System

Abstract

A system is provided.SOLUTION: A system comprising: sensor means for collecting vital data of a user; analysis means for detecting a sudden change in the vital data; notification means for sending a notification to the user when the sudden change is detected; interaction means for receiving a response of the user and obtaining an emotional state of the user; generation means for generating a message according to a method of Anger management based on the response; and message sending means for sending the generated message to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, stress in daily life and the workplace often leads to feelings of anger that cannot be controlled. This can result in poor mental health and a decline in quality of life. Traditional anger management methods rely on individual sessions and self-study, making it difficult to respond in real time. There is a need to solve these problems and provide an effective system that helps users properly manage their anger and maintain their mental health. [Means for solving the problem]

[0005] The present invention solves the above problems by the following means. It provides a sensor means for collecting vital data of a user and includes an analysis means for detecting sudden fluctuations in the vital data. It also provides a notification means for sending a notification to the user based on the information when a sudden fluctuation is detected. It further includes an interaction means for receiving a response from the user and acquiring the user's emotional state, and a generation means for generating a message in accordance with an anger management method based on the response. Finally, it includes a message sending means for sending the generated message to the user. This configuration allows the user to recognize their own emotional fluctuations in real time and take appropriate countermeasures, thereby enabling effective anger management.

[0006] The term "sensor means" refers to a device or module for collecting vital data of a user.

[0007] "Analysis means" refers to software and hardware used to analyze collected vital data and detect sudden fluctuations in the data.

[0008] "Notification means" refers to a device or function for sending a notification to the user when emotional fluctuations are detected.

[0009] "Interaction means" refers to the interface or protocol for receiving responses from the user and understanding the user's emotional state based on that information.

[0010] "Generation means" refers to software or a system for generating a message in accordance with an anger management method based on the user's response.

[0011] "Message sending means" refers to communication equipment or software for sending the generated message to the user.

[0012] "Vital data" refers to data that indicates the physiological state of the user, such as heart rate, galvanic skin response, and body temperature.

[0013] "Emotional fluctuations" refers to a state in which a sudden change is observed in a user's vital data and the change suggests an emotional change.

[0014] "Anger management" refers to psychological training and techniques that help users appropriately control their anger and maintain their mental health. [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 following describes in detail an embodiment of the present invention. The present invention is a system that monitors a user's vital signs, detects emotional fluctuations, and provides an anger management technique. This system is composed of a wearable device (terminal), a central server, and a user.

[0037] 1. Data Collection and Monitoring

[0038] The device is equipped with multiple sensors, including a heart rate sensor, a skin galvanic response sensor, and a body temperature sensor, and uses these sensors to collect the user's vital data in real time. Data collection is set to a high frequency, with data recorded almost every second.

[0039] 2. Data fluctuation analysis and notification

[0040] The device has a built-in program for analyzing the collected vital data. For example, this program compares the average heart rate over the past minute with the heart rate over the past 10 seconds, and if it detects a sudden change (for example, an increase of 10 bpm or more over 10 seconds), it determines that this indicates emotional fluctuations.

[0041] If an emotional shift is detected, the device sends this information to a server, which analyzes the received data and further verifies whether an emotional shift is present.

[0042] 3. User Interaction

[0043] If an emotional change is detected, the server sends a push notification to the device. This notification contains a message such as "Please tell us how you feel right now." The user receives the push notification and selects their own emotion (e.g., anger, sadness, stress, etc.).

[0044] 4. Generate and provide countermeasures

[0045] The emotional information selected by the user is sent from the device to the server. Based on the received information, the server uses a generative AI model to generate a message that follows anger management methods. For example, it generates advice such as, "Practice the six-second rule. Take six deep breaths to regain your composure."

[0046] The generated message is sent from the server to the terminal, which displays the message to the user.

[0047] 5. Practical Use Cases

[0048] As a concrete example, let's say a user is feeling stressed at work. The device detects a sudden increase in heart rate and sends this information to the server. The server checks the emotional fluctuations and sends a push notification to the device saying, "Tell us how you're feeling right now." If the user selects "anger," that information is sent to the server. The server uses a generative AI model to generate a message saying, "Practice the 6-second rule," and sends it to the device. The device displays this message to the user, who then takes a deep breath to reduce stress.

[0049] In this way, the present invention allows users to recognize their own emotional fluctuations in real time and practice appropriate anger management.

[0050] The processing flow will be explained below.

[0051] Step 1:

[0052] The device collects vital data such as the user's heart rate, skin galvanic response, and body temperature. These data are measured every second using sensors and temporarily stored in the internal memory.

[0053] Step 2:

[0054] The device analyzes the stored data at regular intervals (for example, every minute), compares the average heart rate over the past minute with the heart rate over the past 10 seconds, and detects any sudden fluctuations.

[0055] Step 3:

[0056] When the device detects a sudden change in data, it sends the data to the server, including a timestamp, the heart rate at the time of detection, and changes in the electrical response of the skin.

[0057] Step 4:

[0058] The server then analyzes the received data to further determine whether there is any emotional impact, applying machine learning algorithms using multiple data points to determine whether there is any emotional impact.

[0059] Step 5:

[0060] If the server detects any emotional changes, it sends a push notification to the device, which includes the message "Please tell us how you feel right now."

[0061] Step 6:

[0062] Users receive a push notification from their device and can select their emotions, with options including "anger," "sadness," and "stress."

[0063] Step 7:

[0064] The terminal transmits the emotion information selected by the user to the server, and the transmitted data includes the selected emotion, a timestamp, and related vital data.

[0065] Step 8:

[0066] Based on the emotion data received by the server, a generative AI model is used to generate messages based on anger management techniques, such as "Practice the six-second rule. Taking a deep breath will help you regain your composure."

[0067] Step 9:

[0068] The server generates and sends the message to the terminal, which contains specific advice or instructions for the user.

[0069] Step 10:

[0070] The device displays the received message to the user, who then checks the message and takes the necessary action (e.g., take a deep breath).

[0071] In this way, by following specific actions at each step, users can recognize their own emotional fluctuations in real time and practice appropriate anger management.

[0072] Example 1

[0073] 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."

[0074] Conventional emotion monitoring systems have not adequately provided users with a means to recognize their own emotional fluctuations in real time and practice appropriate anger management. Furthermore, they have been unable to respond appropriately to sudden emotional fluctuations, making it difficult for users to manage their emotions. This has resulted in a lack of effective countermeasures for sudden emotional changes, making it difficult to appropriately control emotions such as stress and anger. The present invention aims to solve these problems.

[0075] 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.

[0076] In this invention, the server includes sensor means for collecting biometric data of a user, analysis means for detecting sudden fluctuations in the biometric data, notification means for sending a notification to the user when the sudden fluctuation is detected, dialogue means for receiving a response from the user and acquiring the user's emotional state, generation means for generating a message according to an anger management method using a generative model based on the response, and message transmission means for sending the generated message to the user. This enables the user to recognize their own emotional fluctuations in real time and practice appropriate anger management.

[0077] "Sensor means" is a general term for devices and functions used to collect biometric data of a user.

[0078] "Analysis means" is a general term for devices and functions for detecting and analyzing sudden fluctuations in collected biological data.

[0079] "Notification means" is a general term for devices and functions for sending a notification to a user when a sudden change in biometric data is detected.

[0080] "Dialogue means" is a general term for devices and functions for receiving responses from users and acquiring their emotional state.

[0081] "Generation means" is a general term for devices and functions for creating messages in accordance with anger management methods using a generative model based on a user's response.

[0082] "Message sending means" is a general term for devices and functions for sending generated messages to users.

[0083] "Biometric data" refers to data that reflects a user's physical state, such as a user's heart rate, galvanic skin response, or body temperature.

[0084] A "generative model" refers to an algorithm or system that automatically generates anger management messages based on user responses.

[0085] The following is a detailed description of an embodiment of the present invention. This system collects biometric data, detects emotional fluctuations, and provides users with an anger management method suited to their needs. The system is primarily composed of three elements: a terminal, a server, and a user.

[0086] Data Collection and Monitoring

[0087] The device uses multiple sensors, including a heart rate sensor, a skin galvanic response sensor, and a body temperature sensor, to collect biometric data in real time. These sensor means measure the user's heart rate, skin galvanic response, body temperature, etc., at a frequency of once per second. The collected data is stored in the device and monitored in real time by an analysis means to check for any sudden fluctuations.

[0088] Data fluctuation analysis and notification

[0089] A program built into the device analyzes the collected biometric data. Specifically, it compares the average heart rate over the past minute with the heart rate over the last 10 seconds, and evaluates whether there are any sudden fluctuations (for example, an increase of 10 bpm or more over 10 seconds). If a sudden fluctuation is detected, the device interprets it as an emotional change and sends the data to a server. The server then reanalyzes the received data to confirm whether the fluctuations are continuing.

[0090] User Interaction

[0091] If an emotional change is detected, the server sends a push notification to the device. The notification contains a message such as "Please tell us how you feel right now." The device displays the notification to the user. The user receives the notification and selects their emotional state (e.g., anger, sadness, stress, etc.).

[0092] Generate and provide countermeasures

[0093] Once the user selects an emotion, that information is sent to the server via the device. The server uses a generative model based on the received emotion information to generate an appropriate anger management response. The generative AI model is provided with prompts such as:

[0094] Emotional signals detected. User selected "Anger." Generate appropriate anger management advice.

[0095] The generated message (e.g., "Practice the 6-second rule. Taking a deep breath for 6 seconds will help you regain your composure") is sent from the server to the device, which then displays the message to the user.

[0096] Practical use cases

[0097] As a concrete example, consider a situation where a user is feeling stressed at work. The device detects a sudden increase in heart rate and sends this information to the server. The server checks the emotional fluctuations and sends a push notification to the device asking, "Tell us how you're feeling right now." If the user selects "anger," that information is sent to the server. The server uses a generative AI model to generate a message such as "Practice the 6-second rule" and sends it to the device. The device displays this message to the user, who then takes a deep breath to reduce stress.

[0098] In this way, the present invention allows users to recognize their own emotional fluctuations in real time and practice appropriate anger management.

[0099] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0100] Step 1:

[0101] The device collects biometric data in real time using a heart rate sensor, a skin galvanic response sensor, and a body temperature sensor. Each sensor collects data once per second and stores the data in the device's memory. The input is the data from the sensors (heart rate, skin galvanic response, body temperature), and the output is an array of the collected biometric data.

[0102] Step 2:

[0103] The device analyzes the collected biometric data using a built-in program. It calculates the average heart rate over the past minute and compares it with the heart rate over the most recent 10 seconds. The input is the heart rate data over the past minute and the heart rate data over the most recent 10 seconds, and the output is the difference between the average heart rate. Specifically, the device calculates the average of the data and compares the results.

[0104] Step 3:

[0105] The device checks whether there is a sudden change (for example, an increase of 10 bpm or more in 10 seconds) based on the analysis results of step 2. If a sudden change is detected, this information is sent to the server. The input is the difference in heart rate, and the output is a flag indicating that a sudden change has been detected.

[0106] Step 4:

[0107] The server receives the data sent from the device and re-analyzes it. The analysis involves evaluating additional data to determine whether the fluctuation is incidental or sustained. The input is the vital data sent from the device and a flag indicating a sudden change, and the output is the confirmation of the emotional fluctuation.

[0108] Step 5:

[0109] When the server confirms the emotional fluctuation, it sends a push notification to the device. The notification contains the message "Please tell us how you are feeling right now." The input is the confirmation result of the emotional fluctuation, and the output is the sending of the push notification. Specifically, the server generates a notification and pushes it to the device.

[0110] Step 6:

[0111] A user receives a push notification and selects an emotional state. Example choices include "anger," "sadness," "stress," etc. The input is the push notification, and the output is the user-selected emotional state.

[0112] Step 7:

[0113] The device transmits the emotion information selected by the user to the server. The input is the user's emotion selection, and the output is the transmission of the emotion information to the server. In concrete terms, the device executes code to transmit the information selected by the user to the server.

[0114] Step 8:

[0115] The server receives the emotional information and sends a prompt to the generative AI model based on that information. An example of a prompt is, "A signal indicating emotional fluctuation has been detected. The user selected 'anger'. Please generate appropriate anger management advice." The input is the user's emotional information, and the output is the prompt sent to the generative AI model.

[0116] Step 9:

[0117] The generative AI model generates anger management messages based on prompt sentences. An example of a generated message is "Practice the six-second rule. Taking six deep breaths will help you regain your composure." The input is the prompt sentence, and the output is the generated advice message.

[0118] Step 10:

[0119] The server sends the generated advice message to the device. The input is the advice message from the generative AI model, and the output is the message sent to the device. Specifically, the server generates the advice message and executes the code to send it to the device.

[0120] Step 11:

[0121] The terminal displays the advice message sent from the server to the user. The input is the advice message from the server, and the output is the message displayed to the user. Specifically, the terminal displays the received message on the screen.

[0122] (Application example 1)

[0123] 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."

[0124] Security personnel are frequently exposed to high levels of stress and tension, and therefore require emotional stability. It is necessary to improve work efficiency and maintain mental stability by detecting emotional fluctuations using biometric data and providing appropriate countermeasures in real time. However, conventional systems are limited to detecting fluctuations in vital data and do not go as far as managing emotions or proposing actual countermeasures. Therefore, a system that can quickly respond to personnel's stress and emotional fluctuations and provide appropriate advice is needed.

[0125] 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.

[0126] In this invention, the server includes a sensor means for collecting biometric data of a user, an analysis means for detecting sudden fluctuations in the biometric data, a notification means for sending a notification to the user when the sudden fluctuation is detected, a dialogue means for receiving a response from the user and acquiring the user's emotional state, a generation means for generating a message according to an emotion management method based on the response, a message transmission means for sending the generated message to the user, a generation means for generating a message using a generative AI model based on emotion information to manage fluctuations in the biometric data of security personnel and provide appropriate countermeasures, and a display means for displaying the generated message to the security personnel. This makes it possible to immediately detect sudden fluctuations in the security personnel's biometric data and provide appropriate anger management methods, thereby quickly responding to the security personnel's stress and emotional fluctuations.

[0127] "User" refers to an individual who uses the system of the present invention.

[0128] "Biometric data" refers to data that indicates the user's physical condition, such as heart rate, galvanic skin response, and body temperature.

[0129] "Sensor means" refers to a device for collecting biometric data of a user.

[0130] "Analysis means" refers to a method or device for analyzing collected biometric data and detecting sudden fluctuations therein.

[0131] "Notification means" refers to a device or method for reporting to the user when a sudden change in biometric data is detected.

[0132] "Interaction means" refers to a method or device for receiving responses and obtaining emotional states from a user.

[0133] "Emotional state" refers to a state that indicates the emotions such as anger, sadness, stress, etc. that a user is feeling.

[0134] "Generator" refers to a method or device for automatically creating a message that conforms to emotion management techniques based on a user's response.

[0135] "Message sending means" refers to a means for sending a generated message to a user.

[0136] "Display means" refers to a device or method for a user to view the generated message.

[0137] "Official personnel" refers to people engaged in security work.

[0138] A "generative AI model" refers to a model that uses artificial intelligence to analyze data and generate messages.

[0139] The present invention provides a system that monitors a user's biometric data in real time, detects emotional fluctuations based on that data, and provides an anger management technique. This system is configured to include a sensor, an analysis means, a notification means, a dialogue means, a generation means, and a display means.

[0140] 1. Devices and Data Collection

[0141] The sensor means is installed in smart glasses (such as Google Glass) worn by security personnel, and includes a heart rate sensor, a skin galvanic response sensor, and a body temperature sensor, which collect the personnel's biometric data. The biometric data is collected in real time and recorded in seconds.

[0142] 2. Data Analysis

[0143] The analysis is performed by software (e.g., Python scripts) embedded in the smart glasses, which analyzes the collected biometric data and detects sudden changes (e.g., an increase in heart rate). When these changes are detected, the data is sent to a cloud server using AWS IoT Core.

[0144] 3. Emotional awareness and notification

[0145] The data sent to the cloud server is again analyzed by the analysis means to check for any sudden changes in emotion. If any emotional fluctuations are detected, the notification means sends a push notification to the smart glasses. This notification is a message based on the dialogue means, such as "Please tell us how you are feeling right now."

[0146] 4. Acquiring emotional states

[0147] The worker responds to the push notification and selects their emotional state (e.g., anger, sadness, stress). This emotional information is then sent back to the cloud server.

[0148] 5. Generating strategies for anger management

[0149] After receiving the emotion information, the server uses a generative AI model (such as the OpenAI API) to generate an appropriate message based on emotion management techniques, such as a prompt such as "Take a deep breath for six seconds."

[0150] 6. Sending and Viewing Messages

[0151] The message generated by the generating means is again transmitted to the smart glasses and displayed by the display means to the worker, thereby enabling the worker to implement the provided countermeasure in real time.

[0152] Specific examples

[0153] Suppose a security worker experiences a sudden rise in heart rate during a long shift, putting him or her in a stressful situation. The smart glasses detect this sudden change and send a notification asking the worker to "Enter your current emotions." If the worker selects "Stress," the cloud server generates a countermeasure, and the message "Take six deep breaths" is displayed on the smart glasses.

[0154] Prompt Sentence Examples

[0155] "A sudden change in vital signs has been detected. The immediate emotional state is anger. Please provide appropriate anger management advice."

[0156] As described above, the present invention is a system that monitors a user's biometric data in real time, instantly detects emotional fluctuations, and provides countermeasures, thereby maintaining the mental health of security personnel and improving work efficiency and safety.

[0157] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0158] Step 1:

[0159] The device (smart glasses) collects the wearer's biometric data (heart rate, electrodermal response, body temperature). The biometric data collected in real time by sensors is the input, and this data is recorded in local storage every second.

[0160] Step 2:

[0161] The device's internal analysis means analyzes the collected biometric data. Specifically, a Python script is used to compare the average heart rate over the past minute with the heart rate over the last 10 seconds. If a sudden change (for example, an increase of 10 bpm or more over 10 seconds) is detected, this information is sent to a server using AWS IoT Core. The input is the collected biometric data, and the output is data indicating the sudden change.

[0162] Step 3:

[0163] The server receives the data sent from the terminal and performs further detailed analysis. The server also uses analysis software to check for sudden fluctuations in the data. Once detected, a notification is sent to the terminal using a notification means. The input is the data sent from the terminal, and the output is the notification message to be sent.

[0164] Step 4:

[0165] The device notifies the user of the notification received from the server. It displays a push notification with a message such as "Please tell us your current emotions." The user checks the notification and selects their emotional state (anger, sadness, stress, etc.). The input is the notification from the server, and the output is the emotional information selected by the user.

[0166] Step 5:

[0167] The user's emotional information is again sent from the device to the server. Once the emotional information reaches the server, the server uses a generative AI model to generate an anger management response. An example of a prompt sentence used here is, "A sudden change in vital data has been detected. The current emotional state is anger. Please provide appropriate anger management advice." The input is the user's emotional information, and the output is the generated response message.

[0168] Step 6:

[0169] The message generated by the server is sent back to the terminal. The terminal displays the received message to the user. For example, advice such as "Take a deep breath for six seconds" is displayed. The input is the generated message from the server, and the output is the message displayed to the user.

[0170] As described above, this system monitors users' biometric data in real time and provides appropriate countermeasures when emotional fluctuations are detected, thereby helping to maintain the mental health of security personnel and improving work efficiency.

[0171] 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.

[0172] The following describes in detail the mode for carrying out the present invention. The present invention is a system that monitors a user's vital signs, detects emotional fluctuations, and provides anger management techniques. Furthermore, by combining it with an emotion engine, it is possible to more accurately recognize the user's emotional state and provide optimal countermeasures. This system is composed of a wearable device (terminal), a central server (server), an emotion engine, and a user (user).

[0173] 1. Data Collection and Monitoring

[0174] The device is equipped with multiple sensors, including a heart rate sensor, a skin galvanic response sensor, and a body temperature sensor, and uses these sensors to collect the user's vital data in real time. Data collection is set to a high frequency, with data recorded almost every second.

[0175] 2. Data fluctuation analysis and notification

[0176] The device has a built-in program for analyzing the collected vital data. For example, this program compares the average heart rate over the past minute with the heart rate over the last 10 seconds, and if a sudden change (e.g., an increase of 10 bpm or more over 10 seconds) is detected, it determines that this indicates emotional fluctuations. If emotional fluctuations are detected, the device sends this information to a server. The server analyzes the received data and further verifies whether emotional fluctuations are present.

[0177] 3. User Interaction and Emotion Recognition

[0178] If an emotional change is detected, the server sends a push notification to the device. This notification contains a message such as "Please tell us how you feel right now." When the user receives the push notification and selects their emotion (e.g., anger, sadness, stress, etc.), the selected emotion information is sent from the device to the server. Furthermore, the emotion engine also analyzes the user's response data and selects the most appropriate emotion from multiple emotion categories.

[0179] The emotion engine has the ability to analyze the user's voice and text data, which allows for more accurate emotion recognition. For example, if a user is yelling, the voice data can be analyzed and the emotion can be recognized as "anger."

[0180] 4. Generate and provide countermeasures

[0181] The server uses a generative AI model to generate messages based on anger management techniques based on the emotion data received from the emotion engine. For example, it generates advice such as, "Practice the six-second rule. Take six deep breaths to regain your composure." The generated message is sent from the server to the device, which then displays it to the user.

[0182] 5. Practical Use Cases

[0183] As a concrete example, suppose a user is feeling stressed at work. The device detects a sudden increase in heart rate and sends this information to the server. The server checks the emotional fluctuations and sends a push notification to the device saying, "Tell us how you're feeling right now." If the user selects "anger," that information is sent to the server. At the same time, the emotion engine also analyzes the user's voice and determines that it is "anger." The server uses the generative AI model to generate a message saying, "Practice the 6-second rule," and sends it to the device. The device displays this message to the user, who then takes a deep breath to reduce stress.

[0184] In this way, the present invention allows users to recognize their own emotional fluctuations in real time and utilize an emotion engine to enable more accurate emotion recognition and appropriate anger management.

[0185] The processing flow will be explained below.

[0186] Step 1:

[0187] The device collects vital data such as the user's heart rate, skin galvanic response, and body temperature. The sensor measures the data every second and temporarily stores it in the internal memory.

[0188] Step 2:

[0189] The device analyzes the saved vital data at regular intervals (for example, every minute). For example, it compares the average heart rate over the past minute with the heart rate over the past 10 seconds to detect any sudden fluctuations.

[0190] Step 3:

[0191] When the device detects a sudden change in data, it sends the data to the server, including a timestamp, the heart rate at the time of detection, and the amount of change in the electrical response of the skin.

[0192] Step 4:

[0193] The server then analyzes the received data to further determine if any emotional swings are present, applying machine learning algorithms using multiple data points to determine emotional swings.

[0194] Step 5:

[0195] If the server detects any emotional changes, it sends a push notification to the device, which includes the message "Please tell us how you feel right now."

[0196] Step 6:

[0197] Users receive a push notification from their device and can select their emotion with one tap. Options include "anger," "sadness," and "stress."

[0198] Step 7:

[0199] The device transmits the selected emotion information to the server, including the user's selected emotion, a timestamp, and related vital data.

[0200] Step 8:

[0201] The server sends the received emotion data to the emotion engine, which analyzes the user's selected emotion data and vital data to more accurately recognize the user's emotional state.

[0202] Step 9:

[0203] The emotion engine recognizes the user's emotional state and sends the result back to the server, for example, "anger."

[0204] Step 10:

[0205] Based on the recognition results from the emotion engine, the server uses a generative AI model to generate messages that follow anger management methods, such as advice like "Practice the six-second rule. Taking six deep breaths will help you regain your composure."

[0206] Step 11:

[0207] Sends server-generated messages to the terminal, containing specific advice or instructions to the user.

[0208] Step 12:

[0209] The device displays the received message to the user, who then checks the message and takes the necessary action (e.g., take a deep breath).

[0210] As a result, users can recognize their own emotional fluctuations in real time, and with the help of the emotion engine, they can perform more accurate emotion recognition and practice appropriate anger management.

[0211] Example 2

[0212] 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."

[0213] Currently, there is no system that can monitor users' emotional fluctuations in real time and quickly provide appropriate countermeasures. In particular, there is a need for a system that can detect sudden changes in emotions and provide appropriate anger management techniques based on those changes. It is also difficult to accurately recognize a user's emotional state and immediately suggest countermeasures using push notifications. This makes it difficult to provide effective means for maintaining users' mental health.

[0214] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0215] In this invention, the server includes detection means for collecting biometric data of a user, analysis means for detecting sudden fluctuations in the biometric data, warning means for sending a notification to the user when the sudden fluctuation is detected, communication means for receiving a response from the user and acquiring the user's emotional state, analysis means for analyzing the emotional data based on the response using an emotion engine, generation means for generating a message according to an anger management method using a generative AI model, and transmission means for sending the generated message to the user. This makes it possible to accurately recognize the user's emotional fluctuations in real time and provide appropriate anger management messages based on the emotional fluctuations.

[0216] "User" means any person or entity that uses the system.

[0217] "Biometric data" refers to data relating to the user's physical condition, such as heart rate, galvanic skin response, and body temperature.

[0218] "Detection means" refers to a device or sensor for collecting biometric data of a user.

[0219] "Analysis means" refers to the program and hardware used to analyze collected biological data and detect sudden changes.

[0220] The "warning means" is a function for sending a notification to the user when a sudden change is detected.

[0221] "Communication means" refers to the interface or protocol for receiving the user's responses and obtaining their emotional state.

[0222] "Analysis means" refers to software or hardware that uses an emotion engine to analyze the user's response data (voice data or text data) and recognize the user's emotional state.

[0223] "Generation means" refers to a function for using a generative AI model to create messages that follow anger management methods.

[0224] "Vehicle" refers to the functionality and interface for sending generated messages to users.

[0225] An "emotion engine" is an algorithm and software that analyzes a user's emotions and recognizes their optimal emotional state.

[0226] A "generative AI model" is a machine learning model that generates optimal anger management messages based on the user's emotional state.

[0227] "Push notification" refers to a protocol and method for sending notifications to devices in real time.

[0228] "Emotional fluctuation" is a term that indicates a state in which a sudden change occurs in the user's biometric data.

[0229] This invention is a system that monitors a user's biometric data in real time and provides appropriate anger management countermeasures based on changes in the biometric data using an emotion engine and a generative AI model. This system consists of a wearable device (terminal), a central server (server), an emotion engine, and a user (user).

[0230] The device is equipped with multiple sensors, including a heart rate sensor, a skin galvanic response sensor, and a body temperature sensor. These sensors collect the user's vital data every second. Specifically, the heart rate sensor measures heart rate data every second, and the skin galvanic response sensor records the user's stress level in real time.

[0231] The server receives and analyzes the biometric data sent from the device. Specifically, it compares the average heart rate over the past minute with the heart rate over the past 10 seconds to detect any sudden fluctuations. For example, if the heart rate rises by 10 bpm or more over 10 seconds, it is determined that there is an emotional fluctuation.

[0232] When an emotional change is detected, the server sends a push notification to the device. This notification contains a message such as "Please tell us how you feel right now." When the user receives the push notification and selects their emotion (e.g., anger, sadness, stress, etc.), that information is sent from the device to the server.

[0233] The server uses a generative AI model to generate anger management messages based on the received emotional data and the user's voice and text data analyzed by the emotion engine. For example, it might generate advice such as, "Practice the six-second rule. Taking a deep breath for six seconds will help you regain your composure."

[0234] The generated message is sent from the server to the device, which then displays it to the user, who can then take action such as deep breathing to reduce stress and emotional upheaval.

[0235] As a specific use case, consider a case where a user is experiencing high stress at work. The device detects a sudden increase in heart rate and sends this information to the server. After the server confirms the emotional fluctuations, it sends a push notification to the device. If the user selects "anger," this information is sent to the server. At the same time, the emotion engine analyzes the user's voice and determines it to be "anger." The server uses a generative AI model to generate a message saying, "Practice the 6-second rule," and sends it to the device. The device displays this message to the user, who then takes a deep breath to reduce stress.

[0236] In this way, the present invention realizes a system that allows users to recognize their own emotional fluctuations in real time and provides more accurate emotion recognition and appropriate anger management by utilizing an emotion engine and generative AI model.

[0237] An example prompt is:

[0238] "The user is currently experiencing deep stress and their heart rate has increased by more than 10 bpm in 10 seconds. Generate anger management advice if the user selects anger."

[0239] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0240] Step 1: Collect biometric data

[0241] The terminal collects the user's biometric data using sensors (heart rate sensor, skin galvanic response sensor, body temperature sensor, etc.) built into the wearable device. The data measured by the sensors is periodically recorded in a database within the terminal. The input here is the user's current physical condition, and the output is the collected vital data (heart rate, skin galvanic response, body temperature).

[0242] Step 2: Analyzing biological data

[0243] The device runs a program to analyze the collected vital data. Specifically, it compares the average heart rate over the past minute with the heart rate over the last 10 seconds. Based on this comparison, it determines that a sudden change has been detected, such as when the heart rate increases by 10 bpm or more over a 10-second period. The input is the collected vital data, and the output is the detected change.

[0244] Step 3: Emotional Indications

[0245] If the device detects a change in emotion, it sends that information to the server. The server analyzes the received data, and if an emotion change is confirmed, it sends a push notification to the device. The notification contains the message "Please tell us how you are feeling right now." The input is the change detection result, and the output is the sending of a push notification.

[0246] Step 4: Obtaining the user's emotional response

[0247] The user receives a push notification and selects their emotion (e.g., anger, sadness, stress, etc.). The selected emotion information is sent from the device to the server. The input here is the user's emotional response, and the output is the transmitted emotion information.

[0248] Step 5: Sentiment Analysis

[0249] The server runs an emotion engine and analyzes the user's response data (voice data and text data). For example, it recognizes the user's optimal emotional state from their tone of voice and vocabulary. The input is the user's emotional response and voice data, and the output is the specific emotion recognition result.

[0250] Step 6: Create an Anger Management Message

[0251] The server uses a generative AI model to generate anger management messages based on the emotion recognition results obtained from the emotion engine. For example, it creates advice such as, "Practice the six-second rule. Take six deep breaths to regain your composure." The input is the emotion recognition results, and the output is the generated message.

[0252] Step 7: Sending a message

[0253] The server generates a message and sends it to the terminal, which displays it to the user. The input here is the generated message, and the output is the message displayed to the user.

[0254] Step 8: User Action

[0255] The user takes specific coping actions (e.g., deep breathing) in accordance with the anger management message displayed on the device. This allows the user to control their emotions. The input here is the displayed message, and the output is the user's response.

[0256] (Application example 2)

[0257] 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."

[0258] Conventional systems for security staff lack the means to detect emotional fluctuations in real time and immediately provide appropriate anger management methods, which results in security staff being unable to perform their duties effectively under high stress.

[0259] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: sensor means for collecting vital data of the user; analysis means for detecting sudden fluctuations in the vital data; notification means for sending a notification to the user when the sudden fluctuation is detected; dialogue means for receiving the user's response and acquiring the user's emotional state; generation means for generating a message according to an anger management method based on the response; message sending means for sending the generated message to the user; emotion analysis means for analyzing the emotional state; generative AI model utilization means for generating a message using a generative AI model based on the emotional state analyzed by the emotion analysis means and a prompt sentence; and display means for displaying the generated message on the user's device. This enables security staff to recognize their own emotional fluctuations in real time and immediately practice an appropriate anger management method.

[0260] "Sensor means" refers to a device used to collect vital data of a user.

[0261] "Analysis means" refers to a device or program used to detect sudden fluctuations in collected vital data.

[0262] A "notification means" is a device or program used to send a notification to a user when a sudden change is detected.

[0263] An "interactive means" is a device or program for receiving a user's response and obtaining the user's emotional state.

[0264] The "generating means" is a device or program for generating a message in accordance with an anger management method based on the user's response.

[0265] The "message sending means" is a device or program for sending the generated message to the user.

[0266] "Emotion analysis means" is a device or program for analyzing the emotional state of a user.

[0267] "Generative AI model utilization means" refers to a device or program that uses a prompt sentence to generate a message using a generative AI model based on the emotional state analyzed by the emotion analysis means.

[0268] A "display means" is a device or program for displaying the generated message on a user's device.

[0269] overview

[0270] This invention is a system that monitors emotional fluctuations in real time and provides anger management techniques for security staff. The system collects users' vital data, analyzes sudden fluctuations, and uses a generative AI model to suggest appropriate countermeasures when emotional fluctuations are detected.

[0271] Hardware and Software Configuration

[0272] The system consists of the following main components:

[0273] 1. Sensor means:

[0274] Hardware: Smartwatches, smart glasses, and other wearable devices.

[0275] Function: Collects heart rate, galvanic skin response, and body temperature in real time.

[0276] 2. Analysis method:

[0277] Software: Python programs, data analysis libraries (e.g., pandas, numpy).

[0278] Function: Detects sudden fluctuations in collected vital data.

[0279] 3. Means of notification:

[0280] Hardware / Software: Mobile devices, push notification services.

[0281] Function: Notify the user of emotional fluctuations.

[0282] 4. Means of interaction:

[0283] Software: Conversational interfaces (e.g., chatbots).

[0284] Function: Receives the user's response and captures their emotional state.

[0285] 5. Emotion analysis means:

[0286] Software: Speech analysis engine, text analysis engine (e.g., Google Cloud Speech-to-Text, BERT).

[0287] Function: Analyzes the user's emotional state.

[0288] 6. Generation means:

[0289] Software: Generative AI models (e.g., GPT-3).

[0290] Function: Generates anger management messages using prompts based on emotional state.

[0291] 7. Message sending method:

[0292] Hardware: Mobile devices, wearable devices.

[0293] Function: Displays the generated message.

[0294] Processing flow

[0295] The specific process is as follows:

[0296] 1. Data Collection:

[0297] The sensor means collects vital data such as the user's heart rate, galvanic skin response, body temperature, etc. The data is collected in real time and updated every second.

[0298] 2. Data Analysis:

[0299] The analysis method detects sudden fluctuations in vital data. For example, it compares the average heart rate over the past minute with the heart rate over the past 10 seconds, and if a sudden fluctuation is detected, it determines whether the person is emotionally unstable.

[0300] 3. Notice:

[0301] The notification means sends a push notification to the user, displaying a message saying, "Please tell us how you feel right now."

[0302] 4. Getting user response:

[0303] The user selects their emotion (e.g., anger, sadness, stress, etc.) and responds through a dialogue means. This information is sent to the server.

[0304] 5. Emotion analysis:

[0305] The emotion analysis means analyzes the user's voice data and text data to identify the user's emotional state. For example, if the user is yelling, the voice data is analyzed and the emotion is recognized as "anger."

[0306] 6. Message Creation:

[0307] The generator uses a generative AI model to generate advice based on the emotional state, using prompts such as, "Practice the six-second rule. Take six deep breaths to regain your composure."

[0308] 7. Sending a message:

[0309] A message sending means displays the generated message on the user's device.

[0310] Specific examples

[0311] Consider a situation where a security staff member experiences stress during work and their heart rate suddenly rises. The system detects this change in heart rate and sends a notification to the user. If the user responds with "Anger," the server uses the generative AI model to generate advice such as "Practice the 6-second rule," which is displayed on the user's device.

[0312] Example prompts for generative AI models

[0313] A user's vital data (heart rate: x bpm, galvanic skin response: y, body temperature: z°C) has been collected. A sudden fluctuation has been detected compared to past data, indicating a change in emotion. Analysis by the emotion engine has identified the emotion "anger." Please generate appropriate anger management advice to help the user regain their composure. Please also include specific example messages.

[0314] This allows security staff to recognize their own emotional fluctuations in real time and immediately implement appropriate anger management methods.

[0315] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0316] Step 1:

[0317] Collect user vital data

[0318] Operation: The device (sensor means) collects vital data such as the user's heart rate, skin galvanic response, and body temperature in real time.

[0319] Input: User vital data (heart rate, galvanic skin response, body temperature) obtained from sensors connected to the device.

[0320] Output: Collected vital data (heart rate: x bpm, galvanic skin response: y, temperature: z°C).

[0321] Step 2:

[0322] Analyzing fluctuations in vital data

[0323] Operation: The terminal (analysis means) analyzes the collected vital data and compares the past data with the latest data to detect sudden fluctuations in heart rate.

[0324] Input: Vital data collected in step 1.

[0325] Data processing: Compare the average heart rate over the past minute with the heart rate over the last 10 seconds, and measure whether there is a fluctuation of more than 10 bpm over the 10 seconds.

[0326] Output: If a rapid heart rate fluctuation is detected, this information is generated (e.g., a flag indicating whether a rapid fluctuation was detected).

[0327] Step 3:

[0328] Send notifications to users

[0329] Operation: When the device (notification means) detects a sudden change, it sends a push notification to the user, prompting them to report their current emotional state.

[0330] Input: The sudden change flag, which is the output of step 2.

[0331] Output: Push notification message (e.g. "Tell us how you're feeling right now").

[0332] Step 4:

[0333] Get the user's emotional state

[0334] Action: The user uses the terminal (interaction means) to select an emotional state (e.g., anger, sadness, stress) and send a response.

[0335] Input: The user's selected emotional state.

[0336] Output: The selected emotional state (e.g., anger).

[0337] Step 5:

[0338] Analyzing emotional states

[0339] Operation: The server (emotion analysis means) analyzes the user's response data and identifies the user's emotional state based on the voice data and text data.

[0340] Input: The user's selected emotional state (output of step 4), as well as audio and text data.

[0341] Data computation: Using speech and text analysis engines to accurately recognize emotional states (e.g., determining "anger" from tone of voice).

[0342] Output: Sentiment analysis result (e.g. anger).

[0343] Step 6:

[0344] Generate messages using generative AI models

[0345] Operation: The server (generation means) generates an anger management message using the generative AI model based on the judgment results of the emotion analysis means. The server inputs the prompt sentence into the generative AI model.

[0346] Input: The sentiment analysis results from Step 5, and a prompt (e.g., a prompt for generating a message based on the sentiment analysis results).

[0347] Example prompt sentence:

[0348] A user's vital data (heart rate: x bpm, galvanic skin response: y, body temperature: z°C) has been collected. A sudden fluctuation has been detected compared to past data, indicating a change in emotion. Analysis by the emotion engine has identified the emotion "anger." Please generate appropriate anger management advice to help the user regain their composure. Please also include specific example messages.

[0349] Output: Generated anger management message (e.g., "Practice the six-second rule. Taking six deep breaths will help you regain your composure.").

[0350] Step 7:

[0351] Sending a generated message to a user

[0352] Operation: The terminal (message sending means) displays the generated anger management message on the user's device.

[0353] Input: The generated messages that are the output of Step 6.

[0354] Output: A message displayed on the user's device (e.g., "Practice the 6-second rule. Taking 6 deep breaths will help you regain your composure.").

[0355] 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.

[0356] 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.

[0357] 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.

[0358] [Second embodiment]

[0359] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0360] 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.

[0361] 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).

[0362] 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.

[0363] 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.

[0364] 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).

[0365] 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.

[0366] 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.

[0367] 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.

[0368] 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.

[0369] 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.

[0370] 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."

[0371] The following describes in detail an embodiment of the present invention. The present invention is a system that monitors a user's vital signs, detects emotional fluctuations, and provides an anger management technique. This system is composed of a wearable device (terminal), a central server, and a user.

[0372] 1. Data Collection and Monitoring

[0373] The device is equipped with multiple sensors, including a heart rate sensor, a skin galvanic response sensor, and a body temperature sensor, and uses these sensors to collect the user's vital data in real time. Data collection is set to a high frequency, with data recorded almost every second.

[0374] 2. Data fluctuation analysis and notification

[0375] The device has a built-in program for analyzing the collected vital data. For example, this program compares the average heart rate over the past minute with the heart rate over the past 10 seconds, and if it detects a sudden change (for example, an increase of 10 bpm or more over 10 seconds), it determines that this indicates emotional fluctuations.

[0376] If an emotional shift is detected, the device sends this information to a server, which analyzes the received data and further verifies whether an emotional shift is present.

[0377] 3. User Interaction

[0378] If an emotional change is detected, the server sends a push notification to the device. This notification contains a message such as "Please tell us how you feel right now." The user receives the push notification and selects their own emotion (e.g., anger, sadness, stress, etc.).

[0379] 4. Generate and provide countermeasures

[0380] The emotional information selected by the user is sent from the device to the server. Based on the received information, the server uses a generative AI model to generate a message that follows anger management methods. For example, it generates advice such as, "Practice the six-second rule. Take six deep breaths to regain your composure."

[0381] The generated message is sent from the server to the terminal, which displays the message to the user.

[0382] 5. Practical Use Cases

[0383] As a concrete example, let's say a user is feeling stressed at work. The device detects a sudden increase in heart rate and sends this information to the server. The server checks the emotional fluctuations and sends a push notification to the device saying, "Tell us how you're feeling right now." If the user selects "anger," that information is sent to the server. The server uses a generative AI model to generate a message saying, "Practice the 6-second rule," and sends it to the device. The device displays this message to the user, who then takes a deep breath to reduce stress.

[0384] In this way, the present invention allows users to recognize their own emotional fluctuations in real time and practice appropriate anger management.

[0385] The processing flow will be explained below.

[0386] Step 1:

[0387] The device collects vital data such as the user's heart rate, skin galvanic response, and body temperature. These data are measured every second using sensors and temporarily stored in the internal memory.

[0388] Step 2:

[0389] The device analyzes the stored data at regular intervals (for example, every minute), compares the average heart rate over the past minute with the heart rate over the past 10 seconds, and detects any sudden fluctuations.

[0390] Step 3:

[0391] When the device detects a sudden change in data, it sends the data to the server, including a timestamp, the heart rate at the time of detection, and changes in the electrical response of the skin.

[0392] Step 4:

[0393] The server then analyzes the received data to further determine whether there is any emotional impact, applying machine learning algorithms using multiple data points to determine whether there is any emotional impact.

[0394] Step 5:

[0395] If the server detects any emotional changes, it sends a push notification to the device, which includes the message "Please tell us how you feel right now."

[0396] Step 6:

[0397] Users receive a push notification from their device and can select their emotions, with options including "anger," "sadness," and "stress."

[0398] Step 7:

[0399] The terminal transmits the emotion information selected by the user to the server, and the transmitted data includes the selected emotion, a timestamp, and related vital data.

[0400] Step 8:

[0401] Based on the emotion data received by the server, a generative AI model is used to generate messages based on anger management techniques, such as "Practice the six-second rule. Taking a deep breath will help you regain your composure."

[0402] Step 9:

[0403] The server generates and sends the message to the terminal, which contains specific advice or instructions for the user.

[0404] Step 10:

[0405] The device displays the received message to the user, who then checks the message and takes the necessary action (e.g., take a deep breath).

[0406] In this way, by following specific actions at each step, users can recognize their own emotional fluctuations in real time and practice appropriate anger management.

[0407] Example 1

[0408] 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."

[0409] Conventional emotion monitoring systems have not adequately provided users with a means to recognize their own emotional fluctuations in real time and practice appropriate anger management. Furthermore, they have been unable to respond appropriately to sudden emotional fluctuations, making it difficult for users to manage their emotions. This has resulted in a lack of effective countermeasures for sudden emotional changes, making it difficult to appropriately control emotions such as stress and anger. The present invention aims to solve these problems.

[0410] 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.

[0411] In this invention, the server includes sensor means for collecting biometric data of a user, analysis means for detecting sudden fluctuations in the biometric data, notification means for sending a notification to the user when the sudden fluctuation is detected, dialogue means for receiving a response from the user and acquiring the user's emotional state, generation means for generating a message according to an anger management method using a generative model based on the response, and message transmission means for sending the generated message to the user. This enables the user to recognize their own emotional fluctuations in real time and practice appropriate anger management.

[0412] "Sensor means" is a general term for devices and functions used to collect biometric data of a user.

[0413] "Analysis means" is a general term for devices and functions for detecting and analyzing sudden fluctuations in collected biological data.

[0414] "Notification means" is a general term for devices and functions for sending a notification to a user when a sudden change in biometric data is detected.

[0415] "Dialogue means" is a general term for devices and functions for receiving responses from users and acquiring their emotional state.

[0416] "Generation means" is a general term for devices and functions for creating messages in accordance with anger management methods using a generative model based on a user's response.

[0417] "Message sending means" is a general term for devices and functions for sending generated messages to users.

[0418] "Biometric data" refers to data that reflects a user's physical state, such as a user's heart rate, galvanic skin response, or body temperature.

[0419] A "generative model" refers to an algorithm or system that automatically generates anger management messages based on user responses.

[0420] The following is a detailed description of an embodiment of the present invention. This system collects biometric data, detects emotional fluctuations, and provides users with an anger management method suited to their needs. The system is primarily composed of three elements: a terminal, a server, and a user.

[0421] Data Collection and Monitoring

[0422] The device uses multiple sensors, including a heart rate sensor, a skin galvanic response sensor, and a body temperature sensor, to collect biometric data in real time. These sensor means measure the user's heart rate, skin galvanic response, body temperature, etc., at a frequency of once per second. The collected data is stored in the device and monitored in real time by an analysis means to check for any sudden fluctuations.

[0423] Data fluctuation analysis and notification

[0424] A program built into the device analyzes the collected biometric data. Specifically, it compares the average heart rate over the past minute with the heart rate over the last 10 seconds, and evaluates whether there are any sudden fluctuations (for example, an increase of 10 bpm or more over 10 seconds). If a sudden fluctuation is detected, the device interprets it as an emotional change and sends the data to a server. The server then reanalyzes the received data to confirm whether the fluctuations are continuing.

[0425] User Interaction

[0426] If an emotional change is detected, the server sends a push notification to the device. The notification contains a message such as "Please tell us how you feel right now." The device displays the notification to the user. The user receives the notification and selects their emotional state (e.g., anger, sadness, stress, etc.).

[0427] Generate and provide countermeasures

[0428] Once the user selects an emotion, that information is sent to the server via the device. The server uses a generative model based on the received emotion information to generate an appropriate anger management response. The generative AI model is provided with prompts such as:

[0429] Emotional signals detected. User selected "Anger." Generate appropriate anger management advice.

[0430] The generated message (e.g., "Practice the 6-second rule. Taking a deep breath for 6 seconds will help you regain your composure") is sent from the server to the device, which then displays the message to the user.

[0431] Practical use cases

[0432] As a concrete example, consider a situation where a user is feeling stressed at work. The device detects a sudden increase in heart rate and sends this information to the server. The server checks the emotional fluctuations and sends a push notification to the device asking, "Tell us how you're feeling right now." If the user selects "anger," that information is sent to the server. The server uses a generative AI model to generate a message such as "Practice the 6-second rule" and sends it to the device. The device displays this message to the user, who then takes a deep breath to reduce stress.

[0433] In this way, the present invention allows users to recognize their own emotional fluctuations in real time and practice appropriate anger management.

[0434] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0435] Step 1:

[0436] The device collects biometric data in real time using a heart rate sensor, a skin galvanic response sensor, and a body temperature sensor. Each sensor collects data once per second and stores the data in the device's memory. The input is the data from the sensors (heart rate, skin galvanic response, body temperature), and the output is an array of the collected biometric data.

[0437] Step 2:

[0438] The device analyzes the collected biometric data using a built-in program. It calculates the average heart rate over the past minute and compares it with the heart rate over the most recent 10 seconds. The input is the heart rate data over the past minute and the heart rate data over the most recent 10 seconds, and the output is the difference between the average heart rate. Specifically, the device calculates the average of the data and compares the results.

[0439] Step 3:

[0440] The device checks whether there is a sudden change (for example, an increase of 10 bpm or more in 10 seconds) based on the analysis results of step 2. If a sudden change is detected, this information is sent to the server. The input is the difference in heart rate, and the output is a flag indicating that a sudden change has been detected.

[0441] Step 4:

[0442] The server receives the data sent from the device and re-analyzes it. The analysis involves evaluating additional data to determine whether the fluctuation is incidental or sustained. The input is the vital data sent from the device and a flag indicating a sudden change, and the output is the confirmation of the emotional fluctuation.

[0443] Step 5:

[0444] When the server confirms the emotional fluctuation, it sends a push notification to the device. The notification contains the message "Please tell us how you are feeling right now." The input is the confirmation result of the emotional fluctuation, and the output is the sending of the push notification. Specifically, the server generates a notification and pushes it to the device.

[0445] Step 6:

[0446] A user receives a push notification and selects an emotional state. Example choices include "anger," "sadness," "stress," etc. The input is the push notification, and the output is the user-selected emotional state.

[0447] Step 7:

[0448] The device transmits the emotion information selected by the user to the server. The input is the user's emotion selection, and the output is the transmission of the emotion information to the server. In concrete terms, the device executes code to transmit the information selected by the user to the server.

[0449] Step 8:

[0450] The server receives the emotional information and sends a prompt to the generative AI model based on that information. An example of a prompt is, "A signal indicating emotional fluctuation has been detected. The user selected 'anger'. Please generate appropriate anger management advice." The input is the user's emotional information, and the output is the prompt sent to the generative AI model.

[0451] Step 9:

[0452] The generative AI model generates anger management messages based on prompt sentences. An example of a generated message is "Practice the six-second rule. Taking six deep breaths will help you regain your composure." The input is the prompt sentence, and the output is the generated advice message.

[0453] Step 10:

[0454] The server sends the generated advice message to the device. The input is the advice message from the generative AI model, and the output is the message sent to the device. Specifically, the server generates the advice message and executes the code to send it to the device.

[0455] Step 11:

[0456] The terminal displays the advice message sent from the server to the user. The input is the advice message from the server, and the output is the message displayed to the user. Specifically, the terminal displays the received message on the screen.

[0457] (Application example 1)

[0458] 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."

[0459] Security personnel are frequently exposed to high levels of stress and tension, and therefore require emotional stability. It is necessary to improve work efficiency and maintain mental stability by detecting emotional fluctuations using biometric data and providing appropriate countermeasures in real time. However, conventional systems are limited to detecting fluctuations in vital data and do not go as far as managing emotions or proposing actual countermeasures. Therefore, a system that can quickly respond to personnel's stress and emotional fluctuations and provide appropriate advice is needed.

[0460] 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.

[0461] In this invention, the server includes a sensor means for collecting biometric data of a user, an analysis means for detecting sudden fluctuations in the biometric data, a notification means for sending a notification to the user when the sudden fluctuation is detected, a dialogue means for receiving a response from the user and acquiring the user's emotional state, a generation means for generating a message according to an emotion management method based on the response, a message transmission means for sending the generated message to the user, a generation means for generating a message using a generative AI model based on emotion information to manage fluctuations in the biometric data of security personnel and provide appropriate countermeasures, and a display means for displaying the generated message to the security personnel. This makes it possible to immediately detect sudden fluctuations in the security personnel's biometric data and provide appropriate anger management methods, thereby quickly responding to the security personnel's stress and emotional fluctuations.

[0462] "User" refers to an individual who uses the system of the present invention.

[0463] "Biometric data" refers to data that indicates the user's physical condition, such as heart rate, galvanic skin response, and body temperature.

[0464] "Sensor means" refers to a device for collecting biometric data of a user.

[0465] "Analysis means" refers to a method or device for analyzing collected biometric data and detecting sudden fluctuations therein.

[0466] "Notification means" refers to a device or method for reporting to the user when a sudden change in biometric data is detected.

[0467] "Interaction means" refers to a method or device for receiving responses and obtaining emotional states from a user.

[0468] "Emotional state" refers to a state that indicates the emotions such as anger, sadness, stress, etc. that a user is feeling.

[0469] "Generator" refers to a method or device for automatically creating a message that conforms to emotion management techniques based on a user's response.

[0470] "Message sending means" refers to a means for sending a generated message to a user.

[0471] "Display means" refers to a device or method for a user to view the generated message.

[0472] "Official personnel" refers to people engaged in security work.

[0473] A "generative AI model" refers to a model that uses artificial intelligence to analyze data and generate messages.

[0474] The present invention provides a system that monitors a user's biometric data in real time, detects emotional fluctuations based on that data, and provides an anger management technique. This system is configured to include a sensor, an analysis means, a notification means, a dialogue means, a generation means, and a display means.

[0475] 1. Devices and Data Collection

[0476] The sensor means is installed in smart glasses (such as Google Glass) worn by security personnel, and includes a heart rate sensor, a skin galvanic response sensor, and a body temperature sensor, which collect the personnel's biometric data. The biometric data is collected in real time and recorded in seconds.

[0477] 2. Data Analysis

[0478] The analysis is performed by software (e.g., Python scripts) embedded in the smart glasses, which analyzes the collected biometric data and detects sudden changes (e.g., an increase in heart rate). When these changes are detected, the data is sent to a cloud server using AWS IoT Core.

[0479] 3. Emotional awareness and notification

[0480] The data sent to the cloud server is again analyzed by the analysis means to check for any sudden changes in emotion. If any emotional fluctuations are detected, the notification means sends a push notification to the smart glasses. This notification is a message based on the dialogue means, such as "Please tell us how you are feeling right now."

[0481] 4. Acquiring emotional states

[0482] The worker responds to the push notification and selects their emotional state (e.g., anger, sadness, stress). This emotional information is then sent back to the cloud server.

[0483] 5. Generating strategies for anger management

[0484] After receiving the emotion information, the server uses a generative AI model (such as the OpenAI API) to generate an appropriate message based on emotion management techniques, such as a prompt such as "Take a deep breath for six seconds."

[0485] 6. Sending and Viewing Messages

[0486] The message generated by the generating means is again transmitted to the smart glasses and displayed by the display means to the worker, thereby enabling the worker to implement the provided countermeasure in real time.

[0487] Specific examples

[0488] Suppose a security worker experiences a sudden rise in heart rate during a long shift, putting him or her in a stressful situation. The smart glasses detect this sudden change and send a notification asking the worker to "Enter your current emotions." If the worker selects "Stress," the cloud server generates a countermeasure, and the message "Take six deep breaths" is displayed on the smart glasses.

[0489] Prompt Sentence Examples

[0490] "A sudden change in vital signs has been detected. The immediate emotional state is anger. Please provide appropriate anger management advice."

[0491] As described above, the present invention is a system that monitors a user's biometric data in real time, instantly detects emotional fluctuations, and provides countermeasures, thereby maintaining the mental health of security personnel and improving work efficiency and safety.

[0492] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0493] Step 1:

[0494] The device (smart glasses) collects the wearer's biometric data (heart rate, electrodermal response, body temperature). The biometric data collected in real time by sensors is the input, and this data is recorded in local storage every second.

[0495] Step 2:

[0496] The device's internal analysis means analyzes the collected biometric data. Specifically, a Python script is used to compare the average heart rate over the past minute with the heart rate over the last 10 seconds. If a sudden change (for example, an increase of 10 bpm or more over 10 seconds) is detected, this information is sent to a server using AWS IoT Core. The input is the collected biometric data, and the output is data indicating the sudden change.

[0497] Step 3:

[0498] The server receives the data sent from the terminal and performs further detailed analysis. The server also uses analysis software to check for sudden fluctuations in the data. Once detected, a notification is sent to the terminal using a notification means. The input is the data sent from the terminal, and the output is the notification message to be sent.

[0499] Step 4:

[0500] The device notifies the user of the notification received from the server. It displays a push notification with a message such as "Please tell us your current emotions." The user checks the notification and selects their emotional state (anger, sadness, stress, etc.). The input is the notification from the server, and the output is the emotional information selected by the user.

[0501] Step 5:

[0502] The user's emotional information is again sent from the device to the server. Once the emotional information reaches the server, the server uses a generative AI model to generate an anger management response. An example of a prompt sentence used here is, "A sudden change in vital data has been detected. The current emotional state is anger. Please provide appropriate anger management advice." The input is the user's emotional information, and the output is the generated response message.

[0503] Step 6:

[0504] The message generated by the server is sent back to the terminal. The terminal displays the received message to the user. For example, advice such as "Take a deep breath for six seconds" is displayed. The input is the generated message from the server, and the output is the message displayed to the user.

[0505] As described above, this system monitors users' biometric data in real time and provides appropriate countermeasures when emotional fluctuations are detected, thereby helping to maintain the mental health of security personnel and improving work efficiency.

[0506] 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.

[0507] The following describes in detail the mode for carrying out the present invention. The present invention is a system that monitors a user's vital signs, detects emotional fluctuations, and provides anger management techniques. Furthermore, by combining it with an emotion engine, it is possible to more accurately recognize the user's emotional state and provide optimal countermeasures. This system is composed of a wearable device (terminal), a central server (server), an emotion engine, and a user (user).

[0508] 1. Data Collection and Monitoring

[0509] The device is equipped with multiple sensors, including a heart rate sensor, a skin galvanic response sensor, and a body temperature sensor, and uses these sensors to collect the user's vital data in real time. Data collection is set to a high frequency, with data recorded almost every second.

[0510] 2. Data fluctuation analysis and notification

[0511] The device has a built-in program for analyzing the collected vital data. For example, this program compares the average heart rate over the past minute with the heart rate over the last 10 seconds, and if a sudden change (e.g., an increase of 10 bpm or more over 10 seconds) is detected, it determines that this indicates emotional fluctuations. If emotional fluctuations are detected, the device sends this information to a server. The server analyzes the received data and further verifies whether emotional fluctuations are present.

[0512] 3. User Interaction and Emotion Recognition

[0513] If an emotional change is detected, the server sends a push notification to the device. This notification contains a message such as "Please tell us how you feel right now." When the user receives the push notification and selects their emotion (e.g., anger, sadness, stress, etc.), the selected emotion information is sent from the device to the server. Furthermore, the emotion engine also analyzes the user's response data and selects the most appropriate emotion from multiple emotion categories.

[0514] The emotion engine has the ability to analyze the user's voice and text data, which allows for more accurate emotion recognition. For example, if a user is yelling, the voice data can be analyzed and the emotion can be recognized as "anger."

[0515] 4. Generate and provide countermeasures

[0516] The server uses a generative AI model to generate messages based on anger management techniques based on the emotion data received from the emotion engine. For example, it generates advice such as, "Practice the six-second rule. Take six deep breaths to regain your composure." The generated message is sent from the server to the device, which then displays it to the user.

[0517] 5. Practical Use Cases

[0518] As a concrete example, suppose a user is feeling stressed at work. The device detects a sudden increase in heart rate and sends this information to the server. The server checks the emotional fluctuations and sends a push notification to the device saying, "Tell us how you're feeling right now." If the user selects "anger," that information is sent to the server. At the same time, the emotion engine also analyzes the user's voice and determines that it is "anger." The server uses the generative AI model to generate a message saying, "Practice the 6-second rule," and sends it to the device. The device displays this message to the user, who then takes a deep breath to reduce stress.

[0519] In this way, the present invention allows users to recognize their own emotional fluctuations in real time and utilize an emotion engine to enable more accurate emotion recognition and appropriate anger management.

[0520] The processing flow will be explained below.

[0521] Step 1:

[0522] The device collects vital data such as the user's heart rate, skin galvanic response, and body temperature. The sensor measures the data every second and temporarily stores it in the internal memory.

[0523] Step 2:

[0524] The device analyzes the saved vital data at regular intervals (for example, every minute). For example, it compares the average heart rate over the past minute with the heart rate over the past 10 seconds to detect any sudden fluctuations.

[0525] Step 3:

[0526] When the device detects a sudden change in data, it sends the data to the server, including a timestamp, the heart rate at the time of detection, and the amount of change in the electrical response of the skin.

[0527] Step 4:

[0528] The server then analyzes the received data to further determine if any emotional swings are present, applying machine learning algorithms using multiple data points to determine emotional swings.

[0529] Step 5:

[0530] If the server detects any emotional changes, it sends a push notification to the device, which includes the message "Please tell us how you feel right now."

[0531] Step 6:

[0532] Users receive a push notification from their device and can select their emotion with one tap. Options include "anger," "sadness," and "stress."

[0533] Step 7:

[0534] The device transmits the selected emotion information to the server, including the user's selected emotion, a timestamp, and related vital data.

[0535] Step 8:

[0536] The server sends the received emotion data to the emotion engine, which analyzes the user's selected emotion data and vital data to more accurately recognize the user's emotional state.

[0537] Step 9:

[0538] The emotion engine recognizes the user's emotional state and sends the result back to the server, for example, "anger."

[0539] Step 10:

[0540] Based on the recognition results from the emotion engine, the server uses a generative AI model to generate messages that follow anger management methods, such as advice like "Practice the six-second rule. Taking six deep breaths will help you regain your composure."

[0541] Step 11:

[0542] Sends server-generated messages to the terminal, containing specific advice or instructions to the user.

[0543] Step 12:

[0544] The device displays the received message to the user, who then checks the message and takes the necessary action (e.g., take a deep breath).

[0545] As a result, users can recognize their own emotional fluctuations in real time, and with the help of the emotion engine, they can perform more accurate emotion recognition and practice appropriate anger management.

[0546] Example 2

[0547] 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."

[0548] Currently, there is no system that can monitor users' emotional fluctuations in real time and quickly provide appropriate countermeasures. In particular, there is a need for a system that can detect sudden changes in emotions and provide appropriate anger management techniques based on those changes. It is also difficult to accurately recognize a user's emotional state and immediately suggest countermeasures using push notifications. This makes it difficult to provide effective means for maintaining users' mental health.

[0549] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0550] In this invention, the server includes detection means for collecting biometric data of a user, analysis means for detecting sudden fluctuations in the biometric data, warning means for sending a notification to the user when the sudden fluctuation is detected, communication means for receiving a response from the user and acquiring the user's emotional state, analysis means for analyzing the emotional data based on the response using an emotion engine, generation means for generating a message according to an anger management method using a generative AI model, and transmission means for sending the generated message to the user. This makes it possible to accurately recognize the user's emotional fluctuations in real time and provide appropriate anger management messages based on the emotional fluctuations.

[0551] "User" means any person or entity that uses the system.

[0552] "Biometric data" refers to data relating to the user's physical condition, such as heart rate, galvanic skin response, and body temperature.

[0553] "Detection means" refers to a device or sensor for collecting biometric data of a user.

[0554] "Analysis means" refers to the program and hardware used to analyze collected biological data and detect sudden changes.

[0555] The "warning means" is a function for sending a notification to the user when a sudden change is detected.

[0556] "Communication means" refers to the interface or protocol for receiving the user's responses and obtaining their emotional state.

[0557] "Analysis means" refers to software or hardware that uses an emotion engine to analyze the user's response data (voice data or text data) and recognize the user's emotional state.

[0558] "Generation means" refers to a function for using a generative AI model to create messages that follow anger management methods.

[0559] "Vehicle" refers to the functionality and interface for sending generated messages to users.

[0560] An "emotion engine" is an algorithm and software that analyzes a user's emotions and recognizes their optimal emotional state.

[0561] A "generative AI model" is a machine learning model that generates optimal anger management messages based on the user's emotional state.

[0562] "Push notification" refers to a protocol and method for sending notifications to devices in real time.

[0563] "Emotional fluctuation" is a term that indicates a state in which a sudden change occurs in the user's biometric data.

[0564] This invention is a system that monitors a user's biometric data in real time and provides appropriate anger management countermeasures based on changes in the biometric data using an emotion engine and a generative AI model. This system consists of a wearable device (terminal), a central server (server), an emotion engine, and a user (user).

[0565] The device is equipped with multiple sensors, including a heart rate sensor, a skin galvanic response sensor, and a body temperature sensor. These sensors collect the user's vital data every second. Specifically, the heart rate sensor measures heart rate data every second, and the skin galvanic response sensor records the user's stress level in real time.

[0566] The server receives and analyzes the biometric data sent from the device. Specifically, it compares the average heart rate over the past minute with the heart rate over the past 10 seconds to detect any sudden fluctuations. For example, if the heart rate rises by 10 bpm or more over 10 seconds, it is determined that there is an emotional fluctuation.

[0567] When an emotional change is detected, the server sends a push notification to the device. This notification contains a message such as "Please tell us how you feel right now." When the user receives the push notification and selects their emotion (e.g., anger, sadness, stress, etc.), that information is sent from the device to the server.

[0568] The server uses a generative AI model to generate anger management messages based on the received emotional data and the user's voice and text data analyzed by the emotion engine. For example, it might generate advice such as, "Practice the six-second rule. Taking a deep breath for six seconds will help you regain your composure."

[0569] The generated message is sent from the server to the device, which then displays it to the user, who can then take action such as deep breathing to reduce stress and emotional upheaval.

[0570] As a specific use case, consider a case where a user is experiencing high stress at work. The device detects a sudden increase in heart rate and sends this information to the server. After the server confirms the emotional fluctuations, it sends a push notification to the device. If the user selects "anger," this information is sent to the server. At the same time, the emotion engine analyzes the user's voice and determines it to be "anger." The server uses a generative AI model to generate a message saying, "Practice the 6-second rule," and sends it to the device. The device displays this message to the user, who then takes a deep breath to reduce stress.

[0571] In this way, the present invention realizes a system that allows users to recognize their own emotional fluctuations in real time and provides more accurate emotion recognition and appropriate anger management by utilizing an emotion engine and generative AI model.

[0572] An example prompt is:

[0573] "The user is currently experiencing deep stress and their heart rate has increased by more than 10 bpm in 10 seconds. Generate anger management advice if the user selects anger."

[0574] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0575] Step 1: Collect biometric data

[0576] The terminal collects the user's biometric data using sensors (heart rate sensor, skin galvanic response sensor, body temperature sensor, etc.) built into the wearable device. The data measured by the sensors is periodically recorded in a database within the terminal. The input here is the user's current physical condition, and the output is the collected vital data (heart rate, skin galvanic response, body temperature).

[0577] Step 2: Analyzing biological data

[0578] The device runs a program to analyze the collected vital data. Specifically, it compares the average heart rate over the past minute with the heart rate over the last 10 seconds. Based on this comparison, it determines that a sudden change has been detected, such as when the heart rate increases by 10 bpm or more over a 10-second period. The input is the collected vital data, and the output is the detected change.

[0579] Step 3: Emotional Indications

[0580] If the device detects a change in emotion, it sends that information to the server. The server analyzes the received data, and if an emotion change is confirmed, it sends a push notification to the device. The notification contains the message "Please tell us how you are feeling right now." The input is the change detection result, and the output is the sending of a push notification.

[0581] Step 4: Obtaining the user's emotional response

[0582] The user receives a push notification and selects their emotion (e.g., anger, sadness, stress, etc.). The selected emotion information is sent from the device to the server. The input here is the user's emotional response, and the output is the transmitted emotion information.

[0583] Step 5: Sentiment Analysis

[0584] The server runs an emotion engine and analyzes the user's response data (voice data and text data). For example, it recognizes the user's optimal emotional state from their tone of voice and vocabulary. The input is the user's emotional response and voice data, and the output is the specific emotion recognition result.

[0585] Step 6: Create an Anger Management Message

[0586] The server uses a generative AI model to generate anger management messages based on the emotion recognition results obtained from the emotion engine. For example, it creates advice such as, "Practice the six-second rule. Take six deep breaths to regain your composure." The input is the emotion recognition results, and the output is the generated message.

[0587] Step 7: Sending a message

[0588] The server generates a message and sends it to the terminal, which displays it to the user. The input here is the generated message, and the output is the message displayed to the user.

[0589] Step 8: User Action

[0590] The user takes specific coping actions (e.g., deep breathing) in accordance with the anger management message displayed on the device. This allows the user to control their emotions. The input here is the displayed message, and the output is the user's response.

[0591] (Application example 2)

[0592] 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."

[0593] Conventional systems for security staff lack the means to detect emotional fluctuations in real time and immediately provide appropriate anger management methods, which results in security staff being unable to perform their duties effectively under high stress.

[0594] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: sensor means for collecting vital data of the user; analysis means for detecting sudden fluctuations in the vital data; notification means for sending a notification to the user when the sudden fluctuation is detected; dialogue means for receiving the user's response and acquiring the user's emotional state; generation means for generating a message according to an anger management method based on the response; message sending means for sending the generated message to the user; emotion analysis means for analyzing the emotional state; generative AI model utilization means for generating a message using a generative AI model based on the emotional state analyzed by the emotion analysis means and a prompt sentence; and display means for displaying the generated message on the user's device. This enables security staff to recognize their own emotional fluctuations in real time and immediately practice an appropriate anger management method.

[0595] "Sensor means" refers to a device used to collect vital data of a user.

[0596] "Analysis means" refers to a device or program used to detect sudden fluctuations in collected vital data.

[0597] A "notification means" is a device or program used to send a notification to a user when a sudden change is detected.

[0598] An "interactive means" is a device or program for receiving a user's response and obtaining the user's emotional state.

[0599] The "generating means" is a device or program for generating a message in accordance with an anger management method based on the user's response.

[0600] The "message sending means" is a device or program for sending the generated message to the user.

[0601] "Emotion analysis means" is a device or program for analyzing the emotional state of a user.

[0602] "Generative AI model utilization means" refers to a device or program that uses a prompt sentence to generate a message using a generative AI model based on the emotional state analyzed by the emotion analysis means.

[0603] A "display means" is a device or program for displaying the generated message on a user's device.

[0604] overview

[0605] This invention is a system that monitors emotional fluctuations in real time and provides anger management techniques for security staff. The system collects users' vital data, analyzes sudden fluctuations, and uses a generative AI model to suggest appropriate countermeasures when emotional fluctuations are detected.

[0606] Hardware and Software Configuration

[0607] The system consists of the following main components:

[0608] 1. Sensor means:

[0609] Hardware: Smartwatches, smart glasses, and other wearable devices.

[0610] Function: Collects heart rate, galvanic skin response, and body temperature in real time.

[0611] 2. Analysis method:

[0612] Software: Python programs, data analysis libraries (e.g., pandas, numpy).

[0613] Function: Detects sudden fluctuations in collected vital data.

[0614] 3. Means of notification:

[0615] Hardware / Software: Mobile devices, push notification services.

[0616] Function: Notify the user of emotional fluctuations.

[0617] 4. Means of interaction:

[0618] Software: Conversational interfaces (e.g., chatbots).

[0619] Function: Receives the user's response and captures their emotional state.

[0620] 5. Emotion analysis means:

[0621] Software: Speech analysis engine, text analysis engine (e.g., Google Cloud Speech-to-Text, BERT).

[0622] Function: Analyzes the user's emotional state.

[0623] 6. Generation means:

[0624] Software: Generative AI models (e.g., GPT-3).

[0625] Function: Generates anger management messages using prompts based on emotional state.

[0626] 7. Message sending method:

[0627] Hardware: Mobile devices, wearable devices.

[0628] Function: Displays the generated message.

[0629] Processing flow

[0630] The specific process is as follows:

[0631] 1. Data Collection:

[0632] The sensor means collects vital data such as the user's heart rate, galvanic skin response, body temperature, etc. The data is collected in real time and updated every second.

[0633] 2. Data Analysis:

[0634] The analysis method detects sudden fluctuations in vital data. For example, it compares the average heart rate over the past minute with the heart rate over the past 10 seconds, and if a sudden fluctuation is detected, it determines whether the person is emotionally unstable.

[0635] 3. Notice:

[0636] The notification means sends a push notification to the user, displaying a message saying, "Please tell us how you feel right now."

[0637] 4. Getting user response:

[0638] The user selects their emotion (e.g., anger, sadness, stress, etc.) and responds through a dialogue means. This information is sent to the server.

[0639] 5. Emotion analysis:

[0640] The emotion analysis means analyzes the user's voice data and text data to identify the user's emotional state. For example, if the user is yelling, the voice data is analyzed and the emotion is recognized as "anger."

[0641] 6. Message Creation:

[0642] The generator uses a generative AI model to generate advice based on the emotional state, using prompts such as, "Practice the six-second rule. Take six deep breaths to regain your composure."

[0643] 7. Sending a message:

[0644] A message sending means displays the generated message on the user's device.

[0645] Specific examples

[0646] Consider a situation where a security staff member experiences stress during work and their heart rate suddenly rises. The system detects this change in heart rate and sends a notification to the user. If the user responds with "Anger," the server uses the generative AI model to generate advice such as "Practice the 6-second rule," which is displayed on the user's device.

[0647] Example prompts for generative AI models

[0648] A user's vital data (heart rate: x bpm, galvanic skin response: y, body temperature: z°C) has been collected. A sudden fluctuation has been detected compared to past data, indicating a change in emotion. Analysis by the emotion engine has identified the emotion "anger." Please generate appropriate anger management advice to help the user regain their composure. Please also include specific example messages.

[0649] This allows security staff to recognize their own emotional fluctuations in real time and immediately implement appropriate anger management methods.

[0650] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0651] Step 1:

[0652] Collect user vital data

[0653] Operation: The device (sensor means) collects vital data such as the user's heart rate, skin galvanic response, and body temperature in real time.

[0654] Input: User vital data (heart rate, galvanic skin response, body temperature) obtained from sensors connected to the device.

[0655] Output: Collected vital data (heart rate: x bpm, galvanic skin response: y, temperature: z°C).

[0656] Step 2:

[0657] Analyzing fluctuations in vital data

[0658] Operation: The terminal (analysis means) analyzes the collected vital data and compares the past data with the latest data to detect sudden fluctuations in heart rate.

[0659] Input: Vital data collected in step 1.

[0660] Data processing: Compare the average heart rate over the past minute with the heart rate over the last 10 seconds, and measure whether there is a fluctuation of more than 10 bpm over the 10 seconds.

[0661] Output: If a rapid heart rate fluctuation is detected, this information is generated (e.g., a flag indicating whether a rapid fluctuation was detected).

[0662] Step 3:

[0663] Send notifications to users

[0664] Operation: When the device (notification means) detects a sudden change, it sends a push notification to the user, prompting them to report their current emotional state.

[0665] Input: The sudden change flag, which is the output of step 2.

[0666] Output: Push notification message (e.g. "Tell us how you're feeling right now").

[0667] Step 4:

[0668] Get the user's emotional state

[0669] Action: The user uses the terminal (interaction means) to select an emotional state (e.g., anger, sadness, stress) and send a response.

[0670] Input: The user's selected emotional state.

[0671] Output: The selected emotional state (e.g., anger).

[0672] Step 5:

[0673] Analyzing emotional states

[0674] Operation: The server (emotion analysis means) analyzes the user's response data and identifies the user's emotional state based on the voice data and text data.

[0675] Input: The user's selected emotional state (output of step 4), as well as audio and text data.

[0676] Data computation: Using speech and text analysis engines to accurately recognize emotional states (e.g., determining "anger" from tone of voice).

[0677] Output: Sentiment analysis result (e.g. anger).

[0678] Step 6:

[0679] Generate messages using generative AI models

[0680] Operation: The server (generation means) generates an anger management message using the generative AI model based on the judgment results of the emotion analysis means. The server inputs the prompt sentence into the generative AI model.

[0681] Input: The sentiment analysis results from Step 5, and a prompt (e.g., a prompt for generating a message based on the sentiment analysis results).

[0682] Example prompt sentence:

[0683] A user's vital data (heart rate: x bpm, galvanic skin response: y, body temperature: z°C) has been collected. A sudden fluctuation has been detected compared to past data, indicating a change in emotion. Analysis by the emotion engine has identified the emotion "anger." Please generate appropriate anger management advice to help the user regain their composure. Please also include specific example messages.

[0684] Output: Generated anger management message (e.g., "Practice the six-second rule. Taking six deep breaths will help you regain your composure.").

[0685] Step 7:

[0686] Sending a generated message to a user

[0687] Operation: The terminal (message sending means) displays the generated anger management message on the user's device.

[0688] Input: The generated messages that are the output of Step 6.

[0689] Output: A message displayed on the user's device (e.g., "Practice the 6-second rule. Taking 6 deep breaths will help you regain your composure.").

[0690] 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.

[0691] 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.

[0692] 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.

[0693] [Third embodiment]

[0694] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0695] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0696] 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).

[0697] 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.

[0698] 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.

[0699] 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).

[0700] 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.

[0701] 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.

[0702] 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.

[0703] 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.

[0704] 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.

[0705] 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."

[0706] The following describes in detail an embodiment of the present invention. The present invention is a system that monitors a user's vital signs, detects emotional fluctuations, and provides an anger management technique. This system is composed of a wearable device (terminal), a central server, and a user.

[0707] 1. Data Collection and Monitoring

[0708] The device is equipped with multiple sensors, including a heart rate sensor, a skin galvanic response sensor, and a body temperature sensor, and uses these sensors to collect the user's vital data in real time. Data collection is set to a high frequency, with data recorded almost every second.

[0709] 2. Data fluctuation analysis and notification

[0710] The device has a built-in program for analyzing the collected vital data. For example, this program compares the average heart rate over the past minute with the heart rate over the past 10 seconds, and if it detects a sudden change (for example, an increase of 10 bpm or more over 10 seconds), it determines that this indicates emotional fluctuations.

[0711] If an emotional shift is detected, the device sends this information to a server, which analyzes the received data and further verifies whether an emotional shift is present.

[0712] 3. User Interaction

[0713] If an emotional change is detected, the server sends a push notification to the device. This notification contains a message such as "Please tell us how you feel right now." The user receives the push notification and selects their own emotion (e.g., anger, sadness, stress, etc.).

[0714] 4. Generate and provide countermeasures

[0715] The emotional information selected by the user is sent from the device to the server. Based on the received information, the server uses a generative AI model to generate a message that follows anger management methods. For example, it generates advice such as, "Practice the six-second rule. Take six deep breaths to regain your composure."

[0716] The generated message is sent from the server to the terminal, which displays the message to the user.

[0717] 5. Practical Use Cases

[0718] As a concrete example, let's say a user is feeling stressed at work. The device detects a sudden increase in heart rate and sends this information to the server. The server checks the emotional fluctuations and sends a push notification to the device saying, "Tell us how you're feeling right now." If the user selects "anger," that information is sent to the server. The server uses a generative AI model to generate a message saying, "Practice the 6-second rule," and sends it to the device. The device displays this message to the user, who then takes a deep breath to reduce stress.

[0719] In this way, the present invention allows users to recognize their own emotional fluctuations in real time and practice appropriate anger management.

[0720] The processing flow will be explained below.

[0721] Step 1:

[0722] The device collects vital data such as the user's heart rate, skin galvanic response, and body temperature. These data are measured every second using sensors and temporarily stored in the internal memory.

[0723] Step 2:

[0724] The device analyzes the stored data at regular intervals (for example, every minute), compares the average heart rate over the past minute with the heart rate over the past 10 seconds, and detects any sudden fluctuations.

[0725] Step 3:

[0726] When the device detects a sudden change in data, it sends the data to the server, including a timestamp, the heart rate at the time of detection, and changes in the electrical response of the skin.

[0727] Step 4:

[0728] The server then analyzes the received data to further determine whether there is any emotional impact, applying machine learning algorithms using multiple data points to determine whether there is any emotional impact.

[0729] Step 5:

[0730] If the server detects any emotional changes, it sends a push notification to the device, which includes the message "Please tell us how you feel right now."

[0731] Step 6:

[0732] Users receive a push notification from their device and can select their emotions, with options including "anger," "sadness," and "stress."

[0733] Step 7:

[0734] The terminal transmits the emotion information selected by the user to the server, and the transmitted data includes the selected emotion, a timestamp, and related vital data.

[0735] Step 8:

[0736] Based on the emotion data received by the server, a generative AI model is used to generate messages based on anger management techniques, such as "Practice the six-second rule. Taking a deep breath will help you regain your composure."

[0737] Step 9:

[0738] The server generates and sends the message to the terminal, which contains specific advice or instructions for the user.

[0739] Step 10:

[0740] The device displays the received message to the user, who then checks the message and takes the necessary action (e.g., take a deep breath).

[0741] In this way, by following specific actions at each step, users can recognize their own emotional fluctuations in real time and practice appropriate anger management.

[0742] Example 1

[0743] 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."

[0744] Conventional emotion monitoring systems have not adequately provided users with a means to recognize their own emotional fluctuations in real time and practice appropriate anger management. Furthermore, they have been unable to respond appropriately to sudden emotional fluctuations, making it difficult for users to manage their emotions. This has resulted in a lack of effective countermeasures for sudden emotional changes, making it difficult to appropriately control emotions such as stress and anger. The present invention aims to solve these problems.

[0745] 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.

[0746] In this invention, the server includes sensor means for collecting biometric data of a user, analysis means for detecting sudden fluctuations in the biometric data, notification means for sending a notification to the user when the sudden fluctuation is detected, dialogue means for receiving a response from the user and acquiring the user's emotional state, generation means for generating a message according to an anger management method using a generative model based on the response, and message transmission means for sending the generated message to the user. This enables the user to recognize their own emotional fluctuations in real time and practice appropriate anger management.

[0747] "Sensor means" is a general term for devices and functions used to collect biometric data of a user.

[0748] "Analysis means" is a general term for devices and functions for detecting and analyzing sudden fluctuations in collected biological data.

[0749] "Notification means" is a general term for devices and functions for sending a notification to a user when a sudden change in biometric data is detected.

[0750] "Dialogue means" is a general term for devices and functions for receiving responses from users and acquiring their emotional state.

[0751] "Generation means" is a general term for devices and functions for creating messages in accordance with anger management methods using a generative model based on a user's response.

[0752] "Message sending means" is a general term for devices and functions for sending generated messages to users.

[0753] "Biometric data" refers to data that reflects a user's physical state, such as a user's heart rate, galvanic skin response, or body temperature.

[0754] A "generative model" refers to an algorithm or system that automatically generates anger management messages based on user responses.

[0755] The following is a detailed description of an embodiment of the present invention. This system collects biometric data, detects emotional fluctuations, and provides users with an anger management method suited to their needs. The system is primarily composed of three elements: a terminal, a server, and a user.

[0756] Data Collection and Monitoring

[0757] The device uses multiple sensors, including a heart rate sensor, a skin galvanic response sensor, and a body temperature sensor, to collect biometric data in real time. These sensor means measure the user's heart rate, skin galvanic response, body temperature, etc., at a frequency of once per second. The collected data is stored in the device and monitored in real time by an analysis means to check for any sudden fluctuations.

[0758] Data fluctuation analysis and notification

[0759] A program built into the device analyzes the collected biometric data. Specifically, it compares the average heart rate over the past minute with the heart rate over the last 10 seconds, and evaluates whether there are any sudden fluctuations (for example, an increase of 10 bpm or more over 10 seconds). If a sudden fluctuation is detected, the device interprets it as an emotional change and sends the data to a server. The server then reanalyzes the received data to confirm whether the fluctuations are continuing.

[0760] User Interaction

[0761] If an emotional change is detected, the server sends a push notification to the device. The notification contains a message such as "Please tell us how you feel right now." The device displays the notification to the user. The user receives the notification and selects their emotional state (e.g., anger, sadness, stress, etc.).

[0762] Generate and provide countermeasures

[0763] Once the user selects an emotion, that information is sent to the server via the device. The server uses a generative model based on the received emotion information to generate an appropriate anger management response. The generative AI model is provided with prompts such as:

[0764] Emotional signals detected. User selected "Anger." Generate appropriate anger management advice.

[0765] The generated message (e.g., "Practice the 6-second rule. Taking a deep breath for 6 seconds will help you regain your composure") is sent from the server to the device, which then displays the message to the user.

[0766] Practical use cases

[0767] As a concrete example, consider a situation where a user is feeling stressed at work. The device detects a sudden increase in heart rate and sends this information to the server. The server checks the emotional fluctuations and sends a push notification to the device asking, "Tell us how you're feeling right now." If the user selects "anger," that information is sent to the server. The server uses a generative AI model to generate a message such as "Practice the 6-second rule" and sends it to the device. The device displays this message to the user, who then takes a deep breath to reduce stress.

[0768] In this way, the present invention allows users to recognize their own emotional fluctuations in real time and practice appropriate anger management.

[0769] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0770] Step 1:

[0771] The device collects biometric data in real time using a heart rate sensor, a skin galvanic response sensor, and a body temperature sensor. Each sensor collects data once per second and stores the data in the device's memory. The input is the data from the sensors (heart rate, skin galvanic response, body temperature), and the output is an array of the collected biometric data.

[0772] Step 2:

[0773] The device analyzes the collected biometric data using a built-in program. It calculates the average heart rate over the past minute and compares it with the heart rate over the most recent 10 seconds. The input is the heart rate data over the past minute and the heart rate data over the most recent 10 seconds, and the output is the difference between the average heart rate. Specifically, the device calculates the average of the data and compares the results.

[0774] Step 3:

[0775] The device checks whether there is a sudden change (for example, an increase of 10 bpm or more in 10 seconds) based on the analysis results of step 2. If a sudden change is detected, this information is sent to the server. The input is the difference in heart rate, and the output is a flag indicating that a sudden change has been detected.

[0776] Step 4:

[0777] The server receives the data sent from the device and re-analyzes it. The analysis involves evaluating additional data to determine whether the fluctuation is incidental or sustained. The input is the vital data sent from the device and a flag indicating a sudden change, and the output is the confirmation of the emotional fluctuation.

[0778] Step 5:

[0779] When the server confirms the emotional fluctuation, it sends a push notification to the device. The notification contains the message "Please tell us how you are feeling right now." The input is the confirmation result of the emotional fluctuation, and the output is the sending of the push notification. Specifically, the server generates a notification and pushes it to the device.

[0780] Step 6:

[0781] A user receives a push notification and selects an emotional state. Example choices include "anger," "sadness," "stress," etc. The input is the push notification, and the output is the user-selected emotional state.

[0782] Step 7:

[0783] The device transmits the emotion information selected by the user to the server. The input is the user's emotion selection, and the output is the transmission of the emotion information to the server. In concrete terms, the device executes code to transmit the information selected by the user to the server.

[0784] Step 8:

[0785] The server receives the emotional information and sends a prompt to the generative AI model based on that information. An example of a prompt is, "A signal indicating emotional fluctuation has been detected. The user selected 'anger'. Please generate appropriate anger management advice." The input is the user's emotional information, and the output is the prompt sent to the generative AI model.

[0786] Step 9:

[0787] The generative AI model generates anger management messages based on prompt sentences. An example of a generated message is "Practice the six-second rule. Taking six deep breaths will help you regain your composure." The input is the prompt sentence, and the output is the generated advice message.

[0788] Step 10:

[0789] The server sends the generated advice message to the device. The input is the advice message from the generative AI model, and the output is the message sent to the device. Specifically, the server generates the advice message and executes the code to send it to the device.

[0790] Step 11:

[0791] The terminal displays the advice message sent from the server to the user. The input is the advice message from the server, and the output is the message displayed to the user. Specifically, the terminal displays the received message on the screen.

[0792] (Application example 1)

[0793] 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."

[0794] Security personnel are frequently exposed to high levels of stress and tension, and therefore require emotional stability. It is necessary to improve work efficiency and maintain mental stability by detecting emotional fluctuations using biometric data and providing appropriate countermeasures in real time. However, conventional systems are limited to detecting fluctuations in vital data and do not go as far as managing emotions or proposing actual countermeasures. Therefore, a system that can quickly respond to personnel's stress and emotional fluctuations and provide appropriate advice is needed.

[0795] 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.

[0796] In this invention, the server includes a sensor means for collecting biometric data of a user, an analysis means for detecting sudden fluctuations in the biometric data, a notification means for sending a notification to the user when the sudden fluctuation is detected, a dialogue means for receiving a response from the user and acquiring the user's emotional state, a generation means for generating a message according to an emotion management method based on the response, a message transmission means for sending the generated message to the user, a generation means for generating a message using a generative AI model based on emotion information to manage fluctuations in the biometric data of security personnel and provide appropriate countermeasures, and a display means for displaying the generated message to the security personnel. This makes it possible to immediately detect sudden fluctuations in the security personnel's biometric data and provide appropriate anger management methods, thereby quickly responding to the security personnel's stress and emotional fluctuations.

[0797] "User" refers to an individual who uses the system of the present invention.

[0798] "Biometric data" refers to data that indicates the user's physical condition, such as heart rate, galvanic skin response, and body temperature.

[0799] "Sensor means" refers to a device for collecting biometric data of a user.

[0800] "Analysis means" refers to a method or device for analyzing collected biometric data and detecting sudden fluctuations therein.

[0801] "Notification means" refers to a device or method for reporting to the user when a sudden change in biometric data is detected.

[0802] "Interaction means" refers to a method or device for receiving responses and obtaining emotional states from a user.

[0803] "Emotional state" refers to a state that indicates the emotions such as anger, sadness, stress, etc. that a user is feeling.

[0804] "Generator" refers to a method or device for automatically creating a message that conforms to emotion management techniques based on a user's response.

[0805] "Message sending means" refers to a means for sending a generated message to a user.

[0806] "Display means" refers to a device or method for a user to view the generated message.

[0807] "Official personnel" refers to people engaged in security work.

[0808] A "generative AI model" refers to a model that uses artificial intelligence to analyze data and generate messages.

[0809] The present invention provides a system that monitors a user's biometric data in real time, detects emotional fluctuations based on that data, and provides an anger management technique. This system is configured to include a sensor, an analysis means, a notification means, a dialogue means, a generation means, and a display means.

[0810] 1. Devices and Data Collection

[0811] The sensor means is installed in smart glasses (such as Google Glass) worn by security personnel, and includes a heart rate sensor, a skin galvanic response sensor, and a body temperature sensor, which collect the personnel's biometric data. The biometric data is collected in real time and recorded in seconds.

[0812] 2. Data Analysis

[0813] The analysis is performed by software (e.g., Python scripts) embedded in the smart glasses, which analyzes the collected biometric data and detects sudden changes (e.g., an increase in heart rate). When these changes are detected, the data is sent to a cloud server using AWS IoT Core.

[0814] 3. Emotional awareness and notification

[0815] The data sent to the cloud server is again analyzed by the analysis means to check for any sudden changes in emotion. If any emotional fluctuations are detected, the notification means sends a push notification to the smart glasses. This notification is a message based on the dialogue means, such as "Please tell us how you are feeling right now."

[0816] 4. Acquiring emotional states

[0817] The worker responds to the push notification and selects their emotional state (e.g., anger, sadness, stress). This emotional information is then sent back to the cloud server.

[0818] 5. Generating strategies for anger management

[0819] After receiving the emotion information, the server uses a generative AI model (such as the OpenAI API) to generate an appropriate message based on emotion management techniques, such as a prompt such as "Take a deep breath for six seconds."

[0820] 6. Sending and Viewing Messages

[0821] The message generated by the generating means is again transmitted to the smart glasses and displayed by the display means to the worker, thereby enabling the worker to implement the provided countermeasure in real time.

[0822] Specific examples

[0823] Suppose a security worker experiences a sudden rise in heart rate during a long shift, putting him or her in a stressful situation. The smart glasses detect this sudden change and send a notification asking the worker to "Enter your current emotions." If the worker selects "Stress," the cloud server generates a countermeasure, and the message "Take six deep breaths" is displayed on the smart glasses.

[0824] Prompt Sentence Examples

[0825] "A sudden change in vital signs has been detected. The immediate emotional state is anger. Please provide appropriate anger management advice."

[0826] As described above, the present invention is a system that monitors a user's biometric data in real time, instantly detects emotional fluctuations, and provides countermeasures, thereby maintaining the mental health of security personnel and improving work efficiency and safety.

[0827] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0828] Step 1:

[0829] The device (smart glasses) collects the wearer's biometric data (heart rate, electrodermal response, body temperature). The biometric data collected in real time by sensors is the input, and this data is recorded in local storage every second.

[0830] Step 2:

[0831] The device's internal analysis means analyzes the collected biometric data. Specifically, a Python script is used to compare the average heart rate over the past minute with the heart rate over the last 10 seconds. If a sudden change (for example, an increase of 10 bpm or more over 10 seconds) is detected, this information is sent to a server using AWS IoT Core. The input is the collected biometric data, and the output is data indicating the sudden change.

[0832] Step 3:

[0833] The server receives the data sent from the terminal and performs further detailed analysis. The server also uses analysis software to check for sudden fluctuations in the data. Once detected, a notification is sent to the terminal using a notification means. The input is the data sent from the terminal, and the output is the notification message to be sent.

[0834] Step 4:

[0835] The device notifies the user of the notification received from the server. It displays a push notification with a message such as "Please tell us your current emotions." The user checks the notification and selects their emotional state (anger, sadness, stress, etc.). The input is the notification from the server, and the output is the emotional information selected by the user.

[0836] Step 5:

[0837] The user's emotional information is again sent from the device to the server. Once the emotional information reaches the server, the server uses a generative AI model to generate an anger management response. An example of a prompt sentence used here is, "A sudden change in vital data has been detected. The current emotional state is anger. Please provide appropriate anger management advice." The input is the user's emotional information, and the output is the generated response message.

[0838] Step 6:

[0839] The message generated by the server is sent back to the terminal. The terminal displays the received message to the user. For example, advice such as "Take a deep breath for six seconds" is displayed. The input is the generated message from the server, and the output is the message displayed to the user.

[0840] As described above, this system monitors users' biometric data in real time and provides appropriate countermeasures when emotional fluctuations are detected, thereby helping to maintain the mental health of security personnel and improving work efficiency.

[0841] 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.

[0842] The following describes in detail the mode for carrying out the present invention. The present invention is a system that monitors a user's vital signs, detects emotional fluctuations, and provides anger management techniques. Furthermore, by combining it with an emotion engine, it is possible to more accurately recognize the user's emotional state and provide optimal countermeasures. This system is composed of a wearable device (terminal), a central server (server), an emotion engine, and a user (user).

[0843] 1. Data Collection and Monitoring

[0844] The device is equipped with multiple sensors, including a heart rate sensor, a skin galvanic response sensor, and a body temperature sensor, and uses these sensors to collect the user's vital data in real time. Data collection is set to a high frequency, with data recorded almost every second.

[0845] 2. Data fluctuation analysis and notification

[0846] The device has a built-in program for analyzing the collected vital data. For example, this program compares the average heart rate over the past minute with the heart rate over the last 10 seconds, and if a sudden change (e.g., an increase of 10 bpm or more over 10 seconds) is detected, it determines that this indicates emotional fluctuations. If emotional fluctuations are detected, the device sends this information to a server. The server analyzes the received data and further verifies whether emotional fluctuations are present.

[0847] 3. User Interaction and Emotion Recognition

[0848] If an emotional change is detected, the server sends a push notification to the device. This notification contains a message such as "Please tell us how you feel right now." When the user receives the push notification and selects their emotion (e.g., anger, sadness, stress, etc.), the selected emotion information is sent from the device to the server. Furthermore, the emotion engine also analyzes the user's response data and selects the most appropriate emotion from multiple emotion categories.

[0849] The emotion engine has the ability to analyze the user's voice and text data, which allows for more accurate emotion recognition. For example, if a user is yelling, the voice data can be analyzed and the emotion can be recognized as "anger."

[0850] 4. Generate and provide countermeasures

[0851] The server uses a generative AI model to generate messages based on anger management techniques based on the emotion data received from the emotion engine. For example, it generates advice such as, "Practice the six-second rule. Take six deep breaths to regain your composure." The generated message is sent from the server to the device, which then displays it to the user.

[0852] 5. Practical Use Cases

[0853] As a concrete example, suppose a user is feeling stressed at work. The device detects a sudden increase in heart rate and sends this information to the server. The server checks the emotional fluctuations and sends a push notification to the device saying, "Tell us how you're feeling right now." If the user selects "anger," that information is sent to the server. At the same time, the emotion engine also analyzes the user's voice and determines that it is "anger." The server uses the generative AI model to generate a message saying, "Practice the 6-second rule," and sends it to the device. The device displays this message to the user, who then takes a deep breath to reduce stress.

[0854] In this way, the present invention allows users to recognize their own emotional fluctuations in real time and utilize an emotion engine to enable more accurate emotion recognition and appropriate anger management.

[0855] The processing flow will be explained below.

[0856] Step 1:

[0857] The device collects vital data such as the user's heart rate, skin galvanic response, and body temperature. The sensor measures the data every second and temporarily stores it in the internal memory.

[0858] Step 2:

[0859] The device analyzes the saved vital data at regular intervals (for example, every minute). For example, it compares the average heart rate over the past minute with the heart rate over the past 10 seconds to detect any sudden fluctuations.

[0860] Step 3:

[0861] When the device detects a sudden change in data, it sends the data to the server, including a timestamp, the heart rate at the time of detection, and the amount of change in the electrical response of the skin.

[0862] Step 4:

[0863] The server then analyzes the received data to further determine if any emotional swings are present, applying machine learning algorithms using multiple data points to determine emotional swings.

[0864] Step 5:

[0865] If the server detects any emotional changes, it sends a push notification to the device, which includes the message "Please tell us how you feel right now."

[0866] Step 6:

[0867] Users receive a push notification from their device and can select their emotion with one tap. Options include "anger," "sadness," and "stress."

[0868] Step 7:

[0869] The device transmits the selected emotion information to the server, including the user's selected emotion, a timestamp, and related vital data.

[0870] Step 8:

[0871] The server sends the received emotion data to the emotion engine, which analyzes the user's selected emotion data and vital data to more accurately recognize the user's emotional state.

[0872] Step 9:

[0873] The emotion engine recognizes the user's emotional state and sends the result back to the server, for example, "anger."

[0874] Step 10:

[0875] Based on the recognition results from the emotion engine, the server uses a generative AI model to generate messages that follow anger management methods, such as advice like "Practice the six-second rule. Taking six deep breaths will help you regain your composure."

[0876] Step 11:

[0877] Sends server-generated messages to the terminal, containing specific advice or instructions to the user.

[0878] Step 12:

[0879] The device displays the received message to the user, who then checks the message and takes the necessary action (e.g., take a deep breath).

[0880] As a result, users can recognize their own emotional fluctuations in real time, and with the help of the emotion engine, they can perform more accurate emotion recognition and practice appropriate anger management.

[0881] Example 2

[0882] 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."

[0883] Currently, there is no system that can monitor users' emotional fluctuations in real time and quickly provide appropriate countermeasures. In particular, there is a need for a system that can detect sudden changes in emotions and provide appropriate anger management techniques based on those changes. It is also difficult to accurately recognize a user's emotional state and immediately suggest countermeasures using push notifications. This makes it difficult to provide effective means for maintaining users' mental health.

[0884] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0885] In this invention, the server includes detection means for collecting biometric data of a user, analysis means for detecting sudden fluctuations in the biometric data, warning means for sending a notification to the user when the sudden fluctuation is detected, communication means for receiving a response from the user and acquiring the user's emotional state, analysis means for analyzing the emotional data based on the response using an emotion engine, generation means for generating a message according to an anger management method using a generative AI model, and transmission means for sending the generated message to the user. This makes it possible to accurately recognize the user's emotional fluctuations in real time and provide appropriate anger management messages based on the emotional fluctuations.

[0886] "User" means any person or entity that uses the system.

[0887] "Biometric data" refers to data relating to the user's physical condition, such as heart rate, galvanic skin response, and body temperature.

[0888] "Detection means" refers to a device or sensor for collecting biometric data of a user.

[0889] "Analysis means" refers to the program and hardware used to analyze collected biological data and detect sudden changes.

[0890] The "warning means" is a function for sending a notification to the user when a sudden change is detected.

[0891] "Communication means" refers to the interface or protocol for receiving the user's responses and obtaining their emotional state.

[0892] "Analysis means" refers to software or hardware that uses an emotion engine to analyze the user's response data (voice data or text data) and recognize the user's emotional state.

[0893] "Generation means" refers to a function for using a generative AI model to create messages that follow anger management methods.

[0894] "Vehicle" refers to the functionality and interface for sending generated messages to users.

[0895] An "emotion engine" is an algorithm and software that analyzes a user's emotions and recognizes their optimal emotional state.

[0896] A "generative AI model" is a machine learning model that generates optimal anger management messages based on the user's emotional state.

[0897] "Push notification" refers to a protocol and method for sending notifications to devices in real time.

[0898] "Emotional fluctuation" is a term that indicates a state in which a sudden change occurs in the user's biometric data.

[0899] This invention is a system that monitors a user's biometric data in real time and provides appropriate anger management countermeasures based on changes in the biometric data using an emotion engine and a generative AI model. This system consists of a wearable device (terminal), a central server (server), an emotion engine, and a user (user).

[0900] The device is equipped with multiple sensors, including a heart rate sensor, a skin galvanic response sensor, and a body temperature sensor. These sensors collect the user's vital data every second. Specifically, the heart rate sensor measures heart rate data every second, and the skin galvanic response sensor records the user's stress level in real time.

[0901] The server receives and analyzes the biometric data sent from the device. Specifically, it compares the average heart rate over the past minute with the heart rate over the past 10 seconds to detect any sudden fluctuations. For example, if the heart rate rises by 10 bpm or more over 10 seconds, it is determined that there is an emotional fluctuation.

[0902] When an emotional change is detected, the server sends a push notification to the device. This notification contains a message such as "Please tell us how you feel right now." When the user receives the push notification and selects their emotion (e.g., anger, sadness, stress, etc.), that information is sent from the device to the server.

[0903] The server uses a generative AI model to generate anger management messages based on the received emotional data and the user's voice and text data analyzed by the emotion engine. For example, it might generate advice such as, "Practice the six-second rule. Taking a deep breath for six seconds will help you regain your composure."

[0904] The generated message is sent from the server to the device, which then displays it to the user, who can then take action such as deep breathing to reduce stress and emotional upheaval.

[0905] As a specific use case, consider a case where a user is experiencing high stress at work. The device detects a sudden increase in heart rate and sends this information to the server. After the server confirms the emotional fluctuations, it sends a push notification to the device. If the user selects "anger," this information is sent to the server. At the same time, the emotion engine analyzes the user's voice and determines it to be "anger." The server uses a generative AI model to generate a message saying, "Practice the 6-second rule," and sends it to the device. The device displays this message to the user, who then takes a deep breath to reduce stress.

[0906] In this way, the present invention realizes a system that allows users to recognize their own emotional fluctuations in real time and provides more accurate emotion recognition and appropriate anger management by utilizing an emotion engine and generative AI model.

[0907] An example prompt is:

[0908] "The user is currently experiencing deep stress and their heart rate has increased by more than 10 bpm in 10 seconds. Generate anger management advice if the user selects anger."

[0909] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0910] Step 1: Collect biometric data

[0911] The terminal collects the user's biometric data using sensors (heart rate sensor, skin galvanic response sensor, body temperature sensor, etc.) built into the wearable device. The data measured by the sensors is periodically recorded in a database within the terminal. The input here is the user's current physical condition, and the output is the collected vital data (heart rate, skin galvanic response, body temperature).

[0912] Step 2: Analyzing biological data

[0913] The device runs a program to analyze the collected vital data. Specifically, it compares the average heart rate over the past minute with the heart rate over the last 10 seconds. Based on this comparison, it determines that a sudden change has been detected, such as when the heart rate increases by 10 bpm or more over 10 seconds. The input is the collected vital data, and the output is the detected change.

[0914] Step 3: Emotional Indications

[0915] If the device detects a change in emotion, it sends that information to the server. The server analyzes the received data, and if an emotion change is confirmed, it sends a push notification to the device. The notification contains the message "Please tell us how you are feeling right now." The input is the change detection result, and the output is the sending of a push notification.

[0916] Step 4: Obtaining the user's emotional response

[0917] The user receives a push notification and selects their emotion (e.g., anger, sadness, stress, etc.). The selected emotion information is sent from the device to the server. The input here is the user's emotional response, and the output is the transmitted emotion information.

[0918] Step 5: Sentiment Analysis

[0919] The server runs an emotion engine and analyzes the user's response data (voice data and text data). For example, it recognizes the user's optimal emotional state from their tone of voice and vocabulary. The input is the user's emotional response and voice data, and the output is the specific emotion recognition result.

[0920] Step 6: Create an Anger Management Message

[0921] The server uses a generative AI model to generate anger management messages based on the emotion recognition results obtained from the emotion engine. For example, it creates advice such as, "Practice the six-second rule. Take six deep breaths to regain your composure." The input is the emotion recognition results, and the output is the generated message.

[0922] Step 7: Sending a message

[0923] The server generates a message and sends it to the terminal, which displays it to the user. The input here is the generated message, and the output is the message displayed to the user.

[0924] Step 8: User Action

[0925] The user takes specific coping actions (e.g., deep breathing) in accordance with the anger management message displayed on the device. This allows the user to control their emotions. The input here is the displayed message, and the output is the user's response.

[0926] (Application example 2)

[0927] 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."

[0928] Conventional systems for security staff lack the means to detect emotional fluctuations in real time and immediately provide appropriate anger management methods, which results in security staff being unable to perform their duties effectively under high stress.

[0929] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: sensor means for collecting vital data of the user; analysis means for detecting sudden fluctuations in the vital data; notification means for sending a notification to the user when the sudden fluctuation is detected; dialogue means for receiving the user's response and acquiring the user's emotional state; generation means for generating a message according to an anger management method based on the response; message sending means for sending the generated message to the user; emotion analysis means for analyzing the emotional state; generative AI model utilization means for generating a message using a generative AI model based on the emotional state analyzed by the emotion analysis means and a prompt sentence; and display means for displaying the generated message on the user's device. This enables security staff to recognize their own emotional fluctuations in real time and immediately practice an appropriate anger management method.

[0930] "Sensor means" refers to a device used to collect vital data of a user.

[0931] "Analysis means" refers to a device or program used to detect sudden fluctuations in collected vital data.

[0932] A "notification means" is a device or program used to send a notification to a user when a sudden change is detected.

[0933] An "interactive means" is a device or program for receiving a user's response and obtaining the user's emotional state.

[0934] The "generating means" is a device or program for generating a message in accordance with an anger management method based on the user's response.

[0935] The "message sending means" is a device or program for sending the generated message to the user.

[0936] "Emotion analysis means" is a device or program for analyzing the emotional state of a user.

[0937] "Generative AI model utilization means" refers to a device or program that uses a prompt sentence to generate a message using a generative AI model based on the emotional state analyzed by the emotion analysis means.

[0938] A "display means" is a device or program for displaying the generated message on a user's device.

[0939] overview

[0940] This invention is a system that monitors emotional fluctuations in real time and provides anger management techniques for security staff. The system collects users' vital data, analyzes sudden fluctuations, and uses a generative AI model to suggest appropriate countermeasures when emotional fluctuations are detected.

[0941] Hardware and Software Configuration

[0942] The system consists of the following main components:

[0943] 1. Sensor means:

[0944] Hardware: Smartwatches, smart glasses, and other wearable devices.

[0945] Function: Collects heart rate, galvanic skin response, and body temperature in real time.

[0946] 2. Analysis method:

[0947] Software: Python programs, data analysis libraries (e.g., pandas, numpy).

[0948] Function: Detects sudden fluctuations in collected vital data.

[0949] 3. Means of notification:

[0950] Hardware / Software: Mobile devices, push notification services.

[0951] Function: Notify the user of emotional fluctuations.

[0952] 4. Means of interaction:

[0953] Software: Conversational interfaces (e.g., chatbots).

[0954] Function: Receives the user's response and captures their emotional state.

[0955] 5. Emotion analysis means:

[0956] Software: Speech analysis engine, text analysis engine (e.g., Google Cloud Speech-to-Text, BERT).

[0957] Function: Analyzes the user's emotional state.

[0958] 6. Generation means:

[0959] Software: Generative AI models (e.g., GPT-3).

[0960] Function: Generates anger management messages using prompts based on emotional state.

[0961] 7. Message sending method:

[0962] Hardware: Mobile devices, wearable devices.

[0963] Function: Displays the generated message.

[0964] Processing flow

[0965] The specific process is as follows:

[0966] 1. Data Collection:

[0967] The sensor means collects vital data such as the user's heart rate, galvanic skin response, body temperature, etc. The data is collected in real time and updated every second.

[0968] 2. Data Analysis:

[0969] The analysis method detects sudden fluctuations in vital data. For example, it compares the average heart rate over the past minute with the heart rate over the past 10 seconds, and if a sudden fluctuation is detected, it determines whether the person is emotionally unstable.

[0970] 3. Notice:

[0971] The notification means sends a push notification to the user, displaying a message saying, "Please tell us how you feel right now."

[0972] 4. Getting user response:

[0973] The user selects their emotion (e.g., anger, sadness, stress, etc.) and responds through a dialogue means. This information is sent to the server.

[0974] 5. Emotion analysis:

[0975] The emotion analysis means analyzes the user's voice data and text data to identify the user's emotional state. For example, if the user is yelling, the voice data is analyzed and the emotion is recognized as "anger."

[0976] 6. Message Creation:

[0977] The generator uses a generative AI model to generate advice based on the emotional state, using prompts such as, "Practice the six-second rule. Take six deep breaths to regain your composure."

[0978] 7. Sending a message:

[0979] A message sending means displays the generated message on the user's device.

[0980] Specific examples

[0981] Consider a situation where a security staff member experiences stress during work and their heart rate suddenly rises. The system detects this change in heart rate and sends a notification to the user. If the user responds with "Anger," the server uses the generative AI model to generate advice such as "Practice the 6-second rule," which is displayed on the user's device.

[0982] Example prompts for generative AI models

[0983] A user's vital data (heart rate: x bpm, galvanic skin response: y, body temperature: z°C) has been collected. A sudden fluctuation has been detected compared to past data, indicating a change in emotion. Analysis by the emotion engine has identified the emotion "anger." Please generate appropriate anger management advice to help the user regain their composure. Please also include specific example messages.

[0984] This allows security staff to recognize their own emotional fluctuations in real time and immediately implement appropriate anger management methods.

[0985] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0986] Step 1:

[0987] Collect user vital data

[0988] Operation: The device (sensor means) collects vital data such as the user's heart rate, skin galvanic response, and body temperature in real time.

[0989] Input: User vital data (heart rate, galvanic skin response, body temperature) obtained from sensors connected to the device.

[0990] Output: Collected vital data (heart rate: x bpm, galvanic skin response: y, temperature: z°C).

[0991] Step 2:

[0992] Analyzing fluctuations in vital data

[0993] Operation: The terminal (analysis means) analyzes the collected vital data and compares the past data with the latest data to detect sudden fluctuations in heart rate.

[0994] Input: Vital data collected in step 1.

[0995] Data processing: Compare the average heart rate over the past minute with the heart rate over the last 10 seconds, and measure whether there is a fluctuation of more than 10 bpm over the 10 seconds.

[0996] Output: If a rapid heart rate fluctuation is detected, this information is generated (e.g., a flag indicating whether a rapid fluctuation was detected).

[0997] Step 3:

[0998] Send notifications to users

[0999] Operation: When the device (notification means) detects a sudden change, it sends a push notification to the user, prompting them to report their current emotional state.

[1000] Input: The sudden change flag, which is the output of step 2.

[1001] Output: Push notification message (e.g. "Tell us how you're feeling right now").

[1002] Step 4:

[1003] Get the user's emotional state

[1004] Action: The user uses the terminal (interaction means) to select an emotional state (e.g., anger, sadness, stress) and send a response.

[1005] Input: The user's selected emotional state.

[1006] Output: The selected emotional state (e.g., anger).

[1007] Step 5:

[1008] Analyzing emotional states

[1009] Operation: The server (emotion analysis means) analyzes the user's response data and identifies the user's emotional state based on the voice data and text data.

[1010] Input: The user's selected emotional state (output of step 4), as well as audio and text data.

[1011] Data computation: Using speech and text analysis engines to accurately recognize emotional states (e.g., determining "anger" from tone of voice).

[1012] Output: Sentiment analysis result (e.g. anger).

[1013] Step 6:

[1014] Generate messages using generative AI models

[1015] Operation: The server (generation means) generates an anger management message using the generative AI model based on the judgment results of the emotion analysis means. The server inputs the prompt sentence into the generative AI model.

[1016] Input: The sentiment analysis results from Step 5, and a prompt (e.g., a prompt for generating a message based on the sentiment analysis results).

[1017] Example prompt sentence:

[1018] A user's vital data (heart rate: x bpm, galvanic skin response: y, body temperature: z°C) has been collected. A sudden fluctuation has been detected compared to past data, indicating a change in emotion. Analysis by the emotion engine has identified the emotion "anger." Please generate appropriate anger management advice to help the user regain their composure. Please also include specific example messages.

[1019] Output: Generated anger management message (e.g., "Practice the six-second rule. Taking six deep breaths will help you regain your composure.").

[1020] Step 7:

[1021] Sending a generated message to a user

[1022] Operation: The terminal (message sending means) displays the generated anger management message on the user's device.

[1023] Input: The generated messages that are the output of Step 6.

[1024] Output: A message displayed on the user's device (e.g., "Practice the 6-second rule. Taking 6 deep breaths will help you regain your composure.").

[1025] 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.

[1026] 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.

[1027] 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.

[1028] [Fourth embodiment]

[1029] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1030] 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.

[1031] 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).

[1032] 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.

[1033] 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.

[1034] 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).

[1035] 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.

[1036] 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.

[1037] 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.

[1038] 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.

[1039] 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.

[1040] 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.

[1041] 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."

[1042] The following describes in detail an embodiment of the present invention. The present invention is a system that monitors a user's vital signs, detects emotional fluctuations, and provides an anger management technique. This system is composed of a wearable device (terminal), a central server, and a user.

[1043] 1. Data Collection and Monitoring

[1044] The device is equipped with multiple sensors, including a heart rate sensor, a skin galvanic response sensor, and a body temperature sensor, and uses these sensors to collect the user's vital data in real time. Data collection is set to a high frequency, with data recorded almost every second.

[1045] 2. Data fluctuation analysis and notification

[1046] The device has a built-in program for analyzing the collected vital data. For example, this program compares the average heart rate over the past minute with the heart rate over the past 10 seconds, and if it detects a sudden change (for example, an increase of 10 bpm or more over 10 seconds), it determines that this indicates emotional fluctuations.

[1047] If an emotional shift is detected, the device sends this information to a server, which analyzes the received data and further verifies whether an emotional shift is present.

[1048] 3. User Interaction

[1049] If an emotional change is detected, the server sends a push notification to the device. This notification contains a message such as "Please tell us how you feel right now." The user receives the push notification and selects their own emotion (e.g., anger, sadness, stress, etc.).

[1050] 4. Generate and provide countermeasures

[1051] The emotional information selected by the user is sent from the device to the server. Based on the received information, the server uses a generative AI model to generate a message that follows anger management methods. For example, it generates advice such as, "Practice the six-second rule. Take six deep breaths to regain your composure."

[1052] The generated message is sent from the server to the terminal, which displays the message to the user.

[1053] 5. Practical Use Cases

[1054] As a concrete example, let's say a user is feeling stressed at work. The device detects a sudden increase in heart rate and sends this information to the server. The server checks the emotional fluctuations and sends a push notification to the device saying, "Tell us how you're feeling right now." If the user selects "anger," that information is sent to the server. The server uses a generative AI model to generate a message saying, "Practice the 6-second rule," and sends it to the device. The device displays this message to the user, who then takes a deep breath to reduce stress.

[1055] In this way, the present invention allows users to recognize their own emotional fluctuations in real time and practice appropriate anger management.

[1056] The processing flow will be explained below.

[1057] Step 1:

[1058] The device collects vital data such as the user's heart rate, skin galvanic response, and body temperature. These data are measured every second using sensors and temporarily stored in the internal memory.

[1059] Step 2:

[1060] The device analyzes the stored data at regular intervals (for example, every minute), compares the average heart rate over the past minute with the heart rate over the past 10 seconds, and detects any sudden fluctuations.

[1061] Step 3:

[1062] When the device detects a sudden change in data, it sends the data to the server, including a timestamp, the heart rate at the time of detection, and changes in the electrical response of the skin.

[1063] Step 4:

[1064] The server then analyzes the received data to further determine whether there is any emotional impact, applying machine learning algorithms using multiple data points to determine whether there is any emotional impact.

[1065] Step 5:

[1066] If the server detects any emotional changes, it sends a push notification to the device, which includes the message "Please tell us how you feel right now."

[1067] Step 6:

[1068] Users receive a push notification from their device and can select their emotions, with options including "anger," "sadness," and "stress."

[1069] Step 7:

[1070] The terminal transmits the emotion information selected by the user to the server, and the transmitted data includes the selected emotion, a timestamp, and related vital data.

[1071] Step 8:

[1072] Based on the emotion data received by the server, a generative AI model is used to generate messages based on anger management techniques, such as "Practice the six-second rule. Taking a deep breath will help you regain your composure."

[1073] Step 9:

[1074] The server generates and sends the message to the terminal, which contains specific advice or instructions for the user.

[1075] Step 10:

[1076] The device displays the received message to the user, who then checks the message and takes the necessary action (e.g., take a deep breath).

[1077] In this way, by following specific actions at each step, users can recognize their own emotional fluctuations in real time and practice appropriate anger management.

[1078] Example 1

[1079] 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."

[1080] Conventional emotion monitoring systems have not adequately provided users with a means to recognize their own emotional fluctuations in real time and practice appropriate anger management. Furthermore, they have been unable to respond appropriately to sudden emotional fluctuations, making it difficult for users to manage their emotions. This has resulted in a lack of effective countermeasures for sudden emotional changes, making it difficult to appropriately control emotions such as stress and anger. The present invention aims to solve these problems.

[1081] 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.

[1082] In this invention, the server includes sensor means for collecting biometric data of a user, analysis means for detecting sudden fluctuations in the biometric data, notification means for sending a notification to the user when the sudden fluctuation is detected, dialogue means for receiving a response from the user and acquiring the user's emotional state, generation means for generating a message according to an anger management method using a generative model based on the response, and message transmission means for sending the generated message to the user. This enables the user to recognize their own emotional fluctuations in real time and practice appropriate anger management.

[1083] "Sensor means" is a general term for devices and functions used to collect biometric data of a user.

[1084] "Analysis means" is a general term for devices and functions for detecting and analyzing sudden fluctuations in collected biological data.

[1085] "Notification means" is a general term for devices and functions for sending a notification to a user when a sudden change in biometric data is detected.

[1086] "Dialogue means" is a general term for devices and functions for receiving responses from users and acquiring their emotional state.

[1087] "Generation means" is a general term for devices and functions for creating messages in accordance with anger management methods using a generative model based on a user's response.

[1088] "Message sending means" is a general term for devices and functions for sending generated messages to users.

[1089] "Biometric data" refers to data that reflects a user's physical state, such as a user's heart rate, galvanic skin response, or body temperature.

[1090] A "generative model" refers to an algorithm or system that automatically generates anger management messages based on user responses.

[1091] The following is a detailed description of an embodiment of the present invention. This system collects biometric data, detects emotional fluctuations, and provides users with an anger management method suited to their needs. The system is primarily composed of three elements: a terminal, a server, and a user.

[1092] Data Collection and Monitoring

[1093] The device uses multiple sensors, including a heart rate sensor, a skin galvanic response sensor, and a body temperature sensor, to collect biometric data in real time. These sensor means measure the user's heart rate, skin galvanic response, body temperature, etc., at a frequency of once per second. The collected data is stored in the device and monitored in real time by an analysis means to check for any sudden fluctuations.

[1094] Data fluctuation analysis and notification

[1095] A program built into the device analyzes the collected biometric data. Specifically, it compares the average heart rate over the past minute with the heart rate over the last 10 seconds, and evaluates whether there are any sudden fluctuations (for example, an increase of 10 bpm or more over 10 seconds). If a sudden fluctuation is detected, the device interprets it as an emotional change and sends the data to a server. The server then reanalyzes the received data to confirm whether the fluctuations are continuing.

[1096] User Interaction

[1097] If an emotional change is detected, the server sends a push notification to the device. The notification contains a message such as "Please tell us how you feel right now." The device displays the notification to the user. The user receives the notification and selects their emotional state (e.g., anger, sadness, stress, etc.).

[1098] Generate and provide countermeasures

[1099] Once the user selects an emotion, that information is sent to the server via the device. The server uses a generative model based on the received emotion information to generate an appropriate anger management response. The generative AI model is provided with prompts such as:

[1100] Emotional signals detected. User selected "Anger." Generate appropriate anger management advice.

[1101] The generated message (e.g., "Practice the 6-second rule. Taking a deep breath for 6 seconds will help you regain your composure") is sent from the server to the device, which then displays the message to the user.

[1102] Practical use cases

[1103] As a concrete example, consider a situation where a user is feeling stressed at work. The device detects a sudden increase in heart rate and sends this information to the server. The server checks the emotional fluctuations and sends a push notification to the device asking, "Tell us how you're feeling right now." If the user selects "anger," that information is sent to the server. The server uses a generative AI model to generate a message such as "Practice the 6-second rule" and sends it to the device. The device displays this message to the user, who then takes a deep breath to reduce stress.

[1104] In this way, the present invention allows users to recognize their own emotional fluctuations in real time and practice appropriate anger management.

[1105] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1106] Step 1:

[1107] The device collects biometric data in real time using a heart rate sensor, a skin galvanic response sensor, and a body temperature sensor. Each sensor collects data once per second and stores the data in the device's memory. The input is the data from the sensors (heart rate, skin galvanic response, body temperature), and the output is an array of the collected biometric data.

[1108] Step 2:

[1109] The device analyzes the collected biometric data using a built-in program. It calculates the average heart rate over the past minute and compares it with the heart rate over the most recent 10 seconds. The input is the heart rate data over the past minute and the heart rate data over the most recent 10 seconds, and the output is the difference between the average heart rate. Specifically, the device calculates the average of the data and compares the results.

[1110] Step 3:

[1111] The device checks whether there is a sudden change (for example, an increase of 10 bpm or more in 10 seconds) based on the analysis results of step 2. If a sudden change is detected, this information is sent to the server. The input is the difference in heart rate, and the output is a flag indicating that a sudden change has been detected.

[1112] Step 4:

[1113] The server receives the data sent from the device and re-analyzes it. The analysis involves evaluating additional data to determine whether the fluctuation is incidental or sustained. The input is the vital data sent from the device and a flag indicating a sudden change, and the output is the confirmation of the emotional fluctuation.

[1114] Step 5:

[1115] When the server confirms the emotional fluctuation, it sends a push notification to the device. The notification contains the message "Please tell us how you are feeling right now." The input is the confirmation result of the emotional fluctuation, and the output is the sending of the push notification. Specifically, the server generates a notification and pushes it to the device.

[1116] Step 6:

[1117] A user receives a push notification and selects an emotional state. Example choices include "anger," "sadness," "stress," etc. The input is the push notification, and the output is the user-selected emotional state.

[1118] Step 7:

[1119] The device transmits the emotion information selected by the user to the server. The input is the user's emotion selection, and the output is the transmission of the emotion information to the server. In concrete terms, the device executes code to transmit the information selected by the user to the server.

[1120] Step 8:

[1121] The server receives the emotional information and sends a prompt to the generative AI model based on that information. An example of a prompt is, "A signal indicating emotional fluctuation has been detected. The user selected 'anger'. Please generate appropriate anger management advice." The input is the user's emotional information, and the output is the prompt sent to the generative AI model.

[1122] Step 9:

[1123] The generative AI model generates anger management messages based on prompt sentences. An example of a generated message is "Practice the six-second rule. Taking six deep breaths will help you regain your composure." The input is the prompt sentence, and the output is the generated advice message.

[1124] Step 10:

[1125] The server sends the generated advice message to the device. The input is the advice message from the generative AI model, and the output is the message sent to the device. Specifically, the server generates the advice message and executes the code to send it to the device.

[1126] Step 11:

[1127] The terminal displays the advice message sent from the server to the user. The input is the advice message from the server, and the output is the message displayed to the user. Specifically, the terminal displays the received message on the screen.

[1128] (Application example 1)

[1129] 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."

[1130] Security personnel are frequently exposed to high levels of stress and tension, and therefore require emotional stability. It is necessary to improve work efficiency and maintain mental stability by detecting emotional fluctuations using biometric data and providing appropriate countermeasures in real time. However, conventional systems are limited to detecting fluctuations in vital data and do not go as far as managing emotions or proposing actual countermeasures. Therefore, a system that can quickly respond to personnel's stress and emotional fluctuations and provide appropriate advice is needed.

[1131] 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.

[1132] In this invention, the server includes a sensor means for collecting biometric data of a user, an analysis means for detecting sudden fluctuations in the biometric data, a notification means for sending a notification to the user when the sudden fluctuation is detected, a dialogue means for receiving a response from the user and acquiring the user's emotional state, a generation means for generating a message according to an emotion management method based on the response, a message transmission means for sending the generated message to the user, a generation means for generating a message using a generative AI model based on emotion information to manage fluctuations in the biometric data of security personnel and provide appropriate countermeasures, and a display means for displaying the generated message to the security personnel. This makes it possible to immediately detect sudden fluctuations in the security personnel's biometric data and provide appropriate anger management methods, thereby quickly responding to the security personnel's stress and emotional fluctuations.

[1133] "User" refers to an individual who uses the system of the present invention.

[1134] "Biometric data" refers to data that indicates the user's physical condition, such as heart rate, galvanic skin response, and body temperature.

[1135] "Sensor means" refers to a device for collecting biometric data of a user.

[1136] "Analysis means" refers to a method or device for analyzing collected biometric data and detecting sudden fluctuations therein.

[1137] "Notification means" refers to a device or method for reporting to the user when a sudden change in biometric data is detected.

[1138] "Interaction means" refers to a method or device for receiving responses and obtaining emotional states from a user.

[1139] "Emotional state" refers to a state that indicates the emotions such as anger, sadness, stress, etc. that a user is feeling.

[1140] "Generator" refers to a method or device for automatically creating a message that conforms to emotion management techniques based on a user's response.

[1141] "Message sending means" refers to a means for sending a generated message to a user.

[1142] "Display means" refers to a device or method for a user to view the generated message.

[1143] "Official personnel" refers to people engaged in security work.

[1144] A "generative AI model" refers to a model that uses artificial intelligence to analyze data and generate messages.

[1145] The present invention provides a system that monitors a user's biometric data in real time, detects emotional fluctuations based on that data, and provides an anger management technique. This system is configured to include a sensor, an analysis means, a notification means, a dialogue means, a generation means, and a display means.

[1146] 1. Devices and Data Collection

[1147] The sensor means is installed in smart glasses (such as Google Glass) worn by security personnel, and includes a heart rate sensor, a skin galvanic response sensor, and a body temperature sensor, which collect the personnel's biometric data. The biometric data is collected in real time and recorded in seconds.

[1148] 2. Data Analysis

[1149] The analysis is performed by software (e.g., Python scripts) embedded in the smart glasses, which analyzes the collected biometric data and detects sudden changes (e.g., an increase in heart rate). When these changes are detected, the data is sent to a cloud server using AWS IoT Core.

[1150] 3. Emotional awareness and notification

[1151] The data sent to the cloud server is again analyzed by the analysis means to check for any sudden changes in emotion. If any emotional fluctuations are detected, the notification means sends a push notification to the smart glasses. This notification is a message based on the dialogue means, such as "Please tell us how you are feeling right now."

[1152] 4. Acquiring emotional states

[1153] The worker responds to the push notification and selects their emotional state (e.g., anger, sadness, stress). This emotional information is then sent back to the cloud server.

[1154] 5. Generating strategies for anger management

[1155] After receiving the emotion information, the server uses a generative AI model (such as the OpenAI API) to generate an appropriate message based on emotion management techniques, such as a prompt such as "Take a deep breath for six seconds."

[1156] 6. Sending and Viewing Messages

[1157] The message generated by the generating means is again transmitted to the smart glasses and displayed by the display means to the worker, thereby enabling the worker to implement the provided countermeasure in real time.

[1158] Specific examples

[1159] Suppose a security worker experiences a sudden rise in heart rate during a long shift, putting him or her in a stressful situation. The smart glasses detect this sudden change and send a notification asking the worker to "Enter your current emotions." If the worker selects "Stress," the cloud server generates a countermeasure, and the message "Take six deep breaths" is displayed on the smart glasses.

[1160] Prompt Sentence Examples

[1161] "A sudden change in vital signs has been detected. The immediate emotional state is anger. Please provide appropriate anger management advice."

[1162] As described above, the present invention is a system that monitors a user's biometric data in real time, instantly detects emotional fluctuations, and provides countermeasures, thereby maintaining the mental health of security personnel and improving work efficiency and safety.

[1163] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1164] Step 1:

[1165] The device (smart glasses) collects the wearer's biometric data (heart rate, electrodermal response, body temperature). The biometric data collected in real time by sensors is the input, and this data is recorded in local storage every second.

[1166] Step 2:

[1167] The device's internal analysis means analyzes the collected biometric data. Specifically, a Python script is used to compare the average heart rate over the past minute with the heart rate over the last 10 seconds. If a sudden change (for example, an increase of 10 bpm or more over 10 seconds) is detected, this information is sent to a server using AWS IoT Core. The input is the collected biometric data, and the output is data indicating the sudden change.

[1168] Step 3:

[1169] The server receives the data sent from the terminal and performs further detailed analysis. The server also uses analysis software to check for sudden fluctuations in the data. Once detected, a notification is sent to the terminal using a notification means. The input is the data sent from the terminal, and the output is the notification message to be sent.

[1170] Step 4:

[1171] The device notifies the user of the notification received from the server. It displays a push notification with a message such as "Please tell us your current emotions." The user checks the notification and selects their emotional state (anger, sadness, stress, etc.). The input is the notification from the server, and the output is the emotional information selected by the user.

[1172] Step 5:

[1173] The user's emotional information is again sent from the device to the server. Once the emotional information reaches the server, the server uses a generative AI model to generate an anger management response. An example of a prompt sentence used here is, "A sudden change in vital data has been detected. The current emotional state is anger. Please provide appropriate anger management advice." The input is the user's emotional information, and the output is the generated response message.

[1174] Step 6:

[1175] The message generated by the server is sent back to the terminal. The terminal displays the received message to the user. For example, advice such as "Take a deep breath for six seconds" is displayed. The input is the generated message from the server, and the output is the message displayed to the user.

[1176] As described above, this system monitors users' biometric data in real time and provides appropriate countermeasures when emotional fluctuations are detected, thereby helping to maintain the mental health of security personnel and improving work efficiency.

[1177] 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.

[1178] The following describes in detail the mode for carrying out the present invention. The present invention is a system that monitors a user's vital signs, detects emotional fluctuations, and provides anger management techniques. Furthermore, by combining it with an emotion engine, it is possible to more accurately recognize the user's emotional state and provide optimal countermeasures. This system is composed of a wearable device (terminal), a central server (server), an emotion engine, and a user (user).

[1179] 1. Data Collection and Monitoring

[1180] The device is equipped with multiple sensors, including a heart rate sensor, a skin galvanic response sensor, and a body temperature sensor, and uses these sensors to collect the user's vital data in real time. Data collection is set to a high frequency, with data recorded almost every second.

[1181] 2. Data fluctuation analysis and notification

[1182] The device has a built-in program for analyzing the collected vital data. For example, this program compares the average heart rate over the past minute with the heart rate over the last 10 seconds, and if a sudden change (e.g., an increase of 10 bpm or more over 10 seconds) is detected, it determines that this indicates emotional fluctuations. If emotional fluctuations are detected, the device sends this information to a server. The server analyzes the received data and further verifies whether emotional fluctuations are present.

[1183] 3. User Interaction and Emotion Recognition

[1184] If an emotional change is detected, the server sends a push notification to the device. This notification contains a message such as "Please tell us how you feel right now." When the user receives the push notification and selects their emotion (e.g., anger, sadness, stress, etc.), the selected emotion information is sent from the device to the server. Furthermore, the emotion engine also analyzes the user's response data and selects the most appropriate emotion from multiple emotion categories.

[1185] The emotion engine has the ability to analyze the user's voice and text data, which allows for more accurate emotion recognition. For example, if a user is yelling, the voice data can be analyzed and the emotion can be recognized as "anger."

[1186] 4. Generate and provide countermeasures

[1187] The server uses a generative AI model to generate messages based on anger management techniques based on the emotion data received from the emotion engine. For example, it generates advice such as, "Practice the six-second rule. Take six deep breaths to regain your composure." The generated message is sent from the server to the device, which then displays it to the user.

[1188] 5. Practical Use Cases

[1189] As a concrete example, suppose a user is feeling stressed at work. The device detects a sudden increase in heart rate and sends this information to the server. The server checks the emotional fluctuations and sends a push notification to the device saying, "Tell us how you're feeling right now." If the user selects "anger," that information is sent to the server. At the same time, the emotion engine also analyzes the user's voice and determines that it is "anger." The server uses the generative AI model to generate a message saying, "Practice the 6-second rule," and sends it to the device. The device displays this message to the user, who then takes a deep breath to reduce stress.

[1190] In this way, the present invention allows users to recognize their own emotional fluctuations in real time and utilize an emotion engine to enable more accurate emotion recognition and appropriate anger management.

[1191] The processing flow will be explained below.

[1192] Step 1:

[1193] The device collects vital data such as the user's heart rate, skin galvanic response, and body temperature. The sensor measures the data every second and temporarily stores it in the internal memory.

[1194] Step 2:

[1195] The device analyzes the saved vital data at regular intervals (for example, every minute). For example, it compares the average heart rate over the past minute with the heart rate over the past 10 seconds to detect any sudden fluctuations.

[1196] Step 3:

[1197] When the device detects a sudden change in data, it sends the data to the server, including a timestamp, the heart rate at the time of detection, and the amount of change in the electrical response of the skin.

[1198] Step 4:

[1199] The server then analyzes the received data to further determine if any emotional swings are present, applying machine learning algorithms using multiple data points to determine emotional swings.

[1200] Step 5:

[1201] If the server detects any emotional changes, it sends a push notification to the device, which includes the message "Please tell us how you feel right now."

[1202] Step 6:

[1203] Users receive a push notification from their device and can select their emotion with one tap. Options include "anger," "sadness," and "stress."

[1204] Step 7:

[1205] The device transmits the selected emotion information to the server, including the user's selected emotion, a timestamp, and related vital data.

[1206] Step 8:

[1207] The server sends the received emotion data to the emotion engine, which analyzes the user's selected emotion data and vital data to more accurately recognize the user's emotional state.

[1208] Step 9:

[1209] The emotion engine recognizes the user's emotional state and sends the result back to the server, for example, "anger."

[1210] Step 10:

[1211] Based on the recognition results from the emotion engine, the server uses a generative AI model to generate messages that follow anger management methods, such as advice like "Practice the six-second rule. Taking six deep breaths will help you regain your composure."

[1212] Step 11:

[1213] Sends server-generated messages to the terminal, containing specific advice or instructions to the user.

[1214] Step 12:

[1215] The device displays the received message to the user, who then checks the message and takes the necessary action (e.g., take a deep breath).

[1216] As a result, users can recognize their own emotional fluctuations in real time, and with the help of the emotion engine, they can perform more accurate emotion recognition and practice appropriate anger management.

[1217] Example 2

[1218] 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."

[1219] Currently, there is no system that can monitor users' emotional fluctuations in real time and quickly provide appropriate countermeasures. In particular, there is a need for a system that can detect sudden changes in emotions and provide appropriate anger management techniques based on those changes. It is also difficult to accurately recognize a user's emotional state and immediately suggest countermeasures using push notifications. This makes it difficult to provide effective means for maintaining users' mental health.

[1220] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1221] In this invention, the server includes detection means for collecting biometric data of a user, analysis means for detecting sudden fluctuations in the biometric data, warning means for sending a notification to the user when the sudden fluctuation is detected, communication means for receiving a response from the user and acquiring the user's emotional state, analysis means for analyzing the emotional data based on the response using an emotion engine, generation means for generating a message according to an anger management method using a generative AI model, and transmission means for sending the generated message to the user. This makes it possible to accurately recognize the user's emotional fluctuations in real time and provide appropriate anger management messages based on the emotional fluctuations.

[1222] "User" means any person or entity that uses the system.

[1223] "Biometric data" refers to data relating to the user's physical condition, such as heart rate, galvanic skin response, and body temperature.

[1224] "Detection means" refers to a device or sensor for collecting biometric data of a user.

[1225] "Analysis means" refers to the program and hardware used to analyze collected biological data and detect sudden changes.

[1226] The "warning means" is a function for sending a notification to the user when a sudden change is detected.

[1227] "Communication means" refers to the interface or protocol for receiving the user's responses and obtaining their emotional state.

[1228] "Analysis means" refers to software or hardware that uses an emotion engine to analyze the user's response data (voice data or text data) and recognize the user's emotional state.

[1229] "Generation means" refers to a function for using a generative AI model to create messages that follow anger management methods.

[1230] "Vehicle" refers to the functionality and interface for sending generated messages to users.

[1231] An "emotion engine" is an algorithm and software that analyzes a user's emotions and recognizes their optimal emotional state.

[1232] A "generative AI model" is a machine learning model that generates optimal anger management messages based on the user's emotional state.

[1233] "Push notification" refers to a protocol and method for sending notifications to devices in real time.

[1234] "Emotional fluctuation" is a term that indicates a state in which a sudden change occurs in the user's biometric data.

[1235] This invention is a system that monitors a user's biometric data in real time and provides appropriate anger management countermeasures based on changes in the biometric data using an emotion engine and a generative AI model. This system consists of a wearable device (terminal), a central server (server), an emotion engine, and a user (user).

[1236] The device is equipped with multiple sensors, including a heart rate sensor, a skin galvanic response sensor, and a body temperature sensor. These sensors collect the user's vital data every second. Specifically, the heart rate sensor measures heart rate data every second, and the skin galvanic response sensor records the user's stress level in real time.

[1237] The server receives and analyzes the biometric data sent from the device. Specifically, it compares the average heart rate over the past minute with the heart rate over the past 10 seconds to detect any sudden fluctuations. For example, if the heart rate rises by 10 bpm or more over 10 seconds, it is determined that there is an emotional fluctuation.

[1238] When an emotional change is detected, the server sends a push notification to the device. This notification contains a message such as "Please tell us how you feel right now." When the user receives the push notification and selects their emotion (e.g., anger, sadness, stress, etc.), that information is sent from the device to the server.

[1239] The server uses a generative AI model to generate anger management messages based on the received emotional data and the user's voice and text data analyzed by the emotion engine. For example, it might generate advice such as, "Practice the six-second rule. Taking a deep breath for six seconds will help you regain your composure."

[1240] The generated message is sent from the server to the device, which then displays it to the user, who can then take action such as deep breathing to reduce stress and emotional upheaval.

[1241] As a specific use case, consider a case where a user is experiencing high stress at work. The device detects a sudden increase in heart rate and sends this information to the server. After the server confirms the emotional fluctuations, it sends a push notification to the device. If the user selects "anger," this information is sent to the server. At the same time, the emotion engine analyzes the user's voice and determines it to be "anger." The server uses a generative AI model to generate a message saying, "Practice the 6-second rule," and sends it to the device. The device displays this message to the user, who then takes a deep breath to reduce stress.

[1242] In this way, the present invention realizes a system that allows users to recognize their own emotional fluctuations in real time and provides more accurate emotion recognition and appropriate anger management by utilizing an emotion engine and generative AI model.

[1243] An example prompt is:

[1244] "The user is currently experiencing deep stress and their heart rate has increased by more than 10 bpm in 10 seconds. Generate anger management advice if the user selects anger."

[1245] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1246] Step 1: Collect biometric data

[1247] The terminal collects the user's biometric data using sensors (heart rate sensor, skin galvanic response sensor, body temperature sensor, etc.) built into the wearable device. The data measured by the sensors is periodically recorded in a database within the terminal. The input here is the user's current physical condition, and the output is the collected vital data (heart rate, skin galvanic response, body temperature).

[1248] Step 2: Analyzing biological data

[1249] The device runs a program to analyze the collected vital data. Specifically, it compares the average heart rate over the past minute with the heart rate over the last 10 seconds. Based on this comparison, it determines that a sudden change has been detected, such as when the heart rate increases by 10 bpm or more over 10 seconds. The input is the collected vital data, and the output is the detected change.

[1250] Step 3: Emotional Indications

[1251] If the device detects a change in emotion, it sends that information to the server. The server analyzes the received data, and if an emotion change is confirmed, it sends a push notification to the device. The notification contains the message "Please tell us how you are feeling right now." The input is the change detection result, and the output is the sending of a push notification.

[1252] Step 4: Obtaining the user's emotional response

[1253] The user receives a push notification and selects their emotion (e.g., anger, sadness, stress, etc.). The selected emotion information is sent from the device to the server. The input here is the user's emotional response, and the output is the transmitted emotion information.

[1254] Step 5: Sentiment Analysis

[1255] The server runs an emotion engine and analyzes the user's response data (voice data and text data). For example, it recognizes the user's optimal emotional state from their tone of voice and vocabulary. The input is the user's emotional response and voice data, and the output is the specific emotion recognition result.

[1256] Step 6: Create an Anger Management Message

[1257] The server uses a generative AI model to generate anger management messages based on the emotion recognition results obtained from the emotion engine. For example, it creates advice such as, "Practice the six-second rule. Take six deep breaths to regain your composure." The input is the emotion recognition results, and the output is the generated message.

[1258] Step 7: Sending a message

[1259] The server generates a message and sends it to the terminal, which displays it to the user. The input here is the generated message, and the output is the message displayed to the user.

[1260] Step 8: User Action

[1261] The user takes specific coping actions (e.g., deep breathing) in accordance with the anger management message displayed on the device. This allows the user to control their emotions. The input here is the displayed message, and the output is the user's response.

[1262] (Application example 2)

[1263] 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."

[1264] Conventional systems for security staff lack the means to detect emotional fluctuations in real time and immediately provide appropriate anger management methods, which results in security staff being unable to perform their duties effectively under high stress.

[1265] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: sensor means for collecting vital data of the user; analysis means for detecting sudden fluctuations in the vital data; notification means for sending a notification to the user when the sudden fluctuation is detected; dialogue means for receiving the user's response and acquiring the user's emotional state; generation means for generating a message according to an anger management method based on the response; message sending means for sending the generated message to the user; emotion analysis means for analyzing the emotional state; generative AI model utilization means for generating a message using a generative AI model based on the emotional state analyzed by the emotion analysis means and a prompt sentence; and display means for displaying the generated message on the user's device. This enables security staff to recognize their own emotional fluctuations in real time and immediately practice an appropriate anger management method.

[1266] "Sensor means" refers to a device used to collect vital data of a user.

[1267] "Analysis means" refers to a device or program used to detect sudden fluctuations in collected vital data.

[1268] A "notification means" is a device or program used to send a notification to a user when a sudden change is detected.

[1269] An "interactive means" is a device or program for receiving a user's response and obtaining the user's emotional state.

[1270] The "generating means" is a device or program for generating a message in accordance with an anger management method based on the user's response.

[1271] The "message sending means" is a device or program for sending the generated message to the user.

[1272] "Emotion analysis means" is a device or program for analyzing the emotional state of a user.

[1273] "Generative AI model utilization means" refers to a device or program that uses a prompt sentence to generate a message using a generative AI model based on the emotional state analyzed by the emotion analysis means.

[1274] A "display means" is a device or program for displaying the generated message on a user's device.

[1275] overview

[1276] This invention is a system that monitors emotional fluctuations in real time and provides anger management techniques for security staff. The system collects users' vital data, analyzes sudden fluctuations, and uses a generative AI model to suggest appropriate countermeasures when emotional fluctuations are detected.

[1277] Hardware and Software Configuration

[1278] The system consists of the following main components:

[1279] 1. Sensor means:

[1280] Hardware: Smartwatches, smart glasses, and other wearable devices.

[1281] Function: Collects heart rate, galvanic skin response, and body temperature in real time.

[1282] 2. Analysis method:

[1283] Software: Python programs, data analysis libraries (e.g., pandas, numpy).

[1284] Function: Detects sudden fluctuations in collected vital data.

[1285] 3. Means of notification:

[1286] Hardware / Software: Mobile devices, push notification services.

[1287] Function: Notify the user of emotional fluctuations.

[1288] 4. Means of interaction:

[1289] Software: Conversational interfaces (e.g., chatbots).

[1290] Function: Receives the user's response and captures their emotional state.

[1291] 5. Emotion analysis means:

[1292] Software: Speech analysis engine, text analysis engine (e.g., Google Cloud Speech-to-Text, BERT).

[1293] Function: Analyzes the user's emotional state.

[1294] 6. Generation means:

[1295] Software: Generative AI models (e.g., GPT-3).

[1296] Function: Generates anger management messages using prompts based on emotional state.

[1297] 7. Message sending method:

[1298] Hardware: Mobile devices, wearable devices.

[1299] Function: Displays the generated message.

[1300] Processing flow

[1301] The specific process is as follows:

[1302] 1. Data Collection:

[1303] The sensor means collects vital data such as the user's heart rate, galvanic skin response, body temperature, etc. The data is collected in real time and updated every second.

[1304] 2. Data Analysis:

[1305] The analysis method detects sudden fluctuations in vital data. For example, it compares the average heart rate over the past minute with the heart rate over the past 10 seconds, and if a sudden fluctuation is detected, it determines whether the person is emotionally unstable.

[1306] 3. Notice:

[1307] The notification means sends a push notification to the user, displaying a message saying, "Please tell us how you feel right now."

[1308] 4. Getting user response:

[1309] The user selects their emotion (e.g., anger, sadness, stress, etc.) and responds through a dialogue means. This information is sent to the server.

[1310] 5. Emotion analysis:

[1311] The emotion analysis means analyzes the user's voice data and text data to identify the user's emotional state. For example, if the user is yelling, the voice data is analyzed and the emotion is recognized as "anger."

[1312] 6. Message Creation:

[1313] The generator uses a generative AI model to generate advice based on the emotional state, using prompts such as, "Practice the six-second rule. Take six deep breaths to regain your composure."

[1314] 7. Sending a message:

[1315] A message sending means displays the generated message on the user's device.

[1316] Specific examples

[1317] Consider a situation where a security staff member experiences stress during work and their heart rate suddenly rises. The system detects this change in heart rate and sends a notification to the user. If the user responds with "Anger," the server uses the generative AI model to generate advice such as "Practice the 6-second rule," which is displayed on the user's device.

[1318] Example prompts for generative AI models

[1319] A user's vital data (heart rate: x bpm, galvanic skin response: y, body temperature: z°C) has been collected. A sudden fluctuation has been detected compared to past data, indicating a change in emotion. Analysis by the emotion engine has identified the emotion "anger." Please generate appropriate anger management advice to help the user regain their composure. Please also include specific example messages.

[1320] This allows security staff to recognize their own emotional fluctuations in real time and immediately implement appropriate anger management methods.

[1321] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1322] Step 1:

[1323] Collect user vital data

[1324] Operation: The device (sensor means) collects vital data such as the user's heart rate, skin galvanic response, and body temperature in real time.

[1325] Input: User vital data (heart rate, galvanic skin response, body temperature) obtained from sensors connected to the device.

[1326] Output: Collected vital data (heart rate: x bpm, galvanic skin response: y, temperature: z°C).

[1327] Step 2:

[1328] Analyzing fluctuations in vital data

[1329] Operation: The terminal (analysis means) analyzes the collected vital data and compares the past data with the latest data to detect sudden fluctuations in heart rate.

[1330] Input: Vital data collected in step 1.

[1331] Data processing: Compare the average heart rate over the past minute with the heart rate over the last 10 seconds, and measure whether there is a fluctuation of more than 10 bpm over the 10 seconds.

[1332] Output: If a rapid heart rate fluctuation is detected, this information is generated (e.g., a flag indicating whether a rapid fluctuation was detected).

[1333] Step 3:

[1334] Send notifications to users

[1335] Operation: When the device (notification means) detects a sudden change, it sends a push notification to the user, prompting them to report their current emotional state.

[1336] Input: The sudden change flag, which is the output of step 2.

[1337] Output: Push notification message (e.g. "Tell us how you're feeling right now").

[1338] Step 4:

[1339] Get the user's emotional state

[1340] Action: The user uses the terminal (interaction means) to select an emotional state (e.g., anger, sadness, stress) and send a response.

[1341] Input: The user's selected emotional state.

[1342] Output: The selected emotional state (e.g., anger).

[1343] Step 5:

[1344] Analyzing emotional states

[1345] Operation: The server (emotion analysis means) analyzes the user's response data and identifies the user's emotional state based on the voice data and text data.

[1346] Input: The user's selected emotional state (output of step 4), as well as audio and text data.

[1347] Data computation: Using speech and text analysis engines to accurately recognize emotional states (e.g., determining "anger" from tone of voice).

[1348] Output: Sentiment analysis result (e.g. anger).

[1349] Step 6:

[1350] Generate messages using generative AI models

[1351] Operation: The server (generation means) generates an anger management message using the generative AI model based on the judgment results of the emotion analysis means. The server inputs the prompt sentence into the generative AI model.

[1352] Input: The sentiment analysis results from Step 5, and a prompt (e.g., a prompt for generating a message based on the sentiment analysis results).

[1353] Example prompt sentence:

[1354] A user's vital data (heart rate: x bpm, galvanic skin response: y, body temperature: z°C) has been collected. A sudden fluctuation has been detected compared to past data, indicating a change in emotion. Analysis by the emotion engine has identified the emotion "anger." Please generate appropriate anger management advice to help the user regain their composure. Please also include specific example messages.

[1355] Output: Generated anger management message (e.g., "Practice the six-second rule. Taking six deep breaths will help you regain your composure.").

[1356] Step 7:

[1357] Sending a generated message to a user

[1358] Operation: The terminal (message sending means) displays the generated anger management message on the user's device.

[1359] Input: The generated messages that are the output of Step 6.

[1360] Output: A message displayed on the user's device (e.g., "Practice the 6-second rule. Taking 6 deep breaths will help you regain your composure.").

[1361] 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.

[1362] 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.

[1363] 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.

[1364] 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.

[1365] 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.

[1366] 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.

[1367] 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).

[1368] 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.

[1369] 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."

[1370] 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.

[1371] 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).

[1372] 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.

[1373] 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.

[1374] 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.

[1375] 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.

[1376] 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.

[1377] 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.

[1378] 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.

[1379] 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.

[1380] 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.

[1381] 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.

[1382] The following is further disclosed regarding the above embodiment.

[1383] (Claim 1)

[1384] sensor means for collecting vital data of a user;

[1385] an analysis means for detecting a sudden change in the vital data;

[1386] a notification means for sending a notification to a user when the sudden change is detected;

[1387] interaction means for receiving a user's response and obtaining the user's emotional state;

[1388] a generating means for generating a message according to an anger management method based on the response;

[1389] a message sending means for sending the generated message to a user;

[1390] A system including:

[1391] (Claim 2)

[1392] 10. The system of claim 1, wherein the vital data includes heart rate, galvanic skin response, and body temperature.

[1393] (Claim 3)

[1394] The system of claim 1 , wherein the notification means for sending notifications to the user provides push notifications.

[1395] "Example 1"

[1396] (Claim 1)

[1397] sensor means for collecting biometric data of a user;

[1398] an analysis means for detecting a sudden change in the biometric data;

[1399] a notification means for sending a notification to a user when the sudden change is detected;

[1400] interaction means for receiving a user's response and obtaining the user's emotional state;

[1401] a generating means for generating a message according to an anger management method using a generative model based on the response;

[1402] a message sending means for sending the generated message to a user;

[1403] A system including:

[1404] (Claim 2)

[1405] 10. The system of claim 1, wherein the biometric data includes heart rate, galvanic skin response, and body temperature.

[1406] (Claim 3)

[1407] The system of claim 1 , wherein the notification means for sending notifications to the user provides push notifications.

[1408] "Application Example 1"

[1409] (Claim 1)

[1410] sensor means for collecting biometric data of a user;

[1411] an analysis means for detecting a sudden change in the biometric data;

[1412] a notification means for sending a notification to a user when the sudden change is detected;

[1413] interaction means for receiving a user's response and obtaining the user's emotional state;

[1414] a generating means for generating a message according to a method for emotion management based on the response;

[1415] a message sending means for sending the generated message to a user;

[1416] A generating means for generating a message using a generative AI model based on emotion information in order to manage fluctuations in biometric data by security personnel and provide appropriate countermeasures;

[1417] a display means for displaying the generated message to a worker;

[1418] A system including:

[1419] (Claim 2)

[1420] 10. The system of claim 1, wherein the biometric data includes heart rate, galvanic skin response, and body temperature.

[1421] (Claim 3)

[1422] The system of claim 1, wherein the notification means for sending notifications to the employee provides push notifications.

[1423] "Example 2: Combining Emotion Engines"

[1424] (Claim 1)

[1425] detection means for collecting biometric data of a user;

[1426] analysis means for detecting abrupt fluctuations in the biometric data;

[1427] an alert means for sending a notification to a user when the sudden change is detected;

[1428] communication means for receiving a user's response and obtaining the user's emotional state;

[1429] analysis means for analyzing emotion data using an emotion engine based on the response;

[1430] A generating means for generating a message according to an anger management method using a generative AI model;

[1431] a communication means for transmitting the generated message to a user;

[1432] A system including:

[1433] (Claim 2)

[1434] 10. The system of claim 1, wherein the biometric data includes heart rate, galvanic skin response, and body temperature.

[1435] (Claim 3)

[1436] 10. The system of claim 1, wherein the alerting means for sending a notification to the user provides a push notification.

[1437] "Application example 2 when combining emotion engines"

[1438] (Claim 1)

[1439] sensor means for collecting vital data of a user;

[1440] an analysis means for detecting a sudden change in the vital data;

[1441] a notification means for sending a notification to a user when the sudden change is detected;

[1442] interaction means for receiving a user's response and obtaining the user's emotional state;

[1443] a generating means for generating a message according to an anger management method based on the response;

[1444] a message sending means for sending the generated message to a user;

[1445] emotion analysis means for analyzing the emotional state;

[1446] a generation AI model utilization means for generating a message using a prompt sentence by using a generation AI model based on the emotional state analyzed by the emotion analysis means;

[1447] display means for displaying the generated message on a user's device;

[1448] A system including:

[1449] (Claim 2)

[1450] 10. The system of claim 1, wherein the vital data includes heart rate, galvanic skin response, and body temperature.

[1451] (Claim 3)

[1452] The system of claim 1 , wherein the notification means for sending notifications to the user provides push notifications. [Explanation of symbols]

[1453] 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. sensor means for collecting vital data of a user; an analysis means for detecting a sudden change in the vital data; a notification means for sending a notification to a user when the sudden change is detected; interaction means for receiving a user's response and obtaining the user's emotional state; a generating means for generating a message according to an anger management method based on the response; a message sending means for sending the generated message to a user; A system including:

2. The system of claim 1 , wherein the vital data includes heart rate, galvanic skin response, and body temperature.

3. The system of claim 1 , wherein the notification means for sending notifications to the user provides push notifications.

Images