System

Abstract

A system is provided.SOLUTION: A system includes a means for collecting biological data from a wearable device, a means for transmitting the collected biological data to a server, a means for receiving the biological data in the server and performing preprocessing, a means for analyzing an emotional state and a health state based on a generated AI model using the preprocessed data, a means for generating advice for stress management and relaxation based on an analysis result, and a means for displaying the analysis result and the advice to a user in a terminal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Today's working-age population suffers from a lot of stress and anxiety, making it difficult to accurately assess their psychological and physical health. This makes it difficult to implement appropriate stress management and health care. Furthermore, there is a lack of means to collect and analyze this information in real time and provide effective advice to users. The present invention aims to solve these problems by providing a system that provides appropriate support by more accurately analyzing a user's emotional and health states. [Means for solving the problem]

[0005] The present invention provides a system including means for collecting biometric data from a wearable device, means for transmitting the collected biometric data to a server, means for receiving and preprocessing the biometric data in the server, means for analyzing the emotional state and health state based on a generative AI model using the preprocessed data, means for generating advice for stress management and relaxation based on the analysis results, means for transmitting the generated advice and analysis results to a terminal, and means for displaying the analysis results and advice to a user on the terminal, allowing the user to understand their own health condition in detail and take appropriate measures.

[0006] A "wearable device" is a small electronic device worn by a user to collect biometric data.

[0007] "Biometric data" refers to data that indicates the user's physical condition, such as blood pressure, heart rate, and sleep patterns.

[0008] A "server" is a computer system that receives, stores, analyzes, and transmits data.

[0009] "Preprocessing" refers to processes such as data cleansing, normalization, and missing value processing that are performed before analyzing biological data.

[0010] A "generative AI model" is an artificial intelligence model that uses biometric data to analyze a user's emotional and health state.

[0011] "Analysis" is the process of performing calculations and inferences based on collected biometric data to derive the user's emotional and health states.

[0012] "Advice" is specific instructions or suggestions for stress management and relaxation for the user that are generated based on the analysis results.

[0013] A "terminal" is a computer or smartphone that can be operated directly by a user, and is a device that provides information to the user in cooperation with a server.

[0014] "User interface" refers to the display means through which users can check analysis results and advice via their terminal. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The system of the present invention uses a wearable device, a terminal, and a server to collect and analyze a user's biometric data and provide advice based on their emotional and health states.

[0037] Program processing

[0038] Server program:

[0039] 1. Data Collection Module:

[0040] The server receives biometric data (e.g., blood pressure, heart rate, sleep patterns, etc.) from the wearable device sent from the terminal.

[0041] All received data is stored in a secure database.

[0042] 2. Data Preprocessing Module:

[0043] Before analyzing the biometric data, the server cleanses, normalizes, and processes missing values, thereby ensuring data quality and improving analysis accuracy.

[0044] 3. Analysis module using generative AI models:

[0045] The preprocessed biometric data is input into a generative AI model.

[0046] The generative AI model predicts and analyzes the user's emotional state (e.g., stress level, relaxation state) and health status in real time.

[0047] 4. Advice Generation Module:

[0048] Based on the analysis results, the server generates specific advice for stress management and relaxation.

[0049] Include suggestions for seeking expert advice where necessary.

[0050] 5. Data transmission module:

[0051] The server sends the generated analysis results and advice to the terminal.

[0052] Terminal programs:

[0053] 1. Sensor data collection module:

[0054] The terminal collects biometric data in real time from the wearable device.

[0055] 2. Data transmission module:

[0056] The terminal periodically transmits the collected data to the server.

[0057] 3. Result receiving module:

[0058] Receive analysis results and advice sent from the server.

[0059] 4. User Feedback Module:

[0060] Display the received analysis results and advice to the user.

[0061] If necessary, users can directly seek professional help.

[0062] Specific examples

[0063] Example of user action:

[0064] One night, the user puts on the wearable device and goes to sleep. During the night, the wearable device records their heart rate and sleep patterns. The device transmits this data in real time to the terminal, which receives the data and subsequently transmits it to the server.

[0065] Server processing example:

[0066] The data received overnight is stored in a database on the server and pre-processed. A generative AI model then analyzes the data and identifies when the user's stress level is high. The server generates relaxation advice, including breathing exercises and meditation techniques. The results and advice are then sent to the device.

[0067] Example of terminal processing:

[0068] The next morning, the device receives the analysis results and advice from the server. Through the user interface, the user can review the advice, practice the suggested relaxation techniques, and contact a specialist if necessary.

[0069] The processing flow will be explained below.

[0070] Step 1:

[0071] The user puts on the wearable device

[0072] The user puts on the wearable device, which then goes to work and starts collecting biometric data such as blood pressure, heart rate, and sleep patterns.

[0073] Step 2:

[0074] Device collects data

[0075] The terminal's sensor data collection module receives data from the wearable device in real time.

[0076] Step 3:

[0077] The device sends data

[0078] The terminal's data transmission module preprocesses the collected data (e.g., packet shaping), converts it into an optimal format, and transmits it to the server.

[0079] Step 4:

[0080] The server receives and stores the data

[0081] The server's data collection module receives the data sent from the devices and stores it in a secure database.

[0082] Step 5:

[0083] The server preprocesses the data

[0084] The server's data preprocessing module cleanses, normalizes, and handles missing values ​​on the biometric data, preparing it for data analysis.

[0085] Step 6:

[0086] The server analyzes using the generated AI model

[0087] The server's generated AI model analysis module inputs the preprocessed data and predicts and analyzes the user's emotional state and health condition.

[0088] Step 7:

[0089] Server generates advice

[0090] The server's advice generation module generates specific advice for stress management and relaxation based on the analysis results, including suggestions for expert advice where necessary.

[0091] Step 8:

[0092] The server sends the results and advice

[0093] The server's data transmission module transmits the generated analysis results and advice to the terminal.

[0094] Step 9:

[0095] The device receives the results and advice

[0096] A result receiving module of the terminal receives the data from the server.

[0097] Step 10:

[0098] The device displays feedback to the user

[0099] A user interface module of the terminal displays the received analysis results and advice to the user.

[0100] Step 11:

[0101] User reviews feedback

[0102] The user reviews the analysis and advice, practices the suggested relaxation techniques, and seeks professional help if necessary.

[0103] Step 12:

[0104] Contact an expert

[0105] The terminal provides a means for users to contact experts for advice, allowing them to receive support from appropriate experts.

[0106] Example 1

[0107] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0108] Conventional health management systems only collect and analyze a user's biometric information, but lack the functionality to provide real-time stress management and relaxation advice. Furthermore, the accuracy of the acquired data preprocessing and analysis results is insufficient, making it difficult to provide appropriate advice to the user. The present invention aims to solve these problems by providing a system that can provide immediate and specific advice based on the user's health and emotional state.

[0109] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0110] In this invention, the server includes means for collecting biometric information from the wearable device, means for transferring the collected biometric information to the device, means for transmitting the biometric information from the device to the server, means for receiving the biometric information in the server and performing data cleansing, normalization, and missing value processing, means for analyzing emotions and health based on a generative AI model using the preprocessed data, means for generating advice for stress management and relaxation based on the analysis results and including expert advice as needed, means for transmitting the generated advice and analysis results to the device, and means for displaying the analysis results and advice to the user on the device and enabling the user to practice the suggested relaxation techniques. This makes it possible to provide immediate and specific advice based on the user's health and emotional state.

[0111] A "wearable device" is a device worn by the user that can collect biometric information in real time. Examples include smartwatches and fitness trackers.

[0112] "Biometric information" refers to data about a user's physical condition, including heart rate, blood pressure, and sleep patterns.

[0113] The "terminal" is a relay device that receives biometric information from a wearable device and transmits it to a server. Examples of such devices include smartphones and tablets.

[0114] A "server" is a computer system that manages and processes data received from multiple terminals via a network.

[0115] "Data cleansing" is the process of using software to remove noise and inaccurate values ​​from a dataset to improve the quality of the data.

[0116] "Normalization" is a process of aligning the range and distribution of data in order to standardize the variations in the data.

[0117] "Missing value processing" is the process of filling in missing values ​​in a dataset so that they do not interfere with analysis. Common methods are mean value imputation and neighboring point imputation.

[0118] A "generative AI model" is a model built using machine learning algorithms that analyze input data to derive a specific outcome or prediction.

[0119] "Emotion and health analysis" is the process by which a generative AI model assesses a user's emotional state and health status using collected and pre-processed biometric information.

[0120] "Stress management" refers to providing specific measures and advice to reduce users' stress levels.

[0121] "Relaxation techniques" refer to methods or techniques that help users relieve tension or stress, including breathing exercises and meditation.

[0122] "Expert Advice" means advice based on specialized knowledge and experience for a particular situation, which the System provides as needed.

[0123] This invention relates to a system that uses a wearable device, a terminal, and a server to collect and analyze a user's biometric information and provide advice based on their emotions and health status. This system is composed of multiple modules, each with a specific function.

[0124] Hardware and software used

[0125] This system uses the following hardware and software:

[0126] Wearable devices: Smartwatches, fitness trackers, and other devices worn by users that collect biometric information in real time. Examples include the Apple Watch and Fitbit.

[0127] Terminal: A relay device that receives biometric information from a wearable device and transmits it to a server. Examples include smartphones and tablets. Examples include iPhone (registered trademark) and Android (registered trademark) devices.

[0128] Server: A computer system that manages and processes data received from multiple devices. Software used includes MySQL® as a database, Python's pandas library for data preprocessing and analysis, and TENSORFLOW® for running generative AI models.

[0129] Processing Details

[0130] Data Collection and Transfer

[0131] Users wear a wearable device, and the device collects real-time biometric information such as heart rate, blood pressure, and sleep patterns. For example, when a user wears an Apple Watch, the device measures their heart rate every second.

[0132] The device receives biometric data from the wearable device via Bluetooth connection. This data is temporarily stored in the device's local storage. Specifically, the iPhone receives heart rate data from the Apple Watch via Bluetooth.

[0133] Data submission and preprocessing

[0134] The device sends the collected biometric information to a server over Wi-Fi or mobile data, using the HTTPS protocol to ensure data security.

[0135] The server stores the received biometric information in a MySQL database, then uses the Python pandas library to cleanse, normalize, and process missing values, making the preprocessed data suitable for subsequent analysis.

[0136] Analysis and advice generation using generative AI models

[0137] The server then inputs the preprocessed data into a TensorFlow-based generative AI model to analyze the user's emotions and health in real time. For example, collected heart rate data can be used to predict the user's stress level and relaxation state.

[0138] Based on the analysis results, the server generates specific advice for stress management and relaxation, such as suggesting breathing exercises and meditation techniques to users with high stress levels, and including expert advice if necessary.

[0139] Results submission and user interface

[0140] The server then sends the generated analysis results and advice to the device again using the HTTPS protocol, and the sent data is saved in the device's local storage.

[0141] The device displays the received analysis results and advice in a user interface (e.g., a dedicated app). Users can check the analysis results and advice by opening the dedicated iPhone app. Based on this, users can practice breathing exercises and meditation techniques. They can also contact a specialist if necessary.

[0142] Specific examples

[0143] User processing example

[0144] One night, the user puts on the smartwatch and goes to sleep. During the night, the smartwatch records their heart rate and sleep patterns and transmits this data in real time to their smartphone. The smartphone then transmits the data to a server via Wi-Fi.

[0145] Server processing example

[0146] The data received overnight is stored in a MySQL database on the server, where it is validated and preprocessed using Python's pandas library. A TensorFlow-based generative AI model then analyzes the data and identifies high stress levels. The server then generates relaxation advice, such as breathing exercises or meditation techniques, and sends the results and advice to the device.

[0147] Terminal processing example

[0148] The next morning, the smartphone receives the analysis results and advice from the server and displays them to the user through a dedicated app. The user can then review the advice, practice the suggested relaxation techniques, and contact a specialist if necessary.

[0149] Prompt Sentence Examples

[0150] Last night, I used my smartwatch to record my heart rate and sleep patterns while I was asleep. Based on the results, I would like to know how to relax if my stress level is high.

[0151] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0152] Step 1:

[0153] The user wears a wearable device to collect biometric information, including heart rate, blood pressure, and sleep patterns. This data is stored in the wearable device's internal memory. The input is the user's biometric information, and the output is the data recorded on the wearable device. Specifically, the user wears the smartwatch and goes about their daily activities, and the device automatically collects data.

[0154] Step 2:

[0155] The device receives biometric information from the wearable device via Bluetooth. The received data is temporarily stored in the device's local storage. The input is the biometric information received from the wearable device, and the output is the data stored in the device's local storage. Specifically, the device polls for data via Bluetooth every few seconds and stores the received data in local storage.

[0156] Step 3:

[0157] The device sends the collected biometric information to the server. Wi-Fi or mobile data communication is used for transmission, and the HTTPS protocol is used to ensure security. The input is the biometric information stored in the device's local storage, and the output is the data sent to the server. Specifically, the device uploads the data to the server in batch format at regular intervals (e.g., every 30 minutes).

[0158] Step 4:

[0159] The server stores the received biometric information in a database. MySQL or similar databases are used as the database. The input is the biometric information received from the device, and the output is the data stored in the database. Specifically, the server parses the received data and inserts it into the database in the appropriate format.

[0160] Step 5:

[0161] The server preprocesses the biometric information stored in the database. Preprocessing includes data cleansing, normalization, and missing value processing. The input is the biometric information stored in the database, and the output is the preprocessed data. Specifically, it uses the Python pandas library to complement missing values ​​in the data and remove invalid values.

[0162] Step 6:

[0163] The server inputs the preprocessed data into a generative AI model to analyze emotions and health. TensorFlow can be used as the model. The input is preprocessed biometric information, and the output is the analysis results. Specifically, the server inputs the preprocessed data into a machine learning model to analyze stress levels, health status, and other factors in real time.

[0164] Step 7:

[0165] The server generates advice based on the analysis results. The advice includes stress management and relaxation techniques, and expert advice is also provided if necessary. The input is the analysis results, and the output is the generated advice. Specifically, the server generates text suggesting breathing exercises and meditation methods based on the results of the generative AI model.

[0166] Step 8:

[0167] The server sends the generated analysis results and advice to the device. The HTTPS protocol is used for transmission. The input is the generated advice and analysis results, and the output is the data sent to the device. Specifically, the server periodically pushes the analysis results and advice to the device.

[0168] Step 9:

[0169] The device displays the received analysis results and advice in a user interface. The user can review them and implement the suggested relaxation techniques. The input is the analysis results and advice received from the server, and the output is the information displayed to the user and the user's actions. Specifically, the device app renders the display UI and notifies the user of the results in the notification bar or on the app screen.

[0170] Prompt Sentence Examples

[0171] Last night, I used my smartwatch to record my heart rate and sleep patterns while I was asleep. Based on the results, I would like to know how to relax if my stress level is high.

[0172] (Application example 1)

[0173] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0174] The main function of conventional wearable device systems is to collect the user's biometric data, analyze their health and emotional state, and provide advice. However, they have the problem of not being able to monitor the user's security status and respond in real time if an abnormality is detected. This makes it difficult to quickly ensure the user's safety. To solve this problem, there is a need for a system that can simultaneously monitor the user's security status using the user's biometric data and has the ability to detect and notify abnormalities.

[0175] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0176] In this invention, the server includes means for collecting biometric data from the wearable device, means for transmitting the collected biometric data to the server, means for receiving and preprocessing the biometric data in the server, means for analyzing the emotional state and health state based on a generative AI model using the preprocessed data, means for generating advice for stress management and relaxation based on the analysis results, means for transmitting the generated advice and analysis results to the terminal, means for displaying the analysis results and advice to the user in the terminal, means for monitoring the user's security status and detecting abnormalities, and means for notifying the user of an alert when an abnormality is detected. This enables not only constant health management of the user but also real-time security monitoring and abnormality detection.

[0177] A "wearable device" is an electronic device that is worn on the body and collects personal biometric data in real time.

[0178] "Biometric data" refers to measurements such as heart rate, blood pressure, sleep patterns, pulse, and pupil movement that are used to understand an individual's health or emotional state.

[0179] "Server" is a central computer system that receives collected biometric data and performs pre-processing and analysis.

[0180] "Preprocessing" refers to the process of ensuring data quality by performing processes such as data cleansing, normalization, and missing value processing on biometric data.

[0181] A "generative AI model" is a model that uses machine learning algorithms to analyze biometric data and predict emotional and health states.

[0182] "Emotional state" refers to the user's psychological and emotional state, such as stress level, relaxation state, etc.

[0183] "Health status" refers to the physical condition of the user, as assessed by their heart rate, blood pressure, sleep patterns, etc.

[0184] "Stress management" is the practice of providing users with techniques and methods to reduce stress and improve their well-being.

[0185] "Relaxation techniques" are methods such as breathing exercises, meditation, and massage that help users relax.

[0186] "Advice" refers to specific guidelines or suggestions for action provided to users based on the analysis results.

[0187] A "terminal" is an electronic device that allows the user to communicate with the wearable device and display analysis results and advice.

[0188] "Security status" refers to the environment or situation that affects the user's safety, including the presence of suspicious individuals and abnormal events.

[0189] "Anomaly detection" refers to detecting abnormal conditions based on the user's biometric data and surrounding conditions.

[0190] An "alert" is a warning message or notification that notifies the user of an abnormality or an event that requires attention.

[0191] This invention connects a wearable device, a terminal, and a server to collect and analyze a user's biometric data in real time and provide advice based on their emotional and health status. In particular, it simultaneously monitors the user's security status and can respond in real time if an abnormality is detected.

[0192] Overall system configuration

[0193] 1. Wearable devices

[0194] It has a pulse sensor, a camera (to track pupil movement), a display, and a speaker.

[0195] 2. Terminal

[0196] These devices include smartphones and smart glasses, which collect data, send it to a server, and display analysis results and advice.

[0197] 3. Server

[0198] It uses a database (e.g., AWS® RDS), a generative AI model (e.g., TensorFlow), and a data preprocessing tool (Pandas).

[0199] Program processing details

[0200] Server Processing

[0201] The server receives biometric data collected from the wearable device and stores it in a secure database. It then performs preprocessing, such as data cleansing, normalization, and missing value treatment, to ensure data quality. The preprocessed biometric data is then input into a generative AI model to analyze the emotional state, health status, and security status of the user in real time.

[0202] Based on the analysis results, the server generates advice for stress management, relaxation, and specific actions for security, and sends the advice to the device.

[0203] Terminal handling

[0204] The terminal collects biometric data from the wearable device in real time and periodically transmits it to a server. It receives analysis results and advice from the server and displays them through a user interface. If an abnormality is detected, it notifies the user with visual and audio alerts.

[0205] User Actions

[0206] Users wear a wearable device and biometric data is collected without any special effort on their part. They act according to the advice displayed on the device, and if an abnormality is detected, they can take safe action based on the alert.

[0207] Specific examples

[0208] Example of a prompt for the analysis result:

[0209] "We have detected abnormalities in your heart rate and pupil movement. There is a high possibility that the surrounding area is currently in danger. Please check your surroundings and move to a safe location."

[0210] This system allows users to monitor their own health status and security status in real time, enabling safety management from two angles.

[0211] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0212] Step 1:

[0213] The terminal collects biometric data (e.g., pulse rate, pupil movement) in real time from the wearable device. This input data is obtained by sensors in the wearable device. The terminal temporarily stores the collected biometric data in its memory.

[0214] Step 2:

[0215] The device periodically transmits the collected biometric data, which is a block of the most recent biometric data, to the server, which stores the biometric data received from the device in a secure database.

[0216] Step 3:

[0217] The server performs data cleansing, normalization, and missing value processing on the received biometric data to ensure the quality of the input data, such as removing noise data and scaling the data.

[0218] Step 4:

[0219] The preprocessed biometric data is input into a generative AI model. The model estimates the emotional state, health status, and security status, and outputs the results. The server stores the analysis results from the generative AI model as analysis data.

[0220] Step 5:

[0221] Based on the analysis results, the server generates advice for stress management and relaxation, as well as specific actions for security. These actions are presented as text-based advice and warning messages.

[0222] Step 6:

[0223] The server then sends the generated advice and analysis results to the device. This data includes the analysis results (e.g., the level of stress) and advice as specific actions.

[0224] Step 7:

[0225] The device then displays the analysis results and advice to the user, such as breathing techniques for stress management or actions such as rechecking the surroundings if an abnormality is detected.

[0226] Step 8:

[0227] If necessary, the device will notify the user of an abnormality by visually and audibly alerting them, for example by displaying a warning message on the screen and playing a warning sound through the speaker.

[0228] In this way, the system can monitor the user's health and emotional state in real time while also monitoring their security status, and provide prompt countermeasures if an abnormality occurs. An example of a prompt sentence is, "Abnormalities have been detected in your heart rate and pupil movement. There is a high possibility that your surroundings are currently in a dangerous state. Please check your surroundings and move to a safe location."

[0229] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0230] The system of the present invention uses a wearable device, a terminal, a server, and an emotion engine to collect and analyze a user's biometric data and emotional data, and provide advice based on the user's emotional state and health condition.

[0231] Program processing

[0232] Server program:

[0233] 1. Data Collection Module:

[0234] The server receives data from the wearable device and emotion engine transmitted from the terminal.

[0235] All received data is stored in a secure database.

[0236] 2. Data Preprocessing Module:

[0237] The server cleanses, normalizes, and processes missing values ​​before analyzing biometric and emotional data, ensuring data quality and improving analysis accuracy.

[0238] 3. Analysis module using generative AI models:

[0239] Preprocessed biometric and emotional data is input into a generative AI model.

[0240] The generative AI model predicts and analyzes the user's emotional state (e.g., stress level, relaxation state) and health status in real time.

[0241] 4. Advice Generation Module:

[0242] Based on the analysis results, the server generates specific advice for stress management and relaxation.

[0243] Include suggestions for seeking expert advice where necessary.

[0244] 5. Data transmission module:

[0245] The server sends the generated analysis results and advice to the terminal.

[0246] Terminal programs:

[0247] 1. Sensor data collection module:

[0248] The terminal collects biometric data in real time from the wearable device.

[0249] 2. Emotion data collection module:

[0250] The emotion engine analyzes the user's voice and text data to recognize their emotional state.

[0251] 3. Data transmission module:

[0252] The terminal periodically transmits the collected biometric data and emotion data to the server.

[0253] 4. Result receiving module:

[0254] Receive analysis results and advice sent from the server.

[0255] 5. User Feedback Module:

[0256] Display the received analysis results and advice to the user.

[0257] If necessary, users can directly seek professional help.

[0258] Specific examples

[0259] Example of user action:

[0260] One night, the user puts on the wearable device and goes to sleep. During the night, the wearable device records the user's heart rate and sleep patterns, while the emotion engine analyzes the text data the user has written in a diary app. The device and emotion engine transmit this data to the device in real time, and the device receives the data and sends it to the server.

[0261] Server processing example:

[0262] The biometric and emotional data received overnight is stored in a server database and pre-processed. A generative AI model then analyzes the data and identifies when the user's stress level is high. The server generates relaxation advice, including breathing and meditation techniques. The results and advice are then sent to the device.

[0263] Example of terminal processing:

[0264] The next morning, the device receives the analysis results and advice from the server. Through the user interface, the user can review the advice, practice the suggested relaxation techniques, and contact a specialist if necessary.

[0265] The processing flow will be explained below.

[0266] Step 1:

[0267] The user puts on the wearable device

[0268] The user puts on the wearable device, which then goes to work and starts collecting biometric data such as blood pressure, heart rate, and sleep patterns.

[0269] Step 2:

[0270] Device collects data

[0271] The terminal's sensor data collection module receives data from the wearable device in real time.

[0272] Step 3:

[0273] Emotion engine collects emotional data

[0274] The emotion engine installed in the device analyzes the user's voice and text data to recognize the user's emotional state.

[0275] Step 4:

[0276] The device sends all data

[0277] The terminal's data transmission module preprocesses the collected biometric data and emotion data generated by the emotion engine, converts them into an optimal format, and transmits them to the server.

[0278] Step 5:

[0279] The server receives and stores the data

[0280] The server's data collection module receives the biometric and emotional data sent from the device and stores it all in a secure database.

[0281] Step 6:

[0282] The server preprocesses the data

[0283] The server's data preprocessing module cleanses, normalizes, and handles missing values ​​on biometric and emotional data to prepare it for data analysis.

[0284] Step 7:

[0285] The server analyzes using the generated AI model

[0286] The server's generated AI model analysis module inputs preprocessed biometric and emotional data to predict and analyze the user's emotional state (e.g., stress level, relaxation state) and health condition.

[0287] Step 8:

[0288] Server generates advice

[0289] The server's advice generation module generates specific advice for stress management and relaxation based on the analysis results, including suggestions for expert advice where necessary.

[0290] Step 9:

[0291] The server sends the results and advice

[0292] The server's data transmission module transmits the generated analysis results and advice to the terminal.

[0293] Step 10:

[0294] The device receives the results and advice

[0295] A result receiving module of the terminal receives the data from the server.

[0296] Step 11:

[0297] The device displays feedback to the user

[0298] A user interface module of the terminal displays the received analysis results and advice to the user.

[0299] Step 12:

[0300] Users review and implement feedback

[0301] The user checks the analysis results and recommendations on the device and practices the suggested relaxation techniques (e.g., deep breathing exercises or meditation).

[0302] Step 13:

[0303] Contact an expert

[0304] If necessary, the user can use the contact means to seek advice from an expert through the terminal, thereby receiving support from an appropriate expert.

[0305] Example 2

[0306] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0307] Conventional emotional and health management systems have struggled to effectively collect and analyze users' biometric and emotional data. Furthermore, they lacked the means to quickly provide users with appropriate advice based on the analysis results. This made it difficult for users to effectively implement actions for stress management and relaxation.

[0308] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for transmitting biometric data and emotional data between the terminal and the server, a means for preprocessing the received data, a means for analyzing using a generative AI model, a means for generating advice based on the analysis results, a means for transmitting the advice to the terminal, and a means for contacting an expert. This makes it possible to effectively collect and analyze the user's biometric data and emotional data and quickly provide appropriate advice.

[0309] A "wearable device" is an electronic device that can be worn by a user and is used to collect biometric data in real time.

[0310] "Biometric data" is data relating to a user's physical condition, such as the user's heart rate, body temperature, or sleep patterns.

[0311] "Emotion data" is data related to the user's emotional state that is extracted by analyzing the user's voice data and text data.

[0312] A "terminal" is an electronic device that transmits biometric and emotional data collected from a wearable device to a server and displays analysis results and advice to the user.

[0313] The "server" is a network computer system that receives biometric data and emotional data sent from the terminal, analyzes them, and generates advice.

[0314] "Data cleansing" is a process for removing noise and redundancy from collected data and improving the quality of the data.

[0315] "Normalization" is a process of converting data to a uniform scale in order to equalize data variations and improve analytical accuracy.

[0316] "Missing value handling" is the process of imputing missing values ​​in a dataset or deleting the records.

[0317] A "generative AI model" is an algorithm that uses machine learning technology to analyze a user's biometric and emotional data to predict and analyze their emotional and health states.

[0318] "Analysis results" are information about the user's emotional state and health status obtained by the generative AI model.

[0319] "Advice" refers to specific recommended actions for stress management and relaxation provided to users based on the analysis results.

[0320] "Professional contact" is a feature that helps users contact health and psychological professionals, if necessary.

[0321] The system of the present invention is composed of a wearable device, a terminal, a server, and an emotion engine. The user wears the wearable device to collect biometric and emotional data. This data is sent to the server via the terminal, and the server analyzes the data using a generative AI model to generate advice based on the user's emotional and health states. An embodiment of the system using specific hardware and software is described below.

[0322] Hardware and Software

[0323] Wearable devices

[0324] Overview: A device that collects biometric data such as heart rate, body temperature, and sleep patterns in real time.

[0325] Examples: smartwatches, fitness trackers.

[0326] Terminal

[0327] Overview: A device for collecting data from wearable devices and sending it to a server.

[0328] For example: smartphones, tablets.

[0329] server

[0330] Overview: A network computer system that receives data sent from terminals, analyzes it, and generates advice.

[0331] software:

[0332] Database Management System: AWS DynamoDB

[0333] Programming libraries: Python, Pandas

[0334] Generative AI models: TensorFlow, PyTorch

[0335] Specific operation of the system

[0336] 1. Wearable device operation

[0337] The user wears a wearable device to collect biometric data (heart rate, body temperature, sleep patterns, etc.), which is then transmitted to a terminal via Bluetooth or Wi-Fi.

[0338] 2. Device Operation

[0339] The terminal stores biometric data received from the wearable device and also collects the user's voice and text data, which are then converted into emotional data through an emotion engine.

[0340] 3. Data transmission

[0341] The device sends the collected biometric and emotional data to a server at regular intervals using the HTTPS protocol, and the data is encrypted.

[0342] 4. Data processing on the server

[0343] The server stores the data received from the device in a database, cleansing, normalizing, and processing missing values, and then inputs the preprocessed data into the generative AI model.

[0344] 5. Analysis using generative AI models

[0345] The generative AI model analyzes the data using the following prompt:

[0346] "Based on the user's heart rate data and sleep patterns, as well as the text data analyzed by the emotion engine, assess the user's current stress level and provide appropriate relaxation advice."

[0347] 6. Generating and Sending Advice

[0348] The server generates health advice for the user based on the analysis results obtained from the generative AI model and sends it to the device, including advice on stress management and relaxation techniques.

[0349] 7. User Feedback

[0350] The device receives advice and analysis results from the server and displays them to the user, who can then put these advice into practice and contact an expert if necessary.

[0351] Specific examples

[0352] One night, the user puts on the wearable device and goes to sleep. The device records their nighttime heart rate and sleep patterns, and the emotion engine analyzes text data written by the user in a diary app. This data is sent to a server via the device. The server preprocesses the data and analyzes it using a generative AI model. If the analysis identifies that the user's stress level is high, the server generates relaxation advice. This advice is notified to the user via the device the next morning, and the user can reduce stress by practicing the suggested relaxation techniques.

[0353] This allows users to manage their emotional and health states in real time and receive appropriate advice to maintain a healthier lifestyle.

[0354] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0355] Step 1: Collecting data with wearable devices

[0356] The wearable device collects real-time biometric data such as the user's heart rate, body temperature, and sleep patterns. This data is transmitted to the device via Bluetooth or Wi-Fi. Specifically, the data is measured every five minutes and transferred to the device.

[0357] Input: User biometric data (heart rate, body temperature, sleep patterns, etc.).

[0358] Specific operation: The wearable device takes measurements using biosensors and transmits the data to the terminal.

[0359] Output: Biometric data sent to the device.

[0360] Step 2: Data reception by device and emotion data collection

[0361] The terminal receives biometric data transmitted in real time from the wearable device. At the same time, the emotion engine analyzes the user's voice and text data to recognize their emotional state. The data collected during this process is temporarily stored on the terminal.

[0362] Input: Biometric data sent from the wearable device, user voice data, and text data.

[0363] Specific operation: The terminal receives data from the wearable device and inputs the voice and text data into the emotion engine to analyze the emotional state.

[0364] Output: Biometric and emotional data stored on the device.

[0365] Step 3: Send data

[0366] The device sends the collected biometric and emotional data to a server at regular intervals (e.g., every 15 minutes). The data is encrypted using the HTTPS protocol.

[0367] Input: Biometric and emotional data stored on the device.

[0368] What it does: The device encrypts the collected data and sends it to the server using the HTTPS protocol.

[0369] Output: Biometric and emotional data sent to the server.

[0370] Step 4: Data reception and preprocessing by the server

[0371] The server receives biometric and emotional data sent from the device, stores the data in a database, and performs preprocessing such as data cleansing, normalization, and missing value processing.

[0372] Input: Biometric and emotional data sent from the device.

[0373] Specific operations: Data is stored in a database management system, and data cleansing, normalization, and missing value processing are performed using Python and Pandas.

[0374] Output: Preprocessed data.

[0375] Step 5: Analysis by generative AI model

[0376] The server inputs the preprocessed biometric and emotional data into a generative AI model to analyze the user's emotional and health states.

[0377] Input: Preprocessed biometric and emotion data.

[0378] Prompt text: Based on the user's heart rate data and sleep patterns, as well as the text data analyzed by the emotion engine, assess the user's current stress level and provide appropriate relaxation advice.

[0379] Specific operation: Data is input into a generative AI model (using TensorFlow or PyTorch) and analysis is performed using prompt statements.

[0380] Output: Analysis results (stress level and health status).

[0381] Step 6: Generate and send advice

[0382] The server generates advice for stress management and relaxation based on the analysis results obtained from the generative AI model, and the generated advice and analysis results are sent to the device.

[0383] Input: Analysis results from the generative AI model.

[0384] Specific actions: Generate specific advice on stress management and relaxation and send it to the device using the HTTPS protocol.

[0385] Output: Advice and analysis results sent to the device.

[0386] Step 7: Terminal receives results and user feedback

[0387] The device receives the analysis results and advice sent from the server and displays them to the user through a user interface, which also provides the user with the means to contact an expert if desired.

[0388] Input: Analysis results and advice sent from the server.

[0389] What it does: Stores the received data, displays it in a user interface, and provides a way to contact an expert if needed.

[0390] Output: Analysis results and advice displayed to the user.

[0391] These are the specific steps of the program processing of this system.

[0392] (Application example 2)

[0393] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0394] In current factory work, there is a lack of means to grasp workers' stress levels and health status in real time, which can result in reduced work efficiency. Furthermore, if workers' emotional state cannot be properly managed, excessive stress can continue for long periods of time, increasing safety risks. Furthermore, current factory robots lack the ability to collect biometric and emotional information, making them unable to contribute to worker health management or improved work efficiency. There is a need to solve these problems, improve workers' health, and increase work efficiency.

[0395] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting biometric information from the wearable device, means for transmitting the collected biometric information to the server, means for receiving and preprocessing the biometric information, means for analyzing the emotional state and health state based on a generative artificial intelligence model, means for generating instructions for stress management and relaxation based on the analysis results, and means for transmitting the generated instructions and analysis results to the terminal. This enables real-time collection and analysis of biometric information and emotional information, monitors the worker's health status, and improves work efficiency by providing appropriate instructions. It also enables factory robots to provide support for balancing worker safety and work efficiency.

[0396] A "wearable device" is a portable or wearable device that can collect biometric information by being worn on the body.

[0397] "Biometric information" refers to physiological data such as heart rate, body temperature, and blood pressure, and is data that indicates an individual's health condition.

[0398] "Server" means a central control unit for processing, analyzing and storing collected data.

[0399] "Preprocessing" refers to the process of cleansing, normalizing, and imputing missing values ​​of the collected data.

[0400] A "generative AI model" is an artificial intelligence model that predicts and analyzes emotional and health states based on biometric and emotional information.

[0401] "Emotional information" is data related to emotions extracted from the user's voice, facial expressions, and behavior.

[0402] "Analysis results" are predictions of emotional and health states output by the generative AI model based on biometric and emotional information.

[0403] "Stress management" refers to specific techniques and methods for reducing a user's stress level based on the results of analysis.

[0404] "Relaxation instructions" are specific advice or techniques suggested by the generative AI model to help users relieve stress.

[0405] A "robot" is a mechanical device that performs specific tasks under automatic control or programming.

[0406] A "sensor" is a device that senses a physical phenomenon and converts it into an electrical signal.

[0407] A "camera" is a device that collects visual information and stores and transmits it as image data.

[0408] "Real-time" means that data is collected, transmitted, and analyzed immediately.

[0409] The present invention provides a system for improving work efficiency and safety by using a factory robot to collect and analyze biometric and emotional information of workers. A specific embodiment of this system will be described below.

[0410] System configuration

[0411] This system consists of a wearable device, a terminal, a server, and a factory robot. The specific functions of each element are as follows:

[0412] Wearable devices

[0413] The wearable devices will collect workers' biometric information, such as heart rate and body temperature, in real time, using the latest heart rate monitors and temperature sensors.

[0414] Factory robots

[0415] The factory robots are equipped with sensors and cameras to collect biometric and emotional information from workers, and transmit this data to a terminal in real time.

[0416] Terminal

[0417] The device receives sensor data and emotion data collected from the camera and transmits it to the server. The device itself is equipped with a data transmission module, a result reception module, and a user feedback module.

[0418] server

[0419] The server consists of multiple modules (data collection module, data preprocessing module, analysis module using generative AI model, advice generation module, and data transmission module).

[0420] 1. Data Collection Module:

[0421] The server receives the biometric and emotional information sent from the device and stores it in a secure database. The data is encrypted to ensure its security.

[0422] 2. Data Preprocessing Module:

[0423] The server cleanses, normalizes, and processes missing values ​​on the received data to maintain data quality, using Python and Pandas.

[0424] 3. Analysis module using generative AI models:

[0425] The preprocessed data is input into a generative AI model (e.g., GPT-4 (registered trademark)) to predict and analyze the worker's emotional state and health status.

[0426] 4. Advice Generation Module:

[0427] Based on the analysis results, specific instructions for stress management and relaxation can be generated, as well as instructions for improving work efficiency.

[0428] 5. Data transmission module:

[0429] The generated instructions and analysis results are then sent back to the terminal, and this information is then appropriately fed back to the worker.

[0430] Specific examples

[0431] For example, when Worker A is working continuously in a factory, a robot monitors A's heart rate, body temperature, and facial expressions. This data is sent in real time to the terminal and then to the server. On the server side, the data is preprocessed and analyzed by a generative AI model (GPT-4). If the server determines that Worker A is experiencing high stress, it can immediately generate instructions, including suggestions for breaks and relaxation methods, and provide them as feedback to Worker A via the terminal.

[0432] Prompt Sentence Examples

[0433] Biometric data: Heart rate 75, body temperature 36.5℃, facial expression: nervous, voice: irritated

[0434] Question: Based on this data, what relaxation techniques would be best for the worker?

[0435] In this way, this system efficiently collects and analyzes biometric and emotional information, thereby improving worker health management and work efficiency.

[0436] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0437] Step 1:

[0438] The terminal collects biometric information (heart rate, body temperature, etc.) and emotional information (facial expressions, voice) from wearable devices and factory robots in real time. Each time data collection is completed, a data packet is generated. The terminal temporarily stores the data.

[0439] Step 2:

[0440] The device periodically transmits collected data packets to the server, which include the collection time, biometric information, and emotional information. This allows the server to receive real-time data.

[0441] Step 3:

[0442] The server stores the received data packets in a database and performs data preprocessing. This preprocessing step involves data cleansing (noise removal), normalization (standardization of data), and missing value imputation. For example, Python and Pandas are used to cleanse the data and impute missing values.

[0443] Step 4:

[0444] The server inputs the preprocessed data into a generative AI model (such as GPT-4), which analyzes the emotional and health states according to the prompts. Based on the input data, the AI ​​model generates an output that predicts the stress level and health state.

[0445] Step 5:

[0446] The server generates specific instructions for stress management and relaxation based on the analysis results obtained from the generative AI model. In this step, instructions are generated that include specific advice on breathing techniques, meditation techniques, etc.

[0447] Step 6:

[0448] The server then sends the generated instructions and analysis results to the terminal, allowing the worker to receive the analysis results and instructions in real time.

[0449] Step 7:

[0450] The device receives the analysis results and instructions sent from the server and provides feedback to the worker. This feedback is provided via the device's display or audio output. The worker can then practice the suggested relaxation methods and contact a specialist if necessary.

[0451] Step 8:

[0452] The user follows instructions provided through the device to practice stress management and relaxation techniques, such as deep breathing or short meditation sessions, and data on these practices is again collected through the device.

[0453] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0454] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0455] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0456] [Second embodiment]

[0457] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0458] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0459] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0460] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0461] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0462] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0463] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0464] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0465] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0466] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0467] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0468] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0469] The system of the present invention uses a wearable device, a terminal, and a server to collect and analyze a user's biometric data and provide advice based on their emotional and health states.

[0470] Program processing

[0471] Server program:

[0472] 1. Data Collection Module:

[0473] The server receives biometric data (e.g., blood pressure, heart rate, sleep patterns, etc.) from the wearable device sent from the terminal.

[0474] All received data is stored in a secure database.

[0475] 2. Data Preprocessing Module:

[0476] Before analyzing the biometric data, the server cleanses, normalizes, and processes missing values, thereby ensuring data quality and improving analysis accuracy.

[0477] 3. Analysis module using generative AI models:

[0478] The preprocessed biometric data is input into a generative AI model.

[0479] The generative AI model predicts and analyzes the user's emotional state (e.g., stress level, relaxation state) and health status in real time.

[0480] 4. Advice Generation Module:

[0481] Based on the analysis results, the server generates specific advice for stress management and relaxation.

[0482] Include suggestions for seeking expert advice where necessary.

[0483] 5. Data transmission module:

[0484] The server sends the generated analysis results and advice to the terminal.

[0485] Terminal programs:

[0486] 1. Sensor data collection module:

[0487] The terminal collects biometric data in real time from the wearable device.

[0488] 2. Data transmission module:

[0489] The terminal periodically transmits the collected data to the server.

[0490] 3. Result receiving module:

[0491] Receive analysis results and advice sent from the server.

[0492] 4. User Feedback Module:

[0493] Display the received analysis results and advice to the user.

[0494] If necessary, users can directly seek professional help.

[0495] Specific examples

[0496] Example of user action:

[0497] One night, the user puts on the wearable device and goes to sleep. During the night, the wearable device records their heart rate and sleep patterns. The device transmits this data in real time to the terminal, which receives the data and subsequently transmits it to the server.

[0498] Server processing example:

[0499] The data received overnight is stored in a database on the server and pre-processed. A generative AI model then analyzes the data and identifies when the user's stress level is high. The server generates relaxation advice, including breathing exercises and meditation techniques. The results and advice are then sent to the device.

[0500] Example of terminal processing:

[0501] The next morning, the device receives the analysis results and advice from the server. Through the user interface, the user can review the advice, practice the suggested relaxation techniques, and contact a specialist if necessary.

[0502] The processing flow will be explained below.

[0503] Step 1:

[0504] The user puts on the wearable device

[0505] The user puts on the wearable device, which then goes to work and starts collecting biometric data such as blood pressure, heart rate, and sleep patterns.

[0506] Step 2:

[0507] Device collects data

[0508] The terminal's sensor data collection module receives data from the wearable device in real time.

[0509] Step 3:

[0510] The device sends data

[0511] The terminal's data transmission module preprocesses the collected data (e.g., packet shaping), converts it into an optimal format, and transmits it to the server.

[0512] Step 4:

[0513] The server receives and stores the data

[0514] The server's data collection module receives the data sent from the devices and stores it in a secure database.

[0515] Step 5:

[0516] The server preprocesses the data

[0517] The server's data preprocessing module cleanses, normalizes, and handles missing values ​​on the biometric data, preparing it for data analysis.

[0518] Step 6:

[0519] The server analyzes using the generated AI model

[0520] The server's generated AI model analysis module inputs the preprocessed data and predicts and analyzes the user's emotional state and health condition.

[0521] Step 7:

[0522] Server generates advice

[0523] The server's advice generation module generates specific advice for stress management and relaxation based on the analysis results, including suggestions for expert advice where necessary.

[0524] Step 8:

[0525] The server sends the results and advice

[0526] The server's data transmission module transmits the generated analysis results and advice to the terminal.

[0527] Step 9:

[0528] The device receives the results and advice

[0529] A result receiving module of the terminal receives the data from the server.

[0530] Step 10:

[0531] The device displays feedback to the user

[0532] A user interface module of the terminal displays the received analysis results and advice to the user.

[0533] Step 11:

[0534] User reviews feedback

[0535] The user reviews the analysis and advice, practices the suggested relaxation techniques, and seeks professional help if necessary.

[0536] Step 12:

[0537] Contact an expert

[0538] The terminal provides a means for users to contact experts for advice, allowing them to receive support from appropriate experts.

[0539] Example 1

[0540] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0541] Conventional health management systems only collect and analyze a user's biometric information, but lack the functionality to provide real-time stress management and relaxation advice. Furthermore, the accuracy of the acquired data preprocessing and analysis results is insufficient, making it difficult to provide appropriate advice to the user. The present invention aims to solve these problems by providing a system that can provide immediate and specific advice based on the user's health and emotional state.

[0542] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0543] In this invention, the server includes means for collecting biometric information from the wearable device, means for transferring the collected biometric information to the device, means for transmitting the biometric information from the device to the server, means for receiving the biometric information in the server and performing data cleansing, normalization, and missing value processing, means for analyzing emotions and health based on a generative AI model using the preprocessed data, means for generating advice for stress management and relaxation based on the analysis results and including expert advice as needed, means for transmitting the generated advice and analysis results to the device, and means for displaying the analysis results and advice to the user on the device and enabling the user to practice the suggested relaxation techniques. This makes it possible to provide immediate and specific advice based on the user's health and emotional state.

[0544] A "wearable device" is a device worn by the user that can collect biometric information in real time. Examples include smartwatches and fitness trackers.

[0545] "Biometric information" refers to data about a user's physical condition, including heart rate, blood pressure, and sleep patterns.

[0546] The "terminal" is a relay device that receives biometric information from a wearable device and transmits it to a server. Examples of such devices include smartphones and tablets.

[0547] A "server" is a computer system that manages and processes data received from multiple terminals via a network.

[0548] "Data cleansing" is the process of using software to remove noise and inaccurate values ​​from a dataset to improve the quality of the data.

[0549] "Normalization" is a process of aligning the range and distribution of data in order to standardize the variations in the data.

[0550] "Missing value processing" is the process of filling in missing values ​​in a dataset so that they do not interfere with analysis. Common methods are mean value imputation and neighboring point imputation.

[0551] A "generative AI model" is a model built using machine learning algorithms that analyze input data to derive a specific outcome or prediction.

[0552] "Emotion and health analysis" is the process by which a generative AI model assesses a user's emotional state and health status using collected and pre-processed biometric information.

[0553] "Stress management" refers to providing specific measures and advice to reduce users' stress levels.

[0554] "Relaxation techniques" refer to methods or techniques that help users relieve tension or stress, including breathing exercises and meditation.

[0555] "Expert Advice" means advice based on specialized knowledge and experience for a particular situation, which the System provides as needed.

[0556] This invention relates to a system that uses a wearable device, a terminal, and a server to collect and analyze a user's biometric information and provide advice based on their emotions and health status. This system is composed of multiple modules, each with a specific function.

[0557] Hardware and software used

[0558] This system uses the following hardware and software:

[0559] Wearable devices: Smartwatches, fitness trackers, and other devices worn by users that collect biometric information in real time. Examples include the Apple Watch and Fitbit.

[0560] Terminal: A relay device that receives biometric information from a wearable device and sends it to a server. This includes smartphones and tablets. Specific examples include iPhones and Android devices.

[0561] Server: A computer system that manages and processes data received from multiple devices. The software used includes MySQL as a database, Python's pandas library for data preprocessing and analysis, and TensorFlow for running generative AI models.

[0562] Processing Details

[0563] Data Collection and Transfer

[0564] Users wear a wearable device, and the device collects real-time biometric information such as heart rate, blood pressure, and sleep patterns. For example, when a user wears an Apple Watch, the device measures their heart rate every second.

[0565] The device receives biometric data from the wearable device via Bluetooth connection. This data is temporarily stored in the device's local storage. Specifically, the iPhone receives heart rate data from the Apple Watch via Bluetooth.

[0566] Data submission and preprocessing

[0567] The device sends the collected biometric information to a server over Wi-Fi or mobile data, using the HTTPS protocol to ensure data security.

[0568] The server stores the received biometric information in a MySQL database, then uses the Python pandas library to cleanse, normalize, and process missing values, making the preprocessed data suitable for subsequent analysis.

[0569] Analysis and advice generation using generative AI models

[0570] The server then inputs the preprocessed data into a TensorFlow-based generative AI model to analyze the user's emotions and health in real time. For example, collected heart rate data can be used to predict the user's stress level and relaxation state.

[0571] Based on the analysis results, the server generates specific advice for stress management and relaxation, such as suggesting breathing exercises and meditation techniques to users with high stress levels, and including expert advice if necessary.

[0572] Results submission and user interface

[0573] The server then sends the generated analysis results and advice to the device again using the HTTPS protocol, and the sent data is saved in the device's local storage.

[0574] The device displays the received analysis results and advice in a user interface (e.g., a dedicated app). Users can check the analysis results and advice by opening the dedicated iPhone app. Based on this, users can practice breathing exercises and meditation techniques. They can also contact a specialist if necessary.

[0575] Specific examples

[0576] User processing example

[0577] One night, the user puts on the smartwatch and goes to sleep. During the night, the smartwatch records their heart rate and sleep patterns and transmits this data in real time to their smartphone. The smartphone then transmits the data to a server via Wi-Fi.

[0578] Server processing example

[0579] The data received overnight is stored in a MySQL database on the server, where it is validated and preprocessed using Python's pandas library. A TensorFlow-based generative AI model then analyzes the data and identifies high stress levels. The server then generates relaxation advice, such as breathing exercises or meditation techniques, and sends the results and advice to the device.

[0580] Terminal processing example

[0581] The next morning, the smartphone receives the analysis results and advice from the server and displays them to the user through a dedicated app. The user can then review the advice, practice the suggested relaxation techniques, and contact a specialist if necessary.

[0582] Prompt Sentence Examples

[0583] Last night, I used my smartwatch to record my heart rate and sleep patterns while I was asleep. Based on the results, I would like to know how to relax if my stress level is high.

[0584] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0585] Step 1:

[0586] The user wears a wearable device to collect biometric information, including heart rate, blood pressure, and sleep patterns. This data is stored in the wearable device's internal memory. The input is the user's biometric information, and the output is the data recorded on the wearable device. Specifically, the user wears the smartwatch and goes about their daily activities, and the device automatically collects data.

[0587] Step 2:

[0588] The device receives biometric information from the wearable device via Bluetooth. The received data is temporarily stored in the device's local storage. The input is the biometric information received from the wearable device, and the output is the data stored in the device's local storage. Specifically, the device polls for data via Bluetooth every few seconds and stores the received data in local storage.

[0589] Step 3:

[0590] The device sends the collected biometric information to the server. Wi-Fi or mobile data communication is used for transmission, and the HTTPS protocol is used to ensure security. The input is the biometric information stored in the device's local storage, and the output is the data sent to the server. Specifically, the device uploads the data to the server in batch format at regular intervals (e.g., every 30 minutes).

[0591] Step 4:

[0592] The server stores the received biometric information in a database. MySQL or similar databases are used as the database. The input is the biometric information received from the device, and the output is the data stored in the database. Specifically, the server parses the received data and inserts it into the database in the appropriate format.

[0593] Step 5:

[0594] The server preprocesses the biometric information stored in the database. Preprocessing includes data cleansing, normalization, and missing value processing. The input is the biometric information stored in the database, and the output is the preprocessed data. Specifically, it uses the Python pandas library to complement missing values ​​in the data and remove invalid values.

[0595] Step 6:

[0596] The server inputs the preprocessed data into a generative AI model to analyze emotions and health. TensorFlow can be used as the model. The input is preprocessed biometric information, and the output is the analysis results. Specifically, the server inputs the preprocessed data into a machine learning model to analyze stress levels, health status, and other factors in real time.

[0597] Step 7:

[0598] The server generates advice based on the analysis results. The advice includes stress management and relaxation techniques, and expert advice is also provided if necessary. The input is the analysis results, and the output is the generated advice. Specifically, the server generates text suggesting breathing exercises and meditation methods based on the results of the generative AI model.

[0599] Step 8:

[0600] The server sends the generated analysis results and advice to the device. The HTTPS protocol is used for transmission. The input is the generated advice and analysis results, and the output is the data sent to the device. Specifically, the server periodically pushes the analysis results and advice to the device.

[0601] Step 9:

[0602] The device displays the received analysis results and advice in a user interface. The user can review them and implement the suggested relaxation techniques. The input is the analysis results and advice received from the server, and the output is the information displayed to the user and the user's actions. Specifically, the device app renders the display UI and notifies the user of the results in the notification bar or on the app screen.

[0603] Prompt Sentence Examples

[0604] Last night, I used my smartwatch to record my heart rate and sleep patterns while I was asleep. Based on the results, I would like to know how to relax if my stress level is high.

[0605] (Application example 1)

[0606] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0607] The main function of conventional wearable device systems is to collect the user's biometric data, analyze their health and emotional state, and provide advice. However, they have the problem of not being able to monitor the user's security status and respond in real time if an abnormality is detected. This makes it difficult to quickly ensure the user's safety. To solve this problem, there is a need for a system that can simultaneously monitor the user's security status using the user's biometric data and has the ability to detect and notify abnormalities.

[0608] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0609] In this invention, the server includes means for collecting biometric data from the wearable device, means for transmitting the collected biometric data to the server, means for receiving and preprocessing the biometric data in the server, means for analyzing the emotional state and health state based on a generative AI model using the preprocessed data, means for generating advice for stress management and relaxation based on the analysis results, means for transmitting the generated advice and analysis results to the terminal, means for displaying the analysis results and advice to the user in the terminal, means for monitoring the user's security status and detecting abnormalities, and means for notifying the user of an alert when an abnormality is detected. This enables not only constant health management of the user but also real-time security monitoring and abnormality detection.

[0610] A "wearable device" is an electronic device that is worn on the body and collects personal biometric data in real time.

[0611] "Biometric data" refers to measurements such as heart rate, blood pressure, sleep patterns, pulse, and pupil movement that are used to understand an individual's health or emotional state.

[0612] "Server" is a central computer system that receives collected biometric data and performs pre-processing and analysis.

[0613] "Preprocessing" refers to the process of ensuring data quality by performing processes such as data cleansing, normalization, and missing value processing on biometric data.

[0614] A "generative AI model" is a model that uses machine learning algorithms to analyze biometric data and predict emotional and health states.

[0615] "Emotional state" refers to the user's psychological and emotional state, such as stress level, relaxation state, etc.

[0616] "Health status" refers to the physical condition of the user, as assessed by their heart rate, blood pressure, sleep patterns, etc.

[0617] "Stress management" is the practice of providing users with techniques and methods to reduce stress and improve their well-being.

[0618] "Relaxation techniques" are methods such as breathing exercises, meditation, and massage that help users relax.

[0619] "Advice" refers to specific guidelines or suggestions for action provided to users based on the analysis results.

[0620] A "terminal" is an electronic device that allows the user to communicate with the wearable device and display analysis results and advice.

[0621] "Security status" refers to the environment or situation that affects the user's safety, including the presence of suspicious individuals and abnormal events.

[0622] "Anomaly detection" refers to detecting abnormal conditions based on the user's biometric data and surrounding conditions.

[0623] An "alert" is a warning message or notification that notifies the user of an abnormality or an event that requires attention.

[0624] This invention connects a wearable device, a terminal, and a server to collect and analyze a user's biometric data in real time and provide advice based on their emotional and health status. In particular, it simultaneously monitors the user's security status and can respond in real time if an abnormality is detected.

[0625] Overall system configuration

[0626] 1. Wearable devices

[0627] It has a pulse sensor, a camera (to track pupil movement), a display, and a speaker.

[0628] 2. Terminal

[0629] These devices include smartphones and smart glasses, which collect data, send it to a server, and display analysis results and advice.

[0630] 3. Server

[0631] Use a database (e.g., AWS RDS), a generative AI model (e.g., TensorFlow), and a data preprocessing tool (Pandas).

[0632] Program processing details

[0633] Server Processing

[0634] The server receives biometric data collected from the wearable device and stores it in a secure database. It then performs preprocessing, such as data cleansing, normalization, and missing value treatment, to ensure data quality. The preprocessed biometric data is then input into a generative AI model to analyze the emotional state, health status, and security status of the user in real time.

[0635] Based on the analysis results, the server generates advice for stress management, relaxation, and specific actions for security, and sends the advice to the device.

[0636] Terminal handling

[0637] The terminal collects biometric data from the wearable device in real time and periodically transmits it to a server. It receives analysis results and advice from the server and displays them through a user interface. If an abnormality is detected, it notifies the user with visual and audio alerts.

[0638] User Actions

[0639] Users wear a wearable device and biometric data is collected without any special effort on their part. They act according to the advice displayed on the device, and if an abnormality is detected, they can take safe action based on the alert.

[0640] Specific examples

[0641] Example of a prompt for the analysis result:

[0642] "We have detected abnormalities in your heart rate and pupil movement. There is a high possibility that the surrounding area is currently in danger. Please check your surroundings and move to a safe location."

[0643] This system allows users to monitor their own health status and security status in real time, enabling safety management from two angles.

[0644] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0645] Step 1:

[0646] The terminal collects biometric data (e.g., pulse rate, pupil movement) in real time from the wearable device. This input data is obtained by sensors in the wearable device. The terminal temporarily stores the collected biometric data in its memory.

[0647] Step 2:

[0648] The device periodically transmits the collected biometric data, which is a block of the most recent biometric data, to the server, which stores the biometric data received from the device in a secure database.

[0649] Step 3:

[0650] The server performs data cleansing, normalization, and missing value processing on the received biometric data to ensure the quality of the input data, such as removing noise data and scaling the data.

[0651] Step 4:

[0652] The preprocessed biometric data is input into a generative AI model. The model estimates the emotional state, health status, and security status, and outputs the results. The server stores the analysis results from the generative AI model as analysis data.

[0653] Step 5:

[0654] Based on the analysis results, the server generates advice for stress management and relaxation, as well as specific actions for security. These actions are presented as text-based advice and warning messages.

[0655] Step 6:

[0656] The server then sends the generated advice and analysis results to the device. This data includes the analysis results (e.g., the level of stress) and advice as specific actions.

[0657] Step 7:

[0658] The device then displays the analysis results and advice to the user, such as breathing techniques for stress management or actions such as rechecking the surroundings if an abnormality is detected.

[0659] Step 8:

[0660] If necessary, the device will notify the user of an abnormality by visually and audibly alerting them, for example by displaying a warning message on the screen and playing a warning sound through the speaker.

[0661] In this way, the system can monitor the user's health and emotional state in real time while also monitoring their security status, and provide prompt countermeasures if an abnormality occurs. An example of a prompt sentence is, "Abnormalities have been detected in your heart rate and pupil movement. There is a high possibility that your surroundings are currently in a dangerous state. Please check your surroundings and move to a safe location."

[0662] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0663] The system of the present invention uses a wearable device, a terminal, a server, and an emotion engine to collect and analyze a user's biometric data and emotional data, and provide advice based on the user's emotional state and health condition.

[0664] Program processing

[0665] Server program:

[0666] 1. Data Collection Module:

[0667] The server receives data from the wearable device and emotion engine transmitted from the terminal.

[0668] All received data is stored in a secure database.

[0669] 2. Data Preprocessing Module:

[0670] The server cleanses, normalizes, and processes missing values ​​before analyzing biometric and emotional data, ensuring data quality and improving analysis accuracy.

[0671] 3. Analysis module using generative AI models:

[0672] Preprocessed biometric and emotional data is input into a generative AI model.

[0673] The generative AI model predicts and analyzes the user's emotional state (e.g., stress level, relaxation state) and health status in real time.

[0674] 4. Advice Generation Module:

[0675] Based on the analysis results, the server generates specific advice for stress management and relaxation.

[0676] Include suggestions for seeking expert advice where necessary.

[0677] 5. Data transmission module:

[0678] The server sends the generated analysis results and advice to the terminal.

[0679] Terminal programs:

[0680] 1. Sensor data collection module:

[0681] The terminal collects biometric data in real time from the wearable device.

[0682] 2. Emotion data collection module:

[0683] The emotion engine analyzes the user's voice and text data to recognize their emotional state.

[0684] 3. Data transmission module:

[0685] The terminal periodically transmits the collected biometric data and emotion data to the server.

[0686] 4. Result receiving module:

[0687] Receive analysis results and advice sent from the server.

[0688] 5. User Feedback Module:

[0689] Display the received analysis results and advice to the user.

[0690] If necessary, users can directly seek professional help.

[0691] Specific examples

[0692] Example of user action:

[0693] One night, the user puts on the wearable device and goes to sleep. During the night, the wearable device records the user's heart rate and sleep patterns, while the emotion engine analyzes the text data the user has written in a diary app. The device and emotion engine transmit this data to the device in real time, and the device receives the data and sends it to the server.

[0694] Server processing example:

[0695] The biometric and emotional data received overnight is stored in a server database and pre-processed. A generative AI model then analyzes the data and identifies when the user's stress level is high. The server generates relaxation advice, including breathing and meditation techniques. The results and advice are then sent to the device.

[0696] Example of terminal processing:

[0697] The next morning, the device receives the analysis results and advice from the server. Through the user interface, the user can review the advice, practice the suggested relaxation techniques, and contact a specialist if necessary.

[0698] The processing flow will be explained below.

[0699] Step 1:

[0700] The user puts on the wearable device

[0701] The user puts on the wearable device, which then goes to work and starts collecting biometric data such as blood pressure, heart rate, and sleep patterns.

[0702] Step 2:

[0703] Device collects data

[0704] The terminal's sensor data collection module receives data from the wearable device in real time.

[0705] Step 3:

[0706] Emotion engine collects emotional data

[0707] The emotion engine installed in the device analyzes the user's voice and text data to recognize the user's emotional state.

[0708] Step 4:

[0709] The device sends all data

[0710] The terminal's data transmission module preprocesses the collected biometric data and emotion data generated by the emotion engine, converts them into an optimal format, and transmits them to the server.

[0711] Step 5:

[0712] The server receives and stores the data

[0713] The server's data collection module receives the biometric and emotional data sent from the device and stores it all in a secure database.

[0714] Step 6:

[0715] The server preprocesses the data

[0716] The server's data preprocessing module cleanses, normalizes, and handles missing values ​​on biometric and emotional data to prepare it for data analysis.

[0717] Step 7:

[0718] The server analyzes using the generated AI model

[0719] The server's generated AI model analysis module inputs preprocessed biometric and emotional data to predict and analyze the user's emotional state (e.g., stress level, relaxation state) and health condition.

[0720] Step 8:

[0721] Server generates advice

[0722] The server's advice generation module generates specific advice for stress management and relaxation based on the analysis results, including suggestions for expert advice where necessary.

[0723] Step 9:

[0724] The server sends the results and advice

[0725] The server's data transmission module transmits the generated analysis results and advice to the terminal.

[0726] Step 10:

[0727] The device receives the results and advice

[0728] A result receiving module of the terminal receives the data from the server.

[0729] Step 11:

[0730] The device displays feedback to the user

[0731] A user interface module of the terminal displays the received analysis results and advice to the user.

[0732] Step 12:

[0733] Users review and implement feedback

[0734] The user checks the analysis results and recommendations on the device and practices the suggested relaxation techniques (e.g., deep breathing exercises or meditation).

[0735] Step 13:

[0736] Contact an expert

[0737] If necessary, the user can use the contact means to seek advice from an expert through the terminal, thereby receiving support from an appropriate expert.

[0738] Example 2

[0739] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0740] Conventional emotional and health management systems have struggled to effectively collect and analyze users' biometric and emotional data. Furthermore, they lacked the means to quickly provide users with appropriate advice based on the analysis results. This made it difficult for users to effectively implement actions for stress management and relaxation.

[0741] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for transmitting biometric data and emotional data between the terminal and the server, a means for preprocessing the received data, a means for analyzing using a generative AI model, a means for generating advice based on the analysis results, a means for transmitting the advice to the terminal, and a means for contacting an expert. This makes it possible to effectively collect and analyze the user's biometric data and emotional data and quickly provide appropriate advice.

[0742] A "wearable device" is an electronic device that can be worn by a user and is used to collect biometric data in real time.

[0743] "Biometric data" is data relating to a user's physical condition, such as the user's heart rate, body temperature, or sleep patterns.

[0744] "Emotion data" is data related to the user's emotional state that is extracted by analyzing the user's voice data and text data.

[0745] A "terminal" is an electronic device that transmits biometric and emotional data collected from a wearable device to a server and displays analysis results and advice to the user.

[0746] The "server" is a network computer system that receives biometric data and emotional data sent from the terminal, analyzes them, and generates advice.

[0747] "Data cleansing" is a process for removing noise and redundancy from collected data and improving the quality of the data.

[0748] "Normalization" is a process of converting data to a uniform scale in order to equalize data variations and improve analytical accuracy.

[0749] "Missing value handling" is the process of imputing missing values ​​in a dataset or deleting the records.

[0750] A "generative AI model" is an algorithm that uses machine learning technology to analyze a user's biometric and emotional data to predict and analyze their emotional and health states.

[0751] "Analysis results" are information about the user's emotional state and health status obtained by the generative AI model.

[0752] "Advice" refers to specific recommended actions for stress management and relaxation provided to users based on the analysis results.

[0753] "Professional contact" is a feature that helps users contact health and psychological professionals, if necessary.

[0754] The system of the present invention is composed of a wearable device, a terminal, a server, and an emotion engine. The user wears the wearable device to collect biometric and emotional data. This data is sent to the server via the terminal, and the server analyzes the data using a generative AI model to generate advice based on the user's emotional and health states. An embodiment of the system using specific hardware and software is described below.

[0755] Hardware and Software

[0756] Wearable devices

[0757] Overview: A device that collects biometric data such as heart rate, body temperature, and sleep patterns in real time.

[0758] Examples: smartwatches, fitness trackers.

[0759] Terminal

[0760] Overview: A device for collecting data from wearable devices and sending it to a server.

[0761] For example: smartphones, tablets.

[0762] server

[0763] Overview: A network computer system that receives data sent from terminals, analyzes it, and generates advice.

[0764] software:

[0765] Database Management System: AWS DynamoDB

[0766] Programming libraries: Python, Pandas

[0767] Generative AI models: TensorFlow, PyTorch

[0768] Specific operation of the system

[0769] 1. Wearable device operation

[0770] The user wears a wearable device to collect biometric data (heart rate, body temperature, sleep patterns, etc.), which is then transmitted to a terminal via Bluetooth or Wi-Fi.

[0771] 2. Device Operation

[0772] The terminal stores biometric data received from the wearable device and also collects the user's voice and text data, which are then converted into emotional data through an emotion engine.

[0773] 3. Data transmission

[0774] The device sends the collected biometric and emotional data to a server at regular intervals using the HTTPS protocol, and the data is encrypted.

[0775] 4. Data processing on the server

[0776] The server stores the data received from the device in a database, cleansing, normalizing, and processing missing values, and then inputs the preprocessed data into the generative AI model.

[0777] 5. Analysis using generative AI models

[0778] The generative AI model analyzes the data using the following prompt:

[0779] "Based on the user's heart rate data and sleep patterns, as well as the text data analyzed by the emotion engine, assess the user's current stress level and provide appropriate relaxation advice."

[0780] 6. Generating and Sending Advice

[0781] The server generates health advice for the user based on the analysis results obtained from the generative AI model and sends it to the device, including advice on stress management and relaxation techniques.

[0782] 7. User Feedback

[0783] The device receives advice and analysis results from the server and displays them to the user, who can then put these advice into practice and contact an expert if necessary.

[0784] Specific examples

[0785] One night, the user puts on the wearable device and goes to sleep. The device records their nighttime heart rate and sleep patterns, and the emotion engine analyzes text data written by the user in a diary app. This data is sent to a server via the device. The server preprocesses the data and analyzes it using a generative AI model. If the analysis identifies that the user's stress level is high, the server generates relaxation advice. This advice is notified to the user via the device the next morning, and the user can reduce stress by practicing the suggested relaxation techniques.

[0786] This allows users to manage their emotional and health states in real time and receive appropriate advice to maintain a healthier lifestyle.

[0787] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0788] Step 1: Collecting data with wearable devices

[0789] The wearable device collects real-time biometric data such as the user's heart rate, body temperature, and sleep patterns. This data is transmitted to the device via Bluetooth or Wi-Fi. Specifically, the data is measured every five minutes and transferred to the device.

[0790] Input: User biometric data (heart rate, body temperature, sleep patterns, etc.).

[0791] Specific operation: The wearable device takes measurements using biosensors and transmits the data to the terminal.

[0792] Output: Biometric data sent to the device.

[0793] Step 2: Data reception by device and emotion data collection

[0794] The terminal receives biometric data transmitted in real time from the wearable device. At the same time, the emotion engine analyzes the user's voice and text data to recognize their emotional state. The data collected during this process is temporarily stored on the terminal.

[0795] Input: Biometric data sent from the wearable device, user voice data, and text data.

[0796] Specific operation: The terminal receives data from the wearable device and inputs the voice and text data into the emotion engine to analyze the emotional state.

[0797] Output: Biometric and emotional data stored on the device.

[0798] Step 3: Send data

[0799] The device sends the collected biometric and emotional data to a server at regular intervals (e.g., every 15 minutes). The data is encrypted using the HTTPS protocol.

[0800] Input: Biometric and emotional data stored on the device.

[0801] What it does: The device encrypts the collected data and sends it to the server using the HTTPS protocol.

[0802] Output: Biometric and emotional data sent to the server.

[0803] Step 4: Data reception and preprocessing by the server

[0804] The server receives biometric and emotional data sent from the device, stores the data in a database, and performs preprocessing such as data cleansing, normalization, and missing value processing.

[0805] Input: Biometric and emotional data sent from the device.

[0806] Specific operations: Data is stored in a database management system, and data cleansing, normalization, and missing value processing are performed using Python and Pandas.

[0807] Output: Preprocessed data.

[0808] Step 5: Analysis by generative AI model

[0809] The server inputs the preprocessed biometric and emotional data into a generative AI model to analyze the user's emotional and health states.

[0810] Input: Preprocessed biometric and emotion data.

[0811] Prompt text: Based on the user's heart rate data and sleep patterns, as well as the text data analyzed by the emotion engine, assess the user's current stress level and provide appropriate relaxation advice.

[0812] Specific operation: Data is input into a generative AI model (using TensorFlow or PyTorch) and analysis is performed using prompt statements.

[0813] Output: Analysis results (stress level and health status).

[0814] Step 6: Generate and send advice

[0815] The server generates advice for stress management and relaxation based on the analysis results obtained from the generative AI model, and the generated advice and analysis results are sent to the device.

[0816] Input: Analysis results from the generative AI model.

[0817] Specific actions: Generate specific advice on stress management and relaxation and send it to the device using the HTTPS protocol.

[0818] Output: Advice and analysis results sent to the device.

[0819] Step 7: Terminal receives results and user feedback

[0820] The device receives the analysis results and advice sent from the server and displays them to the user through a user interface, which also provides the user with the means to contact an expert if desired.

[0821] Input: Analysis results and advice sent from the server.

[0822] What it does: Stores the received data, displays it in a user interface, and provides a way to contact an expert if needed.

[0823] Output: Analysis results and advice displayed to the user.

[0824] These are the specific steps of the program processing of this system.

[0825] (Application example 2)

[0826] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0827] In current factory work, there is a lack of means to grasp workers' stress levels and health status in real time, which can result in reduced work efficiency. Furthermore, if workers' emotional state cannot be properly managed, excessive stress can continue for long periods of time, increasing safety risks. Furthermore, current factory robots lack the ability to collect biometric and emotional information, making them unable to contribute to worker health management or improved work efficiency. There is a need to solve these problems, improve workers' health, and increase work efficiency.

[0828] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting biometric information from the wearable device, means for transmitting the collected biometric information to the server, means for receiving and preprocessing the biometric information, means for analyzing the emotional state and health state based on a generative artificial intelligence model, means for generating instructions for stress management and relaxation based on the analysis results, and means for transmitting the generated instructions and analysis results to the terminal. This enables real-time collection and analysis of biometric information and emotional information, monitors the worker's health status, and improves work efficiency by providing appropriate instructions. It also enables factory robots to provide support for balancing worker safety and work efficiency.

[0829] A "wearable device" is a portable or wearable device that can collect biometric information by being worn on the body.

[0830] "Biometric information" refers to physiological data such as heart rate, body temperature, and blood pressure, and is data that indicates an individual's health condition.

[0831] "Server" means a central control unit for processing, analyzing and storing collected data.

[0832] "Preprocessing" refers to the process of cleansing, normalizing, and imputing missing values ​​of the collected data.

[0833] A "generative AI model" is an artificial intelligence model that predicts and analyzes emotional and health states based on biometric and emotional information.

[0834] "Emotional information" is data related to emotions extracted from the user's voice, facial expressions, and behavior.

[0835] "Analysis results" are predictions of emotional and health states output by the generative AI model based on biometric and emotional information.

[0836] "Stress management" refers to specific techniques and methods for reducing a user's stress level based on the results of analysis.

[0837] "Relaxation instructions" are specific advice or techniques suggested by the generative AI model to help users relieve stress.

[0838] A "robot" is a mechanical device that performs specific tasks under automatic control or programming.

[0839] A "sensor" is a device that senses a physical phenomenon and converts it into an electrical signal.

[0840] A "camera" is a device that collects visual information and stores and transmits it as image data.

[0841] "Real-time" means that data is collected, transmitted, and analyzed immediately.

[0842] The present invention provides a system for improving work efficiency and safety by using a factory robot to collect and analyze biometric and emotional information of workers. A specific embodiment of this system will be described below.

[0843] System configuration

[0844] This system consists of a wearable device, a terminal, a server, and a factory robot. The specific functions of each element are as follows:

[0845] Wearable devices

[0846] The wearable devices will collect workers' biometric information, such as heart rate and body temperature, in real time, using the latest heart rate monitors and temperature sensors.

[0847] Factory robots

[0848] The factory robots are equipped with sensors and cameras to collect biometric and emotional information from workers, and transmit this data to a terminal in real time.

[0849] Terminal

[0850] The device receives sensor data and emotion data collected from the camera and transmits it to the server. The device itself is equipped with a data transmission module, a result reception module, and a user feedback module.

[0851] server

[0852] The server consists of multiple modules (data collection module, data preprocessing module, analysis module using generative AI model, advice generation module, and data transmission module).

[0853] 1. Data Collection Module:

[0854] The server receives the biometric and emotional information sent from the device and stores it in a secure database. The data is encrypted to ensure its security.

[0855] 2. Data Preprocessing Module:

[0856] The server cleanses, normalizes, and processes missing values ​​on the received data to maintain data quality, using Python and Pandas.

[0857] 3. Analysis module using generative AI models:

[0858] The preprocessed data is input into a generative AI model (e.g., GPT-4) to predict and analyze the worker's emotional state and health status.

[0859] 4. Advice Generation Module:

[0860] Based on the analysis results, specific instructions for stress management and relaxation can be generated, as well as instructions for improving work efficiency.

[0861] 5. Data transmission module:

[0862] The generated instructions and analysis results are then sent back to the terminal, and this information is then appropriately fed back to the worker.

[0863] Specific examples

[0864] For example, when Worker A is working continuously in a factory, a robot monitors A's heart rate, body temperature, and facial expressions. This data is sent in real time to the terminal and then to the server. On the server side, the data is preprocessed and analyzed by a generative AI model (GPT-4). If the server determines that Worker A is experiencing high stress, it can immediately generate instructions, including suggestions for breaks and relaxation methods, and provide them as feedback to Worker A via the terminal.

[0865] Prompt Sentence Examples

[0866] Biometric data: Heart rate 75, body temperature 36.5℃, facial expression: nervous, voice: irritated

[0867] Question: Based on this data, what relaxation techniques would be best for the worker?

[0868] In this way, this system efficiently collects and analyzes biometric and emotional information, thereby improving worker health management and work efficiency.

[0869] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0870] Step 1:

[0871] The terminal collects biometric information (heart rate, body temperature, etc.) and emotional information (facial expressions, voice) from wearable devices and factory robots in real time. Each time data collection is completed, a data packet is generated. The terminal temporarily stores the data.

[0872] Step 2:

[0873] The device periodically transmits collected data packets to the server, which include the collection time, biometric information, and emotional information. This allows the server to receive real-time data.

[0874] Step 3:

[0875] The server stores the received data packets in a database and performs data preprocessing. This preprocessing step involves data cleansing (noise removal), normalization (standardization of data), and missing value imputation. For example, Python and Pandas are used to cleanse the data and impute missing values.

[0876] Step 4:

[0877] The server inputs the preprocessed data into a generative AI model (such as GPT-4), which analyzes the emotional and health states according to the prompts. Based on the input data, the AI ​​model generates an output that predicts the stress level and health state.

[0878] Step 5:

[0879] The server generates specific instructions for stress management and relaxation based on the analysis results obtained from the generative AI model. In this step, instructions are generated that include specific advice on breathing techniques, meditation techniques, etc.

[0880] Step 6:

[0881] The server then sends the generated instructions and analysis results to the terminal, allowing the worker to receive the analysis results and instructions in real time.

[0882] Step 7:

[0883] The device receives the analysis results and instructions sent from the server and provides feedback to the worker. This feedback is provided via the device's display or audio output. The worker can then practice the suggested relaxation methods and contact a specialist if necessary.

[0884] Step 8:

[0885] The user follows instructions provided through the device to practice stress management and relaxation techniques, such as deep breathing or short meditation sessions, and data on these practices is again collected through the device.

[0886] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0887] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0888] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0889] [Third embodiment]

[0890] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0891] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0892] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0893] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0894] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0895] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0896] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0897] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0898] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0899] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0900] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0901] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0902] The system of the present invention uses a wearable device, a terminal, and a server to collect and analyze a user's biometric data and provide advice based on their emotional and health states.

[0903] Program processing

[0904] Server program:

[0905] 1. Data Collection Module:

[0906] The server receives biometric data (e.g., blood pressure, heart rate, sleep patterns, etc.) from the wearable device sent from the terminal.

[0907] All received data is stored in a secure database.

[0908] 2. Data Preprocessing Module:

[0909] Before analyzing the biometric data, the server cleanses, normalizes, and processes missing values, thereby ensuring data quality and improving analysis accuracy.

[0910] 3. Analysis module using generative AI models:

[0911] The preprocessed biometric data is input into a generative AI model.

[0912] The generative AI model predicts and analyzes the user's emotional state (e.g., stress level, relaxation state) and health status in real time.

[0913] 4. Advice Generation Module:

[0914] Based on the analysis results, the server generates specific advice for stress management and relaxation.

[0915] Include suggestions for seeking expert advice where necessary.

[0916] 5. Data transmission module:

[0917] The server sends the generated analysis results and advice to the terminal.

[0918] Terminal programs:

[0919] 1. Sensor data collection module:

[0920] The terminal collects biometric data in real time from the wearable device.

[0921] 2. Data transmission module:

[0922] The terminal periodically transmits the collected data to the server.

[0923] 3. Result receiving module:

[0924] Receive analysis results and advice sent from the server.

[0925] 4. User Feedback Module:

[0926] Display the received analysis results and advice to the user.

[0927] If necessary, users can directly seek professional help.

[0928] Specific examples

[0929] Example of user action:

[0930] One night, the user puts on the wearable device and goes to sleep. During the night, the wearable device records their heart rate and sleep patterns. The device transmits this data in real time to the terminal, which receives the data and subsequently transmits it to the server.

[0931] Server processing example:

[0932] The data received overnight is stored in a database on the server and pre-processed. A generative AI model then analyzes the data and identifies when the user's stress level is high. The server generates relaxation advice, including breathing exercises and meditation techniques. The results and advice are then sent to the device.

[0933] Example of terminal processing:

[0934] The next morning, the device receives the analysis results and advice from the server. Through the user interface, the user can review the advice, practice the suggested relaxation techniques, and contact a specialist if necessary.

[0935] The processing flow will be explained below.

[0936] Step 1:

[0937] The user puts on the wearable device

[0938] The user puts on the wearable device, which then goes to work and starts collecting biometric data such as blood pressure, heart rate, and sleep patterns.

[0939] Step 2:

[0940] Device collects data

[0941] The terminal's sensor data collection module receives data from the wearable device in real time.

[0942] Step 3:

[0943] The device sends data

[0944] The terminal's data transmission module preprocesses the collected data (e.g., packet shaping), converts it into an optimal format, and transmits it to the server.

[0945] Step 4:

[0946] The server receives and stores the data

[0947] The server's data collection module receives the data sent from the devices and stores it in a secure database.

[0948] Step 5:

[0949] The server preprocesses the data

[0950] The server's data preprocessing module cleanses, normalizes, and handles missing values ​​on the biometric data, preparing it for data analysis.

[0951] Step 6:

[0952] The server analyzes using the generated AI model

[0953] The server's generated AI model analysis module inputs the preprocessed data and predicts and analyzes the user's emotional state and health condition.

[0954] Step 7:

[0955] Server generates advice

[0956] The server's advice generation module generates specific advice for stress management and relaxation based on the analysis results, including suggestions for expert advice where necessary.

[0957] Step 8:

[0958] The server sends the results and advice

[0959] The server's data transmission module transmits the generated analysis results and advice to the terminal.

[0960] Step 9:

[0961] The device receives the results and advice

[0962] A result receiving module of the terminal receives the data from the server.

[0963] Step 10:

[0964] The device displays feedback to the user

[0965] A user interface module of the terminal displays the received analysis results and advice to the user.

[0966] Step 11:

[0967] User reviews feedback

[0968] The user reviews the analysis and advice, practices the suggested relaxation techniques, and seeks professional help if necessary.

[0969] Step 12:

[0970] Contact an expert

[0971] The terminal provides a means for users to contact experts for advice, allowing them to receive support from appropriate experts.

[0972] Example 1

[0973] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0974] Conventional health management systems only collect and analyze a user's biometric information, but lack the functionality to provide real-time stress management and relaxation advice. Furthermore, the accuracy of the acquired data preprocessing and analysis results is insufficient, making it difficult to provide appropriate advice to the user. The present invention aims to solve these problems by providing a system that can provide immediate and specific advice based on the user's health and emotional state.

[0975] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0976] In this invention, the server includes means for collecting biometric information from the wearable device, means for transferring the collected biometric information to the device, means for transmitting the biometric information from the device to the server, means for receiving the biometric information in the server and performing data cleansing, normalization, and missing value processing, means for analyzing emotions and health based on a generative AI model using the preprocessed data, means for generating advice for stress management and relaxation based on the analysis results and including expert advice as needed, means for transmitting the generated advice and analysis results to the device, and means for displaying the analysis results and advice to the user on the device and enabling the user to practice the suggested relaxation techniques. This makes it possible to provide immediate and specific advice based on the user's health and emotional state.

[0977] A "wearable device" is a device worn by the user that can collect biometric information in real time. Examples include smartwatches and fitness trackers.

[0978] "Biometric information" refers to data about a user's physical condition, including heart rate, blood pressure, and sleep patterns.

[0979] The "terminal" is a relay device that receives biometric information from a wearable device and transmits it to a server. Examples of such devices include smartphones and tablets.

[0980] A "server" is a computer system that manages and processes data received from multiple terminals via a network.

[0981] "Data cleansing" is the process of using software to remove noise and inaccurate values ​​from a dataset to improve the quality of the data.

[0982] "Normalization" is a process of aligning the range and distribution of data in order to standardize the variations in the data.

[0983] "Missing value processing" is the process of filling in missing values ​​in a dataset so that they do not interfere with analysis. Common methods are mean value imputation and neighboring point imputation.

[0984] A "generative AI model" is a model built using machine learning algorithms that analyze input data to derive a specific outcome or prediction.

[0985] "Emotion and health analysis" is the process by which a generative AI model assesses a user's emotional state and health status using collected and pre-processed biometric information.

[0986] "Stress management" refers to providing specific measures and advice to reduce users' stress levels.

[0987] "Relaxation techniques" refer to methods or techniques that help users relieve tension or stress, including breathing exercises and meditation.

[0988] "Expert Advice" means advice based on specialized knowledge and experience for a particular situation, which the System provides as needed.

[0989] This invention relates to a system that uses a wearable device, a terminal, and a server to collect and analyze a user's biometric information and provide advice based on their emotions and health status. This system is composed of multiple modules, each with a specific function.

[0990] Hardware and software used

[0991] This system uses the following hardware and software:

[0992] Wearable devices: Smartwatches, fitness trackers, and other devices worn by users that collect biometric information in real time. Examples include the Apple Watch and Fitbit.

[0993] Terminal: A relay device that receives biometric information from a wearable device and sends it to a server. This includes smartphones and tablets. Specific examples include iPhones and Android devices.

[0994] Server: A computer system that manages and processes data received from multiple devices. The software used includes MySQL as a database, Python's pandas library for data preprocessing and analysis, and TensorFlow for running generative AI models.

[0995] Processing Details

[0996] Data Collection and Transfer

[0997] Users wear a wearable device, and the device collects real-time biometric information such as heart rate, blood pressure, and sleep patterns. For example, when a user wears an Apple Watch, the device measures their heart rate every second.

[0998] The device receives biometric data from the wearable device via Bluetooth connection. This data is temporarily stored in the device's local storage. Specifically, the iPhone receives heart rate data from the Apple Watch via Bluetooth.

[0999] Data submission and preprocessing

[1000] The device sends the collected biometric information to a server over Wi-Fi or mobile data, using the HTTPS protocol to ensure data security.

[1001] The server stores the received biometric information in a MySQL database, then uses the Python pandas library to cleanse, normalize, and process missing values, making the preprocessed data suitable for subsequent analysis.

[1002] Analysis and advice generation using generative AI models

[1003] The server then inputs the preprocessed data into a TensorFlow-based generative AI model to analyze the user's emotions and health in real time. For example, collected heart rate data can be used to predict the user's stress level and relaxation state.

[1004] Based on the analysis results, the server generates specific advice for stress management and relaxation, such as suggesting breathing exercises and meditation techniques to users with high stress levels, and including expert advice if necessary.

[1005] Results submission and user interface

[1006] The server then sends the generated analysis results and advice to the device again using the HTTPS protocol, and the sent data is saved in the device's local storage.

[1007] The device displays the received analysis results and advice in a user interface (e.g., a dedicated app). Users can check the analysis results and advice by opening the dedicated iPhone app. Based on this, users can practice breathing exercises and meditation techniques. They can also contact a specialist if necessary.

[1008] Specific examples

[1009] User processing example

[1010] One night, the user puts on the smartwatch and goes to sleep. During the night, the smartwatch records their heart rate and sleep patterns and transmits this data in real time to their smartphone. The smartphone then transmits the data to a server via Wi-Fi.

[1011] Server processing example

[1012] The data received overnight is stored in a MySQL database on the server, where it is validated and preprocessed using Python's pandas library. A TensorFlow-based generative AI model then analyzes the data and identifies high stress levels. The server then generates relaxation advice, such as breathing exercises or meditation techniques, and sends the results and advice to the device.

[1013] Terminal processing example

[1014] The next morning, the smartphone receives the analysis results and advice from the server and displays them to the user through a dedicated app. The user can then review the advice, practice the suggested relaxation techniques, and contact a specialist if necessary.

[1015] Prompt Sentence Examples

[1016] Last night, I used my smartwatch to record my heart rate and sleep patterns while I was asleep. Based on the results, I would like to know how to relax if my stress level is high.

[1017] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1018] Step 1:

[1019] The user wears a wearable device to collect biometric information, including heart rate, blood pressure, and sleep patterns. This data is stored in the wearable device's internal memory. The input is the user's biometric information, and the output is the data recorded on the wearable device. Specifically, the user wears the smartwatch and goes about their daily activities, and the device automatically collects data.

[1020] Step 2:

[1021] The device receives biometric information from the wearable device via Bluetooth. The received data is temporarily stored in the device's local storage. The input is the biometric information received from the wearable device, and the output is the data stored in the device's local storage. Specifically, the device polls for data via Bluetooth every few seconds and stores the received data in local storage.

[1022] Step 3:

[1023] The device sends the collected biometric information to the server. Wi-Fi or mobile data communication is used for transmission, and the HTTPS protocol is used to ensure security. The input is the biometric information stored in the device's local storage, and the output is the data sent to the server. Specifically, the device uploads the data to the server in batch format at regular intervals (e.g., every 30 minutes).

[1024] Step 4:

[1025] The server stores the received biometric information in a database. MySQL or similar databases are used as the database. The input is the biometric information received from the device, and the output is the data stored in the database. Specifically, the server parses the received data and inserts it into the database in the appropriate format.

[1026] Step 5:

[1027] The server preprocesses the biometric information stored in the database. Preprocessing includes data cleansing, normalization, and missing value processing. The input is the biometric information stored in the database, and the output is the preprocessed data. Specifically, it uses the Python pandas library to complement missing values ​​in the data and remove invalid values.

[1028] Step 6:

[1029] The server inputs the preprocessed data into a generative AI model to analyze emotions and health. TensorFlow can be used as the model. The input is preprocessed biometric information, and the output is the analysis results. Specifically, the server inputs the preprocessed data into a machine learning model to analyze stress levels, health status, and other factors in real time.

[1030] Step 7:

[1031] The server generates advice based on the analysis results. The advice includes stress management and relaxation techniques, and expert advice is also provided if necessary. The input is the analysis results, and the output is the generated advice. Specifically, the server generates text suggesting breathing exercises and meditation methods based on the results of the generative AI model.

[1032] Step 8:

[1033] The server sends the generated analysis results and advice to the device. The HTTPS protocol is used for transmission. The input is the generated advice and analysis results, and the output is the data sent to the device. Specifically, the server periodically pushes the analysis results and advice to the device.

[1034] Step 9:

[1035] The device displays the received analysis results and advice in a user interface. The user can review them and implement the suggested relaxation techniques. The input is the analysis results and advice received from the server, and the output is the information displayed to the user and the user's actions. Specifically, the device app renders the display UI and notifies the user of the results in the notification bar or on the app screen.

[1036] Prompt Sentence Examples

[1037] Last night, I used my smartwatch to record my heart rate and sleep patterns while I was asleep. Based on the results, I would like to know how to relax if my stress level is high.

[1038] (Application example 1)

[1039] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1040] The main function of conventional wearable device systems is to collect the user's biometric data, analyze their health and emotional state, and provide advice. However, they have the problem of not being able to monitor the user's security status and respond in real time if an abnormality is detected. This makes it difficult to quickly ensure the user's safety. To solve this problem, there is a need for a system that can simultaneously monitor the user's security status using the user's biometric data and has the ability to detect and notify abnormalities.

[1041] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1042] In this invention, the server includes means for collecting biometric data from the wearable device, means for transmitting the collected biometric data to the server, means for receiving and preprocessing the biometric data in the server, means for analyzing the emotional state and health state based on a generative AI model using the preprocessed data, means for generating advice for stress management and relaxation based on the analysis results, means for transmitting the generated advice and analysis results to the terminal, means for displaying the analysis results and advice to the user in the terminal, means for monitoring the user's security status and detecting abnormalities, and means for notifying the user of an alert when an abnormality is detected. This enables not only constant health management of the user but also real-time security monitoring and abnormality detection.

[1043] A "wearable device" is an electronic device that is worn on the body and collects personal biometric data in real time.

[1044] "Biometric data" refers to measurements such as heart rate, blood pressure, sleep patterns, pulse, and pupil movement that are used to understand an individual's health or emotional state.

[1045] "Server" is a central computer system that receives collected biometric data and performs pre-processing and analysis.

[1046] "Preprocessing" refers to the process of ensuring data quality by performing processes such as data cleansing, normalization, and missing value processing on biometric data.

[1047] A "generative AI model" is a model that uses machine learning algorithms to analyze biometric data and predict emotional and health states.

[1048] "Emotional state" refers to the user's psychological and emotional state, such as stress level, relaxation state, etc.

[1049] "Health status" refers to the physical condition of the user, as assessed by their heart rate, blood pressure, sleep patterns, etc.

[1050] "Stress management" is the practice of providing users with techniques and methods to reduce stress and improve their well-being.

[1051] "Relaxation techniques" are methods such as breathing exercises, meditation, and massage that help users relax.

[1052] "Advice" refers to specific guidelines or suggestions for action provided to users based on the analysis results.

[1053] A "terminal" is an electronic device that allows the user to communicate with the wearable device and display analysis results and advice.

[1054] "Security status" refers to the environment or situation that affects the user's safety, including the presence of suspicious individuals and abnormal events.

[1055] "Anomaly detection" refers to detecting abnormal conditions based on the user's biometric data and surrounding conditions.

[1056] An "alert" is a warning message or notification that notifies the user of an abnormality or an event that requires attention.

[1057] This invention connects a wearable device, a terminal, and a server to collect and analyze a user's biometric data in real time and provide advice based on their emotional and health status. In particular, it simultaneously monitors the user's security status and can respond in real time if an abnormality is detected.

[1058] Overall system configuration

[1059] 1. Wearable devices

[1060] It has a pulse sensor, a camera (to track pupil movement), a display, and a speaker.

[1061] 2. Terminal

[1062] These devices include smartphones and smart glasses, which collect data, send it to a server, and display analysis results and advice.

[1063] 3. Server

[1064] Use a database (e.g., AWS RDS), a generative AI model (e.g., TensorFlow), and a data preprocessing tool (Pandas).

[1065] Program processing details

[1066] Server Processing

[1067] The server receives biometric data collected from the wearable device and stores it in a secure database. It then performs preprocessing, such as data cleansing, normalization, and missing value treatment, to ensure data quality. The preprocessed biometric data is then input into a generative AI model to analyze the emotional state, health status, and security status of the user in real time.

[1068] Based on the analysis results, the server generates advice for stress management, relaxation, and specific actions for security, and sends the advice to the device.

[1069] Terminal handling

[1070] The terminal collects biometric data from the wearable device in real time and periodically transmits it to a server. It receives analysis results and advice from the server and displays them through a user interface. If an abnormality is detected, it notifies the user with visual and audio alerts.

[1071] User Actions

[1072] Users wear a wearable device and biometric data is collected without any special effort on their part. They act according to the advice displayed on the device, and if an abnormality is detected, they can take safe action based on the alert.

[1073] Specific examples

[1074] Example of a prompt for the analysis result:

[1075] "We have detected abnormalities in your heart rate and pupil movement. There is a high possibility that the surrounding area is currently in danger. Please check your surroundings and move to a safe location."

[1076] This system allows users to monitor their own health status and security status in real time, enabling safety management from two angles.

[1077] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1078] Step 1:

[1079] The terminal collects biometric data (e.g., pulse rate, pupil movement) in real time from the wearable device. This input data is obtained by sensors in the wearable device. The terminal temporarily stores the collected biometric data in its memory.

[1080] Step 2:

[1081] The device periodically transmits the collected biometric data, which is a block of the most recent biometric data, to the server, which stores the biometric data received from the device in a secure database.

[1082] Step 3:

[1083] The server performs data cleansing, normalization, and missing value processing on the received biometric data to ensure the quality of the input data, such as removing noise data and scaling the data.

[1084] Step 4:

[1085] The preprocessed biometric data is input into a generative AI model. The model estimates the emotional state, health status, and security status, and outputs the results. The server stores the analysis results from the generative AI model as analysis data.

[1086] Step 5:

[1087] Based on the analysis results, the server generates advice for stress management and relaxation, as well as specific actions for security. These actions are presented as text-based advice and warning messages.

[1088] Step 6:

[1089] The server then sends the generated advice and analysis results to the device. This data includes the analysis results (e.g., the level of stress) and advice as specific actions.

[1090] Step 7:

[1091] The device then displays the analysis results and advice to the user, such as breathing techniques for stress management or actions such as rechecking the surroundings if an abnormality is detected.

[1092] Step 8:

[1093] If necessary, the device will notify the user of an abnormality by visually and audibly alerting them, for example by displaying a warning message on the screen and playing a warning sound through the speaker.

[1094] In this way, the system can monitor the user's health and emotional state in real time while also monitoring their security status, and provide prompt countermeasures if an abnormality occurs. An example of a prompt sentence is, "Abnormalities have been detected in your heart rate and pupil movement. There is a high possibility that your surroundings are currently in a dangerous state. Please check your surroundings and move to a safe location."

[1095] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1096] The system of the present invention uses a wearable device, a terminal, a server, and an emotion engine to collect and analyze a user's biometric data and emotional data, and provide advice based on the user's emotional state and health condition.

[1097] Program processing

[1098] Server program:

[1099] 1. Data Collection Module:

[1100] The server receives data from the wearable device and emotion engine transmitted from the terminal.

[1101] All received data is stored in a secure database.

[1102] 2. Data Preprocessing Module:

[1103] The server cleanses, normalizes, and processes missing values ​​before analyzing biometric and emotional data, ensuring data quality and improving analysis accuracy.

[1104] 3. Analysis module using generative AI models:

[1105] Preprocessed biometric and emotional data is input into a generative AI model.

[1106] The generative AI model predicts and analyzes the user's emotional state (e.g., stress level, relaxation state) and health status in real time.

[1107] 4. Advice Generation Module:

[1108] Based on the analysis results, the server generates specific advice for stress management and relaxation.

[1109] Include suggestions for seeking expert advice where necessary.

[1110] 5. Data transmission module:

[1111] The server sends the generated analysis results and advice to the terminal.

[1112] Terminal programs:

[1113] 1. Sensor data collection module:

[1114] The terminal collects biometric data in real time from the wearable device.

[1115] 2. Emotion data collection module:

[1116] The emotion engine analyzes the user's voice and text data to recognize their emotional state.

[1117] 3. Data transmission module:

[1118] The terminal periodically transmits the collected biometric data and emotion data to the server.

[1119] 4. Result receiving module:

[1120] Receive analysis results and advice sent from the server.

[1121] 5. User Feedback Module:

[1122] Display the received analysis results and advice to the user.

[1123] If necessary, users can directly seek professional help.

[1124] Specific examples

[1125] Example of user action:

[1126] One night, the user puts on the wearable device and goes to sleep. During the night, the wearable device records the user's heart rate and sleep patterns, while the emotion engine analyzes the text data the user has written in a diary app. The device and emotion engine transmit this data to the device in real time, and the device receives the data and sends it to the server.

[1127] Server processing example:

[1128] The biometric and emotional data received overnight is stored in a server database and pre-processed. A generative AI model then analyzes the data and identifies when the user's stress level is high. The server generates relaxation advice, including breathing and meditation techniques. The results and advice are then sent to the device.

[1129] Example of terminal processing:

[1130] The next morning, the device receives the analysis results and advice from the server. Through the user interface, the user can review the advice, practice the suggested relaxation techniques, and contact a specialist if necessary.

[1131] The processing flow will be explained below.

[1132] Step 1:

[1133] The user puts on the wearable device

[1134] The user puts on the wearable device, which then goes to work and starts collecting biometric data such as blood pressure, heart rate, and sleep patterns.

[1135] Step 2:

[1136] Device collects data

[1137] The terminal's sensor data collection module receives data from the wearable device in real time.

[1138] Step 3:

[1139] Emotion engine collects emotional data

[1140] The emotion engine installed in the device analyzes the user's voice and text data to recognize the user's emotional state.

[1141] Step 4:

[1142] The device sends all data

[1143] The terminal's data transmission module preprocesses the collected biometric data and emotion data generated by the emotion engine, converts them into an optimal format, and transmits them to the server.

[1144] Step 5:

[1145] The server receives and stores the data

[1146] The server's data collection module receives the biometric and emotional data sent from the device and stores it all in a secure database.

[1147] Step 6:

[1148] The server preprocesses the data

[1149] The server's data preprocessing module cleanses, normalizes, and handles missing values ​​on biometric and emotional data to prepare it for data analysis.

[1150] Step 7:

[1151] The server analyzes using the generated AI model

[1152] The server's generated AI model analysis module inputs preprocessed biometric and emotional data to predict and analyze the user's emotional state (e.g., stress level, relaxation state) and health condition.

[1153] Step 8:

[1154] Server generates advice

[1155] The server's advice generation module generates specific advice for stress management and relaxation based on the analysis results, including suggestions for expert advice where necessary.

[1156] Step 9:

[1157] The server sends the results and advice

[1158] The server's data transmission module transmits the generated analysis results and advice to the terminal.

[1159] Step 10:

[1160] The device receives the results and advice

[1161] A result receiving module of the terminal receives the data from the server.

[1162] Step 11:

[1163] The device displays feedback to the user

[1164] A user interface module of the terminal displays the received analysis results and advice to the user.

[1165] Step 12:

[1166] Users review and implement feedback

[1167] The user checks the analysis results and recommendations on the device and practices the suggested relaxation techniques (e.g., deep breathing exercises or meditation).

[1168] Step 13:

[1169] Contact an expert

[1170] If necessary, the user can use the contact means to seek advice from an expert through the terminal, thereby receiving support from an appropriate expert.

[1171] Example 2

[1172] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1173] Conventional emotional and health management systems have struggled to effectively collect and analyze users' biometric and emotional data. Furthermore, they lacked the means to quickly provide users with appropriate advice based on the analysis results. This made it difficult for users to effectively implement actions for stress management and relaxation.

[1174] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for transmitting biometric data and emotional data between the terminal and the server, a means for preprocessing the received data, a means for analyzing using a generative AI model, a means for generating advice based on the analysis results, a means for transmitting the advice to the terminal, and a means for contacting an expert. This makes it possible to effectively collect and analyze the user's biometric data and emotional data and quickly provide appropriate advice.

[1175] A "wearable device" is an electronic device that can be worn by a user and is used to collect biometric data in real time.

[1176] "Biometric data" is data relating to a user's physical condition, such as the user's heart rate, body temperature, or sleep patterns.

[1177] "Emotion data" is data related to the user's emotional state that is extracted by analyzing the user's voice data and text data.

[1178] A "terminal" is an electronic device that transmits biometric and emotional data collected from a wearable device to a server and displays analysis results and advice to the user.

[1179] The "server" is a network computer system that receives biometric data and emotional data sent from the terminal, analyzes them, and generates advice.

[1180] "Data cleansing" is a process for removing noise and redundancy from collected data and improving the quality of the data.

[1181] "Normalization" is a process of converting data to a uniform scale in order to equalize data variations and improve analytical accuracy.

[1182] "Missing value handling" is the process of imputing missing values ​​in a dataset or deleting the records.

[1183] A "generative AI model" is an algorithm that uses machine learning technology to analyze a user's biometric and emotional data to predict and analyze their emotional and health states.

[1184] "Analysis results" are information about the user's emotional state and health status obtained by the generative AI model.

[1185] "Advice" refers to specific recommended actions for stress management and relaxation provided to users based on the analysis results.

[1186] "Professional contact" is a feature that helps users contact health and psychological professionals, if necessary.

[1187] The system of the present invention is composed of a wearable device, a terminal, a server, and an emotion engine. The user wears the wearable device to collect biometric and emotional data. This data is sent to the server via the terminal, and the server analyzes the data using a generative AI model to generate advice based on the user's emotional and health states. An embodiment of the system using specific hardware and software is described below.

[1188] Hardware and Software

[1189] Wearable devices

[1190] Overview: A device that collects biometric data such as heart rate, body temperature, and sleep patterns in real time.

[1191] Examples: smartwatches, fitness trackers.

[1192] Terminal

[1193] Overview: A device for collecting data from wearable devices and sending it to a server.

[1194] For example: smartphones, tablets.

[1195] server

[1196] Overview: A network computer system that receives data sent from terminals, analyzes it, and generates advice.

[1197] software:

[1198] Database Management System: AWS DynamoDB

[1199] Programming libraries: Python, Pandas

[1200] Generative AI models: TensorFlow, PyTorch

[1201] Specific operation of the system

[1202] 1. Wearable device operation

[1203] The user wears a wearable device to collect biometric data (heart rate, body temperature, sleep patterns, etc.), which is then transmitted to a terminal via Bluetooth or Wi-Fi.

[1204] 2. Device Operation

[1205] The terminal stores biometric data received from the wearable device and also collects the user's voice and text data, which are then converted into emotional data through an emotion engine.

[1206] 3. Data transmission

[1207] The device sends the collected biometric and emotional data to a server at regular intervals using the HTTPS protocol, and the data is encrypted.

[1208] 4. Data processing on the server

[1209] The server stores the data received from the device in a database, cleansing, normalizing, and processing missing values, and then inputs the preprocessed data into the generative AI model.

[1210] 5. Analysis using generative AI models

[1211] The generative AI model analyzes the data using the following prompt:

[1212] "Based on the user's heart rate data and sleep patterns, as well as the text data analyzed by the emotion engine, assess the user's current stress level and provide appropriate relaxation advice."

[1213] 6. Generating and Sending Advice

[1214] The server generates health advice for the user based on the analysis results obtained from the generative AI model and sends it to the device, including advice on stress management and relaxation techniques.

[1215] 7. User Feedback

[1216] The device receives advice and analysis results from the server and displays them to the user, who can then put these advice into practice and contact an expert if necessary.

[1217] Specific examples

[1218] One night, the user puts on the wearable device and goes to sleep. The device records their nighttime heart rate and sleep patterns, and the emotion engine analyzes text data written by the user in a diary app. This data is sent to a server via the device. The server preprocesses the data and analyzes it using a generative AI model. If the analysis identifies that the user's stress level is high, the server generates relaxation advice. This advice is notified to the user via the device the next morning, and the user can reduce stress by practicing the suggested relaxation techniques.

[1219] This allows users to manage their emotional and health states in real time and receive appropriate advice to maintain a healthier lifestyle.

[1220] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1221] Step 1: Collecting data with wearable devices

[1222] The wearable device collects real-time biometric data such as the user's heart rate, body temperature, and sleep patterns. This data is transmitted to the device via Bluetooth or Wi-Fi. Specifically, the data is measured every five minutes and transferred to the device.

[1223] Input: User biometric data (heart rate, body temperature, sleep patterns, etc.).

[1224] Specific operation: The wearable device takes measurements using biosensors and transmits the data to the terminal.

[1225] Output: Biometric data sent to the device.

[1226] Step 2: Data reception by device and emotion data collection

[1227] The terminal receives biometric data transmitted in real time from the wearable device. At the same time, the emotion engine analyzes the user's voice and text data to recognize their emotional state. The data collected during this process is temporarily stored on the terminal.

[1228] Input: Biometric data sent from the wearable device, user voice data, and text data.

[1229] Specific operation: The terminal receives data from the wearable device and inputs the voice and text data into the emotion engine to analyze the emotional state.

[1230] Output: Biometric and emotional data stored on the device.

[1231] Step 3: Send data

[1232] The device sends the collected biometric and emotional data to a server at regular intervals (e.g., every 15 minutes). The data is encrypted using the HTTPS protocol.

[1233] Input: Biometric and emotional data stored on the device.

[1234] What it does: The device encrypts the collected data and sends it to the server using the HTTPS protocol.

[1235] Output: Biometric and emotional data sent to the server.

[1236] Step 4: Data reception and preprocessing by the server

[1237] The server receives biometric and emotional data sent from the device, stores the data in a database, and performs preprocessing such as data cleansing, normalization, and missing value processing.

[1238] Input: Biometric and emotional data sent from the device.

[1239] Specific operations: Data is stored in a database management system, and data cleansing, normalization, and missing value processing are performed using Python and Pandas.

[1240] Output: Preprocessed data.

[1241] Step 5: Analysis by generative AI model

[1242] The server inputs the preprocessed biometric and emotional data into a generative AI model to analyze the user's emotional and health states.

[1243] Input: Preprocessed biometric and emotion data.

[1244] Prompt text: Based on the user's heart rate data and sleep patterns, as well as the text data analyzed by the emotion engine, assess the user's current stress level and provide appropriate relaxation advice.

[1245] Specific operation: Data is input into a generative AI model (using TensorFlow or PyTorch) and analysis is performed using prompt statements.

[1246] Output: Analysis results (stress level and health status).

[1247] Step 6: Generate and send advice

[1248] The server generates advice for stress management and relaxation based on the analysis results obtained from the generative AI model, and the generated advice and analysis results are sent to the device.

[1249] Input: Analysis results from the generative AI model.

[1250] Specific actions: Generate specific advice on stress management and relaxation and send it to the device using the HTTPS protocol.

[1251] Output: Advice and analysis results sent to the device.

[1252] Step 7: Terminal receives results and user feedback

[1253] The device receives the analysis results and advice sent from the server and displays them to the user through a user interface, which also provides the user with the means to contact an expert if desired.

[1254] Input: Analysis results and advice sent from the server.

[1255] What it does: Stores the received data, displays it in a user interface, and provides a way to contact an expert if needed.

[1256] Output: Analysis results and advice displayed to the user.

[1257] These are the specific steps of the program processing of this system.

[1258] (Application example 2)

[1259] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1260] In current factory work, there is a lack of means to grasp workers' stress levels and health status in real time, which can result in reduced work efficiency. Furthermore, if workers' emotional state cannot be properly managed, excessive stress can continue for long periods of time, increasing safety risks. Furthermore, current factory robots lack the ability to collect biometric and emotional information, making them unable to contribute to worker health management or improved work efficiency. There is a need to solve these problems, improve workers' health, and increase work efficiency.

[1261] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting biometric information from the wearable device, means for transmitting the collected biometric information to the server, means for receiving and preprocessing the biometric information, means for analyzing the emotional state and health state based on a generative artificial intelligence model, means for generating instructions for stress management and relaxation based on the analysis results, and means for transmitting the generated instructions and analysis results to the terminal. This enables real-time collection and analysis of biometric information and emotional information, monitors the worker's health status, and improves work efficiency by providing appropriate instructions. It also enables factory robots to provide support for balancing worker safety and work efficiency.

[1262] A "wearable device" is a portable or wearable device that can collect biometric information by being worn on the body.

[1263] "Biometric information" refers to physiological data such as heart rate, body temperature, and blood pressure, and is data that indicates an individual's health condition.

[1264] "Server" means a central control unit for processing, analyzing and storing collected data.

[1265] "Preprocessing" refers to the process of cleansing, normalizing, and imputing missing values ​​of the collected data.

[1266] A "generative AI model" is an artificial intelligence model that predicts and analyzes emotional and health states based on biometric and emotional information.

[1267] "Emotional information" is data related to emotions extracted from the user's voice, facial expressions, and behavior.

[1268] "Analysis results" are predictions of emotional and health states output by the generative AI model based on biometric and emotional information.

[1269] "Stress management" refers to specific techniques and methods for reducing a user's stress level based on the results of analysis.

[1270] "Relaxation instructions" are specific advice or techniques suggested by the generative AI model to help users relieve stress.

[1271] A "robot" is a mechanical device that performs specific tasks under automatic control or programming.

[1272] A "sensor" is a device that senses a physical phenomenon and converts it into an electrical signal.

[1273] A "camera" is a device that collects visual information and stores and transmits it as image data.

[1274] "Real-time" means that data is collected, transmitted, and analyzed immediately.

[1275] The present invention provides a system for improving work efficiency and safety by using a factory robot to collect and analyze biometric and emotional information of workers. A specific embodiment of this system will be described below.

[1276] System configuration

[1277] This system consists of a wearable device, a terminal, a server, and a factory robot. The specific functions of each element are as follows:

[1278] Wearable devices

[1279] The wearable devices will collect workers' biometric information, such as heart rate and body temperature, in real time, using the latest heart rate monitors and temperature sensors.

[1280] Factory robots

[1281] The factory robots are equipped with sensors and cameras to collect biometric and emotional information from workers, and transmit this data to a terminal in real time.

[1282] Terminal

[1283] The device receives sensor data and emotion data collected from the camera and transmits it to the server. The device itself is equipped with a data transmission module, a result reception module, and a user feedback module.

[1284] server

[1285] The server consists of multiple modules (data collection module, data preprocessing module, analysis module using generative AI model, advice generation module, and data transmission module).

[1286] 1. Data Collection Module:

[1287] The server receives the biometric and emotional information sent from the device and stores it in a secure database. The data is encrypted to ensure its security.

[1288] 2. Data Preprocessing Module:

[1289] The server cleanses, normalizes, and processes missing values ​​on the received data to maintain data quality, using Python and Pandas.

[1290] 3. Analysis module using generative AI models:

[1291] The preprocessed data is input into a generative AI model (e.g., GPT-4) to predict and analyze the worker's emotional state and health status.

[1292] 4. Advice Generation Module:

[1293] Based on the analysis results, specific instructions for stress management and relaxation can be generated, as well as instructions for improving work efficiency.

[1294] 5. Data transmission module:

[1295] The generated instructions and analysis results are then sent back to the terminal, and this information is then appropriately fed back to the worker.

[1296] Specific examples

[1297] For example, when Worker A is working continuously in a factory, a robot monitors A's heart rate, body temperature, and facial expressions. This data is sent in real time to the terminal and then to the server. On the server side, the data is preprocessed and analyzed by a generative AI model (GPT-4). If the server determines that Worker A is experiencing high stress, it can immediately generate instructions, including suggestions for breaks and relaxation methods, and provide them as feedback to Worker A via the terminal.

[1298] Prompt Sentence Examples

[1299] Biometric data: Heart rate 75, body temperature 36.5℃, facial expression: nervous, voice: irritated

[1300] Question: Based on this data, what relaxation techniques would be best for the worker?

[1301] In this way, this system efficiently collects and analyzes biometric and emotional information, thereby improving worker health management and work efficiency.

[1302] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1303] Step 1:

[1304] The terminal collects biometric information (heart rate, body temperature, etc.) and emotional information (facial expressions, voice) from wearable devices and factory robots in real time. Each time data collection is completed, a data packet is generated. The terminal temporarily stores the data.

[1305] Step 2:

[1306] The device periodically transmits collected data packets to the server, which include the collection time, biometric information, and emotional information. This allows the server to receive real-time data.

[1307] Step 3:

[1308] The server stores the received data packets in a database and performs data preprocessing. This preprocessing step involves data cleansing (noise removal), normalization (standardization of data), and missing value imputation. For example, Python and Pandas are used to cleanse the data and impute missing values.

[1309] Step 4:

[1310] The server inputs the preprocessed data into a generative AI model (such as GPT-4), which analyzes the emotional and health states according to the prompts. Based on the input data, the AI ​​model generates an output that predicts the stress level and health state.

[1311] Step 5:

[1312] The server generates specific instructions for stress management and relaxation based on the analysis results obtained from the generative AI model. In this step, instructions are generated that include specific advice on breathing techniques, meditation techniques, etc.

[1313] Step 6:

[1314] The server then sends the generated instructions and analysis results to the terminal, allowing the worker to receive the analysis results and instructions in real time.

[1315] Step 7:

[1316] The device receives the analysis results and instructions sent from the server and provides feedback to the worker. This feedback is provided via the device's display or audio output. The worker can then practice the suggested relaxation methods and contact a specialist if necessary.

[1317] Step 8:

[1318] The user follows instructions provided through the device to practice stress management and relaxation techniques, such as deep breathing or short meditation sessions, and data on these practices is again collected through the device.

[1319] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1320] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1321] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1322] [Fourth embodiment]

[1323] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1324] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1325] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1326] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1327] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1328] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1329] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1330] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1331] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1332] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1333] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1334] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1335] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1336] The system of the present invention uses a wearable device, a terminal, and a server to collect and analyze a user's biometric data and provide advice based on their emotional and health states.

[1337] Program processing

[1338] Server program:

[1339] 1. Data Collection Module:

[1340] The server receives biometric data (e.g., blood pressure, heart rate, sleep patterns, etc.) from the wearable device sent from the terminal.

[1341] All received data is stored in a secure database.

[1342] 2. Data Preprocessing Module:

[1343] Before analyzing the biometric data, the server cleanses, normalizes, and processes missing values, thereby ensuring data quality and improving analysis accuracy.

[1344] 3. Analysis module using generative AI models:

[1345] The preprocessed biometric data is input into a generative AI model.

[1346] The generative AI model predicts and analyzes the user's emotional state (e.g., stress level, relaxation state) and health status in real time.

[1347] 4. Advice Generation Module:

[1348] Based on the analysis results, the server generates specific advice for stress management and relaxation.

[1349] Include suggestions for seeking expert advice where necessary.

[1350] 5. Data transmission module:

[1351] The server sends the generated analysis results and advice to the terminal.

[1352] Terminal programs:

[1353] 1. Sensor data collection module:

[1354] The terminal collects biometric data in real time from the wearable device.

[1355] 2. Data transmission module:

[1356] The terminal periodically transmits the collected data to the server.

[1357] 3. Result receiving module:

[1358] Receive analysis results and advice sent from the server.

[1359] 4. User Feedback Module:

[1360] Display the received analysis results and advice to the user.

[1361] If necessary, users can directly seek professional help.

[1362] Specific examples

[1363] Example of user action:

[1364] One night, the user puts on the wearable device and goes to sleep. During the night, the wearable device records their heart rate and sleep patterns. The device transmits this data in real time to the terminal, which receives the data and subsequently transmits it to the server.

[1365] Server processing example:

[1366] The data received overnight is stored in a database on the server and pre-processed. A generative AI model then analyzes the data and identifies when the user's stress level is high. The server generates relaxation advice, including breathing exercises and meditation techniques. The results and advice are then sent to the device.

[1367] Example of terminal processing:

[1368] The next morning, the device receives the analysis results and advice from the server. Through the user interface, the user can review the advice, practice the suggested relaxation techniques, and contact a specialist if necessary.

[1369] The processing flow will be explained below.

[1370] Step 1:

[1371] The user puts on the wearable device

[1372] The user puts on the wearable device, which then goes to work and starts collecting biometric data such as blood pressure, heart rate, and sleep patterns.

[1373] Step 2:

[1374] Device collects data

[1375] The terminal's sensor data collection module receives data from the wearable device in real time.

[1376] Step 3:

[1377] The device sends data

[1378] The terminal's data transmission module preprocesses the collected data (e.g., packet shaping), converts it into an optimal format, and transmits it to the server.

[1379] Step 4:

[1380] The server receives and stores the data

[1381] The server's data collection module receives the data sent from the devices and stores it in a secure database.

[1382] Step 5:

[1383] The server preprocesses the data

[1384] The server's data preprocessing module cleanses, normalizes, and handles missing values ​​on the biometric data, preparing it for data analysis.

[1385] Step 6:

[1386] The server analyzes using the generated AI model

[1387] The server's generated AI model analysis module inputs the preprocessed data and predicts and analyzes the user's emotional state and health condition.

[1388] Step 7:

[1389] Server generates advice

[1390] The server's advice generation module generates specific advice for stress management and relaxation based on the analysis results, including suggestions for expert advice where necessary.

[1391] Step 8:

[1392] The server sends the results and advice

[1393] The server's data transmission module transmits the generated analysis results and advice to the terminal.

[1394] Step 9:

[1395] The device receives the results and advice

[1396] A result receiving module of the terminal receives the data from the server.

[1397] Step 10:

[1398] The device displays feedback to the user

[1399] A user interface module of the terminal displays the received analysis results and advice to the user.

[1400] Step 11:

[1401] User reviews feedback

[1402] The user reviews the analysis and advice, practices the suggested relaxation techniques, and seeks professional help if necessary.

[1403] Step 12:

[1404] Contact an expert

[1405] The terminal provides a means for users to contact experts for advice, allowing them to receive support from appropriate experts.

[1406] Example 1

[1407] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1408] Conventional health management systems only collect and analyze a user's biometric information, but lack the functionality to provide real-time stress management and relaxation advice. Furthermore, the accuracy of the acquired data preprocessing and analysis results is insufficient, making it difficult to provide appropriate advice to the user. The present invention aims to solve these problems by providing a system that can provide immediate and specific advice based on the user's health and emotional state.

[1409] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1410] In this invention, the server includes means for collecting biometric information from the wearable device, means for transferring the collected biometric information to the device, means for transmitting the biometric information from the device to the server, means for receiving the biometric information in the server and performing data cleansing, normalization, and missing value processing, means for analyzing emotions and health based on a generative AI model using the preprocessed data, means for generating advice for stress management and relaxation based on the analysis results and including expert advice as needed, means for transmitting the generated advice and analysis results to the device, and means for displaying the analysis results and advice to the user on the device and enabling the user to practice the suggested relaxation techniques. This makes it possible to provide immediate and specific advice based on the user's health and emotional state.

[1411] A "wearable device" is a device worn by the user that can collect biometric information in real time. Examples include smartwatches and fitness trackers.

[1412] "Biometric information" refers to data about a user's physical condition, including heart rate, blood pressure, and sleep patterns.

[1413] The "terminal" is a relay device that receives biometric information from a wearable device and transmits it to a server. Examples of such devices include smartphones and tablets.

[1414] A "server" is a computer system that manages and processes data received from multiple terminals via a network.

[1415] "Data cleansing" is the process of using software to remove noise and inaccurate values ​​from a dataset to improve the quality of the data.

[1416] "Normalization" is a process of aligning the range and distribution of data in order to standardize the variations in the data.

[1417] "Missing value processing" is the process of filling in missing values ​​in a dataset so that they do not interfere with analysis. Common methods are mean value imputation and neighboring point imputation.

[1418] A "generative AI model" is a model built using machine learning algorithms that analyze input data to derive a specific outcome or prediction.

[1419] "Emotion and health analysis" is the process by which a generative AI model assesses a user's emotional state and health status using collected and pre-processed biometric information.

[1420] "Stress management" refers to providing specific measures and advice to reduce users' stress levels.

[1421] "Relaxation techniques" refer to methods or techniques that help users relieve tension or stress, including breathing exercises and meditation.

[1422] "Expert Advice" means advice based on specialized knowledge and experience for a particular situation, which the System provides as needed.

[1423] This invention relates to a system that uses a wearable device, a terminal, and a server to collect and analyze a user's biometric information and provide advice based on their emotions and health status. This system is composed of multiple modules, each with a specific function.

[1424] Hardware and software used

[1425] This system uses the following hardware and software:

[1426] Wearable devices: Smartwatches, fitness trackers, and other devices worn by users that collect biometric information in real time. Examples include the Apple Watch and Fitbit.

[1427] Terminal: A relay device that receives biometric information from a wearable device and sends it to a server. This includes smartphones and tablets. Specific examples include iPhones and Android devices.

[1428] Server: A computer system that manages and processes data received from multiple devices. The software used includes MySQL as a database, Python's pandas library for data preprocessing and analysis, and TensorFlow for running generative AI models.

[1429] Processing Details

[1430] Data Collection and Transfer

[1431] Users wear a wearable device, and the device collects real-time biometric information such as heart rate, blood pressure, and sleep patterns. For example, when a user wears an Apple Watch, the device measures their heart rate every second.

[1432] The device receives biometric data from the wearable device via Bluetooth connection. This data is temporarily stored in the device's local storage. Specifically, the iPhone receives heart rate data from the Apple Watch via Bluetooth.

[1433] Data submission and preprocessing

[1434] The device sends the collected biometric information to a server over Wi-Fi or mobile data, using the HTTPS protocol to ensure data security.

[1435] The server stores the received biometric information in a MySQL database, then uses the Python pandas library to cleanse, normalize, and process missing values, making the preprocessed data suitable for subsequent analysis.

[1436] Analysis and advice generation using generative AI models

[1437] The server then inputs the preprocessed data into a TensorFlow-based generative AI model to analyze the user's emotions and health in real time. For example, collected heart rate data can be used to predict the user's stress level and relaxation state.

[1438] Based on the analysis results, the server generates specific advice for stress management and relaxation, such as suggesting breathing exercises and meditation techniques to users with high stress levels, and including expert advice if necessary.

[1439] Results submission and user interface

[1440] The server then sends the generated analysis results and advice to the device again using the HTTPS protocol, and the sent data is saved in the device's local storage.

[1441] The device displays the received analysis results and advice in a user interface (e.g., a dedicated app). Users can check the analysis results and advice by opening the dedicated iPhone app. Based on this, users can practice breathing exercises and meditation techniques. They can also contact a specialist if necessary.

[1442] Specific examples

[1443] User processing example

[1444] One night, the user puts on the smartwatch and goes to sleep. During the night, the smartwatch records their heart rate and sleep patterns and transmits this data in real time to their smartphone. The smartphone then transmits the data to a server via Wi-Fi.

[1445] Server processing example

[1446] The data received overnight is stored in a MySQL database on the server, where it is validated and preprocessed using Python's pandas library. A TensorFlow-based generative AI model then analyzes the data and identifies high stress levels. The server then generates relaxation advice, such as breathing exercises or meditation techniques, and sends the results and advice to the device.

[1447] Terminal processing example

[1448] The next morning, the smartphone receives the analysis results and advice from the server and displays them to the user through a dedicated app. The user can then review the advice, practice the suggested relaxation techniques, and contact a specialist if necessary.

[1449] Prompt Sentence Examples

[1450] Last night, I used my smartwatch to record my heart rate and sleep patterns while I was asleep. Based on the results, I would like to know how to relax if my stress level is high.

[1451] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1452] Step 1:

[1453] The user wears a wearable device to collect biometric information, including heart rate, blood pressure, and sleep patterns. This data is stored in the wearable device's internal memory. The input is the user's biometric information, and the output is the data recorded on the wearable device. Specifically, the user wears the smartwatch and goes about their daily activities, and the device automatically collects data.

[1454] Step 2:

[1455] The device receives biometric information from the wearable device via Bluetooth. The received data is temporarily stored in the device's local storage. The input is the biometric information received from the wearable device, and the output is the data stored in the device's local storage. Specifically, the device polls for data via Bluetooth every few seconds and stores the received data in local storage.

[1456] Step 3:

[1457] The device sends the collected biometric information to the server. Wi-Fi or mobile data communication is used for transmission, and the HTTPS protocol is used to ensure security. The input is the biometric information stored in the device's local storage, and the output is the data sent to the server. Specifically, the device uploads the data to the server in batch format at regular intervals (e.g., every 30 minutes).

[1458] Step 4:

[1459] The server stores the received biometric information in a database. MySQL or similar databases are used as the database. The input is the biometric information received from the device, and the output is the data stored in the database. Specifically, the server parses the received data and inserts it into the database in the appropriate format.

[1460] Step 5:

[1461] The server preprocesses the biometric information stored in the database. Preprocessing includes data cleansing, normalization, and missing value processing. The input is the biometric information stored in the database, and the output is the preprocessed data. Specifically, it uses the Python pandas library to complement missing values ​​in the data and remove invalid values.

[1462] Step 6:

[1463] The server inputs the preprocessed data into a generative AI model to analyze emotions and health. TensorFlow can be used as the model. The input is preprocessed biometric information, and the output is the analysis results. Specifically, the server inputs the preprocessed data into a machine learning model to analyze stress levels, health status, and other factors in real time.

[1464] Step 7:

[1465] The server generates advice based on the analysis results. The advice includes stress management and relaxation techniques, and expert advice is also provided if necessary. The input is the analysis results, and the output is the generated advice. Specifically, the server generates text suggesting breathing exercises and meditation methods based on the results of the generative AI model.

[1466] Step 8:

[1467] The server sends the generated analysis results and advice to the device. The HTTPS protocol is used for transmission. The input is the generated advice and analysis results, and the output is the data sent to the device. Specifically, the server periodically pushes the analysis results and advice to the device.

[1468] Step 9:

[1469] The device displays the received analysis results and advice in a user interface. The user can review them and implement the suggested relaxation techniques. The input is the analysis results and advice received from the server, and the output is the information displayed to the user and the user's actions. Specifically, the device app renders the display UI and notifies the user of the results in the notification bar or on the app screen.

[1470] Prompt Sentence Examples

[1471] Last night, I used my smartwatch to record my heart rate and sleep patterns while I was asleep. Based on the results, I would like to know how to relax if my stress level is high.

[1472] (Application example 1)

[1473] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1474] The main function of conventional wearable device systems is to collect the user's biometric data, analyze their health and emotional state, and provide advice. However, they have the problem of not being able to monitor the user's security status and respond in real time if an abnormality is detected. This makes it difficult to quickly ensure the user's safety. To solve this problem, there is a need for a system that can simultaneously monitor the user's security status using the user's biometric data and has the ability to detect and notify abnormalities.

[1475] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1476] In this invention, the server includes means for collecting biometric data from the wearable device, means for transmitting the collected biometric data to the server, means for receiving and preprocessing the biometric data in the server, means for analyzing the emotional state and health state based on a generative AI model using the preprocessed data, means for generating advice for stress management and relaxation based on the analysis results, means for transmitting the generated advice and analysis results to the terminal, means for displaying the analysis results and advice to the user in the terminal, means for monitoring the user's security status and detecting abnormalities, and means for notifying the user of an alert when an abnormality is detected. This enables not only constant health management of the user but also real-time security monitoring and abnormality detection.

[1477] A "wearable device" is an electronic device that is worn on the body and collects personal biometric data in real time.

[1478] "Biometric data" refers to measurements such as heart rate, blood pressure, sleep patterns, pulse, and pupil movement that are used to understand an individual's health or emotional state.

[1479] "Server" is a central computer system that receives collected biometric data and performs pre-processing and analysis.

[1480] "Preprocessing" refers to the process of ensuring data quality by performing processes such as data cleansing, normalization, and missing value processing on biometric data.

[1481] A "generative AI model" is a model that uses machine learning algorithms to analyze biometric data and predict emotional and health states.

[1482] "Emotional state" refers to the user's psychological and emotional state, such as stress level, relaxation state, etc.

[1483] "Health status" refers to the physical condition of the user, as assessed by their heart rate, blood pressure, sleep patterns, etc.

[1484] "Stress management" is the practice of providing users with techniques and methods to reduce stress and improve their well-being.

[1485] "Relaxation techniques" are methods such as breathing exercises, meditation, and massage that help users relax.

[1486] "Advice" refers to specific guidelines or suggestions for action provided to users based on the analysis results.

[1487] A "terminal" is an electronic device that allows the user to communicate with the wearable device and display analysis results and advice.

[1488] "Security status" refers to the environment or situation that affects the user's safety, including the presence of suspicious individuals and abnormal events.

[1489] "Anomaly detection" refers to detecting abnormal conditions based on the user's biometric data and surrounding conditions.

[1490] An "alert" is a warning message or notification that notifies the user of an abnormality or an event that requires attention.

[1491] This invention connects a wearable device, a terminal, and a server to collect and analyze a user's biometric data in real time and provide advice based on their emotional and health status. In particular, it simultaneously monitors the user's security status and can respond in real time if an abnormality is detected.

[1492] Overall system configuration

[1493] 1. Wearable devices

[1494] It has a pulse sensor, a camera (to track pupil movement), a display, and a speaker.

[1495] 2. Terminal

[1496] These devices include smartphones and smart glasses, which collect data, send it to a server, and display analysis results and advice.

[1497] 3. Server

[1498] Use a database (e.g., AWS RDS), a generative AI model (e.g., TensorFlow), and a data preprocessing tool (Pandas).

[1499] Program processing details

[1500] Server Processing

[1501] The server receives biometric data collected from the wearable device and stores it in a secure database. It then performs preprocessing, such as data cleansing, normalization, and missing value treatment, to ensure data quality. The preprocessed biometric data is then input into a generative AI model to analyze the emotional state, health status, and security status of the user in real time.

[1502] Based on the analysis results, the server generates advice for stress management, relaxation, and specific actions for security, and sends the advice to the device.

[1503] Terminal handling

[1504] The terminal collects biometric data from the wearable device in real time and periodically transmits it to a server. It receives analysis results and advice from the server and displays them through a user interface. If an abnormality is detected, it notifies the user with visual and audio alerts.

[1505] User Actions

[1506] Users wear a wearable device and biometric data is collected without any special effort on their part. They act according to the advice displayed on the device, and if an abnormality is detected, they can take safe action based on the alert.

[1507] Specific examples

[1508] Example of a prompt for the analysis result:

[1509] "We have detected abnormalities in your heart rate and pupil movement. There is a high possibility that the surrounding area is currently in danger. Please check your surroundings and move to a safe location."

[1510] This system allows users to monitor their own health status and security status in real time, enabling safety management from two angles.

[1511] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1512] Step 1:

[1513] The terminal collects biometric data (e.g., pulse rate, pupil movement) in real time from the wearable device. This input data is obtained by sensors in the wearable device. The terminal temporarily stores the collected biometric data in its memory.

[1514] Step 2:

[1515] The device periodically transmits the collected biometric data, which is a block of the most recent biometric data, to the server, which stores the biometric data received from the device in a secure database.

[1516] Step 3:

[1517] The server performs data cleansing, normalization, and missing value processing on the received biometric data to ensure the quality of the input data, such as removing noise data and scaling the data.

[1518] Step 4:

[1519] The preprocessed biometric data is input into a generative AI model. The model estimates the emotional state, health status, and security status, and outputs the results. The server stores the analysis results from the generative AI model as analysis data.

[1520] Step 5:

[1521] Based on the analysis results, the server generates advice for stress management and relaxation, as well as specific actions for security. These actions are presented as text-based advice and warning messages.

[1522] Step 6:

[1523] The server then sends the generated advice and analysis results to the device. This data includes the analysis results (e.g., the level of stress) and advice as specific actions.

[1524] Step 7:

[1525] The device then displays the analysis results and advice to the user, such as breathing techniques for stress management or actions such as rechecking the surroundings if an abnormality is detected.

[1526] Step 8:

[1527] If necessary, the device will notify the user of an abnormality by visually and audibly alerting them, for example by displaying a warning message on the screen and playing a warning sound through the speaker.

[1528] In this way, the system can monitor the user's health and emotional state in real time while also monitoring their security status, and provide prompt countermeasures if an abnormality occurs. An example of a prompt sentence is, "Abnormalities have been detected in your heart rate and pupil movement. There is a high possibility that your surroundings are currently in a dangerous state. Please check your surroundings and move to a safe location."

[1529] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1530] The system of the present invention uses a wearable device, a terminal, a server, and an emotion engine to collect and analyze a user's biometric data and emotional data, and provide advice based on the user's emotional state and health condition.

[1531] Program processing

[1532] Server program:

[1533] 1. Data Collection Module:

[1534] The server receives data from the wearable device and emotion engine transmitted from the terminal.

[1535] All received data is stored in a secure database.

[1536] 2. Data Preprocessing Module:

[1537] The server cleanses, normalizes, and processes missing values ​​before analyzing biometric and emotional data, ensuring data quality and improving analysis accuracy.

[1538] 3. Analysis module using generative AI models:

[1539] Preprocessed biometric and emotional data is input into a generative AI model.

[1540] The generative AI model predicts and analyzes the user's emotional state (e.g., stress level, relaxation state) and health status in real time.

[1541] 4. Advice Generation Module:

[1542] Based on the analysis results, the server generates specific advice for stress management and relaxation.

[1543] Include suggestions for seeking expert advice where necessary.

[1544] 5. Data transmission module:

[1545] The server sends the generated analysis results and advice to the terminal.

[1546] Terminal programs:

[1547] 1. Sensor data collection module:

[1548] The terminal collects biometric data in real time from the wearable device.

[1549] 2. Emotion data collection module:

[1550] The emotion engine analyzes the user's voice and text data to recognize their emotional state.

[1551] 3. Data transmission module:

[1552] The terminal periodically transmits the collected biometric data and emotion data to the server.

[1553] 4. Result receiving module:

[1554] Receive analysis results and advice sent from the server.

[1555] 5. User Feedback Module:

[1556] Display the received analysis results and advice to the user.

[1557] If necessary, users can directly seek professional help.

[1558] Specific examples

[1559] Example of user action:

[1560] One night, the user puts on the wearable device and goes to sleep. During the night, the wearable device records the user's heart rate and sleep patterns, while the emotion engine analyzes the text data the user has written in a diary app. The device and emotion engine transmit this data to the device in real time, and the device receives the data and sends it to the server.

[1561] Server processing example:

[1562] The biometric and emotional data received overnight is stored in a server database and pre-processed. A generative AI model then analyzes the data and identifies when the user's stress level is high. The server generates relaxation advice, including breathing and meditation techniques. The results and advice are then sent to the device.

[1563] Example of terminal processing:

[1564] The next morning, the device receives the analysis results and advice from the server. Through the user interface, the user can review the advice, practice the suggested relaxation techniques, and contact a specialist if necessary.

[1565] The processing flow will be explained below.

[1566] Step 1:

[1567] The user puts on the wearable device

[1568] The user puts on the wearable device, which then goes to work and starts collecting biometric data such as blood pressure, heart rate, and sleep patterns.

[1569] Step 2:

[1570] Device collects data

[1571] The terminal's sensor data collection module receives data from the wearable device in real time.

[1572] Step 3:

[1573] Emotion engine collects emotional data

[1574] The emotion engine installed in the device analyzes the user's voice and text data to recognize the user's emotional state.

[1575] Step 4:

[1576] The device sends all data

[1577] The terminal's data transmission module preprocesses the collected biometric data and emotion data generated by the emotion engine, converts them into an optimal format, and transmits them to the server.

[1578] Step 5:

[1579] The server receives and stores the data

[1580] The server's data collection module receives the biometric and emotional data sent from the device and stores it all in a secure database.

[1581] Step 6:

[1582] The server preprocesses the data

[1583] The server's data preprocessing module cleanses, normalizes, and handles missing values ​​on biometric and emotional data to prepare it for data analysis.

[1584] Step 7:

[1585] The server analyzes using the generated AI model

[1586] The server's generated AI model analysis module inputs preprocessed biometric and emotional data to predict and analyze the user's emotional state (e.g., stress level, relaxation state) and health condition.

[1587] Step 8:

[1588] Server generates advice

[1589] The server's advice generation module generates specific advice for stress management and relaxation based on the analysis results, including suggestions for expert advice where necessary.

[1590] Step 9:

[1591] The server sends the results and advice

[1592] The server's data transmission module transmits the generated analysis results and advice to the terminal.

[1593] Step 10:

[1594] The device receives the results and advice

[1595] A result receiving module of the terminal receives the data from the server.

[1596] Step 11:

[1597] The device displays feedback to the user

[1598] A user interface module of the terminal displays the received analysis results and advice to the user.

[1599] Step 12:

[1600] Users review and implement feedback

[1601] The user checks the analysis results and recommendations on the device and practices the suggested relaxation techniques (e.g., deep breathing exercises or meditation).

[1602] Step 13:

[1603] Contact an expert

[1604] If necessary, the user can use the contact means to seek advice from an expert through the terminal, thereby receiving support from an appropriate expert.

[1605] Example 2

[1606] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1607] Conventional emotional and health management systems have struggled to effectively collect and analyze users' biometric and emotional data. Furthermore, they lacked the means to quickly provide users with appropriate advice based on the analysis results. This made it difficult for users to effectively implement actions for stress management and relaxation.

[1608] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for transmitting biometric data and emotional data between the terminal and the server, a means for preprocessing the received data, a means for analyzing using a generative AI model, a means for generating advice based on the analysis results, a means for transmitting the advice to the terminal, and a means for contacting an expert. This makes it possible to effectively collect and analyze the user's biometric data and emotional data and quickly provide appropriate advice.

[1609] A "wearable device" is an electronic device that can be worn by a user and is used to collect biometric data in real time.

[1610] "Biometric data" is data relating to a user's physical condition, such as the user's heart rate, body temperature, or sleep patterns.

[1611] "Emotion data" is data related to the user's emotional state that is extracted by analyzing the user's voice data and text data.

[1612] A "terminal" is an electronic device that transmits biometric and emotional data collected from a wearable device to a server and displays analysis results and advice to the user.

[1613] The "server" is a network computer system that receives biometric data and emotional data sent from the terminal, analyzes them, and generates advice.

[1614] "Data cleansing" is a process for removing noise and redundancy from collected data and improving the quality of the data.

[1615] "Normalization" is a process of converting data to a uniform scale in order to equalize data variations and improve analytical accuracy.

[1616] "Missing value handling" is the process of imputing missing values ​​in a dataset or deleting the records.

[1617] A "generative AI model" is an algorithm that uses machine learning technology to analyze a user's biometric and emotional data to predict and analyze their emotional and health states.

[1618] "Analysis results" are information about the user's emotional state and health status obtained by the generative AI model.

[1619] "Advice" refers to specific recommended actions for stress management and relaxation provided to users based on the analysis results.

[1620] "Professional contact" is a feature that helps users contact health and psychological professionals, if necessary.

[1621] The system of the present invention is composed of a wearable device, a terminal, a server, and an emotion engine. The user wears the wearable device to collect biometric and emotional data. This data is sent to the server via the terminal, and the server analyzes the data using a generative AI model to generate advice based on the user's emotional and health states. An embodiment of the system using specific hardware and software is described below.

[1622] Hardware and Software

[1623] Wearable devices

[1624] Overview: A device that collects biometric data such as heart rate, body temperature, and sleep patterns in real time.

[1625] Examples: smartwatches, fitness trackers.

[1626] Terminal

[1627] Overview: A device for collecting data from wearable devices and sending it to a server.

[1628] For example: smartphones, tablets.

[1629] server

[1630] Overview: A network computer system that receives data sent from terminals, analyzes it, and generates advice.

[1631] software:

[1632] Database Management System: AWS DynamoDB

[1633] Programming libraries: Python, Pandas

[1634] Generative AI models: TensorFlow, PyTorch

[1635] Specific operation of the system

[1636] 1. Wearable device operation

[1637] The user wears a wearable device to collect biometric data (heart rate, body temperature, sleep patterns, etc.), which is then transmitted to a terminal via Bluetooth or Wi-Fi.

[1638] 2. Device Operation

[1639] The terminal stores biometric data received from the wearable device and also collects the user's voice and text data, which are then converted into emotional data through an emotion engine.

[1640] 3. Data transmission

[1641] The device sends the collected biometric and emotional data to a server at regular intervals using the HTTPS protocol, and the data is encrypted.

[1642] 4. Data processing on the server

[1643] The server stores the data received from the device in a database, cleansing, normalizing, and processing missing values, and then inputs the preprocessed data into the generative AI model.

[1644] 5. Analysis using generative AI models

[1645] The generative AI model analyzes the data using the following prompt:

[1646] "Based on the user's heart rate data and sleep patterns, as well as the text data analyzed by the emotion engine, assess the user's current stress level and provide appropriate relaxation advice."

[1647] 6. Generating and Sending Advice

[1648] The server generates health advice for the user based on the analysis results obtained from the generative AI model and sends it to the device, including advice on stress management and relaxation techniques.

[1649] 7. User Feedback

[1650] The device receives advice and analysis results from the server and displays them to the user, who can then put these advice into practice and contact an expert if necessary.

[1651] Specific examples

[1652] One night, the user puts on the wearable device and goes to sleep. The device records their nighttime heart rate and sleep patterns, and the emotion engine analyzes text data written by the user in a diary app. This data is sent to a server via the device. The server preprocesses the data and analyzes it using a generative AI model. If the analysis identifies that the user's stress level is high, the server generates relaxation advice. This advice is notified to the user via the device the next morning, and the user can reduce stress by practicing the suggested relaxation techniques.

[1653] This allows users to manage their emotional and health states in real time and receive appropriate advice to maintain a healthier lifestyle.

[1654] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1655] Step 1: Collecting data with wearable devices

[1656] The wearable device collects real-time biometric data such as the user's heart rate, body temperature, and sleep patterns. This data is transmitted to the device via Bluetooth or Wi-Fi. Specifically, the data is measured every five minutes and transferred to the device.

[1657] Input: User biometric data (heart rate, body temperature, sleep patterns, etc.).

[1658] Specific operation: The wearable device takes measurements using biosensors and transmits the data to the terminal.

[1659] Output: Biometric data sent to the device.

[1660] Step 2: Data reception by device and emotion data collection

[1661] The terminal receives biometric data transmitted in real time from the wearable device. At the same time, the emotion engine analyzes the user's voice and text data to recognize their emotional state. The data collected during this process is temporarily stored on the terminal.

[1662] Input: Biometric data sent from the wearable device, user voice data, and text data.

[1663] Specific operation: The terminal receives data from the wearable device and inputs the voice and text data into the emotion engine to analyze the emotional state.

[1664] Output: Biometric and emotional data stored on the device.

[1665] Step 3: Send data

[1666] The device sends the collected biometric and emotional data to a server at regular intervals (e.g., every 15 minutes). The data is encrypted using the HTTPS protocol.

[1667] Input: Biometric and emotional data stored on the device.

[1668] What it does: The device encrypts the collected data and sends it to the server using the HTTPS protocol.

[1669] Output: Biometric and emotional data sent to the server.

[1670] Step 4: Data reception and preprocessing by the server

[1671] The server receives biometric and emotional data sent from the device, stores the data in a database, and performs preprocessing such as data cleansing, normalization, and missing value processing.

[1672] Input: Biometric and emotional data sent from the device.

[1673] Specific operations: Data is stored in a database management system, and data cleansing, normalization, and missing value processing are performed using Python and Pandas.

[1674] Output: Preprocessed data.

[1675] Step 5: Analysis by generative AI model

[1676] The server inputs the preprocessed biometric and emotional data into a generative AI model to analyze the user's emotional and health states.

[1677] Input: Preprocessed biometric and emotion data.

[1678] Prompt text: Based on the user's heart rate data and sleep patterns, as well as the text data analyzed by the emotion engine, assess the user's current stress level and provide appropriate relaxation advice.

[1679] Specific operation: Data is input into a generative AI model (using TensorFlow or PyTorch) and analysis is performed using prompt statements.

[1680] Output: Analysis results (stress level and health status).

[1681] Step 6: Generate and send advice

[1682] The server generates advice for stress management and relaxation based on the analysis results obtained from the generative AI model, and the generated advice and analysis results are sent to the device.

[1683] Input: Analysis results from the generative AI model.

[1684] Specific actions: Generate specific advice on stress management and relaxation and send it to the device using the HTTPS protocol.

[1685] Output: Advice and analysis results sent to the device.

[1686] Step 7: Terminal receives results and user feedback

[1687] The device receives the analysis results and advice sent from the server and displays them to the user through a user interface, which also provides the user with the means to contact an expert if desired.

[1688] Input: Analysis results and advice sent from the server.

[1689] What it does: Stores the received data, displays it in a user interface, and provides a way to contact an expert if needed.

[1690] Output: Analysis results and advice displayed to the user.

[1691] These are the specific steps of the program processing of this system.

[1692] (Application example 2)

[1693] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1694] In current factory work, there is a lack of means to grasp workers' stress levels and health status in real time, which can result in reduced work efficiency. Furthermore, if workers' emotional state cannot be properly managed, excessive stress can continue for long periods of time, increasing safety risks. Furthermore, current factory robots lack the ability to collect biometric and emotional information, making them unable to contribute to worker health management or improved work efficiency. There is a need to solve these problems, improve workers' health, and increase work efficiency.

[1695] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting biometric information from the wearable device, means for transmitting the collected biometric information to the server, means for receiving and preprocessing the biometric information, means for analyzing the emotional state and health state based on a generative artificial intelligence model, means for generating instructions for stress management and relaxation based on the analysis results, and means for transmitting the generated instructions and analysis results to the terminal. This enables real-time collection and analysis of biometric information and emotional information, monitors the worker's health status, and improves work efficiency by providing appropriate instructions. It also enables factory robots to provide support for balancing worker safety and work efficiency.

[1696] A "wearable device" is a portable or wearable device that can collect biometric information by being worn on the body.

[1697] "Biometric information" refers to physiological data such as heart rate, body temperature, and blood pressure, and is data that indicates an individual's health condition.

[1698] "Server" means a central control unit for processing, analyzing and storing collected data.

[1699] "Preprocessing" refers to the process of cleansing, normalizing, and imputing missing values ​​of the collected data.

[1700] A "generative AI model" is an artificial intelligence model that predicts and analyzes emotional and health states based on biometric and emotional information.

[1701] "Emotional information" is data related to emotions extracted from the user's voice, facial expressions, and behavior.

[1702] "Analysis results" are predictions of emotional and health states output by the generative AI model based on biometric and emotional information.

[1703] "Stress management" refers to specific techniques and methods for reducing a user's stress level based on the results of analysis.

[1704] "Relaxation instructions" are specific advice or techniques suggested by the generative AI model to help users relieve stress.

[1705] A "robot" is a mechanical device that performs specific tasks under automatic control or programming.

[1706] A "sensor" is a device that senses a physical phenomenon and converts it into an electrical signal.

[1707] A "camera" is a device that collects visual information and stores and transmits it as image data.

[1708] "Real-time" means that data is collected, transmitted, and analyzed immediately.

[1709] The present invention provides a system for improving work efficiency and safety by using a factory robot to collect and analyze biometric and emotional information of workers. A specific embodiment of this system will be described below.

[1710] System configuration

[1711] This system consists of a wearable device, a terminal, a server, and a factory robot. The specific functions of each element are as follows:

[1712] Wearable devices

[1713] The wearable devices will collect workers' biometric information, such as heart rate and body temperature, in real time, using the latest heart rate monitors and temperature sensors.

[1714] Factory robots

[1715] The factory robots are equipped with sensors and cameras to collect biometric and emotional information from workers, and transmit this data to a terminal in real time.

[1716] Terminal

[1717] The device receives sensor data and emotion data collected from the camera and transmits it to the server. The device itself is equipped with a data transmission module, a result reception module, and a user feedback module.

[1718] server

[1719] The server consists of multiple modules (data collection module, data preprocessing module, analysis module using generative AI model, advice generation module, and data transmission module).

[1720] 1. Data Collection Module:

[1721] The server receives the biometric and emotional information sent from the device and stores it in a secure database. The data is encrypted to ensure its security.

[1722] 2. Data Preprocessing Module:

[1723] The server cleanses, normalizes, and processes missing values ​​on the received data to maintain data quality, using Python and Pandas.

[1724] 3. Analysis module using generative AI models:

[1725] The preprocessed data is input into a generative AI model (e.g., GPT-4) to predict and analyze the worker's emotional state and health status.

[1726] 4. Advice Generation Module:

[1727] Based on the analysis results, specific instructions for stress management and relaxation can be generated, as well as instructions for improving work efficiency.

[1728] 5. Data transmission module:

[1729] The generated instructions and analysis results are then sent back to the terminal, and this information is then appropriately fed back to the worker.

[1730] Specific examples

[1731] For example, when Worker A is working continuously in a factory, a robot monitors A's heart rate, body temperature, and facial expressions. This data is sent in real time to the terminal and then to the server. On the server side, the data is preprocessed and analyzed by a generative AI model (GPT-4). If the server determines that Worker A is experiencing high stress, it can immediately generate instructions, including suggestions for breaks and relaxation methods, and provide them as feedback to Worker A via the terminal.

[1732] Prompt Sentence Examples

[1733] Biometric data: Heart rate 75, body temperature 36.5℃, facial expression: nervous, voice: irritated

[1734] Question: Based on this data, what relaxation techniques would be best for the worker?

[1735] In this way, this system efficiently collects and analyzes biometric and emotional information, thereby improving worker health management and work efficiency.

[1736] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1737] Step 1:

[1738] The terminal collects biometric information (heart rate, body temperature, etc.) and emotional information (facial expressions, voice) from wearable devices and factory robots in real time. Each time data collection is completed, a data packet is generated. The terminal temporarily stores the data.

[1739] Step 2:

[1740] The device periodically transmits collected data packets to the server, which include the collection time, biometric information, and emotional information. This allows the server to receive real-time data.

[1741] Step 3:

[1742] The server stores the received data packets in a database and performs data preprocessing. This preprocessing step involves data cleansing (noise removal), normalization (standardization of data), and missing value imputation. For example, Python and Pandas are used to cleanse the data and impute missing values.

[1743] Step 4:

[1744] The server inputs the preprocessed data into a generative AI model (such as GPT-4), which analyzes the emotional and health states according to the prompts. Based on the input data, the AI ​​model generates an output that predicts the stress level and health state.

[1745] Step 5:

[1746] The server generates specific instructions for stress management and relaxation based on the analysis results obtained from the generative AI model. In this step, instructions are generated that include specific advice on breathing techniques, meditation techniques, etc.

[1747] Step 6:

[1748] The server then sends the generated instructions and analysis results to the terminal, allowing the worker to receive the analysis results and instructions in real time.

[1749] Step 7:

[1750] The device receives the analysis results and instructions sent from the server and provides feedback to the worker. This feedback is provided via the device's display or audio output. The worker can then practice the suggested relaxation methods and contact a specialist if necessary.

[1751] Step 8:

[1752] The user follows instructions provided through the device to practice stress management and relaxation techniques, such as deep breathing or short meditation sessions, and data on these practices is again collected through the device.

[1753] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1754] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1755] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1756] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1757] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1758] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1759] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1760] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1761] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1762] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1763] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1764] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1765] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1766] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1767] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1768] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1769] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1770] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1771] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1772] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1773] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1774] The following is further disclosed regarding the above embodiment.

[1775] (Claim 1)

[1776] means for collecting biometric data from the wearable device;

[1777] means for transmitting the collected biometric data to a server;

[1778] means for receiving and pre-processing biometric data in a server;

[1779] means for analyzing emotional and health states based on a generative AI model using the preprocessed data;

[1780] means for generating stress management and relaxation advice based on the analysis results;

[1781] means for transmitting the generated advice and analysis results to the terminal;

[1782] The system includes means for displaying analysis results and advice to the user at the terminal.

[1783] (Claim 2)

[1784] 10. The system of claim 1, wherein the preprocessing includes data cleansing, normalization, and missing value handling.

[1785] (Claim 3)

[1786] 10. The system of claim 1, wherein the generated advice includes stress management and relaxation techniques.

[1787] "Example 1"

[1788] (Claim 1)

[1789] A means for collecting biometric information from a wearable device;

[1790] means for transferring the collected biometric information to a terminal;

[1791] means for transmitting biometric information from the terminal to a server;

[1792] a means for receiving the biometric information in a server and performing data cleansing, normalization, and missing value processing;

[1793] means for analyzing emotions and health based on a generative AI model using the preprocessed data;

[1794] a means for generating stress management and relaxation advice based on the analysis results, including expert advice where appropriate;

[1795] means for transmitting the generated advice and analysis results to the terminal;

[1796] The system includes a means for displaying analysis results and advice to the user on a terminal and enabling the user to practice the suggested relaxation techniques.

[1797] (Claim 2)

[1798] 10. The system of claim 1, wherein preprocessing is performed, including data cleansing, normalization, and missing value handling.

[1799] (Claim 3)

[1800] 10. The system of claim 1, wherein the generated advice includes relaxation techniques such as breathing exercises and meditation.

[1801] "Application Example 1"

[1802] (Claim 1)

[1803] means for collecting biometric data from the wearable device;

[1804] means for transmitting the collected biometric data to a server;

[1805] means for receiving and pre-processing biometric data in a server;

[1806] means for analyzing emotional and health states based on a generative AI model using the preprocessed data;

[1807] means for generating stress management and relaxation advice based on the analysis results;

[1808] means for transmitting the generated advice and analysis results to the terminal;

[1809] means for displaying the analysis results and advice to the user on the device;

[1810] A means of monitoring the user's security status and detecting abnormalities;

[1811] The system includes a means for notifying the user of an alert when an abnormality is detected.

[1812] (Claim 2)

[1813] 10. The system of claim 1, wherein the preprocessing includes data cleansing, normalization, and missing value handling.

[1814] (Claim 3)

[1815] 10. The system of claim 1, wherein the generated advice includes stress management and relaxation techniques.

[1816] "Example 2: Combining Emotion Engines"

[1817] (Claim 1)

[1818] means for collecting biometric data from the wearable device;

[1819] means for transmitting the collected biometric data and emotion data to a terminal;

[1820] means in the terminal for transmitting biometric data and emotion data to a server;

[1821] A server receives biometric data and emotion data, and performs data cleansing, normalization, and missing value processing;

[1822] A means for analyzing the emotional state and health state based on the generated AI model from the pre-processed data;

[1823] means for generating stress management and relaxation advice based on the analysis results;

[1824] means for transmitting the generated advice and analysis results to the terminal;

[1825] means for displaying the analysis results and advice to the user on the device;

[1826] The system includes a means for users to contact an expert if desired.

[1827] (Claim 2)

[1828] 10. The system of claim 1, wherein the preprocessing includes data cleansing, normalization, and missing value handling.

[1829] (Claim 3)

[1830] 10. The system of claim 1, wherein the generated advice includes stress management and relaxation techniques.

[1831] "Application example 2 when combining emotion engines"

[1832] (Claim 1)

[1833] a means for collecting biometric information from the wearable device;

[1834] means for transmitting the collected biometric information to a server;

[1835] a means for receiving and pre-processing biometric information in a server;

[1836] means for analyzing emotional and health states based on a generative artificial intelligence model using the preprocessed data;

[1837] means for generating stress management and relaxation instructions based on the analysis results;

[1838] means for transmitting the generated instructions and analysis results to a terminal;

[1839] means for presenting the analysis results and instructions to the user at the terminal;

[1840] A means for equipping factory robots with sensors and cameras to collect biometric and emotional information from workers;

[1841] means for transmitting the collected data to a server in real time;

[1842] A means to receive analysis results and instructions from the server and provide feedback to the worker.

[1843] A system including:

[1844] (Claim 2)

[1845] 10. The system of claim 1, wherein the preprocessing includes data cleansing, normalization, and missing value handling.

[1846] (Claim 3)

[1847] 10. The system of claim 1, wherein the generated instructions include stress management and relaxation techniques and suggestions for improving factory work efficiency. [Explanation of symbols]

[1848] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for collecting biometric data from the wearable device; means for transmitting the collected biometric data to a server; means for receiving and pre-processing biometric data in a server; means for analyzing emotional and health states based on a generative AI model using the preprocessed data; means for generating stress management and relaxation advice based on the analysis results; means for transmitting the generated advice and analysis results to the terminal; The system includes means for displaying analysis results and advice to the user at the terminal.

2. The system of claim 1 , wherein the preprocessing includes data cleansing, normalization, and missing value handling.

3. The system of claim 1 , wherein the generated advice includes stress management and relaxation techniques.

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