System

Abstract

To provide a system capable of scientifically and continuously tracking lifestyle data, finding a pre-disease at an early stage, and providing an individualized health management plan.SOLUTION: A means in which a user answers a questionnaire related to lifestyle habits through a terminal and transmits answer data to a server, a means in which the server receives the answer data and an AI evaluates a risk of poor health, a means in which the server notifies a user terminal of an evaluation result, a means in which a wearable device collects health data and periodically transmits the health data to the server, a means in which an AI of the server analyzes the health data and generates individualized advice for improving a lifestyle habit and notifies a user of the advice, a means in which the AI of the server detects an abnormal value in an analysis result and notifies a medical expert of the abnormal value, and a means in which the medical expert confirms the analysis result and, A system comprising means for providing the necessary advice and treatment plan and means for the server to process the feedback sent by the user in the app and provide the necessary support.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 today's world, many people are at risk of lifestyle-related diseases and other health risks, making it important to detect pre-disease states early and take measures to prevent them. However, pre-disease states have few noticeable symptoms, making it difficult for ordinary people to detect them themselves in their daily lives. For this reason, there is a need for a system that can scientifically and continuously track lifestyle data, detect pre-disease states early, and provide personalized health management plans. [Means for solving the problem]

[0005] The present invention is a system that includes a means for a user to answer a questionnaire about lifestyle habits via a terminal and send the answer data to a server, a means for the server to receive the answer data and for AI to evaluate pre-disease risk, a means for the server to notify the user terminal of the evaluation results, a means for a wearable device to collect health data and periodically send it to the server, a means for the server's AI to analyze the health data and generate personalized advice for improving habits and notify the user, a means for the server's AI to notify a medical professional if it detects an abnormal value in the analysis results, a means for the medical professional to check the analysis results and provide any necessary advice or treatment plan, and a means for the server to process feedback sent by the user within the app and provide any necessary support.

[0006] This allows for more effective early detection of pre-disease and prevention of its progression, and makes it possible to provide users with personalized health management plans.

[0007] A "questionnaire" is a series of questionnaires provided to users to answer questions about their lifestyle habits.

[0008] "Response data" refers to information that a user has responded to a questionnaire, and is data that is sent to the server.

[0009] "Server" means a central computer system that stores data, performs analysis, and manages communications with user terminals and medical professionals.

[0010] "AI" stands for artificial intelligence, an automated program for analyzing data and assessing risks.

[0011] "Pre-disease risk" is an index that indicates the risk that a user may have health problems before they become ill.

[0012] A "wearable device" is an electronic device that a user wears on a daily basis to collect health data.

[0013] "Health data" refers to physiological information collected by wearable devices, including heart rate, steps taken, and sleep data.

[0014] "Analysis results" refers to the results of the AI ​​analyzing collected health data and questionnaire responses.

[0015] "Individualized habit improvement advice" refers to specific suggestions for improving lifestyle habits that are provided to a specific user based on the analysis results.

[0016] "Abnormal values" are numbers or patterns that, when analyzed by AI, are determined to indicate an abnormality in health status.

[0017] A "health professional" is a doctor or other medical professional who has specialized knowledge about health issues and provides appropriate advice and treatment plans to users.

[0018] "Feedback" refers to opinions and questions submitted within the app by users about their health status and the advice provided.

[0019] "Support" refers to advice, answers, or additional assistance provided to the user by the system or a medical professional. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] The present invention is a personal health plan advisor system that supports the early detection of pre-disease conditions and the prevention of their progression. An embodiment of this system will be described in detail below.

[0042] 1. Online Health Checkup

[0043] The user launches the app through their device and proceeds to a lifestyle questionnaire from the profile entry screen. The device displays the questionnaire items, and the user answers them. Once the answers are completed, the device sends the response data to a server. On the server, AI analyzes the response data and evaluates the risk of developing pre-disease. The evaluation results are sent from the server to the user's device and notified.

[0044] Examples:

[0045] After User A answers a questionnaire about the amount of exercise, diet, and sleep time each day, the server evaluates that "lack of exercise puts you at high risk of cardiovascular disease" and notifies User A of the result.

[0046] 2. Data Tracking and Analysis

[0047] The user wears a wearable device in their daily life. The device periodically records heart rate, steps, sleep data, etc. and sends this data to a server. The server's AI analyzes the received data and evaluates the user's health. Based on the analysis results, the server generates personalized advice on habit improvement and notifies the user.

[0048] Examples:

[0049] User B wears a smartwatch, and based on the data sent from the device, the server's AI determines that "the number of steps per day has not reached the goal for two consecutive weeks." As improvement advice, it sends User B the suggestion to "try adding 30 minutes of walking each day."

[0050] 3. Collaboration with medical professionals

[0051] If the server's AI detects an abnormal value in the analysis results, it flags it and sends a notification to a medical professional. The medical professional then reviews the analysis results through a dedicated portal and provides any necessary advice or treatment plans. This feedback is then sent to the user via the server.

[0052] Examples:

[0053] Based on User C's heart rate data, the server's AI determines that "a persistently high heart rate is observed" and notifies a medical professional. The medical professional then conducts a detailed evaluation and notifies User C of the need for additional testing.

[0054] 4. Feedback and Support

[0055] Users can use the in-app chat feature to send questions or feedback, which the device then sends to the server, which analyzes the user's feedback and, if necessary, connects them with a medical professional to provide appropriate support to the user.

[0056] Examples:

[0057] User D submits feedback about their sleep quality, reporting that they "wake up multiple times each night." The server analyzes the data and returns a result stating that "consultation with a medical professional is recommended." A medical professional reviews the data and provides advice on specific supplements and improving the sleep environment.

[0058] As described above, the personal health plan advisor system of the present invention is a system that tracks a user's lifestyle habits and health data, and evaluates and analyzes pre-disease risks, thereby enabling early detection of pre-disease and individualized health management.

[0059] The processing flow will be explained below.

[0060] Online health check processing steps

[0061] Step 1:

[0062] The user starts the device and opens the Health Plan Advisor app.

[0063] Step 2:

[0064] The terminal displays a profile entry screen to the user, prompting them to enter basic information such as name, age, sex, and medical history.

[0065] Step 3:

[0066] The device displays a questionnaire screen about lifestyle habits, and the user answers questions about eating habits, exercise habits, sleep habits, stress levels, etc.

[0067] Step 4:

[0068] The user completes the survey and clicks the submit button.

[0069] Step 5:

[0070] The terminal transmits the user's response data to the server.

[0071] Step 6:

[0072] The server receives the response data and inputs it into the AI ​​engine.

[0073] Step 7:

[0074] The server's AI analyzes the response data and assesses the risk of developing illness.

[0075] Step 8:

[0076] The server generates the evaluation results and transmits the data to the user terminal.

[0077] Step 9:

[0078] The terminal notifies the user of the evaluation results and displays advice based on the evaluation results.

[0079] Data Tracking and Analysis Process Steps

[0080] Step 1:

[0081] A user wears a wearable device (e.g., a smart watch) in their daily life.

[0082] Step 2:

[0083] The wearable device regularly records health data such as heart rate, steps taken, and sleep data.

[0084] Step 3:

[0085] The wearable device periodically transmits the recorded data to the user's smartphone.

[0086] Step 4:

[0087] The terminal transmits the data received from the wearable terminal to the server at regular intervals.

[0088] Step 5:

[0089] The server receives the wearable data and stores it in a database.

[0090] Step 6:

[0091] The server's AI periodically performs data analysis and evaluates the user's health status.

[0092] Step 7:

[0093] The server's AI generates personalized advice on habit improvement based on the analysis results.

[0094] Step 8:

[0095] The server transmits the generated advice to the user's terminal.

[0096] Step 9:

[0097] The device will notify the user of the advice and display details within the app.

[0098] Steps in the process of working with a medical professional

[0099] Step 1:

[0100] If the server's AI detects an abnormal value in the analysis results, it will raise an abnormality flag.

[0101] Step 2:

[0102] The server notifies the medical professional of the results of the abnormality analysis.

[0103] Step 3:

[0104] Medical professionals are notified through a dedicated portal and can review the analysis results.

[0105] Step 4:

[0106] A medical professional will conduct a detailed assessment and generate further advice and a treatment plan.

[0107] Step 5:

[0108] The server transmits the feedback from the medical professional to the user terminal.

[0109] Step 6:

[0110] The device will notify the user of feedback from medical professionals and display details within the app.

[0111] Feedback and support process steps

[0112] Step 1:

[0113] Users submit questions or comments using the in-app feedback feature.

[0114] Step 2:

[0115] The terminal sends the user's feedback to the server.

[0116] Step 3:

[0117] The server receives the feedback, and the AI ​​handles any content that can be automatically responded to.

[0118] Step 4:

[0119] The server notifies the medical professional of the feedback as needed.

[0120] Step 5:

[0121] Medical professionals will review the feedback and take appropriate action.

[0122] Step 6:

[0123] The server transmits the response results from the medical professional to the user terminal.

[0124] Step 7:

[0125] The device will notify the user of the response results and display details within the app.

[0126] The above are the specific processing steps of the personal health plan advisor system. The system supports the user's health management through each step, realizing early detection of pre-disease and prevention of its progression.

[0127] Example 1

[0128] 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."

[0129] In modern society, problems related to lifestyle habits and health management are on the rise, making it essential to detect pre-disease risks early and prevent their progression. However, conventional health management systems lack personalized advice and collaboration with medical professionals, making it difficult for users to accurately and efficiently understand their own health status. Furthermore, feedback processing tends to be delayed, making it difficult to provide immediate support to users.

[0130] 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.

[0131] In this invention, the server includes a means for allowing a user to answer a questionnaire about lifestyle habits via a terminal and transmitting the answer data to the server, a means for the server to receive the answer data and for an AI model to evaluate pre-disease risk, and a means for the server to notify the user terminal of the evaluation result, thereby enabling the user's lifestyle habit data to be analyzed quickly and accurately, and the pre-disease risk to be evaluated and notified.

[0132] Furthermore, the server includes means for the wearable device to collect health data and periodically transmit it to the server, means for the AI ​​model of the server to analyze the health data and generate personalized habit improvement advice and notify the user, means for notifying a medical professional if the AI ​​model of the server detects an abnormal value in the analysis result, means for the medical professional to check the analysis result and provide necessary advice and a treatment plan, and means for the server to process feedback sent by the user within the app and provide necessary support, thereby making it possible to continuously monitor the user's health condition and provide personalized advice and immediate support.

[0133] "User" refers to an individual or organization that uses the system and provides data for health management and pre-disease risk assessment.

[0134] A "terminal" is an electronic device used by a user, including a smartphone, tablet, or PC.

[0135] A "server" is a computer system that receives, analyzes, and stores data sent by users, and has advanced computing power and data storage capabilities.

[0136] "AI model" refers to the artificial intelligence algorithms and software that analyze user data and generate pre-disease risk assessments and personalized advice.

[0137] A "questionnaire" is a series of questions to collect information about a user's lifestyle habits, including items such as diet, exercise, and sleep.

[0138] "Pre-disease risk" refers to the result of an assessment of whether a user is likely to contract a disease in the future.

[0139] "Evaluation results" refers to information that indicates pre-disease risk and health status as determined by the AI ​​model analyzing the user's data.

[0140] A "wearable device" is an electronic device worn by a user to collect health data, including smartwatches and fitness trackers.

[0141] "Health data" refers to data related to a user's health status, such as heart rate, number of steps, and sleep data.

[0142] "Habit improvement advice" refers to specific behavioral suggestions and advice on lifestyle changes generated by the AI ​​model to help users maintain good health.

[0143] An "outlier" is an unusual value detected by an AI model while analyzing health data, which may indicate a potential health problem.

[0144] A "medical expert" is a doctor or other person with expertise in the medical field who provides advice and treatment plans to the user based on notifications and data from the server.

[0145] "Feedback" means any questions, requests, or health status reports provided by a User through the App.

[0146] "Support" refers to advice, solutions, or problem-solving assistance provided by a server or medical professional in response to user feedback.

[0147] This invention is a personal health plan advisor system that supports the early detection and prevention of pre-disease conditions. The system involves users answering a questionnaire about their lifestyle habits, analyzing the data to assess their pre-disease risk, and providing personalized health management advice.

[0148] Hardware and Software Configuration

[0149] 1. Device: The electronic device used by the user, including a smartphone, tablet, or PC, that answers the survey and receives data from the wearable device.

[0150] 2. Server: A computer system with advanced computing power and data storage capabilities that receives, analyzes, and stores data. It runs AI models such as Google® Cloud AI, Azure® AI, Scikit-learn, and Tensorflow®.

[0151] 3. Wearable devices: Devices that collect user health data, including smartwatches and fitness trackers, which collect data such as heart rate, steps taken, and sleep duration.

[0152] Program processing

[0153] 1. Online Health Checkup

[0154] The user launches the app on their device and answers a questionnaire about their lifestyle habits. The device displays the questionnaire items and records the user's responses. Once the responses are complete, the device sends the data to the server. The server's AI model analyzes the data and evaluates the risk of developing pre-disease. The evaluation results are sent from the server to the user's device.

[0155] Example prompt: "Please complete a survey about your daily exercise, diet, and sleep."

[0156] 2. Data Tracking and Analysis

[0157] The user wears a wearable device, and the device records the heart rate, steps, sleep data, and other data obtained from the device. The device periodically sends this data to a server, where an AI model analyzes the data and evaluates the user's health. Based on the analysis results, the server generates personalized advice on improving habits and notifies the user's device.

[0158] Example prompt: "Please wear your smartwatch and send your data."

[0159] 3. Collaboration with medical professionals

[0160] If the server's AI detects an abnormal value in the analysis results, it flags it and notifies medical professionals. Medical professionals review the analysis results through a dedicated portal and provide necessary advice and treatment plans. This feedback is then sent to the user via the server.

[0161] Example prompt: "Analyze your heart rate data and notify a medical professional of any abnormal values."

[0162] 4. Feedback and Support

[0163] Users can use the in-app chat feature to send questions or feedback, which the device then sends to the server, which analyzes the user's feedback and, if necessary, connects them to a medical professional to provide the user with appropriate support.

[0164] Example prompt: "Submit your feedback or questions via in-app chat."

[0165] Specific examples

[0166] 1. Online Health Check:

[0167] After the user answers a questionnaire about their daily exercise, diet, and sleep, the server assesses whether they are at high risk of cardiovascular disease due to lack of exercise and notifies the user of the results.

[0168] 2. Data Tracking and Analysis:

[0169] When a user wears a smartwatch, the server's AI determines based on the data collected by the smartwatch that the user has not met their daily step goal for two consecutive weeks, and then provides advice on how to improve the situation, suggesting that the user try adding 30 minutes of walking each day.

[0170] 3. Collaboration with medical professionals:

[0171] Based on the user's heart rate data, the server's AI determines that a "persistently high heart rate is observed" and notifies a medical professional. The medical professional then conducts a detailed evaluation and notifies the user of the need for further testing.

[0172] 4. Feedback and Support:

[0173] The user sends feedback such as "I wake up many times every night." The server analyzes the data and returns a result saying, "Consultation with a medical professional is recommended." A medical professional reviews the data and provides recommendations for specific supplements and advice on improving the sleep environment.

[0174] As described above, the personal health plan advisor system of the present invention monitors the user's lifestyle data in detail, and by evaluating pre-disease risks and providing personalized advice, enables early detection of pre-disease and health management.

[0175] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0176] Online health check processing steps

[0177] Step 1:

[0178] The user launches the Personal Health Plan Advisor app on their device and answers a questionnaire about their lifestyle habits.

[0179] Input: User's basic information (name, age, gender, height, weight) and lifestyle data (exercise, diet, sleep, etc.)

[0180] Output: User response data

[0181] Specific operation: The user taps the app to launch it and enters answers to each item in the survey. The device records the user's input.

[0182] Step 2:

[0183] The terminal transmits the user's response data to the server.

[0184] Input: Survey response data

[0185] Output: Response data sent to the server

[0186] Specific operation: The device temporarily stores the user's survey responses and sends the data to a server via the Internet.

[0187] Step 3:

[0188] The AI ​​model on the server analyzes the response data and assesses the risk of developing illness.

[0189] Input: User response data

[0190] Output: Pre-disease risk assessment results

[0191] How it works: The server analyzes the data using Google Cloud AI or Azure AI models and generates a rating such as "high risk of cardiovascular disease."

[0192] Step 4:

[0193] The server generates the evaluation results and sends them to the user's terminal.

[0194] Input: Evaluation result data

[0195] Output: Evaluation results sent to the user's device

[0196] Specific operation: The server sends the generated evaluation results to the user's device, and the device displays the evaluation results via a pop-up or notification function.

[0197] Data Tracking and Analysis Process Steps

[0198] Step 1:

[0199] A user wears a wearable device, which collects health data.

[0200] Input: User's physical activity data (heart rate, steps, sleep data)

[0201] Output: Collected health data

[0202] What it does: A user puts on a smartwatch and the device records their activity data for the day.

[0203] Step 2:

[0204] The terminal acquires health data from the wearable device and transmits it to the server.

[0205] Input: Health data from wearable devices

[0206] Output: Health data sent to the server

[0207] Specific operation: The terminal acquires data from the device using Bluetooth or Wi-Fi and sends it to the server.

[0208] Step 3:

[0209] The server's AI model analyzes the health data and assesses the user's health status.

[0210] Input: Health data (heart rate, steps, sleep data)

[0211] Output: Health status assessment results

[0212] Specific operation: The server analyzes the data using TensorFlow and determines that the number of steps per day has not reached the target for two consecutive weeks.

[0213] Step 4:

[0214] Based on the analysis results, the server generates personalized advice for improving habits and notifies the user.

[0215] Input: Health status assessment results

[0216] Output: Personalized advice on how to improve your habits

[0217] Specific operation: The server generates a suggestion such as "Try adding 30 minutes of walking every day" and notifies the user device.

[0218] Steps in the process of working with a medical professional

[0219] Step 1:

[0220] If the server's AI detects an abnormal value in the analysis results, it will notify medical experts.

[0221] Input: Health data analysis results

[0222] Output: Notification to medical professionals

[0223] What it does: The server flags a "persistently high heart rate" and sends an automatic email notification to a medical professional.

[0224] Step 2:

[0225] Medical professionals will review the analysis results on a dedicated portal and provide necessary advice and treatment plans.

[0226] Input: Abnormal value notification and analysis result data from the server

[0227] Output: Detailed assessment and advice, treatment plan

[0228] How it works: Medical professionals log in to a dedicated portal, check the analysis results, and enter any necessary advice in writing or as a message within the system.

[0229] Step 3:

[0230] The server receives feedback from the medical professional and notifies the user.

[0231] Input: Medical professional advice, treatment plan

[0232] Output: User notification

[0233] Specific operation: The server sends the received advice to the user's device and displays it as a pop-up notification or in-app message.

[0234] Feedback and support process steps

[0235] Step 1:

[0236] Users submit questions or feedback through in-app chat.

[0237] Input: User feedback

[0238] Output: Feedback data sent to the server

[0239] What happens: A user opens the in-app chat, types in a question or feedback, and hits send.

[0240] Step 2:

[0241] The server analyzes the user's feedback and contacts medical professionals as needed.

[0242] Input: User feedback data

[0243] Output: Analysis of feedback and notification to medical professionals

[0244] Specific operation: The server analyzes the text of the feedback and if it determines that "expert consultation is required," it sends an email notification to a medical professional.

[0245] Step 3:

[0246] A medical professional will provide appropriate advice and notify the user through the server.

[0247] Input: Consultation request and feedback data from the server

[0248] Output: Advice to the user

[0249] What happens: A medical professional reviews the feedback and provides appropriate advice (e.g., specific supplement recommendations, sleep improvement strategies), which is then sent to the user by the server.

[0250] This allows the user to receive appropriate support immediately. In this way, the personal health plan advisor system of the present invention closely monitors the user's health condition and provides personalized advice and prompt support.

[0251] (Application example 1)

[0252] 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."

[0253] Early detection of pre-disease risks and appropriate responses are crucial for maintaining and improving a user's health. However, conventional health management systems inadequately collect and analyze users' lifestyle and health data, and have limited collaboration with medical professionals, making it difficult to detect pre-disease risks early or provide personalized health advice. It is also difficult to properly manage users' health data as a security service and provide necessary support in a timely manner. There is a need to solve these problems and achieve effective health management and early pre-disease prevention.

[0254] 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.

[0255] In this invention, the server includes: means for a user to answer a lifestyle questionnaire via a terminal and transmit the response data to the server; means for the server to receive the response data and for an AI to evaluate pre-disease risk; means for the server to notify the user of the evaluation results; means for a wearable device to collect health data and periodically transmit it to the server; means for the server's AI to analyze the health data and generate personalized habit improvement advice and notify the user; means for the server's AI to notify a medical professional if an abnormal value is detected in the analysis results; means for the medical professional to review the analysis results and provide necessary advice and treatment plans; means for the server to process feedback sent by the user within the app and provide necessary support; means for using an AI analysis engine to evaluate the collected health data and generate a pre-disease risk notification for the user; and means for transmitting the generated notification to the user's terminal. This enables efficient collection and analysis of a user's health data and early evaluation and notification of pre-disease risk.

[0256] "User" means an individual who uses the pre-disease risk assessment system.

[0257] A "terminal" is a device that a user uses to answer a questionnaire about lifestyle habits.

[0258] A "server" is a computer system that receives and analyzes questionnaire data and health data from wearable devices.

[0259] "AI" is a system that uses artificial intelligence technology to analyze questionnaire data and health data and evaluate the risk of pre-disease.

[0260] "Pre-disease risk" is the result of assessing the possibility of developing illness in the future, even if the illness has not yet occurred.

[0261] The "evaluation results" are the results of the AI's analysis of pre-disease risk, and are the information notified to the user.

[0262] A "wearable device" is a device that a user wears in their daily life to collect health data (heart rate, number of steps, sleep data, etc.).

[0263] "Habit Improvement Advice" is a lifestyle improvement suggestion provided to users based on their health data analyzed by the server's AI.

[0264] An "abnormal value" is a value that deviates from the normal range in the analysis results of health data.

[0265] A "health professional" is a qualified healthcare professional who provides advice and treatment plans based on a user's health data.

[0266] "Feedback" refers to users submitting opinions or questions about their health or advice within the app.

[0267] A "notification" is information or a warning that the server sends to the user terminal.

[0268] "Analysis engine" is a general term for software that uses AI technology to evaluate and analyze health data.

[0269] The present invention provides a system for analyzing a user's lifestyle and health data, and evaluating and notifying the user of their risk of developing a pre-disease. This system is realized using the following hardware and software.

[0270] Hardware and Software Configuration

[0271] Device: The device (smartphone, tablet, etc.) used by the user to answer the lifestyle questionnaire.

[0272] Server: A computer system (AWS (registered trademark), Google Cloud, etc.) that receives and analyzes questionnaire data and health data sent from wearable devices.

[0273] AI analysis engine: Software (TensorFlow, PyTorch, etc.) for analyzing health data and assessing pre-disease risk.

[0274] Wearable device: A device (such as a smartwatch) that collects user health data (heart rate, steps, sleep data, etc.).

[0275] System operation process

[0276] 1. Data collection: Users answer a questionnaire about their lifestyle habits through their device and send the data to the server. In addition, the wearable device periodically records their heart rate, number of steps, sleep data, etc. and sends this data to the server.

[0277] 2. Data analysis: The server's AI analysis engine analyzes the received questionnaire data and health data to assess the risk of pre-disease.

[0278] 3. Risk assessment and notification: The server assesses the risk of developing pre-disease based on the analysis results and sends a notification to the user's device. For example, the assessment result may say, "You are at high risk of cardiovascular disease due to lack of exercise."

[0279] 4. Habit improvement advice: Based on the analysis results, the server's AI analysis engine generates personalized habit improvement advice and notifies the user. For example, the advice may be, "Try adding 30 minutes of walking every day."

[0280] 5. Collaboration with medical experts: If the server's AI detects an abnormality in the analysis results, it will notify medical experts. Medical experts will review the analysis results through a dedicated portal and provide necessary advice and treatment plans. This feedback will be sent to the user via the server.

[0281] 6. User feedback processing: When users send questions or feedback using the in-app chat function, the data is sent to the server and analyzed by the AI ​​analysis engine. If necessary, medical professionals will be contacted and appropriate support will be provided.

[0282] Specific examples

[0283] For example, User A can use a smartphone to answer a questionnaire about the amount of exercise, diet, and sleep time they get each day, and send heart rate and step count data from a wearable device (smartwatch) to a server. The server's AI analysis engine analyzes this data and evaluates their risk of developing a pre-disease condition. As a result of the evaluation, a notification stating, for example, "Your lack of exercise puts you at high risk of cardiovascular disease" is sent to User A's smartphone. Then, personalized advice on improving habits is sent, such as "Try adding 30 minutes of walking each day."

[0284] Prompt Sentence Examples

[0285] "Based on the user's health data (heart rate, number of steps, sleep time), please assess their risk of illness and provide specific health advice. The following data is from User A's data for the past week."

[0286] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0287] Step 1:

[0288] The user uses a device to answer a questionnaire about their lifestyle habits. The questions in the questionnaire include the amount of exercise, dietary habits, and sleep duration. The device collects the user's input data and sends it to the server in JSON format. The input is the user's questionnaire response data, and the output is JSON-formatted data sent to the server. Once the server receives this data, it is ready to proceed to the next analysis step.

[0289] Step 2:

[0290] The server passes the received survey data to the AI ​​model. The AI ​​model is implemented using TensorFlow or PyTorch and evaluates pre-disease risk based on the survey data. Here, data processing involves normalization and feature selection. The input is the survey data and the output is a pre-disease risk score. The evaluated risk score is stored in a database and used in the next notification step.

[0291] Step 3:

[0292] The server notifies the user's device of the evaluation results. If the pre-disease risk score is high, a message urging special attention is generated. This notification is sent to the user's smartphone as a push notification. The input is the pre-disease risk evaluation score, and the output is the notification message sent to the user's device. This allows the user to receive real-time information about their health status.

[0293] Step 4:

[0294] The wearable device collects the user's health data (heart rate, steps, sleep data, etc.) and sends it to a server. The wearable device periodically records the data and transfers it to a terminal via Bluetooth or Wi-Fi. The input is raw data from the wearable device, and the output is health data sent to the server via the terminal.

[0295] Step 5:

[0296] The server's AI analysis engine evaluates and analyzes the collected health data. Statistical methods and machine learning algorithms are used for the analysis. The AI ​​analysis engine detects outliers and performs trend analysis, and if an abnormality is detected, it records the results as a flag. The input is health data from the wearable device, and the output is the analysis results and an abnormal value flag.

[0297] Step 6:

[0298] Based on the analysis results of the server, personalized advice for improving habits is generated and notified to the user. For example, a user who has not reached their step goal may receive the advice "Try adding 30 minutes of walking each day." The input is the analysis results and the user's health data, and the output is personalized advice for improving habits.

[0299] Step 7:

[0300] If the server's AI detects an abnormal value in the analysis results, it notifies medical professionals. Medical professionals log in to a dedicated portal to check the analysis results and provide any necessary advice or treatment plans. The input is the abnormal value flag and the analysis results, and the output is the notification and advice to the medical professional.

[0301] Step 8:

[0302] Users use the in-app chat function to send questions or feedback. The device sends this to the server, where the AI ​​analysis engine analyzes the content. If necessary, medical professionals are contacted and appropriate support is provided to the user. The input is the user's feedback data, and the output is the analysis results and countermeasures.

[0303] Specific examples

[0304] For example, user A answers a lifestyle questionnaire using a smartphone and collects heart rate and step count data from a smartwatch. This data is sent to a server, and the AI ​​analysis engine evaluates the user as being at high risk for cardiovascular disease due to lack of exercise. The server notifies user A of this result on their smartphone and provides advice such as "Try adding 30 minutes of walking each day."

[0305] 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.

[0306] The present invention provides a system that provides more accurate health management by combining a personal health plan advisor system that supports the early detection of pre-disease conditions and the prevention of their progression with an emotion engine that recognizes the user's emotions. An embodiment of this system will be described in detail below.

[0307] 1. Online Health Checkup

[0308] The user launches the app through their device and proceeds to a lifestyle questionnaire from the profile entry screen. The device displays the questionnaire items, which the user answers. Once the answers are completed, the device sends the response data to the server. On the server, AI analyzes the response data and evaluates the risk of developing pre-disease. The evaluation results are sent from the server to the user's device and notified.

[0309] Examples:

[0310] After User A answers a questionnaire about his / her eating and exercise habits, the server evaluates that he / she is at high risk of cardiovascular disease due to lack of exercise and notifies User A of the result.

[0311] 2. Data Tracking and Analysis

[0312] Users wear a wearable device (e.g., a smartwatch) in their daily lives, and the device periodically records their heart rate, steps, sleep data, etc. This data is periodically sent to a server. The server's AI analyzes the received data and evaluates the user's health status. Based on the analysis results, the server generates personalized advice on habit improvement and notifies the user.

[0313] Examples:

[0314] User B wears a smartwatch, and based on the data sent from the device, the server's AI determines that "the number of steps per day has not reached the goal for two consecutive weeks." As improvement advice, it sends User B the suggestion to "add 30 minutes of walking every day."

[0315] 3. Collaboration with medical professionals

[0316] If the server's AI detects an abnormality in the analysis results, it will flag it and send a notification to a medical professional. The medical professional will review the analysis results through a dedicated portal and provide any necessary advice or treatment plans. Feedback is then sent to the user via the server.

[0317] Examples:

[0318] Based on User C's heart rate data, the server's AI determines that "a persistently high heart rate is observed" and notifies a medical professional. The medical professional then conducts a detailed evaluation and notifies User C of the need for additional testing.

[0319] 4. Feedback and Support

[0320] Users can use the in-app chat feature to send questions or feedback. The device then sends the feedback to the server, which analyzes it and, if necessary, notifies medical professionals to provide appropriate support to the user.

[0321] Examples:

[0322] User D submits feedback about their sleep quality, reporting that they "wake up multiple times each night." The server analyzes the data and determines that "consultation with a medical professional is recommended." A medical professional reviews the data and provides advice on specific supplements and improving their sleep environment.

[0323] 5. Combining Emotion Engines

[0324] While the user is using the app, the emotion engine analyzes the user's voice, facial expressions, and text input to recognize their emotional state. The device collects this data and sends it to a server, where the server's AI adjusts health assessments and improvement advice based on the user's emotional state. This enables more personalized health management.

[0325] Examples:

[0326] When User E sends feedback within the app, the emotion engine detects a state of stress from User E's voice. The server's AI takes this into consideration and provides advice such as "Try deep breathing exercises to reduce stress."

[0327] As described above, the personal health plan advisor system of the present invention tracks a user's lifestyle and health data, and evaluates and analyzes pre-disease risks to realize early detection of pre-disease and personalized health management. In addition, by combining it with an emotion engine, it provides more precise health management that takes into account the user's emotional state.

[0328] The processing flow will be explained below.

[0329] Processing steps of a system that combines emotion engines

[0330] Online health check processing steps

[0331] Step 1:

[0332] The user starts the device and opens the Health Plan Advisor app.

[0333] Step 2:

[0334] The device displays a profile entry screen to the user, prompting them to enter basic information (such as name, age, gender, medical history, etc.).

[0335] Step 3:

[0336] The device displays a questionnaire screen about lifestyle habits, and the user answers questions about eating habits, exercise habits, sleep habits, stress levels, etc.

[0337] Step 4:

[0338] The user completes the survey and clicks the submit button.

[0339] Step 5:

[0340] The terminal transmits the user's response data to the server.

[0341] Step 6:

[0342] The server receives the response data and inputs it into the AI ​​engine.

[0343] Step 7:

[0344] The server's AI analyzes the response data and assesses the risk of developing illness.

[0345] Step 8:

[0346] The server generates the evaluation results and transmits the data to the user terminal.

[0347] Step 9:

[0348] The terminal notifies the user of the evaluation results and displays advice based on the evaluation results.

[0349] Data Tracking and Analysis Process Steps

[0350] Step 1:

[0351] A user wears a wearable device (e.g., a smart watch) in their daily life.

[0352] Step 2:

[0353] The wearable device regularly records health data such as heart rate, steps taken, and sleep data.

[0354] Step 3:

[0355] The wearable device periodically transmits the recorded data to the user's smartphone.

[0356] Step 4:

[0357] The terminal transmits the data received from the wearable terminal to the server at regular intervals.

[0358] Step 5:

[0359] The server receives the wearable data and stores it in a database.

[0360] Step 6:

[0361] The server's AI periodically performs data analysis and evaluates the user's health status.

[0362] Step 7:

[0363] The server's AI generates personalized advice on habit improvement based on the analysis results.

[0364] Step 8:

[0365] The server transmits the generated advice to the user's terminal.

[0366] Step 9:

[0367] The device will notify the user of the advice and display details within the app.

[0368] Steps in the process of working with a medical professional

[0369] Step 1:

[0370] If the server's AI detects an abnormal value in the analysis results, it will raise an abnormality flag.

[0371] Step 2:

[0372] The server notifies the medical professional of the results of the abnormality analysis.

[0373] Step 3:

[0374] Medical professionals are notified through a dedicated portal and can review the analysis results.

[0375] Step 4:

[0376] A medical professional will conduct a detailed assessment and generate further advice and a treatment plan.

[0377] Step 5:

[0378] The server transmits the feedback from the medical professional to the user terminal.

[0379] Step 6:

[0380] The device will notify the user of feedback from medical professionals and display details within the app.

[0381] Feedback and support process steps

[0382] Step 1:

[0383] Users submit questions or comments using the in-app feedback feature.

[0384] Step 2:

[0385] The terminal sends the user's feedback to the server.

[0386] Step 3:

[0387] The server receives the feedback, and the AI ​​handles any content that can be automatically responded to.

[0388] Step 4:

[0389] The server notifies the medical professional of the feedback as needed.

[0390] Step 5:

[0391] Medical professionals will review the feedback and take appropriate action.

[0392] Step 6:

[0393] The server transmits the response results from the medical professional to the user terminal.

[0394] Step 7:

[0395] The device will notify the user of the response results and display details within the app.

[0396] Emotion Engine Processing Steps

[0397] Step 1:

[0398] While the user is using the app, voice, facial expressions, and text input are collected through the device's camera and microphone.

[0399] Step 2:

[0400] The device sends the collected voice, facial expression, and text data to the emotion engine.

[0401] Step 3:

[0402] An emotion engine within the device analyzes this data and recognizes the user's emotional state.

[0403] Step 4:

[0404] The terminal transmits the recognized emotional state data to the server.

[0405] Step 5:

[0406] The server's AI takes into account the user's emotional state and adjusts health assessments and advice on improving habits.

[0407] Step 6:

[0408] The server sends the adjusted advice to the user's terminal.

[0409] Step 7:

[0410] The device will notify the user of tailored advice and display details within the app.

[0411] Examples:

[0412] When User E sends feedback within the app, the emotion engine detects a state of stress from User E's voice. The server's AI takes this into consideration and provides advice such as "Try deep breathing exercises to reduce stress."

[0413] These are the specific processing steps of the personal health plan advisor system that combines the emotion engine. Through these steps, the system will further personalize the user's health management, enabling early detection of pre-disease and prevention of its progression.

[0414] Example 2

[0415] 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."

[0416] The need for prevention of lifestyle-related diseases and health management is rapidly increasing in modern times, but existing health management systems have difficulty in providing advanced personalization that fully takes into account the user's lifestyle and emotional state. In particular, for early detection of pre-disease and prevention of progression, there is a need to comprehensively analyze the user's individual data and emotional state and provide appropriate advice. However, conventional systems lack such advanced functionality.

[0417] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to answer a lifestyle questionnaire via a terminal and transmit the response data to the server; a means for the server to receive the response data and evaluate pre-disease risk using an artificial intelligence; a means for the server to notify the user of the evaluation results; a means for the wearable device to collect health data and periodically transmit it to the server; a means for the server's artificial intelligence to analyze the health data and generate personalized habit improvement advice and notify the user; a means for the server's artificial intelligence to notify an expert if it detects an abnormal value in the analysis results; a means for the expert to review the analysis results and provide necessary advice and a treatment plan; a means for the server to process feedback sent by the user within the app and provide necessary support; a means for the server's artificial intelligence to analyze the user's voice, facial expression, and text input and recognize the user's emotional state; and a means for the server's artificial intelligence to adjust the health assessment and improvement advice taking the emotional state into account. This enables integrated analysis of the user's lifestyle data and emotional state, enabling highly personalized health management.

[0418] "User" refers to an individual who uses this system.

[0419] "Terminal" refers to the electronic device that a user uses to access this system. Specifically, it includes devices such as smartphones, tablets, and personal computers.

[0420] The "questionnaire on lifestyle habits" refers to a questionnaire in the form of questions in which the user answers about habits in daily life such as eating, exercise, and sleep.

[0421] "Server" refers to a computer system for receiving, storing, and analyzing data from users.

[0422] "Artificial intelligence" refers to machine learning algorithms that run on a server and analyze user data to assess pre-disease risk and recognize emotional states.

[0423] "Pre-disease risk" refers to assessing the risk of a user becoming ill before they become ill.

[0424] "Evaluation results" refers to information regarding pre-disease risks and other health conditions generated by artificial intelligence through analysis of the user's data.

[0425] "Wearable devices" refer to health management devices worn by users, including smartwatches and fitness trackers.

[0426] "Health data" refers to information collected by wearable devices, such as heart rate, steps taken, and sleep data.

[0427] "Personalized habit improvement advice" refers to customized health improvement suggestions provided to a user based on the user's particular health condition and lifestyle habits.

[0428] "Experts" refer to qualified individuals with knowledge of medical care and health management, and are responsible for providing advice and treatment plans to users based on notifications from the server.

[0429] "Feedback" refers to information such as opinions, questions, reports, etc. that users submit within the app.

[0430] "Emotional state" refers to the psychological and emotional state a user exhibits while using the app.

[0431] "Emotion engine" refers to a software component that recognizes a user's emotional state by analyzing their voice, facial expressions, and text input.

[0432] The present invention provides a system that provides more accurate health management by combining a personal health plan advisor system that supports the early detection of pre-disease conditions and the prevention of their progression with an emotion engine that recognizes the user's emotions. An embodiment of this system will be described in detail below.

[0433] Online Health Check

[0434] Users launch the app on a device such as a smartphone or tablet. They enter basic information on the app's profile screen, then answer a questionnaire about their lifestyle habits (diet, exercise, sleep, etc.). The device temporarily stores this information and sends the data to a server. The server passes the received data to an analysis engine (AI model) and evaluates the risk of developing pre-disease. The server then notifies the user's device of the evaluation results.

[0435] Examples:

[0436] After User A answers a questionnaire about his / her eating and exercise habits, the server evaluates that he / she is at high risk of cardiovascular disease due to lack of exercise and notifies User A of the result.

[0437] Prompt statement:

[0438] "Tell me about your recent eating and exercise habits."

[0439] Data Tracking and Analysis

[0440] Users wear a wearable device (e.g., a smartwatch) in their daily lives and regularly record their heart rate, steps, sleep data, etc. This data is periodically sent to a server where it is analyzed by AI. Based on the analysis results, the server generates personalized advice for improving habits and notifies the user.

[0441] Examples:

[0442] User B wears a smartwatch, and based on the data sent from the device, the server's AI determines that "the number of steps per day has not reached the goal for two consecutive weeks." As improvement advice, it sends User B the suggestion to "add 30 minutes of walking every day."

[0443] Prompt statement:

[0444] "We've recorded your recent exercise data. To reach your step goal, we recommend you increase your walking time."

[0445] Collaboration with medical professionals

[0446] If the server's AI detects an abnormality in the analysis results, it will send a notification to an expert. The expert will review the analysis results through a dedicated portal and provide any necessary advice or treatment plans. This feedback is then sent to the user via the server.

[0447] Examples:

[0448] Based on User C's heart rate data, the server's AI determines that "a sustained high heart rate is observed" and notifies an expert. The expert then conducts a detailed evaluation and notifies User C of the need for further testing.

[0449] Prompt statement:

[0450] "Get expert advice based on your heart rate data."

[0451] Feedback and Support

[0452] Users can use the in-app chat feature to ask questions or send feedback. The device then sends the feedback to the server, which analyzes it and, if necessary, notifies an expert to provide the user with appropriate support.

[0453] Examples:

[0454] User D submits feedback about their sleep quality, reporting that they "wake up multiple times each night." The server analyzes the data and determines that "expert consultation is recommended." The expert reviews the data and offers advice on specific supplements and improving their sleep environment.

[0455] Prompt statement:

[0456] "We've received your feedback about your sleep issues. Our experts will suggest specific solutions."

[0457] Combining Emotion Engines

[0458] While the user is using the app, the emotion engine analyzes the user's voice, facial expressions, and text input to recognize their emotional state. The device collects this data and sends it to a server, where the server's AI adjusts health assessments and improvement advice based on the user's emotional state. This enables more personalized health management.

[0459] Examples:

[0460] When User E sends feedback within the app, the emotion engine detects the stress level from User E's voice. The server's AI then provides advice such as "Try deep breathing exercises to reduce stress."

[0461] Prompt statement:

[0462] "We've detected your stress level. Try some deep breathing exercises to help you relax."

[0463] This system tracks the user's lifestyle and health data, and evaluates and analyzes pre-disease risks, enabling early detection of pre-disease and personalized health management. In addition, by combining it with an emotion engine, it provides more precise health management that takes into account the user's emotional state.

[0464] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0465] Step 1:

[0466] A user taps the app icon on their smartphone or tablet to launch the app, which takes them to the app's home screen where they can log in or register.

[0467] Input: User login or registration information

[0468] Output: App home screen

[0469] Specific operation: After launching the app, the app logo and login / new registration form will be displayed on the screen.

[0470] Step 2:

[0471] The user enters basic information (age, gender, height, weight, etc.) on the profile entry screen within the app and answers questions about lifestyle habits (e.g., diet, exercise, sleep, etc.). Once the information entry is complete, the device temporarily stores this information.

[0472] Input: User profile information and survey responses

[0473] Output: Temporarily saved data

[0474] Specific operation: Survey questions are displayed on the screen and the user enters answers.

[0475] Step 3:

[0476] The terminal converts the temporarily stored data into a packet format and transmits it to a server via the Internet.

[0477] Input:Temporarily saved data

[0478] Output: Data sent to the server

[0479] Specific behavior: The "Send" button is enabled, and when the user clicks it, the message "Data transmission completed" is displayed on the screen.

[0480] Step 4:

[0481] The server passes the data received from the user to the analysis engine (generative AI model) to evaluate the risk of pre-disease. This analysis generates the risk assessment results.

[0482] Input: User survey data

[0483] Output: Pre-disease risk assessment results

[0484] Specific operation: A new record is generated in the database on the server, and the AI ​​model performs analysis.

[0485] Step 5:

[0486] The server generates a feedback message based on the evaluation result and transmits it to the user terminal.

[0487] Input: Pre-disease risk assessment results

[0488] Output: Feedback message

[0489] Specific actions: If a person is judged to be at risk due to lack of exercise, a message containing specific exercise advice will be generated.

[0490] Step 6:

[0491] Users wear wearable devices such as smartwatches in their daily lives to regularly record their heart rate, number of steps, sleep data, etc. The devices temporarily store this data and periodically transmit it to a server.

[0492] Input: Health data collected by wearable devices

[0493] Output: Health data sent to the server

[0494] How it works: The smartwatch measures your heart rate and steps, and periodically transmits the data.

[0495] Step 7:

[0496] The server generates an AI model that analyzes health data and generates personalized advice for improving habits, which is then sent to the user.

[0497] Input: Health data

[0498] Output: personalized advice on how to improve your habits

[0499] Specific action: If it is determined that the daily step count has not reached the goal, a suggestion is generated to "add 30 minutes of walking every day."

[0500] Step 8:

[0501] If the server-generated AI model detects an abnormality in the analysis results, it will send a notification to the expert, who will then review the analysis results on the portal and provide any necessary advice or treatment plans.

[0502] Input: Outlier analysis results

[0503] Output: Professional notification and advice, treatment plan

[0504] Specific operation: The server sends emails and notifications to the specialist, who then enters evaluation comments and treatment plans into the portal.

[0505] Step 9:

[0506] Users can use the in-app chat feature to send questions or feedback. The device sends the feedback to the server, which analyzes it. If necessary, the feedback is notified to an expert, who can provide the user with appropriate support.

[0507] Input: User feedback

[0508] Output: Any support or expert advice you need

[0509] Specific Actions: After reports of "waking up multiple times each night," experts offer advice on specific supplements and improving the sleep environment.

[0510] Step 10:

[0511] While the user is using the app, the emotion recognition engine analyzes the user's voice, facial expressions, and text input to recognize their emotional state. The device collects this data and sends it to the server.

[0512] Input: User voice, facial expressions, and text input

[0513] Output: Perceived emotional state

[0514] Specific operation: The microphone is activated to detect the stress level from the user's voice, and the camera is activated to analyze facial expressions.

[0515] Step 11:

[0516] The server's generative AI model takes into account the user's emotional state to adjust the health assessment and improvement advice, resulting in more personalized advice for the user.

[0517] Input: Perceived emotional state

[0518] Output: Emotion-based health assessment and improvement advice

[0519] Specific actions: Detects stress levels and provides advice such as "Try deep breathing exercises to reduce stress"

[0520] Through these steps, the system will be able to continuously track and analyze users' lifestyle data and emotional state, providing highly personalized health management.

[0521] (Application example 2)

[0522] 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."

[0523] Conventional health management systems require users to approach the system and provide their response data and health data, and insufficient data can prevent early detection of pre-disease symptoms and appropriate health management. Furthermore, conventional health management systems have difficulty providing advice that takes into account the user's emotional state, creating a need for more precise and effective health management. Furthermore, similar health management is required even during long trips using autonomous vehicles.

[0524] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means. In this invention, the server includes: means for a user to answer a questionnaire about lifestyle habits via a communication terminal and transmit the answer data to the server; means for the server to receive the answer data and evaluate pre-disease risk using artificial intelligence; means for the server to notify the user terminal of the evaluation results; means for a wearable device to collect health data and periodically transmit it to the server; means for the server's artificial intelligence to analyze the health data and generate personalized habit improvement advice and notify the user; means for the server's artificial intelligence to notify a medical professional if it detects an abnormal value in the analysis results; means for the medical professional to check the analysis results and provide necessary advice and / or treatment plans; means for the server to process feedback transmitted by the user within an application and provide necessary support; means for an autonomous vehicle to collect data on the passenger's heart rate, sitting posture, and in-vehicle environment and periodically transmit it to the server; means for a camera and microphone in the autonomous vehicle to analyze the passenger's facial expressions and voice to recognize their emotional state and transmit it to the server; and means for the server's artificial intelligence to generate health management advice taking the passenger's emotional state into consideration and notify the passenger. This will enable users to monitor their health and emotional state in real time and receive appropriate advice while in an autonomous vehicle, helping them maintain their health even during long journeys and providing a comfortable riding experience.

[0525] A "communication terminal" is a device that allows a user to connect to the Internet and send and receive information.

[0526] "Lifestyle habits" refer to the actions and habits that a user performs in their daily life.

[0527] A "server" is a computer system that stores and processes various data on a network and exchanges information with user terminals and other devices.

[0528] "Artificial intelligence" refers to software technology that mimics human intelligence and has functions such as data analysis, prediction, and learning.

[0529] "Pre-disease risk" refers to potential health risks before symptoms appear.

[0530] A "user terminal" is a device operated by a user (e.g., a smartphone, tablet, or PC).

[0531] A "wearable device" is a device (e.g., smartwatch, fitness tracker) worn by a user to collect and record various data.

[0532] "Health data" refers to data that indicates the user's physical condition (e.g., heart rate, number of steps, sleep data, sitting posture, and in-car environmental data).

[0533] "Individualized advice for improving lifestyle habits" refers to specific lifestyle improvement suggestions provided based on each user's individual health condition.

[0534] A "healthcare professional" is a professional (e.g., doctor, nurse, or healthcare provider) who has medical expertise and who assesses, diagnoses, or treats a user's health condition.

[0535] "Feedback" refers to opinions and reports from users, which the system analyzes and takes appropriate action.

[0536] An "autonomous vehicle" is a vehicle that can autonomously navigate roads and transport passengers to their destinations.

[0537] "Sitting posture" is data indicating the position and angle of the user's body when sitting.

[0538] "In-vehicle environment data" refers to environmental data such as temperature, humidity, and noise level inside the vehicle.

[0539] "Facial expression" is data that observes the movements and expressions of the user's face.

[0540] "Voice" is data that observes the characteristics of a user's voice.

[0541] The present invention relates to a system that monitors the health and emotional state of users in autonomous vehicles in real time and provides appropriate health management advice, enabling users to maintain their health and enjoy a comfortable riding experience even during long trips.

[0542] System configuration

[0543] 1. Hardware:

[0544] Communication devices: smartphones, tablets, computers, etc.

[0545] Wearable devices: smartwatches, fitness trackers, etc.

[0546] Cameras built into autonomous vehicles: for facial recognition.

[0547] In-vehicle microphone: For voice recognition.

[0548] On-board computer: for data collection and analysis.

[0549] 2. Software:

[0550] Health management applications: For surveys, data collection, and notifications.

[0551] Emotion recognition engine: such as "EmotionEngine".

[0552] Health data analysis AI: "HealthAnalyzer" etc.

[0553] Server system: Stores and analyzes data.

[0554] Processing Flow

[0555] 1. Collecting survey data

[0556] The user answers a questionnaire about lifestyle habits via a communication terminal.

[0557] The terminal transmits the response data to the server.

[0558] The server uses artificial intelligence to assess the risk of developing pre-disease and notifies the user's device of the results.

[0559] 2. Health Data Collection

[0560] The user wears a wearable device that collects data such as heart rate, steps, and sleep.

[0561] The data is transmitted to a server via an on-board computer in the autonomous vehicle.

[0562] The server's artificial intelligence analyzes the data, generates personalized advice on how to improve habits, and sends it to the user's device.

[0563] 3. Collecting Emotional Data

[0564] Cameras and microphones inside the self-driving vehicle collect the user's facial expressions and voice to analyze their emotional state.

[0565] The data is sent to a server, and artificial intelligence uses the analysis results to generate health management advice that takes emotional state into account.

[0566] The advice is sent to the user terminal.

[0567] Specific examples

[0568] Example 1:

[0569] User A gets into an autonomous vehicle and answers a questionnaire. The server evaluates that "the risk due to lack of exercise is high" and notifies User A's device of the result. The advice is provided to "add 30 minutes of walking every day."

[0570] Example 2:

[0571] The server detects that User B's heart rate has continuously increased while driving. The server determines this to be a "stressed state" and suggests relaxing music in the car.

[0572] Example 3:

[0573] The server analyzes the facial expressions and voice of user C and recognizes that he is feeling stressed. The server generates advice to display on the in-car screen, such as a video of deep breathing exercises.

[0574] Prompt Sentence Examples

[0575] "If a user is emotionally exhausted while in the car, what relaxation methods would you suggest?"

[0576] "What health precautions are recommended for users when sitting for extended periods of time?"

[0577] This allows users to monitor their health and emotional state in real time while in an autonomous vehicle and receive optimal health management advice, thereby maintaining health and providing a comfortable riding experience even during long journeys.

[0578] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0579] Step 1:

[0580] Survey data collection

[0581] Input: The user answers a questionnaire about their lifestyle habits via a communication terminal.

[0582] Operation: The user enters answers to the questionnaire items displayed on the screen of the communication terminal.

[0583] Output: Answer data is generated.

[0584] Step 2:

[0585] Sending survey data

[0586] Input: Survey response data

[0587] How it works: The device sends the answer data to the server via the Internet.

[0588] Output: Survey response data saved on the server

[0589] Step 3:

[0590] Pre-disease risk assessment

[0591] Input: Survey response data on the server

[0592] How it works: The server's AI analyzes the questionnaire response data and evaluates the risk of developing pre-disease. The AI ​​calculates the risk using the user's lifestyle data.

[0593] Output: Pre-disease risk assessment results

[0594] Step 4:

[0595] Notification of pre-disease risk assessment results

[0596] Input: Pre-disease risk assessment results

[0597] Operation: The server notifies the user of the evaluation results via push notification or in-app notification.

[0598] Output: Evaluation result notification displayed on the user's device

[0599] Step 5:

[0600] Health data collection

[0601] Input: Heart rate, step count, and sleep data collected by the user's wearable device

[0602] How it works: The wearable device periodically records the user's health data and transmits it to the car's onboard computer.

[0603] Output: Health data stored in the on-board computer

[0604] Step 6:

[0605] Sending health data

[0606] Input: Health data on the vehicle's computer

[0607] How it works: The on-board computer sends health data to a server via the autonomous vehicle's communications system.

[0608] Output: Health data stored on the server

[0609] Step 7:

[0610] Health data analysis

[0611] Input: Health data on the server

[0612] How it works: The server's artificial intelligence analyzes health data and generates personalized advice for improving habits. Analysis includes data preprocessing, feature extraction, and model application.

[0613] Output: Habit improvement advice

[0614] Step 8:

[0615] Habit improvement advice notifications

[0616] Input: Habit improvement advice

[0617] Behavior: The server sends the generated advice to the user's device via push notification or in-app notification.

[0618] Output: Advice displayed on the user's terminal

[0619] Step 9:

[0620] Collecting Emotional Data

[0621] Input: Facial expression and voice data collected by cameras and microphones inside the autonomous vehicle

[0622] How it works: Cameras inside the vehicle record facial expression data and microphones record audio data in real time.

[0623] Output: Emotion data stored in the on-board computer

[0624] Step 10:

[0625] Sending emotional data

[0626] Input: Emotion data on the onboard computer

[0627] How it works: The onboard computer sends emotion data to a server via the autonomous vehicle's communications system.

[0628] Output: Emotion data stored on the server

[0629] Step 11:

[0630] Emotional Data Analysis

[0631] Input: Emotion data on the server

[0632] How it works: The server's AI analyzes the emotional data and evaluates the user's emotional state using emotion recognition technology.

[0633] Output: Emotional state assessment result

[0634] Step 12:

[0635] Generating Health Care Advice Considering Emotional States

[0636] Input: Emotional state assessment results, health data analysis results

[0637] How it works: The server's artificial intelligence integrates emotional and health status to generate more personalized health care advice.

[0638] Output: Integrated health advice

[0639] Step 13:

[0640] Integrated health management advice notification

[0641] Enter: Integrated Health Care Advice

[0642] How it works: The server sends the integrated advice to the user's device via push notification or in-app notification.

[0643] Output: Consolidated advice displayed on the user's terminal

[0644] 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.

[0645] 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.

[0646] 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.

[0647] [Second embodiment]

[0648] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0649] 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.

[0650] 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).

[0651] 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.

[0652] 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.

[0653] 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).

[0654] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0655] 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.

[0656] 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.

[0657] 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.

[0658] 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.

[0659] 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."

[0660] The present invention is a personal health plan advisor system that supports the early detection of pre-disease conditions and the prevention of their progression. An embodiment of this system will be described in detail below.

[0661] 1. Online Health Checkup

[0662] The user launches the app through their device and proceeds to a lifestyle questionnaire from the profile entry screen. The device displays the questionnaire items, and the user answers them. Once the answers are completed, the device sends the response data to a server. On the server, AI analyzes the response data and evaluates the risk of developing pre-disease. The evaluation results are sent from the server to the user's device and notified.

[0663] Examples:

[0664] After User A answers a questionnaire about the amount of exercise, diet, and sleep time each day, the server evaluates that "lack of exercise puts you at high risk of cardiovascular disease" and notifies User A of the result.

[0665] 2. Data Tracking and Analysis

[0666] The user wears a wearable device in their daily life. The device periodically records heart rate, steps, sleep data, etc. and sends this data to a server. The server's AI analyzes the received data and evaluates the user's health. Based on the analysis results, the server generates personalized advice on habit improvement and notifies the user.

[0667] Examples:

[0668] User B wears a smartwatch, and based on the data sent from the device, the server's AI determines that "the number of steps per day has not reached the goal for two consecutive weeks." As improvement advice, it sends User B the suggestion to "try adding 30 minutes of walking each day."

[0669] 3. Collaboration with medical professionals

[0670] If the server's AI detects an abnormal value in the analysis results, it flags it and sends a notification to a medical professional. The medical professional then reviews the analysis results through a dedicated portal and provides any necessary advice or treatment plans. This feedback is then sent to the user via the server.

[0671] Examples:

[0672] Based on User C's heart rate data, the server's AI determines that "a persistently high heart rate is observed" and notifies a medical professional. The medical professional then conducts a detailed evaluation and notifies User C of the need for additional testing.

[0673] 4. Feedback and Support

[0674] Users can use the in-app chat feature to send questions or feedback, which the device then sends to the server, which analyzes the user's feedback and, if necessary, connects them with a medical professional to provide appropriate support to the user.

[0675] Examples:

[0676] User D submits feedback about their sleep quality, reporting that they "wake up multiple times each night." The server analyzes the data and returns a result stating that "consultation with a medical professional is recommended." A medical professional reviews the data and provides advice on specific supplements and improving the sleep environment.

[0677] As described above, the personal health plan advisor system of the present invention is a system that tracks a user's lifestyle habits and health data, and evaluates and analyzes pre-disease risks, thereby enabling early detection of pre-disease and individualized health management.

[0678] The processing flow will be explained below.

[0679] Online health check processing steps

[0680] Step 1:

[0681] The user starts the device and opens the Health Plan Advisor app.

[0682] Step 2:

[0683] The terminal displays a profile entry screen to the user, prompting them to enter basic information such as name, age, sex, and medical history.

[0684] Step 3:

[0685] The device displays a questionnaire screen about lifestyle habits, and the user answers questions about eating habits, exercise habits, sleep habits, stress levels, etc.

[0686] Step 4:

[0687] The user completes the survey and clicks the submit button.

[0688] Step 5:

[0689] The terminal transmits the user's response data to the server.

[0690] Step 6:

[0691] The server receives the response data and inputs it into the AI ​​engine.

[0692] Step 7:

[0693] The server's AI analyzes the response data and assesses the risk of developing illness.

[0694] Step 8:

[0695] The server generates the evaluation results and transmits the data to the user terminal.

[0696] Step 9:

[0697] The terminal notifies the user of the evaluation results and displays advice based on the evaluation results.

[0698] Data Tracking and Analysis Process Steps

[0699] Step 1:

[0700] A user wears a wearable device (e.g., a smart watch) in their daily life.

[0701] Step 2:

[0702] The wearable device regularly records health data such as heart rate, steps taken, and sleep data.

[0703] Step 3:

[0704] The wearable device periodically transmits the recorded data to the user's smartphone.

[0705] Step 4:

[0706] The terminal transmits the data received from the wearable terminal to the server at regular intervals.

[0707] Step 5:

[0708] The server receives the wearable data and stores it in a database.

[0709] Step 6:

[0710] The server's AI periodically performs data analysis and evaluates the user's health status.

[0711] Step 7:

[0712] The server's AI generates personalized advice on habit improvement based on the analysis results.

[0713] Step 8:

[0714] The server transmits the generated advice to the user's terminal.

[0715] Step 9:

[0716] The device will notify the user of the advice and display details within the app.

[0717] Steps in the process of working with a medical professional

[0718] Step 1:

[0719] If the server's AI detects an abnormal value in the analysis results, it will raise an abnormality flag.

[0720] Step 2:

[0721] The server notifies the medical professional of the results of the abnormality analysis.

[0722] Step 3:

[0723] Medical professionals are notified through a dedicated portal and can review the analysis results.

[0724] Step 4:

[0725] A medical professional will conduct a detailed assessment and generate further advice and a treatment plan.

[0726] Step 5:

[0727] The server transmits the feedback from the medical professional to the user terminal.

[0728] Step 6:

[0729] The device will notify the user of feedback from medical professionals and display details within the app.

[0730] Feedback and support process steps

[0731] Step 1:

[0732] Users submit questions or comments using the in-app feedback feature.

[0733] Step 2:

[0734] The terminal sends the user's feedback to the server.

[0735] Step 3:

[0736] The server receives the feedback, and the AI ​​handles any content that can be automatically responded to.

[0737] Step 4:

[0738] The server notifies the medical professional of the feedback as needed.

[0739] Step 5:

[0740] Medical professionals will review the feedback and take appropriate action.

[0741] Step 6:

[0742] The server transmits the response results from the medical professional to the user terminal.

[0743] Step 7:

[0744] The device will notify the user of the response results and display details within the app.

[0745] The above are the specific processing steps of the personal health plan advisor system. The system supports the user's health management through each step, realizing early detection of pre-disease and prevention of its progression.

[0746] Example 1

[0747] 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."

[0748] In modern society, problems related to lifestyle habits and health management are on the rise, making it essential to detect pre-disease risks early and prevent their progression. However, conventional health management systems lack personalized advice and collaboration with medical professionals, making it difficult for users to accurately and efficiently understand their own health status. Furthermore, feedback processing tends to be delayed, making it difficult to provide immediate support to users.

[0749] 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.

[0750] In this invention, the server includes a means for allowing a user to answer a questionnaire about lifestyle habits via a terminal and transmitting the answer data to the server, a means for the server to receive the answer data and for an AI model to evaluate pre-disease risk, and a means for the server to notify the user terminal of the evaluation result, thereby enabling the user's lifestyle habit data to be analyzed quickly and accurately, and the pre-disease risk to be evaluated and notified.

[0751] Furthermore, the server includes means for the wearable device to collect health data and periodically transmit it to the server, means for the AI ​​model of the server to analyze the health data and generate personalized habit improvement advice and notify the user, means for notifying a medical professional if the AI ​​model of the server detects an abnormal value in the analysis result, means for the medical professional to check the analysis result and provide necessary advice and a treatment plan, and means for the server to process feedback sent by the user within the app and provide necessary support, thereby making it possible to continuously monitor the user's health condition and provide personalized advice and immediate support.

[0752] "User" refers to an individual or organization that uses the system and provides data for health management and pre-disease risk assessment.

[0753] A "terminal" is an electronic device used by a user, including a smartphone, tablet, or PC.

[0754] A "server" is a computer system that receives, analyzes, and stores data sent by users, and has advanced computing power and data storage capabilities.

[0755] "AI model" refers to the artificial intelligence algorithms and software that analyze user data and generate pre-disease risk assessments and personalized advice.

[0756] A "questionnaire" is a series of questions to collect information about a user's lifestyle habits, including items such as diet, exercise, and sleep.

[0757] "Pre-disease risk" refers to the result of an assessment of whether a user is likely to contract a disease in the future.

[0758] "Evaluation results" refers to information that indicates pre-disease risk and health status as determined by the AI ​​model analyzing the user's data.

[0759] A "wearable device" is an electronic device worn by a user to collect health data, including smartwatches and fitness trackers.

[0760] "Health data" refers to data related to a user's health status, such as heart rate, number of steps, and sleep data.

[0761] "Habit improvement advice" refers to specific behavioral suggestions and advice on lifestyle changes generated by the AI ​​model to help users maintain good health.

[0762] An "outlier" is an unusual value detected by an AI model while analyzing health data, which may indicate a potential health problem.

[0763] A "medical expert" is a doctor or other person with expertise in the medical field who provides advice and treatment plans to the user based on notifications and data from the server.

[0764] "Feedback" means any questions, requests, or health status reports provided by a User through the App.

[0765] "Support" refers to advice, solutions, or problem-solving assistance provided by a server or medical professional in response to user feedback.

[0766] This invention is a personal health plan advisor system that supports the early detection and prevention of pre-disease conditions. The system involves users answering a questionnaire about their lifestyle habits, analyzing the data to assess their pre-disease risk, and providing personalized health management advice.

[0767] Hardware and Software Configuration

[0768] 1. Device: The electronic device used by the user, including a smartphone, tablet, or PC, that answers the survey and receives data from the wearable device.

[0769] 2. Server: A computer system with advanced computing power and data storage capabilities that receives, analyzes, and stores data. It runs AI models such as Google Cloud AI, Azure AI, Scikit-learn, and TensorFlow.

[0770] 3. Wearable devices: Devices that collect user health data, including smartwatches and fitness trackers, which collect data such as heart rate, steps taken, and sleep duration.

[0771] Program processing

[0772] 1. Online Health Checkup

[0773] The user launches the app on their device and answers a questionnaire about their lifestyle habits. The device displays the questionnaire items and records the user's responses. Once the responses are complete, the device sends the data to the server. The server's AI model analyzes the data and evaluates the risk of developing pre-disease. The evaluation results are sent from the server to the user's device.

[0774] Example prompt: "Please complete a survey about your daily exercise, diet, and sleep."

[0775] 2. Data Tracking and Analysis

[0776] The user wears a wearable device, and the device records the heart rate, steps, sleep data, and other data obtained from the device. The device periodically sends this data to a server, where an AI model analyzes the data and evaluates the user's health. Based on the analysis results, the server generates personalized advice on improving habits and notifies the user's device.

[0777] Example prompt: "Please wear your smartwatch and send your data."

[0778] 3. Collaboration with medical professionals

[0779] If the server's AI detects an abnormal value in the analysis results, it flags it and notifies medical professionals. Medical professionals review the analysis results through a dedicated portal and provide necessary advice and treatment plans. This feedback is then sent to the user via the server.

[0780] Example prompt: "Analyze your heart rate data and notify a medical professional of any abnormal values."

[0781] 4. Feedback and Support

[0782] Users can use the in-app chat feature to send questions or feedback, which the device then sends to the server, which analyzes the user's feedback and, if necessary, connects them to a medical professional to provide the user with appropriate support.

[0783] Example prompt: "Submit your feedback or questions via in-app chat."

[0784] Specific examples

[0785] 1. Online Health Check:

[0786] After the user answers a questionnaire about their daily exercise, diet, and sleep, the server assesses whether they are at high risk of cardiovascular disease due to lack of exercise and notifies the user of the results.

[0787] 2. Data Tracking and Analysis:

[0788] When a user wears a smartwatch, the server's AI determines based on the data collected by the smartwatch that the user has not met their daily step goal for two consecutive weeks, and then provides advice on how to improve the situation, suggesting that the user try adding 30 minutes of walking each day.

[0789] 3. Collaboration with medical professionals:

[0790] Based on the user's heart rate data, the server's AI determines that a "persistently high heart rate is observed" and notifies a medical professional. The medical professional then conducts a detailed evaluation and notifies the user of the need for further testing.

[0791] 4. Feedback and Support:

[0792] The user sends feedback such as "I wake up many times every night." The server analyzes the data and returns a result saying, "Consultation with a medical professional is recommended." A medical professional reviews the data and provides recommendations for specific supplements and advice on improving the sleep environment.

[0793] As described above, the personal health plan advisor system of the present invention monitors the user's lifestyle data in detail, and by evaluating pre-disease risks and providing personalized advice, enables early detection of pre-disease and health management.

[0794] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0795] Online health check processing steps

[0796] Step 1:

[0797] The user launches the Personal Health Plan Advisor app on their device and answers a questionnaire about their lifestyle habits.

[0798] Input: User's basic information (name, age, gender, height, weight) and lifestyle data (exercise, diet, sleep, etc.)

[0799] Output: User response data

[0800] Specific operation: The user taps the app to launch it and enters answers to each item in the survey. The device records the user's input.

[0801] Step 2:

[0802] The terminal transmits the user's response data to the server.

[0803] Input: Survey response data

[0804] Output: Response data sent to the server

[0805] Specific operation: The device temporarily stores the user's survey responses and sends the data to a server via the Internet.

[0806] Step 3:

[0807] The AI ​​model on the server analyzes the response data and assesses the risk of developing illness.

[0808] Input: User response data

[0809] Output: Pre-disease risk assessment results

[0810] How it works: The server analyzes the data using Google Cloud AI or Azure AI models and generates a rating such as "high risk of cardiovascular disease."

[0811] Step 4:

[0812] The server generates the evaluation results and sends them to the user's terminal.

[0813] Input: Evaluation result data

[0814] Output: Evaluation results sent to the user's device

[0815] Specific operation: The server sends the generated evaluation results to the user's device, and the device displays the evaluation results via a pop-up or notification function.

[0816] Data Tracking and Analysis Process Steps

[0817] Step 1:

[0818] A user wears a wearable device, which collects health data.

[0819] Input: User's physical activity data (heart rate, steps, sleep data)

[0820] Output: Collected health data

[0821] What it does: A user puts on a smartwatch and the device records their activity data for the day.

[0822] Step 2:

[0823] The terminal acquires health data from the wearable device and transmits it to the server.

[0824] Input: Health data from wearable devices

[0825] Output: Health data sent to the server

[0826] Specific operation: The terminal acquires data from the device using Bluetooth or Wi-Fi and sends it to the server.

[0827] Step 3:

[0828] The server's AI model analyzes the health data and assesses the user's health status.

[0829] Input: Health data (heart rate, steps, sleep data)

[0830] Output: Health status assessment results

[0831] Specific operation: The server analyzes the data using TensorFlow and determines that the number of steps per day has not reached the target for two consecutive weeks.

[0832] Step 4:

[0833] Based on the analysis results, the server generates personalized advice for improving habits and notifies the user.

[0834] Input: Health status assessment results

[0835] Output: Personalized advice on how to improve your habits

[0836] Specific operation: The server generates a suggestion such as "Try adding 30 minutes of walking every day" and notifies the user device.

[0837] Steps in the process of working with a medical professional

[0838] Step 1:

[0839] If the server's AI detects an abnormal value in the analysis results, it will notify medical experts.

[0840] Input: Health data analysis results

[0841] Output: Notification to medical professionals

[0842] What it does: The server flags a "persistently high heart rate" and sends an automatic email notification to a medical professional.

[0843] Step 2:

[0844] Medical professionals will review the analysis results on a dedicated portal and provide necessary advice and treatment plans.

[0845] Input: Abnormal value notification and analysis result data from the server

[0846] Output: Detailed assessment and advice, treatment plan

[0847] How it works: Medical professionals log in to a dedicated portal, check the analysis results, and enter any necessary advice in writing or as a message within the system.

[0848] Step 3:

[0849] The server receives feedback from the medical professional and notifies the user.

[0850] Input: Medical professional advice, treatment plan

[0851] Output: User notification

[0852] Specific operation: The server sends the received advice to the user's device and displays it as a pop-up notification or in-app message.

[0853] Feedback and support process steps

[0854] Step 1:

[0855] Users submit questions or feedback through in-app chat.

[0856] Input: User feedback

[0857] Output: Feedback data sent to the server

[0858] What happens: A user opens the in-app chat, types in a question or feedback, and hits send.

[0859] Step 2:

[0860] The server analyzes the user's feedback and contacts medical professionals as needed.

[0861] Input: User feedback data

[0862] Output: Analysis of feedback and notification to medical professionals

[0863] Specific operation: The server analyzes the text of the feedback and if it determines that "expert consultation is required," it sends an email notification to a medical professional.

[0864] Step 3:

[0865] A medical professional will provide appropriate advice and notify the user through the server.

[0866] Input: Consultation request and feedback data from the server

[0867] Output: Advice to the user

[0868] What happens: A medical professional reviews the feedback and provides appropriate advice (e.g., specific supplement recommendations, sleep improvement strategies), which is then sent to the user by the server.

[0869] This allows the user to receive appropriate support immediately. In this way, the personal health plan advisor system of the present invention closely monitors the user's health condition and provides personalized advice and prompt support.

[0870] (Application example 1)

[0871] 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."

[0872] Early detection of pre-disease risks and appropriate responses are crucial for maintaining and improving a user's health. However, conventional health management systems inadequately collect and analyze users' lifestyle and health data, and have limited collaboration with medical professionals, making it difficult to detect pre-disease risks early or provide personalized health advice. It is also difficult to properly manage users' health data as a security service and provide necessary support in a timely manner. There is a need to solve these problems and achieve effective health management and early pre-disease prevention.

[0873] 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.

[0874] In this invention, the server includes: means for a user to answer a lifestyle questionnaire via a terminal and transmit the response data to the server; means for the server to receive the response data and for an AI to evaluate pre-disease risk; means for the server to notify the user of the evaluation results; means for a wearable device to collect health data and periodically transmit it to the server; means for the server's AI to analyze the health data and generate personalized habit improvement advice and notify the user; means for the server's AI to notify a medical professional if an abnormal value is detected in the analysis results; means for the medical professional to review the analysis results and provide necessary advice and treatment plans; means for the server to process feedback sent by the user within the app and provide necessary support; means for using an AI analysis engine to evaluate the collected health data and generate a pre-disease risk notification for the user; and means for transmitting the generated notification to the user's terminal. This enables efficient collection and analysis of a user's health data and early evaluation and notification of pre-disease risk.

[0875] "User" means an individual who uses the pre-disease risk assessment system.

[0876] A "terminal" is a device that a user uses to answer a questionnaire about lifestyle habits.

[0877] A "server" is a computer system that receives and analyzes questionnaire data and health data from wearable devices.

[0878] "AI" is a system that uses artificial intelligence technology to analyze questionnaire data and health data and evaluate the risk of pre-disease.

[0879] "Pre-disease risk" is the result of assessing the possibility of developing illness in the future, even if the illness has not yet occurred.

[0880] The "evaluation results" are the results of the AI's analysis of pre-disease risk, and are the information notified to the user.

[0881] A "wearable device" is a device that a user wears in their daily life to collect health data (heart rate, number of steps, sleep data, etc.).

[0882] "Habit Improvement Advice" is a lifestyle improvement suggestion provided to users based on their health data analyzed by the server's AI.

[0883] An "abnormal value" is a value that deviates from the normal range in the analysis results of health data.

[0884] A "health professional" is a qualified healthcare professional who provides advice and treatment plans based on a user's health data.

[0885] "Feedback" refers to users submitting opinions or questions about their health or advice within the app.

[0886] A "notification" is information or a warning that the server sends to the user terminal.

[0887] "Analysis engine" is a general term for software that uses AI technology to evaluate and analyze health data.

[0888] The present invention provides a system for analyzing a user's lifestyle and health data, and evaluating and notifying the user of their risk of developing a pre-disease. This system is realized using the following hardware and software.

[0889] Hardware and Software Configuration

[0890] Device: The device (smartphone, tablet, etc.) used by the user to answer the lifestyle questionnaire.

[0891] Server: A computer system (AWS, Google Cloud, etc.) that receives and analyzes survey data and health data sent from wearable devices.

[0892] AI analysis engine: Software (TensorFlow, PyTorch, etc.) for analyzing health data and assessing pre-disease risk.

[0893] Wearable device: A device (such as a smartwatch) that collects user health data (heart rate, steps, sleep data, etc.).

[0894] System operation process

[0895] 1. Data collection: Users answer a questionnaire about their lifestyle habits through their device and send the data to the server. In addition, the wearable device periodically records their heart rate, number of steps, sleep data, etc. and sends this data to the server.

[0896] 2. Data analysis: The server's AI analysis engine analyzes the received questionnaire data and health data to assess the risk of pre-disease.

[0897] 3. Risk assessment and notification: The server assesses the risk of developing pre-disease based on the analysis results and sends a notification to the user's device. For example, the assessment result may say, "You are at high risk of cardiovascular disease due to lack of exercise."

[0898] 4. Habit improvement advice: Based on the analysis results, the server's AI analysis engine generates personalized habit improvement advice and notifies the user. For example, the advice may be, "Try adding 30 minutes of walking every day."

[0899] 5. Collaboration with medical experts: If the server's AI detects an abnormality in the analysis results, it will notify medical experts. Medical experts will review the analysis results through a dedicated portal and provide necessary advice and treatment plans. This feedback will be sent to the user via the server.

[0900] 6. User feedback processing: When users send questions or feedback using the in-app chat function, the data is sent to the server and analyzed by the AI ​​analysis engine. If necessary, medical professionals will be contacted and appropriate support will be provided.

[0901] Specific examples

[0902] For example, User A can use a smartphone to answer a questionnaire about the amount of exercise, diet, and sleep time they get each day, and send heart rate and step count data from a wearable device (smartwatch) to a server. The server's AI analysis engine analyzes this data and evaluates their risk of developing a pre-disease condition. As a result of the evaluation, a notification stating, for example, "Your lack of exercise puts you at high risk of cardiovascular disease" is sent to User A's smartphone. Then, personalized advice on improving habits is sent, such as "Try adding 30 minutes of walking each day."

[0903] Prompt Sentence Examples

[0904] "Based on the user's health data (heart rate, number of steps, sleep time), please assess their risk of illness and provide specific health advice. The following data is from User A's data for the past week."

[0905] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0906] Step 1:

[0907] The user uses a device to answer a questionnaire about their lifestyle habits. The questions in the questionnaire include the amount of exercise, dietary habits, and sleep duration. The device collects the user's input data and sends it to the server in JSON format. The input is the user's questionnaire response data, and the output is JSON-formatted data sent to the server. Once the server receives this data, it is ready to proceed to the next analysis step.

[0908] Step 2:

[0909] The server passes the received survey data to the AI ​​model. The AI ​​model is implemented using TensorFlow or PyTorch and evaluates pre-disease risk based on the survey data. Here, data processing involves normalization and feature selection. The input is the survey data and the output is a pre-disease risk score. The evaluated risk score is stored in a database and used in the next notification step.

[0910] Step 3:

[0911] The server notifies the user's device of the evaluation results. If the pre-disease risk score is high, a message urging special attention is generated. This notification is sent to the user's smartphone as a push notification. The input is the pre-disease risk evaluation score, and the output is the notification message sent to the user's device. This allows the user to receive real-time information about their health status.

[0912] Step 4:

[0913] The wearable device collects the user's health data (heart rate, steps, sleep data, etc.) and sends it to a server. The wearable device periodically records the data and transfers it to a terminal via Bluetooth or Wi-Fi. The input is raw data from the wearable device, and the output is health data sent to the server via the terminal.

[0914] Step 5:

[0915] The server's AI analysis engine evaluates and analyzes the collected health data. Statistical methods and machine learning algorithms are used for the analysis. The AI ​​analysis engine detects outliers and performs trend analysis, and if an abnormality is detected, it records the results as a flag. The input is health data from the wearable device, and the output is the analysis results and an abnormal value flag.

[0916] Step 6:

[0917] Based on the analysis results of the server, personalized advice for improving habits is generated and notified to the user. For example, a user who has not reached their step goal may receive the advice "Try adding 30 minutes of walking each day." The input is the analysis results and the user's health data, and the output is personalized advice for improving habits.

[0918] Step 7:

[0919] If the server's AI detects an abnormal value in the analysis results, it notifies medical professionals. Medical professionals log in to a dedicated portal to check the analysis results and provide any necessary advice or treatment plans. The input is the abnormal value flag and the analysis results, and the output is the notification and advice to the medical professional.

[0920] Step 8:

[0921] Users use the in-app chat function to send questions or feedback. The device sends this to the server, where the AI ​​analysis engine analyzes the content. If necessary, medical professionals are contacted and appropriate support is provided to the user. The input is the user's feedback data, and the output is the analysis results and countermeasures.

[0922] Specific examples

[0923] For example, user A answers a lifestyle questionnaire using a smartphone and collects heart rate and step count data from a smartwatch. This data is sent to a server, and the AI ​​analysis engine evaluates the user as being at high risk for cardiovascular disease due to lack of exercise. The server notifies user A of this result on their smartphone and provides advice such as "Try adding 30 minutes of walking each day."

[0924] 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.

[0925] The present invention provides a system that provides more accurate health management by combining a personal health plan advisor system that supports the early detection of pre-disease conditions and the prevention of their progression with an emotion engine that recognizes the user's emotions. An embodiment of this system will be described in detail below.

[0926] 1. Online Health Checkup

[0927] The user launches the app through their device and proceeds to a lifestyle questionnaire from the profile entry screen. The device displays the questionnaire items, which the user answers. Once the answers are completed, the device sends the response data to the server. On the server, AI analyzes the response data and evaluates the risk of developing pre-disease. The evaluation results are sent from the server to the user's device and notified.

[0928] Examples:

[0929] After User A answers a questionnaire about his / her eating and exercise habits, the server evaluates that he / she is at high risk of cardiovascular disease due to lack of exercise and notifies User A of the result.

[0930] 2. Data Tracking and Analysis

[0931] Users wear a wearable device (e.g., a smartwatch) in their daily lives, and the device periodically records their heart rate, steps, sleep data, etc. This data is periodically sent to a server. The server's AI analyzes the received data and evaluates the user's health status. Based on the analysis results, the server generates personalized advice on habit improvement and notifies the user.

[0932] Examples:

[0933] User B wears a smartwatch, and based on the data sent from the device, the server's AI determines that "the number of steps per day has not reached the goal for two consecutive weeks." As improvement advice, it sends User B the suggestion to "add 30 minutes of walking every day."

[0934] 3. Collaboration with medical professionals

[0935] If the server's AI detects an abnormality in the analysis results, it will flag it and send a notification to a medical professional. The medical professional will review the analysis results through a dedicated portal and provide any necessary advice or treatment plans. Feedback is then sent to the user via the server.

[0936] Examples:

[0937] Based on User C's heart rate data, the server's AI determines that "a persistently high heart rate is observed" and notifies a medical professional. The medical professional then conducts a detailed evaluation and notifies User C of the need for additional testing.

[0938] 4. Feedback and Support

[0939] Users can use the in-app chat feature to send questions or feedback. The device then sends the feedback to the server, which analyzes it and, if necessary, notifies medical professionals to provide appropriate support to the user.

[0940] Examples:

[0941] User D submits feedback about their sleep quality, reporting that they "wake up multiple times each night." The server analyzes the data and determines that "consultation with a medical professional is recommended." A medical professional reviews the data and provides advice on specific supplements and improving their sleep environment.

[0942] 5. Combining Emotion Engines

[0943] While the user is using the app, the emotion engine analyzes the user's voice, facial expressions, and text input to recognize their emotional state. The device collects this data and sends it to a server, where the server's AI adjusts health assessments and improvement advice based on the user's emotional state. This enables more personalized health management.

[0944] Examples:

[0945] When User E sends feedback within the app, the emotion engine detects a state of stress from User E's voice. The server's AI takes this into consideration and provides advice such as "Try deep breathing exercises to reduce stress."

[0946] As described above, the personal health plan advisor system of the present invention tracks a user's lifestyle and health data, and evaluates and analyzes pre-disease risks to realize early detection of pre-disease and personalized health management. In addition, by combining it with an emotion engine, it provides more precise health management that takes into account the user's emotional state.

[0947] The processing flow will be explained below.

[0948] Processing steps of a system that combines emotion engines

[0949] Online health check processing steps

[0950] Step 1:

[0951] The user starts the device and opens the Health Plan Advisor app.

[0952] Step 2:

[0953] The device displays a profile entry screen to the user, prompting them to enter basic information (such as name, age, gender, medical history, etc.).

[0954] Step 3:

[0955] The device displays a questionnaire screen about lifestyle habits, and the user answers questions about eating habits, exercise habits, sleep habits, stress levels, etc.

[0956] Step 4:

[0957] The user completes the survey and clicks the submit button.

[0958] Step 5:

[0959] The terminal transmits the user's response data to the server.

[0960] Step 6:

[0961] The server receives the response data and inputs it into the AI ​​engine.

[0962] Step 7:

[0963] The server's AI analyzes the response data and assesses the risk of developing illness.

[0964] Step 8:

[0965] The server generates the evaluation results and transmits the data to the user terminal.

[0966] Step 9:

[0967] The terminal notifies the user of the evaluation results and displays advice based on the evaluation results.

[0968] Data Tracking and Analysis Process Steps

[0969] Step 1:

[0970] A user wears a wearable device (e.g., a smart watch) in their daily life.

[0971] Step 2:

[0972] The wearable device regularly records health data such as heart rate, steps taken, and sleep data.

[0973] Step 3:

[0974] The wearable device periodically transmits the recorded data to the user's smartphone.

[0975] Step 4:

[0976] The terminal transmits the data received from the wearable terminal to the server at regular intervals.

[0977] Step 5:

[0978] The server receives the wearable data and stores it in a database.

[0979] Step 6:

[0980] The server's AI periodically performs data analysis and evaluates the user's health status.

[0981] Step 7:

[0982] The server's AI generates personalized advice on habit improvement based on the analysis results.

[0983] Step 8:

[0984] The server transmits the generated advice to the user's terminal.

[0985] Step 9:

[0986] The device will notify the user of the advice and display details within the app.

[0987] Steps in the process of working with a medical professional

[0988] Step 1:

[0989] If the server's AI detects an abnormal value in the analysis results, it will raise an abnormality flag.

[0990] Step 2:

[0991] The server notifies the medical professional of the results of the abnormality analysis.

[0992] Step 3:

[0993] Medical professionals are notified through a dedicated portal and can review the analysis results.

[0994] Step 4:

[0995] A medical professional will conduct a detailed assessment and generate further advice and a treatment plan.

[0996] Step 5:

[0997] The server transmits the feedback from the medical professional to the user terminal.

[0998] Step 6:

[0999] The device will notify the user of feedback from medical professionals and display details within the app.

[1000] Feedback and support process steps

[1001] Step 1:

[1002] Users submit questions or comments using the in-app feedback feature.

[1003] Step 2:

[1004] The terminal sends the user's feedback to the server.

[1005] Step 3:

[1006] The server receives the feedback, and the AI ​​handles any content that can be automatically responded to.

[1007] Step 4:

[1008] The server notifies the medical professional of the feedback as needed.

[1009] Step 5:

[1010] Medical professionals will review the feedback and take appropriate action.

[1011] Step 6:

[1012] The server transmits the response results from the medical professional to the user terminal.

[1013] Step 7:

[1014] The device will notify the user of the response results and display details within the app.

[1015] Emotion Engine Processing Steps

[1016] Step 1:

[1017] While the user is using the app, voice, facial expressions, and text input are collected through the device's camera and microphone.

[1018] Step 2:

[1019] The device sends the collected voice, facial expression, and text data to the emotion engine.

[1020] Step 3:

[1021] An emotion engine within the device analyzes this data and recognizes the user's emotional state.

[1022] Step 4:

[1023] The terminal transmits the recognized emotional state data to the server.

[1024] Step 5:

[1025] The server's AI takes into account the user's emotional state and adjusts health assessments and advice on improving habits.

[1026] Step 6:

[1027] The server sends the adjusted advice to the user's terminal.

[1028] Step 7:

[1029] The device will notify the user of tailored advice and display details within the app.

[1030] Examples:

[1031] When User E sends feedback within the app, the emotion engine detects a state of stress from User E's voice. The server's AI takes this into consideration and provides advice such as "Try deep breathing exercises to reduce stress."

[1032] These are the specific processing steps of the personal health plan advisor system that combines the emotion engine. Through these steps, the system will further personalize the user's health management, enabling early detection of pre-disease and prevention of its progression.

[1033] Example 2

[1034] 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."

[1035] The need for prevention of lifestyle-related diseases and health management is rapidly increasing in modern times, but existing health management systems have difficulty in providing advanced personalization that fully takes into account the user's lifestyle and emotional state. In particular, for early detection of pre-disease and prevention of progression, there is a need to comprehensively analyze the user's individual data and emotional state and provide appropriate advice. However, conventional systems lack such advanced functionality.

[1036] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to answer a lifestyle questionnaire via a terminal and transmit the response data to the server; a means for the server to receive the response data and evaluate pre-disease risk using an artificial intelligence; a means for the server to notify the user of the evaluation results; a means for the wearable device to collect health data and periodically transmit it to the server; a means for the server's artificial intelligence to analyze the health data and generate personalized habit improvement advice and notify the user; a means for the server's artificial intelligence to notify an expert if it detects an abnormal value in the analysis results; a means for the expert to review the analysis results and provide necessary advice and a treatment plan; a means for the server to process feedback sent by the user within the app and provide necessary support; a means for the server's artificial intelligence to analyze the user's voice, facial expression, and text input and recognize the user's emotional state; and a means for the server's artificial intelligence to adjust the health assessment and improvement advice taking the emotional state into account. This enables integrated analysis of the user's lifestyle data and emotional state, enabling highly personalized health management.

[1037] "User" refers to an individual who uses this system.

[1038] "Terminal" refers to the electronic device that a user uses to access this system. Specifically, it includes devices such as smartphones, tablets, and personal computers.

[1039] The "questionnaire on lifestyle habits" refers to a questionnaire in the form of questions in which the user answers about habits in daily life such as eating, exercise, and sleep.

[1040] "Server" refers to a computer system for receiving, storing, and analyzing data from users.

[1041] "Artificial intelligence" refers to machine learning algorithms that run on a server and analyze user data to assess pre-disease risk and recognize emotional states.

[1042] "Pre-disease risk" refers to assessing the risk of a user becoming ill before they become ill.

[1043] "Evaluation results" refers to information regarding pre-disease risks and other health conditions generated by artificial intelligence through analysis of the user's data.

[1044] "Wearable devices" refer to health management devices worn by users, including smartwatches and fitness trackers.

[1045] "Health data" refers to information collected by wearable devices, such as heart rate, steps taken, and sleep data.

[1046] "Personalized habit improvement advice" refers to customized health improvement suggestions provided to a user based on the user's particular health condition and lifestyle habits.

[1047] "Experts" refer to qualified individuals with knowledge of medical care and health management, and are responsible for providing advice and treatment plans to users based on notifications from the server.

[1048] "Feedback" refers to information such as opinions, questions, reports, etc. that users submit within the app.

[1049] "Emotional state" refers to the psychological and emotional state a user exhibits while using the app.

[1050] "Emotion engine" refers to a software component that recognizes a user's emotional state by analyzing their voice, facial expressions, and text input.

[1051] The present invention provides a system that provides more accurate health management by combining a personal health plan advisor system that supports the early detection of pre-disease conditions and the prevention of their progression with an emotion engine that recognizes the user's emotions. An embodiment of this system will be described in detail below.

[1052] Online Health Check

[1053] Users launch the app on a device such as a smartphone or tablet. They enter basic information on the app's profile screen, then answer a questionnaire about their lifestyle habits (diet, exercise, sleep, etc.). The device temporarily stores this information and sends the data to a server. The server passes the received data to an analysis engine (AI model) and evaluates the risk of developing pre-disease. The server then notifies the user's device of the evaluation results.

[1054] Examples:

[1055] After User A answers a questionnaire about his / her eating and exercise habits, the server evaluates that he / she is at high risk of cardiovascular disease due to lack of exercise and notifies User A of the result.

[1056] Prompt statement:

[1057] "Tell me about your recent eating and exercise habits."

[1058] Data Tracking and Analysis

[1059] Users wear a wearable device (e.g., a smartwatch) in their daily lives and regularly record their heart rate, steps, sleep data, etc. This data is periodically sent to a server where it is analyzed by AI. Based on the analysis results, the server generates personalized advice for improving habits and notifies the user.

[1060] Examples:

[1061] User B wears a smartwatch, and based on the data sent from the device, the server's AI determines that "the number of steps per day has not reached the goal for two consecutive weeks." As improvement advice, it sends User B the suggestion to "add 30 minutes of walking every day."

[1062] Prompt statement:

[1063] "We've recorded your recent exercise data. To reach your step goal, we recommend you increase your walking time."

[1064] Collaboration with medical professionals

[1065] If the server's AI detects an abnormality in the analysis results, it will send a notification to an expert. The expert will review the analysis results through a dedicated portal and provide any necessary advice or treatment plans. This feedback is then sent to the user via the server.

[1066] Examples:

[1067] Based on User C's heart rate data, the server's AI determines that "a sustained high heart rate is observed" and notifies an expert. The expert then conducts a detailed evaluation and notifies User C of the need for further testing.

[1068] Prompt statement:

[1069] "Get expert advice based on your heart rate data."

[1070] Feedback and Support

[1071] Users can use the in-app chat feature to ask questions or send feedback. The device then sends the feedback to the server, which analyzes it and, if necessary, notifies an expert to provide the user with appropriate support.

[1072] Examples:

[1073] User D submits feedback about their sleep quality, reporting that they "wake up multiple times each night." The server analyzes the data and determines that "expert consultation is recommended." The expert reviews the data and offers advice on specific supplements and improving their sleep environment.

[1074] Prompt statement:

[1075] "We've received your feedback about your sleep issues. Our experts will suggest specific solutions."

[1076] Combining Emotion Engines

[1077] While the user is using the app, the emotion engine analyzes the user's voice, facial expressions, and text input to recognize their emotional state. The device collects this data and sends it to a server, where the server's AI adjusts health assessments and improvement advice based on the user's emotional state. This enables more personalized health management.

[1078] Examples:

[1079] When User E sends feedback within the app, the emotion engine detects the stress level from User E's voice. The server's AI then provides advice such as "Try deep breathing exercises to reduce stress."

[1080] Prompt statement:

[1081] "We've detected your stress level. Try some deep breathing exercises to help you relax."

[1082] This system tracks the user's lifestyle and health data, and evaluates and analyzes pre-disease risks, enabling early detection of pre-disease and personalized health management. In addition, by combining it with an emotion engine, it provides more precise health management that takes into account the user's emotional state.

[1083] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1084] Step 1:

[1085] A user taps the app icon on their smartphone or tablet to launch the app, which takes them to the app's home screen where they can log in or register.

[1086] Input: User login or registration information

[1087] Output: App home screen

[1088] Specific operation: After launching the app, the app logo and login / new registration form will be displayed on the screen.

[1089] Step 2:

[1090] The user enters basic information (age, gender, height, weight, etc.) on the profile entry screen within the app and answers questions about lifestyle habits (e.g., diet, exercise, sleep, etc.). Once the information entry is complete, the device temporarily stores this information.

[1091] Input: User profile information and survey responses

[1092] Output: Temporarily saved data

[1093] Specific operation: Survey questions are displayed on the screen and the user enters answers.

[1094] Step 3:

[1095] The terminal converts the temporarily stored data into a packet format and transmits it to a server via the Internet.

[1096] Input:Temporarily saved data

[1097] Output: Data sent to the server

[1098] Specific behavior: The "Send" button is enabled, and when the user clicks it, the message "Data transmission completed" is displayed on the screen.

[1099] Step 4:

[1100] The server passes the data received from the user to the analysis engine (generative AI model) to evaluate the risk of pre-disease. This analysis generates the risk assessment results.

[1101] Input: User survey data

[1102] Output: Pre-disease risk assessment results

[1103] Specific operation: A new record is generated in the database on the server, and the AI ​​model performs analysis.

[1104] Step 5:

[1105] The server generates a feedback message based on the evaluation result and transmits it to the user terminal.

[1106] Input: Pre-disease risk assessment results

[1107] Output: Feedback message

[1108] Specific actions: If a person is judged to be at risk due to lack of exercise, a message containing specific exercise advice will be generated.

[1109] Step 6:

[1110] Users wear wearable devices such as smartwatches in their daily lives to regularly record their heart rate, number of steps, sleep data, etc. The devices temporarily store this data and periodically transmit it to a server.

[1111] Input: Health data collected by wearable devices

[1112] Output: Health data sent to the server

[1113] How it works: The smartwatch measures your heart rate and steps, and periodically transmits the data.

[1114] Step 7:

[1115] The server generates an AI model that analyzes health data and generates personalized advice for improving habits, which is then sent to the user.

[1116] Input: Health data

[1117] Output: personalized advice on how to improve your habits

[1118] Specific action: If it is determined that the daily step count has not reached the goal, a suggestion is generated to "add 30 minutes of walking every day."

[1119] Step 8:

[1120] If the server-generated AI model detects an abnormality in the analysis results, it will send a notification to the expert, who will then review the analysis results on the portal and provide any necessary advice or treatment plans.

[1121] Input: Outlier analysis results

[1122] Output: Professional notification and advice, treatment plan

[1123] Specific operation: The server sends emails and notifications to the specialist, who then enters evaluation comments and treatment plans into the portal.

[1124] Step 9:

[1125] Users can use the in-app chat feature to send questions or feedback. The device sends the feedback to the server, which analyzes it. If necessary, the feedback is notified to an expert, who can provide the user with appropriate support.

[1126] Input: User feedback

[1127] Output: Any support or expert advice you need

[1128] Specific Actions: After reports of "waking up multiple times each night," experts offer advice on specific supplements and improving the sleep environment.

[1129] Step 10:

[1130] While the user is using the app, the emotion recognition engine analyzes the user's voice, facial expressions, and text input to recognize their emotional state. The device collects this data and sends it to the server.

[1131] Input: User voice, facial expressions, and text input

[1132] Output: Perceived emotional state

[1133] Specific operation: The microphone is activated to detect the stress level from the user's voice, and the camera is activated to analyze facial expressions.

[1134] Step 11:

[1135] The server's generative AI model takes into account the user's emotional state to adjust the health assessment and improvement advice, resulting in more personalized advice for the user.

[1136] Input: Perceived emotional state

[1137] Output: Emotion-based health assessment and improvement advice

[1138] Specific actions: Detects stress levels and provides advice such as "Try deep breathing exercises to reduce stress"

[1139] Through these steps, the system will be able to continuously track and analyze users' lifestyle data and emotional state, providing highly personalized health management.

[1140] (Application example 2)

[1141] 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."

[1142] Conventional health management systems require users to approach the system and provide their response data and health data, and insufficient data can prevent early detection of pre-disease symptoms and appropriate health management. Furthermore, conventional health management systems have difficulty providing advice that takes into account the user's emotional state, creating a need for more precise and effective health management. Furthermore, similar health management is required even during long trips using autonomous vehicles.

[1143] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means. In this invention, the server includes: means for a user to answer a questionnaire about lifestyle habits via a communication terminal and transmit the answer data to the server; means for the server to receive the answer data and evaluate pre-disease risk using artificial intelligence; means for the server to notify the user terminal of the evaluation results; means for a wearable device to collect health data and periodically transmit it to the server; means for the server's artificial intelligence to analyze the health data and generate personalized habit improvement advice and notify the user; means for the server's artificial intelligence to notify a medical professional if it detects an abnormal value in the analysis results; means for the medical professional to check the analysis results and provide necessary advice and / or treatment plans; means for the server to process feedback transmitted by the user within an application and provide necessary support; means for an autonomous vehicle to collect data on the passenger's heart rate, sitting posture, and in-vehicle environment and periodically transmit it to the server; means for a camera and microphone in the autonomous vehicle to analyze the passenger's facial expressions and voice to recognize their emotional state and transmit it to the server; and means for the server's artificial intelligence to generate health management advice taking the passenger's emotional state into consideration and notify the passenger. This will enable users to monitor their health and emotional state in real time and receive appropriate advice while in an autonomous vehicle, helping them maintain their health even during long journeys and providing a comfortable riding experience.

[1144] A "communication terminal" is a device that allows a user to connect to the Internet and send and receive information.

[1145] "Lifestyle habits" refer to the actions and habits that a user performs in their daily life.

[1146] A "server" is a computer system that stores and processes various data on a network and exchanges information with user terminals and other devices.

[1147] "Artificial intelligence" refers to software technology that mimics human intelligence and has functions such as data analysis, prediction, and learning.

[1148] "Pre-disease risk" refers to potential health risks before symptoms appear.

[1149] A "user terminal" is a device operated by a user (e.g., a smartphone, tablet, or PC).

[1150] A "wearable device" is a device (e.g., smartwatch, fitness tracker) worn by a user to collect and record various data.

[1151] "Health data" refers to data that indicates the user's physical condition (e.g., heart rate, number of steps, sleep data, sitting posture, and in-car environmental data).

[1152] "Individualized advice for improving lifestyle habits" refers to specific lifestyle improvement suggestions provided based on each user's individual health condition.

[1153] A "healthcare professional" is a professional (e.g., doctor, nurse, or healthcare provider) who has medical expertise and who assesses, diagnoses, or treats a user's health condition.

[1154] "Feedback" refers to opinions and reports from users, which the system analyzes and takes appropriate action.

[1155] An "autonomous vehicle" is a vehicle that can autonomously navigate roads and transport passengers to their destinations.

[1156] "Sitting posture" is data indicating the position and angle of the user's body when sitting.

[1157] "In-vehicle environment data" refers to environmental data such as temperature, humidity, and noise level inside the vehicle.

[1158] "Facial expression" is data that observes the movements and expressions of the user's face.

[1159] "Voice" is data that observes the characteristics of a user's voice.

[1160] The present invention relates to a system that monitors the health and emotional state of users in autonomous vehicles in real time and provides appropriate health management advice, enabling users to maintain their health and enjoy a comfortable riding experience even during long trips.

[1161] System configuration

[1162] 1. Hardware:

[1163] Communication devices: smartphones, tablets, computers, etc.

[1164] Wearable devices: smartwatches, fitness trackers, etc.

[1165] Cameras built into autonomous vehicles: for facial recognition.

[1166] In-vehicle microphone: For voice recognition.

[1167] On-board computer: for data collection and analysis.

[1168] 2. Software:

[1169] Health management applications: For surveys, data collection, and notifications.

[1170] Emotion recognition engine: such as "EmotionEngine".

[1171] Health data analysis AI: "HealthAnalyzer" etc.

[1172] Server system: Stores and analyzes data.

[1173] Processing Flow

[1174] 1. Collecting survey data

[1175] The user answers a questionnaire about lifestyle habits via a communication terminal.

[1176] The terminal transmits the response data to the server.

[1177] The server uses artificial intelligence to assess the risk of developing pre-disease and notifies the user's device of the results.

[1178] 2. Health Data Collection

[1179] The user wears a wearable device that collects data such as heart rate, steps, and sleep.

[1180] The data is transmitted to a server via an on-board computer in the autonomous vehicle.

[1181] The server's artificial intelligence analyzes the data, generates personalized advice on how to improve habits, and sends it to the user's device.

[1182] 3. Collecting Emotional Data

[1183] Cameras and microphones inside the self-driving vehicle collect the user's facial expressions and voice to analyze their emotional state.

[1184] The data is sent to a server, and artificial intelligence uses the analysis results to generate health management advice that takes emotional state into account.

[1185] The advice is sent to the user terminal.

[1186] Specific examples

[1187] Example 1:

[1188] User A gets into an autonomous vehicle and answers a questionnaire. The server evaluates that "the risk due to lack of exercise is high" and notifies User A's device of the result. The advice is provided to "add 30 minutes of walking every day."

[1189] Example 2:

[1190] The server detects that User B's heart rate has continuously increased while driving. The server determines this to be a "stressed state" and suggests relaxing music in the car.

[1191] Example 3:

[1192] The server analyzes the facial expressions and voice of user C and recognizes that he is feeling stressed. The server generates advice to display on the in-car screen, such as a video of deep breathing exercises.

[1193] Prompt Sentence Examples

[1194] "If a user is emotionally exhausted while in the car, what relaxation methods would you suggest?"

[1195] "What health precautions are recommended for users when sitting for extended periods of time?"

[1196] This allows users to monitor their health and emotional state in real time while in an autonomous vehicle and receive optimal health management advice, thereby maintaining health and providing a comfortable riding experience even during long journeys.

[1197] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1198] Step 1:

[1199] Survey data collection

[1200] Input: The user answers a questionnaire about their lifestyle habits via a communication terminal.

[1201] Operation: The user enters answers to the questionnaire items displayed on the screen of the communication terminal.

[1202] Output: Answer data is generated.

[1203] Step 2:

[1204] Sending survey data

[1205] Input: Survey response data

[1206] How it works: The device sends the answer data to the server via the Internet.

[1207] Output: Survey response data saved on the server

[1208] Step 3:

[1209] Pre-disease risk assessment

[1210] Input: Survey response data on the server

[1211] How it works: The server's AI analyzes the questionnaire response data and evaluates the risk of developing pre-disease. The AI ​​calculates the risk using the user's lifestyle data.

[1212] Output: Pre-disease risk assessment results

[1213] Step 4:

[1214] Notification of pre-disease risk assessment results

[1215] Input: Pre-disease risk assessment results

[1216] Operation: The server notifies the user of the evaluation results via push notification or in-app notification.

[1217] Output: Evaluation result notification displayed on the user's device

[1218] Step 5:

[1219] Health data collection

[1220] Input: Heart rate, step count, and sleep data collected by the user's wearable device

[1221] How it works: The wearable device periodically records the user's health data and transmits it to the car's onboard computer.

[1222] Output: Health data stored in the on-board computer

[1223] Step 6:

[1224] Sending health data

[1225] Input: Health data on the vehicle's computer

[1226] How it works: The on-board computer sends health data to a server via the autonomous vehicle's communications system.

[1227] Output: Health data stored on the server

[1228] Step 7:

[1229] Health data analysis

[1230] Input: Health data on the server

[1231] How it works: The server's artificial intelligence analyzes health data and generates personalized advice for improving habits. Analysis includes data preprocessing, feature extraction, and model application.

[1232] Output: Habit improvement advice

[1233] Step 8:

[1234] Habit improvement advice notifications

[1235] Input: Habit improvement advice

[1236] Behavior: The server sends the generated advice to the user's device via push notification or in-app notification.

[1237] Output: Advice displayed on the user's terminal

[1238] Step 9:

[1239] Collecting Emotional Data

[1240] Input: Facial expression and voice data collected by cameras and microphones inside the autonomous vehicle

[1241] How it works: Cameras inside the vehicle record facial expression data and microphones record audio data in real time.

[1242] Output: Emotion data stored in the on-board computer

[1243] Step 10:

[1244] Sending emotional data

[1245] Input: Emotion data on the onboard computer

[1246] How it works: The onboard computer sends emotion data to a server via the autonomous vehicle's communications system.

[1247] Output: Emotion data stored on the server

[1248] Step 11:

[1249] Emotional Data Analysis

[1250] Input: Emotion data on the server

[1251] How it works: The server's AI analyzes the emotional data and evaluates the user's emotional state using emotion recognition technology.

[1252] Output: Emotional state assessment result

[1253] Step 12:

[1254] Generating Health Care Advice Considering Emotional States

[1255] Input: Emotional state assessment results, health data analysis results

[1256] How it works: The server's artificial intelligence integrates emotional and health status to generate more personalized health care advice.

[1257] Output: Integrated health advice

[1258] Step 13:

[1259] Integrated health management advice notification

[1260] Enter: Integrated Health Care Advice

[1261] How it works: The server sends the integrated advice to the user's device via push notification or in-app notification.

[1262] Output: Consolidated advice displayed on the user's terminal

[1263] 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.

[1264] 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.

[1265] 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.

[1266] [Third embodiment]

[1267] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1268] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1269] 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).

[1270] 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.

[1271] 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.

[1272] 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).

[1273] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1274] 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.

[1275] 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.

[1276] 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.

[1277] 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.

[1278] 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."

[1279] The present invention is a personal health plan advisor system that supports the early detection of pre-disease conditions and the prevention of their progression. An embodiment of this system will be described in detail below.

[1280] 1. Online Health Checkup

[1281] The user launches the app through their device and proceeds to a lifestyle questionnaire from the profile entry screen. The device displays the questionnaire items, and the user answers them. Once the answers are completed, the device sends the response data to a server. On the server, AI analyzes the response data and evaluates the risk of developing pre-disease. The evaluation results are sent from the server to the user's device and notified.

[1282] Examples:

[1283] After User A answers a questionnaire about the amount of exercise, diet, and sleep time each day, the server evaluates that "lack of exercise puts you at high risk of cardiovascular disease" and notifies User A of the result.

[1284] 2. Data Tracking and Analysis

[1285] The user wears a wearable device in their daily life. The device periodically records heart rate, steps, sleep data, etc. and sends this data to a server. The server's AI analyzes the received data and evaluates the user's health. Based on the analysis results, the server generates personalized advice on habit improvement and notifies the user.

[1286] Examples:

[1287] User B wears a smartwatch, and based on the data sent from the device, the server's AI determines that "the number of steps per day has not reached the goal for two consecutive weeks." As improvement advice, it sends User B the suggestion to "try adding 30 minutes of walking each day."

[1288] 3. Collaboration with medical professionals

[1289] If the server's AI detects an abnormal value in the analysis results, it flags it and sends a notification to a medical professional. The medical professional then reviews the analysis results through a dedicated portal and provides any necessary advice or treatment plans. This feedback is then sent to the user via the server.

[1290] Examples:

[1291] Based on User C's heart rate data, the server's AI determines that "a persistently high heart rate is observed" and notifies a medical professional. The medical professional then conducts a detailed evaluation and notifies User C of the need for additional testing.

[1292] 4. Feedback and Support

[1293] Users can use the in-app chat feature to send questions or feedback, which the device then sends to the server, which analyzes the user's feedback and, if necessary, connects them with a medical professional to provide appropriate support to the user.

[1294] Examples:

[1295] User D submits feedback about their sleep quality, reporting that they "wake up multiple times each night." The server analyzes the data and returns a result stating that "consultation with a medical professional is recommended." A medical professional reviews the data and provides advice on specific supplements and improving the sleep environment.

[1296] As described above, the personal health plan advisor system of the present invention is a system that tracks a user's lifestyle habits and health data, and evaluates and analyzes pre-disease risks, thereby enabling early detection of pre-disease and individualized health management.

[1297] The processing flow will be explained below.

[1298] Online health check processing steps

[1299] Step 1:

[1300] The user starts the device and opens the Health Plan Advisor app.

[1301] Step 2:

[1302] The terminal displays a profile entry screen to the user, prompting them to enter basic information such as name, age, sex, and medical history.

[1303] Step 3:

[1304] The device displays a questionnaire screen about lifestyle habits, and the user answers questions about eating habits, exercise habits, sleep habits, stress levels, etc.

[1305] Step 4:

[1306] The user completes the survey and clicks the submit button.

[1307] Step 5:

[1308] The terminal transmits the user's response data to the server.

[1309] Step 6:

[1310] The server receives the response data and inputs it into the AI ​​engine.

[1311] Step 7:

[1312] The server's AI analyzes the response data and assesses the risk of developing illness.

[1313] Step 8:

[1314] The server generates the evaluation results and transmits the data to the user terminal.

[1315] Step 9:

[1316] The terminal notifies the user of the evaluation results and displays advice based on the evaluation results.

[1317] Data Tracking and Analysis Process Steps

[1318] Step 1:

[1319] A user wears a wearable device (e.g., a smart watch) in their daily life.

[1320] Step 2:

[1321] The wearable device regularly records health data such as heart rate, steps taken, and sleep data.

[1322] Step 3:

[1323] The wearable device periodically transmits the recorded data to the user's smartphone.

[1324] Step 4:

[1325] The terminal transmits the data received from the wearable terminal to the server at regular intervals.

[1326] Step 5:

[1327] The server receives the wearable data and stores it in a database.

[1328] Step 6:

[1329] The server's AI periodically performs data analysis and evaluates the user's health status.

[1330] Step 7:

[1331] The server's AI generates personalized advice on habit improvement based on the analysis results.

[1332] Step 8:

[1333] The server transmits the generated advice to the user's terminal.

[1334] Step 9:

[1335] The device will notify the user of the advice and display details within the app.

[1336] Steps in the process of working with a medical professional

[1337] Step 1:

[1338] If the server's AI detects an abnormal value in the analysis results, it will raise an abnormality flag.

[1339] Step 2:

[1340] The server notifies the medical professional of the results of the abnormality analysis.

[1341] Step 3:

[1342] Medical professionals are notified through a dedicated portal and can review the analysis results.

[1343] Step 4:

[1344] A medical professional will conduct a detailed assessment and generate further advice and a treatment plan.

[1345] Step 5:

[1346] The server transmits the feedback from the medical professional to the user terminal.

[1347] Step 6:

[1348] The device will notify the user of feedback from medical professionals and display details within the app.

[1349] Feedback and support process steps

[1350] Step 1:

[1351] Users submit questions or comments using the in-app feedback feature.

[1352] Step 2:

[1353] The terminal sends the user's feedback to the server.

[1354] Step 3:

[1355] The server receives the feedback, and the AI ​​handles any content that can be automatically responded to.

[1356] Step 4:

[1357] The server notifies the medical professional of the feedback as needed.

[1358] Step 5:

[1359] Medical professionals will review the feedback and take appropriate action.

[1360] Step 6:

[1361] The server transmits the response results from the medical professional to the user terminal.

[1362] Step 7:

[1363] The device will notify the user of the response results and display details within the app.

[1364] The above are the specific processing steps of the personal health plan advisor system. The system supports the user's health management through each step, realizing early detection of pre-disease and prevention of its progression.

[1365] Example 1

[1366] 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."

[1367] In modern society, problems related to lifestyle habits and health management are on the rise, making it essential to detect pre-disease risks early and prevent their progression. However, conventional health management systems lack personalized advice and collaboration with medical professionals, making it difficult for users to accurately and efficiently understand their own health status. Furthermore, feedback processing tends to be delayed, making it difficult to provide immediate support to users.

[1368] 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.

[1369] In this invention, the server includes a means for allowing a user to answer a questionnaire about lifestyle habits via a terminal and transmitting the answer data to the server, a means for the server to receive the answer data and for an AI model to evaluate pre-disease risk, and a means for the server to notify the user terminal of the evaluation result, thereby enabling the user's lifestyle habit data to be analyzed quickly and accurately, and the pre-disease risk to be evaluated and notified.

[1370] Furthermore, the server includes means for the wearable device to collect health data and periodically transmit it to the server, means for the AI ​​model of the server to analyze the health data and generate personalized habit improvement advice and notify the user, means for notifying a medical professional if the AI ​​model of the server detects an abnormal value in the analysis result, means for the medical professional to check the analysis result and provide necessary advice and a treatment plan, and means for the server to process feedback sent by the user within the app and provide necessary support, thereby making it possible to continuously monitor the user's health condition and provide personalized advice and immediate support.

[1371] "User" refers to an individual or organization that uses the system and provides data for health management and pre-disease risk assessment.

[1372] A "terminal" is an electronic device used by a user, including a smartphone, tablet, or PC.

[1373] A "server" is a computer system that receives, analyzes, and stores data sent by users, and has advanced computing power and data storage capabilities.

[1374] "AI model" refers to the artificial intelligence algorithms and software that analyze user data and generate pre-disease risk assessments and personalized advice.

[1375] A "questionnaire" is a series of questions to collect information about a user's lifestyle habits, including items such as diet, exercise, and sleep.

[1376] "Pre-disease risk" refers to the result of an assessment of whether a user is likely to contract a disease in the future.

[1377] "Evaluation results" refers to information that indicates pre-disease risk and health status as determined by the AI ​​model analyzing the user's data.

[1378] A "wearable device" is an electronic device worn by a user to collect health data, including smartwatches and fitness trackers.

[1379] "Health data" refers to data related to a user's health status, such as heart rate, number of steps, and sleep data.

[1380] "Habit improvement advice" refers to specific behavioral suggestions and advice on lifestyle changes generated by the AI ​​model to help users maintain good health.

[1381] An "outlier" is an unusual value detected by an AI model while analyzing health data, which may indicate a potential health problem.

[1382] A "medical expert" is a doctor or other person with expertise in the medical field who provides advice and treatment plans to the user based on notifications and data from the server.

[1383] "Feedback" means any questions, requests, or health status reports provided by a User through the App.

[1384] "Support" refers to advice, solutions, or problem-solving assistance provided by a server or medical professional in response to user feedback.

[1385] This invention is a personal health plan advisor system that supports the early detection and prevention of pre-disease conditions. The system involves users answering a questionnaire about their lifestyle habits, analyzing the data to assess their pre-disease risk, and providing personalized health management advice.

[1386] Hardware and Software Configuration

[1387] 1. Device: The electronic device used by the user, including a smartphone, tablet, or PC, that answers the survey and receives data from the wearable device.

[1388] 2. Server: A computer system with advanced computing power and data storage capabilities that receives, analyzes, and stores data. It runs AI models such as Google Cloud AI, Azure AI, Scikit-learn, and TensorFlow.

[1389] 3. Wearable devices: Devices that collect user health data, including smartwatches and fitness trackers, which collect data such as heart rate, steps taken, and sleep duration.

[1390] Program processing

[1391] 1. Online Health Checkup

[1392] The user launches the app on their device and answers a questionnaire about their lifestyle habits. The device displays the questionnaire items and records the user's responses. Once the responses are complete, the device sends the data to the server. The server's AI model analyzes the data and evaluates the risk of developing pre-disease. The evaluation results are sent from the server to the user's device.

[1393] Example prompt: "Please complete a survey about your daily exercise, diet, and sleep."

[1394] 2. Data Tracking and Analysis

[1395] The user wears a wearable device, and the device records the heart rate, steps, sleep data, and other data obtained from the device. The device periodically sends this data to a server, where an AI model analyzes the data and evaluates the user's health. Based on the analysis results, the server generates personalized advice on improving habits and notifies the user's device.

[1396] Example prompt: "Please wear your smartwatch and send your data."

[1397] 3. Collaboration with medical professionals

[1398] If the server's AI detects an abnormal value in the analysis results, it flags it and notifies medical professionals. Medical professionals review the analysis results through a dedicated portal and provide necessary advice and treatment plans. This feedback is then sent to the user via the server.

[1399] Example prompt: "Analyze your heart rate data and notify a medical professional of any abnormal values."

[1400] 4. Feedback and Support

[1401] Users can use the in-app chat feature to send questions or feedback, which the device then sends to the server, which analyzes the user's feedback and, if necessary, connects them to a medical professional to provide the user with appropriate support.

[1402] Example prompt: "Submit your feedback or questions via in-app chat."

[1403] Specific examples

[1404] 1. Online Health Check:

[1405] After the user answers a questionnaire about their daily exercise, diet, and sleep, the server assesses whether they are at high risk of cardiovascular disease due to lack of exercise and notifies the user of the results.

[1406] 2. Data Tracking and Analysis:

[1407] When a user wears a smartwatch, the server's AI determines based on the data collected by the smartwatch that the user has not met their daily step goal for two consecutive weeks, and then provides advice on how to improve the situation, suggesting that the user try adding 30 minutes of walking each day.

[1408] 3. Collaboration with medical professionals:

[1409] Based on the user's heart rate data, the server's AI determines that a "persistently high heart rate is observed" and notifies a medical professional. The medical professional then conducts a detailed evaluation and notifies the user of the need for further testing.

[1410] 4. Feedback and Support:

[1411] The user sends feedback such as "I wake up many times every night." The server analyzes the data and returns a result saying, "Consultation with a medical professional is recommended." A medical professional reviews the data and provides recommendations for specific supplements and advice on improving the sleep environment.

[1412] As described above, the personal health plan advisor system of the present invention monitors the user's lifestyle data in detail, and by evaluating pre-disease risks and providing personalized advice, enables early detection of pre-disease and health management.

[1413] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1414] Online health check processing steps

[1415] Step 1:

[1416] The user launches the Personal Health Plan Advisor app on their device and answers a questionnaire about their lifestyle habits.

[1417] Input: User's basic information (name, age, gender, height, weight) and lifestyle data (exercise, diet, sleep, etc.)

[1418] Output: User response data

[1419] Specific operation: The user taps the app to launch it and enters answers to each item in the survey. The device records the user's input.

[1420] Step 2:

[1421] The terminal transmits the user's response data to the server.

[1422] Input: Survey response data

[1423] Output: Response data sent to the server

[1424] Specific operation: The device temporarily stores the user's survey responses and sends the data to a server via the Internet.

[1425] Step 3:

[1426] The AI ​​model on the server analyzes the response data and assesses the risk of developing illness.

[1427] Input: User response data

[1428] Output: Pre-disease risk assessment results

[1429] How it works: The server analyzes the data using Google Cloud AI or Azure AI models and generates a rating such as "high risk of cardiovascular disease."

[1430] Step 4:

[1431] The server generates the evaluation results and sends them to the user's terminal.

[1432] Input: Evaluation result data

[1433] Output: Evaluation results sent to the user's device

[1434] Specific operation: The server sends the generated evaluation results to the user's device, and the device displays the evaluation results via a pop-up or notification function.

[1435] Data Tracking and Analysis Process Steps

[1436] Step 1:

[1437] A user wears a wearable device, which collects health data.

[1438] Input: User's physical activity data (heart rate, steps, sleep data)

[1439] Output: Collected health data

[1440] What it does: A user puts on a smartwatch and the device records their activity data for the day.

[1441] Step 2:

[1442] The terminal acquires health data from the wearable device and transmits it to the server.

[1443] Input: Health data from wearable devices

[1444] Output: Health data sent to the server

[1445] Specific operation: The terminal acquires data from the device using Bluetooth or Wi-Fi and sends it to the server.

[1446] Step 3:

[1447] The server's AI model analyzes the health data and assesses the user's health status.

[1448] Input: Health data (heart rate, steps, sleep data)

[1449] Output: Health status assessment results

[1450] Specific operation: The server analyzes the data using TensorFlow and determines that the number of steps per day has not reached the target for two consecutive weeks.

[1451] Step 4:

[1452] Based on the analysis results, the server generates personalized advice for improving habits and notifies the user.

[1453] Input: Health status assessment results

[1454] Output: Personalized advice on how to improve your habits

[1455] Specific operation: The server generates a suggestion such as "Try adding 30 minutes of walking every day" and notifies the user device.

[1456] Steps in the process of working with a medical professional

[1457] Step 1:

[1458] If the server's AI detects an abnormal value in the analysis results, it will notify medical experts.

[1459] Input: Health data analysis results

[1460] Output: Notification to medical professionals

[1461] What it does: The server flags a "persistently high heart rate" and sends an automatic email notification to a medical professional.

[1462] Step 2:

[1463] Medical professionals will review the analysis results on a dedicated portal and provide necessary advice and treatment plans.

[1464] Input: Abnormal value notification and analysis result data from the server

[1465] Output: Detailed assessment and advice, treatment plan

[1466] How it works: Medical professionals log in to a dedicated portal, check the analysis results, and enter any necessary advice in writing or as a message within the system.

[1467] Step 3:

[1468] The server receives feedback from the medical professional and notifies the user.

[1469] Input: Medical professional advice, treatment plan

[1470] Output: User notification

[1471] Specific operation: The server sends the received advice to the user's device and displays it as a pop-up notification or in-app message.

[1472] Feedback and support process steps

[1473] Step 1:

[1474] Users submit questions or feedback through in-app chat.

[1475] Input: User feedback

[1476] Output: Feedback data sent to the server

[1477] What happens: A user opens the in-app chat, types in a question or feedback, and hits send.

[1478] Step 2:

[1479] The server analyzes the user's feedback and contacts medical professionals as needed.

[1480] Input: User feedback data

[1481] Output: Analysis of feedback and notification to medical professionals

[1482] Specific operation: The server analyzes the text of the feedback and if it determines that "expert consultation is required," it sends an email notification to a medical professional.

[1483] Step 3:

[1484] A medical professional will provide appropriate advice and notify the user through the server.

[1485] Input: Consultation request and feedback data from the server

[1486] Output: Advice to the user

[1487] What happens: A medical professional reviews the feedback and provides appropriate advice (e.g., specific supplement recommendations, sleep improvement strategies), which is then sent to the user by the server.

[1488] This allows the user to receive appropriate support immediately. In this way, the personal health plan advisor system of the present invention closely monitors the user's health condition and provides personalized advice and prompt support.

[1489] (Application example 1)

[1490] 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."

[1491] Early detection of pre-disease risks and appropriate responses are crucial for maintaining and improving a user's health. However, conventional health management systems inadequately collect and analyze users' lifestyle and health data, and have limited collaboration with medical professionals, making it difficult to detect pre-disease risks early or provide personalized health advice. It is also difficult to properly manage users' health data as a security service and provide necessary support in a timely manner. There is a need to solve these problems and achieve effective health management and early pre-disease prevention.

[1492] 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.

[1493] In this invention, the server includes: means for a user to answer a lifestyle questionnaire via a terminal and transmit the response data to the server; means for the server to receive the response data and for an AI to evaluate pre-disease risk; means for the server to notify the user of the evaluation results; means for a wearable device to collect health data and periodically transmit it to the server; means for the server's AI to analyze the health data and generate personalized habit improvement advice and notify the user; means for the server's AI to notify a medical professional if an abnormal value is detected in the analysis results; means for the medical professional to review the analysis results and provide necessary advice and treatment plans; means for the server to process feedback sent by the user within the app and provide necessary support; means for using an AI analysis engine to evaluate the collected health data and generate a pre-disease risk notification for the user; and means for transmitting the generated notification to the user's terminal. This enables efficient collection and analysis of a user's health data and early evaluation and notification of pre-disease risk.

[1494] "User" means an individual who uses the pre-disease risk assessment system.

[1495] A "terminal" is a device that a user uses to answer a questionnaire about lifestyle habits.

[1496] A "server" is a computer system that receives and analyzes questionnaire data and health data from wearable devices.

[1497] "AI" is a system that uses artificial intelligence technology to analyze questionnaire data and health data and evaluate the risk of pre-disease.

[1498] "Pre-disease risk" is the result of assessing the possibility of developing illness in the future, even if the illness has not yet occurred.

[1499] The "evaluation results" are the results of the AI's analysis of pre-disease risk, and are the information notified to the user.

[1500] A "wearable device" is a device that a user wears in their daily life to collect health data (heart rate, number of steps, sleep data, etc.).

[1501] "Habit Improvement Advice" is a lifestyle improvement suggestion provided to users based on their health data analyzed by the server's AI.

[1502] An "abnormal value" is a value that deviates from the normal range in the analysis results of health data.

[1503] A "health professional" is a qualified healthcare professional who provides advice and treatment plans based on a user's health data.

[1504] "Feedback" refers to users submitting opinions or questions about their health or advice within the app.

[1505] A "notification" is information or a warning that the server sends to the user terminal.

[1506] "Analysis engine" is a general term for software that uses AI technology to evaluate and analyze health data.

[1507] The present invention provides a system for analyzing a user's lifestyle and health data, and evaluating and notifying the user of their risk of developing a pre-disease. This system is realized using the following hardware and software.

[1508] Hardware and Software Configuration

[1509] Device: The device (smartphone, tablet, etc.) used by the user to answer the lifestyle questionnaire.

[1510] Server: A computer system (AWS, Google Cloud, etc.) that receives and analyzes survey data and health data sent from wearable devices.

[1511] AI analysis engine: Software (TensorFlow, PyTorch, etc.) for analyzing health data and assessing pre-disease risk.

[1512] Wearable device: A device (such as a smartwatch) that collects user health data (heart rate, steps, sleep data, etc.).

[1513] System operation process

[1514] 1. Data collection: Users answer a questionnaire about their lifestyle habits through their device and send the data to the server. In addition, the wearable device periodically records their heart rate, number of steps, sleep data, etc. and sends this data to the server.

[1515] 2. Data analysis: The server's AI analysis engine analyzes the received questionnaire data and health data to assess the risk of pre-disease.

[1516] 3. Risk assessment and notification: The server assesses the risk of developing pre-disease based on the analysis results and sends a notification to the user's device. For example, the assessment result may say, "You are at high risk of cardiovascular disease due to lack of exercise."

[1517] 4. Habit improvement advice: Based on the analysis results, the server's AI analysis engine generates personalized habit improvement advice and notifies the user. For example, the advice may be, "Try adding 30 minutes of walking every day."

[1518] 5. Collaboration with medical experts: If the server's AI detects an abnormality in the analysis results, it will notify medical experts. Medical experts will review the analysis results through a dedicated portal and provide necessary advice and treatment plans. This feedback will be sent to the user via the server.

[1519] 6. User feedback processing: When users send questions or feedback using the in-app chat function, the data is sent to the server and analyzed by the AI ​​analysis engine. If necessary, medical professionals will be contacted and appropriate support will be provided.

[1520] Specific examples

[1521] For example, User A can use a smartphone to answer a questionnaire about the amount of exercise, diet, and sleep time they get each day, and send heart rate and step count data from a wearable device (smartwatch) to a server. The server's AI analysis engine analyzes this data and evaluates their risk of developing a pre-disease condition. As a result of the evaluation, a notification stating, for example, "Your lack of exercise puts you at high risk of cardiovascular disease" is sent to User A's smartphone. Then, personalized advice on improving habits is sent, such as "Try adding 30 minutes of walking each day."

[1522] Prompt Sentence Examples

[1523] "Based on the user's health data (heart rate, number of steps, sleep time), please assess their risk of illness and provide specific health advice. The following data is from User A's data for the past week."

[1524] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1525] Step 1:

[1526] The user uses a device to answer a questionnaire about their lifestyle habits. The questions in the questionnaire include the amount of exercise, dietary habits, and sleep duration. The device collects the user's input data and sends it to the server in JSON format. The input is the user's questionnaire response data, and the output is JSON-formatted data sent to the server. Once the server receives this data, it is ready to proceed to the next analysis step.

[1527] Step 2:

[1528] The server passes the received survey data to the AI ​​model. The AI ​​model is implemented using TensorFlow or PyTorch and evaluates pre-disease risk based on the survey data. Here, data processing involves normalization and feature selection. The input is the survey data and the output is a pre-disease risk score. The evaluated risk score is stored in a database and used in the next notification step.

[1529] Step 3:

[1530] The server notifies the user's device of the evaluation results. If the pre-disease risk score is high, a message urging special attention is generated. This notification is sent to the user's smartphone as a push notification. The input is the pre-disease risk evaluation score, and the output is the notification message sent to the user's device. This allows the user to receive real-time information about their health status.

[1531] Step 4:

[1532] The wearable device collects the user's health data (heart rate, steps, sleep data, etc.) and sends it to a server. The wearable device periodically records the data and transfers it to a terminal via Bluetooth or Wi-Fi. The input is raw data from the wearable device, and the output is health data sent to the server via the terminal.

[1533] Step 5:

[1534] The server's AI analysis engine evaluates and analyzes the collected health data. Statistical methods and machine learning algorithms are used for the analysis. The AI ​​analysis engine detects outliers and performs trend analysis, and if an abnormality is detected, it records the results as a flag. The input is health data from the wearable device, and the output is the analysis results and an abnormal value flag.

[1535] Step 6:

[1536] Based on the analysis results of the server, personalized advice for improving habits is generated and notified to the user. For example, a user who has not reached their step goal may receive the advice "Try adding 30 minutes of walking each day." The input is the analysis results and the user's health data, and the output is personalized advice for improving habits.

[1537] Step 7:

[1538] If the server's AI detects an abnormal value in the analysis results, it notifies medical professionals. Medical professionals log in to a dedicated portal to check the analysis results and provide any necessary advice or treatment plans. The input is the abnormal value flag and the analysis results, and the output is the notification and advice to the medical professional.

[1539] Step 8:

[1540] Users use the in-app chat function to send questions or feedback. The device sends this to the server, where the AI ​​analysis engine analyzes the content. If necessary, medical professionals are contacted and appropriate support is provided to the user. The input is the user's feedback data, and the output is the analysis results and countermeasures.

[1541] Specific examples

[1542] For example, user A answers a lifestyle questionnaire using a smartphone and collects heart rate and step count data from a smartwatch. This data is sent to a server, and the AI ​​analysis engine evaluates the user as being at high risk for cardiovascular disease due to lack of exercise. The server notifies user A of this result on their smartphone and provides advice such as "Try adding 30 minutes of walking each day."

[1543] 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.

[1544] The present invention provides a system that provides more accurate health management by combining a personal health plan advisor system that supports the early detection of pre-disease conditions and the prevention of their progression with an emotion engine that recognizes the user's emotions. An embodiment of this system will be described in detail below.

[1545] 1. Online Health Checkup

[1546] The user launches the app through their device and proceeds to a lifestyle questionnaire from the profile entry screen. The device displays the questionnaire items, which the user answers. Once the answers are completed, the device sends the response data to the server. On the server, AI analyzes the response data and evaluates the risk of developing pre-disease. The evaluation results are sent from the server to the user's device and notified.

[1547] Examples:

[1548] After User A answers a questionnaire about his / her eating and exercise habits, the server evaluates that he / she is at high risk of cardiovascular disease due to lack of exercise and notifies User A of the result.

[1549] 2. Data Tracking and Analysis

[1550] Users wear a wearable device (e.g., a smartwatch) in their daily lives, and the device periodically records their heart rate, steps, sleep data, etc. This data is periodically sent to a server. The server's AI analyzes the received data and evaluates the user's health status. Based on the analysis results, the server generates personalized advice on habit improvement and notifies the user.

[1551] Examples:

[1552] User B wears a smartwatch, and based on the data sent from the device, the server's AI determines that "the number of steps per day has not reached the goal for two consecutive weeks." As improvement advice, it sends User B the suggestion to "add 30 minutes of walking every day."

[1553] 3. Collaboration with medical professionals

[1554] If the server's AI detects an abnormality in the analysis results, it will flag it and send a notification to a medical professional. The medical professional will review the analysis results through a dedicated portal and provide any necessary advice or treatment plans. Feedback is then sent to the user via the server.

[1555] Examples:

[1556] Based on User C's heart rate data, the server's AI determines that "a persistently high heart rate is observed" and notifies a medical professional. The medical professional then conducts a detailed evaluation and notifies User C of the need for additional testing.

[1557] 4. Feedback and Support

[1558] Users can use the in-app chat feature to send questions or feedback. The device then sends the feedback to the server, which analyzes it and, if necessary, notifies medical professionals to provide appropriate support to the user.

[1559] Examples:

[1560] User D submits feedback about their sleep quality, reporting that they "wake up multiple times each night." The server analyzes the data and determines that "consultation with a medical professional is recommended." A medical professional reviews the data and provides advice on specific supplements and improving their sleep environment.

[1561] 5. Combining Emotion Engines

[1562] While the user is using the app, the emotion engine analyzes the user's voice, facial expressions, and text input to recognize their emotional state. The device collects this data and sends it to a server, where the server's AI adjusts health assessments and improvement advice based on the user's emotional state. This enables more personalized health management.

[1563] Examples:

[1564] When User E sends feedback within the app, the emotion engine detects a state of stress from User E's voice. The server's AI takes this into consideration and provides advice such as "Try deep breathing exercises to reduce stress."

[1565] As described above, the personal health plan advisor system of the present invention tracks a user's lifestyle and health data, and evaluates and analyzes pre-disease risks to realize early detection of pre-disease and personalized health management. In addition, by combining it with an emotion engine, it provides more precise health management that takes into account the user's emotional state.

[1566] The processing flow will be explained below.

[1567] Processing steps of a system that combines emotion engines

[1568] Online health check processing steps

[1569] Step 1:

[1570] The user starts the device and opens the Health Plan Advisor app.

[1571] Step 2:

[1572] The device displays a profile entry screen to the user, prompting them to enter basic information (such as name, age, gender, medical history, etc.).

[1573] Step 3:

[1574] The device displays a questionnaire screen about lifestyle habits, and the user answers questions about eating habits, exercise habits, sleep habits, stress levels, etc.

[1575] Step 4:

[1576] The user completes the survey and clicks the submit button.

[1577] Step 5:

[1578] The terminal transmits the user's response data to the server.

[1579] Step 6:

[1580] The server receives the response data and inputs it into the AI ​​engine.

[1581] Step 7:

[1582] The server's AI analyzes the response data and assesses the risk of developing illness.

[1583] Step 8:

[1584] The server generates the evaluation results and transmits the data to the user terminal.

[1585] Step 9:

[1586] The terminal notifies the user of the evaluation results and displays advice based on the evaluation results.

[1587] Data Tracking and Analysis Process Steps

[1588] Step 1:

[1589] A user wears a wearable device (e.g., a smart watch) in their daily life.

[1590] Step 2:

[1591] The wearable device regularly records health data such as heart rate, steps taken, and sleep data.

[1592] Step 3:

[1593] The wearable device periodically transmits the recorded data to the user's smartphone.

[1594] Step 4:

[1595] The terminal transmits the data received from the wearable terminal to the server at regular intervals.

[1596] Step 5:

[1597] The server receives the wearable data and stores it in a database.

[1598] Step 6:

[1599] The server's AI periodically performs data analysis and evaluates the user's health status.

[1600] Step 7:

[1601] The server's AI generates personalized advice on habit improvement based on the analysis results.

[1602] Step 8:

[1603] The server transmits the generated advice to the user's terminal.

[1604] Step 9:

[1605] The device will notify the user of the advice and display details within the app.

[1606] Steps in the process of working with a medical professional

[1607] Step 1:

[1608] If the server's AI detects an abnormal value in the analysis results, it will raise an abnormality flag.

[1609] Step 2:

[1610] The server notifies the medical professional of the results of the abnormality analysis.

[1611] Step 3:

[1612] Medical professionals are notified through a dedicated portal and can review the analysis results.

[1613] Step 4:

[1614] A medical professional will conduct a detailed assessment and generate further advice and a treatment plan.

[1615] Step 5:

[1616] The server transmits the feedback from the medical professional to the user terminal.

[1617] Step 6:

[1618] The device will notify the user of feedback from medical professionals and display details within the app.

[1619] Feedback and support process steps

[1620] Step 1:

[1621] Users submit questions or comments using the in-app feedback feature.

[1622] Step 2:

[1623] The terminal sends the user's feedback to the server.

[1624] Step 3:

[1625] The server receives the feedback, and the AI ​​handles any content that can be automatically responded to.

[1626] Step 4:

[1627] The server notifies the medical professional of the feedback as needed.

[1628] Step 5:

[1629] Medical professionals will review the feedback and take appropriate action.

[1630] Step 6:

[1631] The server transmits the response results from the medical professional to the user terminal.

[1632] Step 7:

[1633] The device will notify the user of the response results and display details within the app.

[1634] Emotion Engine Processing Steps

[1635] Step 1:

[1636] While the user is using the app, voice, facial expressions, and text input are collected through the device's camera and microphone.

[1637] Step 2:

[1638] The device sends the collected voice, facial expression, and text data to the emotion engine.

[1639] Step 3:

[1640] An emotion engine within the device analyzes this data and recognizes the user's emotional state.

[1641] Step 4:

[1642] The terminal transmits the recognized emotional state data to the server.

[1643] Step 5:

[1644] The server's AI takes into account the user's emotional state and adjusts health assessments and advice on improving habits.

[1645] Step 6:

[1646] The server sends the adjusted advice to the user's terminal.

[1647] Step 7:

[1648] The device will notify the user of tailored advice and display details within the app.

[1649] Examples:

[1650] When User E sends feedback within the app, the emotion engine detects a state of stress from User E's voice. The server's AI takes this into consideration and provides advice such as "Try deep breathing exercises to reduce stress."

[1651] These are the specific processing steps of the personal health plan advisor system that combines the emotion engine. Through these steps, the system will further personalize the user's health management, enabling early detection of pre-disease and prevention of its progression.

[1652] Example 2

[1653] 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."

[1654] The need for prevention of lifestyle-related diseases and health management is rapidly increasing in modern times, but existing health management systems have difficulty in providing advanced personalization that fully takes into account the user's lifestyle and emotional state. In particular, for early detection of pre-disease and prevention of progression, there is a need to comprehensively analyze the user's individual data and emotional state and provide appropriate advice. However, conventional systems lack such advanced functionality.

[1655] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to answer a lifestyle questionnaire via a terminal and transmit the response data to the server; a means for the server to receive the response data and evaluate pre-disease risk using an artificial intelligence; a means for the server to notify the user of the evaluation results; a means for the wearable device to collect health data and periodically transmit it to the server; a means for the server's artificial intelligence to analyze the health data and generate personalized habit improvement advice and notify the user; a means for the server's artificial intelligence to notify an expert if it detects an abnormal value in the analysis results; a means for the expert to review the analysis results and provide necessary advice and a treatment plan; a means for the server to process feedback sent by the user within the app and provide necessary support; a means for the server's artificial intelligence to analyze the user's voice, facial expression, and text input and recognize the user's emotional state; and a means for the server's artificial intelligence to adjust the health assessment and improvement advice taking the emotional state into account. This enables integrated analysis of the user's lifestyle data and emotional state, enabling highly personalized health management.

[1656] "User" refers to an individual who uses this system.

[1657] "Terminal" refers to the electronic device that a user uses to access this system. Specifically, it includes devices such as smartphones, tablets, and personal computers.

[1658] The "questionnaire on lifestyle habits" refers to a questionnaire in the form of questions in which the user answers about habits in daily life such as eating, exercise, and sleep.

[1659] "Server" refers to a computer system for receiving, storing, and analyzing data from users.

[1660] "Artificial intelligence" refers to machine learning algorithms that run on a server and analyze user data to assess pre-disease risk and recognize emotional states.

[1661] "Pre-disease risk" refers to assessing the risk of a user becoming ill before they become ill.

[1662] "Evaluation results" refers to information regarding pre-disease risks and other health conditions generated by artificial intelligence through analysis of the user's data.

[1663] "Wearable devices" refer to health management devices worn by users, including smartwatches and fitness trackers.

[1664] "Health data" refers to information collected by wearable devices, such as heart rate, steps taken, and sleep data.

[1665] "Personalized habit improvement advice" refers to customized health improvement suggestions provided to a user based on the user's particular health condition and lifestyle habits.

[1666] "Experts" refer to qualified individuals with knowledge of medical care and health management, and are responsible for providing advice and treatment plans to users based on notifications from the server.

[1667] "Feedback" refers to information such as opinions, questions, reports, etc. that users submit within the app.

[1668] "Emotional state" refers to the psychological and emotional state a user exhibits while using the app.

[1669] "Emotion engine" refers to a software component that recognizes a user's emotional state by analyzing their voice, facial expressions, and text input.

[1670] The present invention provides a system that provides more accurate health management by combining a personal health plan advisor system that supports the early detection of pre-disease conditions and the prevention of their progression with an emotion engine that recognizes the user's emotions. An embodiment of this system will be described in detail below.

[1671] Online Health Check

[1672] Users launch the app on a device such as a smartphone or tablet. They enter basic information on the app's profile screen, then answer a questionnaire about their lifestyle habits (diet, exercise, sleep, etc.). The device temporarily stores this information and sends the data to a server. The server passes the received data to an analysis engine (AI model) and evaluates the risk of developing pre-disease. The server then notifies the user's device of the evaluation results.

[1673] Examples:

[1674] After User A answers a questionnaire about his / her eating and exercise habits, the server evaluates that he / she is at high risk of cardiovascular disease due to lack of exercise and notifies User A of the result.

[1675] Prompt statement:

[1676] "Tell me about your recent eating and exercise habits."

[1677] Data Tracking and Analysis

[1678] Users wear a wearable device (e.g., a smartwatch) in their daily lives and regularly record their heart rate, steps, sleep data, etc. This data is periodically sent to a server where it is analyzed by AI. Based on the analysis results, the server generates personalized advice for improving habits and notifies the user.

[1679] Examples:

[1680] User B wears a smartwatch, and based on the data sent from the device, the server's AI determines that "the number of steps per day has not reached the goal for two consecutive weeks." As improvement advice, it sends User B the suggestion to "add 30 minutes of walking every day."

[1681] Prompt statement:

[1682] "We've recorded your recent exercise data. To reach your step goal, we recommend you increase your walking time."

[1683] Collaboration with medical professionals

[1684] If the server's AI detects an abnormality in the analysis results, it will send a notification to an expert. The expert will review the analysis results through a dedicated portal and provide any necessary advice or treatment plans. This feedback is then sent to the user via the server.

[1685] Examples:

[1686] Based on User C's heart rate data, the server's AI determines that "a sustained high heart rate is observed" and notifies an expert. The expert then conducts a detailed evaluation and notifies User C of the need for further testing.

[1687] Prompt statement:

[1688] "Get expert advice based on your heart rate data."

[1689] Feedback and Support

[1690] Users can use the in-app chat feature to ask questions or send feedback. The device then sends the feedback to the server, which analyzes it and, if necessary, notifies an expert to provide the user with appropriate support.

[1691] Examples:

[1692] User D submits feedback about their sleep quality, reporting that they "wake up multiple times each night." The server analyzes the data and determines that "expert consultation is recommended." The expert reviews the data and offers advice on specific supplements and improving their sleep environment.

[1693] Prompt statement:

[1694] "We've received your feedback about your sleep issues. Our experts will suggest specific solutions."

[1695] Combining Emotion Engines

[1696] While the user is using the app, the emotion engine analyzes the user's voice, facial expressions, and text input to recognize their emotional state. The device collects this data and sends it to a server, where the server's AI adjusts health assessments and improvement advice based on the user's emotional state. This enables more personalized health management.

[1697] Examples:

[1698] When User E sends feedback within the app, the emotion engine detects the stress level from User E's voice. The server's AI then provides advice such as "Try deep breathing exercises to reduce stress."

[1699] Prompt statement:

[1700] "We've detected your stress level. Try some deep breathing exercises to help you relax."

[1701] This system tracks the user's lifestyle and health data, and evaluates and analyzes pre-disease risks, enabling early detection of pre-disease and personalized health management. In addition, by combining it with an emotion engine, it provides more precise health management that takes into account the user's emotional state.

[1702] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1703] Step 1:

[1704] A user taps the app icon on their smartphone or tablet to launch the app, which takes them to the app's home screen where they can log in or register.

[1705] Input: User login or registration information

[1706] Output: App home screen

[1707] Specific operation: After launching the app, the app logo and login / new registration form will be displayed on the screen.

[1708] Step 2:

[1709] The user enters basic information (age, gender, height, weight, etc.) on the profile entry screen within the app and answers questions about lifestyle habits (e.g., diet, exercise, sleep, etc.). Once the information entry is complete, the device temporarily stores this information.

[1710] Input: User profile information and survey responses

[1711] Output: Temporarily saved data

[1712] Specific operation: Survey questions are displayed on the screen and the user enters answers.

[1713] Step 3:

[1714] The terminal converts the temporarily stored data into a packet format and transmits it to a server via the Internet.

[1715] Input:Temporarily saved data

[1716] Output: Data sent to the server

[1717] Specific behavior: The "Send" button is enabled, and when the user clicks it, the message "Data transmission completed" is displayed on the screen.

[1718] Step 4:

[1719] The server passes the data received from the user to the analysis engine (generative AI model) to evaluate the risk of pre-disease. This analysis generates the risk assessment results.

[1720] Input: User survey data

[1721] Output: Pre-disease risk assessment results

[1722] Specific operation: A new record is generated in the database on the server, and the AI ​​model performs analysis.

[1723] Step 5:

[1724] The server generates a feedback message based on the evaluation result and transmits it to the user terminal.

[1725] Input: Pre-disease risk assessment results

[1726] Output: Feedback message

[1727] Specific actions: If a person is judged to be at risk due to lack of exercise, a message containing specific exercise advice will be generated.

[1728] Step 6:

[1729] Users wear wearable devices such as smartwatches in their daily lives to regularly record their heart rate, number of steps, sleep data, etc. The devices temporarily store this data and periodically transmit it to a server.

[1730] Input: Health data collected by wearable devices

[1731] Output: Health data sent to the server

[1732] How it works: The smartwatch measures your heart rate and steps, and periodically transmits the data.

[1733] Step 7:

[1734] The server generates an AI model that analyzes health data and generates personalized advice for improving habits, which is then sent to the user.

[1735] Input: Health data

[1736] Output: personalized advice on how to improve your habits

[1737] Specific action: If it is determined that the daily step count has not reached the goal, a suggestion is generated to "add 30 minutes of walking every day."

[1738] Step 8:

[1739] If the server-generated AI model detects an abnormality in the analysis results, it will send a notification to the expert, who will then review the analysis results on the portal and provide any necessary advice or treatment plans.

[1740] Input: Outlier analysis results

[1741] Output: Professional notification and advice, treatment plan

[1742] Specific operation: The server sends emails and notifications to the specialist, who then enters evaluation comments and treatment plans into the portal.

[1743] Step 9:

[1744] Users can use the in-app chat feature to send questions or feedback. The device sends the feedback to the server, which analyzes it. If necessary, the feedback is notified to an expert, who can provide the user with appropriate support.

[1745] Input: User feedback

[1746] Output: Any support or expert advice you need

[1747] Specific Actions: After reports of "waking up multiple times each night," experts offer advice on specific supplements and improving the sleep environment.

[1748] Step 10:

[1749] While the user is using the app, the emotion recognition engine analyzes the user's voice, facial expressions, and text input to recognize their emotional state. The device collects this data and sends it to the server.

[1750] Input: User voice, facial expressions, and text input

[1751] Output: Perceived emotional state

[1752] Specific operation: The microphone is activated to detect the stress level from the user's voice, and the camera is activated to analyze facial expressions.

[1753] Step 11:

[1754] The server's generative AI model takes into account the user's emotional state to adjust the health assessment and improvement advice, resulting in more personalized advice for the user.

[1755] Input: Perceived emotional state

[1756] Output: Emotion-based health assessment and improvement advice

[1757] Specific actions: Detects stress levels and provides advice such as "Try deep breathing exercises to reduce stress"

[1758] Through these steps, the system will be able to continuously track and analyze users' lifestyle data and emotional state, providing highly personalized health management.

[1759] (Application example 2)

[1760] 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."

[1761] Conventional health management systems require users to approach the system and provide their response data and health data, and insufficient data can prevent early detection of pre-disease symptoms and appropriate health management. Furthermore, conventional health management systems have difficulty providing advice that takes into account the user's emotional state, creating a need for more precise and effective health management. Furthermore, similar health management is required even during long trips using autonomous vehicles.

[1762] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means. In this invention, the server includes: means for a user to answer a questionnaire about lifestyle habits via a communication terminal and transmit the answer data to the server; means for the server to receive the answer data and evaluate pre-disease risk using artificial intelligence; means for the server to notify the user terminal of the evaluation results; means for a wearable device to collect health data and periodically transmit it to the server; means for the server's artificial intelligence to analyze the health data and generate personalized habit improvement advice and notify the user; means for the server's artificial intelligence to notify a medical professional if it detects an abnormal value in the analysis results; means for the medical professional to check the analysis results and provide necessary advice and / or treatment plans; means for the server to process feedback transmitted by the user within an application and provide necessary support; means for an autonomous vehicle to collect data on the passenger's heart rate, sitting posture, and in-vehicle environment and periodically transmit it to the server; means for a camera and microphone in the autonomous vehicle to analyze the passenger's facial expressions and voice to recognize their emotional state and transmit it to the server; and means for the server's artificial intelligence to generate health management advice taking the passenger's emotional state into consideration and notify the passenger. This will enable users to monitor their health and emotional state in real time and receive appropriate advice while in an autonomous vehicle, helping them maintain their health even during long journeys and providing a comfortable riding experience.

[1763] A "communication terminal" is a device that allows a user to connect to the Internet and send and receive information.

[1764] "Lifestyle habits" refer to the actions and habits that a user performs in their daily life.

[1765] A "server" is a computer system that stores and processes various data on a network and exchanges information with user terminals and other devices.

[1766] "Artificial intelligence" refers to software technology that mimics human intelligence and has functions such as data analysis, prediction, and learning.

[1767] "Pre-disease risk" refers to potential health risks before symptoms appear.

[1768] A "user terminal" is a device operated by a user (e.g., a smartphone, tablet, or PC).

[1769] A "wearable device" is a device (e.g., smartwatch, fitness tracker) worn by a user to collect and record various data.

[1770] "Health data" refers to data that indicates the user's physical condition (e.g., heart rate, number of steps, sleep data, sitting posture, and in-car environmental data).

[1771] "Individualized advice for improving lifestyle habits" refers to specific lifestyle improvement suggestions provided based on each user's individual health condition.

[1772] A "healthcare professional" is a professional (e.g., doctor, nurse, or healthcare provider) who has medical expertise and who assesses, diagnoses, or treats a user's health condition.

[1773] "Feedback" refers to opinions and reports from users, which the system analyzes and takes appropriate action.

[1774] An "autonomous vehicle" is a vehicle that can autonomously navigate roads and transport passengers to their destinations.

[1775] "Sitting posture" is data indicating the position and angle of the user's body when sitting.

[1776] "In-vehicle environment data" refers to environmental data such as temperature, humidity, and noise level inside the vehicle.

[1777] "Facial expression" is data that observes the movements and expressions of the user's face.

[1778] "Voice" is data that observes the characteristics of a user's voice.

[1779] The present invention relates to a system that monitors the health and emotional state of users in autonomous vehicles in real time and provides appropriate health management advice, enabling users to maintain their health and enjoy a comfortable riding experience even during long trips.

[1780] System configuration

[1781] 1. Hardware:

[1782] Communication devices: smartphones, tablets, computers, etc.

[1783] Wearable devices: smartwatches, fitness trackers, etc.

[1784] Cameras built into autonomous vehicles: for facial recognition.

[1785] In-vehicle microphone: For voice recognition.

[1786] On-board computer: for data collection and analysis.

[1787] 2. Software:

[1788] Health management applications: For surveys, data collection, and notifications.

[1789] Emotion recognition engine: such as "EmotionEngine".

[1790] Health data analysis AI: "HealthAnalyzer" etc.

[1791] Server system: Stores and analyzes data.

[1792] Processing Flow

[1793] 1. Collecting survey data

[1794] The user answers a questionnaire about lifestyle habits via a communication terminal.

[1795] The terminal transmits the response data to the server.

[1796] The server uses artificial intelligence to assess the risk of developing pre-disease and notifies the user's device of the results.

[1797] 2. Health Data Collection

[1798] The user wears a wearable device that collects data such as heart rate, steps, and sleep.

[1799] The data is transmitted to a server via an on-board computer in the autonomous vehicle.

[1800] The server's artificial intelligence analyzes the data, generates personalized advice on how to improve habits, and sends it to the user's device.

[1801] 3. Collecting Emotional Data

[1802] Cameras and microphones inside the self-driving vehicle collect the user's facial expressions and voice to analyze their emotional state.

[1803] The data is sent to a server, and artificial intelligence uses the analysis results to generate health management advice that takes emotional state into account.

[1804] The advice is sent to the user terminal.

[1805] Specific examples

[1806] Example 1:

[1807] User A gets into an autonomous vehicle and answers a questionnaire. The server evaluates that "the risk due to lack of exercise is high" and notifies User A's device of the result. The advice is provided to "add 30 minutes of walking every day."

[1808] Example 2:

[1809] The server detects that User B's heart rate has continuously increased while driving. The server determines this to be a "stressed state" and suggests relaxing music in the car.

[1810] Example 3:

[1811] The server analyzes the facial expressions and voice of user C and recognizes that he is feeling stressed. The server generates advice to display on the in-car screen, such as a video of deep breathing exercises.

[1812] Prompt Sentence Examples

[1813] "If a user is emotionally exhausted while in the car, what relaxation methods would you suggest?"

[1814] "What health precautions are recommended for users when sitting for extended periods of time?"

[1815] This allows users to monitor their health and emotional state in real time while in an autonomous vehicle and receive optimal health management advice, thereby maintaining health and providing a comfortable riding experience even during long journeys.

[1816] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1817] Step 1:

[1818] Survey data collection

[1819] Input: The user answers a questionnaire about their lifestyle habits via a communication terminal.

[1820] Operation: The user enters answers to the questionnaire items displayed on the screen of the communication terminal.

[1821] Output: Answer data is generated.

[1822] Step 2:

[1823] Sending survey data

[1824] Input: Survey response data

[1825] How it works: The device sends the answer data to the server via the Internet.

[1826] Output: Survey response data saved on the server

[1827] Step 3:

[1828] Pre-disease risk assessment

[1829] Input: Survey response data on the server

[1830] How it works: The server's AI analyzes the questionnaire response data and evaluates the risk of developing pre-disease. The AI ​​calculates the risk using the user's lifestyle data.

[1831] Output: Pre-disease risk assessment results

[1832] Step 4:

[1833] Notification of pre-disease risk assessment results

[1834] Input: Pre-disease risk assessment results

[1835] Operation: The server notifies the user of the evaluation results via push notification or in-app notification.

[1836] Output: Evaluation result notification displayed on the user's device

[1837] Step 5:

[1838] Health data collection

[1839] Input: Heart rate, step count, and sleep data collected by the user's wearable device

[1840] How it works: The wearable device periodically records the user's health data and transmits it to the car's onboard computer.

[1841] Output: Health data stored in the on-board computer

[1842] Step 6:

[1843] Sending health data

[1844] Input: Health data on the vehicle's computer

[1845] How it works: The on-board computer sends health data to a server via the autonomous vehicle's communications system.

[1846] Output: Health data stored on the server

[1847] Step 7:

[1848] Health data analysis

[1849] Input: Health data on the server

[1850] How it works: The server's artificial intelligence analyzes health data and generates personalized advice for improving habits. Analysis includes data preprocessing, feature extraction, and model application.

[1851] Output: Habit improvement advice

[1852] Step 8:

[1853] Habit improvement advice notifications

[1854] Input: Habit improvement advice

[1855] Behavior: The server sends the generated advice to the user's device via push notification or in-app notification.

[1856] Output: Advice displayed on the user's terminal

[1857] Step 9:

[1858] Collecting Emotional Data

[1859] Input: Facial expression and voice data collected by cameras and microphones inside the autonomous vehicle

[1860] How it works: Cameras inside the vehicle record facial expression data and microphones record audio data in real time.

[1861] Output: Emotion data stored in the on-board computer

[1862] Step 10:

[1863] Sending emotional data

[1864] Input: Emotion data on the onboard computer

[1865] How it works: The onboard computer sends emotion data to a server via the autonomous vehicle's communications system.

[1866] Output: Emotion data stored on the server

[1867] Step 11:

[1868] Emotional Data Analysis

[1869] Input: Emotion data on the server

[1870] How it works: The server's AI analyzes the emotional data and evaluates the user's emotional state using emotion recognition technology.

[1871] Output: Emotional state assessment result

[1872] Step 12:

[1873] Generating Health Care Advice Considering Emotional States

[1874] Input: Emotional state assessment results, health data analysis results

[1875] How it works: The server's artificial intelligence integrates emotional and health status to generate more personalized health care advice.

[1876] Output: Integrated health advice

[1877] Step 13:

[1878] Integrated health management advice notification

[1879] Enter: Integrated Health Care Advice

[1880] How it works: The server sends the integrated advice to the user's device via push notification or in-app notification.

[1881] Output: Consolidated advice displayed on the user's terminal

[1882] 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.

[1883] 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.

[1884] 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.

[1885] [Fourth embodiment]

[1886] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1887] 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.

[1888] 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).

[1889] 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.

[1890] 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.

[1891] 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).

[1892] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1893] 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.

[1894] 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.

[1895] 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.

[1896] 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.

[1897] 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.

[1898] 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."

[1899] The present invention is a personal health plan advisor system that supports the early detection of pre-disease conditions and the prevention of their progression. An embodiment of this system will be described in detail below.

[1900] 1. Online Health Checkup

[1901] The user launches the app through their device and proceeds to a lifestyle questionnaire from the profile entry screen. The device displays the questionnaire items, and the user answers them. Once the answers are completed, the device sends the response data to a server. On the server, AI analyzes the response data and evaluates the risk of developing pre-disease. The evaluation results are sent from the server to the user's device and notified.

[1902] Examples:

[1903] After User A answers a questionnaire about the amount of exercise, diet, and sleep time each day, the server evaluates that "lack of exercise puts you at high risk of cardiovascular disease" and notifies User A of the result.

[1904] 2. Data Tracking and Analysis

[1905] The user wears a wearable device in their daily life. The device periodically records heart rate, steps, sleep data, etc. and sends this data to a server. The server's AI analyzes the received data and evaluates the user's health. Based on the analysis results, the server generates personalized advice on habit improvement and notifies the user.

[1906] Examples:

[1907] User B wears a smartwatch, and based on the data sent from the device, the server's AI determines that "the number of steps per day has not reached the goal for two consecutive weeks." As improvement advice, it sends User B the suggestion to "try adding 30 minutes of walking each day."

[1908] 3. Collaboration with medical professionals

[1909] If the server's AI detects an abnormal value in the analysis results, it flags it and sends a notification to a medical professional. The medical professional then reviews the analysis results through a dedicated portal and provides any necessary advice or treatment plans. This feedback is then sent to the user via the server.

[1910] Examples:

[1911] Based on User C's heart rate data, the server's AI determines that "a persistently high heart rate is observed" and notifies a medical professional. The medical professional then conducts a detailed evaluation and notifies User C of the need for additional testing.

[1912] 4. Feedback and Support

[1913] Users can use the in-app chat feature to send questions or feedback, which the device then sends to the server, which analyzes the user's feedback and, if necessary, connects them with a medical professional to provide appropriate support to the user.

[1914] Examples:

[1915] User D submits feedback about their sleep quality, reporting that they "wake up multiple times each night." The server analyzes the data and returns a result stating that "consultation with a medical professional is recommended." A medical professional reviews the data and provides advice on specific supplements and improving the sleep environment.

[1916] As described above, the personal health plan advisor system of the present invention is a system that tracks a user's lifestyle habits and health data, and evaluates and analyzes pre-disease risks, thereby enabling early detection of pre-disease and individualized health management.

[1917] The processing flow will be explained below.

[1918] Online health check processing steps

[1919] Step 1:

[1920] The user starts the device and opens the Health Plan Advisor app.

[1921] Step 2:

[1922] The terminal displays a profile entry screen to the user, prompting them to enter basic information such as name, age, sex, and medical history.

[1923] Step 3:

[1924] The device displays a questionnaire screen about lifestyle habits, and the user answers questions about eating habits, exercise habits, sleep habits, stress levels, etc.

[1925] Step 4:

[1926] The user completes the survey and clicks the submit button.

[1927] Step 5:

[1928] The terminal transmits the user's response data to the server.

[1929] Step 6:

[1930] The server receives the response data and inputs it into the AI ​​engine.

[1931] Step 7:

[1932] The server's AI analyzes the response data and assesses the risk of developing illness.

[1933] Step 8:

[1934] The server generates the evaluation results and transmits the data to the user terminal.

[1935] Step 9:

[1936] The terminal notifies the user of the evaluation results and displays advice based on the evaluation results.

[1937] Data Tracking and Analysis Process Steps

[1938] Step 1:

[1939] A user wears a wearable device (e.g., a smart watch) in their daily life.

[1940] Step 2:

[1941] The wearable device regularly records health data such as heart rate, steps taken, and sleep data.

[1942] Step 3:

[1943] The wearable device periodically transmits the recorded data to the user's smartphone.

[1944] Step 4:

[1945] The terminal transmits the data received from the wearable terminal to the server at regular intervals.

[1946] Step 5:

[1947] The server receives the wearable data and stores it in a database.

[1948] Step 6:

[1949] The server's AI periodically performs data analysis and evaluates the user's health status.

[1950] Step 7:

[1951] The server's AI generates personalized advice on habit improvement based on the analysis results.

[1952] Step 8:

[1953] The server transmits the generated advice to the user's terminal.

[1954] Step 9:

[1955] The device will notify the user of the advice and display details within the app.

[1956] Steps in the process of working with a medical professional

[1957] Step 1:

[1958] If the server's AI detects an abnormal value in the analysis results, it will raise an abnormality flag.

[1959] Step 2:

[1960] The server notifies the medical professional of the results of the abnormality analysis.

[1961] Step 3:

[1962] Medical professionals are notified through a dedicated portal and can review the analysis results.

[1963] Step 4:

[1964] A medical professional will conduct a detailed assessment and generate further advice and a treatment plan.

[1965] Step 5:

[1966] The server transmits the feedback from the medical professional to the user terminal.

[1967] Step 6:

[1968] The device will notify the user of feedback from medical professionals and display details within the app.

[1969] Feedback and support process steps

[1970] Step 1:

[1971] Users submit questions or comments using the in-app feedback feature.

[1972] Step 2:

[1973] The terminal sends the user's feedback to the server.

[1974] Step 3:

[1975] The server receives the feedback, and the AI ​​handles any content that can be automatically responded to.

[1976] Step 4:

[1977] The server notifies the medical professional of the feedback as needed.

[1978] Step 5:

[1979] Medical professionals will review the feedback and take appropriate action.

[1980] Step 6:

[1981] The server transmits the response results from the medical professional to the user terminal.

[1982] Step 7:

[1983] The device will notify the user of the response results and display details within the app.

[1984] The above are the specific processing steps of the personal health plan advisor system. The system supports the user's health management through each step, realizing early detection of pre-disease and prevention of its progression.

[1985] Example 1

[1986] 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."

[1987] In modern society, problems related to lifestyle habits and health management are on the rise, making it essential to detect pre-disease risks early and prevent their progression. However, conventional health management systems lack personalized advice and collaboration with medical professionals, making it difficult for users to accurately and efficiently understand their own health status. Furthermore, feedback processing tends to be delayed, making it difficult to provide immediate support to users.

[1988] 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.

[1989] In this invention, the server includes a means for allowing a user to answer a questionnaire about lifestyle habits via a terminal and transmitting the answer data to the server, a means for the server to receive the answer data and for an AI model to evaluate pre-disease risk, and a means for the server to notify the user terminal of the evaluation result, thereby enabling the user's lifestyle habit data to be analyzed quickly and accurately, and the pre-disease risk to be evaluated and notified.

[1990] Furthermore, the server includes means for the wearable device to collect health data and periodically transmit it to the server, means for the AI ​​model of the server to analyze the health data and generate personalized habit improvement advice and notify the user, means for notifying a medical professional if the AI ​​model of the server detects an abnormal value in the analysis result, means for the medical professional to check the analysis result and provide necessary advice and a treatment plan, and means for the server to process feedback sent by the user within the app and provide necessary support, thereby making it possible to continuously monitor the user's health condition and provide personalized advice and immediate support.

[1991] "User" refers to an individual or organization that uses the system and provides data for health management and pre-disease risk assessment.

[1992] A "terminal" is an electronic device used by a user, including a smartphone, tablet, or PC.

[1993] A "server" is a computer system that receives, analyzes, and stores data sent by users, and has advanced computing power and data storage capabilities.

[1994] "AI model" refers to the artificial intelligence algorithms and software that analyze user data and generate pre-disease risk assessments and personalized advice.

[1995] A "questionnaire" is a series of questions to collect information about a user's lifestyle habits, including items such as diet, exercise, and sleep.

[1996] "Pre-disease risk" refers to the result of an assessment of whether a user is likely to contract a disease in the future.

[1997] "Evaluation results" refers to information that indicates pre-disease risk and health status as determined by the AI ​​model analyzing the user's data.

[1998] A "wearable device" is an electronic device worn by a user to collect health data, including smartwatches and fitness trackers.

[1999] "Health data" refers to data related to a user's health status, such as heart rate, number of steps, and sleep data.

[2000] "Habit improvement advice" refers to specific behavioral suggestions and advice on lifestyle changes generated by the AI ​​model to help users maintain good health.

[2001] An "outlier" is an unusual value detected by an AI model while analyzing health data, which may indicate a potential health problem.

[2002] A "medical expert" is a doctor or other person with expertise in the medical field who provides advice and treatment plans to the user based on notifications and data from the server.

[2003] "Feedback" means any questions, requests, or health status reports provided by a User through the App.

[2004] "Support" refers to advice, solutions, or problem-solving assistance provided by a server or medical professional in response to user feedback.

[2005] This invention is a personal health plan advisor system that supports the early detection and prevention of pre-disease conditions. The system involves users answering a questionnaire about their lifestyle habits, analyzing the data to assess their pre-disease risk, and providing personalized health management advice.

[2006] Hardware and Software Configuration

[2007] 1. Device: The electronic device used by the user, including a smartphone, tablet, or PC, that answers the survey and receives data from the wearable device.

[2008] 2. Server: A computer system with advanced computing power and data storage capabilities that receives, analyzes, and stores data. It runs AI models such as Google Cloud AI, Azure AI, Scikit-learn, and TensorFlow.

[2009] 3. Wearable devices: Devices that collect user health data, including smartwatches and fitness trackers, which collect data such as heart rate, steps taken, and sleep duration.

[2010] Program processing

[2011] 1. Online Health Checkup

[2012] The user launches the app on their device and answers a questionnaire about their lifestyle habits. The device displays the questionnaire items and records the user's responses. Once the responses are complete, the device sends the data to the server. The server's AI model analyzes the data and evaluates the risk of developing pre-disease. The evaluation results are sent from the server to the user's device.

[2013] Example prompt: "Please complete a survey about your daily exercise, diet, and sleep."

[2014] 2. Data Tracking and Analysis

[2015] The user wears a wearable device, and the device records the heart rate, steps, sleep data, and other data obtained from the device. The device periodically sends this data to a server, where an AI model analyzes the data and evaluates the user's health. Based on the analysis results, the server generates personalized advice on improving habits and notifies the user's device.

[2016] Example prompt: "Please wear your smartwatch and send your data."

[2017] 3. Collaboration with medical professionals

[2018] If the server's AI detects an abnormal value in the analysis results, it flags it and notifies medical professionals. Medical professionals review the analysis results through a dedicated portal and provide necessary advice and treatment plans. This feedback is then sent to the user via the server.

[2019] Example prompt: "Analyze your heart rate data and notify a medical professional of any abnormal values."

[2020] 4. Feedback and Support

[2021] Users can use the in-app chat feature to send questions or feedback, which the device then sends to the server, which analyzes the user's feedback and, if necessary, connects them to a medical professional to provide the user with appropriate support.

[2022] Example prompt: "Submit your feedback or questions via in-app chat."

[2023] Specific examples

[2024] 1. Online Health Check:

[2025] After the user answers a questionnaire about their daily exercise, diet, and sleep, the server assesses whether they are at high risk of cardiovascular disease due to lack of exercise and notifies the user of the results.

[2026] 2. Data Tracking and Analysis:

[2027] When a user wears a smartwatch, the server's AI determines based on the data collected by the smartwatch that the user has not met their daily step goal for two consecutive weeks, and then provides advice on how to improve the situation, suggesting that the user try adding 30 minutes of walking each day.

[2028] 3. Collaboration with medical professionals:

[2029] Based on the user's heart rate data, the server's AI determines that a "persistently high heart rate is observed" and notifies a medical professional. The medical professional then conducts a detailed evaluation and notifies the user of the need for further testing.

[2030] 4. Feedback and Support:

[2031] The user sends feedback such as "I wake up many times every night." The server analyzes the data and returns a result saying, "Consultation with a medical professional is recommended." A medical professional reviews the data and provides recommendations for specific supplements and advice on improving the sleep environment.

[2032] As described above, the personal health plan advisor system of the present invention monitors the user's lifestyle data in detail, and by evaluating pre-disease risks and providing personalized advice, enables early detection of pre-disease and health management.

[2033] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2034] Online health check processing steps

[2035] Step 1:

[2036] The user launches the Personal Health Plan Advisor app on their device and answers a questionnaire about their lifestyle habits.

[2037] Input: User's basic information (name, age, gender, height, weight) and lifestyle data (exercise, diet, sleep, etc.)

[2038] Output: User response data

[2039] Specific operation: The user taps the app to launch it and enters answers to each item in the survey. The device records the user's input.

[2040] Step 2:

[2041] The terminal transmits the user's response data to the server.

[2042] Input: Survey response data

[2043] Output: Response data sent to the server

[2044] Specific operation: The device temporarily stores the user's survey responses and sends the data to a server via the Internet.

[2045] Step 3:

[2046] The AI ​​model on the server analyzes the response data and assesses the risk of developing illness.

[2047] Input: User response data

[2048] Output: Pre-disease risk assessment results

[2049] How it works: The server analyzes the data using Google Cloud AI or Azure AI models and generates a rating such as "high risk of cardiovascular disease."

[2050] Step 4:

[2051] The server generates the evaluation results and sends them to the user's terminal.

[2052] Input: Evaluation result data

[2053] Output: Evaluation results sent to the user's device

[2054] Specific operation: The server sends the generated evaluation results to the user's device, and the device displays the evaluation results via a pop-up or notification function.

[2055] Data Tracking and Analysis Process Steps

[2056] Step 1:

[2057] A user wears a wearable device, which collects health data.

[2058] Input: User's physical activity data (heart rate, steps, sleep data)

[2059] Output: Collected health data

[2060] What it does: A user puts on a smartwatch and the device records their activity data for the day.

[2061] Step 2:

[2062] The terminal acquires health data from the wearable device and transmits it to the server.

[2063] Input: Health data from wearable devices

[2064] Output: Health data sent to the server

[2065] Specific operation: The terminal acquires data from the device using Bluetooth or Wi-Fi and sends it to the server.

[2066] Step 3:

[2067] The server's AI model analyzes the health data and assesses the user's health status.

[2068] Input: Health data (heart rate, steps, sleep data)

[2069] Output: Health status assessment results

[2070] Specific operation: The server analyzes the data using TensorFlow and determines that the number of steps per day has not reached the target for two consecutive weeks.

[2071] Step 4:

[2072] Based on the analysis results, the server generates personalized advice for improving habits and notifies the user.

[2073] Input: Health status assessment results

[2074] Output: Personalized advice on how to improve your habits

[2075] Specific operation: The server generates a suggestion such as "Try adding 30 minutes of walking every day" and notifies the user device.

[2076] Steps in the process of working with a medical professional

[2077] Step 1:

[2078] If the server's AI detects an abnormal value in the analysis results, it will notify medical experts.

[2079] Input: Health data analysis results

[2080] Output: Notification to medical professionals

[2081] What it does: The server flags a "persistently high heart rate" and sends an automatic email notification to a medical professional.

[2082] Step 2:

[2083] Medical professionals will review the analysis results on a dedicated portal and provide necessary advice and treatment plans.

[2084] Input: Abnormal value notification and analysis result data from the server

[2085] Output: Detailed assessment and advice, treatment plan

[2086] How it works: Medical professionals log in to a dedicated portal, check the analysis results, and enter any necessary advice in writing or as a message within the system.

[2087] Step 3:

[2088] The server receives feedback from the medical professional and notifies the user.

[2089] Input: Medical professional advice, treatment plan

[2090] Output: User notification

[2091] Specific operation: The server sends the received advice to the user's device and displays it as a pop-up notification or in-app message.

[2092] Feedback and support process steps

[2093] Step 1:

[2094] Users submit questions or feedback through in-app chat.

[2095] Input: User feedback

[2096] Output: Feedback data sent to the server

[2097] What happens: A user opens the in-app chat, types in a question or feedback, and hits send.

[2098] Step 2:

[2099] The server analyzes the user's feedback and contacts medical professionals as needed.

[2100] Input: User feedback data

[2101] Output: Analysis of feedback and notification to medical professionals

[2102] Specific operation: The server analyzes the text of the feedback and if it determines that "expert consultation is required," it sends an email notification to a medical professional.

[2103] Step 3:

[2104] A medical professional will provide appropriate advice and notify the user through the server.

[2105] Input: Consultation request and feedback data from the server

[2106] Output: Advice to the user

[2107] What happens: A medical professional reviews the feedback and provides appropriate advice (e.g., specific supplement recommendations, sleep improvement strategies), which is then sent to the user by the server.

[2108] This allows the user to receive appropriate support immediately. In this way, the personal health plan advisor system of the present invention closely monitors the user's health condition and provides personalized advice and prompt support.

[2109] (Application example 1)

[2110] 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."

[2111] Early detection of pre-disease risks and appropriate responses are crucial for maintaining and improving a user's health. However, conventional health management systems inadequately collect and analyze users' lifestyle and health data, and have limited collaboration with medical professionals, making it difficult to detect pre-disease risks early or provide personalized health advice. It is also difficult to properly manage users' health data as a security service and provide necessary support in a timely manner. There is a need to solve these problems and achieve effective health management and early pre-disease prevention.

[2112] 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.

[2113] In this invention, the server includes: means for a user to answer a lifestyle questionnaire via a terminal and transmit the response data to the server; means for the server to receive the response data and for an AI to evaluate pre-disease risk; means for the server to notify the user of the evaluation results; means for a wearable device to collect health data and periodically transmit it to the server; means for the server's AI to analyze the health data and generate personalized habit improvement advice and notify the user; means for the server's AI to notify a medical professional if an abnormal value is detected in the analysis results; means for the medical professional to review the analysis results and provide necessary advice and treatment plans; means for the server to process feedback sent by the user within the app and provide necessary support; means for using an AI analysis engine to evaluate the collected health data and generate a pre-disease risk notification for the user; and means for transmitting the generated notification to the user's terminal. This enables efficient collection and analysis of a user's health data and early evaluation and notification of pre-disease risk.

[2114] "User" means an individual who uses the pre-disease risk assessment system.

[2115] A "terminal" is a device that a user uses to answer a questionnaire about lifestyle habits.

[2116] A "server" is a computer system that receives and analyzes questionnaire data and health data from wearable devices.

[2117] "AI" is a system that uses artificial intelligence technology to analyze questionnaire data and health data and evaluate the risk of pre-disease.

[2118] "Pre-disease risk" is the result of assessing the possibility of developing illness in the future, even if the illness has not yet occurred.

[2119] The "evaluation results" are the results of the AI's analysis of pre-disease risk, and are the information notified to the user.

[2120] A "wearable device" is a device that a user wears in their daily life to collect health data (heart rate, number of steps, sleep data, etc.).

[2121] "Habit Improvement Advice" is a lifestyle improvement suggestion provided to users based on their health data analyzed by the server's AI.

[2122] An "abnormal value" is a value that deviates from the normal range in the analysis results of health data.

[2123] A "health professional" is a qualified healthcare professional who provides advice and treatment plans based on a user's health data.

[2124] "Feedback" refers to users submitting opinions or questions about their health or advice within the app.

[2125] A "notification" is information or a warning that the server sends to the user terminal.

[2126] "Analysis engine" is a general term for software that uses AI technology to evaluate and analyze health data.

[2127] The present invention provides a system for analyzing a user's lifestyle and health data, and evaluating and notifying the user of their risk of developing a pre-disease. This system is realized using the following hardware and software.

[2128] Hardware and Software Configuration

[2129] Device: The device (smartphone, tablet, etc.) used by the user to answer the lifestyle questionnaire.

[2130] Server: A computer system (AWS, Google Cloud, etc.) that receives and analyzes survey data and health data sent from wearable devices.

[2131] AI analysis engine: Software (TensorFlow, PyTorch, etc.) for analyzing health data and assessing pre-disease risk.

[2132] Wearable device: A device (such as a smartwatch) that collects user health data (heart rate, steps, sleep data, etc.).

[2133] System operation process

[2134] 1. Data collection: Users answer a questionnaire about their lifestyle habits through their device and send the data to the server. In addition, the wearable device periodically records their heart rate, number of steps, sleep data, etc. and sends this data to the server.

[2135] 2. Data analysis: The server's AI analysis engine analyzes the received questionnaire data and health data to assess the risk of pre-disease.

[2136] 3. Risk assessment and notification: The server assesses the risk of developing pre-disease based on the analysis results and sends a notification to the user's device. For example, the assessment result may say, "You are at high risk of cardiovascular disease due to lack of exercise."

[2137] 4. Habit improvement advice: Based on the analysis results, the server's AI analysis engine generates personalized habit improvement advice and notifies the user. For example, the advice may be, "Try adding 30 minutes of walking every day."

[2138] 5. Collaboration with medical experts: If the server's AI detects an abnormality in the analysis results, it will notify medical experts. Medical experts will review the analysis results through a dedicated portal and provide necessary advice and treatment plans. This feedback will be sent to the user via the server.

[2139] 6. User feedback processing: When users send questions or feedback using the in-app chat function, the data is sent to the server and analyzed by the AI ​​analysis engine. If necessary, medical professionals will be contacted and appropriate support will be provided.

[2140] Specific examples

[2141] For example, User A can use a smartphone to answer a questionnaire about the amount of exercise, diet, and sleep time they get each day, and send heart rate and step count data from a wearable device (smartwatch) to a server. The server's AI analysis engine analyzes this data and evaluates their risk of developing a pre-disease condition. As a result of the evaluation, a notification stating, for example, "Your lack of exercise puts you at high risk of cardiovascular disease" is sent to User A's smartphone. Then, personalized advice on improving habits is sent, such as "Try adding 30 minutes of walking each day."

[2142] Prompt Sentence Examples

[2143] "Based on the user's health data (heart rate, number of steps, sleep time), please assess their risk of illness and provide specific health advice. The following data is from User A's data for the past week."

[2144] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2145] Step 1:

[2146] The user uses a device to answer a questionnaire about their lifestyle habits. The questions in the questionnaire include the amount of exercise, dietary habits, and sleep duration. The device collects the user's input data and sends it to the server in JSON format. The input is the user's questionnaire response data, and the output is JSON-formatted data sent to the server. Once the server receives this data, it is ready to proceed to the next analysis step.

[2147] Step 2:

[2148] The server passes the received survey data to the AI ​​model. The AI ​​model is implemented using TensorFlow or PyTorch and evaluates pre-disease risk based on the survey data. Here, data processing involves normalization and feature selection. The input is the survey data and the output is a pre-disease risk score. The evaluated risk score is stored in a database and used in the next notification step.

[2149] Step 3:

[2150] The server notifies the user's device of the evaluation results. If the pre-disease risk score is high, a message urging special attention is generated. This notification is sent to the user's smartphone as a push notification. The input is the pre-disease risk evaluation score, and the output is the notification message sent to the user's device. This allows the user to receive real-time information about their health status.

[2151] Step 4:

[2152] The wearable device collects the user's health data (heart rate, steps, sleep data, etc.) and sends it to a server. The wearable device periodically records the data and transfers it to a terminal via Bluetooth or Wi-Fi. The input is raw data from the wearable device, and the output is health data sent to the server via the terminal.

[2153] Step 5:

[2154] The server's AI analysis engine evaluates and analyzes the collected health data. Statistical methods and machine learning algorithms are used for the analysis. The AI ​​analysis engine detects outliers and performs trend analysis, and if an abnormality is detected, it records the results as a flag. The input is health data from the wearable device, and the output is the analysis results and an abnormal value flag.

[2155] Step 6:

[2156] Based on the analysis results of the server, personalized advice for improving habits is generated and notified to the user. For example, a user who has not reached their step goal may receive the advice "Try adding 30 minutes of walking each day." The input is the analysis results and the user's health data, and the output is personalized advice for improving habits.

[2157] Step 7:

[2158] If the server's AI detects an abnormal value in the analysis results, it notifies medical professionals. Medical professionals log in to a dedicated portal to check the analysis results and provide any necessary advice or treatment plans. The input is the abnormal value flag and the analysis results, and the output is the notification and advice to the medical professional.

[2159] Step 8:

[2160] Users use the in-app chat function to send questions or feedback. The device sends this to the server, where the AI ​​analysis engine analyzes the content. If necessary, medical professionals are contacted and appropriate support is provided to the user. The input is the user's feedback data, and the output is the analysis results and countermeasures.

[2161] Specific examples

[2162] For example, user A answers a lifestyle questionnaire using a smartphone and collects heart rate and step count data from a smartwatch. This data is sent to a server, and the AI ​​analysis engine evaluates the user as being at high risk for cardiovascular disease due to lack of exercise. The server notifies user A of this result on their smartphone and provides advice such as "Try adding 30 minutes of walking each day."

[2163] 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.

[2164] The present invention provides a system that provides more accurate health management by combining a personal health plan advisor system that supports the early detection of pre-disease conditions and the prevention of their progression with an emotion engine that recognizes the user's emotions. An embodiment of this system will be described in detail below.

[2165] 1. Online Health Checkup

[2166] The user launches the app through their device and proceeds to a lifestyle questionnaire from the profile entry screen. The device displays the questionnaire items, which the user answers. Once the answers are completed, the device sends the response data to the server. On the server, AI analyzes the response data and evaluates the risk of developing pre-disease. The evaluation results are sent from the server to the user's device and notified.

[2167] Examples:

[2168] After User A answers a questionnaire about his / her eating and exercise habits, the server evaluates that he / she is at high risk of cardiovascular disease due to lack of exercise and notifies User A of the result.

[2169] 2. Data Tracking and Analysis

[2170] Users wear a wearable device (e.g., a smartwatch) in their daily lives, and the device periodically records their heart rate, steps, sleep data, etc. This data is periodically sent to a server. The server's AI analyzes the received data and evaluates the user's health status. Based on the analysis results, the server generates personalized advice on habit improvement and notifies the user.

[2171] Examples:

[2172] User B wears a smartwatch, and based on the data sent from the device, the server's AI determines that "the number of steps per day has not reached the goal for two consecutive weeks." As improvement advice, it sends User B the suggestion to "add 30 minutes of walking every day."

[2173] 3. Collaboration with medical professionals

[2174] If the server's AI detects an abnormality in the analysis results, it will flag it and send a notification to a medical professional. The medical professional will review the analysis results through a dedicated portal and provide any necessary advice or treatment plans. Feedback is then sent to the user via the server.

[2175] Examples:

[2176] Based on User C's heart rate data, the server's AI determines that "a persistently high heart rate is observed" and notifies a medical professional. The medical professional then conducts a detailed evaluation and notifies User C of the need for additional testing.

[2177] 4. Feedback and Support

[2178] Users can use the in-app chat feature to send questions or feedback. The device then sends the feedback to the server, which analyzes it and, if necessary, notifies medical professionals to provide appropriate support to the user.

[2179] Examples:

[2180] User D submits feedback about their sleep quality, reporting that they "wake up multiple times each night." The server analyzes the data and determines that "consultation with a medical professional is recommended." A medical professional reviews the data and provides advice on specific supplements and improving their sleep environment.

[2181] 5. Combining Emotion Engines

[2182] While the user is using the app, the emotion engine analyzes the user's voice, facial expressions, and text input to recognize their emotional state. The device collects this data and sends it to a server, where the server's AI adjusts health assessments and improvement advice based on the user's emotional state. This enables more personalized health management.

[2183] Examples:

[2184] When User E sends feedback within the app, the emotion engine detects a state of stress from User E's voice. The server's AI takes this into consideration and provides advice such as "Try deep breathing exercises to reduce stress."

[2185] As described above, the personal health plan advisor system of the present invention tracks a user's lifestyle and health data, and evaluates and analyzes pre-disease risks to realize early detection of pre-disease and personalized health management. In addition, by combining it with an emotion engine, it provides more precise health management that takes into account the user's emotional state.

[2186] The processing flow will be explained below.

[2187] Processing steps of a system that combines emotion engines

[2188] Online health check processing steps

[2189] Step 1:

[2190] The user starts the device and opens the Health Plan Advisor app.

[2191] Step 2:

[2192] The device displays a profile entry screen to the user, prompting them to enter basic information (such as name, age, gender, medical history, etc.).

[2193] Step 3:

[2194] The device displays a questionnaire screen about lifestyle habits, and the user answers questions about eating habits, exercise habits, sleep habits, stress levels, etc.

[2195] Step 4:

[2196] The user completes the survey and clicks the submit button.

[2197] Step 5:

[2198] The terminal transmits the user's response data to the server.

[2199] Step 6:

[2200] The server receives the response data and inputs it into the AI ​​engine.

[2201] Step 7:

[2202] The server's AI analyzes the response data and assesses the risk of developing illness.

[2203] Step 8:

[2204] The server generates the evaluation results and transmits the data to the user terminal.

[2205] Step 9:

[2206] The terminal notifies the user of the evaluation results and displays advice based on the evaluation results.

[2207] Data Tracking and Analysis Process Steps

[2208] Step 1:

[2209] A user wears a wearable device (e.g., a smart watch) in their daily life.

[2210] Step 2:

[2211] The wearable device regularly records health data such as heart rate, steps taken, and sleep data.

[2212] Step 3:

[2213] The wearable device periodically transmits the recorded data to the user's smartphone.

[2214] Step 4:

[2215] The terminal transmits the data received from the wearable terminal to the server at regular intervals.

[2216] Step 5:

[2217] The server receives the wearable data and stores it in a database.

[2218] Step 6:

[2219] The server's AI periodically performs data analysis and evaluates the user's health status.

[2220] Step 7:

[2221] The server's AI generates personalized advice on habit improvement based on the analysis results.

[2222] Step 8:

[2223] The server transmits the generated advice to the user's terminal.

[2224] Step 9:

[2225] The device will notify the user of the advice and display details within the app.

[2226] Steps in the process of working with a medical professional

[2227] Step 1:

[2228] If the server's AI detects an abnormal value in the analysis results, it will raise an abnormality flag.

[2229] Step 2:

[2230] The server notifies the medical professional of the results of the abnormality analysis.

[2231] Step 3:

[2232] Medical professionals are notified through a dedicated portal and can review the analysis results.

[2233] Step 4:

[2234] A medical professional will conduct a detailed assessment and generate further advice and a treatment plan.

[2235] Step 5:

[2236] The server transmits the feedback from the medical professional to the user terminal.

[2237] Step 6:

[2238] The device will notify the user of feedback from medical professionals and display details within the app.

[2239] Feedback and support process steps

[2240] Step 1:

[2241] Users submit questions or comments using the in-app feedback feature.

[2242] Step 2:

[2243] The terminal sends the user's feedback to the server.

[2244] Step 3:

[2245] The server receives the feedback, and the AI ​​handles any content that can be automatically responded to.

[2246] Step 4:

[2247] The server notifies the medical professional of the feedback as needed.

[2248] Step 5:

[2249] Medical professionals will review the feedback and take appropriate action.

[2250] Step 6:

[2251] The server transmits the response results from the medical professional to the user terminal.

[2252] Step 7:

[2253] The device will notify the user of the response results and display details within the app.

[2254] Emotion Engine Processing Steps

[2255] Step 1:

[2256] While the user is using the app, voice, facial expressions, and text input are collected through the device's camera and microphone.

[2257] Step 2:

[2258] The device sends the collected voice, facial expression, and text data to the emotion engine.

[2259] Step 3:

[2260] An emotion engine within the device analyzes this data and recognizes the user's emotional state.

[2261] Step 4:

[2262] The terminal transmits the recognized emotional state data to the server.

[2263] Step 5:

[2264] The server's AI takes into account the user's emotional state and adjusts health assessments and advice on improving habits.

[2265] Step 6:

[2266] The server sends the adjusted advice to the user's terminal.

[2267] Step 7:

[2268] The device will notify the user of tailored advice and display details within the app.

[2269] Examples:

[2270] When User E sends feedback within the app, the emotion engine detects a state of stress from User E's voice. The server's AI takes this into consideration and provides advice such as "Try deep breathing exercises to reduce stress."

[2271] These are the specific processing steps of the personal health plan advisor system that combines the emotion engine. Through these steps, the system will further personalize the user's health management, enabling early detection of pre-disease and prevention of its progression.

[2272] Example 2

[2273] 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."

[2274] The need for prevention of lifestyle-related diseases and health management is rapidly increasing in modern times, but existing health management systems have difficulty in providing advanced personalization that fully takes into account the user's lifestyle and emotional state. In particular, for early detection of pre-disease and prevention of progression, there is a need to comprehensively analyze the user's individual data and emotional state and provide appropriate advice. However, conventional systems lack such advanced functionality.

[2275] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to answer a lifestyle questionnaire via a terminal and transmit the response data to the server; a means for the server to receive the response data and evaluate pre-disease risk using an artificial intelligence; a means for the server to notify the user of the evaluation results; a means for the wearable device to collect health data and periodically transmit it to the server; a means for the server's artificial intelligence to analyze the health data and generate personalized habit improvement advice and notify the user; a means for the server's artificial intelligence to notify an expert if it detects an abnormal value in the analysis results; a means for the expert to review the analysis results and provide necessary advice and a treatment plan; a means for the server to process feedback sent by the user within the app and provide necessary support; a means for the server's artificial intelligence to analyze the user's voice, facial expression, and text input and recognize the user's emotional state; and a means for the server's artificial intelligence to adjust the health assessment and improvement advice taking the emotional state into account. This enables integrated analysis of the user's lifestyle data and emotional state, enabling highly personalized health management.

[2276] "User" refers to an individual who uses this system.

[2277] "Terminal" refers to the electronic device that a user uses to access this system. Specifically, it includes devices such as smartphones, tablets, and personal computers.

[2278] The "questionnaire on lifestyle habits" refers to a questionnaire in the form of questions in which the user answers about habits in daily life such as eating, exercise, and sleep.

[2279] "Server" refers to a computer system for receiving, storing, and analyzing data from users.

[2280] "Artificial intelligence" refers to machine learning algorithms that run on a server and analyze user data to assess pre-disease risk and recognize emotional states.

[2281] "Pre-disease risk" refers to assessing the risk of a user becoming ill before they become ill.

[2282] "Evaluation results" refers to information regarding pre-disease risks and other health conditions generated by artificial intelligence through analysis of the user's data.

[2283] "Wearable devices" refer to health management devices worn by users, including smartwatches and fitness trackers.

[2284] "Health data" refers to information collected by wearable devices, such as heart rate, steps taken, and sleep data.

[2285] "Personalized habit improvement advice" refers to customized health improvement suggestions provided to a user based on the user's particular health condition and lifestyle habits.

[2286] "Experts" refer to qualified individuals with knowledge of medical care and health management, and are responsible for providing advice and treatment plans to users based on notifications from the server.

[2287] "Feedback" refers to information such as opinions, questions, reports, etc. that users submit within the app.

[2288] "Emotional state" refers to the psychological and emotional state a user exhibits while using the app.

[2289] "Emotion engine" refers to a software component that recognizes a user's emotional state by analyzing their voice, facial e...

Claims

1. A means for a user to answer a questionnaire about lifestyle habits through a terminal and transmit the answer data to a server; The server receives the response data and the AI ​​evaluates the risk of pre-disease. A means for the server to notify the user terminal of the evaluation result; A means for the wearable device to collect and periodically transmit health data to a server; The server's AI analyzes health data, generates personalized advice on how to improve habits, and notifies the user. If the server's AI detects an abnormal value in the analysis results, it will notify medical experts. A means for medical professionals to review the analysis results and provide necessary advice and treatment plans; A means for the server to process feedback sent by users within the app and provide necessary support; A system including:

2. 2. The system according to claim 1, further comprising means for the server to notify the user terminal based on the evaluation result.

3. 10. The system of claim 1, wherein the collected health data includes heart rate, step count, and sleep data.

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