System

Abstract

A system is provided.SOLUTION: A system comprising: means for analyzing collected health data to generate an aging prediction model; means for generating a personalized health care recommendation; and means for receiving medical professional advice to modify the health care recommendation.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] As our society ages, health management and nursing care support for the elderly are becoming increasingly important. However, current systems often lack sufficient privacy protection, posing a risk of personal health data leaks. Furthermore, the lack of personalized health management suggestions makes effective health management and nursing care support difficult. Furthermore, the lack of reliable services that reflect the ongoing advice of medical professionals makes it difficult to improve the quality of life for the elderly. [Means for solving the problem]

[0005] This invention solves the above-mentioned problems by providing a system that includes a means for analyzing collected health data to generate an aging prediction model, a means for generating personalized health care proposals, and a means for modifying the health care proposals based on advice from medical professionals. Furthermore, it provides a data management means with a privacy protection function to reduce the risk of data leaks, and a means for periodically updating the aging prediction model. It also includes a means for providing reliable health care and nursing support in cooperation with medical institutions and nursing care facilities.

[0006] "Collected health data" refers to data such as vital signs, lifestyle habits, and medical history provided by users or generated by various wearable devices or linked apps.

[0007] "Analysis" refers to the process of using AI technology to create aging prediction models and health risk assessments based on collected health data.

[0008] "Aging Prediction Model" refers to a mathematical and statistical model for predicting future health risks and the rate of aging progression based on a user's health data.

[0009] "Personalized health management suggestions" refers to suggestions based on the user's health status and aging prediction model, such as specific exercise plans, dietary advice, and preventative health checks.

[0010] "Professional medical advice" refers to the professional opinion or correction provided by a medical professional, such as a doctor or nurse, to a health care recommendation.

[0011] "Privacy protection features" refer to security features designed to prevent unauthorized access or leakage of a user's health data.

[0012] "Leakage risk" refers to the possibility that health data may be illegally accessed and leaked by a third party.

[0013] "Data management measures" refers to the systems and processes for properly storing collected health data, performing necessary processing, and managing it safely.

[0014] "Means for updating aging prediction models" refers to the process of periodically reviewing and improving the accuracy of existing aging prediction models through the collection and analysis of new data.

[0015] "Medical institutions and nursing care facilities" refers to organizations and facilities that provide medical and nursing care services.

[0016] "Reliable health care and nursing care support" refers to the provision of accurate and useful health care and nursing care services that have undergone medical verification. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] This invention relates to a system that collects and analyzes a user's health data and provides personalized health management and care support. Specifically, the invention is embodied in the following manner.

[0039] User registration and health data collection

[0040] 1. When a user uses this system for the first time, they access the initial registration form that appears on the terminal.

[0041] 2. The device prompts the user to enter information such as name, age, gender, medical history, and lifestyle habits.

[0042] 3. After entering and submitting the registration information, the device sends the information to the server, which stores it in a database.

[0043] Real-time health data collection

[0044] 1. If the user consents, the device will collect real-time health data from wearable devices and connected apps.

[0045] 2. The collected health data covers a wide range of topics, including the user's vital signs (heart rate, blood pressure, etc.), number of steps taken, and sleep patterns.

[0046] 3. The device sends this data to the server at regular intervals, and the server stores it in a database.

[0047] Health data analysis and aging prediction

[0048] 1. The server uses the health data stored in the database to perform analysis using AI technology.

[0049] 2. The server generates an aging prediction model for the user based on past health data and existing statistical data.

[0050] 3. The generated aging prediction model predicts the user's future health risks and aging rate, and privacy protection features reduce the risk of information leakage.

[0051] Generate personalized health care recommendations

[0052] 1. The server generates health management proposals tailored to each individual user based on the aging prediction model.

[0053] 2. The suggestions include exercise plans, dietary advice, and recommendations for regular health checks tailored to the user's health condition.

[0054] 3. The server sends the generated suggestions to medical professionals for feedback.

[0055] Reflecting advice from medical professionals

[0056] 1. The medical professional reviews the submitted health care proposal and makes any necessary adjustments.

[0057] 2. The corrected feedback is sent back to the server, which then finalizes the health management plan.

[0058] User feedback and implementation

[0059] 1. The server sends the confirmed health management plan to the terminal.

[0060] 2. The device notifies the user and displays the plan details.

[0061] 3. Users can check their health management plan and set reminders to follow it in their daily lives.

[0062] Ongoing monitoring and feedback

[0063] 1. The server analyzes continuously collected health data in real time and periodically updates the aging prediction model.

[0064] 2. The server adjusts the health management plan accordingly based on the user's health condition and notifies medical professionals as necessary.

[0065] 3. Users can check their health status and suggested actions at any time from their device and manage their health.

[0066] The system is designed to provide personalized health management and care support using advanced technology while protecting user privacy, thereby contributing to solving health management challenges in an aging society.

[0067] The processing flow will be explained below.

[0068] Step 1:

[0069] The user accesses the initial registration form and enters information such as name, age, gender, medical history, and lifestyle habits.

[0070] Step 2:

[0071] The device sends the entered registration information to a server, where the data is encrypted for enhanced privacy protection.

[0072] Step 3:

[0073] The server stores the received registration information in a database and creates an initial user profile.

[0074] Step 4:

[0075] If the user consents, the device will collect real-time health data (such as heart rate, steps, and sleep patterns) from wearable devices and connected apps.

[0076] Step 5:

[0077] The device periodically transmits the collected health data to a server, which also encrypts the data to ensure security.

[0078] Step 6:

[0079] The server stores the received health data in a database and simultaneously provides the data to an AI for data analysis.

[0080] Step 7:

[0081] The AI ​​on the server analyzes the health data and generates an aging prediction model, which is used to predict the user's health risks and aging rate.

[0082] Step 8:

[0083] Based on the generated aging prediction model, the server generates personalized health management recommendations, including exercise plans, dietary advice, and preventative health checks.

[0084] Step 9:

[0085] The server sends the health management suggestions to medical professionals and receives feedback, who review the suggestions and make any necessary adjustments.

[0086] Step 10:

[0087] The server determines the final health management plan based on the feedback from medical professionals, and the finalized plan is stored in a database.

[0088] Step 11:

[0089] The server sends the finalized health management plan to the terminal, which then notifies the user and displays the plan contents.

[0090] Step 12:

[0091] Users can check their health management plan, set reminders to implement it in their daily lives, and manage their health according to the plan.

[0092] Step 13:

[0093] The server analyzes the continuously collected health data in real time and updates the aging prediction model as needed, which is done periodically.

[0094] Step 14:

[0095] The server adjusts the health plan based on the user's health condition and notifies medical professionals if action is required.

[0096] Step 15:

[0097] Users can check their health status and suggested actions at any time through their device and continue to manage their health appropriately.

[0098] Through these processing steps, the system provides personalized health management and care support while protecting the user's privacy.

[0099] Example 1

[0100] 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."

[0101] In an aging society, there is a growing demand for personalized health management and nursing care support. However, existing systems face challenges in providing accurate, real-time health management while protecting user privacy. Furthermore, there is a lack of mechanisms for effectively analyzing collected health data and obtaining efficient feedback from medical professionals. This makes it difficult to properly monitor users' health status and provide appropriate health management.

[0102] 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.

[0103] In this invention, the server includes means for analyzing collected health data and generating an aging prediction model, means for generating personalized health care proposals, means for revising the health care proposals based on advice from medical professionals, means for collecting user registration information and storing it in a database, means for collecting health data from the wearable device in real time and transmitting it to the server, means for periodically analyzing the health data and updating the aging prediction model, and means for notifying the user of an optimized health care plan and setting reminders. This makes it possible to provide highly accurate health care in real time and receive appropriate feedback quickly while protecting the user's privacy.

[0104] "Means for analyzing collected health data and generating an aging prediction model" refers to the function of using an algorithm to create a model that predicts the user's aging progression based on the user's health data collected from wearable devices and linked apps.

[0105] "Means for generating personalized health management proposals" refers to a function that proposes health management plans, such as individually optimized exercise plans and dietary advice, based on each user's health data and aging prediction models.

[0106] "Means for amending health care proposals with the advice of a medical professional" refers to the process of having a medical professional review the generated health care proposals and making amendments as necessary.

[0107] "Means for collecting user registration information and storing it in a database" refers to the function of safely storing personal information entered by users when they first start using the system in a database.

[0108] "Means for collecting health data from a wearable device in real time and transmitting it to a server" refers to a mechanism for collecting a user's health data from a wearable device in real time and transmitting it to a server.

[0109] "Means for periodically analyzing health data and updating the aging prediction model" refers to a function for periodically analyzing continuously collected health data and keeping the aging prediction model up to date.

[0110] "Means for notifying the user of the optimized health management plan and setting reminders" refers to a function that notifies the user of the generated health management plan and supports the user in setting reminders to put the plan into action.

[0111] This invention relates to a system that collects and analyzes a user's health data and provides personalized health management and care support. Specifically, the invention is embodied in the following manner.

[0112] User registration and health data collection

[0113] When a user first registers, they access a form that appears on their device (e.g., a smartphone or tablet). They enter information such as their name, age, gender, medical history, and lifestyle habits into the device and submit it. The device then sends this information to the server, which then stores it in a database.

[0114] Real-time health data collection

[0115] Once a user consents to real-time health data collection, the device will collect health data such as heart rate, blood pressure, number of steps, and sleep patterns from wearable devices (e.g., Fitbit or Apple Watch) and connected apps. The device will then send this data to a server at regular intervals, where it will be stored in a database.

[0116] Health data analysis and aging prediction

[0117] The server uses AI technology to analyze the health data stored in the database. Based on past data and statistical data, the server generates a predictive model for the user's aging. This model makes it possible to predict future health risks and the rate of aging.

[0118] Generate personalized health care recommendations

[0119] Based on the aging prediction model, the server generates personalized health management suggestions for the user, including exercise plans, dietary advice, and recommendations for regular health checkups. The server then sends the suggestions to medical professionals for feedback.

[0120] Reflecting advice from medical professionals

[0121] The medical professional reviews the submitted health care proposal and makes any necessary modifications, and the modified feedback is sent back to the server, which then finalizes the health care plan.

[0122] User feedback and implementation

[0123] The server sends the finalized health management plan to the device, which notifies the user and displays the plan contents. The user can then review the health management plan and set reminders to implement it in their daily lives.

[0124] Ongoing monitoring and feedback

[0125] The server analyzes continuously collected health data in real time and periodically updates the aging prediction model. The server adjusts the health management plan according to the user's health condition and notifies medical professionals as necessary. Users can check their health condition and suggested actions on their device and manage their health.

[0126] The system uses advanced technology to provide personalized health and care support while protecting user privacy. Below are some examples and prompts:

[0127] Specific examples

[0128] User: 60-year-old male with a medical history of hypertension and diabetes.

[0129] Devices: Smartphone and wearable device (Fitbit)

[0130] Health Data: Heart rate, steps, and sleep data collected from wearable devices.

[0131] Prompt Sentence Examples

[0132] A 60-year-old male with a medical history of hypertension and diabetes. Generate personalized health management suggestions based on this user's health data, taking into account data collected from wearable devices (heart rate, steps, sleep data, etc.).

[0133] Thus, the present invention is a system that provides advanced personalized health management while protecting the user's privacy.

[0134] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0135] Step 1:

[0136] When a user uses the system for the first time, he or she accesses the initial registration form displayed on the terminal.

[0137] Input: Information such as the user's name, age, gender, medical history, and lifestyle habits.

[0138] Output: The user information entered into the form.

[0139] What happens: A user opens a web browser or mobile app, fills out a form, and clicks submit.

[0140] Step 2:

[0141] The terminal prompts the user to enter information such as name, age, gender, medical history, and lifestyle habits.

[0142] Input: Information entered by the user.

[0143] Output: A request to send the entered information to the server.

[0144] Specific operation: The device displays an input form and sends the information entered by the user to the server as an HTTPS request.

[0145] Step 3:

[0146] The server stores the received registration information in a database.

[0147] Input: User information sent from the device.

[0148] Output: User profile stored in the database.

[0149] What happens: The server receives the request and stores the information in a database.

[0150] Step 4:

[0151] User consents to real-time health data collection.

[0152] Input: User consent.

[0153] Output: The consent is recorded.

[0154] Specific operation: The consent form is displayed on the device and the user presses the "Agree" button.

[0155] Step 5:

[0156] The device collects health data such as heart rate, blood pressure, steps taken, and sleep patterns from wearable devices and connected apps.

[0157] Input: Real-time data obtained from wearable devices.

[0158] Output: Collected health data.

[0159] Specific behavior: Obtain data from the device via Bluetooth or other communication means.

[0160] Step 6:

[0161] The terminal transmits this data to the server at regular intervals.

[0162] Input: Collected health data.

[0163] Output: The data sent to the server.

[0164] Specific operation: The device collects data using an interval timer and sends an HTTP request to the server.

[0165] Step 7:

[0166] The server uses AI technology to analyze the health data stored in the database.

[0167] Input: Health data in a database.

[0168] Output: Analysis results and aging prediction model.

[0169] What it does: The server analyzes the data using machine learning algorithms.

[0170] Step 8:

[0171] The server generates an aging prediction model based on past data and statistical data.

[0172] Input: Health data, historical data, statistical data.

[0173] Output: Aging prediction model.

[0174] Specific operation: The server trains the machine learning model and generates a new predictive model.

[0175] Step 9:

[0176] The server generates health management proposals tailored to each individual user based on the generated aging prediction model.

[0177] Input: Aging prediction model.

[0178] Output: personalized health care recommendations.

[0179] Specific operation: The server analyzes the model output and generates a health management plan.

[0180] Step 10:

[0181] The server sends the suggestions to medical professionals and asks for their feedback.

[0182] Enter: Healthcare Proposal.

[0183] Output: Healthcare professional feedback.

[0184] Specific operation: The server sends suggestions via email or a dedicated app and receives feedback.

[0185] Step 11:

[0186] A medical professional will review the submitted health care proposal and make any necessary adjustments.

[0187] Enter: Healthcare Proposal.

[0188] Output: Corrected feedback.

[0189] How it works: Medical professionals review and correct suggestions through a dedicated app or web portal.

[0190] Step 12:

[0191] The server finalizes the health management plan and transmits it to the terminal.

[0192] Input: Revised health care proposal.

[0193] Output: Final health care plan.

[0194] Specific operation: The server generates the final plan and sends it to the terminal.

[0195] Step 13:

[0196] The terminal notifies the user and displays the plan details.

[0197] Enter: the final health care plan.

[0198] Output: Inform and display the plan to the user.

[0199] What it does: The device generates a push notification and displays the plan details when the user opens the app.

[0200] Step 14:

[0201] Users can review their health plan and set reminders.

[0202] Enter: the final health care plan.

[0203] Output: The set reminder.

[0204] What happens: The user sets a reminder in the app, and the device sets a notification or alarm.

[0205] Step 15:

[0206] The server continuously analyzes collected health data in real time and updates the aging prediction model.

[0207] Input: New health data.

[0208] Output: Updated aging prediction model.

[0209] What happens: The server analyzes the new data and updates the model parameters.

[0210] Step 16:

[0211] The server adjusts the health care plan according to the user's health status and notifies medical professionals as needed.

[0212] Input: Updated aging prediction model and health data.

[0213] Output: Coordinated health care plan.

[0214] What it does: The server detects changes in health status, adjusts plans, and notifies medical professionals.

[0215] Step 17:

[0216] Users can check their health status and suggested actions at any time from their device and manage their health.

[0217] Input: Health care plan and latest health data.

[0218] Output: User's plan execution and health management.

[0219] Specific behavior: User views the in-app dashboard and takes the required action.

[0220] (Application example 1)

[0221] 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."

[0222] Providing personalized health management and nursing care support is an important challenge in an aging society. However, conventional systems have difficulty collecting and analyzing real-time health data and quickly providing personalized recommendations that reflect the advice of medical professionals. Providing immediate feedback based on the user's health status and continuously updating data while protecting privacy are also challenges. Furthermore, the integration of health management recommendations and feedback in physical stores is insufficient.

[0223] 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.

[0224] In this invention, the server includes a means for analyzing collected health data to generate an aging prediction model, a means for generating personalized health management suggestions, and a means for revising the health management suggestions based on advice from medical professionals. This also includes a means for displaying personalized health management suggestions to customers using health data collected on a dedicated in-store terminal, a means for providing health management suggestions based on health checkup results and feedback from the physical store, a means for displaying the health management suggestions on an in-store display or on the customer's smartphone app, a means for medical professionals to review and revise the generated suggestions, and a means for analyzing continuously collected health data over time and updating the suggestions. This enables the rapid provision of personalized health management and care support in real time, and real-time feedback and continuous monitoring according to the user's health condition. Privacy protection features also reduce the risk of data leakage.

[0225] "Collected health data" refers to information such as heart rate, blood pressure, number of steps, and sleep patterns obtained from users' wearable devices and smartphone apps.

[0226] An "aging prediction model" is a model for predicting a user's future health risks and aging rate based on past health data and existing statistical data.

[0227] "Personalized health management suggestions" refer to suggestions that include exercise plans, dietary advice, and recommendations for regular health checks that are generated based on the user's health condition.

[0228] The "means for modifying health care proposals with the advice of a medical professional" refers to a method by which a medical professional reviews and modifies the generated health care proposals and provides optimized proposals.

[0229] An "in-store dedicated terminal" is an electronic device installed in a physical store for collecting and displaying users' health data.

[0230] "Providing health care suggestions" refers to informing the user of the generated health care suggestions and recommending health care activities based on the suggestions.

[0231] A "store display" is a device installed in a physical store that displays health management suggestions as visual information to customers.

[0232] A "smartphone app" is application software that is installed on a customer's smartphone and can display health management suggestions and obtain feedback.

[0233] "Real-time health data" refers to instantaneous data such as a user's current heart rate, blood pressure, number of steps, and sleep patterns.

[0234] "Data leakage risk" refers to the risk of a user's health data being illegally accessed or leaked to a third party.

[0235] "Privacy protection features" refer to technical measures to safely manage users' personal information and health data and prevent unauthorized access or data leakage.

[0236] This invention is a system that collects and analyzes a user's health data and provides personalized health management and care support. Specifically, the invention can be implemented in the following forms.

[0237] User registration and health data collection

[0238] The server provides a means for first-time users to access the initial registration form displayed on a dedicated terminal in the store. The user enters information such as name, age, gender, medical history, and lifestyle habits. The terminal sends this information to the server, where it is stored in a database on the server.

[0239] Real-time health data collection

[0240] If the user agrees, the device will collect real-time health data (heart rate, blood pressure, steps taken, sleep patterns, etc.) from wearable devices and connected apps. This data will be periodically sent to a server, which will store it in a database.

[0241] Health data analysis and aging prediction

[0242] The server uses the stored health data to perform analysis using AI technology, which generates a user's aging prediction model based on past health data and existing statistical data. This model predicts the user's future health risks and aging rate.

[0243] Generate personalized health care recommendations

[0244] Based on the aging prediction model, the server generates personalized health management suggestions for the user, including exercise plans, dietary advice, and recommendations for regular health checks.

[0245] Reflecting advice from medical professionals

[0246] The server sends the generated health care suggestions to medical experts for their feedback, and the suggestions modified by the medical experts ensure further accuracy and reliability.

[0247] Providing health management suggestions in-store

[0248] The improved health management suggestions are sent from the server to dedicated terminals or in-store displays in the store and are displayed to customers. The suggestions are also linked to the customer's smartphone app, allowing the user to instantly check and implement the suggestions.

[0249] Ongoing monitoring and feedback

[0250] The server continuously analyzes the health data collected in real time and periodically updates the aging prediction model, allowing the system to adjust the health management plan accordingly based on the user's health condition and notify medical professionals as needed, enabling health management based on the latest information.

[0251] Examples of prompt statements

[0252] An example of a specific prompt sentence is, "Generate health management suggestions for a 40-year-old male. Heart rate data for the past week: average 75, blood pressure 120 / 80, average number of steps 10,000, average sleep time 7 hours."

[0253] Hardware and Software Used

[0254] The system uses tablets and in-store displays as dedicated terminals, and connects to wearable devices and smartphone apps. A database management system and software with the functions necessary for AI analysis run on the server side. Using this system, customers can receive health management suggestions in real time and use them to implement them.

[0255] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0256] Step 1:

[0257] The user accesses the initial registration form using a dedicated terminal at the store. The user enters information such as name, age, gender, medical history, and lifestyle habits. The entered information is sent from the terminal to the server, which saves it in a database. The entered data is stored in the user information database.

[0258] Step 2:

[0259] If the user agrees, the device will collect real-time health data (e.g., heart rate, blood pressure, number of steps, sleep patterns) from wearable devices and connected apps. This data will be sent to a server at regular intervals, and the server will store it in a database. The input data is the real-time health data, and the output data is an updated health database.

[0260] Step 3:

[0261] The server uses AI technology to perform analysis based on the health data stored in the database. This analysis generates an aging prediction model for the user, taking into account past health data and existing statistical data. This model predicts the user's future health risks and aging rate. The input data is the collected health data, and the output data is the aging prediction model.

[0262] Step 4:

[0263] The server generates personalized health management suggestions based on the generated aging prediction model. These suggestions include exercise plans, dietary advice, and recommendations for regular health checkups. The input data is the aging prediction model, and the output data is the personalized health management suggestions.

[0264] Step 5:

[0265] The generated health care proposal is sent from the server to a medical expert, who provides advice and makes corrections. The medical expert reviews the proposal and makes corrections as necessary. The corrected proposal is sent back to the server and saved in the database again. The input data is the health care proposal, and the output data is the corrected health care proposal.

[0266] Step 6:

[0267] The final health management proposal is sent from the server to a dedicated terminal or in-store display in the store and displayed to the customer. The proposal is also linked to the customer's smartphone app. The user confirms it and carries out health management. The input data is the revised health management proposal, and the output data is the displayed health management proposal.

[0268] Step 7:

[0269] The server continuously analyzes the health data collected in real time and periodically updates the aging prediction model. This allows the system to adjust the health management plan according to the user's health condition and notify medical professionals again as necessary. The input data is the latest health data, and the output data is the updated aging prediction model and health management suggestions.

[0270] Specific working example:

[0271] If the user is a 40-year-old man and his average heart rate data for the past week is 75, the server will use this information to generate health management suggestions such as "walking 30 minutes every day" and "eating a vegetable-based diet."

[0272] The server-generated suggestions were reviewed by medical experts, who then revised them to "reduce walking time to 20 minutes."

[0273] The revised proposals are displayed on the dedicated device and smartphone app, and the user can confirm and implement them.

[0274] Examples of prompts:

[0275] "Generate health management suggestions for a 40-year-old male. Heart rate data for the past week: average 75, blood pressure 120 / 80, average steps 10,000, average sleep time 7 hours."

[0276] 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.

[0277] This invention relates to a system that collects and analyzes a user's health data and emotional data to provide personalized health management and care support. Specifically, the invention is embodied in the following manner.

[0278] User registration and collection of health and emotional data

[0279] 1. When a user uses the system for the first time, they access the initial registration form displayed on the terminal and enter information such as their name, age, gender, medical history, and lifestyle habits.

[0280] 2. The terminal sends the entered registration information to the server, which stores it in a database.

[0281] 3. If the user agrees, the device will collect real-time health data from wearable devices and connected apps, and simultaneously use the emotion engine to collect the user's emotional data.

[0282] 4. The device periodically sends health and emotion data to the server, which stores this data in a database.

[0283] Analysis of health and emotional data and aging prediction

[0284] 1. The server provides the health and emotional data stored in the database to the AI ​​for analysis.

[0285] 2. The AI ​​on the server analyzes the health and emotional data and generates an aging prediction model, which predicts the user's health risks and aging rate.

[0286] 3. The server generates personalized health recommendations that take emotional data into account, including exercise plans, dietary advice, and preventative health checks that take into account the user's mental health.

[0287] Reflecting advice from medical professionals

[0288] 1. The server sends the generated health management proposal to the medical professional and receives feedback. The medical professional reviews the proposal and makes corrections if necessary.

[0289] 2. The corrected feedback is sent back to the server, which then finalizes the health management plan.

[0290] User feedback and implementation

[0291] 1. The server sends the finalized health management plan to the terminal, which notifies the user and displays the plan details.

[0292] 2. The user checks the health management plan, sets reminders to implement it in daily life, and manages their health according to the plan.

[0293] Ongoing monitoring and feedback

[0294] 1. The server analyzes continuously collected health and emotion data in real time and periodically updates the aging prediction model.

[0295] 2. The server adjusts the health management plan accordingly based on the user's health and emotional state, and notifies medical professionals as needed.

[0296] 3. Users can check their health status, emotional state, and suggested actions at any time through their device, and continue to take appropriate health management measures.

[0297] For example, if a user records their daily emotional state and the emotion engine detects that they are experiencing increased stress, the server will add health management suggestions such as reducing exercise and practicing relaxing breathing exercises. It will also take an approach to improving mental health by recommending a weekly counseling session with a medical professional. In this way, the system provides comprehensive support to improve the user's overall health and happiness.

[0298] By integrating emotional data while protecting user privacy, this system can provide more accurate and personalized health management and nursing care support, thereby improving users' quality of life and contributing to solving health management issues in an aging society.

[0299] The processing flow will be explained below.

[0300] Step 1:

[0301] The user accesses the initial registration form and enters information such as name, age, gender, medical history, and lifestyle habits.

[0302] Step 2:

[0303] The device sends the entered registration information to a server, where the data is encrypted for enhanced privacy protection.

[0304] Step 3:

[0305] The server stores the received registration information in a database and creates an initial user profile.

[0306] Step 4:

[0307] If the user agrees, the device will collect real-time health data (heart rate, steps, sleep patterns, etc.) from the wearable device and connected apps. At the same time, the emotion engine will collect emotional data from the user's facial expressions and tone of voice using the device's built-in camera and microphone.

[0308] Step 5:

[0309] The device periodically transmits the collected health and emotional data to a server, which also encrypts the data to ensure security.

[0310] Step 6:

[0311] The server stores the received health and emotional data in a database and simultaneously provides the data to an AI for data analysis.

[0312] Step 7:

[0313] The server's AI analyzes the health and emotional data to generate an aging prediction model, which predicts the user's health risks and aging rate while also incorporating the emotional data.

[0314] Step 8:

[0315] The server then uses the generated aging prediction model to create personalized health management recommendations, including exercise plans, dietary advice, and preventative health checks that take into account the user's health and emotional state.

[0316] Step 9:

[0317] The server sends the health management suggestions to medical professionals and receives feedback, who review the suggestions and make any necessary adjustments.

[0318] Step 10:

[0319] The server determines the final health care plan based on feedback from medical professionals, which is then stored in a database.

[0320] Step 11:

[0321] The server sends the finalized health management plan to the terminal, which notifies the user and displays the plan contents.

[0322] Step 12:

[0323] Users can check their health management plan, set reminders to carry out their plan in their daily lives, and manage their health according to the plan.

[0324] Step 13:

[0325] The server analyzes continuously collected health and emotion data in real time and updates the aging prediction model as needed, which is updated periodically.

[0326] Step 14:

[0327] The server adjusts the health management plan based on the user's health and emotional state and notifies medical professionals if action is needed.

[0328] Step 15:

[0329] Users can check their health status, emotional state, and suggested actions at any time through their device, and continue to manage their health appropriately.

[0330] Through these processing steps, the system can protect the user's privacy while integrating emotional data to provide more accurate and personalized health management and care support. For example, when a user is experiencing high stress, the emotion engine can detect their stress level and incorporate relaxation activities and counseling suggestions into their health management plan, improving their mental health and quality of life.

[0331] Example 2

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

[0333] While personalized health management and nursing care support are becoming increasingly important in an aging society, existing systems do not take into account the user's emotional state, resulting in insufficient comprehensive health management, including mental health. Furthermore, issues remain, such as protecting the privacy of collected data and lack of real-time performance. Furthermore, there is a lack of accurate recommendations incorporating the advice of medical experts.

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

[0335] In this invention, the server includes: means for analyzing collected health data and emotion data to generate an aging prediction model; means for generating a personalized health management proposal; means for collecting data from the wearable device and linked apps with the user's consent; means for collecting emotion data using an emotion analysis engine; means for revising the health management proposal based on advice from medical professionals; means for periodically analyzing data in real time and updating the health management plan; data management means with a privacy protection function to reduce the risk of data leaks; and means for notifying the end-user terminal of the health management plan. This enables comprehensive analysis of the user's health condition, provides personalized health management while protecting privacy, and enables highly accurate health management incorporating feedback from medical professionals.

[0336] "Health data" is information related to the user's physical condition, and includes indicators such as heart rate, number of steps, blood pressure, and weight.

[0337] "Emotional data" is information related to the user's emotional state, and is obtained through facial recognition and voice analysis.

[0338] A "wearable device" is an electronic device that is worn on the body and is used to collect health data such as heart rate and number of steps taken in real time.

[0339] A "linked app" is software that communicates with wearable devices and other data collection devices to collect and manage data.

[0340] An "aging prediction model" is a model that analyzes health data and emotional data to predict a user's aging risk and rate of progression.

[0341] "Health management suggestions" are suggestions such as personalized exercise plans, dietary advice, and preventative health checks that are generated based on the user's health and emotional state.

[0342] "Emotion analysis engine" is a general term for algorithms and software that use image processing and voice recognition to analyze a user's emotional state.

[0343] A "medical professional" is a person with specialized medical knowledge and qualifications, such as a doctor or nurse, who is responsible for providing opinions and feedback on the user's health management suggestions.

[0344] "Data management measures" refer to methods and technologies for safely and efficiently storing and managing collected data, including encryption and access control.

[0345] An "end-user terminal" is an electronic device such as a computer, smartphone, or tablet that is directly used by a user and displays notifications and information from the system.

[0346] The present invention relates to a system for collecting and analyzing a user's health data and emotional data, and providing personalized health management and nursing care support. Specific embodiments for carrying out the present invention will be described below.

[0347] User Registration

[0348] The user accesses the initial registration form and enters information such as name, age, gender, medical history, and lifestyle habits. The device then sends the collected registration information to the server, which stores this information in a database and creates an individual profile for the user.

[0349] Health and emotional data collection

[0350] Users use wearable devices and linked apps to collect daily health data, including heart rate, step count, and blood pressure. The devices collect this data in real time and periodically send it to a server. Furthermore, the devices are equipped with cameras and voice recognition functions, and use an emotion analysis engine to collect user emotion data. Specifically, the emotion data is analyzed using a face recognition and voice analysis engine that uses OpenCV.

[0351] Data analysis and aging prediction

[0352] The server stores the collected health and emotion data in a database and provides it to an AI for analysis. The AI ​​uses TensorFlow and PyTorch. The AI ​​on the server analyzes this data and generates an aging prediction model, which can predict the user's health risks and aging rate.

[0353] Generate personalized health care recommendations

[0354] The server generates personalized health management suggestions based on the analysis results. These suggestions include exercise plans that take the user's mental health into account, dietary advice, and preventative health check items. Using the generative AI model, suggestions can be generated by inputting specific prompts. For example, the prompt could be, "30-year-old male, software engineer. I've been feeling a bit stressed lately. Please predict health risks based on my heart rate and sleep data and suggest an appropriate health management plan."

[0355] Reflecting advice from medical professionals

[0356] The server sends the generated health management proposal to a medical professional and receives feedback. The medical professional reviews the proposal and makes any necessary corrections. The corrected feedback is sent back to the server, and the final health management plan is finalized.

[0357] User feedback and implementation

[0358] The server sends the finalized health management plan to the device, which notifies the user and displays the plan contents. The user can then check the health management plan, set reminders, and implement the plan in their daily lives.

[0359] Ongoing monitoring and feedback

[0360] The server analyzes continuously collected health and emotional data in real time and periodically updates the aging prediction model. This allows the health management plan to be adjusted appropriately according to the user's health and emotional state. Users can check their health and emotional state, as well as suggested actions, at any time via their device to continue appropriate health management.

[0361] For example, if the emotion engine detects high stress in a user, the server can add health management suggestions such as reducing exercise and practicing relaxing breathing exercises to the user's list of health management suggestions. It also recommends a weekly counseling session with a medical professional, taking an approach to improving the user's mental health. This system can improve the user's overall health and happiness.

[0362] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0363] Step 1:

[0364] The user accesses the initial registration form and enters information such as name, age, sex, medical history, and lifestyle habits.

[0365] Input: User's personal information (name, age, gender, medical history, lifestyle, etc.)

[0366] Output: User registration information is saved on the device

[0367] Specific behavior: The user enters information into the form displayed on the device and clicks the "Register" button.

[0368] Step 2:

[0369] The terminal transmits the user's registration information to the server.

[0370] Input: User registration information (data stored in the device)

[0371] Output: User registration information is sent to the server and stored in the database

[0372] What happens: The device sends registration information to the server using an HTTP POST request.

[0373] Step 3:

[0374] The server stores the registration information in a database.

[0375] Input: User registration information sent to the server

[0376] Output: User registration information stored in the database

[0377] Specific operation: The server uses a database operation API (e.g. MySQL) to insert information into the database.

[0378] Step 4:

[0379] The server collects health data from the wearable device and the associated app.

[0380] Input: Real-time health data (heart rate, steps, blood pressure, etc.) provided by wearable devices and linked apps

[0381] Output: Health data sent to the server

[0382] Specific operation: The device collects data via Bluetooth or API and sends it to the server at regular intervals.

[0383] Step 5:

[0384] The server uses a sentiment analysis engine to collect sentiment data.

[0385] Input: Data (audio, image) from the device's camera and microphone

[0386] Output: Emotion data (happiness, sadness, anger, etc.) sent to the server

[0387] Specific operation: The device performs face recognition and voice analysis in real time using OpenCV and a voice recognition engine, and sends the results to the server.

[0388] Step 6:

[0389] The server provides health and emotional data to the analytical AI.

[0390] Input: Health and emotion data stored on the server

[0391] Output: Aging prediction results and health risk assessment generated by the AI ​​model

[0392] Specific operation: The server inputs data into TensorFlow or PyTorch AI models using Python scripts and outputs the analysis results.

[0393] Step 7:

[0394] A server generates personalized health care recommendations.

[0395] Input: Analysis results of the AI ​​model

[0396] Output: Health management suggestions for each user (exercise plan, dietary advice, etc.)

[0397] Specific operation: The server inputs a prompt to the generated AI model, such as "30-year-old male, software engineer by occupation. Recently, stress has been increasing. Please predict health risks based on heart rate and sleep data and propose an appropriate health management plan." and receives the proposal as output.

[0398] Step 8:

[0399] The server sends the generated health care recommendations to the medical professionals and receives feedback.

[0400] Input: Health management suggestions from a generative AI model

[0401] Output: Modified or approved health care proposal

[0402] What it does: The server sends the proposal to medical professionals via email or a dedicated portal and collects their feedback.

[0403] Step 9:

[0404] The server finalizes the health management plan and sends it to the terminal.

[0405] Input: Healthcare recommendations incorporating feedback from medical professionals

[0406] Output: The final health plan displayed on the user's device.

[0407] Specific operation: The server sends the confirmed plan in JSON format to the device and notifies it.

[0408] Step 10:

[0409] The user checks their health plan and sets reminders.

[0410] Input: Health management plan received on the device

[0411] Output: Set reminders (exercise, meals, counseling sessions, etc.)

[0412] Specific behavior: The user checks the plan details on the device and uses the app's reminder setting function.

[0413] Step 11:

[0414] The server continuously analyzes the data in real time and updates the health management plan accordingly.

[0415] Input: Regularly collected health and emotional data

[0416] Output: Updated health plan

[0417] How it works: The server periodically re-runs the AI ​​model and regenerates a health management plan based on the analysis results.

[0418] (Application example 2)

[0419] 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."

[0420] Conventional health management systems collect and analyze users' health data, but they do not adequately collect real-time emotional data or respond to emergencies based on that data. Furthermore, they lack the functionality to automatically notify users appropriately in the event of an emergency, making it difficult to ensure user safety.

[0421] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing collected health data to generate an aging prediction model, means for generating personalized health management suggestions, means for revising the health management suggestions based on advice from medical professionals, means for automatically notifying in the event of an emergency, means for monitoring the health status in real time based on emotional data, and means for sending emergency alerts to designated contacts. This makes it possible to comprehensively manage the user's health status and emotional state and to respond quickly in the event of an emergency.

[0422] The "means of analyzing collected health data to generate an aging prediction model" is a function that analyzes health data such as heart rate and blood pressure collected from users and creates a model that predicts the progression of aging and health risks based on this data.

[0423] The "means for generating personalized health management suggestions" is a function that takes into consideration each user's health data and emotional data and individually suggests optimal exercise plans, dietary advice, and the like.

[0424] The "means for correcting the health care proposal with the advice of a medical professional" is a function for having a medical professional review the generated health care proposal and correcting it as necessary.

[0425] "Means for automatically notifying in an emergency" is a function for sending an emergency notification when an abnormality is detected in the user's health or emotional state.

[0426] The "means for monitoring health status in real time based on emotional data" is a function that continuously collects emotional data from the user and monitors the user's mental health status in real time based on this data.

[0427] "Means for sending emergency alerts to designated contacts" refers to a feature that automatically sends alerts to pre-defined contacts and emergency services in the event of a user emergency.

[0428] "Data management means with privacy protection functions to reduce the risk of data leakage" refers to security functions that safely manage user data and prevent it from being leaked to the outside.

[0429] The "means for periodically updating the aging prediction model" is a function for periodically maintaining the aging prediction model in the latest state based on newly collected data from users.

[0430] "Means for managing the emergency alert sending log" is a function that saves the history of emergency alerts sent and manages them so that they can be checked later.

[0431] The "means for continuously collecting and analyzing health data and emotional data" is a function for continuously collecting a user's health data and emotional data and analyzing them in real time.

[0432] "Means of collaborating with medical institutions and nursing care facilities to provide highly reliable health management and nursing care support" is a function that shares information with medical institutions and nursing care facilities and provides users with highly accurate health management and nursing care support.

[0433] The present invention relates to a system that collects and analyzes health data and emotional data of a user and provides personalized health management and care support. The present invention can be embodied in the following forms.

[0434] User registration and collection of health and emotional data

[0435] 1. When a user uses the system for the first time, they access the initial registration form displayed on the terminal and enter information such as their name, age, gender, medical history, and lifestyle habits.

[0436] 2. The terminal sends the entered registration information to the server, which stores it in a database.

[0437] 3. If the user agrees, the device will collect real-time health data from wearable devices and connected apps, and simultaneously use the emotion engine to collect the user's emotional data.

[0438] 4. The device periodically sends health and emotion data to the server, which stores this data in a database.

[0439] Analysis of health and emotional data and aging prediction

[0440] 1. The server provides the health and emotional data stored in the database to the AI ​​for analysis.

[0441] 2. The AI ​​on the server analyzes the health and emotional data and generates an aging prediction model, which predicts the user's health risks and aging rate.

[0442] 3. The server generates personalized health recommendations that take emotional data into account, including exercise plans, dietary advice, and preventative health checks that take into account the user's mental health.

[0443] Automatic emergency notification function

[0444] 1. The server monitors the user's collected data in real time and sends an emergency notification if an abnormality is detected. For example, if an abnormally high heart rate is detected at the same time as a state of severe stress, an emergency notification will be sent.

[0445] 2. Emergency Notifications automatically send alerts to designated contacts (e.g., family members or emergency services).

[0446] Reflecting advice from medical professionals

[0447] 1. The server sends the generated health management proposal to the medical professional and receives feedback. The medical professional reviews the proposal and makes corrections if necessary.

[0448] 2. The corrected feedback is sent back to the server, which then finalizes the health management plan.

[0449] User feedback and implementation

[0450] 1. The server sends the finalized health management plan to the terminal, which notifies the user and displays the plan details.

[0451] 2. The user checks the health management plan, sets reminders to implement it in daily life, and manages their health according to the plan.

[0452] Ongoing monitoring and feedback

[0453] 1. The server analyzes continuously collected health and emotion data in real time and periodically updates the aging prediction model.

[0454] 2. The server adjusts the health management plan accordingly based on the user's health and emotional state, and notifies medical professionals as needed.

[0455] 3. Users can check their health status, emotional state, and suggested actions at any time through their device, and continue to take appropriate health management measures.

[0456] Program processing explanation

[0457] Health data collection: The device collects data such as heart rate and blood pressure from wearable devices and sends it to a server.

[0458] Emotion data collection: The device uses an emotion engine to extract emotion data from the user's speech and facial expressions and transmits it to the server.

[0459] Data analysis: The AI ​​in the server analyzes the collected health and emotional data to generate an aging prediction model and personalized health management suggestions.

[0460] Emergency notification: Based on the analysis of health and emotional data, if an abnormality is detected, an emergency alert will be automatically sent to designated contacts.

[0461] Examples of concrete examples and prompts

[0462] Example: If a user experiences a spike in heart rate and elevated stress levels after intense exercise, the system can automatically send out an emergency alert and notify designated contacts.

[0463] Example prompt: "How can I design a security support app that uses health and emotion data to send automatic alerts in emergencies?"

[0464] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0465] Step 1:

[0466] When a user uses the system for the first time, they access the initial registration form displayed on their terminal and enter information such as their name, age, gender, medical history, and lifestyle habits. The terminal sends this information to the server, which then stores it in a database. The input is personal information from the user, and the output is registration in the server's database.

[0467] Step 2:

[0468] If the user agrees to the collection of health and emotional data, the device collects real-time health data from the wearable device and linked apps. At the same time, the device uses an emotion engine to collect emotional data from the user's speech and facial expressions. The health and emotional data are periodically sent to a server, which stores them in a database. The input is the health and emotional data obtained from the wearable device and emotion engine, and the output is stored in the server's database.

[0469] Step 3:

[0470] The server provides the health and emotion data stored in the database to the AI ​​model for analysis. The AI ​​model analyzes these data and generates an aging prediction model. It then creates personalized health care recommendations based on the analysis results. The inputs are health and emotion data, and the outputs are the aging prediction model and health care recommendations.

[0471] Step 4:

[0472] The server sends the generated healthcare proposal to the healthcare professional and receives feedback. The healthcare professional reviews the proposal and makes any necessary modifications. The modified feedback is sent back to the server, which then finalizes the healthcare plan. The input is the generated healthcare proposal and the output is the modified healthcare plan.

[0473] Step 5:

[0474] The server sends the confirmed health management plan to the terminal. The terminal notifies the user and displays the plan contents. The user confirms the health management plan and sets reminders to implement it in daily life. The input is the confirmed health management plan, and the output is the user's confirmation and reminder settings.

[0475] Step 6:

[0476] The server monitors continuously collected health and emotion data in real time, periodically updates the aging prediction model, and, if necessary, adjusts the health management plan accordingly and notifies the user. The input is continuously collected health and emotion data, and the output is an updated aging prediction model and health management plan.

[0477] Step 7:

[0478] The server sends an emergency notification if an abnormality in the user's health or emotional state is detected. The emergency alert is automatically sent to designated contacts (e.g., family members or emergency services). The input is abnormal data collected in real time, and the output is the issuance of an emergency alert.

[0479] 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.

[0480] 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.

[0481] 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.

[0482] [Second embodiment]

[0483] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0484] 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.

[0485] 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).

[0486] 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.

[0487] 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.

[0488] 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).

[0489] 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.

[0490] 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.

[0491] 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.

[0492] 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.

[0493] 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.

[0494] 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."

[0495] This invention relates to a system that collects and analyzes a user's health data and provides personalized health management and care support. Specifically, the invention is embodied in the following manner.

[0496] User registration and health data collection

[0497] 1. When a user uses this system for the first time, they access the initial registration form that appears on the terminal.

[0498] 2. The device prompts the user to enter information such as name, age, gender, medical history, and lifestyle habits.

[0499] 3. After entering and submitting the registration information, the device sends the information to the server, which stores it in a database.

[0500] Real-time health data collection

[0501] 1. If the user consents, the device will collect real-time health data from wearable devices and connected apps.

[0502] 2. The collected health data covers a wide range of topics, including the user's vital signs (heart rate, blood pressure, etc.), number of steps taken, and sleep patterns.

[0503] 3. The device sends this data to the server at regular intervals, and the server stores it in a database.

[0504] Health data analysis and aging prediction

[0505] 1. The server uses the health data stored in the database to perform analysis using AI technology.

[0506] 2. The server generates an aging prediction model for the user based on past health data and existing statistical data.

[0507] 3. The generated aging prediction model predicts the user's future health risks and aging rate, and privacy protection features reduce the risk of information leakage.

[0508] Generate personalized health care recommendations

[0509] 1. The server generates health management proposals tailored to each individual user based on the aging prediction model.

[0510] 2. The suggestions include exercise plans, dietary advice, and recommendations for regular health checks tailored to the user's health condition.

[0511] 3. The server sends the generated suggestions to medical professionals for feedback.

[0512] Reflecting advice from medical professionals

[0513] 1. The medical professional reviews the submitted health care proposal and makes any necessary adjustments.

[0514] 2. The corrected feedback is sent back to the server, which then finalizes the health management plan.

[0515] User feedback and implementation

[0516] 1. The server sends the confirmed health management plan to the terminal.

[0517] 2. The device notifies the user and displays the plan details.

[0518] 3. Users can check their health management plan and set reminders to follow it in their daily lives.

[0519] Ongoing monitoring and feedback

[0520] 1. The server analyzes continuously collected health data in real time and periodically updates the aging prediction model.

[0521] 2. The server adjusts the health management plan accordingly based on the user's health condition and notifies medical professionals as necessary.

[0522] 3. Users can check their health status and suggested actions at any time from their device and manage their health.

[0523] The system is designed to provide personalized health management and care support using advanced technology while protecting user privacy, thereby contributing to solving health management challenges in an aging society.

[0524] The processing flow will be explained below.

[0525] Step 1:

[0526] The user accesses the initial registration form and enters information such as name, age, gender, medical history, and lifestyle habits.

[0527] Step 2:

[0528] The device sends the entered registration information to a server, where the data is encrypted for enhanced privacy protection.

[0529] Step 3:

[0530] The server stores the received registration information in a database and creates an initial user profile.

[0531] Step 4:

[0532] If the user consents, the device will collect real-time health data (such as heart rate, steps, and sleep patterns) from wearable devices and connected apps.

[0533] Step 5:

[0534] The device periodically transmits the collected health data to a server, which also encrypts the data to ensure security.

[0535] Step 6:

[0536] The server stores the received health data in a database and simultaneously provides the data to an AI for data analysis.

[0537] Step 7:

[0538] The AI ​​on the server analyzes the health data and generates an aging prediction model, which is used to predict the user's health risks and aging rate.

[0539] Step 8:

[0540] Based on the generated aging prediction model, the server generates personalized health management recommendations, including exercise plans, dietary advice, and preventative health checks.

[0541] Step 9:

[0542] The server sends the health management suggestions to medical professionals and receives feedback, who review the suggestions and make any necessary adjustments.

[0543] Step 10:

[0544] The server determines the final health management plan based on the feedback from medical professionals, and the finalized plan is stored in a database.

[0545] Step 11:

[0546] The server sends the finalized health management plan to the terminal, which then notifies the user and displays the plan contents.

[0547] Step 12:

[0548] Users can check their health management plan, set reminders to implement it in their daily lives, and manage their health according to the plan.

[0549] Step 13:

[0550] The server analyzes the continuously collected health data in real time and updates the aging prediction model as needed, which is done periodically.

[0551] Step 14:

[0552] The server adjusts the health plan based on the user's health condition and notifies medical professionals if action is required.

[0553] Step 15:

[0554] Users can check their health status and suggested actions at any time through their device and continue to manage their health appropriately.

[0555] Through these processing steps, the system provides personalized health management and care support while protecting the user's privacy.

[0556] Example 1

[0557] 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."

[0558] In an aging society, there is a growing demand for personalized health management and nursing care support. However, existing systems face challenges in providing accurate, real-time health management while protecting user privacy. Furthermore, there is a lack of mechanisms for effectively analyzing collected health data and obtaining efficient feedback from medical professionals. This makes it difficult to properly monitor users' health status and provide appropriate health management.

[0559] 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.

[0560] In this invention, the server includes means for analyzing collected health data and generating an aging prediction model, means for generating personalized health care proposals, means for revising the health care proposals based on advice from medical professionals, means for collecting user registration information and storing it in a database, means for collecting health data from the wearable device in real time and transmitting it to the server, means for periodically analyzing the health data and updating the aging prediction model, and means for notifying the user of an optimized health care plan and setting reminders. This makes it possible to provide highly accurate health care in real time and receive appropriate feedback quickly while protecting the user's privacy.

[0561] "Means for analyzing collected health data and generating an aging prediction model" refers to the function of using an algorithm to create a model that predicts the user's aging progression based on the user's health data collected from wearable devices and linked apps.

[0562] "Means for generating personalized health management proposals" refers to a function that proposes health management plans, such as individually optimized exercise plans and dietary advice, based on each user's health data and aging prediction models.

[0563] "Means for amending health care proposals with the advice of a medical professional" refers to the process of having a medical professional review the generated health care proposals and making amendments as necessary.

[0564] "Means for collecting user registration information and storing it in a database" refers to the function of safely storing personal information entered by users when they first start using the system in a database.

[0565] "Means for collecting health data from a wearable device in real time and transmitting it to a server" refers to a mechanism for collecting a user's health data from a wearable device in real time and transmitting it to a server.

[0566] "Means for periodically analyzing health data and updating the aging prediction model" refers to a function for periodically analyzing continuously collected health data and keeping the aging prediction model up to date.

[0567] "Means for notifying the user of the optimized health management plan and setting reminders" refers to a function that notifies the user of the generated health management plan and supports the user in setting reminders to put the plan into action.

[0568] This invention relates to a system that collects and analyzes a user's health data and provides personalized health management and care support. Specifically, the invention is embodied in the following manner.

[0569] User registration and health data collection

[0570] When a user first registers, they access a form that appears on their device (e.g., a smartphone or tablet). They enter information such as their name, age, gender, medical history, and lifestyle habits into the device and submit it. The device then sends this information to the server, which then stores it in a database.

[0571] Real-time health data collection

[0572] Once a user consents to real-time health data collection, the device will collect health data such as heart rate, blood pressure, number of steps, and sleep patterns from wearable devices (e.g., Fitbit or Apple Watch) and connected apps. The device will then send this data to a server at regular intervals, where it will be stored in a database.

[0573] Health data analysis and aging prediction

[0574] The server uses AI technology to analyze the health data stored in the database. Based on past data and statistical data, the server generates a predictive model for the user's aging. This model makes it possible to predict future health risks and the rate of aging.

[0575] Generate personalized health care recommendations

[0576] Based on the aging prediction model, the server generates personalized health management suggestions for the user, including exercise plans, dietary advice, and recommendations for regular health checkups. The server then sends the suggestions to medical professionals for feedback.

[0577] Reflecting advice from medical professionals

[0578] The medical professional reviews the submitted health care proposal and makes any necessary modifications, and the modified feedback is sent back to the server, which then finalizes the health care plan.

[0579] User feedback and implementation

[0580] The server sends the finalized health management plan to the device, which notifies the user and displays the plan contents. The user can then review the health management plan and set reminders to implement it in their daily lives.

[0581] Ongoing monitoring and feedback

[0582] The server analyzes continuously collected health data in real time and periodically updates the aging prediction model. The server adjusts the health management plan according to the user's health condition and notifies medical professionals as necessary. Users can check their health condition and suggested actions on their device and manage their health.

[0583] The system uses advanced technology to provide personalized health and care support while protecting user privacy. Below are some examples and prompts:

[0584] Specific examples

[0585] User: 60-year-old male with a medical history of hypertension and diabetes.

[0586] Devices: Smartphone and wearable device (Fitbit)

[0587] Health Data: Heart rate, steps, and sleep data collected from wearable devices.

[0588] Prompt Sentence Examples

[0589] A 60-year-old male with a medical history of hypertension and diabetes. Generate personalized health management suggestions based on this user's health data, taking into account data collected from wearable devices (heart rate, steps, sleep data, etc.).

[0590] Thus, the present invention is a system that provides advanced personalized health management while protecting the user's privacy.

[0591] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0592] Step 1:

[0593] When a user uses the system for the first time, he or she accesses the initial registration form displayed on the terminal.

[0594] Input: Information such as the user's name, age, gender, medical history, and lifestyle habits.

[0595] Output: The user information entered into the form.

[0596] What happens: A user opens a web browser or mobile app, fills out a form, and clicks submit.

[0597] Step 2:

[0598] The terminal prompts the user to enter information such as name, age, gender, medical history, and lifestyle habits.

[0599] Input: Information entered by the user.

[0600] Output: A request to send the entered information to the server.

[0601] Specific operation: The device displays an input form and sends the information entered by the user to the server as an HTTPS request.

[0602] Step 3:

[0603] The server stores the received registration information in a database.

[0604] Input: User information sent from the device.

[0605] Output: User profile stored in the database.

[0606] What happens: The server receives the request and stores the information in a database.

[0607] Step 4:

[0608] User consents to real-time health data collection.

[0609] Input: User consent.

[0610] Output: The consent is recorded.

[0611] Specific operation: The consent form is displayed on the device and the user presses the "Agree" button.

[0612] Step 5:

[0613] The device collects health data such as heart rate, blood pressure, steps taken, and sleep patterns from wearable devices and connected apps.

[0614] Input: Real-time data obtained from wearable devices.

[0615] Output: Collected health data.

[0616] Specific behavior: Obtain data from the device via Bluetooth or other communication means.

[0617] Step 6:

[0618] The terminal transmits this data to the server at regular intervals.

[0619] Input: Collected health data.

[0620] Output: The data sent to the server.

[0621] Specific operation: The device collects data using an interval timer and sends an HTTP request to the server.

[0622] Step 7:

[0623] The server uses AI technology to analyze the health data stored in the database.

[0624] Input: Health data in a database.

[0625] Output: Analysis results and aging prediction model.

[0626] What it does: The server analyzes the data using machine learning algorithms.

[0627] Step 8:

[0628] The server generates an aging prediction model based on past data and statistical data.

[0629] Input: Health data, historical data, statistical data.

[0630] Output: Aging prediction model.

[0631] Specific operation: The server trains the machine learning model and generates a new predictive model.

[0632] Step 9:

[0633] The server generates health management proposals tailored to each individual user based on the generated aging prediction model.

[0634] Input: Aging prediction model.

[0635] Output: personalized health care recommendations.

[0636] Specific operation: The server analyzes the model output and generates a health management plan.

[0637] Step 10:

[0638] The server sends the suggestions to medical professionals and asks for their feedback.

[0639] Enter: Healthcare Proposal.

[0640] Output: Healthcare professional feedback.

[0641] Specific operation: The server sends suggestions via email or a dedicated app and receives feedback.

[0642] Step 11:

[0643] A medical professional will review the submitted health care proposal and make any necessary adjustments.

[0644] Enter: Healthcare Proposal.

[0645] Output: Corrected feedback.

[0646] How it works: Medical professionals review and correct suggestions through a dedicated app or web portal.

[0647] Step 12:

[0648] The server finalizes the health management plan and transmits it to the terminal.

[0649] Input: Revised health care proposal.

[0650] Output: Final health care plan.

[0651] Specific operation: The server generates the final plan and sends it to the terminal.

[0652] Step 13:

[0653] The terminal notifies the user and displays the plan details.

[0654] Enter: the final health care plan.

[0655] Output: Inform and display the plan to the user.

[0656] What it does: The device generates a push notification and displays the plan details when the user opens the app.

[0657] Step 14:

[0658] Users can review their health plan and set reminders.

[0659] Enter: the final health care plan.

[0660] Output: The set reminder.

[0661] What happens: The user sets a reminder in the app, and the device sets a notification or alarm.

[0662] Step 15:

[0663] The server continuously analyzes collected health data in real time and updates the aging prediction model.

[0664] Input: New health data.

[0665] Output: Updated aging prediction model.

[0666] What happens: The server analyzes the new data and updates the model parameters.

[0667] Step 16:

[0668] The server adjusts the health care plan according to the user's health status and notifies medical professionals as needed.

[0669] Input: Updated aging prediction model and health data.

[0670] Output: Coordinated health care plan.

[0671] What it does: The server detects changes in health status, adjusts plans, and notifies medical professionals.

[0672] Step 17:

[0673] Users can check their health status and suggested actions at any time from their device and manage their health.

[0674] Input: Health care plan and latest health data.

[0675] Output: User's plan execution and health management.

[0676] Specific behavior: User views the in-app dashboard and takes the required action.

[0677] (Application example 1)

[0678] 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."

[0679] Providing personalized health management and nursing care support is an important challenge in an aging society. However, conventional systems have difficulty collecting and analyzing real-time health data and quickly providing personalized recommendations that reflect the advice of medical professionals. Providing immediate feedback based on the user's health status and continuously updating data while protecting privacy are also challenges. Furthermore, the integration of health management recommendations and feedback in physical stores is insufficient.

[0680] 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.

[0681] In this invention, the server includes a means for analyzing collected health data to generate an aging prediction model, a means for generating personalized health management suggestions, and a means for revising the health management suggestions based on advice from medical professionals. This also includes a means for displaying personalized health management suggestions to customers using health data collected on a dedicated in-store terminal, a means for providing health management suggestions based on health checkup results and feedback from the physical store, a means for displaying the health management suggestions on an in-store display or on the customer's smartphone app, a means for medical professionals to review and revise the generated suggestions, and a means for analyzing continuously collected health data over time and updating the suggestions. This enables the rapid provision of personalized health management and care support in real time, and real-time feedback and continuous monitoring according to the user's health condition. Privacy protection features also reduce the risk of data leakage.

[0682] "Collected health data" refers to information such as heart rate, blood pressure, number of steps, and sleep patterns obtained from users' wearable devices and smartphone apps.

[0683] An "aging prediction model" is a model for predicting a user's future health risks and aging rate based on past health data and existing statistical data.

[0684] "Personalized health management suggestions" refer to suggestions that include exercise plans, dietary advice, and recommendations for regular health checks that are generated based on the user's health condition.

[0685] The "means for modifying health care proposals with the advice of a medical professional" refers to a method by which a medical professional reviews and modifies the generated health care proposals and provides optimized proposals.

[0686] An "in-store dedicated terminal" is an electronic device installed in a physical store for collecting and displaying users' health data.

[0687] "Providing health care suggestions" refers to informing the user of the generated health care suggestions and recommending health care activities based on the suggestions.

[0688] A "store display" is a device installed in a physical store that displays health management suggestions as visual information to customers.

[0689] A "smartphone app" is application software that is installed on a customer's smartphone and can display health management suggestions and obtain feedback.

[0690] "Real-time health data" refers to instantaneous data such as a user's current heart rate, blood pressure, number of steps, and sleep patterns.

[0691] "Data leakage risk" refers to the risk of a user's health data being illegally accessed or leaked to a third party.

[0692] "Privacy protection features" refer to technical measures to safely manage users' personal information and health data and prevent unauthorized access or data leakage.

[0693] This invention is a system that collects and analyzes a user's health data and provides personalized health management and care support. Specifically, the invention can be implemented in the following forms.

[0694] User registration and health data collection

[0695] The server provides a means for first-time users to access the initial registration form displayed on a dedicated terminal in the store. The user enters information such as name, age, gender, medical history, and lifestyle habits. The terminal sends this information to the server, where it is stored in a database on the server.

[0696] Real-time health data collection

[0697] If the user agrees, the device will collect real-time health data (heart rate, blood pressure, steps taken, sleep patterns, etc.) from wearable devices and connected apps. This data will be periodically sent to a server, which will store it in a database.

[0698] Health data analysis and aging prediction

[0699] The server uses the stored health data to perform analysis using AI technology, which generates a user's aging prediction model based on past health data and existing statistical data. This model predicts the user's future health risks and aging rate.

[0700] Generate personalized health care recommendations

[0701] Based on the aging prediction model, the server generates personalized health management suggestions for the user, including exercise plans, dietary advice, and recommendations for regular health checks.

[0702] Reflecting advice from medical professionals

[0703] The server sends the generated health care suggestions to medical experts for their feedback, and the suggestions modified by the medical experts ensure further accuracy and reliability.

[0704] Providing health management suggestions in-store

[0705] The improved health management suggestions are sent from the server to dedicated terminals or in-store displays in the store and are displayed to customers. The suggestions are also linked to the customer's smartphone app, allowing the user to instantly check and implement the suggestions.

[0706] Ongoing monitoring and feedback

[0707] The server continuously analyzes the health data collected in real time and periodically updates the aging prediction model, allowing the system to adjust the health management plan accordingly based on the user's health condition and notify medical professionals as needed, enabling health management based on the latest information.

[0708] Examples of prompt statements

[0709] An example of a specific prompt sentence is, "Generate health management suggestions for a 40-year-old male. Heart rate data for the past week: average 75, blood pressure 120 / 80, average number of steps 10,000, average sleep time 7 hours."

[0710] Hardware and Software Used

[0711] The system uses tablets and in-store displays as dedicated terminals, and connects to wearable devices and smartphone apps. A database management system and software with the functions necessary for AI analysis run on the server side. Using this system, customers can receive health management suggestions in real time and use them to implement them.

[0712] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0713] Step 1:

[0714] The user accesses the initial registration form using a dedicated terminal at the store. The user enters information such as name, age, gender, medical history, and lifestyle habits. The entered information is sent from the terminal to the server, which saves it in a database. The entered data is stored in the user information database.

[0715] Step 2:

[0716] If the user agrees, the device will collect real-time health data (e.g., heart rate, blood pressure, number of steps, sleep patterns) from wearable devices and connected apps. This data will be sent to a server at regular intervals, and the server will store it in a database. The input data is the real-time health data, and the output data is an updated health database.

[0717] Step 3:

[0718] The server uses AI technology to perform analysis based on the health data stored in the database. This analysis generates an aging prediction model for the user, taking into account past health data and existing statistical data. This model predicts the user's future health risks and aging rate. The input data is the collected health data, and the output data is the aging prediction model.

[0719] Step 4:

[0720] The server generates personalized health management suggestions based on the generated aging prediction model. These suggestions include exercise plans, dietary advice, and recommendations for regular health checkups. The input data is the aging prediction model, and the output data is the personalized health management suggestions.

[0721] Step 5:

[0722] The generated health care proposal is sent from the server to a medical expert, who provides advice and makes corrections. The medical expert reviews the proposal and makes corrections as necessary. The corrected proposal is sent back to the server and saved in the database again. The input data is the health care proposal, and the output data is the corrected health care proposal.

[0723] Step 6:

[0724] The final health management proposal is sent from the server to a dedicated terminal or in-store display in the store and displayed to the customer. The proposal is also linked to the customer's smartphone app. The user confirms it and carries out health management. The input data is the revised health management proposal, and the output data is the displayed health management proposal.

[0725] Step 7:

[0726] The server continuously analyzes the health data collected in real time and periodically updates the aging prediction model. This allows the system to adjust the health management plan according to the user's health condition and notify medical professionals again as necessary. The input data is the latest health data, and the output data is the updated aging prediction model and health management suggestions.

[0727] Specific working example:

[0728] If the user is a 40-year-old man and his average heart rate data for the past week is 75, the server will use this information to generate health management suggestions such as "walking 30 minutes every day" and "eating a vegetable-based diet."

[0729] The server-generated suggestions were reviewed by medical experts, who then revised them to "reduce walking time to 20 minutes."

[0730] The revised proposals are displayed on the dedicated device and smartphone app, and the user can confirm and implement them.

[0731] Examples of prompts:

[0732] "Generate health management suggestions for a 40-year-old male. Heart rate data for the past week: average 75, blood pressure 120 / 80, average steps 10,000, average sleep time 7 hours."

[0733] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0734] This invention relates to a system that collects and analyzes a user's health data and emotional data to provide personalized health management and care support. Specifically, the invention is embodied in the following manner.

[0735] User registration and collection of health and emotional data

[0736] 1. When a user uses the system for the first time, they access the initial registration form displayed on the terminal and enter information such as their name, age, gender, medical history, and lifestyle habits.

[0737] 2. The terminal sends the entered registration information to the server, which stores it in a database.

[0738] 3. If the user agrees, the device will collect real-time health data from wearable devices and connected apps, and simultaneously use the emotion engine to collect the user's emotional data.

[0739] 4. The device periodically sends health and emotion data to the server, which stores this data in a database.

[0740] Analysis of health and emotional data and aging prediction

[0741] 1. The server provides the health and emotional data stored in the database to the AI ​​for analysis.

[0742] 2. The AI ​​on the server analyzes the health and emotional data and generates an aging prediction model, which predicts the user's health risks and aging rate.

[0743] 3. The server generates personalized health recommendations that take emotional data into account, including exercise plans, dietary advice, and preventative health checks that take into account the user's mental health.

[0744] Reflecting advice from medical professionals

[0745] 1. The server sends the generated health management proposal to the medical professional and receives feedback. The medical professional reviews the proposal and makes corrections if necessary.

[0746] 2. The corrected feedback is sent back to the server, which then finalizes the health management plan.

[0747] User feedback and implementation

[0748] 1. The server sends the finalized health management plan to the terminal, which notifies the user and displays the plan details.

[0749] 2. The user checks the health management plan, sets reminders to implement it in daily life, and manages their health according to the plan.

[0750] Ongoing monitoring and feedback

[0751] 1. The server analyzes continuously collected health and emotion data in real time and periodically updates the aging prediction model.

[0752] 2. The server adjusts the health management plan accordingly based on the user's health and emotional state, and notifies medical professionals as needed.

[0753] 3. Users can check their health status, emotional state, and suggested actions at any time through their device, and continue to take appropriate health management measures.

[0754] For example, if a user records their daily emotional state and the emotion engine detects that they are experiencing increased stress, the server will add health management suggestions such as reducing exercise and practicing relaxing breathing exercises. It will also take an approach to improving mental health by recommending a weekly counseling session with a medical professional. In this way, the system provides comprehensive support to improve the user's overall health and happiness.

[0755] By integrating emotional data while protecting user privacy, this system can provide more accurate and personalized health management and nursing care support, thereby improving users' quality of life and contributing to solving health management issues in an aging society.

[0756] The processing flow will be explained below.

[0757] Step 1:

[0758] The user accesses the initial registration form and enters information such as name, age, gender, medical history, and lifestyle habits.

[0759] Step 2:

[0760] The device sends the entered registration information to a server, where the data is encrypted for enhanced privacy protection.

[0761] Step 3:

[0762] The server stores the received registration information in a database and creates an initial user profile.

[0763] Step 4:

[0764] If the user agrees, the device will collect real-time health data (heart rate, steps, sleep patterns, etc.) from the wearable device and connected apps. At the same time, the emotion engine will collect emotional data from the user's facial expressions and tone of voice using the device's built-in camera and microphone.

[0765] Step 5:

[0766] The device periodically transmits the collected health and emotional data to a server, which also encrypts the data to ensure security.

[0767] Step 6:

[0768] The server stores the received health and emotional data in a database and simultaneously provides the data to an AI for data analysis.

[0769] Step 7:

[0770] The server's AI analyzes the health and emotional data to generate an aging prediction model, which predicts the user's health risks and aging rate while also incorporating the emotional data.

[0771] Step 8:

[0772] The server then uses the generated aging prediction model to create personalized health management recommendations, including exercise plans, dietary advice, and preventative health checks that take into account the user's health and emotional state.

[0773] Step 9:

[0774] The server sends the health management suggestions to medical professionals and receives feedback, who review the suggestions and make any necessary adjustments.

[0775] Step 10:

[0776] The server determines the final health care plan based on feedback from medical professionals, which is then stored in a database.

[0777] Step 11:

[0778] The server sends the finalized health management plan to the terminal, which notifies the user and displays the plan contents.

[0779] Step 12:

[0780] Users can check their health management plan, set reminders to carry out their plan in their daily lives, and manage their health according to the plan.

[0781] Step 13:

[0782] The server analyzes continuously collected health and emotion data in real time and updates the aging prediction model as needed, which is updated periodically.

[0783] Step 14:

[0784] The server adjusts the health management plan based on the user's health and emotional state and notifies medical professionals if action is needed.

[0785] Step 15:

[0786] Users can check their health status, emotional state, and suggested actions at any time through their device, and continue to manage their health appropriately.

[0787] Through these processing steps, the system can protect the user's privacy while integrating emotional data to provide more accurate and personalized health management and care support. For example, when a user is experiencing high stress, the emotion engine can detect their stress level and incorporate relaxation activities and counseling suggestions into their health management plan, improving their mental health and quality of life.

[0788] Example 2

[0789] 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."

[0790] While personalized health management and nursing care support are becoming increasingly important in an aging society, existing systems do not take into account the user's emotional state, resulting in insufficient comprehensive health management, including mental health. Furthermore, issues remain, such as protecting the privacy of collected data and lack of real-time performance. Furthermore, there is a lack of accurate recommendations incorporating the advice of medical experts.

[0791] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0792] In this invention, the server includes: means for analyzing collected health data and emotion data to generate an aging prediction model; means for generating a personalized health management proposal; means for collecting data from the wearable device and linked apps with the user's consent; means for collecting emotion data using an emotion analysis engine; means for revising the health management proposal based on advice from medical professionals; means for periodically analyzing data in real time and updating the health management plan; data management means with a privacy protection function to reduce the risk of data leaks; and means for notifying the end-user terminal of the health management plan. This enables comprehensive analysis of the user's health condition, provides personalized health management while protecting privacy, and enables highly accurate health management incorporating feedback from medical professionals.

[0793] "Health data" is information related to the user's physical condition, and includes indicators such as heart rate, number of steps, blood pressure, and weight.

[0794] "Emotional data" is information related to the user's emotional state, and is obtained through facial recognition and voice analysis.

[0795] A "wearable device" is an electronic device that is worn on the body and is used to collect health data such as heart rate and number of steps taken in real time.

[0796] A "linked app" is software that communicates with wearable devices and other data collection devices to collect and manage data.

[0797] An "aging prediction model" is a model that analyzes health data and emotional data to predict a user's aging risk and rate of progression.

[0798] "Health management suggestions" are suggestions such as personalized exercise plans, dietary advice, and preventative health checks that are generated based on the user's health and emotional state.

[0799] "Emotion analysis engine" is a general term for algorithms and software that use image processing and voice recognition to analyze a user's emotional state.

[0800] A "medical professional" is a person with specialized medical knowledge and qualifications, such as a doctor or nurse, who is responsible for providing opinions and feedback on the user's health management suggestions.

[0801] "Data management measures" refer to methods and technologies for safely and efficiently storing and managing collected data, including encryption and access control.

[0802] An "end-user terminal" is an electronic device such as a computer, smartphone, or tablet that is directly used by a user and displays notifications and information from the system.

[0803] The present invention relates to a system for collecting and analyzing a user's health data and emotional data, and providing personalized health management and nursing care support. Specific embodiments for carrying out the present invention will be described below.

[0804] User Registration

[0805] The user accesses the initial registration form and enters information such as name, age, gender, medical history, and lifestyle habits. The device then sends the collected registration information to the server, which stores this information in a database and creates an individual profile for the user.

[0806] Health and emotional data collection

[0807] Users use wearable devices and linked apps to collect daily health data, including heart rate, step count, and blood pressure. The devices collect this data in real time and periodically send it to a server. Furthermore, the devices are equipped with cameras and voice recognition functions, and use an emotion analysis engine to collect user emotion data. Specifically, the emotion data is analyzed using a face recognition and voice analysis engine that uses OpenCV.

[0808] Data analysis and aging prediction

[0809] The server stores the collected health and emotion data in a database and provides it to an AI for analysis. The AI ​​uses TensorFlow and PyTorch. The AI ​​on the server analyzes this data and generates an aging prediction model, which can predict the user's health risks and aging rate.

[0810] Generate personalized health care recommendations

[0811] The server generates personalized health management suggestions based on the analysis results. These suggestions include exercise plans that take the user's mental health into account, dietary advice, and preventative health check items. Using the generative AI model, suggestions can be generated by inputting specific prompts. For example, the prompt could be, "30-year-old male, software engineer. I've been feeling a bit stressed lately. Please predict health risks based on my heart rate and sleep data and suggest an appropriate health management plan."

[0812] Reflecting advice from medical professionals

[0813] The server sends the generated health management proposal to a medical professional and receives feedback. The medical professional reviews the proposal and makes any necessary corrections. The corrected feedback is sent back to the server, and the final health management plan is finalized.

[0814] User feedback and implementation

[0815] The server sends the finalized health management plan to the device, which notifies the user and displays the plan contents. The user can then check the health management plan, set reminders, and implement the plan in their daily lives.

[0816] Ongoing monitoring and feedback

[0817] The server analyzes continuously collected health and emotional data in real time and periodically updates the aging prediction model. This allows the health management plan to be adjusted appropriately according to the user's health and emotional state. Users can check their health and emotional state, as well as suggested actions, at any time via their device to continue appropriate health management.

[0818] For example, if the emotion engine detects high stress in a user, the server can add health management suggestions such as reducing exercise and practicing relaxing breathing exercises to the user's list of health management suggestions. It also recommends a weekly counseling session with a medical professional, taking an approach to improving the user's mental health. This system can improve the user's overall health and happiness.

[0819] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0820] Step 1:

[0821] The user accesses the initial registration form and enters information such as name, age, sex, medical history, and lifestyle habits.

[0822] Input: User's personal information (name, age, gender, medical history, lifestyle, etc.)

[0823] Output: User registration information is saved on the device

[0824] Specific behavior: The user enters information into the form displayed on the device and clicks the "Register" button.

[0825] Step 2:

[0826] The terminal transmits the user's registration information to the server.

[0827] Input: User registration information (data stored in the device)

[0828] Output: User registration information is sent to the server and stored in the database

[0829] What happens: The device sends registration information to the server using an HTTP POST request.

[0830] Step 3:

[0831] The server stores the registration information in a database.

[0832] Input: User registration information sent to the server

[0833] Output: User registration information stored in the database

[0834] Specific operation: The server uses a database operation API (e.g. MySQL) to insert information into the database.

[0835] Step 4:

[0836] The server collects health data from the wearable device and the associated app.

[0837] Input: Real-time health data (heart rate, steps, blood pressure, etc.) provided by wearable devices and linked apps

[0838] Output: Health data sent to the server

[0839] Specific operation: The device collects data via Bluetooth or API and sends it to the server at regular intervals.

[0840] Step 5:

[0841] The server uses a sentiment analysis engine to collect sentiment data.

[0842] Input: Data (audio, image) from the device's camera and microphone

[0843] Output: Emotion data (happiness, sadness, anger, etc.) sent to the server

[0844] Specific operation: The device performs face recognition and voice analysis in real time using OpenCV and a voice recognition engine, and sends the results to the server.

[0845] Step 6:

[0846] The server provides health and emotional data to the analytical AI.

[0847] Input: Health and emotion data stored on the server

[0848] Output: Aging prediction results and health risk assessment generated by the AI ​​model

[0849] Specific operation: The server inputs data into TensorFlow or PyTorch AI models using Python scripts and outputs the analysis results.

[0850] Step 7:

[0851] A server generates personalized health care recommendations.

[0852] Input: Analysis results of the AI ​​model

[0853] Output: Health management suggestions for each user (exercise plan, dietary advice, etc.)

[0854] Specific operation: The server inputs a prompt to the generated AI model, such as "30-year-old male, software engineer by occupation. Recently, stress has been increasing. Please predict health risks based on heart rate and sleep data and propose an appropriate health management plan." and receives the proposal as output.

[0855] Step 8:

[0856] The server sends the generated health care recommendations to the medical professionals and receives feedback.

[0857] Input: Health management suggestions from a generative AI model

[0858] Output: Modified or approved health care proposal

[0859] What it does: The server sends the proposal to medical professionals via email or a dedicated portal and collects their feedback.

[0860] Step 9:

[0861] The server finalizes the health management plan and sends it to the terminal.

[0862] Input: Healthcare recommendations incorporating feedback from medical professionals

[0863] Output: The final health plan displayed on the user's device.

[0864] Specific operation: The server sends the confirmed plan in JSON format to the device and notifies it.

[0865] Step 10:

[0866] The user checks their health plan and sets reminders.

[0867] Input: Health management plan received on the device

[0868] Output: Set reminders (exercise, meals, counseling sessions, etc.)

[0869] Specific behavior: The user checks the plan details on the device and uses the app's reminder setting function.

[0870] Step 11:

[0871] The server continuously analyzes the data in real time and updates the health management plan accordingly.

[0872] Input: Regularly collected health and emotional data

[0873] Output: Updated health plan

[0874] How it works: The server periodically re-runs the AI ​​model and regenerates a health management plan based on the analysis results.

[0875] (Application example 2)

[0876] 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."

[0877] Conventional health management systems collect and analyze users' health data, but they do not adequately collect real-time emotional data or respond to emergencies based on that data. Furthermore, they lack the functionality to automatically notify users appropriately in the event of an emergency, making it difficult to ensure user safety.

[0878] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing collected health data to generate an aging prediction model, means for generating personalized health management suggestions, means for revising the health management suggestions based on advice from medical professionals, means for automatically notifying in the event of an emergency, means for monitoring the health status in real time based on emotional data, and means for sending emergency alerts to designated contacts. This makes it possible to comprehensively manage the user's health status and emotional state and to respond quickly in the event of an emergency.

[0879] The "means of analyzing collected health data to generate an aging prediction model" is a function that analyzes health data such as heart rate and blood pressure collected from users and creates a model that predicts the progression of aging and health risks based on this data.

[0880] The "means for generating personalized health management suggestions" is a function that takes into consideration each user's health data and emotional data and individually suggests optimal exercise plans, dietary advice, and the like.

[0881] The "means for correcting the health care proposal with the advice of a medical professional" is a function for having a medical professional review the generated health care proposal and correcting it as necessary.

[0882] "Means for automatically notifying in an emergency" is a function for sending an emergency notification when an abnormality is detected in the user's health or emotional state.

[0883] The "means for monitoring health status in real time based on emotional data" is a function that continuously collects emotional data from the user and monitors the user's mental health status in real time based on this data.

[0884] "Means for sending emergency alerts to designated contacts" refers to a feature that automatically sends alerts to pre-defined contacts and emergency services in the event of a user emergency.

[0885] "Data management means with privacy protection functions to reduce the risk of data leakage" refers to security functions that safely manage user data and prevent it from being leaked to the outside.

[0886] The "means for periodically updating the aging prediction model" is a function for periodically maintaining the aging prediction model in the latest state based on newly collected data from users.

[0887] "Means for managing the emergency alert sending log" is a function that saves the history of emergency alerts sent and manages them so that they can be checked later.

[0888] The "means for continuously collecting and analyzing health data and emotional data" is a function for continuously collecting a user's health data and emotional data and analyzing them in real time.

[0889] "Means of collaborating with medical institutions and nursing care facilities to provide highly reliable health management and nursing care support" is a function that shares information with medical institutions and nursing care facilities and provides users with highly accurate health management and nursing care support.

[0890] The present invention relates to a system that collects and analyzes health data and emotional data of a user and provides personalized health management and care support. The present invention can be embodied in the following forms.

[0891] User registration and collection of health and emotional data

[0892] 1. When a user uses the system for the first time, they access the initial registration form displayed on the terminal and enter information such as their name, age, gender, medical history, and lifestyle habits.

[0893] 2. The terminal sends the entered registration information to the server, which stores it in a database.

[0894] 3. If the user agrees, the device will collect real-time health data from wearable devices and connected apps, and simultaneously use the emotion engine to collect the user's emotional data.

[0895] 4. The device periodically sends health and emotion data to the server, which stores this data in a database.

[0896] Analysis of health and emotional data and aging prediction

[0897] 1. The server provides the health and emotional data stored in the database to the AI ​​for analysis.

[0898] 2. The AI ​​on the server analyzes the health and emotional data and generates an aging prediction model, which predicts the user's health risks and aging rate.

[0899] 3. The server generates personalized health recommendations that take emotional data into account, including exercise plans, dietary advice, and preventative health checks that take into account the user's mental health.

[0900] Automatic emergency notification function

[0901] 1. The server monitors the user's collected data in real time and sends an emergency notification if an abnormality is detected. For example, if an abnormally high heart rate is detected at the same time as a state of severe stress, an emergency notification will be sent.

[0902] 2. Emergency Notifications automatically send alerts to designated contacts (e.g., family members or emergency services).

[0903] Reflecting advice from medical professionals

[0904] 1. The server sends the generated health management proposal to the medical professional and receives feedback. The medical professional reviews the proposal and makes corrections if necessary.

[0905] 2. The corrected feedback is sent back to the server, which then finalizes the health management plan.

[0906] User feedback and implementation

[0907] 1. The server sends the finalized health management plan to the terminal, which notifies the user and displays the plan details.

[0908] 2. The user checks the health management plan, sets reminders to implement it in daily life, and manages their health according to the plan.

[0909] Ongoing monitoring and feedback

[0910] 1. The server analyzes continuously collected health and emotion data in real time and periodically updates the aging prediction model.

[0911] 2. The server adjusts the health management plan accordingly based on the user's health and emotional state, and notifies medical professionals as needed.

[0912] 3. Users can check their health status, emotional state, and suggested actions at any time through their device, and continue to take appropriate health management measures.

[0913] Program processing explanation

[0914] Health data collection: The device collects data such as heart rate and blood pressure from wearable devices and sends it to a server.

[0915] Emotion data collection: The device uses an emotion engine to extract emotion data from the user's speech and facial expressions and transmits it to the server.

[0916] Data analysis: The AI ​​in the server analyzes the collected health and emotional data to generate an aging prediction model and personalized health management suggestions.

[0917] Emergency notification: Based on the analysis of health and emotional data, if an abnormality is detected, an emergency alert will be automatically sent to designated contacts.

[0918] Examples of concrete examples and prompts

[0919] Example: If a user experiences a spike in heart rate and elevated stress levels after intense exercise, the system can automatically send out an emergency alert and notify designated contacts.

[0920] Example prompt: "How can I design a security support app that uses health and emotion data to send automatic alerts in emergencies?"

[0921] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0922] Step 1:

[0923] When a user uses the system for the first time, they access the initial registration form displayed on their terminal and enter information such as their name, age, gender, medical history, and lifestyle habits. The terminal sends this information to the server, which then stores it in a database. The input is personal information from the user, and the output is registration in the server's database.

[0924] Step 2:

[0925] If the user agrees to the collection of health and emotional data, the device collects real-time health data from the wearable device and linked apps. At the same time, the device uses an emotion engine to collect emotional data from the user's speech and facial expressions. The health and emotional data are periodically sent to a server, which stores them in a database. The input is the health and emotional data obtained from the wearable device and emotion engine, and the output is stored in the server's database.

[0926] Step 3:

[0927] The server provides the health and emotion data stored in the database to the AI ​​model for analysis. The AI ​​model analyzes these data and generates an aging prediction model. It then creates personalized health care recommendations based on the analysis results. The inputs are health and emotion data, and the outputs are the aging prediction model and health care recommendations.

[0928] Step 4:

[0929] The server sends the generated healthcare proposal to the healthcare professional and receives feedback. The healthcare professional reviews the proposal and makes any necessary modifications. The modified feedback is sent back to the server, which then finalizes the healthcare plan. The input is the generated healthcare proposal and the output is the modified healthcare plan.

[0930] Step 5:

[0931] The server sends the confirmed health management plan to the terminal. The terminal notifies the user and displays the plan contents. The user confirms the health management plan and sets reminders to implement it in daily life. The input is the confirmed health management plan, and the output is the user's confirmation and reminder settings.

[0932] Step 6:

[0933] The server monitors continuously collected health and emotion data in real time, periodically updates the aging prediction model, and, if necessary, adjusts the health management plan accordingly and notifies the user. The input is continuously collected health and emotion data, and the output is an updated aging prediction model and health management plan.

[0934] Step 7:

[0935] The server sends an emergency notification if an abnormality in the user's health or emotional state is detected. The emergency alert is automatically sent to designated contacts (e.g., family members or emergency services). The input is abnormal data collected in real time, and the output is the issuance of an emergency alert.

[0936] 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.

[0937] 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.

[0938] 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.

[0939] [Third embodiment]

[0940] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0941] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0942] 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).

[0943] 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.

[0944] 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.

[0945] 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).

[0946] 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.

[0947] 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.

[0948] 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.

[0949] 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.

[0950] 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.

[0951] 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."

[0952] This invention relates to a system that collects and analyzes a user's health data and provides personalized health management and care support. Specifically, the invention is embodied in the following manner.

[0953] User registration and health data collection

[0954] 1. When a user uses this system for the first time, they access the initial registration form that appears on the terminal.

[0955] 2. The device prompts the user to enter information such as name, age, gender, medical history, and lifestyle habits.

[0956] 3. After entering and submitting the registration information, the device sends the information to the server, which stores it in a database.

[0957] Real-time health data collection

[0958] 1. If the user consents, the device will collect real-time health data from wearable devices and connected apps.

[0959] 2. The collected health data covers a wide range of topics, including the user's vital signs (heart rate, blood pressure, etc.), number of steps taken, and sleep patterns.

[0960] 3. The device sends this data to the server at regular intervals, and the server stores it in a database.

[0961] Health data analysis and aging prediction

[0962] 1. The server uses the health data stored in the database to perform analysis using AI technology.

[0963] 2. The server generates an aging prediction model for the user based on past health data and existing statistical data.

[0964] 3. The generated aging prediction model predicts the user's future health risks and aging rate, and privacy protection features reduce the risk of information leakage.

[0965] Generate personalized health care recommendations

[0966] 1. The server generates health management proposals tailored to each individual user based on the aging prediction model.

[0967] 2. The suggestions include exercise plans, dietary advice, and recommendations for regular health checks tailored to the user's health condition.

[0968] 3. The server sends the generated suggestions to medical professionals for feedback.

[0969] Reflecting advice from medical professionals

[0970] 1. The medical professional reviews the submitted health care proposal and makes any necessary adjustments.

[0971] 2. The corrected feedback is sent back to the server, which then finalizes the health management plan.

[0972] User feedback and implementation

[0973] 1. The server sends the confirmed health management plan to the terminal.

[0974] 2. The device notifies the user and displays the plan details.

[0975] 3. Users can check their health management plan and set reminders to follow it in their daily lives.

[0976] Ongoing monitoring and feedback

[0977] 1. The server analyzes continuously collected health data in real time and periodically updates the aging prediction model.

[0978] 2. The server adjusts the health management plan accordingly based on the user's health condition and notifies medical professionals as necessary.

[0979] 3. Users can check their health status and suggested actions at any time from their device and manage their health.

[0980] The system is designed to provide personalized health management and care support using advanced technology while protecting user privacy, thereby contributing to solving health management challenges in an aging society.

[0981] The processing flow will be explained below.

[0982] Step 1:

[0983] The user accesses the initial registration form and enters information such as name, age, gender, medical history, and lifestyle habits.

[0984] Step 2:

[0985] The device sends the entered registration information to a server, where the data is encrypted for enhanced privacy protection.

[0986] Step 3:

[0987] The server stores the received registration information in a database and creates an initial user profile.

[0988] Step 4:

[0989] If the user consents, the device will collect real-time health data (such as heart rate, steps, and sleep patterns) from wearable devices and connected apps.

[0990] Step 5:

[0991] The device periodically transmits the collected health data to a server, which also encrypts the data to ensure security.

[0992] Step 6:

[0993] The server stores the received health data in a database and simultaneously provides the data to an AI for data analysis.

[0994] Step 7:

[0995] The AI ​​on the server analyzes the health data and generates an aging prediction model, which is used to predict the user's health risks and aging rate.

[0996] Step 8:

[0997] Based on the generated aging prediction model, the server generates personalized health management recommendations, including exercise plans, dietary advice, and preventative health checks.

[0998] Step 9:

[0999] The server sends the health management suggestions to medical professionals and receives feedback, who review the suggestions and make any necessary adjustments.

[1000] Step 10:

[1001] The server determines the final health management plan based on the feedback from medical professionals, and the finalized plan is stored in a database.

[1002] Step 11:

[1003] The server sends the finalized health management plan to the terminal, which then notifies the user and displays the plan contents.

[1004] Step 12:

[1005] Users can check their health management plan, set reminders to implement it in their daily lives, and manage their health according to the plan.

[1006] Step 13:

[1007] The server analyzes the continuously collected health data in real time and updates the aging prediction model as needed, which is done periodically.

[1008] Step 14:

[1009] The server adjusts the health plan based on the user's health condition and notifies medical professionals if action is required.

[1010] Step 15:

[1011] Users can check their health status and suggested actions at any time through their device and continue to manage their health appropriately.

[1012] Through these processing steps, the system provides personalized health management and care support while protecting the user's privacy.

[1013] Example 1

[1014] 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."

[1015] In an aging society, there is a growing demand for personalized health management and nursing care support. However, existing systems face challenges in providing accurate, real-time health management while protecting user privacy. Furthermore, there is a lack of mechanisms for effectively analyzing collected health data and obtaining efficient feedback from medical professionals. This makes it difficult to properly monitor users' health status and provide appropriate health management.

[1016] 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.

[1017] In this invention, the server includes means for analyzing collected health data and generating an aging prediction model, means for generating personalized health care proposals, means for revising the health care proposals based on advice from medical professionals, means for collecting user registration information and storing it in a database, means for collecting health data from the wearable device in real time and transmitting it to the server, means for periodically analyzing the health data and updating the aging prediction model, and means for notifying the user of an optimized health care plan and setting reminders. This makes it possible to provide highly accurate health care in real time and receive appropriate feedback quickly while protecting the user's privacy.

[1018] "Means for analyzing collected health data and generating an aging prediction model" refers to the function of using an algorithm to create a model that predicts the user's aging progression based on the user's health data collected from wearable devices and linked apps.

[1019] "Means for generating personalized health management proposals" refers to a function that proposes health management plans, such as individually optimized exercise plans and dietary advice, based on each user's health data and aging prediction models.

[1020] "Means for amending health care proposals with the advice of a medical professional" refers to the process of having a medical professional review the generated health care proposals and making amendments as necessary.

[1021] "Means for collecting user registration information and storing it in a database" refers to the function of safely storing personal information entered by users when they first start using the system in a database.

[1022] "Means for collecting health data from a wearable device in real time and transmitting it to a server" refers to a mechanism for collecting a user's health data from a wearable device in real time and transmitting it to a server.

[1023] "Means for periodically analyzing health data and updating the aging prediction model" refers to a function for periodically analyzing continuously collected health data and keeping the aging prediction model up to date.

[1024] "Means for notifying the user of the optimized health management plan and setting reminders" refers to a function that notifies the user of the generated health management plan and supports the user in setting reminders to put the plan into action.

[1025] This invention relates to a system that collects and analyzes a user's health data and provides personalized health management and care support. Specifically, the invention is embodied in the following manner.

[1026] User registration and health data collection

[1027] When a user first registers, they access a form that appears on their device (e.g., a smartphone or tablet). They enter information such as their name, age, gender, medical history, and lifestyle habits into the device and submit it. The device then sends this information to the server, which then stores it in a database.

[1028] Real-time health data collection

[1029] Once a user consents to real-time health data collection, the device will collect health data such as heart rate, blood pressure, number of steps, and sleep patterns from wearable devices (e.g., Fitbit or Apple Watch) and connected apps. The device will then send this data to a server at regular intervals, where it will be stored in a database.

[1030] Health data analysis and aging prediction

[1031] The server uses AI technology to analyze the health data stored in the database. Based on past data and statistical data, the server generates a predictive model for the user's aging. This model makes it possible to predict future health risks and the rate of aging.

[1032] Generate personalized health care recommendations

[1033] Based on the aging prediction model, the server generates personalized health management suggestions for the user, including exercise plans, dietary advice, and recommendations for regular health checkups. The server then sends the suggestions to medical professionals for feedback.

[1034] Reflecting advice from medical professionals

[1035] The medical professional reviews the submitted health care proposal and makes any necessary modifications, and the modified feedback is sent back to the server, which then finalizes the health care plan.

[1036] User feedback and implementation

[1037] The server sends the finalized health management plan to the device, which notifies the user and displays the plan contents. The user can then review the health management plan and set reminders to implement it in their daily lives.

[1038] Ongoing monitoring and feedback

[1039] The server analyzes continuously collected health data in real time and periodically updates the aging prediction model. The server adjusts the health management plan according to the user's health condition and notifies medical professionals as necessary. Users can check their health condition and suggested actions on their device and manage their health.

[1040] The system uses advanced technology to provide personalized health and care support while protecting user privacy. Below are some examples and prompts:

[1041] Specific examples

[1042] User: 60-year-old male with a medical history of hypertension and diabetes.

[1043] Devices: Smartphone and wearable device (Fitbit)

[1044] Health Data: Heart rate, steps, and sleep data collected from wearable devices.

[1045] Prompt Sentence Examples

[1046] A 60-year-old male with a medical history of hypertension and diabetes. Generate personalized health management suggestions based on this user's health data, taking into account data collected from wearable devices (heart rate, steps, sleep data, etc.).

[1047] Thus, the present invention is a system that provides advanced personalized health management while protecting the user's privacy.

[1048] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1049] Step 1:

[1050] When a user uses the system for the first time, he or she accesses the initial registration form displayed on the terminal.

[1051] Input: Information such as the user's name, age, gender, medical history, and lifestyle habits.

[1052] Output: The user information entered into the form.

[1053] What happens: A user opens a web browser or mobile app, fills out a form, and clicks submit.

[1054] Step 2:

[1055] The terminal prompts the user to enter information such as name, age, gender, medical history, and lifestyle habits.

[1056] Input: Information entered by the user.

[1057] Output: A request to send the entered information to the server.

[1058] Specific operation: The device displays an input form and sends the information entered by the user to the server as an HTTPS request.

[1059] Step 3:

[1060] The server stores the received registration information in a database.

[1061] Input: User information sent from the device.

[1062] Output: User profile stored in the database.

[1063] What happens: The server receives the request and stores the information in a database.

[1064] Step 4:

[1065] User consents to real-time health data collection.

[1066] Input: User consent.

[1067] Output: The consent is recorded.

[1068] Specific operation: The consent form is displayed on the device and the user presses the "Agree" button.

[1069] Step 5:

[1070] The device collects health data such as heart rate, blood pressure, steps taken, and sleep patterns from wearable devices and connected apps.

[1071] Input: Real-time data obtained from wearable devices.

[1072] Output: Collected health data.

[1073] Specific behavior: Obtain data from the device via Bluetooth or other communication means.

[1074] Step 6:

[1075] The terminal transmits this data to the server at regular intervals.

[1076] Input: Collected health data.

[1077] Output: The data sent to the server.

[1078] Specific operation: The device collects data using an interval timer and sends an HTTP request to the server.

[1079] Step 7:

[1080] The server uses AI technology to analyze the health data stored in the database.

[1081] Input: Health data in a database.

[1082] Output: Analysis results and aging prediction model.

[1083] What it does: The server analyzes the data using machine learning algorithms.

[1084] Step 8:

[1085] The server generates an aging prediction model based on past data and statistical data.

[1086] Input: Health data, historical data, statistical data.

[1087] Output: Aging prediction model.

[1088] Specific operation: The server trains the machine learning model and generates a new predictive model.

[1089] Step 9:

[1090] The server generates health management proposals tailored to each individual user based on the generated aging prediction model.

[1091] Input: Aging prediction model.

[1092] Output: personalized health care recommendations.

[1093] Specific operation: The server analyzes the model output and generates a health management plan.

[1094] Step 10:

[1095] The server sends the suggestions to medical professionals and asks for their feedback.

[1096] Enter: Healthcare Proposal.

[1097] Output: Healthcare professional feedback.

[1098] Specific operation: The server sends suggestions via email or a dedicated app and receives feedback.

[1099] Step 11:

[1100] A medical professional will review the submitted health care proposal and make any necessary adjustments.

[1101] Enter: Healthcare Proposal.

[1102] Output: Corrected feedback.

[1103] How it works: Medical professionals review and correct suggestions through a dedicated app or web portal.

[1104] Step 12:

[1105] The server finalizes the health management plan and transmits it to the terminal.

[1106] Input: Revised health care proposal.

[1107] Output: Final health care plan.

[1108] Specific operation: The server generates the final plan and sends it to the terminal.

[1109] Step 13:

[1110] The terminal notifies the user and displays the plan details.

[1111] Enter: the final health care plan.

[1112] Output: Inform and display the plan to the user.

[1113] What it does: The device generates a push notification and displays the plan details when the user opens the app.

[1114] Step 14:

[1115] Users can review their health plan and set reminders.

[1116] Enter: the final health care plan.

[1117] Output: The set reminder.

[1118] What happens: The user sets a reminder in the app, and the device sets a notification or alarm.

[1119] Step 15:

[1120] The server continuously analyzes collected health data in real time and updates the aging prediction model.

[1121] Input: New health data.

[1122] Output: Updated aging prediction model.

[1123] What happens: The server analyzes the new data and updates the model parameters.

[1124] Step 16:

[1125] The server adjusts the health care plan according to the user's health status and notifies medical professionals as needed.

[1126] Input: Updated aging prediction model and health data.

[1127] Output: Coordinated health care plan.

[1128] What it does: The server detects changes in health status, adjusts plans, and notifies medical professionals.

[1129] Step 17:

[1130] Users can check their health status and suggested actions at any time from their device and manage their health.

[1131] Input: Health care plan and latest health data.

[1132] Output: User's plan execution and health management.

[1133] Specific behavior: User views the in-app dashboard and takes the required action.

[1134] (Application example 1)

[1135] 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."

[1136] Providing personalized health management and nursing care support is an important challenge in an aging society. However, conventional systems have difficulty collecting and analyzing real-time health data and quickly providing personalized recommendations that reflect the advice of medical professionals. Providing immediate feedback based on the user's health status and continuously updating data while protecting privacy are also challenges. Furthermore, the integration of health management recommendations and feedback in physical stores is insufficient.

[1137] 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.

[1138] In this invention, the server includes a means for analyzing collected health data to generate an aging prediction model, a means for generating personalized health management suggestions, and a means for revising the health management suggestions based on advice from medical professionals. This also includes a means for displaying personalized health management suggestions to customers using health data collected on a dedicated in-store terminal, a means for providing health management suggestions based on health checkup results and feedback from the physical store, a means for displaying the health management suggestions on an in-store display or on the customer's smartphone app, a means for medical professionals to review and revise the generated suggestions, and a means for analyzing continuously collected health data over time and updating the suggestions. This enables the rapid provision of personalized health management and care support in real time, and real-time feedback and continuous monitoring according to the user's health condition. Privacy protection features also reduce the risk of data leakage.

[1139] "Collected health data" refers to information such as heart rate, blood pressure, number of steps, and sleep patterns obtained from users' wearable devices and smartphone apps.

[1140] An "aging prediction model" is a model for predicting a user's future health risks and aging rate based on past health data and existing statistical data.

[1141] "Personalized health management suggestions" refer to suggestions that include exercise plans, dietary advice, and recommendations for regular health checks that are generated based on the user's health condition.

[1142] The "means for modifying health care proposals with the advice of a medical professional" refers to a method by which a medical professional reviews and modifies the generated health care proposals and provides optimized proposals.

[1143] An "in-store dedicated terminal" is an electronic device installed in a physical store for collecting and displaying users' health data.

[1144] "Providing health care suggestions" refers to informing the user of the generated health care suggestions and recommending health care activities based on the suggestions.

[1145] A "store display" is a device installed in a physical store that displays health management suggestions as visual information to customers.

[1146] A "smartphone app" is application software that is installed on a customer's smartphone and can display health management suggestions and obtain feedback.

[1147] "Real-time health data" refers to instantaneous data such as a user's current heart rate, blood pressure, number of steps, and sleep patterns.

[1148] "Data leakage risk" refers to the risk of a user's health data being illegally accessed or leaked to a third party.

[1149] "Privacy protection features" refer to technical measures to safely manage users' personal information and health data and prevent unauthorized access or data leakage.

[1150] This invention is a system that collects and analyzes a user's health data and provides personalized health management and care support. Specifically, the invention can be implemented in the following forms.

[1151] User registration and health data collection

[1152] The server provides a means for first-time users to access the initial registration form displayed on a dedicated terminal in the store. The user enters information such as name, age, gender, medical history, and lifestyle habits. The terminal sends this information to the server, where it is stored in a database on the server.

[1153] Real-time health data collection

[1154] If the user agrees, the device will collect real-time health data (heart rate, blood pressure, steps taken, sleep patterns, etc.) from wearable devices and connected apps. This data will be periodically sent to a server, which will store it in a database.

[1155] Health data analysis and aging prediction

[1156] The server uses the stored health data to perform analysis using AI technology, which generates a user's aging prediction model based on past health data and existing statistical data. This model predicts the user's future health risks and aging rate.

[1157] Generate personalized health care recommendations

[1158] Based on the aging prediction model, the server generates personalized health management suggestions for the user, including exercise plans, dietary advice, and recommendations for regular health checks.

[1159] Reflecting advice from medical professionals

[1160] The server sends the generated health care suggestions to medical experts for their feedback, and the suggestions modified by the medical experts ensure further accuracy and reliability.

[1161] Providing health management suggestions in-store

[1162] The improved health management suggestions are sent from the server to dedicated terminals or in-store displays in the store and are displayed to customers. The suggestions are also linked to the customer's smartphone app, allowing the user to instantly check and implement the suggestions.

[1163] Ongoing monitoring and feedback

[1164] The server continuously analyzes the health data collected in real time and periodically updates the aging prediction model, allowing the system to adjust the health management plan accordingly based on the user's health condition and notify medical professionals as needed, enabling health management based on the latest information.

[1165] Examples of prompt statements

[1166] An example of a specific prompt sentence is, "Generate health management suggestions for a 40-year-old male. Heart rate data for the past week: average 75, blood pressure 120 / 80, average number of steps 10,000, average sleep time 7 hours."

[1167] Hardware and Software Used

[1168] The system uses tablets and in-store displays as dedicated terminals, and connects to wearable devices and smartphone apps. A database management system and software with the functions necessary for AI analysis run on the server side. Using this system, customers can receive health management suggestions in real time and use them to implement them.

[1169] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1170] Step 1:

[1171] The user accesses the initial registration form using a dedicated terminal at the store. The user enters information such as name, age, gender, medical history, and lifestyle habits. The entered information is sent from the terminal to the server, which saves it in a database. The entered data is stored in the user information database.

[1172] Step 2:

[1173] If the user agrees, the device will collect real-time health data (e.g., heart rate, blood pressure, number of steps, sleep patterns) from wearable devices and connected apps. This data will be sent to a server at regular intervals, and the server will store it in a database. The input data is the real-time health data, and the output data is an updated health database.

[1174] Step 3:

[1175] The server uses AI technology to perform analysis based on the health data stored in the database. This analysis generates an aging prediction model for the user, taking into account past health data and existing statistical data. This model predicts the user's future health risks and aging rate. The input data is the collected health data, and the output data is the aging prediction model.

[1176] Step 4:

[1177] The server generates personalized health management suggestions based on the generated aging prediction model. These suggestions include exercise plans, dietary advice, and recommendations for regular health checkups. The input data is the aging prediction model, and the output data is the personalized health management suggestions.

[1178] Step 5:

[1179] The generated health care proposal is sent from the server to a medical expert, who provides advice and makes corrections. The medical expert reviews the proposal and makes corrections as necessary. The corrected proposal is sent back to the server and saved in the database again. The input data is the health care proposal, and the output data is the corrected health care proposal.

[1180] Step 6:

[1181] The final health management proposal is sent from the server to a dedicated terminal or in-store display in the store and displayed to the customer. The proposal is also linked to the customer's smartphone app. The user confirms it and carries out health management. The input data is the revised health management proposal, and the output data is the displayed health management proposal.

[1182] Step 7:

[1183] The server continuously analyzes the health data collected in real time and periodically updates the aging prediction model. This allows the system to adjust the health management plan according to the user's health condition and notify medical professionals again as necessary. The input data is the latest health data, and the output data is the updated aging prediction model and health management suggestions.

[1184] Specific working example:

[1185] If the user is a 40-year-old man and his average heart rate data for the past week is 75, the server will use this information to generate health management suggestions such as "walking 30 minutes every day" and "eating a vegetable-based diet."

[1186] The server-generated suggestions were reviewed by medical experts, who then revised them to "reduce walking time to 20 minutes."

[1187] The revised proposals are displayed on the dedicated device and smartphone app, and the user can confirm and implement them.

[1188] Examples of prompts:

[1189] "Generate health management suggestions for a 40-year-old male. Heart rate data for the past week: average 75, blood pressure 120 / 80, average steps 10,000, average sleep time 7 hours."

[1190] 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.

[1191] This invention relates to a system that collects and analyzes a user's health data and emotional data to provide personalized health management and care support. Specifically, the invention is embodied in the following manner.

[1192] User registration and collection of health and emotional data

[1193] 1. When a user uses the system for the first time, they access the initial registration form displayed on the terminal and enter information such as their name, age, gender, medical history, and lifestyle habits.

[1194] 2. The terminal sends the entered registration information to the server, which stores it in a database.

[1195] 3. If the user agrees, the device will collect real-time health data from wearable devices and connected apps, and simultaneously use the emotion engine to collect the user's emotional data.

[1196] 4. The device periodically sends health and emotion data to the server, which stores this data in a database.

[1197] Analysis of health and emotional data and aging prediction

[1198] 1. The server provides the health and emotional data stored in the database to the AI ​​for analysis.

[1199] 2. The AI ​​on the server analyzes the health and emotional data and generates an aging prediction model, which predicts the user's health risks and aging rate.

[1200] 3. The server generates personalized health recommendations that take emotional data into account, including exercise plans, dietary advice, and preventative health checks that take into account the user's mental health.

[1201] Reflecting advice from medical professionals

[1202] 1. The server sends the generated health management proposal to the medical professional and receives feedback. The medical professional reviews the proposal and makes corrections if necessary.

[1203] 2. The corrected feedback is sent back to the server, which then finalizes the health management plan.

[1204] User feedback and implementation

[1205] 1. The server sends the finalized health management plan to the terminal, which notifies the user and displays the plan details.

[1206] 2. The user checks the health management plan, sets reminders to implement it in daily life, and manages their health according to the plan.

[1207] Ongoing monitoring and feedback

[1208] 1. The server analyzes continuously collected health and emotion data in real time and periodically updates the aging prediction model.

[1209] 2. The server adjusts the health management plan accordingly based on the user's health and emotional state, and notifies medical professionals as needed.

[1210] 3. Users can check their health status, emotional state, and suggested actions at any time through their device, and continue to take appropriate health management measures.

[1211] For example, if a user records their daily emotional state and the emotion engine detects that they are experiencing increased stress, the server will add health management suggestions such as reducing exercise and practicing relaxing breathing exercises. It will also take an approach to improving mental health by recommending a weekly counseling session with a medical professional. In this way, the system provides comprehensive support to improve the user's overall health and happiness.

[1212] By integrating emotional data while protecting user privacy, this system can provide more accurate and personalized health management and nursing care support, thereby improving users' quality of life and contributing to solving health management issues in an aging society.

[1213] The processing flow will be explained below.

[1214] Step 1:

[1215] The user accesses the initial registration form and enters information such as name, age, gender, medical history, and lifestyle habits.

[1216] Step 2:

[1217] The device sends the entered registration information to a server, where the data is encrypted for enhanced privacy protection.

[1218] Step 3:

[1219] The server stores the received registration information in a database and creates an initial user profile.

[1220] Step 4:

[1221] If the user agrees, the device will collect real-time health data (heart rate, steps, sleep patterns, etc.) from the wearable device and connected apps. At the same time, the emotion engine will collect emotional data from the user's facial expressions and tone of voice using the device's built-in camera and microphone.

[1222] Step 5:

[1223] The device periodically transmits the collected health and emotional data to a server, which also encrypts the data to ensure security.

[1224] Step 6:

[1225] The server stores the received health and emotional data in a database and simultaneously provides the data to an AI for data analysis.

[1226] Step 7:

[1227] The server's AI analyzes the health and emotional data to generate an aging prediction model, which predicts the user's health risks and aging rate while also incorporating the emotional data.

[1228] Step 8:

[1229] The server then uses the generated aging prediction model to create personalized health management recommendations, including exercise plans, dietary advice, and preventative health checks that take into account the user's health and emotional state.

[1230] Step 9:

[1231] The server sends the health management suggestions to medical professionals and receives feedback, who review the suggestions and make any necessary adjustments.

[1232] Step 10:

[1233] The server determines the final health care plan based on feedback from medical professionals, which is then stored in a database.

[1234] Step 11:

[1235] The server sends the finalized health management plan to the terminal, which notifies the user and displays the plan contents.

[1236] Step 12:

[1237] Users can check their health management plan, set reminders to carry out their plan in their daily lives, and manage their health according to the plan.

[1238] Step 13:

[1239] The server analyzes continuously collected health and emotion data in real time and updates the aging prediction model as needed, which is updated periodically.

[1240] Step 14:

[1241] The server adjusts the health management plan based on the user's health and emotional state and notifies medical professionals if action is needed.

[1242] Step 15:

[1243] Users can check their health status, emotional state, and suggested actions at any time through their device, and continue to manage their health appropriately.

[1244] Through these processing steps, the system can protect the user's privacy while integrating emotional data to provide more accurate and personalized health management and care support. For example, when a user is experiencing high stress, the emotion engine can detect their stress level and incorporate relaxation activities and counseling suggestions into their health management plan, improving their mental health and quality of life.

[1245] Example 2

[1246] 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."

[1247] While personalized health management and nursing care support are becoming increasingly important in an aging society, existing systems do not take into account the user's emotional state, resulting in insufficient comprehensive health management, including mental health. Furthermore, issues remain, such as protecting the privacy of collected data and lack of real-time performance. Furthermore, there is a lack of accurate recommendations incorporating the advice of medical experts.

[1248] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1249] In this invention, the server includes: means for analyzing collected health data and emotion data to generate an aging prediction model; means for generating a personalized health management proposal; means for collecting data from the wearable device and linked apps with the user's consent; means for collecting emotion data using an emotion analysis engine; means for revising the health management proposal based on advice from medical professionals; means for periodically analyzing data in real time and updating the health management plan; data management means with a privacy protection function to reduce the risk of data leaks; and means for notifying the end-user terminal of the health management plan. This enables comprehensive analysis of the user's health condition, provides personalized health management while protecting privacy, and enables highly accurate health management incorporating feedback from medical professionals.

[1250] "Health data" is information related to the user's physical condition, and includes indicators such as heart rate, number of steps, blood pressure, and weight.

[1251] "Emotional data" is information related to the user's emotional state, and is obtained through facial recognition and voice analysis.

[1252] A "wearable device" is an electronic device that is worn on the body and is used to collect health data such as heart rate and number of steps taken in real time.

[1253] A "linked app" is software that communicates with wearable devices and other data collection devices to collect and manage data.

[1254] An "aging prediction model" is a model that analyzes health data and emotional data to predict a user's aging risk and rate of progression.

[1255] "Health management suggestions" are suggestions such as personalized exercise plans, dietary advice, and preventative health checks that are generated based on the user's health and emotional state.

[1256] "Emotion analysis engine" is a general term for algorithms and software that use image processing and voice recognition to analyze a user's emotional state.

[1257] A "medical professional" is a person with specialized medical knowledge and qualifications, such as a doctor or nurse, who is responsible for providing opinions and feedback on the user's health management suggestions.

[1258] "Data management measures" refer to methods and technologies for safely and efficiently storing and managing collected data, including encryption and access control.

[1259] An "end-user terminal" is an electronic device such as a computer, smartphone, or tablet that is directly used by a user and displays notifications and information from the system.

[1260] The present invention relates to a system for collecting and analyzing a user's health data and emotional data, and providing personalized health management and nursing care support. Specific embodiments for carrying out the present invention will be described below.

[1261] User Registration

[1262] The user accesses the initial registration form and enters information such as name, age, gender, medical history, and lifestyle habits. The device then sends the collected registration information to the server, which stores this information in a database and creates an individual profile for the user.

[1263] Health and emotional data collection

[1264] Users use wearable devices and linked apps to collect daily health data, including heart rate, step count, and blood pressure. The devices collect this data in real time and periodically send it to a server. Furthermore, the devices are equipped with cameras and voice recognition functions, and use an emotion analysis engine to collect user emotion data. Specifically, the emotion data is analyzed using a face recognition and voice analysis engine that uses OpenCV.

[1265] Data analysis and aging prediction

[1266] The server stores the collected health and emotion data in a database and provides it to an AI for analysis. The AI ​​uses TensorFlow and PyTorch. The AI ​​on the server analyzes this data and generates an aging prediction model, which can predict the user's health risks and aging rate.

[1267] Generate personalized health care recommendations

[1268] The server generates personalized health management suggestions based on the analysis results. These suggestions include exercise plans that take the user's mental health into account, dietary advice, and preventative health check items. Using the generative AI model, suggestions can be generated by inputting specific prompts. For example, the prompt could be, "30-year-old male, software engineer. I've been feeling a bit stressed lately. Please predict health risks based on my heart rate and sleep data and suggest an appropriate health management plan."

[1269] Reflecting advice from medical professionals

[1270] The server sends the generated health management proposal to a medical professional and receives feedback. The medical professional reviews the proposal and makes any necessary corrections. The corrected feedback is sent back to the server, and the final health management plan is finalized.

[1271] User feedback and implementation

[1272] The server sends the finalized health management plan to the device, which notifies the user and displays the plan contents. The user can then check the health management plan, set reminders, and implement the plan in their daily lives.

[1273] Ongoing monitoring and feedback

[1274] The server analyzes continuously collected health and emotional data in real time and periodically updates the aging prediction model. This allows the health management plan to be adjusted appropriately according to the user's health and emotional state. Users can check their health and emotional state, as well as suggested actions, at any time via their device to continue appropriate health management.

[1275] For example, if the emotion engine detects high stress in a user, the server can add health management suggestions such as reducing exercise and practicing relaxing breathing exercises to the user's list of health management suggestions. It also recommends a weekly counseling session with a medical professional, taking an approach to improving the user's mental health. This system can improve the user's overall health and happiness.

[1276] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1277] Step 1:

[1278] The user accesses the initial registration form and enters information such as name, age, sex, medical history, and lifestyle habits.

[1279] Input: User's personal information (name, age, gender, medical history, lifestyle, etc.)

[1280] Output: User registration information is saved on the device

[1281] Specific behavior: The user enters information into the form displayed on the device and clicks the "Register" button.

[1282] Step 2:

[1283] The terminal transmits the user's registration information to the server.

[1284] Input: User registration information (data stored in the device)

[1285] Output: User registration information is sent to the server and stored in the database

[1286] What happens: The device sends registration information to the server using an HTTP POST request.

[1287] Step 3:

[1288] The server stores the registration information in a database.

[1289] Input: User registration information sent to the server

[1290] Output: User registration information stored in the database

[1291] Specific operation: The server uses a database operation API (e.g. MySQL) to insert information into the database.

[1292] Step 4:

[1293] The server collects health data from the wearable device and the associated app.

[1294] Input: Real-time health data (heart rate, steps, blood pressure, etc.) provided by wearable devices and linked apps

[1295] Output: Health data sent to the server

[1296] Specific operation: The device collects data via Bluetooth or API and sends it to the server at regular intervals.

[1297] Step 5:

[1298] The server uses a sentiment analysis engine to collect sentiment data.

[1299] Input: Data (audio, image) from the device's camera and microphone

[1300] Output: Emotion data (happiness, sadness, anger, etc.) sent to the server

[1301] Specific operation: The device performs face recognition and voice analysis in real time using OpenCV and a voice recognition engine, and sends the results to the server.

[1302] Step 6:

[1303] The server provides health and emotional data to the analytical AI.

[1304] Input: Health and emotion data stored on the server

[1305] Output: Aging prediction results and health risk assessment generated by the AI ​​model

[1306] Specific operation: The server inputs data into TensorFlow or PyTorch AI models using Python scripts and outputs the analysis results.

[1307] Step 7:

[1308] A server generates personalized health care recommendations.

[1309] Input: Analysis results of the AI ​​model

[1310] Output: Health management suggestions for each user (exercise plan, dietary advice, etc.)

[1311] Specific operation: The server inputs a prompt to the generated AI model, such as "30-year-old male, software engineer by occupation. Recently, stress has been increasing. Please predict health risks based on heart rate and sleep data and propose an appropriate health management plan." and receives the proposal as output.

[1312] Step 8:

[1313] The server sends the generated health care recommendations to the medical professionals and receives feedback.

[1314] Input: Health management suggestions from a generative AI model

[1315] Output: Modified or approved health care proposal

[1316] What it does: The server sends the proposal to medical professionals via email or a dedicated portal and collects their feedback.

[1317] Step 9:

[1318] The server finalizes the health management plan and sends it to the terminal.

[1319] Input: Healthcare recommendations incorporating feedback from medical professionals

[1320] Output: The final health plan displayed on the user's device.

[1321] Specific operation: The server sends the confirmed plan in JSON format to the device and notifies it.

[1322] Step 10:

[1323] The user checks their health plan and sets reminders.

[1324] Input: Health management plan received on the device

[1325] Output: Set reminders (exercise, meals, counseling sessions, etc.)

[1326] Specific behavior: The user checks the plan details on the device and uses the app's reminder setting function.

[1327] Step 11:

[1328] The server continuously analyzes the data in real time and updates the health management plan accordingly.

[1329] Input: Regularly collected health and emotional data

[1330] Output: Updated health plan

[1331] How it works: The server periodically re-runs the AI ​​model and regenerates a health management plan based on the analysis results.

[1332] (Application example 2)

[1333] 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."

[1334] Conventional health management systems collect and analyze users' health data, but they do not adequately collect real-time emotional data or respond to emergencies based on that data. Furthermore, they lack the functionality to automatically notify users appropriately in the event of an emergency, making it difficult to ensure user safety.

[1335] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing collected health data to generate an aging prediction model, means for generating personalized health management suggestions, means for revising the health management suggestions based on advice from medical professionals, means for automatically notifying in the event of an emergency, means for monitoring the health status in real time based on emotional data, and means for sending emergency alerts to designated contacts. This makes it possible to comprehensively manage the user's health status and emotional state and to respond quickly in the event of an emergency.

[1336] The "means of analyzing collected health data to generate an aging prediction model" is a function that analyzes health data such as heart rate and blood pressure collected from users and creates a model that predicts the progression of aging and health risks based on this data.

[1337] The "means for generating personalized health management suggestions" is a function that takes into consideration each user's health data and emotional data and individually suggests optimal exercise plans, dietary advice, and the like.

[1338] The "means for correcting the health care proposal with the advice of a medical professional" is a function for having a medical professional review the generated health care proposal and correcting it as necessary.

[1339] "Means for automatically notifying in an emergency" is a function for sending an emergency notification when an abnormality is detected in the user's health or emotional state.

[1340] The "means for monitoring health status in real time based on emotional data" is a function that continuously collects emotional data from the user and monitors the user's mental health status in real time based on this data.

[1341] "Means for sending emergency alerts to designated contacts" refers to a feature that automatically sends alerts to pre-defined contacts and emergency services in the event of a user emergency.

[1342] "Data management means with privacy protection functions to reduce the risk of data leakage" refers to security functions that safely manage user data and prevent it from being leaked to the outside.

[1343] The "means for periodically updating the aging prediction model" is a function for periodically maintaining the aging prediction model in the latest state based on newly collected data from users.

[1344] "Means for managing the emergency alert sending log" is a function that saves the history of emergency alerts sent and manages them so that they can be checked later.

[1345] The "means for continuously collecting and analyzing health data and emotional data" is a function for continuously collecting a user's health data and emotional data and analyzing them in real time.

[1346] "Means of collaborating with medical institutions and nursing care facilities to provide highly reliable health management and nursing care support" is a function that shares information with medical institutions and nursing care facilities and provides users with highly accurate health management and nursing care support.

[1347] The present invention relates to a system that collects and analyzes health data and emotional data of a user and provides personalized health management and care support. The present invention can be embodied in the following forms.

[1348] User registration and collection of health and emotional data

[1349] 1. When a user uses the system for the first time, they access the initial registration form displayed on the terminal and enter information such as their name, age, gender, medical history, and lifestyle habits.

[1350] 2. The terminal sends the entered registration information to the server, which stores it in a database.

[1351] 3. If the user agrees, the device will collect real-time health data from wearable devices and connected apps, and simultaneously use the emotion engine to collect the user's emotional data.

[1352] 4. The device periodically sends health and emotion data to the server, which stores this data in a database.

[1353] Analysis of health and emotional data and aging prediction

[1354] 1. The server provides the health and emotional data stored in the database to the AI ​​for analysis.

[1355] 2. The AI ​​on the server analyzes the health and emotional data and generates an aging prediction model, which predicts the user's health risks and aging rate.

[1356] 3. The server generates personalized health recommendations that take emotional data into account, including exercise plans, dietary advice, and preventative health checks that take into account the user's mental health.

[1357] Automatic emergency notification function

[1358] 1. The server monitors the user's collected data in real time and sends an emergency notification if an abnormality is detected. For example, if an abnormally high heart rate is detected at the same time as a state of severe stress, an emergency notification will be sent.

[1359] 2. Emergency Notifications automatically send alerts to designated contacts (e.g., family members or emergency services).

[1360] Reflecting advice from medical professionals

[1361] 1. The server sends the generated health management proposal to the medical professional and receives feedback. The medical professional reviews the proposal and makes corrections if necessary.

[1362] 2. The corrected feedback is sent back to the server, which then finalizes the health management plan.

[1363] User feedback and implementation

[1364] 1. The server sends the finalized health management plan to the terminal, which notifies the user and displays the plan details.

[1365] 2. The user checks the health management plan, sets reminders to implement it in daily life, and manages their health according to the plan.

[1366] Ongoing monitoring and feedback

[1367] 1. The server analyzes continuously collected health and emotion data in real time and periodically updates the aging prediction model.

[1368] 2. The server adjusts the health management plan accordingly based on the user's health and emotional state, and notifies medical professionals as needed.

[1369] 3. Users can check their health status, emotional state, and suggested actions at any time through their device, and continue to take appropriate health management measures.

[1370] Program processing explanation

[1371] Health data collection: The device collects data such as heart rate and blood pressure from wearable devices and sends it to a server.

[1372] Emotion data collection: The device uses an emotion engine to extract emotion data from the user's speech and facial expressions and transmits it to the server.

[1373] Data analysis: The AI ​​in the server analyzes the collected health and emotional data to generate an aging prediction model and personalized health management suggestions.

[1374] Emergency notification: Based on the analysis of health and emotional data, if an abnormality is detected, an emergency alert will be automatically sent to designated contacts.

[1375] Examples of concrete examples and prompts

[1376] Example: If a user experiences a spike in heart rate and elevated stress levels after intense exercise, the system can automatically send out an emergency alert and notify designated contacts.

[1377] Example prompt: "How can I design a security support app that uses health and emotion data to send automatic alerts in emergencies?"

[1378] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1379] Step 1:

[1380] When a user uses the system for the first time, they access the initial registration form displayed on their terminal and enter information such as their name, age, gender, medical history, and lifestyle habits. The terminal sends this information to the server, which then stores it in a database. The input is personal information from the user, and the output is registration in the server's database.

[1381] Step 2:

[1382] If the user agrees to the collection of health and emotional data, the device collects real-time health data from the wearable device and linked apps. At the same time, the device uses an emotion engine to collect emotional data from the user's speech and facial expressions. The health and emotional data are periodically sent to a server, which stores them in a database. The input is the health and emotional data obtained from the wearable device and emotion engine, and the output is stored in the server's database.

[1383] Step 3:

[1384] The server provides the health and emotion data stored in the database to the AI ​​model for analysis. The AI ​​model analyzes these data and generates an aging prediction model. It then creates personalized health care recommendations based on the analysis results. The inputs are health and emotion data, and the outputs are the aging prediction model and health care recommendations.

[1385] Step 4:

[1386] The server sends the generated healthcare proposal to the healthcare professional and receives feedback. The healthcare professional reviews the proposal and makes any necessary modifications. The modified feedback is sent back to the server, which then finalizes the healthcare plan. The input is the generated healthcare proposal and the output is the modified healthcare plan.

[1387] Step 5:

[1388] The server sends the confirmed health management plan to the terminal. The terminal notifies the user and displays the plan contents. The user confirms the health management plan and sets reminders to implement it in daily life. The input is the confirmed health management plan, and the output is the user's confirmation and reminder settings.

[1389] Step 6:

[1390] The server monitors continuously collected health and emotion data in real time, periodically updates the aging prediction model, and, if necessary, adjusts the health management plan accordingly and notifies the user. The input is continuously collected health and emotion data, and the output is an updated aging prediction model and health management plan.

[1391] Step 7:

[1392] The server sends an emergency notification if an abnormality in the user's health or emotional state is detected. The emergency alert is automatically sent to designated contacts (e.g., family members or emergency services). The input is abnormal data collected in real time, and the output is the issuance of an emergency alert.

[1393] 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.

[1394] 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.

[1395] 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.

[1396] [Fourth embodiment]

[1397] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1398] 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.

[1399] 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).

[1400] 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.

[1401] 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.

[1402] 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).

[1403] 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.

[1404] 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.

[1405] 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.

[1406] 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.

[1407] 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.

[1408] 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.

[1409] 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."

[1410] This invention relates to a system that collects and analyzes a user's health data and provides personalized health management and care support. Specifically, the invention is embodied in the following manner.

[1411] User registration and health data collection

[1412] 1. When a user uses this system for the first time, they access the initial registration form that appears on the terminal.

[1413] 2. The device prompts the user to enter information such as name, age, gender, medical history, and lifestyle habits.

[1414] 3. After entering and submitting the registration information, the device sends the information to the server, which stores it in a database.

[1415] Real-time health data collection

[1416] 1. If the user consents, the device will collect real-time health data from wearable devices and connected apps.

[1417] 2. The collected health data covers a wide range of topics, including the user's vital signs (heart rate, blood pressure, etc.), number of steps taken, and sleep patterns.

[1418] 3. The device sends this data to the server at regular intervals, and the server stores it in a database.

[1419] Health data analysis and aging prediction

[1420] 1. The server uses the health data stored in the database to perform analysis using AI technology.

[1421] 2. The server generates an aging prediction model for the user based on past health data and existing statistical data.

[1422] 3. The generated aging prediction model predicts the user's future health risks and aging rate, and privacy protection features reduce the risk of information leakage.

[1423] Generate personalized health care recommendations

[1424] 1. The server generates health management proposals tailored to each individual user based on the aging prediction model.

[1425] 2. The suggestions include exercise plans, dietary advice, and recommendations for regular health checks tailored to the user's health condition.

[1426] 3. The server sends the generated suggestions to medical professionals for feedback.

[1427] Reflecting advice from medical professionals

[1428] 1. The medical professional reviews the submitted health care proposal and makes any necessary adjustments.

[1429] 2. The corrected feedback is sent back to the server, which then finalizes the health management plan.

[1430] User feedback and implementation

[1431] 1. The server sends the confirmed health management plan to the terminal.

[1432] 2. The device notifies the user and displays the plan details.

[1433] 3. Users can check their health management plan and set reminders to follow it in their daily lives.

[1434] Ongoing monitoring and feedback

[1435] 1. The server analyzes continuously collected health data in real time and periodically updates the aging prediction model.

[1436] 2. The server adjusts the health management plan accordingly based on the user's health condition and notifies medical professionals as necessary.

[1437] 3. Users can check their health status and suggested actions at any time from their device and manage their health.

[1438] The system is designed to provide personalized health management and care support using advanced technology while protecting user privacy, thereby contributing to solving health management challenges in an aging society.

[1439] The processing flow will be explained below.

[1440] Step 1:

[1441] The user accesses the initial registration form and enters information such as name, age, gender, medical history, and lifestyle habits.

[1442] Step 2:

[1443] The device sends the entered registration information to a server, where the data is encrypted for enhanced privacy protection.

[1444] Step 3:

[1445] The server stores the received registration information in a database and creates an initial user profile.

[1446] Step 4:

[1447] If the user consents, the device will collect real-time health data (such as heart rate, steps, and sleep patterns) from wearable devices and connected apps.

[1448] Step 5:

[1449] The device periodically transmits the collected health data to a server, which also encrypts the data to ensure security.

[1450] Step 6:

[1451] The server stores the received health data in a database and simultaneously provides the data to an AI for data analysis.

[1452] Step 7:

[1453] The AI ​​on the server analyzes the health data and generates an aging prediction model, which is used to predict the user's health risks and aging rate.

[1454] Step 8:

[1455] Based on the generated aging prediction model, the server generates personalized health management recommendations, including exercise plans, dietary advice, and preventative health checks.

[1456] Step 9:

[1457] The server sends the health management suggestions to medical professionals and receives feedback, who review the suggestions and make any necessary adjustments.

[1458] Step 10:

[1459] The server determines the final health management plan based on the feedback from medical professionals, and the finalized plan is stored in a database.

[1460] Step 11:

[1461] The server sends the finalized health management plan to the terminal, which then notifies the user and displays the plan contents.

[1462] Step 12:

[1463] Users can check their health management plan, set reminders to implement it in their daily lives, and manage their health according to the plan.

[1464] Step 13:

[1465] The server analyzes the continuously collected health data in real time and updates the aging prediction model as needed, which is done periodically.

[1466] Step 14:

[1467] The server adjusts the health plan based on the user's health condition and notifies medical professionals if action is required.

[1468] Step 15:

[1469] Users can check their health status and suggested actions at any time through their device and continue to manage their health appropriately.

[1470] Through these processing steps, the system provides personalized health management and care support while protecting the user's privacy.

[1471] Example 1

[1472] 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."

[1473] In an aging society, there is a growing demand for personalized health management and nursing care support. However, existing systems face challenges in providing accurate, real-time health management while protecting user privacy. Furthermore, there is a lack of mechanisms for effectively analyzing collected health data and obtaining efficient feedback from medical professionals. This makes it difficult to properly monitor users' health status and provide appropriate health management.

[1474] 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.

[1475] In this invention, the server includes means for analyzing collected health data and generating an aging prediction model, means for generating personalized health care proposals, means for revising the health care proposals based on advice from medical professionals, means for collecting user registration information and storing it in a database, means for collecting health data from the wearable device in real time and transmitting it to the server, means for periodically analyzing the health data and updating the aging prediction model, and means for notifying the user of an optimized health care plan and setting reminders. This makes it possible to provide highly accurate health care in real time and receive appropriate feedback quickly while protecting the user's privacy.

[1476] "Means for analyzing collected health data and generating an aging prediction model" refers to the function of using an algorithm to create a model that predicts the user's aging progression based on the user's health data collected from wearable devices and linked apps.

[1477] "Means for generating personalized health management proposals" refers to a function that proposes health management plans, such as individually optimized exercise plans and dietary advice, based on each user's health data and aging prediction models.

[1478] "Means for amending health care proposals with the advice of a medical professional" refers to the process of having a medical professional review the generated health care proposals and making amendments as necessary.

[1479] "Means for collecting user registration information and storing it in a database" refers to the function of safely storing personal information entered by users when they first start using the system in a database.

[1480] "Means for collecting health data from a wearable device in real time and transmitting it to a server" refers to a mechanism for collecting a user's health data from a wearable device in real time and transmitting it to a server.

[1481] "Means for periodically analyzing health data and updating the aging prediction model" refers to a function for periodically analyzing continuously collected health data and keeping the aging prediction model up to date.

[1482] "Means for notifying the user of the optimized health management plan and setting reminders" refers to a function that notifies the user of the generated health management plan and supports the user in setting reminders to put the plan into action.

[1483] This invention relates to a system that collects and analyzes a user's health data and provides personalized health management and care support. Specifically, the invention is embodied in the following manner.

[1484] User registration and health data collection

[1485] When a user first registers, they access a form that appears on their device (e.g., a smartphone or tablet). They enter information such as their name, age, gender, medical history, and lifestyle habits into the device and submit it. The device then sends this information to the server, which then stores it in a database.

[1486] Real-time health data collection

[1487] Once a user consents to real-time health data collection, the device will collect health data such as heart rate, blood pressure, number of steps, and sleep patterns from wearable devices (e.g., Fitbit or Apple Watch) and connected apps. The device will then send this data to a server at regular intervals, where it will be stored in a database.

[1488] Health data analysis and aging prediction

[1489] The server uses AI technology to analyze the health data stored in the database. Based on past data and statistical data, the server generates a predictive model for the user's aging. This model makes it possible to predict future health risks and the rate of aging.

[1490] Generate personalized health care recommendations

[1491] Based on the aging prediction model, the server generates personalized health management suggestions for the user, including exercise plans, dietary advice, and recommendations for regular health checkups. The server then sends the suggestions to medical professionals for feedback.

[1492] Reflecting advice from medical professionals

[1493] The medical professional reviews the submitted health care proposal and makes any necessary modifications, and the modified feedback is sent back to the server, which then finalizes the health care plan.

[1494] User feedback and implementation

[1495] The server sends the finalized health management plan to the device, which notifies the user and displays the plan contents. The user can then review the health management plan and set reminders to implement it in their daily lives.

[1496] Ongoing monitoring and feedback

[1497] The server analyzes continuously collected health data in real time and periodically updates the aging prediction model. The server adjusts the health management plan according to the user's health condition and notifies medical professionals as necessary. Users can check their health condition and suggested actions on their device and manage their health.

[1498] The system uses advanced technology to provide personalized health and care support while protecting user privacy. Below are some examples and prompts:

[1499] Specific examples

[1500] User: 60-year-old male with a medical history of hypertension and diabetes.

[1501] Devices: Smartphone and wearable device (Fitbit)

[1502] Health Data: Heart rate, steps, and sleep data collected from wearable devices.

[1503] Prompt Sentence Examples

[1504] A 60-year-old male with a medical history of hypertension and diabetes. Generate personalized health management suggestions based on this user's health data, taking into account data collected from wearable devices (heart rate, steps, sleep data, etc.).

[1505] Thus, the present invention is a system that provides advanced personalized health management while protecting the user's privacy.

[1506] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1507] Step 1:

[1508] When a user uses the system for the first time, he or she accesses the initial registration form displayed on the terminal.

[1509] Input: Information such as the user's name, age, gender, medical history, and lifestyle habits.

[1510] Output: The user information entered into the form.

[1511] What happens: A user opens a web browser or mobile app, fills out a form, and clicks submit.

[1512] Step 2:

[1513] The terminal prompts the user to enter information such as name, age, gender, medical history, and lifestyle habits.

[1514] Input: Information entered by the user.

[1515] Output: A request to send the entered information to the server.

[1516] Specific operation: The device displays an input form and sends the information entered by the user to the server as an HTTPS request.

[1517] Step 3:

[1518] The server stores the received registration information in a database.

[1519] Input: User information sent from the device.

[1520] Output: User profile stored in the database.

[1521] What happens: The server receives the request and stores the information in a database.

[1522] Step 4:

[1523] User consents to real-time health data collection.

[1524] Input: User consent.

[1525] Output: The consent is recorded.

[1526] Specific operation: The consent form is displayed on the device and the user presses the "Agree" button.

[1527] Step 5:

[1528] The device collects health data such as heart rate, blood pressure, steps taken, and sleep patterns from wearable devices and connected apps.

[1529] Input: Real-time data obtained from wearable devices.

[1530] Output: Collected health data.

[1531] Specific behavior: Obtain data from the device via Bluetooth or other communication means.

[1532] Step 6:

[1533] The terminal transmits this data to the server at regular intervals.

[1534] Input: Collected health data.

[1535] Output: The data sent to the server.

[1536] Specific operation: The device collects data using an interval timer and sends an HTTP request to the server.

[1537] Step 7:

[1538] The server uses AI technology to analyze the health data stored in the database.

[1539] Input: Health data in a database.

[1540] Output: Analysis results and aging prediction model.

[1541] What it does: The server analyzes the data using machine learning algorithms.

[1542] Step 8:

[1543] The server generates an aging prediction model based on past data and statistical data.

[1544] Input: Health data, historical data, statistical data.

[1545] Output: Aging prediction model.

[1546] Specific operation: The server trains the machine learning model and generates a new predictive model.

[1547] Step 9:

[1548] The server generates health management proposals tailored to each individual user based on the generated aging prediction model.

[1549] Input: Aging prediction model.

[1550] Output: personalized health care recommendations.

[1551] Specific operation: The server analyzes the model output and generates a health management plan.

[1552] Step 10:

[1553] The server sends the suggestions to medical professionals and asks for their feedback.

[1554] Enter: Healthcare Proposal.

[1555] Output: Healthcare professional feedback.

[1556] Specific operation: The server sends suggestions via email or a dedicated app and receives feedback.

[1557] Step 11:

[1558] A medical professional will review the submitted health care proposal and make any necessary adjustments.

[1559] Enter: Healthcare Proposal.

[1560] Output: Corrected feedback.

[1561] How it works: Medical professionals review and correct suggestions through a dedicated app or web portal.

[1562] Step 12:

[1563] The server finalizes the health management plan and transmits it to the terminal.

[1564] Input: Revised health care proposal.

[1565] Output: Final health care plan.

[1566] Specific operation: The server generates the final plan and sends it to the terminal.

[1567] Step 13:

[1568] The terminal notifies the user and displays the plan details.

[1569] Enter: the final health care plan.

[1570] Output: Inform and display the plan to the user.

[1571] What it does: The device generates a push notification and displays the plan details when the user opens the app.

[1572] Step 14:

[1573] Users can review their health plan and set reminders.

[1574] Enter: the final health care plan.

[1575] Output: The set reminder.

[1576] What happens: The user sets a reminder in the app, and the device sets a notification or alarm.

[1577] Step 15:

[1578] The server continuously analyzes collected health data in real time and updates the aging prediction model.

[1579] Input: New health data.

[1580] Output: Updated aging prediction model.

[1581] What happens: The server analyzes the new data and updates the model parameters.

[1582] Step 16:

[1583] The server adjusts the health care plan according to the user's health status and notifies medical professionals as needed.

[1584] Input: Updated aging prediction model and health data.

[1585] Output: Coordinated health care plan.

[1586] What it does: The server detects changes in health status, adjusts plans, and notifies medical professionals.

[1587] Step 17:

[1588] Users can check their health status and suggested actions at any time from their device and manage their health.

[1589] Input: Health care plan and latest health data.

[1590] Output: User's plan execution and health management.

[1591] Specific behavior: User views the in-app dashboard and takes the required action.

[1592] (Application example 1)

[1593] 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."

[1594] Providing personalized health management and nursing care support is an important challenge in an aging society. However, conventional systems have difficulty collecting and analyzing real-time health data and quickly providing personalized recommendations that reflect the advice of medical professionals. Providing immediate feedback based on the user's health status and continuously updating data while protecting privacy are also challenges. Furthermore, the integration of health management recommendations and feedback in physical stores is insufficient.

[1595] 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.

[1596] In this invention, the server includes a means for analyzing collected health data to generate an aging prediction model, a means for generating personalized health management suggestions, and a means for revising the health management suggestions based on advice from medical professionals. This also includes a means for displaying personalized health management suggestions to customers using health data collected on a dedicated in-store terminal, a means for providing health management suggestions based on health checkup results and feedback from the physical store, a means for displaying the health management suggestions on an in-store display or on the customer's smartphone app, a means for medical professionals to review and revise the generated suggestions, and a means for analyzing continuously collected health data over time and updating the suggestions. This enables the rapid provision of personalized health management and care support in real time, and real-time feedback and continuous monitoring according to the user's health condition. Privacy protection features also reduce the risk of data leakage.

[1597] "Collected health data" refers to information such as heart rate, blood pressure, number of steps, and sleep patterns obtained from users' wearable devices and smartphone apps.

[1598] An "aging prediction model" is a model for predicting a user's future health risks and aging rate based on past health data and existing statistical data.

[1599] "Personalized health management suggestions" refer to suggestions that include exercise plans, dietary advice, and recommendations for regular health checks that are generated based on the user's health condition.

[1600] The "means for modifying health care proposals with the advice of a medical professional" refers to a method by which a medical professional reviews and modifies the generated health care proposals and provides optimized proposals.

[1601] An "in-store dedicated terminal" is an electronic device installed in a physical store for collecting and displaying users' health data.

[1602] "Providing health care suggestions" refers to informing the user of the generated health care suggestions and recommending health care activities based on the suggestions.

[1603] A "store display" is a device installed in a physical store that displays health management suggestions as visual information to customers.

[1604] A "smartphone app" is application software that is installed on a customer's smartphone and can display health management suggestions and obtain feedback.

[1605] "Real-time health data" refers to instantaneous data such as a user's current heart rate, blood pressure, number of steps, and sleep patterns.

[1606] "Data leakage risk" refers to the risk of a user's health data being illegally accessed or leaked to a third party.

[1607] "Privacy protection features" refer to technical measures to safely manage users' personal information and health data and prevent unauthorized access or data leakage.

[1608] This invention is a system that collects and analyzes a user's health data and provides personalized health management and care support. Specifically, the invention can be implemented in the following forms.

[1609] User registration and health data collection

[1610] The server provides a means for first-time users to access the initial registration form displayed on a dedicated terminal in the store. The user enters information such as name, age, gender, medical history, and lifestyle habits. The terminal sends this information to the server, where it is stored in a database on the server.

[1611] Real-time health data collection

[1612] If the user agrees, the device will collect real-time health data (heart rate, blood pressure, steps taken, sleep patterns, etc.) from wearable devices and connected apps. This data will be periodically sent to a server, which will store it in a database.

[1613] Health data analysis and aging prediction

[1614] The server uses the stored health data to perform analysis using AI technology, which generates a user's aging prediction model based on past health data and existing statistical data. This model predicts the user's future health risks and aging rate.

[1615] Generate personalized health care recommendations

[1616] Based on the aging prediction model, the server generates personalized health management suggestions for the user, including exercise plans, dietary advice, and recommendations for regular health checks.

[1617] Reflecting advice from medical professionals

[1618] The server sends the generated health care suggestions to medical experts for their feedback, and the suggestions modified by the medical experts ensure further accuracy and reliability.

[1619] Providing health management suggestions in-store

[1620] The improved health management suggestions are sent from the server to dedicated terminals or in-store displays in the store and are displayed to customers. The suggestions are also linked to the customer's smartphone app, allowing the user to instantly check and implement the suggestions.

[1621] Ongoing monitoring and feedback

[1622] The server continuously analyzes the health data collected in real time and periodically updates the aging prediction model, allowing the system to adjust the health management plan accordingly based on the user's health condition and notify medical professionals as needed, enabling health management based on the latest information.

[1623] Examples of prompt statements

[1624] An example of a specific prompt sentence is, "Generate health management suggestions for a 40-year-old male. Heart rate data for the past week: average 75, blood pressure 120 / 80, average number of steps 10,000, average sleep time 7 hours."

[1625] Hardware and Software Used

[1626] The system uses tablets and in-store displays as dedicated terminals, and connects to wearable devices and smartphone apps. A database management system and software with the functions necessary for AI analysis run on the server side. Using this system, customers can receive health management suggestions in real time and use them to implement them.

[1627] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1628] Step 1:

[1629] The user accesses the initial registration form using a dedicated terminal at the store. The user enters information such as name, age, gender, medical history, and lifestyle habits. The entered information is sent from the terminal to the server, which saves it in a database. The entered data is stored in the user information database.

[1630] Step 2:

[1631] If the user agrees, the device will collect real-time health data (e.g., heart rate, blood pressure, number of steps, sleep patterns) from wearable devices and connected apps. This data will be sent to a server at regular intervals, and the server will store it in a database. The input data is the real-time health data, and the output data is an updated health database.

[1632] Step 3:

[1633] The server uses AI technology to perform analysis based on the health data stored in the database. This analysis generates an aging prediction model for the user, taking into account past health data and existing statistical data. This model predicts the user's future health risks and aging rate. The input data is the collected health data, and the output data is the aging prediction model.

[1634] Step 4:

[1635] The server generates personalized health management suggestions based on the generated aging prediction model. These suggestions include exercise plans, dietary advice, and recommendations for regular health checkups. The input data is the aging prediction model, and the output data is the personalized health management suggestions.

[1636] Step 5:

[1637] The generated health care proposal is sent from the server to a medical expert, who provides advice and makes corrections. The medical expert reviews the proposal and makes corrections as necessary. The corrected proposal is sent back to the server and saved in the database again. The input data is the health care proposal, and the output data is the corrected health care proposal.

[1638] Step 6:

[1639] The final health management proposal is sent from the server to a dedicated terminal or in-store display in the store and displayed to the customer. The proposal is also linked to the customer's smartphone app. The user confirms it and carries out health management. The input data is the revised health management proposal, and the output data is the displayed health management proposal.

[1640] Step 7:

[1641] The server continuously analyzes the health data collected in real time and periodically updates the aging prediction model. This allows the system to adjust the health management plan according to the user's health condition and notify medical professionals again as necessary. The input data is the latest health data, and the output data is the updated aging prediction model and health management suggestions.

[1642] Specific working example:

[1643] If the user is a 40-year-old man and his average heart rate data for the past week is 75, the server will use this information to generate health management suggestions such as "walking 30 minutes every day" and "eating a vegetable-based diet."

[1644] The server-generated suggestions were reviewed by medical experts, who then revised them to "reduce walking time to 20 minutes."

[1645] The revised proposals are displayed on the dedicated device and smartphone app, and the user can confirm and implement them.

[1646] Examples of prompts:

[1647] "Generate health management suggestions for a 40-year-old male. Heart rate data for the past week: average 75, blood pressure 120 / 80, average steps 10,000, average sleep time 7 hours."

[1648] 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.

[1649] This invention relates to a system that collects and analyzes a user's health data and emotional data to provide personalized health management and care support. Specifically, the invention is embodied in the following manner.

[1650] User registration and collection of health and emotional data

[1651] 1. When a user uses the system for the first time, they access the initial registration form displayed on the terminal and enter information such as their name, age, gender, medical history, and lifestyle habits.

[1652] 2. The terminal sends the entered registration information to the server, which stores it in a database.

[1653] 3. If the user agrees, the device will collect real-time health data from wearable devices and connected apps, and simultaneously use the emotion engine to collect the user's emotional data.

[1654] 4. The device periodically sends health and emotion data to the server, which stores this data in a database.

[1655] Analysis of health and emotional data and aging prediction

[1656] 1. The server provides the health and emotional data stored in the database to the AI ​​for analysis.

[1657] 2. The AI ​​on the server analyzes the health and emotional data and generates an aging prediction model, which predicts the user's health risks and aging rate.

[1658] 3. The server generates personalized health recommendations that take emotional data into account, including exercise plans, dietary advice, and preventative health checks that take into account the user's mental health.

[1659] Reflecting advice from medical professionals

[1660] 1. The server sends the generated health management proposal to the medical professional and receives feedback. The medical professional reviews the proposal and makes corrections if necessary.

[1661] 2. The corrected feedback is sent back to the server, which then finalizes the health management plan.

[1662] User feedback and implementation

[1663] 1. The server sends the finalized health management plan to the terminal, which notifies the user and displays the plan details.

[1664] 2. The user checks the health management plan, sets reminders to implement it in daily life, and manages their health according to the plan.

[1665] Ongoing monitoring and feedback

[1666] 1. The server analyzes continuously collected health and emotion data in real time and periodically updates the aging prediction model.

[1667] 2. The server adjusts the health management plan accordingly based on the user's health and emotional state, and notifies medical professionals as needed.

[1668] 3. Users can check their health status, emotional state, and suggested actions at any time through their device, and continue to take appropriate health management measures.

[1669] For example, if a user records their daily emotional state and the emotion engine detects that they are experiencing increased stress, the server will add health management suggestions such as reducing exercise and practicing relaxing breathing exercises. It will also take an approach to improving mental health by recommending a weekly counseling session with a medical professional. In this way, the system provides comprehensive support to improve the user's overall health and happiness.

[1670] By integrating emotional data while protecting user privacy, this system can provide more accurate and personalized health management and nursing care support, thereby improving users' quality of life and contributing to solving health management issues in an aging society.

[1671] The processing flow will be explained below.

[1672] Step 1:

[1673] The user accesses the initial registration form and enters information such as name, age, gender, medical history, and lifestyle habits.

[1674] Step 2:

[1675] The device sends the entered registration information to a server, where the data is encrypted for enhanced privacy protection.

[1676] Step 3:

[1677] The server stores the received registration information in a database and creates an initial user profile.

[1678] Step 4:

[1679] If the user agrees, the device will collect real-time health data (heart rate, steps, sleep patterns, etc.) from the wearable device and connected apps. At the same time, the emotion engine will collect emotional data from the user's facial expressions and tone of voice using the device's built-in camera and microphone.

[1680] Step 5:

[1681] The device periodically transmits the collected health and emotional data to a server, which also encrypts the data to ensure security.

[1682] Step 6:

[1683] The server stores the received health and emotional data in a database and simultaneously provides the data to an AI for data analysis.

[1684] Step 7:

[1685] The server's AI analyzes the health and emotional data to generate an aging prediction model, which predicts the user's health risks and aging rate while also incorporating the emotional data.

[1686] Step 8:

[1687] The server then uses the generated aging prediction model to create personalized health management recommendations, including exercise plans, dietary advice, and preventative health checks that take into account the user's health and emotional state.

[1688] Step 9:

[1689] The server sends the health management suggestions to medical professionals and receives feedback, who review the suggestions and make any necessary adjustments.

[1690] Step 10:

[1691] The server determines the final health care plan based on feedback from medical professionals, which is then stored in a database.

[1692] Step 11:

[1693] The server sends the finalized health management plan to the terminal, which notifies the user and displays the plan contents.

[1694] Step 12:

[1695] Users can check their health management plan, set reminders to carry out their plan in their daily lives, and manage their health according to the plan.

[1696] Step 13:

[1697] The server analyzes continuously collected health and emotion data in real time and updates the aging prediction model as needed, which is updated periodically.

[1698] Step 14:

[1699] The server adjusts the health management plan based on the user's health and emotional state and notifies medical professionals if action is needed.

[1700] Step 15:

[1701] Users can check their health status, emotional state, and suggested actions at any time through their device, and continue to manage their health appropriately.

[1702] Through these processing steps, the system can protect the user's privacy while integrating emotional data to provide more accurate and personalized health management and care support. For example, when a user is experiencing high stress, the emotion engine can detect their stress level and incorporate relaxation activities and counseling suggestions into their health management plan, improving their mental health and quality of life.

[1703] Example 2

[1704] 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."

[1705] While personalized health management and nursing care support are becoming increasingly important in an aging society, existing systems do not take into account the user's emotional state, resulting in insufficient comprehensive health management, including mental health. Furthermore, issues remain, such as protecting the privacy of collected data and lack of real-time performance. Furthermore, there is a lack of accurate recommendations incorporating the advice of medical experts.

[1706] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1707] In this invention, the server includes: means for analyzing collected health data and emotion data to generate an aging prediction model; means for generating a personalized health management proposal; means for collecting data from the wearable device and linked apps with the user's consent; means for collecting emotion data using an emotion analysis engine; means for revising the health management proposal based on advice from medical professionals; means for periodically analyzing data in real time and updating the health management plan; data management means with a privacy protection function to reduce the risk of data leaks; and means for notifying the end-user terminal of the health management plan. This enables comprehensive analysis of the user's health condition, provides personalized health management while protecting privacy, and enables highly accurate health management incorporating feedback from medical professionals.

[1708] "Health data" is information related to the user's physical condition, and includes indicators such as heart rate, number of steps, blood pressure, and weight.

[1709] "Emotional data" is information related to the user's emotional state, and is obtained through facial recognition and voice analysis.

[1710] A "wearable device" is an electronic device that is worn on the body and is used to collect health data such as heart rate and number of steps taken in real time.

[1711] A "linked app" is software that communicates with wearable devices and other data collection devices to collect and manage data.

[1712] An "aging prediction model" is a model that analyzes health data and emotional data to predict a user's aging risk and rate of progression.

[1713] "Health management suggestions" are suggestions such as personalized exercise plans, dietary advice, and preventative health checks that are generated based on the user's health and emotional state.

[1714] "Emotion analysis engine" is a general term for algorithms and software that use image processing and voice recognition to analyze a user's emotional state.

[1715] A "medical professional" is a person with specialized medical knowledge and qualifications, such as a doctor or nurse, who is responsible for providing opinions and feedback on the user's health management suggestions.

[1716] "Data management measures" refer to methods and technologies for safely and efficiently storing and managing collected data, including encryption and access control.

[1717] An "end-user terminal" is an electronic device such as a computer, smartphone, or tablet that is directly used by a user and displays notifications and information from the system.

[1718] The present invention relates to a system for collecting and analyzing a user's health data and emotional data, and providing personalized health management and nursing care support. Specific embodiments for carrying out the present invention will be described below.

[1719] User Registration

[1720] The user accesses the initial registration form and enters information such as name, age, gender, medical history, and lifestyle habits. The device then sends the collected registration information to the server, which stores this information in a database and creates an individual profile for the user.

[1721] Health and emotional data collection

[1722] Users use wearable devices and linked apps to collect daily health data, including heart rate, step count, and blood pressure. The devices collect this data in real time and periodically send it to a server. Furthermore, the devices are equipped with cameras and voice recognition functions, and use an emotion analysis engine to collect user emotion data. Specifically, the emotion data is analyzed using a face recognition and voice analysis engine that uses OpenCV.

[1723] Data analysis and aging prediction

[1724] The server stores the collected health and emotion data in a database and provides it to an AI for analysis. The AI ​​uses TensorFlow and PyTorch. The AI ​​on the server analyzes this data and generates an aging prediction model, which can predict the user's health risks and aging rate.

[1725] Generate personalized health care recommendations

[1726] The server generates personalized health management suggestions based on the analysis results. These suggestions include exercise plans that take the user's mental health into account, dietary advice, and preventative health check items. Using the generative AI model, suggestions can be generated by inputting specific prompts. For example, the prompt could be, "30-year-old male, software engineer. I've been feeling a bit stressed lately. Please predict health risks based on my heart rate and sleep data and suggest an appropriate health management plan."

[1727] Reflecting advice from medical professionals

[1728] The server sends the generated health management proposal to a medical professional and receives feedback. The medical professional reviews the proposal and makes any necessary corrections. The corrected feedback is sent back to the server, and the final health management plan is finalized.

[1729] User feedback and implementation

[1730] The server sends the finalized health management plan to the device, which notifies the user and displays the plan contents. The user can then check the health management plan, set reminders, and implement the plan in their daily lives.

[1731] Ongoing monitoring and feedback

[1732] The server analyzes continuously collected health and emotional data in real time and periodically updates the aging prediction model. This allows the health management plan to be adjusted appropriately according to the user's health and emotional state. Users can check their health and emotional state, as well as suggested actions, at any time via their device to continue appropriate health management.

[1733] For example, if the emotion engine detects high stress in a user, the server can add health management suggestions such as reducing exercise and practicing relaxing breathing exercises to the user's list of health management suggestions. It also recommends a weekly counseling session with a medical professional, taking an approach to improving the user's mental health. This system can improve the user's overall health and happiness.

[1734] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1735] Step 1:

[1736] The user accesses the initial registration form and enters information such as name, age, sex, medical history, and lifestyle habits.

[1737] Input: User's personal information (name, age, gender, medical history, lifestyle, etc.)

[1738] Output: User registration information is saved on the device

[1739] Specific behavior: The user enters information into the form displayed on the device and clicks the "Register" button.

[1740] Step 2:

[1741] The terminal transmits the user's registration information to the server.

[1742] Input: User registration information (data stored in the device)

[1743] Output: User registration information is sent to the server and stored in the database

[1744] What happens: The device sends registration information to the server using an HTTP POST request.

[1745] Step 3:

[1746] The server stores the registration information in a database.

[1747] Input: User registration information sent to the server

[1748] Output: User registration information stored in the database

[1749] Specific operation: The server uses a database operation API (e.g. MySQL) to insert information into the database.

[1750] Step 4:

[1751] The server collects health data from the wearable device and the associated app.

[1752] Input: Real-time health data (heart rate, steps, blood pressure, etc.) provided by wearable devices and linked apps

[1753] Output: Health data sent to the server

[1754] Specific operation: The device collects data via Bluetooth or API and sends it to the server at regular intervals.

[1755] Step 5:

[1756] The server uses a sentiment analysis engine to collect sentiment data.

[1757] Input: Data (audio, image) from the device's camera and microphone

[1758] Output: Emotion data (happiness, sadness, anger, etc.) sent to the server

[1759] Specific operation: The device performs face recognition and voice analysis in real time using OpenCV and a voice recognition engine, and sends the results to the server.

[1760] Step 6:

[1761] The server provides health and emotional data to the analytical AI.

[1762] Input: Health and emotion data stored on the server

[1763] Output: Aging prediction results and health risk assessment generated by the AI ​​model

[1764] Specific operation: The server inputs data into TensorFlow or PyTorch AI models using Python scripts and outputs the analysis results.

[1765] Step 7:

[1766] A server generates personalized health care recommendations.

[1767] Input: Analysis results of the AI ​​model

[1768] Output: Health management suggestions for each user (exercise plan, dietary advice, etc.)

[1769] Specific operation: The server inputs a prompt to the generated AI model, such as "30-year-old male, software engineer by occupation. Recently, stress has been increasing. Please predict health risks based on heart rate and sleep data and propose an appropriate health management plan." and receives the proposal as output.

[1770] Step 8:

[1771] The server sends the generated health care recommendations to the medical professionals and receives feedback.

[1772] Input: Health management suggestions from a generative AI model

[1773] Output: Modified or approved health care proposal

[1774] What it does: The server sends the proposal to medical professionals via email or a dedicated portal and collects their feedback.

[1775] Step 9:

[1776] The server finalizes the health management plan and sends it to the terminal.

[1777] Input: Healthcare recommendations incorporating feedback from medical professionals

[1778] Output: The final health plan displayed on the user's device.

[1779] Specific operation: The server sends the confirmed plan in JSON format to the device and notifies it.

[1780] Step 10:

[1781] The user checks their health plan and sets reminders.

[1782] Input: Health management plan received on the device

[1783] Output: Set reminders (exercise, meals, counseling sessions, etc.)

[1784] Specific behavior: The user checks the plan details on the device and uses the app's reminder setting function.

[1785] Step 11:

[1786] The server continuously analyzes the data in real time and updates the health management plan accordingly.

[1787] Input: Regularly collected health and emotional data

[1788] Output: Updated health plan

[1789] How it works: The server periodically re-runs the AI ​​model and regenerates a health management plan based on the analysis results.

[1790] (Application example 2)

[1791] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1792] Conventional health management systems collect and analyze users' health data, but they do not adequately collect real-time emotional data or respond to emergencies based on that data. Furthermore, they lack the functionality to automatically notify users appropriately in the event of an emergency, making it difficult to ensure user safety.

[1793] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing collected health data to generate an aging prediction model, means for generating personalized health management suggestions, means for revising the health management suggestions based on advice from medical professionals, means for automatically notifying in the event of an emergency, means for monitoring the health status in real time based on emotional data, and means for sending emergency alerts to designated contacts. This makes it possible to comprehensively manage the user's health status and emotional state and to respond quickly in the event of an emergency.

[1794] The "means of analyzing collected health data to generate an aging prediction model" is a function that analyzes health data such as heart rate and blood pressure collected from users and creates a model that predicts the progression of aging and health risks based on this data.

[1795] The "means for generating personalized health management suggestions" is a function that takes into consideration each user's health data and emotional data and individually suggests optimal exercise plans, dietary advice, and the like.

[1796] The "means for correcting the health care proposal with the advice of a medical professional" is a function for having a medical professional review the generated health care proposal and correcting it as necessary.

[1797] "Means for automatically notifying in an emergency" is a function for sending an emergency notification when an abnormality is detected in the user's health or emotional state.

[1798] The "means for monitoring health status in real time based on emotional data" is a function that continuously collects emotional data from the user and monitors the user's mental health status in real time based on this data.

[1799] "Means for sending emergency alerts to designated contacts" refers to a feature that automatically sends alerts to pre-defined contacts and emergency services in the event of a user emergency.

[1800] "Data management means with privacy protection functions to reduce the risk of data leakage" refers to security functions that safely manage user data and prevent it from being leaked to the outside.

[1801] The "means for periodically updating the aging prediction model" is a function for periodically maintaining the aging prediction model in the latest state based on newly collected data from users.

[1802] "Means for managing the emergency alert sending log" is a function that saves the history of emergency alerts sent and manages them so that they can be checked later.

[1803] The "means for continuously collecting and analyzing health data and emotional data" is a function for continuously collecting a user's health data and emotional data and analyzing them in real time.

[1804] "Means of collaborating with medical institutions and nursing care facilities to provide highly reliable health management and nursing care support" is a function that shares information with medical institutions and nursing care facilities and provides users with highly accurate health management and nursing care support.

[1805] The present invention relates to a system that collects and analyzes health data and emotional data of a user and provides personalized health management and care support. The present invention can be embodied in the following forms.

[1806] User registration and collection of health and emotional data

[1807] 1. When a user uses the system for the first time, they access the initial registration form displayed on the terminal and enter information such as their name, age, gender, medical history, and lifestyle habits.

[1808] 2. The terminal sends the entered registration information to the server, which stores it in a database.

[1809] 3. If the user agrees, the device will collect real-time health data from wearable devices and connected apps, and simultaneously use the emotion engine to collect the user's emotional data.

[1810] 4. The device periodically sends health and emotion data to the server, which stores this data in a database.

[1811] Analysis of health and emotional data and aging prediction

[1812] 1. The server provides the health and emotional data stored in the database to the AI ​​for analysis.

[1813] 2. The AI ​​on the server analyzes the health and emotional data and generates an aging prediction model, which predicts the user's health risks and aging rate.

[1814] 3. The server generates personalized health recommendations that take emotional data into account, including exercise plans, dietary advice, and preventative health checks that take into account the user's mental health.

[1815] Automatic emergency notification function

[1816] 1. The server monitors the user's collected data in real time and sends an emergency notification if an abnormality is detected. For example, if an abnormally high heart rate is detected at the same time as a state of severe stress, an emergency notification will be sent.

[1817] 2. Emergency Notifications automatically send alerts to designated contacts (e.g., family members or emergency services).

[1818] Reflecting advice from medical professionals

[1819] 1. The server sends the generated health management proposal to the medical professional and receives feedback. The medical professional reviews the proposal and makes corrections if necessary.

[1820] 2. The corrected feedback is sent back to the server, which then finalizes the health management plan.

[1821] User feedback and implementation

[1822] 1. The server sends the finalized health management plan to the terminal, which notifies the user and displays the plan details.

[1823] 2. The user checks the health management plan, sets reminders to implement it in daily life, and manages their health according to the plan.

[1824] Ongoing monitoring and feedback

[1825] 1. The server analyzes continuously collected health and emotion data in real time and periodically updates the aging prediction model.

[1826] 2. The server adjusts the health management plan accordingly based on the user's health and emotional state, and notifies medical professionals as needed.

[1827] 3. Users can check their health status, emotional state, and suggested actions at any time through their device, and continue to take appropriate health management measures.

[1828] Program processing explanation

[1829] Health data collection: The device collects data such as heart rate and blood pressure from wearable devices and sends it to a server.

[1830] Emotion data collection: The device uses an emotion engine to extract emotion data from the user's speech and facial expressions and transmits it to the server.

[1831] Data analysis: The AI ​​in the server analyzes the collected health and emotional data to generate an aging prediction model and personalized health management suggestions.

[1832] Emergency notification: Based on the analysis of health and emotional data, if an abnormality is detected, an emergency alert will be automatically sent to designated contacts.

[1833] Examples of concrete examples and prompts

[1834] Example: If a user experiences a spike in heart rate and elevated stress levels after intense exercise, the system can automatically send out an emergency alert and notify designated contacts.

[1835] Example prompt: "How can I design a security support app that uses health and emotion data to send automatic alerts in emergencies?"

[1836] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1837] Step 1:

[1838] When a user uses the system for the first time, they access the initial registration form displayed on their terminal and enter information such as their name, age, gender, medical history, and lifestyle habits. The terminal sends this information to the server, which then stores it in a database. The input is personal information from the user, and the output is registration in the server's database.

[1839] Step 2:

[1840] If the user agrees to the collection of health and emotional data, the device collects real-time health data from the wearable device and linked apps. At the same time, the device uses an emotion engine to collect emotional data from the user's speech and facial expressions. The health and emotional data are periodically sent to a server, which stores them in a database. The input is the health and emotional data obtained from the wearable device and emotion engine, and the output is stored in the server's database.

[1841] Step 3:

[1842] The server provides the health and emotion data stored in the database to the AI ​​model for analysis. The AI ​​model analyzes these data and generates an aging prediction model. It then creates personalized health care recommendations based on the analysis results. The inputs are health and emotion data, and the outputs are the aging prediction model and health care recommendations.

[1843] Step 4:

[1844] The server sends the generated healthcare proposal to the healthcare professional and receives feedback. The healthcare professional reviews the proposal and makes any necessary modifications. The modified feedback is sent back to the server, which then finalizes the healthcare plan. The input is the generated healthcare proposal and the output is the modified healthcare plan.

[1845] Step 5:

[1846] The server sends the confirmed health management plan to the terminal. The terminal notifies the user and displays the plan contents. The user confirms the health management plan and sets reminders to implement it in daily life. The input is the confirmed health management plan, and the output is the user's confirmation and reminder settings.

[1847] Step 6:

[1848] The server monitors continuously collected health and emotion data in real time, periodically updates the aging prediction model, and, if necessary, adjusts the health management plan accordingly and notifies the user. The input is continuously collected health and emotion data, and the output is an updated aging prediction model and health management plan.

[1849] Step 7:

[1850] The server sends an emergency notification if an abnormality in the user's health or emotional state is detected. The emergency alert is automatically sent to designated contacts (e.g., family members or emergency services). The input is abnormal data collected in real time, and the output is the issuance of an emergency alert.

[1851] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1852] 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.

[1853] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1854] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1855] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1856] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1857] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1858] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1859] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1860] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1861] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1862] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1863] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1864] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1865] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1866] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1867] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1868] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1869] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1870] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1871] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1872] The following is further disclosed regarding the above embodiment.

[1873] (Claim 1)

[1874] a means for analyzing the collected health data to generate an aging prediction model;

[1875] means for generating personalized health care recommendations;

[1876] A system that includes a means for modifying health care recommendations with the advice of a medical professional.

[1877] (Claim 2)

[1878] Data management measures to protect privacy and reduce the risk of data leaks;

[1879] 10. The system of claim 1, further comprising means for periodically updating the aging prediction model.

[1880] (Claim 3)

[1881] 2. The system according to claim 1, further comprising means for providing reliable health management and care support in cooperation with medical institutions and care facilities.

[1882] "Example 1"

[1883] (Claim 1)

[1884] a means for analyzing the collected health data to generate an aging prediction model;

[1885] means for generating personalized health care recommendations;

[1886] a means of amending health care proposals with the advice of medical professionals;

[1887] a means for collecting and storing user registration information in a database;

[1888] A means for collecting health data from the wearable device in real time and transmitting the data to a server;

[1889] A means to regularly analyze health data and update aging prediction models;

[1890] means for informing the user of the optimized health management plan and setting reminders;

[1891] A system including:

[1892] (Claim 2)

[1893] Data management measures to protect privacy and reduce the risk of data leaks;

[1894] 10. The system of claim 1, further comprising means for periodically updating the aging prediction model.

[1895] (Claim 3)

[1896] 2. The system according to claim 1, further comprising means for providing reliable health management and care support in cooperation with medical institutions and care facilities.

[1897] "Application Example 1"

[1898] (Claim 1)

[1899] a means for analyzing the collected health data to generate an aging prediction model;

[1900] means for generating personalized health care recommendations;

[1901] a means of amending health care proposals with the advice of medical professionals;

[1902] A means for displaying personalized health management suggestions to customers using health data collected by a dedicated terminal in the store;

[1903] A means to provide health management suggestions based on health checkup results and feedback from physical stores,

[1904] A means to display health management suggestions on store displays and customer smartphone apps,

[1905] A means for medical professionals to review and correct the generated suggestions;

[1906] A system that includes a means to analyze continuously collected health data over time and update recommendations.

[1907] (Claim 2)

[1908] Data management measures to protect privacy and reduce the risk of data leaks;

[1909] 10. The system of claim 1, further comprising means for periodically updating the aging prediction model.

[1910] (Claim 3)

[1911] 2. The system according to claim 1, further comprising means for providing reliable health management and care support in cooperation with medical institutions and care facilities.

[1912] "Example 2: Combining Emotion Engines"

[1913] (Claim 1)

[1914] means for analyzing the collected health data and emotion data to generate an aging prediction model;

[1915] means for generating personalized health care recommendations;

[1916] A means for collecting data from wearable devices and associated apps with user consent;

[1917] a means for collecting emotion data utilizing an emotion analysis engine;

[1918] a means of amending health care proposals with the advice of medical professionals;

[1919] A means to regularly analyze real-time data and update health management plans;

[1920] Data management measures to protect privacy and reduce the risk of data leaks;

[1921] means for notifying an end user terminal of the health management plan;

[1922] ...

[1923] A system including:

[1924] (Claim 2)

[1925] 2. The system according to claim 1, further comprising means for providing reliable health management and care support in cooperation with medical institutions and care facilities.

[1926] (Claim 3)

[1927] 10. The system of claim 1, including means for utilizing continuous feedback from the end-user terminal to adjust the health management plan accordingly.

[1928] "Application example 2 when combining emotion engines"

[1929] (Claim 1)

[1930] a means for analyzing the collected health data to generate an aging prediction model;

[1931] means for generating personalized health care recommendations;

[1932] a means of amending health care proposals with the advice of medical professionals;

[1933] A means for automatic notification in the event of an emergency;

[1934] A means of monitoring health status in real time based on emotional data,

[1935] a means of transmitting emergency alerts to designated contacts;

[1936] A system including:

[1937] (Claim 2)

[1938] Data management measures to protect privacy and reduce the risk of data leaks;

[1939] a means of periodically updating the aging prediction model;

[1940] a means for managing a log of emergency alerts;

[1941] 10. The system of claim 1, further comprising means for collecting and analyzing continuous health and emotional data.

[1942] (Claim 3)

[1943] 2. The system according to claim 1, further comprising means for providing reliable health management and care support in cooperation with medical institutions and care facilities. [Explanation of symbols]

[1944] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for analyzing the collected health data to generate an aging prediction model; means for generating personalized health care recommendations; A system that includes a means for modifying health care recommendations with the advice of a medical professional.

2. Data management measures to protect privacy and reduce the risk of data leaks; 10. The system of claim 1, further comprising means for periodically updating the aging prediction model.

3. 2. The system according to claim 1, further comprising means for providing highly reliable health management and care support in cooperation with medical institutions and care facilities.

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