer is sent to the user's device.

[1686] Step 7:

[1687] Meal plan generation and delivery

[1688] How it works: The server's AI analysis module generates an individually customized meal plan and sends it to the user's device.

[1689] Input: Health analysis results and individual health status

[1690] Data processing and calculation: AI generates optimal meal plans based on the user's health condition. For example, it will recommend low-calorie meals for users who need to lose weight, and nutritious meals for users who need to recover from fatigue.

[1691] Output: The customized meal plan is sent to the user's device.

[1692] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1693] The system of the present invention provides a mechanism for inputting a user's identification information, centrally managing health information, analyzing the user's health status using an algorithm, generating appropriate advice, and transmitting it to the user's device. Furthermore, it incorporates an emotion engine that analyzes the user's emotional state and adjusts the advice and suggestions. The system's program processing is explained below with specific examples.

[1694] Basic configuration

[1695] This system consists of a user device (e.g., a smartphone), a server, an AI analysis module, and an emotion engine. Users input health information and emotional state through a smartphone app and send it to the server. The server analyzes the received data and provides advice and information to the user.

[1696] Enter user identification information

[1697] The user launches the smartphone app and scans their My Number and health insurance card. The smartphone encodes this data and sends it to the server, which receives it and authenticates the user by checking it against a database.

[1698] Collection of health information

[1699] The device periodically prompts the user to enter their health checkup results and lifestyle habits. The user enters this information into a smartphone app, and the device then sends it to a server.

[1700] Collecting Emotional Data

[1701] The device provides an interface for inputting the user's emotional state. The user inputs the emotional state (e.g., stress, happiness, etc.), and the device transmits it to the server.

[1702] Centralized management and analysis of health information

[1703] The server stores the received health and emotional information in a database, and an algorithm uses this data to analyze the user's health status and generate advice based on the user's current health and emotional state.

[1704] Generating and Sending Advice

[1705] The AI ​​analysis module on the server generates personalized advice based on the user's health information and emotional state. For example, if the user is feeling stressed, the AI ​​will suggest ways to relieve stress. This advice is then sent to the device and notified to the user.

[1706] Chatbot functionality

[1707] The device accepts health-related questions from users. These questions are sent to a server, where an AI chatbot generates answers. The AI ​​consults relevant medical databases and historical data to provide appropriate answers. These answers are then sent back to the device and displayed to the user.

[1708] Specific examples

[1709] For example, if a user types a question such as "I've been feeling stressed lately," the device sends this question to the server, where the AI ​​chatbot analyzes the question. The emotion engine determines the stress level from the user's input and behavior, and the AI ​​provides advice such as "breathing techniques to relax." This information is sent to the device and notified to the user.

[1710] Exercise program suggestions

[1711] When a user receives an alert indicating insufficient exercise, the server's AI analysis module generates an exercise program based on the user's health and emotional state. For example, it may suggest light exercise for a depressed user, or a more vigorous exercise program for a healthy user. These exercise programs are then sent to the device and notified to the user.

[1712] As described above, this system centrally manages a user's health and emotional information and analyzes it using an algorithm to provide individually customized advice and treatments. This not only improves the efficiency of health management and extends the user's healthy lifespan, but also provides more personalized support that responds to the user's emotions.

[1713] The processing flow will be explained below.

[1714] Step 1:

[1715] The user launches the smartphone app and uses the camera function to scan their My Number and health insurance card.

[1716] Step 2:

[1717] The device encodes the scanned data and sends it to the server as the user's identification information.

[1718] Step 3:

[1719] The server compares the received identification information with a database and authenticates the user. If authentication is successful, the server sends a notification of authentication completion to the terminal.

[1720] Step 4:

[1721] The device receives a notification that authentication is complete, displays a message on the app screen indicating successful authentication, and the user can proceed to the next step.

[1722] Step 5:

[1723] The device periodically sends notifications to the user prompting them to enter their health checkup results and lifestyle habits data.

[1724] Step 6:

[1725] Users receive a notification and enter information such as health check results, daily diet, exercise, and sleep time into a smartphone app.

[1726] Step 7:

[1727] The terminal formats the entered information and sends it to the server.

[1728] Step 8:

[1729] The server stores the received health information in a database, allowing users' health information to be managed centrally.

[1730] Step 9:

[1731] The device provides an interface for inputting the user's emotional state. The user inputs the emotional state (e.g., stress, happiness, etc.), and the device transmits it to the server.

[1732] Step 10:

[1733] The server stores the received emotion information in a database.

[1734] Step 11:

[1735] An AI analysis module on the server analyzes the stored health and emotional information and compares it with the user's current health status and past data.

[1736] Step 12:

[1737] The AI ​​analysis module evaluates the user's health risks based on the analysis results. For example, it identifies the user's future risk of high blood pressure based on their blood pressure data and lifestyle habits.

[1738] Step 13:

[1739] The server generates appropriate advice, preventative measures, and treatments based on the evaluation results, and an emotion engine adjusts the advice content based on the user's emotional state.

[1740] Step 14:

[1741] The server sends the generated advice and preventative measures to the device.

[1742] Step 15:

[1743] The device will notify the user of the advice and preventative measures it receives and display them on the app screen, allowing the user to use this information to improve their lifestyle habits.

[1744] Step 16:

[1745] The device accepts health-related questions from the user, who then enters and submits the question.

[1746] Step 17:

[1747] The terminal sends the entered question to the server.

[1748] Step 18:

[1749] The server's AI chatbot analyzes the question and references relevant medical databases and past user health information.

[1750] Step 19:

[1751] The AI ​​chatbot generates an appropriate response, which the server then sends to the device.

[1752] Step 20:

[1753] The device receives the response and displays it to the user.

[1754] Step 21:

[1755] When a user receives an alert indicating insufficient exercise, the AI ​​analysis module in the server generates an individually customized exercise program, and the emotion engine adjusts the program appropriately based on the user's emotional state.

[1756] Step 22:

[1757] The server transmits the generated exercise program to the terminal.

[1758] Step 23:

[1759] The device notifies the user of the exercise program and displays details on the app screen, and the user follows the suggested exercise program to perform daily exercise.

[1760] Example 2

[1761] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1762] In modern society, it is becoming increasingly important to centrally manage each individual's health and emotional state and provide appropriate advice. However, conventional health management systems have struggled to provide personalized advice that fully takes into account the user's emotional state. Furthermore, they lacked technology to efficiently authenticate and securely manage user identification information. This made it difficult to provide individually customized health support.

[1763] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1764] In this invention, the server includes means for inputting user identification information, means for centrally managing health information, means for analyzing the user's health information using an algorithm and generating advice according to the user's health condition, means for analyzing the user's emotional state and adjusting the advice and suggestions, means for providing an interface for inputting data, and means for transmitting the health information and advice to the user's terminal. This makes it possible to manage the user's health information and emotional information in an integrated manner and provide advice optimized for each individual condition.

[1765] "User" refers to any individual or organization that uses this system.

[1766] "Identification information" is data for uniquely identifying a user, and includes, for example, information such as a My Number or health insurance card.

[1767] "Health information" is data that indicates the user's health condition, and includes information such as weight, blood pressure, pulse rate, dietary content, amount of exercise, and sleep time.

[1768] An "algorithm" refers to a specific procedure for performing processing according to a certain calculation procedure.

[1769] "Emotional state" is data that indicates the psychological state of the user, and includes, for example, stress level, happiness level, tiredness, and the like.

[1770] "Interface" refers to a device or means for inputting or outputting data.

[1771] "Terminal" means an electronic device that allows a user to input data or receive information, including, for example, a smartphone or tablet.

[1772] A "server" refers to a computer system that processes and stores data over a network.

[1773] "AI analysis module" refers to software or hardware that uses artificial intelligence to analyze data and generate processing results.

[1774] A "database" refers to a system that organizes and stores information according to certain standards and allows it to be retrieved as needed.

[1775] The system of the present invention mainly manages and analyzes a user's identification information, health information, and emotional state to provide individually customized health advice. This system is composed of a user terminal, a server, an AI analysis module, and an emotion engine.

[1776] Enter user identification information

[1777] The user launches the smartphone app and scans their My Number and health insurance card with the camera. The device encodes this data using OCR (Optical Character Recognition) technology and sends it to the server using a secure protocol (e.g., HTTPS). The server compares the received data with a database and authenticates the user. If authentication is successful, the server starts a user session and allows subsequent data transmission.

[1778] Collection of health information

[1779] The device periodically uses push notifications to prompt the user to enter their health checkup results and lifestyle data (e.g., diet, exercise, and sleep duration). After the user has entered this information, the device encrypts the input data and sends it back to the server. The server verifies the received data and stores it in a database.

[1780] Collecting Emotional Data

[1781] The device displays options and sliders for inputting emotional states. After the user inputs their current emotional state (e.g., stress level, happiness, fatigue), and confirms the data, the device sends it to the server, which then performs further analysis based on the received emotional data.

[1782] Storage and analysis of health and emotional information

[1783] The server stores the received health and emotional information in a database. The AI ​​analysis module processes this data in real time and uses predictive models to analyze the user's health and emotional state. The analysis results in the calculation of the user's health risks and emotional trends.

[1784] Generating and Sending Advice

[1785] The server uses a generative AI model to customize personalized advice based on the user's health and emotional information. For example, if a user inputs a high stress level, the AI ​​model generates specific suggestions such as "try deep breathing" or "take a short walk." This advice is sent to the device and displayed to the user as a notification.

[1786] Chatbot functionality

[1787] Users enter health-related questions into the app as text. For example, if they ask, "How can I improve the quality of my sleep?", the device sends the question to the server. The server uses an AI chatbot module to analyze the question and generate an appropriate answer by referencing relevant medical databases and past question data. This answer is then sent to the device and displayed to the user.

[1788] Exercise program suggestions

[1789] When a user receives an alert about lack of exercise, the server's AI analysis module generates an appropriate exercise program based on the user's health condition, emotional state, and past exercise history. For example, it might suggest a program such as "10 minutes of light stretching" for a user who tends to feel depressed, or "30 minutes of light jogging" for a user who appears healthy. The exercise program is sent to the device and notified to the user.

[1790] Specific examples

[1791] For example, if a user types a question such as "I've been feeling stressed lately," the device sends this question to the server, where the AI ​​chatbot analyzes the question. The emotion engine determines the stress level from the user's input and behavior, and the AI ​​provides advice such as "breathing techniques to relax" or "do some light exercise." This information is sent to the device and notified to the user.

[1792] Example prompt: "I've been feeling stressed lately. How can I relieve this?"

[1793] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1794] Step 1: Enter user identification information

[1795] The user launches the smartphone app and scans their My Number and health insurance card with the camera. The device uses OCR technology to encode this information and sends it to the server via a secure protocol (HTTPS). The server compares the received identification information with a database and authenticates the user. If authentication is successful, the server starts a user session and allows subsequent data transmission.

[1796] input:

[1797] Image data of the user's My Number and health insurance card

[1798] output:

[1799] Encoded identity information, authentication result (success or failure)

[1800] Specific behavior:

[1801] Extracts text data from images using OCR technology and encodes it. Sends data securely using HTTPS and authenticates it against a database.

[1802] Step 2: Gathering health information

[1803] The device periodically uses push notifications to prompt the user to enter health checkup results and lifestyle habit data. The user enters data such as dietary habits, exercise, and sleep duration into a smartphone app. The device encrypts the entered data and sends it to the server. The server verifies the received data and stores it in a database.

[1804] input:

[1805] Health checkup results and lifestyle data entered by the user

[1806] output:

[1807] Encrypted health information stored in a database

[1808] Specific behavior:

[1809] Push notifications prompt users to enter data, encrypt the data and send it in real time. Received data is verified and accurately stored in the database.

[1810] Step 3: Collecting emotion data

[1811] Using the interface displayed on the device, the user inputs their current emotional state (e.g., stress level, happiness, fatigue), and the device securely transmits this data to the server, which then analyzes the received emotional data and stores the necessary information in a database.

[1812] input:

[1813] Emotional state data entered by the user

[1814] output:

[1815] Encrypted emotion data, stored in a database

[1816] Specific behavior:

[1817] Emotional information is collected from the user via the interface, encrypted and transmitted, and then analyzed and stored after reception.

[1818] Step 4: Storing and analyzing health and emotional information

[1819] The server stores the received health and emotional information in a database. The AI ​​analysis module uses this data to analyze the user's health and emotional state in real time and calculates risk assessments and trends using predictive models.

[1820] input:

[1821] Encrypted health and emotional information

[1822] output:

[1823] Analysis results (health risks, emotional trends)

[1824] Specific behavior:

[1825] The AI ​​analysis module analyzes the received data and uses predictive models to calculate risk assessments and trends of the user's health and emotional state.

[1826] Step 5: Generate and send advice

[1827] Using a generative AI model installed on the server, the system generates personalized advice based on the user's health and emotional information. For example, a user with a high stress level will be given specific suggestions such as "try taking deep breaths." This advice is sent to the device and notified to the user.

[1828] input:

[1829] Analysis results

[1830] output:

[1831] Personalized advice

[1832] Specific behavior:

[1833] The generative AI model creates appropriate advice based on the analysis results and sends it to the user's device.

[1834] Step 6: Chatbot functionality

[1835] Users input health-related questions into the app, such as "How can I improve the quality of my sleep?" The device sends the question to the server, where the AI ​​chatbot analyzes the question, references relevant information, and generates an appropriate answer. The generated answer is then sent back to the device and displayed to the user.

[1836] input:

[1837] User Questions

[1838] output:

[1839] Chatbot Answers

[1840] Specific behavior:

[1841] The chatbot function analyzes the user's question and generates an answer by referring to a related medical database and a database of past questions.

[1842] Step 7: Propose an exercise program

[1843] When a user receives an alert indicating lack of exercise, the server's AI analysis module generates an appropriate exercise program based on the user's health condition, emotional state, and past exercise data. For example, a depressed user might be recommended a 10-minute light stretching program, while a lively user might be recommended a 30-minute light jog. These exercise programs are then sent to the device and notified to the user.

[1844] input:

[1845] Analysis results, past exercise data

[1846] output:

[1847] Exercise program suggestions

[1848] Specific behavior:

[1849] The AI ​​analysis module generates an exercise program based on the data and notifies the user of the optimal exercise suggestions via the device.

[1850] (Application example 2)

[1851] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1852] Conventional health management systems can centrally manage a user's health information and provide advice, but they are unable to provide appropriate advice or meal menus that take into account the user's emotional state.In addition, they do not integrate a function to suggest meal menus based on the user's health and emotional state and to easily order them, making it difficult to improve the convenience of health management for users, and this needs to be improved.

[1853] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1854] In this invention, the server includes means for inputting user identification information, means for centrally managing user health information, means for analyzing the user's health information using an algorithm and generating advice based on the user's health condition, means for analyzing the user's emotional state, means for generating advice and a meal menu individually customized based on the user's health condition and emotional state and providing this to the user, and means for ordering the generated meal menu through a food delivery platform. This enables more personalized health management and meal suggestions based on the user's health condition and emotional state.

[1855] "User identification information" is unique information that identifies a user.

[1856] "Health information" refers to data related to the user's health condition, including health checkup results and lifestyle data.

[1857] An "algorithm" is a procedure and calculation method for analyzing a user's health information and generating advice based on their health condition.

[1858] "Emotional state" refers to a user's psychological state, such as stress, happiness, or anger.

[1859] "Centralized management" means integrating multiple user data and managing it efficiently.

[1860] "Advice" refers to specific recommendations or suggestions for improvement based on the user's health or emotional state.

[1861] "Health Status" refers to the user's current physical health, assessed based on various health information.

[1862] "User's device" refers to a communication device that is directly used by the user, such as a smartphone or tablet.

[1863] A "food delivery platform" is a system that provides a series of services from when users order food online until the food is delivered.

[1864] "Customized advice" refers to advice that is individually tailored to each user based on their health and emotional state.

[1865] "Meal Menu" refers to the specific dishes and combinations of ingredients offered to Users.

[1866] The present invention provides a system that improves the efficiency of health management and dietary suggestions for users and provides individually customized advice. The specific configuration and operation of the system will be described below.

[1867] System configuration

[1868] The system consists of the following elements:

[1869] 1. User device: A device operated by a user, such as a smartphone or tablet.

[1870] 2. Server: The central system that processes and stores data.

[1871] 3. Database: Data storage for centralized management of user identity, health information, and emotional state.

[1872] 4. AI analysis module: Analyzes health information and emotional state using machine learning models such as TensorFlow.

[1873] 5. Emotion Analysis Engine: Analyzes the user's emotional state using the Emotion API.

[1874] 6. Food delivery platform: An online system that allows users to order customized meal menus.

[1875] Processing flow

[1876] Entering and authenticating user identity

[1877] Through a smartphone app, users enter identifying information, such as government identification and a scan of their health insurance card, which is encoded and sent to a server that authenticates the user against a database.

[1878] Health and emotional data collection

[1879] The user's device periodically requests input of health checkup results and lifestyle habits data. Emotional states are also input, and this data is sent to the server. Emotional states are analyzed using the Emotion API.

[1880] Unified management and analysis of health and emotional information

[1881] The server stores the received health and emotional information in a database, and uses an AI analysis module to analyze this data and generate advice based on the user's current health and emotional state.

[1882] Generate and provide advice and meal menus

[1883] The AI ​​analysis module on the server generates personalized advice and meal menus based on the user's health information and emotional state, and these advice and meal menus are sent to the user's device.

[1884] Ordering food from the menu

[1885] Users can easily order the provided meal menu through the food delivery platform, and the order information is sent to the food delivery platform via the server, and the food is delivered to the user.

[1886] Specific use cases

[1887] For example, if a user inputs "I've been feeling stressed lately" and records "lack of exercise" as health information, the system will suggest appropriate meal options based on this. Suggested options include "mint tea to relieve stress" and "vegetable soup that's easy to digest." Users can order these options with the touch of a button.

[1888] Prompt Sentence Examples

[1889] "The user has entered 'I've been feeling stressed lately.' Health information has been recorded as 'not getting enough exercise.' Based on this, please suggest an appropriate meal menu."

[1890] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1891] Step 1:

[1892] Entering and authenticating user identity

[1893] Users launch the smartphone app and scan their government-issued identification and health insurance card information.

[1894] Input: Identification information (official identification information, medical insurance card information)

[1895] Output: Authentication request data (encoded identity information)

[1896] The server receives this, compares it with the database, authenticates the user, and returns the authentication result to the user's device.

[1897] Behavior: If the user is successfully authenticated, the primary functionality of the application becomes available.

[1898] Step 2:

[1899] Health and emotional data collection

[1900] The user device periodically requests input of health checkup results and lifestyle habits. The user also inputs their emotional state. This data is sent from the user device to the server.

[1901] Input: Health information (health checkup results, lifestyle data), emotional data (stress level, happiness level)

[1902] Output: Health information and emotion data (data sent to the server)

[1903] The server stores the received data in a database.

[1904] How it works: Users enter health and emotional data through the app and verify that the data is sent accurately to the server.

[1905] Step 3:

[1906] Analysis of information stored in the database

[1907] The server inputs the stored health and emotional information into an AI analysis module (using TensorFlow).

[1908] Input: Stored health and emotional information (data from database)

[1909] Output: Analysis results (health status, emotional status)

[1910] The AI ​​analysis module analyzes the user's health and emotional state and generates results.

[1911] How it works: The AI ​​model processes the data to analyze the user's health and emotional state.

[1912] Step 4:

[1913] Advice and meal menu generation

[1914] The server generates appropriate advice and meal menus based on the analysis results.

[1915] Input: Analysis results (health status, emotional status)

[1916] Output: Customized advice and meal menu (data sent to user device)

[1917] The generated advice and meal menu are sent to the user's device.

[1918] How it works: The server uses the analysis results to provide the user with optimal advice and meal plans.

[1919] Step 5:

[1920] Ordering food from the menu

[1921] Users can view the offered meal menu and place their order through the food delivery platform.

[1922] Input: Customized meal menu (displayed on user's device)

[1923] Output: Order data (data sent to food delivery platform)

[1924] The user terminal sends the order to the server, which then forwards the order data to the food delivery platform.

[1925] How it works: When the user presses the bulk order button, the specified meal menu is sent to the delivery service.

[1926] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1927] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1928] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1929] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1930] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1931] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1932] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1933] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1934] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1935] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1936] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1937] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1938] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1939] 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.

[1940] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1941] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1942] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1943] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1944] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1945] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1946] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1947] The following is further disclosed regarding the above embodiment.

[1948] (Claim 1)

[1949] a means for inputting user identification information;

[1950] A means of centrally managing users' health information,

[1951] A means for analyzing a user's health information using an algorithm and generating advice according to the user's health condition;

[1952] means for transmitting said health information and advice to a user's terminal;

[1953] A system including:

[1954] (Claim 2)

[1955] 10. The system of claim 1, further comprising means for authenticating the identity of the user.

[1956] (Claim 3)

[1957] 10. The system of claim 1, further comprising means for using My Number and health insurance card information as user identification information.

[1958] (Claim 4)

[1959] 10. The system of claim 1, further comprising means for collecting lifestyle data and health checkup results as health information of the user.

[1960] (Claim 5)

[1961] 10. The system of claim 1, wherein the analysis means further comprises means for predicting health risks using an algorithm and suggesting preventative measures and treatments.

[1962] (Claim 6)

[1963] 10. The system of claim 1, further comprising means for displaying advice on a user's terminal and providing a chatbot function for accepting and answering questions from the user.

[1964] (Claim 7)

[1965] The system of claim 1 , further comprising means for generating and transmitting an exercise program to a user's terminal.

[1966] "Example 1"

[1967] (Claim 1)

[1968] a means for inputting user identification information;

[1969] A means of centrally managing users' health information,

[1970] A means for analyzing a user's health information using an algorithm and generating advice according to the user's health condition;

[1971] means for transmitting said health information and advice to a user's terminal;

[1972] A means for generating answers to questions entered by users using a generative AI model;

[1973] means for generating an exercise program based on the user's health status;

[1974] A system including:

[1975] (Claim 2)

[1976] 10. The system of claim 1, further comprising means for authenticating the identity of the user.

[1977] (Claim 3)

[1978] 10. The system of claim 1, further comprising means for using the identification number and authentication information as the user's identity.

[1979] "Application Example 1"

[1980] (Claim 1)

[1981] a means for inputting user identification information;

[1982] A means of centrally managing users' health information,

[1983] A means for analyzing a user's health information using an algorithm and generating advice according to the user's health condition;

[1984] means for transmitting said health information and advice to a user's terminal;

[1985] means for generating and transmitting a health-based meal plan to a user's device;

[1986] A system including:

[1987] (Claim 2)

[1988] 10. The system of claim 1, further comprising means for authenticating the identity of the user.

[1989] (Claim 3)

[1990] 10. The system of claim 1, further comprising means for using My Number and health insurance card information as user identification information.

[1991] "Example 2: Combining Emotion Engines"

[1992] (Claim 1)

[1993] means for inputting user identification information;

[1994] A means for centrally managing user health information;

[1995] A means for analyzing a user's health information using an algorithm and generating advice according to the user's health condition;

[1996] A means of analyzing emotional states and adjusting advice and suggestions;

[1997] means for providing an interface for inputting data;

[1998] means for transmitting the health information and advice to a user's terminal;

[1999] A system including:

[2000] (Claim 2)

[2001] 10. The system of claim 1, further comprising means for authenticating the identity of the user.

[2002] (Claim 3)

[2003] 10. The system of claim 1, further comprising means for using the identification number and authentication information as the user's identification information.

[2004] "Application example 2 when combining emotion engines"

[2005] (Claim 1)

[2006] a means for inputting user identification information;

[2007] A means of centrally managing users' health information,

[2008] A means for analyzing a user's health information using an algorithm and generating advice according to the user's health condition;

[2009] means for transmitting said health information and advice to a user's terminal;

[2010] a means for analyzing the emotional state of a user;

[2011] A means for generating and providing personalized advice and meal menus to the user based on the user's health and emotional state;

[2012] A means for ordering the generated meal menu through a food delivery platform;

[2013] A system including:

[2014] (Claim 2)

[2015] 10. The system of claim 1, further comprising means for authenticating the identity of the user.

[2016] (Claim 3)

[2017] 10. The system of claim 1, further comprising means for using official identification and health insurance card information as the user's identification information. [Explanation of symbols]

[2018] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for inputting user identification information; A means of centrally managing users' health information, A means for analyzing a user's health information using an algorithm and generating advice according to the user's health condition; means for transmitting said health information and advice to a user's terminal; A system including:

2. The system of claim 1 further comprising means for authenticating the identity of the user.

3. The system of claim 1 further comprising means for using My Number and health insurance card information as user identification information.

4. The system of claim 1 , further comprising means for collecting lifestyle data and health checkup results as the user's health information.

5. The system of claim 1 , wherein the analysis means further comprises means for predicting health risks using an algorithm and suggesting preventative measures and treatments.

6. The system according to claim 1 , further comprising means for displaying advice on a user's terminal and providing a chatbot function for accepting and answering questions from the user.

7. The system of claim 1 , further comprising means for generating and transmitting an exercise program to a user's terminal.

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