system

Abstract

We provide the system. [Solution] Means for collecting and managing users' biometric data, A means of analyzing biological data to generate an individually optimized health management plan, A means for receiving and analyzing real-time monitoring data and providing an alarm when an anomaly is detected, Means of providing guidance on diet and exercise based on a health management plan, A means of incorporating information from medical institutions and adjusting health management plans, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In personal health management, it is required to appropriately integrate genetic information, lifestyle habits, and real-time biological data, and provide an optimal health plan based on them. However, with conventional methods, it is difficult to manage these data in a unified and real-time manner and provide optimal guidance for individuals. Also, it is not sufficient to link the health management plan with feedback from medical institutions and make flexible adjustments according to individual needs. As a result, there is a lack of an effective approach to personal health promotion and disease prevention.

Means for Solving the Problems

[0005] This invention provides a system that comprehensively collects and analyzes a user's biological data to generate an individually optimized health management plan. This system includes functions for detecting abnormalities through real-time monitoring and issuing immediate alerts, providing dietary and exercise guidance based on the health plan, and incorporating feedback from medical institutions to adjust the health management plan. Furthermore, it provides immediate and personalized support by generating interactive responses to user questions. This effectively promotes individual health improvement and disease prevention.

[0006] A "user" refers to an individual who uses the system, and whose biometric data is collected and analyzed by the system.

[0007] "Biological data" refers to data about a user's health status, including information such as heart rate, blood pressure, sleep patterns, and genetic information.

[0008] "Analysis" refers to the evaluation and computational processing of data performed to generate individually optimized health management plans based on collected biological data.

[0009] A "personalized health management plan" refers to a plan that includes recommendations for maintaining and promoting health tailored to the user, based on the user's biometric data.

[0010] "Monitoring" refers to the process of continuously observing and collecting user health-related data using wearable devices or other means.

[0011] An "alert" refers to a warning message that is sent to the user when an anomaly is detected based on the results of real-time monitoring.

[0012] "Dietary and exercise guidance" refers to activities that provide guidelines for diet and physical activity that are recommended based on the user's health condition and goals.

[0013] "Feedback from medical institutions" refers to information about the user's health status obtained through health checkups and other activities conducted at medical institutions, and this information is reflected in the health management plan. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the 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.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0028] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention relates to a comprehensive management system for supporting individual health management. The system has the functionality to collect and analyze the user's biometric data and provide an individually optimized health management plan.

[0036] First, users wear a wearable device to collect daily biological data such as heart rate, blood pressure, and sleep patterns. The collected data is then transferred to a terminal using wireless communication such as Bluetooth.

[0037] Next, the device receives the biological data and transfers it to a cloud server. This ensures that the data is securely stored and can be used for later analysis.

[0038] The server analyzes data collected on the cloud and generates personalized health management plans using its proprietary algorithms. These plans include guidance on diet and exercise for maintaining and promoting health. For example, users at high risk of diabetes may be advised to follow a low-carbohydrate diet, while users who are sedentary may be suggested to incorporate exercise three times a week.

[0039] Real-time monitoring is also one of the important functions of this invention. The terminal continuously receives data from the wearable device and transmits it to the server. Based on this data, the server detects abnormal values ​​and immediately issues an alert to the user as needed. For example, if a higher-than-normal heart rate is recorded for an extended period, an alert is issued pointing to the possibility of stress or illness.

[0040] Furthermore, the server collaborates with medical institutions to import the results of users' regular health checkups and incorporate them into their health management plans. This enables more precise and timely health management. Users may also be recommended to consult a specialist if necessary.

[0041] Furthermore, user inquiries can be answered immediately through the system's built-in chatbot function. For example, in response to a question such as "What would you recommend for dinner tonight?", the system will provide a response in natural language, such as suggesting a "low-carbohydrate menu with plenty of vegetables" based on a plan generated by the server.

[0042] Thus, in this embodiment, it is possible to utilize users' biodata to provide healthcare tailored to individual needs and contribute to disease prevention and health promotion.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user puts on a wearable device and begins collecting biometric data. The device continuously measures information such as heart rate, blood pressure, and sleep patterns, and transmits this data to the terminal.

[0046] Step 2:

[0047] The device temporarily stores biological data received from wearable devices and uploads this data to a cloud server at regular intervals. A secure communication protocol is used for the upload.

[0048] Step 3:

[0049] The server receives the collected biological data and stores it in a database. After receiving the data, it uses an AI algorithm to analyze the data and evaluate each user's health status.

[0050] Step 4:

[0051] Based on the analysis results, the server generates a personalized health management plan. This plan includes suggestions for diet and exercise, and is customized to the user's health goals.

[0052] Step 5:

[0053] The generated health management plan is sent to the device and the user is notified. The user is encouraged to manage their daily health according to the plan.

[0054] Step 6:

[0055] The terminal continuously receives real-time data from wearable devices and transfers it to a server. This data is used for anomaly detection.

[0056] Step 7:

[0057] The server analyzes real-time data and immediately generates an alarm if an anomaly is detected. The alarm is sent to the user via their terminal, and necessary actions are recommended.

[0058] Step 8:

[0059] The server receives health checkup results from medical institutions and incorporates them into the health management plan. This ensures the plan is updated based on the latest health status.

[0060] Step 9:

[0061] The server responds to user questions in natural language via the device's chatbot function. Immediate feedback is provided to support the user's health.

[0062] (Example 1)

[0063] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0064] Current health management systems are insufficient in efficiently and comprehensively managing users' biometric information and providing individually optimized health management plans. Furthermore, real-time anomaly detection and alerting, as well as the integration of diagnostic data from medical institutions, are incomplete, making it difficult to properly maintain users' health status. Additionally, there is a lack of means to respond quickly and accurately to user inquiries.

[0065] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0066] In this invention, the server includes means for acquiring and managing the user's biometric information, means for automatically acquiring the user's biometric information from a wearable device, and means for providing question-answering services using a generative AI model. This enables real-time management of the user's health status, immediate detection and warning of abnormalities, and more precise health management through the provision of personalized health management plans and integration of diagnostic data with medical institutions. Furthermore, it reduces health-related anxiety by responding quickly to inquiries from the user.

[0067] "User biometric information" refers to data that indicates the user's physiological state, such as heart rate, blood pressure, and sleep patterns.

[0068] A "health management plan" is a plan that includes recommended nutrition and exercise guidance to maintain and promote the user's health, based on collected biological information.

[0069] "Anomaly detection" involves monitoring the user's biometric information and notifying the user of any values ​​that exceed the normal range.

[0070] An "alert" is an alert that is sent to the user when an anomaly is detected, warning the user of a potential health problem.

[0071] "Information from medical institutions" refers to the results of diagnoses and tests received by the user at medical institutions, and is data used to improve health management plans.

[0072] A "wearable device" is an electronic device that is worn on a user's body to continuously collect biological information.

[0073] A "generative AI model" is an artificial intelligence technology used to generate appropriate responses to natural language inquiries from users.

[0074] "Cloud-based computing" refers to a computing method that analyzes data on remote servers connected via the internet and centrally manages resources.

[0075] This invention provides a system that collects and manages users' biometric information in real time to support personal health management and provides each user with an optimized health management plan. The hardware used includes wearable devices, mobile terminals, and cloud servers.

[0076] The user wears a wearable device that measures heart rate, blood pressure, sleep patterns, etc. This device uses wireless technology such as Bluetooth to transmit data to a mobile device that is physically nearby.

[0077] The device temporarily stores the received data and transfers the information to the cloud server via an available network. This process is designed to ensure that the data is stored securely and quickly on the cloud server.

[0078] The server analyzes vast amounts of biological information collected on the cloud and generates individually optimized health management plans using its proprietary algorithms. The server focuses on providing personalized advice based on exercise and dietary data. For example, based on daily data, it might provide health guidance such as, "Today, reduce your carbohydrate intake and focus on a vegetable-based diet."

[0079] Furthermore, this invention features a user question answering function that utilizes a generative AI model. The server uses natural language processing to appropriately respond to user prompts. For example, if a user inputs "Please give me some advice on future weight management," it can return a highly effective response such as, "We recommend maintaining your current exercise level while reducing your total calorie intake by 15%."

[0080] In this way, this system effectively utilizes users' health data and proposes health promotion plans tailored to individual needs, thereby comprehensively supporting users' health.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The user wears a wearable device to detect biometric information such as heart rate, blood pressure, and sleep patterns. This device uses Bluetooth communication to transmit the collected data to a nearby terminal. (Input) User's biometric information. (Output) Data transfer to the terminal. The wearable device measures data at specific time intervals and sends it to the terminal in real time.

[0084] Step 2:

[0085] The terminal receives biological information transmitted from the wearable device and temporarily stores it in memory. The stored data is securely transferred to a cloud server via the internet. (Input) Biological information from the wearable device. (Output) Data transfer to the cloud server. The terminal sends the accumulated data in batch processing at regular intervals.

[0086] Step 3:

[0087] The server analyzes biometric data received on the cloud and generates a personalized health management plan using its own algorithms. This plan includes individualized advice on diet and exercise. (Input) User's biometric data stored in the cloud. (Output) Individually optimized health management plan. For example, the server might detect insufficient exercise from past data and generate advice such as, "We recommend exercising for 30 minutes three times a week."

[0088] Step 4:

[0089] The terminal notifies the user of the generated health management plan and continues real-time monitoring. If an abnormal value is detected, an alert is immediately sent to the user. (Input) Health management plan and real-time data generated by the server. (Output) Health management plan notification and alert. The terminal continuously monitors the data and immediately notifies the user of any abnormalities.

[0090] Step 5:

[0091] The server utilizes a generative AI model to generate appropriate responses to user inquiries. For example, if a user sends the prompt "Please give me advice on what to eat today," the server will respond with instructions such as "For today's meal, please choose low-carbohydrate foods that are mainly vegetables." (Input) User prompt. (Output) Response from the generative AI model. The generative AI performs natural language analysis to derive the optimal response.

[0092] (Application Example 1)

[0093] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] For the elderly, it is crucial to detect changes in their health status early and provide appropriate health management. However, it is difficult for individuals to operate a system that collects and analyzes biometric data on a daily basis and responds quickly to abnormalities. Furthermore, there is a problem in that maintaining health is difficult because exercise and dietary suggestions tailored to individual health conditions are not provided.

[0095] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0096] In this invention, the server includes means for collecting and managing the user's biometric data, means for generating an individually optimized health management policy, and means for immediately making an emergency contact if an abnormal value is detected in an elderly person. This makes it possible to continuously monitor the health status of elderly people and provide appropriate health management.

[0097] "User" refers to an individual who uses a health management system, and may include elderly people in particular.

[0098] "Biometric data" refers to information about the user's physical condition, such as heart rate, blood pressure, steps taken, and sleep patterns.

[0099] A "personally optimized health management policy" refers to a health management plan that is optimized for each individual, based on the user's biometric data, with the aim of maintaining and improving their health.

[0100] "Real-time monitoring data" refers to data that continuously collects users' biometric data and can be analyzed immediately.

[0101] "Means of providing an alert when an anomaly is detected" refers to a function that notifies the user to take notice when an abnormal value is detected in the user's biometric data.

[0102] "Exercise and dietary guidance" refers to advice that suggests appropriate exercise and dietary content based on the user's health condition.

[0103] "Means of incorporating information from medical institutions and adjusting health management policies" refers to a function that adjusts the health management plan as needed based on information such as the user's health checkup results obtained from medical institutions.

[0104] A "wearable device" refers to a device that a user wears to record biometric data, and may include heart rate monitors and fitness trackers.

[0105] The system implementing this invention is designed for the health management of users, including the elderly. Users wear wearable devices that record heart rate, blood pressure, steps, and sleep patterns. This data is transmitted to a terminal using Bluetooth. The terminal uploads this data to a cloud platform. Specifically, this system uses AWS® Lambda as a cloud service to efficiently manage the data. The data is stored in AWS DynamoDB, and the server analyzes the data in real time.

[0106] The server uses a generative AI model based on data analyzed for each user to create an individually optimized health management plan. This generative AI model is implemented using OpenAI® GPT-3® and other technologies, and has the ability to respond to user questions in an interactive format. For example, if a user asks, "What kind of light exercise is recommended when I don't get enough exercise?", the server will suggest appropriate exercises based on the data analysis results.

[0107] Furthermore, the server is designed to immediately provide users with alarms and notifications if it detects abnormal values. For example, if a user's heart rate exceeds the normal range, a notification such as "Your heart rate remains high. Please take deep breaths and calm down" will be sent. In addition, the system has a function to adjust health management policies based on the results of health checkups and other data.

[0108] Examples of prompts for the generating AI model include, "What light exercises would you recommend for a 70-year-old who doesn't get enough exercise?" and "What should I do if my heart rate is higher than normal?" By combining detailed data analysis with AI technology in this way, it is possible to provide specific and practical health management services tailored to individual users.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The user wears a wearable device to record heart rate, blood pressure, steps, and sleep patterns. This collects biometric data as input to the wearable device. The wearable device then transmits this data to the terminal via Bluetooth.

[0112] Step 2:

[0113] The device uploads the received biometric data to the cloud platform. Specifically, the device sends data to the cloud, where it is stored in AWS Lambda. During this process, the device converts the data format to JSON and standardizes the data labels. The output is then saved to a database in the cloud.

[0114] Step 3:

[0115] The server analyzes biometric data stored in the cloud in real time. It retrieves data from AWS DynamoDB and performs calculations to detect anomalies using statistical methods. If an anomaly is detected, it generates an alarm message and sends a notification to the user. The output includes a flag indicating an anomaly and an alarm message.

[0116] Step 4:

[0117] The server utilizes a generative AI model to generate personalized health management plans from analyzed data. Using OpenAI GPT-3, it takes data as prompts and outputs appropriate health management procedures. Based on the health management plan generated by the AI ​​model, it provides specific health guidance tailored to each individual user.

[0118] Step 5:

[0119] When a user inputs a question to the generative AI model through their device, the server processes the question and sends it to the model as a prompt. The generative AI model then generates an appropriate response and returns it to the device as output. This allows the user to obtain appropriate health management advice and information.

[0120] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0121] This invention proposes a system that combines the collection, management, and analysis of biological data with an emotion engine to comprehensively support the user's health. The emotion engine enables the integrated evaluation of the user's biological information and emotional state, providing a individually optimized health management plan.

[0122] First, users collect biometric data such as heart rate, blood pressure, and sleep patterns using a wearable device. This data is transmitted to a terminal via a dedicated application. Furthermore, users can periodically input their emotional state using the terminal, or recognize their emotions in real time using the terminal's camera and voice analysis functions.

[0123] The device transfers the collected biological and emotional data to a server in the cloud. The server receives the data at runtime and stores it in an up-to-date database.

[0124] The server uses AI algorithms and an emotion engine to analyze the user's health status in detail. The emotion engine assesses the user's stress level and well-being, and incorporates this into a health management plan. For example, if a high stress level is detected, relaxation activities and mental care suggestions are provided.

[0125] Based on the analysis results, the server generates a personalized health management plan for each user. This plan includes diet, exercise, and stress management and mental health improvement measures based on emotional state. The generated plan is then notified to the user in real time via their device.

[0126] Furthermore, user questions are answered quickly through the chatbot function built into the device. For example, in response to a question like, "I'm feeling down today, what should I do?", feedback based on the analysis results of the emotion engine is provided in natural language, such as, "Try some light exercise or meditation. Even a short walk can be effective in improving your mood."

[0127] The device also continuously monitors the user's biometric data and sends newly acquired data to the server, enabling timely updates to the plan. This process allows the system to provide dynamic health management and support tailored to the user's condition, improving overall health and well-being.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] Users wear wearable devices to collect biometric data such as heart rate, blood pressure, and sleep patterns. Furthermore, the smartphone's camera and microphone are used for emotion recognition, and an emotion engine analyzes facial expressions and voice tone in real time.

[0131] Step 2:

[0132] The device temporarily stores data collected from wearable devices and emotion recognition systems, and uploads it to a server in the cloud at regular intervals. A secure communication protocol is used for the upload.

[0133] Step 3:

[0134] The server receives biological and emotional data from the cloud and stores it in a database. The received data is analyzed using AI algorithms and an emotion engine to evaluate the user's health and emotional state.

[0135] Step 4:

[0136] Based on the analysis results, the server generates a personalized health management plan. This plan includes suggestions for diet, exercise, stress management, and mental care. For example, if emotional data indicates high stress levels, yoga or meditation practice might be recommended.

[0137] Step 5:

[0138] The generated health management plan is sent to the device and notified to the user. The user can then use this plan as a guide to manage their daily activities and health.

[0139] Step 6:

[0140] The terminal continuously receives data from wearable devices and emotion recognition systems and transfers it to the server. This data is used for real-time anomaly detection and continuous plan updates.

[0141] Step 7:

[0142] The server analyzes new data and immediately generates an alert if it detects any anomalies related to emotions. For example, if a high level of stress is detected, it sends a notification to the user via their device recommending relaxation or urging them to consult a medical professional.

[0143] Step 8:

[0144] The server uses natural language processing to answer user questions via the device's chatbot function. Depending on the content of the question, it provides accurate feedback based on the user's emotional state.

[0145] (Example 2)

[0146] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0147] In modern society, personal health management is crucial, but conventional health management systems are generally based on limited data, making it difficult to provide individually optimized plans. Furthermore, the lack of a function to comprehensively evaluate emotional states and biological information makes it difficult to properly manage health risks caused by stress and emotional fluctuations. In addition, real-time monitoring and feedback are essential, but many systems fail to provide this.

[0148] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0149] In this invention, the server includes means for analyzing biological and emotional information to generate individually optimized health maintenance plans, means for receiving and analyzing real-time monitored information and providing warnings when anomalies are detected, and means for analyzing emotional information and making suggestions based on emotional state. As a result, users can receive individually optimized health management, comprehensively evaluate their emotional and biological health states, and manage their health risks in real time.

[0150] "Biological information" refers to data obtained from the human body that indicates health conditions, such as heart rate, blood pressure, and sleep patterns.

[0151] "Management" is the process of organizing, storing, and utilizing collected data as needed.

[0152] "Emotional information" refers to data that indicates the user's emotional state, and is acquired through camera and voice analysis.

[0153] "Analysis" is the process of examining collected data, giving it meaning, and deriving individualized health maintenance plans.

[0154] A "health maintenance plan" is a plan consisting of guidelines and suggestions for diet, exercise, and stress management that are optimized for each individual user.

[0155] "Real-time monitoring" is a process of continuously and immediately checking and analyzing data from users.

[0156] A "warning" is information that notifies the user and urges them to pay attention when an abnormal situation is detected.

[0157] A "suggestion" is a set of specific action guidelines based on the analysis results, outlining how users should manage their health.

[0158] This invention provides a system that collects and analyzes biometric and emotional information by coordinating a wearable device, a terminal, and a server to support user health management. The wearable device used is assumed to be a general-purpose smart device capable of measuring heart rate, blood pressure, and sleep patterns. The terminal is a smartphone or tablet, with a dedicated application installed on these devices.

[0159] The user first uses a wearable device to acquire biometric information. This data is transmitted to the terminal via Bluetooth or Wi-Fi. The user can then use the terminal to input their emotional information into a dedicated application or to record their emotions in real time using the terminal's camera and microphone.

[0160] The device transfers biometric and emotional information to a server in the cloud. Upon receiving the data, the server analyzes it using an AI algorithm and an emotion engine. The AI ​​algorithm provides a detailed assessment of the user's health status, while the emotion engine analyzes changes in emotions, and these findings are then incorporated into a health maintenance plan.

[0161] Based on the analysis results, the server generates a health maintenance plan optimized for the user and notifies the user of its contents in real time via their device. The user can adjust their diet and exercise according to the plan displayed on their device. In addition, if an abnormality is detected, the server will issue a warning to the user and prompt further action.

[0162] Furthermore, users can use the chatbot function built into the device to ask questions about their concerns and health. The device utilizes a generative AI model to generate conversational responses in natural language. For example, if a user asks, "I'm feeling down today, what should I do?", the system will provide specific advice such as, "I recommend light exercise or a walk in nature."

[0163] Examples of prompt messages include, "A high stress level has been detected. Please suggest relaxation activities," and "The user is reporting emotional fatigue. What advice should you provide?" In this way, the system provides integrated support for the user's health and emotions, enabling real-time health management.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The user uses a wearable device to acquire biometric information such as heart rate, blood pressure, and sleep patterns. The input is data acquired from the wearable device, which is then transmitted to the terminal via Bluetooth or Wi-Fi. The terminal then stores this biometric information in a dedicated application.

[0167] Step 2:

[0168] The user inputs their emotional information using an application on their device. This input can be text or voice information about the user's current feelings and stress level. The device uses its camera and microphone to perform voice analysis and facial recognition, recognizing and storing this information as emotional data.

[0169] Step 3:

[0170] The device transfers collected biological and emotional information to a server in the cloud. The input consists of this information stored on the device. The server stores the received data in a database. The transfer uses secure communication protocols to ensure data protection.

[0171] Step 4:

[0172] The server analyzes data using AI algorithms and an emotion engine. The input consists of biological and emotional information stored in a database. By analyzing this data, the server quantifies the user's health status and identifies stress levels and happiness levels.

[0173] Step 5:

[0174] The server generates a personalized health maintenance plan based on the analysis results. The output includes a plan that includes suggestions for diet and exercise, as well as methods for stress management. The server then uses a generated AI model to formulate the plan and sends it to the user's device.

[0175] Step 6:

[0176] The device notifies the user of the health maintenance plan received from the server. The output is the plan details displayed on the device's screen. The user can review the notification and obtain specific action guidelines through the application.

[0177] Step 7:

[0178] Based on real-time monitored information, the server sends a warning to the user via the terminal if an anomaly is detected. The input consists of new biological information and the results of AI analysis. This triggers an action to alert the user and instruct them to take the necessary action.

[0179] Step 8:

[0180] When a user asks a question, the device's chatbot function uses a generated AI model to produce a response. The input is the user's question, and the output is an appropriate answer in natural language. This allows the device to provide quick advice.

[0181] (Application Example 2)

[0182] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0183] To provide personalized health management and improve users' quality of life, it is necessary to collect and comprehensively analyze both biological and emotional data. However, conventional systems are limited to the collection and analysis of biological data, making it difficult to provide individually optimized health management plans that take into account the user's emotional state. Furthermore, there is a need to realize health support that utilizes devices that users use on a daily basis.

[0184] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0185] In this invention, the server includes means for collecting and managing the user's biological and emotional data, means for generating an individually optimized health management plan through analysis using an emotion engine, and means equipped with a device that functions as a health support device. This enables appropriate health management and guidance based on the user's physiological and emotional state.

[0186] A "user" refers to an individual who uses a specific health management system.

[0187] "Biological data" refers to measurements that indicate an individual's health status, such as heart rate, blood pressure, and sleep patterns.

[0188] "Emotional data" refers to information about an individual's emotional state obtained through voice and facial expression analysis.

[0189] An "emotion engine" refers to software that analyzes emotional data and evaluates the user's emotional state.

[0190] A "personalized health management plan" refers to a plan that develops guidance tailored to each individual user's health maintenance and improvement goals, based on their biological and emotional data.

[0191] A "health support device" refers to a device used at home to assist users in managing their health.

[0192] In the system for implementing this invention, the user first uses a health support device equipped with sensors for acquiring biological data. The sensors measure and collect data on heart rate, blood pressure, and sleep patterns in real time. In parallel, the device has a built-in camera and microphone for collecting emotional data, which is obtained from facial expressions and voice. Based on this, the system comprehensively assesses the user's health status.

[0193] The device temporarily stores this data locally and transfers it to the server via wireless communication. The server then runs AI algorithms and an emotion engine to further analyze this data in a cloud environment. This process uses machine learning libraries such as TENSORFLOW® to analyze the data, and emotional states are evaluated using the IBM Watson® API. Based on the analysis results, an optimal health management plan is generated for the user, and necessary guidance is provided to the user via the device.

[0194] As a concrete example, if data analysis reveals that a user has recently been experiencing stress, the server will provide relaxation methods and meditation recommendations through the health support device. Furthermore, it will utilize a generative AI model to provide the user with appropriate feedback in natural language, tailored to their emotional state. For example, it might use a prompt such as, "Based on the user's emotional state and biometric data today, please begin the analysis to provide the next optimal health support suggestion." This prompt serves as a guide for the system in determining the best next steps for the user.

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The device continuously collects biometric data such as the user's heart rate, blood pressure, and sleep patterns through a health support device. This data is stored locally using Bluetooth. The input is analog data from sensors. This data is converted to digital, and any invalid data is filtered out before output.

[0198] Step 2:

[0199] The user collects emotional data using the device's camera and microphone. Specifically, facial recognition and voice analysis are used to detect the user's emotional state in real time. The input consists of camera images and voice data, which the emotion engine uses to analyze emotional attributes such as amusement and tension, and generates emotional data as output.

[0200] Step 3:

[0201] The terminal transmits collected biological and emotional data to the server via wireless communication. The server receives this data and records it in a database. The input is a digital signal from the terminal, which is then processed and output by the server for storage in the database.

[0202] Step 4:

[0203] The server analyzes biological data using TensorFlow in the cloud and emotional data using the IBM Watson API. The input is data stored on the server, and data mining and machine learning algorithms are used to learn data patterns and produce analysis results as output.

[0204] Step 5:

[0205] The server generates an optimal health management plan for the user based on the analysis results. The input is the analysis results from machine learning, and the generating AI model uses this to construct appropriate health guidance content, resulting in an individually optimized health management plan as output.

[0206] Step 6:

[0207] The terminal notifies the user of the health management plan sent from the server. The input is plan information from the server, which the terminal receives and displays in a user-friendly format using natural language processing. The output is visual information and audio instructions for the user.

[0208] Step 7:

[0209] Users can ask follow-up questions through the chatbot function built into their device. The input is a question from the user, which is analyzed by an emotion engine, and an AI model is used to generate an appropriate answer, which is then presented to the user as output.

[0210] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0211] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0212] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0216] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0217] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0218] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0219] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0220] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0221] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0222] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0223] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0224] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0225] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0226] This invention relates to a comprehensive management system for supporting individual health management. The system has the functionality to collect and analyze the user's biometric data and provide an individually optimized health management plan.

[0227] First, users wear a wearable device to collect daily biological data such as heart rate, blood pressure, and sleep patterns. The collected data is then transferred to a terminal using wireless communication such as Bluetooth.

[0228] Next, the device receives the biological data and transfers it to a cloud server. This ensures that the data is securely stored and can be used for later analysis.

[0229] The server analyzes data collected on the cloud and generates personalized health management plans using its proprietary algorithms. These plans include guidance on diet and exercise for maintaining and promoting health. For example, users at high risk of diabetes may be advised to follow a low-carbohydrate diet, while users who are sedentary may be suggested to incorporate exercise three times a week.

[0230] Real-time monitoring is also one of the important functions of this invention. The terminal continuously receives data from the wearable device and transmits it to the server. Based on this data, the server detects abnormal values ​​and immediately issues an alert to the user as needed. For example, if a higher-than-normal heart rate is recorded for an extended period, an alert is issued pointing to the possibility of stress or illness.

[0231] Furthermore, the server collaborates with medical institutions to import the results of users' regular health checkups and incorporate them into their health management plans. This enables more precise and timely health management. Users may also be recommended to consult a specialist if necessary.

[0232] Furthermore, user inquiries can be answered immediately through the system's built-in chatbot function. For example, in response to a question such as "What would you recommend for dinner tonight?", the system will provide a response in natural language, such as suggesting a "low-carbohydrate menu with plenty of vegetables" based on a plan generated by the server.

[0233] Thus, in this embodiment, it is possible to utilize users' biodata to provide healthcare tailored to individual needs and contribute to disease prevention and health promotion.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] The user puts on a wearable device and begins collecting biometric data. The device continuously measures information such as heart rate, blood pressure, and sleep patterns, and transmits this data to the terminal.

[0237] Step 2:

[0238] The device temporarily stores biological data received from wearable devices and uploads this data to a cloud server at regular intervals. A secure communication protocol is used for the upload.

[0239] Step 3:

[0240] The server receives the collected biological data and stores it in a database. After receiving the data, it uses an AI algorithm to analyze the data and evaluate each user's health status.

[0241] Step 4:

[0242] Based on the analysis results, the server generates a personalized health management plan. This plan includes suggestions for diet and exercise, and is customized to the user's health goals.

[0243] Step 5:

[0244] The generated health management plan is sent to the device and the user is notified. The user is encouraged to manage their daily health according to the plan.

[0245] Step 6:

[0246] The terminal continuously receives real-time data from wearable devices and transfers it to a server. This data is used for anomaly detection.

[0247] Step 7:

[0248] The server analyzes real-time data and immediately generates an alarm if an anomaly is detected. The alarm is sent to the user via their terminal, and necessary actions are recommended.

[0249] Step 8:

[0250] The server receives health checkup results from medical institutions and incorporates them into the health management plan. This ensures the plan is updated based on the latest health status.

[0251] Step 9:

[0252] The server responds to user questions in natural language via the device's chatbot function. Immediate feedback is provided to support the user's health.

[0253] (Example 1)

[0254] Next, we will describe Example 1. 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."

[0255] Current health management systems are insufficient in efficiently and comprehensively managing users' biometric information and providing individually optimized health management plans. Furthermore, real-time anomaly detection and alerting, as well as the integration of diagnostic data from medical institutions, are incomplete, making it difficult to properly maintain users' health status. Additionally, there is a lack of means to respond quickly and accurately to user inquiries.

[0256] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0257] In this invention, the server includes means for acquiring and managing the user's biometric information, means for automatically acquiring the user's biometric information from a wearable device, and means for providing question-answering services using a generative AI model. This enables real-time management of the user's health status, immediate detection and warning of abnormalities, and more precise health management through the provision of personalized health management plans and integration of diagnostic data with medical institutions. Furthermore, it reduces health-related anxiety by responding quickly to inquiries from the user.

[0258] "User biometric information" refers to data that indicates the user's physiological state, such as heart rate, blood pressure, and sleep patterns.

[0259] A "health management plan" is a plan that includes recommended nutrition and exercise guidance to maintain and promote the user's health, based on collected biological information.

[0260] "Anomaly detection" involves monitoring the user's biometric information and notifying the user of any values ​​that exceed the normal range.

[0261] An "alert" is an alert that is sent to the user when an anomaly is detected, warning the user of a potential health problem.

[0262] "Information from medical institutions" refers to the results of diagnoses and tests received by the user at medical institutions, and is data used to improve health management plans.

[0263] A "wearable device" is an electronic device that is worn on a user's body to continuously collect biological information.

[0264] A "generative AI model" is an artificial intelligence technology used to generate appropriate responses to natural language inquiries from users.

[0265] "Cloud-based computing" refers to a computing method that analyzes data on remote servers connected via the internet and centrally manages resources.

[0266] This invention provides a system that collects and manages users' biometric information in real time to support personal health management and provides each user with an optimized health management plan. The hardware used includes wearable devices, mobile terminals, and cloud servers.

[0267] The user wears a wearable device that measures heart rate, blood pressure, sleep patterns, etc. This device uses wireless technology such as Bluetooth to transmit data to a mobile device that is physically nearby.

[0268] The device temporarily stores the received data and transfers the information to the cloud server via an available network. This process is designed to ensure that the data is stored securely and quickly on the cloud server.

[0269] The server analyzes vast amounts of biological information collected on the cloud and generates individually optimized health management plans using its proprietary algorithms. The server focuses on providing personalized advice based on exercise and dietary data. For example, based on daily data, it might provide health guidance such as, "Today, reduce your carbohydrate intake and focus on a vegetable-based diet."

[0270] Furthermore, this invention features a user question answering function that utilizes a generative AI model. The server uses natural language processing to appropriately respond to user prompts. For example, if a user inputs "Please give me some advice on future weight management," it can return a highly effective response such as, "We recommend maintaining your current exercise level while reducing your total calorie intake by 15%."

[0271] In this way, this system effectively utilizes users' health data and proposes health promotion plans tailored to individual needs, thereby comprehensively supporting users' health.

[0272] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0273] Step 1:

[0274] The user wears a wearable device to detect biometric information such as heart rate, blood pressure, and sleep patterns. This device uses Bluetooth communication to transmit the collected data to a nearby terminal. (Input) User's biometric information. (Output) Data transfer to the terminal. The wearable device measures data at specific time intervals and sends it to the terminal in real time.

[0275] Step 2:

[0276] The terminal receives biological information transmitted from the wearable device and temporarily stores it in memory. The stored data is securely transferred to a cloud server via the internet. (Input) Biological information from the wearable device. (Output) Data transfer to the cloud server. The terminal sends the accumulated data in batch processing at regular intervals.

[0277] Step 3:

[0278] The server analyzes biometric data received on the cloud and generates a personalized health management plan using its own algorithms. This plan includes individualized advice on diet and exercise. (Input) User's biometric data stored in the cloud. (Output) Individually optimized health management plan. For example, the server might detect insufficient exercise from past data and generate advice such as, "We recommend exercising for 30 minutes three times a week."

[0279] Step 4:

[0280] The terminal notifies the user of the generated health management plan and continues real-time monitoring. If an abnormal value is detected, an alarm is immediately sent to the user. (Input) Health management plan and real-time data generated by the server. (Output) Notification of the health management plan and alarm. The terminal continuously monitors the data and immediately notifies the user of any abnormalities.

[0281] Step 5:

[0282] The server utilizes the generated AI model to generate an appropriate response to the user's inquiry. For example, when the user sends a prompt sentence such as "Please give me advice on today's diet", the server gives an instruction such as "Select a low-carbohydrate diet centered on vegetables for today's meal". (Input) Prompt sentence from the user. (Output) Response by the generated AI model. The generated AI performs natural language analysis to derive an optimal response.

[0283] (Application Example 1)

[0284] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0285] For the elderly, it is important to detect changes in their health status early and provide appropriate health management. However, it is difficult for an individual to operate a system that routinely collects and analyzes biological data and responds promptly in case of abnormalities. In addition, there is a problem that it is difficult to maintain health because proposals for exercise and diet according to individual health status are not provided.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0287] In this invention, the server includes means for collecting and managing the user's biometric data, means for generating an individually optimized health management policy, and means for immediately making an emergency contact if an abnormal value is detected in an elderly person. This makes it possible to continuously monitor the health status of elderly people and provide appropriate health management.

[0288] "User" refers to an individual who uses a health management system, and may include elderly people in particular.

[0289] "Biometric data" refers to information about the user's physical condition, such as heart rate, blood pressure, steps taken, and sleep patterns.

[0290] A "personally optimized health management policy" refers to a health management plan that is optimized for each individual, based on the user's biometric data, with the aim of maintaining and improving their health.

[0291] "Real-time monitoring data" refers to data that continuously collects users' biometric data and can be analyzed immediately.

[0292] "Means of providing an alert when an anomaly is detected" refers to a function that notifies the user to take notice when an abnormal value is detected in the user's biometric data.

[0293] "Exercise and dietary guidance" refers to advice that suggests appropriate exercise and dietary content based on the user's health condition.

[0294] "Means of incorporating information from medical institutions and adjusting health management policies" refers to a function that adjusts the health management plan as needed based on information such as the user's health checkup results obtained from medical institutions.

[0295] A "wearable device" refers to a device that a user wears to record biometric data, and may include heart rate monitors and fitness trackers.

[0296] The system implementing this invention is designed for the health management of users, including the elderly. Users wear wearable devices that record heart rate, blood pressure, steps, and sleep patterns. This data is transmitted to a terminal using Bluetooth. The terminal uploads this data to a cloud platform. Specifically, this system uses AWS Lambda as a cloud service to efficiently manage the data. The data is stored in AWS DynamoDB, and the server analyzes the data in real time.

[0297] The server uses a generative AI model based on data analyzed for each user to create an individually optimized health management plan. This generative AI model is implemented using OpenAI GPT-3 and has the ability to respond to user questions in an interactive format. For example, if a user asks, "What kind of light exercise is recommended when I don't get enough exercise?", the server will suggest appropriate exercises based on the data analysis results.

[0298] Furthermore, the server is designed to immediately provide users with alarms and notifications if it detects abnormal values. For example, if a user's heart rate exceeds the normal range, a notification such as "Your heart rate remains high. Please take deep breaths and calm down" will be sent. In addition, the system has a function to adjust health management policies based on the results of health checkups and other data.

[0299] Examples of prompts for the generating AI model include, "What light exercises would you recommend for a 70-year-old who doesn't get enough exercise?" and "What should I do if my heart rate is higher than normal?" By combining detailed data analysis with AI technology in this way, it is possible to provide specific and practical health management services tailored to individual users.

[0300] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0301] Step 1:

[0302] The user wears a wearable device to record their heart rate, blood pressure, step count, and sleep pattern. As a result, biometric data is collected as input by the wearable device. The wearable device transmits the data to the terminal via Bluetooth.

[0303] Step 2:

[0304] The terminal uploads the received biometric data to the cloud platform. Specifically, the data is sent from the terminal to the cloud and stored in AWS Lambda. In this process, the terminal converts the data format to JSON and performs processing to standardize the data labels. It is saved in the database within the cloud as output.

[0305] Step 3:

[0306] The server analyzes the biometric data stored in the cloud in real-time. It retrieves the data from AWS DynamoDB and performs calculations to detect outliers using statistical methods. If an outlier is detected, an alert message is generated and sent as a notification to the user. As output, a flag indicating an anomaly and an alert message are generated.

[0307] Step 4:

[0308] The server utilizes a generated AI model to generate an individualized optimized health management plan from the analyzed data. It uses OpenAI GPT-3, inputs the data as a prompt, and outputs appropriate health management procedures. Based on the health management plan generated by the AI model, specific health guidance is provided as output for each individual user.

[0309] Step 5:

[0310] When the user inputs a question to the generated AI model through the terminal, the server processes the question and sends it as a prompt to the model. The generated AI model generates an appropriate response to this and returns it as output to the terminal. As a result, the user can obtain advice and information regarding appropriate health management.

[0311] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0312] This invention proposes a system that combines the collection, management, and analysis of biological data with an emotion engine to comprehensively support the user's health. The emotion engine enables the integrated evaluation of the user's biological information and emotional state, providing a individually optimized health management plan.

[0313] First, users collect biometric data such as heart rate, blood pressure, and sleep patterns using a wearable device. This data is transmitted to a terminal via a dedicated application. Furthermore, users can periodically input their emotional state using the terminal, or recognize their emotions in real time using the terminal's camera and voice analysis functions.

[0314] The device transfers the collected biological and emotional data to a server in the cloud. The server receives the data at runtime and stores it in an up-to-date database.

[0315] The server uses AI algorithms and an emotion engine to analyze the user's health status in detail. The emotion engine assesses the user's stress level and well-being, and incorporates this into a health management plan. For example, if a high stress level is detected, relaxation activities and mental care suggestions are provided.

[0316] Based on the analysis results, the server generates a personalized health management plan for each user. This plan includes diet, exercise, and stress management and mental health improvement measures based on emotional state. The generated plan is then notified to the user in real time via their device.

[0317] Furthermore, user questions are answered quickly through the chatbot function built into the device. For example, in response to a question like, "I'm feeling down today, what should I do?", feedback based on the analysis results of the emotion engine is provided in natural language, such as, "Try some light exercise or meditation. Even a short walk can be effective in improving your mood."

[0318] The device also continuously monitors the user's biometric data and sends newly acquired data to the server, enabling timely updates to the plan. This process allows the system to provide dynamic health management and support tailored to the user's condition, improving overall health and well-being.

[0319] The following describes the processing flow.

[0320] Step 1:

[0321] Users wear wearable devices to collect biometric data such as heart rate, blood pressure, and sleep patterns. Furthermore, the smartphone's camera and microphone are used for emotion recognition, and an emotion engine analyzes facial expressions and voice tone in real time.

[0322] Step 2:

[0323] The device temporarily stores data collected from wearable devices and emotion recognition systems, and uploads it to a server in the cloud at regular intervals. A secure communication protocol is used for the upload.

[0324] Step 3:

[0325] The server receives biological and emotional data from the cloud and stores it in a database. The received data is analyzed using AI algorithms and an emotion engine to evaluate the user's health and emotional state.

[0326] Step 4:

[0327] Based on the analysis results, the server generates a personalized health management plan. This plan includes suggestions for diet, exercise, stress management, and mental care. For example, if emotional data indicates high stress levels, yoga or meditation practice might be recommended.

[0328] Step 5:

[0329] The generated health management plan is sent to the device and notified to the user. The user can then use this plan as a guide to manage their daily activities and health.

[0330] Step 6:

[0331] The terminal continuously receives data from wearable devices and emotion recognition systems and transfers it to the server. This data is used for real-time anomaly detection and continuous plan updates.

[0332] Step 7:

[0333] The server analyzes new data and immediately generates an alert if it detects any anomalies related to emotions. For example, if a high level of stress is detected, it sends a notification to the user via their device recommending relaxation or urging them to consult a medical professional.

[0334] Step 8:

[0335] The server uses natural language processing to answer user questions via the device's chatbot function. Depending on the content of the question, it provides accurate feedback based on the user's emotional state.

[0336] (Example 2)

[0337] Next, we will describe Example 2. 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".

[0338] In modern society, personal health management is crucial, but conventional health management systems are generally based on limited data, making it difficult to provide individually optimized plans. Furthermore, the lack of a function to comprehensively evaluate emotional states and biological information makes it difficult to properly manage health risks caused by stress and emotional fluctuations. In addition, real-time monitoring and feedback are essential, but many systems fail to provide this.

[0339] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0340] In this invention, the server includes means for analyzing biological and emotional information to generate individually optimized health maintenance plans, means for receiving and analyzing real-time monitored information and providing warnings when anomalies are detected, and means for analyzing emotional information and making suggestions based on emotional state. As a result, users can receive individually optimized health management, comprehensively evaluate their emotional and biological health states, and manage their health risks in real time.

[0341] "Biological information" refers to data obtained from the human body that indicates health conditions, such as heart rate, blood pressure, and sleep patterns.

[0342] "Management" is the process of organizing, storing, and utilizing collected data as needed.

[0343] "Emotional information" refers to data that indicates the user's emotional state, and is acquired through camera and voice analysis.

[0344] "Analysis" is the process of examining collected data, giving it meaning, and deriving individualized health maintenance plans.

[0345] A "health maintenance plan" is a plan consisting of guidelines and suggestions for diet, exercise, and stress management that are optimized for each individual user.

[0346] "Real-time monitoring" is a process of continuously and immediately checking and analyzing data from users.

[0347] A "warning" is information that notifies the user and urges them to pay attention when an abnormal situation is detected.

[0348] A "suggestion" is a set of specific action guidelines based on the analysis results, outlining how users should manage their health.

[0349] This invention provides a system that collects and analyzes biometric and emotional information by coordinating a wearable device, a terminal, and a server to support user health management. The wearable device used is assumed to be a general-purpose smart device capable of measuring heart rate, blood pressure, and sleep patterns. The terminal is a smartphone or tablet, with a dedicated application installed on these devices.

[0350] The user first uses a wearable device to acquire biometric information. This data is transmitted to the terminal via Bluetooth or Wi-Fi. The user can then use the terminal to input their emotional information into a dedicated application or to record their emotions in real time using the terminal's camera and microphone.

[0351] The device transfers biometric and emotional information to a server in the cloud. Upon receiving the data, the server analyzes it using an AI algorithm and an emotion engine. The AI ​​algorithm provides a detailed assessment of the user's health status, while the emotion engine analyzes changes in emotions, and these findings are then incorporated into a health maintenance plan.

[0352] Based on the analysis results, the server generates a health maintenance plan optimized for the user and notifies the user of its contents in real time via their device. The user can adjust their diet and exercise according to the plan displayed on their device. In addition, if an abnormality is detected, the server will issue a warning to the user and prompt further action.

[0353] Furthermore, users can use the chatbot function built into the device to ask questions about their concerns and health. The device utilizes a generative AI model to generate conversational responses in natural language. For example, if a user asks, "I'm feeling down today, what should I do?", the system will provide specific advice such as, "I recommend light exercise or a walk in nature."

[0354] Examples of prompt messages include, "A high stress level has been detected. Please suggest relaxation activities," and "The user is reporting emotional fatigue. What advice should you provide?" In this way, the system provides integrated support for the user's health and emotions, enabling real-time health management.

[0355] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0356] Step 1:

[0357] The user uses a wearable device to acquire biometric information such as heart rate, blood pressure, and sleep patterns. The input is data acquired from the wearable device, which is then transmitted to the terminal via Bluetooth or Wi-Fi. The terminal then stores this biometric information in a dedicated application.

[0358] Step 2:

[0359] The user inputs their emotional information using an application on their device. This input can be text or voice information about the user's current feelings and stress level. The device uses its camera and microphone to perform voice analysis and facial recognition, recognizing and storing this information as emotional data.

[0360] Step 3:

[0361] The device transfers collected biological and emotional information to a server in the cloud. The input consists of this information stored on the device. The server stores the received data in a database. The transfer uses secure communication protocols to ensure data protection.

[0362] Step 4:

[0363] The server analyzes data using AI algorithms and an emotion engine. The input consists of biological and emotional information stored in a database. By analyzing this data, the server quantifies the user's health status and identifies stress levels and happiness levels.

[0364] Step 5:

[0365] The server generates a personalized health maintenance plan based on the analysis results. The output includes a plan that includes suggestions for diet and exercise, as well as methods for stress management. The server then uses a generated AI model to formulate the plan and sends it to the user's device.

[0366] Step 6:

[0367] The device notifies the user of the health maintenance plan received from the server. The output is the plan details displayed on the device's screen. The user can review the notification and obtain specific action guidelines through the application.

[0368] Step 7:

[0369] Based on real-time monitored information, the server sends a warning to the user via the terminal if an anomaly is detected. The input consists of new biological information and the results of AI analysis. This triggers an action to alert the user and instruct them to take the necessary action.

[0370] Step 8:

[0371] When a user asks a question, the device's chatbot function uses a generated AI model to produce a response. The input is the user's question, and the output is an appropriate answer in natural language. This allows the device to provide quick advice.

[0372] (Application Example 2)

[0373] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0374] To provide personalized health management and improve users' quality of life, it is necessary to collect and comprehensively analyze both biological and emotional data. However, conventional systems are limited to the collection and analysis of biological data, making it difficult to provide individually optimized health management plans that take into account the user's emotional state. Furthermore, there is a need to realize health support that utilizes devices that users use on a daily basis.

[0375] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0376] In this invention, the server includes means for collecting and managing the user's biological and emotional data, means for generating an individually optimized health management plan through analysis using an emotion engine, and means equipped with a device that functions as a health support device. This enables appropriate health management and guidance based on the user's physiological and emotional state.

[0377] A "user" refers to an individual who uses a specific health management system.

[0378] "Biological data" refers to measurements that indicate an individual's health status, such as heart rate, blood pressure, and sleep patterns.

[0379] "Emotional data" refers to information about an individual's emotional state obtained through voice and facial expression analysis.

[0380] An "emotion engine" refers to software that analyzes emotional data and evaluates the user's emotional state.

[0381] A "personalized health management plan" refers to a plan that develops guidance tailored to each individual user's health maintenance and improvement goals, based on their biological and emotional data.

[0382] A "health support device" refers to a device used at home to assist users in managing their health.

[0383] In the system for implementing this invention, the user first uses a health support device equipped with sensors for acquiring biological data. The sensors measure and collect data on heart rate, blood pressure, and sleep patterns in real time. In parallel, the device has a built-in camera and microphone for collecting emotional data, which is obtained from facial expressions and voice. Based on this, the system comprehensively assesses the user's health status.

[0384] The device temporarily stores this data locally and transfers it to the server via wireless communication. The server then runs AI algorithms and an emotion engine to further analyze this data in a cloud environment. This process uses machine learning libraries such as TensorFlow to analyze the data, and emotional states are evaluated using the IBM Watson API. Based on the analysis results, an optimal health management plan is generated for the user, and necessary guidance is provided to the user via the device.

[0385] As a concrete example, if data analysis reveals that a user has recently been experiencing stress, the server will provide relaxation methods and meditation recommendations through the health support device. Furthermore, it will utilize a generative AI model to provide the user with appropriate feedback in natural language, tailored to their emotional state. For example, it might use a prompt such as, "Based on the user's emotional state and biometric data today, please begin the analysis to provide the next optimal health support suggestion." This prompt serves as a guide for the system in determining the best next steps for the user.

[0386] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0387] Step 1:

[0388] The device continuously collects biometric data such as the user's heart rate, blood pressure, and sleep patterns through a health support device. This data is stored locally using Bluetooth. The input is analog data from sensors. This data is converted to digital, and any invalid data is filtered out before output.

[0389] Step 2:

[0390] The user collects emotional data using the device's camera and microphone. Specifically, facial recognition and voice analysis are used to detect the user's emotional state in real time. The input consists of camera images and voice data, which the emotion engine uses to analyze emotional attributes such as amusement and tension, and generates emotional data as output.

[0391] Step 3:

[0392] The terminal transmits collected biological and emotional data to the server via wireless communication. The server receives this data and records it in a database. The input is a digital signal from the terminal, which is then processed and output by the server for storage in the database.

[0393] Step 4:

[0394] The server analyzes biological data using TensorFlow in the cloud and emotional data using the IBM Watson API. The input is data stored on the server, and data mining and machine learning algorithms are used to learn data patterns and produce analysis results as output.

[0395] Step 5:

[0396] The server generates an optimal health management plan for the user based on the analysis results. The input is the analysis results from machine learning, and the generating AI model uses this to construct appropriate health guidance content, resulting in an individually optimized health management plan as output.

[0397] Step 6:

[0398] The terminal notifies the user of the health management plan sent from the server. The input is plan information from the server, which the terminal receives and displays in a user-friendly format using natural language processing. The output is visual information and audio instructions for the user.

[0399] Step 7:

[0400] Users can ask follow-up questions through the chatbot function built into their device. The input is a question from the user, which is analyzed by an emotion engine, and an AI model is used to generate an appropriate answer, which is then presented to the user as output.

[0401] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0402] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0403] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0404] [Third Embodiment]

[0405] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0406] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0407] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0408] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0409] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0410] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0411] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0412] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0413] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0414] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0415] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0416] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0417] This invention relates to a comprehensive management system for supporting individual health management. The system has the functionality to collect and analyze the user's biometric data and provide an individually optimized health management plan.

[0418] First, users wear a wearable device to collect daily biological data such as heart rate, blood pressure, and sleep patterns. The collected data is then transferred to a terminal using wireless communication such as Bluetooth.

[0419] Next, the device receives the biological data and transfers it to a cloud server. This ensures that the data is securely stored and can be used for later analysis.

[0420] The server analyzes data collected on the cloud and generates personalized health management plans using its proprietary algorithms. These plans include guidance on diet and exercise for maintaining and promoting health. For example, users at high risk of diabetes may be advised to follow a low-carbohydrate diet, while users who are sedentary may be suggested to incorporate exercise three times a week.

[0421] Real-time monitoring is also one of the important functions of this invention. The terminal continuously receives data from the wearable device and transmits it to the server. Based on this data, the server detects abnormal values ​​and immediately issues an alert to the user as needed. For example, if a higher-than-normal heart rate is recorded for an extended period, an alert is issued pointing to the possibility of stress or illness.

[0422] Furthermore, the server collaborates with medical institutions to import the results of users' regular health checkups and incorporate them into their health management plans. This enables more precise and timely health management. Users may also be recommended to consult a specialist if necessary.

[0423] Furthermore, user inquiries can be answered immediately through the system's built-in chatbot function. For example, in response to a question such as "What would you recommend for dinner tonight?", the system will provide a response in natural language, such as suggesting a "low-carbohydrate menu with plenty of vegetables" based on a plan generated by the server.

[0424] Thus, in this embodiment, it is possible to utilize users' biodata to provide healthcare tailored to individual needs and contribute to disease prevention and health promotion.

[0425] The following describes the processing flow.

[0426] Step 1:

[0427] The user puts on a wearable device and begins collecting biometric data. The device continuously measures information such as heart rate, blood pressure, and sleep patterns, and transmits this data to the terminal.

[0428] Step 2:

[0429] The device temporarily stores biological data received from wearable devices and uploads this data to a cloud server at regular intervals. A secure communication protocol is used for the upload.

[0430] Step 3:

[0431] The server receives the collected biological data and stores it in a database. After receiving the data, it uses an AI algorithm to analyze the data and evaluate each user's health status.

[0432] Step 4:

[0433] Based on the analysis results, the server generates a personalized health management plan. This plan includes suggestions for diet and exercise, and is customized to the user's health goals.

[0434] Step 5:

[0435] The generated health management plan is sent to the device and the user is notified. The user is encouraged to manage their daily health according to the plan.

[0436] Step 6:

[0437] The terminal continuously receives real-time data from wearable devices and transfers it to a server. This data is used for anomaly detection.

[0438] Step 7:

[0439] The server analyzes real-time data and immediately generates an alarm if an anomaly is detected. The alarm is sent to the user via their terminal, and necessary actions are recommended.

[0440] Step 8:

[0441] The server receives health checkup results from medical institutions and incorporates them into the health management plan. This ensures the plan is updated based on the latest health status.

[0442] Step 9:

[0443] The server responds to user questions in natural language via the device's chatbot function. Immediate feedback is provided to support the user's health.

[0444] (Example 1)

[0445] Next, we will describe Example 1. 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."

[0446] Current health management systems are insufficient in efficiently and comprehensively managing users' biometric information and providing individually optimized health management plans. Furthermore, real-time anomaly detection and alerting, as well as the integration of diagnostic data from medical institutions, are incomplete, making it difficult to properly maintain users' health status. Additionally, there is a lack of means to respond quickly and accurately to user inquiries.

[0447] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0448] In this invention, the server includes means for acquiring and managing the user's biometric information, means for automatically acquiring the user's biometric information from a wearable device, and means for providing question-answering services using a generative AI model. This enables real-time management of the user's health status, immediate detection and warning of abnormalities, and more precise health management through the provision of personalized health management plans and integration of diagnostic data with medical institutions. Furthermore, it reduces health-related anxiety by responding quickly to inquiries from the user.

[0449] "User biometric information" refers to data that indicates the user's physiological state, such as heart rate, blood pressure, and sleep patterns.

[0450] A "health management plan" is a plan that includes recommended nutrition and exercise guidance to maintain and promote the user's health, based on collected biological information.

[0451] "Anomaly detection" involves monitoring the user's biometric information and notifying the user of any values ​​that exceed the normal range.

[0452] An "alert" is an alert that is sent to the user when an anomaly is detected, warning the user of a potential health problem.

[0453] "Information from medical institutions" refers to the results of diagnoses and tests received by the user at medical institutions, and is data used to improve health management plans.

[0454] A "wearable device" is an electronic device that is worn on a user's body to continuously collect biological information.

[0455] A "generative AI model" is an artificial intelligence technology used to generate appropriate responses to natural language inquiries from users.

[0456] "Cloud-based computing" refers to a computing method that analyzes data on remote servers connected via the internet and centrally manages resources.

[0457] This invention provides a system that collects and manages users' biometric information in real time to support personal health management and provides each user with an optimized health management plan. The hardware used includes wearable devices, mobile terminals, and cloud servers.

[0458] The user wears a wearable device that measures heart rate, blood pressure, sleep patterns, etc. This device uses wireless technology such as Bluetooth to transmit data to a mobile device that is physically nearby.

[0459] The device temporarily stores the received data and transfers the information to the cloud server via an available network. This process is designed to ensure that the data is stored securely and quickly on the cloud server.

[0460] The server analyzes vast amounts of biological information collected on the cloud and generates individually optimized health management plans using its proprietary algorithms. The server focuses on providing personalized advice based on exercise and dietary data. For example, based on daily data, it might provide health guidance such as, "Today, reduce your carbohydrate intake and focus on a vegetable-based diet."

[0461] Furthermore, this invention features a user question answering function that utilizes a generative AI model. The server uses natural language processing to appropriately respond to user prompts. For example, if a user inputs "Please give me some advice on future weight management," it can return a highly effective response such as, "We recommend maintaining your current exercise level while reducing your total calorie intake by 15%."

[0462] In this way, this system effectively utilizes users' health data and proposes health promotion plans tailored to individual needs, thereby comprehensively supporting users' health.

[0463] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0464] Step 1:

[0465] The user wears a wearable device to detect biometric information such as heart rate, blood pressure, and sleep patterns. This device uses Bluetooth communication to transmit the collected data to a nearby terminal. (Input) User's biometric information. (Output) Data transfer to the terminal. The wearable device measures data at specific time intervals and sends it to the terminal in real time.

[0466] Step 2:

[0467] The terminal receives biological information transmitted from the wearable device and temporarily stores it in memory. The stored data is securely transferred to a cloud server via the internet. (Input) Biological information from the wearable device. (Output) Data transfer to the cloud server. The terminal sends the accumulated data in batch processing at regular intervals.

[0468] Step 3:

[0469] The server analyzes biometric data received on the cloud and generates a personalized health management plan using its own algorithms. This plan includes individualized advice on diet and exercise. (Input) User's biometric data stored in the cloud. (Output) Individually optimized health management plan. For example, the server might detect insufficient exercise from past data and generate advice such as, "We recommend exercising for 30 minutes three times a week."

[0470] Step 4:

[0471] The terminal notifies the user of the generated health management plan and continues real-time monitoring. If an abnormal value is detected, an alert is immediately sent to the user. (Input) Health management plan and real-time data generated by the server. (Output) Health management plan notification and alert. The terminal continuously monitors the data and immediately notifies the user of any abnormalities.

[0472] Step 5:

[0473] The server utilizes a generative AI model to generate appropriate responses to user inquiries. For example, if a user sends the prompt "Please give me advice on what to eat today," the server will respond with instructions such as "For today's meal, please choose low-carbohydrate foods that are mainly vegetables." (Input) User prompt. (Output) Response from the generative AI model. The generative AI performs natural language analysis to derive the optimal response.

[0474] (Application Example 1)

[0475] Next, we will explain Application Example 1. In the following explanation, 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."

[0476] For the elderly, it is crucial to detect changes in their health status early and provide appropriate health management. However, it is difficult for individuals to operate a system that collects and analyzes biometric data on a daily basis and responds quickly to abnormalities. Furthermore, there is a problem in that maintaining health is difficult because exercise and dietary suggestions tailored to individual health conditions are not provided.

[0477] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0478] In this invention, the server includes means for collecting and managing the user's biometric data, means for generating an individually optimized health management policy, and means for immediately making an emergency contact if an abnormal value is detected in an elderly person. This makes it possible to continuously monitor the health status of elderly people and provide appropriate health management.

[0479] "User" refers to an individual who uses a health management system, and may include elderly people in particular.

[0480] "Biometric data" refers to information about the user's physical condition, such as heart rate, blood pressure, steps taken, and sleep patterns.

[0481] A "personally optimized health management policy" refers to a health management plan that is optimized for each individual, based on the user's biometric data, with the aim of maintaining and improving their health.

[0482] "Real-time monitoring data" refers to data that continuously collects users' biometric data and can be analyzed immediately.

[0483] "Means of providing an alert when an anomaly is detected" refers to a function that notifies the user to take notice when an abnormal value is detected in the user's biometric data.

[0484] "Exercise and dietary guidance" refers to advice that suggests appropriate exercise and dietary content based on the user's health condition.

[0485] "Means of incorporating information from medical institutions and adjusting health management policies" refers to a function that adjusts the health management plan as needed based on information such as the user's health checkup results obtained from medical institutions.

[0486] A "wearable device" refers to a device that a user wears to record biometric data, and may include heart rate monitors and fitness trackers.

[0487] The system implementing this invention is designed for the health management of users, including the elderly. Users wear wearable devices that record heart rate, blood pressure, steps, and sleep patterns. This data is transmitted to a terminal using Bluetooth. The terminal uploads this data to a cloud platform. Specifically, this system uses AWS Lambda as a cloud service to efficiently manage the data. The data is stored in AWS DynamoDB, and the server analyzes the data in real time.

[0488] The server uses a generative AI model based on data analyzed for each user to create an individually optimized health management plan. This generative AI model is implemented using OpenAI GPT-3 and has the ability to respond to user questions in an interactive format. For example, if a user asks, "What kind of light exercise is recommended when I don't get enough exercise?", the server will suggest appropriate exercises based on the data analysis results.

[0489] Furthermore, the server is designed to immediately provide users with alarms and notifications if it detects abnormal values. For example, if a user's heart rate exceeds the normal range, a notification such as "Your heart rate remains high. Please take deep breaths and calm down" will be sent. In addition, the system has a function to adjust health management policies based on the results of health checkups and other data.

[0490] Examples of prompts for the generating AI model include, "What light exercises would you recommend for a 70-year-old who doesn't get enough exercise?" and "What should I do if my heart rate is higher than normal?" By combining detailed data analysis with AI technology in this way, it is possible to provide specific and practical health management services tailored to individual users.

[0491] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0492] Step 1:

[0493] The user wears a wearable device to record heart rate, blood pressure, steps, and sleep patterns. This collects biometric data as input to the wearable device. The wearable device then transmits this data to the terminal via Bluetooth.

[0494] Step 2:

[0495] The device uploads the received biometric data to the cloud platform. Specifically, the device sends data to the cloud, where it is stored in AWS Lambda. During this process, the device converts the data format to JSON and standardizes the data labels. The output is then saved to a database in the cloud.

[0496] Step 3:

[0497] The server analyzes biometric data stored in the cloud in real time. It retrieves data from AWS DynamoDB and performs calculations to detect anomalies using statistical methods. If an anomaly is detected, it generates an alarm message and sends a notification to the user. The output includes a flag indicating an anomaly and an alarm message.

[0498] Step 4:

[0499] The server utilizes a generative AI model to generate personalized health management plans from analyzed data. Using OpenAI GPT-3, it takes data as prompts and outputs appropriate health management procedures. Based on the health management plan generated by the AI ​​model, it provides specific health guidance tailored to each individual user.

[0500] Step 5:

[0501] When a user inputs a question to the generative AI model through their device, the server processes the question and sends it to the model as a prompt. The generative AI model then generates an appropriate response and returns it to the device as output. This allows the user to obtain appropriate health management advice and information.

[0502] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0503] This invention proposes a system that combines the collection, management, and analysis of biological data with an emotion engine to comprehensively support the user's health. The emotion engine enables the integrated evaluation of the user's biological information and emotional state, providing a individually optimized health management plan.

[0504] First, users collect biometric data such as heart rate, blood pressure, and sleep patterns using a wearable device. This data is transmitted to a terminal via a dedicated application. Furthermore, users can periodically input their emotional state using the terminal, or recognize their emotions in real time using the terminal's camera and voice analysis functions.

[0505] The device transfers the collected biological and emotional data to a server in the cloud. The server receives the data at runtime and stores it in an up-to-date database.

[0506] The server uses AI algorithms and an emotion engine to analyze the user's health status in detail. The emotion engine assesses the user's stress level and well-being, and incorporates this into a health management plan. For example, if a high stress level is detected, relaxation activities and mental care suggestions are provided.

[0507] Based on the analysis results, the server generates a personalized health management plan for each user. This plan includes diet, exercise, and stress management and mental health improvement measures based on emotional state. The generated plan is then notified to the user in real time via their device.

[0508] Furthermore, user questions are answered quickly through the chatbot function built into the device. For example, in response to a question like, "I'm feeling down today, what should I do?", feedback based on the analysis results of the emotion engine is provided in natural language, such as, "Try some light exercise or meditation. Even a short walk can be effective in improving your mood."

[0509] The device also continuously monitors the user's biometric data and sends newly acquired data to the server, enabling timely updates to the plan. This process allows the system to provide dynamic health management and support tailored to the user's condition, improving overall health and well-being.

[0510] The following describes the processing flow.

[0511] Step 1:

[0512] Users wear wearable devices to collect biometric data such as heart rate, blood pressure, and sleep patterns. Furthermore, the smartphone's camera and microphone are used for emotion recognition, and an emotion engine analyzes facial expressions and voice tone in real time.

[0513] Step 2:

[0514] The device temporarily stores data collected from wearable devices and emotion recognition systems, and uploads it to a server in the cloud at regular intervals. A secure communication protocol is used for the upload.

[0515] Step 3:

[0516] The server receives biological and emotional data from the cloud and stores it in a database. The received data is analyzed using AI algorithms and an emotion engine to evaluate the user's health and emotional state.

[0517] Step 4:

[0518] Based on the analysis results, the server generates a personalized health management plan. This plan includes suggestions for diet, exercise, stress management, and mental care. For example, if emotional data indicates high stress levels, yoga or meditation practice might be recommended.

[0519] Step 5:

[0520] The generated health management plan is sent to the device and notified to the user. The user can then use this plan as a guide to manage their daily activities and health.

[0521] Step 6:

[0522] The terminal continuously receives data from wearable devices and emotion recognition systems and transfers it to the server. This data is used for real-time anomaly detection and continuous plan updates.

[0523] Step 7:

[0524] The server analyzes new data and immediately generates an alert if it detects any anomalies related to emotions. For example, if a high level of stress is detected, it sends a notification to the user via their device recommending relaxation or urging them to consult a medical professional.

[0525] Step 8:

[0526] The server uses natural language processing to answer user questions via the device's chatbot function. Depending on the content of the question, it provides accurate feedback based on the user's emotional state.

[0527] (Example 2)

[0528] Next, we will describe Example 2. 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."

[0529] In modern society, personal health management is crucial, but conventional health management systems are generally based on limited data, making it difficult to provide individually optimized plans. Furthermore, the lack of a function to comprehensively evaluate emotional states and biological information makes it difficult to properly manage health risks caused by stress and emotional fluctuations. In addition, real-time monitoring and feedback are essential, but many systems fail to provide this.

[0530] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0531] In this invention, the server includes means for analyzing biological and emotional information to generate individually optimized health maintenance plans, means for receiving and analyzing real-time monitored information and providing warnings when anomalies are detected, and means for analyzing emotional information and making suggestions based on emotional state. As a result, users can receive individually optimized health management, comprehensively evaluate their emotional and biological health states, and manage their health risks in real time.

[0532] "Biological information" refers to data obtained from the human body that indicates health conditions, such as heart rate, blood pressure, and sleep patterns.

[0533] "Management" is the process of organizing, storing, and utilizing collected data as needed.

[0534] "Emotional information" refers to data that indicates the user's emotional state, and is acquired through camera and voice analysis.

[0535] "Analysis" is the process of examining collected data, giving it meaning, and deriving individualized health maintenance plans.

[0536] A "health maintenance plan" is a plan consisting of guidelines and suggestions for diet, exercise, and stress management that are optimized for each individual user.

[0537] "Real-time monitoring" is a process of continuously and immediately checking and analyzing data from users.

[0538] A "warning" is information that notifies the user and urges them to pay attention when an abnormal situation is detected.

[0539] A "suggestion" is a set of specific action guidelines based on the analysis results, outlining how users should manage their health.

[0540] This invention provides a system that collects and analyzes biometric and emotional information by coordinating a wearable device, a terminal, and a server to support user health management. The wearable device used is assumed to be a general-purpose smart device capable of measuring heart rate, blood pressure, and sleep patterns. The terminal is a smartphone or tablet, with a dedicated application installed on these devices.

[0541] The user first uses a wearable device to acquire biometric information. This data is transmitted to the terminal via Bluetooth or Wi-Fi. The user can then use the terminal to input their emotional information into a dedicated application or to record their emotions in real time using the terminal's camera and microphone.

[0542] The device transfers biometric and emotional information to a server in the cloud. Upon receiving the data, the server analyzes it using an AI algorithm and an emotion engine. The AI ​​algorithm provides a detailed assessment of the user's health status, while the emotion engine analyzes changes in emotions, and these findings are then incorporated into a health maintenance plan.

[0543] Based on the analysis results, the server generates a health maintenance plan optimized for the user and notifies the user of its contents in real time via their device. The user can adjust their diet and exercise according to the plan displayed on their device. In addition, if an abnormality is detected, the server will issue a warning to the user and prompt further action.

[0544] Furthermore, users can use the chatbot function built into the device to ask questions about their concerns and health. The device utilizes a generative AI model to generate conversational responses in natural language. For example, if a user asks, "I'm feeling down today, what should I do?", the system will provide specific advice such as, "I recommend light exercise or a walk in nature."

[0545] Examples of prompt messages include, "A high stress level has been detected. Please suggest relaxation activities," and "The user is reporting emotional fatigue. What advice should you provide?" In this way, the system provides integrated support for the user's health and emotions, enabling real-time health management.

[0546] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0547] Step 1:

[0548] The user uses a wearable device to acquire biometric information such as heart rate, blood pressure, and sleep patterns. The input is data acquired from the wearable device, which is then transmitted to the terminal via Bluetooth or Wi-Fi. The terminal then stores this biometric information in a dedicated application.

[0549] Step 2:

[0550] The user inputs their emotional information using an application on their device. This input can be text or voice information about the user's current feelings and stress level. The device uses its camera and microphone to perform voice analysis and facial recognition, recognizing and storing this information as emotional data.

[0551] Step 3:

[0552] The device transfers collected biological and emotional information to a server in the cloud. The input consists of this information stored on the device. The server stores the received data in a database. The transfer uses secure communication protocols to ensure data protection.

[0553] Step 4:

[0554] The server analyzes data using AI algorithms and an emotion engine. The input consists of biological and emotional information stored in a database. By analyzing this data, the server quantifies the user's health status and identifies stress levels and happiness levels.

[0555] Step 5:

[0556] The server generates a personalized health maintenance plan based on the analysis results. The output includes a plan that includes suggestions for diet and exercise, as well as methods for stress management. The server then uses a generated AI model to formulate the plan and sends it to the user's device.

[0557] Step 6:

[0558] The device notifies the user of the health maintenance plan received from the server. The output is the plan details displayed on the device's screen. The user can review the notification and obtain specific action guidelines through the application.

[0559] Step 7:

[0560] Based on real-time monitored information, the server sends a warning to the user via the terminal if an anomaly is detected. The input consists of new biological information and the results of AI analysis. This triggers an action to alert the user and instruct them to take the necessary action.

[0561] Step 8:

[0562] When a user asks a question, the device's chatbot function uses a generated AI model to produce a response. The input is the user's question, and the output is an appropriate answer in natural language. This allows the device to provide quick advice.

[0563] (Application Example 2)

[0564] Next, we will explain application example 2. In the following explanation, 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."

[0565] To provide personalized health management and improve users' quality of life, it is necessary to collect and comprehensively analyze both biological and emotional data. However, conventional systems are limited to the collection and analysis of biological data, making it difficult to provide individually optimized health management plans that take into account the user's emotional state. Furthermore, there is a need to realize health support that utilizes devices that users use on a daily basis.

[0566] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0567] In this invention, the server includes means for collecting and managing the user's biological and emotional data, means for generating an individually optimized health management plan through analysis using an emotion engine, and means equipped with a device that functions as a health support device. This enables appropriate health management and guidance based on the user's physiological and emotional state.

[0568] A "user" refers to an individual who uses a specific health management system.

[0569] "Biological data" refers to measurements that indicate an individual's health status, such as heart rate, blood pressure, and sleep patterns.

[0570] "Emotional data" refers to information about an individual's emotional state obtained through voice and facial expression analysis.

[0571] An "emotion engine" refers to software that analyzes emotional data and evaluates the user's emotional state.

[0572] A "personalized health management plan" refers to a plan that develops guidance tailored to each individual user's health maintenance and improvement goals, based on their biological and emotional data.

[0573] A "health support device" refers to a device used at home to assist users in managing their health.

[0574] In the system for implementing this invention, the user first uses a health support device equipped with sensors for acquiring biological data. The sensors measure and collect data on heart rate, blood pressure, and sleep patterns in real time. In parallel, the device has a built-in camera and microphone for collecting emotional data, which is obtained from facial expressions and voice. Based on this, the system comprehensively assesses the user's health status.

[0575] The device temporarily stores this data locally and transfers it to the server via wireless communication. The server then runs AI algorithms and an emotion engine to further analyze this data in a cloud environment. This process uses machine learning libraries such as TensorFlow to analyze the data, and emotional states are evaluated using the IBM Watson API. Based on the analysis results, an optimal health management plan is generated for the user, and necessary guidance is provided to the user via the device.

[0576] As a concrete example, if data analysis reveals that a user has recently been experiencing stress, the server will provide relaxation methods and meditation recommendations through the health support device. Furthermore, it will utilize a generative AI model to provide the user with appropriate feedback in natural language, tailored to their emotional state. For example, it might use a prompt such as, "Based on the user's emotional state and biometric data today, please begin the analysis to provide the next optimal health support suggestion." This prompt serves as a guide for the system in determining the best next steps for the user.

[0577] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0578] Step 1:

[0579] The device continuously collects biometric data such as the user's heart rate, blood pressure, and sleep patterns through a health support device. This data is stored locally using Bluetooth. The input is analog data from sensors. This data is converted to digital, and any invalid data is filtered out before output.

[0580] Step 2:

[0581] The user collects emotional data using the device's camera and microphone. Specifically, facial recognition and voice analysis are used to detect the user's emotional state in real time. The input consists of camera images and voice data, which the emotion engine uses to analyze emotional attributes such as amusement and tension, and generates emotional data as output.

[0582] Step 3:

[0583] The terminal transmits collected biological and emotional data to the server via wireless communication. The server receives this data and records it in a database. The input is a digital signal from the terminal, which is then processed and output by the server for storage in the database.

[0584] Step 4:

[0585] The server analyzes biological data using TensorFlow in the cloud and emotional data using the IBM Watson API. The input is data stored on the server, and data mining and machine learning algorithms are used to learn data patterns and produce analysis results as output.

[0586] Step 5:

[0587] The server generates an optimal health management plan for the user based on the analysis results. The input is the analysis results from machine learning, and the generating AI model uses this to construct appropriate health guidance content, resulting in an individually optimized health management plan as output.

[0588] Step 6:

[0589] The terminal notifies the user of the health management plan sent from the server. The input is plan information from the server, which the terminal receives and displays in a user-friendly format using natural language processing. The output is visual information and audio instructions for the user.

[0590] Step 7:

[0591] Users can ask follow-up questions through the chatbot function built into their device. The input is a question from the user, which is analyzed by an emotion engine, and an AI model is used to generate an appropriate answer, which is then presented to the user as output.

[0592] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0593] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0594] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0595] [Fourth Embodiment]

[0596] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0597] As shown in Figure 7, the 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.

[0598] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0599] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0600] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0601] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0602] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0603] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0604] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0605] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0606] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0607] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0608] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0609] This invention relates to a comprehensive management system for supporting individual health management. The system has the functionality to collect and analyze the user's biometric data and provide an individually optimized health management plan.

[0610] First, users wear a wearable device to collect daily biological data such as heart rate, blood pressure, and sleep patterns. The collected data is then transferred to a terminal using wireless communication such as Bluetooth.

[0611] Next, the device receives the biological data and transfers it to a cloud server. This ensures that the data is securely stored and can be used for later analysis.

[0612] The server analyzes data collected on the cloud and generates personalized health management plans using its proprietary algorithms. These plans include guidance on diet and exercise for maintaining and promoting health. For example, users at high risk of diabetes may be advised to follow a low-carbohydrate diet, while users who are sedentary may be suggested to incorporate exercise three times a week.

[0613] Real-time monitoring is also one of the important functions of this invention. The terminal continuously receives data from the wearable device and transmits it to the server. Based on this data, the server detects abnormal values ​​and immediately issues an alert to the user as needed. For example, if a higher-than-normal heart rate is recorded for an extended period, an alert is issued pointing to the possibility of stress or illness.

[0614] Furthermore, the server collaborates with medical institutions to import the results of users' regular health checkups and incorporate them into their health management plans. This enables more precise and timely health management. Users may also be recommended to consult a specialist if necessary.

[0615] Furthermore, user inquiries can be answered immediately through the system's built-in chatbot function. For example, in response to a question such as "What would you recommend for dinner tonight?", the system will provide a response in natural language, such as suggesting a "low-carbohydrate menu with plenty of vegetables" based on a plan generated by the server.

[0616] Thus, in this embodiment, it is possible to utilize users' biodata to provide healthcare tailored to individual needs and contribute to disease prevention and health promotion.

[0617] The following describes the processing flow.

[0618] Step 1:

[0619] The user puts on a wearable device and begins collecting biometric data. The device continuously measures information such as heart rate, blood pressure, and sleep patterns, and transmits this data to the terminal.

[0620] Step 2:

[0621] The device temporarily stores biological data received from wearable devices and uploads this data to a cloud server at regular intervals. A secure communication protocol is used for the upload.

[0622] Step 3:

[0623] The server receives the collected biological data and stores it in a database. After receiving the data, it uses an AI algorithm to analyze the data and evaluate each user's health status.

[0624] Step 4:

[0625] Based on the analysis results, the server generates a personalized health management plan. This plan includes suggestions for diet and exercise, and is customized to the user's health goals.

[0626] Step 5:

[0627] The generated health management plan is sent to the device and the user is notified. The user is encouraged to manage their daily health according to the plan.

[0628] Step 6:

[0629] The terminal continuously receives real-time data from wearable devices and transfers it to a server. This data is used for anomaly detection.

[0630] Step 7:

[0631] The server analyzes real-time data and immediately generates an alarm if an anomaly is detected. The alarm is sent to the user via their terminal, and necessary actions are recommended.

[0632] Step 8:

[0633] The server receives health checkup results from medical institutions and incorporates them into the health management plan. This ensures the plan is updated based on the latest health status.

[0634] Step 9:

[0635] The server responds to user questions in natural language via the device's chatbot function. Immediate feedback is provided to support the user's health.

[0636] (Example 1)

[0637] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0638] Current health management systems are insufficient in efficiently and comprehensively managing users' biometric information and providing individually optimized health management plans. Furthermore, real-time anomaly detection and alerting, as well as the integration of diagnostic data from medical institutions, are incomplete, making it difficult to properly maintain users' health status. Additionally, there is a lack of means to respond quickly and accurately to user inquiries.

[0639] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0640] In this invention, the server includes means for acquiring and managing the user's biometric information, means for automatically acquiring the user's biometric information from a wearable device, and means for providing question-answering services using a generative AI model. This enables real-time management of the user's health status, immediate detection and warning of abnormalities, and more precise health management through the provision of personalized health management plans and integration of diagnostic data with medical institutions. Furthermore, it reduces health-related anxiety by responding quickly to inquiries from the user.

[0641] "User biometric information" refers to data that indicates the user's physiological state, such as heart rate, blood pressure, and sleep patterns.

[0642] A "health management plan" is a plan that includes recommended nutrition and exercise guidance to maintain and promote the user's health, based on collected biological information.

[0643] "Anomaly detection" involves monitoring the user's biometric information and notifying the user of any values ​​that exceed the normal range.

[0644] An "alert" is an alert that is sent to the user when an anomaly is detected, warning the user of a potential health problem.

[0645] "Information from medical institutions" refers to the results of diagnoses and tests received by the user at medical institutions, and is data used to improve health management plans.

[0646] A "wearable device" is an electronic device that is worn on a user's body to continuously collect biological information.

[0647] A "generative AI model" is an artificial intelligence technology used to generate appropriate responses to natural language inquiries from users.

[0648] "Cloud-based computing" refers to a computing method that analyzes data on remote servers connected via the internet and centrally manages resources.

[0649] This invention provides a system that collects and manages users' biometric information in real time to support personal health management and provides each user with an optimized health management plan. The hardware used includes wearable devices, mobile terminals, and cloud servers.

[0650] The user wears a wearable device that measures heart rate, blood pressure, sleep patterns, etc. This device uses wireless technology such as Bluetooth to transmit data to a mobile device that is physically nearby.

[0651] The device temporarily stores the received data and transfers the information to the cloud server via an available network. This process is designed to ensure that the data is stored securely and quickly on the cloud server.

[0652] The server analyzes vast amounts of biological information collected on the cloud and generates individually optimized health management plans using its proprietary algorithms. The server focuses on providing personalized advice based on exercise and dietary data. For example, based on daily data, it might provide health guidance such as, "Today, reduce your carbohydrate intake and focus on a vegetable-based diet."

[0653] Furthermore, this invention features a user question answering function that utilizes a generative AI model. The server uses natural language processing to appropriately respond to user prompts. For example, if a user inputs "Please give me some advice on future weight management," it can return a highly effective response such as, "We recommend maintaining your current exercise level while reducing your total calorie intake by 15%."

[0654] In this way, this system effectively utilizes users' health data and proposes health promotion plans tailored to individual needs, thereby comprehensively supporting users' health.

[0655] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0656] Step 1:

[0657] The user wears a wearable device to detect biometric information such as heart rate, blood pressure, and sleep patterns. This device uses Bluetooth communication to transmit the collected data to a nearby terminal. (Input) User's biometric information. (Output) Data transfer to the terminal. The wearable device measures data at specific time intervals and sends it to the terminal in real time.

[0658] Step 2:

[0659] The terminal receives biological information transmitted from the wearable device and temporarily stores it in memory. The stored data is securely transferred to a cloud server via the internet. (Input) Biological information from the wearable device. (Output) Data transfer to the cloud server. The terminal sends the accumulated data in batch processing at regular intervals.

[0660] Step 3:

[0661] The server analyzes biometric data received on the cloud and generates a personalized health management plan using its own algorithms. This plan includes individualized advice on diet and exercise. (Input) User's biometric data stored in the cloud. (Output) Individually optimized health management plan. For example, the server might detect insufficient exercise from past data and generate advice such as, "We recommend exercising for 30 minutes three times a week."

[0662] Step 4:

[0663] The terminal notifies the user of the generated health management plan and continues real-time monitoring. If an abnormal value is detected, an alert is immediately sent to the user. (Input) Health management plan and real-time data generated by the server. (Output) Health management plan notification and alert. The terminal continuously monitors the data and immediately notifies the user of any abnormalities.

[0664] Step 5:

[0665] The server utilizes a generative AI model to generate appropriate responses to user inquiries. For example, if a user sends the prompt "Please give me advice on what to eat today," the server will respond with instructions such as "For today's meal, please choose low-carbohydrate foods that are mainly vegetables." (Input) User prompt. (Output) Response from the generative AI model. The generative AI performs natural language analysis to derive the optimal response.

[0666] (Application Example 1)

[0667] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0668] For the elderly, it is crucial to detect changes in their health status early and provide appropriate health management. However, it is difficult for individuals to operate a system that collects and analyzes biometric data on a daily basis and responds quickly to abnormalities. Furthermore, there is a problem in that maintaining health is difficult because exercise and dietary suggestions tailored to individual health conditions are not provided.

[0669] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0670] In this invention, the server includes means for collecting and managing the user's biometric data, means for generating an individually optimized health management policy, and means for immediately making an emergency contact if an abnormal value is detected in an elderly person. This makes it possible to continuously monitor the health status of elderly people and provide appropriate health management.

[0671] "User" refers to an individual who uses a health management system, and may include elderly people in particular.

[0672] "Biometric data" refers to information about the user's physical condition, such as heart rate, blood pressure, steps taken, and sleep patterns.

[0673] A "personally optimized health management policy" refers to a health management plan that is optimized for each individual, based on the user's biometric data, with the aim of maintaining and improving their health.

[0674] "Real-time monitoring data" refers to data that continuously collects users' biometric data and can be analyzed immediately.

[0675] "Means of providing an alert when an anomaly is detected" refers to a function that notifies the user to take notice when an abnormal value is detected in the user's biometric data.

[0676] "Exercise and dietary guidance" refers to advice that suggests appropriate exercise and dietary content based on the user's health condition.

[0677] "Means of incorporating information from medical institutions and adjusting health management policies" refers to a function that adjusts the health management plan as needed based on information such as the user's health checkup results obtained from medical institutions.

[0678] A "wearable device" refers to a device that a user wears to record biometric data, and may include heart rate monitors and fitness trackers.

[0679] The system implementing this invention is designed for the health management of users, including the elderly. Users wear wearable devices that record heart rate, blood pressure, steps, and sleep patterns. This data is transmitted to a terminal using Bluetooth. The terminal uploads this data to a cloud platform. Specifically, this system uses AWS Lambda as a cloud service to efficiently manage the data. The data is stored in AWS DynamoDB, and the server analyzes the data in real time.

[0680] The server uses a generative AI model based on data analyzed for each user to create an individually optimized health management plan. This generative AI model is implemented using OpenAI GPT-3 and has the ability to respond to user questions in an interactive format. For example, if a user asks, "What kind of light exercise is recommended when I don't get enough exercise?", the server will suggest appropriate exercises based on the data analysis results.

[0681] Furthermore, the server is designed to immediately provide users with alarms and notifications if it detects abnormal values. For example, if a user's heart rate exceeds the normal range, a notification such as "Your heart rate remains high. Please take deep breaths and calm down" will be sent. In addition, the system has a function to adjust health management policies based on the results of health checkups and other data.

[0682] Examples of prompts for the generating AI model include, "What light exercises would you recommend for a 70-year-old who doesn't get enough exercise?" and "What should I do if my heart rate is higher than normal?" By combining detailed data analysis with AI technology in this way, it is possible to provide specific and practical health management services tailored to individual users.

[0683] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0684] Step 1:

[0685] The user wears a wearable device to record heart rate, blood pressure, steps, and sleep patterns. This collects biometric data as input to the wearable device. The wearable device then transmits this data to the terminal via Bluetooth.

[0686] Step 2:

[0687] The device uploads the received biometric data to the cloud platform. Specifically, the device sends data to the cloud, where it is stored in AWS Lambda. During this process, the device converts the data format to JSON and standardizes the data labels. The output is then saved to a database in the cloud.

[0688] Step 3:

[0689] The server analyzes biometric data stored in the cloud in real time. It retrieves data from AWS DynamoDB and performs calculations to detect anomalies using statistical methods. If an anomaly is detected, it generates an alarm message and sends a notification to the user. The output includes a flag indicating an anomaly and an alarm message.

[0690] Step 4:

[0691] The server utilizes a generative AI model to generate personalized health management plans from analyzed data. Using OpenAI GPT-3, it takes data as prompts and outputs appropriate health management procedures. Based on the health management plan generated by the AI ​​model, it provides specific health guidance tailored to each individual user.

[0692] Step 5:

[0693] When a user inputs a question to the generative AI model through their device, the server processes the question and sends it to the model as a prompt. The generative AI model then generates an appropriate response and returns it to the device as output. This allows the user to obtain appropriate health management advice and information.

[0694] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0695] This invention proposes a system that combines the collection, management, and analysis of biological data with an emotion engine to comprehensively support the user's health. The emotion engine enables the integrated evaluation of the user's biological information and emotional state, providing a individually optimized health management plan.

[0696] First, users collect biometric data such as heart rate, blood pressure, and sleep patterns using a wearable device. This data is transmitted to a terminal via a dedicated application. Furthermore, users can periodically input their emotional state using the terminal, or recognize their emotions in real time using the terminal's camera and voice analysis functions.

[0697] The device transfers the collected biological and emotional data to a server in the cloud. The server receives the data at runtime and stores it in an up-to-date database.

[0698] The server uses AI algorithms and an emotion engine to analyze the user's health status in detail. The emotion engine assesses the user's stress level and well-being, and incorporates this into a health management plan. For example, if a high stress level is detected, relaxation activities and mental care suggestions are provided.

[0699] Based on the analysis results, the server generates a personalized health management plan for each user. This plan includes diet, exercise, and stress management and mental health improvement measures based on emotional state. The generated plan is then notified to the user in real time via their device.

[0700] Furthermore, user questions are answered quickly through the chatbot function built into the device. For example, in response to a question like, "I'm feeling down today, what should I do?", feedback based on the analysis results of the emotion engine is provided in natural language, such as, "Try some light exercise or meditation. Even a short walk can be effective in improving your mood."

[0701] The device also continuously monitors the user's biometric data and sends newly acquired data to the server, enabling timely updates to the plan. This process allows the system to provide dynamic health management and support tailored to the user's condition, improving overall health and well-being.

[0702] The following describes the processing flow.

[0703] Step 1:

[0704] Users wear wearable devices to collect biometric data such as heart rate, blood pressure, and sleep patterns. Furthermore, the smartphone's camera and microphone are used for emotion recognition, and an emotion engine analyzes facial expressions and voice tone in real time.

[0705] Step 2:

[0706] The device temporarily stores data collected from wearable devices and emotion recognition systems, and uploads it to a server in the cloud at regular intervals. A secure communication protocol is used for the upload.

[0707] Step 3:

[0708] The server receives biological and emotional data from the cloud and stores it in a database. The received data is analyzed using AI algorithms and an emotion engine to evaluate the user's health and emotional state.

[0709] Step 4:

[0710] Based on the analysis results, the server generates a personalized health management plan. This plan includes suggestions for diet, exercise, stress management, and mental care. For example, if emotional data indicates high stress levels, yoga or meditation practice might be recommended.

[0711] Step 5:

[0712] The generated health management plan is sent to the device and notified to the user. The user can then use this plan as a guide to manage their daily activities and health.

[0713] Step 6:

[0714] The terminal continuously receives data from wearable devices and emotion recognition systems and transfers it to the server. This data is used for real-time anomaly detection and continuous plan updates.

[0715] Step 7:

[0716] The server analyzes new data and immediately generates an alert if it detects any anomalies related to emotions. For example, if a high level of stress is detected, it sends a notification to the user via their device recommending relaxation or urging them to consult a medical professional.

[0717] Step 8:

[0718] The server uses natural language processing to answer user questions via the device's chatbot function. Depending on the content of the question, it provides accurate feedback based on the user's emotional state.

[0719] (Example 2)

[0720] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0721] In modern society, personal health management is crucial, but conventional health management systems are generally based on limited data, making it difficult to provide individually optimized plans. Furthermore, the lack of a function to comprehensively evaluate emotional states and biological information makes it difficult to properly manage health risks caused by stress and emotional fluctuations. In addition, real-time monitoring and feedback are essential, but many systems fail to provide this.

[0722] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0723] In this invention, the server includes means for analyzing biological and emotional information to generate individually optimized health maintenance plans, means for receiving and analyzing real-time monitored information and providing warnings when anomalies are detected, and means for analyzing emotional information and making suggestions based on emotional state. As a result, users can receive individually optimized health management, comprehensively evaluate their emotional and biological health states, and manage their health risks in real time.

[0724] "Biological information" refers to data obtained from the human body that indicates health conditions, such as heart rate, blood pressure, and sleep patterns.

[0725] "Management" is the process of organizing, storing, and utilizing collected data as needed.

[0726] "Emotional information" refers to data that indicates the user's emotional state, and is acquired through camera and voice analysis.

[0727] "Analysis" is the process of examining collected data, giving it meaning, and deriving individualized health maintenance plans.

[0728] A "health maintenance plan" is a plan consisting of guidelines and suggestions for diet, exercise, and stress management that are optimized for each individual user.

[0729] "Real-time monitoring" is a process of continuously and immediately checking and analyzing data from users.

[0730] A "warning" is information that notifies the user and urges them to pay attention when an abnormal situation is detected.

[0731] A "suggestion" is a set of specific action guidelines based on the analysis results, outlining how users should manage their health.

[0732] This invention provides a system that collects and analyzes biometric and emotional information by coordinating a wearable device, a terminal, and a server to support user health management. The wearable device used is assumed to be a general-purpose smart device capable of measuring heart rate, blood pressure, and sleep patterns. The terminal is a smartphone or tablet, with a dedicated application installed on these devices.

[0733] The user first uses a wearable device to acquire biometric information. This data is transmitted to the terminal via Bluetooth or Wi-Fi. The user can then use the terminal to input their emotional information into a dedicated application or to record their emotions in real time using the terminal's camera and microphone.

[0734] The device transfers biometric and emotional information to a server in the cloud. Upon receiving the data, the server analyzes it using an AI algorithm and an emotion engine. The AI ​​algorithm provides a detailed assessment of the user's health status, while the emotion engine analyzes changes in emotions, and these findings are then incorporated into a health maintenance plan.

[0735] Based on the analysis results, the server generates a health maintenance plan optimized for the user and notifies the user of its contents in real time via their device. The user can adjust their diet and exercise according to the plan displayed on their device. In addition, if an abnormality is detected, the server will issue a warning to the user and prompt further action.

[0736] Furthermore, users can use the chatbot function built into the device to ask questions about their concerns and health. The device utilizes a generative AI model to generate conversational responses in natural language. For example, if a user asks, "I'm feeling down today, what should I do?", the system will provide specific advice such as, "I recommend light exercise or a walk in nature."

[0737] Examples of prompt messages include, "A high stress level has been detected. Please suggest relaxation activities," and "The user is reporting emotional fatigue. What advice should you provide?" In this way, the system provides integrated support for the user's health and emotions, enabling real-time health management.

[0738] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0739] Step 1:

[0740] The user uses a wearable device to acquire biometric information such as heart rate, blood pressure, and sleep patterns. The input is data acquired from the wearable device, which is then transmitted to the terminal via Bluetooth or Wi-Fi. The terminal then stores this biometric information in a dedicated application.

[0741] Step 2:

[0742] The user inputs their emotional information using an application on their device. This input can be text or voice information about the user's current feelings and stress level. The device uses its camera and microphone to perform voice analysis and facial recognition, recognizing and storing this information as emotional data.

[0743] Step 3:

[0744] The device transfers collected biological and emotional information to a server in the cloud. The input consists of this information stored on the device. The server stores the received data in a database. The transfer uses secure communication protocols to ensure data protection.

[0745] Step 4:

[0746] The server analyzes data using AI algorithms and an emotion engine. The input consists of biological and emotional information stored in a database. By analyzing this data, the server quantifies the user's health status and identifies stress levels and happiness levels.

[0747] Step 5:

[0748] The server generates a personalized health maintenance plan based on the analysis results. The output includes a plan that includes suggestions for diet and exercise, as well as methods for stress management. The server then uses a generated AI model to formulate the plan and sends it to the user's device.

[0749] Step 6:

[0750] The device notifies the user of the health maintenance plan received from the server. The output is the plan details displayed on the device's screen. The user can review the notification and obtain specific action guidelines through the application.

[0751] Step 7:

[0752] Based on real-time monitored information, the server sends a warning to the user via the terminal if an anomaly is detected. The input consists of new biological information and the results of AI analysis. This triggers an action to alert the user and instruct them to take the necessary action.

[0753] Step 8:

[0754] When a user asks a question, the device's chatbot function uses a generated AI model to produce a response. The input is the user's question, and the output is an appropriate answer in natural language. This allows the device to provide quick advice.

[0755] (Application Example 2)

[0756] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0757] To provide personalized health management and improve users' quality of life, it is necessary to collect and comprehensively analyze both biological and emotional data. However, conventional systems are limited to the collection and analysis of biological data, making it difficult to provide individually optimized health management plans that take into account the user's emotional state. Furthermore, there is a need to realize health support that utilizes devices that users use on a daily basis.

[0758] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0759] In this invention, the server includes means for collecting and managing the user's biological and emotional data, means for generating an individually optimized health management plan through analysis using an emotion engine, and means equipped with a device that functions as a health support device. This enables appropriate health management and guidance based on the user's physiological and emotional state.

[0760] A "user" refers to an individual who uses a specific health management system.

[0761] "Biological data" refers to measurements that indicate an individual's health status, such as heart rate, blood pressure, and sleep patterns.

[0762] "Emotional data" refers to information about an individual's emotional state obtained through voice and facial expression analysis.

[0763] An "emotion engine" refers to software that analyzes emotional data and evaluates the user's emotional state.

[0764] A "personalized health management plan" refers to a plan that develops guidance tailored to each individual user's health maintenance and improvement goals, based on their biological and emotional data.

[0765] A "health support device" refers to a device used at home to assist users in managing their health.

[0766] In the system for implementing this invention, the user first uses a health support device equipped with sensors for acquiring biological data. The sensors measure and collect data on heart rate, blood pressure, and sleep patterns in real time. In parallel, the device has a built-in camera and microphone for collecting emotional data, which is obtained from facial expressions and voice. Based on this, the system comprehensively assesses the user's health status.

[0767] The device temporarily stores this data locally and transfers it to the server via wireless communication. The server then runs AI algorithms and an emotion engine to further analyze this data in a cloud environment. This process uses machine learning libraries such as TensorFlow to analyze the data, and emotional states are evaluated using the IBM Watson API. Based on the analysis results, an optimal health management plan is generated for the user, and necessary guidance is provided to the user via the device.

[0768] As a concrete example, if data analysis reveals that a user has recently been experiencing stress, the server will provide relaxation methods and meditation recommendations through the health support device. Furthermore, it will utilize a generative AI model to provide the user with appropriate feedback in natural language, tailored to their emotional state. For example, it might use a prompt such as, "Based on the user's emotional state and biometric data today, please begin the analysis to provide the next optimal health support suggestion." This prompt serves as a guide for the system in determining the best next steps for the user.

[0769] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0770] Step 1:

[0771] The device continuously collects biometric data such as the user's heart rate, blood pressure, and sleep patterns through a health support device. This data is stored locally using Bluetooth. The input is analog data from sensors. This data is converted to digital, and any invalid data is filtered out before output.

[0772] Step 2:

[0773] The user collects emotional data using the device's camera and microphone. Specifically, facial recognition and voice analysis are used to detect the user's emotional state in real time. The input consists of camera images and voice data, which the emotion engine uses to analyze emotional attributes such as amusement and tension, and generates emotional data as output.

[0774] Step 3:

[0775] The terminal transmits collected biological and emotional data to the server via wireless communication. The server receives this data and records it in a database. The input is a digital signal from the terminal, which is then processed and output by the server for storage in the database.

[0776] Step 4:

[0777] The server analyzes biological data using TensorFlow in the cloud and emotional data using the IBM Watson API. The input is data stored on the server, and data mining and machine learning algorithms are used to learn data patterns and produce analysis results as output.

[0778] Step 5:

[0779] The server generates an optimal health management plan for the user based on the analysis results. The input is the analysis results from machine learning, and the generating AI model uses this to construct appropriate health guidance content, resulting in an individually optimized health management plan as output.

[0780] Step 6:

[0781] The terminal notifies the user of the health management plan sent from the server. The input is plan information from the server, which the terminal receives and displays in a user-friendly format using natural language processing. The output is visual information and audio instructions for the user.

[0782] Step 7:

[0783] Users can ask follow-up questions through the chatbot function built into their device. The input is a question from the user, which is analyzed by an emotion engine, and an AI model is used to generate an appropriate answer, which is then presented to the user as output.

[0784] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0785] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0786] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0787] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0788] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0789] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0790] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0791] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0792] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0793] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0794] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0795] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0796] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0797] 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.

[0798] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0799] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0800] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0801] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0802] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0803] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0804] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0805] The following is further disclosed regarding the embodiments described above.

[0806] (Claim 1)

[0807] Means for collecting and managing users' biometric data,

[0808] A means of analyzing biological data to generate an individually optimized health management plan,

[0809] A means for receiving and analyzing real-time monitoring data and providing an alarm when an anomaly is detected,

[0810] Means of providing guidance on diet and exercise based on a health management plan,

[0811] A means of incorporating information from medical institutions and adjusting health management plans,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, comprising means for generating interactive responses to user questions.

[0815] (Claim 3)

[0816] The system according to claim 1, comprising means for automatically acquiring the user's biometric data from a wearable device.

[0817] "Example 1"

[0818] (Claim 1)

[0819] Means for acquiring and managing users' biometric information,

[0820] A means of analyzing biological information to generate an individually optimized health management plan,

[0821] A means for receiving and analyzing real-time monitoring information and providing an alarm when an anomaly is detected,

[0822] Means of providing nutrition and exercise guidance based on a health management plan,

[0823] A means of incorporating information from medical institutions and adjusting health management plans,

[0824] A means of automatically acquiring the user's biometric information from a wearable device,

[0825] A means of securely transferring user information to the cloud using wireless communication,

[0826] A means of analyzing user-specific health data through computation on a cloud platform,

[0827] A system including means for providing question answering using a generative AI model.

[0828] (Claim 2)

[0829] The system according to claim 1, comprising means for generating interactive responses to user questions.

[0830] (Claim 3)

[0831] The system according to claim 1, comprising means for integrating diagnostic data from medical institutions linked with users.

[0832] "Application Example 1"

[0833] (Claim 1)

[0834] Means for collecting and managing users' biometric data,

[0835] A means of analyzing biometric data to generate an individually optimized health management plan,

[0836] A means for receiving and analyzing real-time monitoring data and providing an alarm when an anomaly is detected,

[0837] Means of providing guidance on diet and exercise based on health management policies,

[0838] A means of incorporating information from medical institutions and adjusting health management policies,

[0839] A means of suggesting exercise and diet based on the health condition of elderly people,

[0840] A means of immediately making an emergency contact if an abnormal value is detected,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, comprising means for generating interactive responses to user questions.

[0844] (Claim 3)

[0845] The system according to claim 1, comprising means for automatically acquiring the user's biometric data from a wearable device.

[0846] "Example 2 of combining an emotion engine"

[0847] (Claim 1)

[0848] Means for acquiring and managing biological information,

[0849] A means for analyzing biological and emotional information to generate individually optimized health maintenance plans,

[0850] A means of receiving and analyzing real-time monitored information and providing warnings when anomalies are detected,

[0851] Means of providing guidance on diet and physical activity based on a health maintenance plan,

[0852] A means of integrating information from medical institutions and adjusting health maintenance plans,

[0853] A means of analyzing emotional information and making suggestions based on emotional states,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, having a function to generate responses to user questions.

[0857] (Claim 3)

[0858] The system according to claim 1, comprising means for automatically acquiring biological information from a wearable device.

[0859] "Application example 2 when combining with an emotional engine"

[0860] (Claim 1)

[0861] Means for collecting and managing users' biometric and emotional data,

[0862] A method for generating personalized health management plans by analyzing biological and emotional data,

[0863] A means of receiving and analyzing data in real time and providing an alarm when an anomaly is detected,

[0864] A means of providing lifestyle guidance based on a health management plan,

[0865] A means of incorporating information from various sources and adjusting health management plans,

[0866] A means equipped with a device that functions as a health support device,

[0867] A system that includes this.

[0868] (Claim 2)

[0869] The system according to claim 1, comprising means for generating interactive responses using an emotion engine in response to user questions.

[0870] (Claim 3)

[0871] The system according to claim 1, comprising means for automatically acquiring the user's biological data from a detection device. [Explanation of Symbols]

[0872] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for collecting and managing users' biometric data, A means of analyzing biological data to generate an individually optimized health management plan, A means for receiving and analyzing real-time monitoring data and providing an alarm when an anomaly is detected, Means of providing guidance on diet and exercise based on a health management plan, A means of incorporating information from medical institutions and adjusting health management plans, A system that includes this.

2. The system according to claim 1, comprising means for generating interactive responses to user questions.

3. The system according to claim 1, comprising means for automatically acquiring the user's biological data from a wearable device.

Images