System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting personalized physiological data; means for transmitting the physiological data to a cloud environment; means for using a generative model for analyzing the physiological data and evaluating a health condition of a user in the cloud environment; means for generating a personalized health care plan based on an analysis result of the generative model; means for transmitting the health care plan to a terminal of the user; means for transmitting an execution result and feedback of the health care plan to the cloud environment again; and means for analyzing the feedback and updating the health care plan.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] Japan's aging society demands personalized healthcare solutions to maintain health and independence, but the current system is not adequately addressing this need. Specifically, there is a lack of a system to provide appropriate exercise, diet, and wellness programs for each elderly person to improve their quality of life. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing the following means: A system including: means for collecting individual biometric data; means for transmitting the biometric data to a cloud environment; means for using a generative model in the cloud environment to analyze the biometric data and evaluate the user's health status; means for generating an individual health management plan based on the analysis results of the generative model; means for transmitting the health management plan to the user's device; means for transmitting implementation results and feedback of the health management plan back to the cloud environment; and means for analyzing the feedback and updating the health management plan. This makes it possible to provide an exercise, diet, and wellness program optimized for each elderly person, supporting their health and independence.

[0006] "Biometric data" refers to individual data such as blood pressure, weight, heart rate, and number of steps that are collected to assess a user's health status.

[0007] A "cloud environment" is a system that utilizes data storage and computing resources provided over the Internet.

[0008] A "generative model" is a machine learning algorithm that analyzes a user's health status based on large amounts of data and generates an individualized health management plan.

[0009] A "health management plan" is a plan that includes exercise, diet, and wellness programs created based on the user's health condition.

[0010] A "terminal" is an electronic device used by a user, such as a smartphone or tablet.

[0011] "Feedback" refers to data that a user inputs into the system, such as the results of implementing a health management plan and their impressions.

[0012] A "wearable device" is a wristwatch- or wristband-type electronic device worn by a user, which automatically collects biometric data. [Brief explanation of the drawings]

[0013] [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

[0014] 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.

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

[0016] 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).

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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."

[0021] [First embodiment]

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

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0028] 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.

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

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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."

[0034] This invention relates to a system that uses generative AI to customize health management for elderly people in Japan. The system collects biometric data from users, transmits it to a cloud environment, and analyzes it to provide personalized health management plans. The following describes the program and processing of this system.

[0035] Program processing explanation

[0036] 1. Data Collection

[0037] User: Measures blood pressure every morning and enters the results into a dedicated smartphone app. Also, uses a wearable device to automatically record daily steps and heart rate.

[0038] Example: A user uses a blood pressure monitor in the morning and enters the measurement value into a dedicated app, while the smartwatch simultaneously collects 5,000 steps.

[0039] 2. Data Transmission

[0040] Terminal: Periodically sends collected biometric data (blood pressure, number of steps, heart rate, etc.) to a cloud server.

[0041] Example: A smartphone uses Wi-Fi or mobile data to upload blood pressure data or step count data to a cloud server.

[0042] 3. Data Analysis

[0043] Server: Using a generative model on the cloud, analyzes the collected biometric data and assesses the user's health status.

[0044] Example: The server tracks blood pressure and step count data for the past 30 days and analyzes patterns of changes in physical condition.

[0045] 4. Plan Generation

[0046] Server: Based on the results of data analysis, it generates a health management plan including individualized exercise, diet, and wellness programs.

[0047] Example: A server uses user data to generate a low-salt meal plan and a plan recommending 30 minutes of walking each day.

[0048] 5. Send results

[0049] Server: Sends the generated health management plan to the user's terminal.

[0050] Example: The generated plan is sent in JSON format to a smartphone app and notified to the user.

[0051] 6. Feedback Collection

[0052] User: Implements a health management plan and enters the results and feedback into a dedicated app.

[0053] Terminal: Sends the feedback data back to the cloud server.

[0054] Example: After a user completes a walk, they enter the time it took and their impressions into the app, and their smartphone uploads the data to a cloud server.

[0055] 7. Feedback analysis and plan updates

[0056] Server: Analyzes the feedback data and reflects it in the next health management plan.

[0057] Example: The server analyzes the feedback data and, if walking is effective, adjusts the exercise, such as slightly increasing the amount of exercise.

[0058] Through these measures, the system provides personalized medical solutions to maintain the health and independence of older adults and improve their quality of life, enabling users to more effectively manage their daily health.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] User: Measures blood pressure every morning and enters the results into a dedicated smartphone app. The user also wears a wearable device that automatically records the number of steps taken and heart rate each day.

[0062] Specific operation: The user uses a blood pressure monitor and inputs the measurement value (e.g., 120 / 80 mmHg) into the app. The wearable device counts 5,000 steps and simultaneously records heart rate data.

[0063] Step 2:

[0064] Terminal: Sends collected biometric data to a cloud server at regular intervals.

[0065] How it works: Your smartphone uses Wi-Fi or mobile data to upload blood pressure and step count data to a cloud server. The data is then SSL encrypted before being sent.

[0066] Step 3:

[0067] Server: Using a generative model on the cloud, analyzes the transmitted biometric data and assesses the user's health status.

[0068] Specific operation: The server performs trend analysis of blood pressure data from the past 30 days and detects abnormal values. For example, if the average blood pressure exceeds 130 / 85 for 10 days or more, it is determined to be mild hypertension.

[0069] Step 4:

[0070] Server: Generates an individual health management plan based on the analysis results.

[0071] What it does: The generative model creates a health plan for the user with recommendations for diet, exercise, and mental health, such as a low-sodium diet, 30 minutes of walking daily, and relaxation exercises for stress management.

[0072] Step 5:

[0073] Server: Sends the generated health management plan to the user's terminal.

[0074] Specific operation: The generated plan is packaged in JSON format and sent to the smartphone app using the HTTPS protocol. The plan is then displayed to the user as a notification.

[0075] Step 6:

[0076] User: Carry out daily health management according to the received health management plan.

[0077] Specific actions: The user refers to the smartphone app, changes dinner to a low-salt dish, and begins a 30-minute walk.

[0078] Step 7:

[0079] User: Enter the results of the health management plan and feedback into a dedicated app.

[0080] Specific behavior: After completing a walk, the user enters the time it took and their feelings into the app. For example, they might enter, "After 30 minutes of walking, I feel refreshed."

[0081] Step 8:

[0082] Terminal: Sends the feedback data back to the cloud server.

[0083] Specific operation: The feedback data collected by the smartphone is encrypted and uploaded to a cloud server.

[0084] Step 9:

[0085] Server: Analyzes the feedback data and updates the next health management plan.

[0086] Specific actions: The AI ​​evaluates the feedback data, and if walking has a positive effect, it will make adjustments such as slightly increasing the amount of exercise. This will be reflected in the next plan.

[0087] Through the above steps, it is possible to provide an optimal health management plan tailored to the individual health condition of each user, thereby improving the quality of life of elderly people.

[0088] Example 1

[0089] 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."

[0090] The present invention relates to a system that efficiently analyzes collected biometric data and provides users with customized health management plans as a means of individually optimizing health management for elderly people. Conventional health management systems have struggled to customize plans to accommodate individual health conditions, and generic plans often fail to produce satisfactory results. Furthermore, they lacked a mechanism for continuously improving plans using feedback data, making it difficult to accurately manage users' health conditions.

[0091] 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.

[0092] In this invention, the server includes means for collecting individual biometric data, means for transmitting the biometric data to a cloud environment, means for analyzing the biometric data in the cloud environment and using a generative model to evaluate the user's health condition, means for generating an individual health management plan based on the analysis results of the generative model, means for transmitting the health management plan to the user's device, means for transmitting implementation results and feedback of the health management plan back to the cloud environment, means for analyzing the feedback and updating the health management plan, means for measuring blood pressure every morning and inputting it into a smartphone app, means for automatically recording step count and heart rate data using a wearable device and means for transmitting the data to the device, and means for generating a health management plan that is periodically improved based on the analysis. This makes it possible to individually evaluate the user's health condition and provide an appropriate health management plan, and to continuously improve the plan through feedback.

[0093] "Individual biometric data" refers to data relating to physical conditions such as blood pressure, number of steps, and heart rate collected from individual users.

[0094] A "cloud environment" is a collection of remote servers used to store, analyze, and process data over the Internet.

[0095] A "generative model" is an artificial intelligence algorithm that predicts and generates results based on data analysis.

[0096] A "health plan" is a set of specific instructions, including exercise, diet, wellness activities, etc., to improve a user's health.

[0097] A "terminal" is a hardware device (e.g., smartphone, tablet) used by a user to input, process, transmit, etc. data.

[0098] A "wearable device" is an electronic device that can be worn to automatically record daily physical activity.

[0099] "Feedback" is data on the implementation results and impressions provided by the user after implementing the health management plan.

[0100] The present invention is a system for individually optimizing health management for elderly people. This system analyzes biometric data collected from users in a cloud environment and provides customized health management plans using a generative AI model. The system's program processing is described in detail below.

[0101] First, the user collects biometric data using a smartphone app and a wearable device. Every morning, the user measures their blood pressure with a blood pressure monitor and enters the results into the smartphone app. The wearable device (e.g., a smartwatch) also automatically records their daily steps and heart rate. This data is periodically sent from the device to a cloud server.

[0102] The cloud server analyzes the received biometric data using a generative AI model. This analysis is performed using Python scripts and machine learning libraries such as TensorFlow and PyTorch. The analysis results in an assessment of the user's health status. For example, blood pressure and step count data from the past 30 days can be tracked to identify patterns of fluctuations in health status.

[0103] The server then generates a personalized health management plan based on the analysis results. This plan includes exercise, diet, and wellness activities. For example, the analysis results may recommend a low-salt diet plan and 30 minutes of walking daily. The plan is formatted in JSON and pushed to the user's smartphone app.

[0104] The user carries out the health management plan they receive and enters the results and feedback into a dedicated app. For example, after completing a walk, they can enter the time it took and the effects they felt in text. The device then sends this feedback data back to the cloud server.

[0105] The server analyzes the feedback data and updates the health management plan based on the analysis. This process is reflected in the next health management plan. For example, if walking was effective, adjustments such as slightly increasing the amount of exercise will be made.

[0106] In this way, the system of the present invention effectively supports health management for the elderly by collecting and analyzing the user's biometric data and providing an individually customized health management plan, enabling the user to manage their health more effectively through this system.

[0107] keyword:

[0108] Generative AI model, prompt sentence

[0109] Example prompt sentence:

[0110] Use your biometric data (blood pressure, steps, heart rate) to create an AI model to generate a personalized health plan.

[0111] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0112] Step 1: Data collection

[0113] User: Wakes up in the morning, measures blood pressure with a blood pressure monitor, and enters the results into a dedicated smartphone app. The input data is saved on the device.

[0114] Input: Blood pressure data measured by the user

[0115] Output: Blood pressure data stored in a smartphone app

[0116] User: Wears a wearable device (smartwatch) that automatically records daily steps and heart rate data, which is then periodically sent to a smartphone.

[0117] Input: User's daily activity data (steps, heart rate)

[0118] Output: Step count and heart rate data sent to your smartphone

[0119] Step 2: Send data

[0120] Device: Collected biometric data (blood pressure, number of steps, heart rate) is periodically sent to a cloud server. Data is uploaded securely and quickly using Wi-Fi or mobile data.

[0121] Input: Biometric data stored on the smartphone

[0122] Output: Biometric data sent to the cloud server

[0123] Step 3: Data analysis

[0124] Server: Analyzes the submitted biometric data using a generative AI model in the cloud. Health status is assessed using Python scripts and machine learning libraries such as TensorFlow and PyTorch.

[0125] Input: Biometric data stored on a cloud server

[0126] Output: User's health status assessment result

[0127] Step 4: Generate a plan

[0128] Server: Based on the analysis results, it generates a health management plan that includes a personalized exercise, diet, and wellness program, such as a low-salt meal plan and a daily walking plan.

[0129] Input: Health status assessment results

[0130] Output: A personalized health plan

[0131] Step 5: Send results

[0132] Server: Formats the generated health management plan in JSON format and sends it to the user's smartphone app via push notification.

[0133] Enter: Customized Health Care Plan

[0134] Output: Health plan notification sent to smartphone app

[0135] Step 6: Gather feedback

[0136] User: Implements a health management plan and inputs the results and feedback into a dedicated app. For example, after walking, the user enters the time it took and their impressions.

[0137] Input: User feedback data

[0138] Output: Feedback data stored in a smartphone app

[0139] Terminal: Sends the feedback data back to the cloud server.

[0140] Input: Feedback data stored in the smartphone app

[0141] Output: Feedback data sent to the cloud server

[0142] Step 7: Analyze feedback and update plans

[0143] Server: Analyzes the feedback data and reflects it in the next health management plan. The generative AI model incorporates new data to update and continuously optimize the plan.

[0144] Input: Feedback data

[0145] Output: Updated individual health care plan

[0146] (Application example 1)

[0147] 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."

[0148] Health management for the elderly requires providing plans that meet individual needs, but conventional systems have difficulty linking with food delivery services, such as individual food selection and home delivery, making it impossible to achieve effective health management. In particular, a system is needed that provides individualized healthy eating plans in a way that is easily accessible to the elderly.

[0149] 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.

[0150] In this invention, the server includes means for collecting individual biometric data, means for transmitting the biometric data to a cloud environment, means for analyzing the biometric data in the cloud environment and using a generative model for evaluating the user's health status, means for generating an individual health management plan based on the analysis results of the generative model, means for transmitting the health management plan to the user's terminal, means for transmitting implementation results and feedback of the health management plan back to the cloud environment, means for analyzing the feedback and updating the health management plan, and means for generating a meal plan based on the health management plan and automatically ordering meals in cooperation with a food delivery service. This makes it possible to provide an individualized healthy meal plan to an elderly person and have it delivered to their home via a food delivery service.

[0151] "Biometric data" refers to data related to the user's health condition, including blood pressure, number of steps, heart rate, and the like.

[0152] A "cloud environment" is a computing resource and data storage environment consisting of a group of remote servers accessible via the Internet.

[0153] A "generative model" is an artificial intelligence algorithm that analyzes collected data and generates an individual plan from the results.

[0154] "Health Management Plan" means a plan that includes exercise, diet, and health activities designed to improve and maintain the User's health.

[0155] "Feedback" is information about the results, impressions, and effects of the health management plan implemented by the user.

[0156] A "food delivery service" is a service that delivers meals to a specific location and delivers meals to users based on a personalized healthy eating plan.

[0157] A "terminal" is an electronic device that allows a user to input data, receive notifications, send feedback, etc.

[0158] "Analysis" is the process of processing and calculating collected data to derive results.

[0159] The present invention relates to a system that collects individual biometric data, generates a health management plan, and provides meals in cooperation with a food delivery service. This system supports the health management of elderly people and improves their quality of life by providing personalized meal plans.

[0160] Hardware and software used

[0161] Hardware: Smartphones, wearable devices (smartwatches, etc.)

[0162] Software: Smartphone app, generative AI model, cloud server, food delivery system

[0163] Data collection

[0164] Users measure their blood pressure every morning and enter the data into a smartphone app. Wearable devices (e.g., smartwatches) also automatically record their daily steps and heart rate, thereby collecting biometric data.

[0165] Data transmission

[0166] The collected biometric data is sent to a cloud server via the smartphone, which then uploads the blood pressure and step count data to the cloud server using Wi-Fi or mobile data.

[0167] Data analysis

[0168] The collected biometric data is analyzed on a cloud server using a generative AI model to assess the user's health. For example, the generative AI model tracks blood pressure and step count data from the past 30 days and analyzes patterns of changes in physical condition.

[0169] Health management plan generation

[0170] Based on the results of the data analysis, the generative AI model generates a health management plan that includes a personalized exercise, diet, and wellness program, such as a low-salt diet plan and a plan recommending 30 minutes of walking every day.

[0171] Send results

[0172] The generated health management plan is sent in JSON format to a smartphone app and notified to the user.

[0173] Food delivery collaboration

[0174] Based on the generated health management plan, the app will automatically order appropriate meals through a food delivery service. Specifically, the app will automatically order low-sodium meals from the food delivery service.

[0175] Feedback collection

[0176] Users enter their impressions and effects of the provided meal into the app, and the data is then uploaded to the cloud server, allowing user feedback to be collected.

[0177] Feedback analysis and plan updates

[0178] The cloud server analyzes the collected feedback data and reflects it in the next health management plan. For example, if walking is effective, it will make adjustments such as slightly increasing the amount of exercise.

[0179] Specific examples

[0180] For example, suppose User A enters their morning blood pressure and records the number of steps they took that day on their smartwatch. This data is sent from the smartphone app to a cloud server, where it is analyzed by a generative AI model. Based on the analysis results, the AI ​​generates a low-salt meal plan, and this data is automatically sent to a food delivery service. After User A receives their dinner, they can enter their post-meal impressions into the app, and this feedback is reflected in their next meal plan.

[0181] Prompt Sentence Examples

[0182] "Generate a low-sodium, high-protein healthy diet plan based on user biometric data: blood pressure (120 / 80), steps (5000), heart rate (70 bpm), and 30 days of data analysis."

[0183] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0184] Step 1: Data collection

[0185] The user inputs the results of their blood pressure measurement every morning. A wearable device (such as a smartwatch) automatically records the number of steps taken and heart rate each day. This allows the user's biometric data (input: blood pressure, number of steps, heart rate) to be collected.

[0186] Step 2: Send data

[0187] The device (smartphone) sends the collected biometric data to a cloud server. The smartphone uploads blood pressure data and step count data to the cloud server via Wi-Fi or mobile data communication (input: biometric data, output: data stored on the cloud server).

[0188] Step 3: Data analysis

[0189] The server analyzes the biometric data using a generative AI model on the cloud. The generative AI model tracks, for example, blood pressure data and step count data from the past 30 days and evaluates the user's health status (input: biometric data, output: health status evaluation results). This data processing includes trend analysis and analysis of fluctuation patterns.

[0190] Step 4: Create a health management plan

[0191] Based on the analysis results of the generative AI model, the server generates a health management plan including an individualized exercise, diet, and wellness program (input: health status assessment results, output: health management plan). Specifically, a low-salt meal plan and a plan recommending 30 minutes of walking every day are generated.

[0192] Step 5: Send results

[0193] The server sends the generated health management plan in JSON format to the smartphone app, which notifies the user (input: health management plan, output: plan displayed on smartphone app).

[0194] Step 6: Food delivery integration

[0195] The device (smartphone) then works with a food delivery service to automatically order appropriate meals based on the generated health management plan. Specifically, low-salt meals are automatically ordered from the food delivery service (input: health management plan, output: order data for the food delivery service).

[0196] Step 7: Gather feedback

[0197] The user inputs their impressions and the effects of the provided meal into a smartphone app. The device (smartphone) then uploads the data back to the cloud server (input: feedback data, output: feedback data saved on the cloud server).

[0198] Step 8: Analyze feedback and update plans

[0199] The server analyzes the collected feedback data and reflects it in the next health management plan (input: feedback data, output: updated health management plan). Specifically, if walking is effective, adjustments will be made, such as slightly increasing the amount of exercise. This data calculation includes analyzing the effectiveness of the feedback and optimizing the plan based on the results.

[0200] 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.

[0201] This invention relates to a system that uses generative AI and an emotion engine to customize health management for elderly people in Japan. The system collects biometric and emotional state data from users, transmits it to a cloud environment for analysis, and provides personalized health management plans. The following describes the program and processing of this system.

[0202] Program processing explanation

[0203] 1. Data Collection

[0204] User: Measures blood pressure every morning and enters the results into a dedicated smartphone app. The user also wears a wearable device that automatically records the number of steps taken and heart rate each day. The device also uses the microphone and camera on the smartphone and dedicated device to collect audio and video data, which are then sent to the emotion engine.

[0205] Example: A user uses a blood pressure monitor in the morning and inputs the measurement (e.g., 120 / 80 mmHg) into the app, while the smartwatch counts 5,000 steps and monitors facial expressions with the camera.

[0206] 2. Data Transmission

[0207] Terminal: The terminal transmits the collected biometric data and emotional state data to the cloud server at regular intervals.

[0208] Example: A smartphone uses Wi-Fi or mobile data to upload blood pressure data, step count data, and emotional evaluation data to a cloud server. The data is then sent using SSL encryption.

[0209] 3. Data Analysis

[0210] Server: Using a cloud-based generative model and emotion engine, analyzes the transmitted biometric and emotional state data and evaluates the user's health and emotional state.

[0211] Example: The server analyzes blood pressure and step count data from the past 30 days and uses voice analysis and facial expression recognition to assess the user's emotional state. For example, if the average blood pressure exceeds 130 / 85 for 10 days or more, it is determined to be mild hypertension, and if the user is feeling stressed, the server will strengthen the stress management plan.

[0212] 4. Plan Generation

[0213] Server: Generates an individual health management plan based on the analysis results.

[0214] Example: A generative model and emotion engine create a health plan for the user with recommendations for diet, exercise, and mental health, such as a low-sodium diet, 30 minutes of walking daily, and relaxation exercises to reduce stress.

[0215] 5. Send results

[0216] Server: Sends the generated health management plan to the user's terminal.

[0217] Example: The generated plan is packaged in JSON format and sent to a smartphone app using the HTTPS protocol. The plan is displayed to the user as a notification.

[0218] 6. Feedback Collection

[0219] User: Implements a health management plan and enters the results and feedback into a dedicated app.

[0220] Terminal: Sends the feedback data back to the cloud server.

[0221] Example: After completing a walk, the user enters the time taken and their feelings into the app, and the smartphone uploads the data to a cloud server.

[0222] 7. Feedback analysis and plan updates

[0223] Server: Analyzes the feedback data and emotion evaluation data and updates the next health management plan.

[0224] Example: The AI ​​and emotion engine evaluate the feedback data and, if walking has a positive effect, adjust the exercise by slightly increasing the amount of exercise. If the emotion evaluation indicates that the user is feeling tired, the relaxation element will be strengthened.

[0225] Through these measures, the system provides personalized medical solutions to maintain the health and independence of elderly people and improve their quality of life.The introduction of an emotion engine enables comprehensive health management that takes into account the user's emotional state.

[0226] The processing flow will be explained below.

[0227] Step 1:

[0228] User: Measures blood pressure every morning and enters the results into a dedicated smartphone app. The wearable device also automatically records daily steps and heart rate. Furthermore, the emotion engine recognizes the user's emotional state through audio and video.

[0229] How it works: The user uses a blood pressure monitor and inputs the measurement (e.g., 120 / 80 mmHg) into the app. The wearable device counts 5,000 steps and simultaneously records heart rate data. The smartphone camera captures the user's facial expressions, and the microphone collects audio data.

[0230] Step 2:

[0231] Terminal: The terminal transmits the collected biometric data and emotional state data to the cloud server at regular intervals.

[0232] How it works: Your smartphone uses Wi-Fi or mobile data to upload blood pressure data, step count data, heart rate data, facial expression capture data, and voice data to a cloud server. The data is sent using SSL encryption.

[0233] Step 3:

[0234] Server: Using a cloud-based generative model and emotion engine, analyzes the transmitted biometric and emotional state data and evaluates the user's health and emotional state.

[0235] How it works: The server performs trend analysis on blood pressure and step count data from the past 30 days to detect abnormal values. The emotion engine evaluates the user's emotional state using voice analysis and facial expression recognition. For example, if the average blood pressure exceeds 130 / 85 for 10 days or more, it is determined to be mild hypertension, and facial expression analysis is used to detect stress.

[0236] Step 4:

[0237] Server: Generates an individual health management plan based on the analysis results.

[0238] How it works: The generative model and emotion engine create a health plan for the user, including recommendations for diet, exercise, and mental health. For example, it suggests a low-salt diet plan for mild high blood pressure, 30 minutes of walking daily, and relaxation exercises to reduce stress.

[0239] Step 5:

[0240] Server: Sends the generated health management plan to the user's terminal.

[0241] Specific operation: The generated plan is packaged in JSON format and sent to the smartphone app using the HTTPS protocol. The plan is then displayed to the user as a notification.

[0242] Step 6:

[0243] User: Carry out daily health management according to the received health management plan.

[0244] Specific actions: The user consults a smartphone app to change their dinner to a low-salt dish, begin a 30-minute walk, and perform relaxation exercises as instructed by the app.

[0245] Step 7:

[0246] User: Enters the results of the health management plan and feedback into a dedicated app. Emotional state is also recorded at the same time.

[0247] Specific operation: After completing a walk, the user enters the time it took and their feelings into the app. For example, they can enter "After 30 minutes of walking, I feel refreshed." The app also records the user's facial expressions and voice.

[0248] Step 8:

[0249] Terminal: Sends the feedback data and emotional state data back to the cloud server.

[0250] Specific operation: The smartphone encrypts the collected feedback data and emotional state data and uploads them to a cloud server.

[0251] Step 9:

[0252] Server: Analyzes the feedback data and emotional state data and updates the next health management plan.

[0253] Specific behavior: The AI ​​and emotion engine evaluate the feedback data and, if walking has a positive effect, adjust the exercise by slightly increasing it. If the emotion engine detects stress or fatigue, it will reinforce relaxation exercises and rest plans.

[0254] Through the above steps, it is possible to provide an optimal health management plan tailored to the individual health and emotional state of each user, thereby improving the quality of life of elderly people.

[0255] Example 2

[0256] 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."

[0257] Current health management systems typically collect only a user's biometric data and provide a health management plan based on the collected data. However, these systems do not take the user's emotional state into account and are unable to generate a plan that fully reflects the impact of stress and mental state on health. This can result in ineffective health management plans. Furthermore, because emotional state is not taken into account when updating a health management plan based on feedback, it is difficult to provide a plan optimized for the user's condition.

[0258] 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.

[0259] In this invention, the server includes means for collecting individual biometric data and emotional state data, means for transmitting the biometric data and emotional state data to a cloud environment, means for analyzing the data in the cloud environment and using a generative model and an emotion engine for evaluating the user's health condition and emotional state, means for generating an individual health management plan based on the analysis results of the generative model and the emotion engine, means for transmitting the health management plan to the user's terminal, means for transmitting implementation results and feedback of the health management plan back to the cloud environment, and means for analyzing the feedback and emotional state data and updating the health management plan, thereby enabling comprehensive health management that takes the user's emotional state into consideration.

[0260] "Biometric data" refers to information about the user's physical condition, such as blood pressure, heart rate, and number of steps taken.

[0261] "Emotional state data" is information about the emotional state obtained by analyzing the user's voice and facial expressions.

[0262] A "cloud environment" is a computer network that stores, manages, and analyzes data over the Internet.

[0263] A "generative model" is an artificial intelligence model that assesses and analyzes a user's health status based on their biometric and emotional state data.

[0264] An "emotion engine" is a software program that analyzes and evaluates a user's emotional state based on their voice and video data.

[0265] A "health plan" is a plan that provides personalized health care guidance and recommendations based on an analysis of a user's biometric and emotional state data.

[0266] A "wearable device" is a device worn by a user that automatically collects biometric data.

[0267] "Audio and video collection device" means a device that collects a user's audio and video.

[0268] "Feedback" is data that records the results and impressions of the user when implementing the health management plan.

[0269] The present invention relates to a system that uses a generative AI model and an emotion engine to individually customize health management for elderly people in Japan. The system collects biometric and emotional state data from users, transmits it to a cloud environment for analysis, and provides personalized health management plans. Specific embodiments of the system are described below.

[0270] First, the user measures their blood pressure every morning and enters the results into a dedicated smartphone app. This smartphone app also has a function that automatically records the user's daily steps and heart rate when the user wears a wearable device. Furthermore, the app collects audio and video using the microphone and camera on the smartphone and dedicated device, and sends them to the emotion engine. Specifically, we imagine a scenario in which the user uses a blood pressure monitor in the morning to input the measurement value (e.g., 120 / 80 mmHg) into the app, and the smartwatch counts 5,000 steps and monitors facial expressions using the camera.

[0271] The device then transmits the collected biometric and emotional state data to a cloud server at regular intervals. The smartphone then uploads the blood pressure, step count, and emotional assessment data to the cloud server using Wi-Fi or mobile data. This transmission is securely performed using SSL encryption.

[0272] The server analyzes the transmitted biometric and emotional state data using a cloud-based generative model and emotion engine. This evaluates the user's health and emotional state. Specifically, the server analyzes trends in blood pressure and step count data from the past 30 days, and assesses the user's emotional state using voice analysis and facial expression recognition. For example, if the average blood pressure exceeds 130 / 85 for 10 or more consecutive days, it will determine that the user has mild hypertension, and if the user is feeling stressed, it will strengthen the stress management plan.

[0273] Based on the analysis results, the server generates a personalized health management plan. The generative model and emotion engine create a health management plan that includes recommendations for the user's diet, exercise, and mental health. For example, it suggests a low-salt diet plan, 30 minutes of walking every day, and relaxation exercises to reduce stress.

[0274] The generated health management plan is sent from the server to the user's device. The plan is packaged in JSON format and sent to the smartphone app using the HTTPS protocol. The plan is displayed to the user as a notification, and details can be viewed within the smartphone app.

[0275] The user follows the provided health management plan and enters the results and feedback into a dedicated app. Specifically, after completing a walk, the user enters the time it took and how they felt into the app. This feedback data is then sent from the smartphone to the cloud server.

[0276] Finally, the server analyzes the feedback data and emotional state data to update the next health management plan. The AI ​​and emotion engine evaluate the feedback data and make adjustments, such as slightly increasing the amount of exercise if walking has had a positive effect. If the emotion evaluation indicates that the user is feeling tired, the server will strengthen relaxation elements.

[0277] In this system, the following prompt sentences can be input into the generative AI model:

[0278] "Please explain in detail the process by which a user measures their blood pressure every morning and enters the results into a dedicated app."

[0279] "Please show us how the cloud server can safely process the collected data."

[0280] "Please provide a detailed description of the process for integrating and analyzing biometric data and emotional data."

[0281] "Please provide an example of a generated health plan and explain how it will be communicated."

[0282] Through these measures, the system provides personalized medical solutions to maintain the health and independence of elderly people and improve their quality of life.The introduction of an emotion engine realizes comprehensive health management that also takes into account the user's emotional state.

[0283] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0284] Step 1:

[0285] Data collection

[0286] User: Measures blood pressure every morning and enters the values ​​into a dedicated smartphone app. In addition, wears a wearable device to automatically record daily steps and heart rate, and collects audio and video using the microphone and camera on the smartphone or dedicated device.

[0287] Input: Blood pressure data (e.g., 120 / 80 mmHg) from a sphygmomanometer, step count and heart rate data from a wearable device, and audio and video data from a smartphone's microphone and camera.

[0288] Output: Biometric and emotional data collected on a smartphone app.

[0289] How it works: The user measures their blood pressure in the morning and enters the value into a smartphone app. At the same time, the wearable device records 5,000 steps and their heart rate, while the smartphone's camera monitors facial expressions and the microphone collects audio data.

[0290] Step 2:

[0291] Data transmission

[0292] Terminal: Sends collected biometric data and emotional state data to a cloud server at regular intervals.

[0293] Input: Biometric and emotional data collected in a smartphone app.

[0294] Output: Data sent to the cloud server.

[0295] How it works: The smartphone uses Wi-Fi or mobile data to upload measured blood pressure data, step count data from the wearable device, and emotional evaluation data to a cloud server. The data is SSL encrypted.

[0296] Step 3:

[0297] Data analysis

[0298] Server: Using a cloud-based generative model and emotion engine, analyzes the transmitted biometric and emotional state data and evaluates the user's health and emotional state.

[0299] Input: Biometric and emotional data uploaded to a cloud server.

[0300] Output: Assessment results on the user's health and emotional state.

[0301] Specific operation: The server performs trend analysis of blood pressure data and step count data from the past 30 days, and evaluates the user's emotional state using voice analysis and facial expression recognition technology. For example, it can detect a tendency toward high blood pressure from blood pressure data and sense stress from voice data.

[0302] Step 4:

[0303] Plan Generation

[0304] Server: Generates an individual health management plan based on the analysis results.

[0305] Input: Assessment results on the user's health and emotional state.

[0306] Output: A personalized health care plan.

[0307] How it works: The generative model and emotion engine create a health plan for the user, including recommendations for diet, exercise, and mental health. For example, it suggests a low-sodium diet, 30 minutes of walking daily, and relaxation exercises to reduce stress.

[0308] Step 5:

[0309] Send results

[0310] Server: Sends the generated health management plan to the user's terminal.

[0311] Input: The generated health care plan.

[0312] Output: The health plan sent to the user's device.

[0313] Specific operation: The generated plan is packaged in JSON format and sent to the smartphone app using the HTTPS protocol. The plan is displayed to the user as a notification, and details can be viewed within the smartphone app.

[0314] Step 6:

[0315] Feedback collection

[0316] User: Implements a health management plan and enters the results and feedback into a dedicated app.

[0317] Input: Health management plan implementation results and feedback.

[0318] Output: Feedback data aggregated in a smartphone app.

[0319] Specific behavior: After the user completes a walk, they enter the time it took and their feelings into the app (e.g., "I walked for 30 minutes and felt great").

[0320] Terminal: Sends the feedback data back to the cloud server.

[0321] Input: Feedback data aggregated in a smartphone app.

[0322] Output: Feedback data sent to the cloud server.

[0323] Specific operation: The smartphone uploads the input feedback data to a cloud server using Wi-Fi or mobile data communication under SSL encryption.

[0324] Step 7:

[0325] Feedback analysis and plan updates

[0326] Server: Analyzes the feedback data and emotional state data and updates the health management plan.

[0327] Input: Feedback data and emotional state data.

[0328] Output: Updated health care plan.

[0329] Specific operation: The AI ​​and emotion engine evaluate the feedback data and, for example, if it determines that walking has had a positive effect, it will make adjustments such as increasing the amount of exercise. If the emotion evaluation determines that the user is tired, it will strengthen relaxation elements.

[0330] (Application example 2)

[0331] 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."

[0332] For elderly health management, it is important to accurately assess individual biometric data and emotional states and provide appropriate health management plans. However, conventional systems provide health management plans without considering the user's emotional state, which results in insufficient management of health risks caused by stress and emotional changes. Furthermore, to ensure a comfortable and healthy shopping experience for elderly people, virtual shopping recommendations based on their health state are required.

[0333] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0334] In this invention, the server includes a means for evaluating biometric data and emotional states using a generative model and an emotion engine to generate an individualized health management plan and virtual shopping recommendations, a means for transmitting the implementation results and feedback of the user's health management plan and virtual shopping recommendations back to the cloud environment for analysis, and a means for updating the health management plan and virtual shopping recommendations based on the feedback, thereby enabling comprehensive management of the elderly's health and emotional states and providing an optimal shopping experience according to their health status.

[0335] "Biometric data" refers to data relating to the user's physical condition, such as blood pressure, heart rate, and number of steps.

[0336] "Cloud environment" is a general term for servers and data storage provided via the Internet.

[0337] A "generative model" refers to an algorithm for predicting and generating new data based on past data.

[0338] An "emotion engine" is software that analyzes data such as voice and facial expressions to evaluate the user's emotional state.

[0339] "Health Management Plan" means a plan that includes individually customized exercise, diet, and mental health recommendations to maintain or improve a User's health.

[0340] "Virtual shopping recommendation" is a system that recommends appropriate products and shopping experiences based on the user's health and emotional state.

[0341] "Feedback" means the evaluation and results of the health management plan or virtual shopping recommendations implemented by the user.

[0342] "Wearable devices" is a general term for electronic devices that can collect physical data by being worn, and includes smartwatches and health monitoring devices.

[0343] In the embodiment of the present invention, the system operates in an integrated manner with the server, terminals, and users working together.

[0344] Data collection

[0345] Users utilize wearable devices such as blood pressure monitors, smartwatches, smart glasses, and head-mounted displays to collect biometric and emotional data. For example, a user measures their blood pressure at home every morning and enters the values ​​(e.g., 120 / 80 mmHg) into a smartphone app. The smartwatch automatically records the number of steps taken each day (e.g., 5,000 steps) and heart rate, while the smart glasses and head-mounted display collect heart rate and facial expression data.

[0346] Data transmission

[0347] The device sends the collected biometric and emotional data to a cloud server at regular intervals. The data is SSL encrypted and sent via Wi-Fi or mobile data. For example, a smartphone uploads blood pressure data, step count data, and emotional evaluation data to the cloud server.

[0348] Data analysis

[0349] The server uses a cloud-based generative AI model and emotion engine to analyze the biometric and emotional state data and assess the user's health and emotional state. For example, it analyzes trends in blood pressure and step count data from the past 30 days and uses voice analysis and facial expression recognition to assess the user's emotional state. Specifically, if the average blood pressure exceeds 130 / 85 for 10 or more consecutive days, it is determined to be mild hypertension and will recommend a strengthened stress management plan.

[0350] Plan Generation

[0351] The server generates a health management plan and virtual shopping recommendations based on the analysis results, which include optimal recommendations for the user's diet, exercise, and mental health. Specifically, the server suggests a low-salt meal plan, 30 minutes of walking daily, and relaxation exercises to reduce stress, as well as virtual shopping recommendations based on the user's health status.

[0352] Send results

[0353] The server sends the generated health management plan and virtual shopping recommendations to the user's device. The generated plan is packaged in JSON format and sent to the device app using the HTTPS protocol. The user is then notified of the results.

[0354] Feedback collection and analysis

[0355] The user acts according to the provided health management plan and virtual shopping recommendations, and enters the results and feedback into a dedicated app. The device then sends the feedback data back to the cloud server. The server analyzes the feedback data and new emotional data and updates the next health management plan and virtual shopping recommendations. For example, if walking has had a positive effect, the server increases the amount of exercise, and if the user feels tired, it strengthens relaxation elements.

[0356] Hardware and Software Used

[0357] Hardware: smart glasses, head-mounted displays, smartwatches

[0358] Software: Cloud services (AWS Lambda, Azure Functions), speech recognition API (Google Cloud Speech-to-Text), facial expression recognition library (OpenCV, dlib)

[0359] Data processing: time series analysis of heart rate data, image recognition of facial expression data, acoustic analysis of voice data

[0360] Data calculation: Generative AI model predicts user health status, emotion engine evaluates stress level

[0361] Prompt Sentence Examples

[0362] Use the following dataset to predict a user's health and emotional state and generate personalized shopping recommendations.

[0363] Hourly heart rate data (HR)

[0364] Real-time facial expression data (EXPRESSION)

[0365] Voice tone analysis result (VOICE_TONE)

[0366] Past shopping history and feedback

[0367] example:

[0368] HR: [75, 80, 78, 85, 90]

[0369] EXPRESSION: {"happy": 0.3, "neutral": 0.6, "sad": 0.1}

[0370] VOICE_TONE: {"calm": 0.7, "stress": 0.3}

[0371] Recommendations: Provide relaxing music and recommend a health food section.

[0372] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0373] Step 1:

[0374] Users use wearable devices (e.g., smartwatches, smart glasses) to collect biometric and emotional data. Specifically, users measure their blood pressure every morning and enter the values ​​into a smartphone app. At the same time, the smartwatch automatically records their daily steps and heart rate, and the smart glasses collect facial expression data. These input data (blood pressure, steps, heart rate, and facial expression) are the initial data to be used for analysis.

[0375] Step 2:

[0376] The device sends the collected biometric and emotional data to a cloud server. The data is sent using Wi-Fi or mobile data communication and SSL encryption is used. The input data in this step is the various data collected in step 1, and is securely sent to the cloud server as processing results.

[0377] Step 3:

[0378] The server analyzes the transmitted biometric and emotional data using a cloud-based generative AI model and emotion engine. This analysis evaluates the user's health and emotional state. Specifically, it performs trend analysis on blood pressure and step count data from the past 30 days, and evaluates the emotional state using voice analysis and facial expression recognition. The input data in this step is the data transmitted to the cloud, and the output results are the evaluation results of the user's health and emotional state.

[0379] Step 4:

[0380] The server generates an individualized health management plan and virtual shopping recommendations based on the analysis results. The generative model and emotion engine create a health management plan that combines recommendations for diet, exercise, and mental health, and also generates virtual shopping recommendations. For example, a low-salt meal plan, 30 minutes of walking daily, and relaxation exercises to relieve stress may be suggested. The input data for this step are the evaluation results obtained in step 3, and the output is a health management plan and shopping recommendations.

[0381] Step 5:

[0382] The server sends the generated health management plan and virtual shopping recommendations to the user's device. The HTTPS protocol is used for transmission, and the data is packaged in JSON format. The device receives this data and notifies the user. The input data of this step are the generated plan and recommendations, and the output is a notification sent to the user's device.

[0383] Step 6:

[0384] The user acts according to the generated health management plan and virtual shopping recommendations, and then inputs the results and feedback into a dedicated app. The input data is the implementation results of the health management plan and feedback on the shopping experience. This data is used for reanalysis in subsequent steps.

[0385] Step 7:

[0386] The device retransmits the feedback data entered by the user to the cloud server. The data is SSL encrypted and transmitted as described above. The input data for this step is the user's feedback, and the output is the data to be transmitted to the cloud server.

[0387] Step 8:

[0388] The server analyzes the feedback data and updated emotion data sent to the cloud server and updates the health management plan and virtual shopping recommendations. Based on the feedback data, the server regenerates an optimal plan according to the user's health and emotional state. For example, if walking has a positive effect, the server increases the amount of exercise, and if the user feels tired, it strengthens the relaxation element. The input data of steps is the feedback data, and the output results are the updated plan and recommendations.

[0389] 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.

[0390] 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.

[0391] 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.

[0392] [Second embodiment]

[0393] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0394] 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.

[0395] 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).

[0396] 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.

[0397] 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.

[0398] 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).

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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.

[0404] 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."

[0405] This invention relates to a system that uses generative AI to customize health management for elderly people in Japan. The system collects biometric data from users, transmits it to a cloud environment, and analyzes it to provide personalized health management plans. The following describes the program and processing of this system.

[0406] Program processing explanation

[0407] 1. Data Collection

[0408] User: Measures blood pressure every morning and enters the results into a dedicated smartphone app. Also, uses a wearable device to automatically record daily steps and heart rate.

[0409] Example: A user uses a blood pressure monitor in the morning and enters the measurement value into a dedicated app, while the smartwatch simultaneously collects 5,000 steps.

[0410] 2. Data Transmission

[0411] Terminal: Periodically sends collected biometric data (blood pressure, number of steps, heart rate, etc.) to a cloud server.

[0412] Example: A smartphone uses Wi-Fi or mobile data to upload blood pressure data or step count data to a cloud server.

[0413] 3. Data Analysis

[0414] Server: Using a generative model on the cloud, analyzes the collected biometric data and assesses the user's health status.

[0415] Example: The server tracks blood pressure and step count data for the past 30 days and analyzes patterns of changes in physical condition.

[0416] 4. Plan Generation

[0417] Server: Based on the results of data analysis, it generates a health management plan including individualized exercise, diet, and wellness programs.

[0418] Example: A server uses user data to generate a low-salt meal plan and a plan recommending 30 minutes of walking each day.

[0419] 5. Send results

[0420] Server: Sends the generated health management plan to the user's terminal.

[0421] Example: The generated plan is sent in JSON format to a smartphone app and notified to the user.

[0422] 6. Feedback Collection

[0423] User: Implements a health management plan and enters the results and feedback into a dedicated app.

[0424] Terminal: Sends the feedback data back to the cloud server.

[0425] Example: After a user completes a walk, they enter the time it took and their impressions into the app, and their smartphone uploads the data to a cloud server.

[0426] 7. Feedback analysis and plan updates

[0427] Server: Analyzes the feedback data and reflects it in the next health management plan.

[0428] Example: The server analyzes the feedback data and, if walking is effective, adjusts the exercise, such as slightly increasing the amount of exercise.

[0429] Through these measures, the system provides personalized medical solutions to maintain the health and independence of older adults and improve their quality of life, enabling users to more effectively manage their daily health.

[0430] The processing flow will be explained below.

[0431] Step 1:

[0432] User: Measures blood pressure every morning and enters the results into a dedicated smartphone app. The user also wears a wearable device that automatically records the number of steps taken and heart rate each day.

[0433] Specific operation: The user uses a blood pressure monitor and inputs the measurement value (e.g., 120 / 80 mmHg) into the app. The wearable device counts 5,000 steps and simultaneously records heart rate data.

[0434] Step 2:

[0435] Terminal: Sends collected biometric data to a cloud server at regular intervals.

[0436] How it works: Your smartphone uses Wi-Fi or mobile data to upload blood pressure and step count data to a cloud server. The data is then SSL encrypted before being sent.

[0437] Step 3:

[0438] Server: Using a generative model on the cloud, analyzes the transmitted biometric data and assesses the user's health status.

[0439] Specific operation: The server performs trend analysis of blood pressure data from the past 30 days and detects abnormal values. For example, if the average blood pressure exceeds 130 / 85 for 10 days or more, it is determined to be mild hypertension.

[0440] Step 4:

[0441] Server: Generates an individual health management plan based on the analysis results.

[0442] What it does: The generative model creates a health plan for the user with recommendations for diet, exercise, and mental health, such as a low-sodium diet, 30 minutes of walking daily, and relaxation exercises for stress management.

[0443] Step 5:

[0444] Server: Sends the generated health management plan to the user's terminal.

[0445] Specific operation: The generated plan is packaged in JSON format and sent to the smartphone app using the HTTPS protocol. The plan is then displayed to the user as a notification.

[0446] Step 6:

[0447] User: Carry out daily health management according to the received health management plan.

[0448] Specific actions: The user refers to the smartphone app, changes dinner to a low-salt dish, and begins a 30-minute walk.

[0449] Step 7:

[0450] User: Enter the results of the health management plan and feedback into a dedicated app.

[0451] Specific behavior: After completing a walk, the user enters the time it took and their feelings into the app. For example, they might enter, "After 30 minutes of walking, I feel refreshed."

[0452] Step 8:

[0453] Terminal: Sends the feedback data back to the cloud server.

[0454] Specific operation: The feedback data collected by the smartphone is encrypted and uploaded to a cloud server.

[0455] Step 9:

[0456] Server: Analyzes the feedback data and updates the next health management plan.

[0457] Specific actions: The AI ​​evaluates the feedback data, and if walking has a positive effect, it will make adjustments such as slightly increasing the amount of exercise. This will be reflected in the next plan.

[0458] Through the above steps, it is possible to provide an optimal health management plan tailored to the individual health condition of each user, thereby improving the quality of life of elderly people.

[0459] Example 1

[0460] 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."

[0461] The present invention relates to a system that efficiently analyzes collected biometric data and provides users with customized health management plans as a means of individually optimizing health management for elderly people. Conventional health management systems have struggled to customize plans to accommodate individual health conditions, and generic plans often fail to produce satisfactory results. Furthermore, they lacked a mechanism for continuously improving plans using feedback data, making it difficult to accurately manage users' health conditions.

[0462] 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.

[0463] In this invention, the server includes means for collecting individual biometric data, means for transmitting the biometric data to a cloud environment, means for analyzing the biometric data in the cloud environment and using a generative model to evaluate the user's health condition, means for generating an individual health management plan based on the analysis results of the generative model, means for transmitting the health management plan to the user's device, means for transmitting implementation results and feedback of the health management plan back to the cloud environment, means for analyzing the feedback and updating the health management plan, means for measuring blood pressure every morning and inputting it into a smartphone app, means for automatically recording step count and heart rate data using a wearable device and means for transmitting the data to the device, and means for generating a health management plan that is periodically improved based on the analysis. This makes it possible to individually evaluate the user's health condition and provide an appropriate health management plan, and to continuously improve the plan through feedback.

[0464] "Individual biometric data" refers to data relating to physical conditions such as blood pressure, number of steps, and heart rate collected from individual users.

[0465] A "cloud environment" is a collection of remote servers used to store, analyze, and process data over the Internet.

[0466] A "generative model" is an artificial intelligence algorithm that predicts and generates results based on data analysis.

[0467] A "health plan" is a set of specific instructions, including exercise, diet, wellness activities, etc., to improve a user's health.

[0468] A "terminal" is a hardware device (e.g., smartphone, tablet) used by a user to input, process, transmit, etc. data.

[0469] A "wearable device" is an electronic device that can be worn to automatically record daily physical activity.

[0470] "Feedback" is data on the implementation results and impressions provided by the user after implementing the health management plan.

[0471] The present invention is a system for individually optimizing health management for elderly people. This system analyzes biometric data collected from users in a cloud environment and provides customized health management plans using a generative AI model. The system's program processing is described in detail below.

[0472] First, the user collects biometric data using a smartphone app and a wearable device. Every morning, the user measures their blood pressure with a blood pressure monitor and enters the results into the smartphone app. The wearable device (e.g., a smartwatch) also automatically records their daily steps and heart rate. This data is periodically sent from the device to a cloud server.

[0473] The cloud server analyzes the received biometric data using a generative AI model. This analysis is performed using Python scripts and machine learning libraries such as TensorFlow and PyTorch. The analysis results in an assessment of the user's health status. For example, blood pressure and step count data from the past 30 days can be tracked to identify patterns of fluctuations in health status.

[0474] The server then generates a personalized health management plan based on the analysis results. This plan includes exercise, diet, and wellness activities. For example, the analysis results may recommend a low-salt diet plan and 30 minutes of walking daily. The plan is formatted in JSON and pushed to the user's smartphone app.

[0475] The user carries out the health management plan they receive and enters the results and feedback into a dedicated app. For example, after completing a walk, they can enter the time it took and the effects they felt in text. The device then sends this feedback data back to the cloud server.

[0476] The server analyzes the feedback data and updates the health management plan based on the analysis. This process is reflected in the next health management plan. For example, if walking was effective, adjustments such as slightly increasing the amount of exercise will be made.

[0477] In this way, the system of the present invention effectively supports health management for the elderly by collecting and analyzing the user's biometric data and providing an individually customized health management plan, enabling users to manage their health more effectively through this system.

[0478] keyword:

[0479] Generative AI model, prompt sentence

[0480] Example prompt sentence:

[0481] Use your biometric data (blood pressure, steps, heart rate) to create an AI model to generate a personalized health plan.

[0482] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0483] Step 1: Data collection

[0484] User: Wakes up in the morning, measures blood pressure with a blood pressure monitor, and enters the results into a dedicated smartphone app. The input data is saved on the device.

[0485] Input: Blood pressure data measured by the user

[0486] Output: Blood pressure data stored in a smartphone app

[0487] User: Wears a wearable device (smartwatch) that automatically records daily steps and heart rate data, which is periodically sent to a smartphone.

[0488] Input: User's daily activity data (steps, heart rate)

[0489] Output: Step count and heart rate data sent to your smartphone

[0490] Step 2: Send data

[0491] Device: Collected biometric data (blood pressure, number of steps, heart rate) is periodically sent to a cloud server. Data is uploaded securely and quickly using Wi-Fi or mobile data.

[0492] Input: Biometric data stored on the smartphone

[0493] Output: Biometric data sent to the cloud server

[0494] Step 3: Data analysis

[0495] Server: Analyzes the submitted biometric data using a generative AI model in the cloud. Health status is assessed using Python scripts and machine learning libraries such as TensorFlow and PyTorch.

[0496] Input: Biometric data stored on a cloud server

[0497] Output: User's health status assessment result

[0498] Step 4: Generate a plan

[0499] Server: Based on the analysis results, it generates a health management plan that includes a personalized exercise, diet, and wellness program, such as a low-salt meal plan and a daily walking plan.

[0500] Input: Health status assessment results

[0501] Output: A personalized health plan

[0502] Step 5: Send results

[0503] Server: Formats the generated health management plan in JSON format and sends it to the user's smartphone app via push notification.

[0504] Enter: Customized Health Care Plan

[0505] Output: Health plan notification sent to smartphone app

[0506] Step 6: Gather feedback

[0507] User: Implements a health management plan and inputs the results and feedback into a dedicated app. For example, after walking, the user enters the time it took and their impressions.

[0508] Input: User feedback data

[0509] Output: Feedback data stored in a smartphone app

[0510] Terminal: Sends the feedback data back to the cloud server.

[0511] Input: Feedback data stored in the smartphone app

[0512] Output: Feedback data sent to the cloud server

[0513] Step 7: Analyze feedback and update plans

[0514] Server: Analyzes the feedback data and reflects it in the next health management plan. The generative AI model incorporates new data to update and continuously optimize the plan.

[0515] Input: Feedback data

[0516] Output: Updated individual health care plan

[0517] (Application example 1)

[0518] 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."

[0519] Health management for the elderly requires providing plans that meet individual needs, but conventional systems have difficulty linking with food delivery services, such as individual food selection and home delivery, making it impossible to achieve effective health management. In particular, a system is needed that provides individualized healthy eating plans in a way that is easily accessible to the elderly.

[0520] 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.

[0521] In this invention, the server includes means for collecting individual biometric data, means for transmitting the biometric data to a cloud environment, means for analyzing the biometric data in the cloud environment and using a generative model for evaluating the user's health status, means for generating an individual health management plan based on the analysis results of the generative model, means for transmitting the health management plan to the user's terminal, means for transmitting implementation results and feedback of the health management plan back to the cloud environment, means for analyzing the feedback and updating the health management plan, and means for generating a meal plan based on the health management plan and automatically ordering meals in cooperation with a food delivery service. This makes it possible to provide an individualized healthy meal plan to an elderly person and have it delivered to their home via a food delivery service.

[0522] "Biometric data" refers to data related to the user's health condition, including blood pressure, number of steps, heart rate, and the like.

[0523] A "cloud environment" is a computing resource and data storage environment consisting of a group of remote servers accessible via the Internet.

[0524] A "generative model" is an artificial intelligence algorithm that analyzes collected data and generates an individual plan from the results.

[0525] "Health Management Plan" means a plan that includes exercise, diet, and health activities designed to improve and maintain the User's health.

[0526] "Feedback" is information about the results, impressions, and effects of the health management plan implemented by the user.

[0527] A "food delivery service" is a service that delivers meals to a specific location and delivers meals to users based on a personalized healthy eating plan.

[0528] A "terminal" is an electronic device that allows a user to input data, receive notifications, send feedback, etc.

[0529] "Analysis" is the process of processing and calculating collected data to derive results.

[0530] The present invention relates to a system that collects individual biometric data, generates a health management plan, and provides meals in cooperation with a food delivery service. This system supports the health management of elderly people and improves their quality of life by providing personalized meal plans.

[0531] Hardware and software used

[0532] Hardware: Smartphones, wearable devices (smartwatches, etc.)

[0533] Software: Smartphone app, generative AI model, cloud server, food delivery system

[0534] Data collection

[0535] Users measure their blood pressure every morning and enter the data into a smartphone app. Wearable devices (e.g., smartwatches) also automatically record their daily steps and heart rate, thereby collecting biometric data.

[0536] Data transmission

[0537] The collected biometric data is sent to a cloud server via the smartphone, which then uploads the blood pressure and step count data to the cloud server using Wi-Fi or mobile data.

[0538] Data analysis

[0539] The collected biometric data is analyzed on a cloud server using a generative AI model to assess the user's health. For example, the generative AI model tracks blood pressure and step count data from the past 30 days and analyzes patterns of changes in physical condition.

[0540] Health management plan generation

[0541] Based on the results of the data analysis, the generative AI model generates a health management plan that includes a personalized exercise, diet, and wellness program, such as a low-salt diet plan and a plan recommending 30 minutes of walking every day.

[0542] Send results

[0543] The generated health management plan is sent in JSON format to a smartphone app and notified to the user.

[0544] Food delivery collaboration

[0545] Based on the generated health management plan, the app will automatically order appropriate meals through a food delivery service. Specifically, the app will automatically order low-sodium meals from the food delivery service.

[0546] Feedback collection

[0547] Users enter their impressions and effects of the provided meal into the app, and the data is then uploaded to the cloud server, allowing user feedback to be collected.

[0548] Feedback analysis and plan updates

[0549] The cloud server analyzes the collected feedback data and reflects it in the next health management plan. For example, if walking is effective, it will make adjustments such as slightly increasing the amount of exercise.

[0550] Specific examples

[0551] For example, suppose User A enters their morning blood pressure and records the number of steps they took that day on their smartwatch. This data is sent from the smartphone app to a cloud server, where it is analyzed by a generative AI model. Based on the analysis results, the AI ​​generates a low-salt meal plan, and this data is automatically sent to a food delivery service. After User A receives their dinner, they can enter their post-meal impressions into the app, and this feedback is reflected in their next meal plan.

[0552] Prompt Sentence Examples

[0553] "Generate a low-sodium, high-protein healthy diet plan based on user biometric data: blood pressure (120 / 80), steps (5000), heart rate (70 bpm), and 30 days of data analysis."

[0554] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0555] Step 1: Data collection

[0556] The user inputs the results of their blood pressure measurement every morning. A wearable device (such as a smartwatch) automatically records the number of steps taken and heart rate each day. This allows the user's biometric data (input: blood pressure, number of steps, heart rate) to be collected.

[0557] Step 2: Send data

[0558] The device (smartphone) sends the collected biometric data to a cloud server. The smartphone uploads blood pressure data and step count data to the cloud server via Wi-Fi or mobile data communication (input: biometric data, output: data stored on the cloud server).

[0559] Step 3: Data analysis

[0560] The server analyzes the biometric data using a generative AI model on the cloud. The generative AI model tracks, for example, blood pressure data and step count data from the past 30 days and evaluates the user's health status (input: biometric data, output: health status evaluation results). This data processing includes trend analysis and analysis of fluctuation patterns.

[0561] Step 4: Create a health management plan

[0562] Based on the analysis results of the generative AI model, the server generates a health management plan including an individualized exercise, diet, and wellness program (input: health status assessment results, output: health management plan). Specifically, a low-salt meal plan and a plan recommending 30 minutes of walking every day are generated.

[0563] Step 5: Send results

[0564] The server sends the generated health management plan in JSON format to the smartphone app, which notifies the user (input: health management plan, output: plan displayed on smartphone app).

[0565] Step 6: Food delivery integration

[0566] The device (smartphone) then works with a food delivery service to automatically order appropriate meals based on the generated health management plan. Specifically, low-salt meals are automatically ordered from the food delivery service (input: health management plan, output: order data for the food delivery service).

[0567] Step 7: Gather feedback

[0568] The user inputs their impressions and the effects of the provided meal into a smartphone app. The device (smartphone) then uploads the data back to the cloud server (input: feedback data, output: feedback data saved on the cloud server).

[0569] Step 8: Analyze feedback and update plans

[0570] The server analyzes the collected feedback data and reflects it in the next health management plan (input: feedback data, output: updated health management plan). Specifically, if walking is effective, adjustments will be made, such as slightly increasing the amount of exercise. This data calculation includes analyzing the effectiveness of the feedback and optimizing the plan based on the results.

[0571] 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.

[0572] This invention relates to a system that uses generative AI and an emotion engine to customize health management for elderly people in Japan. The system collects biometric and emotional state data from users, transmits it to a cloud environment for analysis, and provides personalized health management plans. The following describes the program and processing of this system.

[0573] Program processing explanation

[0574] 1. Data Collection

[0575] User: Measures blood pressure every morning and enters the results into a dedicated smartphone app. The user also wears a wearable device that automatically records the number of steps taken and heart rate each day. The device also uses the microphone and camera on the smartphone and dedicated device to collect audio and video data, which are then sent to the emotion engine.

[0576] Example: A user uses a blood pressure monitor in the morning and inputs the measurement (e.g., 120 / 80 mmHg) into the app, while the smartwatch counts 5,000 steps and monitors facial expressions with the camera.

[0577] 2. Data Transmission

[0578] Terminal: The terminal transmits the collected biometric data and emotional state data to the cloud server at regular intervals.

[0579] Example: A smartphone uses Wi-Fi or mobile data to upload blood pressure data, step count data, and emotional evaluation data to a cloud server. The data is then sent using SSL encryption.

[0580] 3. Data Analysis

[0581] Server: Using a cloud-based generative model and emotion engine, analyzes the transmitted biometric and emotional state data and evaluates the user's health and emotional state.

[0582] Example: The server analyzes blood pressure and step count data from the past 30 days and uses voice analysis and facial expression recognition to assess the user's emotional state. For example, if the average blood pressure exceeds 130 / 85 for 10 days or more, it is determined to be mild hypertension, and if the user is feeling stressed, the server will strengthen the stress management plan.

[0583] 4. Plan Generation

[0584] Server: Generates an individual health management plan based on the analysis results.

[0585] Example: A generative model and emotion engine create a health plan for the user with recommendations for diet, exercise, and mental health, such as a low-sodium diet, 30 minutes of walking daily, and relaxation exercises to reduce stress.

[0586] 5. Send results

[0587] Server: Sends the generated health management plan to the user's terminal.

[0588] Example: The generated plan is packaged in JSON format and sent to a smartphone app using the HTTPS protocol. The plan is displayed to the user as a notification.

[0589] 6. Feedback Collection

[0590] User: Implements a health management plan and enters the results and feedback into a dedicated app.

[0591] Terminal: Sends the feedback data back to the cloud server.

[0592] Example: After completing a walk, the user enters the time taken and their feelings into the app, and the smartphone uploads the data to a cloud server.

[0593] 7. Feedback analysis and plan updates

[0594] Server: Analyzes the feedback data and emotion evaluation data and updates the next health management plan.

[0595] Example: The AI ​​and emotion engine evaluate the feedback data and, if walking has a positive effect, adjust the exercise by slightly increasing the amount of exercise. If the emotion evaluation indicates that the user is feeling tired, the relaxation element will be strengthened.

[0596] Through these measures, the system provides personalized medical solutions to maintain the health and independence of elderly people and improve their quality of life.The introduction of an emotion engine enables comprehensive health management that takes into account the user's emotional state.

[0597] The processing flow will be explained below.

[0598] Step 1:

[0599] User: Measures blood pressure every morning and enters the results into a dedicated smartphone app. The wearable device also automatically records daily steps and heart rate. Furthermore, the emotion engine recognizes the user's emotional state through audio and video.

[0600] How it works: The user uses a blood pressure monitor and inputs the measurement (e.g., 120 / 80 mmHg) into the app. The wearable device counts 5,000 steps and simultaneously records heart rate data. The smartphone camera captures the user's facial expressions, and the microphone collects audio data.

[0601] Step 2:

[0602] Terminal: The terminal transmits the collected biometric data and emotional state data to the cloud server at regular intervals.

[0603] How it works: Your smartphone uses Wi-Fi or mobile data to upload blood pressure data, step count data, heart rate data, facial expression capture data, and voice data to a cloud server. The data is sent using SSL encryption.

[0604] Step 3:

[0605] Server: Using a cloud-based generative model and emotion engine, analyzes the transmitted biometric and emotional state data and evaluates the user's health and emotional state.

[0606] How it works: The server performs trend analysis on blood pressure and step count data from the past 30 days to detect abnormal values. The emotion engine evaluates the user's emotional state using voice analysis and facial expression recognition. For example, if the average blood pressure exceeds 130 / 85 for 10 days or more, it is determined to be mild hypertension, and facial expression analysis is used to detect stress.

[0607] Step 4:

[0608] Server: Generates an individual health management plan based on the analysis results.

[0609] How it works: The generative model and emotion engine create a health plan for the user, including recommendations for diet, exercise, and mental health. For example, it might suggest a low-salt diet for mild hypertension, 30 minutes of walking daily, and relaxation exercises to reduce stress.

[0610] Step 5:

[0611] Server: Sends the generated health management plan to the user's terminal.

[0612] Specific operation: The generated plan is packaged in JSON format and sent to the smartphone app using the HTTPS protocol. The plan is then displayed to the user as a notification.

[0613] Step 6:

[0614] User: Carry out daily health management according to the received health management plan.

[0615] Specific actions: The user consults a smartphone app to change their dinner to a low-salt dish, begin a 30-minute walk, and perform relaxation exercises as instructed by the app.

[0616] Step 7:

[0617] User: Enters the results of the health management plan and feedback into a dedicated app. Emotional state is also recorded at the same time.

[0618] Specific operation: After completing a walk, the user enters the time it took and their feelings into the app. For example, they can enter "After 30 minutes of walking, I feel refreshed." The app also records the user's facial expressions and voice.

[0619] Step 8:

[0620] Terminal: Sends the feedback data and emotional state data back to the cloud server.

[0621] Specific operation: The smartphone encrypts the collected feedback data and emotional state data and uploads them to a cloud server.

[0622] Step 9:

[0623] Server: Analyzes the feedback data and emotional state data and updates the next health management plan.

[0624] Specific behavior: The AI ​​and emotion engine evaluate the feedback data and, if walking has a positive effect, adjust the exercise by slightly increasing it. If the emotion engine detects stress or fatigue, it will reinforce relaxation exercises and rest plans.

[0625] Through the above steps, it is possible to provide an optimal health management plan tailored to the individual health and emotional state of each user, thereby improving the quality of life of elderly people.

[0626] Example 2

[0627] 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."

[0628] Current health management systems typically collect only a user's biometric data and provide a health management plan based on the collected data. However, these systems do not take the user's emotional state into account and are unable to generate a plan that fully reflects the impact of stress and mental state on health. This can result in ineffective health management plans. Furthermore, because emotional state is not taken into account when updating a health management plan based on feedback, it is difficult to provide a plan optimized for the user's condition.

[0629] 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.

[0630] In this invention, the server includes means for collecting individual biometric data and emotional state data, means for transmitting the biometric data and emotional state data to a cloud environment, means for analyzing the data in the cloud environment and using a generative model and an emotion engine for evaluating the user's health condition and emotional state, means for generating an individual health management plan based on the analysis results of the generative model and the emotion engine, means for transmitting the health management plan to the user's terminal, means for transmitting implementation results and feedback of the health management plan back to the cloud environment, and means for analyzing the feedback and emotional state data and updating the health management plan, thereby enabling comprehensive health management that takes the user's emotional state into consideration.

[0631] "Biometric data" refers to information about the user's physical condition, such as blood pressure, heart rate, and number of steps taken.

[0632] "Emotional state data" is information about the emotional state obtained by analyzing the user's voice and facial expressions.

[0633] A "cloud environment" is a computer network that stores, manages, and analyzes data over the Internet.

[0634] A "generative model" is an artificial intelligence model that assesses and analyzes a user's health status based on their biometric and emotional state data.

[0635] An "emotion engine" is a software program that analyzes and evaluates a user's emotional state based on their voice and video data.

[0636] A "health plan" is a plan that provides personalized health care guidance and recommendations based on an analysis of a user's biometric and emotional state data.

[0637] A "wearable device" is a device worn by a user that automatically collects biometric data.

[0638] "Audio and video collection device" means a device that collects a user's audio and video.

[0639] "Feedback" is data that records the results and impressions of the user when implementing the health management plan.

[0640] The present invention relates to a system that uses a generative AI model and an emotion engine to individually customize health management for elderly people in Japan. The system collects biometric and emotional state data from users, transmits it to a cloud environment for analysis, and provides personalized health management plans. Specific embodiments of the system are described below.

[0641] First, the user measures their blood pressure every morning and enters the results into a dedicated smartphone app. This smartphone app also has a function that automatically records the user's daily steps and heart rate when the user wears a wearable device. Furthermore, the app collects audio and video using the microphone and camera on the smartphone and dedicated device, and sends them to the emotion engine. Specifically, we imagine a scenario in which the user uses a blood pressure monitor in the morning to input the measurement value (e.g., 120 / 80 mmHg) into the app, and the smartwatch counts 5,000 steps and monitors facial expressions using the camera.

[0642] The device then transmits the collected biometric and emotional state data to a cloud server at regular intervals. The smartphone then uploads the blood pressure, step count, and emotional assessment data to the cloud server using Wi-Fi or mobile data. This transmission is securely performed using SSL encryption.

[0643] The server analyzes the transmitted biometric and emotional state data using a cloud-based generative model and emotion engine. This evaluates the user's health and emotional state. Specifically, the server analyzes trends in blood pressure and step count data from the past 30 days, and assesses the user's emotional state using voice analysis and facial expression recognition. For example, if the average blood pressure exceeds 130 / 85 for 10 or more consecutive days, it will determine that the user has mild hypertension, and if the user is feeling stressed, it will strengthen the stress management plan.

[0644] Based on the analysis results, the server generates a personalized health management plan. The generative model and emotion engine create a health management plan that includes recommendations for the user's diet, exercise, and mental health. For example, it suggests a low-salt diet plan, 30 minutes of walking every day, and relaxation exercises to reduce stress.

[0645] The generated health management plan is sent from the server to the user's device. The plan is packaged in JSON format and sent to the smartphone app using the HTTPS protocol. The plan is displayed to the user as a notification, and details can be viewed within the smartphone app.

[0646] The user follows the provided health management plan and enters the results and feedback into a dedicated app. Specifically, after completing a walk, the user enters the time it took and how they felt into the app. This feedback data is then sent from the smartphone to the cloud server.

[0647] Finally, the server analyzes the feedback data and emotional state data to update the next health management plan. The AI ​​and emotion engine evaluate the feedback data and make adjustments, such as slightly increasing the amount of exercise if walking has had a positive effect. If the emotion evaluation indicates that the user is feeling tired, the server will strengthen relaxation elements.

[0648] In this system, the following prompt sentences can be input into the generative AI model:

[0649] "Please explain in detail the process by which a user measures their blood pressure every morning and enters the results into a dedicated app."

[0650] "Please show us how the cloud server can safely process the collected data."

[0651] "Please provide a detailed description of the process for integrating and analyzing biometric data and emotional data."

[0652] "Please provide an example of a generated health plan and explain how it will be communicated."

[0653] Through these measures, the system provides personalized medical solutions to maintain the health and independence of elderly people and improve their quality of life.The introduction of an emotion engine realizes comprehensive health management that also takes into account the user's emotional state.

[0654] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0655] Step 1:

[0656] Data collection

[0657] User: Measures blood pressure every morning and enters the values ​​into a dedicated smartphone app. In addition, wears a wearable device to automatically record daily steps and heart rate, and collects audio and video using the microphone and camera on the smartphone or dedicated device.

[0658] Input: Blood pressure data (e.g., 120 / 80 mmHg) from a sphygmomanometer, step count and heart rate data from a wearable device, and audio and video data from a smartphone's microphone and camera.

[0659] Output: Biometric and emotional data collected on a smartphone app.

[0660] How it works: The user measures their blood pressure in the morning and enters the value into a smartphone app. At the same time, the wearable device records 5,000 steps and their heart rate, while the smartphone's camera monitors facial expressions and the microphone collects audio data.

[0661] Step 2:

[0662] Data transmission

[0663] Terminal: Sends collected biometric data and emotional state data to a cloud server at regular intervals.

[0664] Input: Biometric and emotional data collected in a smartphone app.

[0665] Output: Data sent to the cloud server.

[0666] How it works: The smartphone uses Wi-Fi or mobile data to upload measured blood pressure data, step count data from the wearable device, and emotional evaluation data to a cloud server. The data is SSL encrypted.

[0667] Step 3:

[0668] Data analysis

[0669] Server: Using a cloud-based generative model and emotion engine, analyzes the transmitted biometric and emotional state data and evaluates the user's health and emotional state.

[0670] Input: Biometric and emotional data uploaded to a cloud server.

[0671] Output: Assessment results on the user's health and emotional state.

[0672] Specific operation: The server performs trend analysis of blood pressure data and step count data from the past 30 days, and evaluates the user's emotional state using voice analysis and facial expression recognition technology. For example, it can detect a tendency toward high blood pressure from blood pressure data and sense stress from voice data.

[0673] Step 4:

[0674] Plan Generation

[0675] Server: Generates an individual health management plan based on the analysis results.

[0676] Input: Assessment results on the user's health and emotional state.

[0677] Output: A personalized health care plan.

[0678] How it works: The generative model and emotion engine create a health plan for the user, including recommendations for diet, exercise, and mental health. For example, it suggests a low-sodium diet, 30 minutes of walking daily, and relaxation exercises to reduce stress.

[0679] Step 5:

[0680] Send results

[0681] Server: Sends the generated health management plan to the user's terminal.

[0682] Input: The generated health care plan.

[0683] Output: The health plan sent to the user's device.

[0684] Specific operation: The generated plan is packaged in JSON format and sent to the smartphone app using the HTTPS protocol. The plan is displayed to the user as a notification, and details can be viewed within the smartphone app.

[0685] Step 6:

[0686] Feedback collection

[0687] User: Implements a health management plan and enters the results and feedback into a dedicated app.

[0688] Input: Health management plan implementation results and feedback.

[0689] Output: Feedback data aggregated in a smartphone app.

[0690] Specific behavior: After the user completes a walk, they enter the time it took and their feelings into the app (e.g., "I walked for 30 minutes and felt great").

[0691] Terminal: Sends the feedback data back to the cloud server.

[0692] Input: Feedback data aggregated in a smartphone app.

[0693] Output: Feedback data sent to the cloud server.

[0694] Specific operation: The smartphone uploads the input feedback data to a cloud server using Wi-Fi or mobile data communication under SSL encryption.

[0695] Step 7:

[0696] Feedback analysis and plan updates

[0697] Server: Analyzes the feedback data and emotional state data and updates the health management plan.

[0698] Input: Feedback data and emotional state data.

[0699] Output: Updated health care plan.

[0700] Specific operation: The AI ​​and emotion engine evaluate the feedback data and, for example, if it determines that walking has had a positive effect, it will make adjustments such as increasing the amount of exercise. If the emotion evaluation determines that the user is tired, it will strengthen relaxation elements.

[0701] (Application example 2)

[0702] 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."

[0703] For elderly health management, it is important to accurately assess individual biometric data and emotional states and provide appropriate health management plans. However, conventional systems provide health management plans without considering the user's emotional state, which results in insufficient management of health risks caused by stress and emotional changes. Furthermore, to ensure a comfortable and healthy shopping experience for elderly people, virtual shopping recommendations based on their health state are required.

[0704] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0705] In this invention, the server includes a means for evaluating biometric data and emotional states using a generative model and an emotion engine to generate an individualized health management plan and virtual shopping recommendations, a means for transmitting the implementation results and feedback of the user's health management plan and virtual shopping recommendations back to the cloud environment for analysis, and a means for updating the health management plan and virtual shopping recommendations based on the feedback, thereby enabling comprehensive management of the elderly's health and emotional states and providing an optimal shopping experience according to their health status.

[0706] "Biometric data" refers to data relating to the user's physical condition, such as blood pressure, heart rate, and number of steps.

[0707] "Cloud environment" is a general term for servers and data storage provided via the Internet.

[0708] A "generative model" refers to an algorithm for predicting and generating new data based on past data.

[0709] An "emotion engine" is software that analyzes data such as voice and facial expressions to evaluate the user's emotional state.

[0710] "Health Management Plan" means a plan that includes individually customized exercise, diet, and mental health recommendations to maintain or improve a User's health.

[0711] "Virtual shopping recommendation" is a system that recommends appropriate products and shopping experiences based on the user's health and emotional state.

[0712] "Feedback" means the evaluation and results of the health management plan or virtual shopping recommendations implemented by the user.

[0713] "Wearable devices" is a general term for electronic devices that can collect physical data by being worn, and includes smartwatches and health monitoring devices.

[0714] In the embodiment of the present invention, the system operates in an integrated manner with the server, terminals, and users working together.

[0715] Data collection

[0716] Users utilize wearable devices such as blood pressure monitors, smartwatches, smart glasses, and head-mounted displays to collect biometric and emotional data. For example, a user measures their blood pressure at home every morning and enters the values ​​(e.g., 120 / 80 mmHg) into a smartphone app. The smartwatch automatically records the number of steps taken each day (e.g., 5,000 steps) and heart rate, while the smart glasses and head-mounted display collect heart rate and facial expression data.

[0717] Data transmission

[0718] The device sends the collected biometric and emotional data to a cloud server at regular intervals. The data is SSL encrypted and sent via Wi-Fi or mobile data. For example, a smartphone uploads blood pressure data, step count data, and emotional evaluation data to the cloud server.

[0719] Data analysis

[0720] The server uses a cloud-based generative AI model and emotion engine to analyze the biometric and emotional state data and assess the user's health and emotional state. For example, it analyzes trends in blood pressure and step count data from the past 30 days and uses voice analysis and facial expression recognition to assess the user's emotional state. Specifically, if the average blood pressure exceeds 130 / 85 for 10 or more consecutive days, it is determined to be mild hypertension and will recommend a strengthened stress management plan.

[0721] Plan Generation

[0722] The server generates a health management plan and virtual shopping recommendations based on the analysis results, which include optimal recommendations for the user's diet, exercise, and mental health. Specifically, the server suggests a low-salt meal plan, 30 minutes of walking daily, and relaxation exercises to reduce stress, as well as virtual shopping recommendations based on the user's health status.

[0723] Send results

[0724] The server sends the generated health management plan and virtual shopping recommendations to the user's device. The generated plan is packaged in JSON format and sent to the device app using the HTTPS protocol. The user is then notified of the results.

[0725] Feedback collection and analysis

[0726] The user acts according to the provided health management plan and virtual shopping recommendations, and enters the results and feedback into a dedicated app. The device then sends the feedback data back to the cloud server. The server analyzes the feedback data and new emotional data and updates the next health management plan and virtual shopping recommendations. For example, if walking has had a positive effect, the server increases the amount of exercise, and if the user feels tired, it strengthens relaxation elements.

[0727] Hardware and Software Used

[0728] Hardware: smart glasses, head-mounted displays, smartwatches

[0729] Software: Cloud services (AWS Lambda, Azure Functions), speech recognition API (Google Cloud Speech-to-Text), facial expression recognition library (OpenCV, dlib)

[0730] Data processing: time series analysis of heart rate data, image recognition of facial expression data, acoustic analysis of voice data

[0731] Data calculation: Generative AI model predicts user health status, emotion engine evaluates stress level

[0732] Prompt Sentence Examples

[0733] Use the following dataset to predict a user's health and emotional state and generate personalized shopping recommendations.

[0734] Hourly heart rate data (HR)

[0735] Real-time facial expression data (EXPRESSION)

[0736] Voice tone analysis result (VOICE_TONE)

[0737] Past shopping history and feedback

[0738] example:

[0739] HR: [75, 80, 78, 85, 90]

[0740] EXPRESSION: {"happy": 0.3, "neutral": 0.6, "sad": 0.1}

[0741] VOICE_TONE: {"calm": 0.7, "stress": 0.3}

[0742] Recommendations: Provide relaxing music and recommend a health food section.

[0743] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0744] Step 1:

[0745] Users use wearable devices (e.g., smartwatches, smart glasses) to collect biometric and emotional data. Specifically, users measure their blood pressure every morning and enter the values ​​into a smartphone app. At the same time, the smartwatch automatically records their daily steps and heart rate, and the smart glasses collect facial expression data. These input data (blood pressure, steps, heart rate, and facial expression) are the initial data to be used for analysis.

[0746] Step 2:

[0747] The device sends the collected biometric and emotional data to a cloud server. The data is sent using Wi-Fi or mobile data communication and SSL encryption is used. The input data in this step is the various data collected in step 1, and is securely sent to the cloud server as processing results.

[0748] Step 3:

[0749] The server analyzes the transmitted biometric and emotional data using a cloud-based generative AI model and emotion engine. This analysis evaluates the user's health and emotional state. Specifically, it performs trend analysis on blood pressure and step count data from the past 30 days, and evaluates the emotional state using voice analysis and facial expression recognition. The input data in this step is the data transmitted to the cloud, and the output results are the evaluation results of the user's health and emotional state.

[0750] Step 4:

[0751] The server generates an individualized health management plan and virtual shopping recommendations based on the analysis results. The generative model and emotion engine create a health management plan that combines recommendations for diet, exercise, and mental health, and also generates virtual shopping recommendations. For example, a low-salt meal plan, 30 minutes of walking daily, and relaxation exercises to relieve stress may be suggested. The input data for this step are the evaluation results obtained in step 3, and the output is a health management plan and shopping recommendations.

[0752] Step 5:

[0753] The server sends the generated health management plan and virtual shopping recommendations to the user's device. The HTTPS protocol is used for transmission, and the data is packaged in JSON format. The device receives this data and notifies the user. The input data of this step are the generated plan and recommendations, and the output is a notification sent to the user's device.

[0754] Step 6:

[0755] The user acts according to the generated health management plan and virtual shopping recommendations, and then inputs the results and feedback into a dedicated app. The input data is the implementation results of the health management plan and feedback on the shopping experience. This data is used for reanalysis in subsequent steps.

[0756] Step 7:

[0757] The device retransmits the feedback data entered by the user to the cloud server. The data is SSL encrypted and transmitted as described above. The input data for this step is the user's feedback, and the output is the data to be transmitted to the cloud server.

[0758] Step 8:

[0759] The server analyzes the feedback data and updated emotion data sent to the cloud server and updates the health management plan and virtual shopping recommendations. Based on the feedback data, the server regenerates an optimal plan according to the user's health and emotional state. For example, if walking has a positive effect, the server increases the amount of exercise, and if the user feels tired, it strengthens the relaxation element. The input data of steps is the feedback data, and the output results are the updated plan and recommendations.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] [Third embodiment]

[0764] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0765] 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.

[0766] 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).

[0767] 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.

[0768] 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.

[0769] 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).

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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."

[0776] This invention relates to a system that uses generative AI to customize health management for elderly people in Japan. The system collects biometric data from users, transmits it to a cloud environment, and analyzes it to provide personalized health management plans. The following describes the program and processing of this system.

[0777] Program processing explanation

[0778] 1. Data Collection

[0779] User: Measures blood pressure every morning and enters the results into a dedicated smartphone app. Also, uses a wearable device to automatically record daily steps and heart rate.

[0780] Example: A user uses a blood pressure monitor in the morning and enters the measurement value into a dedicated app, while the smartwatch simultaneously collects 5,000 steps.

[0781] 2. Data Transmission

[0782] Terminal: Periodically sends collected biometric data (blood pressure, number of steps, heart rate, etc.) to a cloud server.

[0783] Example: A smartphone uses Wi-Fi or mobile data to upload blood pressure data or step count data to a cloud server.

[0784] 3. Data Analysis

[0785] Server: Using a generative model on the cloud, analyzes the collected biometric data and assesses the user's health status.

[0786] Example: The server tracks blood pressure and step count data for the past 30 days and analyzes patterns of changes in physical condition.

[0787] 4. Plan Generation

[0788] Server: Based on the results of data analysis, it generates a health management plan including individualized exercise, diet, and wellness programs.

[0789] Example: A server uses user data to generate a low-salt meal plan and a plan recommending 30 minutes of walking each day.

[0790] 5. Send results

[0791] Server: Sends the generated health management plan to the user's terminal.

[0792] Example: The generated plan is sent in JSON format to a smartphone app and notified to the user.

[0793] 6. Feedback Collection

[0794] User: Implements a health management plan and enters the results and feedback into a dedicated app.

[0795] Terminal: Sends the feedback data back to the cloud server.

[0796] Example: After a user completes a walk, they enter the time it took and their impressions into the app, and their smartphone uploads the data to a cloud server.

[0797] 7. Feedback analysis and plan updates

[0798] Server: Analyzes the feedback data and reflects it in the next health management plan.

[0799] Example: The server analyzes the feedback data and, if walking is effective, adjusts the exercise, such as slightly increasing the amount of exercise.

[0800] Through these measures, the system provides personalized medical solutions to maintain the health and independence of older adults and improve their quality of life, enabling users to more effectively manage their daily health.

[0801] The processing flow will be explained below.

[0802] Step 1:

[0803] User: Measures blood pressure every morning and enters the results into a dedicated smartphone app. The user also wears a wearable device that automatically records the number of steps taken and heart rate each day.

[0804] Specific operation: The user uses a blood pressure monitor and inputs the measurement value (e.g., 120 / 80 mmHg) into the app. The wearable device counts 5,000 steps and simultaneously records heart rate data.

[0805] Step 2:

[0806] Terminal: Sends collected biometric data to a cloud server at regular intervals.

[0807] How it works: Your smartphone uses Wi-Fi or mobile data to upload blood pressure and step count data to a cloud server. The data is then SSL encrypted before being sent.

[0808] Step 3:

[0809] Server: Using a generative model on the cloud, analyzes the transmitted biometric data and assesses the user's health status.

[0810] Specific operation: The server performs trend analysis of blood pressure data from the past 30 days and detects abnormal values. For example, if the average blood pressure exceeds 130 / 85 for 10 days or more, it is determined to be mild hypertension.

[0811] Step 4:

[0812] Server: Generates an individual health management plan based on the analysis results.

[0813] What it does: The generative model creates a health plan for the user with recommendations for diet, exercise, and mental health, such as a low-sodium diet, 30 minutes of walking daily, and relaxation exercises for stress management.

[0814] Step 5:

[0815] Server: Sends the generated health management plan to the user's terminal.

[0816] Specific operation: The generated plan is packaged in JSON format and sent to the smartphone app using the HTTPS protocol. The plan is then displayed to the user as a notification.

[0817] Step 6:

[0818] User: Carry out daily health management according to the received health management plan.

[0819] Specific actions: The user refers to the smartphone app, changes dinner to a low-salt dish, and begins a 30-minute walk.

[0820] Step 7:

[0821] User: Enter the results of the health management plan and feedback into a dedicated app.

[0822] Specific behavior: After completing a walk, the user enters the time it took and their feelings into the app. For example, they might enter, "After 30 minutes of walking, I feel refreshed."

[0823] Step 8:

[0824] Terminal: Sends the feedback data back to the cloud server.

[0825] Specific operation: The feedback data collected by the smartphone is encrypted and uploaded to a cloud server.

[0826] Step 9:

[0827] Server: Analyzes the feedback data and updates the next health management plan.

[0828] Specific actions: The AI ​​evaluates the feedback data, and if walking has a positive effect, it will make adjustments such as slightly increasing the amount of exercise. This will be reflected in the next plan.

[0829] Through the above steps, it is possible to provide an optimal health management plan tailored to the individual health condition of each user, thereby improving the quality of life of elderly people.

[0830] Example 1

[0831] 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."

[0832] The present invention relates to a system that efficiently analyzes collected biometric data and provides users with customized health management plans as a means of individually optimizing health management for elderly people. Conventional health management systems have struggled to customize plans to accommodate individual health conditions, and generic plans often fail to produce satisfactory results. Furthermore, they lacked a mechanism for continuously improving plans using feedback data, making it difficult to accurately manage users' health conditions.

[0833] 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.

[0834] In this invention, the server includes means for collecting individual biometric data, means for transmitting the biometric data to a cloud environment, means for analyzing the biometric data in the cloud environment and using a generative model to evaluate the user's health condition, means for generating an individual health management plan based on the analysis results of the generative model, means for transmitting the health management plan to the user's device, means for transmitting implementation results and feedback of the health management plan back to the cloud environment, means for analyzing the feedback and updating the health management plan, means for measuring blood pressure every morning and inputting it into a smartphone app, means for automatically recording step count and heart rate data using a wearable device and means for transmitting the data to the device, and means for generating a health management plan that is periodically improved based on the analysis. This makes it possible to individually evaluate the user's health condition and provide an appropriate health management plan, and to continuously improve the plan through feedback.

[0835] "Individual biometric data" refers to data relating to physical conditions such as blood pressure, number of steps, and heart rate collected from individual users.

[0836] A "cloud environment" is a collection of remote servers used to store, analyze, and process data over the Internet.

[0837] A "generative model" is an artificial intelligence algorithm that predicts and generates results based on data analysis.

[0838] A "health plan" is a set of specific instructions, including exercise, diet, wellness activities, etc., to improve a user's health.

[0839] A "terminal" is a hardware device (e.g., smartphone, tablet) used by a user to input, process, transmit, etc. data.

[0840] A "wearable device" is an electronic device that can be worn to automatically record daily physical activity.

[0841] "Feedback" is data on the implementation results and impressions provided by the user after implementing the health management plan.

[0842] The present invention is a system for individually optimizing health management for elderly people. This system analyzes biometric data collected from users in a cloud environment and provides customized health management plans using a generative AI model. The system's program processing is described in detail below.

[0843] First, the user collects biometric data using a smartphone app and a wearable device. Every morning, the user measures their blood pressure with a blood pressure monitor and enters the results into the smartphone app. The wearable device (e.g., a smartwatch) also automatically records their daily steps and heart rate. This data is periodically sent from the device to a cloud server.

[0844] The cloud server analyzes the received biometric data using a generative AI model. This analysis is performed using Python scripts and machine learning libraries such as TensorFlow and PyTorch. The analysis results in an assessment of the user's health status. For example, blood pressure and step count data from the past 30 days can be tracked to identify patterns of fluctuations in health status.

[0845] The server then generates a personalized health management plan based on the analysis results. This plan includes exercise, diet, and wellness activities. For example, the analysis results may recommend a low-salt diet plan and 30 minutes of walking daily. The plan is formatted in JSON and pushed to the user's smartphone app.

[0846] The user carries out the health management plan they receive and enters the results and feedback into a dedicated app. For example, after completing a walk, they can enter the time it took and the effects they felt in text. The device then sends this feedback data back to the cloud server.

[0847] The server analyzes the feedback data and updates the health management plan based on the analysis. This process is reflected in the next health management plan. For example, if walking was effective, adjustments such as slightly increasing the amount of exercise will be made.

[0848] In this way, the system of the present invention effectively supports health management for the elderly by collecting and analyzing the user's biometric data and providing an individually customized health management plan, enabling the user to manage their health more effectively through this system.

[0849] keyword:

[0850] Generative AI model, prompt sentence

[0851] Example prompt sentence:

[0852] Use your biometric data (blood pressure, steps, heart rate) to create an AI model to generate a personalized health plan.

[0853] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0854] Step 1: Data collection

[0855] User: Wakes up in the morning, measures blood pressure with a blood pressure monitor, and enters the results into a dedicated smartphone app. The input data is saved on the device.

[0856] Input: Blood pressure data measured by the user

[0857] Output: Blood pressure data stored in a smartphone app

[0858] User: Wears a wearable device (smartwatch) that automatically records daily steps and heart rate data, which is then periodically sent to a smartphone.

[0859] Input: User's daily activity data (steps, heart rate)

[0860] Output: Step count and heart rate data sent to your smartphone

[0861] Step 2: Send data

[0862] Device: Collected biometric data (blood pressure, number of steps, heart rate) is periodically sent to a cloud server. Data is uploaded securely and quickly using Wi-Fi or mobile data.

[0863] Input: Biometric data stored on the smartphone

[0864] Output: Biometric data sent to the cloud server

[0865] Step 3: Data analysis

[0866] Server: Analyzes the submitted biometric data using a generative AI model in the cloud. Health status is assessed using Python scripts and machine learning libraries such as TensorFlow and PyTorch.

[0867] Input: Biometric data stored on a cloud server

[0868] Output: User's health status assessment result

[0869] Step 4: Generate a plan

[0870] Server: Based on the analysis results, it generates a health management plan that includes a personalized exercise, diet, and wellness program, such as a low-salt meal plan and a daily walking plan.

[0871] Input: Health status assessment results

[0872] Output: A personalized health plan

[0873] Step 5: Send results

[0874] Server: Formats the generated health management plan in JSON format and sends it to the user's smartphone app via push notification.

[0875] Enter: Customized Health Care Plan

[0876] Output: Health plan notification sent to smartphone app

[0877] Step 6: Gather feedback

[0878] User: Implements a health management plan and inputs the results and feedback into a dedicated app. For example, after walking, the user enters the time it took and their impressions.

[0879] Input: User feedback data

[0880] Output: Feedback data stored in a smartphone app

[0881] Terminal: Sends the feedback data back to the cloud server.

[0882] Input: Feedback data stored in the smartphone app

[0883] Output: Feedback data sent to the cloud server

[0884] Step 7: Analyze feedback and update plans

[0885] Server: Analyzes the feedback data and reflects it in the next health management plan. The generative AI model incorporates new data to update and continuously optimize the plan.

[0886] Input: Feedback data

[0887] Output: Updated individual health care plan

[0888] (Application example 1)

[0889] 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."

[0890] Health management for the elderly requires providing plans that meet individual needs, but conventional systems have difficulty linking with food delivery services, such as individual food selection and home delivery, making it impossible to achieve effective health management. In particular, a system is needed that provides individualized healthy eating plans in a way that is easily accessible to the elderly.

[0891] 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.

[0892] In this invention, the server includes means for collecting individual biometric data, means for transmitting the biometric data to a cloud environment, means for analyzing the biometric data in the cloud environment and using a generative model for evaluating the user's health status, means for generating an individual health management plan based on the analysis results of the generative model, means for transmitting the health management plan to the user's terminal, means for transmitting implementation results and feedback of the health management plan back to the cloud environment, means for analyzing the feedback and updating the health management plan, and means for generating a meal plan based on the health management plan and automatically ordering meals in cooperation with a food delivery service. This makes it possible to provide an individualized healthy meal plan to an elderly person and have it delivered to their home via a food delivery service.

[0893] "Biometric data" refers to data related to the user's health condition, including blood pressure, number of steps, heart rate, and the like.

[0894] A "cloud environment" is a computing resource and data storage environment consisting of a group of remote servers accessible via the Internet.

[0895] A "generative model" is an artificial intelligence algorithm that analyzes collected data and generates an individual plan from the results.

[0896] "Health Management Plan" means a plan that includes exercise, diet, and health activities designed to improve and maintain the User's health.

[0897] "Feedback" is information about the results, impressions, and effects of the health management plan implemented by the user.

[0898] A "food delivery service" is a service that delivers meals to a specific location and delivers meals to users based on a personalized healthy eating plan.

[0899] A "terminal" is an electronic device that allows a user to input data, receive notifications, send feedback, etc.

[0900] "Analysis" is the process of processing and calculating collected data to derive results.

[0901] The present invention relates to a system that collects individual biometric data, generates a health management plan, and provides meals in cooperation with a food delivery service. This system supports the health management of elderly people and improves their quality of life by providing personalized meal plans.

[0902] Hardware and software used

[0903] Hardware: Smartphones, wearable devices (smartwatches, etc.)

[0904] Software: Smartphone app, generative AI model, cloud server, food delivery system

[0905] Data collection

[0906] Users measure their blood pressure every morning and enter the data into a smartphone app. Wearable devices (e.g., smartwatches) also automatically record their daily steps and heart rate, thereby collecting biometric data.

[0907] Data transmission

[0908] The collected biometric data is sent to a cloud server via the smartphone, which then uploads the blood pressure and step count data to the cloud server using Wi-Fi or mobile data.

[0909] Data analysis

[0910] The collected biometric data is analyzed on a cloud server using a generative AI model to assess the user's health. For example, the generative AI model tracks blood pressure and step count data from the past 30 days and analyzes patterns of changes in physical condition.

[0911] Health management plan generation

[0912] Based on the results of the data analysis, the generative AI model generates a health management plan that includes a personalized exercise, diet, and wellness program, such as a low-salt diet plan and a plan recommending 30 minutes of walking every day.

[0913] Send results

[0914] The generated health management plan is sent in JSON format to a smartphone app and notified to the user.

[0915] Food delivery collaboration

[0916] Based on the generated health management plan, the app will automatically order appropriate meals through a food delivery service. Specifically, the app will automatically order low-sodium meals from the food delivery service.

[0917] Feedback collection

[0918] Users enter their impressions and effects of the provided meal into the app, and the data is then uploaded to the cloud server, allowing user feedback to be collected.

[0919] Feedback analysis and plan updates

[0920] The cloud server analyzes the collected feedback data and reflects it in the next health management plan. For example, if walking is effective, it will make adjustments such as slightly increasing the amount of exercise.

[0921] Specific examples

[0922] For example, suppose User A enters their morning blood pressure and records the number of steps they took that day on their smartwatch. This data is sent from the smartphone app to a cloud server, where it is analyzed by a generative AI model. Based on the analysis results, the AI ​​generates a low-salt meal plan, and this data is automatically sent to a food delivery service. After User A receives their dinner, they can enter their post-meal impressions into the app, and this feedback is reflected in their next meal plan.

[0923] Prompt Sentence Examples

[0924] "Generate a low-sodium, high-protein healthy diet plan based on user biometric data: blood pressure (120 / 80), steps (5000), heart rate (70 bpm), and 30 days of data analysis."

[0925] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0926] Step 1: Data collection

[0927] The user inputs the results of their blood pressure measurement every morning. A wearable device (such as a smartwatch) automatically records the number of steps taken and heart rate each day. This allows the user's biometric data (input: blood pressure, number of steps, heart rate) to be collected.

[0928] Step 2: Send data

[0929] The device (smartphone) sends the collected biometric data to a cloud server. The smartphone uploads blood pressure data and step count data to the cloud server via Wi-Fi or mobile data communication (input: biometric data, output: data stored on the cloud server).

[0930] Step 3: Data analysis

[0931] The server analyzes the biometric data using a generative AI model on the cloud. The generative AI model tracks, for example, blood pressure data and step count data from the past 30 days and evaluates the user's health status (input: biometric data, output: health status evaluation results). This data processing includes trend analysis and analysis of fluctuation patterns.

[0932] Step 4: Create a health management plan

[0933] Based on the analysis results of the generative AI model, the server generates a health management plan including an individualized exercise, diet, and wellness program (input: health status assessment results, output: health management plan). Specifically, a low-salt meal plan and a plan recommending 30 minutes of walking every day are generated.

[0934] Step 5: Send results

[0935] The server sends the generated health management plan in JSON format to the smartphone app, which notifies the user (input: health management plan, output: plan displayed on smartphone app).

[0936] Step 6: Food delivery integration

[0937] The device (smartphone) then works with a food delivery service to automatically order appropriate meals based on the generated health management plan. Specifically, low-salt meals are automatically ordered from the food delivery service (input: health management plan, output: order data for the food delivery service).

[0938] Step 7: Gather feedback

[0939] The user inputs their impressions and the effects of the provided meal into a smartphone app. The device (smartphone) then uploads the data back to the cloud server (input: feedback data, output: feedback data saved on the cloud server).

[0940] Step 8: Analyze feedback and update plans

[0941] The server analyzes the collected feedback data and reflects it in the next health management plan (input: feedback data, output: updated health management plan). Specifically, if walking is effective, adjustments will be made, such as slightly increasing the amount of exercise. This data calculation includes analyzing the effectiveness of the feedback and optimizing the plan based on the results.

[0942] 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.

[0943] This invention relates to a system that uses generative AI and an emotion engine to customize health management for elderly people in Japan. The system collects biometric and emotional state data from users, transmits it to a cloud environment for analysis, and provides personalized health management plans. The following describes the program and processing of this system.

[0944] Program processing explanation

[0945] 1. Data Collection

[0946] User: Measures blood pressure every morning and enters the results into a dedicated smartphone app. The user also wears a wearable device that automatically records the number of steps taken and heart rate each day. The device also uses the microphone and camera on the smartphone and dedicated device to collect audio and video data, which are then sent to the emotion engine.

[0947] Example: A user uses a blood pressure monitor in the morning and inputs the measurement (e.g., 120 / 80 mmHg) into the app, while the smartwatch counts 5,000 steps and monitors facial expressions with the camera.

[0948] 2. Data Transmission

[0949] Terminal: The terminal transmits the collected biometric data and emotional state data to the cloud server at regular intervals.

[0950] Example: A smartphone uses Wi-Fi or mobile data to upload blood pressure data, step count data, and emotional evaluation data to a cloud server. The data is then sent using SSL encryption.

[0951] 3. Data Analysis

[0952] Server: Using a cloud-based generative model and emotion engine, analyzes the transmitted biometric and emotional state data and evaluates the user's health and emotional state.

[0953] Example: The server analyzes blood pressure and step count data from the past 30 days and uses voice analysis and facial expression recognition to assess the user's emotional state. For example, if the average blood pressure exceeds 130 / 85 for 10 days or more, it is determined to be mild hypertension, and if the user is feeling stressed, the server will strengthen the stress management plan.

[0954] 4. Plan Generation

[0955] Server: Generates an individual health management plan based on the analysis results.

[0956] Example: A generative model and emotion engine create a health plan for the user with recommendations for diet, exercise, and mental health, such as a low-sodium diet, 30 minutes of walking daily, and relaxation exercises to reduce stress.

[0957] 5. Send results

[0958] Server: Sends the generated health management plan to the user's terminal.

[0959] Example: The generated plan is packaged in JSON format and sent to a smartphone app using the HTTPS protocol. The plan is displayed to the user as a notification.

[0960] 6. Feedback Collection

[0961] User: Implements a health management plan and enters the results and feedback into a dedicated app.

[0962] Terminal: Sends the feedback data back to the cloud server.

[0963] Example: After completing a walk, the user enters the time taken and their feelings into the app, and the smartphone uploads the data to a cloud server.

[0964] 7. Feedback analysis and plan updates

[0965] Server: Analyzes the feedback data and emotion evaluation data and updates the next health management plan.

[0966] Example: The AI ​​and emotion engine evaluate the feedback data and, for example, if walking has a positive effect, make adjustments such as slightly increasing the amount of exercise. If the emotion evaluation indicates that the user feels tired, strengthen the relaxation element.

[0967] Through these measures, the system provides personalized medical solutions to maintain the health and independence of elderly people and improve their quality of life.The introduction of an emotion engine enables comprehensive health management that takes into account the user's emotional state.

[0968] The processing flow will be explained below.

[0969] Step 1:

[0970] User: Measures blood pressure every morning and enters the results into a dedicated smartphone app. The wearable device also automatically records daily steps and heart rate. Furthermore, the emotion engine recognizes the user's emotional state through audio and video.

[0971] How it works: The user uses a blood pressure monitor and inputs the measurement (e.g., 120 / 80 mmHg) into the app. The wearable device counts 5,000 steps and simultaneously records heart rate data. The smartphone camera captures the user's facial expressions, and the microphone collects audio data.

[0972] Step 2:

[0973] Terminal: The terminal transmits the collected biometric data and emotional state data to the cloud server at regular intervals.

[0974] How it works: Your smartphone uses Wi-Fi or mobile data to upload blood pressure data, step count data, heart rate data, facial expression capture data, and voice data to a cloud server. The data is sent using SSL encryption.

[0975] Step 3:

[0976] Server: Using a cloud-based generative model and emotion engine, analyzes the transmitted biometric and emotional state data and evaluates the user's health and emotional state.

[0977] How it works: The server performs trend analysis on blood pressure and step count data from the past 30 days to detect abnormal values. The emotion engine evaluates the user's emotional state using voice analysis and facial expression recognition. For example, if the average blood pressure exceeds 130 / 85 for 10 days or more, it is determined to be mild hypertension, and facial expression analysis is used to detect stress.

[0978] Step 4:

[0979] Server: Generates an individual health management plan based on the analysis results.

[0980] How it works: The generative model and emotion engine create a health plan for the user, including recommendations for diet, exercise, and mental health. For example, it suggests a low-salt diet plan for mild high blood pressure, 30 minutes of walking daily, and relaxation exercises to reduce stress.

[0981] Step 5:

[0982] Server: Sends the generated health management plan to the user's terminal.

[0983] Specific operation: The generated plan is packaged in JSON format and sent to the smartphone app using the HTTPS protocol. The plan is then displayed to the user as a notification.

[0984] Step 6:

[0985] User: Carry out daily health management according to the received health management plan.

[0986] Specific actions: The user consults a smartphone app to change their dinner to a low-salt dish, begin a 30-minute walk, and perform relaxation exercises as instructed by the app.

[0987] Step 7:

[0988] User: Enters the results of the health management plan and feedback into a dedicated app. Emotional state is also recorded at the same time.

[0989] Specific operation: After completing a walk, the user enters the time it took and their feelings into the app. For example, they can enter "After 30 minutes of walking, I feel refreshed." The app also records the user's facial expressions and voice.

[0990] Step 8:

[0991] Terminal: Sends the feedback data and emotional state data back to the cloud server.

[0992] Specific operation: The smartphone encrypts the collected feedback data and emotional state data and uploads them to a cloud server.

[0993] Step 9:

[0994] Server: Analyzes the feedback data and emotional state data and updates the next health management plan.

[0995] Specific behavior: The AI ​​and emotion engine evaluate the feedback data and, if walking has a positive effect, adjust the exercise by slightly increasing it. If the emotion engine detects stress or fatigue, it will reinforce relaxation exercises and rest plans.

[0996] Through the above steps, it is possible to provide an optimal health management plan tailored to the individual health and emotional state of each user, thereby improving the quality of life of elderly people.

[0997] Example 2

[0998] 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."

[0999] Current health management systems typically collect only a user's biometric data and provide a health management plan based on the collected data. However, these systems do not take the user's emotional state into account and are unable to generate a plan that fully reflects the impact of stress and mental state on health. This can result in ineffective health management plans. Furthermore, because emotional state is not taken into account when updating a health management plan based on feedback, it is difficult to provide a plan optimized for the user's condition.

[1000] 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.

[1001] In this invention, the server includes means for collecting individual biometric data and emotional state data, means for transmitting the biometric data and emotional state data to a cloud environment, means for analyzing the data in the cloud environment and using a generative model and an emotion engine for evaluating the user's health condition and emotional state, means for generating an individual health management plan based on the analysis results of the generative model and the emotion engine, means for transmitting the health management plan to the user's terminal, means for transmitting implementation results and feedback of the health management plan back to the cloud environment, and means for analyzing the feedback and emotional state data and updating the health management plan, thereby enabling comprehensive health management that takes the user's emotional state into consideration.

[1002] "Biometric data" refers to information about the user's physical condition, such as blood pressure, heart rate, and number of steps taken.

[1003] "Emotional state data" is information about the emotional state obtained by analyzing the user's voice and facial expressions.

[1004] A "cloud environment" is a computer network that stores, manages, and analyzes data over the Internet.

[1005] A "generative model" is an artificial intelligence model that assesses and analyzes a user's health status based on their biometric and emotional state data.

[1006] An "emotion engine" is a software program that analyzes and evaluates a user's emotional state based on their voice and video data.

[1007] A "health plan" is a plan that provides personalized health care guidance and recommendations based on an analysis of a user's biometric and emotional state data.

[1008] A "wearable device" is a device worn by a user that automatically collects biometric data.

[1009] "Audio and video collection device" means a device that collects a user's audio and video.

[1010] "Feedback" is data that records the results and impressions of the user when implementing the health management plan.

[1011] The present invention relates to a system that uses a generative AI model and an emotion engine to individually customize health management for elderly people in Japan. The system collects biometric and emotional state data from users, transmits it to a cloud environment for analysis, and provides personalized health management plans. Specific embodiments of the system are described below.

[1012] First, the user measures their blood pressure every morning and enters the results into a dedicated smartphone app. This smartphone app also has a function that automatically records the user's daily steps and heart rate when the user wears a wearable device. Furthermore, the app collects audio and video using the microphone and camera on the smartphone and dedicated device, and sends them to the emotion engine. Specifically, we imagine a scenario in which the user uses a blood pressure monitor in the morning to input the measurement value (e.g., 120 / 80 mmHg) into the app, and the smartwatch counts 5,000 steps and monitors facial expressions using the camera.

[1013] The device then transmits the collected biometric and emotional state data to a cloud server at regular intervals. The smartphone then uploads the blood pressure, step count, and emotional assessment data to the cloud server using Wi-Fi or mobile data. This transmission is securely performed using SSL encryption.

[1014] The server analyzes the transmitted biometric and emotional state data using a cloud-based generative model and emotion engine. This evaluates the user's health and emotional state. Specifically, the server analyzes trends in blood pressure and step count data from the past 30 days, and assesses the user's emotional state using voice analysis and facial expression recognition. For example, if the average blood pressure exceeds 130 / 85 for 10 or more consecutive days, it will determine that the user has mild hypertension, and if the user is feeling stressed, it will strengthen the stress management plan.

[1015] Based on the analysis results, the server generates a personalized health management plan. The generative model and emotion engine create a health management plan that includes recommendations for the user's diet, exercise, and mental health. For example, it suggests a low-salt diet plan, 30 minutes of walking every day, and relaxation exercises to reduce stress.

[1016] The generated health management plan is sent from the server to the user's device. The plan is packaged in JSON format and sent to the smartphone app using the HTTPS protocol. The plan is displayed to the user as a notification, and details can be viewed within the smartphone app.

[1017] The user follows the provided health management plan and enters the results and feedback into a dedicated app. Specifically, after completing a walk, the user enters the time it took and how they felt into the app. This feedback data is then sent from the smartphone to the cloud server.

[1018] Finally, the server analyzes the feedback data and emotional state data to update the next health management plan. The AI ​​and emotion engine evaluate the feedback data and make adjustments, such as slightly increasing the amount of exercise if walking has had a positive effect. If the emotion evaluation indicates that the user is feeling tired, the server will strengthen relaxation elements.

[1019] In this system, the following prompt sentences can be input into the generative AI model:

[1020] "Please explain in detail the process by which a user measures their blood pressure every morning and enters the results into a dedicated app."

[1021] "Please show us how the cloud server can safely process the collected data."

[1022] "Please provide a detailed description of the process for integrating and analyzing biometric data and emotional data."

[1023] "Please provide an example of a generated health plan and explain how it will be communicated."

[1024] Through these measures, the system provides personalized medical solutions to maintain the health and independence of elderly people and improve their quality of life.The introduction of an emotion engine realizes comprehensive health management that also takes into account the user's emotional state.

[1025] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1026] Step 1:

[1027] Data collection

[1028] User: Measures blood pressure every morning and enters the values ​​into a dedicated smartphone app. In addition, wears a wearable device to automatically record daily steps and heart rate, and collects audio and video using the microphone and camera on the smartphone or dedicated device.

[1029] Input: Blood pressure data (e.g., 120 / 80 mmHg) from a sphygmomanometer, step count and heart rate data from a wearable device, and audio and video data from a smartphone's microphone and camera.

[1030] Output: Biometric and emotional data collected on a smartphone app.

[1031] How it works: The user measures their blood pressure in the morning and enters the value into a smartphone app. At the same time, the wearable device records 5,000 steps and their heart rate, while the smartphone's camera monitors facial expressions and the microphone collects audio data.

[1032] Step 2:

[1033] Data transmission

[1034] Terminal: Sends collected biometric data and emotional state data to a cloud server at regular intervals.

[1035] Input: Biometric and emotional data collected in a smartphone app.

[1036] Output: Data sent to the cloud server.

[1037] How it works: The smartphone uses Wi-Fi or mobile data to upload measured blood pressure data, step count data from the wearable device, and emotional evaluation data to a cloud server. The data is SSL encrypted.

[1038] Step 3:

[1039] Data analysis

[1040] Server: Using a cloud-based generative model and emotion engine, analyzes the transmitted biometric and emotional state data and evaluates the user's health and emotional state.

[1041] Input: Biometric and emotional data uploaded to a cloud server.

[1042] Output: Assessment results on the user's health and emotional state.

[1043] Specific operation: The server performs trend analysis of blood pressure data and step count data from the past 30 days, and evaluates the user's emotional state using voice analysis and facial expression recognition technology. For example, it can detect a tendency toward high blood pressure from blood pressure data and sense stress from voice data.

[1044] Step 4:

[1045] Plan Generation

[1046] Server: Generates an individual health management plan based on the analysis results.

[1047] Input: Assessment results on the user's health and emotional state.

[1048] Output: A personalized health care plan.

[1049] How it works: The generative model and emotion engine create a health plan for the user, including recommendations for diet, exercise, and mental health. For example, it suggests a low-sodium diet, 30 minutes of walking daily, and relaxation exercises to reduce stress.

[1050] Step 5:

[1051] Send results

[1052] Server: Sends the generated health management plan to the user's terminal.

[1053] Input: The generated health care plan.

[1054] Output: The health plan sent to the user's device.

[1055] Specific operation: The generated plan is packaged in JSON format and sent to the smartphone app using the HTTPS protocol. The plan is displayed to the user as a notification, and details can be viewed within the smartphone app.

[1056] Step 6:

[1057] Feedback collection

[1058] User: Implements a health management plan and enters the results and feedback into a dedicated app.

[1059] Input: Health management plan implementation results and feedback.

[1060] Output: Feedback data aggregated in a smartphone app.

[1061] Specific behavior: After the user completes a walk, they enter the time it took and their feelings into the app (e.g., "I walked for 30 minutes and felt great").

[1062] Terminal: Sends the feedback data back to the cloud server.

[1063] Input: Feedback data aggregated in a smartphone app.

[1064] Output: Feedback data sent to the cloud server.

[1065] Specific operation: The smartphone uploads the input feedback data to a cloud server using Wi-Fi or mobile data communication under SSL encryption.

[1066] Step 7:

[1067] Feedback analysis and plan updates

[1068] Server: Analyzes the feedback data and emotional state data and updates the health management plan.

[1069] Input: Feedback data and emotional state data.

[1070] Output: Updated health care plan.

[1071] Specific operation: The AI ​​and emotion engine evaluate the feedback data and, for example, if it determines that walking has had a positive effect, it will make adjustments such as increasing the amount of exercise. If the emotion evaluation determines that the user is tired, it will strengthen relaxation elements.

[1072] (Application example 2)

[1073] 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."

[1074] For elderly health management, it is important to accurately assess individual biometric data and emotional states and provide appropriate health management plans. However, conventional systems provide health management plans without considering the user's emotional state, which results in insufficient management of health risks caused by stress and emotional changes. Furthermore, to ensure a comfortable and healthy shopping experience for elderly people, virtual shopping recommendations based on their health state are required.

[1075] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1076] In this invention, the server includes a means for evaluating biometric data and emotional states using a generative model and an emotion engine to generate an individualized health management plan and virtual shopping recommendations, a means for transmitting the implementation results and feedback of the user's health management plan and virtual shopping recommendations back to the cloud environment for analysis, and a means for updating the health management plan and virtual shopping recommendations based on the feedback, thereby enabling comprehensive management of the elderly's health and emotional states and providing an optimal shopping experience according to their health status.

[1077] "Biometric data" refers to data relating to the user's physical condition, such as blood pressure, heart rate, and number of steps.

[1078] "Cloud environment" is a general term for servers and data storage provided via the Internet.

[1079] A "generative model" refers to an algorithm for predicting and generating new data based on past data.

[1080] An "emotion engine" is software that analyzes data such as voice and facial expressions to evaluate the user's emotional state.

[1081] "Health Management Plan" means a plan that includes individually customized exercise, diet, and mental health recommendations to maintain or improve a User's health.

[1082] "Virtual shopping recommendation" is a system that recommends appropriate products and shopping experiences based on the user's health and emotional state.

[1083] "Feedback" means the evaluation and results of the health management plan or virtual shopping recommendations implemented by the user.

[1084] "Wearable devices" is a general term for electronic devices that can collect physical data by being worn, and includes smartwatches and health monitoring devices.

[1085] In the embodiment of the present invention, the system operates in an integrated manner with the server, terminals, and users working together.

[1086] Data collection

[1087] Users utilize wearable devices such as blood pressure monitors, smartwatches, smart glasses, and head-mounted displays to collect biometric and emotional data. For example, a user measures their blood pressure at home every morning and enters the values ​​(e.g., 120 / 80 mmHg) into a smartphone app. The smartwatch automatically records the number of steps taken each day (e.g., 5,000 steps) and heart rate, while the smart glasses and head-mounted display collect heart rate and facial expression data.

[1088] Data transmission

[1089] The device sends the collected biometric and emotional data to a cloud server at regular intervals. The data is SSL encrypted and sent via Wi-Fi or mobile data. For example, a smartphone uploads blood pressure data, step count data, and emotional evaluation data to the cloud server.

[1090] Data analysis

[1091] The server uses a cloud-based generative AI model and emotion engine to analyze the biometric and emotional state data and assess the user's health and emotional state. For example, it analyzes trends in blood pressure and step count data from the past 30 days and uses voice analysis and facial expression recognition to assess the user's emotional state. Specifically, if the average blood pressure exceeds 130 / 85 for 10 or more consecutive days, it is determined to be mild hypertension and will recommend a strengthened stress management plan.

[1092] Plan Generation

[1093] The server generates a health management plan and virtual shopping recommendations based on the analysis results, which include optimal recommendations for the user's diet, exercise, and mental health. Specifically, the server suggests a low-salt meal plan, 30 minutes of walking daily, and relaxation exercises to reduce stress, as well as virtual shopping recommendations based on the user's health status.

[1094] Send results

[1095] The server sends the generated health management plan and virtual shopping recommendations to the user's device. The generated plan is packaged in JSON format and sent to the device app using the HTTPS protocol. The user is then notified of the results.

[1096] Feedback collection and analysis

[1097] The user acts according to the provided health management plan and virtual shopping recommendations, and enters the results and feedback into a dedicated app. The device then sends the feedback data back to the cloud server. The server analyzes the feedback data and new emotional data and updates the next health management plan and virtual shopping recommendations. For example, if walking has had a positive effect, the server increases the amount of exercise, and if the user feels tired, it strengthens relaxation elements.

[1098] Hardware and Software Used

[1099] Hardware: smart glasses, head-mounted displays, smartwatches

[1100] Software: Cloud services (AWS Lambda, Azure Functions), speech recognition API (Google Cloud Speech-to-Text), facial expression recognition library (OpenCV, dlib)

[1101] Data processing: time series analysis of heart rate data, image recognition of facial expression data, acoustic analysis of voice data

[1102] Data calculation: Generative AI model predicts user health status, emotion engine evaluates stress level

[1103] Prompt Sentence Examples

[1104] Use the following dataset to predict a user's health and emotional state and generate personalized shopping recommendations.

[1105] Hourly heart rate data (HR)

[1106] Real-time facial expression data (EXPRESSION)

[1107] Voice tone analysis result (VOICE_TONE)

[1108] Past shopping history and feedback

[1109] example:

[1110] HR: [75, 80, 78, 85, 90]

[1111] EXPRESSION: {"happy": 0.3, "neutral": 0.6, "sad": 0.1}

[1112] VOICE_TONE: {"calm": 0.7, "stress": 0.3}

[1113] Recommendations: Provide relaxing music and recommend a health food section.

[1114] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1115] Step 1:

[1116] Users use wearable devices (e.g., smartwatches, smart glasses) to collect biometric and emotional data. Specifically, users measure their blood pressure every morning and enter the values ​​into a smartphone app. At the same time, the smartwatch automatically records their daily steps and heart rate, and the smart glasses collect facial expression data. These input data (blood pressure, steps, heart rate, and facial expression) are the initial data to be used for analysis.

[1117] Step 2:

[1118] The device sends the collected biometric and emotional data to a cloud server. The data is sent using Wi-Fi or mobile data communication and SSL encryption is used. The input data in this step is the various data collected in step 1, and is securely sent to the cloud server as processing results.

[1119] Step 3:

[1120] The server analyzes the transmitted biometric and emotional data using a cloud-based generative AI model and emotion engine. This analysis evaluates the user's health and emotional state. Specifically, it performs trend analysis on blood pressure and step count data from the past 30 days, and evaluates the emotional state using voice analysis and facial expression recognition. The input data in this step is the data transmitted to the cloud, and the output results are the evaluation results of the user's health and emotional state.

[1121] Step 4:

[1122] The server generates an individualized health management plan and virtual shopping recommendations based on the analysis results. The generative model and emotion engine create a health management plan that combines recommendations for diet, exercise, and mental health, and also generates virtual shopping recommendations. For example, a low-salt meal plan, 30 minutes of walking daily, and relaxation exercises to relieve stress may be suggested. The input data for this step are the evaluation results obtained in step 3, and the output is a health management plan and shopping recommendations.

[1123] Step 5:

[1124] The server sends the generated health management plan and virtual shopping recommendations to the user's device. The HTTPS protocol is used for transmission, and the data is packaged in JSON format. The device receives this data and notifies the user. The input data of this step are the generated plan and recommendations, and the output is a notification sent to the user's device.

[1125] Step 6:

[1126] The user acts according to the generated health management plan and virtual shopping recommendations, and then inputs the results and feedback into a dedicated app. The input data is the implementation results of the health management plan and feedback on the shopping experience. This data is used for reanalysis in subsequent steps.

[1127] Step 7:

[1128] The device retransmits the feedback data entered by the user to the cloud server. The data is SSL encrypted and transmitted as described above. The input data for this step is the user's feedback, and the output is the data to be transmitted to the cloud server.

[1129] Step 8:

[1130] The server analyzes the feedback data and updated emotion data sent to the cloud server and updates the health management plan and virtual shopping recommendations. Based on the feedback data, the server regenerates an optimal plan according to the user's health and emotional state. For example, if walking has a positive effect, the server increases the amount of exercise, and if the user feels tired, it strengthens the relaxation element. The input data of steps is the feedback data, and the output results are the updated plan and recommendations.

[1131] 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.

[1132] 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.

[1133] 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.

[1134] [Fourth embodiment]

[1135] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1136] 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.

[1137] 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).

[1138] 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.

[1139] 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.

[1140] 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).

[1141] 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.

[1142] 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.

[1143] 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.

[1144] 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.

[1145] 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.

[1146] 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.

[1147] 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."

[1148] This invention relates to a system that uses generative AI to customize health management for elderly people in Japan. The system collects biometric data from users, transmits it to a cloud environment, and analyzes it to provide personalized health management plans. The following describes the program and processing of this system.

[1149] Program processing explanation

[1150] 1. Data Collection

[1151] User: Measures blood pressure every morning and enters the results into a dedicated smartphone app. Also, uses a wearable device to automatically record daily steps and heart rate.

[1152] Example: A user uses a blood pressure monitor in the morning and enters the measurement value into a dedicated app, while the smartwatch simultaneously collects 5,000 steps.

[1153] 2. Data Transmission

[1154] Terminal: Periodically sends collected biometric data (blood pressure, number of steps, heart rate, etc.) to a cloud server.

[1155] Example: A smartphone uses Wi-Fi or mobile data to upload blood pressure data or step count data to a cloud server.

[1156] 3. Data Analysis

[1157] Server: Using a generative model on the cloud, analyzes the collected biometric data and assesses the user's health status.

[1158] Example: The server tracks blood pressure and step count data for the past 30 days and analyzes patterns of changes in physical condition.

[1159] 4. Plan Generation

[1160] Server: Based on the results of data analysis, it generates a health management plan including individualized exercise, diet, and wellness programs.

[1161] Example: A server uses user data to generate a low-salt meal plan and a plan recommending 30 minutes of walking each day.

[1162] 5. Send results

[1163] Server: Sends the generated health management plan to the user's terminal.

[1164] Example: The generated plan is sent in JSON format to a smartphone app and notified to the user.

[1165] 6. Feedback Collection

[1166] User: Implements a health management plan and enters the results and feedback into a dedicated app.

[1167] Terminal: Sends the feedback data back to the cloud server.

[1168] Example: After a user completes a walk, they enter the time it took and their impressions into the app, and their smartphone uploads the data to a cloud server.

[1169] 7. Feedback analysis and plan updates

[1170] Server: Analyzes the feedback data and reflects it in the next health management plan.

[1171] Example: The server analyzes the feedback data and, if walking is effective, adjusts the exercise, such as slightly increasing the amount of exercise.

[1172] Through these measures, the system provides personalized medical solutions to maintain the health and independence of older adults and improve their quality of life, enabling users to more effectively manage their daily health.

[1173] The processing flow will be explained below.

[1174] Step 1:

[1175] User: Measures blood pressure every morning and enters the results into a dedicated smartphone app. The user also wears a wearable device that automatically records the number of steps taken and heart rate each day.

[1176] Specific operation: The user uses a blood pressure monitor and inputs the measurement value (e.g., 120 / 80 mmHg) into the app. The wearable device counts 5,000 steps and simultaneously records heart rate data.

[1177] Step 2:

[1178] Terminal: Sends collected biometric data to a cloud server at regular intervals.

[1179] How it works: Your smartphone uses Wi-Fi or mobile data to upload blood pressure and step count data to a cloud server. The data is then SSL encrypted before being sent.

[1180] Step 3:

[1181] Server: Using a generative model on the cloud, analyzes the transmitted biometric data and assesses the user's health status.

[1182] Specific operation: The server performs trend analysis of blood pressure data from the past 30 days and detects abnormal values. For example, if the average blood pressure exceeds 130 / 85 for 10 days or more, it is determined to be mild hypertension.

[1183] Step 4:

[1184] Server: Generates an individual health management plan based on the analysis results.

[1185] What it does: The generative model creates a health plan for the user with recommendations for diet, exercise, and mental health, such as a low-sodium diet, 30 minutes of walking daily, and relaxation exercises for stress management.

[1186] Step 5:

[1187] Server: Sends the generated health management plan to the user's terminal.

[1188] Specific operation: The generated plan is packaged in JSON format and sent to the smartphone app using the HTTPS protocol. The plan is then displayed to the user as a notification.

[1189] Step 6:

[1190] User: Carry out daily health management according to the received health management plan.

[1191] Specific actions: The user refers to the smartphone app, changes dinner to a low-salt dish, and begins a 30-minute walk.

[1192] Step 7:

[1193] User: Enter the results of the health management plan and feedback into a dedicated app.

[1194] Specific behavior: After completing a walk, the user enters the time it took and their feelings into the app. For example, they might enter, "After 30 minutes of walking, I feel refreshed."

[1195] Step 8:

[1196] Terminal: Sends the feedback data back to the cloud server.

[1197] Specific operation: The feedback data collected by the smartphone is encrypted and uploaded to a cloud server.

[1198] Step 9:

[1199] Server: Analyzes the feedback data and updates the next health management plan.

[1200] Specific actions: The AI ​​evaluates the feedback data, and if walking has a positive effect, it will make adjustments such as slightly increasing the amount of exercise. This will be reflected in the next plan.

[1201] Through the above steps, it is possible to provide an optimal health management plan tailored to the individual health condition of each user, thereby improving the quality of life of elderly people.

[1202] Example 1

[1203] 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."

[1204] The present invention relates to a system that efficiently analyzes collected biometric data and provides users with customized health management plans as a means of individually optimizing health management for elderly people. Conventional health management systems have struggled to customize plans to accommodate individual health conditions, and generic plans often fail to produce satisfactory results. Furthermore, they lacked a mechanism for continuously improving plans using feedback data, making it difficult to accurately manage users' health conditions.

[1205] 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.

[1206] In this invention, the server includes means for collecting individual biometric data, means for transmitting the biometric data to a cloud environment, means for analyzing the biometric data in the cloud environment and using a generative model to evaluate the user's health condition, means for generating an individual health management plan based on the analysis results of the generative model, means for transmitting the health management plan to the user's device, means for transmitting implementation results and feedback of the health management plan back to the cloud environment, means for analyzing the feedback and updating the health management plan, means for measuring blood pressure every morning and inputting it into a smartphone app, means for automatically recording step count and heart rate data using a wearable device and means for transmitting the data to the device, and means for generating a health management plan that is periodically improved based on the analysis. This makes it possible to individually evaluate the user's health condition and provide an appropriate health management plan, and to continuously improve the plan through feedback.

[1207] "Individual biometric data" refers to data relating to physical conditions such as blood pressure, number of steps, and heart rate collected from individual users.

[1208] A "cloud environment" is a collection of remote servers used to store, analyze, and process data over the Internet.

[1209] A "generative model" is an artificial intelligence algorithm that predicts and generates results based on data analysis.

[1210] A "health plan" is a set of specific instructions, including exercise, diet, wellness activities, etc., to improve a user's health.

[1211] A "terminal" is a hardware device (e.g., smartphone, tablet) used by a user to input, process, transmit, etc. data.

[1212] A "wearable device" is an electronic device that can be worn to automatically record daily physical activity.

[1213] "Feedback" is data on the implementation results and impressions provided by the user after implementing the health management plan.

[1214] The present invention is a system for individually optimizing health management for elderly people. This system analyzes biometric data collected from users in a cloud environment and provides customized health management plans using a generative AI model. The system's program processing is described in detail below.

[1215] First, the user collects biometric data using a smartphone app and a wearable device. Every morning, the user measures their blood pressure with a blood pressure monitor and enters the results into the smartphone app. The wearable device (e.g., a smartwatch) also automatically records their daily steps and heart rate. This data is periodically sent from the device to a cloud server.

[1216] The cloud server analyzes the received biometric data using a generative AI model. This analysis is performed using Python scripts and machine learning libraries such as TensorFlow and PyTorch. The analysis results in an assessment of the user's health status. For example, blood pressure and step count data from the past 30 days can be tracked to identify patterns of fluctuations in health status.

[1217] The server then generates a personalized health management plan based on the analysis results. This plan includes exercise, diet, and wellness activities. For example, the analysis results may recommend a low-salt diet plan and 30 minutes of walking daily. The plan is formatted in JSON and pushed to the user's smartphone app.

[1218] The user carries out the health management plan they receive and enters the results and feedback into a dedicated app. For example, after completing a walk, they can enter the time it took and the effects they felt in text. The device then sends this feedback data back to the cloud server.

[1219] The server analyzes the feedback data and updates the health management plan based on the analysis. This process is reflected in the next health management plan. For example, if walking was effective, adjustments such as slightly increasing the amount of exercise will be made.

[1220] In this way, the system of the present invention effectively supports health management for the elderly by collecting and analyzing the user's biometric data and providing an individually customized health management plan, enabling the user to manage their health more effectively through this system.

[1221] keyword:

[1222] Generative AI model, prompt sentence

[1223] Example prompt sentence:

[1224] Use your biometric data (blood pressure, steps, heart rate) to create an AI model to generate a personalized health plan.

[1225] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1226] Step 1: Data collection

[1227] User: Wakes up in the morning, measures blood pressure with a blood pressure monitor, and enters the results into a dedicated smartphone app. The input data is saved on the device.

[1228] Input: Blood pressure data measured by the user

[1229] Output: Blood pressure data stored in a smartphone app

[1230] User: Wears a wearable device (smartwatch) that automatically records daily steps and heart rate data, which is then periodically sent to a smartphone.

[1231] Input: User's daily activity data (steps, heart rate)

[1232] Output: Step count and heart rate data sent to your smartphone

[1233] Step 2: Send data

[1234] Device: Collected biometric data (blood pressure, number of steps, heart rate) is periodically sent to a cloud server. Data is uploaded securely and quickly using Wi-Fi or mobile data.

[1235] Input: Biometric data stored on the smartphone

[1236] Output: Biometric data sent to the cloud server

[1237] Step 3: Data analysis

[1238] Server: Analyzes the submitted biometric data using a generative AI model in the cloud. Health status is assessed using Python scripts and machine learning libraries such as TensorFlow and PyTorch.

[1239] Input: Biometric data stored on a cloud server

[1240] Output: User's health status assessment result

[1241] Step 4: Generate a plan

[1242] Server: Based on the analysis results, it generates a health management plan that includes a personalized exercise, diet, and wellness program, such as a low-salt meal plan and a daily walking plan.

[1243] Input: Health status assessment results

[1244] Output: A personalized health plan

[1245] Step 5: Send results

[1246] Server: Formats the generated health management plan in JSON format and sends it to the user's smartphone app via push notification.

[1247] Enter: Customized Health Care Plan

[1248] Output: Health plan notification sent to smartphone app

[1249] Step 6: Gather feedback

[1250] User: Implements a health management plan and inputs the results and feedback into a dedicated app. For example, after walking, the user inputs the time it took and their impressions.

[1251] Input: User feedback data

[1252] Output: Feedback data stored in a smartphone app

[1253] Terminal: Sends the feedback data back to the cloud server.

[1254] Input: Feedback data stored in the smartphone app

[1255] Output: Feedback data sent to the cloud server

[1256] Step 7: Analyze feedback and update plans

[1257] Server: Analyzes the feedback data and reflects it in the next health management plan. The generative AI model incorporates new data to update and continuously optimize the plan.

[1258] Input: Feedback data

[1259] Output: Updated individual health care plan

[1260] (Application example 1)

[1261] 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."

[1262] Health management for the elderly requires providing plans that meet individual needs, but conventional systems have difficulty linking with food delivery services, such as individual food selection and home delivery, making it impossible to achieve effective health management. In particular, a system is needed that provides individualized healthy eating plans in a way that is easily accessible to the elderly.

[1263] 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.

[1264] In this invention, the server includes means for collecting individual biometric data, means for transmitting the biometric data to a cloud environment, means for analyzing the biometric data in the cloud environment and using a generative model for evaluating the user's health status, means for generating an individual health management plan based on the analysis results of the generative model, means for transmitting the health management plan to the user's terminal, means for transmitting implementation results and feedback of the health management plan back to the cloud environment, means for analyzing the feedback and updating the health management plan, and means for generating a meal plan based on the health management plan and automatically ordering meals in cooperation with a food delivery service. This makes it possible to provide an individualized healthy meal plan to an elderly person and have it delivered to their home via a food delivery service.

[1265] "Biometric data" refers to data related to the user's health condition, including blood pressure, number of steps, heart rate, and the like.

[1266] A "cloud environment" is a computing resource and data storage environment consisting of a group of remote servers accessible via the Internet.

[1267] A "generative model" is an artificial intelligence algorithm that analyzes collected data and generates an individual plan from the results.

[1268] "Health Management Plan" means a plan that includes exercise, diet, and health activities designed to improve and maintain the User's health.

[1269] "Feedback" is information about the results, impressions, and effects of the health management plan implemented by the user.

[1270] A "food delivery service" is a service that delivers meals to a specific location and delivers meals to users based on a personalized healthy eating plan.

[1271] A "terminal" is an electronic device that allows a user to input data, receive notifications, send feedback, etc.

[1272] "Analysis" is the process of processing and calculating collected data to derive results.

[1273] The present invention relates to a system that collects individual biometric data, generates a health management plan, and provides meals in cooperation with a food delivery service. This system supports the health management of elderly people and improves their quality of life by providing personalized meal plans.

[1274] Hardware and software used

[1275] Hardware: Smartphones, wearable devices (smartwatches, etc.)

[1276] Software: Smartphone app, generative AI model, cloud server, food delivery system

[1277] Data collection

[1278] Users measure their blood pressure every morning and enter the data into a smartphone app. Wearable devices (e.g., smartwatches) also automatically record their daily steps and heart rate, thereby collecting biometric data.

[1279] Data transmission

[1280] The collected biometric data is sent to a cloud server via the smartphone, which then uploads the blood pressure and step count data to the cloud server using Wi-Fi or mobile data.

[1281] Data analysis

[1282] The collected biometric data is analyzed on a cloud server using a generative AI model to assess the user's health. For example, the generative AI model tracks blood pressure and step count data from the past 30 days and analyzes patterns of changes in physical condition.

[1283] Health management plan generation

[1284] Based on the results of the data analysis, the generative AI model generates a health management plan that includes a personalized exercise, diet, and wellness program, such as a low-salt diet plan and a plan recommending 30 minutes of walking every day.

[1285] Send results

[1286] The generated health management plan is sent in JSON format to a smartphone app and notified to the user.

[1287] Food delivery collaboration

[1288] Based on the generated health management plan, the app will automatically order appropriate meals through a food delivery service. Specifically, the app will automatically order low-sodium meals from the food delivery service.

[1289] Feedback collection

[1290] Users enter their impressions and effects of the provided meal into the app, and the data is then uploaded to the cloud server, allowing user feedback to be collected.

[1291] Feedback analysis and plan updates

[1292] The cloud server analyzes the collected feedback data and reflects it in the next health management plan. For example, if walking is effective, it will make adjustments such as slightly increasing the amount of exercise.

[1293] Specific examples

[1294] For example, suppose User A enters their morning blood pressure and records the number of steps they took that day on their smartwatch. This data is sent from the smartphone app to a cloud server, where it is analyzed by a generative AI model. Based on the analysis results, the AI ​​generates a low-salt meal plan, and this data is automatically sent to a food delivery service. After User A receives their dinner, they can enter their post-meal impressions into the app, and this feedback is reflected in their next meal plan.

[1295] Prompt Sentence Examples

[1296] "Generate a low-sodium, high-protein healthy diet plan based on user biometric data: blood pressure (120 / 80), steps (5000), heart rate (70 bpm), and 30 days of data analysis."

[1297] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1298] Step 1: Data collection

[1299] The user inputs the results of their blood pressure measurement every morning. A wearable device (such as a smartwatch) automatically records the number of steps taken and heart rate each day. This allows the user's biometric data (input: blood pressure, number of steps, heart rate) to be collected.

[1300] Step 2: Send data

[1301] The device (smartphone) sends the collected biometric data to a cloud server. The smartphone uploads blood pressure data and step count data to the cloud server via Wi-Fi or mobile data communication (input: biometric data, output: data stored on the cloud server).

[1302] Step 3: Data analysis

[1303] The server analyzes the biometric data using a generative AI model on the cloud. The generative AI model tracks, for example, blood pressure data and step count data from the past 30 days and evaluates the user's health status (input: biometric data, output: health status evaluation results). This data processing includes trend analysis and analysis of fluctuation patterns.

[1304] Step 4: Create a health management plan

[1305] Based on the analysis results of the generative AI model, the server generates a health management plan including an individualized exercise, diet, and wellness program (input: health status assessment results, output: health management plan). Specifically, a low-salt meal plan and a plan recommending 30 minutes of walking every day are generated.

[1306] Step 5: Send results

[1307] The server sends the generated health management plan in JSON format to the smartphone app, which notifies the user (input: health management plan, output: plan displayed on smartphone app).

[1308] Step 6: Food delivery integration

[1309] The device (smartphone) then works with a food delivery service to automatically order appropriate meals based on the generated health management plan. Specifically, low-salt meals are automatically ordered from the food delivery service (input: health management plan, output: order data for the food delivery service).

[1310] Step 7: Gather feedback

[1311] The user inputs their impressions and the effects of the provided meal into a smartphone app. The device (smartphone) then uploads the data back to the cloud server (input: feedback data, output: feedback data saved on the cloud server).

[1312] Step 8: Analyze feedback and update plans

[1313] The server analyzes the collected feedback data and reflects it in the next health management plan (input: feedback data, output: updated health management plan). Specifically, if walking is effective, adjustments will be made, such as slightly increasing the amount of exercise. This data calculation includes analyzing the effectiveness of the feedback and optimizing the plan based on the results.

[1314] 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.

[1315] This invention relates to a system that uses generative AI and an emotion engine to customize health management for elderly people in Japan. The system collects biometric and emotional state data from users, transmits it to a cloud environment for analysis, and provides personalized health management plans. The following describes the program and processing of this system.

[1316] Program processing explanation

[1317] 1. Data Collection

[1318] User: Measures blood pressure every morning and enters the results into a dedicated smartphone app. The user also wears a wearable device that automatically records the number of steps taken and heart rate each day. The device also uses the microphone and camera on the smartphone and dedicated device to collect audio and video data, which are then sent to the emotion engine.

[1319] Example: A user uses a blood pressure monitor in the morning and inputs the measurement (e.g., 120 / 80 mmHg) into the app, while the smartwatch counts 5,000 steps and monitors facial expressions with the camera.

[1320] 2. Data Transmission

[1321] Terminal: The terminal transmits the collected biometric data and emotional state data to the cloud server at regular intervals.

[1322] Example: A smartphone uses Wi-Fi or mobile data to upload blood pressure data, step count data, and emotional evaluation data to a cloud server. The data is then sent using SSL encryption.

[1323] 3. Data Analysis

[1324] Server: Using a cloud-based generative model and emotion engine, analyzes the transmitted biometric and emotional state data and evaluates the user's health and emotional state.

[1325] Example: The server analyzes blood pressure and step count data from the past 30 days and uses voice analysis and facial expression recognition to assess the user's emotional state. For example, if the average blood pressure exceeds 130 / 85 for 10 days or more, it is determined to be mild hypertension, and if the user is feeling stressed, the server will strengthen the stress management plan.

[1326] 4. Plan Generation

[1327] Server: Generates an individual health management plan based on the analysis results.

[1328] Example: A generative model and emotion engine create a health plan for the user with recommendations for diet, exercise, and mental health, such as a low-sodium diet, 30 minutes of walking daily, and relaxation exercises to reduce stress.

[1329] 5. Send results

[1330] Server: Sends the generated health management plan to the user's terminal.

[1331] Example: The generated plan is packaged in JSON format and sent to a smartphone app using the HTTPS protocol. The plan is displayed to the user as a notification.

[1332] 6. Feedback Collection

[1333] User: Implements a health management plan and enters the results and feedback into a dedicated app.

[1334] Terminal: Sends the feedback data back to the cloud server.

[1335] Example: After completing a walk, the user enters the time taken and their feelings into the app, and the smartphone uploads the data to a cloud server.

[1336] 7. Feedback analysis and plan updates

[1337] Server: Analyzes the feedback data and emotion evaluation data and updates the next health management plan.

[1338] Example: The AI ​​and emotion engine evaluate the feedback data and, if walking has a positive effect, adjust the exercise by slightly increasing the amount of exercise. If the emotion evaluation indicates that the user is feeling tired, the relaxation element will be strengthened.

[1339] Through these measures, the system provides personalized medical solutions to maintain the health and independence of elderly people and improve their quality of life.The introduction of an emotion engine enables comprehensive health management that takes into account the user's emotional state.

[1340] The processing flow will be explained below.

[1341] Step 1:

[1342] User: Measures blood pressure every morning and enters the results into a dedicated smartphone app. The wearable device also automatically records daily steps and heart rate. Furthermore, the emotion engine recognizes the user's emotional state through audio and video.

[1343] How it works: The user uses a blood pressure monitor and inputs the measurement (e.g., 120 / 80 mmHg) into the app. The wearable device counts 5,000 steps and simultaneously records heart rate data. The smartphone camera captures the user's facial expressions, and the microphone collects audio data.

[1344] Step 2:

[1345] Terminal: The terminal transmits the collected biometric data and emotional state data to the cloud server at regular intervals.

[1346] How it works: Your smartphone uses Wi-Fi or mobile data to upload blood pressure data, step count data, heart rate data, facial expression capture data, and voice data to a cloud server. The data is sent using SSL encryption.

[1347] Step 3:

[1348] Server: Using a cloud-based generative model and emotion engine, analyzes the transmitted biometric and emotional state data and evaluates the user's health and emotional state.

[1349] How it works: The server performs trend analysis on blood pressure and step count data from the past 30 days to detect abnormal values. The emotion engine evaluates the user's emotional state using voice analysis and facial expression recognition. For example, if the average blood pressure exceeds 130 / 85 for 10 days or more, it is determined to be mild hypertension, and facial expression analysis is used to detect stress.

[1350] Step 4:

[1351] Server: Generates an individual health management plan based on the analysis results.

[1352] How it works: The generative model and emotion engine create a health plan for the user, including recommendations for diet, exercise, and mental health. For example, it might suggest a low-salt diet for mild hypertension, 30 minutes of walking daily, and relaxation exercises to reduce stress.

[1353] Step 5:

[1354] Server: Sends the generated health management plan to the user's terminal.

[1355] Specific operation: The generated plan is packaged in JSON format and sent to the smartphone app using the HTTPS protocol. The plan is then displayed to the user as a notification.

[1356] Step 6:

[1357] User: Carry out daily health management according to the received health management plan.

[1358] Specific actions: The user consults a smartphone app to change their dinner to a low-salt dish, begin a 30-minute walk, and perform relaxation exercises as instructed by the app.

[1359] Step 7:

[1360] User: Enters the results of the health management plan and feedback into a dedicated app. Emotional state is also recorded at the same time.

[1361] Specific operation: After completing a walk, the user enters the time it took and their feelings into the app. For example, they can enter "After 30 minutes of walking, I feel refreshed." The app also records the user's facial expressions and voice.

[1362] Step 8:

[1363] Terminal: Sends the feedback data and emotional state data back to the cloud server.

[1364] Specific operation: The smartphone encrypts the collected feedback data and emotional state data and uploads them to a cloud server.

[1365] Step 9:

[1366] Server: Analyzes the feedback data and emotional state data and updates the next health management plan.

[1367] Specific behavior: The AI ​​and emotion engine evaluate the feedback data and, if walking has a positive effect, adjust the exercise by slightly increasing it. If the emotion engine detects stress or fatigue, it will reinforce relaxation exercises and rest plans.

[1368] Through the above steps, it is possible to provide an optimal health management plan tailored to the individual health and emotional state of each user, thereby improving the quality of life of elderly people.

[1369] Example 2

[1370] 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."

[1371] Current health management systems typically collect only a user's biometric data and provide a health management plan based on the collected data. However, these systems do not take the user's emotional state into account and are unable to generate a plan that fully reflects the impact of stress and mental state on health. This can result in ineffective health management plans. Furthermore, because emotional state is not taken into account when updating a health management plan based on feedback, it is difficult to provide a plan optimized for the user's condition.

[1372] 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.

[1373] In this invention, the server includes means for collecting individual biometric data and emotional state data, means for transmitting the biometric data and emotional state data to a cloud environment, means for analyzing the data in the cloud environment and using a generative model and an emotion engine for evaluating the user's health condition and emotional state, means for generating an individual health management plan based on the analysis results of the generative model and the emotion engine, means for transmitting the health management plan to the user's terminal, means for transmitting implementation results and feedback of the health management plan back to the cloud environment, and means for analyzing the feedback and emotional state data and updating the health management plan, thereby enabling comprehensive health management that takes the user's emotional state into consideration.

[1374] "Biometric data" refers to information about the user's physical condition, such as blood pressure, heart rate, and number of steps taken.

[1375] "Emotional state data" is information about the emotional state obtained by analyzing the user's voice and facial expressions.

[1376] A "cloud environment" is a computer network that stores, manages, and analyzes data over the Internet.

[1377] A "generative model" is an artificial intelligence model that assesses and analyzes a user's health status based on their biometric and emotional state data.

[1378] An "emotion engine" is a software program that analyzes and evaluates a user's emotional state based on their voice and video data.

[1379] A "health management plan" is a plan that provides personalized health management guidance and recommendations based on an analysis of the user's biometric data and emotional state data.

[1380] A "wearable device" is a device worn by a user that automatically collects biometric data.

[1381] "Audio and video collection device" means a device that collects a user's audio and video.

[1382] "Feedback" is data that records the results and impressions of the user when implementing the health management plan.

[1383] The present invention relates to a system that uses a generative AI model and an emotion engine to individually customize health management for elderly people in Japan. The system collects biometric and emotional state data from users, transmits it to a cloud environment for analysis, and provides personalized health management plans. Specific embodiments of the system are described below.

[1384] First, the user measures their blood pressure every morning and enters the results into a dedicated smartphone app. This smartphone app also has a function that automatically records the user's daily steps and heart rate when the user wears a wearable device. Furthermore, the app collects audio and video using the microphone and camera on the smartphone and dedicated device, and sends them to the emotion engine. Specifically, we imagine a scenario in which the user uses a blood pressure monitor in the morning to input the measurement value (e.g., 120 / 80 mmHg) into the app, and the smartwatch counts 5,000 steps and monitors facial expressions using the camera.

[1385] The device then transmits the collected biometric and emotional state data to a cloud server at regular intervals. The smartphone then uploads the blood pressure, step count, and emotional assessment data to the cloud server using Wi-Fi or mobile data. This transmission is securely performed using SSL encryption.

[1386] The server analyzes the transmitted biometric and emotional state data using a cloud-based generative model and emotion engine. This evaluates the user's health and emotional state. Specifically, the server analyzes trends in blood pressure and step count data from the past 30 days, and assesses the user's emotional state using voice analysis and facial expression recognition. For example, if the average blood pressure exceeds 130 / 85 for 10 or more consecutive days, it will determine that the user has mild hypertension, and if the user is feeling stressed, it will strengthen the stress management plan.

[1387] Based on the analysis results, the server generates a personalized health management plan. The generative model and emotion engine create a health management plan that includes recommendations for the user's diet, exercise, and mental health. For example, it suggests a low-salt diet plan, 30 minutes of walking every day, and relaxation exercises to reduce stress.

[1388] The generated health management plan is sent from the server to the user's device. The plan is packaged in JSON format and sent to the smartphone app using the HTTPS protocol. The plan is displayed to the user as a notification, and details can be viewed within the smartphone app.

[1389] The user follows the provided health management plan and enters the results and feedback into a dedicated app. Specifically, after completing a walk, the user enters the time it took and how they felt into the app. This feedback data is then sent from the smartphone to the cloud server.

[1390] Finally, the server analyzes the feedback data and emotional state data to update the next health management plan. The AI ​​and emotion engine evaluate the feedback data and make adjustments, such as slightly increasing the amount of exercise if walking has had a positive effect. If the emotion evaluation indicates that the user is feeling tired, the server will strengthen relaxation elements.

[1391] In this system, the following prompt sentences can be input into the generative AI model:

[1392] "Please explain in detail the process by which a user measures their blood pressure every morning and enters the results into a dedicated app."

[1393] "Please show us how the cloud server can safely process the collected data."

[1394] "Please provide a detailed description of the process for integrating and analyzing biometric data and emotional data."

[1395] "Please provide an example of a generated health plan and explain how it will be communicated."

[1396] Through these measures, the system provides personalized medical solutions to maintain the health and independence of elderly people and improve their quality of life.The introduction of an emotion engine realizes comprehensive health management that also takes into account the user's emotional state.

[1397] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1398] Step 1:

[1399] Data collection

[1400] User: Measures blood pressure every morning and enters the values ​​into a dedicated smartphone app. In addition, the user wears a wearable device that automatically records the number of steps taken and heart rate each day, and collects audio and video using the microphone and camera on the smartphone or dedicated device.

[1401] Input: Blood pressure data (e.g., 120 / 80 mmHg) from a sphygmomanometer, step count and heart rate data from a wearable device, and audio and video data from a smartphone's microphone and camera.

[1402] Output: Biometric and emotional data collected in a smartphone app.

[1403] How it works: The user measures their blood pressure in the morning and enters the value into a smartphone app. At the same time, the wearable device records 5,000 steps and their heart rate, while the smartphone's camera monitors facial expressions and the microphone collects audio data.

[1404] Step 2:

[1405] Data transmission

[1406] Terminal: Sends collected biometric data and emotional state data to a cloud server at regular intervals.

[1407] Input: Biometric and emotional data collected in a smartphone app.

[1408] Output: Data sent to the cloud server.

[1409] How it works: The smartphone uses Wi-Fi or mobile data to upload measured blood pressure data, step count data from the wearable device, and emotional evaluation data to a cloud server. The data is SSL encrypted.

[1410] Step 3:

[1411] Data analysis

[1412] Server: Using a cloud-based generative model and emotion engine, analyzes the transmitted biometric and emotional state data and evaluates the user's health and emotional state.

[1413] Input: Biometric and emotional data uploaded to a cloud server.

[1414] Output: Assessment results on the user's health and emotional state.

[1415] Specific operation: The server performs trend analysis of blood pressure data and step count data from the past 30 days, and evaluates the user's emotional state using voice analysis and facial expression recognition technology. For example, it can detect a tendency toward high blood pressure from blood pressure data and sense stress from voice data.

[1416] Step 4:

[1417] Plan Generation

[1418] Server: Generates an individual health management plan based on the analysis results.

[1419] Input: Assessment results on the user's health and emotional state.

[1420] Output: A personalized health care plan.

[1421] How it works: The generative model and emotion engine create a health plan for the user, including recommendations for diet, exercise, and mental health. For example, it suggests a low-sodium diet, 30 minutes of walking daily, and relaxation exercises to reduce stress.

[1422] Step 5:

[1423] Send results

[1424] Server: Sends the generated health management plan to the user's terminal.

[1425] Input: The generated health care plan.

[1426] Output: The health plan sent to the user's device.

[1427] Specific operation: The generated plan is packaged in JSON format and sent to the smartphone app using the HTTPS protocol. The plan is displayed to the user as a notification, and details can be viewed within the smartphone app.

[1428] Step 6:

[1429] Feedback collection

[1430] User: Implements a health management plan and enters the results and feedback into a dedicated app.

[1431] Input: Health management plan implementation results and feedback.

[1432] Output: Feedback data aggregated in a smartphone app.

[1433] Specific behavior: After the user completes a walk, they enter the time it took and their feelings into the app (e.g., "I walked for 30 minutes and felt great").

[1434] Terminal: Sends the feedback data back to the cloud server.

[1435] Input: Feedback data aggregated in a smartphone app.

[1436] Output: Feedback data sent to the cloud server.

[1437] Specific operation: The smartphone uploads the input feedback data to a cloud server using Wi-Fi or mobile data communication under SSL encryption.

[1438] Step 7:

[1439] Feedback analysis and plan updates

[1440] Server: Analyzes the feedback data and emotional state data and updates the health management plan.

[1441] Input: Feedback data and emotional state data.

[1442] Output: Updated health care plan.

[1443] Specific operation: The AI ​​and emotion engine evaluate the feedback data and, for example, if it determines that walking has had a positive effect, it will make adjustments such as increasing the amount of exercise. If the emotion evaluation determines that the user is tired, it will strengthen relaxation elements.

[1444] (Application example 2)

[1445] 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."

[1446] For elderly health management, it is important to accurately assess individual biometric data and emotional states and provide appropriate health management plans. However, conventional systems provide health management plans without considering the user's emotional state, which results in insufficient management of health risks caused by stress and emotional changes. Furthermore, to ensure a comfortable and healthy shopping experience for elderly people, virtual shopping recommendations based on their health state are required.

[1447] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1448] In this invention, the server includes a means for evaluating biometric data and emotional states using a generative model and an emotion engine to generate an individualized health management plan and virtual shopping recommendations, a means for transmitting the implementation results and feedback of the user's health management plan and virtual shopping recommendations back to the cloud environment for analysis, and a means for updating the health management plan and virtual shopping recommendations based on the feedback, thereby enabling comprehensive management of the elderly's health and emotional states and providing an optimal shopping experience according to their health status.

[1449] "Biometric data" refers to data relating to the user's physical condition, such as blood pressure, heart rate, and number of steps.

[1450] "Cloud environment" is a general term for servers and data storage provided via the Internet.

[1451] A "generative model" refers to an algorithm for predicting and generating new data based on past data.

[1452] An "emotion engine" is software that analyzes data such as voice and facial expressions to evaluate the user's emotional state.

[1453] "Health Management Plan" means a plan that includes individually customized exercise, diet, and mental health recommendations to maintain or improve a User's health.

[1454] "Virtual shopping recommendation" is a system that recommends appropriate products and shopping experiences based on the user's health and emotional state.

[1455] "Feedback" means the evaluation and results of the health management plan or virtual shopping recommendations implemented by the user.

[1456] "Wearable devices" is a general term for electronic devices that can collect physical data by being worn, and includes smartwatches and health monitoring devices.

[1457] In the embodiment of the present invention, the system operates in an integrated manner with the server, terminals, and users working together.

[1458] Data collection

[1459] Users utilize wearable devices such as blood pressure monitors, smartwatches, smart glasses, and head-mounted displays to collect biometric and emotional data. For example, a user measures their blood pressure at home every morning and enters the values ​​(e.g., 120 / 80 mmHg) into a smartphone app. The smartwatch automatically records the number of steps taken each day (e.g., 5,000 steps) and heart rate, while the smart glasses and head-mounted display collect heart rate and facial expression data.

[1460] Data transmission

[1461] The device sends the collected biometric and emotional data to a cloud server at regular intervals. The data is SSL encrypted and sent via Wi-Fi or mobile data. For example, a smartphone uploads blood pressure data, step count data, and emotional evaluation data to the cloud server.

[1462] Data analysis

[1463] The server uses a cloud-based generative AI model and emotion engine to analyze the biometric and emotional state data and assess the user's health and emotional state. For example, it analyzes trends in blood pressure and step count data from the past 30 days and uses voice analysis and facial expression recognition to assess the user's emotional state. Specifically, if the average blood pressure exceeds 130 / 85 for 10 or more consecutive days, it is determined to be mild hypertension and will recommend a strengthened stress management plan.

[1464] Plan Generation

[1465] The server generates a health management plan and virtual shopping recommendations based on the analysis results, which include optimal recommendations for the user's diet, exercise, and mental health. Specifically, the server suggests a low-salt meal plan, 30 minutes of walking daily, and relaxation exercises to reduce stress, as well as virtual shopping recommendations based on the user's health status.

[1466] Send results

[1467] The server sends the generated health management plan and virtual shopping recommendations to the user's device. The generated plan is packaged in JSON format and sent to the device app using the HTTPS protocol. The user is then notified of the results.

[1468] Feedback collection and analysis

[1469] The user acts according to the provided health management plan and virtual shopping recommendations, and enters the results and feedback into a dedicated app. The device then sends the feedback data back to the cloud server. The server analyzes the feedback data and new emotional data and updates the next health management plan and virtual shopping recommendations. For example, if walking has had a positive effect, the server increases the amount of exercise, and if the user feels tired, it strengthens relaxation elements.

[1470] Hardware and Software Used

[1471] Hardware: smart glasses, head-mounted displays, smartwatches

[1472] Software: Cloud services (AWS Lambda, Azure Functions), speech recognition API (Google Cloud Speech-to-Text), facial expression recognition library (OpenCV, dlib)

[1473] Data processing: time series analysis of heart rate data, image recognition of facial expression data, acoustic analysis of voice data

[1474] Data calculation: Generative AI model predicts user health status, emotion engine evaluates stress level

[1475] Prompt Sentence Examples

[1476] Use the following dataset to predict a user's health and emotional state and generate personalized shopping recommendations.

[1477] Hourly heart rate data (HR)

[1478] Real-time facial expression data (EXPRESSION)

[1479] Voice tone analysis result (VOICE_TONE)

[1480] Past shopping history and feedback

[1481] example:

[1482] HR: [75, 80, 78, 85, 90]

[1483] EXPRESSION: {"happy": 0.3, "neutral": 0.6, "sad": 0.1}

[1484] VOICE_TONE: {"calm": 0.7, "stress": 0.3}

[1485] Recommendations: Provide relaxing music and recommend a health food section.

[1486] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1487] Step 1:

[1488] Users use wearable devices (e.g., smartwatches, smart glasses) to collect biometric and emotional data. Specifically, users measure their blood pressure every morning and enter the values ​​into a smartphone app. At the same time, the smartwatch automatically records their daily steps and heart rate, and the smart glasses collect facial expression data. These input data (blood pressure, steps, heart rate, and facial expression) are the initial data to be used for analysis.

[1489] Step 2:

[1490] The device sends the collected biometric and emotional data to a cloud server. The data is sent using Wi-Fi or mobile data communication and SSL encryption is used. The input data in this step is the various data collected in step 1, and is securely sent to the cloud server as processing results.

[1491] Step 3:

[1492] The server analyzes the transmitted biometric and emotional data using a cloud-based generative AI model and emotion engine. This analysis evaluates the user's health and emotional state. Specifically, it performs trend analysis on blood pressure and step count data from the past 30 days, and evaluates the emotional state using voice analysis and facial expression recognition. The input data in this step is the data transmitted to the cloud, and the output results are the evaluation results of the user's health and emotional state.

[1493] Step 4:

[1494] The server generates an individualized health management plan and virtual shopping recommendations based on the analysis results. The generative model and emotion engine create a health management plan that combines recommendations for diet, exercise, and mental health, and also generates virtual shopping recommendations. For example, a low-salt meal plan, 30 minutes of walking daily, and relaxation exercises to relieve stress may be suggested. The input data for this step are the evaluation results obtained in step 3, and the output is a health management plan and shopping recommendations.

[1495] Step 5:

[1496] The server sends the generated health management plan and virtual shopping recommendations to the user's device. The HTTPS protocol is used for transmission, and the data is packaged in JSON format. The device receives this data and notifies the user. The input data of this step are the generated plan and recommendations, and the output is a notification sent to the user's device.

[1497] Step 6:

[1498] The user acts according to the generated health management plan and virtual shopping recommendations, and then inputs the results and feedback into a dedicated app. The input data is the implementation results of the health management plan and feedback on the shopping experience. This data is used for reanalysis in subsequent steps.

[1499] Step 7:

[1500] The device retransmits the feedback data entered by the user to the cloud server. The data is SSL encrypted and transmitted as described above. The input data for this step is the user's feedback, and the output is the data to be transmitted to the cloud server.

[1501] Step 8:

[1502] The server analyzes the feedback data and updated emotion data sent to the cloud server and updates the health management plan and virtual shopping recommendations. Based on the feedback data, the server regenerates an optimal plan according to the user's health and emotional state. For example, if walking has a positive effect, the server increases the amount of exercise, and if the user feels tired, it strengthens the relaxation element. The input data of steps is the feedback data, and the output results are the updated plan and recommendations.

[1503] 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.

[1504] 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.

[1505] 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.

[1506] 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.

[1507] 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.

[1508] 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.

[1509] 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).

[1510] 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.

[1511] 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."

[1512] 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.

[1513] 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).

[1514] 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.

[1515] 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.

[1516] 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.

[1517] 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.

[1518] 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.

[1519] 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.

[1520] 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.

[1521] 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.

[1522] 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.

[1523] 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.

[1524] The following is further disclosed regarding the above embodiment.

[1525] (Claim 1)

[1526] a means for collecting individual biometric data;

[1527] means for transmitting the biometric data to a cloud environment;

[1528] means for analyzing the biometric data in the cloud environment and using a generative model to assess the health status of the user;

[1529] A means for generating an individual health management plan based on the analysis result of the generative model;

[1530] means for transmitting the health management plan to a user's terminal;

[1531] means for transmitting the implementation results and feedback of the health management plan back to the cloud environment;

[1532] means for analyzing the feedback and updating the health management plan;

[1533] A system including:

[1534] (Claim 2)

[1535] 10. The system of claim 1, further comprising means for customizing exercise programs, meal plans, and wellness activities according to the user's health status.

[1536] (Claim 3)

[1537] 10. The system of claim 1, further comprising means for automatically collecting some or all of the biometric data using a wearable device.

[1538] "Example 1"

[1539] (Claim 1)

[1540] a means for collecting individual biometric data;

[1541] means for transmitting the biometric data to a cloud environment;

[1542] a means for analyzing the biometric data in the cloud environment and using a generative model to assess the health status of the user;

[1543] A means for generating an individual health management plan based on the analysis result of the generative model;

[1544] means for transmitting the health management plan to a user's terminal;

[1545] means for transmitting the implementation results and feedback of the health management plan back to the cloud environment;

[1546] means for analyzing the feedback and updating the health management plan;

[1547] A method to measure blood pressure every morning and enter it into a smartphone app,

[1548] A means for automatically recording step count and heart rate data using a wearable device and a means for transmitting the data to a terminal;

[1549] means for generating periodically improved health care plans based on said analysis;

[1550] A system including:

[1551] (Claim 2)

[1552] 10. The system of claim 1, further comprising means for customizing exercise programs, meal plans, and wellness activities according to the user's health status.

[1553] (Claim 3)

[1554] 10. The system of claim 1, further comprising means for automatically collecting some or all of the biometric data using a wearable device and means for transmitting the data to a cloud environment.

[1555] "Application Example 1"

[1556] (Claim 1)

[1557] a means for collecting individual biometric data;

[1558] means for transmitting the biometric data to a cloud environment;

[1559] means for analyzing the biometric data in the cloud environment and using a generative model to assess the health status of the user;

[1560] A means for generating an individual health management plan based on the analysis result of the generative model;

[1561] means for transmitting the health management plan to a user's terminal;

[1562] means for transmitting the implementation results and feedback of the health management plan back to the cloud environment;

[1563] means for analyzing the feedback and updating the health management plan;

[1564] a means for generating a meal plan based on the health management plan and automatically ordering meals in cooperation with a food delivery service;

[1565] A system including:

[1566] (Claim 2)

[1567] 10. The system of claim 1, further comprising means for customizing exercise programs, meal plans, and wellness activities according to the user's health status.

[1568] (Claim 3)

[1569] 10. The system of claim 1, further comprising means for automatically collecting some or all of the biometric data using a wearable device.

[1570] "Example 2: Combining Emotion Engines"

[1571] (Claim 1)

[1572] means for collecting individual biometric and emotional state data;

[1573] means for transmitting said biometric data and emotional state data to a cloud environment;

[1574] means in the cloud environment for analyzing the data and using a generative model and emotion engine to assess the user's health and emotional state;

[1575] means for generating an individual health management plan based on the analysis results of the generative model and the emotion engine;

[1576] means for transmitting the health management plan to a user's terminal;

[1577] means for transmitting the implementation results and feedback of the health management plan back to the cloud environment;

[1578] means for analyzing the feedback and emotional state data to update the health management plan;

[1579] A system including:

[1580] (Claim 2)

[1581] 10. The system of claim 1, further comprising means for customizing exercise programs, meal plans, and wellness activities according to the user's health and emotional state.

[1582] (Claim 3)

[1583] 10. The system of claim 1, further comprising means for automatically collecting some or all of the biometric data and emotional state data using a wearable device and an audio-visual collection device.

[1584] "Application example 2 when combining emotion engines"

[1585] (Claim 1)

[1586] a means for collecting individual biometric data;

[1587] means for transmitting the biometric data to a cloud environment;

[1588] means in the cloud environment for analyzing the biometric data and using a generative model and emotion engine to assess the user's health and emotional state;

[1589] means for generating a personalized health care plan and virtual shopping recommendations based on the analysis results of the generative model and the emotion engine;

[1590] means for transmitting the health management plan and virtual shopping recommendations to a user's terminal;

[1591] means for transmitting the implementation results and feedback of the health management plan and virtual shopping recommendations back to the cloud environment;

[1592] means for analyzing the feedback to update the health care plan and virtual shopping recommendations;

[1593] A system including:

[1594] (Claim 2)

[1595] 10. The system of claim 1, further comprising means for customizing recommendations in the virtual store according to the user's health and emotional state.

[1596] (Claim 3)

[1597] 10. The system of claim 1, further comprising means for automatically collecting some or all of the biometric data and emotional state data using a wearable device. [Explanation of symbols]

[1598] 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 collecting individual biometric data; means for transmitting the biometric data to a cloud environment; means for analyzing the biometric data in the cloud environment and using a generative model to assess the health status of the user; A means for generating an individual health management plan based on the analysis result of the generative model; means for transmitting the health management plan to a user's terminal; means for transmitting the implementation results and feedback of the health management plan back to the cloud environment; means for analyzing the feedback and updating the health management plan; A system including:

2. The system of claim 1 , further comprising means for customizing exercise programs, meal plans, and wellness activities according to the user's health status.

3. The system of claim 1 , further comprising means for automatically collecting some or all of the biometric data using a wearable device.

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