System

Abstract

A system is provided.SOLUTION: This system includes a means for collecting the health information of a user, a means for performing an interview based on the health information collected by using artificial intelligence, a means for providing life guidance to the user based on the interview result, and a means for managing the progress of the life habit of the user through an appliance.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] In recent years, people have become increasingly health-conscious, and there is growing interest in understanding and improving their health status on a daily basis. However, traditional health checkups are conducted periodically, making it difficult to manage health in real time. Furthermore, because they do not provide personalized lifestyle guidance, the advice they provide is often general, making it difficult to receive appropriate guidance tailored to each individual's needs. In addition, progress management is cumbersome, and there is no easy way to track exercise data or lifestyle changes. This creates challenges that make continuous health management difficult. [Means for solving the problem]

[0005] In order to solve this problem, the present invention provides the following means.

[0006] The system includes a means for collecting a user's health information, a means for conducting interviews based on the collected health information using artificial intelligence, a means for providing the user with lifestyle advice based on the interview results, and a means for managing the user's lifestyle progress through an appliance. The appliance includes a wearable device, which can collect the user's exercise data and biometric data in real time. The artificial intelligence uses big data to assess the user's health risks and generate follow-up questions, providing personalized and highly accurate lifestyle advice. This allows the user to understand their health condition in real time, receive effective lifestyle advice, and easily manage their progress.

[0007] "User" refers to an individual who uses the system to manage their own health.

[0008] "Health information" refers to data such as a user's weight, diet, exercise habits, living environment, and past medical history.

[0009] "Collection means" refers to devices or software that collect health information through questionnaires or sensors.

[0010] "Artificial intelligence" refers to computer programs or systems that use machine learning and big data analytics to evaluate and analyze health information.

[0011] "Interview method" refers to the process by which AI provides follow-up questions and advice based on the user's health information.

[0012] "Lifestyle guidance" refers to specific advice and action plans on diet, exercise, sleep, etc. given to improve health.

[0013] "Appliance" refers to a terminal or device, particularly a wearable device, used to record and manage a user's progress in their lifestyle habits.

[0014] "Big data" refers to the technology for analyzing vast amounts of health information and related data, as well as the data collection itself.

[0015] "Health risks" refer to diseases and health problems predicted based on the user's health information. [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] ---

[0038] This invention is a system that helps users lead healthy lives. The system is mainly composed of three main parts: a terminal, a server, and a user. Here, the processing of the system's program is explained in natural language, and its operation is described in detail with concrete examples.

[0039] System Configuration

[0040] The system consists of a device (such as a smartphone or tablet) that users access, a server that analyzes the data and provides guidance, and ultimately the user who receives health management.

[0041] 1. Display and enter information on the medical questionnaire

[0042] The device displays a screen for entering health information, including weight, diet, exercise habits, and past medical history.

[0043] Users enter their health information and transmit it to a server via their device, where the data is securely encrypted.

[0044] Examples:

[0045] The user opens the app on their smartphone, enters their weight (70 kg), meal details (breakfast: bread and coffee, lunch: salad and sandwich, dinner: fish and rice), and jogging three times a week (30 minutes) on the health information input screen, and presses the send button.

[0046] 2. Interview with AI

[0047] The server receives the submitted health information and analyzes it using artificial intelligence (AI), which uses the collected data to assess health risks and generate follow-up questions as needed.

[0048] The terminal displays follow-up questions from the server to the user, who answers the questions and transmits the answers to the server via the terminal.

[0049] Examples:

[0050] The server analyzes the user's health information, calculates the user's BMI, and sends a follow-up question about the need for weight management to the device. The user answers "Yes" to the question displayed on the device, "Have you been feeling tired easily recently?", and the data is sent.

[0051] 3. Creating and providing lifestyle guidance

[0052] The server then analyzes the answers to follow-up questions again and generates personalized lifestyle advice for the user, including dietary suggestions, exercise plans, and sleep advice.

[0053] The device provides the generated lifestyle advice to the user in real time, and the user can easily check the advice content through the app.

[0054] Examples:

[0055] Based on the analysis results, the server generates specific lifestyle advice for the user, such as "take protein for breakfast" and "recommend strength training once a week," and sends it to the device. The user can then confirm the advice displayed on the device.

[0056] 4. Progress Management

[0057] The device works in conjunction with wearable devices to collect the user's exercise and biometric data, including the number of steps taken, distance traveled, and heart rate.

[0058] The device sends the collected data to a server that analyzes it to monitor lifestyle progress and provide further guidance as needed.

[0059] Examples:

[0060] The user puts on the wearable device and goes jogging. The data (5km run distance, heart rate, etc.) is recorded in the app and automatically sent from the device to the server. Based on the data, the server suggests "increasing the frequency of exercise to four times a week," and a notification is displayed on the device.

[0061] The above is a specific embodiment for carrying out the invention. This system makes it easier for users to receive personalized lifestyle advice and to manage their health on an ongoing basis.

[0062] The processing flow will be explained below.

[0063] ---

[0064] Step 1:

[0065] The device displays a health information entry screen, which includes input items such as the user's weight, diet, exercise habits, living environment, and past medical history.

[0066] Step 2:

[0067] The user enters health information and presses buttons such as "Confirm" and "Send" to send the data. This input data is encrypted and securely sent from the device to the server.

[0068] Step 3:

[0069] The server stores the received health information in a database, then passes the data to an artificial intelligence (AI) that begins assessing basic health risks.

[0070] Step 4:

[0071] The server uses AI to assess health risks and generate follow-up questions, if needed, that are customized based on the user's health information.

[0072] Step 5:

[0073] The device then prompts the user with follow-up questions from the server, including additional health information and lifestyle details.

[0074] Step 6:

[0075] The user answers the follow-up questions and sends the answers to the server via their device, where the answer data is again encrypted and securely sent to the server.

[0076] Step 7:

[0077] The server analyzes the answers to follow-up questions and integrates all the data to generate lifestyle advice. The AI ​​creates a specific action plan (e.g., dietary improvements, exercise plan, sleep advice).

[0078] Step 8:

[0079] The server transmits the generated lifestyle guidance to the terminal. The lifestyle guidance information is presented in real time and provided in a format that the user can access immediately.

[0080] Step 9:

[0081] The device displays the generated lifestyle advice to the user, who then checks the advice and follows the specific action plan within the application.

[0082] Step 10:

[0083] The terminal connects to a wearable device and collects the user's exercise and biological data, including the number of steps taken, distance traveled, heart rate, and sleep data.

[0084] Step 11:

[0085] The device sends the collected data to the server, which periodically transfers the data to the server and stores it as user progress information.

[0086] Step 12:

[0087] The server analyzes the progress data and generates personalized feedback for each user's lifestyle habits. If real-time guidance or changes to the action plan are needed, the server generates new suggestions.

[0088] Step 13:

[0089] The device will notify the user with new feedback and updated lifestyle advice, and the user can check their progress through the app and adjust their behavior according to the new advice.

[0090] ---

[0091] By taking these steps, the system can provide users with personalized, ongoing, and accurate health guidance and monitor progress in real time.

[0092] Example 1

[0093] 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."

[0094] In recent years, the increase in lifestyle-related diseases and stress-related diseases has become a social problem, creating a demand for personalized health management. However, current health management systems only provide information to everyone at once and do not provide specific guidance tailored to each user's individual lifestyle habits or health risks. As a result, users find it difficult to maintain sustainable health management and are unable to achieve effective health improvement. The present invention aims to solve these problems by providing a system that provides customized health management tailored to the user's lifestyle habits.

[0095] 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.

[0096] In this invention, the server includes means for collecting health information of the user, means for conducting an interview based on the collected health information using a generative AI model and generating follow-up questions, means for collecting and analyzing responses from the user based on the generated follow-up questions, means for providing the user with customized lifestyle advice based on the interview results and analysis results, and means for managing the progress of the user's lifestyle habits through the appliance and providing more specific advice as needed, thereby enabling specific and individualized lifestyle advice according to the user's health condition.

[0097] "User" refers to an individual who uses the health care system.

[0098] "Health information" refers to data about an individual's health condition and lifestyle, such as weight, diet, exercise habits, and past medical history.

[0099] A "generative AI model" refers to a system that uses artificial intelligence to analyze data and generate questions.

[0100] "Interview" refers to the process of using a generative AI model to collect information interactively based on the user's health information.

[0101] "Follow-up questions" refer to questions that are asked to obtain additional information that needs to be collected based on the initial health information or interview results.

[0102] "Analysis" refers to the process of analyzing collected data and responses to assess health risks and generate appropriate advice.

[0103] "Lifestyle guidance" refers to specific guidance provided to users based on the analysis results to improve their lifestyle habits, such as diet, exercise, and sleep.

[0104] "Appliance" refers to a hardware device used in a health care system, including, among other things, wearable devices.

[0105] A "wearable device" refers to a digital device worn by the user that is capable of collecting exercise data and biometric data.

[0106] "Large-scale data" refers to a large amount of data, including a wide range of datasets such as health information and lifestyle data.

[0107] That's all.

[0108] This invention is a system that supports users in living a healthy lifestyle. The system is broadly divided into three main parts: a terminal, a server, and a user. Specific embodiments are described below.

[0109] System Configuration

[0110] The system consists of a terminal accessed by the user, a server that analyzes the data and provides guidance, and ultimately the user who receives health management.

[0111] Terminal

[0112] The terminal is a device such as a smartphone or tablet. The terminal displays a health information input screen, collects health information from the user, and sends it to the server. The terminal also displays follow-up questions and lifestyle advice from the server and provides them in a form that the user can check.

[0113] server

[0114] The server is the central component for analyzing the received health information. It uses generative AI models (e.g., OpenAI's GPT-4) to analyze the collected data and generate follow-up questions and customized lifestyle advice. The server also analyzes exercise and biometric data provided in real time to monitor progress.

[0115] User

[0116] Users are individuals who use the system to input their own health information. They use a terminal to enter their health information, answer follow-up questions, and confirm lifestyle advice. They also wear a wearable device to provide daily exercise data and biometric data to the system.

[0117] Data entry and analysis

[0118] Entering health information

[0119] The device displays a health information input screen to the user. The device used here is a smartphone or tablet. Detailed health information such as weight, diet, exercise habits, and past medical history is entered.

[0120] The user enters their health information and presses the send button. The input data is temporarily stored on the device, then securely encrypted and sent to the server.

[0121] Analyze data and generate follow-up questions

[0122] The server decrypts the received data and uses large-scale data to analyze it using a generative AI model, which is then used to assess health risks and generate necessary follow-up questions.

[0123] The terminal displays follow-up questions from the server to the user, who answers these questions and sends them back to the server via the terminal.

[0124] Creating and providing lifestyle guidance

[0125] The server then analyzes the follow-up questions and generates personalized lifestyle advice for the user, including dietary suggestions, exercise plans, and sleep tips.

[0126] The device provides the generated lifestyle advice to the user in real time, and the user can check the advice content through the app.

[0127] Progress management

[0128] Wearable device integration

[0129] The device works in conjunction with wearable devices (such as Fitbit and other smartwatches) to collect the user's exercise data and biometric data (such as steps taken, distance traveled, and heart rate).

[0130] The device encrypts the collected data and sends it to the server.

[0131] The server analyzes this data, monitors the user's lifestyle progress, and provides further specific guidance as needed.

[0132] Examples of prompt statements

[0133] The following are examples of prompts that users can use when receiving support from AI, which will allow the generative AI model to generate appropriate questions and advice.

[0134] "Please tell me your recent jogging record."

[0135] "What foods do you eat for breakfast?"

[0136] "Have you been feeling tired easily lately?"

[0137] The above is a concrete example of how to implement this healthy lifestyle support system. This system makes it easier for users to receive personalized lifestyle guidance and to manage their health on an ongoing basis.

[0138] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0139] Processing flow

[0140] Step 1: Enter your health information

[0141] The device displays a health information entry screen to the user, where detailed health information such as weight, diet, exercise habits, and past medical history can be entered.

[0142] Specific operation: The device launches the application and displays the health information input screen.

[0143] Input: Health information entered by the user (e.g., weight 70 kg, bread and coffee for breakfast, jogging three times a week).

[0144] Output: Encrypted health information data.

[0145] Step 2: Submit your health information

[0146] The user enters their health information and presses the send button. The data is temporarily stored on the device and then securely encrypted and sent to the server.

[0147] Specific operation: The user completes the input and presses the "Send" button. The device AES encrypts the data and generates a request to send to the server.

[0148] Input: The submit button pressed by the user and the health information entered.

[0149] Output: Encrypted health information data sent to the server.

[0150] Step 3: Receiving and analyzing data

[0151] The server decrypts the received data and stores it in a database, after which it uses a generative AI model to analyze the data and generate follow-up questions.

[0152] What it does: The server decrypts the data and stores it in a database. A Python script is used to invoke a generative AI model (e.g., OpenAI's GPT-4) to analyze the health information.

[0153] Input: Encrypted health information data.

[0154] Output: Decoded health information and generated follow-up questions (e.g., "Have you been feeling tired lately?").

[0155] Step 4: View follow-up questions

[0156] The terminal displays the follow-up questions received from the server to the user, who answers these questions and sends them back to the server via the terminal.

[0157] Specific operation: The device receives and displays a question from the server. The user answers the question, and the device sends the answer back to the server.

[0158] Input: The generated follow-up question (e.g., "Have you been feeling tired lately?") and the user's answer (e.g., "Yes").

[0159] Output: User response data.

[0160] Step 5: Create a life lesson

[0161] The server again analyzes the answers to the follow-up questions and generates lifestyle advice customized for the user.

[0162] Specific operation: The server inputs the response data into a generative AI model and generates lifestyle advice based on the analysis results (e.g., "Add protein to your breakfast").

[0163] Input: User response data.

[0164] Output: Customized lifestyle guidance data.

[0165] Step 6: Providing lifestyle guidance

[0166] The device provides the generated lifestyle advice to the user in real time, and the user can check the advice content through the app.

[0167] Specific operation: The device receives lifestyle guidance data from the server and displays it to the user on the application.

[0168] Input: Customized life coaching data.

[0169] Output: A lifestyle advice screen viewable by the user.

[0170] Step 7: Collect and send exercise data

[0171] The terminal works in conjunction with a wearable device to collect the user's exercise and biometric data, which is then encrypted and sent to a server.

[0172] Specific operation: The terminal collects data from the wearable device via Bluetooth, encrypts it, and sends it to the server.

[0173] Input: Exercise data from wearable devices (e.g., steps, distance traveled, heart rate).

[0174] Output: Encrypted exercise data.

[0175] Step 8: Track your progress

[0176] The server analyzes the received exercise data and manages the user's progress in lifestyle habits, providing more specific guidance as needed.

[0177] Specific operation: The server analyzes the exercise data and generates new guidance tailored to the user's lifestyle (e.g., "Increase the frequency of exercise to four times a week").

[0178] Input: Exercise data from a wearable device.

[0179] Output: New lifestyle guidance data according to progress.

[0180] This is the flow of the system's program processing, which allows users to receive detailed and individualized lifestyle advice and enable continuous health management.

[0181] (Application example 1)

[0182] 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."

[0183] Conventional health management systems primarily function to collect users' health information and provide lifestyle advice, but lack the mechanisms to actually encourage users to act on that advice. There was also a need to reduce the effort required for users to purchase suggested products and services. In particular, the financial procedures required to promote the use of health foods and exercise facilities were complicated, preventing users from following the recommendations.

[0184] 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.

[0185] In this invention, the server includes means for collecting health information of the user, means for conducting interviews based on the health information collected using artificial intelligence, means for providing the user with lifestyle advice based on the interview results, means for managing the progress of the user's lifestyle habits through an appliance, means for making electronic payments for products and services based on the health information, and means for making it easy to purchase the suggested products and services. This enables the user to immediately take action based on the advice and to more effectively experience the effects of health management.

[0186] "User" refers to an individual who uses this system to manage health information and receive lifestyle guidance.

[0187] "Health information" refers to data provided by users, such as weight, diet, exercise habits, and past medical history.

[0188] "Artificial intelligence" refers to technology that performs advanced calculations such as data analysis and pattern recognition to provide appropriate lifestyle guidance and generate questions.

[0189] "Interview" refers to the process of analyzing health information collected using artificial intelligence and asking follow-up questions and giving advice to users.

[0190] "Lifestyle guidance" refers to specific methods and suggestions for making users' lives healthier, such as suggestions for improving their diet, exercise plans, and sleep advice, based on the user's health information and the results of the interview.

[0191] "Progress" refers to the health management activities carried out by the user and their results, including exercise data and biometric data.

[0192] "Appliance" refers to a device or equipment used to collect and manage user health data, including, for example, a smartphone or wearable device.

[0193] A "wearable device" refers to an electronic device worn by a user, which collects exercise data and biometric data in real time.

[0194] "Big data" refers to large data sets and the technology for analyzing them to extract useful information and patterns.

[0195] "Electronic payment" refers to the process by which a consumer pays for goods or services using digital means.

[0196] "Products and services" refers to supplements, healthy foods, and use of fitness facilities suggested based on health management guidance.

[0197] The details of the system including the actions of the server, terminals and users are described below for the embodiment of the present invention.

[0198] The system mainly consists of a device (such as a smartphone) that users access, a server that analyzes data and provides guidance, and ultimately the user who receives health management.

[0199] 1. Collection of health information

[0200] The server prompts users to enter their health information using a device such as a smartphone. This health information includes weight, diet, exercise habits, and past medical history. The user enters this information into their device and sends it to the server. The transmitted data is securely encrypted.

[0201] As a concrete example, a user opens a smartphone app and inputs their weight (70 kg), meal details (breakfast: bread and coffee, lunch: salad and sandwich, dinner: fish and rice), and jogging three times a week (30 minutes). Then, they press the send button to send the data to the server.

[0202] 2. AI-based analysis and interviews

[0203] The server receives the health information and analyzes it using a generative AI model. The AI ​​evaluates health risks based on the collected data and generates follow-up questions as needed. The device displays the follow-up questions sent from the server to the user and sends the answers back to the server.

[0204] For example, the server analyzes health information, calculates the user's BMI, and sends a follow-up question about the need for weight management to the device. The user answers "yes" to the question displayed on the device, "Have you been feeling tired easily recently?", and the message is sent.

[0205] 3. Lifestyle guidance and electronic payment

[0206] The server analyzes the answers to the follow-up questions and generates personalized lifestyle advice for the user, including dietary suggestions, exercise plans, and sleep advice, as well as a means to electronically pay for suggested products and services (such as supplements or fitness facilities) based on the health information.

[0207] The device provides users with the generated lifestyle advice and suggested products and services in real time, and users can check the advice through the app and easily purchase the suggested products and services.

[0208] For example, the server sends advice such as "take protein for breakfast" and "recommend strength training once a week" to the terminal. It also suggests purchasing protein (3,000 yen), and the user completes the purchase procedure by making an electronic payment on the spot.

[0209] 4. Progress management and follow-up

[0210] The server analyzes data (such as number of steps, distance traveled, and heart rate) obtained from the wearable device to monitor the user's health management progress. Based on this data, the server provides further appropriate health guidance and follow-up.

[0211] As a concrete example, a user puts on a wearable device and goes jogging. The app records the distance traveled (5km), heart rate, and other data, and automatically sends the data from the device to a server. Based on the analysis results, the server suggests increasing the frequency of exercise to four times a week, and a notification is displayed on the device.

[0212] Example prompt sentence:

[0213] Analyze the user's health status based on the following input data and suggest necessary advice and actions:

[0214] Weight: 70kg

[0215] Meals:

[0216] Breakfast: Bread and coffee

[0217] Lunch: Salad and sandwich

[0218] Dinner: Fish and rice

[0219] Exercise habits: Jogging three times a week (30 minutes)

[0220] As described above, this system makes it easier for users to receive personalized lifestyle advice and actually take action, making it possible for them to experience the benefits of health management over time.

[0221] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0222] Step 1:

[0223] The server prompts users to enter their health information using a smartphone or tablet device. Users enter data such as weight, diet, exercise habits, and past medical history, and this data is sent from the device to the server. The data is encrypted before being transmitted.

[0224] Input: User's health information (e.g., weight 70 kg, dietary details, exercise habits)

[0225] Data processing / calculation: Check the format of input data and encrypt it

[0226] Output: Encrypted health information data

[0227] Step 2:

[0228] The server receives the submitted health information and analyzes it using a generative AI model, assessing health risks based on the input data and generating follow-up questions as needed.

[0229] Input: Encrypted health information data

[0230] Data processing / computation: Decode data, assess health risks, generate follow-up questions

[0231] Output: Health risk assessment results, follow-up questions

[0232] Step 3:

[0233] The terminal displays the follow-up questions sent from the server to the user, who answers the questions and sends the answers to the server via the terminal.

[0234] Input: Health risk assessment results, follow-up questions

[0235] Data processing / calculation: Displaying follow-up questions to users, collecting and encrypting answer data

[0236] Output: Encrypted follow-up question answers

[0237] Step 4:

[0238] The server receives the answers to the follow-up questions, analyzes them again using the generative AI model, and generates optimal lifestyle advice for the user, including dietary suggestions, exercise plans, and sleep advice, as well as electronic payment methods for the suggested products and services.

[0239] Input: Encrypted follow-up question response data

[0240] Data processing / calculation: Decryption of response data, detailed analysis, generation of lifestyle guidance and electronic payment proposals

[0241] Output: Lifestyle guidance, suggested products and services (with electronic payment methods)

[0242] Step 5:

[0243] The device provides users with the generated lifestyle advice and suggested products and services in real time, and users can check the advice through the app and easily purchase the suggested products and services.

[0244] Input: Lifestyle guidance, suggested products and services

[0245] Data processing / calculation: User display, electronic payment processing

[0246] Output: Lifestyle advice provided to the user, products and services purchased

[0247] Step 6:

[0248] The server analyzes the exercise and biological data obtained from the wearable device to manage the user's health management progress, and provides further appropriate health guidance and follow-up as needed.

[0249] Input: Exercise data and biometric data from wearable devices

[0250] Data processing / calculation: Data analysis, progress management, generation of additional health guidance and follow-up

[0251] Output: Progress management results, additional health guidance and follow-up

[0252] 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.

[0253] ---

[0254] This invention is a system that supports users in living a healthy lifestyle. The system collects the user's health information, analyzes their health status using artificial intelligence and an emotion engine, and provides personalized lifestyle advice based on the results. It also manages the user's progress in real time and provides appropriate advice that takes their emotional state into account. The system's main components are a terminal, a server, and a user.

[0255] System Configuration

[0256] 1. Display and enter information on the medical questionnaire

[0257] The device displays a health information entry screen, where the user enters information such as weight, diet, exercise habits, and past medical history.

[0258] The user enters the necessary health information and presses the "Send" button to send the data. The data is encrypted and securely sent from the device to the server.

[0259] Examples:

[0260] The user opens the smartphone app, enters their weight (70 kg), breakfast (bread and coffee), and exercise habits (30 minutes of jogging three times a week) on the health information input screen, and presses the "Submit" button.

[0261] 2. Evaluation by AI and emotion engine

[0262] The server stores the received health information in a database and passes the data to the artificial intelligence (AI) and emotion engine.

[0263] The server's AI assesses health risks based on collected data, while the emotion engine recognizes the user's emotional state from facial expressions, tone of voice, and text input.

[0264] Examples:

[0265] The server analyzes the user's medical data to assess their BMI and risk of lack of exercise, while the emotion engine uses the smartphone's camera and microphone to recognize whether the user is feeling stressed.

[0266] 3. Follow-up questions

[0267] The server generates follow-up questions based on the health risk assessment results and emotional state, and the questions are customized based on the user's current health and emotional state.

[0268] The terminal displays the generated follow-up questions to the user, who answers the questions and transmits the answers from the terminal to the server.

[0269] Examples:

[0270] The server generates follow-up questions such as "Have you been feeling tired lately?" and "How much stress do you usually feel?" and displays them on the device. The user enters and submits the answers.

[0271] 4. Creating and providing lifestyle guidance

[0272] The server then analyzes the follow-up questions and the user's emotional state again to generate optimal lifestyle advice, including dietary suggestions, exercise plans, and sleep advice.

[0273] The device displays the generated lifestyle advice to the user, who can check the advice in real time and apply it to their own behavior.

[0274] Examples:

[0275] The server generates specific lifestyle advice such as "Eat protein for breakfast" and "Try strength training once a week." It also includes advice that takes into account emotional states, such as "Try meditating to relieve stress." These instructions are displayed on the device and notified to the user.

[0276] 5. Progress Management

[0277] The terminal connects to the wearable device and collects the user's exercise and biological data, which is then sent to a server and managed as the user's progress.

[0278] The server analyzes the progress data and provides further guidance or corrections as needed. The emotion engine also monitors changes in the user's emotional state and generates adaptive advice.

[0279] Examples:

[0280] The user puts on the wearable device and goes jogging, and the data is recorded in the app. This data is sent to the server and saved as progress. The server then suggests, "Increase your exercise frequency to four times a week." If the emotion engine detects an increase in stress levels, the app will provide new guidance, such as, "Try yoga to relax."

[0281] The above is a specific embodiment for carrying out the invention. This system allows users to receive personalized lifestyle advice in real time and to perform comprehensive health management, including their emotional state.

[0282] The processing flow will be explained below.

[0283] ---

[0284] Step 1:

[0285] The device displays a screen for entering health information, including weight, diet, exercise habits, living environment, and past medical history.

[0286] Step 2:

[0287] The user enters their health information and presses the "Confirm" and "Send" buttons to send the data. The data is encrypted and securely sent from the device to the server.

[0288] Step 3:

[0289] The server stores the received health information in a database and passes the data to the artificial intelligence (AI) and emotion engine, which then begin processing simultaneously.

[0290] Step 4:

[0291] The server's AI will assess health risks based on health information, analyzing indicators such as BMI, exercise habits, and dietary balance.

[0292] Step 5:

[0293] Using the device's built-in camera and microphone, the emotion engine analyzes the user's facial expressions, tone of voice, and text input to recognize the user's emotional state.

[0294] Step 6:

[0295] The server integrates the AI-based health risk assessment results with the emotional state of the emotion engine to generate follow-up questions, which are customized based on the user's current health and emotional state.

[0296] Step 7:

[0297] The device displays follow-up questions from the server to the user, who answers them and sends the answers from the device to the server, where they are again encrypted and securely transmitted to the server.

[0298] Step 8:

[0299] The server receives and analyzes the answers to follow-up questions. Here, the AI ​​integrates the answer data with health information to generate optimal lifestyle advice. The emotion engine also provides advice based on the user's emotions based on this analysis process.

[0300] Step 9:

[0301] The server then sends the generated lifestyle guidance and emotion-based advice to the device, including advice on improving diet, exercise suggestions, and stress management.

[0302] Step 10:

[0303] The device displays the generated lifestyle guidance and advice to the user, who then checks the advice in real time and implements the action plan.

[0304] Step 11:

[0305] The device connects to wearable devices to collect the user's exercise and biometric data, including steps taken, distance traveled, heart rate, and sleep data.

[0306] Step 12:

[0307] The device sends the collected data to the server, which periodically transfers the data to the server and stores it as the user's progress.

[0308] Step 13:

[0309] The server analyzes the progress data and provides further guidance or modification of the action plan based on the progress. The emotion engine monitors changes in the user's emotional state and updates appropriate feedback and advice.

[0310] Step 14:

[0311] The device notifies the user of new feedback and updated lifestyle advice, and the user can check their progress through the app and adjust their behavior according to the new advice.

[0312] ---

[0313] Through these steps, the system can provide users with personalized, continuous and accurate health guidance, and manage their progress and emotions in real time.

[0314] Example 2

[0315] 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."

[0316] In modern society, there are few systems that simultaneously monitor users' health and emotional states, and personalized lifestyle advice is lacking. This makes it easy for many users to overlook health risks and makes it difficult to effectively improve their lifestyles. Furthermore, there is no system that provides adaptive advice that takes users' emotional states into account, making it difficult to understand and improve a user's overall health.

[0317] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting health information of the user, means for performing an assessment based on the collected health information using artificial intelligence, means for generating follow-up questions based on the assessment results and the emotional state, means for collecting and analyzing the user's answers to the generated follow-up questions, means for providing lifestyle advice based on the user's answers and the emotional state, means for managing the user's lifestyle progress through an appliance, and means for monitoring the user's emotional state using an emotion engine and providing adaptive advice. This enables the user to receive personalized lifestyle advice in real time and achieve comprehensive health management including the emotional state.

[0318] "Health information" refers to various data used to understand the user's health condition, such as the user's weight, diet, exercise habits, and past medical history.

[0319] "Artificial intelligence" is a technology that enables computer systems to mimic human intelligence to process, evaluate, and analyze information.

[0320] "Evaluation" is the process of analyzing and determining the user's health risks and condition based on collected health information.

[0321] "Emotional state" indicates the user's psychological state and is determined from facial expressions, tone of voice, text input, and the like.

[0322] "Follow-up questions" are questions that require additional answers from the user based on the health risk assessment results or emotional state.

[0323] "Lifestyle guidance" refers to instructions such as dietary suggestions, exercise plans, and sleep advice provided based on the user's health and emotional state.

[0324] "Appliance" refers to any equipment or device used to collect and transmit health data, including wearable devices.

[0325] "Emotion engine" is a general term for technologies and systems that analyze and recognize users' psychological emotions.

[0326] "Adaptive advice" refers to advice that is individually tailored to the user based on their current health data and emotional state.

[0327] This invention is a system that helps users lead healthy and emotionally balanced lives. The system collects the user's health information and emotional state, analyzes it using artificial intelligence and an emotion engine, and provides personalized lifestyle advice based on the results. Furthermore, the system manages the user's progress in real time and provides appropriate advice that takes into account the user's emotional state.

[0328] System Configuration

[0329] The system consists of devices, servers, and users. Specific hardware and software components include devices such as smartphones and tablets, cloud servers, wearable devices equipped with various sensors, and health applications that users use on a daily basis.

[0330] Collection of health information

[0331] The device displays a health information input screen, and the user enters information such as weight, diet, exercise habits, and past medical history. This information is encrypted and sent from the device to the server. For example, a user opens a smartphone app, enters their weight as "70 kg," their breakfast as "bread and coffee," and their exercise habits as "30 minutes of jogging three times a week," and presses the "Send" button.

[0332] Data encryption and transmission

[0333] The device encrypts the entered health information using the AES encryption method and sends it to the server via HTTPS. The server then decrypts the received encrypted data and stores it in a database.

[0334] Evaluation by AI and emotion engine

[0335] The server passes the stored health information to an artificial intelligence (AI) and emotion engine. The AI ​​assesses health risks based on the collected data (using, for example, TENSORFLOW (registered trademark) and PyTorch), and the emotion engine recognizes the user's emotional state from their facial expressions, tone of voice, and text input. For example, it evaluates the user's BMI and risk of lack of exercise, and the emotion engine analyzes their stress level using the smartphone's camera and microphone.

[0336] Follow-up question generation

[0337] The server generates follow-up questions based on the health risk assessment results and emotional state. These questions are customized based on the user's current health and emotional state and displayed on the device. For example, the server generates questions such as "Have you been feeling tired easily recently?" or "How much stress do you usually feel?", and the user enters and submits the answers.

[0338] Creating and providing lifestyle guidance

[0339] The server then analyzes the user's responses to the follow-up questions and their emotional state again to generate optimal lifestyle advice. This advice includes suggestions for improving their diet, exercise plans, and sleep advice. The device displays the generated lifestyle advice to the user, who can review it in real time and apply it to their own behavior. For example, specific advice such as "Eat protein for breakfast," "Try strength training once a week," and "Try meditating to relieve stress" is provided.

[0340] Progress monitoring and adaptive advice

[0341] The terminal works in conjunction with a wearable device to collect the user's exercise data and biometric data in real time and send it to a server. This data is stored on the server and managed as progress. The server analyzes the progress data and provides further guidance or corrections as needed. The emotion engine monitors changes in the user's emotional state and generates adaptive advice. For example, based on the user's exercise frequency data, it may suggest "increase your exercise frequency to four times a week," or if it detects an increase in stress levels, it may provide new advice such as "try yoga to relax."

[0342] The above is a specific embodiment for carrying out the invention. This system allows users to receive personalized lifestyle advice in real time and enables comprehensive health management, including emotional state.

[0343] Prompt Sentence Examples

[0344] "Please explain in natural language the process flow of a system that collects a user's health information (e.g., weight, diet, exercise habits), assesses their stress level using an emotion engine, and provides personalized health guidance."

[0345] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0346] Program processing flow and specific explanation

[0347] Step 1:

[0348] The device displays a health information input screen. The user inputs information such as weight, diet, exercise habits, and past medical history. The input includes data entered by the user (weight, diet, exercise, and medical history).

[0349] Specific behavior:

[0350] The device displays a "health data entry" screen on the smartphone or tablet screen, where users can enter information such as their weight (70 kg), breakfast (bread and coffee), and exercise habits (30 minutes of jogging three times a week), then press the "send" button.

[0351] Step 2:

[0352] The terminal encrypts the input data using the AES encryption method. The input includes the data entered by the user in step 1.

[0353] Specific behavior:

[0354] The terminal handles the user's input data and encrypts it using an encryption algorithm called AES (Advanced Encryption Standard), generating encrypted data.

[0355] Step 3:

[0356] The device sends encrypted data to the server using HTTPS communication. The input is encrypted health data.

[0357] Specific behavior:

[0358] The device sends the encrypted health data to the server via HTTPS (Hypertext Transfer Protocol Secure) communication. This communication protocol ensures that the data is transmitted securely. After transmission, the data arrives at the server.

[0359] Step 4:

[0360] The server stores the received health data in a database. The input is the encrypted health data arriving at the server.

[0361] Specific behavior:

[0362] The server decrypts the received data and stores it in a database. The database uses a relational database system (RDBMS) such as SQL (Structured Query Language). The stored data is generated.

[0363] Step 5:

[0364] The server passes the data to an artificial intelligence (AI) to assess health risks. The inputs are health data stored in a database and an AI model.

[0365] Specific behavior:

[0366] The server inputs the stored data into an AI model (e.g., a deep learning model using TensorFlow or PyTorch) to evaluate the user's health risks (e.g., BMI value, risk of lack of exercise), and generates an evaluation result.

[0367] Step 6:

[0368] The server passes data to the emotion engine to recognize the user's emotional state. The inputs are the evaluation results, the user's camera footage, and audio data.

[0369] Specific behavior:

[0370] The server uses the user's camera footage and audio data to call an emotion engine (e.g., IBM Watson® or Microsoft® Azure® Emotion API) to recognize the user's emotional state, generating emotional state data.

[0371] Step 7:

[0372] The server generates follow-up questions based on the health risk assessment results and emotional state. The inputs are the assessment results and emotional state data.

[0373] Specific behavior:

[0374] The server handles the evaluation results of the AI ​​model and emotion engine and generates follow-up questions based on this information. The questions are dynamically generated based on templates, and the generated follow-up questions are sent to the device.

[0375] Step 8:

[0376] The terminal displays the generated follow-up question to the user, and has as input the generated follow-up question.

[0377] Specific behavior:

[0378] The device displays follow-up questions such as "Have you been feeling tired lately?" and "How stressed do you usually feel?" and is ready for the user to enter their answers.

[0379] Step 9:

[0380] The user answers the follow-up questions and sends the answers from the device to the server. The user's answer data is entered into the device.

[0381] Specific behavior:

[0382] The user enters an answer into the question form and presses the "Submit" button to send the answer to the server. The sent answer reaches the server.

[0383] Step 10:

[0384] The server analyzes the follow-up question answers and the emotional state again and generates optimal lifestyle advice. The inputs are the follow-up question answer data and the emotional state.

[0385] Specific behavior:

[0386] The server then uses the AI ​​model again to generate lifestyle advice based on the user's answers and emotional state, and provides the advice to the user.

[0387] Step 11:

[0388] The terminal displays the generated lifestyle guidance to the user. The generated lifestyle guidance data is input.

[0389] Specific behavior:

[0390] The device displays specific lifestyle advice such as "Eat protein for breakfast" and "Try strength training once a week." The user is then ready to confirm the advice.

[0391] Step 12:

[0392] The terminal connects to the wearable device, collects the user's exercise data and biological data in real time, and transmits it to the server. The input includes data obtained from the wearable device.

[0393] Specific behavior:

[0394] The device receives data from a wearable device (e.g., a smartwatch or fitness tracker), records the data in the app, and then sends the recorded data to a server.

[0395] Step 13:

[0396] The server analyzes the collected data and provides further guidance or corrections as needed. As input, the collected progress data is stored on the server.

[0397] Specific behavior:

[0398] The server then re-analyzes the stored data using an AI model to generate new lifestyle advice based on progress, and adjusts the advice as necessary.

[0399] Step 14:

[0400] The server uses an emotion engine to monitor changes in the user's emotional state and generate adaptive advice. The input is the user's emotional state data.

[0401] Specific behavior:

[0402] The server periodically calls the emotion engine to check changes in the user's emotional state, and based on the results, new advice, such as "Try yoga to relax," is generated and provided to the user.

[0403] (Application example 2)

[0404] 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."

[0405] Conventional health management systems collect and analyze users' health information and provide lifestyle advice, but because they are unable to take into account the user's emotional state, the lifestyle advice provided is uniform and it is difficult to provide optimal advice to each individual user.In addition, since they do not have a means to collect health information in real time using IoT devices in physical stores, there is an issue that they cannot provide immediate health management in physical stores.

[0406] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting health information of the user, means for conducting an interview based on the health information collected using artificial intelligence, means for providing lifestyle advice to the user based on the interview results, means for managing the progress of the user's lifestyle habits through an appliance, means for analyzing the user's emotional state using an emotion engine and reflecting the analysis in the lifestyle advice, and means for collecting health information using IoT devices in a physical store. This enables lifestyle advice based on the user's emotional state, and enables real-time collection of health information and immediate advice to be provided in a physical store.

[0407] "User" refers to a person who provides health information to the system and receives lifestyle guidance.

[0408] "Health information" refers to data about the user's health status, such as weight, blood pressure, diet, exercise habits, and past medical history.

[0409] "Artificial intelligence" refers to information processing technology that analyzes collected health information and assesses users' health risks.

[0410] "Interview" refers to a means of providing direct guidance and follow-up to users based on the results of health status analysis by artificial intelligence.

[0411] "Lifestyle guidance" refers to specific advice on diet, exercise, sleep, etc. provided to users based on health information and interviews.

[0412] "Progress management" refers to methods for tracking changes in daily lifestyle habits and health status and providing necessary support.

[0413] "Appliance" refers to any device or system used to collect health information and manage progress.

[0414] A "wearable device" refers to a device worn by the user that collects exercise data and biometric data in real time.

[0415] An "emotion engine" refers to technology that analyzes a user's emotional state from facial expressions, tone of voice, text input, etc.

[0416] "IoT devices" refer to physical store equipment such as scales, blood pressure monitors, and fitness machines that can send and receive data over the internet.

[0417] "Big data" refers to data processing techniques for analyzing huge data sets, and the results of this analysis are used to assess health risks.

[0418] This invention is a system that helps users lead healthy lives. The system collects the user's health information, analyzes the user's health status using artificial intelligence and an emotion engine, and provides personalized lifestyle advice based on the results. Furthermore, the system manages the user's progress in real time and provides appropriate advice taking into account the user's emotional state.

[0419] System Configuration

[0420] 1. Collection of health information

[0421] The device displays a health information entry screen, where the user enters information such as weight, diet, exercise habits, and past medical history.

[0422] The user enters the necessary health information and presses the "Send" button to send the data. The data is encrypted and securely sent from the device to the server.

[0423] Examples:

[0424] The user opens a fitness gym download app, enters their weight (70 kg), breakfast contents (bread and coffee), and exercise habits (30 minutes of jogging three times a week) on the health information input screen, and presses the "Submit" button.

[0425] 2. Evaluation by AI and emotion engine

[0426] The server stores the received health information in a database and passes the data to the artificial intelligence (AI) and emotion engine.

[0427] The server's AI assesses health risks based on collected data, while the emotion engine recognizes the user's emotional state from facial expressions, tone of voice, and text input.

[0428] Examples:

[0429] The server analyzes the user's medical data to assess their BMI and risk of lack of exercise, while the emotion engine uses the smartphone's camera and microphone to recognize whether the user is feeling stressed.

[0430] 3. Follow-up questions

[0431] The server generates follow-up questions based on the health risk assessment results and emotional state, and the questions are customized based on the user's current health and emotional state.

[0432] The terminal displays the generated follow-up questions to the user, who answers the questions and transmits the answers from the terminal to the server.

[0433] Examples:

[0434] The server generates follow-up questions such as "Have you been feeling tired lately?" and "How much stress do you usually feel?" and displays them on the device. The user enters and submits the answers.

[0435] 4. Creating and providing lifestyle guidance

[0436] The server then analyzes the follow-up questions and the user's emotional state again to generate optimal lifestyle advice, including dietary suggestions, exercise plans, and sleep advice.

[0437] The device displays the generated lifestyle advice to the user, who can check the advice in real time and apply it to their own behavior.

[0438] Examples:

[0439] The server generates specific lifestyle advice such as "Eat protein for breakfast" and "Try strength training once a week." It also includes advice that takes into account emotional states, such as "Try meditating to relieve stress." These instructions are displayed on the device and notified to the user.

[0440] 5. Progress Management

[0441] The terminal connects to the wearable device and collects the user's exercise and biological data, which is then sent to a server and managed as the user's progress.

[0442] The server analyzes the progress data and provides further guidance or corrections as needed. The emotion engine also monitors changes in the user's emotional state and generates adaptive advice.

[0443] Examples:

[0444] The user puts on the wearable device and goes jogging, and the data is recorded in the app. This data is sent to the server and saved as progress. The server then suggests, "Increase your exercise frequency to four times a week." If the emotion engine detects an increase in stress levels, the app will provide new guidance, such as, "Try yoga to relax."

[0445] Prompt Sentence Examples

[0446] User ID: 12345

[0447] Weight: 70kg

[0448] Blood pressure: 135 / 85

[0449] Stress level: 80

[0450] "I recommend taking protein and doing yoga for relaxation."

[0451] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0452] Step 1:

[0453] The device displays a health information entry screen. The user enters information such as weight, diet, exercise habits, and past medical history, and then presses the "Submit" button.

[0454] Input: Health information (e.g., weight 70kg, breakfast contents, exercise habits)

[0455] Output: Encrypted health information data

[0456] Specific operation: The terminal displays an input screen, and when the user enters information and presses the "Send" button, the data is encrypted and sent to the server.

[0457] Step 2:

[0458] The server stores the received health information in a database and passes the data to the artificial intelligence (AI) and emotion engine.

[0459] Input: Encrypted health information data

[0460] Output: Analysis results of health information

[0461] Specific operation: The server stores the received data in a database, then sends the data to the AI ​​and emotion engine, where analysis begins.

[0462] Step 3:

[0463] The server's AI evaluates health risks based on health information, and the emotion engine recognizes the user's emotional state.

[0464] Input: Health information in the database

[0465] Output: Health risk assessment and emotional state analysis results

[0466] How it works: The AI ​​analyzes BMI and past medical history to assess health risks, while the emotion engine analyzes facial expressions and tone of voice to recognize emotional states.

[0467] Step 4:

[0468] The server generates follow-up questions based on the health risk assessment results and emotional state and sends them to the terminal.

[0469] Input: Health risk assessment and emotional state analysis results

[0470] Output: Follow-up question

[0471] What it does: Based on your health risks and emotional state, the AI ​​generates customized follow-up questions and sends them to your device.

[0472] Step 5:

[0473] The terminal displays follow-up questions to the user, who answers the questions and sends the answers to the server.

[0474] Input: Answer to follow-up question

[0475] Output: Response data

[0476] Specific behavior: The device displays a follow-up question, the user enters an answer, and sends it back to the server.

[0477] Step 6:

[0478] The server analyzes the answers to the follow-up questions and the emotional state, generates lifestyle advice, and sends it to the terminal.

[0479] Input: Answers to follow-up questions and emotional state analysis results

[0480] Output: Lifestyle guidance suggestions

[0481] Specific operation: AI analyzes the response data and emotional state, generates personalized lifestyle advice, and sends the advice to the device.

[0482] Step 7:

[0483] The terminal displays the received lifestyle advice to the user, who then follows the advice.

[0484] Input: Lifestyle guidance suggestions

[0485] Output: Check and execute the instruction

[0486] Specific operation: The device notifies the user of lifestyle guidance, and the user confirms and implements the content.

[0487] Step 8:

[0488] The terminal works in conjunction with the wearable device to collect the user's exercise data and biometric data and send it to a server.

[0489] Input: Wearable device data

[0490] Output: Exercise data and biological data

[0491] Specific operation: The terminal acquires data from the wearable device and sends it to the server.

[0492] Step 9:

[0493] The server analyzes the progress data, potentially providing further guidance or corrections, and also monitors changes in emotional state.

[0494] Input: Movement data, biometric data, emotional state data

[0495] Output: Additional lifestyle and / or remedial instructions

[0496] Specific behavior: Based on the progress, the AI ​​generates additional guidance, and the emotion engine analyzes changes in the user's emotional state and generates advice as needed.

[0497] Step 10:

[0498] The device notifies the user of additional lifestyle guidance and correction instructions, and the user responds accordingly.

[0499] Input: Additional lifestyle guidance and / or correction instructions

[0500] Output: New lifestyle advice implemented

[0501] Specific behavior: The device notifies the user of new instruction content, and the user acts accordingly.

[0502] 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.

[0503] 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.

[0504] 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.

[0505] [Second embodiment]

[0506] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0507] 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.

[0508] 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).

[0509] 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.

[0510] 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.

[0511] 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).

[0512] 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.

[0513] 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.

[0514] 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.

[0515] 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.

[0516] 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.

[0517] 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."

[0518] ---

[0519] This invention is a system that helps users lead healthy lives. The system is mainly composed of three main parts: a terminal, a server, and a user. Here, the processing of the system's program is explained in natural language, and its operation is described in detail with concrete examples.

[0520] System Configuration

[0521] The system consists of a device (such as a smartphone or tablet) that users access, a server that analyzes the data and provides guidance, and ultimately the user who receives health management.

[0522] 1. Display and enter information on the medical questionnaire

[0523] The device displays a screen for entering health information, including weight, diet, exercise habits, and past medical history.

[0524] Users enter their health information and transmit it to a server via their device, where the data is securely encrypted.

[0525] Examples:

[0526] The user opens the app on their smartphone, enters their weight (70 kg), meal details (breakfast: bread and coffee, lunch: salad and sandwich, dinner: fish and rice), and jogging three times a week (30 minutes) on the health information input screen, and presses the send button.

[0527] 2. Interview with AI

[0528] The server receives the submitted health information and analyzes it using artificial intelligence (AI), which uses the collected data to assess health risks and generate follow-up questions as needed.

[0529] The terminal displays follow-up questions from the server to the user, who answers the questions and transmits the answers to the server via the terminal.

[0530] Examples:

[0531] The server analyzes the user's health information, calculates the user's BMI, and sends a follow-up question about the need for weight management to the device. The user answers "Yes" to the question displayed on the device, "Have you been feeling tired easily recently?", and the data is sent.

[0532] 3. Creating and providing lifestyle guidance

[0533] The server then analyzes the answers to follow-up questions again and generates personalized lifestyle advice for the user, including dietary suggestions, exercise plans, and sleep advice.

[0534] The device provides the generated lifestyle advice to the user in real time, and the user can easily check the advice content through the app.

[0535] Examples:

[0536] Based on the analysis results, the server generates specific lifestyle advice for the user, such as "take protein for breakfast" and "recommend strength training once a week," and sends it to the device. The user can then confirm the advice displayed on the device.

[0537] 4. Progress Management

[0538] The device works in conjunction with wearable devices to collect the user's exercise and biometric data, including the number of steps taken, distance traveled, and heart rate.

[0539] The device sends the collected data to a server that analyzes it to monitor lifestyle progress and provide further guidance as needed.

[0540] Examples:

[0541] The user puts on the wearable device and goes jogging. The data (5km run distance, heart rate, etc.) is recorded in the app and automatically sent from the device to the server. Based on the data, the server suggests "increasing the frequency of exercise to four times a week," and a notification is displayed on the device.

[0542] The above is a specific embodiment for carrying out the invention. This system makes it easier for users to receive personalized lifestyle advice and to manage their health on an ongoing basis.

[0543] The processing flow will be explained below.

[0544] ---

[0545] Step 1:

[0546] The device displays a health information entry screen, which includes input items such as the user's weight, diet, exercise habits, living environment, and past medical history.

[0547] Step 2:

[0548] The user enters health information and presses buttons such as "Confirm" and "Send" to send the data. This input data is encrypted and securely sent from the device to the server.

[0549] Step 3:

[0550] The server stores the received health information in a database, then passes the data to an artificial intelligence (AI) that begins assessing basic health risks.

[0551] Step 4:

[0552] The server uses AI to assess health risks and generate follow-up questions, if needed, that are customized based on the user's health information.

[0553] Step 5:

[0554] The device then prompts the user with follow-up questions from the server, including additional health information and lifestyle details.

[0555] Step 6:

[0556] The user answers the follow-up questions and sends the answers to the server via their device, where the answer data is again encrypted and securely sent to the server.

[0557] Step 7:

[0558] The server analyzes the answers to follow-up questions and integrates all the data to generate lifestyle advice. The AI ​​creates a specific action plan (e.g., dietary improvements, exercise plan, sleep advice).

[0559] Step 8:

[0560] The server transmits the generated lifestyle guidance to the terminal. The lifestyle guidance information is presented in real time and provided in a format that the user can access immediately.

[0561] Step 9:

[0562] The device displays the generated lifestyle advice to the user, who then checks the advice and follows the specific action plan within the application.

[0563] Step 10:

[0564] The terminal connects to a wearable device and collects the user's exercise and biological data, including the number of steps taken, distance traveled, heart rate, and sleep data.

[0565] Step 11:

[0566] The device sends the collected data to the server, which periodically transfers the data to the server and stores it as user progress information.

[0567] Step 12:

[0568] The server analyzes the progress data and generates personalized feedback for each user's lifestyle habits. If real-time guidance or changes to the action plan are needed, the server generates new suggestions.

[0569] Step 13:

[0570] The device will notify the user with new feedback and updated lifestyle advice, and the user can check their progress through the app and adjust their behavior according to the new advice.

[0571] ---

[0572] By taking these steps, the system can provide users with personalized, ongoing, and accurate health guidance and monitor progress in real time.

[0573] Example 1

[0574] 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."

[0575] In recent years, the increase in lifestyle-related diseases and stress-related diseases has become a social problem, creating a demand for personalized health management. However, current health management systems only provide information to everyone at once and do not provide specific guidance tailored to each user's individual lifestyle habits or health risks. As a result, users find it difficult to maintain sustainable health management and are unable to achieve effective health improvement. The present invention aims to solve these problems by providing a system that provides customized health management tailored to the user's lifestyle habits.

[0576] 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.

[0577] In this invention, the server includes means for collecting health information of the user, means for conducting an interview based on the collected health information using a generative AI model and generating follow-up questions, means for collecting and analyzing responses from the user based on the generated follow-up questions, means for providing the user with customized lifestyle advice based on the interview results and analysis results, and means for managing the progress of the user's lifestyle habits through the appliance and providing more specific advice as needed, thereby enabling specific and individualized lifestyle advice according to the user's health condition.

[0578] "User" refers to an individual who uses the health care system.

[0579] "Health information" refers to data about an individual's health condition and lifestyle, such as weight, diet, exercise habits, and past medical history.

[0580] A "generative AI model" refers to a system that uses artificial intelligence to analyze data and generate questions.

[0581] "Interview" refers to the process of using a generative AI model to collect information interactively based on the user's health information.

[0582] "Follow-up questions" refer to questions that are asked to obtain additional information that needs to be collected based on the initial health information or interview results.

[0583] "Analysis" refers to the process of analyzing collected data and responses to assess health risks and generate appropriate advice.

[0584] "Lifestyle guidance" refers to specific guidance provided to users based on the analysis results to improve their lifestyle habits, such as diet, exercise, and sleep.

[0585] "Appliance" refers to a hardware device used in a health care system, including, among other things, wearable devices.

[0586] A "wearable device" refers to a digital device worn by the user that is capable of collecting exercise data and biometric data.

[0587] "Large-scale data" refers to a large amount of data, including a wide range of datasets such as health information and lifestyle data.

[0588] That's all.

[0589] This invention is a system that supports users in living a healthy lifestyle. The system is broadly divided into three main parts: a terminal, a server, and a user. Specific embodiments are described below.

[0590] System Configuration

[0591] The system consists of a terminal accessed by the user, a server that analyzes the data and provides guidance, and ultimately the user who receives health management.

[0592] Terminal

[0593] The terminal is a device such as a smartphone or tablet. The terminal displays a health information input screen, collects health information from the user, and sends it to the server. The terminal also displays follow-up questions and lifestyle advice from the server and provides them in a form that the user can check.

[0594] server

[0595] The server is the central component for analyzing the received health information. It uses generative AI models (e.g., OpenAI's GPT-4) to analyze the collected data and generate follow-up questions and customized lifestyle advice. The server also analyzes exercise and biometric data provided in real time to monitor progress.

[0596] User

[0597] Users are individuals who use the system to input their own health information. They use a terminal to enter their health information, answer follow-up questions, and confirm lifestyle advice. They also wear a wearable device to provide daily exercise data and biometric data to the system.

[0598] Data entry and analysis

[0599] Entering health information

[0600] The device displays a health information input screen to the user. The device used here is a smartphone or tablet. Detailed health information such as weight, diet, exercise habits, and past medical history is entered.

[0601] The user enters their health information and presses the send button. The input data is temporarily stored on the device, then securely encrypted and sent to the server.

[0602] Analyze data and generate follow-up questions

[0603] The server decrypts the received data and uses large-scale data to analyze it using a generative AI model, which is then used to assess health risks and generate necessary follow-up questions.

[0604] The terminal displays follow-up questions from the server to the user, who answers these questions and sends them back to the server via the terminal.

[0605] Creating and providing lifestyle guidance

[0606] The server then analyzes the follow-up questions and generates personalized lifestyle advice for the user, including dietary suggestions, exercise plans, and sleep tips.

[0607] The device provides the generated lifestyle advice to the user in real time, and the user can check the advice content through the app.

[0608] Progress management

[0609] Wearable device integration

[0610] The device works in conjunction with wearable devices (such as Fitbit and other smartwatches) to collect the user's exercise data and biometric data (such as steps taken, distance traveled, and heart rate).

[0611] The device encrypts the collected data and sends it to the server.

[0612] The server analyzes this data, monitors the user's lifestyle progress, and provides further specific guidance as needed.

[0613] Examples of prompt statements

[0614] The following are examples of prompts that users can use when receiving support from AI, which will allow the generative AI model to generate appropriate questions and advice.

[0615] "Please tell me your recent jogging record."

[0616] "What foods do you eat for breakfast?"

[0617] "Have you been feeling tired easily lately?"

[0618] The above is a concrete example of how to implement this healthy lifestyle support system. This system makes it easier for users to receive personalized lifestyle guidance and to manage their health on an ongoing basis.

[0619] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0620] Processing flow

[0621] Step 1: Enter your health information

[0622] The device displays a health information entry screen to the user, where detailed health information such as weight, diet, exercise habits, and past medical history can be entered.

[0623] Specific operation: The device launches the application and displays the health information input screen.

[0624] Input: Health information entered by the user (e.g., weight 70 kg, bread and coffee for breakfast, jogging three times a week).

[0625] Output: Encrypted health information data.

[0626] Step 2: Submit your health information

[0627] The user enters their health information and presses the send button. The data is temporarily stored on the device and then securely encrypted and sent to the server.

[0628] Specific operation: The user completes the input and presses the "Send" button. The device AES encrypts the data and generates a request to send to the server.

[0629] Input: The submit button pressed by the user and the health information entered.

[0630] Output: Encrypted health information data sent to the server.

[0631] Step 3: Receiving and analyzing data

[0632] The server decrypts the received data and stores it in a database, after which it uses a generative AI model to analyze the data and generate follow-up questions.

[0633] What it does: The server decrypts the data and stores it in a database. A Python script is used to invoke a generative AI model (e.g., OpenAI's GPT-4) to analyze the health information.

[0634] Input: Encrypted health information data.

[0635] Output: Decoded health information and generated follow-up questions (e.g., "Have you been feeling tired lately?").

[0636] Step 4: View follow-up questions

[0637] The terminal displays the follow-up questions received from the server to the user, who answers these questions and sends them back to the server via the terminal.

[0638] Specific operation: The device receives and displays a question from the server. The user answers the question, and the device sends the answer back to the server.

[0639] Input: The generated follow-up question (e.g., "Have you been feeling tired lately?") and the user's answer (e.g., "Yes").

[0640] Output: User response data.

[0641] Step 5: Create a life lesson

[0642] The server again analyzes the answers to the follow-up questions and generates lifestyle advice customized for the user.

[0643] Specific operation: The server inputs the response data into a generative AI model and generates lifestyle advice based on the analysis results (e.g., "Add protein to your breakfast").

[0644] Input: User response data.

[0645] Output: Customized lifestyle guidance data.

[0646] Step 6: Providing lifestyle guidance

[0647] The device provides the generated lifestyle advice to the user in real time, and the user can check the advice content through the app.

[0648] Specific operation: The device receives lifestyle guidance data from the server and displays it to the user on the application.

[0649] Input: Customized life coaching data.

[0650] Output: A lifestyle advice screen viewable by the user.

[0651] Step 7: Collect and send exercise data

[0652] The terminal works in conjunction with a wearable device to collect the user's exercise and biometric data, which is then encrypted and sent to a server.

[0653] Specific operation: The terminal collects data from the wearable device via Bluetooth, encrypts it, and sends it to the server.

[0654] Input: Exercise data from wearable devices (e.g., steps, distance traveled, heart rate).

[0655] Output: Encrypted exercise data.

[0656] Step 8: Track your progress

[0657] The server analyzes the received exercise data and manages the user's progress in lifestyle habits, providing more specific guidance as needed.

[0658] Specific operation: The server analyzes the exercise data and generates new guidance tailored to the user's lifestyle (e.g., "Increase the frequency of exercise to four times a week").

[0659] Input: Exercise data from a wearable device.

[0660] Output: New lifestyle guidance data according to progress.

[0661] This is the flow of the system's program processing, which allows users to receive detailed and individualized lifestyle advice and enable continuous health management.

[0662] (Application example 1)

[0663] 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."

[0664] Conventional health management systems primarily function to collect users' health information and provide lifestyle advice, but lack the mechanisms to actually encourage users to act on that advice. There was also a need to reduce the effort required for users to purchase suggested products and services. In particular, the financial procedures required to promote the use of health foods and exercise facilities were complicated, preventing users from following the recommendations.

[0665] 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.

[0666] In this invention, the server includes means for collecting health information of the user, means for conducting interviews based on the health information collected using artificial intelligence, means for providing the user with lifestyle advice based on the interview results, means for managing the progress of the user's lifestyle habits through an appliance, means for making electronic payments for products and services based on the health information, and means for making it easy to purchase the suggested products and services. This enables the user to immediately take action based on the advice and to more effectively experience the effects of health management.

[0667] "User" refers to an individual who uses this system to manage health information and receive lifestyle guidance.

[0668] "Health information" refers to data provided by users, such as weight, diet, exercise habits, and past medical history.

[0669] "Artificial intelligence" refers to technology that performs advanced calculations such as data analysis and pattern recognition to provide appropriate lifestyle guidance and generate questions.

[0670] "Interview" refers to the process of analyzing health information collected using artificial intelligence and asking follow-up questions and giving advice to users.

[0671] "Lifestyle guidance" refers to specific methods and suggestions for making users' lives healthier, such as suggestions for improving their diet, exercise plans, and sleep advice, based on the user's health information and the results of the interview.

[0672] "Progress" refers to the health management activities carried out by the user and their results, including exercise data and biometric data.

[0673] "Appliance" refers to a device or equipment used to collect and manage user health data, including, for example, a smartphone or wearable device.

[0674] A "wearable device" refers to an electronic device worn by a user, which collects exercise data and biometric data in real time.

[0675] "Big data" refers to large data sets and the technology for analyzing them to extract useful information and patterns.

[0676] "Electronic payment" refers to the process by which a consumer pays for goods or services using digital means.

[0677] "Products and services" refers to supplements, healthy foods, and use of fitness facilities suggested based on health management guidance.

[0678] The details of the system including the actions of the server, terminals and users are described below for the embodiment of the present invention.

[0679] The system mainly consists of a device (such as a smartphone) that users access, a server that analyzes data and provides guidance, and ultimately the user who receives health management.

[0680] 1. Collection of health information

[0681] The server prompts users to enter their health information using a device such as a smartphone. This health information includes weight, diet, exercise habits, and past medical history. The user enters this information into their device and sends it to the server. The transmitted data is securely encrypted.

[0682] As a concrete example, a user opens a smartphone app and inputs their weight (70 kg), meal details (breakfast: bread and coffee, lunch: salad and sandwich, dinner: fish and rice), and jogging three times a week (30 minutes). Then, they press the send button to send the data to the server.

[0683] 2. AI-based analysis and interviews

[0684] The server receives the health information and analyzes it using a generative AI model. The AI ​​evaluates health risks based on the collected data and generates follow-up questions as needed. The device displays the follow-up questions sent from the server to the user and sends the answers back to the server.

[0685] For example, the server analyzes health information, calculates the user's BMI, and sends a follow-up question about the need for weight management to the device. The user answers "yes" to the question displayed on the device, "Have you been feeling tired easily recently?", and the message is sent.

[0686] 3. Lifestyle guidance and electronic payment

[0687] The server analyzes the answers to the follow-up questions and generates personalized lifestyle advice for the user, including dietary suggestions, exercise plans, and sleep advice, as well as a means to electronically pay for suggested products and services (such as supplements or fitness facilities) based on the health information.

[0688] The device provides users with the generated lifestyle advice and suggested products and services in real time, and users can check the advice through the app and easily purchase the suggested products and services.

[0689] For example, the server sends advice such as "take protein for breakfast" and "recommend strength training once a week" to the terminal. It also suggests purchasing protein (3,000 yen), and the user completes the purchase procedure by making an electronic payment on the spot.

[0690] 4. Progress management and follow-up

[0691] The server analyzes data (such as number of steps, distance traveled, and heart rate) obtained from the wearable device to monitor the user's health management progress. Based on this data, the server provides further appropriate health guidance and follow-up.

[0692] As a concrete example, a user puts on a wearable device and goes jogging. The app records the distance traveled (5km), heart rate, and other data, and automatically sends the data from the device to a server. Based on the analysis results, the server suggests increasing the frequency of exercise to four times a week, and a notification is displayed on the device.

[0693] Example prompt sentence:

[0694] Analyze the user's health status based on the following input data and suggest necessary advice and actions:

[0695] Weight: 70kg

[0696] Meals:

[0697] Breakfast: Bread and coffee

[0698] Lunch: Salad and sandwich

[0699] Dinner: Fish and rice

[0700] Exercise habits: Jogging three times a week (30 minutes)

[0701] As described above, this system makes it easier for users to receive personalized lifestyle advice and actually take action, making it possible for them to experience the benefits of health management over time.

[0702] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0703] Step 1:

[0704] The server prompts users to enter their health information using a smartphone or tablet device. Users enter data such as weight, diet, exercise habits, and past medical history, and this data is sent from the device to the server. The data is encrypted before being transmitted.

[0705] Input: User's health information (e.g., weight 70 kg, dietary details, exercise habits)

[0706] Data processing / calculation: Check the format of input data and encrypt it

[0707] Output: Encrypted health information data

[0708] Step 2:

[0709] The server receives the submitted health information and analyzes it using a generative AI model, assessing health risks based on the input data and generating follow-up questions as needed.

[0710] Input: Encrypted health information data

[0711] Data processing / computation: Decode data, assess health risks, generate follow-up questions

[0712] Output: Health risk assessment results, follow-up questions

[0713] Step 3:

[0714] The terminal displays the follow-up questions sent from the server to the user, who answers the questions and sends the answers to the server via the terminal.

[0715] Input: Health risk assessment results, follow-up questions

[0716] Data processing / calculation: Displaying follow-up questions to users, collecting and encrypting answer data

[0717] Output: Encrypted follow-up question answers

[0718] Step 4:

[0719] The server receives the answers to the follow-up questions, analyzes them again using the generative AI model, and generates optimal lifestyle advice for the user, including dietary suggestions, exercise plans, and sleep advice, as well as electronic payment methods for the suggested products and services.

[0720] Input: Encrypted follow-up question response data

[0721] Data processing / calculation: Decryption of response data, detailed analysis, generation of lifestyle guidance and electronic payment proposals

[0722] Output: Lifestyle guidance, suggested products and services (with electronic payment methods)

[0723] Step 5:

[0724] The device provides users with the generated lifestyle advice and suggested products and services in real time, and users can check the advice through the app and easily purchase the suggested products and services.

[0725] Input: Lifestyle guidance, suggested products and services

[0726] Data processing / calculation: User display, electronic payment processing

[0727] Output: Lifestyle advice provided to the user, products and services purchased

[0728] Step 6:

[0729] The server analyzes the exercise and biological data obtained from the wearable device to manage the user's health management progress, and provides further appropriate health guidance and follow-up as needed.

[0730] Input: Exercise data and biometric data from wearable devices

[0731] Data processing / calculation: Data analysis, progress management, generation of additional health guidance and follow-up

[0732] Output: Progress management results, additional health guidance and follow-up

[0733] 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.

[0734] ---

[0735] This invention is a system that supports users in living a healthy lifestyle. The system collects the user's health information, analyzes their health status using artificial intelligence and an emotion engine, and provides personalized lifestyle advice based on the results. It also manages the user's progress in real time and provides appropriate advice that takes their emotional state into account. The system's main components are a terminal, a server, and a user.

[0736] System Configuration

[0737] 1. Display and enter information on the medical questionnaire

[0738] The device displays a health information entry screen, where the user enters information such as weight, diet, exercise habits, and past medical history.

[0739] The user enters the necessary health information and presses the "Send" button to send the data. The data is encrypted and securely sent from the device to the server.

[0740] Examples:

[0741] The user opens the smartphone app, enters their weight (70 kg), breakfast (bread and coffee), and exercise habits (30 minutes of jogging three times a week) on the health information input screen, and presses the "Submit" button.

[0742] 2. Evaluation by AI and emotion engine

[0743] The server stores the received health information in a database and passes the data to the artificial intelligence (AI) and emotion engine.

[0744] The server's AI assesses health risks based on collected data, while the emotion engine recognizes the user's emotional state from facial expressions, tone of voice, and text input.

[0745] Examples:

[0746] The server analyzes the user's medical data to assess their BMI and risk of lack of exercise, while the emotion engine uses the smartphone's camera and microphone to recognize whether the user is feeling stressed.

[0747] 3. Follow-up questions

[0748] The server generates follow-up questions based on the health risk assessment results and emotional state, and the questions are customized based on the user's current health and emotional state.

[0749] The terminal displays the generated follow-up questions to the user, who answers the questions and transmits the answers from the terminal to the server.

[0750] Examples:

[0751] The server generates follow-up questions such as "Have you been feeling tired lately?" and "How much stress do you usually feel?" and displays them on the device. The user enters and submits the answers.

[0752] 4. Creating and providing lifestyle guidance

[0753] The server then analyzes the follow-up questions and the user's emotional state again to generate optimal lifestyle advice, including dietary suggestions, exercise plans, and sleep advice.

[0754] The device displays the generated lifestyle advice to the user, who can check the advice in real time and apply it to their own behavior.

[0755] Examples:

[0756] The server generates specific lifestyle advice such as "Eat protein for breakfast" and "Try strength training once a week." It also includes advice that takes into account emotional states, such as "Try meditating to relieve stress." These instructions are displayed on the device and notified to the user.

[0757] 5. Progress Management

[0758] The terminal connects to the wearable device and collects the user's exercise and biological data, which is then sent to a server and managed as the user's progress.

[0759] The server analyzes the progress data and provides further guidance or corrections as needed. The emotion engine also monitors changes in the user's emotional state and generates adaptive advice.

[0760] Examples:

[0761] The user puts on the wearable device and goes jogging, and the data is recorded in the app. This data is sent to the server and saved as progress. The server then suggests, "Increase your exercise frequency to four times a week." If the emotion engine detects an increase in stress levels, the app will provide new guidance, such as, "Try yoga to relax."

[0762] The above is a specific embodiment for carrying out the invention. This system allows users to receive personalized lifestyle advice in real time and to perform comprehensive health management, including their emotional state.

[0763] The processing flow will be explained below.

[0764] ---

[0765] Step 1:

[0766] The device displays a screen for entering health information, including weight, diet, exercise habits, living environment, and past medical history.

[0767] Step 2:

[0768] The user enters their health information and presses the "Confirm" and "Send" buttons to send the data. The data is encrypted and securely sent from the device to the server.

[0769] Step 3:

[0770] The server stores the received health information in a database and passes the data to the artificial intelligence (AI) and emotion engine, which then begin processing simultaneously.

[0771] Step 4:

[0772] The server's AI will assess health risks based on health information, analyzing indicators such as BMI, exercise habits, and dietary balance.

[0773] Step 5:

[0774] Using the device's built-in camera and microphone, the emotion engine analyzes the user's facial expressions, tone of voice, and text input to recognize the user's emotional state.

[0775] Step 6:

[0776] The server integrates the AI-based health risk assessment results with the emotional state of the emotion engine to generate follow-up questions, which are customized based on the user's current health and emotional state.

[0777] Step 7:

[0778] The device displays follow-up questions from the server to the user, who answers them and sends the answers from the device to the server, where they are again encrypted and securely transmitted to the server.

[0779] Step 8:

[0780] The server receives and analyzes the answers to follow-up questions. Here, the AI ​​integrates the answer data with health information to generate optimal lifestyle advice. The emotion engine also provides advice based on the user's emotions based on this analysis process.

[0781] Step 9:

[0782] The server then sends the generated lifestyle guidance and emotion-based advice to the device, including advice on improving diet, exercise suggestions, and stress management.

[0783] Step 10:

[0784] The device displays the generated lifestyle guidance and advice to the user, who then checks the advice in real time and implements the action plan.

[0785] Step 11:

[0786] The device connects to wearable devices to collect the user's exercise and biometric data, including steps taken, distance traveled, heart rate, and sleep data.

[0787] Step 12:

[0788] The device sends the collected data to the server, which periodically transfers the data to the server and stores it as the user's progress.

[0789] Step 13:

[0790] The server analyzes the progress data and provides further guidance or modification of the action plan based on the progress. The emotion engine monitors changes in the user's emotional state and updates appropriate feedback and advice.

[0791] Step 14:

[0792] The device notifies the user of new feedback and updated lifestyle advice, and the user can check their progress through the app and adjust their behavior according to the new advice.

[0793] ---

[0794] Through these steps, the system can provide users with personalized, continuous and accurate health guidance, and manage their progress and emotions in real time.

[0795] Example 2

[0796] 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."

[0797] In modern society, there are few systems that simultaneously monitor users' health and emotional states, and personalized lifestyle advice is lacking. This makes it easy for many users to overlook health risks and makes it difficult to effectively improve their lifestyles. Furthermore, there is no system that provides adaptive advice that takes users' emotional states into account, making it difficult to understand and improve a user's overall health.

[0798] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting health information of the user, means for performing an assessment based on the collected health information using artificial intelligence, means for generating follow-up questions based on the assessment results and the emotional state, means for collecting and analyzing the user's answers to the generated follow-up questions, means for providing lifestyle advice based on the user's answers and the emotional state, means for managing the user's lifestyle progress through an appliance, and means for monitoring the user's emotional state using an emotion engine and providing adaptive advice. This enables the user to receive personalized lifestyle advice in real time and achieve comprehensive health management including the emotional state.

[0799] "Health information" refers to various data used to understand the user's health condition, such as the user's weight, diet, exercise habits, and past medical history.

[0800] "Artificial intelligence" is a technology that enables computer systems to mimic human intelligence to process, evaluate, and analyze information.

[0801] "Evaluation" is the process of analyzing and determining the user's health risks and condition based on collected health information.

[0802] "Emotional state" indicates the user's psychological state and is determined from facial expressions, tone of voice, text input, and the like.

[0803] "Follow-up questions" are questions that require additional answers from the user based on the health risk assessment results or emotional state.

[0804] "Lifestyle guidance" refers to instructions such as dietary suggestions, exercise plans, and sleep advice provided based on the user's health and emotional state.

[0805] "Appliance" refers to any equipment or device used to collect and transmit health data, including wearable devices.

[0806] "Emotion engine" is a general term for technologies and systems that analyze and recognize users' psychological emotions.

[0807] "Adaptive advice" refers to advice that is individually tailored to the user based on their current health data and emotional state.

[0808] This invention is a system that helps users lead healthy and emotionally balanced lives. The system collects the user's health information and emotional state, analyzes it using artificial intelligence and an emotion engine, and provides personalized lifestyle advice based on the results. Furthermore, the system manages the user's progress in real time and provides appropriate advice that takes into account the user's emotional state.

[0809] System Configuration

[0810] The system consists of devices, servers, and users. Specific hardware and software components include devices such as smartphones and tablets, cloud servers, wearable devices equipped with various sensors, and health applications that users use on a daily basis.

[0811] Collection of health information

[0812] The device displays a health information input screen, and the user enters information such as weight, diet, exercise habits, and past medical history. This information is encrypted and sent from the device to the server. For example, a user opens a smartphone app, enters their weight as "70 kg," their breakfast as "bread and coffee," and their exercise habits as "30 minutes of jogging three times a week," and presses the "Send" button.

[0813] Data encryption and transmission

[0814] The device encrypts the entered health information using the AES encryption method and sends it to the server via HTTPS. The server then decrypts the received encrypted data and stores it in a database.

[0815] Evaluation by AI and emotion engine

[0816] The server passes the stored health information to an artificial intelligence (AI) and emotion engine. The AI ​​assesses health risks based on the collected data (using, for example, TensorFlow and PyTorch), and the emotion engine recognizes the user's emotional state from their facial expressions, tone of voice, and text input. For example, it evaluates the user's BMI and risk of lack of exercise, and the emotion engine analyzes their stress level using the smartphone's camera and microphone.

[0817] Follow-up question generation

[0818] The server generates follow-up questions based on the health risk assessment results and emotional state. These questions are customized based on the user's current health and emotional state and displayed on the device. For example, the server generates questions such as "Have you been feeling tired easily recently?" or "How much stress do you usually feel?", and the user enters and submits the answers.

[0819] Creating and providing lifestyle guidance

[0820] The server then analyzes the user's responses to the follow-up questions and their emotional state again to generate optimal lifestyle advice. This advice includes suggestions for improving their diet, exercise plans, and sleep advice. The device displays the generated lifestyle advice to the user, who can review it in real time and apply it to their own behavior. For example, specific advice such as "Eat protein for breakfast," "Try strength training once a week," and "Try meditating to relieve stress" is provided.

[0821] Progress monitoring and adaptive advice

[0822] The terminal works in conjunction with a wearable device to collect the user's exercise data and biometric data in real time and send it to a server. This data is stored on the server and managed as progress. The server analyzes the progress data and provides further guidance or corrections as needed. The emotion engine monitors changes in the user's emotional state and generates adaptive advice. For example, based on the user's exercise frequency data, it may suggest "increase your exercise frequency to four times a week," or if it detects an increase in stress levels, it may provide new advice such as "try yoga to relax."

[0823] The above is a specific embodiment for carrying out the invention. This system allows users to receive personalized lifestyle advice in real time and enables comprehensive health management, including emotional state.

[0824] Prompt Sentence Examples

[0825] "Please explain in natural language the process flow of a system that collects a user's health information (e.g., weight, diet, exercise habits), assesses their stress level using an emotion engine, and provides personalized health guidance."

[0826] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0827] Program processing flow and specific explanation

[0828] Step 1:

[0829] The device displays a health information input screen. The user inputs information such as weight, diet, exercise habits, and past medical history. The input includes data entered by the user (weight, diet, exercise, and medical history).

[0830] Specific behavior:

[0831] The device displays a "health data entry" screen on the smartphone or tablet screen, where users can enter information such as their weight (70 kg), breakfast (bread and coffee), and exercise habits (30 minutes of jogging three times a week), then press the "send" button.

[0832] Step 2:

[0833] The terminal encrypts the input data using the AES encryption method. The input includes the data entered by the user in step 1.

[0834] Specific behavior:

[0835] The terminal handles the user's input data and encrypts it using an encryption algorithm called AES (Advanced Encryption Standard), generating encrypted data.

[0836] Step 3:

[0837] The device sends encrypted data to the server using HTTPS communication. The input is encrypted health data.

[0838] Specific behavior:

[0839] The device sends the encrypted health data to the server via HTTPS (Hypertext Transfer Protocol Secure) communication. This communication protocol ensures that the data is transmitted securely. After transmission, the data arrives at the server.

[0840] Step 4:

[0841] The server stores the received health data in a database. The input is the encrypted health data arriving at the server.

[0842] Specific behavior:

[0843] The server decrypts the received data and stores it in a database. The database uses a relational database system (RDBMS) such as SQL (Structured Query Language). The stored data is generated.

[0844] Step 5:

[0845] The server passes the data to an artificial intelligence (AI) to assess health risks. The inputs are health data stored in a database and an AI model.

[0846] Specific behavior:

[0847] The server inputs the stored data into an AI model (e.g., a deep learning model using TensorFlow or PyTorch) to evaluate the user's health risks (e.g., BMI value, risk of lack of exercise), and generates an evaluation result.

[0848] Step 6:

[0849] The server passes data to the emotion engine to recognize the user's emotional state. The inputs are the evaluation results, the user's camera footage, and audio data.

[0850] Specific behavior:

[0851] The server uses the user's camera footage and audio data to call an emotion engine (e.g., IBM Watson or Microsoft Azure Emotion API) to recognize the user's emotional state, generating emotional state data.

[0852] Step 7:

[0853] The server generates follow-up questions based on the health risk assessment results and emotional state. The inputs are the assessment results and emotional state data.

[0854] Specific behavior:

[0855] The server handles the evaluation results of the AI ​​model and emotion engine and generates follow-up questions based on this information. The questions are dynamically generated based on templates, and the generated follow-up questions are sent to the device.

[0856] Step 8:

[0857] The terminal displays the generated follow-up question to the user, and has as input the generated follow-up question.

[0858] Specific behavior:

[0859] The device displays follow-up questions such as "Have you been feeling tired lately?" and "How stressed do you usually feel?" and is ready for the user to enter their answers.

[0860] Step 9:

[0861] The user answers the follow-up questions and sends the answers from the device to the server. The user's answer data is entered into the device.

[0862] Specific behavior:

[0863] The user enters an answer into the question form and presses the "Submit" button to send the answer to the server. The sent answer reaches the server.

[0864] Step 10:

[0865] The server analyzes the follow-up question answers and the emotional state again and generates optimal lifestyle advice. The inputs are the follow-up question answer data and the emotional state.

[0866] Specific behavior:

[0867] The server then uses the AI ​​model again to generate lifestyle advice based on the user's answers and emotional state, and provides the advice to the user.

[0868] Step 11:

[0869] The terminal displays the generated lifestyle guidance to the user. The generated lifestyle guidance data is input.

[0870] Specific behavior:

[0871] The device displays specific lifestyle advice such as "Eat protein for breakfast" and "Try strength training once a week." The user is then ready to confirm the advice.

[0872] Step 12:

[0873] The terminal connects to the wearable device, collects the user's exercise data and biological data in real time, and transmits it to the server. The input includes data obtained from the wearable device.

[0874] Specific behavior:

[0875] The device receives data from a wearable device (e.g., a smartwatch or fitness tracker), records the data in the app, and then sends the recorded data to a server.

[0876] Step 13:

[0877] The server analyzes the collected data and provides further guidance or corrections as needed. As input, the collected progress data is stored on the server.

[0878] Specific behavior:

[0879] The server then re-analyzes the stored data using an AI model to generate new lifestyle advice based on progress, and adjusts the advice as necessary.

[0880] Step 14:

[0881] The server uses an emotion engine to monitor changes in the user's emotional state and generate adaptive advice. The input is the user's emotional state data.

[0882] Specific behavior:

[0883] The server periodically calls the emotion engine to check changes in the user's emotional state, and based on the results, new advice, such as "Try yoga to relax," is generated and provided to the user.

[0884] (Application example 2)

[0885] 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."

[0886] Conventional health management systems collect and analyze users' health information and provide lifestyle advice, but because they are unable to take into account the user's emotional state, the lifestyle advice provided is uniform and it is difficult to provide optimal advice to each individual user.In addition, since they do not have a means to collect health information in real time using IoT devices in physical stores, there is an issue that they cannot provide immediate health management in physical stores.

[0887] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting health information of the user, means for conducting an interview based on the health information collected using artificial intelligence, means for providing lifestyle advice to the user based on the interview results, means for managing the progress of the user's lifestyle habits through an appliance, means for analyzing the user's emotional state using an emotion engine and reflecting the analysis in the lifestyle advice, and means for collecting health information using IoT devices in a physical store. This enables lifestyle advice based on the user's emotional state, and enables real-time collection of health information and immediate advice to be provided in a physical store.

[0888] "User" refers to a person who provides health information to the system and receives lifestyle guidance.

[0889] "Health information" refers to data about the user's health status, such as weight, blood pressure, diet, exercise habits, and past medical history.

[0890] "Artificial intelligence" refers to information processing technology that analyzes collected health information and assesses users' health risks.

[0891] "Interview" refers to a means of providing direct guidance and follow-up to users based on the results of health status analysis by artificial intelligence.

[0892] "Lifestyle guidance" refers to specific advice on diet, exercise, sleep, etc. provided to users based on health information and interviews.

[0893] "Progress management" refers to methods for tracking changes in daily lifestyle habits and health status and providing necessary support.

[0894] "Appliance" refers to any device or system used to collect health information and manage progress.

[0895] A "wearable device" refers to a device worn by the user that collects exercise data and biometric data in real time.

[0896] An "emotion engine" refers to technology that analyzes a user's emotional state from facial expressions, tone of voice, text input, etc.

[0897] "IoT devices" refer to physical store equipment such as scales, blood pressure monitors, and fitness machines that can send and receive data over the internet.

[0898] "Big data" refers to data processing techniques for analyzing huge data sets, and the results of this analysis are used to assess health risks.

[0899] This invention is a system that helps users lead healthy lives. The system collects the user's health information, analyzes the user's health status using artificial intelligence and an emotion engine, and provides personalized lifestyle advice based on the results. Furthermore, the system manages the user's progress in real time and provides appropriate advice taking into account the user's emotional state.

[0900] System Configuration

[0901] 1. Collection of health information

[0902] The device displays a health information entry screen, where the user enters information such as weight, diet, exercise habits, and past medical history.

[0903] The user enters the necessary health information and presses the "Send" button to send the data. The data is encrypted and securely sent from the device to the server.

[0904] Examples:

[0905] The user opens a fitness gym download app, enters their weight (70 kg), breakfast contents (bread and coffee), and exercise habits (30 minutes of jogging three times a week) on the health information input screen, and presses the "Submit" button.

[0906] 2. Evaluation by AI and emotion engine

[0907] The server stores the received health information in a database and passes the data to the artificial intelligence (AI) and emotion engine.

[0908] The server's AI assesses health risks based on collected data, while the emotion engine recognizes the user's emotional state from facial expressions, tone of voice, and text input.

[0909] Examples:

[0910] The server analyzes the user's medical data to assess their BMI and risk of lack of exercise, while the emotion engine uses the smartphone's camera and microphone to recognize whether the user is feeling stressed.

[0911] 3. Follow-up questions

[0912] The server generates follow-up questions based on the health risk assessment results and emotional state, and the questions are customized based on the user's current health and emotional state.

[0913] The terminal displays the generated follow-up questions to the user, who answers the questions and transmits the answers from the terminal to the server.

[0914] Examples:

[0915] The server generates follow-up questions such as "Have you been feeling tired lately?" and "How much stress do you usually feel?" and displays them on the device. The user enters and submits the answers.

[0916] 4. Creating and providing lifestyle guidance

[0917] The server then analyzes the follow-up questions and the user's emotional state again to generate optimal lifestyle advice, including dietary suggestions, exercise plans, and sleep advice.

[0918] The device displays the generated lifestyle advice to the user, who can check the advice in real time and apply it to their own behavior.

[0919] Examples:

[0920] The server generates specific lifestyle advice such as "Eat protein for breakfast" and "Try strength training once a week." It also includes advice that takes into account emotional states, such as "Try meditating to relieve stress." These instructions are displayed on the device and notified to the user.

[0921] 5. Progress Management

[0922] The terminal connects to the wearable device and collects the user's exercise and biological data, which is then sent to a server and managed as the user's progress.

[0923] The server analyzes the progress data and provides further guidance or corrections as needed. The emotion engine also monitors changes in the user's emotional state and generates adaptive advice.

[0924] Examples:

[0925] The user puts on the wearable device and goes jogging, and the data is recorded in the app. This data is sent to the server and saved as progress. The server then suggests, "Increase your exercise frequency to four times a week." If the emotion engine detects an increase in stress levels, the app will provide new guidance, such as, "Try yoga to relax."

[0926] Prompt Sentence Examples

[0927] User ID: 12345

[0928] Weight: 70kg

[0929] Blood pressure: 135 / 85

[0930] Stress level: 80

[0931] "I recommend taking protein and doing yoga for relaxation."

[0932] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0933] Step 1:

[0934] The device displays a health information entry screen. The user enters information such as weight, diet, exercise habits, and past medical history, and then presses the "Submit" button.

[0935] Input: Health information (e.g., weight 70kg, breakfast contents, exercise habits)

[0936] Output: Encrypted health information data

[0937] Specific operation: The terminal displays an input screen, and when the user enters information and presses the "Send" button, the data is encrypted and sent to the server.

[0938] Step 2:

[0939] The server stores the received health information in a database and passes the data to the artificial intelligence (AI) and emotion engine.

[0940] Input: Encrypted health information data

[0941] Output: Analysis results of health information

[0942] Specific operation: The server stores the received data in a database, then sends the data to the AI ​​and emotion engine, where analysis begins.

[0943] Step 3:

[0944] The server's AI evaluates health risks based on health information, and the emotion engine recognizes the user's emotional state.

[0945] Input: Health information in the database

[0946] Output: Health risk assessment and emotional state analysis results

[0947] How it works: The AI ​​analyzes BMI and past medical history to assess health risks, while the emotion engine analyzes facial expressions and tone of voice to recognize emotional states.

[0948] Step 4:

[0949] The server generates follow-up questions based on the health risk assessment results and emotional state and sends them to the terminal.

[0950] Input: Health risk assessment and emotional state analysis results

[0951] Output: Follow-up question

[0952] What it does: Based on your health risks and emotional state, the AI ​​generates customized follow-up questions and sends them to your device.

[0953] Step 5:

[0954] The terminal displays follow-up questions to the user, who answers the questions and sends the answers to the server.

[0955] Input: Answer to follow-up question

[0956] Output: Response data

[0957] Specific behavior: The device displays a follow-up question, the user enters an answer, and sends it back to the server.

[0958] Step 6:

[0959] The server analyzes the answers to the follow-up questions and the emotional state, generates lifestyle advice, and sends it to the terminal.

[0960] Input: Answers to follow-up questions and emotional state analysis results

[0961] Output: Lifestyle guidance suggestions

[0962] Specific operation: AI analyzes the response data and emotional state, generates personalized lifestyle advice, and sends the advice to the device.

[0963] Step 7:

[0964] The terminal displays the received lifestyle advice to the user, who then follows the advice.

[0965] Input: Lifestyle guidance suggestions

[0966] Output: Check and execute the instruction

[0967] Specific operation: The device notifies the user of lifestyle guidance, and the user confirms and implements the content.

[0968] Step 8:

[0969] The terminal works in conjunction with the wearable device to collect the user's exercise data and biometric data and send it to a server.

[0970] Input: Wearable device data

[0971] Output: Exercise data and biological data

[0972] Specific operation: The terminal acquires data from the wearable device and sends it to the server.

[0973] Step 9:

[0974] The server analyzes the progress data, potentially providing further guidance or corrections, and also monitors changes in emotional state.

[0975] Input: Movement data, biometric data, emotional state data

[0976] Output: Additional lifestyle and / or remedial instructions

[0977] Specific behavior: Based on the progress, the AI ​​generates additional guidance, and the emotion engine analyzes changes in the user's emotional state and generates advice as needed.

[0978] Step 10:

[0979] The device notifies the user of additional lifestyle guidance and correction instructions, and the user responds accordingly.

[0980] Input: Additional lifestyle guidance and / or correction instructions

[0981] Output: New lifestyle advice implemented

[0982] Specific behavior: The device notifies the user of new instruction content, and the user acts accordingly.

[0983] 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.

[0984] 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.

[0985] 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.

[0986] [Third embodiment]

[0987] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0988] 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.

[0989] 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).

[0990] 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.

[0991] 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.

[0992] 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).

[0993] 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.

[0994] 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.

[0995] 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.

[0996] 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.

[0997] 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.

[0998] 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."

[0999] ---

[1000] This invention is a system that helps users lead healthy lives. The system is mainly composed of three main parts: a terminal, a server, and a user. Here, the processing of the system's program is explained in natural language, and its operation is described in detail with concrete examples.

[1001] System Configuration

[1002] The system consists of a device (such as a smartphone or tablet) that users access, a server that analyzes the data and provides guidance, and ultimately the user who receives health management.

[1003] 1. Display and enter information on the medical questionnaire

[1004] The device displays a screen for entering health information, including weight, diet, exercise habits, and past medical history.

[1005] Users enter their health information and transmit it to a server via their device, where the data is securely encrypted.

[1006] Examples:

[1007] The user opens the app on their smartphone, enters their weight (70 kg), meal details (breakfast: bread and coffee, lunch: salad and sandwich, dinner: fish and rice), and jogging three times a week (30 minutes) on the health information input screen, and presses the send button.

[1008] 2. Interview with AI

[1009] The server receives the submitted health information and analyzes it using artificial intelligence (AI), which uses the collected data to assess health risks and generate follow-up questions as needed.

[1010] The terminal displays follow-up questions from the server to the user, who answers the questions and transmits the answers to the server via the terminal.

[1011] Examples:

[1012] The server analyzes the user's health information, calculates the user's BMI, and sends a follow-up question about the need for weight management to the device. The user answers "Yes" to the question displayed on the device, "Have you been feeling tired easily recently?", and the data is sent.

[1013] 3. Creating and providing lifestyle guidance

[1014] The server then analyzes the answers to follow-up questions again and generates personalized lifestyle advice for the user, including dietary suggestions, exercise plans, and sleep advice.

[1015] The device provides the generated lifestyle advice to the user in real time, and the user can easily check the advice content through the app.

[1016] Examples:

[1017] Based on the analysis results, the server generates specific lifestyle advice for the user, such as "take protein for breakfast" and "recommend strength training once a week," and sends it to the device. The user can then confirm the advice displayed on the device.

[1018] 4. Progress Management

[1019] The device works in conjunction with wearable devices to collect the user's exercise and biometric data, including the number of steps taken, distance traveled, and heart rate.

[1020] The device sends the collected data to a server that analyzes it to monitor lifestyle progress and provide further guidance as needed.

[1021] Examples:

[1022] The user puts on the wearable device and goes jogging. The data (5km run distance, heart rate, etc.) is recorded in the app and automatically sent from the device to the server. Based on the data, the server suggests "increasing the frequency of exercise to four times a week," and a notification is displayed on the device.

[1023] The above is a specific embodiment for carrying out the invention. This system makes it easier for users to receive personalized lifestyle advice and to manage their health on an ongoing basis.

[1024] The processing flow will be explained below.

[1025] ---

[1026] Step 1:

[1027] The device displays a health information entry screen, which includes input items such as the user's weight, diet, exercise habits, living environment, and past medical history.

[1028] Step 2:

[1029] The user enters health information and presses buttons such as "Confirm" and "Send" to send the data. This input data is encrypted and securely sent from the device to the server.

[1030] Step 3:

[1031] The server stores the received health information in a database, then passes the data to an artificial intelligence (AI) that begins assessing basic health risks.

[1032] Step 4:

[1033] The server uses AI to assess health risks and generate follow-up questions, if needed, that are customized based on the user's health information.

[1034] Step 5:

[1035] The device then prompts the user with follow-up questions from the server, including additional health information and lifestyle details.

[1036] Step 6:

[1037] The user answers the follow-up questions and sends the answers to the server via their device, where the answer data is again encrypted and securely sent to the server.

[1038] Step 7:

[1039] The server analyzes the answers to follow-up questions and integrates all the data to generate lifestyle advice. The AI ​​creates a specific action plan (e.g., dietary improvements, exercise plan, sleep advice).

[1040] Step 8:

[1041] The server transmits the generated lifestyle guidance to the terminal. The lifestyle guidance information is presented in real time and provided in a format that the user can access immediately.

[1042] Step 9:

[1043] The device displays the generated lifestyle advice to the user, who then checks the advice and follows the specific action plan within the application.

[1044] Step 10:

[1045] The terminal connects to a wearable device and collects the user's exercise and biological data, including the number of steps taken, distance traveled, heart rate, and sleep data.

[1046] Step 11:

[1047] The device sends the collected data to the server, which periodically transfers the data to the server and stores it as user progress information.

[1048] Step 12:

[1049] The server analyzes the progress data and generates personalized feedback for each user's lifestyle habits. If real-time guidance or changes to the action plan are needed, the server generates new suggestions.

[1050] Step 13:

[1051] The device will notify the user with new feedback and updated lifestyle advice, and the user can check their progress through the app and adjust their behavior according to the new advice.

[1052] ---

[1053] By taking these steps, the system can provide users with personalized, ongoing, and accurate health guidance and monitor progress in real time.

[1054] Example 1

[1055] 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."

[1056] In recent years, the increase in lifestyle-related diseases and stress-related diseases has become a social problem, creating a demand for personalized health management. However, current health management systems only provide information to everyone at once and do not provide specific guidance tailored to each user's individual lifestyle habits or health risks. As a result, users find it difficult to maintain sustainable health management and are unable to achieve effective health improvement. The present invention aims to solve these problems by providing a system that provides customized health management tailored to the user's lifestyle habits.

[1057] 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.

[1058] In this invention, the server includes means for collecting health information of the user, means for conducting an interview based on the collected health information using a generative AI model and generating follow-up questions, means for collecting and analyzing responses from the user based on the generated follow-up questions, means for providing the user with customized lifestyle advice based on the interview results and analysis results, and means for managing the progress of the user's lifestyle habits through the appliance and providing more specific advice as needed, thereby enabling specific and individualized lifestyle advice according to the user's health condition.

[1059] "User" refers to an individual who uses the health care system.

[1060] "Health information" refers to data about an individual's health condition and lifestyle, such as weight, diet, exercise habits, and past medical history.

[1061] A "generative AI model" refers to a system that uses artificial intelligence to analyze data and generate questions.

[1062] "Interview" refers to the process of using a generative AI model to collect information interactively based on the user's health information.

[1063] "Follow-up questions" refer to questions that are asked to obtain additional information that needs to be collected based on the initial health information or interview results.

[1064] "Analysis" refers to the process of analyzing collected data and responses to assess health risks and generate appropriate advice.

[1065] "Lifestyle guidance" refers to specific guidance provided to users based on the analysis results to improve their lifestyle habits, such as diet, exercise, and sleep.

[1066] "Appliance" refers to a hardware device used in a health care system, including, among other things, wearable devices.

[1067] A "wearable device" refers to a digital device worn by the user that is capable of collecting exercise data and biometric data.

[1068] "Large-scale data" refers to a large amount of data, including a wide range of datasets such as health information and lifestyle data.

[1069] That's all.

[1070] This invention is a system that supports users in living a healthy lifestyle. The system is broadly divided into three main parts: a terminal, a server, and a user. Specific embodiments are described below.

[1071] System Configuration

[1072] The system consists of a terminal accessed by the user, a server that analyzes the data and provides guidance, and ultimately the user who receives health management.

[1073] Terminal

[1074] The terminal is a device such as a smartphone or tablet. The terminal displays a health information input screen, collects health information from the user, and sends it to the server. The terminal also displays follow-up questions and lifestyle advice from the server and provides them in a form that the user can check.

[1075] server

[1076] The server is the central component for analyzing the received health information. It uses generative AI models (e.g., OpenAI's GPT-4) to analyze the collected data and generate follow-up questions and customized lifestyle advice. The server also analyzes exercise and biometric data provided in real time to monitor progress.

[1077] User

[1078] Users are individuals who use the system to input their own health information. They use a terminal to enter their health information, answer follow-up questions, and confirm lifestyle advice. They also wear a wearable device to provide daily exercise data and biometric data to the system.

[1079] Data entry and analysis

[1080] Entering health information

[1081] The device displays a health information input screen to the user. The device used here is a smartphone or tablet. Detailed health information such as weight, diet, exercise habits, and past medical history is entered.

[1082] The user enters their health information and presses the send button. The input data is temporarily stored on the device, then securely encrypted and sent to the server.

[1083] Analyze data and generate follow-up questions

[1084] The server decrypts the received data and uses large-scale data to analyze it using a generative AI model, which is then used to assess health risks and generate necessary follow-up questions.

[1085] The terminal displays follow-up questions from the server to the user, who answers these questions and sends them back to the server via the terminal.

[1086] Creating and providing lifestyle guidance

[1087] The server then analyzes the follow-up questions and generates personalized lifestyle advice for the user, including dietary suggestions, exercise plans, and sleep tips.

[1088] The device provides the generated lifestyle advice to the user in real time, and the user can check the advice content through the app.

[1089] Progress management

[1090] Wearable device integration

[1091] The device works in conjunction with wearable devices (such as Fitbit and other smartwatches) to collect the user's exercise data and biometric data (such as steps taken, distance traveled, and heart rate).

[1092] The device encrypts the collected data and sends it to the server.

[1093] The server analyzes this data, monitors the user's lifestyle progress, and provides further specific guidance as needed.

[1094] Examples of prompt statements

[1095] The following are examples of prompts that users can use when receiving support from AI, which will allow the generative AI model to generate appropriate questions and advice.

[1096] "Please tell me your recent jogging record."

[1097] "What foods do you eat for breakfast?"

[1098] "Have you been feeling tired easily lately?"

[1099] The above is a concrete example of how to implement this healthy lifestyle support system. This system makes it easier for users to receive personalized lifestyle guidance and to manage their health on an ongoing basis.

[1100] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1101] Processing flow

[1102] Step 1: Enter your health information

[1103] The device displays a health information entry screen to the user, where detailed health information such as weight, diet, exercise habits, and past medical history can be entered.

[1104] Specific operation: The device launches the application and displays the health information input screen.

[1105] Input: Health information entered by the user (e.g., weight 70 kg, bread and coffee for breakfast, jogging three times a week).

[1106] Output: Encrypted health information data.

[1107] Step 2: Submit your health information

[1108] The user enters their health information and presses the send button. The data is temporarily stored on the device and then securely encrypted and sent to the server.

[1109] Specific operation: The user completes the input and presses the "Send" button. The device AES encrypts the data and generates a request to send to the server.

[1110] Input: The submit button pressed by the user and the health information entered.

[1111] Output: Encrypted health information data sent to the server.

[1112] Step 3: Receiving and analyzing data

[1113] The server decrypts the received data and stores it in a database, after which it uses a generative AI model to analyze the data and generate follow-up questions.

[1114] What it does: The server decrypts the data and stores it in a database. A Python script is used to invoke a generative AI model (e.g., OpenAI's GPT-4) to analyze the health information.

[1115] Input: Encrypted health information data.

[1116] Output: Decoded health information and generated follow-up questions (e.g., "Have you been feeling tired lately?").

[1117] Step 4: View follow-up questions

[1118] The terminal displays the follow-up questions received from the server to the user, who answers these questions and sends them back to the server via the terminal.

[1119] Specific operation: The device receives and displays a question from the server. The user answers the question, and the device sends the answer back to the server.

[1120] Input: The generated follow-up question (e.g., "Have you been feeling tired lately?") and the user's answer (e.g., "Yes").

[1121] Output: User response data.

[1122] Step 5: Create a life lesson

[1123] The server again analyzes the answers to the follow-up questions and generates lifestyle advice customized for the user.

[1124] Specific operation: The server inputs the response data into a generative AI model and generates lifestyle advice based on the analysis results (e.g., "Add protein to your breakfast").

[1125] Input: User response data.

[1126] Output: Customized lifestyle guidance data.

[1127] Step 6: Providing lifestyle guidance

[1128] The device provides the generated lifestyle advice to the user in real time, and the user can check the advice content through the app.

[1129] Specific operation: The device receives lifestyle guidance data from the server and displays it to the user on the application.

[1130] Input: Customized life coaching data.

[1131] Output: A lifestyle advice screen viewable by the user.

[1132] Step 7: Collect and send exercise data

[1133] The terminal works in conjunction with a wearable device to collect the user's exercise and biometric data, which is then encrypted and sent to a server.

[1134] Specific operation: The terminal collects data from the wearable device via Bluetooth, encrypts it, and sends it to the server.

[1135] Input: Exercise data from wearable devices (e.g., steps, distance traveled, heart rate).

[1136] Output: Encrypted exercise data.

[1137] Step 8: Track your progress

[1138] The server analyzes the received exercise data and manages the user's progress in lifestyle habits, providing more specific guidance as needed.

[1139] Specific operation: The server analyzes the exercise data and generates new guidance tailored to the user's lifestyle (e.g., "Increase the frequency of exercise to four times a week").

[1140] Input: Exercise data from a wearable device.

[1141] Output: New lifestyle guidance data according to progress.

[1142] This is the flow of the system's program processing, which allows users to receive detailed and individualized lifestyle advice and enable continuous health management.

[1143] (Application example 1)

[1144] 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."

[1145] Conventional health management systems primarily function to collect users' health information and provide lifestyle advice, but lack the mechanisms to actually encourage users to act on that advice. There was also a need to reduce the effort required for users to purchase suggested products and services. In particular, the financial procedures required to promote the use of health foods and exercise facilities were complicated, preventing users from following the recommendations.

[1146] 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.

[1147] In this invention, the server includes means for collecting health information of the user, means for conducting interviews based on the health information collected using artificial intelligence, means for providing the user with lifestyle advice based on the interview results, means for managing the progress of the user's lifestyle habits through an appliance, means for making electronic payments for products and services based on the health information, and means for making it easy to purchase the suggested products and services. This enables the user to immediately take action based on the advice and to more effectively experience the effects of health management.

[1148] "User" refers to an individual who uses this system to manage health information and receive lifestyle guidance.

[1149] "Health information" refers to data provided by users, such as weight, diet, exercise habits, and past medical history.

[1150] "Artificial intelligence" refers to technology that performs advanced calculations such as data analysis and pattern recognition to provide appropriate lifestyle guidance and generate questions.

[1151] "Interview" refers to the process of analyzing health information collected using artificial intelligence and asking follow-up questions and giving advice to users.

[1152] "Lifestyle guidance" refers to specific methods and suggestions for making users' lives healthier, such as suggestions for improving their diet, exercise plans, and sleep advice, based on the user's health information and the results of the interview.

[1153] "Progress" refers to the health management activities carried out by the user and their results, including exercise data and biometric data.

[1154] "Appliance" refers to a device or equipment used to collect and manage user health data, including, for example, a smartphone or wearable device.

[1155] A "wearable device" refers to an electronic device worn by a user, which collects exercise data and biometric data in real time.

[1156] "Big data" refers to large data sets and the technology for analyzing them to extract useful information and patterns.

[1157] "Electronic payment" refers to the process by which a consumer pays for goods or services using digital means.

[1158] "Products and services" refers to supplements, healthy foods, and use of fitness facilities suggested based on health management guidance.

[1159] The details of the system including the actions of the server, terminals and users are described below for the embodiment of the present invention.

[1160] The system mainly consists of a device (such as a smartphone) that users access, a server that analyzes data and provides guidance, and ultimately the user who receives health management.

[1161] 1. Collection of health information

[1162] The server prompts users to enter their health information using a device such as a smartphone. This health information includes weight, diet, exercise habits, and past medical history. The user enters this information into their device and sends it to the server. The transmitted data is securely encrypted.

[1163] As a concrete example, a user opens a smartphone app and inputs their weight (70 kg), meal details (breakfast: bread and coffee, lunch: salad and sandwich, dinner: fish and rice), and jogging three times a week (30 minutes). Then, they press the send button to send the data to the server.

[1164] 2. AI-based analysis and interviews

[1165] The server receives the health information and analyzes it using a generative AI model. The AI ​​evaluates health risks based on the collected data and generates follow-up questions as needed. The device displays the follow-up questions sent from the server to the user and sends the answers back to the server.

[1166] For example, the server analyzes health information, calculates the user's BMI, and sends a follow-up question about the need for weight management to the device. The user answers "yes" to the question displayed on the device, "Have you been feeling tired easily recently?", and the message is sent.

[1167] 3. Lifestyle guidance and electronic payment

[1168] The server analyzes the answers to the follow-up questions and generates personalized lifestyle advice for the user, including dietary suggestions, exercise plans, and sleep advice, as well as a means to electronically pay for suggested products and services (such as supplements or fitness facilities) based on the health information.

[1169] The device provides users with the generated lifestyle advice and suggested products and services in real time, and users can check the advice through the app and easily purchase the suggested products and services.

[1170] For example, the server sends advice such as "take protein for breakfast" and "recommend strength training once a week" to the terminal. It also suggests purchasing protein (3,000 yen), and the user completes the purchase procedure by making an electronic payment on the spot.

[1171] 4. Progress management and follow-up

[1172] The server analyzes data (such as number of steps, distance traveled, and heart rate) obtained from the wearable device to monitor the user's health management progress. Based on this data, the server provides further appropriate health guidance and follow-up.

[1173] As a concrete example, a user puts on a wearable device and goes jogging. The app records the distance traveled (5km), heart rate, and other data, and automatically sends the data from the device to a server. Based on the analysis results, the server suggests increasing the frequency of exercise to four times a week, and a notification is displayed on the device.

[1174] Example prompt sentence:

[1175] Analyze the user's health status based on the following input data and suggest necessary advice and actions:

[1176] Weight: 70kg

[1177] Meals:

[1178] Breakfast: Bread and coffee

[1179] Lunch: Salad and sandwich

[1180] Dinner: Fish and rice

[1181] Exercise habits: Jogging three times a week (30 minutes)

[1182] As described above, this system makes it easier for users to receive personalized lifestyle advice and actually take action, making it possible for them to experience the benefits of health management over time.

[1183] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1184] Step 1:

[1185] The server prompts users to enter their health information using a smartphone or tablet device. Users enter data such as weight, diet, exercise habits, and past medical history, and this data is sent from the device to the server. The data is encrypted before being transmitted.

[1186] Input: User's health information (e.g., weight 70 kg, dietary details, exercise habits)

[1187] Data processing / calculation: Check the format of input data and encrypt it

[1188] Output: Encrypted health information data

[1189] Step 2:

[1190] The server receives the submitted health information and analyzes it using a generative AI model, assessing health risks based on the input data and generating follow-up questions as needed.

[1191] Input: Encrypted health information data

[1192] Data processing / computation: Decode data, assess health risks, generate follow-up questions

[1193] Output: Health risk assessment results, follow-up questions

[1194] Step 3:

[1195] The terminal displays the follow-up questions sent from the server to the user, who answers the questions and sends the answers to the server via the terminal.

[1196] Input: Health risk assessment results, follow-up questions

[1197] Data processing / calculation: Displaying follow-up questions to users, collecting and encrypting answer data

[1198] Output: Encrypted follow-up question answers

[1199] Step 4:

[1200] The server receives the answers to the follow-up questions, analyzes them again using the generative AI model, and generates optimal lifestyle advice for the user, including dietary suggestions, exercise plans, and sleep advice, as well as electronic payment methods for the suggested products and services.

[1201] Input: Encrypted follow-up question response data

[1202] Data processing / calculation: Decryption of response data, detailed analysis, generation of lifestyle guidance and electronic payment proposals

[1203] Output: Lifestyle guidance, suggested products and services (with electronic payment methods)

[1204] Step 5:

[1205] The device provides users with the generated lifestyle advice and suggested products and services in real time, and users can check the advice through the app and easily purchase the suggested products and services.

[1206] Input: Lifestyle guidance, suggested products and services

[1207] Data processing / calculation: User display, electronic payment processing

[1208] Output: Lifestyle advice provided to the user, products and services purchased

[1209] Step 6:

[1210] The server analyzes the exercise and biological data obtained from the wearable device to manage the user's health management progress, and provides further appropriate health guidance and follow-up as needed.

[1211] Input: Exercise data and biometric data from wearable devices

[1212] Data processing / calculation: Data analysis, progress management, generation of additional health guidance and follow-up

[1213] Output: Progress management results, additional health guidance and follow-up

[1214] 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.

[1215] ---

[1216] This invention is a system that supports users in living a healthy lifestyle. The system collects the user's health information, analyzes their health status using artificial intelligence and an emotion engine, and provides personalized lifestyle advice based on the results. It also manages the user's progress in real time and provides appropriate advice that takes their emotional state into account. The system's main components are a terminal, a server, and a user.

[1217] System Configuration

[1218] 1. Display and enter information on the medical questionnaire

[1219] The device displays a health information entry screen, where the user enters information such as weight, diet, exercise habits, and past medical history.

[1220] The user enters the necessary health information and presses the "Send" button to send the data. The data is encrypted and securely sent from the device to the server.

[1221] Examples:

[1222] The user opens the smartphone app, enters their weight (70 kg), breakfast (bread and coffee), and exercise habits (30 minutes of jogging three times a week) on the health information input screen, and presses the "Submit" button.

[1223] 2. Evaluation by AI and emotion engine

[1224] The server stores the received health information in a database and passes the data to the artificial intelligence (AI) and emotion engine.

[1225] The server's AI assesses health risks based on collected data, while the emotion engine recognizes the user's emotional state from facial expressions, tone of voice, and text input.

[1226] Examples:

[1227] The server analyzes the user's medical data to assess their BMI and risk of lack of exercise, while the emotion engine uses the smartphone's camera and microphone to recognize whether the user is feeling stressed.

[1228] 3. Follow-up questions

[1229] The server generates follow-up questions based on the health risk assessment results and emotional state, and the questions are customized based on the user's current health and emotional state.

[1230] The terminal displays the generated follow-up questions to the user, who answers the questions and transmits the answers from the terminal to the server.

[1231] Examples:

[1232] The server generates follow-up questions such as "Have you been feeling tired lately?" and "How much stress do you usually feel?" and displays them on the device. The user enters and submits the answers.

[1233] 4. Creating and providing lifestyle guidance

[1234] The server then analyzes the follow-up questions and the user's emotional state again to generate optimal lifestyle advice, including dietary suggestions, exercise plans, and sleep advice.

[1235] The device displays the generated lifestyle advice to the user, who can check the advice in real time and apply it to their own behavior.

[1236] Examples:

[1237] The server generates specific lifestyle advice such as "Eat protein for breakfast" and "Try strength training once a week." It also includes advice that takes into account emotional states, such as "Try meditating to relieve stress." These instructions are displayed on the device and notified to the user.

[1238] 5. Progress Management

[1239] The terminal connects to the wearable device and collects the user's exercise and biological data, which is then sent to a server and managed as the user's progress.

[1240] The server analyzes the progress data and provides further guidance or corrections as needed. The emotion engine also monitors changes in the user's emotional state and generates adaptive advice.

[1241] Examples:

[1242] The user puts on the wearable device and goes jogging, and the data is recorded in the app. This data is sent to the server and saved as progress. The server then suggests, "Increase your exercise frequency to four times a week." If the emotion engine detects an increase in stress levels, the app will provide new guidance, such as, "Try yoga to relax."

[1243] The above is a specific embodiment for carrying out the invention. This system allows users to receive personalized lifestyle advice in real time and to perform comprehensive health management, including their emotional state.

[1244] The processing flow will be explained below.

[1245] ---

[1246] Step 1:

[1247] The device displays a screen for entering health information, including weight, diet, exercise habits, living environment, and past medical history.

[1248] Step 2:

[1249] The user enters their health information and presses the "Confirm" and "Send" buttons to send the data. The data is encrypted and securely sent from the device to the server.

[1250] Step 3:

[1251] The server stores the received health information in a database and passes the data to the artificial intelligence (AI) and emotion engine, which then begin processing simultaneously.

[1252] Step 4:

[1253] The server's AI will assess health risks based on health information, analyzing indicators such as BMI, exercise habits, and dietary balance.

[1254] Step 5:

[1255] Using the device's built-in camera and microphone, the emotion engine analyzes the user's facial expressions, tone of voice, and text input to recognize the user's emotional state.

[1256] Step 6:

[1257] The server integrates the AI-based health risk assessment results with the emotional state of the emotion engine to generate follow-up questions, which are customized based on the user's current health and emotional state.

[1258] Step 7:

[1259] The device displays follow-up questions from the server to the user, who answers them and sends the answers from the device to the server, where they are again encrypted and securely transmitted to the server.

[1260] Step 8:

[1261] The server receives and analyzes the answers to follow-up questions. Here, the AI ​​integrates the answer data with health information to generate optimal lifestyle advice. The emotion engine also provides advice based on the user's emotions based on this analysis process.

[1262] Step 9:

[1263] The server then sends the generated lifestyle guidance and emotion-based advice to the device, including advice on improving diet, exercise suggestions, and stress management.

[1264] Step 10:

[1265] The device displays the generated lifestyle guidance and advice to the user, who then checks the advice in real time and implements the action plan.

[1266] Step 11:

[1267] The device connects to wearable devices to collect the user's exercise and biometric data, including steps taken, distance traveled, heart rate, and sleep data.

[1268] Step 12:

[1269] The device sends the collected data to the server, which periodically transfers the data to the server and stores it as the user's progress.

[1270] Step 13:

[1271] The server analyzes the progress data and provides further guidance or modification of the action plan based on the progress. The emotion engine monitors changes in the user's emotional state and updates appropriate feedback and advice.

[1272] Step 14:

[1273] The device notifies the user of new feedback and updated lifestyle advice, and the user can check their progress through the app and adjust their behavior according to the new advice.

[1274] ---

[1275] Through these steps, the system can provide users with personalized, continuous and accurate health guidance, and manage their progress and emotions in real time.

[1276] Example 2

[1277] 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."

[1278] In modern society, there are few systems that simultaneously monitor users' health and emotional states, and personalized lifestyle advice is lacking. This makes it easy for many users to overlook health risks and makes it difficult to effectively improve their lifestyles. Furthermore, there is no system that provides adaptive advice that takes users' emotional states into account, making it difficult to understand and improve a user's overall health.

[1279] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting health information of the user, means for performing an assessment based on the collected health information using artificial intelligence, means for generating follow-up questions based on the assessment results and the emotional state, means for collecting and analyzing the user's answers to the generated follow-up questions, means for providing lifestyle advice based on the user's answers and the emotional state, means for managing the user's lifestyle progress through an appliance, and means for monitoring the user's emotional state using an emotion engine and providing adaptive advice. This enables the user to receive personalized lifestyle advice in real time and achieve comprehensive health management including the emotional state.

[1280] "Health information" refers to various data used to understand the user's health condition, such as the user's weight, diet, exercise habits, and past medical history.

[1281] "Artificial intelligence" is a technology that enables computer systems to mimic human intelligence to process, evaluate, and analyze information.

[1282] "Evaluation" is the process of analyzing and determining the user's health risks and condition based on collected health information.

[1283] "Emotional state" indicates the user's psychological state and is determined from facial expressions, tone of voice, text input, and the like.

[1284] "Follow-up questions" are questions that require additional answers from the user based on the health risk assessment results or emotional state.

[1285] "Lifestyle guidance" refers to instructions such as dietary suggestions, exercise plans, and sleep advice provided based on the user's health and emotional state.

[1286] "Appliance" refers to any equipment or device used to collect and transmit health data, including wearable devices.

[1287] "Emotion engine" is a general term for technologies and systems that analyze and recognize users' psychological emotions.

[1288] "Adaptive advice" refers to advice that is individually tailored to the user based on their current health data and emotional state.

[1289] This invention is a system that helps users lead healthy and emotionally balanced lives. The system collects the user's health information and emotional state, analyzes it using artificial intelligence and an emotion engine, and provides personalized lifestyle advice based on the results. Furthermore, the system manages the user's progress in real time and provides appropriate advice that takes into account the user's emotional state.

[1290] System Configuration

[1291] The system consists of devices, servers, and users. Specific hardware and software components include devices such as smartphones and tablets, cloud servers, wearable devices equipped with various sensors, and health applications that users use on a daily basis.

[1292] Collection of health information

[1293] The device displays a health information input screen, and the user enters information such as weight, diet, exercise habits, and past medical history. This information is encrypted and sent from the device to the server. For example, a user opens a smartphone app, enters their weight as "70 kg," their breakfast as "bread and coffee," and their exercise habits as "30 minutes of jogging three times a week," and presses the "Send" button.

[1294] Data encryption and transmission

[1295] The device encrypts the entered health information using the AES encryption method and sends it to the server via HTTPS. The server then decrypts the received encrypted data and stores it in a database.

[1296] Evaluation by AI and emotion engine

[1297] The server passes the stored health information to an artificial intelligence (AI) and emotion engine. The AI ​​assesses health risks based on the collected data (using, for example, TensorFlow and PyTorch), and the emotion engine recognizes the user's emotional state from their facial expressions, tone of voice, and text input. For example, it evaluates the user's BMI and risk of lack of exercise, and the emotion engine analyzes their stress level using the smartphone's camera and microphone.

[1298] Follow-up question generation

[1299] The server generates follow-up questions based on the health risk assessment results and emotional state. These questions are customized based on the user's current health and emotional state and displayed on the device. For example, the server generates questions such as "Have you been feeling tired easily recently?" or "How much stress do you usually feel?", and the user enters and submits the answers.

[1300] Creating and providing lifestyle guidance

[1301] The server then analyzes the user's responses to the follow-up questions and their emotional state again to generate optimal lifestyle advice. This advice includes suggestions for improving their diet, exercise plans, and sleep advice. The device displays the generated lifestyle advice to the user, who can review it in real time and apply it to their own behavior. For example, specific advice such as "Eat protein for breakfast," "Try strength training once a week," and "Try meditating to relieve stress" is provided.

[1302] Progress monitoring and adaptive advice

[1303] The terminal works in conjunction with a wearable device to collect the user's exercise data and biometric data in real time and send it to a server. This data is stored on the server and managed as progress. The server analyzes the progress data and provides further guidance or corrections as needed. The emotion engine monitors changes in the user's emotional state and generates adaptive advice. For example, based on the user's exercise frequency data, it may suggest "increase your exercise frequency to four times a week," or if it detects an increase in stress levels, it may provide new advice such as "try yoga to relax."

[1304] The above is a specific embodiment for carrying out the invention. This system allows users to receive personalized lifestyle advice in real time and enables comprehensive health management, including emotional state.

[1305] Prompt Sentence Examples

[1306] "Please explain in natural language the process flow of a system that collects a user's health information (e.g., weight, diet, exercise habits), assesses their stress level using an emotion engine, and provides personalized health guidance."

[1307] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1308] Program processing flow and specific explanation

[1309] Step 1:

[1310] The device displays a health information input screen. The user inputs information such as weight, diet, exercise habits, and past medical history. The input includes data entered by the user (weight, diet, exercise, and medical history).

[1311] Specific behavior:

[1312] The device displays a "health data entry" screen on the smartphone or tablet screen, where users can enter information such as their weight (70 kg), breakfast (bread and coffee), and exercise habits (30 minutes of jogging three times a week), then press the "send" button.

[1313] Step 2:

[1314] The terminal encrypts the input data using the AES encryption method. The input includes the data entered by the user in step 1.

[1315] Specific behavior:

[1316] The terminal handles the user's input data and encrypts it using an encryption algorithm called AES (Advanced Encryption Standard), generating encrypted data.

[1317] Step 3:

[1318] The device sends encrypted data to the server using HTTPS communication. The input is encrypted health data.

[1319] Specific behavior:

[1320] The device sends the encrypted health data to the server via HTTPS (Hypertext Transfer Protocol Secure) communication. This communication protocol ensures that the data is transmitted securely. After transmission, the data arrives at the server.

[1321] Step 4:

[1322] The server stores the received health data in a database. The input is the encrypted health data arriving at the server.

[1323] Specific behavior:

[1324] The server decrypts the received data and stores it in a database. The database uses a relational database system (RDBMS) such as SQL (Structured Query Language). The stored data is generated.

[1325] Step 5:

[1326] The server passes the data to an artificial intelligence (AI) to assess health risks. The inputs are health data stored in a database and an AI model.

[1327] Specific behavior:

[1328] The server inputs the stored data into an AI model (e.g., a deep learning model using TensorFlow or PyTorch) to evaluate the user's health risks (e.g., BMI value, risk of lack of exercise), and generates an evaluation result.

[1329] Step 6:

[1330] The server passes data to the emotion engine to recognize the user's emotional state. The inputs are the evaluation results, the user's camera footage, and audio data.

[1331] Specific behavior:

[1332] The server uses the user's camera footage and audio data to call an emotion engine (e.g., IBM Watson or Microsoft Azure Emotion API) to recognize the user's emotional state, generating emotional state data.

[1333] Step 7:

[1334] The server generates follow-up questions based on the health risk assessment results and emotional state. The inputs are the assessment results and emotional state data.

[1335] Specific behavior:

[1336] The server handles the evaluation results of the AI ​​model and emotion engine and generates follow-up questions based on this information. The questions are dynamically generated based on templates, and the generated follow-up questions are sent to the device.

[1337] Step 8:

[1338] The terminal displays the generated follow-up question to the user, and has as input the generated follow-up question.

[1339] Specific behavior:

[1340] The device displays follow-up questions such as "Have you been feeling tired lately?" and "How stressed do you usually feel?" and is ready for the user to enter their answers.

[1341] Step 9:

[1342] The user answers the follow-up questions and sends the answers from the device to the server. The user's answer data is entered into the device.

[1343] Specific behavior:

[1344] The user enters an answer into the question form and presses the "Submit" button to send the answer to the server. The sent answer reaches the server.

[1345] Step 10:

[1346] The server analyzes the follow-up question answers and the emotional state again and generates optimal lifestyle advice. The inputs are the follow-up question answer data and the emotional state.

[1347] Specific behavior:

[1348] The server then uses the AI ​​model again to generate lifestyle advice based on the user's answers and emotional state, and provides the advice to the user.

[1349] Step 11:

[1350] The terminal displays the generated lifestyle guidance to the user. The generated lifestyle guidance data is input.

[1351] Specific behavior:

[1352] The device displays specific lifestyle advice such as "Eat protein for breakfast" and "Try strength training once a week." The user is then ready to confirm the advice.

[1353] Step 12:

[1354] The terminal connects to the wearable device, collects the user's exercise data and biological data in real time, and transmits it to the server. The input includes data obtained from the wearable device.

[1355] Specific behavior:

[1356] The device receives data from a wearable device (e.g., a smartwatch or fitness tracker), records the data in the app, and then sends the recorded data to a server.

[1357] Step 13:

[1358] The server analyzes the collected data and provides further guidance or corrections as needed. As input, the collected progress data is stored on the server.

[1359] Specific behavior:

[1360] The server then re-analyzes the stored data using an AI model to generate new lifestyle advice based on progress, and adjusts the advice as necessary.

[1361] Step 14:

[1362] The server uses an emotion engine to monitor changes in the user's emotional state and generate adaptive advice. The input is the user's emotional state data.

[1363] Specific behavior:

[1364] The server periodically calls the emotion engine to check changes in the user's emotional state, and based on the results, new advice, such as "Try yoga to relax," is generated and provided to the user.

[1365] (Application example 2)

[1366] 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."

[1367] Conventional health management systems collect and analyze users' health information and provide lifestyle advice, but because they are unable to take into account the user's emotional state, the lifestyle advice provided is uniform and it is difficult to provide optimal advice to each individual user.In addition, since they do not have a means to collect health information in real time using IoT devices in physical stores, there is an issue that they cannot provide immediate health management in physical stores.

[1368] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting health information of the user, means for conducting an interview based on the health information collected using artificial intelligence, means for providing lifestyle advice to the user based on the interview results, means for managing the progress of the user's lifestyle habits through an appliance, means for analyzing the user's emotional state using an emotion engine and reflecting the analysis in the lifestyle advice, and means for collecting health information using IoT devices in a physical store. This enables lifestyle advice based on the user's emotional state, and enables real-time collection of health information and immediate advice to be provided in a physical store.

[1369] "User" refers to a person who provides health information to the system and receives lifestyle guidance.

[1370] "Health information" refers to data about the user's health status, such as weight, blood pressure, diet, exercise habits, and past medical history.

[1371] "Artificial intelligence" refers to information processing technology that analyzes collected health information and assesses users' health risks.

[1372] "Interview" refers to a means of providing direct guidance and follow-up to users based on the results of health status analysis by artificial intelligence.

[1373] "Lifestyle guidance" refers to specific advice on diet, exercise, sleep, etc. provided to users based on health information and interviews.

[1374] "Progress management" refers to methods for tracking changes in daily lifestyle habits and health status and providing necessary support.

[1375] "Appliance" refers to any device or system used to collect health information and manage progress.

[1376] A "wearable device" refers to a device worn by the user that collects exercise data and biometric data in real time.

[1377] An "emotion engine" refers to technology that analyzes a user's emotional state from facial expressions, tone of voice, text input, etc.

[1378] "IoT devices" refer to physical store equipment such as scales, blood pressure monitors, and fitness machines that can send and receive data over the internet.

[1379] "Big data" refers to data processing techniques for analyzing huge data sets, and the results of this analysis are used to assess health risks.

[1380] This invention is a system that helps users lead healthy lives. The system collects the user's health information, analyzes the user's health status using artificial intelligence and an emotion engine, and provides personalized lifestyle advice based on the results. Furthermore, the system manages the user's progress in real time and provides appropriate advice taking into account the user's emotional state.

[1381] System Configuration

[1382] 1. Collection of health information

[1383] The device displays a health information entry screen, where the user enters information such as weight, diet, exercise habits, and past medical history.

[1384] The user enters the necessary health information and presses the "Send" button to send the data. The data is encrypted and securely sent from the device to the server.

[1385] Examples:

[1386] The user opens a fitness gym download app, enters their weight (70 kg), breakfast contents (bread and coffee), and exercise habits (30 minutes of jogging three times a week) on the health information input screen, and presses the "Submit" button.

[1387] 2. Evaluation by AI and emotion engine

[1388] The server stores the received health information in a database and passes the data to the artificial intelligence (AI) and emotion engine.

[1389] The server's AI assesses health risks based on collected data, while the emotion engine recognizes the user's emotional state from facial expressions, tone of voice, and text input.

[1390] Examples:

[1391] The server analyzes the user's medical data to assess their BMI and risk of lack of exercise, while the emotion engine uses the smartphone's camera and microphone to recognize whether the user is feeling stressed.

[1392] 3. Follow-up questions

[1393] The server generates follow-up questions based on the health risk assessment results and emotional state, and the questions are customized based on the user's current health and emotional state.

[1394] The terminal displays the generated follow-up questions to the user, who answers the questions and transmits the answers from the terminal to the server.

[1395] Examples:

[1396] The server generates follow-up questions such as "Have you been feeling tired lately?" and "How much stress do you usually feel?" and displays them on the device. The user enters and submits the answers.

[1397] 4. Creating and providing lifestyle guidance

[1398] The server then analyzes the follow-up questions and the user's emotional state again to generate optimal lifestyle advice, including dietary suggestions, exercise plans, and sleep advice.

[1399] The device displays the generated lifestyle advice to the user, who can check the advice in real time and apply it to their own behavior.

[1400] Examples:

[1401] The server generates specific lifestyle advice such as "Eat protein for breakfast" and "Try strength training once a week." It also includes advice that takes into account emotional states, such as "Try meditating to relieve stress." These instructions are displayed on the device and notified to the user.

[1402] 5. Progress Management

[1403] The terminal connects to the wearable device and collects the user's exercise and biological data, which is then sent to a server and managed as the user's progress.

[1404] The server analyzes the progress data and provides further guidance or corrections as needed. The emotion engine also monitors changes in the user's emotional state and generates adaptive advice.

[1405] Examples:

[1406] The user puts on the wearable device and goes jogging, and the data is recorded in the app. This data is sent to the server and saved as progress. The server then suggests, "Increase your exercise frequency to four times a week." If the emotion engine detects an increase in stress levels, the app will provide new guidance, such as, "Try yoga to relax."

[1407] Prompt Sentence Examples

[1408] User ID: 12345

[1409] Weight: 70kg

[1410] Blood pressure: 135 / 85

[1411] Stress level: 80

[1412] "I recommend taking protein and doing yoga for relaxation."

[1413] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1414] Step 1:

[1415] The device displays a health information entry screen. The user enters information such as weight, diet, exercise habits, and past medical history, and then presses the "Submit" button.

[1416] Input: Health information (e.g., weight 70kg, breakfast contents, exercise habits)

[1417] Output: Encrypted health information data

[1418] Specific operation: The terminal displays an input screen, and when the user enters information and presses the "Send" button, the data is encrypted and sent to the server.

[1419] Step 2:

[1420] The server stores the received health information in a database and passes the data to the artificial intelligence (AI) and emotion engine.

[1421] Input: Encrypted health information data

[1422] Output: Analysis results of health information

[1423] Specific operation: The server stores the received data in a database, then sends the data to the AI ​​and emotion engine, where analysis begins.

[1424] Step 3:

[1425] The server's AI evaluates health risks based on health information, and the emotion engine recognizes the user's emotional state.

[1426] Input: Health information in the database

[1427] Output: Health risk assessment and emotional state analysis results

[1428] How it works: The AI ​​analyzes BMI and past medical history to assess health risks, while the emotion engine analyzes facial expressions and tone of voice to recognize emotional states.

[1429] Step 4:

[1430] The server generates follow-up questions based on the health risk assessment results and emotional state and sends them to the terminal.

[1431] Input: Health risk assessment and emotional state analysis results

[1432] Output: Follow-up question

[1433] What it does: Based on your health risks and emotional state, the AI ​​generates customized follow-up questions and sends them to your device.

[1434] Step 5:

[1435] The terminal displays follow-up questions to the user, who answers the questions and sends the answers to the server.

[1436] Input: Answer to follow-up question

[1437] Output: Response data

[1438] Specific behavior: The device displays a follow-up question, the user enters an answer, and sends it back to the server.

[1439] Step 6:

[1440] The server analyzes the answers to the follow-up questions and the emotional state, generates lifestyle advice, and sends it to the terminal.

[1441] Input: Answers to follow-up questions and emotional state analysis results

[1442] Output: Lifestyle guidance suggestions

[1443] Specific operation: AI analyzes the response data and emotional state, generates personalized lifestyle advice, and sends the advice to the device.

[1444] Step 7:

[1445] The terminal displays the received lifestyle advice to the user, who then follows the advice.

[1446] Input: Lifestyle guidance suggestions

[1447] Output: Check and execute the instruction

[1448] Specific operation: The device notifies the user of lifestyle guidance, and the user confirms and implements the content.

[1449] Step 8:

[1450] The terminal works in conjunction with the wearable device to collect the user's exercise data and biometric data and send it to a server.

[1451] Input: Wearable device data

[1452] Output: Exercise data and biological data

[1453] Specific operation: The terminal acquires data from the wearable device and sends it to the server.

[1454] Step 9:

[1455] The server analyzes the progress data, potentially providing further guidance or corrections, and also monitors changes in emotional state.

[1456] Input: Movement data, biometric data, emotional state data

[1457] Output: Additional lifestyle and / or remedial instructions

[1458] Specific behavior: Based on the progress, the AI ​​generates additional guidance, and the emotion engine analyzes changes in the user's emotional state and generates advice as needed.

[1459] Step 10:

[1460] The device notifies the user of additional lifestyle guidance and correction instructions, and the user responds accordingly.

[1461] Input: Additional lifestyle guidance and / or correction instructions

[1462] Output: New lifestyle advice implemented

[1463] Specific behavior: The device notifies the user of new instruction content, and the user acts accordingly.

[1464] 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.

[1465] 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.

[1466] 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.

[1467] [Fourth embodiment]

[1468] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1469] 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.

[1470] 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).

[1471] 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.

[1472] 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.

[1473] 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).

[1474] 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.

[1475] 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.

[1476] 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.

[1477] 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.

[1478] 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.

[1479] 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.

[1480] 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."

[1481] ---

[1482] This invention is a system that helps users lead healthy lives. The system is mainly composed of three main parts: a terminal, a server, and a user. Here, the processing of the system's program is explained in natural language, and its operation is described in detail with concrete examples.

[1483] System Configuration

[1484] The system consists of a device (such as a smartphone or tablet) that users access, a server that analyzes the data and provides guidance, and ultimately the user who receives health management.

[1485] 1. Display and enter information on the medical questionnaire

[1486] The device displays a screen for entering health information, including weight, diet, exercise habits, and past medical history.

[1487] Users enter their health information and transmit it to a server via their device, where the data is securely encrypted.

[1488] Examples:

[1489] The user opens the app on their smartphone, enters their weight (70 kg), meal details (breakfast: bread and coffee, lunch: salad and sandwich, dinner: fish and rice), and jogging three times a week (30 minutes) on the health information input screen, and presses the send button.

[1490] 2. Interview with AI

[1491] The server receives the submitted health information and analyzes it using artificial intelligence (AI), which uses the collected data to assess health risks and generate follow-up questions as needed.

[1492] The terminal displays follow-up questions from the server to the user, who answers the questions and transmits the answers to the server via the terminal.

[1493] Examples:

[1494] The server analyzes the user's health information, calculates the user's BMI, and sends a follow-up question about the need for weight management to the device. The user answers "Yes" to the question displayed on the device, "Have you been feeling tired easily recently?", and the data is sent.

[1495] 3. Creating and providing lifestyle guidance

[1496] The server then analyzes the answers to follow-up questions again and generates personalized lifestyle advice for the user, including dietary suggestions, exercise plans, and sleep advice.

[1497] The device provides the generated lifestyle advice to the user in real time, and the user can easily check the advice content through the app.

[1498] Examples:

[1499] Based on the analysis results, the server generates specific lifestyle advice for the user, such as "take protein for breakfast" and "recommend strength training once a week," and sends it to the device. The user can then confirm the advice displayed on the device.

[1500] 4. Progress Management

[1501] The device works in conjunction with wearable devices to collect the user's exercise and biometric data, including the number of steps taken, distance traveled, and heart rate.

[1502] The device sends the collected data to a server that analyzes it to monitor lifestyle progress and provide further guidance as needed.

[1503] Examples:

[1504] The user puts on the wearable device and goes jogging. The data (5km run distance, heart rate, etc.) is recorded in the app and automatically sent from the device to the server. Based on the data, the server suggests "increasing the frequency of exercise to four times a week," and a notification is displayed on the device.

[1505] The above is a specific embodiment for carrying out the invention. This system makes it easier for users to receive personalized lifestyle advice and to manage their health on an ongoing basis.

[1506] The processing flow will be explained below.

[1507] ---

[1508] Step 1:

[1509] The device displays a health information entry screen, which includes input items such as the user's weight, diet, exercise habits, living environment, and past medical history.

[1510] Step 2:

[1511] The user enters health information and presses buttons such as "Confirm" and "Send" to send the data. This input data is encrypted and securely sent from the device to the server.

[1512] Step 3:

[1513] The server stores the received health information in a database, then passes the data to an artificial intelligence (AI) that begins assessing basic health risks.

[1514] Step 4:

[1515] The server uses AI to assess health risks and generate follow-up questions, if needed, that are customized based on the user's health information.

[1516] Step 5:

[1517] The device then prompts the user with follow-up questions from the server, including additional health information and lifestyle details.

[1518] Step 6:

[1519] The user answers the follow-up questions and sends the answers to the server via their device, where the answer data is again encrypted and securely sent to the server.

[1520] Step 7:

[1521] The server analyzes the answers to follow-up questions and integrates all the data to generate lifestyle advice. The AI ​​creates a specific action plan (e.g., dietary improvements, exercise plan, sleep advice).

[1522] Step 8:

[1523] The server transmits the generated lifestyle guidance to the terminal. The lifestyle guidance information is presented in real time and provided in a format that the user can access immediately.

[1524] Step 9:

[1525] The device displays the generated lifestyle advice to the user, who then checks the advice and follows the specific action plan within the application.

[1526] Step 10:

[1527] The terminal connects to a wearable device and collects the user's exercise and biological data, including the number of steps taken, distance traveled, heart rate, and sleep data.

[1528] Step 11:

[1529] The device sends the collected data to the server, which periodically transfers the data to the server and stores it as user progress information.

[1530] Step 12:

[1531] The server analyzes the progress data and generates personalized feedback for each user's lifestyle habits. If real-time guidance or changes to the action plan are needed, the server generates new suggestions.

[1532] Step 13:

[1533] The device will notify the user with new feedback and updated lifestyle advice, and the user can check their progress through the app and adjust their behavior according to the new advice.

[1534] ---

[1535] By taking these steps, the system can provide users with personalized, ongoing, and accurate health guidance and monitor progress in real time.

[1536] Example 1

[1537] 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."

[1538] In recent years, the increase in lifestyle-related diseases and stress-related diseases has become a social problem, creating a demand for personalized health management. However, current health management systems only provide information to everyone at once and do not provide specific guidance tailored to each user's individual lifestyle habits or health risks. As a result, users find it difficult to maintain sustainable health management and are unable to achieve effective health improvement. The present invention aims to solve these problems by providing a system that provides customized health management tailored to the user's lifestyle habits.

[1539] 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.

[1540] In this invention, the server includes means for collecting health information of the user, means for conducting an interview based on the collected health information using a generative AI model and generating follow-up questions, means for collecting and analyzing responses from the user based on the generated follow-up questions, means for providing the user with customized lifestyle advice based on the interview results and analysis results, and means for managing the progress of the user's lifestyle habits through the appliance and providing more specific advice as needed, thereby enabling specific and individualized lifestyle advice according to the user's health condition.

[1541] "User" refers to an individual who uses the health care system.

[1542] "Health information" refers to data about an individual's health condition and lifestyle, such as weight, diet, exercise habits, and past medical history.

[1543] A "generative AI model" refers to a system that uses artificial intelligence to analyze data and generate questions.

[1544] "Interview" refers to the process of using a generative AI model to collect information interactively based on the user's health information.

[1545] "Follow-up questions" refer to questions that are asked to obtain additional information that needs to be collected based on the initial health information or interview results.

[1546] "Analysis" refers to the process of analyzing collected data and responses to assess health risks and generate appropriate advice.

[1547] "Lifestyle guidance" refers to specific guidance provided to users based on the analysis results to improve their lifestyle habits, such as diet, exercise, and sleep.

[1548] "Appliance" refers to a hardware device used in a health care system, including, among other things, wearable devices.

[1549] A "wearable device" refers to a digital device worn by the user that is capable of collecting exercise data and biometric data.

[1550] "Large-scale data" refers to a large amount of data, including a wide range of datasets such as health information and lifestyle data.

[1551] That's all.

[1552] This invention is a system that supports users in living a healthy lifestyle. The system is broadly divided into three main parts: a terminal, a server, and a user. Specific embodiments are described below.

[1553] System Configuration

[1554] The system consists of a terminal accessed by the user, a server that analyzes the data and provides guidance, and ultimately the user who receives health management.

[1555] Terminal

[1556] The terminal is a device such as a smartphone or tablet. The terminal displays a health information input screen, collects health information from the user, and sends it to the server. The terminal also displays follow-up questions and lifestyle advice from the server and provides them in a form that the user can check.

[1557] server

[1558] The server is the central component for analyzing the received health information. It uses generative AI models (e.g., OpenAI's GPT-4) to analyze the collected data and generate follow-up questions and customized lifestyle advice. The server also analyzes exercise and biometric data provided in real time to monitor progress.

[1559] User

[1560] Users are individuals who use the system to input their own health information. They use a terminal to enter their health information, answer follow-up questions, and confirm lifestyle advice. They also wear a wearable device to provide daily exercise data and biometric data to the system.

[1561] Data entry and analysis

[1562] Entering health information

[1563] The device displays a health information input screen to the user. The device used here is a smartphone or tablet. Detailed health information such as weight, diet, exercise habits, and past medical history is entered.

[1564] The user enters their health information and presses the send button. The input data is temporarily stored on the device, then securely encrypted and sent to the server.

[1565] Analyze data and generate follow-up questions

[1566] The server decrypts the received data and uses large-scale data to analyze it using a generative AI model, which is then used to assess health risks and generate necessary follow-up questions.

[1567] The terminal displays follow-up questions from the server to the user, who answers these questions and sends them back to the server via the terminal.

[1568] Creating and providing lifestyle guidance

[1569] The server then analyzes the follow-up questions and generates personalized lifestyle advice for the user, including dietary suggestions, exercise plans, and sleep tips.

[1570] The device provides the generated lifestyle advice to the user in real time, and the user can check the advice content through the app.

[1571] Progress management

[1572] Wearable device integration

[1573] The device works in conjunction with wearable devices (such as Fitbit and other smartwatches) to collect the user's exercise data and biometric data (such as steps taken, distance traveled, and heart rate).

[1574] The device encrypts the collected data and sends it to the server.

[1575] The server analyzes this data, monitors the user's lifestyle progress, and provides further specific guidance as needed.

[1576] Examples of prompt statements

[1577] The following are examples of prompts that users can use when receiving support from AI, which will allow the generative AI model to generate appropriate questions and advice.

[1578] "Please tell me your recent jogging record."

[1579] "What foods do you eat for breakfast?"

[1580] "Have you been feeling tired easily lately?"

[1581] The above is a concrete example of how to implement this healthy lifestyle support system. This system makes it easier for users to receive personalized lifestyle guidance and to manage their health on an ongoing basis.

[1582] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1583] Processing flow

[1584] Step 1: Enter your health information

[1585] The device displays a health information entry screen to the user, where detailed health information such as weight, diet, exercise habits, and past medical history can be entered.

[1586] Specific operation: The device launches the application and displays the health information input screen.

[1587] Input: Health information entered by the user (e.g., weight 70 kg, bread and coffee for breakfast, jogging three times a week).

[1588] Output: Encrypted health information data.

[1589] Step 2: Submit your health information

[1590] The user enters their health information and presses the send button. The data is temporarily stored on the device and then securely encrypted and sent to the server.

[1591] Specific operation: The user completes the input and presses the "Send" button. The device AES encrypts the data and generates a request to send to the server.

[1592] Input: The submit button pressed by the user and the health information entered.

[1593] Output: Encrypted health information data sent to the server.

[1594] Step 3: Receiving and analyzing data

[1595] The server decrypts the received data and stores it in a database, after which it uses a generative AI model to analyze the data and generate follow-up questions.

[1596] What it does: The server decrypts the data and stores it in a database. A Python script is used to invoke a generative AI model (e.g., OpenAI's GPT-4) to analyze the health information.

[1597] Input: Encrypted health information data.

[1598] Output: Decoded health information and generated follow-up questions (e.g., "Have you been feeling tired lately?").

[1599] Step 4: View follow-up questions

[1600] The terminal displays the follow-up questions received from the server to the user, who answers these questions and sends them back to the server via the terminal.

[1601] Specific operation: The device receives and displays a question from the server. The user answers the question, and the device sends the answer back to the server.

[1602] Input: The generated follow-up question (e.g., "Have you been feeling tired lately?") and the user's answer (e.g., "Yes").

[1603] Output: User response data.

[1604] Step 5: Create a life lesson

[1605] The server again analyzes the answers to the follow-up questions and generates lifestyle advice customized for the user.

[1606] Specific operation: The server inputs the response data into a generative AI model and generates lifestyle advice based on the analysis results (e.g., "Add protein to your breakfast").

[1607] Input: User response data.

[1608] Output: Customized lifestyle guidance data.

[1609] Step 6: Providing lifestyle guidance

[1610] The device provides the generated lifestyle advice to the user in real time, and the user can check the advice content through the app.

[1611] Specific operation: The device receives lifestyle guidance data from the server and displays it to the user on the application.

[1612] Input: Customized life coaching data.

[1613] Output: A lifestyle advice screen viewable by the user.

[1614] Step 7: Collect and send exercise data

[1615] The terminal works in conjunction with a wearable device to collect the user's exercise and biometric data, which is then encrypted and sent to a server.

[1616] Specific operation: The terminal collects data from the wearable device via Bluetooth, encrypts it, and sends it to the server.

[1617] Input: Exercise data from wearable devices (e.g., steps, distance traveled, heart rate).

[1618] Output: Encrypted exercise data.

[1619] Step 8: Track your progress

[1620] The server analyzes the received exercise data and manages the user's progress in lifestyle habits, providing more specific guidance as needed.

[1621] Specific operation: The server analyzes the exercise data and generates new guidance tailored to the user's lifestyle (e.g., "Increase the frequency of exercise to four times a week").

[1622] Input: Exercise data from a wearable device.

[1623] Output: New lifestyle guidance data according to progress.

[1624] This is the flow of the system's program processing, which allows users to receive detailed and individualized lifestyle advice and enable continuous health management.

[1625] (Application example 1)

[1626] 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."

[1627] Conventional health management systems primarily function to collect users' health information and provide lifestyle advice, but lack the mechanisms to actually encourage users to act on that advice. There was also a need to reduce the effort required for users to purchase suggested products and services. In particular, the financial procedures required to promote the use of health foods and exercise facilities were complicated, preventing users from following the recommendations.

[1628] 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.

[1629] In this invention, the server includes means for collecting health information of the user, means for conducting interviews based on the health information collected using artificial intelligence, means for providing the user with lifestyle advice based on the interview results, means for managing the progress of the user's lifestyle habits through an appliance, means for making electronic payments for products and services based on the health information, and means for making it easy to purchase the suggested products and services. This enables the user to immediately take action based on the advice and to more effectively experience the effects of health management.

[1630] "User" refers to an individual who uses this system to manage health information and receive lifestyle guidance.

[1631] "Health information" refers to data provided by users, such as weight, diet, exercise habits, and past medical history.

[1632] "Artificial intelligence" refers to technology that performs advanced calculations such as data analysis and pattern recognition to provide appropriate lifestyle guidance and generate questions.

[1633] "Interview" refers to the process of analyzing health information collected using artificial intelligence and asking follow-up questions and giving advice to users.

[1634] "Lifestyle guidance" refers to specific methods and suggestions for making users' lives healthier, such as suggestions for improving their diet, exercise plans, and sleep advice, based on the user's health information and the results of the interview.

[1635] "Progress" refers to the health management activities carried out by the user and their results, including exercise data and biometric data.

[1636] "Appliance" refers to a device or equipment used to collect and manage user health data, including, for example, a smartphone or wearable device.

[1637] A "wearable device" refers to an electronic device worn by a user, which collects exercise data and biometric data in real time.

[1638] "Big data" refers to large data sets and the technology for analyzing them to extract useful information and patterns.

[1639] "Electronic payment" refers to the process by which a consumer pays for goods or services using digital means.

[1640] "Products and services" refers to supplements, healthy foods, and use of fitness facilities suggested based on health management guidance.

[1641] The details of the system including the actions of the server, terminals and users are described below for the embodiment of the present invention.

[1642] The system mainly consists of a device (such as a smartphone) that users access, a server that analyzes data and provides guidance, and ultimately the user who receives health management.

[1643] 1. Collection of health information

[1644] The server prompts users to enter their health information using a device such as a smartphone. This health information includes weight, diet, exercise habits, and past medical history. The user enters this information into their device and sends it to the server. The transmitted data is securely encrypted.

[1645] As a concrete example, a user opens a smartphone app and inputs their weight (70 kg), meal details (breakfast: bread and coffee, lunch: salad and sandwich, dinner: fish and rice), and jogging three times a week (30 minutes). Then, they press the send button to send the data to the server.

[1646] 2. AI-based analysis and interviews

[1647] The server receives the health information and analyzes it using a generative AI model. The AI ​​evaluates health risks based on the collected data and generates follow-up questions as needed. The device displays the follow-up questions sent from the server to the user and sends the answers back to the server.

[1648] For example, the server analyzes health information, calculates the user's BMI, and sends a follow-up question about the need for weight management to the device. The user answers "yes" to the question displayed on the device, "Have you been feeling tired easily recently?", and the message is sent.

[1649] 3. Lifestyle guidance and electronic payment

[1650] The server analyzes the answers to the follow-up questions and generates personalized lifestyle advice for the user, including dietary suggestions, exercise plans, and sleep advice, as well as a means to electronically pay for suggested products and services (such as supplements or fitness facilities) based on the health information.

[1651] The device provides users with the generated lifestyle advice and suggested products and services in real time, and users can check the advice through the app and easily purchase the suggested products and services.

[1652] For example, the server sends advice such as "take protein for breakfast" and "recommend strength training once a week" to the terminal. It also suggests purchasing protein (3,000 yen), and the user completes the purchase procedure by making an electronic payment on the spot.

[1653] 4. Progress management and follow-up

[1654] The server analyzes data (such as number of steps, distance traveled, and heart rate) obtained from the wearable device to monitor the user's health management progress. Based on this data, the server provides further appropriate health guidance and follow-up.

[1655] As a concrete example, a user puts on a wearable device and goes jogging. The app records the distance traveled (5km), heart rate, and other data, and automatically sends the data from the device to a server. Based on the analysis results, the server suggests increasing the frequency of exercise to four times a week, and a notification is displayed on the device.

[1656] Example prompt sentence:

[1657] Analyze the user's health status based on the following input data and suggest necessary advice and actions:

[1658] Weight: 70kg

[1659] Meals:

[1660] Breakfast: Bread and coffee

[1661] Lunch: Salad and sandwich

[1662] Dinner: Fish and rice

[1663] Exercise habits: Jogging three times a week (30 minutes)

[1664] As described above, this system makes it easier for users to receive personalized lifestyle advice and actually take action, making it possible for them to experience the benefits of health management over time.

[1665] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1666] Step 1:

[1667] The server prompts users to enter their health information using a smartphone or tablet device. Users enter data such as weight, diet, exercise habits, and past medical history, and this data is sent from the device to the server. The data is encrypted before being transmitted.

[1668] Input: User's health information (e.g., weight 70 kg, dietary details, exercise habits)

[1669] Data processing / calculation: Check the format of input data and encrypt it

[1670] Output: Encrypted health information data

[1671] Step 2:

[1672] The server receives the submitted health information and analyzes it using a generative AI model, assessing health risks based on the input data and generating follow-up questions as needed.

[1673] Input: Encrypted health information data

[1674] Data processing / computation: Decode data, assess health risks, generate follow-up questions

[1675] Output: Health risk assessment results, follow-up questions

[1676] Step 3:

[1677] The terminal displays the follow-up questions sent from the server to the user, who answers the questions and sends the answers to the server via the terminal.

[1678] Input: Health risk assessment results, follow-up questions

[1679] Data processing / calculation: Displaying follow-up questions to users, collecting and encrypting answer data

[1680] Output: Encrypted follow-up question answers

[1681] Step 4:

[1682] The server receives the answers to the follow-up questions, analyzes them again using the generative AI model, and generates optimal lifestyle advice for the user, including dietary suggestions, exercise plans, and sleep advice, as well as electronic payment methods for the suggested products and services.

[1683] Input: Encrypted follow-up question response data

[1684] Data processing / calculation: Decryption of response data, detailed analysis, generation of lifestyle guidance and electronic payment proposals

[1685] Output: Lifestyle guidance, suggested products and services (with electronic payment methods)

[1686] Step 5:

[1687] The device provides users with the generated lifestyle advice and suggested products and services in real time, and users can check the advice through the app and easily purchase the suggested products and services.

[1688] Input: Lifestyle guidance, suggested products and services

[1689] Data processing / calculation: User display, electronic payment processing

[1690] Output: Lifestyle advice provided to the user, products and services purchased

[1691] Step 6:

[1692] The server analyzes the exercise and biological data obtained from the wearable device to manage the user's health management progress, and provides further appropriate health guidance and follow-up as needed.

[1693] Input: Exercise data and biometric data from wearable devices

[1694] Data processing / calculation: Data analysis, progress management, generation of additional health guidance and follow-up

[1695] Output: Progress management results, additional health guidance and follow-up

[1696] 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.

[1697] ---

[1698] This invention is a system that supports users in living a healthy lifestyle. The system collects the user's health information, analyzes their health status using artificial intelligence and an emotion engine, and provides personalized lifestyle advice based on the results. It also manages the user's progress in real time and provides appropriate advice that takes their emotional state into account. The system's main components are a terminal, a server, and a user.

[1699] System Configuration

[1700] 1. Display and enter information on the medical questionnaire

[1701] The device displays a health information entry screen, where the user enters information such as weight, diet, exercise habits, and past medical history.

[1702] The user enters the necessary health information and presses the "Send" button to send the data. The data is encrypted and securely sent from the device to the server.

[1703] Examples:

[1704] The user opens the smartphone app, enters their weight (70 kg), breakfast (bread and coffee), and exercise habits (30 minutes of jogging three times a week) on the health information input screen, and presses the "Submit" button.

[1705] 2. Evaluation by AI and emotion engine

[1706] The server stores the received health information in a database and passes the data to the artificial intelligence (AI) and emotion engine.

[1707] The server's AI assesses health risks based on collected data, while the emotion engine recognizes the user's emotional state from facial expressions, tone of voice, and text input.

[1708] Examples:

[1709] The server analyzes the user's medical data to assess their BMI and risk of lack of exercise, while the emotion engine uses the smartphone's camera and microphone to recognize whether the user is feeling stressed.

[1710] 3. Follow-up questions

[1711] The server generates follow-up questions based on the health risk assessment results and emotional state, and the questions are customized based on the user's current health and emotional state.

[1712] The terminal displays the generated follow-up questions to the user, who answers the questions and transmits the answers from the terminal to the server.

[1713] Examples:

[1714] The server generates follow-up questions such as "Have you been feeling tired lately?" and "How much stress do you usually feel?" and displays them on the device. The user enters and submits the answers.

[1715] 4. Creating and providing lifestyle guidance

[1716] The server then analyzes the follow-up questions and the user's emotional state again to generate optimal lifestyle advice, including dietary suggestions, exercise plans, and sleep advice.

[1717] The device displays the generated lifestyle advice to the user, who can check the advice in real time and apply it to their own behavior.

[1718] Examples:

[1719] The server generates specific lifestyle advice such as "Eat protein for breakfast" and "Try strength training once a week." It also includes advice that takes into account emotional states, such as "Try meditating to relieve stress." These instructions are displayed on the device and notified to the user.

[1720] 5. Progress Management

[1721] The terminal connects to the wearable device and collects the user's exercise and biological data, which is then sent to a server and managed as the user's progress.

[1722] The server analyzes the progress data and provides further guidance or corrections as needed. The emotion engine also monitors changes in the user's emotional state and generates adaptive advice.

[1723] Examples:

[1724] The user puts on the wearable device and goes jogging, and the data is recorded in the app. This data is sent to the server and saved as progress. The server then suggests, "Increase your exercise frequency to four times a week." If the emotion engine detects an increase in stress levels, the app will provide new guidance, such as, "Try yoga to relax."

[1725] The above is a specific embodiment for carrying out the invention. This system allows users to receive personalized lifestyle advice in real time and to perform comprehensive health management, including their emotional state.

[1726] The processing flow will be explained below.

[1727] ---

[1728] Step 1:

[1729] The device displays a screen for entering health information, including weight, diet, exercise habits, living environment, and past medical history.

[1730] Step 2:

[1731] The user enters their health information and presses the "Confirm" and "Send" buttons to send the data. The data is encrypted and securely sent from the device to the server.

[1732] Step 3:

[1733] The server stores the received health information in a database and passes the data to the artificial intelligence (AI) and emotion engine, which then begin processing simultaneously.

[1734] Step 4:

[1735] The server's AI will assess health risks based on health information, analyzing indicators such as BMI, exercise habits, and dietary balance.

[1736] Step 5:

[1737] Using the device's built-in camera and microphone, the emotion engine analyzes the user's facial expressions, tone of voice, and text input to recognize the user's emotional state.

[1738] Step 6:

[1739] The server integrates the AI-based health risk assessment results with the emotional state of the emotion engine to generate follow-up questions, which are customized based on the user's current health and emotional state.

[1740] Step 7:

[1741] The device displays follow-up questions from the server to the user, who answers them and sends the answers from the device to the server, where they are again encrypted and securely transmitted to the server.

[1742] Step 8:

[1743] The server receives and analyzes the answers to follow-up questions. Here, the AI ​​integrates the answer data with health information to generate optimal lifestyle advice. The emotion engine also provides advice based on the user's emotions based on this analysis process.

[1744] Step 9:

[1745] The server then sends the generated lifestyle guidance and emotion-based advice to the device, including advice on improving diet, exercise suggestions, and stress management.

[1746] Step 10:

[1747] The device displays the generated lifestyle guidance and advice to the user, who then checks the advice in real time and implements the action plan.

[1748] Step 11:

[1749] The device connects to wearable devices to collect the user's exercise and biometric data, including steps taken, distance traveled, heart rate, and sleep data.

[1750] Step 12:

[1751] The device sends the collected data to the server, which periodically transfers the data to the server and stores it as the user's progress.

[1752] Step 13:

[1753] The server analyzes the progress data and provides further guidance or modification of the action plan based on the progress. The emotion engine monitors changes in the user's emotional state and updates appropriate feedback and advice.

[1754] Step 14:

[1755] The device notifies the user of new feedback and updated lifestyle advice, and the user can check their progress through the app and adjust their behavior according to the new advice.

[1756] ---

[1757] Through these steps, the system can provide users with personalized, continuous and accurate health guidance, and manage their progress and emotions in real time.

[1758] Example 2

[1759] 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."

[1760] In modern society, there are few systems that simultaneously monitor users' health and emotional states, and personalized lifestyle advice is lacking. This makes it easy for many users to overlook health risks and makes it difficult to effectively improve their lifestyles. Furthermore, there is no system that provides adaptive advice that takes users' emotional states into account, making it difficult to understand and improve a user's overall health.

[1761] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting health information of the user, means for performing an assessment based on the collected health information using artificial intelligence, means for generating follow-up questions based on the assessment results and the emotional state, means for collecting and analyzing the user's answers to the generated follow-up questions, means for providing lifestyle advice based on the user's answers and the emotional state, means for managing the user's lifestyle progress through an appliance, and means for monitoring the user's emotional state using an emotion engine and providing adaptive advice. This enables the user to receive personalized lifestyle advice in real time and achieve comprehensive health management including the emotional state.

[1762] "Health information" refers to various data used to understand the user's health condition, such as the user's weight, diet, exercise habits, and past medical history.

[1763] "Artificial intelligence" is a technology that enables computer systems to mimic human intelligence to process, evaluate, and analyze information.

[1764] "Evaluation" is the process of analyzing and determining the user's health risks and condition based on collected health information.

[1765] "Emotional state" indicates the user's psychological state and is determined from facial expressions, tone of voice, text input, and the like.

[1766] "Follow-up questions" are questions that require additional answers from the user based on the health risk assessment results or emotional state.

[1767] "Lifestyle guidance" refers to instructions such as dietary suggestions, exercise plans, and sleep advice provided based on the user's health and emotional state.

[1768] "Appliance" refers to any equipment or device used to collect and transmit health data, including wearable devices.

[1769] "Emotion engine" is a general term for technologies and systems that analyze and recognize users' psychological emotions.

[1770] "Adaptive advice" refers to advice that is individually tailored to the user based on their current health data and emotional state.

[1771] This invention is a system that helps users lead healthy and emotionally balanced lives. The system collects the user's health information and emotional state, analyzes it using artificial intelligence and an emotion engine, and provides personalized lifestyle advice based on the results. Furthermore, the system manages the user's progress in real time and provides appropriate advice that takes into account the user's emotional state.

[1772] System Configuration

[1773] The system consists of devices, servers, and users. Specific hardware and software components include devices such as smartphones and tablets, cloud servers, wearable devices equipped with various sensors, and health applications that users use on a daily basis.

[1774] Collection of health information

[1775] The device displays a health information input screen, and the user enters information such as weight, diet, exercise habits, and past medical history. This information is encrypted and sent from the device to the server. For example, a user opens a smartphone app, enters their weight as "70 kg," their breakfast as "bread and coffee," and their exercise habits as "30 minutes of jogging three times a week," and presses the "Send" button.

[1776] Data encryption and transmission

[1777] The device encrypts the entered health information using the AES encryption method and sends it to the server via HTTPS. The server then decrypts the received encrypted data and stores it in a database.

[1778] Evaluation by AI and emotion engine

[1779] The server passes the stored health information to an artificial intelligence (AI) and emotion engine. The AI ​​assesses health risks based on the collected data (using, for example, TensorFlow and PyTorch), and the emotion engine recognizes the user's emotional state from their facial expressions, tone of voice, and text input. For example, it evaluates the user's BMI and risk of lack of exercise, and the emotion engine analyzes their stress level using the smartphone's camera and microphone.

[1780] Follow-up question generation

[1781] The server generates follow-up questions based on the health risk assessment results and emotional state. These questions are customized based on the user's current health and emotional state and displayed on the device. For example, the server generates questions such as "Have you been feeling tired easily recently?" or "How much stress do you usually feel?", and the user enters and submits the answers.

[1782] Creating and providing lifestyle guidance

[1783] The server then analyzes the user's responses to the follow-up questions and their emotional state again to generate optimal lifestyle advice. This advice includes suggestions for improving their diet, exercise plans, and sleep advice. The device displays the generated lifestyle advice to the user, who can review it in real time and apply it to their own behavior. For example, specific advice such as "Eat protein for breakfast," "Try strength training once a week," and "Try meditating to relieve stress" is provided.

[1784] Progress monitoring and adaptive advice

[1785] The terminal works in conjunction with a wearable device to collect the user's exercise data and biometric data in real time and send it to a server. This data is stored on the server and managed as progress. The server analyzes the progress data and provides further guidance or corrections as needed. The emotion engine monitors changes in the user's emotional state and generates adaptive advice. For example, based on the user's exercise frequency data, it may suggest "increase your exercise frequency to four times a week," or if it detects an increase in stress levels, it may provide new advice such as "try yoga to relax."

[1786] The above is a specific embodiment for carrying out the invention. This system allows users to receive personalized lifestyle advice in real time and enables comprehensive health management, including emotional state.

[1787] Prompt Sentence Examples

[1788] "Please explain in natural language the process flow of a system that collects a user's health information (e.g., weight, diet, exercise habits), assesses their stress level using an emotion engine, and provides personalized health guidance."

[1789] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1790] Program processing flow and specific explanation

[1791] Step 1:

[1792] The device displays a health information input screen. The user inputs information such as weight, diet, exercise habits, and past medical history. The input includes data entered by the user (weight, diet, exercise, and medical history).

[1793] Specific behavior:

[1794] The device displays a "health data entry" screen on the smartphone or tablet screen, where users can enter information such as their weight (70 kg), breakfast (bread and coffee), and exercise habits (30 minutes of jogging three times a week), then press the "send" button.

[1795] Step 2:

[1796] The terminal encrypts the input data using the AES encryption method. The input includes the data entered by the user in step 1.

[1797] Specific behavior:

[1798] The terminal handles the user's input data and encrypts it using an encryption algorithm called AES (Advanced Encryption Standard), generating encrypted data.

[1799] Step 3:

[1800] The device sends encrypted data to the server using HTTPS communication. The input is encrypted health data.

[1801] Specific behavior:

[1802] The device sends the encrypted health data to the server via HTTPS (Hypertext Transfer Protocol Secure) communication. This communication protocol ensures that the data is transmitted securely. After transmission, the data arrives at the server.

[1803] Step 4:

[1804] The server stores the received health data in a database. The input is the encrypted health data arriving at the server.

[1805] Specific behavior:

[1806] The server decrypts the received data and stores it in a database. The database uses a relational database system (RDBMS) such as SQL (Structured Query Language). The stored data is generated.

[1807] Step 5:

[1808] The server passes the data to an artificial intelligence (AI) to assess health risks. The inputs are health data stored in a database and an AI model.

[1809] Specific behavior:

[1810] The server inputs the stored data into an AI model (e.g., a deep learning model using TensorFlow or PyTorch) to evaluate the user's health risks (e.g., BMI value, risk of lack of exercise), and generates an evaluation result.

[1811] Step 6:

[1812] The server passes data to the emotion engine to recognize the user's emotional state. The inputs are the evaluation results, the user's camera footage, and audio data.

[1813] Specific behavior:

[1814] The server uses the user's camera footage and audio data to call an emotion engine (e.g., IBM Watson or Microsoft Azure Emotion API) to recognize the user's emotional state, generating emotional state data.

[1815] Step 7:

[1816] The server generates follow-up questions based on the health risk assessment results and emotional state. The inputs are the assessment results and emotional state data.

[1817] Specific behavior:

[1818] The server handles the evaluation results of the AI ​​model and emotion engine and generates follow-up questions based on this information. The questions are dynamically generated based on templates, and the generated follow-up questions are sent to the device.

[1819] Step 8:

[1820] The terminal displays the generated follow-up question to the user, and has as input the generated follow-up question.

[1821] Specific behavior:

[1822] The device displays follow-up questions such as "Have you been feeling tired lately?" and "How stressed do you usually feel?" and is ready for the user to enter their answers.

[1823] Step 9:

[1824] The user answers the follow-up questions and sends the answers from the device to the server. The user's answer data is entered into the device.

[1825] Specific behavior:

[1826] The user enters an answer into the question form and presses the "Submit" button to send the answer to the server. The sent answer reaches the server.

[1827] Step 10:

[1828] The server analyzes the follow-up question answers and the emotional state again and generates optimal lifestyle advice. The inputs are the follow-up question answer data and the emotional state.

[1829] Specific behavior:

[1830] The server then uses the AI ​​model again to generate lifestyle advice based on the user's answers and emotional state, and provides the advice to the user.

[1831] Step 11:

[1832] The terminal displays the generated lifestyle guidance to the user. The generated lifestyle guidance data is input.

[1833] Specific behavior:

[1834] The device displays specific lifestyle advice such as "Eat protein for breakfast" and "Try strength training once a week." The user is then ready to confirm the advice.

[1835] Step 12:

[1836] The terminal connects to the wearable device, collects the user's exercise data and biological data in real time, and transmits it to the server. The input includes data obtained from the wearable device.

[1837] Specific behavior:

[1838] The device receives data from a wearable device (e.g., a smartwatch or fitness tracker), records the data in the app, and then sends the recorded data to a server.

[1839] Step 13:

[1840] The server analyzes the collected data and provides further guidance or corrections as needed. As input, the collected progress data is stored on the server.

[1841] Specific behavior:

[1842] The server then re-analyzes the stored data using an AI model to generate new lifestyle advice based on progress, and adjusts the advice as necessary.

[1843] Step 14:

[1844] The server uses an emotion engine to monitor changes in the user's emotional state and generate adaptive advice. The input is the user's emotional state data.

[1845] Specific behavior:

[1846] The server periodically calls the emotion engine to check changes in the user's emotional state, and based on the results, new advice, such as "Try yoga to relax," is generated and provided to the user.

[1847] (Application example 2)

[1848] 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."

[1849] Conventional health management systems collect and analyze users' health information and provide lifestyle advice, but because they are unable to take into account the user's emotional state, the lifestyle advice provided is uniform and it is difficult to provide optimal advice to each individual user.In addition, since they do not have a means to collect health information in real time using IoT devices in physical stores, there is an issue that they cannot provide immediate health management in physical stores.

[1850] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting health information of the user, means for conducting an interview based on the health information collected using artificial intelligence, means for providing lifestyle advice to the user based on the interview results, means for managing the progress of the user's lifestyle habits through an appliance, means for analyzing the user's emotional state using an emotion engine and reflecting the analysis in the lifestyle advice, and means for collecting health information using IoT devices in a physical store. This enables lifestyle advice based on the user's emotional state, and enables real-time collection of health information and immediate advice to be provided in a physical store.

[1851] "User" refers to a person who provides health information to the system and receives lifestyle guidance.

[1852] "Health information" refers to data about the user's health status, such as weight, blood pressure, diet, exercise habits, and past medical history.

[1853] "Artificial intelligence" refers to information processing technology that analyzes collected health information and assesses users' health risks.

[1854] "Interview" refers to a means of providing direct guidance and follow-up to users based on the results of health status analysis by artificial intelligence.

[1855] "Lifestyle guidance" refers to specific advice on diet, exercise, sleep, etc. provided to users based on health information and interviews.

[1856] "Progress management" refers to methods for tracking changes in daily lifestyle habits and health status and providing necessary support.

[1857] "Appliance" refers to any device or system used to collect health information and manage progress.

[1858] A "wearable device" refers to a device worn by the user that collects exercise data and biometric data in real time.

[1859] An "emotion engine" refers to technology that analyzes a user's emotional state from facial expressions, tone of voice, text input, etc.

[1860] "IoT devices" refer to physical store equipment such as scales, blood pressure monitors, and fitness machines that can send and receive data over the internet.

[1861] "Big data" refers to data processing techniques for analyzing huge data sets, and the results of this analysis are used to assess health risks.

[1862] This invention is a system that helps users lead healthy lives. The system collects the user's health information, analyzes the user's health status using artificial intelligence and an emotion engine, and provides personalized lifestyle advice based on the results. Furthermore, the system manages the user's progress in real time and provides appropriate advice taking into account the user's emotional state.

[1863] System Configuration

[1864] 1. Collection of health information

[1865] The device displays a health information entry screen, where the user enters information such as weight, diet, exercise habits, and past medical history.

[1866] The user enters the necessary health information and presses the "Send" button to send the data. The data is encrypted and securely sent from the device to the server.

[1867] Examples:

[1868] The user opens a fitness gym download app, enters their weight (70 kg), breakfast contents (bread and coffee), and exercise habits (30 minutes of jogging three times a week) on the health information input screen, and presses the "Submit" button.

[1869] 2. Evaluation by AI and emotion engine

[1870] The server stores the received health information in a database and passes the data to the artificial intelligence (AI) and emotion engine.

[1871] The server's AI assesses health risks based on collected data, while the emotion engine recognizes the user's emotional state from facial expressions, tone of voice, and text input.

[1872] Examples:

[1873] The server analyzes the user's medical data to assess their BMI and risk of lack of exercise, while the emotion engine uses the smartphone's camera and microphone to recognize whether the user is feeling stressed.

[1874] 3. Follow-up questions

[1875] The server generates follow-up questions based on the health risk assessment results and emotional state, and the questions are customized based on the user's current health and emotional state.

[1876] The terminal displays the generated follow-up questions to the user, who answers the questions and transmits the answers from the terminal to the server.

[1877] Examples:

[1878] The server generates follow-up questions such as "Have you been feeling tired lately?" and "How much stress do you usually feel?" and displays them on the device. The user enters and submits the answers.

[1879] 4. Creating and providing lifestyle guidance

[1880] The server then analyzes the follow-up questions and the user's emotional state again to generate optimal lifestyle advice, including dietary suggestions, exercise plans, and sleep advice.

[1881] The device displays the generated lifestyle advice to the user, who can check the advice in real time and apply it to their own behavior.

[1882] Examples:

[1883] The server generates specific lifestyle advice such as "Eat protein for breakfast" and "Try strength training once a week." It also includes advice that takes into account emotional states, such as "Try meditating to relieve stress." These instructions are displayed on the device and notified to the user.

[1884] 5. Progress Management

[1885] The terminal connects to the wearable device and collects the user's exercise and biological data, which is then sent to a server and managed as the user's progress.

[1886] The server analyzes the progress data and provides further guidance or corrections as needed. The emotion engine also monitors changes in the user's emotional state and generates adaptive advice.

[1887] Examples:

[1888] The user puts on the wearable device and goes jogging, and the data is recorded in the app. This data is sent to the server and saved as progress. The server then suggests, "Increase your exercise frequency to four times a week." If the emotion engine detects an increase in stress levels, the app will provide new guidance, such as, "Try yoga to relax."

[1889] Prompt Sentence Examples

[1890] User ID: 12345

[1891] Weight: 70kg

[1892] Blood pressure: 135 / 85

[1893] Stress level: 80

[1894] "I recommend taking protein and doing yoga for relaxation."

[1895] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1896] Step 1:

[1897] The device displays a health information entry screen. The user enters information such as weight, diet, exercise habits, and past medical history, and then presses the "Submit" button.

[1898] Input: Health information (e.g., weight 70kg, breakfast contents, exercise habits)

[1899] Output: Encrypted health information data

[1900] Specific operation: The terminal displays an input screen, and when the user enters information and presses the "Send" button, the data is encrypted and sent to the server.

[1901] Step 2:

[1902] The server stores the received health information in a database and passes the data to the artificial intelligence (AI) and emotion engine.

[1903] Input: Encrypted health information data

[1904] Output: Analysis results of health information

[1905] Specific operation: The server stores the received data in a database, then sends the data to the AI ​​and emotion engine, where analysis begins.

[1906] Step 3:

[1907] The server's AI evaluates health risks based on health information, and the emotion engine recognizes the user's emotional state.

[1908] Input: Health information in the database

[1909] Output: Health risk assessment and emotional state analysis results

[1910] How it works: The AI ​​analyzes BMI and past medical history to assess health risks, while the emotion engine analyzes facial expressions and tone of voice to recognize emotional states.

[1911] Step 4:

[1912] The server generates follow-up questions based on the health risk assessment results and emotional state and sends them to the terminal.

[1913] Input: Health risk assessment and emotional state analysis results

[1914] Output: Follow-up question

[1915] What it does: Based on your health risks and emotional state, the AI ​​generates customized follow-up questions and sends them to your device.

[1916] Step 5:

[1917] The terminal displays follow-up questions to the user, who answers the questions and sends the answers to the server.

[1918] Input: Answer to follow-up question

[1919] Output: Response data

[1920] Specific behavior: The device displays a follow-up question, the user enters an answer, and sends it back to the server.

[1921] Step 6:

[1922] The server analyzes the answers to the follow-up questions and the emotional state, generates lifestyle advice, and sends it to the terminal.

[1923] Input: Answers to follow-up questions and emotional state analysis results

[1924] Output: Lifestyle guidance suggestions

[1925] Specific operation: AI analyzes the response data and emotional state, generates personalized lifestyle advice, and sends the advice to the device.

[1926] Step 7:

[1927] The terminal displays the received lifestyle advice to the user, who then follows the advice.

[1928] Input: Lifestyle guidance suggestions

[1929] Output: Check and execute the instruction

[1930] Specific operation: The device notifies the user of lifestyle guidance, and the user confirms and implements the content.

[1931] Step 8:

[1932] The terminal works in conjunction with the wearable device to collect the user's exercise data and biometric data and send it to a server.

[1933] Input: Wearable device data

[1934] Output: Exercise data and biological data

[1935] Specific operation: The terminal acquires data from the wearable device and sends it to the server.

[1936] Step 9:

[1937] The server analyzes the progress data, potentially providing further guidance or corrections, and also monitors changes in emotional state.

[1938] Input: Movement data, biometric data, emotional state data

[1939] Output: Additional lifestyle and / or remedial instructions

[1940] Specific behavior: Based on the progress, the AI ​​generates additional guidance, and the emotion engine analyzes changes in the user's emotional state and generates advice as needed.

[1941] Step 10:

[1942] The device notifies the user of additional lifestyle guidance and correction instructions, and the user responds accordingly.

[1943] Input: Additional lifestyle guidance and / or correction instructions

[1944] Output: New lifestyle advice implemented

[1945] Specific behavior: The device notifies the user of new instruction content, and the user acts accordingly.

[1946] 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.

[1947] 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.

[1948] 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.

[1949] 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.

[1950] 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.

[1951] 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.

[1952] 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).

[1953] 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.

[1954] 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."

[1955] 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.

[1956] 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).

[1957] 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.

[1958] 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.

[1959] 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.

[1960] 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.

[1961] 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.

[1962] 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.

[1963] 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.

[1964] 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.

[1965] 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.

[1966] 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.

[1967] The following is further disclosed regarding the above embodiment.

[1968] (Claim 1)

[1969] A means of collecting health information about users;

[1970] A means of conducting interviews based on health information collected using artificial intelligence;

[1971] A means of providing lifestyle guidance to users based on the results of the interviews,

[1972] A means of managing the progress of the user's lifestyle habits through an appliance;

[1973] A system including:

[1974] (Claim 2)

[1975] The system of claim 1, wherein the appliance includes a wearable device that collects the user's exercise data and biometric data in real time.

[1976] (Claim 3)

[1977] 2. The system of claim 1, wherein the artificial intelligence utilizes big data to assess a user's health risks and generate follow-up questions.

[1978] "Example 1"

[1979] (Claim 1)

[1980] A means of collecting health information about users;

[1981] a means for conducting interviews and generating follow-up questions based on the collected health information using artificial intelligence;

[1982] a means for collecting user responses based on the generated follow-up questions for further analysis;

[1983] A means of providing users with customized lifestyle guidance based on the results of interviews and analysis;

[1984] A means to manage the user's lifestyle progress through the appliance and provide more specific guidance as needed;

[1985] A system including:

[1986] (Claim 2)

[1987] The system of claim 1, wherein the appliance includes a wearable device that collects the user's exercise data and biometric data in real time.

[1988] (Claim 3)

[1989] 2. The system of claim 1, wherein the artificial intelligence utilizes large-scale data to assess a user's health risks and generate follow-up questions.

[1990] That's all.

[1991] "Application Example 1"

[1992] (Claim 1)

[1993] A means of collecting health information about users;

[1994] A means of conducting interviews based on health information collected using artificial intelligence;

[1995] A means of providing lifestyle guidance to users based on the results of the interviews,

[1996] A means of managing the progress of the user's lifestyle habits through an appliance;

[1997] a means of electronically paying for goods and services based on health information;

[1998] A means to facilitate the purchase of proposed goods and services;

[1999] A system including:

[2000] (Claim 2)

[2001] The system of claim 1, wherein the appliance includes a wearable device that collects the user's exercise data and biometric data in real time.

[2002] (Claim 3)

[2003] 2. The system of claim 1, wherein the artificial intelligence utilizes big data to assess a user's health risks and generate follow-up quest...

Claims

1. A means of collecting health information about users; A means of conducting interviews based on health information collected using artificial intelligence; A means of providing lifestyle guidance to users based on the results of the interviews, A means of managing the progress of the user's lifestyle habits through an appliance; A system including:

2. The system of claim 1 , wherein the appliance includes a wearable device that collects exercise data and biometric data of the user in real time.

3. The system of claim 1 , wherein the artificial intelligence utilizes big data to assess a user's health risks and generate follow-up questions.

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