System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting heart rate and electrocardiogram data in real time using a wearable device worn by a user; means for transmitting the collected data to a terminal; means for the terminal to upload the data to a server; means for the server to analyze the received data and transmit an alert to the terminal when an abnormality is detected; and means for the terminal to notify the user by audio and video when the alert is received.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] In modern society, many people lose their lives every day to sudden cardiac death. While such incidents can be prevented by detecting abnormalities early and taking appropriate measures, in many cases the abnormalities remain unnoticed. In particular, the lack of routine means to identify the signs of cardiac arrest or abnormal heartbeats makes it difficult to respond quickly and appropriately. Current devices and systems lack the functionality to monitor heart rate and electrocardiograms in real time, and to immediately detect and notify abnormalities. Furthermore, there is a lack of systems that provide appropriate guidance on how to respond. Given this background, there is a need for a system that can detect cardiac abnormalities early and provide appropriate notification and guidance. [Means for solving the problem]

[0005] To solve the above-mentioned problems, the present invention provides the following means. First, heart rate and electrocardiogram data are collected in real time using a wearable device worn by the user. Next, the collected data is sent to a terminal. The terminal uploads this data to a server, which analyzes the received data. The server is equipped with an artificial intelligence model for detecting abnormalities and uses this model to precisely analyze the data. If an abnormality is detected, the server immediately sends an alert to the terminal. Upon receiving the alert, the terminal notifies the user of the abnormality via audio and video and also displays instructions on life-saving measures. This series of steps enables the user and those around them to take prompt action and prevent sudden cardiac death.

[0006] A "wearable device" is an electronic device that can be worn by the user and has the function of collecting biometric data such as heart rate and electrocardiogram in real time.

[0007] "Heart rate" is an indicator of the number of times the heart beats within a certain period of time, and is usually expressed in beats per minute (bpm).

[0008] An "electrocardiogram" is data that displays the electrical activity of the heart as a waveform, and is used to detect heart rhythms and abnormalities.

[0009] A "terminal" is an electronic device (such as a smartphone or tablet) that receives data sent from a wearable device and uploads it to a server.

[0010] A "server" is a computer system that receives and analyzes data sent from a terminal, and has the function of sending an alert to the terminal if an abnormality is detected.

[0011] An "artificial intelligence model" is an algorithm or learning model used to analyze collected data and detect anomalies.

[0012] An "alert" is a notification sent to a terminal when the server detects an abnormality, and includes a message such as audio or video to notify the user of the abnormality.

[0013] The "lifesaving treatment guide" is a guideline that instructs the user on the appropriate course of action when an abnormality is detected, and is displayed in audio and visual form.

[0014] "Real-time" means that data collection and analysis are immediate and without delay. [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 present invention relates to a system that uses a wearable device worn by a user to collect heart rate and electrocardiogram data in real time, and if an abnormality is detected, quickly notifies the user and people around them and guides them to take appropriate measures. Detailed embodiments for implementing this system are described below.

[0037] System Structure

[0038] This system consists of a wearable device, a terminal, and a server. Each of these components functions as follows:

[0039] Wearable devices

[0040] The wearable device, equipped with a heart rate sensor and an electrocardiogram sensor, continuously collects real-time biometric data from the user, which is then wirelessly transmitted to a terminal at regular intervals.

[0041] Terminal

[0042] The terminal is a computing device such as a smartphone or tablet that receives the biometric data sent from the wearable device. The terminal temporarily stores this data and periodically uploads it to the server. The terminal also has the ability to receive alerts from the server and display audio and video to notify the user.

[0043] server

[0044] The server is equipped with an artificial intelligence model for receiving and analyzing biometric data sent from the device. The server analyzes the biometric data and sends an alert to the device if an abnormality is detected. The alert also includes detailed information about the abnormality and appropriate countermeasures.

[0045] Program processing

[0046] Data collection

[0047] When a user wears the wearable device, it starts collecting heart rate and electrocardiogram data. For example, if the user is jogging, the heart rate and electrocardiogram waveforms are recorded in real time. This collected data is stored in the wearable device's internal memory for a certain period of time and then transmitted to the terminal via wireless communication.

[0048] Sending data

[0049] The terminal uploads the heart rate and electrocardiogram data received from the wearable device to the server at a predetermined interval (e.g., every minute). The terminal monitors the communication status and ensures that the connection to the server is established. If the connection is unstable, the data will be retransmitted after a stable connection is established.

[0050] Data analysis

[0051] The server receives the biometric data sent from the device in real time and analyzes it using an artificial intelligence model. During the analysis process, a sudden increase in heart rate or an abnormal electrocardiogram waveform is detected. For example, if the heart rate significantly exceeds the normal range or if an arrhythmia is detected on the electrocardiogram, the server will determine that an abnormality has occurred.

[0052] Generate alerts

[0053] If an abnormality is detected, the server immediately generates an alert and sends it to the device. The alert includes the specific type of abnormality, the urgency level, and recommended actions to take. For example, if there is a sudden increase in heart rate, the alert will state, "A sudden increase in heart rate has been detected. Please rest immediately and contact emergency services."

[0054] Alert Notification

[0055] When the device receives an alert from the server, it notifies the user via audio and video. The user can check the alert and take appropriate action by following the device's guidance. For example, if the user receives an alert that a sudden increase in heart rate has been detected, the device will provide audio and video guidance on how to perform CPR and contact emergency services.

[0056] Specific examples

[0057] Scenario 1: Heart rate spikes during everyday activities

[0058] When a user wears a wearable device during daily activities, the device continuously monitors their heart rate and electrocardiogram. For example, suppose a user's heart rate rises to 130 bpm while climbing stairs, causing an abnormality in the electrocardiogram. This data is sent to the device and then uploaded to a server. The server analyzes the data and, if it detects an abnormality, sends an alert to the device. The device issues a voice notification saying, "A sudden rise in heart rate has been detected. Please rest," and displays a video guide to first aid on the screen. In this way, the user and those around them can respond quickly and take action to save their precious life.

[0059] The present invention provides a system that reduces the risk of sudden cardiac death and ensures the safety of the user through this series of steps.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] Subject: User

[0063] The user wears a wearable device. The device has built-in heart rate and electrocardiogram sensors and collects biometric data in real time. For example, when the user starts jogging, the device measures heart rate and electrocardiogram data at regular intervals.

[0064] Step 2:

[0065] Subject: Wearable devices

[0066] The wearable device wirelessly transmits collected heart rate and electrocardiogram data to the terminal, where the data is temporarily stored in a buffer and then transmitted in batches at appropriate times, ensuring data continuity.

[0067] Step 3:

[0068] Subject: Terminal

[0069] The terminal receives and temporarily stores data sent from the wearable device. The data is set to be uploaded to the server at regular intervals (e.g., every minute). The terminal also monitors the connection status to the server to ensure that the connection is established. If the connection is unstable, a retry mechanism is implemented.

[0070] Step 4:

[0071] Subject: Terminal

[0072] The device uploads the stored data to the server, and if an error occurs, the error is logged and the device tries again later, thus ensuring the integrity and security of the data.

[0073] Step 5:

[0074] Subject: Server

[0075] The server receives the biometric data sent from the device, after which the data is stored in a database and prepared for analysis.

[0076] Step 6:

[0077] Subject: Server

[0078] The server analyzes the received data using an artificial intelligence model. Specifically, it examines the heart rate and electrocardiogram waveform to detect abnormal patterns or out-of-range values. For example, if the heart rate suddenly increases or an irregular heartbeat is detected, it determines that an abnormality has occurred.

[0079] Step 7:

[0080] Subject: Server

[0081] If an abnormality is detected, the server generates an alert and sends it to the terminal, which includes the specific type of abnormality, its urgency, and recommended actions to take.

[0082] Step 8:

[0083] Subject: Terminal

[0084] When the device receives an alert from the server, it notifies the user with audio and video, for example, by playing a message saying, "A sudden increase in heart rate has been detected. Please rest immediately," and by displaying life-saving measures on the screen.

[0085] Step 9:

[0086] Subject: User

[0087] The user follows the instructions on the device. In some cases, the alert can be shared with people around them to ask for a quick response. For example, a video guiding the user through the steps to call 119 can be displayed, allowing the user and people around them to take appropriate action.

[0088] This series of processes reduces the risk of sudden cardiac death and ensures user safety.

[0089] Example 1

[0090] 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."

[0091] Current health management systems using wearable devices have issues with the accuracy and speed of response when collecting data in real time and detecting abnormalities. It can also be difficult to promptly notify users of the appropriate response method when an abnormality is detected. Furthermore, if data transmission becomes unstable, necessary information may not be transmitted to the server in a timely manner, which risks delaying the detection and response of abnormalities. A system that solves these issues and increases user safety is needed.

[0092] 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.

[0093] In this invention, the server includes means for analyzing data in real time and using an artificial intelligence model to detect sudden increases in heart rate and abnormal electrocardiogram waveforms, means for the terminal to monitor communication status and retransmit data, and means for collecting heart rate and electrocardiogram data in real time using a wearable device worn by the user, thereby enabling highly accurate data analysis and abnormality detection, rapid user notification, and stable data transmission.

[0094] "User" refers to a person who wears a wearable device in order to use it.

[0095] "Wearable device" refers to a device worn by the user that collects heart rate and electrocardiogram data in real time.

[0096] "Heart rate" refers to the number of heartbeats per unit time, and is an important indicator for evaluating the health of a living organism.

[0097] "Electrocardiogram data" refers to data that records the electrical activity of the heart as a waveform, and is used to understand the condition of the heart in detail.

[0098] "Terminal" refers to a computing device for receiving data collected from a wearable device and uploading it to a server.

[0099] "Server" refers to a system that receives and analyzes biometric data sent from a terminal and sends an alert to the terminal if an abnormality is detected.

[0100] "Analysis" refers to the process by which the server uses the generative AI model to evaluate the collected biometric data and determine whether there are any abnormalities.

[0101] "Generative AI models" refer to artificial intelligence models used to analyze collected biometric data, and include techniques such as deep learning.

[0102] An "alert" refers to a message sent by the server to notify the terminal and user of an abnormality detected by the server.

[0103] "Notification" refers to the process by which the device communicates the contents of the alert to the user via audio and video.

[0104] "Retransmission" refers to the process in which a terminal attempts to send data to a server again when data transmission is unstable.

[0105] The system of the present invention is composed of a wearable device worn by a user, a terminal, and a server. Each of these elements functions as follows.

[0106] Wearable devices

[0107] A wearable device worn by a user is equipped with a heart rate sensor and an electrocardiogram sensor. The device collects the user's heart rate and electrocardiogram data in real time. For example, the wearable device records the user's heart rate every second and simultaneously measures the electrocardiogram data. This data is temporarily stored in the device's internal memory and transmitted to a terminal using wireless communication technology such as Bluetooth.

[0108] Terminal

[0109] The terminal is a computing device such as a smartphone or tablet held by the user. This terminal receives and temporarily stores the biometric data transmitted from the wearable device. It also uploads the data from the wearable device to a server at regular intervals (e.g., every minute). The terminal has the function of monitoring the communication status and, if data transmission is unstable, attempting to retransmit as soon as a stable connection is established.

[0110] The device also receives alerts from the server and notifies the user via audio and video. For example, if the device receives an abnormality notification, it will play a message such as "A sudden increase in heart rate has been detected. Please rest and contact emergency services immediately," and display emergency response instructions on the screen.

[0111] server

[0112] The server receives and analyzes biometric data sent from the device using a generative AI model implemented using machine learning frameworks such as TensorFlow and PyTorch.

[0113] The server analyzes the data in real time, and if it detects a sudden increase in heart rate or an abnormal electrocardiogram waveform, it judges it to be an abnormality. For example, if the heart rate significantly exceeds the normal range or if an arrhythmia is detected on the electrocardiogram, the server immediately determines this to be an abnormality and generates an alert. This alert is sent to the device and includes specific instructions to prompt the user to take appropriate action.

[0114] Specific examples

[0115] Scenario 1: Heart rate spikes during everyday activities

[0116] When a user wears a wearable device during daily activities, the device continuously monitors their heart rate and electrocardiogram. For example, suppose a user's heart rate rises to 130 bpm while climbing stairs, causing an abnormality in the electrocardiogram. This data is sent to the device and then uploaded to a server. The server analyzes the data and, if it detects an abnormality, sends an alert to the device. The device then issues a voice notification saying, "A sudden rise in heart rate has been detected. Please rest," and displays a video guide to first aid on the screen.

[0117] Prompt Sentence Examples

[0118] Here are some example prompts to give to a generative AI model:

[0119] A wearable device collects real-time heart rate and electrocardiogram data from a person going about their daily activities. For example, if a heart rate exceeds 130 bpm or an arrhythmia is detected in the electrocardiogram, it is deemed an abnormality and an alert is sent to the user. The alert will include details of the abnormality and a guide for emergency response. Please explain what kind of system is needed.

[0120] The above is the specific content for carrying out the invention, and describes in detail how the system is constructed and operated at each step.

[0121] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0122] Step 1: Data collection

[0123] When a user puts on a wearable device, the device starts collecting heart rate and electrocardiogram data. The input is the user's biometric information, and the output is digital heart rate and electrocardiogram data. The device's sensors measure the heart rate every second, and electrocardiogram data is collected simultaneously. For example, if the user is jogging, the heart rate measurement and electrocardiogram recording continue in real time.

[0124] Step 2: Send data

[0125] The terminal receives data collected from the wearable device via wireless communication such as Bluetooth. The input is the data sent from the wearable device, and the output is the data temporarily stored on the terminal. For example, if the terminal is a smartphone, it continuously receives data from the device and prepares to send it to the server at regular intervals (e.g., every minute).

[0126] Step 3: Upload data

[0127] The device uploads the received data to the server. The input is the collected data stored in the device, and the output is the data stored on the server. The device sends the data to the server using Wi-Fi or 4G communication. For example, if the communication situation is unstable, the device will try to resend the data until the connection is stable. This ensures that the data is uploaded reliably.

[0128] Step 4: Data analysis

[0129] The server analyzes the received biometric data. The input is the data uploaded to the server, and the output is the analysis result. The server uses a generative AI model (e.g., using TensorFlow or PyTorch) to detect sudden increases in heart rate or abnormal ECG waveforms. For example, if there is a sudden increase in heart rate or an arrhythmia is detected on the ECG, the server immediately detects the abnormality.

[0130] Step 5: Alert Generation

[0131] When the server detects an abnormality, it generates an alert. The input is the data analysis result, and the output is an alert message. The alert includes the specific type of abnormality, the urgency, and the recommended response method. For example, if there is a sudden increase in heart rate, the server generates a message saying, "A sudden increase in heart rate has been detected. Please rest immediately and contact emergency services."

[0132] Step 6: Sending an alert

[0133] The server sends the generated alert to the terminal. The input is the generated alert message, and the output is the alert transferred to the terminal. The server immediately sends the alert message to the terminal and prepares to notify the user.

[0134] Step 7: Alert Notifications

[0135] The device notifies the user of the received alert. The input is the alert notification from the server, and the output is an audio and video notification to the user. For example, when a smartphone receives an alert message, it immediately issues an audio notification and also displays emergency response instructions on the screen. It plays an audio message saying, "A sudden increase in heart rate has been detected. Please rest," and displays instructions on the screen such as, "Please perform life-saving measures."

[0136] (Application example 1)

[0137] 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."

[0138] Many people currently use wearable devices to monitor their health, but these devices typically lack the ability to detect abnormalities in real time or provide insufficient countermeasures. This prevents users from taking appropriate action even when they detect an abnormality, making it difficult to avoid serious health risks. Furthermore, existing systems do not effectively utilize the advanced artificial intelligence models required for data analysis, resulting in low accuracy in detecting abnormalities.

[0139] 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.

[0140] In this invention, the server includes a means for collecting heart rate and electrocardiogram data in real time, a means for analyzing this data and using an artificial intelligence model to detect abnormalities, and a means for sending an alert to the terminal when an abnormality is detected. This enables accurate detection of abnormalities and a rapid response. In addition, when the terminal receives an alert, it can notify the user via audio and video and provide guidance on life-saving measures and appropriate countermeasures (e.g., rest, emergency contact information, cardiopulmonary resuscitation guide).

[0141] A "wearable device" is a device that can collect biometric data in real time by being worn by the user.

[0142] "Heart rate" is a numerical value that indicates the number of times the heart beats per unit time.

[0143] "Electrocardiogram data" refers to data that records the electrical activity of the heart.

[0144] A "terminal" is a computing device for receiving data from a wearable device and uploading the data to a server.

[0145] The "server" is a central processing unit that analyzes the biometric data sent from the terminal and sends an alert if an abnormality is detected.

[0146] An "alert" is a warning signal that notifies the user when an abnormality is detected.

[0147] "Audio and video notification" refers to a means of transmitting alerts from the server to the user in the form of audio and video.

[0148] The "Guide for Lifesaving Treatment" is a guide that instructs the user on the appropriate treatment method when an abnormality is detected.

[0149] An "artificial intelligence model" is an analytical algorithm that uses artificial intelligence to allow the server to analyze biometric data and detect abnormalities.

[0150] "Real-time" means that data collection and analysis are carried out immediately, without delay.

[0151] This invention is a real-time biometric data monitoring system consisting of a wearable device, a terminal, and a server. This system collects a user's heart rate and electrocardiogram data in real time and can respond quickly if an abnormality is detected.

[0152] System Structure

[0153] Wearable devices

[0154] The wearable device, which is equipped with a heart rate sensor and an electrocardiogram sensor, continuously collects the user's biometric data in real time and transmits the data wirelessly to a terminal.

[0155] Terminal

[0156] The terminal is a computing device such as a smartphone or tablet that receives the biometric data sent from the wearable device. The terminal temporarily stores the received data and uploads it to the server at regular intervals. It also receives alerts from the server and notifies the user via audio and video.

[0157] server

[0158] The server is equipped with an artificial intelligence model for analyzing biometric data. The server receives the biometric data sent from the device and detects sudden increases in heart rate and abnormal electrocardiogram waveforms. If an abnormality is detected, the server immediately generates an alert and sends it to the device.

[0159] Program processing

[0160] Data collection and transmission

[0161] The heart rate and electrocardiogram data collected by the wearable device are sent to the terminal at regular intervals. For example, if the user is jogging, the heart rate and electrocardiogram waveform are recorded in real time and this data is transferred to the terminal.

[0162] Data analysis

[0163] The server receives the biometric data sent from the device and analyzes it using an artificial intelligence model. This analysis detects, for example, a sudden increase in heart rate or an abnormal electrocardiogram waveform. If an abnormality is detected, the server immediately generates an alert.

[0164] Alert Notification

[0165] When an alert is generated, the server sends it to the device. When the device receives the alert, it notifies the user with audio and video. If necessary, it also displays a guide on life-saving measures. For example, it may say, "A sudden increase in heart rate has been detected. Please rest immediately and contact an emergency department."

[0166] Specific examples

[0167] Scenario 1: Heart rate spikes during everyday activities

[0168] When a user wears a wearable device during daily activities, the device continuously monitors their heart rate and electrocardiogram. For example, suppose a user's heart rate rises to 130 bpm while climbing stairs, causing an abnormality in the electrocardiogram. This data is sent to the device and then uploaded to a server. The server analyzes the data and, if it detects an abnormality, sends an alert to the device. The device issues a voice notification saying, "A sudden rise in heart rate has been detected. Please rest," and displays a video guide to first aid on the screen. In this way, the user and those around them can respond quickly and take action to save their precious life.

[0169] Prompt Sentence Examples

[0170] "Generate a guide to what to do if your heart rate spikes while jogging and an arrhythmia is detected on your electrocardiogram."

[0171] The above is an example of an embodiment of the present invention, which enables a user to monitor their health condition in real time and take prompt and appropriate action when an abnormality is detected.

[0172] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0173] Step 1: Collect data

[0174] A wearable device worn by the user collects heart rate and electrocardiogram data in real time. The device continuously acquires these data using heart rate and electrocardiogram sensors and stores them in its internal memory for a short period of time. These data are then transmitted to a terminal via wireless communication such as Bluetooth. The input is the user's biometric data, and the output is the heart rate and electrocardiogram data collected by the wearable device.

[0175] Step 2: Receiving and storing data

[0176] The terminal receives the biometric data transmitted from the wearable device. The received data is temporarily stored in the terminal's local storage. The input is the heart rate and electrocardiogram data transmitted from the wearable device, and the output is the biometric data stored in the terminal.

[0177] Step 3: Send data to the server

[0178] The device uploads the accumulated biometric data to the server at regular intervals (e.g., every minute). When uploading, the data is sent to the server via an HTTP POST request. The input is the biometric data accumulated in the device, and the output is the biometric data sent to the server. The device also monitors the communication status and retries data transmission if it is unstable.

[0179] Step 4: Analyze the data

[0180] The server analyzes the biometric data received from the device. In the process, it uses a generative AI model to detect sudden increases in heart rate and abnormal electrocardiogram waveforms. The input is the biometric data sent from the device, and the output is a judgment result on whether an abnormality was detected.

[0181] Step 5: Generate an alert

[0182] If an anomaly is detected, the server generates an alert. The alert includes the type of anomaly, its urgency, and recommended actions to take. The input is the result of data analysis, and the output is the generation of an alert. This information is sent to the terminal in the next step.

[0183] Step 6: Sending an alert

[0184] The server sends the generated alert to the terminal. The sending method is HTTP PUSH notification, etc. The input is the generated alert information, and the output is the alert sent to the terminal.

[0185] Step 7: User Notification

[0186] The device notifies the user via audio and video based on the alert received from the server. It also displays video and audio guidance to guide the user on the appropriate course of action. The input is the alert information from the server, and the output is a notification and guidance display to the user. For example, the notification may say, "A sudden increase in heart rate has been detected. Please rest immediately and contact an emergency department."

[0187] The above is the processing flow of the system of the present invention. This system enables users to monitor their own health condition in real time and respond quickly and appropriately if an abnormality occurs.

[0188] 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.

[0189] The present invention relates to a system that uses a wearable device worn by the user to collect heart rate and electrocardiogram data in real time, and if an abnormality is detected, quickly notifies the user and those around them and guides them in taking appropriate measures. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system improves analysis accuracy and provides more personalized responses.

[0190] System Structure

[0191] This system consists of a wearable device, a terminal, a server, and an emotion engine. Each of these components functions as follows:

[0192] Wearable devices

[0193] The wearable device, which is equipped with a heart rate sensor, an electrocardiogram sensor, and an emotion engine, continuously collects the user's real-time biometric and emotional data, which is then wirelessly transmitted to the device at regular intervals.

[0194] Terminal

[0195] The terminal is a computing device such as a smartphone or tablet that receives biometric and emotional data transmitted from the wearable device. The terminal temporarily stores this data and periodically uploads it to the server. The terminal also receives alerts from the server and displays audio and video to notify the user.

[0196] server

[0197] The server is equipped with an artificial intelligence model for receiving and analyzing biometric data and emotional data sent from the device. The server analyzes the biometric data and sends an alert to the device if an abnormality is detected. The alert also includes detailed information about the abnormality and appropriate countermeasures.

[0198] Emotion Engine

[0199] The emotion engine is a system for recognizing a user's emotional state, assessing the user's emotions in real time using data from voice analysis, facial expression recognition, and other sensors. The emotion data collected by the emotion engine complements the analysis of heart rate and electrocardiogram data and is used to improve the accuracy of anomaly detection models.

[0200] Program processing

[0201] Data collection

[0202] When a user puts on a wearable device, the device starts collecting heart rate and electrocardiogram data. For example, if the user is jogging, the device records heart rate, electrocardiogram waveforms, and emotional data (such as stress levels) in real time. This collected data is stored in the wearable device's internal memory for a certain period of time and then transmitted wirelessly to the terminal.

[0203] Sending data

[0204] The terminal uploads the heart rate, electrocardiogram data, and emotion data received from the wearable device to the server at a predetermined interval (e.g., every minute). The terminal monitors the communication status and confirms that the connection to the server is established. If the connection is unstable, a retry mechanism is implemented.

[0205] Data analysis

[0206] The server receives biometric and emotional data sent from the device in real time and analyzes it using an artificial intelligence model. During the analysis process, it checks for sudden increases in heart rate, abnormal electrocardiogram waveforms, and stress or anxiety levels from emotional data. For example, if the heart rate significantly exceeds the normal range, if arrhythmia is detected on the electrocardiogram, or if the emotion engine detects a high stress state, the server will determine that an abnormality has occurred.

[0207] Generate alerts

[0208] If an abnormality is detected, the server immediately generates an alert and sends it to the device. This alert includes the specific type of abnormality, the urgency level, and a recommended course of action. For example, if a sudden increase in heart rate occurs, the alert may state, "A sudden increase in heart rate has been detected. Please rest immediately and contact emergency services." Different responses may be recommended based on emotional data.

[0209] Alert Notification

[0210] When the device receives an alert from the server, it notifies the user via audio and video. The user can confirm the alert and take appropriate action by following the device's guidance. The device also displays customized guidance based on the user's current emotional state based on emotional data.

[0211] Specific examples

[0212] Scenario 1: Rapid heart rate and high stress during everyday activities

[0213] When a user wears a wearable device during daily activities, the device continuously monitors heart rate, electrocardiogram, and emotional data. For example, suppose a user's heart rate rises to 130 bpm during a meeting, an abnormality appears on the electrocardiogram, and the emotional engine detects a high level of stress. This data is sent to the device and then uploaded to the server. The server analyzes the data and, if it detects an abnormality, sends an alert to the device. The device issues a voice notification saying, "A rapid rise in heart rate and high level of stress have been detected. Please take deep breaths and rest," and displays a video of relaxation techniques on the screen. In this way, the user and those around them can respond quickly and take action to protect their precious lives.

[0214] Through this series of steps, the present invention provides a system that reduces the risk of sudden cardiac death and ensures the safety of users. In addition, by combining it with an emotion engine, it becomes possible to respond more precisely to the individual state of the user.

[0215] The processing flow will be explained below.

[0216] Step 1:

[0217] Subject: User

[0218] The user wears a wearable device, which has a built-in heart rate sensor, an electrocardiogram sensor, and an emotion engine to collect biometric and emotional data in real time. For example, when a user starts jogging, their heart rate, electrocardiogram waveform, and emotional data (such as stress level) are continuously measured.

[0219] Step 2:

[0220] Subject: Wearable devices

[0221] The wearable device wirelessly transmits collected heart rate, electrocardiogram, and emotion data to the terminal, where the data is temporarily stored in a buffer and then transmitted in batches at appropriate times, ensuring data continuity without delay.

[0222] Step 3:

[0223] Subject: Terminal

[0224] The terminal receives and temporarily stores data sent from the wearable device. The data is uploaded to the server at regular intervals (e.g., every minute). The terminal also monitors the communication status to ensure that the connection to the server is established. If the connection is unstable, a retry mechanism is implemented.

[0225] Step 4:

[0226] Subject: Terminal

[0227] The device uploads all stored data to the server in bulk, and if an error occurs, it logs the error and attempts to retransmit it later, thus ensuring data integrity and security.

[0228] Step 5:

[0229] Subject: Server

[0230] The server receives the biometric and emotional data sent from the device, stores it in a database, and prepares it for analysis.

[0231] Step 6:

[0232] Subject: Server

[0233] The server analyzes the received data using an artificial intelligence model. This model simultaneously examines heart rate, ECG waveforms, and emotional data to detect abnormal patterns and out-of-range values. For example, it comprehensively determines a sudden increase in heart rate, the occurrence of arrhythmia, and a state of high stress from the emotional engine.

[0234] Step 7:

[0235] Subject: Server

[0236] If an abnormality is detected as a result of the analysis, the server immediately generates an alert and sends it to the device. This alert includes the specific type of abnormality, the urgency level, and recommended actions to take. For example, an alert could be generated stating, "A rapid rise in heart rate and high stress state have been detected. Please rest immediately and contact emergency services if necessary."

[0237] Step 8:

[0238] Subject: Terminal

[0239] When the device receives an alert from the server, it notifies the user with audio and visual notifications, such as "A sudden increase in heart rate has been detected. Please take a deep breath and remain calm," and displays relaxation techniques and first aid procedures on the screen.

[0240] Step 9:

[0241] Subject: User

[0242] The user follows the instructions on the device. In some cases, the alert can be shared with people around them to ask for a quick response. For example, a video guiding the user through the steps to call 119 can be displayed, allowing the user and those around them to take appropriate action. It can also display relaxation techniques and psychological support based on the user's emotional state.

[0243] This series of processes enables comprehensive monitoring of the user's health and emotional state, reducing the risk of sudden cardiac death and ensuring the user's safety.

[0244] Example 2

[0245] 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."

[0246] Conventional wearable devices and related systems have limited accuracy in analyzing heart rate and electrocardiogram data, which can lead to false alarms and delays in detecting abnormalities. They also lack the ability to customize responses based on the user's emotional state, making it difficult to respond appropriately to individual user conditions. Furthermore, in environments with unstable communication, data transmission failures can lead to delays and reduced accuracy in data analysis.

[0247] 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 means for collecting heart rate and electrocardiogram data in real time, means for transmitting the data to the terminal at predetermined intervals, means for the terminal to temporarily store the data and upload it to the server at predetermined intervals, means for analyzing the received data using an artificial intelligence model and sending an alert to the terminal if an abnormality is detected, means for notifying the user by audio and video when the terminal receives the alert, and means for generating an alert including detailed information about the abnormality and how to deal with it. This enables accurate collection and analysis of data even in cases where data transmission fails or in an environment with unstable communication, thereby increasing user safety and enabling individual responses based on emotional data.

[0248] Understood. Below are the definitions of the important words included in the rewritten claims.

[0249] A "wearable device" is a device that can be worn by a user and collects biometric information such as heart rate, electrocardiogram data, and emotional data in real time.

[0250] "Heart rate" is biological information that indicates the number of heartbeats per unit time, and is generally expressed as the number of beats per minute.

[0251] "Electrocardiogram data" refers to data that records the electrical activity of the heart and is used to analyze the rhythm and strength of heartbeats.

[0252] "Emotion data" is data that indicates the user's emotional state, and is a numerical representation of psychological states such as stress level and joy, anger, sadness, and happiness.

[0253] A "terminal" is a device that receives data sent from a wearable device, temporarily stores the data, and sends it to a server, and is a smartphone or tablet-like computing device.

[0254] A "server" is a computer system that receives data sent from a terminal, analyzes it using an artificial intelligence model, and generates and sends an alert to the terminal if an abnormality is detected.

[0255] An "artificial intelligence model" is a mathematical algorithm or machine learning technique used by the server to analyze data, and is used to improve the accuracy of anomaly detection and data analysis.

[0256] An "alert" is a warning message that is sent to the user when an abnormality is detected, and includes the specific nature of the abnormality, the urgency level, and a recommended method of dealing with the problem.

[0257] "Audio and video notification" is a method of notifying the user by audio guidance or displaying a visual message on the screen when the terminal receives an alert.

[0258] "Detailed information about anomaly" is information that indicates the specific content of the detected anomaly, the cause of its occurrence, the extent of its impact, and so on.

[0259] "Countermeasures" are guidelines that indicate specific actions and procedures that users should take in response to detected abnormalities.

[0260] This invention relates to a system that uses a wearable device worn by the user to collect heart rate and electrocardiogram data in real time, and if an abnormality is detected, quickly notifies the user and those around them and guides them in taking appropriate measures. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system improves analysis accuracy and enables more personalized responses. This system consists of a wearable device, a terminal, a server, and an emotion engine.

[0261] Wearable devices

[0262] The wearable device worn by the user has a built-in heart rate sensor, an electrocardiogram sensor, and an emotion engine. The device continuously collects the user's real-time biometric and emotional data. The collected data is wirelessly transmitted to the terminal at regular intervals, using wireless communication technologies such as Bluetooth and Wi-Fi. The collected data includes heart rate, electrocardiogram waveforms, and stress levels, and is temporarily stored in the wearable device's internal memory.

[0263] Terminal

[0264] The terminal is a computing device such as a smartphone or tablet that receives biometric and emotional data transmitted from the wearable device. The terminal temporarily stores this data and periodically uploads it to the server. The terminal also receives alerts from the server and displays audio and video to notify the user.

[0265] server

[0266] The server is equipped with an artificial intelligence model for receiving and analyzing the biometric and emotional data sent from the device. Machine learning frameworks such as TensorFlow and PyTorch can be used for the analysis. The server analyzes the biometric data and sends an alert to the device if an abnormality is detected. The alert also includes detailed information about the abnormality and appropriate countermeasures.

[0267] Emotion Engine

[0268] The emotion engine is a system for recognizing a user's emotional state, assessing the user's emotions in real time using data from voice analysis, facial expression recognition, and other sensors. The emotion data collected by the emotion engine complements the analysis of heart rate and electrocardiogram data and is used to improve the accuracy of anomaly detection models.

[0269] Specific examples

[0270] When a user wears a wearable device during daily activities, the device continuously monitors heart rate, electrocardiogram, and emotional data. For example, suppose a user's heart rate rises to 130 bpm during a meeting, an abnormality appears on the electrocardiogram, and the emotional engine detects a high level of stress. This data is sent to the device and then uploaded to the server. The server analyzes the data and, if it detects an abnormality, sends an alert to the device. The device issues a voice notification saying, "A rapid rise in heart rate and high level of stress have been detected. Please take deep breaths and rest," and displays a video of relaxation techniques on the screen. In this way, the user and those around them can respond quickly and take action to protect their precious lives.

[0271] Prompt Sentence Examples

[0272] Below are some example prompts for inputting specific scenarios for this system into the generative AI model.

[0273] If the wearable device detects a heart rate of 130 bpm and a high stress state while the user is performing daily activities, please explain in detail how and what part of the system analyzes the data and generates an alert.

[0274] This invention makes it possible to provide a system that reduces the risk of sudden cardiac death and ensures user safety. In addition, by combining it with an emotion engine, it becomes possible to provide more precise responses according to the individual state of the user.

[0275] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0276] Understood. Below, I will explain the process flow of the system program step by step.

[0277] Step 1:

[0278] The user puts on the wearable device. The wearable device activates the heart rate sensor, electrocardiogram sensor, and emotion engine to collect heart rate, electrocardiogram, and emotion data in real time. This data is temporarily stored in the internal memory of the wearable device.

[0279] Input: User's heart rate, ECG data, and emotion data.

[0280] Output: Storage of biometric and emotional data in the wearable device.

[0281] Step 2:

[0282] At regular intervals (e.g., every 30 seconds), the wearable device transmits the collected data to the terminal via wireless communication (e.g., Bluetooth). The terminal receives the data and returns an acknowledgement message (ACK) to the device.

[0283] Input: Biometric and emotional data from wearable devices.

[0284] Output: Temporarily stored data in the terminal and a transmission confirmation message.

[0285] Step 3:

[0286] The device temporarily stores the received data in a local buffer. The device uploads this data to the server at regular intervals (e.g., every minute). If the communication situation is unstable, there is a mechanism to retry.

[0287] Input: All data from the wearable device.

[0288] Output: Data transfer to the server.

[0289] Step 4:

[0290] The server receives the data sent from the device and stores it in data storage. When the server receives the data in real time, it analyzes the data using a generative AI model (e.g., TensorFlow).

[0291] Input: Biometric and emotional data from the device.

[0292] Output: Analysis results and analysis log.

[0293] Step 5:

[0294] The server analyzes the received data and detects abnormal patterns, such as a sudden increase in heart rate, abnormal ECG waveforms, or stress levels based on emotional data. If an abnormality is detected based on this analysis, an alert is generated.

[0295] Input: Biometric and emotional data.

[0296] Output: Anomaly detection results and generated alerts.

[0297] Step 6:

[0298] The generated alert contains the specific details of the abnormality, the urgency level, and the recommended action to take. The server sends this alert to the terminal. It will continue to retry until it receives confirmation of the completion of the transmission.

[0299] Input: Anomaly detection results.

[0300] Output: The alert sent to the terminal.

[0301] Step 7:

[0302] When the device receives an alert from the server, it notifies the user with audio and video. The user confirms the alert and follows the instructions. During this time, the device provides customized guidance based on the user's emotional data.

[0303] Input: Alert from the server.

[0304] Output: Audio notification and visual guide to the user.

[0305] The above are the specific processing steps of the program of this system.

[0306] (Application example 2)

[0307] 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."

[0308] In modern society, sudden illness and deterioration of health due to stress have become a social problem. In particular, abnormalities in heart rate or electrocardiogram data while driving a car pose a risk of causing a serious accident. Furthermore, drivers themselves often do not notice abnormalities, so a method for responding quickly is required. Conventional health monitoring systems are limited to detecting and notifying abnormalities in real time, but do not respond to actual driving operations. This poses the issue of a lack of means to ensure driver safety when an abnormality is detected.

[0309] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0310] In this invention, the server includes means for collecting heart rate and electrocardiogram data in real time using a wearable device worn by the user, means for transmitting the collected data to the terminal, means for the terminal to upload the data to the server, means for the server to analyze the received data and send an alert to the terminal if an abnormality is detected, means for notifying the user by audio and video when the terminal receives the alert, and means for controlling the vehicle's autonomous driving system using means for linking with other devices if an abnormality is detected. This makes it possible to immediately transition the autonomous driving system to safety mode when an abnormality is detected in the driver's heart rate or electrocardiogram, thereby quickly ensuring the driver's safety.

[0311] A "wearable device" is a device worn by the user that collects heart rate and electrocardiogram data in real time.

[0312] "Heart rate" is a biometric indicator that indicates the number of times the heart beats within a certain period of time.

[0313] "Electrocardiogram data" refers to data that records the electrical activity of the heart, and is information used to detect the condition of the myocardium and abnormalities in cardiac rhythm.

[0314] A "terminal" is a computing device that receives data transmitted from a wearable device, temporarily stores it, and uploads it to a server.

[0315] The "server" is a data processing device that receives and analyzes biometric data uploaded from a terminal, and generates and sends an alert to the terminal if an abnormality is detected.

[0316] "Means of cooperation with other devices" refers to the means of communication and operation for controlling the vehicle's autonomous driving system when an abnormality is detected.

[0317] An "alert" is a message that notifies the user of an abnormality and recommended countermeasures when an abnormality is detected based on analyzed data.

[0318] An "autonomous driving system" is a system that has the function of automatically driving and controlling a vehicle.

[0319] The present invention is a system that uses a wearable device worn by the user to collect heart rate and electrocardiogram data in real time, and provides the ability to respond quickly in cooperation with the control system of an autonomous vehicle when an abnormality is detected.

[0320] System configuration

[0321] This system consists of a wearable device, a terminal, a server, and a control system for the autonomous vehicle. Specifically, each component functions as follows:

[0322] Wearable devices

[0323] The wearable device collects the user's heart rate, electrocardiogram data, and emotional data, and also includes an emotion engine, which transmits the collected data wirelessly to the device in real time.

[0324] Terminal

[0325] The terminal is a computing device that temporarily stores collected data and uploads it to a server at regular intervals. The terminal receives data sent from the wearable device and receives alerts from the server to notify the user.

[0326] server

[0327] The server is equipped with an artificial intelligence model to analyze data sent from the device. If the server detects an abnormality, it immediately generates an alert and sends it to the device. This alert contains detailed information about the abnormality and how to deal with it.

[0328] Linking with other devices

[0329] The autonomous vehicle's control system will receive alerts from the server and have the ability to coordinate with the server to safely stop the vehicle if the driver's health condition deteriorates.

[0330] Specific operation explanation

[0331] Data collection and transmission

[0332] Once the user puts on the wearable device, it starts collecting heart rate, electrocardiogram, and emotional data, which are then periodically sent to the device, which then uploads the data to a cloud server at regular intervals.

[0333] Data analysis and alert generation

[0334] The server analyzes the data received from the device in real time. The artificial intelligence model used utilizes cloud analysis services such as Google Cloud AI, AWS, and Microsoft Azure. If an abnormality is detected, the server immediately generates an alert and sends it to the vehicle's autonomous driving control system.

[0335] Autonomous Driving System Control

[0336] When the autonomous vehicle's control system receives an alert from the server, it takes action to transition to safety mode. Specifically, it reduces the vehicle's speed and stops it at an appropriate location. The driver is notified via voice and display. This is to quickly ensure safety in the event of a sudden change in the driver's health condition.

[0337] Specific examples

[0338] scenario

[0339] Consider a situation where a driver is operating an autonomous vehicle and their heart rate spikes to 120 bpm, causing the emotion engine to detect a state of high stress.

[0340] 1. Data collection: Heart rate and electrocardiogram data obtained from the wearable device are sent to the terminal and uploaded to the server.

[0341] 2. Data analysis: The server analyzes the received data and detects any anomalies.

[0342] 3. Alert generation: The server detects an anomaly and immediately generates an alert and sends it to the terminal and the autonomous vehicle's control system.

[0343] 4. Response: The autonomous driving system switches to safety mode and notifies the driver that "rapid heart rate and high stress have been detected. An emergency stop will be made immediately."

[0344] Prompt Sentence Examples

[0345] Design a system that will transition to a safety mode and issue an emergency stop alert if the driver's heart rate exceeds 120 bpm and their emotional state is recognized as high stress. This includes a function that will link the autonomous vehicle's control system with wearable devices to collect and analyze data in real time and automatically respond if an abnormality occurs.

[0346] According to the present invention, when an abnormality occurs in the driver's heart rate or electrocardiogram, the autonomous vehicle can be controlled quickly and appropriately to ensure the safety of the driver.

[0347] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0348] Step 1:

[0349] Data collection

[0350] When a user wears the wearable device, it starts collecting heart rate, electrocardiogram (ECG) data, and emotional data, which are then transmitted to a device via Bluetooth. The inputs are data on heart rate, ECG waveforms, and emotional state, and the output is the periodic transmission of these data to the device.

[0351] Step 2:

[0352] Data transmission and storage

[0353] The terminal temporarily stores data received from the wearable device. This data is uploaded to the server at regular intervals. The input is the biometric data received from the wearable device, and the output is the data uploaded to the server. The communication status is monitored during data transmission, and if it fails, the terminal retries.

[0354] Step 3:

[0355] Data analysis

[0356] The server receives data uploaded from the device and analyzes it in real time. A generative AI model (e.g., a TensorFlow model) is used for the analysis. The input is the heart rate, ECG data, and emotion data sent from the device, and the output is the analysis results and the presence or absence of abnormalities. Specifically, the server detects sudden increases in heart rate, abnormal ECG waveforms, and high stress states.

[0357] Step 4:

[0358] Alert Generation

[0359] When the server detects an abnormality, it generates an alert and sends it to the device and the autonomous vehicle's control system. The input is the results of data analysis and details of the detected abnormality, and the output is an alert message. Specifically, a message such as "A sudden increase in heart rate has been detected. The vehicle will immediately enter safety mode" is generated.

[0360] Step 5:

[0361] Alert notifications and driving control

[0362] The terminal receives the alert from the server and notifies the user via audio and video. At the same time, the autonomous vehicle's control system also receives the alert and transitions to safety mode. The input is the alert message sent from the server, and the output is a notification to the user and a change in vehicle behavior. Specifically, the autonomous vehicle reduces its speed and stops at an appropriate location.

[0363] Step 6:

[0364] Contacting a medical institution

[0365] Furthermore, if necessary, the terminal will activate an automatic contact function to a medical institution. The input is an abnormality detection message from the server, and the output is contact information to the medical institution. Specifically, the terminal will notify the medical institution of the driver's current location and condition.

[0366] This series of processing steps makes it possible to respond quickly and appropriately when an abnormality occurs in the driver's heart rate or electrocardiogram.

[0367] 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.

[0368] 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.

[0369] 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.

[0370] [Second embodiment]

[0371] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0372] 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.

[0373] 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).

[0374] 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.

[0375] 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.

[0376] 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).

[0377] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0378] 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.

[0379] 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.

[0380] 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.

[0381] 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.

[0382] 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."

[0383] The present invention relates to a system that uses a wearable device worn by a user to collect heart rate and electrocardiogram data in real time, and if an abnormality is detected, quickly notifies the user and people around them and guides them to take appropriate measures. Detailed embodiments for implementing this system are described below.

[0384] System Structure

[0385] This system consists of a wearable device, a terminal, and a server. Each of these components functions as follows:

[0386] Wearable devices

[0387] The wearable device, equipped with a heart rate sensor and an electrocardiogram sensor, continuously collects real-time biometric data from the user, which is then wirelessly transmitted to a terminal at regular intervals.

[0388] Terminal

[0389] The terminal is a computing device such as a smartphone or tablet that receives the biometric data sent from the wearable device. The terminal temporarily stores this data and periodically uploads it to the server. The terminal also has the ability to receive alerts from the server and display audio and video to notify the user.

[0390] server

[0391] The server is equipped with an artificial intelligence model for receiving and analyzing biometric data sent from the device. The server analyzes the biometric data and sends an alert to the device if an abnormality is detected. The alert also includes detailed information about the abnormality and appropriate countermeasures.

[0392] Program processing

[0393] Data collection

[0394] When a user wears the wearable device, it starts collecting heart rate and electrocardiogram data. For example, if the user is jogging, the heart rate and electrocardiogram waveforms are recorded in real time. This collected data is stored in the wearable device's internal memory for a certain period of time and then transmitted to the terminal via wireless communication.

[0395] Sending data

[0396] The terminal uploads the heart rate and electrocardiogram data received from the wearable device to the server at a predetermined interval (e.g., every minute). The terminal monitors the communication status and ensures that the connection to the server is established. If the connection is unstable, the data will be retransmitted after a stable connection is established.

[0397] Data analysis

[0398] The server receives the biometric data sent from the device in real time and analyzes it using an artificial intelligence model. During the analysis process, a sudden increase in heart rate or an abnormal electrocardiogram waveform is detected. For example, if the heart rate significantly exceeds the normal range or if an arrhythmia is detected on the electrocardiogram, the server will determine that an abnormality has occurred.

[0399] Generate alerts

[0400] If an abnormality is detected, the server immediately generates an alert and sends it to the device. The alert includes the specific type of abnormality, the urgency level, and recommended actions to take. For example, if there is a sudden increase in heart rate, the alert will state, "A sudden increase in heart rate has been detected. Please rest immediately and contact emergency services."

[0401] Alert Notification

[0402] When the device receives an alert from the server, it notifies the user via audio and video. The user can check the alert and take appropriate action by following the device's guidance. For example, if the user receives an alert that a sudden increase in heart rate has been detected, the device will provide audio and video guidance on how to perform CPR and contact emergency services.

[0403] Specific examples

[0404] Scenario 1: Heart rate spikes during everyday activities

[0405] When a user wears a wearable device during daily activities, the device continuously monitors their heart rate and electrocardiogram. For example, suppose a user's heart rate rises to 130 bpm while climbing stairs, causing an abnormality in the electrocardiogram. This data is sent to the device and then uploaded to a server. The server analyzes the data and, if it detects an abnormality, sends an alert to the device. The device issues a voice notification saying, "A sudden rise in heart rate has been detected. Please rest," and displays a video guide to first aid on the screen. In this way, the user and those around them can respond quickly and take action to save their precious life.

[0406] The present invention provides a system that reduces the risk of sudden cardiac death and ensures the safety of the user through this series of steps.

[0407] The processing flow will be explained below.

[0408] Step 1:

[0409] Subject: User

[0410] The user wears a wearable device. The device has built-in heart rate and electrocardiogram sensors and collects biometric data in real time. For example, when the user starts jogging, the device measures heart rate and electrocardiogram data at regular intervals.

[0411] Step 2:

[0412] Subject: Wearable devices

[0413] The wearable device wirelessly transmits collected heart rate and electrocardiogram data to the terminal, where the data is temporarily stored in a buffer and then transmitted in batches at appropriate times, ensuring data continuity.

[0414] Step 3:

[0415] Subject: Terminal

[0416] The terminal receives and temporarily stores data sent from the wearable device. The data is set to be uploaded to the server at regular intervals (e.g., every minute). The terminal also monitors the connection status to the server to ensure that the connection is established. If the connection is unstable, a retry mechanism is implemented.

[0417] Step 4:

[0418] Subject: Terminal

[0419] The device uploads the stored data to the server, and if an error occurs, the error is logged and the device tries again later, thus ensuring the integrity and security of the data.

[0420] Step 5:

[0421] Subject: Server

[0422] The server receives the biometric data sent from the device, after which the data is stored in a database and prepared for analysis.

[0423] Step 6:

[0424] Subject: Server

[0425] The server analyzes the received data using an artificial intelligence model. Specifically, it examines the heart rate and electrocardiogram waveform to detect abnormal patterns or out-of-range values. For example, if the heart rate suddenly increases or an irregular heartbeat is detected, it determines that an abnormality has occurred.

[0426] Step 7:

[0427] Subject: Server

[0428] If an abnormality is detected, the server generates an alert and sends it to the terminal, which includes the specific type of abnormality, its urgency, and recommended actions to take.

[0429] Step 8:

[0430] Subject: Terminal

[0431] When the device receives an alert from the server, it notifies the user with audio and video, for example, by playing a message saying, "A sudden increase in heart rate has been detected. Please rest immediately," and by displaying life-saving measures on the screen.

[0432] Step 9:

[0433] Subject: User

[0434] The user follows the instructions on the device. In some cases, the alert can be shared with people around them to ask for a quick response. For example, a video guiding the user through the steps to call 119 can be displayed, allowing the user and people around them to take appropriate action.

[0435] This series of processes reduces the risk of sudden cardiac death and ensures user safety.

[0436] Example 1

[0437] 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."

[0438] Current health management systems using wearable devices have issues with the accuracy and speed of response when collecting data in real time and detecting abnormalities. It can also be difficult to promptly notify users of the appropriate response method when an abnormality is detected. Furthermore, if data transmission becomes unstable, necessary information may not be transmitted to the server in a timely manner, which risks delaying the detection and response of abnormalities. A system that solves these issues and increases user safety is needed.

[0439] 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.

[0440] In this invention, the server includes means for analyzing data in real time and using an artificial intelligence model to detect sudden increases in heart rate and abnormal electrocardiogram waveforms, means for the terminal to monitor communication status and retransmit data, and means for collecting heart rate and electrocardiogram data in real time using a wearable device worn by the user, thereby enabling highly accurate data analysis and abnormality detection, rapid user notification, and stable data transmission.

[0441] "User" refers to a person who wears a wearable device in order to use it.

[0442] "Wearable device" refers to a device worn by the user that collects heart rate and electrocardiogram data in real time.

[0443] "Heart rate" refers to the number of heartbeats per unit time, and is an important indicator for evaluating the health of a living organism.

[0444] "Electrocardiogram data" refers to data that records the electrical activity of the heart as a waveform, and is used to understand the condition of the heart in detail.

[0445] "Terminal" refers to a computing device for receiving data collected from a wearable device and uploading it to a server.

[0446] "Server" refers to a system that receives and analyzes biometric data sent from a terminal and sends an alert to the terminal if an abnormality is detected.

[0447] "Analysis" refers to the process by which the server uses the generative AI model to evaluate the collected biometric data and determine whether there are any abnormalities.

[0448] "Generative AI models" refer to artificial intelligence models used to analyze collected biometric data, and include techniques such as deep learning.

[0449] An "alert" refers to a message sent by the server to notify the terminal and user of an abnormality detected by the server.

[0450] "Notification" refers to the process by which the device communicates the contents of the alert to the user via audio and video.

[0451] "Retransmission" refers to the process in which a terminal attempts to send data to a server again when data transmission is unstable.

[0452] The system of the present invention is composed of a wearable device worn by a user, a terminal, and a server. Each of these elements functions as follows.

[0453] Wearable devices

[0454] A wearable device worn by a user is equipped with a heart rate sensor and an electrocardiogram sensor. The device collects the user's heart rate and electrocardiogram data in real time. For example, the wearable device records the user's heart rate every second and simultaneously measures the electrocardiogram data. This data is temporarily stored in the device's internal memory and transmitted to a terminal using wireless communication technology such as Bluetooth.

[0455] Terminal

[0456] The terminal is a computing device such as a smartphone or tablet held by the user. This terminal receives and temporarily stores the biometric data transmitted from the wearable device. It also uploads the data from the wearable device to a server at regular intervals (e.g., every minute). The terminal has the function of monitoring the communication status and, if data transmission is unstable, attempting to retransmit as soon as a stable connection is established.

[0457] The device also receives alerts from the server and notifies the user via audio and video. For example, if the device receives an abnormality notification, it will play a message such as "A sudden increase in heart rate has been detected. Please rest and contact emergency services immediately," and display emergency response instructions on the screen.

[0458] server

[0459] The server receives and analyzes biometric data sent from the device using a generative AI model implemented using machine learning frameworks such as TensorFlow and PyTorch.

[0460] The server analyzes the data in real time, and if it detects a sudden increase in heart rate or an abnormal electrocardiogram waveform, it judges it to be an abnormality. For example, if the heart rate significantly exceeds the normal range or if an arrhythmia is detected on the electrocardiogram, the server immediately determines this to be an abnormality and generates an alert. This alert is sent to the device and includes specific instructions to prompt the user to take appropriate action.

[0461] Specific examples

[0462] Scenario 1: Heart rate spikes during everyday activities

[0463] When a user wears a wearable device during daily activities, the device continuously monitors their heart rate and electrocardiogram. For example, suppose a user's heart rate rises to 130 bpm while climbing stairs, causing an abnormality in the electrocardiogram. This data is sent to the device and then uploaded to a server. The server analyzes the data and, if it detects an abnormality, sends an alert to the device. The device then issues a voice notification saying, "A sudden rise in heart rate has been detected. Please rest," and displays a video guide to first aid on the screen.

[0464] Prompt Sentence Examples

[0465] Here are some example prompts to give to a generative AI model:

[0466] A wearable device collects real-time heart rate and electrocardiogram data from a person going about their daily activities. For example, if a heart rate exceeds 130 bpm or an arrhythmia is detected in the electrocardiogram, it is deemed an abnormality and an alert is sent to the user. The alert will include details of the abnormality and a guide for emergency response. Please explain what kind of system is needed.

[0467] The above is the specific content for carrying out the invention, and describes in detail how the system is constructed and operated at each step.

[0468] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0469] Step 1: Data collection

[0470] When a user puts on a wearable device, the device starts collecting heart rate and electrocardiogram data. The input is the user's biometric information, and the output is digital heart rate and electrocardiogram data. The device's sensors measure the heart rate every second, and electrocardiogram data is collected simultaneously. For example, if the user is jogging, the heart rate measurement and electrocardiogram recording continue in real time.

[0471] Step 2: Send data

[0472] The terminal receives data collected from the wearable device via wireless communication such as Bluetooth. The input is the data sent from the wearable device, and the output is the data temporarily stored on the terminal. For example, if the terminal is a smartphone, it continuously receives data from the device and prepares to send it to the server at regular intervals (e.g., every minute).

[0473] Step 3: Upload data

[0474] The device uploads the received data to the server. The input is the collected data stored in the device, and the output is the data stored on the server. The device sends the data to the server using Wi-Fi or 4G communication. For example, if the communication situation is unstable, the device will try to resend the data until the connection is stable. This ensures that the data is uploaded reliably.

[0475] Step 4: Data analysis

[0476] The server analyzes the received biometric data. The input is the data uploaded to the server, and the output is the analysis result. The server uses a generative AI model (e.g., using TensorFlow or PyTorch) to detect sudden increases in heart rate or abnormal ECG waveforms. For example, if there is a sudden increase in heart rate or an arrhythmia is detected on the ECG, the server immediately detects the abnormality.

[0477] Step 5: Alert Generation

[0478] When the server detects an abnormality, it generates an alert. The input is the data analysis result, and the output is an alert message. The alert includes the specific type of abnormality, the urgency, and the recommended response method. For example, if there is a sudden increase in heart rate, the server generates a message saying, "A sudden increase in heart rate has been detected. Please rest immediately and contact emergency services."

[0479] Step 6: Sending an alert

[0480] The server sends the generated alert to the terminal. The input is the generated alert message, and the output is the alert transferred to the terminal. The server immediately sends the alert message to the terminal and prepares to notify the user.

[0481] Step 7: Alert Notifications

[0482] The device notifies the user of the received alert. The input is the alert notification from the server, and the output is an audio and video notification to the user. For example, when a smartphone receives an alert message, it immediately issues an audio notification and also displays emergency response instructions on the screen. It plays an audio message saying, "A sudden increase in heart rate has been detected. Please rest," and displays instructions on the screen such as, "Please perform life-saving measures."

[0483] (Application example 1)

[0484] 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."

[0485] Many people currently use wearable devices to monitor their health, but these devices typically lack the ability to detect abnormalities in real time or provide insufficient countermeasures. This prevents users from taking appropriate action even when they detect an abnormality, making it difficult to avoid serious health risks. Furthermore, existing systems do not effectively utilize the advanced artificial intelligence models required for data analysis, resulting in low accuracy in detecting abnormalities.

[0486] 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.

[0487] In this invention, the server includes a means for collecting heart rate and electrocardiogram data in real time, a means for analyzing this data and using an artificial intelligence model to detect abnormalities, and a means for sending an alert to the terminal when an abnormality is detected. This enables accurate detection of abnormalities and a rapid response. In addition, when the terminal receives an alert, it can notify the user via audio and video and provide guidance on life-saving measures and appropriate countermeasures (e.g., rest, emergency contact information, cardiopulmonary resuscitation guide).

[0488] A "wearable device" is a device that can collect biometric data in real time by being worn by the user.

[0489] "Heart rate" is a numerical value that indicates the number of times the heart beats per unit time.

[0490] "Electrocardiogram data" refers to data that records the electrical activity of the heart.

[0491] A "terminal" is a computing device for receiving data from a wearable device and uploading the data to a server.

[0492] The "server" is a central processing unit that analyzes the biometric data sent from the terminal and sends an alert if an abnormality is detected.

[0493] An "alert" is a warning signal that notifies the user when an abnormality is detected.

[0494] "Audio and video notification" refers to a means of transmitting alerts from the server to the user in the form of audio and video.

[0495] The "Guide for Lifesaving Treatment" is a guide that instructs the user on the appropriate treatment method when an abnormality is detected.

[0496] An "artificial intelligence model" is an analytical algorithm that uses artificial intelligence to allow the server to analyze biometric data and detect abnormalities.

[0497] "Real-time" means that data collection and analysis are carried out immediately, without delay.

[0498] This invention is a real-time biometric data monitoring system consisting of a wearable device, a terminal, and a server. This system collects a user's heart rate and electrocardiogram data in real time and can respond quickly if an abnormality is detected.

[0499] System Structure

[0500] Wearable devices

[0501] The wearable device, which is equipped with a heart rate sensor and an electrocardiogram sensor, continuously collects the user's biometric data in real time and transmits the data wirelessly to a terminal.

[0502] Terminal

[0503] The terminal is a computing device such as a smartphone or tablet that receives the biometric data sent from the wearable device. The terminal temporarily stores the received data and uploads it to the server at regular intervals. It also receives alerts from the server and notifies the user via audio and video.

[0504] server

[0505] The server is equipped with an artificial intelligence model for analyzing biometric data. The server receives the biometric data sent from the device and detects sudden increases in heart rate and abnormal electrocardiogram waveforms. If an abnormality is detected, the server immediately generates an alert and sends it to the device.

[0506] Program processing

[0507] Data collection and transmission

[0508] The heart rate and electrocardiogram data collected by the wearable device are sent to the terminal at regular intervals. For example, if the user is jogging, the heart rate and electrocardiogram waveform are recorded in real time and this data is transferred to the terminal.

[0509] Data analysis

[0510] The server receives the biometric data sent from the device and analyzes it using an artificial intelligence model. This analysis detects, for example, a sudden increase in heart rate or an abnormal electrocardiogram waveform. If an abnormality is detected, the server immediately generates an alert.

[0511] Alert Notification

[0512] When an alert is generated, the server sends it to the device. When the device receives the alert, it notifies the user with audio and video. If necessary, it also displays a guide on life-saving measures. For example, it may say, "A sudden increase in heart rate has been detected. Please rest immediately and contact an emergency department."

[0513] Specific examples

[0514] Scenario 1: Heart rate spikes during everyday activities

[0515] When a user wears a wearable device during daily activities, the device continuously monitors their heart rate and electrocardiogram. For example, suppose a user's heart rate rises to 130 bpm while climbing stairs, causing an abnormality in the electrocardiogram. This data is sent to the device and then uploaded to a server. The server analyzes the data and, if it detects an abnormality, sends an alert to the device. The device issues a voice notification saying, "A sudden rise in heart rate has been detected. Please rest," and displays a video guide to first aid on the screen. In this way, the user and those around them can respond quickly and take action to save their precious life.

[0516] Prompt Sentence Examples

[0517] "Generate a guide to what to do if your heart rate spikes while jogging and an arrhythmia is detected on your electrocardiogram."

[0518] The above is an example of an embodiment of the present invention, which enables a user to monitor their health condition in real time and take prompt and appropriate action when an abnormality is detected.

[0519] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0520] Step 1: Collect data

[0521] A wearable device worn by the user collects heart rate and electrocardiogram data in real time. The device continuously acquires these data using heart rate and electrocardiogram sensors and stores them in its internal memory for a short period of time. These data are then transmitted to a terminal via wireless communication such as Bluetooth. The input is the user's biometric data, and the output is the heart rate and electrocardiogram data collected by the wearable device.

[0522] Step 2: Receiving and storing data

[0523] The terminal receives the biometric data transmitted from the wearable device. The received data is temporarily stored in the terminal's local storage. The input is the heart rate and electrocardiogram data transmitted from the wearable device, and the output is the biometric data stored in the terminal.

[0524] Step 3: Send data to the server

[0525] The device uploads the accumulated biometric data to the server at regular intervals (e.g., every minute). When uploading, the data is sent to the server via an HTTP POST request. The input is the biometric data accumulated in the device, and the output is the biometric data sent to the server. The device also monitors the communication status and retries data transmission if it is unstable.

[0526] Step 4: Analyze the data

[0527] The server analyzes the biometric data received from the device. In the process, it uses a generative AI model to detect sudden increases in heart rate and abnormal electrocardiogram waveforms. The input is the biometric data sent from the device, and the output is a judgment result on whether an abnormality was detected.

[0528] Step 5: Generate an alert

[0529] If an anomaly is detected, the server generates an alert. The alert includes the type of anomaly, its urgency, and recommended actions to take. The input is the result of data analysis, and the output is the generation of an alert. This information is sent to the terminal in the next step.

[0530] Step 6: Sending an alert

[0531] The server sends the generated alert to the terminal. The sending method is HTTP PUSH notification, etc. The input is the generated alert information, and the output is the alert sent to the terminal.

[0532] Step 7: User Notification

[0533] The device notifies the user via audio and video based on the alert received from the server. It also displays video and audio guidance to guide the user on the appropriate course of action. The input is the alert information from the server, and the output is a notification and guidance display to the user. For example, the notification may say, "A sudden increase in heart rate has been detected. Please rest immediately and contact an emergency department."

[0534] The above is the processing flow of the system of the present invention. This system enables users to monitor their own health condition in real time and respond quickly and appropriately if an abnormality occurs.

[0535] 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.

[0536] The present invention relates to a system that uses a wearable device worn by the user to collect heart rate and electrocardiogram data in real time, and if an abnormality is detected, quickly notifies the user and those around them and guides them in taking appropriate measures. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system improves analysis accuracy and provides more personalized responses.

[0537] System Structure

[0538] This system consists of a wearable device, a terminal, a server, and an emotion engine. Each of these components functions as follows:

[0539] Wearable devices

[0540] The wearable device, which is equipped with a heart rate sensor, an electrocardiogram sensor, and an emotion engine, continuously collects the user's real-time biometric and emotional data, which is then wirelessly transmitted to the device at regular intervals.

[0541] Terminal

[0542] The terminal is a computing device such as a smartphone or tablet that receives biometric and emotional data transmitted from the wearable device. The terminal temporarily stores this data and periodically uploads it to the server. The terminal also receives alerts from the server and displays audio and video to notify the user.

[0543] server

[0544] The server is equipped with an artificial intelligence model for receiving and analyzing biometric data and emotional data sent from the device. The server analyzes the biometric data and sends an alert to the device if an abnormality is detected. The alert also includes detailed information about the abnormality and appropriate countermeasures.

[0545] Emotion Engine

[0546] The emotion engine is a system for recognizing a user's emotional state, assessing the user's emotions in real time using data from voice analysis, facial expression recognition, and other sensors. The emotion data collected by the emotion engine complements the analysis of heart rate and electrocardiogram data and is used to improve the accuracy of anomaly detection models.

[0547] Program processing

[0548] Data collection

[0549] When a user puts on a wearable device, the device starts collecting heart rate and electrocardiogram data. For example, if the user is jogging, the device records heart rate, electrocardiogram waveforms, and emotional data (such as stress levels) in real time. This collected data is stored in the wearable device's internal memory for a certain period of time and then transmitted wirelessly to the terminal.

[0550] Sending data

[0551] The terminal uploads the heart rate, electrocardiogram data, and emotion data received from the wearable device to the server at a predetermined interval (e.g., every minute). The terminal monitors the communication status and confirms that the connection to the server is established. If the connection is unstable, a retry mechanism is implemented.

[0552] Data analysis

[0553] The server receives biometric and emotional data sent from the device in real time and analyzes it using an artificial intelligence model. During the analysis process, it checks for sudden increases in heart rate, abnormal electrocardiogram waveforms, and stress or anxiety levels from emotional data. For example, if the heart rate significantly exceeds the normal range, if arrhythmia is detected on the electrocardiogram, or if the emotion engine detects a high stress state, the server will determine that an abnormality has occurred.

[0554] Generate alerts

[0555] If an abnormality is detected, the server immediately generates an alert and sends it to the device. This alert includes the specific type of abnormality, the urgency level, and a recommended course of action. For example, if a sudden increase in heart rate occurs, the alert may state, "A sudden increase in heart rate has been detected. Please rest immediately and contact emergency services." Different responses may be recommended based on emotional data.

[0556] Alert Notification

[0557] When the device receives an alert from the server, it notifies the user via audio and video. The user can confirm the alert and take appropriate action by following the device's guidance. The device also displays customized guidance based on the user's current emotional state based on emotional data.

[0558] Specific examples

[0559] Scenario 1: Rapid heart rate and high stress during everyday activities

[0560] When a user wears a wearable device during daily activities, the device continuously monitors heart rate, electrocardiogram, and emotional data. For example, suppose a user's heart rate rises to 130 bpm during a meeting, an abnormality appears on the electrocardiogram, and the emotional engine detects a high level of stress. This data is sent to the device and then uploaded to the server. The server analyzes the data and, if it detects an abnormality, sends an alert to the device. The device issues a voice notification saying, "A rapid rise in heart rate and high level of stress have been detected. Please take deep breaths and rest," and displays a video of relaxation techniques on the screen. In this way, the user and those around them can respond quickly and take action to protect their precious lives.

[0561] Through this series of steps, the present invention provides a system that reduces the risk of sudden cardiac death and ensures the safety of users. In addition, by combining it with an emotion engine, it becomes possible to respond more precisely to the individual state of the user.

[0562] The processing flow will be explained below.

[0563] Step 1:

[0564] Subject: User

[0565] The user wears a wearable device, which has a built-in heart rate sensor, an electrocardiogram sensor, and an emotion engine to collect biometric and emotional data in real time. For example, when a user starts jogging, their heart rate, electrocardiogram waveform, and emotional data (such as stress level) are continuously measured.

[0566] Step 2:

[0567] Subject: Wearable devices

[0568] The wearable device wirelessly transmits collected heart rate, electrocardiogram, and emotion data to the terminal, where the data is temporarily stored in a buffer and then transmitted in batches at appropriate times, ensuring data continuity without delay.

[0569] Step 3:

[0570] Subject: Terminal

[0571] The terminal receives and temporarily stores data sent from the wearable device. The data is uploaded to the server at regular intervals (e.g., every minute). The terminal also monitors the communication status to ensure that the connection to the server is established. If the connection is unstable, a retry mechanism is implemented.

[0572] Step 4:

[0573] Subject: Terminal

[0574] The device uploads all stored data to the server in bulk, and if an error occurs, it logs the error and attempts to retransmit it later, thus ensuring data integrity and security.

[0575] Step 5:

[0576] Subject: Server

[0577] The server receives the biometric and emotional data sent from the device, stores it in a database, and prepares it for analysis.

[0578] Step 6:

[0579] Subject: Server

[0580] The server analyzes the received data using an artificial intelligence model. This model simultaneously examines heart rate, ECG waveforms, and emotional data to detect abnormal patterns and out-of-range values. For example, it comprehensively determines a sudden increase in heart rate, the occurrence of arrhythmia, and a state of high stress from the emotional engine.

[0581] Step 7:

[0582] Subject: Server

[0583] If an abnormality is detected as a result of the analysis, the server immediately generates an alert and sends it to the device. This alert includes the specific type of abnormality, the urgency level, and recommended actions to take. For example, an alert could be generated stating, "A rapid rise in heart rate and high stress state have been detected. Please rest immediately and contact emergency services if necessary."

[0584] Step 8:

[0585] Subject: Terminal

[0586] When the device receives an alert from the server, it notifies the user with audio and visual notifications, such as "A sudden increase in heart rate has been detected. Please take a deep breath and remain calm," and displays relaxation techniques and first aid procedures on the screen.

[0587] Step 9:

[0588] Subject: User

[0589] The user follows the instructions on the device. In some cases, the alert can be shared with people around them to ask for a quick response. For example, a video guiding the user through the steps to call 119 can be displayed, allowing the user and those around them to take appropriate action. It can also display relaxation techniques and psychological support based on the user's emotional state.

[0590] This series of processes enables comprehensive monitoring of the user's health and emotional state, reducing the risk of sudden cardiac death and ensuring the user's safety.

[0591] Example 2

[0592] 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."

[0593] Conventional wearable devices and related systems have limited accuracy in analyzing heart rate and electrocardiogram data, which can lead to false alarms and delays in detecting abnormalities. They also lack the ability to customize responses based on the user's emotional state, making it difficult to respond appropriately to individual user conditions. Furthermore, in environments with unstable communication, data transmission failures can lead to delays and reduced accuracy in data analysis.

[0594] 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 means for collecting heart rate and electrocardiogram data in real time, means for transmitting the data to the terminal at predetermined intervals, means for the terminal to temporarily store the data and upload it to the server at predetermined intervals, means for analyzing the received data using an artificial intelligence model and sending an alert to the terminal if an abnormality is detected, means for notifying the user by audio and video when the terminal receives the alert, and means for generating an alert including detailed information about the abnormality and how to deal with it. This enables accurate collection and analysis of data even in cases where data transmission fails or in an environment with unstable communication, thereby increasing user safety and enabling individual responses based on emotional data.

[0595] Understood. Below are the definitions of the important words included in the rewritten claims.

[0596] A "wearable device" is a device that can be worn by a user and collects biometric information such as heart rate, electrocardiogram data, and emotional data in real time.

[0597] "Heart rate" is biological information that indicates the number of heartbeats per unit time, and is generally expressed as the number of beats per minute.

[0598] "Electrocardiogram data" refers to data that records the electrical activity of the heart and is used to analyze the rhythm and strength of heartbeats.

[0599] "Emotion data" is data that indicates the user's emotional state, and is a numerical representation of psychological states such as stress level and joy, anger, sadness, and happiness.

[0600] A "terminal" is a device that receives data sent from a wearable device, temporarily stores the data, and sends it to a server, and is a smartphone or tablet-like computing device.

[0601] A "server" is a computer system that receives data sent from a terminal, analyzes it using an artificial intelligence model, and generates and sends an alert to the terminal if an abnormality is detected.

[0602] An "artificial intelligence model" is a mathematical algorithm or machine learning technique used by the server to analyze data, and is used to improve the accuracy of anomaly detection and data analysis.

[0603] An "alert" is a warning message that is sent to the user when an abnormality is detected, and includes the specific nature of the abnormality, the urgency level, and a recommended method of dealing with the problem.

[0604] "Audio and video notification" is a method of notifying the user by audio guidance or displaying a visual message on the screen when the terminal receives an alert.

[0605] "Detailed information about anomaly" is information that indicates the specific content of the detected anomaly, the cause of its occurrence, the extent of its impact, and so on.

[0606] "Countermeasures" are guidelines that indicate specific actions and procedures that users should take in response to detected abnormalities.

[0607] This invention relates to a system that uses a wearable device worn by the user to collect heart rate and electrocardiogram data in real time, and if an abnormality is detected, quickly notifies the user and those around them and guides them in taking appropriate measures. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system improves analysis accuracy and enables more personalized responses. This system consists of a wearable device, a terminal, a server, and an emotion engine.

[0608] Wearable devices

[0609] The wearable device worn by the user has a built-in heart rate sensor, an electrocardiogram sensor, and an emotion engine. The device continuously collects the user's real-time biometric and emotional data. The collected data is wirelessly transmitted to the terminal at regular intervals, using wireless communication technologies such as Bluetooth and Wi-Fi. The collected data includes heart rate, electrocardiogram waveforms, and stress levels, and is temporarily stored in the wearable device's internal memory.

[0610] Terminal

[0611] The terminal is a computing device such as a smartphone or tablet that receives biometric and emotional data transmitted from the wearable device. The terminal temporarily stores this data and periodically uploads it to the server. The terminal also receives alerts from the server and displays audio and video to notify the user.

[0612] server

[0613] The server is equipped with an artificial intelligence model for receiving and analyzing the biometric and emotional data sent from the device. Machine learning frameworks such as TensorFlow and PyTorch can be used for the analysis. The server analyzes the biometric data and sends an alert to the device if an abnormality is detected. The alert also includes detailed information about the abnormality and appropriate countermeasures.

[0614] Emotion Engine

[0615] The emotion engine is a system for recognizing a user's emotional state, assessing the user's emotions in real time using data from voice analysis, facial expression recognition, and other sensors. The emotion data collected by the emotion engine complements the analysis of heart rate and electrocardiogram data and is used to improve the accuracy of anomaly detection models.

[0616] Specific examples

[0617] When a user wears a wearable device during daily activities, the device continuously monitors heart rate, electrocardiogram, and emotional data. For example, suppose a user's heart rate rises to 130 bpm during a meeting, an abnormality appears on the electrocardiogram, and the emotional engine detects a high level of stress. This data is sent to the device and then uploaded to the server. The server analyzes the data and, if it detects an abnormality, sends an alert to the device. The device issues a voice notification saying, "A rapid rise in heart rate and high level of stress have been detected. Please take deep breaths and rest," and displays a video of relaxation techniques on the screen. In this way, the user and those around them can respond quickly and take action to protect their precious lives.

[0618] Prompt Sentence Examples

[0619] Below are some example prompts for inputting specific scenarios for this system into the generative AI model.

[0620] If the wearable device detects a heart rate of 130 bpm and a high stress state while the user is performing daily activities, please explain in detail how and what part of the system analyzes the data and generates an alert.

[0621] This invention makes it possible to provide a system that reduces the risk of sudden cardiac death and ensures user safety. In addition, by combining it with an emotion engine, it becomes possible to provide more precise responses according to the individual state of the user.

[0622] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0623] Understood. Below, I will explain the process flow of the system program step by step.

[0624] Step 1:

[0625] The user puts on the wearable device. The wearable device activates the heart rate sensor, electrocardiogram sensor, and emotion engine to collect heart rate, electrocardiogram, and emotion data in real time. This data is temporarily stored in the internal memory of the wearable device.

[0626] Input: User's heart rate, ECG data, and emotion data.

[0627] Output: Storage of biometric and emotional data in the wearable device.

[0628] Step 2:

[0629] At regular intervals (e.g., every 30 seconds), the wearable device transmits the collected data to the terminal via wireless communication (e.g., Bluetooth). The terminal receives the data and returns an acknowledgement message (ACK) to the device.

[0630] Input: Biometric and emotional data from wearable devices.

[0631] Output: Temporarily stored data in the terminal and a transmission confirmation message.

[0632] Step 3:

[0633] The device temporarily stores the received data in a local buffer. The device uploads this data to the server at regular intervals (e.g., every minute). If the communication situation is unstable, there is a mechanism to retry.

[0634] Input: All data from the wearable device.

[0635] Output: Data transfer to the server.

[0636] Step 4:

[0637] The server receives the data sent from the device and stores it in data storage. When the server receives the data in real time, it analyzes the data using a generative AI model (e.g., TensorFlow).

[0638] Input: Biometric and emotional data from the device.

[0639] Output: Analysis results and analysis log.

[0640] Step 5:

[0641] The server analyzes the received data and detects abnormal patterns, such as a sudden increase in heart rate, abnormal ECG waveforms, or stress levels based on emotional data. If an abnormality is detected based on this analysis, an alert is generated.

[0642] Input: Biometric and emotional data.

[0643] Output: Anomaly detection results and generated alerts.

[0644] Step 6:

[0645] The generated alert contains the specific details of the abnormality, the urgency level, and the recommended action to take. The server sends this alert to the terminal. It will continue to retry until it receives confirmation of the completion of the transmission.

[0646] Input: Anomaly detection results.

[0647] Output: The alert sent to the terminal.

[0648] Step 7:

[0649] When the device receives an alert from the server, it notifies the user with audio and video. The user confirms the alert and follows the instructions. During this time, the device provides customized guidance based on the user's emotional data.

[0650] Input: Alert from the server.

[0651] Output: Audio notification and visual guide to the user.

[0652] The above are the specific processing steps of the program of this system.

[0653] (Application example 2)

[0654] 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."

[0655] In modern society, sudden illness and deterioration of health due to stress have become a social problem. In particular, abnormalities in heart rate or electrocardiogram data while driving a car pose a risk of causing a serious accident. Furthermore, drivers themselves often do not notice abnormalities, so a method for responding quickly is required. Conventional health monitoring systems are limited to detecting and notifying abnormalities in real time, but do not respond to actual driving operations. This poses the issue of a lack of means to ensure driver safety when an abnormality is detected.

[0656] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0657] In this invention, the server includes means for collecting heart rate and electrocardiogram data in real time using a wearable device worn by the user, means for transmitting the collected data to the terminal, means for the terminal to upload the data to the server, means for the server to analyze the received data and send an alert to the terminal if an abnormality is detected, means for notifying the user by audio and video when the terminal receives the alert, and means for controlling the vehicle's autonomous driving system using means for linking with other devices if an abnormality is detected. This makes it possible to immediately transition the autonomous driving system to safety mode when an abnormality is detected in the driver's heart rate or electrocardiogram, thereby quickly ensuring the driver's safety.

[0658] A "wearable device" is a device worn by the user that collects heart rate and electrocardiogram data in real time.

[0659] "Heart rate" is a biometric indicator that indicates the number of times the heart beats within a certain period of time.

[0660] "Electrocardiogram data" refers to data that records the electrical activity of the heart, and is information used to detect the condition of the myocardium and abnormalities in cardiac rhythm.

[0661] A "terminal" is a computing device that receives data transmitted from a wearable device, temporarily stores it, and uploads it to a server.

[0662] The "server" is a data processing device that receives and analyzes biometric data uploaded from a terminal, and generates and sends an alert to the terminal if an abnormality is detected.

[0663] "Means of cooperation with other devices" refers to the means of communication and operation for controlling the vehicle's autonomous driving system when an abnormality is detected.

[0664] An "alert" is a message that notifies the user of an abnormality and recommended countermeasures when an abnormality is detected based on analyzed data.

[0665] An "autonomous driving system" is a system that has the function of automatically driving and controlling a vehicle.

[0666] The present invention is a system that uses a wearable device worn by the user to collect heart rate and electrocardiogram data in real time, and provides the ability to respond quickly in cooperation with the control system of an autonomous vehicle when an abnormality is detected.

[0667] System configuration

[0668] This system consists of a wearable device, a terminal, a server, and a control system for the autonomous vehicle. Specifically, each component functions as follows:

[0669] Wearable devices

[0670] The wearable device collects the user's heart rate, electrocardiogram data, and emotional data, and also includes an emotion engine, which transmits the collected data wirelessly to the device in real time.

[0671] Terminal

[0672] The terminal is a computing device that temporarily stores collected data and uploads it to a server at regular intervals. The terminal receives data sent from the wearable device and receives alerts from the server to notify the user.

[0673] server

[0674] The server is equipped with an artificial intelligence model to analyze data sent from the device. If the server detects an abnormality, it immediately generates an alert and sends it to the device. This alert contains detailed information about the abnormality and how to deal with it.

[0675] Linking with other devices

[0676] The autonomous vehicle's control system will receive alerts from the server and have the ability to coordinate with the server to safely stop the vehicle if the driver's health condition deteriorates.

[0677] Specific operation explanation

[0678] Data collection and transmission

[0679] Once the user puts on the wearable device, it starts collecting heart rate, electrocardiogram, and emotional data, which are then periodically sent to the device, which then uploads the data to a cloud server at regular intervals.

[0680] Data analysis and alert generation

[0681] The server analyzes the data received from the device in real time. The artificial intelligence model used utilizes cloud analysis services such as Google Cloud AI, AWS, and Microsoft Azure. If an abnormality is detected, the server immediately generates an alert and sends it to the vehicle's autonomous driving control system.

[0682] Autonomous Driving System Control

[0683] When the autonomous vehicle's control system receives an alert from the server, it takes action to transition to safety mode. Specifically, it reduces the vehicle's speed and stops it at an appropriate location. The driver is notified via voice and display. This is to quickly ensure safety in the event of a sudden change in the driver's health condition.

[0684] Specific examples

[0685] scenario

[0686] Consider a situation where a driver is operating an autonomous vehicle and their heart rate spikes to 120 bpm, causing the emotion engine to detect a state of high stress.

[0687] 1. Data collection: Heart rate and electrocardiogram data obtained from the wearable device are sent to the terminal and uploaded to the server.

[0688] 2. Data analysis: The server analyzes the received data and detects any anomalies.

[0689] 3. Alert generation: The server detects an anomaly and immediately generates an alert and sends it to the terminal and the autonomous vehicle's control system.

[0690] 4. Response: The autonomous driving system switches to safety mode and notifies the driver that "rapid heart rate and high stress have been detected. An emergency stop will be made immediately."

[0691] Prompt Sentence Examples

[0692] Design a system that will transition to a safety mode and issue an emergency stop alert if the driver's heart rate exceeds 120 bpm and their emotional state is recognized as high stress. This includes a function that will link the autonomous vehicle's control system with wearable devices to collect and analyze data in real time and automatically respond if an abnormality occurs.

[0693] According to the present invention, when an abnormality occurs in the driver's heart rate or electrocardiogram, the autonomous vehicle can be controlled quickly and appropriately to ensure the safety of the driver.

[0694] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0695] Step 1:

[0696] Data collection

[0697] When a user wears the wearable device, it starts collecting heart rate, electrocardiogram (ECG) data, and emotional data, which are then transmitted to a device via Bluetooth. The inputs are data on heart rate, ECG waveforms, and emotional state, and the output is the periodic transmission of these data to the device.

[0698] Step 2:

[0699] Data transmission and storage

[0700] The terminal temporarily stores data received from the wearable device. This data is uploaded to the server at regular intervals. The input is the biometric data received from the wearable device, and the output is the data uploaded to the server. The communication status is monitored during data transmission, and if it fails, the terminal retries.

[0701] Step 3:

[0702] Data analysis

[0703] The server receives data uploaded from the device and analyzes it in real time. A generative AI model (e.g., a TensorFlow model) is used for the analysis. The input is the heart rate, ECG data, and emotion data sent from the device, and the output is the analysis results and the presence or absence of abnormalities. Specifically, the server detects sudden increases in heart rate, abnormal ECG waveforms, and high stress states.

[0704] Step 4:

[0705] Alert Generation

[0706] When the server detects an abnormality, it generates an alert and sends it to the device and the autonomous vehicle's control system. The input is the results of data analysis and details of the detected abnormality, and the output is an alert message. Specifically, a message such as "A sudden increase in heart rate has been detected. The vehicle will immediately enter safety mode" is generated.

[0707] Step 5:

[0708] Alert notifications and driving control

[0709] The terminal receives the alert from the server and notifies the user via audio and video. At the same time, the autonomous vehicle's control system also receives the alert and transitions to safety mode. The input is the alert message sent from the server, and the output is a notification to the user and a change in vehicle behavior. Specifically, the autonomous vehicle reduces its speed and stops at an appropriate location.

[0710] Step 6:

[0711] Contacting a medical institution

[0712] Furthermore, if necessary, the terminal will activate an automatic contact function to a medical institution. The input is an abnormality detection message from the server, and the output is contact information to the medical institution. Specifically, the terminal will notify the medical institution of the driver's current location and condition.

[0713] This series of processing steps makes it possible to respond quickly and appropriately when an abnormality occurs in the driver's heart rate or electrocardiogram.

[0714] 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.

[0715] 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.

[0716] 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.

[0717] [Third embodiment]

[0718] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0719] 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.

[0720] 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).

[0721] 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.

[0722] 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.

[0723] 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).

[0724] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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."

[0730] The present invention relates to a system that uses a wearable device worn by a user to collect heart rate and electrocardiogram data in real time, and if an abnormality is detected, quickly notifies the user and people around them and guides them to take appropriate measures. Detailed embodiments for implementing this system are described below.

[0731] System Structure

[0732] This system consists of a wearable device, a terminal, and a server. Each of these components functions as follows:

[0733] Wearable devices

[0734] The wearable device, equipped with a heart rate sensor and an electrocardiogram sensor, continuously collects real-time biometric data from the user, which is then wirelessly transmitted to a terminal at regular intervals.

[0735] Terminal

[0736] The terminal is a computing device such as a smartphone or tablet that receives the biometric data sent from the wearable device. The terminal temporarily stores this data and periodically uploads it to the server. The terminal also has the ability to receive alerts from the server and display audio and video to notify the user.

[0737] server

[0738] The server is equipped with an artificial intelligence model for receiving and analyzing biometric data sent from the device. The server analyzes the biometric data and sends an alert to the device if an abnormality is detected. The alert also includes detailed information about the abnormality and appropriate countermeasures.

[0739] Program processing

[0740] Data collection

[0741] When a user wears the wearable device, it starts collecting heart rate and electrocardiogram data. For example, if the user is jogging, the heart rate and electrocardiogram waveforms are recorded in real time. This collected data is stored in the wearable device's internal memory for a certain period of time and then transmitted to the terminal via wireless communication.

[0742] Sending data

[0743] The terminal uploads the heart rate and electrocardiogram data received from the wearable device to the server at a predetermined interval (e.g., every minute). The terminal monitors the communication status and ensures that the connection to the server is established. If the connection is unstable, the data will be retransmitted after a stable connection is established.

[0744] Data analysis

[0745] The server receives the biometric data sent from the device in real time and analyzes it using an artificial intelligence model. During the analysis process, a sudden increase in heart rate or an abnormal electrocardiogram waveform is detected. For example, if the heart rate significantly exceeds the normal range or if an arrhythmia is detected on the electrocardiogram, the server will determine that an abnormality has occurred.

[0746] Generate alerts

[0747] If an abnormality is detected, the server immediately generates an alert and sends it to the device. The alert includes the specific type of abnormality, the urgency level, and recommended actions to take. For example, if there is a sudden increase in heart rate, the alert will state, "A sudden increase in heart rate has been detected. Please rest immediately and contact emergency services."

[0748] Alert Notification

[0749] When the device receives an alert from the server, it notifies the user via audio and video. The user can check the alert and take appropriate action by following the device's guidance. For example, if the user receives an alert that a sudden increase in heart rate has been detected, the device will provide audio and video guidance on how to perform CPR and contact emergency services.

[0750] Specific examples

[0751] Scenario 1: Heart rate spikes during everyday activities

[0752] When a user wears a wearable device during daily activities, the device continuously monitors their heart rate and electrocardiogram. For example, suppose a user's heart rate rises to 130 bpm while climbing stairs, causing an abnormality in the electrocardiogram. This data is sent to the device and then uploaded to a server. The server analyzes the data and, if it detects an abnormality, sends an alert to the device. The device issues a voice notification saying, "A sudden rise in heart rate has been detected. Please rest," and displays a video guide to first aid on the screen. In this way, the user and those around them can respond quickly and take action to save their precious life.

[0753] The present invention provides a system that reduces the risk of sudden cardiac death and ensures the safety of the user through this series of steps.

[0754] The processing flow will be explained below.

[0755] Step 1:

[0756] Subject: User

[0757] The user wears a wearable device. The device has built-in heart rate and electrocardiogram sensors and collects biometric data in real time. For example, when the user starts jogging, the device measures heart rate and electrocardiogram data at regular intervals.

[0758] Step 2:

[0759] Subject: Wearable devices

[0760] The wearable device wirelessly transmits collected heart rate and electrocardiogram data to the terminal, where the data is temporarily stored in a buffer and then transmitted in batches at appropriate times, ensuring data continuity.

[0761] Step 3:

[0762] Subject: Terminal

[0763] The terminal receives and temporarily stores data sent from the wearable device. The data is set to be uploaded to the server at regular intervals (e.g., every minute). The terminal also monitors the connection status to the server to ensure that the connection is established. If the connection is unstable, a retry mechanism is implemented.

[0764] Step 4:

[0765] Subject: Terminal

[0766] The device uploads the stored data to the server, and if an error occurs, the error is logged and the device tries again later, thus ensuring the integrity and security of the data.

[0767] Step 5:

[0768] Subject: Server

[0769] The server receives the biometric data sent from the device, after which the data is stored in a database and prepared for analysis.

[0770] Step 6:

[0771] Subject: Server

[0772] The server analyzes the received data using an artificial intelligence model. Specifically, it examines the heart rate and electrocardiogram waveform to detect abnormal patterns or out-of-range values. For example, if the heart rate suddenly increases or an irregular heartbeat is detected, it determines that an abnormality has occurred.

[0773] Step 7:

[0774] Subject: Server

[0775] If an abnormality is detected, the server generates an alert and sends it to the terminal, which includes the specific type of abnormality, its urgency, and recommended actions to take.

[0776] Step 8:

[0777] Subject: Terminal

[0778] When the device receives an alert from the server, it notifies the user with audio and video, for example, by playing a message saying, "A sudden increase in heart rate has been detected. Please rest immediately," and by displaying life-saving measures on the screen.

[0779] Step 9:

[0780] Subject: User

[0781] The user follows the instructions on the device. In some cases, the alert can be shared with people around them to ask for a quick response. For example, a video guiding the user through the steps to call 119 can be displayed, allowing the user and people around them to take appropriate action.

[0782] This series of processes reduces the risk of sudden cardiac death and ensures user safety.

[0783] Example 1

[0784] 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."

[0785] Current health management systems using wearable devices have issues with the accuracy and speed of response when collecting data in real time and detecting abnormalities. It can also be difficult to promptly notify users of the appropriate response method when an abnormality is detected. Furthermore, if data transmission becomes unstable, necessary information may not be transmitted to the server in a timely manner, which risks delaying the detection and response of abnormalities. A system that solves these issues and increases user safety is needed.

[0786] 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.

[0787] In this invention, the server includes means for analyzing data in real time and using an artificial intelligence model to detect sudden increases in heart rate and abnormal electrocardiogram waveforms, means for the terminal to monitor communication status and retransmit data, and means for collecting heart rate and electrocardiogram data in real time using a wearable device worn by the user, thereby enabling highly accurate data analysis and abnormality detection, rapid user notification, and stable data transmission.

[0788] "User" refers to a person who wears a wearable device in order to use it.

[0789] "Wearable device" refers to a device worn by the user that collects heart rate and electrocardiogram data in real time.

[0790] "Heart rate" refers to the number of heartbeats per unit time, and is an important indicator for evaluating the health of a living organism.

[0791] "Electrocardiogram data" refers to data that records the electrical activity of the heart as a waveform, and is used to understand the condition of the heart in detail.

[0792] "Terminal" refers to a computing device for receiving data collected from a wearable device and uploading it to a server.

[0793] "Server" refers to a system that receives and analyzes biometric data sent from a terminal and sends an alert to the terminal if an abnormality is detected.

[0794] "Analysis" refers to the process by which the server uses the generative AI model to evaluate the collected biometric data and determine whether there are any abnormalities.

[0795] "Generative AI models" refer to artificial intelligence models used to analyze collected biometric data, and include techniques such as deep learning.

[0796] An "alert" refers to a message sent by the server to notify the terminal and user of an abnormality detected by the server.

[0797] "Notification" refers to the process by which the device communicates the contents of the alert to the user via audio and video.

[0798] "Retransmission" refers to the process in which a terminal attempts to send data to a server again when data transmission is unstable.

[0799] The system of the present invention is composed of a wearable device worn by a user, a terminal, and a server. Each of these elements functions as follows.

[0800] Wearable devices

[0801] A wearable device worn by a user is equipped with a heart rate sensor and an electrocardiogram sensor. The device collects the user's heart rate and electrocardiogram data in real time. For example, the wearable device records the user's heart rate every second and simultaneously measures the electrocardiogram data. This data is temporarily stored in the device's internal memory and transmitted to a terminal using wireless communication technology such as Bluetooth.

[0802] Terminal

[0803] The terminal is a computing device such as a smartphone or tablet held by the user. This terminal receives and temporarily stores the biometric data transmitted from the wearable device. It also uploads the data from the wearable device to a server at regular intervals (e.g., every minute). The terminal has the function of monitoring the communication status and, if data transmission is unstable, attempting to retransmit as soon as a stable connection is established.

[0804] The device also receives alerts from the server and notifies the user via audio and video. For example, if the device receives an abnormality notification, it will play a message such as "A sudden increase in heart rate has been detected. Please rest and contact emergency services immediately," and display emergency response instructions on the screen.

[0805] server

[0806] The server receives and analyzes biometric data sent from the device using a generative AI model implemented using machine learning frameworks such as TensorFlow and PyTorch.

[0807] The server analyzes the data in real time, and if it detects a sudden increase in heart rate or an abnormal electrocardiogram waveform, it judges it to be an abnormality. For example, if the heart rate significantly exceeds the normal range or if an arrhythmia is detected on the electrocardiogram, the server immediately determines this to be an abnormality and generates an alert. This alert is sent to the device and includes specific instructions to prompt the user to take appropriate action.

[0808] Specific examples

[0809] Scenario 1: Heart rate spikes during everyday activities

[0810] When a user wears a wearable device during daily activities, the device continuously monitors their heart rate and electrocardiogram. For example, suppose a user's heart rate rises to 130 bpm while climbing stairs, causing an abnormality in the electrocardiogram. This data is sent to the device and then uploaded to a server. The server analyzes the data and, if it detects an abnormality, sends an alert to the device. The device then issues a voice notification saying, "A sudden rise in heart rate has been detected. Please rest," and displays a video guide to first aid on the screen.

[0811] Prompt Sentence Examples

[0812] Here are some example prompts to give to a generative AI model:

[0813] A wearable device collects real-time heart rate and electrocardiogram data from a person going about their daily activities. For example, if a heart rate exceeds 130 bpm or an arrhythmia is detected in the electrocardiogram, it is deemed an abnormality and an alert is sent to the user. The alert will include details of the abnormality and a guide for emergency response. Please explain what kind of system is needed.

[0814] The above is the specific content for carrying out the invention, and describes in detail how the system is constructed and operated at each step.

[0815] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0816] Step 1: Data collection

[0817] When a user puts on a wearable device, the device starts collecting heart rate and electrocardiogram data. The input is the user's biometric information, and the output is digital heart rate and electrocardiogram data. The device's sensors measure the heart rate every second, and electrocardiogram data is collected simultaneously. For example, if the user is jogging, the heart rate measurement and electrocardiogram recording continue in real time.

[0818] Step 2: Send data

[0819] The terminal receives data collected from the wearable device via wireless communication such as Bluetooth. The input is the data sent from the wearable device, and the output is the data temporarily stored on the terminal. For example, if the terminal is a smartphone, it continuously receives data from the device and prepares to send it to the server at regular intervals (e.g., every minute).

[0820] Step 3: Upload data

[0821] The device uploads the received data to the server. The input is the collected data stored in the device, and the output is the data stored on the server. The device sends the data to the server using Wi-Fi or 4G communication. For example, if the communication situation is unstable, the device will try to resend the data until the connection is stable. This ensures that the data is uploaded reliably.

[0822] Step 4: Data analysis

[0823] The server analyzes the received biometric data. The input is the data uploaded to the server, and the output is the analysis result. The server uses a generative AI model (e.g., using TensorFlow or PyTorch) to detect sudden increases in heart rate or abnormal ECG waveforms. For example, if there is a sudden increase in heart rate or an arrhythmia is detected on the ECG, the server immediately detects the abnormality.

[0824] Step 5: Alert Generation

[0825] When the server detects an abnormality, it generates an alert. The input is the data analysis result, and the output is an alert message. The alert includes the specific type of abnormality, the urgency, and the recommended response method. For example, if there is a sudden increase in heart rate, the server generates a message saying, "A sudden increase in heart rate has been detected. Please rest immediately and contact emergency services."

[0826] Step 6: Sending an alert

[0827] The server sends the generated alert to the terminal. The input is the generated alert message, and the output is the alert transferred to the terminal. The server immediately sends the alert message to the terminal and prepares to notify the user.

[0828] Step 7: Alert Notifications

[0829] The device notifies the user of the received alert. The input is the alert notification from the server, and the output is an audio and video notification to the user. For example, when a smartphone receives an alert message, it immediately issues an audio notification and also displays emergency response instructions on the screen. It plays an audio message saying, "A sudden increase in heart rate has been detected. Please rest," and displays instructions on the screen such as, "Please perform life-saving measures."

[0830] (Application example 1)

[0831] 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."

[0832] Many people currently use wearable devices to monitor their health, but these devices typically lack the ability to detect abnormalities in real time or provide insufficient countermeasures. This prevents users from taking appropriate action even when they detect an abnormality, making it difficult to avoid serious health risks. Furthermore, existing systems do not effectively utilize the advanced artificial intelligence models required for data analysis, resulting in low accuracy in detecting abnormalities.

[0833] 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.

[0834] In this invention, the server includes a means for collecting heart rate and electrocardiogram data in real time, a means for analyzing this data and using an artificial intelligence model to detect abnormalities, and a means for sending an alert to the terminal when an abnormality is detected. This enables accurate detection of abnormalities and a rapid response. In addition, when the terminal receives an alert, it can notify the user via audio and video and provide guidance on life-saving measures and appropriate countermeasures (e.g., rest, emergency contact information, cardiopulmonary resuscitation guide).

[0835] A "wearable device" is a device that can collect biometric data in real time by being worn by the user.

[0836] "Heart rate" is a numerical value that indicates the number of times the heart beats per unit time.

[0837] "Electrocardiogram data" refers to data that records the electrical activity of the heart.

[0838] A "terminal" is a computing device for receiving data from a wearable device and uploading the data to a server.

[0839] The "server" is a central processing unit that analyzes the biometric data sent from the terminal and sends an alert if an abnormality is detected.

[0840] An "alert" is a warning signal that notifies the user when an abnormality is detected.

[0841] "Audio and video notification" refers to a means of transmitting alerts from the server to the user in the form of audio and video.

[0842] The "Guide for Lifesaving Treatment" is a guide that instructs the user on the appropriate treatment method when an abnormality is detected.

[0843] An "artificial intelligence model" is an analytical algorithm that uses artificial intelligence to allow the server to analyze biometric data and detect abnormalities.

[0844] "Real-time" means that data collection and analysis are carried out immediately, without delay.

[0845] This invention is a real-time biometric data monitoring system consisting of a wearable device, a terminal, and a server. This system collects a user's heart rate and electrocardiogram data in real time and can respond quickly if an abnormality is detected.

[0846] System Structure

[0847] Wearable devices

[0848] The wearable device, which is equipped with a heart rate sensor and an electrocardiogram sensor, continuously collects the user's biometric data in real time and transmits the data wirelessly to a terminal.

[0849] Terminal

[0850] The terminal is a computing device such as a smartphone or tablet that receives the biometric data sent from the wearable device. The terminal temporarily stores the received data and uploads it to the server at regular intervals. It also receives alerts from the server and notifies the user via audio and video.

[0851] server

[0852] The server is equipped with an artificial intelligence model for analyzing biometric data. The server receives the biometric data sent from the device and detects sudden increases in heart rate and abnormal electrocardiogram waveforms. If an abnormality is detected, the server immediately generates an alert and sends it to the device.

[0853] Program processing

[0854] Data collection and transmission

[0855] The heart rate and electrocardiogram data collected by the wearable device are sent to the terminal at regular intervals. For example, if the user is jogging, the heart rate and electrocardiogram waveform are recorded in real time and this data is transferred to the terminal.

[0856] Data analysis

[0857] The server receives the biometric data sent from the device and analyzes it using an artificial intelligence model. This analysis detects, for example, a sudden increase in heart rate or an abnormal electrocardiogram waveform. If an abnormality is detected, the server immediately generates an alert.

[0858] Alert Notification

[0859] When an alert is generated, the server sends it to the device. When the device receives the alert, it notifies the user with audio and video. If necessary, it also displays a guide on life-saving measures. For example, it may say, "A sudden increase in heart rate has been detected. Please rest immediately and contact an emergency department."

[0860] Specific examples

[0861] Scenario 1: Heart rate spikes during everyday activities

[0862] When a user wears a wearable device during daily activities, the device continuously monitors their heart rate and electrocardiogram. For example, suppose a user's heart rate rises to 130 bpm while climbing stairs, causing an abnormality in the electrocardiogram. This data is sent to the device and then uploaded to a server. The server analyzes the data and, if it detects an abnormality, sends an alert to the device. The device issues a voice notification saying, "A sudden rise in heart rate has been detected. Please rest," and displays a video guide to first aid on the screen. In this way, the user and those around them can respond quickly and take action to save their precious life.

[0863] Prompt Sentence Examples

[0864] "Generate a guide to what to do if your heart rate spikes while jogging and an arrhythmia is detected on your electrocardiogram."

[0865] The above is an example of an embodiment of the present invention, which enables a user to monitor their health condition in real time and take prompt and appropriate action when an abnormality is detected.

[0866] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0867] Step 1: Collect data

[0868] A wearable device worn by the user collects heart rate and electrocardiogram data in real time. The device continuously acquires these data using heart rate and electrocardiogram sensors and stores them in its internal memory for a short period of time. These data are then transmitted to a terminal via wireless communication such as Bluetooth. The input is the user's biometric data, and the output is the heart rate and electrocardiogram data collected by the wearable device.

[0869] Step 2: Receiving and storing data

[0870] The terminal receives the biometric data transmitted from the wearable device. The received data is temporarily stored in the terminal's local storage. The input is the heart rate and electrocardiogram data transmitted from the wearable device, and the output is the biometric data stored in the terminal.

[0871] Step 3: Send data to the server

[0872] The device uploads the accumulated biometric data to the server at regular intervals (e.g., every minute). When uploading, the data is sent to the server via an HTTP POST request. The input is the biometric data accumulated in the device, and the output is the biometric data sent to the server. The device also monitors the communication status and retries data transmission if it is unstable.

[0873] Step 4: Analyze the data

[0874] The server analyzes the biometric data received from the device. In the process, it uses a generative AI model to detect sudden increases in heart rate and abnormal electrocardiogram waveforms. The input is the biometric data sent from the device, and the output is a judgment result on whether an abnormality was detected.

[0875] Step 5: Generate an alert

[0876] If an anomaly is detected, the server generates an alert. The alert includes the type of anomaly, its urgency, and recommended actions to take. The input is the result of data analysis, and the output is the generation of an alert. This information is sent to the terminal in the next step.

[0877] Step 6: Sending an alert

[0878] The server sends the generated alert to the terminal. The sending method is HTTP PUSH notification, etc. The input is the generated alert information, and the output is the alert sent to the terminal.

[0879] Step 7: User Notification

[0880] The device notifies the user via audio and video based on the alert received from the server. It also displays video and audio guidance to guide the user on the appropriate course of action. The input is the alert information from the server, and the output is a notification and guidance display to the user. For example, the notification may say, "A sudden increase in heart rate has been detected. Please rest immediately and contact an emergency department."

[0881] The above is the processing flow of the system of the present invention. This system enables users to monitor their own health condition in real time and respond quickly and appropriately if an abnormality occurs.

[0882] 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.

[0883] The present invention relates to a system that uses a wearable device worn by the user to collect heart rate and electrocardiogram data in real time, and if an abnormality is detected, quickly notifies the user and those around them and guides them in taking appropriate measures. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system improves analysis accuracy and provides more personalized responses.

[0884] System Structure

[0885] This system consists of a wearable device, a terminal, a server, and an emotion engine. Each of these components functions as follows:

[0886] Wearable devices

[0887] The wearable device, which is equipped with a heart rate sensor, an electrocardiogram sensor, and an emotion engine, continuously collects the user's real-time biometric and emotional data, which is then wirelessly transmitted to the device at regular intervals.

[0888] Terminal

[0889] The terminal is a computing device such as a smartphone or tablet that receives biometric and emotional data transmitted from the wearable device. The terminal temporarily stores this data and periodically uploads it to the server. The terminal also receives alerts from the server and displays audio and video to notify the user.

[0890] server

[0891] The server is equipped with an artificial intelligence model for receiving and analyzing biometric data and emotional data sent from the device. The server analyzes the biometric data and sends an alert to the device if an abnormality is detected. The alert also includes detailed information about the abnormality and appropriate countermeasures.

[0892] Emotion Engine

[0893] The emotion engine is a system for recognizing a user's emotional state, assessing the user's emotions in real time using data from voice analysis, facial expression recognition, and other sensors. The emotion data collected by the emotion engine complements the analysis of heart rate and electrocardiogram data and is used to improve the accuracy of anomaly detection models.

[0894] Program processing

[0895] Data collection

[0896] When a user puts on a wearable device, the device starts collecting heart rate and electrocardiogram data. For example, if the user is jogging, the device records heart rate, electrocardiogram waveforms, and emotional data (such as stress levels) in real time. This collected data is stored in the wearable device's internal memory for a certain period of time and then transmitted wirelessly to the terminal.

[0897] Sending data

[0898] The terminal uploads the heart rate, electrocardiogram data, and emotion data received from the wearable device to the server at a predetermined interval (e.g., every minute). The terminal monitors the communication status and confirms that the connection to the server is established. If the connection is unstable, a retry mechanism is implemented.

[0899] Data analysis

[0900] The server receives biometric and emotional data sent from the device in real time and analyzes it using an artificial intelligence model. During the analysis process, it checks for sudden increases in heart rate, abnormal electrocardiogram waveforms, and stress or anxiety levels from emotional data. For example, if the heart rate significantly exceeds the normal range, if arrhythmia is detected on the electrocardiogram, or if the emotion engine detects a high stress state, the server will determine that an abnormality has occurred.

[0901] Generate alerts

[0902] If an abnormality is detected, the server immediately generates an alert and sends it to the device. This alert includes the specific type of abnormality, the urgency level, and a recommended course of action. For example, if a sudden increase in heart rate occurs, the alert may state, "A sudden increase in heart rate has been detected. Please rest immediately and contact emergency services." Different responses may be recommended based on emotional data.

[0903] Alert Notification

[0904] When the device receives an alert from the server, it notifies the user via audio and video. The user can confirm the alert and take appropriate action by following the device's guidance. The device also displays customized guidance based on the user's current emotional state based on emotional data.

[0905] Specific examples

[0906] Scenario 1: Rapid heart rate and high stress during everyday activities

[0907] When a user wears a wearable device during daily activities, the device continuously monitors heart rate, electrocardiogram, and emotional data. For example, suppose a user's heart rate rises to 130 bpm during a meeting, an abnormality appears on the electrocardiogram, and the emotional engine detects a high level of stress. This data is sent to the device and then uploaded to the server. The server analyzes the data and, if it detects an abnormality, sends an alert to the device. The device issues a voice notification saying, "A rapid rise in heart rate and high level of stress have been detected. Please take deep breaths and rest," and displays a video of relaxation techniques on the screen. In this way, the user and those around them can respond quickly and take action to protect their precious lives.

[0908] Through this series of steps, the present invention provides a system that reduces the risk of sudden cardiac death and ensures the safety of users. In addition, by combining it with an emotion engine, it becomes possible to respond more precisely to the individual state of the user.

[0909] The processing flow will be explained below.

[0910] Step 1:

[0911] Subject: User

[0912] The user wears a wearable device, which has a built-in heart rate sensor, an electrocardiogram sensor, and an emotion engine to collect biometric and emotional data in real time. For example, when a user starts jogging, their heart rate, electrocardiogram waveform, and emotional data (such as stress level) are continuously measured.

[0913] Step 2:

[0914] Subject: Wearable devices

[0915] The wearable device wirelessly transmits collected heart rate, electrocardiogram, and emotion data to the terminal, where the data is temporarily stored in a buffer and then transmitted in batches at appropriate times, ensuring data continuity without delay.

[0916] Step 3:

[0917] Subject: Terminal

[0918] The terminal receives and temporarily stores data sent from the wearable device. The data is uploaded to the server at regular intervals (e.g., every minute). The terminal also monitors the communication status to ensure that the connection to the server is established. If the connection is unstable, a retry mechanism is implemented.

[0919] Step 4:

[0920] Subject: Terminal

[0921] The device uploads all stored data to the server in bulk, and if an error occurs, it logs the error and attempts to retransmit it later, thus ensuring data integrity and security.

[0922] Step 5:

[0923] Subject: Server

[0924] The server receives the biometric and emotional data sent from the device, stores it in a database, and prepares it for analysis.

[0925] Step 6:

[0926] Subject: Server

[0927] The server analyzes the received data using an artificial intelligence model. This model simultaneously examines heart rate, ECG waveforms, and emotional data to detect abnormal patterns and out-of-range values. For example, it comprehensively determines a sudden increase in heart rate, the occurrence of arrhythmia, and a state of high stress from the emotional engine.

[0928] Step 7:

[0929] Subject: Server

[0930] If an abnormality is detected as a result of the analysis, the server immediately generates an alert and sends it to the device. This alert includes the specific type of abnormality, the urgency level, and recommended actions to take. For example, an alert could be generated stating, "A rapid rise in heart rate and high stress state have been detected. Please rest immediately and contact emergency services if necessary."

[0931] Step 8:

[0932] Subject: Terminal

[0933] When the device receives an alert from the server, it notifies the user with audio and visual notifications, such as "A sudden increase in heart rate has been detected. Please take a deep breath and remain calm," and displays relaxation techniques and first aid procedures on the screen.

[0934] Step 9:

[0935] Subject: User

[0936] The user follows the instructions on the device. In some cases, the alert can be shared with people around them to ask for a quick response. For example, a video guiding the user through the steps to call 119 can be displayed, allowing the user and those around them to take appropriate action. It can also display relaxation techniques and psychological support based on the user's emotional state.

[0937] This series of processes enables comprehensive monitoring of the user's health and emotional state, reducing the risk of sudden cardiac death and ensuring the user's safety.

[0938] Example 2

[0939] 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."

[0940] Conventional wearable devices and related systems have limited accuracy in analyzing heart rate and electrocardiogram data, which can lead to false alarms and delays in detecting abnormalities. They also lack the ability to customize responses based on the user's emotional state, making it difficult to respond appropriately to individual user conditions. Furthermore, in environments with unstable communication, data transmission failures can lead to delays and reduced accuracy in data analysis.

[0941] 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 means for collecting heart rate and electrocardiogram data in real time, means for transmitting the data to the terminal at predetermined intervals, means for the terminal to temporarily store the data and upload it to the server at predetermined intervals, means for analyzing the received data using an artificial intelligence model and sending an alert to the terminal if an abnormality is detected, means for notifying the user by audio and video when the terminal receives the alert, and means for generating an alert including detailed information about the abnormality and how to deal with it. This enables accurate collection and analysis of data even in cases where data transmission fails or in an environment with unstable communication, thereby increasing user safety and enabling individual responses based on emotional data.

[0942] Understood. Below are the definitions of the important words included in the rewritten claims.

[0943] A "wearable device" is a device that can be worn by a user and collects biometric information such as heart rate, electrocardiogram data, and emotional data in real time.

[0944] "Heart rate" is biological information that indicates the number of heartbeats per unit time, and is generally expressed as the number of beats per minute.

[0945] "Electrocardiogram data" refers to data that records the electrical activity of the heart and is used to analyze the rhythm and strength of heartbeats.

[0946] "Emotion data" is data that indicates the user's emotional state, and is a numerical representation of psychological states such as stress level and joy, anger, sadness, and happiness.

[0947] A "terminal" is a device that receives data sent from a wearable device, temporarily stores the data, and sends it to a server, and is a smartphone or tablet-like computing device.

[0948] A "server" is a computer system that receives data sent from a terminal, analyzes it using an artificial intelligence model, and generates and sends an alert to the terminal if an abnormality is detected.

[0949] An "artificial intelligence model" is a mathematical algorithm or machine learning technique used by the server to analyze data, and is used to improve the accuracy of anomaly detection and data analysis.

[0950] An "alert" is a warning message that is sent to the user when an abnormality is detected, and includes the specific nature of the abnormality, the urgency level, and a recommended method of dealing with the problem.

[0951] "Audio and video notification" is a method of notifying the user by audio guidance or displaying a visual message on the screen when the terminal receives an alert.

[0952] "Detailed information about anomaly" is information that indicates the specific content of the detected anomaly, the cause of its occurrence, the extent of its impact, and so on.

[0953] "Countermeasures" are guidelines that indicate specific actions and procedures that users should take in response to detected abnormalities.

[0954] This invention relates to a system that uses a wearable device worn by the user to collect heart rate and electrocardiogram data in real time, and if an abnormality is detected, quickly notifies the user and those around them and guides them in taking appropriate measures. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system improves analysis accuracy and enables more personalized responses. This system consists of a wearable device, a terminal, a server, and an emotion engine.

[0955] Wearable devices

[0956] The wearable device worn by the user has a built-in heart rate sensor, an electrocardiogram sensor, and an emotion engine. The device continuously collects the user's real-time biometric and emotional data. The collected data is wirelessly transmitted to the terminal at regular intervals, using wireless communication technologies such as Bluetooth and Wi-Fi. The collected data includes heart rate, electrocardiogram waveforms, and stress levels, and is temporarily stored in the wearable device's internal memory.

[0957] Terminal

[0958] The terminal is a computing device such as a smartphone or tablet that receives biometric and emotional data transmitted from the wearable device. The terminal temporarily stores this data and periodically uploads it to the server. The terminal also receives alerts from the server and displays audio and video to notify the user.

[0959] server

[0960] The server is equipped with an artificial intelligence model for receiving and analyzing the biometric and emotional data sent from the device. Machine learning frameworks such as TensorFlow and PyTorch can be used for the analysis. The server analyzes the biometric data and sends an alert to the device if an abnormality is detected. The alert also includes detailed information about the abnormality and appropriate countermeasures.

[0961] Emotion Engine

[0962] The emotion engine is a system for recognizing a user's emotional state, assessing the user's emotions in real time using data from voice analysis, facial expression recognition, and other sensors. The emotion data collected by the emotion engine complements the analysis of heart rate and electrocardiogram data and is used to improve the accuracy of anomaly detection models.

[0963] Specific examples

[0964] When a user wears a wearable device during daily activities, the device continuously monitors heart rate, electrocardiogram, and emotional data. For example, suppose a user's heart rate rises to 130 bpm during a meeting, an abnormality appears on the electrocardiogram, and the emotional engine detects a high level of stress. This data is sent to the device and then uploaded to the server. The server analyzes the data and, if it detects an abnormality, sends an alert to the device. The device issues a voice notification saying, "A rapid rise in heart rate and high level of stress have been detected. Please take deep breaths and rest," and displays a video of relaxation techniques on the screen. In this way, the user and those around them can respond quickly and take action to protect their precious lives.

[0965] Prompt Sentence Examples

[0966] Below are some example prompts for inputting specific scenarios for this system into the generative AI model.

[0967] If the wearable device detects a heart rate of 130 bpm and a high stress state while the user is performing daily activities, please explain in detail how and what part of the system analyzes the data and generates an alert.

[0968] This invention makes it possible to provide a system that reduces the risk of sudden cardiac death and ensures user safety. In addition, by combining it with an emotion engine, it becomes possible to provide more precise responses according to the individual state of the user.

[0969] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0970] Understood. Below, I will explain the process flow of the system program step by step.

[0971] Step 1:

[0972] The user puts on the wearable device. The wearable device activates the heart rate sensor, electrocardiogram sensor, and emotion engine to collect heart rate, electrocardiogram, and emotion data in real time. This data is temporarily stored in the internal memory of the wearable device.

[0973] Input: User's heart rate, ECG data, and emotion data.

[0974] Output: Storage of biometric and emotional data in the wearable device.

[0975] Step 2:

[0976] At regular intervals (e.g., every 30 seconds), the wearable device transmits the collected data to the terminal via wireless communication (e.g., Bluetooth). The terminal receives the data and returns an acknowledgement message (ACK) to the device.

[0977] Input: Biometric and emotional data from wearable devices.

[0978] Output: Temporarily stored data in the terminal and a transmission confirmation message.

[0979] Step 3:

[0980] The device temporarily stores the received data in a local buffer. The device uploads this data to the server at regular intervals (e.g., every minute). If the communication situation is unstable, there is a mechanism to retry.

[0981] Input: All data from the wearable device.

[0982] Output: Data transfer to the server.

[0983] Step 4:

[0984] The server receives the data sent from the device and stores it in data storage. When the server receives the data in real time, it analyzes the data using a generative AI model (e.g., TensorFlow).

[0985] Input: Biometric and emotional data from the device.

[0986] Output: Analysis results and analysis log.

[0987] Step 5:

[0988] The server analyzes the received data and detects abnormal patterns, such as a sudden increase in heart rate, abnormal ECG waveforms, or stress levels based on emotional data. If an abnormality is detected based on this analysis, an alert is generated.

[0989] Input: Biometric and emotional data.

[0990] Output: Anomaly detection results and generated alerts.

[0991] Step 6:

[0992] The generated alert contains the specific details of the abnormality, the urgency level, and the recommended action to take. The server sends this alert to the terminal. It will continue to retry until it receives confirmation of the completion of the transmission.

[0993] Input: Anomaly detection results.

[0994] Output: The alert sent to the terminal.

[0995] Step 7:

[0996] When the device receives an alert from the server, it notifies the user with audio and video. The user confirms the alert and follows the instructions. During this time, the device provides customized guidance based on the user's emotional data.

[0997] Input: Alert from the server.

[0998] Output: Audio notification and visual guide to the user.

[0999] The above are the specific processing steps of the program of this system.

[1000] (Application example 2)

[1001] 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."

[1002] In modern society, sudden illness and deterioration of health due to stress have become a social problem. In particular, abnormalities in heart rate or electrocardiogram data while driving a car pose a risk of causing a serious accident. Furthermore, drivers themselves often do not notice abnormalities, so a method for responding quickly is required. Conventional health monitoring systems are limited to detecting and notifying abnormalities in real time, but do not respond to actual driving operations. This poses the issue of a lack of means to ensure driver safety when an abnormality is detected.

[1003] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1004] In this invention, the server includes means for collecting heart rate and electrocardiogram data in real time using a wearable device worn by the user, means for transmitting the collected data to the terminal, means for the terminal to upload the data to the server, means for the server to analyze the received data and send an alert to the terminal if an abnormality is detected, means for notifying the user by audio and video when the terminal receives the alert, and means for controlling the vehicle's autonomous driving system using means for linking with other devices if an abnormality is detected. This makes it possible to immediately transition the autonomous driving system to safety mode when an abnormality is detected in the driver's heart rate or electrocardiogram, thereby quickly ensuring the driver's safety.

[1005] A "wearable device" is a device worn by the user that collects heart rate and electrocardiogram data in real time.

[1006] "Heart rate" is a biometric indicator that indicates the number of times the heart beats within a certain period of time.

[1007] "Electrocardiogram data" refers to data that records the electrical activity of the heart, and is information used to detect the condition of the myocardium and abnormalities in cardiac rhythm.

[1008] A "terminal" is a computing device that receives data transmitted from a wearable device, temporarily stores it, and uploads it to a server.

[1009] The "server" is a data processing device that receives and analyzes biometric data uploaded from a terminal, and generates and sends an alert to the terminal if an abnormality is detected.

[1010] "Means of cooperation with other devices" refers to the means of communication and operation for controlling the vehicle's autonomous driving system when an abnormality is detected.

[1011] An "alert" is a message that notifies the user of an abnormality and recommended countermeasures when an abnormality is detected based on analyzed data.

[1012] An "autonomous driving system" is a system that has the function of automatically driving and controlling a vehicle.

[1013] The present invention is a system that uses a wearable device worn by the user to collect heart rate and electrocardiogram data in real time, and provides the ability to respond quickly in cooperation with the control system of an autonomous vehicle when an abnormality is detected.

[1014] System configuration

[1015] This system consists of a wearable device, a terminal, a server, and a control system for the autonomous vehicle. Specifically, each component functions as follows:

[1016] Wearable devices

[1017] The wearable device collects the user's heart rate, electrocardiogram data, and emotional data, and also includes an emotion engine, which transmits the collected data wirelessly to the device in real time.

[1018] Terminal

[1019] The terminal is a computing device that temporarily stores collected data and uploads it to a server at regular intervals. The terminal receives data sent from the wearable device and receives alerts from the server to notify the user.

[1020] server

[1021] The server is equipped with an artificial intelligence model to analyze data sent from the device. If the server detects an abnormality, it immediately generates an alert and sends it to the device. This alert contains detailed information about the abnormality and how to deal with it.

[1022] Linking with other devices

[1023] The autonomous vehicle's control system will receive alerts from the server and have the ability to coordinate with the server to safely stop the vehicle if the driver's health condition deteriorates.

[1024] Specific operation explanation

[1025] Data collection and transmission

[1026] Once the user puts on the wearable device, it starts collecting heart rate, electrocardiogram, and emotional data, which are then periodically sent to the device, which then uploads the data to a cloud server at regular intervals.

[1027] Data analysis and alert generation

[1028] The server analyzes the data received from the device in real time. The artificial intelligence model used utilizes cloud analysis services such as Google Cloud AI, AWS, and Microsoft Azure. If an abnormality is detected, the server immediately generates an alert and sends it to the vehicle's autonomous driving control system.

[1029] Autonomous Driving System Control

[1030] When the autonomous vehicle's control system receives an alert from the server, it takes action to transition to safety mode. Specifically, it reduces the vehicle's speed and stops it at an appropriate location. The driver is notified via voice and display. This is to quickly ensure safety in the event of a sudden change in the driver's health condition.

[1031] Specific examples

[1032] scenario

[1033] Consider a situation where a driver is operating an autonomous vehicle and their heart rate spikes to 120 bpm, causing the emotion engine to detect a state of high stress.

[1034] 1. Data collection: Heart rate and electrocardiogram data obtained from the wearable device are sent to the terminal and uploaded to the server.

[1035] 2. Data analysis: The server analyzes the received data and detects any anomalies.

[1036] 3. Alert generation: The server detects an anomaly and immediately generates an alert and sends it to the terminal and the autonomous vehicle's control system.

[1037] 4. Response: The autonomous driving system switches to safety mode and notifies the driver that "rapid heart rate and high stress have been detected. An emergency stop will be made immediately."

[1038] Prompt Sentence Examples

[1039] Design a system that will transition to a safety mode and issue an emergency stop alert if the driver's heart rate exceeds 120 bpm and their emotional state is recognized as high stress. This includes a function that will link the autonomous vehicle's control system with wearable devices to collect and analyze data in real time and automatically respond if an abnormality occurs.

[1040] According to the present invention, when an abnormality occurs in the driver's heart rate or electrocardiogram, the autonomous vehicle can be controlled quickly and appropriately to ensure the safety of the driver.

[1041] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1042] Step 1:

[1043] Data collection

[1044] When a user wears the wearable device, it starts collecting heart rate, electrocardiogram (ECG) data, and emotional data, which are then transmitted to a device via Bluetooth. The inputs are data on heart rate, ECG waveforms, and emotional state, and the output is the periodic transmission of these data to the device.

[1045] Step 2:

[1046] Data transmission and storage

[1047] The terminal temporarily stores data received from the wearable device. This data is uploaded to the server at regular intervals. The input is the biometric data received from the wearable device, and the output is the data uploaded to the server. The communication status is monitored during data transmission, and if it fails, the terminal retries.

[1048] Step 3:

[1049] Data analysis

[1050] The server receives data uploaded from the device and analyzes it in real time. A generative AI model (e.g., a TensorFlow model) is used for the analysis. The input is the heart rate, ECG data, and emotion data sent from the device, and the output is the analysis results and the presence or absence of abnormalities. Specifically, the server detects sudden increases in heart rate, abnormal ECG waveforms, and high stress states.

[1051] Step 4:

[1052] Alert Generation

[1053] When the server detects an abnormality, it generates an alert and sends it to the device and the autonomous vehicle's control system. The input is the results of data analysis and details of the detected abnormality, and the output is an alert message. Specifically, a message such as "A sudden increase in heart rate has been detected. The vehicle will immediately enter safety mode" is generated.

[1054] Step 5:

[1055] Alert notifications and driving control

[1056] The terminal receives the alert from the server and notifies the user via audio and video. At the same time, the autonomous vehicle's control system also receives the alert and transitions to safety mode. The input is the alert message sent from the server, and the output is a notification to the user and a change in vehicle behavior. Specifically, the autonomous vehicle reduces its speed and stops at an appropriate location.

[1057] Step 6:

[1058] Contacting a medical institution

[1059] Furthermore, if necessary, the terminal will activate an automatic contact function to a medical institution. The input is an abnormality detection message from the server, and the output is contact information to the medical institution. Specifically, the terminal will notify the medical institution of the driver's current location and condition.

[1060] This series of processing steps makes it possible to respond quickly and appropriately when an abnormality occurs in the driver's heart rate or electrocardiogram.

[1061] 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.

[1062] 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.

[1063] 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.

[1064] [Fourth embodiment]

[1065] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1066] 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.

[1067] 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).

[1068] 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.

[1069] 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.

[1070] 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).

[1071] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1072] 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.

[1073] 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.

[1074] 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.

[1075] 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.

[1076] 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.

[1077] 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."

[1078] The present invention relates to a system that uses a wearable device worn by a user to collect heart rate and electrocardiogram data in real time, and if an abnormality is detected, quickly notifies the user and people around them and guides them to take appropriate measures. Detailed embodiments for implementing this system are described below.

[1079] System Structure

[1080] This system consists of a wearable device, a terminal, and a server. Each of these components functions as follows:

[1081] Wearable devices

[1082] The wearable device, equipped with a heart rate sensor and an electrocardiogram sensor, continuously collects real-time biometric data from the user, which is then wirelessly transmitted to a terminal at regular intervals.

[1083] Terminal

[1084] The terminal is a computing device such as a smartphone or tablet that receives the biometric data sent from the wearable device. The terminal temporarily stores this data and periodically uploads it to the server. The terminal also has the ability to receive alerts from the server and display audio and video to notify the user.

[1085] server

[1086] The server is equipped with an artificial intelligence model for receiving and analyzing biometric data sent from the device. The server analyzes the biometric data and sends an alert to the device if an abnormality is detected. The alert also includes detailed information about the abnormality and appropriate countermeasures.

[1087] Program processing

[1088] Data collection

[1089] When a user wears the wearable device, it starts collecting heart rate and electrocardiogram data. For example, if the user is jogging, the heart rate and electrocardiogram waveforms are recorded in real time. This collected data is stored in the wearable device's internal memory for a certain period of time and then transmitted to the terminal via wireless communication.

[1090] Sending data

[1091] The terminal uploads the heart rate and electrocardiogram data received from the wearable device to the server at a predetermined interval (e.g., every minute). The terminal monitors the communication status and ensures that the connection to the server is established. If the connection is unstable, the data will be retransmitted after a stable connection is established.

[1092] Data analysis

[1093] The server receives the biometric data sent from the device in real time and analyzes it using an artificial intelligence model. During the analysis process, a sudden increase in heart rate or an abnormal electrocardiogram waveform is detected. For example, if the heart rate significantly exceeds the normal range or if an arrhythmia is detected on the electrocardiogram, the server will determine that an abnormality has occurred.

[1094] Generate alerts

[1095] If an abnormality is detected, the server immediately generates an alert and sends it to the device. The alert includes the specific type of abnormality, the urgency level, and recommended actions to take. For example, if there is a sudden increase in heart rate, the alert will state, "A sudden increase in heart rate has been detected. Please rest immediately and contact emergency services."

[1096] Alert Notification

[1097] When the device receives an alert from the server, it notifies the user via audio and video. The user can check the alert and take appropriate action by following the device's guidance. For example, if the user receives an alert that a sudden increase in heart rate has been detected, the device will provide audio and video guidance on how to perform CPR and contact emergency services.

[1098] Specific examples

[1099] Scenario 1: Heart rate spikes during everyday activities

[1100] When a user wears a wearable device during daily activities, the device continuously monitors their heart rate and electrocardiogram. For example, suppose a user's heart rate rises to 130 bpm while climbing stairs, causing an abnormality in the electrocardiogram. This data is sent to the device and then uploaded to a server. The server analyzes the data and, if it detects an abnormality, sends an alert to the device. The device issues a voice notification saying, "A sudden rise in heart rate has been detected. Please rest," and displays a video guide to first aid on the screen. In this way, the user and those around them can respond quickly and take action to save their precious life.

[1101] The present invention provides a system that reduces the risk of sudden cardiac death and ensures the safety of the user through this series of steps.

[1102] The processing flow will be explained below.

[1103] Step 1:

[1104] Subject: User

[1105] The user wears a wearable device. The device has built-in heart rate and electrocardiogram sensors and collects biometric data in real time. For example, when the user starts jogging, the device measures heart rate and electrocardiogram data at regular intervals.

[1106] Step 2:

[1107] Subject: Wearable devices

[1108] The wearable device wirelessly transmits collected heart rate and electrocardiogram data to the terminal, where the data is temporarily stored in a buffer and then transmitted in batches at appropriate times, ensuring data continuity.

[1109] Step 3:

[1110] Subject: Terminal

[1111] The terminal receives and temporarily stores data sent from the wearable device. The data is set to be uploaded to the server at regular intervals (e.g., every minute). The terminal also monitors the connection status to the server to ensure that the connection is established. If the connection is unstable, a retry mechanism is implemented.

[1112] Step 4:

[1113] Subject: Terminal

[1114] The device uploads the stored data to the server, and if an error occurs, the error is logged and the device tries again later, thus ensuring the integrity and security of the data.

[1115] Step 5:

[1116] Subject: Server

[1117] The server receives the biometric data sent from the device, after which the data is stored in a database and prepared for analysis.

[1118] Step 6:

[1119] Subject: Server

[1120] The server analyzes the received data using an artificial intelligence model. Specifically, it examines the heart rate and electrocardiogram waveform to detect abnormal patterns or out-of-range values. For example, if the heart rate suddenly increases or an irregular heartbeat is detected, it determines that an abnormality has occurred.

[1121] Step 7:

[1122] Subject: Server

[1123] If an abnormality is detected, the server generates an alert and sends it to the terminal, which includes the specific type of abnormality, its urgency, and recommended actions to take.

[1124] Step 8:

[1125] Subject: Terminal

[1126] When the device receives an alert from the server, it notifies the user with audio and video, for example, by playing a message saying, "A sudden increase in heart rate has been detected. Please rest immediately," and by displaying life-saving measures on the screen.

[1127] Step 9:

[1128] Subject: User

[1129] The user follows the instructions on the device. In some cases, the alert can be shared with people around them to ask for a quick response. For example, a video guiding the user through the steps to call 119 can be displayed, allowing the user and people around them to take appropriate action.

[1130] This series of processes reduces the risk of sudden cardiac death and ensures user safety.

[1131] Example 1

[1132] 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."

[1133] Current health management systems using wearable devices have issues with the accuracy and speed of response when collecting data in real time and detecting abnormalities. It can also be difficult to promptly notify users of the appropriate response method when an abnormality is detected. Furthermore, if data transmission becomes unstable, necessary information may not be transmitted to the server in a timely manner, which risks delaying the detection and response of abnormalities. A system that solves these issues and increases user safety is needed.

[1134] 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.

[1135] In this invention, the server includes means for analyzing data in real time and using an artificial intelligence model to detect sudden increases in heart rate and abnormal electrocardiogram waveforms, means for the terminal to monitor communication status and retransmit data, and means for collecting heart rate and electrocardiogram data in real time using a wearable device worn by the user, thereby enabling highly accurate data analysis and abnormality detection, rapid user notification, and stable data transmission.

[1136] "User" refers to a person who wears a wearable device in order to use it.

[1137] "Wearable device" refers to a device worn by the user that collects heart rate and electrocardiogram data in real time.

[1138] "Heart rate" refers to the number of heartbeats per unit time, and is an important indicator for evaluating the health of a living organism.

[1139] "Electrocardiogram data" refers to data that records the electrical activity of the heart as a waveform, and is used to understand the condition of the heart in detail.

[1140] "Terminal" refers to a computing device for receiving data collected from a wearable device and uploading it to a server.

[1141] "Server" refers to a system that receives and analyzes biometric data sent from a terminal and sends an alert to the terminal if an abnormality is detected.

[1142] "Analysis" refers to the process by which the server uses the generative AI model to evaluate the collected biometric data and determine whether there are any abnormalities.

[1143] "Generative AI models" refer to artificial intelligence models used to analyze collected biometric data, and include techniques such as deep learning.

[1144] An "alert" refers to a message sent by the server to notify the terminal and user of an abnormality detected by the server.

[1145] "Notification" refers to the process by which the device communicates the contents of the alert to the user via audio and video.

[1146] "Retransmission" refers to the process in which a terminal attempts to send data to a server again when data transmission is unstable.

[1147] The system of the present invention is composed of a wearable device worn by a user, a terminal, and a server. Each of these elements functions as follows.

[1148] Wearable devices

[1149] A wearable device worn by a user is equipped with a heart rate sensor and an electrocardiogram sensor. The device collects the user's heart rate and electrocardiogram data in real time. For example, the wearable device records the user's heart rate every second and simultaneously measures the electrocardiogram data. This data is temporarily stored in the device's internal memory and transmitted to a terminal using wireless communication technology such as Bluetooth.

[1150] Terminal

[1151] The terminal is a computing device such as a smartphone or tablet held by the user. This terminal receives and temporarily stores the biometric data transmitted from the wearable device. It also uploads the data from the wearable device to a server at regular intervals (e.g., every minute). The terminal has the function of monitoring the communication status and, if data transmission is unstable, attempting to retransmit as soon as a stable connection is established.

[1152] The device also receives alerts from the server and notifies the user via audio and video. For example, if the device receives an abnormality notification, it will play a message such as "A sudden increase in heart rate has been detected. Please rest and contact emergency services immediately," and display emergency response instructions on the screen.

[1153] server

[1154] The server receives and analyzes biometric data sent from the device using a generative AI model implemented using machine learning frameworks such as TensorFlow and PyTorch.

[1155] The server analyzes the data in real time, and if it detects a sudden increase in heart rate or an abnormal electrocardiogram waveform, it judges it to be an abnormality. For example, if the heart rate significantly exceeds the normal range or if an arrhythmia is detected on the electrocardiogram, the server immediately determines this to be an abnormality and generates an alert. This alert is sent to the device and includes specific instructions to prompt the user to take appropriate action.

[1156] Specific examples

[1157] Scenario 1: Heart rate spikes during everyday activities

[1158] When a user wears a wearable device during daily activities, the device continuously monitors their heart rate and electrocardiogram. For example, suppose a user's heart rate rises to 130 bpm while climbing stairs, causing an abnormality in the electrocardiogram. This data is sent to the device and then uploaded to a server. The server analyzes the data and, if it detects an abnormality, sends an alert to the device. The device then issues a voice notification saying, "A sudden rise in heart rate has been detected. Please rest," and displays a video guide to first aid on the screen.

[1159] Prompt Sentence Examples

[1160] Here are some example prompts to give to a generative AI model:

[1161] A wearable device collects real-time heart rate and electrocardiogram data from a person going about their daily activities. For example, if a heart rate exceeds 130 bpm or an arrhythmia is detected in the electrocardiogram, it is deemed an abnormality and an alert is sent to the user. The alert will include details of the abnormality and a guide for emergency response. Please explain what kind of system is needed.

[1162] The above is the specific content for carrying out the invention, and describes in detail how the system is constructed and operated at each step.

[1163] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1164] Step 1: Data collection

[1165] When a user puts on a wearable device, the device starts collecting heart rate and electrocardiogram data. The input is the user's biometric information, and the output is digital heart rate and electrocardiogram data. The device's sensors measure the heart rate every second, and electrocardiogram data is collected simultaneously. For example, if the user is jogging, the heart rate measurement and electrocardiogram recording continue in real time.

[1166] Step 2: Send data

[1167] The terminal receives data collected from the wearable device via wireless communication such as Bluetooth. The input is the data sent from the wearable device, and the output is the data temporarily stored on the terminal. For example, if the terminal is a smartphone, it continuously receives data from the device and prepares to send it to the server at regular intervals (e.g., every minute).

[1168] Step 3: Upload data

[1169] The device uploads the received data to the server. The input is the collected data stored in the device, and the output is the data stored on the server. The device sends the data to the server using Wi-Fi or 4G communication. For example, if the communication situation is unstable, the device will try to resend the data until the connection is stable. This ensures that the data is uploaded reliably.

[1170] Step 4: Data analysis

[1171] The server analyzes the received biometric data. The input is the data uploaded to the server, and the output is the analysis result. The server uses a generative AI model (e.g., using TensorFlow or PyTorch) to detect sudden increases in heart rate or abnormal ECG waveforms. For example, if there is a sudden increase in heart rate or an arrhythmia is detected on the ECG, the server immediately detects the abnormality.

[1172] Step 5: Alert Generation

[1173] When the server detects an abnormality, it generates an alert. The input is the data analysis result, and the output is an alert message. The alert includes the specific type of abnormality, the urgency, and the recommended response method. For example, if there is a sudden increase in heart rate, the server generates a message saying, "A sudden increase in heart rate has been detected. Please rest immediately and contact emergency services."

[1174] Step 6: Sending an alert

[1175] The server sends the generated alert to the terminal. The input is the generated alert message, and the output is the alert transferred to the terminal. The server immediately sends the alert message to the terminal and prepares to notify the user.

[1176] Step 7: Alert Notifications

[1177] The device notifies the user of the received alert. The input is the alert notification from the server, and the output is an audio and video notification to the user. For example, when a smartphone receives an alert message, it immediately issues an audio notification and also displays emergency response instructions on the screen. It plays an audio message saying, "A sudden increase in heart rate has been detected. Please rest," and displays instructions on the screen such as, "Please perform life-saving measures."

[1178] (Application example 1)

[1179] 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."

[1180] Many people currently use wearable devices to monitor their health, but these devices typically lack the ability to detect abnormalities in real time or provide insufficient countermeasures. This prevents users from taking appropriate action even when they detect an abnormality, making it difficult to avoid serious health risks. Furthermore, existing systems do not effectively utilize the advanced artificial intelligence models required for data analysis, resulting in low accuracy in detecting abnormalities.

[1181] 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.

[1182] In this invention, the server includes a means for collecting heart rate and electrocardiogram data in real time, a means for analyzing this data and using an artificial intelligence model to detect abnormalities, and a means for sending an alert to the terminal when an abnormality is detected. This enables accurate detection of abnormalities and a rapid response. In addition, when the terminal receives an alert, it can notify the user via audio and video and provide guidance on life-saving measures and appropriate countermeasures (e.g., rest, emergency contact information, cardiopulmonary resuscitation guide).

[1183] A "wearable device" is a device that can collect biometric data in real time by being worn by the user.

[1184] "Heart rate" is a numerical value that indicates the number of times the heart beats per unit time.

[1185] "Electrocardiogram data" refers to data that records the electrical activity of the heart.

[1186] A "terminal" is a computing device for receiving data from a wearable device and uploading the data to a server.

[1187] The "server" is a central processing unit that analyzes the biometric data sent from the terminal and sends an alert if an abnormality is detected.

[1188] An "alert" is a warning signal that notifies the user when an abnormality is detected.

[1189] "Audio and video notification" refers to a means of transmitting alerts from the server to the user in the form of audio and video.

[1190] The "Guide for Lifesaving Treatment" is a guide that instructs the user on the appropriate treatment method when an abnormality is detected.

[1191] An "artificial intelligence model" is an analytical algorithm that uses artificial intelligence to allow the server to analyze biometric data and detect abnormalities.

[1192] "Real-time" means that data collection and analysis are carried out immediately, without delay.

[1193] This invention is a real-time biometric data monitoring system consisting of a wearable device, a terminal, and a server. This system collects a user's heart rate and electrocardiogram data in real time and can respond quickly if an abnormality is detected.

[1194] System Structure

[1195] Wearable devices

[1196] The wearable device, which is equipped with a heart rate sensor and an electrocardiogram sensor, continuously collects the user's biometric data in real time and transmits the data wirelessly to a terminal.

[1197] Terminal

[1198] The terminal is a computing device such as a smartphone or tablet that receives the biometric data sent from the wearable device. The terminal temporarily stores the received data and uploads it to the server at regular intervals. It also receives alerts from the server and notifies the user via audio and video.

[1199] server

[1200] The server is equipped with an artificial intelligence model for analyzing biometric data. The server receives the biometric data sent from the device and detects sudden increases in heart rate and abnormal electrocardiogram waveforms. If an abnormality is detected, the server immediately generates an alert and sends it to the device.

[1201] Program processing

[1202] Data collection and transmission

[1203] The heart rate and electrocardiogram data collected by the wearable device are sent to the terminal at regular intervals. For example, if the user is jogging, the heart rate and electrocardiogram waveform are recorded in real time and this data is transferred to the terminal.

[1204] Data analysis

[1205] The server receives the biometric data sent from the device and analyzes it using an artificial intelligence model. This analysis detects, for example, a sudden increase in heart rate or an abnormal electrocardiogram waveform. If an abnormality is detected, the server immediately generates an alert.

[1206] Alert Notification

[1207] When an alert is generated, the server sends it to the device. When the device receives the alert, it notifies the user with audio and video. If necessary, it also displays a guide on life-saving measures. For example, it may say, "A sudden increase in heart rate has been detected. Please rest immediately and contact an emergency department."

[1208] Specific examples

[1209] Scenario 1: Heart rate spikes during everyday activities

[1210] When a user wears a wearable device during daily activities, the device continuously monitors their heart rate and electrocardiogram. For example, suppose a user's heart rate rises to 130 bpm while climbing stairs, causing an abnormality in the electrocardiogram. This data is sent to the device and then uploaded to a server. The server analyzes the data and, if it detects an abnormality, sends an alert to the device. The device issues a voice notification saying, "A sudden rise in heart rate has been detected. Please rest," and displays a video guide to first aid on the screen. In this way, the user and those around them can respond quickly and take action to save their precious life.

[1211] Prompt Sentence Examples

[1212] "Generate a guide to what to do if your heart rate spikes while jogging and an arrhythmia is detected on your electrocardiogram."

[1213] The above is an example of an embodiment of the present invention, which enables a user to monitor their health condition in real time and take prompt and appropriate action when an abnormality is detected.

[1214] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1215] Step 1: Collect data

[1216] A wearable device worn by the user collects heart rate and electrocardiogram data in real time. The device continuously acquires these data using heart rate and electrocardiogram sensors and stores them in its internal memory for a short period of time. These data are then transmitted to a terminal via wireless communication such as Bluetooth. The input is the user's biometric data, and the output is the heart rate and electrocardiogram data collected by the wearable device.

[1217] Step 2: Receiving and storing data

[1218] The terminal receives the biometric data transmitted from the wearable device. The received data is temporarily stored in the terminal's local storage. The input is the heart rate and electrocardiogram data transmitted from the wearable device, and the output is the biometric data stored in the terminal.

[1219] Step 3: Send data to the server

[1220] The device uploads the accumulated biometric data to the server at regular intervals (e.g., every minute). When uploading, the data is sent to the server via an HTTP POST request. The input is the biometric data accumulated in the device, and the output is the biometric data sent to the server. The device also monitors the communication status and retries data transmission if it is unstable.

[1221] Step 4: Analyze the data

[1222] The server analyzes the biometric data received from the device. In the process, it uses a generative AI model to detect sudden increases in heart rate and abnormal electrocardiogram waveforms. The input is the biometric data sent from the device, and the output is a judgment result on whether an abnormality was detected.

[1223] Step 5: Generate an alert

[1224] If an anomaly is detected, the server generates an alert. The alert includes the type of anomaly, its urgency, and recommended actions to take. The input is the result of data analysis, and the output is the generation of an alert. This information is sent to the terminal in the next step.

[1225] Step 6: Sending an alert

[1226] The server sends the generated alert to the terminal. The sending method is HTTP PUSH notification, etc. The input is the generated alert information, and the output is the alert sent to the terminal.

[1227] Step 7: User Notification

[1228] The device notifies the user via audio and video based on the alert received from the server. It also displays video and audio guidance to guide the user on the appropriate course of action. The input is the alert information from the server, and the output is a notification and guidance display to the user. For example, the notification may say, "A sudden increase in heart rate has been detected. Please rest immediately and contact an emergency department."

[1229] The above is the processing flow of the system of the present invention. This system enables users to monitor their own health condition in real time and respond quickly and appropriately if an abnormality occurs.

[1230] 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.

[1231] The present invention relates to a system that uses a wearable device worn by the user to collect heart rate and electrocardiogram data in real time, and if an abnormality is detected, quickly notifies the user and those around them and guides them in taking appropriate measures. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system improves analysis accuracy and provides more personalized responses.

[1232] System Structure

[1233] This system consists of a wearable device, a terminal, a server, and an emotion engine. Each of these components functions as follows:

[1234] Wearable devices

[1235] The wearable device, which is equipped with a heart rate sensor, an electrocardiogram sensor, and an emotion engine, continuously collects the user's real-time biometric and emotional data, which is then wirelessly transmitted to the device at regular intervals.

[1236] Terminal

[1237] The terminal is a computing device such as a smartphone or tablet that receives biometric and emotional data transmitted from the wearable device. The terminal temporarily stores this data and periodically uploads it to the server. The terminal also receives alerts from the server and displays audio and video to notify the user.

[1238] server

[1239] The server is equipped with an artificial intelligence model for receiving and analyzing biometric data and emotional data sent from the device. The server analyzes the biometric data and sends an alert to the device if an abnormality is detected. The alert also includes detailed information about the abnormality and appropriate countermeasures.

[1240] Emotion Engine

[1241] The emotion engine is a system for recognizing a user's emotional state, assessing the user's emotions in real time using data from voice analysis, facial expression recognition, and other sensors. The emotion data collected by the emotion engine complements the analysis of heart rate and electrocardiogram data and is used to improve the accuracy of anomaly detection models.

[1242] Program processing

[1243] Data collection

[1244] When a user puts on a wearable device, the device starts collecting heart rate and electrocardiogram data. For example, if the user is jogging, the device records heart rate, electrocardiogram waveforms, and emotional data (such as stress levels) in real time. This collected data is stored in the wearable device's internal memory for a certain period of time and then transmitted wirelessly to the terminal.

[1245] Sending data

[1246] The terminal uploads the heart rate, electrocardiogram data, and emotion data received from the wearable device to the server at a predetermined interval (e.g., every minute). The terminal monitors the communication status and confirms that the connection to the server is established. If the connection is unstable, a retry mechanism is implemented.

[1247] Data analysis

[1248] The server receives biometric and emotional data sent from the device in real time and analyzes it using an artificial intelligence model. During the analysis process, it checks for sudden increases in heart rate, abnormal electrocardiogram waveforms, and stress or anxiety levels from emotional data. For example, if the heart rate significantly exceeds the normal range, if arrhythmia is detected on the electrocardiogram, or if the emotion engine detects a high stress state, the server will determine that an abnormality has occurred.

[1249] Generate alerts

[1250] If an abnormality is detected, the server immediately generates an alert and sends it to the device. This alert includes the specific type of abnormality, the urgency level, and a recommended course of action. For example, if a sudden increase in heart rate occurs, the alert may state, "A sudden increase in heart rate has been detected. Please rest immediately and contact emergency services." Different responses may be recommended based on emotional data.

[1251] Alert Notification

[1252] When the device receives an alert from the server, it notifies the user via audio and video. The user can confirm the alert and take appropriate action by following the device's guidance. The device also displays customized guidance based on the user's current emotional state based on emotional data.

[1253] Specific examples

[1254] Scenario 1: Rapid heart rate and high stress during everyday activities

[1255] When a user wears a wearable device during daily activities, the device continuously monitors heart rate, electrocardiogram, and emotional data. For example, suppose a user's heart rate rises to 130 bpm during a meeting, an abnormality appears on the electrocardiogram, and the emotional engine detects a high level of stress. This data is sent to the device and then uploaded to the server. The server analyzes the data and, if it detects an abnormality, sends an alert to the device. The device issues a voice notification saying, "A rapid rise in heart rate and high level of stress have been detected. Please take deep breaths and rest," and displays a video of relaxation techniques on the screen. In this way, the user and those around them can respond quickly and take action to protect their precious lives.

[1256] Through this series of steps, the present invention provides a system that reduces the risk of sudden cardiac death and ensures the safety of users. In addition, by combining it with an emotion engine, it becomes possible to respond more precisely to the individual state of the user.

[1257] The processing flow will be explained below.

[1258] Step 1:

[1259] Subject: User

[1260] The user wears a wearable device, which has a built-in heart rate sensor, an electrocardiogram sensor, and an emotion engine to collect biometric and emotional data in real time. For example, when a user starts jogging, their heart rate, electrocardiogram waveform, and emotional data (such as stress level) are continuously measured.

[1261] Step 2:

[1262] Subject: Wearable devices

[1263] The wearable device wirelessly transmits collected heart rate, electrocardiogram, and emotion data to the terminal, where the data is temporarily stored in a buffer and then transmitted in batches at appropriate times, ensuring data continuity without delay.

[1264] Step 3:

[1265] Subject: Terminal

[1266] The terminal receives and temporarily stores data sent from the wearable device. The data is uploaded to the server at regular intervals (e.g., every minute). The terminal also monitors the communication status to ensure that the connection to the server is established. If the connection is unstable, a retry mechanism is implemented.

[1267] Step 4:

[1268] Subject: Terminal

[1269] The device uploads all stored data to the server in bulk, and if an error occurs, it logs the error and attempts to retransmit it later, thus ensuring data integrity and security.

[1270] Step 5:

[1271] Subject: Server

[1272] The server receives the biometric and emotional data sent from the device, stores it in a database, and prepares it for analysis.

[1273] Step 6:

[1274] Subject: Server

[1275] The server analyzes the received data using an artificial intelligence model. This model simultaneously examines heart rate, ECG waveforms, and emotional data to detect abnormal patterns and out-of-range values. For example, it comprehensively determines a sudden increase in heart rate, the occurrence of arrhythmia, and a state of high stress from the emotional engine.

[1276] Step 7:

[1277] Subject: Server

[1278] If an abnormality is detected as a result of the analysis, the server immediately generates an alert and sends it to the device. This alert includes the specific type of abnormality, the urgency level, and recommended actions to take. For example, an alert could be generated stating, "A rapid rise in heart rate and high stress state have been detected. Please rest immediately and contact emergency services if necessary."

[1279] Step 8:

[1280] Subject: Terminal

[1281] When the device receives an alert from the server, it notifies the user with audio and visual notifications, such as "A sudden increase in heart rate has been detected. Please take a deep breath and remain calm," and displays relaxation techniques and first aid procedures on the screen.

[1282] Step 9:

[1283] Subject: User

[1284] The user follows the instructions on the device. In some cases, the alert can be shared with people around them to ask for a quick response. For example, a video guiding the user through the steps to call 119 can be displayed, allowing the user and those around them to take appropriate action. It can also display relaxation techniques and psychological support based on the user's emotional state.

[1285] This series of processes enables comprehensive monitoring of the user's health and emotional state, reducing the risk of sudden cardiac death and ensuring the user's safety.

[1286] Example 2

[1287] 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."

[1288] Conventional wearable devices and related systems have limited accuracy in analyzing heart rate and electrocardiogram data, which can lead to false alarms and delays in detecting abnormalities. They also lack the ability to customize responses based on the user's emotional state, making it difficult to respond appropriately to individual user conditions. Furthermore, in environments with unstable communication, data transmission failures can lead to delays and reduced accuracy in data analysis.

[1289] 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 means for collecting heart rate and electrocardiogram data in real time, means for transmitting the data to the terminal at predetermined intervals, means for the terminal to temporarily store the data and upload it to the server at predetermined intervals, means for analyzing the received data using an artificial intelligence model and sending an alert to the terminal if an abnormality is detected, means for notifying the user by audio and video when the terminal receives the alert, and means for generating an alert including detailed information about the abnormality and how to deal with it. This enables accurate collection and analysis of data even in cases where data transmission fails or in an environment with unstable communication, thereby increasing user safety and enabling individual responses based on emotional data.

[1290] Understood. Below are the definitions of the important words included in the rewritten claims.

[1291] A "wearable device" is a device that can be worn by a user and collects biometric information such as heart rate, electrocardiogram data, and emotional data in real time.

[1292] "Heart rate" is biological information that indicates the number of heartbeats per unit time, and is generally expressed as the number of beats per minute.

[1293] "Electrocardiogram data" refers to data that records the electrical activity of the heart and is used to analyze the rhythm and strength of heartbeats.

[1294] "Emotion data" is data that indicates the user's emotional state, and is a numerical representation of psychological states such as stress level and joy, anger, sadness, and happiness.

[1295] A "terminal" is a device that receives data sent from a wearable device, temporarily stores the data, and sends it to a server, and is a smartphone or tablet-like computing device.

[1296] A "server" is a computer system that receives data sent from a terminal, analyzes it using an artificial intelligence model, and generates and sends an alert to the terminal if an abnormality is detected.

[1297] An "artificial intelligence model" is a mathematical algorithm or machine learning technique used by the server to analyze data, and is used to improve the accuracy of anomaly detection and data analysis.

[1298] An "alert" is a warning message that is sent to the user when an abnormality is detected, and includes the specific nature of the abnormality, the urgency level, and a recommended method of dealing with the problem.

[1299] "Audio and video notification" is a method of notifying the user by audio guidance or displaying a visual message on the screen when the terminal receives an alert.

[1300] "Detailed information about anomaly" is information that indicates the specific content of the detected anomaly, the cause of its occurrence, the extent of its impact, and so on.

[1301] "Countermeasures" are guidelines that indicate specific actions and procedures that users should take in response to detected abnormalities.

[1302] This invention relates to a system that uses a wearable device worn by the user to collect heart rate and electrocardiogram data in real time, and if an abnormality is detected, quickly notifies the user and those around them and guides them in taking appropriate measures. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system improves analysis accuracy and enables more personalized responses. This system consists of a wearable device, a terminal, a server, and an emotion engine.

[1303] Wearable devices

[1304] The wearable device worn by the user has a built-in heart rate sensor, an electrocardiogram sensor, and an emotion engine. The device continuously collects the user's real-time biometric and emotional data. The collected data is wirelessly transmitted to the terminal at regular intervals, using wireless communication technologies such as Bluetooth and Wi-Fi. The collected data includes heart rate, electrocardiogram waveforms, and stress levels, and is temporarily stored in the wearable device's internal memory.

[1305] Terminal

[1306] The terminal is a computing device such as a smartphone or tablet that receives biometric and emotional data transmitted from the wearable device. The terminal temporarily stores this data and periodically uploads it to the server. The terminal also receives alerts from the server and displays audio and video to notify the user.

[1307] server

[1308] The server is equipped with an artificial intelligence model for receiving and analyzing the biometric and emotional data sent from the device. Machine learning frameworks such as TensorFlow and PyTorch can be used for the analysis. The server analyzes the biometric data and sends an alert to the device if an abnormality is detected. The alert also includes detailed information about the abnormality and appropriate countermeasures.

[1309] Emotion Engine

[1310] The emotion engine is a system for recognizing a user's emotional state, assessing the user's emotions in real time using data from voice analysis, facial expression recognition, and other sensors. The emotion data collected by the emotion engine complements the analysis of heart rate and electrocardiogram data and is used to improve the accuracy of anomaly detection models.

[1311] Specific examples

[1312] When a user wears a wearable device during daily activities, the device continuously monitors heart rate, electrocardiogram, and emotional data. For example, suppose a user's heart rate rises to 130 bpm during a meeting, an abnormality appears on the electrocardiogram, and the emotional engine detects a high level of stress. This data is sent to the device and then uploaded to the server. The server analyzes the data and, if it detects an abnormality, sends an alert to the device. The device issues a voice notification saying, "A rapid rise in heart rate and high level of stress have been detected. Please take deep breaths and rest," and displays a video of relaxation techniques on the screen. In this way, the user and those around them can respond quickly and take action to protect their precious lives.

[1313] Prompt Sentence Examples

[1314] Below are some example prompts for inputting specific scenarios for this system into the generative AI model.

[1315] If the wearable device detects a heart rate of 130 bpm and a high stress state while the user is performing daily activities, please explain in detail how and what part of the system analyzes the data and generates an alert.

[1316] This invention makes it possible to provide a system that reduces the risk of sudden cardiac death and ensures user safety. In addition, by combining it with an emotion engine, it becomes possible to provide more precise responses according to the individual state of the user.

[1317] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1318] Understood. Below, I will explain the process flow of the system program step by step.

[1319] Step 1:

[1320] The user puts on the wearable device. The wearable device activates the heart rate sensor, electrocardiogram sensor, and emotion engine to collect heart rate, electrocardiogram, and emotion data in real time. This data is temporarily stored in the internal memory of the wearable device.

[1321] Input: User's heart rate, ECG data, and emotion data.

[1322] Output: Storage of biometric and emotional data in the wearable device.

[1323] Step 2:

[1324] At regular intervals (e.g., every 30 seconds), the wearable device transmits the collected data to the terminal via wireless communication (e.g., Bluetooth). The terminal receives the data and returns an acknowledgement message (ACK) to the device.

[1325] Input: Biometric and emotional data from wearable devices.

[1326] Output: Temporarily stored data in the terminal and a transmission confirmation message.

[1327] Step 3:

[1328] The device temporarily stores the received data in a local buffer. The device uploads this data to the server at regular intervals (e.g., every minute). If the communication situation is unstable, there is a mechanism to retry.

[1329] Input: All data from the wearable device.

[1330] Output: Data transfer to the server.

[1331] Step 4:

[1332] The server receives the data sent from the device and stores it in data storage. When the server receives the data in real time, it analyzes the data using a generative AI model (e.g., TensorFlow).

[1333] Input: Biometric and emotional data from the device.

[1334] Output: Analysis results and analysis log.

[1335] Step 5:

[1336] The server analyzes the received data and detects abnormal patterns, such as a sudden increase in heart rate, abnormal ECG waveforms, or stress levels based on emotional data. If an abnormality is detected based on this analysis, an alert is generated.

[1337] Input: Biometric and emotional data.

[1338] Output: Anomaly detection results and generated alerts.

[1339] Step 6:

[1340] The generated alert contains the specific details of the abnormality, the urgency level, and the recommended action to take. The server sends this alert to the terminal. It will continue to retry until it receives confirmation of the completion of the transmission.

[1341] Input: Anomaly detection results.

[1342] Output: The alert sent to the terminal.

[1343] Step 7:

[1344] When the device receives an alert from the server, it notifies the user with audio and video. The user confirms the alert and follows the instructions. During this time, the device provides customized guidance based on the user's emotional data.

[1345] Input: Alert from the server.

[1346] Output: Audio notification and visual guide to the user.

[1347] The above are the specific processing steps of the program of this system.

[1348] (Application example 2)

[1349] 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."

[1350] In modern society, sudden illness and deterioration of health due to stress have become a social problem. In particular, abnormalities in heart rate or electrocardiogram data while driving a car pose a risk of causing a serious accident. Furthermore, drivers themselves often do not notice abnormalities, so a method for responding quickly is required. Conventional health monitoring systems are limited to detecting and notifying abnormalities in real time, but do not respond to actual driving operations. This poses the issue of a lack of means to ensure driver safety when an abnormality is detected.

[1351] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1352] In this invention, the server includes means for collecting heart rate and electrocardiogram data in real time using a wearable device worn by the user, means for transmitting the collected data to the terminal, means for the terminal to upload the data to the server, means for the server to analyze the received data and send an alert to the terminal if an abnormality is detected, means for notifying the user by audio and video when the terminal receives the alert, and means for controlling the vehicle's autonomous driving system using means for linking with other devices if an abnormality is detected. This makes it possible to immediately transition the autonomous driving system to safety mode when an abnormality is detected in the driver's heart rate or electrocardiogram, thereby quickly ensuring the driver's safety.

[1353] A "wearable device" is a device worn by the user that collects heart rate and electrocardiogram data in real time.

[1354] "Heart rate" is a biometric indicator that indicates the number of times the heart beats within a certain period of time.

[1355] "Electrocardiogram data" refers to data that records the electrical activity of the heart, and is information used to detect the condition of the myocardium and abnormalities in cardiac rhythm.

[1356] A "terminal" is a computing device that receives data transmitted from a wearable device, temporarily stores it, and uploads it to a server.

[1357] The "server" is a data processing device that receives and analyzes biometric data uploaded from a terminal, and generates and sends an alert to the terminal if an abnormality is detected.

[1358] "Means of cooperation with other devices" refers to the means of communication and operation for controlling the vehicle's autonomous driving system when an abnormality is detected.

[1359] An "alert" is a message that notifies the user of an abnormality and recommended countermeasures when an abnormality is detected based on analyzed data.

[1360] An "autonomous driving system" is a system that has the function of automatically driving and controlling a vehicle.

[1361] The present invention is a system that uses a wearable device worn by the user to collect heart rate and electrocardiogram data in real time, and provides the ability to respond quickly in cooperation with the control system of an autonomous vehicle when an abnormality is detected.

[1362] System configuration

[1363] This system consists of a wearable device, a terminal, a server, and a control system for the autonomous vehicle. Specifically, each component functions as follows:

[1364] Wearable devices

[1365] The wearable device collects the user's heart rate, electrocardiogram data, and emotional data, and also includes an emotion engine, which transmits the collected data wirelessly to the device in real time.

[1366] Terminal

[1367] The terminal is a computing device that temporarily stores collected data and uploads it to a server at regular intervals. The terminal receives data sent from the wearable device and receives alerts from the server to notify the user.

[1368] server

[1369] The server is equipped with an artificial intelligence model to analyze data sent from the device. If the server detects an abnormality, it immediately generates an alert and sends it to the device. This alert contains detailed information about the abnormality and how to deal with it.

[1370] Linking with other devices

[1371] The autonomous vehicle's control system will receive alerts from the server and have the ability to coordinate with the server to safely stop the vehicle if the driver's health condition deteriorates.

[1372] Specific operation explanation

[1373] Data collection and transmission

[1374] Once the user puts on the wearable device, it starts collecting heart rate, electrocardiogram, and emotional data, which are then periodically sent to the device, which then uploads the data to a cloud server at regular intervals.

[1375] Data analysis and alert generation

[1376] The server analyzes the data received from the device in real time. The artificial intelligence model used utilizes cloud analysis services such as Google Cloud AI, AWS, and Microsoft Azure. If an abnormality is detected, the server immediately generates an alert and sends it to the vehicle's autonomous driving control system.

[1377] Autonomous Driving System Control

[1378] When the autonomous vehicle's control system receives an alert from the server, it takes action to transition to safety mode. Specifically, it reduces the vehicle's speed and stops it at an appropriate location. The driver is notified via voice and display. This is to quickly ensure safety in the event of a sudden change in the driver's health condition.

[1379] Specific examples

[1380] scenario

[1381] Consider a situation where a driver is operating an autonomous vehicle and their heart rate spikes to 120 bpm, causing the emotion engine to detect a state of high stress.

[1382] 1. Data collection: Heart rate and electrocardiogram data obtained from the wearable device are sent to the terminal and uploaded to the server.

[1383] 2. Data analysis: The server analyzes the received data and detects any anomalies.

[1384] 3. Alert generation: The server detects an anomaly and immediately generates an alert and sends it to the terminal and the autonomous vehicle's control system.

[1385] 4. Response: The autonomous driving system switches to safety mode and notifies the driver that "rapid heart rate and high stress have been detected. An emergency stop will be made immediately."

[1386] Prompt Sentence Examples

[1387] Design a system that will transition to a safety mode and issue an emergency stop alert if the driver's heart rate exceeds 120 bpm and their emotional state is recognized as high stress. This includes a function that will link the autonomous vehicle's control system with wearable devices to collect and analyze data in real time and automatically respond if an abnormality occurs.

[1388] According to the present invention, when an abnormality occurs in the driver's heart rate or electrocardiogram, the autonomous vehicle can be controlled quickly and appropriately to ensure the safety of the driver.

[1389] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1390] Step 1:

[1391] Data collection

[1392] When a user wears the wearable device, it starts collecting heart rate, electrocardiogram (ECG) data, and emotional data, which are then transmitted to a device via Bluetooth. The inputs are data on heart rate, ECG waveforms, and emotional state, and the output is the periodic transmission of these data to the device.

[1393] Step 2:

[1394] Data transmission and storage

[1395] The terminal temporarily stores data received from the wearable device. This data is uploaded to the server at regular intervals. The input is the biometric data received from the wearable device, and the output is the data uploaded to the server. The communication status is monitored during data transmission, and if it fails, the terminal retries.

[1396] Step 3:

[1397] Data analysis

[1398] The server receives data uploaded from the device and analyzes it in real time. A generative AI model (e.g., a TensorFlow model) is used for the analysis. The input is the heart rate, ECG data, and emotion data sent from the device, and the output is the analysis results and the presence or absence of abnormalities. Specifically, the server detects sudden increases in heart rate, abnormal ECG waveforms, and high stress states.

[1399] Step 4:

[1400] Alert Generation

[1401] When the server detects an abnormality, it generates an alert and sends it to the device and the autonomous vehicle's control system. The input is the results of data analysis and details of the detected abnormality, and the output is an alert message. Specifically, a message such as "A sudden increase in heart rate has been detected. The vehicle will immediately enter safety mode" is generated.

[1402] Step 5:

[1403] Alert notifications and driving control

[1404] The terminal receives the alert from the server and notifies the user via audio and video. At the same time, the autonomous vehicle's control system also receives the alert and transitions to safety mode. The input is the alert message sent from the server, and the output is a notification to the user and a change in vehicle behavior. Specifically, the autonomous vehicle reduces its speed and stops at an appropriate location.

[1405] Step 6:

[1406] Contacting a medical institution

[1407] Furthermore, if necessary, the terminal will activate an automatic contact function to a medical institution. The input is an abnormality detection message from the server, and the output is contact information to the medical institution. Specifically, the terminal will notify the medical institution of the driver's current location and condition.

[1408] This series of processing steps makes it possible to respond quickly and appropriately when an abnormality occurs in the driver's heart rate or electrocardiogram.

[1409] 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.

[1410] 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.

[1411] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1412] 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.

[1413] 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.

[1414] 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.

[1415] 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).

[1416] 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.

[1417] 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."

[1418] 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.

[1419] 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).

[1420] 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.

[1421] 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.

[1422] 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.

[1423] 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.

[1424] 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.

[1425] 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.

[1426] 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.

[1427] 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.

[1428] 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.

[1429] 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.

[1430] The following is further disclosed regarding the above embodiment.

[1431] (Claim 1)

[1432] means for collecting heart rate and electrocardiogram data in real time using a wearable device worn by a user;

[1433] means for transmitting the collected data to a terminal;

[1434] a means by which the terminal uploads data to a server;

[1435] A means for the server to analyze the received data and send an alert to the terminal if an abnormality is detected;

[1436] means for providing audio and visual notification to the user when the device receives an alert;

[1437] A system including:

[1438] (Claim 2)

[1439] 2. The system according to claim 1, wherein the terminal comprises means for displaying a guide regarding lifesaving measures to the user after receiving the alert.

[1440] (Claim 3)

[1441] 10. The system of claim 1, wherein the server comprises means for using an artificial intelligence model to detect anomalies.

[1442] "Example 1"

[1443] (Claim 1)

[1444] means for collecting heart rate and electrocardiogram data in real time using a wearable device worn by a user;

[1445] means for transmitting the collected data to a terminal;

[1446] a means by which the terminal uploads data to a server;

[1447] A means for the server to analyze the received data and send an alert to the terminal if an abnormality is detected;

[1448] means for providing audio and visual notification to the user when the device receives an alert;

[1449] using an artificial intelligence model to analyze the data in real time and detect sudden increases in heart rate and abnormal electrocardiogram waveforms;

[1450] A system including:

[1451] (Claim 2)

[1452] 2. The system according to claim 1, wherein the terminal comprises means for displaying a guide regarding lifesaving measures to the user after receiving the alert.

[1453] (Claim 3)

[1454] 2. The system according to claim 1, wherein the terminal comprises means for monitoring communication status and for retransmitting data.

[1455] "Application Example 1"

[1456] (Claim 1)

[1457] means for collecting heart rate and electrocardiogram data in real time using a wearable device worn by a user;

[1458] means for transmitting the collected data to a terminal;

[1459] a means by which the terminal uploads data to a server;

[1460] A means for the server to analyze the received data and send an alert to the terminal if an abnormality is detected;

[1461] means for providing audio and visual notification to the user when the device receives an alert;

[1462] a means for providing users with guidance on life-saving procedures;

[1463] A means to display appropriate countermeasures (e.g., rest, emergency contact method, cardiopulmonary resuscitation guide) when an abnormality is detected, and

[1464] A system including:

[1465] (Claim 2)

[1466] 2. The system according to claim 1, wherein the terminal comprises means for displaying a guide regarding lifesaving measures to the user after receiving the alert.

[1467] (Claim 3)

[1468] 10. The system of claim 1, wherein the server comprises means for using an artificial intelligence model to detect anomalies.

[1469] "Example 2: Combining Emotion Engines"

[1470] Understood. We will extract the technically novel parts from the description of the system's program processing, combine them with the original patent claims, and rewrite them taking into account the content of the new invention.

[1471] (Claim 1)

[1472] means for collecting heart rate and electrocardiogram data in real time using a wearable device worn by a user;

[1473] means for transmitting the collected data to a terminal at predetermined intervals;

[1474] A means for the terminal to temporarily store data and upload it to a server at predetermined intervals;

[1475] A means for the server to analyze the received data using an artificial intelligence model and send an alert to the terminal if an abnormality is detected;

[1476] means for providing audio and visual notification to the user when the device receives an alert;

[1477] A means of generating an alert containing detailed information about the anomaly and how to address it;

[1478] A system including:

[1479] (Claim 2)

[1480] 2. The system according to claim 1, further comprising means for displaying a guide on lifesaving measures to the user after the terminal receives an alert, as well as a guide customized based on emotion data.

[1481] (Claim 3)

[1482] 2. The system according to claim 1, wherein the server comprises means for analyzing emotion data in addition to analyzing heart rate and electrocardiogram data to detect abnormalities.

[1483] "Application example 2 when combining emotion engines"

[1484] (Claim 1)

[1485] means for collecting heart rate and electrocardiogram data in real time using a wearable device worn by a user;

[1486] means for transmitting the collected data to a terminal;

[1487] a means by which the terminal uploads data to a server;

[1488] A means for the server to analyze the received data and send an alert to the terminal if an abnormality is detected;

[1489] means for providing audio and visual notification to the user when the device receives an alert;

[1490] A means for controlling an autonomous driving system of a vehicle when an abnormality is detected using a means for linking with other devices;

[1491] A system including:

[1492] (Claim 2)

[1493] 2. The system according to claim 1, wherein the terminal comprises means for displaying a guide regarding lifesaving measures to the user after receiving the alert.

[1494] (Claim 3)

[1495] 10. The system of claim 1, wherein the server comprises means for using an artificial intelligence model to detect anomalies. [Explanation of symbols]

[1496] 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 heart rate and electrocardiogram data in real time using a wearable device worn by a user; means for transmitting the collected data to a terminal; a means by which the terminal uploads data to a server; A means for the server to analyze the received data and send an alert to the terminal if an abnormality is detected; means for providing audio and visual notification to the user when the device receives an alert; A system including:

2. The system according to claim 1 , wherein the terminal comprises means for displaying a guide regarding lifesaving measures to the user after receiving the alert.

3. 10. The system of claim 1, wherein the server comprises means for using an artificial intelligence model to detect anomalies.

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