system

Abstract

We provide the system. [Solution] A means for collecting the user's biometric data, A means for transmitting the biometric data to a cloud server, The aforementioned cloud server analyzes the biometric data and generates personalized health advice, A means of providing the generated health advice to the user, A means of answering health-related questions through interaction with an avatar, A means of sharing a summary of vital information with a physician based on the user's selection, A system that includes this.

Description

Technical Field

[0005] , ,

[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, the method including: 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 that responds 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] When modern people conduct health management, they often face busy lives, time constraints, lack of appropriate health information and knowledge, and furthermore, stress and mental health problems. In such a situation, it is difficult to receive individual health advice and support, and effective health management is difficult to achieve. In addition, although there are a large number of health management applications, it is difficult to integrally manage various data because each application handles data independently. Furthermore, when a user shares vital information with a doctor, it is difficult to organize and summarize the information. There is a need to solve these problems.

Means for Solving the Problems

[0005] This invention provides a next-generation health support system that assists in health management. The system includes means for collecting the user's biometric data (heart rate, steps, sleep duration, etc.) and means for transmitting said biometric data to a cloud server. The cloud server also includes means for analyzing said biometric data and generating personalized health advice. Furthermore, it includes means for providing the generated health advice to the user and means for answering health-related questions through interaction with an avatar. This allows the user to obtain health information in real time. In addition, it includes means for receiving data from other health management applications and managing it integrally, thereby achieving data centralization. Furthermore, it includes means for sharing a summary of vital information with a doctor based on the user's selection, facilitating information sharing between the user and the doctor. This enables comprehensive and effective health management and can solve the various challenges in health management faced by modern people.

[0006] A "user" refers to an individual who uses this system to manage their own health data and receive advice.

[0007] "Biometric data" refers to data obtained from the user's body, specifically including heart rate, steps taken, and sleep duration.

[0008] A "cloud server" is a server located remotely via the internet, used to receive, store, and analyze transmitted biometric data.

[0009] "Health advice" refers to specific instructions and recommendations regarding lifestyle habits and health management, generated based on the user's biometric data.

[0010] An "avatar" refers to a virtual character that interacts with the user within the system and provides information and advice.

[0011] A "health management application" is software used to collect, manage, and analyze users' health data, and includes other applications that work in conjunction with this system.

[0012] "Vital information" refers to important data related to the user's life support, such as heart rate, blood pressure, and body temperature.

[0013] "Relaxation advice" refers to suggestions for specific methods and activities to reduce user stress and improve mental health.

[0014] A "machine learning model" refers to a computer algorithm used to perform analysis and predictions based on collected data.

[0015] A "Natural Language Processing (NLP) engine" refers to a technology that analyzes text and audio data entered by a user and understands its meaning. [Brief explanation of the drawing]

[0016] [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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0017] 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.

[0018] First, the terms used in the following description will be described.

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of 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), and the like.

[0020] 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.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention is a system for supporting users' health management and is configured as follows: The user uses a dedicated smartwatch and a smartphone application. The smartwatch collects biometric data such as heart rate, steps taken, and sleep duration in real time. The collected data is transmitted to a cloud server via the smartphone application. The cloud server analyzes the biometric data and generates personalized health advice.

[0038] Collection and transmission of health data

[0039] Device (Smartwatch): The smartwatch collects biometric data such as the user's heart rate, steps taken, and sleep duration in real time. This data is transmitted to a smartphone application via Bluetooth or other means.

[0040] Device (smartphone): The smartphone application receives data sent from the smartwatch and transmits it to a cloud server via the internet.

[0041] Data analysis and generation of personalized advice

[0042] Server: The cloud server stores the received biometric data in a secure database. Next, machine learning models and algorithms are used to analyze the data in detail and assess the user's health status. Based on this assessment, personalized health advice and meal plans are generated.

[0043] Interacting with an avatar

[0044] Device (Smartphone): The avatar within the smartphone application can answer health-related questions through interaction with the user. When the user enters a question into the application, the content is sent to the server.

[0045] Server: The server uses a natural language processing (NLP) engine to analyze the user's question and generate an appropriate answer. The generated answer is then sent to the terminal.

[0046] Device (smartphone): An avatar within the application provides answers to the user. Through this interaction, the user can obtain useful information about health.

[0047] Integration with other health management apps

[0048] Server: The cloud server can receive data from other health management applications and integrate it into the existing database for management. This integration enables centralized management of users' health data.

[0049] Stress management and mental health support

[0050] Devices (smartwatch and smartphone): The smartwatch continuously monitors the user's heart rate variability and estimates their stress level. The smartphone application sends this data to a cloud server.

[0051] Server: If a high stress level is detected, the cloud server generates specific advice to support relaxation and mental health. Based on this, the user can take appropriate action.

[0052] Sharing information with doctors

[0053] User: If a user wants to share vital information with a doctor, they make that request through the smartphone application.

[0054] Server: The cloud server uses generative AI to summarize the collected vital information and generate a report. The generated report is then sent to the physician after user review. This process enables efficient information sharing between the user and the physician.

[0055] Specific example

[0056] Example 1: Nighttime health monitoring

[0057] Device (Smartwatch): At night, the smartwatch collects the user's sleep data. This includes detailed data such as sleep depth and sleep cycles.

[0058] Device (smartphone): In the morning, data from the smartwatch is sent to the smartphone. The data is then sent to the cloud server.

[0059] Server: The cloud server analyzes this data and generates advice such as, "According to your recent sleep data, you tend to be getting insufficient deep sleep. We recommend that you establish a routine of going to bed at a specific time to ensure you get quality sleep."

[0060] Device (smartphone): The avatar provides advice to the user and suggests specific ways to improve.

[0061] Example 2: Stress Management

[0062] Device (smartwatch): During the day, the smartwatch monitors the user's heart rate variability and detects high-stress states.

[0063] Server: The cloud server evaluates stress levels based on accumulated data and generates advice such as, "Recent data has detected a high stress level. We recommend taking some time to relax by taking deep breaths or meditating."

[0064] Device (smartphone): An avatar provides the user with stress-relief advice and explains in detail how to put it into practice.

[0065] As described above, the present invention is a system that supports users' health management and provides users with valuable information through data analysis and the generation of personalized advice.

[0066] The following describes the processing flow.

[0067] Step 1:

[0068] Device (smartwatch): Collects biometric data such as heart rate, steps taken, and sleep duration from the user in real time. Sensors within the smartwatch periodically measure and temporarily store this data.

[0069] Step 2:

[0070] The device (smartphone) communicates with the smartwatch via Bluetooth and periodically transfers the collected biometric data to the smartphone. The smartphone checks the received data and verifies its integrity.

[0071] Step 3:

[0072] Device (smartphone): Smartphones transmit data to cloud servers via the internet. During this process, protocols are used to encrypt the data and maintain security.

[0073] Step 4:

[0074] Server: The cloud server stores the received biometric data in a secure database. Before saving, it verifies the data format and performs data cleaning, correcting for missing or outlier values.

[0075] Step 5:

[0076] Server: The server processes the stored data using an analysis algorithm to assess the user's health status. For example, it might use a machine learning model to estimate stress levels from the user's heart rate variability.

[0077] Step 6:

[0078] Server: Generates personalized health advice and meal plans based on the results of machine learning models and data analysis. This process takes into account the user's past data and current health status.

[0079] Step 7:

[0080] Server: Formats the data and sends it to the smartphone application to provide the generated advice and plans to the user.

[0081] Step 8:

[0082] Device (Smartphone): The smartphone application displays received advice and plans to the user. An avatar provides advice via text or voice as needed.

[0083] Step 9:

[0084] User: Begin a conversation with the avatar and enter health-related questions. For example, ask, "How has your sleep been lately?"

[0085] Step 10:

[0086] Device (smartphone): Retrieves user questions as text data and sends it to the cloud server.

[0087] Step 11:

[0088] Server: Uses a natural language processing (NLP) engine to analyze user questions and understand their intent. Refers to stored data and past advice logs to generate appropriate answers.

[0089] Step 12:

[0090] Server: Sends the generated answer based on the question to the smartphone application.

[0091] Step 13:

[0092] Device (smartphone): The avatar provides answers to the user and completes the conversation. This allows the user to obtain the necessary information in real time.

[0093] Step 14:

[0094] Server: Handles integration to receive data from other health management applications. Uses APIs to retrieve additional data and manage it in an integrated manner.

[0095] Step 15:

[0096] Server: Integrates data from other applications received and performs a more detailed analysis of the user's overall health status. Based on the results, it generates further advice.

[0097] Step 16:

[0098] Devices (smartwatch and smartphone): The smartwatch continuously monitors the user's heart rate variability and sends stress level data to the smartphone. The smartphone then sends this data to a cloud server.

[0099] Step 17:

[0100] Server: If a high-stress state is detected, the cloud server generates and provides relaxation and mental health advice to the user.

[0101] Step 18:

[0102] User: The app requests the sharing of vital information with a doctor as needed. The smartphone application sends this request to the cloud server.

[0103] Step 19:

[0104] Server: Uses generative AI to summarize the user's vital information and generate a report. After user confirmation, the report is sent to the doctor.

[0105] (Example 1)

[0106] 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."

[0107] The present invention aims to provide a system that efficiently and effectively supports users' health management. Existing health management systems lack sufficient collection and analysis of biometric data, as well as adequate feedback to users, resulting in insufficient personalized health advice. Furthermore, they lack support for stress management and mental health care, and there is no easy way for users to share vital information with healthcare professionals.

[0108] 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.

[0109] In this invention, the server includes means for analyzing the user's biometric data and generating personalized health advice, means for answering health-related questions through interaction with an avatar, and means for sharing a summary of vital information with healthcare professionals. This allows the user to understand their health status in real time and receive necessary health advice. Furthermore, comprehensive health management is achieved through stress level monitoring and the provision of mental health care advice. In addition, the means for sharing information with healthcare professionals enables prompt and appropriate medical responses.

[0110] "Biometric data" refers to data that indicates a user's physical functions and state, such as heart rate, steps taken, and sleep duration.

[0111] A "mobile terminal" is a portable communication device with data communication capabilities, and specifically includes smartphones and tablet devices.

[0112] A "cloud server" refers to a remote server that stores and processes data via the internet.

[0113] "Health advice" refers to specific instructions or suggestions for improving or maintaining a user's health.

[0114] An "avatar" is a digital representation that acts on behalf of a user on a computer and has interactive functions such as conversation.

[0115] "Healthcare professionals" refer to individuals with specialized knowledge and qualifications related to medicine and health, such as doctors and nurses.

[0116] "Health management software" refers to application software used to monitor and manage a user's health status.

[0117] "Stress level" is an indicator that shows the degree of stress a user experiences.

[0118] "Mental health care" refers to care methods and activities aimed at maintaining or improving a user's mental health.

[0119] "Vital information" refers to data on the body's basic physiological functions, such as heart rate, body temperature, and respiratory rate.

[0120] This invention is a system for supporting user health management and is implemented using the following hardware and software.

[0121] Hardware and software to be used

[0122] terminal

[0123] Smartwatch: A device for collecting biometric data such as heart rate, steps taken, and sleep duration. Built-in sensors include heart rate sensors and accelerometers.

[0124] Smartphone: A mobile device used to send data collected from a smartwatch to a cloud server, and a platform for running applications.

[0125] server

[0126] Cloud servers are remote servers used for storing and analyzing data over the internet. Key software includes databases (e.g., AWS RDS, Google Cloud Firestore), machine learning models (e.g., TensorFlow, PyTorch), and natural language processing engines (e.g., BERT, GPT-3).

[0127] Data collection and transmission

[0128] Device (smartwatch)

[0129] Smartwatches collect biometric data such as the user's heart rate, steps taken, and sleep duration in real time. This data is transmitted to a smartphone using Bluetooth technology.

[0130] Device (smartphone)

[0131] The smartphone receives data from the smartwatch via Bluetooth and sends it to a cloud server over the internet. A dedicated application is installed on the smartphone, and this app manages the receiving and sending of data.

[0132] Data storage and analysis

[0133] server

[0134] The cloud server receives biometric data transmitted from smartphones via the internet and stores it in a secure database. The stored data is analyzed using machine learning models (e.g., TensorFlow, PyTorch). Based on the analysis results, the user's health status is evaluated and personalized health advice is generated.

[0135] Interacting with an avatar

[0136] Device (smartphone)

[0137] The smartphone application features an avatar that answers health-related questions through interaction with the user. When the user enters a question into the application, the content is sent to the server.

[0138] server

[0139] The server uses a natural language processing engine (e.g., BERT, GPT-3) to analyze the user's question and generate an appropriate answer. The generated answer is then sent back to the smartphone.

[0140] Device (smartphone)

[0141] An avatar within a smartphone application provides the user with generated responses. This interactive feature allows users to easily obtain useful health-related information.

[0142] Integration with other health management software

[0143] server

[0144] The cloud server receives data from other health management software (e.g., Google Fit, Apple HealthKit) and integrates it into the existing database for management. This integration allows for centralized management of users' health data.

[0145] Stress management and mental health support

[0146] Devices (smartwatches and smartphones)

[0147] The smartwatch monitors the user's heart rate variability and sends the data to the smartphone. The smartphone then sends this data to a cloud server.

[0148] server

[0149] The cloud server assesses stress levels and generates specific advice to support relaxation and mental health. This generated advice is then sent to the user's smartphone.

[0150] Sharing information with healthcare professionals

[0151] User

[0152] If a user wants to share vital information with a healthcare professional, they can make that request through a smartphone application.

[0153] server

[0154] The cloud server uses a generative AI model (e.g., GPT-4) to summarize the collected vital information and generate a report. This report is then reviewed by the user and sent to healthcare professionals.

[0155] Specific example

[0156] Collection of sleep data and generation of advice

[0157] (Specific example)

[0158] At night, the smartwatch collects the user's sleep data. It records cycles of deep and light sleep and generates detailed data. In the morning, the data is sent to the smartphone and then transferred to a cloud server. The cloud server analyzes the data and generates advice such as, "According to your recent sleep data, you are not getting enough deep sleep. We recommend that you establish a habit of going to bed at a specific time." An avatar on the smartphone then conveys this advice to the user.

[0159] As illustrated by the above specific example, the present invention is a system that supports users' health management and provides useful information to users by offering advanced data analysis and personalized health advice.

[0160] Stress management practices

[0161] (Example of a prompt message)

[0162] "I've been under a lot of stress lately. Do you have any recommendations for coping strategies?"

[0163] When a user enters a question using such a prompt, the server analyzes it, generates appropriate advice, and provides it to the user.

[0164] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0165] Step 1: Data Collection

[0166] Device (smartwatch)

[0167] Smartwatches collect biometric data such as the user's heart rate, steps taken, and sleep duration in real time. Heart rate is recorded every second, and steps are tallied every hour. Sleep data is recorded every 30 minutes during the night, showing the state of deep sleep and light sleep.

[0168] Input: User's real-time biometric data

[0169] Output: Biometric data stored in the smartwatch

[0170] Specifically, the smartwatch's heart rate sensor detects the heart rate, and the accelerometer measures the number of steps. This data is temporarily stored in the built-in memory.

[0171] Step 2: Send

[0172] Device (smartwatch)

[0173] Smartwatches transmit collected biometric data to smartphones using Bluetooth connectivity.

[0174] Input: Biometric data stored in the smartwatch

[0175] Output: Biometric data transferred to a smartphone

[0176] Specifically, the smartwatch attempts to transmit data every minute, transferring the data to the smartphone in batches via Bluetooth.

[0177] Step 3: Send data to the cloud

[0178] Device (smartphone)

[0179] Smartphones receive data via Bluetooth and send it to a cloud server over the internet. A dedicated application is installed on the smartphone, which manages the receiving and sending of data.

[0180] Input: Biometric data received from a smartwatch

[0181] Output: Biometric data sent to the cloud server

[0182] Specifically, the smartphone app runs in the background, and once an internet connection is established, it uploads the biometric data to a cloud server.

[0183] Step 4: Save Data

[0184] server

[0185] The cloud server receives data sent from smartphones via the internet and stores it in a secure database. Data integrity checks and encryption are performed before storage.

[0186] Input: Biometric data sent from a smartphone

[0187] Output: Biometric data stored in a secure database

[0188] Specifically, when biometric data arrives at the cloud server, its integrity is checked and it is encrypted (e.g., AES-256) before being inserted into the database.

[0189] Step 5: Data Analysis

[0190] server

[0191] The cloud server analyzes data using machine learning models (e.g., TensorFlow, PyTorch) to assess the user's health status. This analysis includes data preprocessing, model input, and retrieval of evaluation results.

[0192] Input: Biometric data stored in the database

[0193] Output: Evaluation results regarding the user's health status

[0194] Specifically, the process involves pre-processing of biometric data to remove noise and normalize it before it is input to a machine learning model. The model then outputs an evaluation score of the user's health status and any abnormal trends as analysis results.

[0195] Step 6: Generate Advice

[0196] server

[0197] The server generates personalized health advice for users based on the evaluation results of machine learning models. This process utilizes a natural language generation engine (e.g., GPT-4).

[0198] Input: Health status assessment results

[0199] Output: Personalized health advice

[0200] Specifically, the evaluation results are templated, and a generative AI model is used to generate health advice in natural language. For example, advice such as, "According to recent data, you are not getting enough deep sleep. We recommend that you establish a habit of going to bed at a specific time," might be generated.

[0201] Step 7: Avatar-based interaction

[0202] Device (smartphone)

[0203] When a user enters a health-related question for their avatar, the information is sent to the server.

[0204] Input: Question entered by the user

[0205] Output: Question data sent to the server

[0206] Specifically, the smartphone application sends user input to the server in text format.

[0207] server

[0208] The server analyzes the question using a natural language processing engine (e.g., BERT, GPT-3) and generates an appropriate answer. The generated answer is then sent back to the smartphone.

[0209] Input: Question data submitted by the user

[0210] Output: Response to the user

[0211] Specifically, the natural language processing engine analyzes the content of the question, the sentence generation engine creates the optimal answer, and sends it back to the smartphone in text format.

[0212] Device (smartphone)

[0213] An avatar within the application provides the user with the generated response. The response can also be read aloud using a speech synthesis engine.

[0214] Input: Response data sent from the server

[0215] Output: Answers provided by the user

[0216] Specifically, the avatar displays text on the screen and, when necessary, outputs speech using a speech synthesis engine.

[0217] Step 8: Sharing information with healthcare professionals

[0218] User

[0219] If a user wants to share vital information with a healthcare professional, they can make a request through the smartphone application.

[0220] Input: Request for information sharing

[0221] Output: Sending a sharing request to the server

[0222] Specifically, the user instructs the app to share vital information, and a request is sent to the server.

[0223] server

[0224] The cloud server uses a generative AI model (e.g., GPT-4) to summarize vital information and generate a report. This report is then sent to healthcare professionals after user review.

[0225] Input: Vital signs and information sharing request

[0226] Output: Summary report sent to healthcare professionals

[0227] Specifically, the system analyzes and summarizes the user's vital signs and generates a report in PDF format. After user confirmation, the report is sent to the healthcare professional's email address.

[0228] The above outlines the specific processing steps of this system's program. By detailing what processing and calculations are performed on the input data at each step and what the final output is, the overall operation of the system becomes clear.

[0229] (Application Example 1)

[0230] 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."

[0231] Effectively monitoring the health and stress levels of factory workers is crucial for improving productivity and ensuring safety. However, current systems have limitations in collecting and analyzing individual workers' health data in real time and providing appropriate advice and improvement measures. Therefore, a new system is needed to efficiently and effectively manage worker health.

[0232] 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.

[0233] In this invention, the server includes means for collecting heart rate and movement data of workers in a factory and monitoring the work environment; means for analyzing the collected data and generating health advice and safety improvement measures based on the work environment; and means for providing the generated advice and improvement measures via in-factory displays and voice guidance systems. This makes it possible to monitor the health status of workers in the factory in real time and provide appropriate advice and improvement measures.

[0234] "Biometric data" refers to data that indicates the user's physical condition, such as heart rate, movement data, and posture.

[0235] A "cloud server" is a remote server used to store, analyze, and manage data via the internet.

[0236] "Health advice" refers to specific suggestions and instructions generated based on analyzed biometric data to support the user's health management.

[0237] An "avatar" is a virtual character or agent used to interact with users.

[0238] "Vital information" refers to key biometric data that indicates vital functions, such as the user's heart rate, body temperature, and blood pressure.

[0239] A "factory" is a facility where manufacturing and production activities take place.

[0240] A "worker" is a person who engages in production activities within a factory.

[0241] "Motion data" refers to data that shows the physical movements and activity levels of a worker.

[0242] "Safety improvement measures" refer to specific proposals and measures to ensure worker safety and improve the working environment.

[0243] A "display" is a device used to visually display information.

[0244] A "voice guidance system" is a system that provides information and instructions using voice.

[0245] The present invention is a system for supporting the health management of workers in a factory, and is configured as follows: The system includes a factory robot, a data collection server, a cloud server, and a display or voice guidance system.

[0246] Collection and transmission of health data

[0247] Terminal (factory robot):

[0248] Factory robots are equipped with sensors and cameras to collect worker heart rate and movement data. This data is collected in real time. The factory robots transmit the collected data to a data collection server. Wireless communication technologies such as Bluetooth are used for this purpose.

[0249] Terminal (data collection server):

[0250] The data collection server receives data transmitted from factory robots and sends it to a cloud server via the internet. This ensures that the data is analyzed in a secure environment.

[0251] Data analysis and generation of personalized advice

[0252] Server (Cloud Server):

[0253] The cloud server securely stores the received biometric data and analyzes it in detail using machine learning models (e.g., TensorFlow or PyTorch). Based on this analysis, it evaluates the health and safety status of workers and generates personalized health advice and safety improvement measures.

[0254] Interacting with an avatar

[0255] Terminal (factory robot):

[0256] The factory robots are equipped with displays and speakers, allowing avatars to interact with workers and answer health-related questions. When a worker enters a question into the application, the information is sent to a server.

[0257] Server (Cloud Server):

[0258] The cloud server uses a natural language processing (NLP) engine to analyze the worker's questions and generate appropriate answers. These answers are then provided to the worker through the robot's display or speaker.

[0259] Stress management and mental health support

[0260] Terminals (factory robots and data collection servers):

[0261] Factory robots continuously monitor workers' heart rate variability and estimate their stress levels. Data collection servers transmit this data to cloud servers.

[0262] Server (Cloud Server):

[0263] If the cloud server detects a high stress level, it generates specific advice to support relaxation and mental health. Based on this, workers can take appropriate action.

[0264] Specific example

[0265] Example 1:

[0266] Suppose a worker considering whether or not to take a break asks a factory robot, "Should I take a break?" This question is collected by the robot and sent to a server. The server analyzes recent data, confirms that the stress level is high, and then generates advice such as, "Based on recent data, we recommend that you take a break." This advice is then communicated to the worker through the robot's display or speaker.

[0267] Example of a prompt:

[0268] "Generate advice to assess stress levels based on the worker's heart rate and activity data."

[0269] "Please analyze the following data and provide health advice: Heart rate: 80, Activity data: 'active', Posture: 'correct'"

[0270] Thus, the present invention is a system that supports the health management of workers in a factory in real time and provides valuable information to workers through data analysis and the generation of individual advice.

[0271] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0272] Step 1:

[0273] Data collection

[0274] The terminal (factory robot) uses sensors and cameras to collect worker heart rate and movement data in real time. The collected biometric data is transmitted to a data collection server using wireless communication technology such as Bluetooth. The input here is the biometric data acquired from the sensors and cameras, and the output is the data transferred to the data collection server.

[0275] Step 2:

[0276] Data transmission

[0277] The terminal (data collection server) receives biometric data transmitted from factory robots and sends it to a cloud server via the internet. The input is biometric data transmitted from factory robots, and the output is biometric data sent to the cloud server.

[0278] Step 3:

[0279] Data analysis and advice generation

[0280] The server (cloud server) stores the received biometric data in a secure database. The stored data is analyzed using machine learning models (using TensorFlow or PyTorch) to evaluate the user's health status and stress level. Appropriate health advice and safety improvement measures are then generated. The input is the biometric data stored on the cloud server, and the output is the generated health advice and safety improvement measures.

[0281] Step 4:

[0282] Interacting with an avatar

[0283] The terminal (factory robot) uses a display and a speaker to enable the avatar to provide health advice and safety improvement measures to the operator. When the operator enters a question into the robot, the question is sent to the cloud server. The server analyzes the question using a natural language processing (NLP) engine and generates an appropriate answer. The answer is provided to the operator through the robot. The input at this step is the content of the question from the operator, and the output is the answer generated by the server.

[0284] Step 5:

[0285] Stress management and mental health support

[0286] The terminal (factory robot and data collection server) continuously monitors the operator's heart rate variability and sends the data to the cloud server. When the server determines that the stress level is high, it generates advice for relaxation and mental health support. The advice is provided through the display and speaker of the factory robot. The input at this step is the heart rate variability data, and the output is the generated relaxation advice.

[0287] In this way, each step continues with data collection, transmission, analysis, advice generation, and provision to the user, constituting a system for real-time health management of the operator.

[0288] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0289] This invention is a system for supporting users' health management, and by combining it with an emotion engine, it provides more personalized health advice. The following describes a specific embodiment for carrying out this invention. The user uses a dedicated smartwatch and a smartphone application. The smartwatch collects biometric data such as heart rate, steps, and sleep duration in real time and transmits it to a cloud server via the smartphone. The cloud server analyzes the biometric data and generates individual health advice.

[0290] Furthermore, the system incorporates an emotion engine that recognizes the user's emotional state and reflects it in health advice. This allows for the provision of optimal advice tailored to the user's physical and mental condition.

[0291] Collection and transmission of health data

[0292] Device (Smartwatch): The smartwatch collects biometric data such as the user's heart rate, steps taken, and sleep duration in real time. The emotion engine also collects data such as voice and facial expressions to recognize the user's emotional state. This data is transmitted to the smartphone application via Bluetooth or other means.

[0293] Device (smartphone): The smartphone application receives biometric and emotional data transmitted from the smartwatch and sends it to a cloud server via the internet.

[0294] Data analysis and generation of personalized advice

[0295] Server: The cloud server securely stores received biometric and emotional data in a database. Before data is stored, the data format is validated and data cleaning is performed, correcting for missing or outlier values. The server uses machine learning models and algorithms to analyze the data in detail and assess the user's health and emotional state.

[0296] Server: Generates personalized health advice and meal plans based on the user's health and emotional state. For example, if a high-stress state is detected, it will also generate advice on optimal relaxation methods and mental health care for that user.

[0297] Interacting with an avatar

[0298] Device (Smartphone): The avatar within the smartphone application answers health-related questions through dialogue with the user. When the user enters a question into the application, the content is sent to the server. The emotion engine recognizes emotions from the user's input, speech, and facial expressions, and adjusts the content of the dialogue accordingly.

[0299] Server: The server uses a natural language processing (NLP) engine to analyze the user's question and generate an appropriate answer. It also incorporates the results of an emotion engine to generate the optimal answer based on the user's emotional state. The generated answer is then sent to the smartphone application.

[0300] Device (Smartphone): An avatar within the application provides emotionally sensitive responses to the user. Through this interaction, the user can receive real-time, emotion-based health advice.

[0301] Integration with other health management apps

[0302] Server: The cloud server receives data from other health management applications and integrates and manages it in the existing database. This integration enables centralized management of users' health data, allowing for more accurate advice to be provided.

[0303] Stress management and mental health support

[0304] Terminal (smartwatch and smartphone): The smartwatch continuously monitors the user's heart rate fluctuations and estimates the stress level. The smartphone application sends this data to the cloud server.

[0305] Server: When a high-stress state is detected, the cloud server generates relaxation and mental health advice and provides it to the user in combination with the emotion engine. Based on this, the user can take appropriate actions.

[0306] Information sharing with doctors

[0307] User: When the user wants to share vital information with a doctor, the request is made on the smartphone application.

[0308] Server: The cloud server uses generative AI to summarize the collected vital information and generate a report. Then, the generated report is sent to the doctor after the user's confirmation. This procedure enables efficient information sharing between the user and the doctor.

[0309] Specific examples

[0310] Example 1: Health monitoring and emotion recognition during the day

[0311] Terminal (smartwatch): During the day, the smartwatch collects the user's heart rate, step count, and sleep data. At the same time, the emotion engine recognizes the emotion from the user's voice and expression.

[0312] Terminal (smartphone): Sends the collected biometric data and emotion data to the cloud server.

[0313] Server: The cloud server analyzes this data and determines if the user is experiencing stress. Taking the user's emotional state into consideration, it generates advice such as, "You have been in a stressful situation recently. We recommend you relax by taking deep breaths or a short walk."

[0314] Device (smartphone): The avatar provides emotionally sensitive advice to the user via text and voice.

[0315] Example 2: Sharing information with doctors

[0316] User: The user requests to share vital information with a doctor for a health checkup.

[0317] Server: The cloud server summarizes biometric and emotional data and compiles it into a report. This report is then sent to the doctor after user review.

[0318] Doctor: Based on the received report, the doctor will make a comprehensive assessment of the user's health condition and take appropriate measures.

[0319] As described above, the present invention, which incorporates an emotion engine, is a system that supports users' health management and provides advanced health support that takes into account emotional states through data analysis and the generation of personalized advice.

[0320] The following describes the processing flow.

[0321] Step 1:

[0322] Device (smartwatch): Collects biometric data such as the user's heart rate, steps taken, and sleep duration in real time. Sensors within the smartwatch periodically measure and temporarily store this data.

[0323] Step 2:

[0324] Device (Smartwatch): The smartwatch has a built-in emotion engine that recognizes emotions from the user's voice, facial expressions, and gestures. The collected emotion data is temporarily stored along with biometric data.

[0325] Step 3:

[0326] The device (smartphone) communicates with the smartwatch via Bluetooth and periodically transfers collected biometric and emotional data to the smartphone. The smartphone checks the received data and verifies its integrity.

[0327] Step 4:

[0328] Device (smartphone): Smartphones transmit data to cloud servers via the internet. During this process, protocols are used to encrypt the data and maintain security.

[0329] Step 5:

[0330] Server: The cloud server securely stores received biometric and emotional data in a database. Before saving, it validates the data format, cleans the data, and corrects for missing or outlier values.

[0331] Step 6:

[0332] Server: The server processes the stored data using analytical algorithms to evaluate the user's health and emotional state. For example, it might use a machine learning model to estimate stress levels from the user's heart rate variability.

[0333] Step 7:

[0334] Server: Generates personalized health advice and meal plans based on the results of machine learning models and data analysis. This process considers not only the user's past data and current health status, but also their emotional state.

[0335] Step 8:

[0336] Server: Formats the data and sends it to the smartphone application to provide the generated advice and plans to the user.

[0337] Step 9:

[0338] Device (Smartphone): The smartphone application displays received advice and plans to the user. If necessary, an avatar provides emotionally sensitive advice via text or voice.

[0339] Step 10:

[0340] User: Begin a conversation with the avatar and enter health-related questions. For example, ask, "How has your sleep been lately?"

[0341] Step 11:

[0342] Device (smartphone): Retrieves user questions as text data and sends it to the cloud server.

[0343] Step 12:

[0344] Server: Uses a natural language processing (NLP) engine to analyze user questions and understand their intent. Refers to stored biometric data, sentiment data, and past advice logs to generate appropriate answers.

[0345] Step 13:

[0346] Server: Sends the answers generated based on the questions to the smartphone application. At this time, it also considers the results of the emotion engine to generate the optimal answer according to the user's emotional state.

[0347] Step 14:

[0348] Device (smartphone): The avatar provides the user with emotionally sensitive responses and completes the conversation. This allows the user to obtain the necessary information in real time.

[0349] Step 15:

[0350] Server: Handles integration to receive data from other health management applications. Uses APIs to retrieve additional data and manage it in an integrated manner.

[0351] Step 16:

[0352] Server: Integrates data from other applications received and performs a more detailed analysis of the user's overall health status. Based on the results, it generates further advice.

[0353] Step 17:

[0354] Devices (smartwatch and smartphone): The smartwatch continuously monitors the user's heart rate variability and sends stress level data to the smartphone. The smartphone then sends this data to a cloud server.

[0355] Step 18:

[0356] Server: If a high-stress state is detected, the cloud server generates relaxation and mental health advice, taking emotional data into consideration, and provides it to the user.

[0357] Step 19:

[0358] User: The app requests the sharing of vital information with a doctor as needed. The smartphone application sends this request to the cloud server.

[0359] Step 20:

[0360] Server: Uses generative AI to summarize the user's vital information and generate a report. After user confirmation, the report is sent to the doctor.

[0361] (Example 2)

[0362] 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".

[0363] Traditional health management systems focus on collecting and analyzing users' biometric data, but they fail to adequately provide health advice that takes emotional states into account. This makes it difficult to accurately assess users' psychological health and provide personalized advice. Furthermore, the insufficient functionality for integrating data from multiple health management applications makes it challenging to achieve comprehensive health management for users.

[0364] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting biometric data such as heart rate, steps, and sleep time; means for transmitting the biometric data and emotional data to a cloud server via a smartphone; means for analyzing the biometric data and emotional data at the cloud server and generating individual health advice; means for providing the generated health advice to the user; means for answering health-related questions through dialogue with the user using an avatar; means for sharing a summary of vital information and emotional data with medical professionals; means for receiving data from other health management software and managing it integrally; and means for monitoring the user's stress level and psychological health status and providing relaxation and mental care advice as needed. This enables integrated analysis of the user's biometric data and emotional data, and allows for the provision of individual health advice. Furthermore, by integrally managing data from multiple health management applications, comprehensive health management of the user becomes possible.

[0365] "Biometric data" refers to data that indicates the user's physical condition, such as heart rate, steps taken, and sleep duration.

[0366] "Emotional data" refers to data obtained from the user's voice, facial expressions, etc., that indicates the user's emotional state.

[0367] A "cloud server" is a computer system that operates as a remote server accessible via the internet, performing tasks such as data storage, analysis, and advice generation.

[0368] "Health advice" is generated by a cloud server analyzing biometric and emotional data, and provides information and specific suggestions for actions that can help users maintain and improve their health.

[0369] An "avatar" is a virtual character with which a user interacts through a smartphone application, and is used to answer questions about health.

[0370] "Vital information" refers to important biometric data related to life support, such as the user's heart rate, blood pressure, and body temperature.

[0371] A "medical professional" is a professional qualified to monitor, diagnose, and treat a user's health condition, and includes doctors and nurses.

[0372] "Integrated management" means consolidating data collected from different health management software and devices into a single system and managing it as a whole.

[0373] "Stress level" refers to the degree of psychological and physiological stress estimated from the user's heart rate variability, etc.

[0374] "Mental care" refers to advice and suggestions for specific implementation methods to maintain and improve the user's psychological health.

[0375] This invention is a system for supporting users' health management, and by combining it with an emotion engine, it provides more personalized health advice. Specific embodiments of this invention are described below.

[0376] Hardware and software to be used

[0377] Hardware:

[0378] Smartwatch (equipped with sensors to collect heart rate, steps, and sleep time)

[0379] Smartphone (with Bluetooth and internet connectivity)

[0380] software:

[0381] Smartphone application (equipped with data transmission and avatar interaction functions)

[0382] Cloud servers (data storage, data analysis, machine learning models, sentiment engines, natural language processing engines)

[0383] System-wide operation overview

[0384] Device (smartwatch):

[0385] Smartwatches collect biometric data such as heart rate, steps taken, and sleep duration in real time. They also collect emotional data such as voice and facial expressions, and transmit this data to a smartphone.

[0386] Device (smartphone):

[0387] The smartphone application receives biometric and emotional data transmitted from the smartwatch and sends it to a cloud server via the internet.

[0388] server:

[0389] The cloud server securely stores the received biometric and emotional data in a database. The stored data undergoes data format validation and data cleaning, correcting for missing or outlier values. Machine learning models and algorithms are then used to analyze the data and assess the user's health and emotional state. Based on these states, personalized health advice, relaxation methods, and mental health care advice are generated.

[0390] Device (smartphone):

[0391] The generated health advice and emotionally sensitive responses are delivered to the user through an avatar in a smartphone application. The avatar uses a natural language processing engine to interact with the user. When the user enters a question, the content is sent to the server, and the most appropriate response is generated, taking into account the results of the emotion engine's analysis.

[0392] Specific examples of operation

[0393] Example 1: Daytime health monitoring and emotion recognition

[0394] Device (smartwatch):

[0395] Smartwatches collect heart rate, step count, and sleep data throughout the day.

[0396] The emotion engine recognizes emotions from voice and facial expressions.

[0397] Device (smartphone):

[0398] Biometric and emotional data collected from the smartwatch are sent to a cloud server.

[0399] server:

[0400] The cloud server analyzes this data and determines if the user is experiencing stress. It also considers the user's emotional state and generates advice such as, "You've been in a stressful situation recently. We recommend relaxing by taking deep breaths or a short walk."

[0401] Device (smartphone):

[0402] The avatar provides users with emotionally sensitive advice via text and voice.

[0403] Example 2: Sharing information with doctors

[0404] User:

[0405] I would like to share my vital signs with my doctor for a health checkup.

[0406] server:

[0407] The cloud server summarizes biometric and emotional data and compiles it into a report. This report is then sent to the doctor after user review.

[0408] Doctor:

[0409] Based on the received reports, we will comprehensively assess the user's health status and take appropriate measures.

[0410] Example of a prompt

[0411] "How do recent heart rate fluctuations affect things?"

[0412] "What are some ways to relieve stress?"

[0413] "Please give me some advice on how to improve the quality of my sleep last night."

[0414] This invention is an advanced health management system incorporating an emotion engine, which provides personalized health advice through integrated analysis of the user's biometric and emotional data. It also aims to achieve comprehensive health management for the user by integrating data from other health management applications.

[0415] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0416] Step 1: Collection of biometric and emotional data

[0417] Device (smartwatch):

[0418] Smartwatches collect biometric data such as heart rate, steps taken, and sleep duration in real time. They also recognize emotional states from data such as voice and facial expressions.

[0419] Input: User biometric data (heart rate, steps, sleep patterns) and emotional data (voice, facial expressions)

[0420] Output: Collected biometric and emotional data

[0421] Specific operation: A heart rate sensor measures heart rate every 10 seconds, and a pedometer sensor counts steps every minute. A small camera and microphone analyze voice and facial expressions, providing information to the emotion engine.

[0422] Step 2: Send

[0423] Device (smartwatch):

[0424] The smartwatch transmits data to the smartphone via Bluetooth.

[0425] Device (smartphone):

[0426] The data received by the smartphone is sent to a cloud server via the internet.

[0427] Input: Biometric and emotional data transmitted from the smartwatch.

[0428] Output: Data sent to the cloud server

[0429] Specific operation: The smartwatch sends batch data to the smartphone every 30 minutes, and the smartphone uploads the data to the cloud server in real time.

[0430] Step 3: Data storage and preprocessing

[0431] server:

[0432] The cloud server stores the received data in a secure database. It verifies the data format, performs data cleaning, and corrects missing or outlier values.

[0433] Input: Biometric data and emotional data transmitted from a smartphone

[0434] Output: Verified and cleaned data

[0435] Specific operation: Daily batch processing verifies the data format and filters out abnormal heart rate values ​​(e.g., 300 bpm) using an anomaly detection algorithm, and then formats the data into a standard format.

[0436] Step 4: Data Analysis

[0437] server:

[0438] We use machine learning models and algorithms to perform detailed analyses and evaluate the user's health and emotional state.

[0439] Input: Verified and cleaned biometric and emotional data

[0440] Output: Assessment results of health and emotional state

[0441] Specific operation: A machine learning model on a cloud server analyzes data every night to calculate seven-day health and emotional trends.

[0442] Step 5: Generating personalized advice

[0443] server:

[0444] Based on the user's health and emotional state, it generates personalized health advice, relaxation methods, and mental health care advice.

[0445] Input: Assessment results of health and emotional status

[0446] Output: Personalized health advice

[0447] Specific operation: If a high-stress state is detected, the server generates text and video links suggesting "deep breathing exercises" and "5 minutes of meditation," and sends them to the smartphone.

[0448] Step 6: Interacting with the Avatar

[0449] Device (smartphone):

[0450] The avatar answers health-related questions through interaction with the user. The user's questions are sent to the server.

[0451] server:

[0452] The system uses a natural language processing engine to analyze questions and then incorporates the results of a sentiment engine to generate the optimal answer.

[0453] Input: User's question data

[0454] Output: Emotionally sensitive response

[0455] Specific operation: When a user enters "I haven't been sleeping well lately" into the app, the server analyzes the sleep data and generates advice such as "avoid using your smartphone before going to bed."

[0456] Step 7: Integration with other health management apps

[0457] server:

[0458] It receives data from other health management applications and manages it centrally.

[0459] Input: Data from other health management applications

[0460] Output: Integrated health data

[0461] Specific operation: Receives sleep data from other apps, integrates that data with existing watch data, and uses it for comprehensive analysis.

[0462] Step 8: Stress Management and Mental Health Support

[0463] Devices (smartwatches and smartphones):

[0464] The smartwatch continuously monitors heart rate variability and estimates stress levels. The smartphone sends this data to a cloud server.

[0465] server:

[0466] If a high-stress state is detected, it generates relaxation and mental health advice.

[0467] Input: Heart rate variability data

[0468] Output: Advice on relaxation and mental health care.

[0469] Specific operation: Heart rate variability is measured every minute, and if a high-stress state lasting more than two hours is detected, the cloud server generates mental health advice.

[0470] Step 9: Sharing information with doctors

[0471] User:

[0472] A request is made to share vital information with a doctor using a smartphone application.

[0473] server:

[0474] The system summarizes biometric and emotional data, generates a report, and sends it to a doctor after user review.

[0475] Input: Biometric data and emotional data

[0476] Output: Summarized report

[0477] Specific operation: When the user selects "Send information to doctor," the system summarizes the data from the past three months and generates a PDF report. The report includes a graph of heart rate and emotional fluctuations.

[0478] (Application Example 2)

[0479] 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".

[0480] Traditional health management systems provide health advice based on users' biometric data, but they do not take into account security risks associated with users' emotional states or stress levels. Therefore, they fail to prevent suspicious behavior caused by stress or anxiety, making it difficult to guarantee overall safety.

[0481] 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. In this invention, the server includes means for collecting the user's biometric data, means for transmitting the biometric data to a cloud server, means for analyzing the biometric data on the cloud server and generating individual health advice, means for providing the generated health advice to the user, means for answering health-related questions through dialogue with an avatar, means for sharing a summary of vital information with a doctor based on the user's selection, means for evaluating the user's security risk and generating a security alert when an abnormal stress level or emotional state is detected, and means for notifying the user of the security alert and encouraging safe behavior. This enables comprehensive support that considers not only the user's health management but also security risks due to emotional state and stress.

[0482] Definitions of important words

[0483] "User biometric data" refers to an individual's physiological information, such as heart rate, steps taken, and sleep duration.

[0484] A "cloud server" refers to a collection of remote servers that store and analyze data via the internet.

[0485] "Personalized health advice" refers to health management guidelines optimized for a specific individual, provided based on the user's biometric data and emotional state.

[0486] An "avatar" refers to a virtual character that interacts with the user and provides information within an application.

[0487] "Vital information" refers to key bodily data related to maintaining life, such as heart rate, blood pressure, and body temperature.

[0488] "Security risk" refers to potential dangers to an individual's safety that can be inferred from a user's biometric data and emotional state.

[0489] A "security alert" refers to a warning that notifies users of potential security risks, generated based on their biometric data and emotional state.

[0490] A "Natural Language Processing (NLP) engine" refers to software that understands meaning from text and audio and analyzes human natural language.

[0491] Modes for carrying out the invention

[0492] This invention is a system that comprehensively supports the user's health and security. Specific embodiments for carrying out this invention are described below.

[0493] System Configuration

[0494] This system consists of a smartwatch that collects the user's biometric data, a smartphone that transmits the data, a cloud server, and an avatar that provides advice and security alerts to the user.

[0495] Data collection and transmission

[0496] The device (smartwatch) collects biometric data such as the user's heart rate, steps taken, and sleep duration in real time. It also collects emotional data from voice and facial expressions. This data is transmitted to the smartphone via Bluetooth.

[0497] The device (smartphone) receives data transmitted from the smartwatch and sends it to a cloud server via the internet.

[0498] Data Analysis

[0499] The server (cloud server) stores the received biometric and emotional data in a secure database. First, the data format is validated and the data is cleaned, correcting for missing or outlier values. Then, machine learning models and algorithms are used to analyze the data and evaluate the user's health and emotional state.

[0500] Generating health advice and security alerts

[0501] The server generates personalized health advice based on the analysis results. For example, if a high-stress state is detected, it will generate advice on relaxation methods and mental health care.

[0502] Furthermore, the system assesses security risks and generates security alerts if abnormal stress levels or emotional states are detected. These advisories and alerts are communicated to the user. Notifications are displayed within the smartphone application and delivered via an avatar.

[0503] User interaction

[0504] The avatar on the device (smartphone) answers health-related questions through interaction with the user. When the user enters a question into the application, the content is sent to the server. The emotion engine recognizes emotions from the user's entered text, speech, and facial expressions.

[0505] The server uses a natural language processing (NLP) engine to analyze the user's question and generate an appropriate answer. The generated answer is then sent back to the smartphone, where an avatar presents it to the user in an emotionally sensitive manner.

[0506] Specific examples and prompt statements

[0507] Specific example

[0508] For example, suppose a user is about to open their door upon returning home, and their smartwatch detects a sudden increase in heart rate and a high level of stress. In this case, the cloud server analyzes the data and generates a security alert. A notification is sent to the smartphone application saying, "Please relax. Check your surroundings for any suspicious activity and open the door only after you are sure it is safe."

[0509] Example of a prompt

[0510] "Please check your current heart rate and stress level."

[0511] "You are under high stress. Take a deep breath and ensure your safety before proceeding."

[0512] Through this configuration, comprehensive assessment of user health and security risks is performed, enabling the provision of individually optimized advice and alerts.

[0513] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0514] Program processing steps

[0515] Step 1:

[0516] The device (smartwatch) collects the user's heart rate, steps, sleep duration, and emotional data in real time. This data is temporarily stored within the smartwatch. Input is the user's biometric and emotional data, and output is data transfer to the smartphone.

[0517] Step 2:

[0518] The device (smartphone) receives data from the smartwatch via Bluetooth. The smartphone checks the received data and sends it to a cloud server via the internet. The input is biometric and emotional data transferred from the smartwatch, and the output is the transmission of data to the cloud server.

[0519] Step 3:

[0520] The server (cloud server) stores the received data in a secure database. Next, it performs data format validation and data cleaning, correcting for missing or outlier values. The input is data sent from a smartphone, and the output is clean data that can be analyzed.

[0521] Step 4:

[0522] The server uses a machine learning model to analyze data and assess the user's health and emotional state. The analysis results include stress levels and normal / abnormal health status. The input is clean data, and the output is the assessment of the user's health and emotional state.

[0523] Step 5:

[0524] The server generates personalized health advice based on the analysis results. For example, if a high-stress state is detected, it will generate advice on relaxation methods and mental health care. The input is the health assessment results, and the output is health advice text.

[0525] Step 6:

[0526] The server assesses the user's security risks and generates security alerts if abnormal stress levels or emotional states are detected. Inputs are health and emotional assessment results, and outputs are security alerts.

[0527] Step 7:

[0528] The server sends the generated health advice and security alerts to the smartphone application. The input is the health advice and security alerts, and the output is the transmission of data to the smartphone.

[0529] Step 8:

[0530] The device (smartphone) notifies the user of received health advice and security alerts through an avatar. For example, the avatar provides emotionally sensitive advice to the user, such as, "Please relax. Check your surroundings for any suspicious activity and open the door only after you have confirmed that it is safe." The input is the advice and alerts sent from the server, and the output is the notification to the user.

[0531] Step 9:

[0532] Users ask health and security-related questions to an avatar via a smartphone application. The input is the user's question, and the output is the avatar's response.

[0533] Step 10:

[0534] The server receives the user's question and analyzes it using a natural language processing (NLP) engine. An emotion engine determines the user's emotional state and generates an appropriate response. The input is the user's question, and the output is the appropriate response.

[0535] Step 11:

[0536] The device (smartphone) notifies the user of the response received from the server via an avatar. The avatar provides instructions such as, "Take a deep breath and proceed only after confirming your safety." The input is the response from the server, and the output is the notification to the user.

[0537] In this way, data is collected, transmitted, and analyzed throughout each step and provided to the user as health advice and security alerts. Specific prompts include messages such as, "Check your current heart rate and stress level," and "You are experiencing high stress; take a deep breath and ensure your safety before proceeding."

[0538] 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.

[0539] 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.

[0540] 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.

[0541] [Second Embodiment]

[0542] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0543] 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.

[0544] 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).

[0545] 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.

[0546] 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.

[0547] 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).

[0548] 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.

[0549] 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.

[0550] 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.

[0551] 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.

[0552] 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.

[0553] 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".

[0554] This invention is a system for supporting users' health management and is configured as follows: The user uses a dedicated smartwatch and a smartphone application. The smartwatch collects biometric data such as heart rate, steps taken, and sleep duration in real time. The collected data is transmitted to a cloud server via the smartphone application. The cloud server analyzes the biometric data and generates personalized health advice.

[0555] Collection and transmission of health data

[0556] Device (Smartwatch): The smartwatch collects biometric data such as the user's heart rate, steps taken, and sleep duration in real time. This data is transmitted to a smartphone application via Bluetooth or other means.

[0557] Device (smartphone): The smartphone application receives data sent from the smartwatch and transmits it to a cloud server via the internet.

[0558] Data analysis and generation of personalized advice

[0559] Server: The cloud server stores the received biometric data in a secure database. Next, machine learning models and algorithms are used to analyze the data in detail and assess the user's health status. Based on this assessment, personalized health advice and meal plans are generated.

[0560] Interacting with an avatar

[0561] Device (Smartphone): The avatar within the smartphone application can answer health-related questions through interaction with the user. When the user enters a question into the application, the content is sent to the server.

[0562] Server: The server uses a natural language processing (NLP) engine to analyze the user's question and generate an appropriate answer. The generated answer is then sent to the terminal.

[0563] Device (smartphone): An avatar within the application provides answers to the user. Through this interaction, the user can obtain useful information about health.

[0564] Integration with other health management apps

[0565] Server: The cloud server can receive data from other health management applications and integrate it into the existing database for management. This integration enables centralized management of users' health data.

[0566] Stress management and mental health support

[0567] Devices (smartwatch and smartphone): The smartwatch continuously monitors the user's heart rate variability and estimates their stress level. The smartphone application sends this data to a cloud server.

[0568] Server: If a high stress level is detected, the cloud server generates specific advice to support relaxation and mental health. Based on this, the user can take appropriate action.

[0569] Sharing information with doctors

[0570] User: If a user wants to share vital information with a doctor, they make that request through the smartphone application.

[0571] Server: The cloud server uses generative AI to summarize the collected vital information and generate a report. The generated report is then sent to the physician after user review. This process enables efficient information sharing between the user and the physician.

[0572] Specific example

[0573] Example 1: Nighttime health monitoring

[0574] Device (Smartwatch): At night, the smartwatch collects the user's sleep data. This includes detailed data such as sleep depth and sleep cycles.

[0575] Device (smartphone): In the morning, data from the smartwatch is sent to the smartphone. The data is then sent to the cloud server.

[0576] Server: The cloud server analyzes this data and generates advice such as, "According to your recent sleep data, you tend to be getting insufficient deep sleep. We recommend that you establish a routine of going to bed at a specific time to ensure you get quality sleep."

[0577] Device (smartphone): The avatar provides advice to the user and suggests specific ways to improve.

[0578] Example 2: Stress Management

[0579] Device (smartwatch): During the day, the smartwatch monitors the user's heart rate variability and detects high-stress states.

[0580] Server: The cloud server evaluates stress levels based on accumulated data and generates advice such as, "Recent data has detected a high stress level. We recommend taking some time to relax by taking deep breaths or meditating."

[0581] Device (smartphone): An avatar provides the user with stress-relief advice and explains in detail how to put it into practice.

[0582] As described above, the present invention is a system that supports users' health management and provides users with valuable information through data analysis and the generation of personalized advice.

[0583] The following describes the processing flow.

[0584] Step 1:

[0585] Device (smartwatch): Collects biometric data such as heart rate, steps taken, and sleep duration from the user in real time. Sensors within the smartwatch periodically measure and temporarily store this data.

[0586] Step 2:

[0587] The device (smartphone) communicates with the smartwatch via Bluetooth and periodically transfers the collected biometric data to the smartphone. The smartphone checks the received data and verifies its integrity.

[0588] Step 3:

[0589] Device (smartphone): Smartphones transmit data to cloud servers via the internet. During this process, protocols are used to encrypt the data and maintain security.

[0590] Step 4:

[0591] Server: The cloud server stores the received biometric data in a secure database. Before saving, it verifies the data format and performs data cleaning, correcting for missing or outlier values.

[0592] Step 5:

[0593] Server: The server processes the stored data using an analysis algorithm to assess the user's health status. For example, it might use a machine learning model to estimate stress levels from the user's heart rate variability.

[0594] Step 6:

[0595] Server: Generates personalized health advice and meal plans based on the results of machine learning models and data analysis. This process takes into account the user's past data and current health status.

[0596] Step 7:

[0597] Server: Formats the data and sends it to the smartphone application to provide the generated advice and plans to the user.

[0598] Step 8:

[0599] Device (Smartphone): The smartphone application displays received advice and plans to the user. An avatar provides advice via text or voice as needed.

[0600] Step 9:

[0601] User: Begin a conversation with the avatar and enter health-related questions. For example, ask, "How has your sleep been lately?"

[0602] Step 10:

[0603] Device (smartphone): Retrieves user questions as text data and sends it to the cloud server.

[0604] Step 11:

[0605] Server: Uses a natural language processing (NLP) engine to analyze user questions and understand their intent. Refers to stored data and past advice logs to generate appropriate answers.

[0606] Step 12:

[0607] Server: Sends the generated answer based on the question to the smartphone application.

[0608] Step 13:

[0609] Device (smartphone): The avatar provides answers to the user and completes the conversation. This allows the user to obtain the necessary information in real time.

[0610] Step 14:

[0611] Server: Handles integration to receive data from other health management applications. Uses APIs to retrieve additional data and manage it in an integrated manner.

[0612] Step 15:

[0613] Server: Integrates data from other applications received and performs a more detailed analysis of the user's overall health status. Based on the results, it generates further advice.

[0614] Step 16:

[0615] Devices (smartwatch and smartphone): The smartwatch continuously monitors the user's heart rate variability and sends stress level data to the smartphone. The smartphone then sends this data to a cloud server.

[0616] Step 17:

[0617] Server: If a high-stress state is detected, the cloud server generates and provides relaxation and mental health advice to the user.

[0618] Step 18:

[0619] User: The app requests the sharing of vital information with a doctor as needed. The smartphone application sends this request to the cloud server.

[0620] Step 19:

[0621] Server: Uses generative AI to summarize the user's vital information and generate a report. After user confirmation, the report is sent to the doctor.

[0622] (Example 1)

[0623] 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".

[0624] The present invention aims to provide a system that efficiently and effectively supports users' health management. Existing health management systems lack sufficient collection and analysis of biometric data, as well as adequate feedback to users, resulting in insufficient personalized health advice. Furthermore, they lack support for stress management and mental health care, and there is no easy way for users to share vital information with healthcare professionals.

[0625] 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.

[0626] In this invention, the server includes means for analyzing the user's biometric data and generating personalized health advice, means for answering health-related questions through interaction with an avatar, and means for sharing a summary of vital information with healthcare professionals. This allows the user to understand their health status in real time and receive necessary health advice. Furthermore, comprehensive health management is achieved through stress level monitoring and the provision of mental health care advice. In addition, the means for sharing information with healthcare professionals enables prompt and appropriate medical responses.

[0627] "Biometric data" refers to data that indicates a user's physical functions and state, such as heart rate, steps taken, and sleep duration.

[0628] A "mobile terminal" is a portable communication device with data communication capabilities, and specifically includes smartphones and tablet devices.

[0629] A "cloud server" refers to a remote server that stores and processes data via the internet.

[0630] "Health advice" refers to specific instructions or suggestions for improving or maintaining a user's health.

[0631] An "avatar" is a digital representation that acts on behalf of a user on a computer and has interactive functions such as conversation.

[0632] "Healthcare professionals" refer to individuals with specialized knowledge and qualifications related to medicine and health, such as doctors and nurses.

[0633] "Health management software" refers to application software used to monitor and manage a user's health status.

[0634] "Stress level" is an indicator that shows the degree of stress a user experiences.

[0635] "Mental health care" refers to care methods and activities aimed at maintaining or improving a user's mental health.

[0636] "Vital information" refers to data on the body's basic physiological functions, such as heart rate, body temperature, and respiratory rate.

[0637] This invention is a system for supporting user health management and is implemented using the following hardware and software.

[0638] Hardware and software to be used

[0639] terminal

[0640] Smartwatch: A device for collecting biometric data such as heart rate, steps taken, and sleep duration. Built-in sensors include heart rate sensors and accelerometers.

[0641] Smartphone: A mobile device used to send data collected from a smartwatch to a cloud server, and a platform for running applications.

[0642] server

[0643] Cloud servers are remote servers used for storing and analyzing data over the internet. Key software includes databases (e.g., AWS RDS, Google Cloud Firestore), machine learning models (e.g., TensorFlow, PyTorch), and natural language processing engines (e.g., BERT, GPT-3).

[0644] Data collection and transmission

[0645] Device (smartwatch)

[0646] Smartwatches collect biometric data such as the user's heart rate, steps taken, and sleep duration in real time. This data is transmitted to a smartphone using Bluetooth technology.

[0647] Device (smartphone)

[0648] The smartphone receives data from the smartwatch via Bluetooth and sends it to a cloud server over the internet. A dedicated application is installed on the smartphone, and this app manages the receiving and sending of data.

[0649] Data storage and analysis

[0650] server

[0651] The cloud server receives biometric data transmitted from smartphones via the internet and stores it in a secure database. The stored data is analyzed using machine learning models (e.g., TensorFlow, PyTorch). Based on the analysis results, the user's health status is evaluated and personalized health advice is generated.

[0652] Interacting with an avatar

[0653] Device (smartphone)

[0654] The smartphone application features an avatar that answers health-related questions through interaction with the user. When the user enters a question into the application, the content is sent to the server.

[0655] server

[0656] The server uses a natural language processing engine (e.g., BERT, GPT-3) to analyze the user's question and generate an appropriate answer. The generated answer is then sent back to the smartphone.

[0657] Device (smartphone)

[0658] An avatar within a smartphone application provides the user with generated responses. This interactive feature allows users to easily obtain useful health-related information.

[0659] Integration with other health management software

[0660] server

[0661] The cloud server receives data from other health management software (e.g., Google Fit, Apple HealthKit) and integrates it into the existing database for management. This integration allows for centralized management of users' health data.

[0662] Stress management and mental health support

[0663] Devices (smartwatches and smartphones)

[0664] The smartwatch monitors the user's heart rate variability and sends the data to the smartphone. The smartphone then sends this data to a cloud server.

[0665] server

[0666] The cloud server assesses stress levels and generates specific advice to support relaxation and mental health. This generated advice is then sent to the user's smartphone.

[0667] Sharing information with healthcare professionals

[0668] User

[0669] If a user wants to share vital information with a healthcare professional, they can make that request through a smartphone application.

[0670] server

[0671] The cloud server uses a generative AI model (e.g., GPT-4) to summarize the collected vital information and generate a report. This report is then reviewed by the user and sent to healthcare professionals.

[0672] Specific example

[0673] Collection of sleep data and generation of advice

[0674] (Specific example)

[0675] At night, the smartwatch collects the user's sleep data. It records cycles of deep and light sleep and generates detailed data. In the morning, the data is sent to the smartphone and then transferred to a cloud server. The cloud server analyzes the data and generates advice such as, "According to your recent sleep data, you are not getting enough deep sleep. We recommend that you establish a habit of going to bed at a specific time." An avatar on the smartphone then conveys this advice to the user.

[0676] As illustrated by the above specific example, the present invention is a system that supports users' health management and provides useful information to users by offering advanced data analysis and personalized health advice.

[0677] Stress management practices

[0678] (Example of a prompt message)

[0679] "I've been under a lot of stress lately. Do you have any recommendations for coping strategies?"

[0680] When a user enters a question using such a prompt, the server analyzes it, generates appropriate advice, and provides it to the user.

[0681] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0682] Step 1: Data Collection

[0683] Device (smartwatch)

[0684] Smartwatches collect biometric data such as the user's heart rate, steps taken, and sleep duration in real time. Heart rate is recorded every second, and steps are tallied every hour. Sleep data is recorded every 30 minutes during the night, showing the state of deep sleep and light sleep.

[0685] Input: User's real-time biometric data

[0686] Output: Biometric data stored in the smartwatch

[0687] Specifically, the smartwatch's heart rate sensor detects the heart rate, and the accelerometer measures the number of steps. This data is temporarily stored in the built-in memory.

[0688] Step 2: Send

[0689] Device (smartwatch)

[0690] Smartwatches transmit collected biometric data to smartphones using Bluetooth connectivity.

[0691] Input: Biometric data stored in the smartwatch

[0692] Output: Biometric data transferred to a smartphone

[0693] Specifically, the smartwatch attempts to transmit data every minute, transferring the data to the smartphone in batches via Bluetooth.

[0694] Step 3: Send data to the cloud

[0695] Device (smartphone)

[0696] Smartphones receive data via Bluetooth and send it to a cloud server over the internet. A dedicated application is installed on the smartphone, which manages the receiving and sending of data.

[0697] Input: Biometric data received from a smartwatch

[0698] Output: Biometric data sent to the cloud server

[0699] Specifically, the smartphone app runs in the background, and once an internet connection is established, it uploads the biometric data to a cloud server.

[0700] Step 4: Save Data

[0701] server

[0702] The cloud server receives data sent from smartphones via the internet and stores it in a secure database. Data integrity checks and encryption are performed before storage.

[0703] Input: Biometric data sent from a smartphone

[0704] Output: Biometric data stored in a secure database

[0705] Specifically, when biometric data arrives at the cloud server, its integrity is checked and it is encrypted (e.g., AES-256) before being inserted into the database.

[0706] Step 5: Data Analysis

[0707] server

[0708] The cloud server analyzes data using machine learning models (e.g., TensorFlow, PyTorch) to assess the user's health status. This analysis includes data preprocessing, model input, and retrieval of evaluation results.

[0709] Input: Biometric data stored in the database

[0710] Output: Evaluation results regarding the user's health status

[0711] Specifically, the biometric data is pre-processed to remove noise and normalize it before being input into a machine learning model. The model then outputs an evaluation score of the user's health status and trends indicating abnormalities as part of its analysis results.

[0712] Step 6: Generate Advice

[0713] server

[0714] The server generates personalized health advice for users based on the evaluation results of machine learning models. This process utilizes a natural language generation engine (e.g., GPT-4).

[0715] Input: Health status assessment results

[0716] Output: Personalized health advice

[0717] Specifically, the evaluation results are templated, and a generative AI model is used to generate health advice in natural language. For example, advice such as, "According to recent data, you are not getting enough deep sleep. We recommend that you establish a habit of going to bed at a specific time," might be generated.

[0718] Step 7: Avatar-based interaction

[0719] Device (smartphone)

[0720] When a user enters a health-related question for their avatar, the information is sent to the server.

[0721] Input: Question entered by the user

[0722] Output: Question data sent to the server

[0723] Specifically, the smartphone application sends user input to the server in text format.

[0724] server

[0725] The server analyzes the question using a natural language processing engine (e.g., BERT, GPT-3) and generates an appropriate answer. The generated answer is then sent back to the smartphone.

[0726] Input: Question data submitted by the user

[0727] Output: Response to the user

[0728] Specifically, the natural language processing engine analyzes the content of the question, the sentence generation engine creates the optimal answer, and sends it back to the smartphone in text format.

[0729] Device (smartphone)

[0730] An avatar within the application provides the user with the generated response. The response can also be read aloud using a speech synthesis engine.

[0731] Input: Response data sent from the server

[0732] Output: Answers provided by the user

[0733] Specifically, the avatar displays text on the screen and, when necessary, outputs speech using a speech synthesis engine.

[0734] Step 8: Sharing information with healthcare professionals

[0735] User

[0736] If a user wants to share vital information with a healthcare professional, they can make a request through the smartphone application.

[0737] Input: Request for information sharing

[0738] Output: Sending a sharing request to the server

[0739] Specifically, the user instructs the app to share vital information, and a request is sent to the server.

[0740] server

[0741] The cloud server uses a generative AI model (e.g., GPT-4) to summarize vital information and generate a report. This report is then sent to healthcare professionals after user review.

[0742] Input: Vital signs and information sharing request

[0743] Output: Summary report sent to healthcare professionals

[0744] Specifically, the system analyzes and summarizes the user's vital signs and generates a report in PDF format. After user confirmation, the report is sent to the healthcare professional's email address.

[0745] The above outlines the specific processing steps of this system's program. By detailing what processing and calculations are performed on the input data at each step and what the final output is, the overall operation of the system becomes clear.

[0746] (Application Example 1)

[0747] 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 glasses 214 will be referred to as the "terminal."

[0748] Effectively monitoring the health and stress levels of factory workers is crucial for improving productivity and ensuring safety. However, current systems have limitations in collecting and analyzing individual workers' health data in real time and providing appropriate advice and improvement measures. Therefore, a new system is needed to efficiently and effectively manage worker health.

[0749] 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.

[0750] In this invention, the server includes means for collecting heart rate and movement data of workers in a factory and monitoring the work environment; means for analyzing the collected data and generating health advice and safety improvement measures based on the work environment; and means for providing the generated advice and improvement measures via in-factory displays and voice guidance systems. This makes it possible to monitor the health status of workers in the factory in real time and provide appropriate advice and improvement measures.

[0751] "Biometric data" refers to data that indicates the user's physical condition, such as heart rate, movement data, and posture.

[0752] A "cloud server" is a remote server used to store, analyze, and manage data via the internet.

[0753] "Health advice" refers to specific suggestions and instructions generated based on analyzed biometric data to support the user's health management.

[0754] An "avatar" is a virtual character or agent used to interact with users.

[0755] "Vital information" refers to key biometric data that indicates vital functions, such as the user's heart rate, body temperature, and blood pressure.

[0756] A "factory" is a facility where manufacturing and production activities take place.

[0757] A "worker" is a person who engages in production activities within a factory.

[0758] "Motion data" refers to data that shows the physical movements and activity levels of a worker.

[0759] "Safety improvement measures" refer to specific proposals and measures to ensure worker safety and improve the working environment.

[0760] A "display" is a device used to visually display information.

[0761] A "voice guidance system" is a system that provides information and instructions using voice.

[0762] The present invention is a system for supporting the health management of workers in a factory, and is configured as follows: The system includes a factory robot, a data collection server, a cloud server, and a display or voice guidance system.

[0763] Collection and transmission of health data

[0764] Terminal (factory robot):

[0765] Factory robots are equipped with sensors and cameras to collect worker heart rate and movement data. This data is collected in real time. The factory robots transmit the collected data to a data collection server. Wireless communication technologies such as Bluetooth are used for this purpose.

[0766] Terminal (data collection server):

[0767] The data collection server receives data transmitted from factory robots and sends it to a cloud server via the internet. This ensures that the data is analyzed in a secure environment.

[0768] Data analysis and generation of personalized advice

[0769] Server (Cloud Server):

[0770] The cloud server securely stores the received biometric data and analyzes it in detail using machine learning models (e.g., TensorFlow or PyTorch). Based on this analysis, it evaluates the health and safety status of workers and generates personalized health advice and safety improvement measures.

[0771] Interacting with an avatar

[0772] Terminal (factory robot):

[0773] The factory robots are equipped with displays and speakers, allowing avatars to interact with workers and answer health-related questions. When a worker enters a question into the application, the information is sent to a server.

[0774] Server (Cloud Server):

[0775] The cloud server uses a natural language processing (NLP) engine to analyze the worker's questions and generate appropriate answers. These answers are then provided to the worker through the robot's display or speaker.

[0776] Stress management and mental health support

[0777] Terminals (factory robots and data collection servers):

[0778] Factory robots continuously monitor workers' heart rate variability and estimate their stress levels. Data collection servers transmit this data to cloud servers.

[0779] Server (Cloud Server):

[0780] If the cloud server detects a high stress level, it generates specific advice to support relaxation and mental health. Based on this, workers can take appropriate action.

[0781] Specific example

[0782] Example 1:

[0783] Suppose a worker considering whether or not to take a break asks a factory robot, "Should I take a break?" This question is collected by the robot and sent to a server. The server analyzes recent data, confirms that the stress level is high, and then generates advice such as, "Based on recent data, we recommend that you take a break." This advice is then communicated to the worker through the robot's display or speaker.

[0784] Example of a prompt:

[0785] "Generate advice to assess stress levels based on the worker's heart rate and activity data."

[0786] "Please analyze the following data and provide health advice: Heart rate: 80, Activity data: 'active', Posture: 'correct'"

[0787] Thus, the present invention is a system that supports the health management of workers in a factory in real time and provides valuable information to workers through data analysis and the generation of individual advice.

[0788] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0789] Step 1:

[0790] Data collection

[0791] The terminal (factory robot) uses sensors and cameras to collect worker heart rate and movement data in real time. The collected biometric data is transmitted to a data collection server using wireless communication technology such as Bluetooth. The input here is the biometric data acquired from the sensors and cameras, and the output is the data transferred to the data collection server.

[0792] Step 2:

[0793] Data transmission

[0794] The terminal (data collection server) receives biometric data transmitted from factory robots and sends it to a cloud server via the internet. The input is biometric data transmitted from factory robots, and the output is biometric data sent to the cloud server.

[0795] Step 3:

[0796] Data analysis and advice generation

[0797] The server (cloud server) stores the received biometric data in a secure database. The stored data is analyzed using machine learning models (using TensorFlow or PyTorch) to evaluate the user's health status and stress level. Appropriate health advice and safety improvement measures are then generated. The input is the biometric data stored on the cloud server, and the output is the generated health advice and safety improvement measures.

[0798] Step 4:

[0799] Interacting with an avatar

[0800] The terminal (factory robot) uses a display and speakers to have an avatar provide health advice and safety improvement measures to workers. When a worker inputs a question into the robot, the question is sent to a cloud server. The server uses a natural language processing (NLP) engine to analyze the question and generate an appropriate answer. The answer is then provided to the worker through the robot. In this step, the input is the question from the worker, and the output is the answer generated by the server.

[0801] Step 5:

[0802] Stress management and mental health support

[0803] The terminal (factory robot and data collection server) continuously monitors the worker's heart rate variability and transmits the data to a cloud server. If the server determines that the stress level is high, it generates advice for relaxation and mental health support. The advice is delivered through the factory robot's display and speakers. In this step, the input is heart rate variability data, and the output is the generated relaxation advice.

[0804] Thus, each step—data collection, transmission, analysis, advice generation, and delivery to the user—constitutes a system for real-time worker health management.

[0805] 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.

[0806] This invention is a system for supporting users' health management, and by combining it with an emotion engine, it provides more personalized health advice. The following describes a specific embodiment for carrying out this invention. The user uses a dedicated smartwatch and a smartphone application. The smartwatch collects biometric data such as heart rate, steps, and sleep duration in real time and transmits it to a cloud server via the smartphone. The cloud server analyzes the biometric data and generates individual health advice.

[0807] Furthermore, the system incorporates an emotion engine that recognizes the user's emotional state and reflects it in health advice. This allows for the provision of optimal advice tailored to the user's physical and mental condition.

[0808] Collection and transmission of health data

[0809] Device (Smartwatch): The smartwatch collects biometric data such as the user's heart rate, steps taken, and sleep duration in real time. The emotion engine also collects data such as voice and facial expressions to recognize the user's emotional state. This data is transmitted to the smartphone application via Bluetooth or other means.

[0810] Device (smartphone): The smartphone application receives biometric and emotional data transmitted from the smartwatch and sends it to a cloud server via the internet.

[0811] Data analysis and generation of personalized advice

[0812] Server: The cloud server securely stores received biometric and emotional data in a database. Before data is stored, the data format is validated and data cleaning is performed, correcting for missing or outlier values. The server uses machine learning models and algorithms to analyze the data in detail and assess the user's health and emotional state.

[0813] Server: Generates personalized health advice and meal plans based on the user's health and emotional state. For example, if a high-stress state is detected, it will also generate advice on optimal relaxation methods and mental health care for that user.

[0814] Interacting with an avatar

[0815] Device (Smartphone): The avatar within the smartphone application answers health-related questions through dialogue with the user. When the user enters a question into the application, the content is sent to the server. The emotion engine recognizes emotions from the user's input, speech, and facial expressions, and adjusts the content of the dialogue accordingly.

[0816] Server: The server uses a natural language processing (NLP) engine to analyze the user's question and generate an appropriate answer. It also incorporates the results of an emotion engine to generate the optimal answer based on the user's emotional state. The generated answer is then sent to the smartphone application.

[0817] Device (Smartphone): An avatar within the application provides emotionally sensitive responses to the user. Through this interaction, the user can receive real-time, emotion-based health advice.

[0818] Integration with other health management apps

[0819] Server: The cloud server receives data from other health management applications and integrates and manages it in the existing database. This integration enables centralized management of users' health data, allowing for more accurate advice to be provided.

[0820] Stress management and mental health support

[0821] Devices (smartwatch and smartphone): The smartwatch continuously monitors the user's heart rate variability and estimates their stress level. The smartphone application sends this data to a cloud server.

[0822] Server: If a high-stress state is detected, the cloud server generates relaxation and mental health advice, which is then combined with the emotion engine and provided to the user. Based on this, the user can take appropriate action.

[0823] Sharing information with doctors

[0824] User: If a user wants to share vital information with a doctor, they make that request through the smartphone application.

[0825] Server: The cloud server uses generative AI to summarize the collected vital information and generate a report. The generated report is then sent to the physician after user review. This process enables efficient information sharing between the user and the physician.

[0826] Specific example

[0827] Example 1: Daytime health monitoring and emotion recognition

[0828] Device (Smartwatch): During the day, the smartwatch collects the user's heart rate, steps, and sleep data. Simultaneously, the emotion engine recognizes the user's emotions from their voice and facial expressions.

[0829] Device (smartphone): Sends collected biometric and emotional data to a cloud server.

[0830] Server: The cloud server analyzes this data and determines if the user is experiencing stress. Taking the user's emotional state into consideration, it generates advice such as, "You have been in a stressful situation recently. We recommend you relax by taking deep breaths or a short walk."

[0831] Device (smartphone): The avatar provides emotionally sensitive advice to the user via text and voice.

[0832] Example 2: Sharing information with doctors

[0833] User: The user requests to share vital information with a doctor for a health checkup.

[0834] Server: The cloud server summarizes biometric and emotional data and compiles it into a report. This report is then sent to the doctor after user review.

[0835] Doctor: Based on the received report, the doctor will make a comprehensive assessment of the user's health condition and take appropriate measures.

[0836] As described above, the present invention, which incorporates an emotion engine, is a system that supports users' health management and provides advanced health support that takes into account emotional states through data analysis and the generation of personalized advice.

[0837] The following describes the processing flow.

[0838] Step 1:

[0839] Device (smartwatch): Collects biometric data such as the user's heart rate, steps taken, and sleep duration in real time. Sensors within the smartwatch periodically measure and temporarily store this data.

[0840] Step 2:

[0841] Device (Smartwatch): The smartwatch has a built-in emotion engine that recognizes emotions from the user's voice, facial expressions, and gestures. The collected emotion data is temporarily stored along with biometric data.

[0842] Step 3:

[0843] The device (smartphone) communicates with the smartwatch via Bluetooth and periodically transfers collected biometric and emotional data to the smartphone. The smartphone checks the received data and verifies its integrity.

[0844] Step 4:

[0845] Device (smartphone): Smartphones transmit data to cloud servers via the internet. During this process, protocols are used to encrypt the data and maintain security.

[0846] Step 5:

[0847] Server: The cloud server securely stores received biometric and emotional data in a database. Before saving, it validates the data format, cleans the data, and corrects for missing or outlier values.

[0848] Step 6:

[0849] Server: The server processes the stored data using analytical algorithms to evaluate the user's health and emotional state. For example, it might use a machine learning model to estimate stress levels from the user's heart rate variability.

[0850] Step 7:

[0851] Server: Generates personalized health advice and meal plans based on the results of machine learning models and data analysis. This process considers not only the user's past data and current health status, but also their emotional state.

[0852] Step 8:

[0853] Server: Formats the data and sends it to the smartphone application to provide the generated advice and plans to the user.

[0854] Step 9:

[0855] Device (Smartphone): The smartphone application displays received advice and plans to the user. If necessary, an avatar provides emotionally sensitive advice via text or voice.

[0856] Step 10:

[0857] User: Begin a conversation with the avatar and enter health-related questions. For example, ask, "How has your sleep been lately?"

[0858] Step 11:

[0859] Device (smartphone): Retrieves user questions as text data and sends it to the cloud server.

[0860] Step 12:

[0861] Server: Uses a natural language processing (NLP) engine to analyze user questions and understand their intent. Refers to stored biometric data, sentiment data, and past advice logs to generate appropriate answers.

[0862] Step 13:

[0863] Server: Sends the answers generated based on the questions to the smartphone application. At this time, it also considers the results of the emotion engine to generate the optimal answer according to the user's emotional state.

[0864] Step 14:

[0865] Device (smartphone): The avatar provides the user with emotionally sensitive responses and completes the conversation. This allows the user to obtain the necessary information in real time.

[0866] Step 15:

[0867] Server: Handles integration to receive data from other health management applications. Uses APIs to retrieve additional data and manage it in an integrated manner.

[0868] Step 16:

[0869] Server: Integrates data from other applications received and performs a more detailed analysis of the user's overall health status. Based on the results, it generates further advice.

[0870] Step 17:

[0871] Devices (smartwatch and smartphone): The smartwatch continuously monitors the user's heart rate variability and sends stress level data to the smartphone. The smartphone then sends this data to a cloud server.

[0872] Step 18:

[0873] Server: If a high-stress state is detected, the cloud server generates relaxation and mental health advice, taking emotional data into consideration, and provides it to the user.

[0874] Step 19:

[0875] User: The app requests the sharing of vital information with a doctor as needed. The smartphone application sends this request to the cloud server.

[0876] Step 20:

[0877] Server: Uses generative AI to summarize the user's vital information and generate a report. After user confirmation, the report is sent to the doctor.

[0878] (Example 2)

[0879] 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".

[0880] Traditional health management systems focus on collecting and analyzing users' biometric data, but they fail to adequately provide health advice that takes emotional states into account. This makes it difficult to accurately assess users' psychological health and provide personalized advice. Furthermore, the insufficient functionality for integrating data from multiple health management applications makes it challenging to achieve comprehensive health management for users.

[0881] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting biometric data such as heart rate, steps, and sleep time; means for transmitting the biometric data and emotional data to a cloud server via a smartphone; means for analyzing the biometric data and emotional data at the cloud server and generating individual health advice; means for providing the generated health advice to the user; means for answering health-related questions through dialogue with the user using an avatar; means for sharing a summary of vital information and emotional data with medical professionals; means for receiving data from other health management software and managing it integrally; and means for monitoring the user's stress level and psychological health status and providing relaxation and mental care advice as needed. This enables integrated analysis of the user's biometric data and emotional data, and allows for the provision of individual health advice. Furthermore, by integrally managing data from multiple health management applications, comprehensive health management of the user becomes possible.

[0882] "Biometric data" refers to data that indicates the user's physical condition, such as heart rate, steps taken, and sleep duration.

[0883] "Emotional data" refers to data obtained from the user's voice, facial expressions, etc., that indicates the user's emotional state.

[0884] A "cloud server" is a computer system that operates as a remote server accessible via the internet, performing tasks such as data storage, analysis, and advice generation.

[0885] "Health advice" is generated by a cloud server analyzing biometric and emotional data, and provides information and specific suggestions for actions that can help users maintain and improve their health.

[0886] An "avatar" is a virtual character with which a user interacts through a smartphone application, and is used to answer questions about health.

[0887] "Vital information" refers to important biometric data related to life support, such as the user's heart rate, blood pressure, and body temperature.

[0888] A "medical professional" is a professional qualified to monitor, diagnose, and treat a user's health condition, and includes doctors and nurses.

[0889] "Integrated management" means consolidating data collected from different health management software and devices into a single system and managing it as a whole.

[0890] "Stress level" refers to the degree of psychological and physiological stress estimated from the user's heart rate variability, etc.

[0891] "Mental care" refers to advice and suggestions for specific implementation methods to maintain and improve the user's psychological health.

[0892] This invention is a system for supporting users' health management, and by combining it with an emotion engine, it provides more personalized health advice. Specific embodiments of this invention are described below.

[0893] Hardware and software to be used

[0894] Hardware:

[0895] Smartwatch (equipped with sensors to collect heart rate, steps, and sleep time)

[0896] Smartphone (with Bluetooth and internet connectivity)

[0897] software:

[0898] Smartphone application (equipped with data transmission and avatar interaction functions)

[0899] Cloud servers (data storage, data analysis, machine learning models, sentiment engines, natural language processing engines)

[0900] System-wide operation overview

[0901] Device (smartwatch):

[0902] Smartwatches collect biometric data such as heart rate, steps taken, and sleep duration in real time. They also collect emotional data such as voice and facial expressions, and transmit this data to a smartphone.

[0903] Device (smartphone):

[0904] The smartphone application receives biometric and emotional data transmitted from the smartwatch and sends it to a cloud server via the internet.

[0905] server:

[0906] The cloud server securely stores the received biometric and emotional data in a database. The stored data undergoes data format validation and data cleaning, correcting for missing or outlier values. Machine learning models and algorithms are then used to analyze the data and assess the user's health and emotional state. Based on these states, personalized health advice, relaxation methods, and mental health care advice are generated.

[0907] Device (smartphone):

[0908] The generated health advice and emotionally sensitive responses are delivered to the user through an avatar in a smartphone application. The avatar uses a natural language processing engine to interact with the user. When the user enters a question, the content is sent to the server, and the most appropriate response is generated, taking into account the results of the emotion engine's analysis.

[0909] Specific examples of operation

[0910] Example 1: Daytime health monitoring and emotion recognition

[0911] Device (smartwatch):

[0912] Smartwatches collect heart rate, step count, and sleep data throughout the day.

[0913] The emotion engine recognizes emotions from voice and facial expressions.

[0914] Device (smartphone):

[0915] Biometric and emotional data collected from the smartwatch are sent to a cloud server.

[0916] server:

[0917] The cloud server analyzes this data and determines if the user is experiencing stress. It also considers the user's emotional state and generates advice such as, "You've been in a stressful situation recently. We recommend relaxing by taking deep breaths or a short walk."

[0918] Device (smartphone):

[0919] The avatar provides users with emotionally sensitive advice via text and voice.

[0920] Example 2: Sharing information with doctors

[0921] User:

[0922] I would like to share my vital signs with my doctor for a health checkup.

[0923] server:

[0924] The cloud server summarizes biometric and emotional data and compiles it into a report. This report is then sent to the doctor after user review.

[0925] Doctor:

[0926] Based on the received reports, we will comprehensively assess the user's health status and take appropriate measures.

[0927] Example of a prompt

[0928] "How do recent heart rate fluctuations affect things?"

[0929] "What are some ways to relieve stress?"

[0930] "Please give me some advice on how to improve the quality of my sleep last night."

[0931] This invention is an advanced health management system incorporating an emotion engine, which provides personalized health advice through integrated analysis of the user's biometric and emotional data. It also aims to achieve comprehensive health management for the user by integrating data from other health management applications.

[0932] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0933] Step 1: Collection of biometric and emotional data

[0934] Device (smartwatch):

[0935] Smartwatches collect biometric data such as heart rate, steps taken, and sleep duration in real time. They also recognize emotional states from data such as voice and facial expressions.

[0936] Input: User biometric data (heart rate, steps, sleep patterns) and emotional data (voice, facial expressions)

[0937] Output: Collected biometric and emotional data

[0938] Specific operation: A heart rate sensor measures heart rate every 10 seconds, and a pedometer sensor counts steps every minute. A small camera and microphone analyze voice and facial expressions, providing information to the emotion engine.

[0939] Step 2: Send

[0940] Device (smartwatch):

[0941] The smartwatch transmits data to the smartphone via Bluetooth.

[0942] Device (smartphone):

[0943] The data received by the smartphone is sent to a cloud server via the internet.

[0944] Input: Biometric and emotional data transmitted from the smartwatch.

[0945] Output: Data sent to the cloud server

[0946] Specific operation: The smartwatch sends batch data to the smartphone every 30 minutes, and the smartphone uploads the data to the cloud server in real time.

[0947] Step 3: Data storage and preprocessing

[0948] server:

[0949] The cloud server stores the received data in a secure database. It verifies the data format, performs data cleaning, and corrects missing or outlier values.

[0950] Input: Biometric data and emotional data transmitted from a smartphone

[0951] Output: Verified and cleaned data

[0952] Specific operation: Daily batch processing verifies the data format and filters out abnormal heart rate values ​​(e.g., 300 bpm) using an anomaly detection algorithm, and then formats the data into a standard format.

[0953] Step 4: Data Analysis

[0954] server:

[0955] We use machine learning models and algorithms to perform detailed analyses and evaluate the user's health and emotional state.

[0956] Input: Verified and cleaned biometric and emotional data

[0957] Output: Assessment results of health and emotional state

[0958] Specific operation: A machine learning model on a cloud server analyzes data every night to calculate seven-day health and emotional trends.

[0959] Step 5: Generating personalized advice

[0960] server:

[0961] Based on the user's health and emotional state, it generates personalized health advice, relaxation methods, and mental health care advice.

[0962] Input: Assessment results of health and emotional status

[0963] Output: Personalized health advice

[0964] Specific operation: If a high-stress state is detected, the server generates text and video links suggesting "deep breathing exercises" and "5 minutes of meditation," and sends them to the smartphone.

[0965] Step 6: Interacting with the Avatar

[0966] Device (smartphone):

[0967] The avatar answers health-related questions through interaction with the user. The user's questions are sent to the server.

[0968] server:

[0969] The system uses a natural language processing engine to analyze questions and then incorporates the results of a sentiment engine to generate the optimal answer.

[0970] Input: User's question data

[0971] Output: Emotionally sensitive response

[0972] Specific operation: When a user enters "I haven't been sleeping well lately" into the app, the server analyzes the sleep data and generates advice such as "avoid using your smartphone before going to bed."

[0973] Step 7: Integration with other health management apps

[0974] server:

[0975] It receives data from other health management applications and manages it centrally.

[0976] Input: Data from other health management applications

[0977] Output: Integrated health data

[0978] Specific operation: Receives sleep data from other apps, integrates that data with existing watch data, and uses it for comprehensive analysis.

[0979] Step 8: Stress Management and Mental Health Support

[0980] Devices (smartwatches and smartphones):

[0981] The smartwatch continuously monitors heart rate variability and estimates stress levels. The smartphone sends this data to a cloud server.

[0982] server:

[0983] If a high-stress state is detected, it generates relaxation and mental health advice.

[0984] Input: Heart rate variability data

[0985] Output: Advice on relaxation and mental health care.

[0986] Specific operation: Heart rate variability is measured every minute, and if a high-stress state lasting more than two hours is detected, the cloud server generates mental health advice.

[0987] Step 9: Sharing information with doctors

[0988] User:

[0989] A request is made to share vital information with a doctor using a smartphone application.

[0990] server:

[0991] The system summarizes biometric and emotional data, generates a report, and sends it to a doctor after user review.

[0992] Input: Biometric data and emotional data

[0993] Output: Summarized report

[0994] Specific operation: When the user selects "Send information to doctor," the system summarizes the data from the past three months and generates a PDF report. The report includes a graph of heart rate and emotional fluctuations.

[0995] (Application Example 2)

[0996] 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."

[0997] Traditional health management systems provide health advice based on users' biometric data, but they do not take into account security risks associated with users' emotional states or stress levels. Therefore, they fail to prevent suspicious behavior caused by stress or anxiety, making it difficult to guarantee overall safety.

[0998] 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. In this invention, the server includes means for collecting the user's biometric data, means for transmitting the biometric data to a cloud server, means for analyzing the biometric data on the cloud server and generating individual health advice, means for providing the generated health advice to the user, means for answering health-related questions through dialogue with an avatar, means for sharing a summary of vital information with a doctor based on the user's selection, means for evaluating the user's security risk and generating a security alert when an abnormal stress level or emotional state is detected, and means for notifying the user of the security alert and encouraging safe behavior. This enables comprehensive support that considers not only the user's health management but also security risks due to emotional state and stress.

[0999] Definitions of important words

[1000] "User biometric data" refers to an individual's physiological information, such as heart rate, steps taken, and sleep duration.

[1001] A "cloud server" refers to a collection of remote servers that store and analyze data via the internet.

[1002] "Personalized health advice" refers to health management guidelines optimized for a specific individual, provided based on the user's biometric data and emotional state.

[1003] An "avatar" refers to a virtual character that interacts with the user and provides information within an application.

[1004] "Vital information" refers to key bodily data related to maintaining life, such as heart rate, blood pressure, and body temperature.

[1005] "Security risk" refers to potential dangers to an individual's safety that can be inferred from a user's biometric data and emotional state.

[1006] A "security alert" refers to a warning that notifies users of potential security risks, generated based on their biometric data and emotional state.

[1007] A "Natural Language Processing (NLP) engine" refers to software that understands meaning from text and audio and analyzes human natural language.

[1008] Modes for carrying out the invention

[1009] This invention is a system that comprehensively supports the user's health and security. Specific embodiments for carrying out this invention are described below.

[1010] System Configuration

[1011] This system consists of a smartwatch that collects the user's biometric data, a smartphone that transmits the data, a cloud server, and an avatar that provides advice and security alerts to the user.

[1012] Data collection and transmission

[1013] The device (smartwatch) collects biometric data such as the user's heart rate, steps taken, and sleep duration in real time. It also collects emotional data from voice and facial expressions. This data is transmitted to the smartphone via Bluetooth.

[1014] The device (smartphone) receives data transmitted from the smartwatch and sends it to a cloud server via the internet.

[1015] Data Analysis

[1016] The server (cloud server) stores the received biometric and emotional data in a secure database. First, the data format is validated and the data is cleaned, correcting for missing or outlier values. Then, machine learning models and algorithms are used to analyze the data and evaluate the user's health and emotional state.

[1017] Generating health advice and security alerts

[1018] The server generates personalized health advice based on the analysis results. For example, if a high-stress state is detected, it will generate advice on relaxation methods and mental health care.

[1019] Furthermore, the system assesses security risks and generates security alerts if abnormal stress levels or emotional states are detected. These advisories and alerts are communicated to the user. Notifications are displayed within the smartphone application and delivered via an avatar.

[1020] User interaction

[1021] The avatar on the device (smartphone) answers health-related questions through interaction with the user. When the user enters a question into the application, the content is sent to the server. The emotion engine recognizes emotions from the user's entered text, speech, and facial expressions.

[1022] The server uses a natural language processing (NLP) engine to analyze the user's question and generate an appropriate answer. The generated answer is then sent back to the smartphone, where an avatar presents it to the user in an emotionally sensitive manner.

[1023] Specific examples and prompt statements

[1024] Specific example

[1025] For example, suppose a user is about to open their door upon returning home, and their smartwatch detects a sudden increase in heart rate and a high level of stress. In this case, the cloud server analyzes the data and generates a security alert. A notification is sent to the smartphone application saying, "Please relax. Check your surroundings for any suspicious activity and open the door only after you are sure it is safe."

[1026] Example of a prompt

[1027] "Please check your current heart rate and stress level."

[1028] "You are under high stress. Take a deep breath and ensure your safety before proceeding."

[1029] Through this configuration, comprehensive assessment of user health and security risks is performed, enabling the provision of individually optimized advice and alerts.

[1030] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1031] Program processing steps

[1032] Step 1:

[1033] The device (smartwatch) collects the user's heart rate, steps, sleep duration, and emotional data in real time. This data is temporarily stored within the smartwatch. Input is the user's biometric and emotional data, and output is data transfer to the smartphone.

[1034] Step 2:

[1035] The device (smartphone) receives data from the smartwatch via Bluetooth. The smartphone checks the received data and sends it to a cloud server via the internet. The input is biometric and emotional data transferred from the smartwatch, and the output is the transmission of data to the cloud server.

[1036] Step 3:

[1037] The server (cloud server) stores the received data in a secure database. Next, it performs data format validation and data cleaning, correcting for missing or outlier values. The input is data sent from a smartphone, and the output is clean data that can be analyzed.

[1038] Step 4:

[1039] The server uses a machine learning model to analyze data and assess the user's health and emotional state. The analysis results include stress levels and normal / abnormal health status. The input is clean data, and the output is the assessment of the user's health and emotional state.

[1040] Step 5:

[1041] The server generates personalized health advice based on the analysis results. For example, if a high-stress state is detected, it will generate advice on relaxation methods and mental health care. The input is the health assessment results, and the output is health advice text.

[1042] Step 6:

[1043] The server assesses the user's security risks and generates security alerts if abnormal stress levels or emotional states are detected. Inputs are health and emotional assessment results, and outputs are security alerts.

[1044] Step 7:

[1045] The server sends the generated health advice and security alerts to the smartphone application. The input is the health advice and security alerts, and the output is the transmission of data to the smartphone.

[1046] Step 8:

[1047] The device (smartphone) notifies the user of received health advice and security alerts through an avatar. For example, the avatar provides emotionally sensitive advice to the user, such as, "Please relax. Check your surroundings for any suspicious activity and open the door only after you have confirmed that it is safe." The input is the advice and alerts sent from the server, and the output is the notification to the user.

[1048] Step 9:

[1049] Users ask health and security-related questions to an avatar via a smartphone application. The input is the user's question, and the output is the avatar's response.

[1050] Step 10:

[1051] The server receives the user's question and analyzes it using a natural language processing (NLP) engine. An emotion engine determines the user's emotional state and generates an appropriate response. The input is the user's question, and the output is the appropriate response.

[1052] Step 11:

[1053] The device (smartphone) notifies the user of the response received from the server via an avatar. The avatar provides instructions such as, "Take a deep breath and proceed only after confirming your safety." The input is the response from the server, and the output is the notification to the user.

[1054] In this way, data is collected, transmitted, and analyzed throughout each step and provided to the user as health advice and security alerts. Specific prompts include messages such as, "Check your current heart rate and stress level," and "You are experiencing high stress; take a deep breath and ensure your safety before proceeding."

[1055] 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.

[1056] 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.

[1057] 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.

[1058] [Third Embodiment]

[1059] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1060] 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.

[1061] 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).

[1062] 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.

[1063] 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.

[1064] 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).

[1065] 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.

[1066] 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.

[1067] 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.

[1068] 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.

[1069] 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.

[1070] 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".

[1071] This invention is a system for supporting users' health management and is configured as follows: The user uses a dedicated smartwatch and a smartphone application. The smartwatch collects biometric data such as heart rate, steps taken, and sleep duration in real time. The collected data is transmitted to a cloud server via the smartphone application. The cloud server analyzes the biometric data and generates personalized health advice.

[1072] Collection and transmission of health data

[1073] Device (Smartwatch): The smartwatch collects biometric data such as the user's heart rate, steps taken, and sleep duration in real time. This data is transmitted to a smartphone application via Bluetooth or other means.

[1074] Device (smartphone): The smartphone application receives data sent from the smartwatch and transmits it to a cloud server via the internet.

[1075] Data analysis and generation of personalized advice

[1076] Server: The cloud server stores the received biometric data in a secure database. Next, machine learning models and algorithms are used to analyze the data in detail and assess the user's health status. Based on this assessment, personalized health advice and meal plans are generated.

[1077] Interacting with an avatar

[1078] Device (Smartphone): The avatar within the smartphone application can answer health-related questions through interaction with the user. When the user enters a question into the application, the content is sent to the server.

[1079] Server: The server uses a natural language processing (NLP) engine to analyze the user's question and generate an appropriate answer. The generated answer is then sent to the terminal.

[1080] Device (smartphone): An avatar within the application provides answers to the user. Through this interaction, the user can obtain useful information about health.

[1081] Integration with other health management apps

[1082] Server: The cloud server can receive data from other health management applications and integrate it into the existing database for management. This integration enables centralized management of users' health data.

[1083] Stress management and mental health support

[1084] Devices (smartwatch and smartphone): The smartwatch continuously monitors the user's heart rate variability and estimates their stress level. The smartphone application sends this data to a cloud server.

[1085] Server: If a high stress level is detected, the cloud server generates specific advice to support relaxation and mental health. Based on this, the user can take appropriate action.

[1086] Sharing information with doctors

[1087] User: If a user wants to share vital information with a doctor, they make that request through the smartphone application.

[1088] Server: The cloud server uses generative AI to summarize the collected vital information and generate a report. The generated report is then sent to the physician after user review. This process enables efficient information sharing between the user and the physician.

[1089] Specific example

[1090] Example 1: Nighttime health monitoring

[1091] Device (Smartwatch): At night, the smartwatch collects the user's sleep data. This includes detailed data such as sleep depth and sleep cycles.

[1092] Device (smartphone): In the morning, data from the smartwatch is sent to the smartphone. The data is then sent to the cloud server.

[1093] Server: The cloud server analyzes this data and generates advice such as, "According to your recent sleep data, you tend to be getting insufficient deep sleep. We recommend that you establish a routine of going to bed at a specific time to ensure you get quality sleep."

[1094] Device (smartphone): The avatar provides advice to the user and suggests specific ways to improve.

[1095] Example 2: Stress Management

[1096] Device (smartwatch): During the day, the smartwatch monitors the user's heart rate variability and detects high-stress states.

[1097] Server: The cloud server evaluates stress levels based on accumulated data and generates advice such as, "Recent data has detected a high stress level. We recommend taking some time to relax by taking deep breaths or meditating."

[1098] Device (smartphone): An avatar provides the user with stress-relief advice and explains in detail how to put it into practice.

[1099] As described above, the present invention is a system that supports users' health management and provides users with valuable information through data analysis and the generation of personalized advice.

[1100] The following describes the processing flow.

[1101] Step 1:

[1102] Device (smartwatch): Collects biometric data such as heart rate, steps taken, and sleep duration from the user in real time. Sensors within the smartwatch periodically measure and temporarily store this data.

[1103] Step 2:

[1104] The device (smartphone) communicates with the smartwatch via Bluetooth and periodically transfers the collected biometric data to the smartphone. The smartphone checks the received data and verifies its integrity.

[1105] Step 3:

[1106] Device (smartphone): Smartphones transmit data to cloud servers via the internet. During this process, protocols are used to encrypt the data and maintain security.

[1107] Step 4:

[1108] Server: The cloud server stores the received biometric data in a secure database. Before saving, it verifies the data format and performs data cleaning, correcting for missing or outlier values.

[1109] Step 5:

[1110] Server: The server processes the stored data using an analysis algorithm to assess the user's health status. For example, it might use a machine learning model to estimate stress levels from the user's heart rate variability.

[1111] Step 6:

[1112] Server: Generates personalized health advice and meal plans based on the results of machine learning models and data analysis. This process takes into account the user's past data and current health status.

[1113] Step 7:

[1114] Server: Formats the data and sends it to the smartphone application to provide the generated advice and plans to the user.

[1115] Step 8:

[1116] Device (Smartphone): The smartphone application displays received advice and plans to the user. An avatar provides advice via text or voice as needed.

[1117] Step 9:

[1118] User: Begin a conversation with the avatar and enter health-related questions. For example, ask, "How has your sleep been lately?"

[1119] Step 10:

[1120] Device (smartphone): Retrieves user questions as text data and sends it to the cloud server.

[1121] Step 11:

[1122] Server: Uses a natural language processing (NLP) engine to analyze user questions and understand their intent. Refers to stored data and past advice logs to generate appropriate answers.

[1123] Step 12:

[1124] Server: Sends the generated answer based on the question to the smartphone application.

[1125] Step 13:

[1126] Device (smartphone): The avatar provides answers to the user and completes the conversation. This allows the user to obtain the necessary information in real time.

[1127] Step 14:

[1128] Server: Handles integration to receive data from other health management applications. Uses APIs to retrieve additional data and manage it in an integrated manner.

[1129] Step 15:

[1130] Server: Integrates data from other applications received and performs a more detailed analysis of the user's overall health status. Based on the results, it generates further advice.

[1131] Step 16:

[1132] Devices (smartwatch and smartphone): The smartwatch continuously monitors the user's heart rate variability and sends stress level data to the smartphone. The smartphone then sends this data to a cloud server.

[1133] Step 17:

[1134] Server: If a high-stress state is detected, the cloud server generates and provides relaxation and mental health advice to the user.

[1135] Step 18:

[1136] User: The app requests the sharing of vital information with a doctor as needed. The smartphone application sends this request to the cloud server.

[1137] Step 19:

[1138] Server: Uses generative AI to summarize the user's vital information and generate a report. After user confirmation, the report is sent to the doctor.

[1139] (Example 1)

[1140] 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."

[1141] The present invention aims to provide a system that efficiently and effectively supports users' health management. Existing health management systems lack sufficient collection and analysis of biometric data, as well as adequate feedback to users, resulting in insufficient personalized health advice. Furthermore, they lack support for stress management and mental health care, and there is no easy way for users to share vital information with healthcare professionals.

[1142] 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.

[1143] In this invention, the server includes means for analyzing the user's biometric data and generating personalized health advice, means for answering health-related questions through interaction with an avatar, and means for sharing a summary of vital information with healthcare professionals. This allows the user to understand their health status in real time and receive necessary health advice. Furthermore, comprehensive health management is achieved through stress level monitoring and the provision of mental health care advice. In addition, the means for sharing information with healthcare professionals enables prompt and appropriate medical responses.

[1144] "Biometric data" refers to data that indicates a user's physical functions and state, such as heart rate, steps taken, and sleep duration.

[1145] A "mobile terminal" is a portable communication device with data communication capabilities, and specifically includes smartphones and tablet devices.

[1146] A "cloud server" refers to a remote server that stores and processes data via the internet.

[1147] "Health advice" refers to specific instructions or suggestions for improving or maintaining a user's health.

[1148] An "avatar" is a digital representation that acts on behalf of a user on a computer and has interactive functions such as conversation.

[1149] "Healthcare professionals" refer to individuals with specialized knowledge and qualifications related to medicine and health, such as doctors and nurses.

[1150] "Health management software" refers to application software used to monitor and manage a user's health status.

[1151] "Stress level" is an indicator that shows the degree of stress a user experiences.

[1152] "Mental health care" refers to care methods and activities aimed at maintaining or improving a user's mental health.

[1153] "Vital information" refers to data on the body's basic physiological functions, such as heart rate, body temperature, and respiratory rate.

[1154] This invention is a system for supporting user health management and is implemented using the following hardware and software.

[1155] Hardware and software to be used

[1156] terminal

[1157] Smartwatch: A device for collecting biometric data such as heart rate, steps taken, and sleep duration. Built-in sensors include heart rate sensors and accelerometers.

[1158] Smartphone: A mobile device used to send data collected from a smartwatch to a cloud server, and a platform for running applications.

[1159] server

[1160] Cloud servers are remote servers used for storing and analyzing data over the internet. Key software includes databases (e.g., AWS RDS, Google Cloud Firestore), machine learning models (e.g., TensorFlow, PyTorch), and natural language processing engines (e.g., BERT, GPT-3).

[1161] Data collection and transmission

[1162] Device (smartwatch)

[1163] Smartwatches collect biometric data such as the user's heart rate, steps taken, and sleep duration in real time. This data is transmitted to a smartphone using Bluetooth technology.

[1164] Device (smartphone)

[1165] The smartphone receives data from the smartwatch via Bluetooth and sends it to a cloud server over the internet. A dedicated application is installed on the smartphone, and this app manages the receiving and sending of data.

[1166] Data storage and analysis

[1167] server

[1168] The cloud server receives biometric data transmitted from smartphones via the internet and stores it in a secure database. The stored data is analyzed using machine learning models (e.g., TensorFlow, PyTorch). Based on the analysis results, the user's health status is evaluated and personalized health advice is generated.

[1169] Interacting with an avatar

[1170] Device (smartphone)

[1171] The smartphone application features an avatar that answers health-related questions through interaction with the user. When the user enters a question into the application, the content is sent to the server.

[1172] server

[1173] The server uses a natural language processing engine (e.g., BERT, GPT-3) to analyze the user's question and generate an appropriate answer. The generated answer is then sent back to the smartphone.

[1174] Device (smartphone)

[1175] An avatar within a smartphone application provides the user with generated responses. This interactive feature allows users to easily obtain useful health-related information.

[1176] Integration with other health management software

[1177] server

[1178] The cloud server receives data from other health management software (e.g., Google Fit, Apple HealthKit) and integrates it into the existing database for management. This integration allows for centralized management of users' health data.

[1179] Stress management and mental health support

[1180] Devices (smartwatches and smartphones)

[1181] The smartwatch monitors the user's heart rate variability and sends the data to the smartphone. The smartphone then sends this data to a cloud server.

[1182] server

[1183] The cloud server assesses stress levels and generates specific advice to support relaxation and mental health. This generated advice is then sent to the user's smartphone.

[1184] Sharing information with healthcare professionals

[1185] User

[1186] If a user wants to share vital information with a healthcare professional, they can make that request through a smartphone application.

[1187] server

[1188] The cloud server uses a generative AI model (e.g., GPT-4) to summarize the collected vital information and generate a report. This report is then reviewed by the user and sent to healthcare professionals.

[1189] Specific example

[1190] Collection of sleep data and generation of advice

[1191] (Specific example)

[1192] At night, the smartwatch collects the user's sleep data. It records cycles of deep and light sleep and generates detailed data. In the morning, the data is sent to the smartphone and then transferred to a cloud server. The cloud server analyzes the data and generates advice such as, "According to your recent sleep data, you are not getting enough deep sleep. We recommend that you establish a habit of going to bed at a specific time." An avatar on the smartphone then conveys this advice to the user.

[1193] As illustrated by the above specific example, the present invention is a system that supports users' health management and provides useful information to users by offering advanced data analysis and personalized health advice.

[1194] Stress management practices

[1195] (Example of a prompt message)

[1196] "I've been under a lot of stress lately. Do you have any recommendations for coping strategies?"

[1197] When a user enters a question using such a prompt, the server analyzes it, generates appropriate advice, and provides it to the user.

[1198] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1199] Step 1: Data Collection

[1200] Device (smartwatch)

[1201] Smartwatches collect biometric data such as the user's heart rate, steps taken, and sleep duration in real time. Heart rate is recorded every second, and steps are tallied every hour. Sleep data is recorded every 30 minutes during the night, showing the state of deep sleep and light sleep.

[1202] Input: User's real-time biometric data

[1203] Output: Biometric data stored in the smartwatch

[1204] Specifically, the smartwatch's heart rate sensor detects the heart rate, and the accelerometer measures the number of steps. This data is temporarily stored in the built-in memory.

[1205] Step 2: Send

[1206] Device (smartwatch)

[1207] Smartwatches transmit collected biometric data to smartphones using Bluetooth connectivity.

[1208] Input: Biometric data stored in the smartwatch

[1209] Output: Biometric data transferred to a smartphone

[1210] Specifically, the smartwatch attempts to transmit data every minute, transferring the data to the smartphone in batches via Bluetooth.

[1211] Step 3: Send data to the cloud

[1212] Device (smartphone)

[1213] Smartphones receive data via Bluetooth and send it to a cloud server over the internet. A dedicated application is installed on the smartphone, which manages the receiving and sending of data.

[1214] Input: Biometric data received from a smartwatch

[1215] Output: Biometric data sent to the cloud server

[1216] Specifically, the smartphone app runs in the background, and once an internet connection is established, it uploads the biometric data to a cloud server.

[1217] Step 4: Save Data

[1218] server

[1219] The cloud server receives data sent from smartphones via the internet and stores it in a secure database. Data integrity checks and encryption are performed before storage.

[1220] Input: Biometric data sent from a smartphone

[1221] Output: Biometric data stored in a secure database

[1222] Specifically, when biometric data arrives at the cloud server, its integrity is checked and it is encrypted (e.g., AES-256) before being inserted into the database.

[1223] Step 5: Data Analysis

[1224] server

[1225] The cloud server analyzes data using machine learning models (e.g., TensorFlow, PyTorch) to assess the user's health status. This analysis includes data preprocessing, model input, and retrieval of evaluation results.

[1226] Input: Biometric data stored in the database

[1227] Output: Evaluation results regarding the user's health status

[1228] Specifically, the biometric data is pre-processed to remove noise and normalize it before being input into a machine learning model. The model then outputs an evaluation score of the user's health status and trends indicating abnormalities as part of its analysis results.

[1229] Step 6: Generate Advice

[1230] server

[1231] The server generates personalized health advice for users based on the evaluation results of machine learning models. This process utilizes a natural language generation engine (e.g., GPT-4).

[1232] Input: Health status assessment results

[1233] Output: Personalized health advice

[1234] Specifically, the evaluation results are templated, and a generative AI model is used to generate health advice in natural language. For example, advice such as, "According to recent data, you are not getting enough deep sleep. We recommend that you establish a habit of going to bed at a specific time," might be generated.

[1235] Step 7: Avatar-based interaction

[1236] Device (smartphone)

[1237] When a user enters a health-related question for their avatar, the information is sent to the server.

[1238] Input: Question entered by the user

[1239] Output: Question data sent to the server

[1240] Specifically, the smartphone application sends user input to the server in text format.

[1241] server

[1242] The server analyzes the question using a natural language processing engine (e.g., BERT, GPT-3) and generates an appropriate answer. The generated answer is then sent back to the smartphone.

[1243] Input: Question data submitted by the user

[1244] Output: Response to the user

[1245] Specifically, the natural language processing engine analyzes the content of the question, the sentence generation engine creates the optimal answer, and sends it back to the smartphone in text format.

[1246] Device (smartphone)

[1247] An avatar within the application provides the user with the generated response. The response can also be read aloud using a speech synthesis engine.

[1248] Input: Response data sent from the server

[1249] Output: Answers provided by the user

[1250] Specifically, the avatar displays text on the screen and, when necessary, outputs speech using a speech synthesis engine.

[1251] Step 8: Sharing information with healthcare professionals

[1252] User

[1253] If a user wants to share vital information with a healthcare professional, they can make a request through the smartphone application.

[1254] Input: Request for information sharing

[1255] Output: Sending a sharing request to the server

[1256] Specifically, the user instructs the app to share vital information, and a request is sent to the server.

[1257] server

[1258] The cloud server uses a generative AI model (e.g., GPT-4) to summarize vital information and generate a report. This report is then sent to healthcare professionals after user review.

[1259] Input: Vital signs and information sharing request

[1260] Output: Summary report sent to healthcare professionals

[1261] Specifically, the system analyzes and summarizes the user's vital signs and generates a report in PDF format. After user confirmation, the report is sent to the healthcare professional's email address.

[1262] The above outlines the specific processing steps of this system's program. By detailing what processing and calculations are performed on the input data at each step and what the final output is, the overall operation of the system becomes clear.

[1263] (Application Example 1)

[1264] 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."

[1265] Effectively monitoring the health and stress levels of factory workers is crucial for improving productivity and ensuring safety. However, current systems have limitations in collecting and analyzing individual workers' health data in real time and providing appropriate advice and improvement measures. Therefore, a new system is needed to efficiently and effectively manage worker health.

[1266] 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.

[1267] In this invention, the server includes means for collecting heart rate and movement data of workers in a factory and monitoring the work environment; means for analyzing the collected data and generating health advice and safety improvement measures based on the work environment; and means for providing the generated advice and improvement measures via in-factory displays and voice guidance systems. This makes it possible to monitor the health status of workers in the factory in real time and provide appropriate advice and improvement measures.

[1268] "Biometric data" refers to data that indicates the user's physical condition, such as heart rate, movement data, and posture.

[1269] A "cloud server" is a remote server used to store, analyze, and manage data via the internet.

[1270] "Health advice" refers to specific suggestions and instructions generated based on analyzed biometric data to support the user's health management.

[1271] An "avatar" is a virtual character or agent used to interact with users.

[1272] "Vital information" refers to key biometric data that indicates vital functions, such as the user's heart rate, body temperature, and blood pressure.

[1273] A "factory" is a facility where manufacturing and production activities take place.

[1274] A "worker" is a person who engages in production activities within a factory.

[1275] "Motion data" refers to data that shows the physical movements and activity levels of a worker.

[1276] "Safety improvement measures" refer to specific proposals and measures to ensure worker safety and improve the working environment.

[1277] A "display" is a device used to visually display information.

[1278] A "voice guidance system" is a system that provides information and instructions using voice.

[1279] The present invention is a system for supporting the health management of workers in a factory, and is configured as follows: The system includes a factory robot, a data collection server, a cloud server, and a display or voice guidance system.

[1280] Collection and transmission of health data

[1281] Terminal (factory robot):

[1282] Factory robots are equipped with sensors and cameras to collect worker heart rate and movement data. This data is collected in real time. The factory robots transmit the collected data to a data collection server. Wireless communication technologies such as Bluetooth are used for this purpose.

[1283] Terminal (data collection server):

[1284] The data collection server receives data transmitted from factory robots and sends it to a cloud server via the internet. This ensures that the data is analyzed in a secure environment.

[1285] Data analysis and generation of personalized advice

[1286] Server (Cloud Server):

[1287] The cloud server securely stores the received biometric data and analyzes it in detail using machine learning models (e.g., TensorFlow or PyTorch). Based on this analysis, it evaluates the health and safety status of workers and generates personalized health advice and safety improvement measures.

[1288] Interacting with an avatar

[1289] Terminal (factory robot):

[1290] The factory robots are equipped with displays and speakers, allowing avatars to interact with workers and answer health-related questions. When a worker enters a question into the application, the information is sent to a server.

[1291] Server (Cloud Server):

[1292] The cloud server uses a natural language processing (NLP) engine to analyze the worker's questions and generate appropriate answers. These answers are then provided to the worker through the robot's display or speaker.

[1293] Stress management and mental health support

[1294] Terminals (factory robots and data collection servers):

[1295] Factory robots continuously monitor workers' heart rate variability and estimate their stress levels. Data collection servers transmit this data to cloud servers.

[1296] Server (Cloud Server):

[1297] If the cloud server detects a high stress level, it generates specific advice to support relaxation and mental health. Based on this, workers can take appropriate action.

[1298] Specific example

[1299] Example 1:

[1300] Suppose a worker considering whether or not to take a break asks a factory robot, "Should I take a break?" This question is collected by the robot and sent to a server. The server analyzes recent data, confirms that the stress level is high, and then generates advice such as, "Based on recent data, we recommend that you take a break." This advice is then communicated to the worker through the robot's display or speaker.

[1301] Example of a prompt:

[1302] "Generate advice to assess stress levels based on the worker's heart rate and activity data."

[1303] "Please analyze the following data and provide health advice: Heart rate: 80, Activity data: 'active', Posture: 'correct'"

[1304] Thus, the present invention is a system that supports the health management of workers in a factory in real time and provides valuable information to workers through data analysis and the generation of individual advice.

[1305] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1306] Step 1:

[1307] Data collection

[1308] The terminal (factory robot) uses sensors and cameras to collect worker heart rate and movement data in real time. The collected biometric data is transmitted to a data collection server using wireless communication technology such as Bluetooth. The input here is the biometric data acquired from the sensors and cameras, and the output is the data transferred to the data collection server.

[1309] Step 2:

[1310] Data transmission

[1311] The terminal (data collection server) receives biometric data transmitted from factory robots and sends it to a cloud server via the internet. The input is biometric data transmitted from factory robots, and the output is biometric data sent to the cloud server.

[1312] Step 3:

[1313] Data analysis and advice generation

[1314] The server (cloud server) stores the received biometric data in a secure database. The stored data is analyzed using machine learning models (using TensorFlow or PyTorch) to evaluate the user's health status and stress level. Appropriate health advice and safety improvement measures are then generated. The input is the biometric data stored on the cloud server, and the output is the generated health advice and safety improvement measures.

[1315] Step 4:

[1316] Interacting with an avatar

[1317] The terminal (factory robot) uses a display and speakers to have an avatar provide health advice and safety improvement measures to workers. When a worker inputs a question into the robot, the question is sent to a cloud server. The server uses a natural language processing (NLP) engine to analyze the question and generate an appropriate answer. The answer is then provided to the worker through the robot. In this step, the input is the question from the worker, and the output is the answer generated by the server.

[1318] Step 5:

[1319] Stress management and mental health support

[1320] The terminal (factory robot and data collection server) continuously monitors the worker's heart rate variability and transmits the data to a cloud server. If the server determines that the stress level is high, it generates advice for relaxation and mental health support. The advice is delivered through the factory robot's display and speakers. In this step, the input is heart rate variability data, and the output is the generated relaxation advice.

[1321] Thus, each step—data collection, transmission, analysis, advice generation, and delivery to the user—constitutes a system for real-time worker health management.

[1322] 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.

[1323] This invention is a system for supporting users' health management, and by combining it with an emotion engine, it provides more personalized health advice. The following describes a specific embodiment for carrying out this invention. The user uses a dedicated smartwatch and a smartphone application. The smartwatch collects biometric data such as heart rate, steps, and sleep duration in real time and transmits it to a cloud server via the smartphone. The cloud server analyzes the biometric data and generates individual health advice.

[1324] Furthermore, the system incorporates an emotion engine that recognizes the user's emotional state and reflects it in health advice. This allows for the provision of optimal advice tailored to the user's physical and mental condition.

[1325] Collection and transmission of health data

[1326] Device (Smartwatch): The smartwatch collects biometric data such as the user's heart rate, steps taken, and sleep duration in real time. The emotion engine also collects data such as voice and facial expressions to recognize the user's emotional state. This data is transmitted to the smartphone application via Bluetooth or other means.

[1327] Device (smartphone): The smartphone application receives biometric and emotional data transmitted from the smartwatch and sends it to a cloud server via the internet.

[1328] Data analysis and generation of personalized advice

[1329] Server: The cloud server securely stores received biometric and emotional data in a database. Before data is stored, the data format is validated and data cleaning is performed, correcting for missing or outlier values. The server uses machine learning models and algorithms to analyze the data in detail and assess the user's health and emotional state.

[1330] Server: Generates personalized health advice and meal plans based on the user's health and emotional state. For example, if a high-stress state is detected, it will also generate advice on optimal relaxation methods and mental health care for that user.

[1331] Interacting with an avatar

[1332] Device (Smartphone): The avatar within the smartphone application answers health-related questions through dialogue with the user. When the user enters a question into the application, the content is sent to the server. The emotion engine recognizes emotions from the user's input, speech, and facial expressions, and adjusts the content of the dialogue accordingly.

[1333] Server: The server uses a natural language processing (NLP) engine to analyze the user's question and generate an appropriate answer. It also incorporates the results of an emotion engine to generate the optimal answer based on the user's emotional state. The generated answer is then sent to the smartphone application.

[1334] Device (Smartphone): An avatar within the application provides emotionally sensitive responses to the user. Through this interaction, the user can receive real-time, emotion-based health advice.

[1335] Integration with other health management apps

[1336] Server: The cloud server receives data from other health management applications and integrates and manages it in the existing database. This integration enables centralized management of users' health data, allowing for more accurate advice to be provided.

[1337] Stress management and mental health support

[1338] Devices (smartwatch and smartphone): The smartwatch continuously monitors the user's heart rate variability and estimates their stress level. The smartphone application sends this data to a cloud server.

[1339] Server: If a high-stress state is detected, the cloud server generates relaxation and mental health advice, which is then combined with the emotion engine and provided to the user. Based on this, the user can take appropriate action.

[1340] Sharing information with doctors

[1341] User: If a user wants to share vital information with a doctor, they make that request through the smartphone application.

[1342] Server: The cloud server uses generative AI to summarize the collected vital information and generate a report. The generated report is then sent to the physician after user review. This process enables efficient information sharing between the user and the physician.

[1343] Specific example

[1344] Example 1: Daytime health monitoring and emotion recognition

[1345] Device (Smartwatch): During the day, the smartwatch collects the user's heart rate, steps, and sleep data. Simultaneously, the emotion engine recognizes the user's emotions from their voice and facial expressions.

[1346] Device (smartphone): Sends collected biometric and emotional data to a cloud server.

[1347] Server: The cloud server analyzes this data and determines if the user is experiencing stress. Taking the user's emotional state into consideration, it generates advice such as, "You have been in a stressful situation recently. We recommend you relax by taking deep breaths or a short walk."

[1348] Device (smartphone): The avatar provides emotionally sensitive advice to the user via text and voice.

[1349] Example 2: Sharing information with doctors

[1350] User: The user requests to share vital information with a doctor for a health checkup.

[1351] Server: The cloud server summarizes biometric and emotional data and compiles it into a report. This report is then sent to the doctor after user review.

[1352] Doctor: Based on the received report, the doctor will make a comprehensive assessment of the user's health condition and take appropriate measures.

[1353] As described above, the present invention, which incorporates an emotion engine, is a system that supports users' health management and provides advanced health support that takes into account emotional states through data analysis and the generation of personalized advice.

[1354] The following describes the processing flow.

[1355] Step 1:

[1356] Device (smartwatch): Collects biometric data such as the user's heart rate, steps taken, and sleep duration in real time. Sensors within the smartwatch periodically measure and temporarily store this data.

[1357] Step 2:

[1358] Device (Smartwatch): The smartwatch has a built-in emotion engine that recognizes emotions from the user's voice, facial expressions, and gestures. The collected emotion data is temporarily stored along with biometric data.

[1359] Step 3:

[1360] The device (smartphone) communicates with the smartwatch via Bluetooth and periodically transfers collected biometric and emotional data to the smartphone. The smartphone checks the received data and verifies its integrity.

[1361] Step 4:

[1362] Device (smartphone): Smartphones transmit data to cloud servers via the internet. During this process, protocols are used to encrypt the data and maintain security.

[1363] Step 5:

[1364] Server: The cloud server stores received biometric and emotional data in a secure database. Before saving, it validates the data format, cleans the data, and corrects for missing or outlier values.

[1365] Step 6:

[1366] Server: The server processes the stored data using an analysis algorithm to evaluate the user's health and emotional state. For example, it might use a machine learning model to estimate stress levels from the user's heart rate variability.

[1367] Step 7:

[1368] Server: Generates personalized health advice and meal plans based on the results of machine learning models and data analysis. This process considers not only the user's past data and current health status, but also their emotional state.

[1369] Step 8:

[1370] Server: Formats the data and sends it to the smartphone application to provide the generated advice and plans to the user.

[1371] Step 9:

[1372] Device (Smartphone): The smartphone application displays received advice and plans to the user. If necessary, an avatar provides emotionally sensitive advice via text or voice.

[1373] Step 10:

[1374] User: Begin a conversation with the avatar and enter health-related questions. For example, ask, "How has your sleep been lately?"

[1375] Step 11:

[1376] Device (smartphone): Retrieves user questions as text data and sends it to the cloud server.

[1377] Step 12:

[1378] Server: Uses a natural language processing (NLP) engine to analyze user questions and understand their intent. Refers to stored biometric data, sentiment data, and past advice logs to generate appropriate answers.

[1379] Step 13:

[1380] Server: Sends the answers generated based on the questions to the smartphone application. At this time, it also considers the results of the emotion engine to generate the optimal answer according to the user's emotional state.

[1381] Step 14:

[1382] Device (smartphone): The avatar provides the user with emotionally sensitive responses and completes the conversation. This allows the user to obtain the necessary information in real time.

[1383] Step 15:

[1384] Server: Handles integration to receive data from other health management applications. Uses APIs to retrieve additional data and manage it in an integrated manner.

[1385] Step 16:

[1386] Server: Integrates data from other applications received and performs a more detailed analysis of the user's overall health status. Based on the results, it generates further advice.

[1387] Step 17:

[1388] Devices (smartwatch and smartphone): The smartwatch continuously monitors the user's heart rate variability and sends stress level data to the smartphone. The smartphone then sends this data to a cloud server.

[1389] Step 18:

[1390] Server: If a high-stress state is detected, the cloud server generates relaxation and mental health advice, taking emotional data into consideration, and provides it to the user.

[1391] Step 19:

[1392] User: The app requests the sharing of vital information with a doctor as needed. The smartphone application sends this request to the cloud server.

[1393] Step 20:

[1394] Server: Uses generative AI to summarize the user's vital information and generate a report. After user confirmation, the report is sent to the doctor.

[1395] (Example 2)

[1396] 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."

[1397] Traditional health management systems focus on collecting and analyzing users' biometric data, but they fail to adequately provide health advice that takes emotional states into account. This makes it difficult to accurately assess users' psychological health and provide personalized advice. Furthermore, the insufficient functionality for integrating data from multiple health management applications makes it challenging to achieve comprehensive health management for users.

[1398] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting biometric data such as heart rate, steps, and sleep time; means for transmitting the biometric data and emotional data to a cloud server via a smartphone; means for analyzing the biometric data and emotional data at the cloud server and generating individual health advice; means for providing the generated health advice to the user; means for answering health-related questions through dialogue with the user using an avatar; means for sharing a summary of vital information and emotional data with medical professionals; means for receiving data from other health management software and managing it integrally; and means for monitoring the user's stress level and psychological health status and providing relaxation and mental care advice as needed. This enables integrated analysis of the user's biometric data and emotional data, and allows for the provision of individual health advice. Furthermore, by integrally managing data from multiple health management applications, comprehensive health management of the user becomes possible.

[1399] "Biometric data" refers to data that indicates the user's physical condition, such as heart rate, steps taken, and sleep duration.

[1400] "Emotional data" refers to data obtained from the user's voice, facial expressions, etc., that indicates the user's emotional state.

[1401] A "cloud server" is a computer system that operates as a remote server accessible via the internet, performing tasks such as data storage, analysis, and advice generation.

[1402] "Health advice" is generated by a cloud server analyzing biometric and emotional data, and provides information and specific implementation suggestions that are useful for maintaining and improving the user's health.

[1403] An "avatar" is a virtual character with which a user interacts through a smartphone application, used to answer health-related questions.

[1404] "Vital information" refers to important biometric data related to life support, such as the user's heart rate, blood pressure, and body temperature.

[1405] A "medical professional" is a professional qualified to monitor, diagnose, and treat a user's health condition, and includes doctors and nurses.

[1406] "Integrated management" means consolidating data collected from different health management software and devices into a single system and managing it as a whole.

[1407] "Stress level" refers to the degree of psychological and physiological stress estimated from the user's heart rate variability, etc.

[1408] "Mental care" refers to advice and suggestions for specific implementation methods to maintain and improve the user's psychological health.

[1409] This invention is a system for supporting users' health management, and by combining it with an emotion engine, it provides more personalized health advice. Specific embodiments of this invention are described below.

[1410] Hardware and software to be used

[1411] Hardware:

[1412] Smartwatch (equipped with sensors to collect heart rate, steps, and sleep time)

[1413] Smartphone (with Bluetooth and internet connectivity)

[1414] software:

[1415] Smartphone application (equipped with data transmission and avatar interaction functions)

[1416] Cloud servers (data storage, data analysis, machine learning models, sentiment engines, natural language processing engines)

[1417] System-wide operation overview

[1418] Device (smartwatch):

[1419] Smartwatches collect biometric data such as heart rate, steps taken, and sleep duration in real time. They also collect emotional data such as voice and facial expressions, and transmit this data to a smartphone.

[1420] Device (smartphone):

[1421] The smartphone application receives biometric and emotional data transmitted from the smartwatch and sends it to a cloud server via the internet.

[1422] server:

[1423] The cloud server securely stores the received biometric and emotional data in a database. The stored data undergoes data format validation and data cleaning, correcting for missing or outlier values. Machine learning models and algorithms are then used to analyze the data and assess the user's health and emotional state. Based on these states, personalized health advice, relaxation methods, and mental health care advice are generated.

[1424] Device (smartphone):

[1425] The generated health advice and emotionally sensitive responses are delivered to the user through an avatar in a smartphone application. The avatar uses a natural language processing engine to interact with the user. When the user enters a question, the content is sent to the server, and the most appropriate response is generated, taking into account the results of the emotion engine's analysis.

[1426] Specific examples of operation

[1427] Example 1: Daytime health monitoring and emotion recognition

[1428] Device (smartwatch):

[1429] Smartwatches collect heart rate, step count, and sleep data throughout the day.

[1430] The emotion engine recognizes emotions from voice and facial expressions.

[1431] Device (smartphone):

[1432] Biometric and emotional data collected from the smartwatch are sent to a cloud server.

[1433] server:

[1434] The cloud server analyzes this data and determines if the user is experiencing stress. It also considers the user's emotional state and generates advice such as, "You've been in a stressful situation recently. We recommend relaxing by taking deep breaths or a short walk."

[1435] Device (smartphone):

[1436] The avatar provides users with emotionally sensitive advice via text and voice.

[1437] Example 2: Sharing information with doctors

[1438] User:

[1439] I would like to share my vital signs with my doctor for a health checkup.

[1440] server:

[1441] The cloud server summarizes biometric and emotional data and compiles it into a report. This report is then sent to the doctor after user review.

[1442] Doctor:

[1443] Based on the received reports, we will comprehensively assess the user's health status and take appropriate measures.

[1444] Example of a prompt

[1445] "How do recent heart rate fluctuations affect things?"

[1446] "What are some ways to relieve stress?"

[1447] "Please give me some advice on how to improve the quality of my sleep last night."

[1448] This invention is an advanced health management system incorporating an emotion engine, which provides personalized health advice through integrated analysis of the user's biometric and emotional data. It also aims to achieve comprehensive health management for the user by integrating data from other health management applications.

[1449] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1450] Step 1: Collection of biometric and emotional data

[1451] Device (smartwatch):

[1452] Smartwatches collect biometric data such as heart rate, steps taken, and sleep duration in real time. They also recognize emotional states from data such as voice and facial expressions.

[1453] Input: User biometric data (heart rate, steps, sleep patterns) and emotional data (voice, facial expressions)

[1454] Output: Collected biometric and emotional data

[1455] Specific operation: A heart rate sensor measures heart rate every 10 seconds, and a pedometer sensor counts steps every minute. A small camera and microphone analyze voice and facial expressions, providing information to the emotion engine.

[1456] Step 2: Send

[1457] Device (smartwatch):

[1458] The smartwatch transmits data to the smartphone via Bluetooth.

[1459] Device (smartphone):

[1460] The data received by the smartphone is sent to a cloud server via the internet.

[1461] Input: Biometric and emotional data transmitted from the smartwatch.

[1462] Output: Data sent to the cloud server

[1463] Specific operation: The smartwatch sends batch data to the smartphone every 30 minutes, and the smartphone uploads the data to the cloud server in real time.

[1464] Step 3: Data storage and preprocessing

[1465] server:

[1466] The cloud server stores the received data in a secure database. It verifies the data format, performs data cleaning, and corrects missing or outlier values.

[1467] Input: Biometric data and emotional data transmitted from a smartphone

[1468] Output: Verified and cleaned data

[1469] Specific operation: Daily batch processing verifies the data format and filters out abnormal heart rate values ​​(e.g., 300 bpm) using an anomaly detection algorithm, and then formats the data into a standard format.

[1470] Step 4: Data Analysis

[1471] server:

[1472] We use machine learning models and algorithms to perform detailed analyses and evaluate the user's health and emotional state.

[1473] Input: Verified and cleaned biometric and emotional data

[1474] Output: Assessment results of health and emotional state

[1475] Specific operation: A machine learning model on a cloud server analyzes data every night to calculate seven-day health and emotional trends.

[1476] Step 5: Generating personalized advice

[1477] server:

[1478] Based on the user's health and emotional state, it generates personalized health advice, relaxation methods, and mental health care advice.

[1479] Input: Assessment results of health and emotional status

[1480] Output: Personalized health advice

[1481] Specific operation: If a high-stress state is detected, the server generates text and video links suggesting "deep breathing exercises" and "5 minutes of meditation," and sends them to the smartphone.

[1482] Step 6: Interacting with the Avatar

[1483] Device (smartphone):

[1484] The avatar answers health-related questions through interaction with the user. The user's questions are sent to the server.

[1485] server:

[1486] The system uses a natural language processing engine to analyze questions and then incorporates the results of a sentiment engine to generate the optimal answer.

[1487] Input: User's question data

[1488] Output: Emotionally sensitive response

[1489] Specific operation: When a user enters "I haven't been sleeping well lately" into the app, the server analyzes the sleep data and generates advice such as "avoid using your smartphone before going to bed."

[1490] Step 7: Integration with other health management apps

[1491] server:

[1492] It receives data from other health management applications and manages it centrally.

[1493] Input: Data from other health management applications

[1494] Output: Integrated health data

[1495] Specific operation: Receives sleep data from other apps, integrates that data with existing watch data, and uses it for comprehensive analysis.

[1496] Step 8: Stress Management and Mental Health Support

[1497] Devices (smartwatches and smartphones):

[1498] The smartwatch continuously monitors heart rate variability and estimates stress levels. The smartphone sends this data to a cloud server.

[1499] server:

[1500] If a high-stress state is detected, it generates relaxation and mental health advice.

[1501] Input: Heart rate variability data

[1502] Output: Advice on relaxation and mental health care.

[1503] Specific operation: Heart rate variability is measured every minute, and if a high-stress state lasting more than two hours is detected, the cloud server generates mental health advice.

[1504] Step 9: Sharing information with doctors

[1505] User:

[1506] A request is made to share vital information with a doctor using a smartphone application.

[1507] server:

[1508] The system summarizes biometric and emotional data, generates a report, and sends it to a doctor after user review.

[1509] Input: Biometric data and emotional data

[1510] Output: Summarized report

[1511] Specific operation: When the user selects "Send information to doctor," the system summarizes the data from the past three months and generates a PDF report. The report includes a graph of heart rate and emotional fluctuations.

[1512] (Application Example 2)

[1513] 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."

[1514] Traditional health management systems provide health advice based on users' biometric data, but they do not take into account security risks associated with users' emotional states or stress levels. Therefore, they fail to prevent suspicious behavior caused by stress or anxiety, making it difficult to guarantee overall safety.

[1515] 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. In this invention, the server includes means for collecting the user's biometric data, means for transmitting the biometric data to a cloud server, means for analyzing the biometric data on the cloud server and generating individual health advice, means for providing the generated health advice to the user, means for answering health-related questions through dialogue with an avatar, means for sharing a summary of vital information with a doctor based on the user's selection, means for evaluating the user's security risk and generating a security alert when an abnormal stress level or emotional state is detected, and means for notifying the user of the security alert and encouraging safe behavior. This enables comprehensive support that considers not only the user's health management but also security risks due to emotional state and stress.

[1516] Definitions of important words

[1517] "User biometric data" refers to an individual's physiological information, such as heart rate, steps taken, and sleep duration.

[1518] A "cloud server" refers to a collection of remote servers that store and analyze data via the internet.

[1519] "Personalized health advice" refers to health management guidelines optimized for a specific individual, provided based on the user's biometric data and emotional state.

[1520] An "avatar" refers to a virtual character that interacts with the user and provides information within an application.

[1521] "Vital information" refers to key bodily data related to maintaining life, such as heart rate, blood pressure, and body temperature.

[1522] "Security risk" refers to potential dangers to an individual's safety that can be inferred from a user's biometric data and emotional state.

[1523] A "security alert" refers to a warning that notifies users of potential security risks, generated based on their biometric data and emotional state.

[1524] A "Natural Language Processing (NLP) engine" refers to software that understands meaning from text and audio and analyzes human natural language.

[1525] Modes for carrying out the invention

[1526] This invention is a system that comprehensively supports the user's health and security. Specific embodiments for carrying out this invention are described below.

[1527] System Configuration

[1528] This system consists of a smartwatch that collects the user's biometric data, a smartphone that transmits the data, a cloud server, and an avatar that provides advice and security alerts to the user.

[1529] Data collection and transmission

[1530] The device (smartwatch) collects biometric data such as the user's heart rate, steps taken, and sleep duration in real time. It also collects emotional data from voice and facial expressions. This data is transmitted to the smartphone via Bluetooth.

[1531] The device (smartphone) receives data transmitted from the smartwatch and sends it to a cloud server via the internet.

[1532] Data Analysis

[1533] The server (cloud server) stores the received biometric and emotional data in a secure database. First, the data format is validated and the data is cleaned, correcting for missing or outlier values. Then, machine learning models and algorithms are used to analyze the data and evaluate the user's health and emotional state.

[1534] Generating health advice and security alerts

[1535] The server generates personalized health advice based on the analysis results. For example, if a high-stress state is detected, it will generate advice on relaxation methods and mental health care.

[1536] Furthermore, the system assesses security risks and generates security alerts if abnormal stress levels or emotional states are detected. These advisories and alerts are communicated to the user. Notifications are displayed within the smartphone application and delivered via an avatar.

[1537] User interaction

[1538] The avatar on the device (smartphone) answers health-related questions through interaction with the user. When the user enters a question into the application, the content is sent to the server. The emotion engine recognizes emotions from the user's entered text, speech, and facial expressions.

[1539] The server uses a natural language processing (NLP) engine to analyze the user's question and generate an appropriate answer. The generated answer is then sent back to the smartphone, where an avatar presents it to the user in an emotionally sensitive manner.

[1540] Specific examples and prompt statements

[1541] Specific example

[1542] For example, suppose a user is about to open their door upon returning home, and their smartwatch detects a sudden increase in heart rate and a high level of stress. In this case, the cloud server analyzes the data and generates a security alert. A notification is sent to the smartphone application saying, "Please relax. Check your surroundings for any suspicious activity and open the door only after you are sure it is safe."

[1543] Example of a prompt

[1544] "Please check your current heart rate and stress level."

[1545] "You are under high stress. Take a deep breath and ensure your safety before proceeding."

[1546] Through this configuration, comprehensive assessment of user health and security risks is conducted, enabling the provision of individually optimized advice and alerts.

[1547] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1548] Program processing steps

[1549] Step 1:

[1550] The device (smartwatch) collects the user's heart rate, steps, sleep duration, and emotional data in real time. This data is temporarily stored within the smartwatch. Input is the user's biometric and emotional data, and output is data transfer to the smartphone.

[1551] Step 2:

[1552] The device (smartphone) receives data from the smartwatch via Bluetooth. The smartphone checks the received data and sends it to a cloud server via the internet. The input is biometric and emotional data transferred from the smartwatch, and the output is the transmission of data to the cloud server.

[1553] Step 3:

[1554] The server (cloud server) stores the received data in a secure database. Next, it performs data format validation and data cleaning, correcting for missing or outlier values. The input is data sent from a smartphone, and the output is clean data that can be analyzed.

[1555] Step 4:

[1556] The server uses a machine learning model to analyze data and assess the user's health and emotional state. The analysis results include stress levels and normal / abnormal health status. The input is clean data, and the output is the assessment of the user's health and emotional state.

[1557] Step 5:

[1558] The server generates personalized health advice based on the analysis results. For example, if a high-stress state is detected, it will generate advice on relaxation methods and mental health care. The input is the health assessment results, and the output is health advice text.

[1559] Step 6:

[1560] The server assesses the user's security risks and generates security alerts if abnormal stress levels or emotional states are detected. Inputs are health and emotional assessment results, and outputs are security alerts.

[1561] Step 7:

[1562] The server sends the generated health advice and security alerts to the smartphone application. The input is the health advice and security alerts, and the output is the transmission of data to the smartphone.

[1563] Step 8:

[1564] The device (smartphone) notifies the user of received health advice and security alerts through an avatar. For example, the avatar provides emotionally sensitive advice to the user, such as, "Please relax. Check your surroundings for any suspicious activity and open the door only after you have confirmed that it is safe." The input is the advice and alerts sent from the server, and the output is the notification to the user.

[1565] Step 9:

[1566] Users ask health and security-related questions to an avatar via a smartphone application. The input is the user's question, and the output is the avatar's response.

[1567] Step 10:

[1568] The server receives the user's question and analyzes it using a natural language processing (NLP) engine. An emotion engine determines the user's emotional state and generates an appropriate response. The input is the user's question, and the output is the appropriate response.

[1569] Step 11:

[1570] The device (smartphone) notifies the user of the response received from the server via an avatar. The avatar provides instructions such as, "Take a deep breath and proceed only after confirming your safety." The input is the response from the server, and the output is the notification to the user.

[1571] In this way, data is collected, transmitted, and analyzed throughout each step and provided to the user as health advice and security alerts. Specific prompts include messages such as, "Check your current heart rate and stress level," and "You are experiencing high stress; take a deep breath and ensure your safety before proceeding."

[1572] 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.

[1573] 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.

[1574] 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.

[1575] [Fourth Embodiment]

[1576] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1577] 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.

[1578] 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).

[1579] 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.

[1580] 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.

[1581] 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).

[1582] 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.

[1583] 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.

[1584] 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.

[1585] 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.

[1586] 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.

[1587] 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.

[1588] 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".

[1589] This invention is a system for supporting users' health management and is configured as follows: The user uses a dedicated smartwatch and a smartphone application. The smartwatch collects biometric data such as heart rate, steps taken, and sleep duration in real time. The collected data is transmitted to a cloud server via the smartphone application. The cloud server analyzes the biometric data and generates personalized health advice.

[1590] Collection and transmission of health data

[1591] Device (Smartwatch): The smartwatch collects biometric data such as the user's heart rate, steps taken, and sleep duration in real time. This data is transmitted to a smartphone application via Bluetooth or other means.

[1592] Device (smartphone): The smartphone application receives data sent from the smartwatch and transmits it to a cloud server via the internet.

[1593] Data analysis and generation of personalized advice

[1594] Server: The cloud server stores the received biometric data in a secure database. Next, machine learning models and algorithms are used to analyze the data in detail and assess the user's health status. Based on this assessment, personalized health advice and meal plans are generated.

[1595] Interacting with an avatar

[1596] Device (Smartphone): The avatar within the smartphone application can answer health-related questions through interaction with the user. When the user enters a question into the application, the content is sent to the server.

[1597] Server: The server uses a natural language processing (NLP) engine to analyze the user's question and generate an appropriate answer. The generated answer is then sent to the terminal.

[1598] Device (smartphone): An avatar within the application provides answers to the user. Through this interaction, the user can obtain useful information about health.

[1599] Integration with other health management apps

[1600] Server: The cloud server can receive data from other health management applications and integrate it into the existing database for management. This integration enables centralized management of users' health data.

[1601] Stress management and mental health support

[1602] Devices (smartwatch and smartphone): The smartwatch continuously monitors the user's heart rate variability and estimates their stress level. The smartphone application sends this data to a cloud server.

[1603] Server: If a high stress level is detected, the cloud server generates specific advice to support relaxation and mental health. Based on this, the user can take appropriate action.

[1604] Sharing information with doctors

[1605] User: If a user wants to share vital information with a doctor, they make that request through the smartphone application.

[1606] Server: The cloud server uses generative AI to summarize the collected vital information and generate a report. The generated report is then sent to the physician after user review. This process enables efficient information sharing between the user and the physician.

[1607] Specific example

[1608] Example 1: Nighttime health monitoring

[1609] Device (Smartwatch): At night, the smartwatch collects the user's sleep data. This includes detailed data such as sleep depth and sleep cycles.

[1610] Device (smartphone): In the morning, data from the smartwatch is sent to the smartphone. The data is then sent to the cloud server.

[1611] Server: The cloud server analyzes this data and generates advice such as, "According to your recent sleep data, you tend to be getting insufficient deep sleep. We recommend that you establish a routine of going to bed at a specific time to ensure you get quality sleep."

[1612] Device (smartphone): The avatar provides advice to the user and suggests specific ways to improve.

[1613] Example 2: Stress Management

[1614] Device (smartwatch): During the day, the smartwatch monitors the user's heart rate variability and detects high-stress states.

[1615] Server: The cloud server evaluates stress levels based on accumulated data and generates advice such as, "Recent data has detected a high stress level. We recommend taking some time to relax by taking deep breaths or meditating."

[1616] Device (smartphone): An avatar provides the user with stress-relief advice and explains in detail how to put it into practice.

[1617] As described above, the present invention is a system that supports users' health management and provides users with valuable information through data analysis and the generation of personalized advice.

[1618] The following describes the processing flow.

[1619] Step 1:

[1620] Device (smartwatch): Collects biometric data such as heart rate, steps taken, and sleep duration from the user in real time. Sensors within the smartwatch periodically measure and temporarily store this data.

[1621] Step 2:

[1622] The device (smartphone) communicates with the smartwatch via Bluetooth and periodically transfers the collected biometric data to the smartphone. The smartphone checks the received data and verifies its integrity.

[1623] Step 3:

[1624] Device (smartphone): Smartphones transmit data to cloud servers via the internet. During this process, protocols are used to encrypt the data and maintain security.

[1625] Step 4:

[1626] Server: The cloud server stores the received biometric data in a secure database. Before saving, it verifies the data format and performs data cleaning, correcting for missing or outlier values.

[1627] Step 5:

[1628] Server: The server processes the stored data using an analysis algorithm to assess the user's health status. For example, it might use a machine learning model to estimate stress levels from the user's heart rate variability.

[1629] Step 6:

[1630] Server: Generates personalized health advice and meal plans based on the results of machine learning models and data analysis. This process takes into account the user's past data and current health status.

[1631] Step 7:

[1632] Server: Formats the data and sends it to the smartphone application to provide the generated advice and plans to the user.

[1633] Step 8:

[1634] Device (Smartphone): The smartphone application displays received advice and plans to the user. An avatar provides advice via text or voice as needed.

[1635] Step 9:

[1636] User: Begin a conversation with the avatar and enter health-related questions. For example, ask, "How has your sleep been lately?"

[1637] Step 10:

[1638] Device (smartphone): Retrieves user questions as text data and sends it to the cloud server.

[1639] Step 11:

[1640] Server: Uses a natural language processing (NLP) engine to analyze user questions and understand their intent. Refers to stored data and past advice logs to generate appropriate answers.

[1641] Step 12:

[1642] Server: Sends the generated answer based on the question to the smartphone application.

[1643] Step 13:

[1644] Device (smartphone): The avatar provides answers to the user and completes the conversation. This allows the user to obtain the necessary information in real time.

[1645] Step 14:

[1646] Server: Handles integration to receive data from other health management applications. Uses APIs to retrieve additional data and manage it in an integrated manner.

[1647] Step 15:

[1648] Server: Integrates data from other applications received and performs a more detailed analysis of the user's overall health status. Based on the results, it generates further advice.

[1649] Step 16:

[1650] Devices (smartwatch and smartphone): The smartwatch continuously monitors the user's heart rate variability and sends stress level data to the smartphone. The smartphone then sends this data to a cloud server.

[1651] Step 17:

[1652] Server: If a high-stress state is detected, the cloud server generates and provides relaxation and mental health advice to the user.

[1653] Step 18:

[1654] User: The app requests the sharing of vital information with a doctor as needed. The smartphone application sends this request to the cloud server.

[1655] Step 19:

[1656] Server: Uses generative AI to summarize the user's vital information and generate a report. After user confirmation, the report is sent to the doctor.

[1657] (Example 1)

[1658] 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".

[1659] The present invention aims to provide a system that efficiently and effectively supports users' health management. Existing health management systems lack sufficient collection and analysis of biometric data, as well as adequate feedback to users, resulting in insufficient personalized health advice. Furthermore, they lack support for stress management and mental health care, and there is no easy way for users to share vital information with healthcare professionals.

[1660] 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.

[1661] In this invention, the server includes means for analyzing the user's biometric data and generating personalized health advice, means for answering health-related questions through interaction with an avatar, and means for sharing a summary of vital information with healthcare professionals. This allows the user to understand their health status in real time and receive necessary health advice. Furthermore, comprehensive health management is achieved through stress level monitoring and the provision of mental health care advice. In addition, the means for sharing information with healthcare professionals enables prompt and appropriate medical responses.

[1662] "Biometric data" refers to data that indicates a user's physical functions and state, such as heart rate, steps taken, and sleep duration.

[1663] A "mobile terminal" is a portable communication device with data communication capabilities, and specifically includes smartphones and tablet devices.

[1664] A "cloud server" refers to a remote server that stores and processes data via the internet.

[1665] "Health advice" refers to specific instructions or suggestions for improving or maintaining a user's health.

[1666] An "avatar" is a digital representation that acts on behalf of a user on a computer and has interactive functions such as conversation.

[1667] "Healthcare professionals" refer to individuals with specialized knowledge and qualifications related to medicine and health, such as doctors and nurses.

[1668] "Health management software" refers to application software used to monitor and manage a user's health status.

[1669] "Stress level" is an indicator that shows the degree of stress a user experiences.

[1670] "Mental health care" refers to care methods and activities aimed at maintaining or improving a user's mental health.

[1671] "Vital information" refers to data on the body's basic physiological functions, such as heart rate, body temperature, and respiratory rate.

[1672] This invention is a system for supporting user health management and is implemented using the following hardware and software.

[1673] Hardware and software to be used

[1674] terminal

[1675] Smartwatch: A device for collecting biometric data such as heart rate, steps taken, and sleep duration. Built-in sensors include heart rate sensors and accelerometers.

[1676] Smartphone: A mobile device used to send data collected from a smartwatch to a cloud server, and a platform for running applications.

[1677] server

[1678] Cloud servers are remote servers used for storing and analyzing data over the internet. Key software includes databases (e.g., AWS RDS, Google Cloud Firestore), machine learning models (e.g., TensorFlow, PyTorch), and natural language processing engines (e.g., BERT, GPT-3).

[1679] Data collection and transmission

[1680] Device (smartwatch)

[1681] Smartwatches collect biometric data such as the user's heart rate, steps taken, and sleep duration in real time. This data is transmitted to a smartphone using Bluetooth technology.

[1682] Device (smartphone)

[1683] The smartphone receives data from the smartwatch via Bluetooth and sends it to a cloud server over the internet. A dedicated application is installed on the smartphone, and this app manages the receiving and sending of data.

[1684] Data storage and analysis

[1685] server

[1686] The cloud server receives biometric data transmitted from smartphones via the internet and stores it in a secure database. The stored data is analyzed using machine learning models (e.g., TensorFlow, PyTorch). Based on the analysis results, the user's health status is evaluated and personalized health advice is generated.

[1687] Interacting with an avatar

[1688] Device (smartphone)

[1689] The smartphone application features an avatar that answers health-related questions through interaction with the user. When the user enters a question into the application, the content is sent to the server.

[1690] server

[1691] The server uses a natural language processing engine (e.g., BERT, GPT-3) to analyze the user's question and generate an appropriate answer. The generated answer is then sent back to the smartphone.

[1692] Device (smartphone)

[1693] An avatar within a smartphone application provides the user with generated responses. This interactive feature allows users to easily obtain useful health-related information.

[1694] Integration with other health management software

[1695] server

[1696] The cloud server receives data from other health management software (e.g., Google Fit, Apple HealthKit) and integrates it into the existing database for management. This integration allows for centralized management of users' health data.

[1697] Stress management and mental health support

[1698] Devices (smartwatches and smartphones)

[1699] The smartwatch monitors the user's heart rate variability and sends the data to the smartphone. The smartphone then sends this data to a cloud server.

[1700] server

[1701] The cloud server assesses stress levels and generates specific advice to support relaxation and mental health. This generated advice is then sent to the user's smartphone.

[1702] Sharing information with healthcare professionals

[1703] User

[1704] If a user wants to share vital information with a healthcare professional, they can make that request through a smartphone application.

[1705] server

[1706] The cloud server uses a generative AI model (e.g., GPT-4) to summarize the collected vital information and generate a report. This report is then reviewed by the user and sent to healthcare professionals.

[1707] Specific example

[1708] Collection of sleep data and generation of advice

[1709] (Specific example)

[1710] At night, the smartwatch collects the user's sleep data. It records cycles of deep and light sleep and generates detailed data. In the morning, the data is sent to the smartphone and then transferred to a cloud server. The cloud server analyzes the data and generates advice such as, "According to your recent sleep data, you are not getting enough deep sleep. We recommend that you establish a habit of going to bed at a specific time." An avatar on the smartphone then conveys this advice to the user.

[1711] As illustrated by the above specific example, the present invention is a system that supports users' health management and provides useful information to users by offering advanced data analysis and personalized health advice.

[1712] Stress management practices

[1713] (Example of a prompt message)

[1714] "I've been under a lot of stress lately. Do you have any recommendations for coping strategies?"

[1715] When a user enters a question using such a prompt, the server analyzes it, generates appropriate advice, and provides it to the user.

[1716] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1717] Step 1: Data Collection

[1718] Device (smartwatch)

[1719] Smartwatches collect biometric data such as the user's heart rate, steps taken, and sleep duration in real time. Heart rate is recorded every second, and steps are tallied every hour. Sleep data is recorded every 30 minutes during the night, showing the state of deep sleep and light sleep.

[1720] Input: User's real-time biometric data

[1721] Output: Biometric data stored in the smartwatch

[1722] Specifically, the smartwatch's heart rate sensor detects the heart rate, and the accelerometer measures the number of steps. This data is temporarily stored in the built-in memory.

[1723] Step 2: Send

[1724] Device (smartwatch)

[1725] Smartwatches transmit collected biometric data to smartphones using Bluetooth connectivity.

[1726] Input: Biometric data stored in the smartwatch

[1727] Output: Biometric data transferred to a smartphone

[1728] Specifically, the smartwatch attempts to transmit data every minute, transferring the data to the smartphone in batches via Bluetooth.

[1729] Step 3: Send data to the cloud

[1730] Device (smartphone)

[1731] Smartphones receive data via Bluetooth and send it to a cloud server over the internet. A dedicated application is installed on the smartphone, which manages the receiving and sending of data.

[1732] Input: Biometric data received from a smartwatch

[1733] Output: Biometric data sent to the cloud server

[1734] Specifically, the smartphone app runs in the background, and once an internet connection is established, it uploads the biometric data to a cloud server.

[1735] Step 4: Save Data

[1736] server

[1737] The cloud server receives data sent from smartphones via the internet and stores it in a secure database. Data integrity checks and encryption are performed before storage.

[1738] Input: Biometric data sent from a smartphone

[1739] Output: Biometric data stored in a secure database

[1740] Specifically, when biometric data arrives at the cloud server, its integrity is checked and it is encrypted (e.g., AES-256) before being inserted into the database.

[1741] Step 5: Data Analysis

[1742] server

[1743] The cloud server analyzes data using machine learning models (e.g., TensorFlow, PyTorch) to assess the user's health status. This analysis includes data preprocessing, model input, and retrieval of evaluation results.

[1744] Input: Biometric data stored in the database

[1745] Output: Evaluation results regarding the user's health status

[1746] Specifically, the biometric data is pre-processed to remove noise and normalize it before being input into a machine learning model. The model then outputs an evaluation score of the user's health status and trends indicating abnormalities as part of its analysis results.

[1747] Step 6: Generate Advice

[1748] server

[1749] The server generates personalized health advice for users based on the evaluation results of machine learning models. This process utilizes a natural language generation engine (e.g., GPT-4).

[1750] Input: Health status assessment results

[1751] Output: Personalized health advice

[1752] Specifically, the evaluation results are templated, and a generative AI model is used to generate health advice in natural language. For example, advice such as, "According to recent data, you are not getting enough deep sleep. We recommend that you establish a habit of going to bed at a specific time," might be generated.

[1753] Step 7: Avatar-based interaction

[1754] Device (smartphone)

[1755] When a user enters a health-related question for their avatar, the information is sent to the server.

[1756] Input: Question entered by the user

[1757] Output: Question data sent to the server

[1758] Specifically, the smartphone application sends user input to the server in text format.

[1759] server

[1760] The server analyzes the question using a natural language processing engine (e.g., BERT, GPT-3) and generates an appropriate answer. The generated answer is then sent back to the smartphone.

[1761] Input: Question data submitted by the user

[1762] Output: Response to the user

[1763] Specifically, the natural language processing engine analyzes the content of the question, the sentence generation engine creates the optimal answer, and sends it back to the smartphone in text format.

[1764] Device (smartphone)

[1765] An avatar within the application provides the user with the generated response. The response can also be read aloud using a speech synthesis engine.

[1766] Input: Response data sent from the server

[1767] Output: Answers provided by the user

[1768] Specifically, the avatar displays text on the screen and, when necessary, outputs speech using a speech synthesis engine.

[1769] Step 8: Sharing information with healthcare professionals

[1770] User

[1771] If a user wants to share vital information with a healthcare professional, they can make a request through the smartphone application.

[1772] Input: Request for information sharing

[1773] Output: Sending a sharing request to the server

[1774] Specifically, the user instructs the app to share vital information, and a request is sent to the server.

[1775] server

[1776] The cloud server uses a generative AI model (e.g., GPT-4) to summarize vital information and generate a report. This report is then sent to healthcare professionals after user review.

[1777] Input: Vital signs and information sharing request

[1778] Output: Summary report sent to healthcare professionals

[1779] Specifically, the system analyzes and summarizes the user's vital signs and generates a report in PDF format. After user confirmation, the report is sent to the healthcare professional's email address.

[1780] The above outlines the specific processing steps of this system's program. By detailing what processing and calculations are performed on the input data at each step and what the final output is, the overall operation of the system becomes clear.

[1781] (Application Example 1)

[1782] 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".

[1783] Effectively monitoring the health and stress levels of factory workers is crucial for improving productivity and ensuring safety. However, current systems have limitations in collecting and analyzing individual workers' health data in real time and providing appropriate advice and improvement measures. Therefore, a new system is needed to efficiently and effectively manage worker health.

[1784] 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.

[1785] In this invention, the server includes means for collecting heart rate and movement data of workers in a factory and monitoring the work environment; means for analyzing the collected data and generating health advice and safety improvement measures based on the work environment; and means for providing the generated advice and improvement measures via in-factory displays and voice guidance systems. This makes it possible to monitor the health status of workers in the factory in real time and provide appropriate advice and improvement measures.

[1786] "Biometric data" refers to data that indicates the user's physical condition, such as heart rate, movement data, and posture.

[1787] A "cloud server" is a remote server used to store, analyze, and manage data via the internet.

[1788] "Health advice" refers to specific suggestions and instructions generated based on analyzed biometric data to support the user's health management.

[1789] An "avatar" is a virtual character or agent used to interact with users.

[1790] "Vital information" refers to key biometric data that indicates vital functions, such as the user's heart rate, body temperature, and blood pressure.

[1791] A "factory" is a facility where manufacturing and production activities take place.

[1792] A "worker" is a person who engages in production activities within a factory.

[1793] "Motion data" refers to data that shows the physical movements and activity levels of a worker.

[1794] "Safety improvement measures" refer to specific proposals and measures to ensure worker safety and improve the working environment.

[1795] A "display" is a device used to visually display information.

[1796] A "voice guidance system" is a system that provides information and instructions using voice.

[1797] The present invention is a system for supporting the health management of workers in a factory, and is configured as follows: The system includes a factory robot, a data collection server, a cloud server, and a display or voice guidance system.

[1798] Collection and transmission of health data

[1799] Terminal (factory robot):

[1800] Factory robots are equipped with sensors and cameras to collect worker heart rate and movement data. This data is collected in real time. The factory robots transmit the collected data to a data collection server. Wireless communication technologies such as Bluetooth are used for this purpose.

[1801] Terminal (data collection server):

[1802] The data collection server receives data transmitted from factory robots and sends it to a cloud server via the internet. This ensures that the data is analyzed in a secure environment.

[1803] Data analysis and generation of personalized advice

[1804] Server (Cloud Server):

[1805] The cloud server securely stores the received biometric data and analyzes it in detail using machine learning models (e.g., TensorFlow or PyTorch). Based on this analysis, it evaluates the health and safety status of workers and generates personalized health advice and safety improvement measures.

[1806] Interacting with an avatar

[1807] Terminal (factory robot):

[1808] The factory robots are equipped with displays and speakers, allowing avatars to interact with workers and answer health-related questions. When a worker enters a question into the application, the information is sent to a server.

[1809] Server (Cloud Server):

[1810] The cloud server uses a natural language processing (NLP) engine to analyze the worker's questions and generate appropriate answers. These answers are then provided to the worker through the robot's display or speaker.

[1811] Stress management and mental health support

[1812] Terminals (factory robots and data collection servers):

[1813] Factory robots continuously monitor workers' heart rate variability and estimate their stress levels. Data collection servers transmit this data to cloud servers.

[1814] Server (Cloud Server):

[1815] If the cloud server detects a high stress level, it generates specific advice to support relaxation and mental health. Based on this, workers can take appropriate action.

[1816] Specific example

[1817] Example 1:

[1818] Suppose a worker considering whether or not to take a break asks a factory robot, "Should I take a break?" This question is collected by the robot and sent to a server. The server analyzes recent data, confirms that the stress level is high, and then generates advice such as, "Based on recent data, we recommend that you take a break." This advice is then communicated to the worker through the robot's display or speaker.

[1819] Example of a prompt:

[1820] "Generate advice to assess stress levels based on the worker's heart rate and activity data."

[1821] "Please analyze the following data and provide health advice: Heart rate: 80, Activity data: 'active', Posture: 'correct'"

[1822] Thus, the present invention is a system that supports the health management of workers in a factory in real time and provides valuable information to workers through data analysis and the generation of individual advice.

[1823] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1824] Step 1:

[1825] Data collection

[1826] The terminal (factory robot) uses sensors and cameras to collect worker heart rate and movement data in real time. The collected biometric data is transmitted to a data collection server using wireless communication technology such as Bluetooth. The input here is the biometric data acquired from the sensors and cameras, and the output is the data transferred to the data collection server.

[1827] Step 2:

[1828] Data transmission

[1829] The terminal (data collection server) receives biometric data transmitted from factory robots and sends it to a cloud server via the internet. The input is biometric data transmitted from factory robots, and the output is biometric data sent to the cloud server.

[1830] Step 3:

[1831] Data analysis and advice generation

[1832] The server (cloud server) stores the received biometric data in a secure database. The stored data is analyzed using machine learning models (using TensorFlow or PyTorch) to evaluate the user's health status and stress level. Appropriate health advice and safety improvement measures are then generated. The input is the biometric data stored on the cloud server, and the output is the generated health advice and safety improvement measures.

[1833] Step 4:

[1834] Interacting with an avatar

[1835] The terminal (factory robot) uses a display and speakers to have an avatar provide health advice and safety improvement measures to workers. When a worker inputs a question into the robot, the question is sent to a cloud server. The server uses a natural language processing (NLP) engine to analyze the question and generate an appropriate answer. The answer is then provided to the worker through the robot. In this step, the input is the question from the worker, and the output is the answer generated by the server.

[1836] Step 5:

[1837] Stress management and mental health support

[1838] The terminal (factory robot and data collection server) continuously monitors the worker's heart rate variability and transmits the data to a cloud server. If the server determines that the stress level is high, it generates advice for relaxation and mental health support. The advice is delivered through the factory robot's display and speakers. In this step, the input is heart rate variability data, and the output is the generated relaxation advice.

[1839] Thus, each step—data collection, transmission, analysis, advice generation, and delivery to the user—constitutes a system for real-time worker health management.

[1840] 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.

[1841] This invention is a system for supporting users' health management, and by combining it with an emotion engine, it provides more personalized health advice. The following describes a specific embodiment for carrying out this invention. The user uses a dedicated smartwatch and a smartphone application. The smartwatch collects biometric data such as heart rate, steps, and sleep duration in real time and transmits it to a cloud server via the smartphone. The cloud server analyzes the biometric data and generates individual health advice.

[1842] Furthermore, the system incorporates an emotion engine that recognizes the user's emotional state and reflects it in health advice. This allows for the provision of optimal advice tailored to the user's physical and mental condition.

[1843] Collection and transmission of health data

[1844] Device (Smartwatch): The smartwatch collects biometric data such as the user's heart rate, steps taken, and sleep duration in real time. The emotion engine also collects data such as voice and facial expressions to recognize the user's emotional state. This data is transmitted to the smartphone application via Bluetooth or other means.

[1845] Device (smartphone): The smartphone application receives biometric and emotional data transmitted from the smartwatch and sends it to a cloud server via the internet.

[1846] Data analysis and generation of personalized advice

[1847] Server: The cloud server securely stores received biometric and emotional data in a database. Before data is stored, the data format is validated and data cleaning is performed, correcting for missing or outlier values. The server uses machine learning models and algorithms to analyze the data in detail and assess the user's health and emotional state.

[1848] Server: Generates personalized health advice and meal plans based on the user's health and emotional state. For example, if a high-stress state is detected, it will also generate advice on optimal relaxation methods and mental health care for that user.

[1849] Interacting with an avatar

[1850] Device (Smartphone): The avatar within the smartphone application answers health-related questions through dialogue with the user. When the user enters a question into the application, the content is sent to the server. The emotion engine recognizes emotions from the user's input, speech, and facial expressions, and adjusts the content of the dialogue accordingly.

[1851] Server: The server uses a natural language processing (NLP) engine to analyze the user's question and generate an appropriate answer. It also incorporates the results of an emotion engine to generate the optimal answer based on the user's emotional state. The generated answer is then sent to the smartphone application.

[1852] Device (Smartphone): An avatar within the application provides emotionally sensitive responses to the user. Through this interaction, the user can receive real-time, emotion-based health advice.

[1853] Integration with other health management apps

[1854] Server: The cloud server receives data from other health management applications and integrates and manages it in the existing database. This integration enables centralized management of users' health data, allowing for more accurate advice to be provided.

[1855] Stress management and mental health support

[1856] Devices (smartwatch and smartphone): The smartwatch continuously monitors the user's heart rate variability and estimates their stress level. The smartphone application sends this data to a cloud server.

[1857] Server: If a high-stress state is detected, the cloud server generates relaxation and mental health advice, which is then combined with the emotion engine and provided to the user. Based on this, the user can take appropriate action.

[1858] Sharing information with doctors

[1859] User: If a user wants to share vital information with a doctor, they make that request through the smartphone application.

[1860] Server: The cloud server uses generative AI to summarize the collected vital information and generate a report. The generated report is then sent to the physician after user review. This process enables efficient information sharing between the user and the physician.

[1861] Specific example

[1862] Example 1: Daytime health monitoring and emotion recognition

[1863] Device (Smartwatch): During the day, the smartwatch collects the user's heart rate, steps, and sleep data. Simultaneously, the emotion engine recognizes the user's emotions from their voice and facial expressions.

[1864] Device (smartphone): Sends collected biometric and emotional data to a cloud server.

[1865] Server: The cloud server analyzes this data and determines if the user is experiencing stress. Taking the user's emotional state into consideration, it generates advice such as, "You have been in a stressful situation recently. We recommend you relax by taking deep breaths or a short walk."

[1866] Device (smartphone): The avatar provides emotionally sensitive advice to the user via text and voice.

[1867] Example 2: Sharing information with doctors

[1868] User: The user requests to share vital information with a doctor for a health checkup.

[1869] Server: The cloud server summarizes biometric and emotional data and compiles it into a report. This report is then sent to the doctor after user review.

[1870] Doctor: Based on the received report, the doctor will make a comprehensive assessment of the user's health condition and take appropriate measures.

[1871] As described above, the present invention, which incorporates an emotion engine, is a system that supports users' health management and provides advanced health support that takes into account emotional states through data analysis and the generation of personalized advice.

[1872] The following describes the processing flow.

[1873] Step 1:

[1874] Device (smartwatch): Collects biometric data such as the user's heart rate, steps taken, and sleep duration in real time. Sensors within the smartwatch periodically measure and temporarily store this data.

[1875] Step 2:

[1876] Device (Smartwatch): The smartwatch has a built-in emotion engine that recognizes emotions from the user's voice, facial expressions, and gestures. The collected emotion data is temporarily stored along with biometric data.

[1877] Step 3:

[1878] The device (smartphone) communicates with the smartwatch via Bluetooth and periodically transfers collected biometric and emotional data to the smartphone. The smartphone checks the received data and verifies its integrity.

[1879] Step 4:

[1880] Device (smartphone): Smartphones transmit data to cloud servers via the internet. During this process, protocols are used to encrypt the data and maintain security.

[1881] Step 5:

[1882] Server: The cloud server stores received biometric and emotional data in a secure database. Before saving, it validates the data format, cleans the data, and corrects for missing or outlier values.

[1883] Step 6:

[1884] Server: The server processes the stored data using an analysis algorithm to evaluate the user's health and emotional state. For example, it might use a machine learning model to estimate stress levels from the user's heart rate variability.

[1885] Step 7:

[1886] Server: Generates personalized health advice and meal plans based on the results of machine learning models and data analysis. This process considers not only the user's past data and current health status, but also their emotional state.

[1887] Step 8:

[1888] Server: Formats the data and sends it to the smartphone application to provide the generated advice and plans to the user.

[1889] Step 9:

[1890] Device (Smartphone): The smartphone application displays received advice and plans to the user. If necessary, an avatar provides emotionally sensitive advice via text or voice.

[1891] Step 10:

[1892] User: Begin a conversation with the avatar and enter health-related questions. For example, ask, "How has your sleep been lately?"

[1893] Step 11:

[1894] Device (smartphone): Retrieves user questions as text data and sends it to the cloud server.

[1895] Step 12:

[1896] Server: Uses a natural language processing (NLP) engine to analyze user questions and understand their intent. Refers to stored biometric data, sentiment data, and past advice logs to generate appropriate answers.

[1897] Step 13:

[1898] Server: Sends the answers generated based on the questions to the smartphone application. At this time, it also considers the results of the emotion engine to generate the optimal answer according to the user's emotional state.

[1899] Step 14:

[1900] Device (smartphone): The avatar provides the user with emotionally sensitive responses and completes the conversation. This allows the user to obtain the necessary information in real time.

[1901] Step 15:

[1902] Server: Handles integration to receive data from other health management applications. Uses APIs to retrieve additional data and manage it in an integrated manner.

[1903] Step 16:

[1904] Server: Integrates data from other applications received and performs a more detailed analysis of the user's overall health status. Based on the results, it generates further advice.

[1905] Step 17:

[1906] Devices (smartwatch and smartphone): The smartwatch continuously monitors the user's heart rate variability and sends stress level data to the smartphone. The smartphone then sends this data to a cloud server.

[1907] Step 18:

[1908] Server: If a high-stress state is detected, the cloud server generates relaxation and mental health advice, taking emotional data into consideration, and provides it to the user.

[1909] Step 19:

[1910] User: The app requests the sharing of vital information with a doctor as needed. The smartphone application sends this request to the cloud server.

[1911] Step 20:

[1912] Server: Uses generative AI to summarize the user's vital information and generate a report. After user confirmation, the report is sent to the doctor.

[1913] (Example 2)

[1914] 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".

[1915] Traditional health management systems focus on collecting and analyzing users' biometric data, but they fail to adequately provide health advice that takes emotional states into account. This makes it difficult to accurately assess users' psychological health and provide personalized advice. Furthermore, the insufficient functionality for integrating data from multiple health management applications makes it challenging to achieve comprehensive health management for users.

[1916] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting biometric data such as heart rate, steps, and sleep time; means for transmitting the biometric data and emotional data to a cloud server via a smartphone; means for analyzing the biometric data and emotional data at the cloud server and generating individual health advice; means for providing the generated health advice to the user; means for answering health-related questions through dialogue with the user using an avatar; means for sharing a summary of vital information and emotional data with medical professionals; means for receiving data from other health management software and managing it integrally; and means for monitoring the user's stress level and psychological health status and providing relaxation and mental care advice as needed. This enables integrated analysis of the user's biometric data and emotional data, and allows for the provision of individual health advice. Furthermore, by integrally managing data from multiple health management applications, comprehensive health management of the user becomes possible.

[1917] "Biometric data" refers to data that indicates the user's physical condition, such as heart rate, steps taken, and sleep duration.

[1918] "Emotional data" refers to data obtained from the user's voice, facial expressions, etc., that indicates the user's emotional state.

[1919] A "cloud server" is a computer system that operates as a remote server accessible via the internet, performing tasks such as data storage, analysis, and advice generation.

[1920] "Health advice" is generated by a cloud server analyzing biometric and emotional data, and provides information and specific implementation suggestions that are useful for maintaining and improving the user's health.

[1921] An "avatar" is a virtual character with which a user interacts through a smartphone application, used to answer health-related questions.

[1922] "Vital information" refers to important biometric data related to life support, such as the user's heart rate, blood pressure, and body temperature.

[1923] A "medical professional" is a professional qualified to monitor, diagnose, and treat a user's health condition, and includes doctors and nurses.

[1924] "Integrated management" means consolidating data collected from different health management software and devices into a single system and managing it as a whole.

[1925] "Stress level" refers to the degree of psychological and physiological stress estimated from the user's heart rate variability, etc.

[1926] "Mental care" refers to advice and suggestions for specific implementation methods to maintain and improve the user's psychological health.

[1927] This invention is a system for supporting users' health management, and by combining it with an emotion engine, it provides more personalized health advice. Specific embodiments of this invention are described below.

[1928] Hardware and software to be used

[1929] Hardware:

[1930] Smartwatch (equipped with sensors to collect heart rate, steps, and sleep time)

[1931] Smartphone (with Bluetooth and internet connectivity)

[1932] software:

[1933] Smartphone application (equipped with data transmission and avatar interaction functions)

[1934] Cloud servers (data storage, data analysis, machine learning models, sentiment engines, natural language processing engines)

[1935] System-wide operation overview

[1936] Device (smartwatch):

[1937] Smartwatches collect biometric data such as heart rate, steps taken, and sleep duration in real time. They also collect emotional data such as voice and facial expressions, and transmit this data to a smartphone.

[1938] Device (smartphone):

[1939] The smartphone application receives biometric and emotional data transmitted from the smartwatch and sends it to a cloud server via the internet.

[1940] server:

[1941] The cloud server securely stores the received biometric and emotional data in a database. The stored data undergoes data format validation and data cleaning, correcting for missing or outlier values. Machine learning models and algorithms are then used to analyze the data and assess the user's health and emotional state. Based on these states, personalized health advice, relaxation methods, and mental health care advice are generated.

[1942] Device (smartphone):

[1943] The generated health advice and emotionally sensitive responses are delivered to the user through an avatar in a smartphone application. The avatar uses a natural language processing engine to interact with the user. When the user enters a question, the content is sent to the server, and the most appropriate response is generated, taking into account the results of the emotion engine's analysis.

[1944] Specific examples of operation

[1945] Example 1: Daytime health monitoring and emotion recognition

[1946] Device (smartwatch):

[1947] Smartwatches collect heart rate, step count, and sleep data throughout the day.

[1948] The emotion engine recognizes emotions from voice and facial expressions.

[1949] Device (smartphone):

[1950] Biometric and emotional data collected from the smartwatch are sent to a cloud server.

[1951] server:

[1952] The cloud server analyzes this data and determines if the user is experiencing stress. It also considers the user's emotional state and generates advice such as, "You've been in a stressful situation recently. We recommend relaxing by taking deep breaths or a short walk."

[1953] Device (smartphone):

[1954] The avatar provides users with emotionally sensitive advice via text and voice.

[1955] Example 2: Sharing information with doctors

[1956] User:

[1957] I would like to share my vital signs with my doctor for a health checkup.

[1958] server:

[1959] The cloud server summarizes biometric and emotional data and compiles it into a report. This report is then sent to the doctor after user review.

[1960] Doctor:

[1961] Based on the received reports, we will comprehensively assess the user's health status and take appropriate measures.

[1962] Example of a prompt

[1963] "How do recent heart rate fluctuations affect things?"

[1964] "What are some ways to relieve stress?"

[1965] "Please give me some advice on how to improve the quality of my sleep last night."

[1966] This invention is an advanced health management system incorporating an emotion engine, which provides personalized health advice through integrated analysis of the user's biometric and emotional data. It also aims to achieve comprehensive health management for the user by integrating data from other health management applications.

[1967] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1968] Step 1: Collection of biometric and emotional data

[1969] Device (smartwatch):

[1970] Smartwatches collect biometric data such as heart rate, steps taken, and sleep duration in real time. They also recognize emotional states from data such as voice and facial expressions.

[1971] Input: User biometric data (heart rate, steps, sleep patterns) and emotional data (voice, facial expressions)

[1972] Output: Collected biometric and emotional data

[1973] Specific operation: A heart rate sensor measures heart rate every 10 seconds, and a pedometer sensor counts steps every minute. A small camera and microphone analyze voice and facial expressions, providing information to the emotion engine.

[1974] Step 2: Send

[1975] Device (smartwatch):

[1976] The smartwatch transmits data to the smartphone via Bluetooth.

[1977] Device (smartphone):

[1978] The data received by the smartphone is sent to a cloud server via the internet.

[1979] Input: Biometric and emotional data transmitted from the smartwatch.

[1980] Output: Data sent to the cloud server

[1981] Specific operation: The smartwatch sends batch data to the smartphone every 30 minutes, and the smartphone uploads the data to the cloud server in real time.

[1982] Step 3: Data storage and preprocessing

[1983] server:

[1984] The cloud server stores the received data in a secure database. It verifies the data format, performs data cleaning, and corrects missing or outlier values.

[1985] Input: Biometric data and emotional data transmitted from a smartphone

[1986] Output: Verified and cleaned data

[1987] Specific operation: Daily batch processing verifies the data format and filters out abnormal heart rate values ​​(e.g., 300 bpm) using an anomaly detection algorithm, and then formats the data into a standard format.

[1988] Step 4: Data Analysis

[1989] server:

[1990] We use machine learning models and algorithms to perform detailed analyses and evaluate the user's health and emotional state.

[1991] Input: Verified and cleaned biometric and emotional data

[1992] Output: Assessment results of health and emotional state

[1993] Specific operation: A machine learning model on a cloud server analyzes data every night to calculate seven-day health and emotional trends.

[1994] Step 5: Generating personalized advice

[1995] server:

[1996] Based on the user's health and emotional state, it generates personalized health advice, relaxation methods, and mental health care advice.

[1997] Input: Assessment results of health and emotional status

[1998] Output: Personalized health advice

[1999] Specific operation: If a high-stress state is detected, the server generates text and video links suggesting "deep breathing exercises" and "5 minutes of meditation," and sends them to the smartphone.

[2000] Step 6: Interacting with the Avatar

[2001] Device (smartphone):

[2002] The avatar answers health-related questions through interaction with the user. The user's questions are sent to the server.

[2003] server:

[2004] The system uses a natural language processing engine to analyze questions and then incorporates the results of a sentiment engine to generate the optimal answer.

[2005] Input: User's question data

[2006] Output: Emotionally sensitive response

[2007] Specific operation: When a user enters "I haven't been sleeping well lately" into the app, the server analyzes the sleep data and generates advice such as "avoid using your smartphone before going to bed."

[2008] Step 7: Integration with other health management apps

[2009] server:

[2010] It receives data from other health management applications and manages it centrally.

[2011] Input: Data from other health management applications

[2012] Output: Integrated health data

[2013] Specific operation: Receives sleep data from other apps, integrates that data with existing watch data, and uses it for comprehensive analysis.

[2014] Step 8: Stress Management and Mental Health Support

[2015] Devices (smartwatches and smartphones):

[2016] The smartwatch continuously monitors heart rate variability and estimates stress levels. The smartphone sends this data to a cloud server.

[2017] server:

[2018] If a high-stress state is detected, it generates relaxation and mental health advice.

[2019] Input: Heart rate variability data

[2020] Output: Advice on relaxation and mental health care.

[2021] Specific operation: Heart rate variability is measured every minute, and if a high-stress state lasting more than two hours is detected, the cloud server generates mental health advice.

[2022] Step 9: Sharing information with doctors

[2023] User:

[2024] A request is made to share vital information with a doctor using a smartphone application.

[2025] server:

[2026] The system summarizes biometric and emotional data, generates a report, and sends it to a doctor after user review.

[2027] Input: Biometric data and emotional data

[2028] Output: Summarized report

[2029] Specific operation: When the user selects "Send information to doctor," the system summarizes the data from the past three months and generates a PDF report. The report includes a graph of heart rate and emotional fluctuations.

[2030] (Application Example 2)

[2031] 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".

[2032] Traditional health management systems provide health advice based on users' biometric data, but they do not take into account security risks associated with users' emotional states or stress levels. Therefore, they fail to prevent suspicious behavior caused by stress or anxiety, making it difficult to guarantee overall safety.

[2033] 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. In this invention, the server includes means for collecting the user's biometric data, means for transmitting the biometric data to a cloud server, means for analyzing the biometric data on the cloud server and generating individual health advice, means for providing the generated health advice to the user, means for answering health-related questions through dialogue with an avatar, means for sharing a summary of vital information with a doctor based on the user's selection, means for evaluating the user's security risk and generating a security alert when an abnormal stress level or emotional state is detected, and means for notifying the user of the security alert and encouraging safe behavior. This enables comprehensive support that considers not only the user's health management but also security risks due to emotional state and stress.

[2034] Definitions of important words

[2035] "User biometric data" refers to an individual's physiological information, such as heart rate, steps taken, and sleep duration.

[2036] A "cloud server" refers to a collection of remote servers that store and analyze data via the internet.

[2037] "Personalized health advice" refers to health management guidelines optimized for a specific individual, provided based on the user's biometric data and emotional state.

[2038] An "avatar" refers to a virtual character that interacts with the user and provides information within an application.

[2039] "Vital information" refers to key bodily data related to maintaining life, such as heart rate, blood pressure, and body temperature.

[2040] "Security risk" refers to potential dangers to an individual's safety that can be inferred from a user's biometric data and emotional state.

[2041] A "security alert" refers to a warning that notifies users of potential security risks, generated based on their biometric data and emotional state.

[2042] A "Natural Language Processing (NLP) engine" refers to software that understands meaning from text and audio and analyzes human natural language.

[2043] Modes for carrying out the invention

[2044] This invention is a system that comprehensively supports the user's health and security. Specific embodiments for carrying out this invention are described below.

[2045] System Configuration

[2046] This system consists of a smartwatch that collects the user's biometric data, a smartphone that transmits the data, a cloud server, and an avatar that provides advice and security alerts to the user.

[2047] Data collection and transmission

[2048] The device (smartwatch) collects biometric data such as the user's heart rate, steps taken, and sleep duration in real time. It also collects emotional data from voice and facial expressions. This data is transmitted to the smartphone via Bluetooth.

[2049] The device (smartphone) receives data transmitted from the smartwatch and sends it to a cloud server via the internet.

[2050] Data Analysis

[2051] The server (cloud server) stores the received biometric and emotional data in a secure database. First, the data format is validated and the data is cleaned, correcting for missing or outlier values. Then, machine learning models and algorithms are used to analyze the data and evaluate the user's health and emotional state.

[2052] Generating health advice and security alerts

[2053] The server generates personalized health advice based on the analysis results. For example, if a high-stress state is detected, it will generate advice on relaxation methods and mental health care.

[2054] Furthermore, the system assesses security risks and generates security alerts if abnormal stress levels or emotional states are detected. These advisories and alerts are communicated to the user. Notifications are displayed within the smartphone application and delivered via an avatar.

[2055] User interaction

[2056] The avatar on the device (smartphone) answers health-related questions through interaction with the user. When the user enters a question into the application, the content is sent to the server. The emotion engine recognizes emotions from the user's entered text, speech, and facial expressions.

[2057] The server uses a natural language processing (NLP) engine to analyze the user's question and generate an appropriate answer. The generated answer is then sent back to the smartphone, where an avatar presents it to the user in an emotionally sensitive manner.

[2058] Specific examples and prompt statements

[2059] Specific example

[2060] For example, suppose a user is about to open their door upon returning home, and their smartwatch detects a sudden increase in heart rate and a high level of stress. In this case, the cloud server analyzes the data and generates a security alert. A notification is sent to the smartphone application saying, "Please relax. Check your surroundings for any suspicious activity and open the door only after you are sure it is safe."

[2061] Example of a prompt

[2062] "Please check your current heart rate and stress level."

[2063] "You are under high stress. Take a deep breath and ensure your safety before proceeding."

[2064] Through this configuration, comprehensive assessment of user health and security risks is conducted, enabling the provision of individually optimized advice and alerts.

[2065] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[2066] Program processing steps

[2067] Step 1:

[2068] The device (smartwatch) collects the user's heart rate, steps, sleep duration, and emotional data in real time. This data is temporarily stored within the smartwatch. Input is the user's biometric and emotional data, and output is data transfer to the smartphone.

[2069] Step 2:

[2070] The device (smartphone) receives data from the smartwatch via Bluetooth. The smartphone checks the received data and sends it to a cloud server via the internet. The input is biometric and emotional data transferred from the smartwatch, and the output is the transmission of data to the cloud server.

[2071] Step 3:

[2072] The server (cloud server) stores the received data in a secure database. Next, it performs data format validation and data cleaning, correcting for missing or outlier values. The input is data sent from a smartphone, and the output is clean data that can be analyzed.

[2073] Step 4:

[2074] The server uses a machine learning model to analyze data and assess the user's health and emotional state. The analysis results include stress levels and normal / abnormal health status. The input is clean data, and the output is the assessment of the user's health and emotional state.

[2075] Step 5:

[2076] The server generates personalized health advice based on the analysis results. For example, if a high-stress state is detected, it will generate advice on relaxation methods and mental health care. The input is the health assessment results, and the output is health advice text.

[2077] Step 6:

[2078] The server assesses the user's security risks and generates security alerts if abnormal stress levels or emotional states are detected. Inputs are health and emotional assessment results, and outputs are security alerts.

[2079] Step 7:

[2080] The server sends the generated health advice and security alerts to the smartphone application. The input is the health advice and security alerts, and the output is the transmission of data to the smartphone.

[2081] Step 8:

[2082] The device (smartphone) notifies the user of received health advice and security alerts through an avatar. For example, the avatar provides emotionally sensitive advice to the user, such as, "Please relax. Check your surroundings for any suspicious activity and open the door only after you have confirmed that it is safe." The input is the advice and alerts sent from the server, and the output is the notification to the user.

[2083] Step 9:

[2084] Users ask health and security-related questions to an avatar via a smartphone application. The input is the user's question, and the output is the avatar's response.

[2085] Step 10:

[2086] The server receives the user's question and analyzes it using a natural language processing (NLP) engine. An emotion engine determines the user's emotional state and generates an appropriate response. The input is the user's question, and the output is the appropriate response.

[2087] Step 11:

[2088] The device (smartphone) notifies the user of the response received from the server via an avatar. The avatar provides instructions such as, "Take a deep breath and proceed only after confirming your safety." The input is the response from the server, and the output is the notification to the user.

[2089] In this way, data is collected, transmitted, and analyzed throughout each step and provided to the user as health advice and security alerts. Specific prompts include messages such as, "Check your current heart rate and stress level," and "You are experiencing high stress; take a deep breath and ensure your safety before proceeding."

[2090] 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.

[2091] 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.

[2092] 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.

[2093] 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.

[2094] 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 ...

Claims

1. Means for collecting user biometric data, A means for transmitting the biometric data to a cloud server, The aforementioned cloud server analyzes the biometric data and generates personalized health advice, A means of providing the generated health advice to the user, A means of answering health-related questions through interaction with an avatar, A means of sharing a summary of vital information with a physician based on the user's selection, A system that includes this.

2. The system according to claim 1, further comprising means for receiving data from other health management apps and managing it in an integrated manner.

3. The system according to claim 1, further comprising means for monitoring the user's stress level and mental health status and providing relaxation and mental health care advice as needed.

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