system

Abstract

Provide a system. The data collection unit includes means for acquiring physiological data and voice data of a user in real time; a data analysis unit for processing and analyzing the acquired physiological data and voice data to evaluate a health condition; an anomaly detection unit for detecting a health anomaly based on the analysis result from the data analysis unit; a notification unit that notifies a predetermined notification destination based on the abnormality detected by the abnormality detection unit; A system including:

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 recent years, wearable devices have increasingly been used for health management and early detection of illnesses, but problems remain that prevent these devices from becoming widespread. The main factors include the inconvenience of constantly wearing the device, the hassle of users managing their own data, and the accuracy of disease detection. Furthermore, due to a lack of established collaboration with medical institutions, appropriate and prompt measures may not be taken even if an abnormality is detected. The present invention aims to solve these problems. [Means for solving the problem]

[0005] The present invention provides a system having the following configuration.

[0006] The data collection unit acquires the user's physiological data and voice data in real time, and the data analysis unit processes and analyzes the acquired physiological data and voice data to evaluate their health condition. Furthermore, the anomaly detection unit detects health abnormalities based on the analysis results from the data analysis unit, and the notification unit notifies designated recipients based on the abnormalities detected by the anomaly detection unit. This reduces the burden on the user even if the device is worn at all times, and the collected data is automatically analyzed, with prompt notification being sent when an abnormality is detected. In addition, by using voice separation technology, only the necessary voice signals can be accurately extracted and optimal health advice can be provided to each individual, enabling highly accurate health management while reducing the burden on the user.

[0007] A "data collection unit" is a means for acquiring physiological data and audio data of a user in real time.

[0008] The "data analysis unit" is a means for processing and analyzing the acquired physiological data and audio data to assess the health condition.

[0009] The "abnormality detection unit" is a means for detecting abnormal health conditions based on the analysis results from the data analysis unit.

[0010] The "notification unit" is a means for notifying a predetermined notification destination based on an abnormality detected by the abnormality detection unit.

[0011] "Speech separation technology" is a technology that extracts only the necessary audio signals from collected audio data with high accuracy.

[0012] The "voice generation unit" is a means for generating and providing optimal health advice to the user based on the results of analysis by the data analysis unit.

[0013] An "abnormality" is a condition that is different from the normal state of health of the user and indicates a sign of a medical condition or a significant change.

[0014] "Health status" is an assessment of the user's overall physical condition and medical condition based on physiological and voice data. [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 an earhook-type wearable device, which is a system that acquires a user's physiological and voice data in real time, analyzes the data, and evaluates their health condition. This system has the function of immediately notifying the user if an abnormality is detected, and supports the user's daily health management.

[0037] Server Processing

[0038] Data collection and storage

[0039] The server receives physiological and audio data from the device in real time and stores it in a database, including heart rate, body temperature, and other data.

[0040] Data analysis

[0041] The server analyzes the received data using algorithms that evaluate heart rate fluctuations, voice data separation, and changes in body temperature, among other things, to assess the user's current health status and determine whether there are any abnormalities.

[0042] Anomaly detection and notification

[0043] The server detects abnormalities based on the analysis results, and if an abnormality is detected, the server immediately sends a notification to the user, their family, or medical professionals, including the results of the data analysis and details of the specific abnormal condition.

[0044] Terminal handling

[0045] Biometric data collection

[0046] The device is worn in the user's ear and collects physiological data such as heart rate, body temperature, and everyday sounds in real time. This data is acquired by internal sensors.

[0047] Data transmission

[0048] The terminals send the collected data to the server at regular intervals, and the data is time-stamped, making it easier for the server to process it.

[0049] User Feedback

[0050] The device then provides the user with feedback based on the analysis results obtained from the server. This feedback is provided in the form of voice, and specific advice such as "Your heart rate is high. Please rest."

[0051] User Action

[0052] Wearing the device

[0053] The user wears the earhook-type device, which automatically starts up and begins collecting physiological and audio data.

[0054] Receiving feedback

[0055] The user receives feedback from the device and adjusts their behavior accordingly. For example, if an abnormality is detected, the device recommends taking specific action, such as visiting a medical institution.

[0056] Specific examples

[0057] For example, when a user wears the device in their ear, the device collects their heart rate, body temperature, and everyday sounds. This data is periodically sent to a server, where it is analyzed in real time. Based on the analysis results, if the heart rate is higher than normal, for example, the device will provide voice feedback such as "Your heart rate is high. Please take a deep breath." By following this feedback and taking a deep breath, the user can stabilize their heart rate.

[0058] As described above, the system of the present invention effectively supports health management by monitoring the user's health condition in real time and immediately notifying the user if an abnormality occurs. This system allows the user to constantly understand their own health condition and quickly take appropriate measures.

[0059] The processing flow will be explained below.

[0060] Server Processing

[0061] Step 1: Receiving and storing data

[0062] The server receives the physiological data and voice data transmitted from the terminal and stores the received data in a database.

[0063] The server receives the data over the network.

[0064] The received data is stored in a database.

[0065] Step 2: Data analysis

[0066] The server analyzes the stored data and evaluates the user's health condition.

[0067] Retrieve data from the database.

[0068] Analytical algorithms are used to process heart rate, body temperature, voice data, etc.

[0069] Evaluate health status and generate analytical results.

[0070] Step 3: Anomaly detection

[0071] The server detects health abnormalities based on the analysis results.

[0072] Detect outliers from the analysis results.

[0073] If an abnormality is detected, the abnormal data is determined.

[0074] Step 4: Notification

[0075] The server will notify you if an abnormality is detected.

[0076] Decide who to notify (user, family, medical professionals).

[0077] Sends notifications containing details of abnormal conditions and analysis results.

[0078] Terminal handling

[0079] Step 1: Collect biometric data

[0080] The device measures the user's heart rate, body temperature, and daily sounds in real time.

[0081] Measure your heart rate using a sensor.

[0082] Body temperature is measured using a sensor.

[0083] Daily sounds are recorded using a microphone.

[0084] Step 2: Send data to the server

[0085] The terminal transmits the collected biometric data and voice data to the server.

[0086] Packetize the data.

[0087] Send data to the server periodically.

[0088] Step 3: Receive analysis results from the server

[0089] The terminal receives the analysis result from the server.

[0090] Receives analysis result data sent from the server.

[0091] The received analysis results are saved in the internal memory.

[0092] Step 4: User feedback

[0093] The terminal provides feedback to the user based on the analysis results from the server.

[0094] Analyze the results and determine the necessary actions.

[0095] Provides audio feedback through a speaker.

[0096] User Action

[0097] Step 1: Wear the device

[0098] The user wears the ear hook type device on their ear.

[0099] Place the device in your ear.

[0100] Check that it is installed correctly.

[0101] Step 2: Verify your biometric data

[0102] The user checks the feedback provided by the terminal.

[0103] Listen to the feedback audio message.

[0104] Adjust your behavior based on the feedback.

[0105] Step 3: Take Healthy Actions

[0106] The user manages their health based on the feedback.

[0107] Follow the advice provided (e.g., take deep breaths, take breaks).

[0108] If necessary, consult a medical institution.

[0109] The above are the specific processing steps in the system of the present invention. This process allows the user's health condition to be monitored in real time, and if an abnormality is detected, a notification is sent immediately. This enables early health management and preventative measures.

[0110] Example 1

[0111] 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."

[0112] Existing health management systems lack the ability to monitor users' physiological data in real time and quickly detect and notify abnormalities. Furthermore, they often fail to provide appropriate feedback or health advice based on analysis results. This makes it difficult for users to constantly monitor their own health status and quickly take appropriate measures.

[0113] 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.

[0114] In this invention, the server includes a data collection unit for acquiring a user's physiological data and voice data in real time, a data communication unit for transmitting the acquired physiological data and voice data to a central computer, a data storage unit for storing the transmitted data with time information, a data analysis unit for analyzing the stored physiological data and voice data using a machine learning algorithm and evaluating the user's health status, an anomaly detection unit for detecting a health abnormality based on the analysis results from the data analysis unit, a notification unit for notifying a predetermined notification destination based on the abnormality detected by the anomaly detection unit, and a feedback unit for providing audio feedback to the user based on the abnormality detection result and the analysis result. This enables the user's physiological data to be monitored in real time and abnormalities to be quickly detected and notified. Furthermore, by providing effective feedback based on the analysis results, the user can constantly understand their health status and quickly take appropriate measures.

[0115] A "data collection unit" is a device or means for acquiring physiological and audio data of a user in real time.

[0116] "Physiological data" refers to data related to the user's physical functions and health condition, such as heart rate, body temperature, and everyday sounds.

[0117] "Voice data" refers to all voice signals collected from a user, and is the data that serves as the basis for extracting only the necessary voice signals.

[0118] A "data communications unit" is a device or means for transmitting acquired physiological and audio data to a central computer in real time.

[0119] The "data storage unit" is a storage device or means for storing transmitted physiological data and audio data with time information.

[0120] "Data analysis unit" refers to a device or means for analyzing stored physiological data and audio data using machine learning algorithms to assess health status.

[0121] "Machine learning algorithms" are mathematical methods and models that use AI technology to analyze data and detect patterns and anomalies.

[0122] The "abnormality detection unit" is a device or means for detecting a health abnormality based on the analysis results from the data analysis unit.

[0123] The "notification unit" is a device or means for notifying a predetermined notification destination based on an abnormality detected by the abnormality detection unit.

[0124] The "feedback unit" is a device or means for providing audio feedback to the user based on the anomaly detection results and analysis results.

[0125] A "central computer" is a central information processing system that analyzes and stores data.

[0126] "Time information" is information that indicates the time when data was collected and transmitted.

[0127] The following hardware and software are used to implement the present invention.

[0128] Server Configuration

[0129] The server configuration includes a data collection unit, a data communication unit, a data storage unit, a data analysis unit, an anomaly detection unit, a notification unit, and a feedback unit.

[0130] Hardware used

[0131] Central computer: Information processing system that analyzes and stores data

[0132] Storage: MySQL® database

[0133] Software used

[0134] Programming language: Python, JavaScript (registered trademark)

[0135] Machine learning framework: TENSORFLOW (registered trademark)

[0136] Database Management System: MySQL

[0137] Communication method: Node.js

[0138] Device configuration

[0139] The terminal configuration includes collecting biometric data, transmitting data, and providing feedback.

[0140] Hardware used

[0141] Sensors: Heart rate sensor, temperature sensor, microphone

[0142] Communication devices: Bluetooth communication module, Wi-Fi module

[0143] Speaker: A device that provides audio feedback

[0144] User Roles

[0145] The user's primary role is to place the device in their ear and receive feedback.

[0146] System processing details

[0147] Server Processing

[0148] The server receives physiological and audio data sent from the device via Bluetooth. It then stores this data in a MySQL database and adds a timestamp to facilitate analysis. It then analyzes the data using Python and TensorFlow, detecting any abnormalities based on the analysis results. If an abnormality is detected, it activates a notification system via Node.js to notify the user, their family, and medical professionals. It also provides feedback to the user based on the analysis results.

[0149] Terminal handling

[0150] The device uses sensors to collect physiological data such as heart rate, body temperature, and everyday sounds in real time. This data is sent to a smartphone via Bluetooth, and the smartphone then transfers the data to a server via Wi-Fi. The device receives the server's analysis results and provides voice feedback such as "Your heart rate is high. Please take a deep breath" through the speaker.

[0151] User Action

[0152] The user begins collecting physiological and audio data by wearing the earhook device, and adjusts their behavior accordingly based on the audio feedback provided by the device.

[0153] Specific examples

[0154] For example, when a user wears the device in their ear, the device collects heart rate, body temperature, and everyday sounds, and sends them to a smartphone via Bluetooth. The smartphone then sends the data to a server via Wi-Fi, where it is analyzed using TensorFlow. If the analysis determines that the heart rate is higher than normal, the device's speaker will provide audio feedback saying, "Your heart rate is high. Please take a deep breath." By following this feedback and taking deep breaths, the user can stabilize their heart rate.

[0155] Prompt Sentence Examples

[0156] Please provide the Python code for a program that sends the user's physiological and audio data to a server, analyzes the data in real time, and evaluates the user's health condition.

[0157] As described above, this system monitors the user's health condition in real time, quickly detecting and notifying abnormalities. In addition, by providing effective feedback based on the analysis results, users can constantly understand their own health condition and quickly take appropriate measures.

[0158] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0159] Step 1: Wear the device

[0160] The user wears the ear hook type device on their ear. This automatically starts the device and starts collecting physiological and audio data. Specifically, the heart rate sensor, temperature sensor, and microphone start working and data collection begins. The input is "user wearing" and the output is "sensor activation."

[0161] Step 2: Collect biometric data

[0162] The device uses a heart rate sensor, temperature sensor, and microphone to collect the user's heart rate, body temperature, and everyday sounds in real time, allowing the device to constantly monitor the user's physiological state. The input is "physiological data and audio data acquired by the sensors," and the output is "physiological data and audio data collected in real time."

[0163] Step 3: Sending data

[0164] The device sends the collected data to a smartphone via Bluetooth, and the smartphone transfers the data to a server via Wi-Fi. A timestamp is added, making it easier for the server to process the data. The input is the "collected physiological data and voice data," and the output is the "data sent to the server."

[0165] Step 4: Save your data

[0166] The server stores the received physiological and audio data in a MySQL database. The important thing here is that the data is time-stamped, which makes it possible to analyze it later along a timeline. The input is "data sent to the server" and the output is "data stored in the database."

[0167] Step 5: Analyze the data

[0168] The server analyzes the stored data using Python and TensorFlow. Specifically, it runs algorithms that analyze heart rate variability, separate audio data, and evaluate changes in body temperature. This analysis evaluates the user's current health status. The input is the data stored in the database, and the output is the analysis results.

[0169] Step 6: Anomaly detection

[0170] The server detects anomalies based on the analysis results. If an anomaly is detected, the details of the anomaly and the corresponding data are recorded in a log. For example, if the analysis results determine that the heart rate is abnormally high, this is detected as an anomaly. The input is the "analysis results" and the output is the "anomaly detection results."

[0171] Step 7: Sending notifications

[0172] The server uses Node.js to send notifications to users, their families, and medical professionals based on the anomaly detection results. The notifications include the results of data analysis and details of the specific abnormality. The input is the "anomaly detection results," and the output is "notifications to recipients."

[0173] Step 8: Provide feedback

[0174] The device provides voice feedback to the user based on the analysis results obtained from the server. Specifically, a message such as "Your heart rate is high. Please rest" is provided through the speaker. The input is "analysis results and abnormality detection results," and the output is "voice feedback."

[0175] (Application example 1)

[0176] 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."

[0177] Conventional health monitoring systems are primarily limited to detecting and notifying physiological abnormalities, making it difficult to detect and respond appropriately to security risks such as stress, abnormal behavior, and dangerous environments. Furthermore, many of these systems rely on a single notification method, and lack a mechanism for quickly sending notifications to users' mobile devices in the event of an emergency.

[0178] 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.

[0179] In this invention, the server includes a data collection unit for acquiring physiological data and voice data of a user in real time, a data analysis unit for processing and analyzing the acquired physiological data and voice data to evaluate the health condition, an anomaly detection unit for detecting an abnormality in health based on the analysis result from the data analysis unit, a notification unit for notifying a predetermined notification destination based on the abnormality detected by the anomaly detection unit, a means for analyzing security risks and detecting stress, abnormal behavior, and dangerous environments, and a means for sending a notification to the user's mobile device when a danger is detected. This enables comprehensive health and safety monitoring, including security risks, and enables prompt and appropriate notification in emergencies.

[0180] A "data collection unit" is a device that has the function of acquiring physiological data and voice data of a user in real time.

[0181] The "data analysis unit" is a device that processes and analyzes acquired physiological data and audio data to evaluate health status.

[0182] The "abnormality detection unit" is a device that has the function of detecting health abnormalities based on the analysis results from the data analysis unit.

[0183] The "notification unit" is a device that has the function of notifying a predetermined notification destination based on an abnormality detected by the abnormality detection unit.

[0184] "Security risks" refer to risks that affect user safety, such as stress, abnormal behavior, and dangerous environments.

[0185] "Mobile terminal" refers to an information and communication device that a user carries with them at all times, such as a smartphone.

[0186] "Server" refers to the central computer system that collects, analyzes, and notifies data.

[0187] The present invention relates to an earhook-type wearable device that collects a user's physiological and voice data in real time, analyzes the data, and evaluates their health status and security risks. This system has the function of immediately notifying users if an abnormality is detected, and supports the user's daily health and safety management.

[0188] Server Processing

[0189] Data collection and storage

[0190] The server receives physiological and audio data from the device in real time and stores it in a database, including heart rate, body temperature, and other data.

[0191] Data analysis

[0192] The server analyzes the received data using algorithms that detect heart rate fluctuations, voice data separation, changes in body temperature, and stress or abnormal behavior. This allows the server to assess the user's current health and security risks and determine whether there are any abnormalities. For example, the server can use a generative AI model to detect psychological stress.

[0193] Anomaly detection and notification

[0194] The server detects various abnormalities based on the analysis results. If an abnormality is detected, the server immediately sends a notification to the user, their family, or medical professionals. This notification includes the results of the data analysis and details of the specific abnormal condition. For example, if the heart rate is abnormally high, the server will notify the user, saying, "Your heart rate is high. Please rest."

[0195] Terminal handling

[0196] Biometric data collection

[0197] The device is worn in the user's ear and collects physiological data such as heart rate, body temperature, and everyday sounds in real time. This data is acquired by internal sensors.

[0198] Data transmission

[0199] The terminals send the collected data to the server at regular intervals, and the data is time-stamped, making it easier for the server to process it.

[0200] User Feedback

[0201] The device then provides feedback to the user based on the analysis results obtained from the server. This feedback is provided in the form of voice, and specific advice such as "Your heart rate is high. Please rest." If a security risk is detected, the device will notify the user, saying, "You may be in a dangerous environment. Please be careful."

[0202] User Action

[0203] Wearing the device

[0204] The user wears the earhook-type device, which automatically starts up and begins collecting physiological and audio data.

[0205] Receiving feedback

[0206] The user receives feedback from the device and adjusts their behavior accordingly. For example, if an abnormality is detected, the device recommends taking specific action, such as visiting a medical institution.

[0207] Specific examples

[0208] For example, when a user wears the device in their ear, the device collects their heart rate, body temperature, and everyday sounds. This data is periodically sent to a server, where it is analyzed in real time. Based on the analysis results, if the heart rate is higher than normal, for example, the device will provide voice feedback saying, "Your heart rate is high. Please take a deep breath." If stress is detected, the device will send a notification saying, "Your stress is increasing. Please relax." By following this feedback and taking a deep breath, the user can stabilize their heart rate.

[0209] Generative AI model prompt example

[0210] The next analysis will receive the user's heart rate data and determine if it is an abnormal value. If the heart rate is not within the range of 60 to 100, notify the user that "An abnormal heart rate has been detected." Below is an example of heart rate data.

[0211] Heart rate: 110

[0212] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0213] Step 1:

[0214] The terminal collects physiological and audio data from the user in real time.

[0215] (Input) Heart rate data, body temperature data, and voice data from sensors.

[0216] (Processing) Data collection by sensors.

[0217] (Output) Physiological and audio data are stored in internal memory.

[0218] Step 2:

[0219] The terminal sends the collected data to the server at regular intervals.

[0220] (Input) Physiological and audio data collected in step 1.

[0221] (Processing) The data is formatted into packets, timestamps are added, and the packets are sent to the server.

[0222] (Output) Physiological and audio data received on the server side.

[0223] Step 3:

[0224] The server stores the received data in a database.

[0225] (Input) Physiological and audio data sent from sensors.

[0226] (Processing) Save received data in a database.

[0227] (Output) Physiological and audio data stored in a database.

[0228] Step 4:

[0229] The server analyzes the stored data.

[0230] (Input) Physiological and audio data stored in a database.

[0231] (Processing) The data analysis unit analyzes heart rate fluctuations, audio data separation, and changes in body temperature. Stress and abnormal behavior are also detected using generative AI models.

[0232] (Output) Health and security risk assessment results.

[0233] Step 5:

[0234] The server detects anomalies based on the analysis results.

[0235] (Input) Evaluation results of data analysis.

[0236] (Processing) Detect outliers using an anomaly detection algorithm.

[0237] (Output) The result of the anomaly detection.

[0238] Step 6:

[0239] If the server detects an abnormality, it sends a notification to the user's mobile device.

[0240] (Input) Anomaly detection results.

[0241] (Processing) Generate notification content and send it to the mobile device.

[0242] (Output) The notification sent to the user's mobile device.

[0243] Step 7:

[0244] The terminal provides feedback to the user based on the analysis results of the server.

[0245] (Input) The notification content sent from the server.

[0246] (Processing) Audio and visual notifications.

[0247] (Output) Providing feedback to the user.

[0248] Step 8:

[0249] The user receives feedback provided by the device and adjusts their behavior accordingly.

[0250] (Input) Feedback from the device.

[0251] (Processing) Adjusting behavior based on feedback content.

[0252] (Output) Change in user behavior (e.g., taking a deep breath, seeking medical attention, etc.).

[0253] 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.

[0254] The present invention relates to an earhook-type wearable device and a system that acquires a user's physiological and voice data in real time, analyzes the data, and evaluates their health condition. The present invention also incorporates an emotion engine to recognize the user's emotional state and manage their health based on this. This system immediately notifies the user if an abnormality is detected, supporting the user's daily health management.

[0255] Server Processing

[0256] Data collection and storage

[0257] The server receives physiological and voice data from the device in real time and stores it in a database. This data includes heart rate, body temperature, and everyday sounds. Furthermore, it uses an emotion engine to detect the user's emotions from the collected voice data.

[0258] Data analysis

[0259] The server analyzes the stored data using algorithms that include heart rate variability, body temperature changes, audio data separation, and emotional state recognition and evaluation, which then evaluates the user's current health and emotional state.

[0260] Anomaly detection and notification

[0261] The server detects abnormalities in health and emotions based on the analysis results. If an abnormality is detected, the server immediately sends a notification to the user, their family, or medical professionals. The notification includes the results of the data analysis and details of the abnormal condition.

[0262] Terminal handling

[0263] Biometric data collection

[0264] The device measures and generates data on the user's heart rate, body temperature, and everyday sounds in real time, as well as collecting data needed to detect the user's emotional state.

[0265] Data transmission

[0266] The device transmits the collected physiological data, voice data, and emotional state data to a server at regular intervals.

[0267] User Feedback

[0268] The device then provides the user with feedback based on the analysis results obtained from the server. The feedback is provided in the form of voice, and specific advice is provided, such as "Your heart rate is high. Take a deep breath" or "You seem to be stressed. Relax."

[0269] User Action

[0270] Wearing the device

[0271] The user wears the earhook-type device, which automatically starts up and begins collecting physiological, audio, and emotional data.

[0272] Receiving feedback

[0273] Users can then adjust their behavior based on the feedback provided by the device. For example, if an abnormality is detected, specific actions such as seeking medical attention may be recommended. Users may also receive advice based on their emotional state, helping them to practice stress management and relaxation techniques.

[0274] Specific examples

[0275] For example, when a user wears the device in their ear, the device collects their heart rate, body temperature, and everyday sounds. This data is periodically sent to a server, where it is analyzed in real time. Based on the analysis results, if the heart rate is higher than normal, for example, the device will provide voice feedback such as, "Your heart rate is high. Please take a deep breath." At the same time, if the device determines that the user is feeling stressed based on the tone and pace of their voice, it will add the advice, "You seem stressed. Relax." By following this feedback and taking deep breaths, the user can stabilize their heart rate and reduce stress.

[0276] As described above, the system of the present invention effectively supports health and stress management by monitoring the user's health and emotional state in real time and immediately notifying the user if any abnormalities occur. This system allows the user to constantly understand their own health and emotional state and quickly take appropriate measures.

[0277] The processing flow will be explained below.

[0278] Server Processing

[0279] Step 1: Receiving and storing data

[0280] The server receives physiological and audio data from the device in real time and stores the data in a database.

[0281] The server receives the data over the network.

[0282] The received data is stored in a database.

[0283] Step 2: Data analysis

[0284] The server analyzes the stored data and evaluates the user's health condition.

[0285] Retrieve data from the database.

[0286] Heart rate, body temperature, and audio data are processed using analytical algorithms.

[0287] Recognize the user's emotional state from voice data using an emotion engine.

[0288] Evaluate health and emotional states and generate analytical results.

[0289] Step 3: Anomaly detection

[0290] The server detects health and emotional abnormalities based on the analysis results.

[0291] Detect outliers from the analysis results.

[0292] If an abnormality is detected, the abnormal data is determined.

[0293] Step 4: Notification

[0294] The server will notify you if an abnormality is detected.

[0295] Decide who to notify (user, family, medical professionals).

[0296] Sends notifications containing details of abnormal conditions and analysis results.

[0297] Terminal handling

[0298] Step 1: Collect biometric data

[0299] The device measures the user's heart rate, body temperature, and everyday sounds in real time and generates data.

[0300] Measure your heart rate using a sensor.

[0301] Body temperature is measured using a sensor.

[0302] Daily sounds are recorded using a microphone.

[0303] Step 2: Collecting emotion data

[0304] The device collects emotional data from the user's tone and pace of voice.

[0305] Analyzes voice data and uses an emotion engine to recognize emotional states.

[0306] Record the recognized emotion data.

[0307] Step 3: Send data to the server

[0308] The terminal transmits the collected physiological data, voice data, and emotion data to a server.

[0309] Packetize the data.

[0310] Send data to the server periodically.

[0311] Step 4: Receive analysis results from the server

[0312] The terminal receives the analysis result from the server.

[0313] Receives analysis result data sent from the server.

[0314] The received analysis results are saved in the internal memory.

[0315] Step 5: User feedback

[0316] The terminal provides feedback to the user based on the analysis results from the server.

[0317] Analyze the results and determine the necessary actions.

[0318] Provides audio feedback through a speaker.

[0319] User Action

[0320] Step 1: Wear the device

[0321] The user wears the ear hook type device on their ear.

[0322] Place the device in your ear.

[0323] Check that it is installed correctly.

[0324] Step 2: Check biometric and emotional data

[0325] The user checks the feedback provided by the terminal.

[0326] Listen to the feedback audio message.

[0327] Adjust your behavior based on the feedback.

[0328] Step 3: Take healthy actions and manage your emotions

[0329] Users manage their health and emotions based on the feedback.

[0330] Follow the advice provided (e.g., take deep breaths, take breaks).

[0331] Practice stress management and relaxation techniques.

[0332] If necessary, consult a medical institution.

[0333] Specific examples

[0334] For example, when a user wears the device in their ear, the device collects emotional data from their heart rate, body temperature, everyday sounds, and even voice. This data is periodically sent to a server, where it is analyzed in real time. Based on the analysis results, if the heart rate is higher than normal, for example, the device will provide voice feedback such as "Your heart rate is high. Take a deep breath." At the same time, if the device determines that the user is feeling stressed based on the tone and pace of their voice, it will add advice such as "You seem stressed. Relax." By following this feedback and taking deep breaths and practicing relaxation techniques, the user can stabilize their heart rate and reduce stress.

[0335] The above are the specific processing steps in the system of the present invention. This process allows the user's health and emotional state to be monitored in real time, and if an abnormality is detected, a notification is sent immediately. This allows for early health management and emotional management at the same time.

[0336] Example 2

[0337] 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."

[0338] In recent years, personal health management has become increasingly important. However, existing systems lack the ability to adequately monitor physiological data and emotional states in real time, making it difficult to respond quickly when abnormalities occur. Furthermore, there are limited means for providing appropriate feedback and advice to users. This makes it difficult for users to effectively manage their own health and emotional states. To address these issues, a system that comprehensively collects, analyzes, and provides feedback in real time is needed.

[0339] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a data collection unit that acquires physiological data and voice data of the user in real time, a data analysis unit that processes and analyzes the acquired physiological data and voice data to evaluate the health and emotional states, an anomaly detection unit that detects health and emotional abnormalities based on the analysis results from the data analysis unit, a notification unit that notifies a predetermined notification destination based on the abnormality detected by the anomaly detection unit, and a voice feedback unit that provides specific health advice to the user based on the results of the data analysis unit. This enables real-time monitoring of the health and emotional states and the provision of appropriate feedback and advice. Furthermore, early detection of abnormalities and rapid response enable the user to effectively manage their own health and emotional states.

[0340] A "data collection unit" is a device that has the function of acquiring physiological data and voice data of a user in real time.

[0341] "Physiological data" refers to data that indicates the user's physical condition, such as heart rate and body temperature.

[0342] "Voice data" refers to acoustic data that includes the user's voice and surrounding sounds.

[0343] The "data analysis unit" is a device that processes and analyzes acquired physiological and audio data to evaluate health and emotional states.

[0344] "Health status" is an indicator of a user's physical condition, and is primarily evaluated by heart rate, body temperature, etc.

[0345] "Emotional state" is an index that indicates the user's psychological state, and is primarily evaluated by the tone and pace of the voice data.

[0346] The "anomaly detection unit" is a device that has the function of detecting health and emotional abnormalities based on the analysis results from the data analysis unit.

[0347] The "notification unit" is a device that has the function of notifying a predetermined notification destination based on an abnormality detected by the abnormality detection unit.

[0348] The "voice feedback unit" is a device that has the function of providing specific health advice to the user based on the results of the data analysis unit.

[0349] "Health Advice" means specific instructions or suggestions provided to a User to improve or maintain their health.

[0350] "Emotion management advice" is specific instructions or suggestions provided to help users achieve psychological well-being.

[0351] The present invention is a system for collecting physiological data and audio data of a user in real time using an earhook-type wearable device, and analyzing the data to evaluate the health and emotional state of the user, which includes a data collection unit, a data analysis unit, an anomaly detection unit, a notification unit, and an audio feedback unit.

[0352] Hardware and Software Configuration

[0353] Data Acquisition Unit

[0354] The data collection unit is built into the earhook device. Its main hardware includes a heart rate sensor to measure heart rate, a temperature sensor to measure body temperature, and a microphone to collect sounds and voices. The audio data is filtered by an audio processor to extract only the necessary data. This data is then transmitted in real time to a server via Bluetooth or Wi-Fi.

[0355] Data Analysis Unit

[0356] The server functions as a data analysis unit. The acquired physiological and audio data is first stored in a database. Then, heart rate fluctuations and changes in body temperature are analyzed using Python's SciPy and NumPy libraries. The emotional state is evaluated from the audio data using IBM Watson (registered trademark) sentiment analysis API. The results of the data analysis are used for the next step, anomaly detection.

[0357] Anomaly Detection Unit

[0358] The server runs an algorithm to detect health and emotional anomalies based on the results of the data analysis unit. The algorithm is designed to compare the analysis results with pre-set thresholds and detect any anomalies that occur. Detected anomalies are immediately passed to the notification unit.

[0359] Notification Unit

[0360] The notification unit is responsible for sending necessary notifications based on the anomalies detected by the anomaly detection unit. These notifications are sent via SMS or email using APIs such as Twilio. The notifications include details of the anomaly and the analysis results.

[0361] Audio Feedback Unit

[0362] The device also has a built-in voice feedback unit that receives feedback from the server and notifies the user via voice. The feedback includes specific health advice, such as "Your heart rate is high, take a deep breath" or "You seem stressed, relax." This allows the user to take action in real time according to their health and emotional state.

[0363] Adding specific examples

[0364] For example, when a user wears an earhook device, their heart rate, body temperature, and everyday sounds are collected in real time. This data is periodically sent from the device to a server, where it is analyzed. If the analysis detects a high heart rate or stress, the device will provide voice feedback such as "Your heart rate is high. Please take a deep breath" or "You seem stressed. Please relax."

[0365] Prompt Sentence Examples

[0366] "How can we build a system where an earhook device attached to the ear measures heart rate, body temperature, and everyday sounds in real time, and analyzes the user's emotional state and provides advice?"

[0367] This system allows users to constantly monitor their own health and emotional state and take appropriate action quickly.

[0368] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0369] Step 1:

[0370] When the user wears the earhook device, the device automatically starts up and collects physiological data (heart rate, body temperature) and audio data (life sounds, voice) in real time. The collected data is temporarily stored in the built-in memory. The input is the user's physiological data and audio data, and the output is a buffer of the collected data.

[0371] Step 2:

[0372] The device reads the collected physiological and audio data from the buffer at regular intervals (e.g., every 30 seconds) and transmits it to the server using Bluetooth or Wi-Fi. The data is encrypted before transmission to ensure communication security. The input is the data read from the buffer, and the output is the encrypted data packet.

[0373] Step 3:

[0374] The server decompresses the data packets received from the terminal and stores them in a database. This process also formats the data, checks for duplicates, and filters outliers. The input is the encrypted data packet, and the output is the normal data stored in the database.

[0375] Step 4:

[0376] The server uses Python's SciPy and NumPy libraries to analyze the stored physiological and audio data. Heart rate fluctuations and changes in body temperature are smoothed using the moving average method, and frequency components are extracted from the audio data using a Fast Fourier Transform (FFT). The emotional state of the audio data is also recognized using an emotion analysis API. The input is the physiological and audio data stored in the database, and the output is the analysis results (health status and emotional state).

[0377] Step 5:

[0378] The server detects anomalies based on the analysis results. If the health or emotional state exceeds a preset threshold, it is deemed to be an anomaly. When an anomaly is detected, the data is immediately passed to the notification unit. The input is the analysis results, and the output is the anomaly detection result.

[0379] Step 6:

[0380] The server uses APIs such as Twilio to send notifications via SMS or email to users, their families, and medical professionals based on the anomaly detection results. The notifications include the results of data analysis and details of the anomaly. The input is the anomaly detection result, and the output is the sent notification message.

[0381] Step 7:

[0382] The terminal receives feedback from the server and notifies the user through the voice feedback unit. For example, if the heart rate is high, voice feedback such as "Your heart rate is high, please take a deep breath" is given. Also, depending on the emotional state, advice such as "You seem to be stressed, please relax" is given. The input is the feedback result from the server, and the output is the voice feedback.

[0383] Through the processing flow of these steps, the system of the present invention can monitor the user's health and emotional state in real time, quickly notify the user if an abnormality occurs, and provide the user with appropriate advice.

[0384] (Application example 2)

[0385] 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."

[0386] Conventional wearable devices mainly collect physiological data from users, but lack the ability to monitor their emotional state. Furthermore, their ability to detect abnormalities in real time and provide appropriate advice is incomplete, limiting their use in security services, where stress management and rapid notification of abnormalities are essential. As a result, there is a need for a means to effectively monitor the health and emotional state of employees, such as security guards.

[0387] 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.

[0388] In this invention, the server includes a data collection unit that acquires physiological data and voice data of a user in real time, a data analysis unit that processes and analyzes the acquired physiological data and voice data to evaluate the health and emotional states, an anomaly detection unit that detects health and emotional abnormalities based on the analysis results from the data analysis unit, a notification unit that notifies a predetermined notification destination based on the abnormality detected by the anomaly detection unit, and a user interface unit that provides specific feedback to the user based on the analysis results. This makes it possible to monitor the health and emotional states of security guards in real time and to provide immediate countermeasures when an abnormality is detected.

[0389] definition statement

[0390] A "data collection unit" is a device that acquires physiological and audio data of a user in real time.

[0391] The "data analysis unit" is a device that processes and analyzes acquired physiological and audio data to assess health and emotional states.

[0392] The "anomaly detection unit" is a device that detects health and emotional abnormalities based on the analysis results from the data analysis unit.

[0393] The "notification unit" is a device that notifies a predetermined notification destination based on an abnormality detected by the abnormality detection unit.

[0394] A "user interface unit" is a device that provides specific feedback to the user based on the analysis results.

[0395] "Audio separation technology" is a technology that extracts only the necessary audio signals from audio data.

[0396] The "voice generation unit" is a device that generates and provides to the user advice on optimal health and emotional state for an individual based on the analysis results.

[0397] MODE FOR CARRYING OUT THE INVENTION

[0398] System Overview

[0399] The system of this invention collects and analyzes physiological and voice data of security guards in real time by having them wear earhook-type wearable devices. The system evaluates the health and emotional state of the security guards and immediately notifies them appropriately if any abnormalities are detected. It also provides specific feedback to the security guards based on the analysis results.

[0400] Hardware used

[0401] Ear hook wearable device: Worn by security guards, it collects physiological and audio data.

[0402] Server: Collects, stores, and analyzes data.

[0403] Smartphone: The device through which security guards receive notifications.

[0404] Software used

[0405] Programming language: Python

[0406] Database: MySQL

[0407] Analysis algorithm: Heart rate variability analysis, voice recognition

[0408] Emotion engine: Deep Learning model (TensorFlow)

[0409] Data exchange and analysis

[0410] In this system, data is exchanged and analyzed through the following steps:

[0411] Terminal handling

[0412] The wearable device collects the guard's heart rate, body temperature, sound, and emotional data in real time, and transmits the collected data to a server at regular intervals. The device also provides feedback to the guard based on the analysis results from the server.

[0413] Server Processing

[0414] The server receives physiological and audio data sent from the device in real time and stores it in a MySQL database. The stored data is then analyzed by an emotion engine using TensorFlow. Specific analysis tasks include heart rate variability analysis, changes in body temperature, audio data segmentation, and emotional state recognition. Health and emotional abnormalities are detected based on the analysis results, and if an abnormality is detected, the server immediately sends a notification to the security guard's smartphone. The notification includes specific advice such as "Your heart rate is high. Please take a break" or "Your stress is increasing. Please try to relax."

[0415] Prompt Sentence Examples

[0416] Use the following as an example of a prompt for a generative AI model:

[0417] "Please display the prediction results of the emotion engine that estimates the user's stress level from their voice data."

[0418] Specific examples

[0419] For example, when security guard A is wearing the earhook device, the device detects his heart rate of 120 bpm, body temperature of 37.5°C, and stress level from his voice. The server receives this data and evaluates his health and emotional state based on the analysis results. If the anomaly detection unit detects an abnormal heart rate and stress level, the server immediately sends a notification to security guard A's smartphone, providing feedback such as, "Your heart rate is high. Please take a break."

[0420] As described above, the system of the present invention effectively supports health and stress management for security guards, and is capable of detecting abnormalities in real time and responding immediately. This system allows security guards to constantly monitor their own health and emotional state and quickly take appropriate measures.

[0421] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0422] Program processing flow

[0423] Step 1: Data collection

[0424] The device collects the guard's heart rate, body temperature, daily sounds, and emotional data.

[0425] Input: Security guard's biometric information (heart rate, body temperature), daily sounds, voice input

[0426] Output: Collected biometric and audio data

[0427] Specific operation: The earhook wearable device collects data using built-in sensors (heart rate sensor, temperature sensor) and microphone. The device prepares to send the data to a server at regular intervals.

[0428] Step 2: Sending data

[0429] The terminal transmits the collected physiological data, audio data, and emotional state data to a server.

[0430] Input: Biometric and audio data collected in step 1

[0431] Output: Data packets sent to the server

[0432] Specific operation: The device sends the collected data to the server at regular intervals using Wi-Fi or mobile data communication.

[0433] Step 3: Save your data

[0434] The server stores the received physiological data and voice data in a database.

[0435] Input: Biometric data and voice data sent from the device

[0436] Output: Data stored in the database

[0437] What happens: The Python script runs a SQL query against the MySQL database to insert the received data.

[0438] Step 4: Data analysis

[0439] The server analyzes the stored data.

[0440] Input: Biometric and audio data stored in a database

[0441] Output: Health and emotional state assessment results

[0442] How it works: A program written in Python uses TensorFlow to analyze audio data to detect emotional states, and runs algorithms that analyze heart rate and temperature data to assess outliers.

[0443] Step 5: Anomaly detection

[0444] The anomaly detection unit detects anomalies based on the analysis results.

[0445] Input: Health and emotional state assessment results

[0446] Output: Anomaly detection results

[0447] Specific operation: If the evaluation result exceeds a specified threshold, the Python code sets an abnormality flag and determines that an abnormality has occurred.

[0448] Step 6: Notification

[0449] The server sends a notification to the security guard's smartphone based on the abnormality detection results.

[0450] Input: Anomaly detection result

[0451] Output: Notification sent to your phone

[0452] Specific operation: The notification function sends an SMS or push notification to the security guard's smartphone informing them of an abnormality and providing specific advice.

[0453] Step 7: Provide feedback

[0454] The terminal provides specific feedback to the security guard based on the analysis results obtained from the server.

[0455] Input: Analysis results and notification content

[0456] Output: Feedback to security guards (audio advice)

[0457] Specific operation: The device uses its voice output function to provide advice to the security guard, such as "Your heart rate is high. Please take a break" or "Your stress is increasing. Please try to relax."

[0458] 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.

[0459] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0460] 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.

[0461] [Second embodiment]

[0462] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0463] 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.

[0464] 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).

[0465] 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.

[0466] 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.

[0467] 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).

[0468] 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.

[0469] 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.

[0470] 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.

[0471] 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.

[0472] 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.

[0473] 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."

[0474] The present invention relates to an earhook-type wearable device, which is a system that acquires a user's physiological and voice data in real time, analyzes the data, and evaluates their health condition. This system has the function of immediately notifying the user if an abnormality is detected, and supports the user's daily health management.

[0475] Server Processing

[0476] Data collection and storage

[0477] The server receives physiological and audio data from the device in real time and stores it in a database, including heart rate, body temperature, and other data.

[0478] Data analysis

[0479] The server analyzes the received data using algorithms that evaluate heart rate fluctuations, voice data separation, and changes in body temperature, among other things, to assess the user's current health status and determine whether there are any abnormalities.

[0480] Anomaly detection and notification

[0481] The server detects abnormalities based on the analysis results, and if an abnormality is detected, the server immediately sends a notification to the user, their family, or medical professionals, including the results of the data analysis and details of the specific abnormal condition.

[0482] Terminal handling

[0483] Biometric data collection

[0484] The device is worn in the user's ear and collects physiological data such as heart rate, body temperature, and everyday sounds in real time. This data is acquired by internal sensors.

[0485] Data transmission

[0486] The terminals send the collected data to the server at regular intervals, and the data is time-stamped, making it easier for the server to process it.

[0487] User Feedback

[0488] The device then provides the user with feedback based on the analysis results obtained from the server. This feedback is provided in the form of voice, and specific advice such as "Your heart rate is high. Please rest."

[0489] User Action

[0490] Wearing the device

[0491] The user wears the earhook-type device, which automatically starts up and begins collecting physiological and audio data.

[0492] Receiving feedback

[0493] The user receives feedback from the device and adjusts their behavior accordingly. For example, if an abnormality is detected, the device recommends taking specific action, such as visiting a medical institution.

[0494] Specific examples

[0495] For example, when a user wears the device in their ear, the device collects their heart rate, body temperature, and everyday sounds. This data is periodically sent to a server, where it is analyzed in real time. Based on the analysis results, if the heart rate is higher than normal, for example, the device will provide voice feedback such as "Your heart rate is high. Please take a deep breath." By following this feedback and taking a deep breath, the user can stabilize their heart rate.

[0496] As described above, the system of the present invention effectively supports health management by monitoring the user's health condition in real time and immediately notifying the user if an abnormality occurs. This system allows the user to constantly understand their own health condition and quickly take appropriate measures.

[0497] The processing flow will be explained below.

[0498] Server Processing

[0499] Step 1: Receiving and storing data

[0500] The server receives the physiological data and voice data transmitted from the terminal and stores the received data in a database.

[0501] The server receives the data over the network.

[0502] The received data is stored in a database.

[0503] Step 2: Data analysis

[0504] The server analyzes the stored data and evaluates the user's health condition.

[0505] Retrieve data from the database.

[0506] Analytical algorithms are used to process heart rate, body temperature, voice data, etc.

[0507] Evaluate health status and generate analytical results.

[0508] Step 3: Anomaly detection

[0509] The server detects health abnormalities based on the analysis results.

[0510] Detect outliers from the analysis results.

[0511] If an abnormality is detected, the abnormal data is determined.

[0512] Step 4: Notification

[0513] The server will notify you if an abnormality is detected.

[0514] Decide who to notify (user, family, medical professionals).

[0515] Sends notifications containing details of abnormal conditions and analysis results.

[0516] Terminal handling

[0517] Step 1: Collect biometric data

[0518] The device measures the user's heart rate, body temperature, and daily sounds in real time.

[0519] Measure your heart rate using a sensor.

[0520] Body temperature is measured using a sensor.

[0521] Daily sounds are recorded using a microphone.

[0522] Step 2: Send data to the server

[0523] The terminal transmits the collected biometric data and voice data to the server.

[0524] Packetize the data.

[0525] Send data to the server periodically.

[0526] Step 3: Receive analysis results from the server

[0527] The terminal receives the analysis result from the server.

[0528] Receives analysis result data sent from the server.

[0529] The received analysis results are saved in the internal memory.

[0530] Step 4: User feedback

[0531] The terminal provides feedback to the user based on the analysis results from the server.

[0532] Analyze the results and determine the necessary actions.

[0533] Provides audio feedback through a speaker.

[0534] User Action

[0535] Step 1: Wear the device

[0536] The user wears the ear hook type device on their ear.

[0537] Place the device in your ear.

[0538] Check that it is installed correctly.

[0539] Step 2: Verify your biometric data

[0540] The user checks the feedback provided by the terminal.

[0541] Listen to the feedback audio message.

[0542] Adjust your behavior based on the feedback.

[0543] Step 3: Take Healthy Actions

[0544] The user manages their health based on the feedback.

[0545] Follow the advice provided (e.g., take deep breaths, take breaks).

[0546] If necessary, consult a medical institution.

[0547] The above are the specific processing steps in the system of the present invention. This process allows the user's health condition to be monitored in real time, and if an abnormality is detected, a notification is sent immediately. This enables early health management and preventative measures.

[0548] Example 1

[0549] 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."

[0550] Existing health management systems lack the ability to monitor users' physiological data in real time and quickly detect and notify abnormalities. Furthermore, they often fail to provide appropriate feedback or health advice based on analysis results. This makes it difficult for users to constantly monitor their own health status and quickly take appropriate measures.

[0551] 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.

[0552] In this invention, the server includes a data collection unit for acquiring a user's physiological data and voice data in real time, a data communication unit for transmitting the acquired physiological data and voice data to a central computer, a data storage unit for storing the transmitted data with time information, a data analysis unit for analyzing the stored physiological data and voice data using a machine learning algorithm and evaluating the user's health status, an anomaly detection unit for detecting a health abnormality based on the analysis results from the data analysis unit, a notification unit for notifying a predetermined notification destination based on the abnormality detected by the anomaly detection unit, and a feedback unit for providing audio feedback to the user based on the abnormality detection result and the analysis result. This enables the user's physiological data to be monitored in real time and abnormalities to be quickly detected and notified. Furthermore, by providing effective feedback based on the analysis results, the user can constantly understand their health status and quickly take appropriate measures.

[0553] A "data collection unit" is a device or means for acquiring physiological and audio data of a user in real time.

[0554] "Physiological data" refers to data related to the user's physical functions and health condition, such as heart rate, body temperature, and everyday sounds.

[0555] "Voice data" refers to all voice signals collected from a user, and is the data that serves as the basis for extracting only the necessary voice signals.

[0556] A "data communications unit" is a device or means for transmitting acquired physiological and audio data to a central computer in real time.

[0557] The "data storage unit" is a storage device or means for storing transmitted physiological data and audio data with time information.

[0558] "Data analysis unit" refers to a device or means for analyzing stored physiological data and audio data using machine learning algorithms to assess health status.

[0559] "Machine learning algorithms" are mathematical methods and models that use AI technology to analyze data and detect patterns and anomalies.

[0560] The "abnormality detection unit" is a device or means for detecting a health abnormality based on the analysis results from the data analysis unit.

[0561] The "notification unit" is a device or means for notifying a predetermined notification destination based on an abnormality detected by the abnormality detection unit.

[0562] The "feedback unit" is a device or means for providing audio feedback to the user based on the anomaly detection results and analysis results.

[0563] A "central computer" is a central information processing system that analyzes and stores data.

[0564] "Time information" is information that indicates the time when data was collected and transmitted.

[0565] The following hardware and software are used to implement the present invention.

[0566] Server Configuration

[0567] The server configuration includes a data collection unit, a data communication unit, a data storage unit, a data analysis unit, an anomaly detection unit, a notification unit, and a feedback unit.

[0568] Hardware used

[0569] Central computer: Information processing system that analyzes and stores data

[0570] Storage: MySQL database

[0571] Software used

[0572] Programming languages: Python, JavaScript

[0573] Machine learning framework: TensorFlow

[0574] Database Management System: MySQL

[0575] Communication method: Node.js

[0576] Device configuration

[0577] The terminal configuration includes collecting biometric data, transmitting data, and providing feedback.

[0578] Hardware used

[0579] Sensors: Heart rate sensor, temperature sensor, microphone

[0580] Communication devices: Bluetooth communication module, Wi-Fi module

[0581] Speaker: A device that provides audio feedback

[0582] User Roles

[0583] The user's primary role is to place the device in their ear and receive feedback.

[0584] System processing details

[0585] Server Processing

[0586] The server receives physiological and audio data sent from the device via Bluetooth. It then stores this data in a MySQL database and adds a timestamp to facilitate analysis. It then analyzes the data using Python and TensorFlow, detecting any abnormalities based on the analysis results. If an abnormality is detected, it activates a notification system via Node.js to notify the user, their family, and medical professionals. It also provides feedback to the user based on the analysis results.

[0587] Terminal handling

[0588] The device uses sensors to collect physiological data such as heart rate, body temperature, and everyday sounds in real time. This data is sent to a smartphone via Bluetooth, and the smartphone then transfers the data to a server via Wi-Fi. The device receives the server's analysis results and provides voice feedback such as "Your heart rate is high. Please take a deep breath" through the speaker.

[0589] User Action

[0590] The user begins collecting physiological and audio data by wearing the earhook device, and adjusts their behavior accordingly based on the audio feedback provided by the device.

[0591] Specific examples

[0592] For example, when a user wears the device in their ear, the device collects heart rate, body temperature, and everyday sounds, and sends them to a smartphone via Bluetooth. The smartphone then sends the data to a server via Wi-Fi, where it is analyzed using TensorFlow. If the analysis determines that the heart rate is higher than normal, the device's speaker will provide audio feedback saying, "Your heart rate is high. Please take a deep breath." By following this feedback and taking deep breaths, the user can stabilize their heart rate.

[0593] Prompt Sentence Examples

[0594] Please provide the Python code for a program that sends the user's physiological and audio data to a server, analyzes the data in real time, and evaluates the user's health condition.

[0595] As described above, this system monitors the user's health condition in real time, quickly detecting and notifying abnormalities. In addition, by providing effective feedback based on the analysis results, users can constantly understand their own health condition and quickly take appropriate measures.

[0596] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0597] Step 1: Wear the device

[0598] The user wears the ear hook type device on their ear. This automatically starts the device and starts collecting physiological and audio data. Specifically, the heart rate sensor, temperature sensor, and microphone start working and data collection begins. The input is "user wearing" and the output is "sensor activation."

[0599] Step 2: Collect biometric data

[0600] The device uses a heart rate sensor, temperature sensor, and microphone to collect the user's heart rate, body temperature, and everyday sounds in real time, allowing the device to constantly monitor the user's physiological state. The input is "physiological data and audio data acquired by the sensors," and the output is "physiological data and audio data collected in real time."

[0601] Step 3: Sending data

[0602] The device sends the collected data to a smartphone via Bluetooth, and the smartphone transfers the data to a server via Wi-Fi. A timestamp is added, making it easier for the server to process the data. The input is the "collected physiological data and voice data," and the output is the "data sent to the server."

[0603] Step 4: Save your data

[0604] The server stores the received physiological and audio data in a MySQL database. The important thing here is that the data is time-stamped, which makes it possible to analyze it later along a timeline. The input is "data sent to the server" and the output is "data stored in the database."

[0605] Step 5: Analyze the data

[0606] The server analyzes the stored data using Python and TensorFlow. Specifically, it runs algorithms that analyze heart rate variability, separate audio data, and evaluate changes in body temperature. This analysis evaluates the user's current health status. The input is the data stored in the database, and the output is the analysis results.

[0607] Step 6: Anomaly detection

[0608] The server detects anomalies based on the analysis results. If an anomaly is detected, the details of the anomaly and the corresponding data are recorded in a log. For example, if the analysis results determine that the heart rate is abnormally high, this is detected as an anomaly. The input is the "analysis results" and the output is the "anomaly detection results."

[0609] Step 7: Sending notifications

[0610] The server uses Node.js to send notifications to users, their families, and medical professionals based on the anomaly detection results. The notifications include the results of data analysis and details of the specific abnormality. The input is the "anomaly detection results," and the output is "notifications to recipients."

[0611] Step 8: Provide feedback

[0612] The device provides voice feedback to the user based on the analysis results obtained from the server. Specifically, a message such as "Your heart rate is high. Please rest" is provided through the speaker. The input is "analysis results and abnormality detection results," and the output is "voice feedback."

[0613] (Application example 1)

[0614] 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."

[0615] Conventional health monitoring systems are primarily limited to detecting and notifying physiological abnormalities, making it difficult to detect and respond appropriately to security risks such as stress, abnormal behavior, and dangerous environments. Furthermore, many of these systems rely on a single notification method, and lack a mechanism for quickly sending notifications to users' mobile devices in the event of an emergency.

[0616] 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.

[0617] In this invention, the server includes a data collection unit for acquiring physiological data and voice data of a user in real time, a data analysis unit for processing and analyzing the acquired physiological data and voice data to evaluate the health condition, an anomaly detection unit for detecting an abnormality in health based on the analysis result from the data analysis unit, a notification unit for notifying a predetermined notification destination based on the abnormality detected by the anomaly detection unit, a means for analyzing security risks and detecting stress, abnormal behavior, and dangerous environments, and a means for sending a notification to the user's mobile device when a danger is detected. This enables comprehensive health and safety monitoring, including security risks, and enables prompt and appropriate notification in emergencies.

[0618] A "data collection unit" is a device that has the function of acquiring physiological data and voice data of a user in real time.

[0619] The "data analysis unit" is a device that processes and analyzes acquired physiological data and audio data to evaluate health status.

[0620] The "abnormality detection unit" is a device that has the function of detecting health abnormalities based on the analysis results from the data analysis unit.

[0621] The "notification unit" is a device that has the function of notifying a predetermined notification destination based on an abnormality detected by the abnormality detection unit.

[0622] "Security risks" refer to risks that affect user safety, such as stress, abnormal behavior, and dangerous environments.

[0623] "Mobile terminal" refers to an information and communication device that a user carries with them at all times, such as a smartphone.

[0624] "Server" refers to the central computer system that collects, analyzes, and notifies data.

[0625] The present invention relates to an earhook-type wearable device that collects a user's physiological and voice data in real time, analyzes the data, and evaluates their health status and security risks. This system has the function of immediately notifying users if an abnormality is detected, and supports the user's daily health and safety management.

[0626] Server Processing

[0627] Data collection and storage

[0628] The server receives physiological and audio data from the device in real time and stores it in a database, including heart rate, body temperature, and other data.

[0629] Data analysis

[0630] The server analyzes the received data using algorithms that detect heart rate fluctuations, voice data separation, changes in body temperature, and stress or abnormal behavior. This allows the server to assess the user's current health and security risks and determine whether there are any abnormalities. For example, the server can use a generative AI model to detect psychological stress.

[0631] Anomaly detection and notification

[0632] The server detects various abnormalities based on the analysis results. If an abnormality is detected, the server immediately sends a notification to the user, their family, or medical professionals. This notification includes the results of the data analysis and details of the specific abnormal condition. For example, if the heart rate is abnormally high, the server will notify the user, saying, "Your heart rate is high. Please rest."

[0633] Terminal handling

[0634] Biometric data collection

[0635] The device is worn in the user's ear and collects physiological data such as heart rate, body temperature, and everyday sounds in real time. This data is acquired by internal sensors.

[0636] Data transmission

[0637] The terminals send the collected data to the server at regular intervals, and the data is time-stamped, making it easier for the server to process it.

[0638] User Feedback

[0639] The device then provides feedback to the user based on the analysis results obtained from the server. This feedback is provided in the form of voice, and specific advice such as "Your heart rate is high. Please rest." If a security risk is detected, the device will notify the user, saying, "You may be in a dangerous environment. Please be careful."

[0640] User Action

[0641] Wearing the device

[0642] The user wears the earhook-type device, which automatically starts up and begins collecting physiological and audio data.

[0643] Receiving feedback

[0644] The user receives feedback from the device and adjusts their behavior accordingly. For example, if an abnormality is detected, the device recommends taking specific action, such as visiting a medical institution.

[0645] Specific examples

[0646] For example, when a user wears the device in their ear, the device collects their heart rate, body temperature, and everyday sounds. This data is periodically sent to a server, where it is analyzed in real time. Based on the analysis results, if the heart rate is higher than normal, for example, the device will provide voice feedback saying, "Your heart rate is high. Please take a deep breath." If stress is detected, the device will send a notification saying, "Your stress is increasing. Please relax." By following this feedback and taking a deep breath, the user can stabilize their heart rate.

[0647] Generative AI model prompt example

[0648] The next analysis will receive the user's heart rate data and determine if it is an abnormal value. If the heart rate is not within the range of 60 to 100, notify the user that "An abnormal heart rate has been detected." Below is an example of heart rate data.

[0649] Heart rate: 110

[0650] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0651] Step 1:

[0652] The terminal collects physiological and audio data from the user in real time.

[0653] (Input) Heart rate data, body temperature data, and voice data from sensors.

[0654] (Processing) Data collection by sensors.

[0655] (Output) Physiological and audio data are stored in internal memory.

[0656] Step 2:

[0657] The terminal sends the collected data to the server at regular intervals.

[0658] (Input) Physiological and audio data collected in step 1.

[0659] (Processing) The data is formatted into packets, timestamps are added, and the packets are sent to the server.

[0660] (Output) Physiological and audio data received on the server side.

[0661] Step 3:

[0662] The server stores the received data in a database.

[0663] (Input) Physiological and audio data sent from sensors.

[0664] (Processing) Save received data in a database.

[0665] (Output) Physiological and audio data stored in a database.

[0666] Step 4:

[0667] The server analyzes the stored data.

[0668] (Input) Physiological and audio data stored in a database.

[0669] (Processing) The data analysis unit analyzes heart rate fluctuations, audio data separation, and changes in body temperature. Stress and abnormal behavior are also detected using generative AI models.

[0670] (Output) Health and security risk assessment results.

[0671] Step 5:

[0672] The server detects anomalies based on the analysis results.

[0673] (Input) Evaluation results of data analysis.

[0674] (Processing) Detect outliers using an anomaly detection algorithm.

[0675] (Output) The result of the anomaly detection.

[0676] Step 6:

[0677] If the server detects an abnormality, it sends a notification to the user's mobile device.

[0678] (Input) Anomaly detection results.

[0679] (Processing) Generate notification content and send it to the mobile device.

[0680] (Output) The notification sent to the user's mobile device.

[0681] Step 7:

[0682] The terminal provides feedback to the user based on the analysis results of the server.

[0683] (Input) The notification content sent from the server.

[0684] (Processing) Audio and visual notifications.

[0685] (Output) Providing feedback to the user.

[0686] Step 8:

[0687] The user receives feedback provided by the device and adjusts their behavior accordingly.

[0688] (Input) Feedback from the device.

[0689] (Processing) Adjusting behavior based on feedback content.

[0690] (Output) Change in user behavior (e.g., taking a deep breath, seeking medical attention, etc.).

[0691] 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.

[0692] The present invention relates to an earhook-type wearable device and a system that acquires a user's physiological and voice data in real time, analyzes the data, and evaluates their health condition. The present invention also incorporates an emotion engine to recognize the user's emotional state and manage their health based on this. This system immediately notifies the user if an abnormality is detected, supporting the user's daily health management.

[0693] Server Processing

[0694] Data collection and storage

[0695] The server receives physiological and voice data from the device in real time and stores it in a database. This data includes heart rate, body temperature, and everyday sounds. Furthermore, it uses an emotion engine to detect the user's emotions from the collected voice data.

[0696] Data analysis

[0697] The server analyzes the stored data using algorithms that include heart rate variability, body temperature changes, audio data separation, and emotional state recognition and evaluation, which then evaluates the user's current health and emotional state.

[0698] Anomaly detection and notification

[0699] The server detects abnormalities in health and emotions based on the analysis results. If an abnormality is detected, the server immediately sends a notification to the user, their family, or medical professionals. The notification includes the results of the data analysis and details of the abnormal condition.

[0700] Terminal handling

[0701] Biometric data collection

[0702] The device measures and generates data on the user's heart rate, body temperature, and everyday sounds in real time, as well as collecting data needed to detect the user's emotional state.

[0703] Data transmission

[0704] The device transmits the collected physiological data, voice data, and emotional state data to a server at regular intervals.

[0705] User Feedback

[0706] The device then provides the user with feedback based on the analysis results obtained from the server. The feedback is provided in the form of voice, and specific advice is provided, such as "Your heart rate is high. Take a deep breath" or "You seem to be stressed. Relax."

[0707] User Action

[0708] Wearing the device

[0709] The user wears the earhook-type device, which automatically starts up and begins collecting physiological, audio, and emotional data.

[0710] Receiving feedback

[0711] Users can then adjust their behavior based on the feedback provided by the device. For example, if an abnormality is detected, specific actions such as seeking medical attention may be recommended. Users may also receive advice based on their emotional state, helping them to practice stress management and relaxation techniques.

[0712] Specific examples

[0713] For example, when a user wears the device in their ear, the device collects their heart rate, body temperature, and everyday sounds. This data is periodically sent to a server, where it is analyzed in real time. Based on the analysis results, if the heart rate is higher than normal, for example, the device will provide voice feedback such as, "Your heart rate is high. Please take a deep breath." At the same time, if the device determines that the user is feeling stressed based on the tone and pace of their voice, it will add the advice, "You seem stressed. Relax." By following this feedback and taking deep breaths, the user can stabilize their heart rate and reduce stress.

[0714] As described above, the system of the present invention effectively supports health and stress management by monitoring the user's health and emotional state in real time and immediately notifying the user if any abnormalities occur. This system allows the user to constantly understand their own health and emotional state and quickly take appropriate measures.

[0715] The processing flow will be explained below.

[0716] Server Processing

[0717] Step 1: Receiving and storing data

[0718] The server receives physiological and audio data from the device in real time and stores the data in a database.

[0719] The server receives the data over the network.

[0720] The received data is stored in a database.

[0721] Step 2: Data analysis

[0722] The server analyzes the stored data and evaluates the user's health condition.

[0723] Retrieve data from the database.

[0724] Heart rate, body temperature, and audio data are processed using analytical algorithms.

[0725] Recognize the user's emotional state from voice data using an emotion engine.

[0726] Evaluate health and emotional states and generate analytical results.

[0727] Step 3: Anomaly detection

[0728] The server detects health and emotional abnormalities based on the analysis results.

[0729] Detect outliers from the analysis results.

[0730] If an abnormality is detected, the abnormal data is determined.

[0731] Step 4: Notification

[0732] The server will notify you if an abnormality is detected.

[0733] Decide who to notify (user, family, medical professionals).

[0734] Sends notifications containing details of abnormal conditions and analysis results.

[0735] Terminal handling

[0736] Step 1: Collect biometric data

[0737] The device measures the user's heart rate, body temperature, and everyday sounds in real time and generates data.

[0738] Measure your heart rate using a sensor.

[0739] Body temperature is measured using a sensor.

[0740] Daily sounds are recorded using a microphone.

[0741] Step 2: Collecting emotion data

[0742] The device collects emotional data from the user's tone and pace of voice.

[0743] Analyzes voice data and uses an emotion engine to recognize emotional states.

[0744] Record the recognized emotion data.

[0745] Step 3: Send data to the server

[0746] The terminal transmits the collected physiological data, voice data, and emotion data to a server.

[0747] Packetize the data.

[0748] Send data to the server periodically.

[0749] Step 4: Receive analysis results from the server

[0750] The terminal receives the analysis result from the server.

[0751] Receives analysis result data sent from the server.

[0752] The received analysis results are saved in the internal memory.

[0753] Step 5: User feedback

[0754] The terminal provides feedback to the user based on the analysis results from the server.

[0755] Analyze the results and determine the necessary actions.

[0756] Provides audio feedback through a speaker.

[0757] User Action

[0758] Step 1: Wear the device

[0759] The user wears the ear hook type device on their ear.

[0760] Place the device in your ear.

[0761] Check that it is installed correctly.

[0762] Step 2: Check biometric and emotional data

[0763] The user checks the feedback provided by the terminal.

[0764] Listen to the feedback audio message.

[0765] Adjust your behavior based on the feedback.

[0766] Step 3: Take healthy actions and manage your emotions

[0767] Users manage their health and emotions based on the feedback.

[0768] Follow the advice provided (e.g., take deep breaths, take breaks).

[0769] Practice stress management and relaxation techniques.

[0770] If necessary, consult a medical institution.

[0771] Specific examples

[0772] For example, when a user wears the device in their ear, the device collects emotional data from their heart rate, body temperature, everyday sounds, and even voice. This data is periodically sent to a server, where it is analyzed in real time. Based on the analysis results, if the heart rate is higher than normal, for example, the device will provide voice feedback such as "Your heart rate is high. Take a deep breath." At the same time, if the device determines that the user is feeling stressed based on the tone and pace of their voice, it will add advice such as "You seem stressed. Relax." By following this feedback and taking deep breaths and practicing relaxation techniques, the user can stabilize their heart rate and reduce stress.

[0773] The above are the specific processing steps in the system of the present invention. This process allows the user's health and emotional state to be monitored in real time, and if an abnormality is detected, a notification is sent immediately. This allows for early health management and emotional management at the same time.

[0774] Example 2

[0775] 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."

[0776] In recent years, personal health management has become increasingly important. However, existing systems lack the ability to adequately monitor physiological data and emotional states in real time, making it difficult to respond quickly when abnormalities occur. Furthermore, there are limited means for providing appropriate feedback and advice to users. This makes it difficult for users to effectively manage their own health and emotional states. To address these issues, a system that comprehensively collects, analyzes, and provides feedback in real time is needed.

[0777] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a data collection unit that acquires physiological data and voice data of the user in real time, a data analysis unit that processes and analyzes the acquired physiological data and voice data to evaluate the health and emotional states, an anomaly detection unit that detects health and emotional abnormalities based on the analysis results from the data analysis unit, a notification unit that notifies a predetermined notification destination based on the abnormality detected by the anomaly detection unit, and a voice feedback unit that provides specific health advice to the user based on the results of the data analysis unit. This enables real-time monitoring of the health and emotional states and the provision of appropriate feedback and advice. Furthermore, early detection of abnormalities and rapid response enable the user to effectively manage their own health and emotional states.

[0778] A "data collection unit" is a device that has the function of acquiring physiological data and voice data of a user in real time.

[0779] "Physiological data" refers to data that indicates the user's physical condition, such as heart rate and body temperature.

[0780] "Voice data" refers to acoustic data that includes the user's voice and surrounding sounds.

[0781] The "data analysis unit" is a device that processes and analyzes acquired physiological and audio data to evaluate health and emotional states.

[0782] "Health status" is an indicator of a user's physical condition, and is primarily evaluated by heart rate, body temperature, etc.

[0783] "Emotional state" is an index that indicates the user's psychological state, and is primarily evaluated by the tone and pace of the voice data.

[0784] The "anomaly detection unit" is a device that has the function of detecting health and emotional abnormalities based on the analysis results from the data analysis unit.

[0785] The "notification unit" is a device that has the function of notifying a predetermined notification destination based on an abnormality detected by the abnormality detection unit.

[0786] The "voice feedback unit" is a device that has the function of providing specific health advice to the user based on the results of the data analysis unit.

[0787] "Health Advice" means specific instructions or suggestions provided to a User to improve or maintain their health.

[0788] "Emotion management advice" is specific instructions or suggestions provided to help users achieve psychological well-being.

[0789] The present invention is a system for collecting physiological data and audio data of a user in real time using an earhook-type wearable device, and analyzing the data to evaluate the health and emotional state of the user, which includes a data collection unit, a data analysis unit, an anomaly detection unit, a notification unit, and an audio feedback unit.

[0790] Hardware and Software Configuration

[0791] Data Acquisition Unit

[0792] The data collection unit is built into the earhook device. Its main hardware includes a heart rate sensor to measure heart rate, a temperature sensor to measure body temperature, and a microphone to collect sounds and voices. The audio data is filtered by an audio processor to extract only the necessary data. This data is then transmitted in real time to a server via Bluetooth or Wi-Fi.

[0793] Data Analysis Unit

[0794] The server functions as a data analysis unit. The acquired physiological and audio data is first stored in a database. Then, using Python's SciPy and NumPy libraries, heart rate fluctuations and changes in body temperature are analyzed. The emotional state is evaluated from the audio data using IBM Watson's sentiment analysis API. The results of the data analysis are used for the next step, anomaly detection.

[0795] Anomaly Detection Unit

[0796] The server runs an algorithm to detect health and emotional anomalies based on the results of the data analysis unit. The algorithm is designed to compare the analysis results with pre-set thresholds and detect any anomalies that occur. Detected anomalies are immediately passed to the notification unit.

[0797] Notification Unit

[0798] The notification unit is responsible for sending necessary notifications based on the anomalies detected by the anomaly detection unit. These notifications are sent via SMS or email using APIs such as Twilio. The notifications include details of the anomaly and the analysis results.

[0799] Audio Feedback Unit

[0800] The device also has a built-in voice feedback unit that receives feedback from the server and notifies the user via voice. The feedback includes specific health advice, such as "Your heart rate is high, take a deep breath" or "You seem stressed, relax." This allows the user to take action in real time according to their health and emotional state.

[0801] Adding specific examples

[0802] For example, when a user wears an earhook device, their heart rate, body temperature, and everyday sounds are collected in real time. This data is periodically sent from the device to a server, where it is analyzed. If the analysis detects a high heart rate or stress, the device will provide voice feedback such as "Your heart rate is high. Please take a deep breath" or "You seem stressed. Please relax."

[0803] Prompt Sentence Examples

[0804] "How can we build a system where an earhook device attached to the ear measures heart rate, body temperature, and everyday sounds in real time, and analyzes the user's emotional state and provides advice?"

[0805] This system allows users to constantly monitor their own health and emotional state and take appropriate action quickly.

[0806] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0807] Step 1:

[0808] When the user wears the earhook device, the device automatically starts up and collects physiological data (heart rate, body temperature) and audio data (life sounds, voice) in real time. The collected data is temporarily stored in the built-in memory. The input is the user's physiological data and audio data, and the output is a buffer of the collected data.

[0809] Step 2:

[0810] The device reads the collected physiological and audio data from the buffer at regular intervals (e.g., every 30 seconds) and transmits it to the server using Bluetooth or Wi-Fi. The data is encrypted before transmission to ensure communication security. The input is the data read from the buffer, and the output is the encrypted data packet.

[0811] Step 3:

[0812] The server decompresses the data packets received from the terminal and stores them in a database. This process also formats the data, checks for duplicates, and filters outliers. The input is the encrypted data packet, and the output is the normal data stored in the database.

[0813] Step 4:

[0814] The server uses Python's SciPy and NumPy libraries to analyze the stored physiological and audio data. Heart rate fluctuations and changes in body temperature are smoothed using the moving average method, and frequency components are extracted from the audio data using a Fast Fourier Transform (FFT). The emotional state of the audio data is also recognized using an emotion analysis API. The input is the physiological and audio data stored in the database, and the output is the analysis results (health status and emotional state).

[0815] Step 5:

[0816] The server detects anomalies based on the analysis results. If the health or emotional state exceeds a preset threshold, it is deemed to be an anomaly. When an anomaly is detected, the data is immediately passed to the notification unit. The input is the analysis results, and the output is the anomaly detection result.

[0817] Step 6:

[0818] The server uses APIs such as Twilio to send notifications via SMS or email to users, their families, and medical professionals based on the anomaly detection results. The notifications include the results of data analysis and details of the anomaly. The input is the anomaly detection result, and the output is the sent notification message.

[0819] Step 7:

[0820] The terminal receives feedback from the server and notifies the user through the voice feedback unit. For example, if the heart rate is high, voice feedback such as "Your heart rate is high, please take a deep breath" is given. Also, depending on the emotional state, advice such as "You seem to be stressed, please relax" is given. The input is the feedback result from the server, and the output is the voice feedback.

[0821] Through the processing flow of these steps, the system of the present invention can monitor the user's health and emotional state in real time, quickly notify the user if an abnormality occurs, and provide the user with appropriate advice.

[0822] (Application example 2)

[0823] 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."

[0824] Conventional wearable devices mainly collect physiological data from users, but lack the ability to monitor their emotional state. Furthermore, their ability to detect abnormalities in real time and provide appropriate advice is incomplete, limiting their use in security services, where stress management and rapid notification of abnormalities are essential. As a result, there is a need for a means to effectively monitor the health and emotional state of employees, such as security guards.

[0825] 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.

[0826] In this invention, the server includes a data collection unit that acquires physiological data and voice data of a user in real time, a data analysis unit that processes and analyzes the acquired physiological data and voice data to evaluate the health and emotional states, an anomaly detection unit that detects health and emotional abnormalities based on the analysis results from the data analysis unit, a notification unit that notifies a predetermined notification destination based on the abnormality detected by the anomaly detection unit, and a user interface unit that provides specific feedback to the user based on the analysis results. This makes it possible to monitor the health and emotional states of security guards in real time and to provide immediate countermeasures when an abnormality is detected.

[0827] definition statement

[0828] A "data collection unit" is a device that acquires physiological and audio data of a user in real time.

[0829] The "data analysis unit" is a device that processes and analyzes acquired physiological and audio data to assess health and emotional states.

[0830] The "anomaly detection unit" is a device that detects health and emotional abnormalities based on the analysis results from the data analysis unit.

[0831] The "notification unit" is a device that notifies a predetermined notification destination based on an abnormality detected by the abnormality detection unit.

[0832] A "user interface unit" is a device that provides specific feedback to the user based on the analysis results.

[0833] "Audio separation technology" is a technology that extracts only the necessary audio signals from audio data.

[0834] The "voice generation unit" is a device that generates and provides to the user advice on optimal health and emotional state for an individual based on the analysis results.

[0835] MODE FOR CARRYING OUT THE INVENTION

[0836] System Overview

[0837] The system of this invention collects and analyzes physiological and voice data of security guards in real time by having them wear earhook-type wearable devices. The system evaluates the health and emotional state of the security guards and immediately notifies them appropriately if any abnormalities are detected. It also provides specific feedback to the security guards based on the analysis results.

[0838] Hardware used

[0839] Ear hook wearable device: Worn by security guards, it collects physiological and audio data.

[0840] Server: Collects, stores, and analyzes data.

[0841] Smartphone: The device through which security guards receive notifications.

[0842] Software used

[0843] Programming language: Python

[0844] Database: MySQL

[0845] Analysis algorithm: Heart rate variability analysis, voice recognition

[0846] Emotion engine: Deep Learning model (TensorFlow)

[0847] Data exchange and analysis

[0848] In this system, data is exchanged and analyzed through the following steps:

[0849] Terminal handling

[0850] The wearable device collects the guard's heart rate, body temperature, sound, and emotional data in real time, and transmits the collected data to a server at regular intervals. The device also provides feedback to the guard based on the analysis results from the server.

[0851] Server Processing

[0852] The server receives physiological and audio data sent from the device in real time and stores it in a MySQL database. The stored data is then analyzed by an emotion engine using TensorFlow. Specific analysis tasks include heart rate variability analysis, changes in body temperature, audio data segmentation, and emotional state recognition. Health and emotional abnormalities are detected based on the analysis results, and if an abnormality is detected, the server immediately sends a notification to the security guard's smartphone. The notification includes specific advice such as "Your heart rate is high. Please take a break" or "Your stress is increasing. Please try to relax."

[0853] Prompt Sentence Examples

[0854] Use the following as an example of a prompt for a generative AI model:

[0855] "Please display the prediction results of the emotion engine that estimates the user's stress level from their voice data."

[0856] Specific examples

[0857] For example, when security guard A is wearing the earhook device, the device detects his heart rate of 120 bpm, body temperature of 37.5°C, and stress level from his voice. The server receives this data and evaluates his health and emotional state based on the analysis results. If the anomaly detection unit detects an abnormal heart rate and stress level, the server immediately sends a notification to security guard A's smartphone, providing feedback such as, "Your heart rate is high. Please take a break."

[0858] As described above, the system of the present invention effectively supports health and stress management for security guards, and is capable of detecting abnormalities in real time and responding immediately. This system allows security guards to constantly monitor their own health and emotional state and quickly take appropriate measures.

[0859] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0860] Program processing flow

[0861] Step 1: Data collection

[0862] The device collects the guard's heart rate, body temperature, daily sounds, and emotional data.

[0863] Input: Security guard's biometric information (heart rate, body temperature), daily sounds, voice input

[0864] Output: Collected biometric and audio data

[0865] Specific operation: The earhook wearable device collects data using built-in sensors (heart rate sensor, temperature sensor) and microphone. The device prepares to send the data to a server at regular intervals.

[0866] Step 2: Sending data

[0867] The terminal transmits the collected physiological data, audio data, and emotional state data to a server.

[0868] Input: Biometric and audio data collected in step 1

[0869] Output: Data packets sent to the server

[0870] Specific operation: The device sends the collected data to the server at regular intervals using Wi-Fi or mobile data communication.

[0871] Step 3: Save your data

[0872] The server stores the received physiological data and voice data in a database.

[0873] Input: Biometric data and voice data sent from the device

[0874] Output: Data stored in the database

[0875] What happens: The Python script runs a SQL query against the MySQL database to insert the received data.

[0876] Step 4: Data analysis

[0877] The server analyzes the stored data.

[0878] Input: Biometric and audio data stored in a database

[0879] Output: Health and emotional state assessment results

[0880] How it works: A program written in Python uses TensorFlow to analyze audio data to detect emotional states, and runs algorithms that analyze heart rate and temperature data to assess outliers.

[0881] Step 5: Anomaly detection

[0882] The anomaly detection unit detects anomalies based on the analysis results.

[0883] Input: Health and emotional state assessment results

[0884] Output: Anomaly detection results

[0885] Specific operation: If the evaluation result exceeds a specified threshold, the Python code sets an abnormality flag and determines that an abnormality has occurred.

[0886] Step 6: Notification

[0887] The server sends a notification to the security guard's smartphone based on the abnormality detection results.

[0888] Input: Anomaly detection result

[0889] Output: Notification sent to your phone

[0890] Specific operation: The notification function sends an SMS or push notification to the security guard's smartphone informing them of an abnormality and providing specific advice.

[0891] Step 7: Provide feedback

[0892] The terminal provides specific feedback to the security guard based on the analysis results obtained from the server.

[0893] Input: Analysis results and notification content

[0894] Output: Feedback to security guards (audio advice)

[0895] Specific operation: The device uses its voice output function to provide advice to the security guard, such as "Your heart rate is high. Please take a break" or "Your stress is increasing. Please try to relax."

[0896] 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.

[0897] 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.

[0898] 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.

[0899] [Third embodiment]

[0900] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0901] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0902] 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).

[0903] 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.

[0904] 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.

[0905] 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).

[0906] 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.

[0907] 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.

[0908] 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.

[0909] 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.

[0910] 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.

[0911] 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."

[0912] The present invention relates to an earhook-type wearable device, which is a system that acquires a user's physiological and voice data in real time, analyzes the data, and evaluates their health condition. This system has the function of immediately notifying the user if an abnormality is detected, and supports the user's daily health management.

[0913] Server Processing

[0914] Data collection and storage

[0915] The server receives physiological and audio data from the device in real time and stores it in a database, including heart rate, body temperature, and other data.

[0916] Data analysis

[0917] The server analyzes the received data using algorithms that evaluate heart rate fluctuations, voice data separation, and changes in body temperature, among other things, to assess the user's current health status and determine whether there are any abnormalities.

[0918] Anomaly detection and notification

[0919] The server detects abnormalities based on the analysis results, and if an abnormality is detected, the server immediately sends a notification to the user, their family, or medical professionals, including the results of the data analysis and details of the specific abnormal condition.

[0920] Terminal handling

[0921] Biometric data collection

[0922] The device is worn in the user's ear and collects physiological data such as heart rate, body temperature, and everyday sounds in real time. This data is acquired by internal sensors.

[0923] Data transmission

[0924] The terminals send the collected data to the server at regular intervals, and the data is time-stamped, making it easier for the server to process it.

[0925] User Feedback

[0926] The device then provides the user with feedback based on the analysis results obtained from the server. This feedback is provided in the form of voice, and specific advice such as "Your heart rate is high. Please rest."

[0927] User Action

[0928] Wearing the device

[0929] The user wears the earhook-type device, which automatically starts up and begins collecting physiological and audio data.

[0930] Receiving feedback

[0931] The user receives feedback from the device and adjusts their behavior accordingly. For example, if an abnormality is detected, the device recommends taking specific action, such as visiting a medical institution.

[0932] Specific examples

[0933] For example, when a user wears the device in their ear, the device collects their heart rate, body temperature, and everyday sounds. This data is periodically sent to a server, where it is analyzed in real time. Based on the analysis results, if the heart rate is higher than normal, for example, the device will provide voice feedback such as "Your heart rate is high. Please take a deep breath." By following this feedback and taking a deep breath, the user can stabilize their heart rate.

[0934] As described above, the system of the present invention effectively supports health management by monitoring the user's health condition in real time and immediately notifying the user if an abnormality occurs. This system allows the user to constantly understand their own health condition and quickly take appropriate measures.

[0935] The processing flow will be explained below.

[0936] Server Processing

[0937] Step 1: Receiving and storing data

[0938] The server receives the physiological data and voice data transmitted from the terminal and stores the received data in a database.

[0939] The server receives the data over the network.

[0940] The received data is stored in a database.

[0941] Step 2: Data analysis

[0942] The server analyzes the stored data and evaluates the user's health condition.

[0943] Retrieve data from the database.

[0944] Analytical algorithms are used to process heart rate, body temperature, voice data, etc.

[0945] Evaluate health status and generate analytical results.

[0946] Step 3: Anomaly detection

[0947] The server detects health abnormalities based on the analysis results.

[0948] Detect outliers from the analysis results.

[0949] If an abnormality is detected, the abnormal data is determined.

[0950] Step 4: Notification

[0951] The server will notify you if an abnormality is detected.

[0952] Decide who to notify (user, family, medical professionals).

[0953] Sends notifications containing details of abnormal conditions and analysis results.

[0954] Terminal handling

[0955] Step 1: Collect biometric data

[0956] The device measures the user's heart rate, body temperature, and daily sounds in real time.

[0957] Measure your heart rate using a sensor.

[0958] Body temperature is measured using a sensor.

[0959] Daily sounds are recorded using a microphone.

[0960] Step 2: Send data to the server

[0961] The terminal transmits the collected biometric data and voice data to the server.

[0962] Packetize the data.

[0963] Send data to the server periodically.

[0964] Step 3: Receive analysis results from the server

[0965] The terminal receives the analysis result from the server.

[0966] Receives analysis result data sent from the server.

[0967] The received analysis results are saved in the internal memory.

[0968] Step 4: User feedback

[0969] The terminal provides feedback to the user based on the analysis results from the server.

[0970] Analyze the results and determine the necessary actions.

[0971] Provides audio feedback through a speaker.

[0972] User Action

[0973] Step 1: Wear the device

[0974] The user wears the ear hook type device on their ear.

[0975] Place the device in your ear.

[0976] Check that it is installed correctly.

[0977] Step 2: Verify your biometric data

[0978] The user checks the feedback provided by the terminal.

[0979] Listen to the feedback audio message.

[0980] Adjust your behavior based on the feedback.

[0981] Step 3: Take Healthy Actions

[0982] The user manages their health based on the feedback.

[0983] Follow the advice provided (e.g., take deep breaths, take breaks).

[0984] If necessary, consult a medical institution.

[0985] The above are the specific processing steps in the system of the present invention. This process allows the user's health condition to be monitored in real time, and if an abnormality is detected, a notification is sent immediately. This enables early health management and preventative measures.

[0986] Example 1

[0987] 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."

[0988] Existing health management systems lack the ability to monitor users' physiological data in real time and quickly detect and notify abnormalities. Furthermore, they often fail to provide appropriate feedback or health advice based on analysis results. This makes it difficult for users to constantly monitor their own health status and quickly take appropriate measures.

[0989] 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.

[0990] In this invention, the server includes a data collection unit for acquiring a user's physiological data and voice data in real time, a data communication unit for transmitting the acquired physiological data and voice data to a central computer, a data storage unit for storing the transmitted data with time information, a data analysis unit for analyzing the stored physiological data and voice data using a machine learning algorithm and evaluating the user's health status, an anomaly detection unit for detecting a health abnormality based on the analysis results from the data analysis unit, a notification unit for notifying a predetermined notification destination based on the abnormality detected by the anomaly detection unit, and a feedback unit for providing audio feedback to the user based on the abnormality detection result and the analysis result. This enables the user's physiological data to be monitored in real time and abnormalities to be quickly detected and notified. Furthermore, by providing effective feedback based on the analysis results, the user can constantly understand their health status and quickly take appropriate measures.

[0991] A "data collection unit" is a device or means for acquiring physiological and audio data of a user in real time.

[0992] "Physiological data" refers to data related to the user's physical functions and health condition, such as heart rate, body temperature, and everyday sounds.

[0993] "Voice data" refers to all voice signals collected from a user, and is the data that serves as the basis for extracting only the necessary voice signals.

[0994] A "data communications unit" is a device or means for transmitting acquired physiological and audio data to a central computer in real time.

[0995] The "data storage unit" is a storage device or means for storing transmitted physiological data and audio data with time information.

[0996] "Data analysis unit" refers to a device or means for analyzing stored physiological data and audio data using machine learning algorithms to assess health status.

[0997] "Machine learning algorithms" are mathematical methods and models that use AI technology to analyze data and detect patterns and anomalies.

[0998] The "abnormality detection unit" is a device or means for detecting a health abnormality based on the analysis results from the data analysis unit.

[0999] The "notification unit" is a device or means for notifying a predetermined notification destination based on an abnormality detected by the abnormality detection unit.

[1000] The "feedback unit" is a device or means for providing audio feedback to the user based on the anomaly detection results and analysis results.

[1001] A "central computer" is a central information processing system that analyzes and stores data.

[1002] "Time information" is information that indicates the time when data was collected and transmitted.

[1003] The following hardware and software are used to implement the present invention.

[1004] Server Configuration

[1005] The server configuration includes a data collection unit, a data communication unit, a data storage unit, a data analysis unit, an anomaly detection unit, a notification unit, and a feedback unit.

[1006] Hardware used

[1007] Central computer: Information processing system that analyzes and stores data

[1008] Storage: MySQL database

[1009] Software used

[1010] Programming languages: Python, JavaScript

[1011] Machine learning framework: TensorFlow

[1012] Database Management System: MySQL

[1013] Communication method: Node.js

[1014] Device configuration

[1015] The terminal configuration includes collecting biometric data, transmitting data, and providing feedback.

[1016] Hardware used

[1017] Sensors: Heart rate sensor, temperature sensor, microphone

[1018] Communication devices: Bluetooth communication module, Wi-Fi module

[1019] Speaker: A device that provides audio feedback

[1020] User Roles

[1021] The user's primary role is to place the device in their ear and receive feedback.

[1022] System processing details

[1023] Server Processing

[1024] The server receives physiological and audio data sent from the device via Bluetooth. It then stores this data in a MySQL database and adds a timestamp to facilitate analysis. It then analyzes the data using Python and TensorFlow, detecting any abnormalities based on the analysis results. If an abnormality is detected, it activates a notification system via Node.js to notify the user, their family, and medical professionals. It also provides feedback to the user based on the analysis results.

[1025] Terminal handling

[1026] The device uses sensors to collect physiological data such as heart rate, body temperature, and everyday sounds in real time. This data is sent to a smartphone via Bluetooth, and the smartphone then transfers the data to a server via Wi-Fi. The device receives the server's analysis results and provides voice feedback such as "Your heart rate is high. Please take a deep breath" through the speaker.

[1027] User Action

[1028] The user begins collecting physiological and audio data by wearing the earhook device, and adjusts their behavior accordingly based on the audio feedback provided by the device.

[1029] Specific examples

[1030] For example, when a user wears the device in their ear, the device collects heart rate, body temperature, and everyday sounds, and sends them to a smartphone via Bluetooth. The smartphone then sends the data to a server via Wi-Fi, where it is analyzed using TensorFlow. If the analysis determines that the heart rate is higher than normal, the device's speaker will provide audio feedback saying, "Your heart rate is high. Please take a deep breath." By following this feedback and taking deep breaths, the user can stabilize their heart rate.

[1031] Prompt Sentence Examples

[1032] Please provide the Python code for a program that sends the user's physiological and audio data to a server, analyzes the data in real time, and evaluates the user's health condition.

[1033] As described above, this system monitors the user's health condition in real time, quickly detecting and notifying abnormalities. In addition, by providing effective feedback based on the analysis results, users can constantly understand their own health condition and quickly take appropriate measures.

[1034] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1035] Step 1: Wear the device

[1036] The user wears the ear hook type device on their ear. This automatically starts the device and starts collecting physiological and audio data. Specifically, the heart rate sensor, temperature sensor, and microphone start working and data collection begins. The input is "user wearing" and the output is "sensor activation."

[1037] Step 2: Collect biometric data

[1038] The device uses a heart rate sensor, temperature sensor, and microphone to collect the user's heart rate, body temperature, and everyday sounds in real time, allowing the device to constantly monitor the user's physiological state. The input is "physiological data and audio data acquired by the sensors," and the output is "physiological data and audio data collected in real time."

[1039] Step 3: Sending data

[1040] The device sends the collected data to a smartphone via Bluetooth, and the smartphone transfers the data to a server via Wi-Fi. A timestamp is added, making it easier for the server to process the data. The input is the "collected physiological data and voice data," and the output is the "data sent to the server."

[1041] Step 4: Save your data

[1042] The server stores the received physiological and audio data in a MySQL database. The important thing here is that the data is time-stamped, which makes it possible to analyze it later along a timeline. The input is "data sent to the server" and the output is "data stored in the database."

[1043] Step 5: Analyze the data

[1044] The server analyzes the stored data using Python and TensorFlow. Specifically, it runs algorithms that analyze heart rate variability, separate audio data, and evaluate changes in body temperature. This analysis evaluates the user's current health status. The input is the data stored in the database, and the output is the analysis results.

[1045] Step 6: Anomaly detection

[1046] The server detects anomalies based on the analysis results. If an anomaly is detected, the details of the anomaly and the corresponding data are recorded in a log. For example, if the analysis results determine that the heart rate is abnormally high, this is detected as an anomaly. The input is the "analysis results" and the output is the "anomaly detection results."

[1047] Step 7: Sending notifications

[1048] The server uses Node.js to send notifications to users, their families, and medical professionals based on the anomaly detection results. The notifications include the results of data analysis and details of the specific abnormality. The input is the "anomaly detection results," and the output is "notifications to recipients."

[1049] Step 8: Provide feedback

[1050] The device provides voice feedback to the user based on the analysis results obtained from the server. Specifically, a message such as "Your heart rate is high. Please rest" is provided through the speaker. The input is "analysis results and abnormality detection results," and the output is "voice feedback."

[1051] (Application example 1)

[1052] 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."

[1053] Conventional health monitoring systems are primarily limited to detecting and notifying physiological abnormalities, making it difficult to detect and respond appropriately to security risks such as stress, abnormal behavior, and dangerous environments. Furthermore, many of these systems rely on a single notification method, and lack a mechanism for quickly sending notifications to users' mobile devices in the event of an emergency.

[1054] 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.

[1055] In this invention, the server includes a data collection unit for acquiring physiological data and voice data of a user in real time, a data analysis unit for processing and analyzing the acquired physiological data and voice data to evaluate the health condition, an anomaly detection unit for detecting an abnormality in health based on the analysis result from the data analysis unit, a notification unit for notifying a predetermined notification destination based on the abnormality detected by the anomaly detection unit, a means for analyzing security risks and detecting stress, abnormal behavior, and dangerous environments, and a means for sending a notification to the user's mobile device when a danger is detected. This enables comprehensive health and safety monitoring, including security risks, and enables prompt and appropriate notification in emergencies.

[1056] A "data collection unit" is a device that has the function of acquiring physiological data and voice data of a user in real time.

[1057] The "data analysis unit" is a device that processes and analyzes acquired physiological data and audio data to evaluate health status.

[1058] The "abnormality detection unit" is a device that has the function of detecting health abnormalities based on the analysis results from the data analysis unit.

[1059] The "notification unit" is a device that has the function of notifying a predetermined notification destination based on an abnormality detected by the abnormality detection unit.

[1060] "Security risks" refer to risks that affect user safety, such as stress, abnormal behavior, and dangerous environments.

[1061] "Mobile terminal" refers to an information and communication device that a user carries with them at all times, such as a smartphone.

[1062] "Server" refers to the central computer system that collects, analyzes, and notifies data.

[1063] The present invention relates to an earhook-type wearable device that collects a user's physiological and voice data in real time, analyzes the data, and evaluates their health status and security risks. This system has the function of immediately notifying users if an abnormality is detected, and supports the user's daily health and safety management.

[1064] Server Processing

[1065] Data collection and storage

[1066] The server receives physiological and audio data from the device in real time and stores it in a database, including heart rate, body temperature, and other data.

[1067] Data analysis

[1068] The server analyzes the received data using algorithms that detect heart rate fluctuations, voice data separation, changes in body temperature, and stress or abnormal behavior. This allows the server to assess the user's current health and security risks and determine whether there are any abnormalities. For example, the server can use a generative AI model to detect psychological stress.

[1069] Anomaly detection and notification

[1070] The server detects various abnormalities based on the analysis results. If an abnormality is detected, the server immediately sends a notification to the user, their family, or medical professionals. This notification includes the results of the data analysis and details of the specific abnormal condition. For example, if the heart rate is abnormally high, the server will notify the user, saying, "Your heart rate is high. Please rest."

[1071] Terminal handling

[1072] Biometric data collection

[1073] The device is worn in the user's ear and collects physiological data such as heart rate, body temperature, and everyday sounds in real time. This data is acquired by internal sensors.

[1074] Data transmission

[1075] The terminals send the collected data to the server at regular intervals, and the data is time-stamped, making it easier for the server to process it.

[1076] User Feedback

[1077] The device then provides feedback to the user based on the analysis results obtained from the server. This feedback is provided in the form of voice, and specific advice such as "Your heart rate is high. Please rest." If a security risk is detected, the device will notify the user, saying, "You may be in a dangerous environment. Please be careful."

[1078] User Action

[1079] Wearing the device

[1080] The user wears the earhook-type device, which automatically starts up and begins collecting physiological and audio data.

[1081] Receiving feedback

[1082] The user receives feedback from the device and adjusts their behavior accordingly. For example, if an abnormality is detected, the device recommends taking specific action, such as visiting a medical institution.

[1083] Specific examples

[1084] For example, when a user wears the device in their ear, the device collects their heart rate, body temperature, and everyday sounds. This data is periodically sent to a server, where it is analyzed in real time. Based on the analysis results, if the heart rate is higher than normal, for example, the device will provide voice feedback saying, "Your heart rate is high. Please take a deep breath." If stress is detected, the device will send a notification saying, "Your stress is increasing. Please relax." By following this feedback and taking a deep breath, the user can stabilize their heart rate.

[1085] Generative AI model prompt example

[1086] The next analysis will receive the user's heart rate data and determine if it is an abnormal value. If the heart rate is not within the range of 60 to 100, notify the user that "An abnormal heart rate has been detected." Below is an example of heart rate data.

[1087] Heart rate: 110

[1088] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1089] Step 1:

[1090] The terminal collects physiological and audio data from the user in real time.

[1091] (Input) Heart rate data, body temperature data, and voice data from sensors.

[1092] (Processing) Data collection by sensors.

[1093] (Output) Physiological and audio data are stored in internal memory.

[1094] Step 2:

[1095] The terminal sends the collected data to the server at regular intervals.

[1096] (Input) Physiological and audio data collected in step 1.

[1097] (Processing) The data is formatted into packets, timestamps are added, and the packets are sent to the server.

[1098] (Output) Physiological and audio data received on the server side.

[1099] Step 3:

[1100] The server stores the received data in a database.

[1101] (Input) Physiological and audio data sent from sensors.

[1102] (Processing) Save received data in a database.

[1103] (Output) Physiological and audio data stored in a database.

[1104] Step 4:

[1105] The server analyzes the stored data.

[1106] (Input) Physiological and audio data stored in a database.

[1107] (Processing) The data analysis unit analyzes heart rate fluctuations, audio data separation, and changes in body temperature. Stress and abnormal behavior are also detected using generative AI models.

[1108] (Output) Health and security risk assessment results.

[1109] Step 5:

[1110] The server detects anomalies based on the analysis results.

[1111] (Input) Evaluation results of data analysis.

[1112] (Processing) Detect outliers using an anomaly detection algorithm.

[1113] (Output) The result of the anomaly detection.

[1114] Step 6:

[1115] If the server detects an abnormality, it sends a notification to the user's mobile device.

[1116] (Input) Anomaly detection results.

[1117] (Processing) Generate notification content and send it to the mobile device.

[1118] (Output) The notification sent to the user's mobile device.

[1119] Step 7:

[1120] The terminal provides feedback to the user based on the analysis results of the server.

[1121] (Input) The notification content sent from the server.

[1122] (Processing) Audio and visual notifications.

[1123] (Output) Providing feedback to the user.

[1124] Step 8:

[1125] The user receives feedback provided by the device and adjusts their behavior accordingly.

[1126] (Input) Feedback from the device.

[1127] (Processing) Adjusting behavior based on feedback content.

[1128] (Output) Change in user behavior (e.g., taking a deep breath, seeking medical attention, etc.).

[1129] 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.

[1130] The present invention relates to an earhook-type wearable device and a system that acquires a user's physiological and voice data in real time, analyzes the data, and evaluates their health condition. The present invention also incorporates an emotion engine to recognize the user's emotional state and manage their health based on this. This system immediately notifies the user if an abnormality is detected, supporting the user's daily health management.

[1131] Server Processing

[1132] Data collection and storage

[1133] The server receives physiological and voice data from the device in real time and stores it in a database. This data includes heart rate, body temperature, and everyday sounds. Furthermore, it uses an emotion engine to detect the user's emotions from the collected voice data.

[1134] Data analysis

[1135] The server analyzes the stored data using algorithms that include heart rate variability, body temperature changes, audio data separation, and emotional state recognition and evaluation, which then evaluates the user's current health and emotional state.

[1136] Anomaly detection and notification

[1137] The server detects abnormalities in health and emotions based on the analysis results. If an abnormality is detected, the server immediately sends a notification to the user, their family, or medical professionals. The notification includes the results of the data analysis and details of the abnormal condition.

[1138] Terminal handling

[1139] Biometric data collection

[1140] The device measures and generates data on the user's heart rate, body temperature, and everyday sounds in real time, as well as collecting data needed to detect the user's emotional state.

[1141] Data transmission

[1142] The device transmits the collected physiological data, voice data, and emotional state data to a server at regular intervals.

[1143] User Feedback

[1144] The device then provides the user with feedback based on the analysis results obtained from the server. The feedback is provided in the form of voice, and specific advice is provided, such as "Your heart rate is high. Take a deep breath" or "You seem to be stressed. Relax."

[1145] User Action

[1146] Wearing the device

[1147] The user wears the earhook-type device, which automatically starts up and begins collecting physiological, audio, and emotional data.

[1148] Receiving feedback

[1149] Users can then adjust their behavior based on the feedback provided by the device. For example, if an abnormality is detected, specific actions such as seeking medical attention may be recommended. Users may also receive advice based on their emotional state, helping them to practice stress management and relaxation techniques.

[1150] Specific examples

[1151] For example, when a user wears the device in their ear, the device collects their heart rate, body temperature, and everyday sounds. This data is periodically sent to a server, where it is analyzed in real time. Based on the analysis results, if the heart rate is higher than normal, for example, the device will provide voice feedback such as, "Your heart rate is high. Please take a deep breath." At the same time, if the device determines that the user is feeling stressed based on the tone and pace of their voice, it will add the advice, "You seem stressed. Relax." By following this feedback and taking deep breaths, the user can stabilize their heart rate and reduce stress.

[1152] As described above, the system of the present invention effectively supports health and stress management by monitoring the user's health and emotional state in real time and immediately notifying the user if any abnormalities occur. This system allows the user to constantly understand their own health and emotional state and quickly take appropriate measures.

[1153] The processing flow will be explained below.

[1154] Server Processing

[1155] Step 1: Receiving and storing data

[1156] The server receives physiological and audio data from the device in real time and stores the data in a database.

[1157] The server receives the data over the network.

[1158] The received data is stored in a database.

[1159] Step 2: Data analysis

[1160] The server analyzes the stored data and evaluates the user's health condition.

[1161] Retrieve data from the database.

[1162] Heart rate, body temperature, and audio data are processed using analytical algorithms.

[1163] Recognize the user's emotional state from voice data using an emotion engine.

[1164] Evaluate health and emotional states and generate analytical results.

[1165] Step 3: Anomaly detection

[1166] The server detects health and emotional abnormalities based on the analysis results.

[1167] Detect outliers from the analysis results.

[1168] If an abnormality is detected, the abnormal data is determined.

[1169] Step 4: Notification

[1170] The server will notify you if an abnormality is detected.

[1171] Decide who to notify (user, family, medical professionals).

[1172] Sends notifications containing details of abnormal conditions and analysis results.

[1173] Terminal handling

[1174] Step 1: Collect biometric data

[1175] The device measures the user's heart rate, body temperature, and everyday sounds in real time and generates data.

[1176] Measure your heart rate using a sensor.

[1177] Body temperature is measured using a sensor.

[1178] Daily sounds are recorded using a microphone.

[1179] Step 2: Collecting emotion data

[1180] The device collects emotional data from the user's tone and pace of voice.

[1181] Analyzes voice data and uses an emotion engine to recognize emotional states.

[1182] Record the recognized emotion data.

[1183] Step 3: Send data to the server

[1184] The terminal transmits the collected physiological data, voice data, and emotion data to a server.

[1185] Packetize the data.

[1186] Send data to the server periodically.

[1187] Step 4: Receive analysis results from the server

[1188] The terminal receives the analysis result from the server.

[1189] Receives analysis result data sent from the server.

[1190] The received analysis results are saved in the internal memory.

[1191] Step 5: User feedback

[1192] The terminal provides feedback to the user based on the analysis results from the server.

[1193] Analyze the results and determine the necessary actions.

[1194] Provides audio feedback through a speaker.

[1195] User Action

[1196] Step 1: Wear the device

[1197] The user wears the ear hook type device on their ear.

[1198] Place the device in your ear.

[1199] Check that it is installed correctly.

[1200] Step 2: Check biometric and emotional data

[1201] The user checks the feedback provided by the terminal.

[1202] Listen to the feedback audio message.

[1203] Adjust your behavior based on the feedback.

[1204] Step 3: Take healthy actions and manage your emotions

[1205] Users manage their health and emotions based on the feedback.

[1206] Follow the advice provided (e.g., take deep breaths, take breaks).

[1207] Practice stress management and relaxation techniques.

[1208] If necessary, consult a medical institution.

[1209] Specific examples

[1210] For example, when a user wears the device in their ear, the device collects emotional data from their heart rate, body temperature, everyday sounds, and even voice. This data is periodically sent to a server, where it is analyzed in real time. Based on the analysis results, if the heart rate is higher than normal, for example, the device will provide voice feedback such as "Your heart rate is high. Take a deep breath." At the same time, if the device determines that the user is feeling stressed based on the tone and pace of their voice, it will add advice such as "You seem stressed. Relax." By following this feedback and taking deep breaths and practicing relaxation techniques, the user can stabilize their heart rate and reduce stress.

[1211] The above are the specific processing steps in the system of the present invention. This process allows the user's health and emotional state to be monitored in real time, and if an abnormality is detected, a notification is sent immediately. This allows for early health management and emotional management at the same time.

[1212] Example 2

[1213] 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."

[1214] In recent years, personal health management has become increasingly important. However, existing systems lack the ability to adequately monitor physiological data and emotional states in real time, making it difficult to respond quickly when abnormalities occur. Furthermore, there are limited means for providing appropriate feedback and advice to users. This makes it difficult for users to effectively manage their own health and emotional states. To address these issues, a system that comprehensively collects, analyzes, and provides feedback in real time is needed.

[1215] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a data collection unit that acquires physiological data and voice data of the user in real time, a data analysis unit that processes and analyzes the acquired physiological data and voice data to evaluate the health and emotional states, an anomaly detection unit that detects health and emotional abnormalities based on the analysis results from the data analysis unit, a notification unit that notifies a predetermined notification destination based on the abnormality detected by the anomaly detection unit, and a voice feedback unit that provides specific health advice to the user based on the results of the data analysis unit. This enables real-time monitoring of the health and emotional states and the provision of appropriate feedback and advice. Furthermore, early detection of abnormalities and rapid response enable the user to effectively manage their own health and emotional states.

[1216] A "data collection unit" is a device that has the function of acquiring physiological data and voice data of a user in real time.

[1217] "Physiological data" refers to data that indicates the user's physical condition, such as heart rate and body temperature.

[1218] "Voice data" refers to acoustic data that includes the user's voice and surrounding sounds.

[1219] The "data analysis unit" is a device that processes and analyzes acquired physiological and audio data to evaluate health and emotional states.

[1220] "Health status" is an indicator of a user's physical condition, and is primarily evaluated by heart rate, body temperature, etc.

[1221] "Emotional state" is an index that indicates the user's psychological state, and is primarily evaluated by the tone and pace of the voice data.

[1222] The "anomaly detection unit" is a device that has the function of detecting health and emotional abnormalities based on the analysis results from the data analysis unit.

[1223] The "notification unit" is a device that has the function of notifying a predetermined notification destination based on an abnormality detected by the abnormality detection unit.

[1224] The "voice feedback unit" is a device that has the function of providing specific health advice to the user based on the results of the data analysis unit.

[1225] "Health Advice" means specific instructions or suggestions provided to a User to improve or maintain their health.

[1226] "Emotion management advice" is specific instructions or suggestions provided to help users achieve psychological well-being.

[1227] The present invention is a system for collecting physiological data and audio data of a user in real time using an earhook-type wearable device, and analyzing the data to evaluate the health and emotional state of the user, which includes a data collection unit, a data analysis unit, an anomaly detection unit, a notification unit, and an audio feedback unit.

[1228] Hardware and Software Configuration

[1229] Data Acquisition Unit

[1230] The data collection unit is built into the earhook device. Its main hardware includes a heart rate sensor to measure heart rate, a temperature sensor to measure body temperature, and a microphone to collect sounds and voices. The audio data is filtered by an audio processor to extract only the necessary data. This data is then transmitted in real time to a server via Bluetooth or Wi-Fi.

[1231] Data Analysis Unit

[1232] The server functions as a data analysis unit. The acquired physiological and audio data is first stored in a database. Then, using Python's SciPy and NumPy libraries, heart rate fluctuations and changes in body temperature are analyzed. The emotional state is evaluated from the audio data using IBM Watson's sentiment analysis API. The results of the data analysis are used for the next step, anomaly detection.

[1233] Anomaly Detection Unit

[1234] The server runs an algorithm to detect health and emotional anomalies based on the results of the data analysis unit. The algorithm is designed to compare the analysis results with pre-set thresholds and detect any anomalies that occur. Detected anomalies are immediately passed to the notification unit.

[1235] Notification Unit

[1236] The notification unit is responsible for sending necessary notifications based on the anomalies detected by the anomaly detection unit. These notifications are sent via SMS or email using APIs such as Twilio. The notifications include details of the anomaly and the analysis results.

[1237] Audio Feedback Unit

[1238] The device also has a built-in voice feedback unit that receives feedback from the server and notifies the user via voice. The feedback includes specific health advice, such as "Your heart rate is high, take a deep breath" or "You seem stressed, relax." This allows the user to take action in real time according to their health and emotional state.

[1239] Adding specific examples

[1240] For example, when a user wears an earhook device, their heart rate, body temperature, and everyday sounds are collected in real time. This data is periodically sent from the device to a server, where it is analyzed. If the analysis detects a high heart rate or stress, the device will provide voice feedback such as "Your heart rate is high. Please take a deep breath" or "You seem stressed. Please relax."

[1241] Prompt Sentence Examples

[1242] "How can we build a system where an earhook device attached to the ear measures heart rate, body temperature, and everyday sounds in real time, and analyzes the user's emotional state and provides advice?"

[1243] This system allows users to constantly monitor their own health and emotional state and take appropriate action quickly.

[1244] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1245] Step 1:

[1246] When the user wears the earhook device, the device automatically starts up and collects physiological data (heart rate, body temperature) and audio data (life sounds, voice) in real time. The collected data is temporarily stored in the built-in memory. The input is the user's physiological data and audio data, and the output is a buffer of the collected data.

[1247] Step 2:

[1248] The device reads the collected physiological and audio data from the buffer at regular intervals (e.g., every 30 seconds) and transmits it to the server using Bluetooth or Wi-Fi. The data is encrypted before transmission to ensure communication security. The input is the data read from the buffer, and the output is the encrypted data packet.

[1249] Step 3:

[1250] The server decompresses the data packets received from the terminal and stores them in a database. This process also formats the data, checks for duplicates, and filters outliers. The input is the encrypted data packet, and the output is the normal data stored in the database.

[1251] Step 4:

[1252] The server uses Python's SciPy and NumPy libraries to analyze the stored physiological and audio data. Heart rate fluctuations and changes in body temperature are smoothed using the moving average method, and frequency components are extracted from the audio data using a Fast Fourier Transform (FFT). The emotional state of the audio data is also recognized using an emotion analysis API. The input is the physiological and audio data stored in the database, and the output is the analysis results (health status and emotional state).

[1253] Step 5:

[1254] The server detects anomalies based on the analysis results. If the health or emotional state exceeds a preset threshold, it is deemed to be an anomaly. When an anomaly is detected, the data is immediately passed to the notification unit. The input is the analysis results, and the output is the anomaly detection result.

[1255] Step 6:

[1256] The server uses APIs such as Twilio to send notifications via SMS or email to users, their families, and medical professionals based on the anomaly detection results. The notifications include the results of data analysis and details of the anomaly. The input is the anomaly detection result, and the output is the sent notification message.

[1257] Step 7:

[1258] The terminal receives feedback from the server and notifies the user through the voice feedback unit. For example, if the heart rate is high, voice feedback such as "Your heart rate is high, please take a deep breath" is given. Also, depending on the emotional state, advice such as "You seem to be stressed, please relax" is given. The input is the feedback result from the server, and the output is the voice feedback.

[1259] Through the processing flow of these steps, the system of the present invention can monitor the user's health and emotional state in real time, quickly notify the user if an abnormality occurs, and provide the user with appropriate advice.

[1260] (Application example 2)

[1261] 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."

[1262] Conventional wearable devices mainly collect physiological data from users, but lack the ability to monitor their emotional state. Furthermore, their ability to detect abnormalities in real time and provide appropriate advice is incomplete, limiting their use in security services, where stress management and rapid notification of abnormalities are essential. As a result, there is a need for a means to effectively monitor the health and emotional state of employees, such as security guards.

[1263] 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.

[1264] In this invention, the server includes a data collection unit that acquires physiological data and voice data of a user in real time, a data analysis unit that processes and analyzes the acquired physiological data and voice data to evaluate the health and emotional states, an anomaly detection unit that detects health and emotional abnormalities based on the analysis results from the data analysis unit, a notification unit that notifies a predetermined notification destination based on the abnormality detected by the anomaly detection unit, and a user interface unit that provides specific feedback to the user based on the analysis results. This makes it possible to monitor the health and emotional states of security guards in real time and to provide immediate countermeasures when an abnormality is detected.

[1265] definition statement

[1266] A "data collection unit" is a device that acquires physiological and audio data of a user in real time.

[1267] The "data analysis unit" is a device that processes and analyzes acquired physiological and audio data to assess health and emotional states.

[1268] The "anomaly detection unit" is a device that detects health and emotional abnormalities based on the analysis results from the data analysis unit.

[1269] The "notification unit" is a device that notifies a predetermined notification destination based on an abnormality detected by the abnormality detection unit.

[1270] A "user interface unit" is a device that provides specific feedback to the user based on the analysis results.

[1271] "Audio separation technology" is a technology that extracts only the necessary audio signals from audio data.

[1272] The "voice generation unit" is a device that generates and provides to the user advice on optimal health and emotional state for an individual based on the analysis results.

[1273] MODE FOR CARRYING OUT THE INVENTION

[1274] System Overview

[1275] The system of this invention collects and analyzes physiological and voice data of security guards in real time by having them wear earhook-type wearable devices. The system evaluates the health and emotional state of the security guards and immediately notifies them appropriately if any abnormalities are detected. It also provides specific feedback to the security guards based on the analysis results.

[1276] Hardware used

[1277] Ear hook wearable device: Worn by security guards, it collects physiological and audio data.

[1278] Server: Collects, stores, and analyzes data.

[1279] Smartphone: The device through which security guards receive notifications.

[1280] Software used

[1281] Programming language: Python

[1282] Database: MySQL

[1283] Analysis algorithm: Heart rate variability analysis, voice recognition

[1284] Emotion engine: Deep Learning model (TensorFlow)

[1285] Data exchange and analysis

[1286] In this system, data is exchanged and analyzed through the following steps:

[1287] Terminal handling

[1288] The wearable device collects the guard's heart rate, body temperature, sound, and emotional data in real time, and transmits the collected data to a server at regular intervals. The device also provides feedback to the guard based on the analysis results from the server.

[1289] Server Processing

[1290] The server receives physiological and audio data sent from the device in real time and stores it in a MySQL database. The stored data is then analyzed by an emotion engine using TensorFlow. Specific analysis tasks include heart rate variability analysis, changes in body temperature, audio data segmentation, and emotional state recognition. Health and emotional abnormalities are detected based on the analysis results, and if an abnormality is detected, the server immediately sends a notification to the security guard's smartphone. The notification includes specific advice such as "Your heart rate is high. Please take a break" or "Your stress is increasing. Please try to relax."

[1291] Prompt Sentence Examples

[1292] Use the following as an example of a prompt for a generative AI model:

[1293] "Please display the prediction results of the emotion engine that estimates the user's stress level from their voice data."

[1294] Specific examples

[1295] For example, when security guard A is wearing the earhook device, the device detects his heart rate of 120 bpm, body temperature of 37.5°C, and stress level from his voice. The server receives this data and evaluates his health and emotional state based on the analysis results. If the anomaly detection unit detects an abnormal heart rate and stress level, the server immediately sends a notification to security guard A's smartphone, providing feedback such as, "Your heart rate is high. Please take a break."

[1296] As described above, the system of the present invention effectively supports health and stress management for security guards, and is capable of detecting abnormalities in real time and responding immediately. This system allows security guards to constantly monitor their own health and emotional state and quickly take appropriate measures.

[1297] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1298] Program processing flow

[1299] Step 1: Data collection

[1300] The device collects the guard's heart rate, body temperature, daily sounds, and emotional data.

[1301] Input: Security guard's biometric information (heart rate, body temperature), daily sounds, voice input

[1302] Output: Collected biometric and audio data

[1303] Specific operation: The earhook wearable device collects data using built-in sensors (heart rate sensor, temperature sensor) and microphone. The device prepares to send the data to a server at regular intervals.

[1304] Step 2: Sending data

[1305] The terminal transmits the collected physiological data, audio data, and emotional state data to a server.

[1306] Input: Biometric and audio data collected in step 1

[1307] Output: Data packets sent to the server

[1308] Specific operation: The device sends the collected data to the server at regular intervals using Wi-Fi or mobile data communication.

[1309] Step 3: Save your data

[1310] The server stores the received physiological data and voice data in a database.

[1311] Input: Biometric data and voice data sent from the device

[1312] Output: Data stored in the database

[1313] What happens: The Python script runs a SQL query against the MySQL database to insert the received data.

[1314] Step 4: Data analysis

[1315] The server analyzes the stored data.

[1316] Input: Biometric and audio data stored in a database

[1317] Output: Health and emotional state assessment results

[1318] How it works: A program written in Python uses TensorFlow to analyze audio data to detect emotional states, and runs algorithms that analyze heart rate and temperature data to assess outliers.

[1319] Step 5: Anomaly detection

[1320] The anomaly detection unit detects anomalies based on the analysis results.

[1321] Input: Health and emotional state assessment results

[1322] Output: Anomaly detection results

[1323] Specific operation: If the evaluation result exceeds a specified threshold, the Python code sets an abnormality flag and determines that an abnormality has occurred.

[1324] Step 6: Notification

[1325] The server sends a notification to the security guard's smartphone based on the abnormality detection results.

[1326] Input: Anomaly detection result

[1327] Output: Notification sent to your phone

[1328] Specific operation: The notification function sends an SMS or push notification to the security guard's smartphone informing them of an abnormality and providing specific advice.

[1329] Step 7: Provide feedback

[1330] The terminal provides specific feedback to the security guard based on the analysis results obtained from the server.

[1331] Input: Analysis results and notification content

[1332] Output: Feedback to security guards (audio advice)

[1333] Specific operation: The device uses its voice output function to provide advice to the security guard, such as "Your heart rate is high. Please take a break" or "Your stress is increasing. Please try to relax."

[1334] 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.

[1335] 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.

[1336] 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.

[1337] [Fourth embodiment]

[1338] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1339] 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.

[1340] 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).

[1341] 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.

[1342] 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.

[1343] 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).

[1344] 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.

[1345] 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.

[1346] 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.

[1347] 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.

[1348] 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.

[1349] 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.

[1350] 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."

[1351] The present invention relates to an earhook-type wearable device, which is a system that acquires a user's physiological and voice data in real time, analyzes the data, and evaluates their health condition. This system has the function of immediately notifying the user if an abnormality is detected, and supports the user's daily health management.

[1352] Server Processing

[1353] Data collection and storage

[1354] The server receives physiological and audio data from the device in real time and stores it in a database, including heart rate, body temperature, and other data.

[1355] Data analysis

[1356] The server analyzes the received data using algorithms that evaluate heart rate fluctuations, voice data separation, and changes in body temperature, among other things, to assess the user's current health status and determine whether there are any abnormalities.

[1357] Anomaly detection and notification

[1358] The server detects abnormalities based on the analysis results, and if an abnormality is detected, the server immediately sends a notification to the user, their family, or medical professionals, including the results of the data analysis and details of the specific abnormal condition.

[1359] Terminal handling

[1360] Biometric data collection

[1361] The device is worn in the user's ear and collects physiological data such as heart rate, body temperature, and everyday sounds in real time. This data is acquired by internal sensors.

[1362] Data transmission

[1363] The terminals send the collected data to the server at regular intervals, and the data is time-stamped, making it easier for the server to process it.

[1364] User Feedback

[1365] The device then provides the user with feedback based on the analysis results obtained from the server. This feedback is provided in the form of voice, and specific advice such as "Your heart rate is high. Please rest."

[1366] User Action

[1367] Wearing the device

[1368] The user wears the earhook-type device, which automatically starts up and begins collecting physiological and audio data.

[1369] Receiving feedback

[1370] The user receives feedback from the device and adjusts their behavior accordingly. For example, if an abnormality is detected, the device recommends taking specific action, such as visiting a medical institution.

[1371] Specific examples

[1372] For example, when a user wears the device in their ear, the device collects their heart rate, body temperature, and everyday sounds. This data is periodically sent to a server, where it is analyzed in real time. Based on the analysis results, if the heart rate is higher than normal, for example, the device will provide voice feedback such as "Your heart rate is high. Please take a deep breath." By following this feedback and taking a deep breath, the user can stabilize their heart rate.

[1373] As described above, the system of the present invention effectively supports health management by monitoring the user's health condition in real time and immediately notifying the user if an abnormality occurs. This system allows the user to constantly understand their own health condition and quickly take appropriate measures.

[1374] The processing flow will be explained below.

[1375] Server Processing

[1376] Step 1: Receiving and storing data

[1377] The server receives the physiological data and voice data transmitted from the terminal and stores the received data in a database.

[1378] The server receives the data over the network.

[1379] The received data is stored in a database.

[1380] Step 2: Data analysis

[1381] The server analyzes the stored data and evaluates the user's health condition.

[1382] Retrieve data from the database.

[1383] Analytical algorithms are used to process heart rate, body temperature, voice data, etc.

[1384] Evaluate health status and generate analytical results.

[1385] Step 3: Anomaly detection

[1386] The server detects health abnormalities based on the analysis results.

[1387] Detect outliers from the analysis results.

[1388] If an abnormality is detected, the abnormal data is determined.

[1389] Step 4: Notification

[1390] The server will notify you if an abnormality is detected.

[1391] Decide who to notify (user, family, medical professionals).

[1392] Sends notifications containing details of abnormal conditions and analysis results.

[1393] Terminal handling

[1394] Step 1: Collect biometric data

[1395] The device measures the user's heart rate, body temperature, and daily sounds in real time.

[1396] Measure your heart rate using a sensor.

[1397] Body temperature is measured using a sensor.

[1398] Daily sounds are recorded using a microphone.

[1399] Step 2: Send data to the server

[1400] The terminal transmits the collected biometric data and voice data to the server.

[1401] Packetize the data.

[1402] Send data to the server periodically.

[1403] Step 3: Receive analysis results from the server

[1404] The terminal receives the analysis result from the server.

[1405] Receives analysis result data sent from the server.

[1406] The received analysis results are saved in the internal memory.

[1407] Step 4: User feedback

[1408] The terminal provides feedback to the user based on the analysis results from the server.

[1409] Analyze the results and determine the necessary actions.

[1410] Provides audio feedback through a speaker.

[1411] User Action

[1412] Step 1: Wear the device

[1413] The user wears the ear hook type device on their ear.

[1414] Place the device in your ear.

[1415] Check that it is installed correctly.

[1416] Step 2: Verify your biometric data

[1417] The user checks the feedback provided by the terminal.

[1418] Listen to the feedback audio message.

[1419] Adjust your behavior based on the feedback.

[1420] Step 3: Take Healthy Actions

[1421] The user manages their health based on the feedback.

[1422] Follow the advice provided (e.g., take deep breaths, take breaks).

[1423] If necessary, consult a medical institution.

[1424] The above are the specific processing steps in the system of the present invention. This process allows the user's health condition to be monitored in real time, and if an abnormality is detected, a notification is sent immediately. This enables early health management and preventative measures.

[1425] Example 1

[1426] 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."

[1427] Existing health management systems lack the ability to monitor users' physiological data in real time and quickly detect and notify abnormalities. Furthermore, they often fail to provide appropriate feedback or health advice based on analysis results. This makes it difficult for users to constantly monitor their own health status and quickly take appropriate measures.

[1428] 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.

[1429] In this invention, the server includes a data collection unit for acquiring a user's physiological data and voice data in real time, a data communication unit for transmitting the acquired physiological data and voice data to a central computer, a data storage unit for storing the transmitted data with time information, a data analysis unit for analyzing the stored physiological data and voice data using a machine learning algorithm and evaluating the user's health status, an anomaly detection unit for detecting a health abnormality based on the analysis results from the data analysis unit, a notification unit for notifying a predetermined notification destination based on the abnormality detected by the anomaly detection unit, and a feedback unit for providing audio feedback to the user based on the abnormality detection result and the analysis result. This enables the user's physiological data to be monitored in real time and abnormalities to be quickly detected and notified. Furthermore, by providing effective feedback based on the analysis results, the user can constantly understand their health status and quickly take appropriate measures.

[1430] A "data collection unit" is a device or means for acquiring physiological and audio data of a user in real time.

[1431] "Physiological data" refers to data related to the user's physical functions and health condition, such as heart rate, body temperature, and everyday sounds.

[1432] "Voice data" refers to all voice signals collected from a user, and is the data that serves as the basis for extracting only the necessary voice signals.

[1433] A "data communications unit" is a device or means for transmitting acquired physiological and audio data to a central computer in real time.

[1434] The "data storage unit" is a storage device or means for storing transmitted physiological data and audio data with time information.

[1435] "Data analysis unit" refers to a device or means for analyzing stored physiological data and audio data using machine learning algorithms to assess health status.

[1436] "Machine learning algorithms" are mathematical methods and models that use AI technology to analyze data and detect patterns and anomalies.

[1437] The "abnormality detection unit" is a device or means for detecting a health abnormality based on the analysis results from the data analysis unit.

[1438] The "notification unit" is a device or means for notifying a predetermined notification destination based on an abnormality detected by the abnormality detection unit.

[1439] The "feedback unit" is a device or means for providing audio feedback to the user based on the anomaly detection results and analysis results.

[1440] A "central computer" is a central information processing system that analyzes and stores data.

[1441] "Time information" is information that indicates the time when data was collected and transmitted.

[1442] The following hardware and software are used to implement the present invention.

[1443] Server Configuration

[1444] The server configuration includes a data collection unit, a data communication unit, a data storage unit, a data analysis unit, an anomaly detection unit, a notification unit, and a feedback unit.

[1445] Hardware used

[1446] Central computer: Information processing system that analyzes and stores data

[1447] Storage: MySQL database

[1448] Software used

[1449] Programming languages: Python, JavaScript

[1450] Machine learning framework: TensorFlow

[1451] Database Management System: MySQL

[1452] Communication method: Node.js

[1453] Device configuration

[1454] The terminal configuration includes collecting biometric data, transmitting data, and providing feedback.

[1455] Hardware used

[1456] Sensors: Heart rate sensor, temperature sensor, microphone

[1457] Communication devices: Bluetooth communication module, Wi-Fi module

[1458] Speaker: A device that provides audio feedback

[1459] User Roles

[1460] The user's primary role is to place the device in their ear and receive feedback.

[1461] System processing details

[1462] Server Processing

[1463] The server receives physiological and audio data sent from the device via Bluetooth. It then stores this data in a MySQL database and adds a timestamp to facilitate analysis. It then analyzes the data using Python and TensorFlow, detecting any abnormalities based on the analysis results. If an abnormality is detected, it activates a notification system via Node.js to notify the user, their family, and medical professionals. It also provides feedback to the user based on the analysis results.

[1464] Terminal handling

[1465] The device uses sensors to collect physiological data such as heart rate, body temperature, and everyday sounds in real time. This data is sent to a smartphone via Bluetooth, and the smartphone then transfers the data to a server via Wi-Fi. The device receives the server's analysis results and provides voice feedback such as "Your heart rate is high. Please take a deep breath" through the speaker.

[1466] User Action

[1467] The user begins collecting physiological and audio data by wearing the earhook device, and adjusts their behavior accordingly based on the audio feedback provided by the device.

[1468] Specific examples

[1469] For example, when a user wears the device in their ear, the device collects heart rate, body temperature, and everyday sounds, and sends them to a smartphone via Bluetooth. The smartphone then sends the data to a server via Wi-Fi, where it is analyzed using TensorFlow. If the analysis determines that the heart rate is higher than normal, the device's speaker will provide audio feedback saying, "Your heart rate is high. Please take a deep breath." By following this feedback and taking deep breaths, the user can stabilize their heart rate.

[1470] Prompt Sentence Examples

[1471] Please provide the Python code for a program that sends the user's physiological and audio data to a server, analyzes the data in real time, and evaluates the user's health condition.

[1472] As described above, this system monitors the user's health condition in real time, quickly detecting and notifying abnormalities. In addition, by providing effective feedback based on the analysis results, users can constantly understand their own health condition and quickly take appropriate measures.

[1473] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1474] Step 1: Wear the device

[1475] The user wears the ear hook type device on their ear. This automatically starts the device and starts collecting physiological and audio data. Specifically, the heart rate sensor, temperature sensor, and microphone start working and data collection begins. The input is "user wearing" and the output is "sensor activation."

[1476] Step 2: Collect biometric data

[1477] The device uses a heart rate sensor, temperature sensor, and microphone to collect the user's heart rate, body temperature, and everyday sounds in real time, allowing the device to constantly monitor the user's physiological state. The input is "physiological data and audio data acquired by the sensors," and the output is "physiological data and audio data collected in real time."

[1478] Step 3: Sending data

[1479] The device sends the collected data to a smartphone via Bluetooth, and the smartphone transfers the data to a server via Wi-Fi. A timestamp is added, making it easier for the server to process the data. The input is the "collected physiological data and voice data," and the output is the "data sent to the server."

[1480] Step 4: Save your data

[1481] The server stores the received physiological and audio data in a MySQL database. The important thing here is that the data is time-stamped, which makes it possible to analyze it later along a timeline. The input is "data sent to the server" and the output is "data stored in the database."

[1482] Step 5: Analyze the data

[1483] The server analyzes the stored data using Python and TensorFlow. Specifically, it runs algorithms that analyze heart rate variability, separate audio data, and evaluate changes in body temperature. This analysis evaluates the user's current health status. The input is the data stored in the database, and the output is the analysis results.

[1484] Step 6: Anomaly detection

[1485] The server detects anomalies based on the analysis results. If an anomaly is detected, the details of the anomaly and the corresponding data are recorded in a log. For example, if the analysis results determine that the heart rate is abnormally high, this is detected as an anomaly. The input is the "analysis results" and the output is the "anomaly detection results."

[1486] Step 7: Sending notifications

[1487] The server uses Node.js to send notifications to users, their families, and medical professionals based on the anomaly detection results. The notifications include the results of data analysis and details of the specific abnormality. The input is the "anomaly detection results," and the output is "notifications to recipients."

[1488] Step 8: Provide feedback

[1489] The device provides voice feedback to the user based on the analysis results obtained from the server. Specifically, a message such as "Your heart rate is high. Please rest" is provided through the speaker. The input is "analysis results and abnormality detection results," and the output is "voice feedback."

[1490] (Application example 1)

[1491] 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."

[1492] Conventional health monitoring systems are primarily limited to detecting and notifying physiological abnormalities, making it difficult to detect and respond appropriately to security risks such as stress, abnormal behavior, and dangerous environments. Furthermore, many of these systems rely on a single notification method, and lack a mechanism for quickly sending notifications to users' mobile devices in the event of an emergency.

[1493] 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.

[1494] In this invention, the server includes a data collection unit for acquiring physiological data and voice data of a user in real time, a data analysis unit for processing and analyzing the acquired physiological data and voice data to evaluate the health condition, an anomaly detection unit for detecting an abnormality in health based on the analysis result from the data analysis unit, a notification unit for notifying a predetermined notification destination based on the abnormality detected by the anomaly detection unit, a means for analyzing security risks and detecting stress, abnormal behavior, and dangerous environments, and a means for sending a notification to the user's mobile device when a danger is detected. This enables comprehensive health and safety monitoring, including security risks, and enables prompt and appropriate notification in emergencies.

[1495] A "data collection unit" is a device that has the function of acquiring physiological data and voice data of a user in real time.

[1496] The "data analysis unit" is a device that processes and analyzes acquired physiological data and audio data to evaluate health status.

[1497] The "abnormality detection unit" is a device that has the function of detecting health abnormalities based on the analysis results from the data analysis unit.

[1498] The "notification unit" is a device that has the function of notifying a predetermined notification destination based on an abnormality detected by the abnormality detection unit.

[1499] "Security risks" refer to risks that affect user safety, such as stress, abnormal behavior, and dangerous environments.

[1500] "Mobile terminal" refers to an information and communication device that a user carries with them at all times, such as a smartphone.

[1501] "Server" refers to the central computer system that collects, analyzes, and notifies data.

[1502] The present invention relates to an earhook-type wearable device that collects a user's physiological and voice data in real time, analyzes the data, and evaluates their health status and security risks. This system has the function of immediately notifying users if an abnormality is detected, and supports the user's daily health and safety management.

[1503] Server Processing

[1504] Data collection and storage

[1505] The server receives physiological and audio data from the device in real time and stores it in a database, including heart rate, body temperature, and other data.

[1506] Data analysis

[1507] The server analyzes the received data using algorithms that detect heart rate fluctuations, voice data separation, changes in body temperature, and stress or abnormal behavior. This allows the server to assess the user's current health and security risks and determine whether there are any abnormalities. For example, the server can use a generative AI model to detect psychological stress.

[1508] Anomaly detection and notification

[1509] The server detects various abnormalities based on the analysis results. If an abnormality is detected, the server immediately sends a notification to the user, their family, or medical professionals. This notification includes the results of the data analysis and details of the specific abnormal condition. For example, if the heart rate is abnormally high, the server will notify the user, saying, "Your heart rate is high. Please rest."

[1510] Terminal handling

[1511] Biometric data collection

[1512] The device is worn in the user's ear and collects physiological data such as heart rate, body temperature, and everyday sounds in real time. This data is acquired by internal sensors.

[1513] Data transmission

[1514] The terminals send the collected data to the server at regular intervals, and the data is time-stamped, making it easier for the server to process it.

[1515] User Feedback

[1516] The device then provides feedback to the user based on the analysis results obtained from the server. This feedback is provided in the form of voice, and specific advice such as "Your heart rate is high. Please rest." If a security risk is detected, the device will notify the user, saying, "You may be in a dangerous environment. Please be careful."

[1517] User Action

[1518] Wearing the device

[1519] The user wears the earhook-type device, which automatically starts up and begins collecting physiological and audio data.

[1520] Receiving feedback

[1521] The user receives feedback from the device and adjusts their behavior accordingly. For example, if an abnormality is detected, the device recommends taking specific action, such as visiting a medical institution.

[1522] Specific examples

[1523] For example, when a user wears the device in their ear, the device collects their heart rate, body temperature, and everyday sounds. This data is periodically sent to a server, where it is analyzed in real time. Based on the analysis results, if the heart rate is higher than normal, for example, the device will provide voice feedback saying, "Your heart rate is high. Please take a deep breath." If stress is detected, the device will send a notification saying, "Your stress is increasing. Please relax." By following this feedback and taking a deep breath, the user can stabilize their heart rate.

[1524] Generative AI model prompt example

[1525] The next analysis will receive the user's heart rate data and determine if it is an abnormal value. If the heart rate is not within the range of 60 to 100, notify the user that "An abnormal heart rate has been detected." Below is an example of heart rate data.

[1526] Heart rate: 110

[1527] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1528] Step 1:

[1529] The terminal collects physiological and audio data from the user in real time.

[1530] (Input) Heart rate data, body temperature data, and voice data from sensors.

[1531] (Processing) Data collection by sensors.

[1532] (Output) Physiological and audio data are stored in internal memory.

[1533] Step 2:

[1534] The terminal sends the collected data to the server at regular intervals.

[1535] (Input) Physiological and audio data collected in step 1.

[1536] (Processing) The data is formatted into packets, timestamps are added, and the packets are sent to the server.

[1537] (Output) Physiological and audio data received on the server side.

[1538] Step 3:

[1539] The server stores the received data in a database.

[1540] (Input) Physiological and audio data sent from sensors.

[1541] (Processing) Save received data in a database.

[1542] (Output) Physiological and audio data stored in a database.

[1543] Step 4:

[1544] The server analyzes the stored data.

[1545] (Input) Physiological and audio data stored in a database.

[1546] (Processing) The data analysis unit analyzes heart rate fluctuations, audio data separation, and changes in body temperature. Stress and abnormal behavior are also detected using generative AI models.

[1547] (Output) Health and security risk assessment results.

[1548] Step 5:

[1549] The server detects anomalies based on the analysis results.

[1550] (Input) Evaluation results of data analysis.

[1551] (Processing) Detect outliers using an anomaly detection algorithm.

[1552] (Output) The result of the anomaly detection.

[1553] Step 6:

[1554] If the server detects an abnormality, it sends a notification to the user's mobile device.

[1555] (Input) Anomaly detection results.

[1556] (Processing) Generate notification content and send it to the mobile device.

[1557] (Output) The notification sent to the user's mobile device.

[1558] Step 7:

[1559] The terminal provides feedback to the user based on the analysis results of the server.

[1560] (Input) The notification content sent from the server.

[1561] (Processing) Audio and visual notifications.

[1562] (Output) Providing feedback to the user.

[1563] Step 8:

[1564] The user receives feedback provided by the device and adjusts their behavior accordingly.

[1565] (Input) Feedback from the device.

[1566] (Processing) Adjusting behavior based on feedback content.

[1567] (Output) Change in user behavior (e.g., taking a deep breath, seeking medical attention, etc.).

[1568] 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.

[1569] The present invention relates to an earhook-type wearable device and a system that acquires a user's physiological and voice data in real time, analyzes the data, and evaluates their health condition. The present invention also incorporates an emotion engine to recognize the user's emotional state and manage their health based on this. This system immediately notifies the user if an abnormality is detected, supporting the user's daily health management.

[1570] Server Processing

[1571] Data collection and storage

[1572] The server receives physiological and voice data from the device in real time and stores it in a database. This data includes heart rate, body temperature, and everyday sounds. Furthermore, it uses an emotion engine to detect the user's emotions from the collected voice data.

[1573] Data analysis

[1574] The server analyzes the stored data using algorithms that include heart rate variability, body temperature changes, audio data separation, and emotional state recognition and evaluation, which then evaluates the user's current health and emotional state.

[1575] Anomaly detection and notification

[1576] The server detects abnormalities in health and emotions based on the analysis results. If an abnormality is detected, the server immediately sends a notification to the user, their family, or medical professionals. The notification includes the results of the data analysis and details of the abnormal condition.

[1577] Terminal handling

[1578] Biometric data collection

[1579] The device measures and generates data on the user's heart rate, body temperature, and everyday sounds in real time, as well as collecting data needed to detect the user's emotional state.

[1580] Data transmission

[1581] The device transmits the collected physiological data, voice data, and emotional state data to a server at regular intervals.

[1582] User Feedback

[1583] The device then provides the user with feedback based on the analysis results obtained from the server. The feedback is provided in the form of voice, and specific advice is provided, such as "Your heart rate is high. Take a deep breath" or "You seem to be stressed. Relax."

[1584] User Action

[1585] Wearing the device

[1586] The user wears the earhook-type device, which automatically starts up and begins collecting physiological, audio, and emotional data.

[1587] Receiving feedback

[1588] Users can then adjust their behavior based on the feedback provided by the device. For example, if an abnormality is detected, specific actions such as seeking medical attention may be recommended. Users may also receive advice based on their emotional state, helping them to practice stress management and relaxation techniques.

[1589] Specific examples

[1590] For example, when a user wears the device in their ear, the device collects their heart rate, body temperature, and everyday sounds. This data is periodically sent to a server, where it is analyzed in real time. Based on the analysis results, if the heart rate is higher than normal, for example, the device will provide voice feedback such as, "Your heart rate is high. Please take a deep breath." At the same time, if the device determines that the user is feeling stressed based on the tone and pace of their voice, it will add the advice, "You seem stressed. Relax." By following this feedback and taking deep breaths, the user can stabilize their heart rate and reduce stress.

[1591] As described above, the system of the present invention effectively supports health and stress management by monitoring the user's health and emotional state in real time and immediately notifying the user if any abnormalities occur. This system allows the user to constantly understand their own health and emotional state and quickly take appropriate measures.

[1592] The processing flow will be explained below.

[1593] Server Processing

[1594] Step 1: Receiving and storing data

[1595] The server receives physiological and audio data from the device in real time and stores the data in a database.

[1596] The server receives the data over the network.

[1597] The received data is stored in a database.

[1598] Step 2: Data analysis

[1599] The server analyzes the stored data and evaluates the user's health condition.

[1600] Retrieve data from the database.

[1601] Heart rate, body temperature, and audio data are processed using analytical algorithms.

[1602] Recognize the user's emotional state from voice data using an emotion engine.

[1603] Evaluate health and emotional states and generate analytical results.

[1604] Step 3: Anomaly detection

[1605] The server detects health and emotional abnormalities based on the analysis results.

[1606] Detect outliers from the analysis results.

[1607] If an abnormality is detected, the abnormal data is determined.

[1608] Step 4: Notification

[1609] The server will notify you if an abnormality is detected.

[1610] Decide who to notify (user, family, medical professionals).

[1611] Sends notifications containing details of abnormal conditions and analysis results.

[1612] Terminal handling

[1613] Step 1: Collect biometric data

[1614] The device measures the user's heart rate, body temperature, and everyday sounds in real time and generates data.

[1615] Measure your heart rate using a sensor.

[1616] Body temperature is measured using a sensor.

[1617] Daily sounds are recorded using a microphone.

[1618] Step 2: Collecting emotion data

[1619] The device collects emotional data from the user's tone and pace of voice.

[1620] Analyzes voice data and uses an emotion engine to recognize emotional states.

[1621] Record the recognized emotion data.

[1622] Step 3: Send data to the server

[1623] The terminal transmits the collected physiological data, voice data, and emotion data to a server.

[1624] Packetize the data.

[1625] Send data to the server periodically.

[1626] Step 4: Receive analysis results from the server

[1627] The terminal receives the analysis result from the server.

[1628] Receives analysis result data sent from the server.

[1629] The received analysis results are saved in the internal memory.

[1630] Step 5: User feedback

[1631] The terminal provides feedback to the user based on the analysis results from the server.

[1632] Analyze the results and determine the necessary actions.

[1633] Provides audio feedback through a speaker.

[1634] User Action

[1635] Step 1: Wear the device

[1636] The user wears the ear hook type device on their ear.

[1637] Place the device in your ear.

[1638] Check that it is installed correctly.

[1639] Step 2: Check biometric and emotional data

[1640] The user checks the feedback provided by the terminal.

[1641] Listen to the feedback audio message.

[1642] Adjust your behavior based on the feedback.

[1643] Step 3: Take healthy actions and manage your emotions

[1644] Users manage their health and emotions based on the feedback.

[1645] Follow the advice provided (e.g., take deep breaths, take breaks).

[1646] Practice stress management and relaxation techniques.

[1647] If necessary, consult a medical institution.

[1648] Specific examples

[1649] For example, when a user wears the device in their ear, the device collects emotional data from their heart rate, body temperature, everyday sounds, and even voice. This data is periodically sent to a server, where it is analyzed in real time. Based on the analysis results, if the heart rate is higher than normal, for example, the device will provide voice feedback such as "Your heart rate is high. Take a deep breath." At the same time, if the device determines that the user is feeling stressed based on the tone and pace of their voice, it will add advice such as "You seem stressed. Relax." By following this feedback and taking deep breaths and practicing relaxation techniques, the user can stabilize their heart rate and reduce stress.

[1650] The above are the specific processing steps in the system of the present invention. This process allows the user's health and emotional state to be monitored in real time, and if an abnormality is detected, a notification is sent immediately. This allows for early health management and emotional management at the same time.

[1651] Example 2

[1652] 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."

[1653] In recent years, personal health management has become increasingly important. However, existing systems lack the ability to adequately monitor physiological data and emotional states in real time, making it difficult to respond quickly when abnormalities occur. Furthermore, there are limited means for providing appropriate feedback and advice to users. This makes it difficult for users to effectively manage their own health and emotional states. To address these issues, a system that comprehensively collects, analyzes, and provides feedback in real time is needed.

[1654] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a data collection unit that acquires physiological data and voice data of the user in real time, a data analysis unit that processes and analyzes the acquired physiological data and voice data to evaluate the health and emotional states, an anomaly detection unit that detects health and emotional abnormalities based on the analysis results from the data analysis unit, a notification unit that notifies a predetermined notification destination based on the abnormality detected by the anomaly detection unit, and a voice feedback unit that provides specific health advice to the user based on the results of the data analysis unit. This enables real-time monitoring of the health and emotional states and the provision of appropriate feedback and advice. Furthermore, early detection of abnormalities and rapid response enable the user to effectively manage their own health and emotional states.

[1655] A "data collection unit" is a device that has the function of acquiring physiological data and voice data of a user in real time.

[1656] "Physiological data" refers to data that indicates the user's physical condition, such as heart rate and body temperature.

[1657] "Voice data" refers to acoustic data that includes the user's voice and surrounding sounds.

[1658] The "data analysis unit" is a device that processes and analyzes acquired physiological and audio data to evaluate health and emotional states.

[1659] "Health status" is an indicator of a user's physical condition, and is primarily evaluated by heart rate, body temperature, etc.

[1660] "Emotional state" is an index that indicates the user's psychological state, and is primarily evaluated by the tone and pace of the voice data.

[1661] The "anomaly detection unit" is a device that has the function of detecting health and emotional abnormalities based on the analysis results from the data analysis unit.

[1662] The "notification unit" is a device that has the function of notifying a predetermined notification destination based on an abnormality detected by the abnormality detection unit.

[1663] The "voice feedback unit" is a device that has the function of providing specific health advice to the user based on the results of the data analysis unit.

[1664] "Health Advice" means specific instructions or suggestions provided to a User to improve or maintain their health.

[1665] "Emotion management advice" is specific instructions or suggestions provided to help users achieve psychological well-being.

[1666] The present invention is a system for collecting physiological data and audio data of a user in real time using an earhook-type wearable device, and analyzing the data to evaluate the health and emotional state of the user, which includes a data collection unit, a data analysis unit, an anomaly detection unit, a notification unit, and an audio feedback unit.

[1667] Hardware and Software Configuration

[1668] Data Acquisition Unit

[1669] The data collection unit is built into the earhook device. Its main hardware includes a heart rate sensor to measure heart rate, a temperature sensor to measure body temperature, and a microphone to collect sounds and voices. The audio data is filtered by an audio processor to extract only the necessary data. This data is then transmitted in real time to a server via Bluetooth or Wi-Fi.

[1670] Data Analysis Unit

[1671] The server functions as a data analysis unit. The acquired physiological and audio data is first stored in a database. Then, using Python's SciPy and NumPy libraries, heart rate fluctuations and changes in body temperature are analyzed. The emotional state is evaluated from the audio data using IBM Watson's sentiment analysis API. The results of the data analysis are used for the next step, anomaly detection.

[1672] Anomaly Detection Unit

[1673] The server runs an algorithm to detect health and emotional anomalies based on the results of the data analysis unit. The algorithm is designed to compare the analysis results with pre-set thresholds and detect any anomalies that occur. Detected anomalies are immediately passed to the notification unit.

[1674] Notification Unit

[1675] The notification unit is responsible for sending necessary notifications based on the anomalies detected by the anomaly detection unit. These notifications are sent via SMS or email using APIs such as Twilio. The notifications include details of the anomaly and the analysis results.

[1676] Audio Feedback Unit

[1677] The device also has a built-in voice feedback unit that receives feedback from the server and notifies the user via voice. The feedback includes specific health advice, such as "Your heart rate is high, take a deep breath" or "You seem stressed, relax." This allows the user to take action in real time according to their health and emotional state.

[1678] Adding specific examples

[1679] For example, when a user wears an earhook device, their heart rate, body temperature, and everyday sounds are collected in real time. This data is periodically sent from the device to a server, where it is analyzed. If the analysis detects a high heart rate or stress, the device will provide voice feedback such as "Your heart rate is high. Please take a deep breath" or "You seem stressed. Please relax."

[1680] Prompt Sentence Examples

[1681] "How can we build a system where an earhook device attached to the ear measures heart rate, body temperature, and everyday sounds in real time, and analyzes the user's emotional state and provides advice?"

[1682] This system allows users to constantly monitor their own health and emotional state and take appropriate action quickly.

[1683] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1684] Step 1:

[1685] When the user wears the earhook device, the device automatically starts up and collects physiological data (heart rate, body temperature) and audio data (life sounds, voice) in real time. The collected data is temporarily stored in the built-in memory. The input is the user's physiological data and audio data, and the output is a buffer of the collected data.

[1686] Step 2:

[1687] The device reads the collected physiological and audio data from the buffer at regular intervals (e.g., every 30 seconds) and transmits it to the server using Bluetooth or Wi-Fi. The data is encrypted before transmission to ensure communication security. The input is the data read from the buffer, and the output is the encrypted data packet.

[1688] Step 3:

[1689] The server decompresses the data packets received from the terminal and stores them in a database. This process also formats the data, checks for duplicates, and filters outliers. The input is the encrypted data packet, and the output is the normal data stored in the database.

[1690] Step 4:

[1691] The server uses Python's SciPy and NumPy libraries to analyze the stored physiological and audio data. Heart rate fluctuations and changes in body temperature are smoothed using the moving average method, and frequency components are extracted from the audio data using a Fast Fourier Transform (FFT). The emotional state of the audio data is also recognized using an emotion analysis API. The input is the physiological and audio data stored in the database, and the output is the analysis results (health status and emotional state).

[1692] Step 5:

[1693] The server detects anomalies based on the analysis results. If the health or emotional state exceeds a preset threshold, it is deemed to be an anomaly. When an anomaly is detected, the data is immediately passed to the notification unit. The input is the analysis results, and the output is the anomaly detection result.

[1694] Step 6:

[1695] The server uses APIs such as Twilio to send notifications via SMS or email to users, their families, and medical professionals based on the anomaly detection results. The notifications include the results of data analysis and details of the anomaly. The input is the anomaly detection result, and the output is the sent notification message.

[1696] Step 7:

[1697] The terminal receives feedback from the server and notifies the user through the voice feedback unit. For example, if the heart rate is high, voice feedback such as "Your heart rate is high, please take a deep breath" is given. Also, depending on the emotional state, advice such as "You seem to be stressed, please relax" is given. The input is the feedback result from the server, and the output is the voice feedback.

[1698] Through the processing flow of these steps, the system of the present invention can monitor the user's health and emotional state in real time, quickly notify the user if an abnormality occurs, and provide the user with appropriate advice.

[1699] (Application example 2)

[1700] 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."

[1701] Conventional wearable devices mainly collect physiological data from users, but lack the ability to monitor their emotional state. Furthermore, their ability to detect abnormalities in real time and provide appropriate advice is incomplete, limiting their use in security services, where stress management and rapid notification of abnormalities are essential. As a result, there is a need for a means to effectively monitor the health and emotional state of employees, such as security guards.

[1702] 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.

[1703] In this invention, the server includes a data collection unit that acquires physiological data and voice data of a user in real time, a data analysis unit that processes and analyzes the acquired physiological data and voice data to evaluate the health and emotional states, an anomaly detection unit that detects health and emotional abnormalities based on the analysis results from the data analysis unit, a notification unit that notifies a predetermined notification destination based on the abnormality detected by the anomaly detection unit, and a user interface unit that provides specific feedback to the user based on the analysis results. This makes it possible to monitor the health and emotional states of security guards in real time and to provide immediate countermeasures when an abnormality is detected.

[1704] definition statement

[1705] A "data collection unit" is a device that acquires physiological and audio data of a user in real time.

[1706] The "data analysis unit" is a device that processes and analyzes acquired physiological and audio data to assess health and emotional states.

[1707] The "anomaly detection unit" is a device that detects health and emotional abnormalities based on the analysis results from the data analysis unit.

[1708] The "notification unit" is a device that notifies a predetermined notification destination based on an abnormality detected by the abnormality detection unit.

[1709] A "user interface unit" is a device that provides specific feedback to the user based on the analysis results.

[1710] "Audio separation technology" is a technology that extracts only the necessary audio signals from audio data.

[1711] The "voice generation unit" is a device that generates and provides to the user advice on optimal health and emotional state for an individual based on the analysis results.

[1712] MODE FOR CARRYING OUT THE INVENTION

[1713] System Overview

[1714] The system of this invention collects and analyzes physiological and voice data of security guards in real time by having them wear earhook-type wearable devices. The system evaluates the health and emotional state of the security guards and immediately notifies them appropriately if any abnormalities are detected. It also provides specific feedback to the security guards based on the analysis results.

[1715] Hardware used

[1716] Ear hook wearable device: Worn by security guards, it collects physiological and audio data.

[1717] Server: Collects, stores, and analyzes data.

[1718] Smartphone: The device through which security guards receive notifications.

[1719] Software used

[1720] Programming language: Python

[1721] Database: MySQL

[1722] Analysis algorithm: Heart rate variability analysis, voice recognition

[1723] Emotion engine: Deep Learning model (TensorFlow)

[1724] Data exchange and analysis

[1725] In this system, data is exchanged and analyzed through the following steps:

[1726] Terminal handling

[1727] The wearable device collects the guard's heart rate, body temperature, sound, and emotional data in real time, and transmits the collected data to a server at regular intervals. The device also provides feedback to the guard based on the analysis results from the server.

[1728] Server Processing

[1729] The server receives physiological and audio data sent from the device in real time and stores it in a MySQL database. The stored data is then analyzed by an emotion engine using TensorFlow. Specific analysis tasks include heart rate variability analysis, changes in body temperature, audio data segmentation, and emotional state recognition. Health and emotional abnormalities are detected based on the analysis results, and if an abnormality is detected, the server immediately sends a notification to the security guard's smartphone. The notification includes specific advice such as "Your heart rate is high. Please take a break" or "Your stress is increasing. Please try to relax."

[1730] Prompt Sentence Examples

[1731] Use the following as an example of a prompt for a generative AI model:

[1732] "Please display the prediction results of the emotion engine that estimates the user's stress level from their voice data."

[1733] Specific examples

[1734] For example, when security guard A is wearing the earhook device, the device detects his heart rate of 120 bpm, body temperature of 37.5°C, and stress level from his voice. The server receives this data and evaluates his health and emotional state based on the analysis results. If the anomaly detection unit detects an abnormal heart rate and stress level, the server immediately sends a notification to security guard A's smartphone, providing feedback such as, "Your heart rate is high. Please take a break."

[1735] As described above, the system of the present invention effectively supports health and stress management for security guards, and is capable of detecting abnormalities in real time and responding immediately. This system allows security guards to constantly monitor their own health and emotional state and quickly take appropriate measures.

[1736] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1737] Program processing flow

[1738] Step 1: Data collection

[1739] The device collects the guard's heart rate, body temperature, daily sounds, and emotional data.

[1740] Input: Security guard's biometric information (heart rate, body temperature), daily sounds, voice input

[1741] Output: Collected biometric and audio data

[1742] Specific operation: The earhook wearable device collects data using built-in sensors (heart rate sensor, temperature sensor) and microphone. The device prepares to send the data to a server at regular intervals.

[1743] Step 2: Sending data

[1744] The terminal transmits the collected physiological data, audio data, and emotional state data to a server.

[1745] Input: Biometric and audio data collected in step 1

[1746] Output: Data packets sent to the server

[1747] Specific operation: The device sends the collected data to the server at regular intervals using Wi-Fi or mobile data communication.

[1748] Step 3: Save your data

[1749] The server stores the received physiological data and voice data in a database.

[1750] Input: Biometric data and voice data sent from the device

[1751] Output: Data stored in the database

[1752] What happens: The Python script runs a SQL query against the MySQL database to insert the received data.

[1753] Step 4: Data analysis

[1754] The server analyzes the stored data.

[1755] Input: Biometric and audio data stored in a database

[1756] Output: Health and emotional state assessment results

[1757] How it works: A program written in Python uses TensorFlow to analyze audio data to detect emotional states, and runs algorithms that analyze heart rate and temperature data to assess outliers.

[1758] Step 5: Anomaly detection

[1759] The anomaly detection unit detects anomalies based on the analysis results.

[1760] Input: Health and emotional state assessment results

[1761] Output: Anomaly detection results

[1762] Specific operation: If the evaluation result exceeds a specified threshold, the Python code sets an abnormality flag and determines that an abnormality has occurred.

[1763] Step 6: Notification

[1764] The server sends a notification to the security guard's smartphone based on the abnormality detection results.

[1765] Input: Anomaly detection result

[1766] Output: Notification sent to your phone

[1767] Specific operation: The notification function sends an SMS or push notification to the security guard's smartphone informing them of an abnormality and providing specific advice.

[1768] Step 7: Provide feedback

[1769] The terminal provides specific feedback to the security guard based on the analysis results obtained from the server.

[1770] Input: Analysis results and notification content

[1771] Output: Feedback to security guards (audio advice)

[1772] Specific operation: The device uses its voice output function to provide advice to the security guard, such as "Your heart rate is high. Please take a break" or "Your stress is increasing. Please try to relax."

[1773] 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.

[1774] 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.

[1775] 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.

[1776] 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.

[1777] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1778] 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.

[1779] 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).

[1780] 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.

[1781] 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."

[1782] 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.

[1783] 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).

[1784] 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.

[1785] 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.

[1786] 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.

[1787] 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.

[1788] 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.

[1789] 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.

[1790] 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.

[1791] 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.

[1792] 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.

[1793] 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.

[1794] The following is further disclosed regarding the above embodiment.

[1795] (Claim 1)

[1796] a data collection unit for acquiring physiological data and voice data of the user in real time;

[1797] a data analysis unit for processing and analyzing the acquired physiological data and voice data to evaluate a health condition;

[1798] an anomaly detection unit for detecting a health anomaly based on the analysis result from the data analysis unit;

[1799] a notification unit that notifies a predetermined notification destination based on the abnormality detected by the abnormality detection unit;

[1800] A system including:

[1801] (Claim 2)

[1802] 10. The system of claim 1, further comprising means for extracting only required audio signals from the audio data collected by the data collection unit using an audio separation technique.

[1803] (Claim 3)

[1804] 10. The system according to claim 1, further comprising a voice generation unit that generates optimal health advice for an individual based on the results of the analysis performed by the data analysis unit and provides the advice to the user.

[1805] "Example 1"

[1806] (Claim 1)

[1807] a data collection unit for acquiring physiological data and voice data of the user in real time;

[1808] a data communication unit for transmitting the acquired physiological data and audio data to a central computer;

[1809] a data storage unit for storing the transmitted data together with time information;

[1810] a data analysis unit for analyzing the stored physiological data and audio data using a machine learning algorithm to evaluate a health condition;

[1811] an anomaly detection unit for detecting a health anomaly based on the analysis result from the data analysis unit;

[1812] a notification unit that notifies a predetermined notification destination based on the abnormality detected by the abnormality detection unit;

[1813] a feedback unit for providing a user with audio feedback based on the anomaly detection result and the analysis result;

[1814] A system including:

[1815] (Claim 2)

[1816] 10. The system of claim 1, further comprising means for extracting only required audio signals from the audio data collected by the data collection unit using an audio separation technique.

[1817] (Claim 3)

[1818] 10. The system according to claim 1, further comprising a voice generation unit that generates optimal health advice for an individual based on the results of the analysis performed by the data analysis unit and provides the advice to the user.

[1819] "Application Example 1"

[1820] (Claim 1)

[1821] a data collection unit for acquiring physiological data and voice data of the user in real time;

[1822] a data analysis unit for processing and analyzing the acquired physiological data and voice data to evaluate a health condition;

[1823] an anomaly detection unit for detecting a health anomaly based on the analysis result from the data analysis unit;

[1824] a notification unit that notifies a predetermined notification destination based on the abnormality detected by the abnormality detection unit;

[1825] A means of analyzing security risks and detecting stress, abnormal behavior, and dangerous environments;

[1826] means for sending a notification to a user's mobile device when a danger is detected;

[1827] A system including:

[1828] (Claim 2)

[1829] 10. The system of claim 1, further comprising means for extracting only required audio signals from the audio data collected by the data collection unit using an audio separation technique.

[1830] (Claim 3)

[1831] 10. The system according to claim 1, further comprising a voice generation unit that generates optimal health advice for an individual based on the results of the analysis performed by the data analysis unit and provides the advice to the user.

[1832] "Example 2: Combining Emotion Engines"

[1833] (Claim 1)

[1834] a data collection unit for acquiring physiological data and voice data of the user in real time;

[1835] a data analysis unit for processing and analyzing the acquired physiological data and audio data to assess health and emotional states;

[1836] an anomaly detection unit for detecting health anomalies and emotional anomalies based on the analysis results from the data analysis unit;

[1837] a notification unit that notifies a predetermined notification destination based on the abnormality detected by the abnormality detection unit;

[1838] a means for the voice feedback unit to provide specific health advice to the user based on the results of the data analysis unit;

[1839] A system including:

[1840] (Claim 2)

[1841] 10. The system of claim 1, further comprising means for extracting only required audio signals from the audio data collected by the data collection unit using an audio separation technique.

[1842] (Claim 3)

[1843] 10. The system according to claim 1, further comprising a voice generation unit that generates optimal health advice and emotion management advice for an individual based on the results of the analysis by the data analysis unit and provides the advice to the user.

[1844] "Application example 2 when combining emotion engines"

[1845] Claims

[1846] (Claim 1)

[1847] a data collection unit for acquiring physiological data and voice data of the user in real time;

[1848] a data analysis unit for processing and analyzing the acquired physiological data and audio data to assess health and emotional states;

[1849] an anomaly detection unit for detecting health anomalies and emotional anomalies based on the analysis results from the data analysis unit;

[1850] a notification unit that notifies a predetermined notification destination based on the abnormality detected by the abnormality detection unit;

[1851] a means for the user interface unit to provide specific feedback to the user based on the analysis results;

[1852] A system including:

[1853] (Claim 2)

[1854] 10. The system of claim 1, further comprising means for extracting only required audio signals from the audio data collected by the data collection unit using an audio separation technique.

[1855] (Claim 3)

[1856] 10. The system according to claim 1, further comprising a voice generation unit that generates and provides to the user advice regarding optimal health and emotional states for an individual based on the results of the analysis by the data analysis unit. [Explanation of symbols]

[1857] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a data collection unit for acquiring physiological data and voice data of the user in real time; a data analysis unit for processing and analyzing the acquired physiological data and voice data to evaluate a health condition; an anomaly detection unit for detecting a health anomaly based on the analysis result from the data analysis unit; a notification unit that notifies a predetermined notification destination based on the abnormality detected by the abnormality detection unit; A system including:

2. 2. The system of claim 1, further comprising means for extracting only a required audio signal from the audio data collected by the data collection unit using an audio separation technique.

3. The system according to claim 1 , further comprising a voice generation unit that generates optimal health advice for an individual based on the results of the analysis performed by the data analysis unit and provides the advice to the user.

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