System

Abstract

A system is provided.SOLUTION: A system comprising: means for acquiring pulse data; means for acquiring body temperature data; means for acquiring perspiration data; means for acquiring sound data; means for analyzing the acquired pulse data, body temperature data, perspiration data, and sound data; means for determining whether a user is in an angry state based on an analysis result; and means for sending a notification to the user if it is determined that the user is in an angry state.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] Minor disputes in crowded public places, especially on trains, can not only cause delays and disruption to train operations, but can also have a negative impact on the lives and work of many people. In particular, individuals who cause trouble can face legal issues, such as compensation for damages, and this can become a source of unhappiness that spreads throughout society. This invention aims to prevent such disputes in public places, particularly by detecting people's anger early and encouraging self-control. [Means for solving the problem]

[0005] The present invention provides a system including the following means.

[0006] Means of obtaining pulse data

[0007] Means of obtaining body temperature data

[0008] Means of acquiring sweat data

[0009] A means of acquiring audio data

[0010] A means for analyzing the acquired pulse data, body temperature data, sweat data, and voice data

[0011] A means to determine whether the user is in an angry state based on the analysis results

[0012] Means for sending a notification to a user when the user is determined to be in an angry state

[0013] The system further includes a means for sending a message generated based on the analysis results, and a means for sending notifications to at least the smartwatch and smartphone. This system detects a user's anger early and sends a message urging the user to exercise self-control at an appropriate time, thereby preventing trouble in public places.

[0014] "Pulse data" is information relating to the number of beats of the human heart.

[0015] "Body temperature data" is information indicating the temperature of the human body.

[0016] "Sweat data" is information about sweat secreted from the human body.

[0017] "Voice data" is information obtained by recording the voice uttered by the user.

[0018] "Analyzing means" are the technical means for analyzing and processing the acquired data.

[0019] The "means for determining" is a technical means for identifying a particular state based on the analysis results.

[0020] "Means for sending notifications" are the technical means for sending specific messages or alerts to users.

[0021] The "means for generating a message" refers to the technical means for creating the content of a notification to the user.

[0022] A "smartwatch" is a computer device worn on the wrist and equipped with various sensors.

[0023] A "smartphone" is a multi-function mobile phone that is a device that can connect to the Internet and use applications. [Brief explanation of the drawings]

[0024] [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

[0025] 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.

[0026] First, the terms used in the following description will be explained.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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."

[0032] [First embodiment]

[0033] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0034] 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.

[0035] 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).

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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."

[0045] This invention relates to an anger management AI system that prevents trouble in crowded public places, especially on trains, etc. This system is characterized by sending notifications and messages urging self-control at appropriate times when the user becomes angry.

[0046] System Configuration

[0047] The system includes the following components:

[0048] 1. Devices (smartwatches, smartphones)

[0049] How pulse data is acquired: A sensor built into the device measures the user's pulse in real time.

[0050] Body temperature data acquisition method: A temperature sensor built into the device measures the user's body temperature in real time.

[0051] Method for acquiring sweat data: A sensor built into the device detects the user's sweating status in real time.

[0052] Means for acquiring voice data: The device's microphone records the user's voice and saves it as an audio file.

[0053] 2. Server

[0054] Data analysis means: The server receives the acquired pulse data, body temperature data, sweat data and voice data and analyzes them.

[0055] Anger detection means: Based on the analysis results, it is determined whether the user is in an angry state.

[0056] Notification generating means: When it is determined that the user is in an angry state, a message for sending a notification to the user is generated.

[0057] Notification sending means: Sends the generated notification message to the smartwatch or smartphone.

[0058] Explanation of program processing

[0059] 1. Initial Setup

[0060] The device registers the user's basic data (normal pulse, body temperature, sweat level) as the initial setting, and the user can select the type of notification (vibration, voice, text) according to their preference.

[0061] 2. Data collection

[0062] The device periodically collects pulse, body temperature, and sweat data. Audio data is also recorded in real time using a microphone and sent to a server.

[0063] 3. Data Analysis

[0064] The server receives the transmitted data and analyzes it, assessing how much the pulse rate has deviated from normal, whether body temperature has risen, and whether sweating has increased. It also converts the voice data into text and uses natural language processing (NLP) to detect keywords and tones that indicate anger.

[0065] 4. Anger detection

[0066] The server determines whether the user is in an angry state based on the analysis results. For example, if the pulse rate is 30% higher than normal, the body temperature is 0.5 degrees higher, and the voice data contains keywords indicating anger, the server determines that the user is in an angry state.

[0067] 5. Generate and send notifications

[0068] If the server detects the user's anger, it generates an appropriate notification message, such as "Take a deep breath and calm down" or a joke like "If you thought the train was crowded, you're here to see me today!" The generated message is sent to the smartwatch or smartphone.

[0069] The device receives the generated notification and presents it to the user: the smartwatch vibrates and displays a message, and the smartphone plays a message or joke aloud.

[0070] Specific examples

[0071] For example, suppose a user becomes irritated on a train, their pulse rate and body temperature rise, they start sweating, and they say things like, "Stop messing around!" or "Why is it so crowded?" In this case, the device collects this data and sends it to the server. The server analyzes the data and, if it determines that the user is angry, it generates an appropriate message and sends it to the device. Finally, the device tries to calm the user down by telling them to "take a deep breath and calm down" or telling them a joke.

[0072] By using this system, it is possible to prevent trouble in public places and provide a safe and comfortable environment for users and those around them.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] The device registers the user's basic data (normal pulse, body temperature, sweat level) as the initial setting, and the user also selects the type of notification (vibration, voice, text) according to their preference.

[0076] Step 2:

[0077] The device periodically (for example, every minute) collects the user's pulse, body temperature, and sweat data. At the same time, the device's microphone records the user's voice in real time and saves it as audio data.

[0078] Step 3:

[0079] The terminal transmits the collected pulse data, body temperature data, sweat data, and voice data to a server at regular intervals (for example, every minute).

[0080] Step 4:

[0081] The server analyzes the received data in real time. Specifically, it analyzes pulse data and calculates variations from normal baseline values. It also calculates variations from normal baseline values ​​for body temperature data and analyzes sweat data.

[0082] Step 5:

[0083] The server converts the speech data into text and uses natural language processing (NLP) to detect keywords and tones that indicate anger, such as phrases like "Don't mess with me!" or "Why is it so busy?"

[0084] Step 6:

[0085] The server comprehensively evaluates the analysis results and determines whether the user is in an angry state. If the pulse rate is 30% or more higher than normal, the body temperature is 0.5 degrees higher, and the voice data contains keywords related to anger, the server determines that the user is in an angry state.

[0086] Step 7:

[0087] If the server determines that the user is angry, it generates an appropriate notification message, such as "Take a deep breath and calm down" or a joke like "If you thought the train was crowded, you're here to see me today!"

[0088] Step 8:

[0089] The server sends the generated notification message to the device, which then sends the notification to the user's smartwatch and smartphone.

[0090] Step 9:

[0091] The device will present the received notification to the user: the smartwatch will vibrate and display the message "Take a deep breath and calm down," and the smartphone will play a joke.

[0092] Step 10:

[0093] The user receives a notification and can take action, for example, by taking a deep breath or listening to a joke to calm down.

[0094] Step 11:

[0095] The server again collects and analyzes the data to observe the user's reaction. If it determines that the user's anger continues, it will send additional notifications or advice, such as a reminder like "You're almost at the station!"

[0096] These steps will help detect user anger early and take appropriate measures to prevent trouble in public places.

[0097] Example 1

[0098] 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."

[0099] Anger-related troubles in public places, especially on trains, are a major social problem. Conventional technologies lack the means to monitor a user's emotional state in real time and send notifications at the appropriate time, making it difficult to prevent trouble before it occurs. Furthermore, there was no technology that efficiently analyzed a user's physiological and voice data and quickly detected anger based on the analysis results.

[0100] 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.

[0101] In this invention, the server includes means for acquiring pulse data, means for acquiring body temperature data, means for acquiring sweat data, means for acquiring voice data, means for registering basic data as initial settings, means for analyzing the acquired pulse data, body temperature data, sweat data, and voice data, means for converting the voice data into text and analyzing it using natural language processing, means for determining whether the user is in an angry state based on the analysis result, and means for sending a notification to the user when it is determined that the user is in an angry state. This makes it possible to monitor the user's emotional state in real time, quickly detect an anger state, and send a notification at an appropriate time.

[0102] "Pulse data" is data measured using the user's heart rate and heart rhythm as indicators.

[0103] "Body temperature data" is data obtained by measuring the user's body temperature.

[0104] "Sweat data" is data obtained by measuring the amount of sweat on the user's skin surface using a sensor.

[0105] "Voice data" refers to sound data of a user's voice collected using a recording device.

[0106] "Basic data" is reference data that indicates the user's normal state, such as normal pulse rate, body temperature, and sweat level.

[0107] "Analysis" is a computational process for determining the user's state and emotions based on various acquired data.

[0108] "Natural language processing" is a technology that converts voice data into text and analyzes that text to understand emotions and intent.

[0109] The "angry state" is a state in which the user is emotionally aroused and feels angry.

[0110] A "notification" is a means of conveying some information or message to a user.

[0111] "Server" refers to a computer system that handles processes such as data analysis, decision-making, and notification generation and transmission.

[0112] A "terminal" is a device that collects data from users and communicates with servers, and primarily includes smartwatches and smartphones.

[0113] This invention relates to an anger management AI system that prevents trouble in crowded public places, especially on trains, etc. This system is characterized by sending notifications and messages urging self-control at appropriate times when the user becomes angry.

[0114] System Configuration

[0115] The system includes the following components:

[0116] 1. Devices (smartwatches, smartphones)

[0117] How pulse data is acquired: A sensor built into the device measures the user's pulse in real time.

[0118] Body temperature data acquisition method: A temperature sensor built into the device measures the user's body temperature in real time.

[0119] Method for acquiring sweat data: A sensor built into the device detects the user's sweating status in real time.

[0120] Means for acquiring voice data: The device's microphone records the user's voice and saves it as an audio file.

[0121] A means for registering basic data as initial settings: The user registers their normal pulse rate, body temperature, and sweat level as initial settings.

[0122] 2. Server

[0123] Data analysis means: The server receives the acquired pulse data, body temperature data, sweat data and voice data and analyzes them.

[0124] Speech-to-text conversion and natural language processing means: Convert speech data into text and use natural language processing (NLP) to analyze keywords and tones that indicate anger.

[0125] Anger detection means: Based on the analysis results, it is determined whether the user is in an angry state.

[0126] Notification generating means: When it is determined that the user is in an angry state, a message for sending a notification to the user is generated.

[0127] Notification sending means: Sends the generated notification message to the smartwatch or smartphone.

[0128] Specific Examples

[0129] For example, imagine a user getting irritated on a train, their pulse rate and body temperature rising, sweating increasing, and uttering phrases like "Stop messing around!" or "Why is it so crowded?" At this time, the device collects this data and sends it to the server. The server analyzes the data and, if it determines that the user is angry, generates an appropriate message and sends it to the device. Finally, the device tries to calm the user down by telling them to "take a deep breath and calm down" or telling them a joke.

[0130] Prompt Sentence Examples

[0131] Below is an example of input to a generative AI model to illustrate this system.

[0132] "Please explain an anger management AI system for controlling anger in public places. This system collects and analyzes the user's biometric and voice data in real time. If an abnormality is detected, it sends a message to the user urging them to control themselves. Please explain in detail what data is collected, how it is analyzed, and what kind of notification is sent."

[0133] By using this system, it is possible to prevent trouble in public places and provide a safe and comfortable environment for users and those around them.

[0134] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0135] Step 1:

[0136] Initial Setup

[0137] When the user starts the system for the first time, they input their basic data (normal pulse, body temperature, and sweat level) using a smartphone app. This basic data is then saved on the device. The user can also select the type of notification (vibration, voice, or text). The input is the user's normal physiological data and notification preferences, and the output is the settings saved on the device.

[0138] Specific behavior:

[0139] 1. The user launches the smartphone app.

[0140] 2. The user enters pulse rate, body temperature, and sweat level according to the screen where basic data is entered.

[0141] 3. On the screen where the user selects the type of notification, select "Vibrate," "Voice," or "Text."

[0142] Step 2:

[0143] Data collection

[0144] The device collects physiological data (pulse, body temperature, sweat) and voice data at regular intervals. The sensor measures this data in real time and temporarily stores it in internal memory. The data is then sent to a server at regular intervals. The input is the physiological data and voice data acquired in real time, and the output is the data sent to the server.

[0145] Specific behavior:

[0146] 1. The device measures pulse rate, body temperature, and sweat data every minute.

[0147] 2. The device's microphone records the user's voice in real time and saves it as an audio file.

[0148] 3. The device sends the collected data to a server via the Internet every five minutes.

[0149] Step 3:

[0150] Data analysis

[0151] The server receives the transmitted data and begins analysis. First, pulse data is evaluated for fluctuations over time to detect deviations from normal baseline values. Next, body temperature data is monitored for temperature fluctuations and compared to baseline values. Sweat data is analyzed in a similar manner to evaluate increases in sweating. Finally, the voice data is converted into text and analyzed using natural language processing (NLP) for keywords and tones that indicate anger. The input is the various physiological data and voice data transmitted to the server, and the output is the analysis results.

[0152] Specific behavior:

[0153] 1. The server analyzes the pulse data and evaluates variations from baseline.

[0154] 2. The server analyzes the body temperature data and determines the degree of temperature rise.

[0155] 3. The server analyzes the sweat data and evaluates the increase in sweat rate.

[0156] 4. The server converts the audio data into text and uses NLP to detect keywords and tones that indicate anger.

[0157] Step 4:

[0158] Anger detection

[0159] The server determines whether the user is in an angry state based on the analysis results. For example, if the pulse rate is 30% higher than normal, the body temperature is 0.5 degrees higher, and the voice data contains anger-related keywords such as "Don't mess with me" or "Why," the server determines the user is in an angry state. The input is the analysis results, and the output is the anger state determination result.

[0160] Specific behavior:

[0161] 1. The server comprehensively evaluates the analysis results of pulse, body temperature, and sweat data.

[0162] 2. The server uses the results of the voice data analysis to determine the emotional state.

[0163] 3. The server determines whether the user's emotional state is angry.

[0164] Step 5:

[0165] Notification generation and delivery

[0166] If the server detects the user's anger, it generates an appropriate notification message. For example, it generates a joke such as "Take a deep breath and calm down" or "Everyone's here to see me today!" This notification message is sent to the smartwatch or smartphone. The input is the anger state judgment result, and the output is the generated notification message.

[0167] Specific behavior:

[0168] 1. The server generates an appropriate notification message for the user.

[0169] 2. The server sends the generated notification message to the terminal.

[0170] 3. The terminal receives the notification message and presents it to the user.

[0171] 4. The smartwatch will vibrate and display a message, and the smartphone will play the message aloud.

[0172] Through the above steps, the anger management AI system of the present invention can monitor the user's emotional state in real time and encourage self-control at the appropriate time, thereby preventing trouble in public places.

[0173] (Application example 1)

[0174] 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."

[0175] In modern brick-and-mortar stores, when customers feel stressed or angry, it often negatively impacts the atmosphere in the store and other customers. However, it is difficult for on-site staff to sense the situation in real time and respond appropriately. Therefore, a method is needed to detect customers' emotional states in real time and respond appropriately.

[0176] 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.

[0177] In this invention, the server includes means for acquiring pulse data, means for acquiring body temperature data, means for acquiring sweat data, means for acquiring voice data, means for analyzing the acquired pulse data, body temperature data, sweat data, and voice data, means for determining whether the user is in an angry state based on the analysis results, means for sending a notification to the user when it is determined that the user is in an angry state, means for sending a notification to the user's smart electronic device in a physical store, and message generation means using a generative AI model to ensure that the notification includes a specific message and specific suggestions such as promoting relaxation or offering a special discount. This makes it possible to automatically notify customers in a physical store of appropriate messages and suggestions in real time when they feel stressed or angry.

[0178] "Pulse data" is data indicating the user's heart rate, and refers to the number of times the user's heart beats measured in real time.

[0179] "Body temperature data" is data indicating the user's body temperature, and refers to the internal and external temperatures of the user's body measured in real time.

[0180] "Sweat data" is data indicating the amount of sweat produced by the user, and refers to the amount of sweat secreted from the user's skin measured in real time.

[0181] "Voice data" refers to data indicating the content of a user's speech, and refers to the characteristics of the user's words and voice collected in real time.

[0182] The "analysis means" is a means for analyzing the various acquired data and evaluating the emotional state of the user based on the data.

[0183] The "determination means" is a means for determining whether the user is in an angry state based on the analysis result obtained by the analysis means.

[0184] The "notification means" is a means for conveying an appropriate message to the user when it is determined that the user is in an angry state.

[0185] "Smart electronic devices" refer to portable devices that users use on a daily basis, such as smartwatches and smartphones.

[0186] A "generative AI model" is a model that uses artificial intelligence to generate specific messages and suggestions, allowing for personalized messages to be generated for users.

[0187] The "message generation means" is a means of generating appropriate messages and suggestions according to the user's state, and uses a generative AI model.

[0188] The present invention is a system for detecting stress and anger in real time in a brick-and-mortar store and responding appropriately using the user's smart electronic devices, a cloud server, and a generative AI model.

[0189] System Configuration

[0190] The system includes the following components:

[0191] 1. Smart Electronics

[0192] This includes smartwatches and smartphones.

[0193] How to obtain pulse data: The user's heart rate is obtained in real time using the sensors in the smartwatch.

[0194] Body temperature data acquisition method: The user's body temperature is acquired in real time using a temperature sensor in a smartwatch or smartphone.

[0195] Means of acquiring sweat data: The amount of sweat a user generates is acquired in real time using sensors in smart electronic devices.

[0196] Method of acquiring voice data: The smartphone's microphone is used to collect the user's speech in real time.

[0197] 2. Server

[0198] Data analysis means: The server receives and analyzes pulse, body temperature, sweat, and voice data sent from smart electronic devices.

[0199] Anger detection means: Based on the analysis results, it is determined whether the user is in an angry state.

[0200] Notification generator: If the user is determined to be in an angry state, an appropriate message is generated using a generative AI model.

[0201] Notification sending method: Send the generated message to your smartwatch or smartphone.

[0202] Hardware and software used

[0203] Hardware:

[0204] Smartwatch (e.g. Samsung Galaxy Watch)

[0205] Smartphone (e.g. iPhone 13)

[0206] Cloud server (e.g. AWS EC2 instance)

[0207] software:

[0208] TensorFlow: Used to implement an anger detection model based on pulse, body temperature, and sweat data.

[0209] Twilio: Used to send notification messages to users' smart electronic devices.

[0210] OpenAI GPT-3.5: Used to generate response messages using a generative AI model.

[0211] Specific example explanation

[0212] For example, if a customer in a physical store becomes stressed due to a long wait or crowding, the system collects the customer's pulse, body temperature, and sweat rate in real time, and also records what they say with a microphone. This data is sent to a server for analysis. If the server determines that the customer is angry based on the analysis results, it uses a generative AI model (OpenAI GPT-3.5) to generate a message such as "Take a deep breath and calm down" or a suggestion message offering a special discount coupon.

[0213] Example prompt for a generative AI model:

[0214] "Generate a soothing message for a person who is likely angry or stressed:"

[0215] Finally, this generated message is sent via Twilio to a smartwatch or smartphone, for example, a notification will appear on the customer's smartwatch saying, "Enjoy a relaxed shopping experience with our special coupons," which will calm the customer and maintain a good atmosphere in the store.

[0216] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0217] Step 1:

[0218] The device collects the user's pulse, body temperature, sweat, and voice data in real time. Specifically, sensors within the smartwatch measure the pulse and body temperature and detect the amount of sweat. The smartphone's microphone also collects surrounding sounds and sends this data to a cloud server. The input is various vital data and voice data, and the output is data sent to the server.

[0219] Step 2:

[0220] The server receives the data acquired by the data analysis means and analyzes each data. Specifically, it uses TensorFlow to analyze pulse, body temperature, and sweat data and evaluates how much these data deviate from baseline values. It also converts voice data into text and uses natural language processing (NLP) to detect emotional tone and keywords that indicate anger. The input is the data sent from the device, and the output is the analysis results.

[0221] Step 3:

[0222] The server determines whether the user is in an angry state based on the analysis results. Specific criteria include pulse rate 30% or more higher than normal, body temperature 0.5 degrees higher, increased sweating, and the voice data containing keywords indicating anger. The input is the analysis results, and the output is the anger state determination.

[0223] Step 4:

[0224] If the server determines that the user is in an angry state, it uses a generative AI model (OpenAI GPT-3.5) to generate an appropriate message. Specifically, the prompt "Generate a soothing message for a person who is likely angry or stressed:" is input into the generative AI model, which outputs messages such as "Take a deep breath and calm down" or "Use a special coupon and enjoy a relaxing shopping experience." The input is the determination of the user's angry state, and the output is the generated message.

[0225] Step 5:

[0226] The server sends the generated message to the user's device as a notification. Specifically, it uses Twilio to send the message to a smartwatch or smartphone. The user's smartwatch vibrates and displays the generated message. The smartphone plays the message aloud. The input is the generated message, and the output is a notification to the user's device.

[0227] Step 6:

[0228] The user receives a notification from the device and checks the message displayed on the screen or the voice guidance, which allows the user to take a deep breath or use a special coupon to reduce stress or anger. The input is the notification to the device, and the output is the user's reaction.

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

[0230] This invention relates to an anger management AI system that prevents trouble in crowded public places, especially on trains, etc. This system is characterized by sending notifications and messages urging self-control at appropriate times when the user becomes angry, and its effectiveness is enhanced by combining it with an emotion engine.

[0231] System Configuration

[0232] The system includes the following components:

[0233] 1. Devices (smartwatches, smartphones)

[0234] How pulse data is acquired: A sensor built into the device measures the user's pulse in real time.

[0235] Body temperature data acquisition method: A temperature sensor built into the device measures the user's body temperature in real time.

[0236] Method for acquiring sweat data: A sensor built into the device detects the user's sweating status in real time.

[0237] Means for acquiring voice data: The device's microphone records the user's voice and saves it as an audio file.

[0238] Means for acquiring image data and video data (if necessary): The smartphone camera captures the user's facial expressions and movements and sends the data to the server.

[0239] 2. Server

[0240] Data analysis means: The server receives the acquired pulse data, body temperature data, sweat data, voice data, image data and video data and analyzes them.

[0241] Anger detection: Based on the analysis results, it is determined whether the user is in an angry state. The emotion engine plays an important role here.

[0242] Emotion engine: Analyzes voice data, pulse data, body temperature data, sweat data, as well as image and video data to identify the user's emotions in detail.

[0243] Notification generation means: If the user is determined to be in an angry state, a message is generated to notify the user. Based on the analysis results of the emotion engine, a notification is generated that matches the user's emotions.

[0244] Notification sending means: Sends the generated notification message to the smartwatch or smartphone.

[0245] Explanation of program processing

[0246] 1. Initial Setup

[0247] The device registers the user's basic data (normal pulse, body temperature, sweat level) as the initial setting, and the user can select the type of notification (vibration, voice, text) according to their preference.

[0248] 2. Data collection

[0249] The device periodically (for example, every minute) collects the user's pulse, body temperature, and sweat data. At the same time, the device's microphone records the user's voice in real time and saves it as audio data. Furthermore, the smartphone's camera captures the user's facial expressions and movements as needed and saves them as image and video data.

[0250] 3. Data transmission

[0251] The collected data is sent to the server at regular intervals (for example, every minute). This data includes pulse data, body temperature data, sweat data, voice data, image data, and video data.

[0252] 4. Data Analysis

[0253] The server analyzes the transmitted data in real time. Specifically, it calculates and evaluates fluctuations in pulse, body temperature, and sweat data from their normal baseline values. The emotion engine analyzes the audio, image, and video data and uses natural language processing (NLP) to detect keywords and tones that indicate anger, as well as the user's facial expressions and actions.

[0254] 5. Anger detection

[0255] The server comprehensively evaluates the analysis results and determines whether the user is in an angry state. If the pulse rate is 30% or more higher than normal, the body temperature is 0.5 degrees or more higher, and the voice data or image data contains keywords or facial expressions that indicate anger, the user is determined to be in an angry state.

[0256] 6. Generating and Sending Notifications

[0257] If the server detects that the user is angry, it generates an appropriate notification message. Based on the analysis results of the emotion engine, it generates a notification that matches the user's emotions. For example, it generates a message such as "Take a deep breath and calm down" or a joke such as "If you think the train is crowded, everyone came to see me today!" The generated message is sent to the smartwatch or smartphone.

[0258] The device receives the generated notification and presents it to the user: the smartwatch vibrates and displays the message "Take a deep breath and calm down," and the smartphone plays a joke aloud.

[0259] Specific examples

[0260] For example, suppose a user becomes irritated on a train, their pulse rate and body temperature rise, they start sweating, they say things like "Stop messing around!" or "Why is it so crowded?", and their expression becomes grim. In this case, the device collects this data and sends it to the server. The server analyzes this data and, if it determines that the user is angry using its emotion engine, it generates an appropriate message and sends it to the device. Finally, the device tries to calm the user down by telling them to "take a deep breath and calm down" or telling a joke.

[0261] This system can detect user anger early and take appropriate measures to prevent trouble in public places, providing a safe and comfortable environment for users and those around them.

[0262] The processing flow will be explained below.

[0263] Step 1:

[0264] The device registers the user's basic data (normal pulse, body temperature, sweat level) as the initial setting, and the user also selects the type of notification (vibration, voice, text) according to their preference.

[0265] Step 2:

[0266] The device periodically (for example, every minute) collects the user's pulse, body temperature, and sweat data. At the same time, the device's microphone records the user's voice in real time and saves it as audio data. If necessary, the smartphone camera captures the user's facial expressions and movements, collecting image and video data.

[0267] Step 3:

[0268] The terminal transmits the collected pulse data, body temperature data, sweat data, voice data, image data, and video data to a server at regular intervals (for example, every minute).

[0269] Step 4:

[0270] The server analyzes the received data in real time. It analyzes pulse data to calculate variations from normal baseline values, as well as body temperature and sweat data.

[0271] Step 5:

[0272] The server converts the voice data into text and uses natural language processing (NLP) to detect keywords and tones that indicate anger, while simultaneously analyzing image and video data to identify emotions from the user's facial expressions and movements.

[0273] Step 6:

[0274] The emotion engine comprehensively evaluates the analysis results and determines the user's emotional state. If the pulse rate is 30% or more higher than normal, the body temperature is 0.5 degrees higher, and the voice and image data contain keywords or facial expressions that indicate anger, the user is determined to be in an angry state.

[0275] Step 7:

[0276] If the server detects that the user is angry, it generates an appropriate notification message based on the analysis results of the emotion engine. For example, it generates a message like "Take a deep breath and calm down" or a joke like "I thought the train was crowded, but everyone came to see me today!"

[0277] Step 8:

[0278] The server sends the generated notification message to the device, which then sends the notification to the user's smartwatch and smartphone.

[0279] Step 9:

[0280] The device receives the notification and presents it to the user accordingly: the smartwatch vibrates and displays the message "Take a deep breath and calm down," and the smartphone plays a joke aloud.

[0281] Step 10:

[0282] The user receives a notification and can take action, for example, by taking a deep breath or listening to a joke to calm down.

[0283] Step 11:

[0284] The server again collects and analyzes the data to observe the user's reaction. If it determines that the user's anger continues, it will send additional notifications or advice, such as a reminder like "You're almost at the station!"

[0285] These steps will help detect user anger early and take appropriate measures to prevent trouble in public places.

[0286] Example 2

[0287] 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."

[0288] In modern public places, especially on trains, users often become angry due to crowding and stress. This can cause trouble and have a negative impact on those around them, so there is a need for a system that can detect users' anger early and encourage appropriate responses.

[0289] 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.

[0290] In this invention, the server includes means for acquiring pulse data, means for acquiring body temperature data, means for acquiring sweat data, means for acquiring voice data, means for acquiring image data and video data, means for analyzing the acquired pulse data, body temperature data, sweat data, voice data, image data and video data, means for determining whether the user is in an angry state based on the analysis results, and means for sending a notification to the user when it is determined that the user is in an angry state. This makes it possible to monitor the user's emotional state in detail and in real time and send notifications at appropriate times.

[0291] "Pulse data" is biological information that indicates the user's heart rate.

[0292] "Body temperature data" is biological information indicating the user's body temperature.

[0293] "Sweat data" is biological information that indicates the amount of sweat of the user and the associated humidity of the skin.

[0294] "Voice data" is digital information that records the user's speech or voice.

[0295] "Image data" is digital information of a still image capturing the user's facial expression and posture.

[0296] "Video data" is digital information of moving images of the user's facial expressions and movements.

[0297] "Emotion engine" is a general term for algorithms and software that analyzes audio data, image data, video data, and various biometric data to determine the user's emotional state.

[0298] "Notification" means a message, alert, or information sent to a user to prompt the user to take action.

[0299] A "personal digital assistant" is a portable electronic device capable of data communication, such as a smartphone or tablet.

[0300] A "mobile device" is an electronic device worn on the wrist, such as a smartwatch.

[0301] This invention relates to an anger management AI system that prevents trouble in crowded public places, especially on trains, etc. This system is characterized by sending notifications and messages urging self-control at appropriate times when the user becomes angry, and its effectiveness is enhanced by combining it with an emotion engine.

[0302] System Configuration

[0303] The system includes the following components:

[0304] 1. Devices (smartwatches, smartphones)

[0305] How pulse data is acquired: A sensor built into the device measures the user's pulse in real time.

[0306] Body temperature data acquisition method: A temperature sensor built into the device measures the user's body temperature in real time.

[0307] Method for acquiring sweat data: A sensor built into the device detects the user's sweating status in real time.

[0308] Means for acquiring voice data: The device's microphone records the user's voice and saves it as an audio file.

[0309] Means for acquiring image data and video data (if necessary): The smartphone camera captures the user's facial expressions and movements and sends the data to the server.

[0310] 2. Server

[0311] Data analysis means: The server receives the acquired pulse data, body temperature data, sweat data, voice data, image data and video data and analyzes them.

[0312] Anger detection: Based on the analysis results, it is determined whether the user is in an angry state. The emotion engine plays an important role here.

[0313] Emotion engine: Analyzes voice data, pulse data, body temperature data, sweat data, as well as image and video data to identify the user's emotions in detail.

[0314] Notification generation means: If the user is determined to be in an angry state, a message is generated to notify the user. Based on the analysis results of the emotion engine, a notification is generated that matches the user's emotions.

[0315] Notification sending means: Sends the generated notification message to the smartwatch or smartphone.

[0316] Detailed Description of the Invention

[0317] Data collection

[0318] The device collects the user's pulse, body temperature, and sweat data every minute. For example, when a user is riding a train, the smartwatch's sensor measures the user's pulse, and the body temperature sensor measures the user's skin temperature. The microphone constantly monitors and records the user's speech in real time. If necessary, the smartphone's camera records the user's facial expressions and movements.

[0319] Data transmission and analysis

[0320] The device periodically transmits the collected data to a server. The server analyzes the received data in real time and evaluates fluctuations from baseline in pulse rate, body temperature, and sweat data. The emotion engine analyzes audio, image, and video data and uses natural language processing (NLP) technology to identify keywords and tones that indicate anger, as well as the user's facial expressions and actions.

[0321] Anger detection and notification generation

[0322] The server comprehensively evaluates the analysis results, and if it determines that the user is angry, it generates an appropriate notification message and sends it to the device. Based on the analysis results of the emotion engine, it generates a notification that matches the user's emotions. For example, it generates a message such as "Take a deep breath and calm down" or a joke such as "If you think the train is crowded, everyone came to see me today!" These messages are sent to the smartwatch or smartphone.

[0323] User Notification

[0324] The device receives the notification and presents it to the user. The smartwatch vibrates and displays the message "Take a deep breath and calm down," while the smartphone plays a joke. The goal is for the user to receive the notification and calm down immediately.

[0325] Specific examples

[0326] For example, suppose a user becomes irritated on a train, their pulse rate and body temperature rise, they start sweating, they say things like "Stop messing around!" or "Why is it so crowded?", and their expression becomes grim. In this case, the device collects this data and sends it to the server. The server analyzes this data and, if it determines that the user is angry using its emotion engine, it generates an appropriate message and sends it to the device. Finally, the device tries to calm the user down by telling them to "take a deep breath and calm down" or telling a joke.

[0327] Prompt Sentence Examples

[0328] Here are some examples of prompts to input to a generative AI model:

[0329] If the user is frustrated and expressing anger, generate a conversational message to defuse the situation. For example, if the user's pulse rate is 30% higher than normal, their body temperature is more than half a degree higher, and their voice recording contains the words "It's so busy!", generate a message or joke to calm the user.

[0330] This system can prevent trouble in public places and provide a safe and comfortable environment for users and those around them.

[0331] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0332] Step 1: Initial Setup

[0333] When a user uses the system for the first time, the device collects the user's basic data. The user wears the smartwatch and remains still for a few minutes to measure their normal pulse, body temperature, and sweat rate. Once the basic data is collected, the smartphone app opens and the user selects the type of notification (vibration, voice, text). This information is stored on the server as initial setting data.

[0334] Input: User's biometric information, notification settings

[0335] Output: Initial setting data

[0336] Step 2: Collect data

[0337] The device collects the user's pulse, body temperature, and sweat data every minute. For example, while riding a train, the smartwatch's sensor measures the user's pulse, and the body temperature sensor measures the user's skin temperature. The device's built-in microphone also records the user's voice in real time. If necessary, the smartphone's camera captures the user's facial expressions and movements.

[0338] Input: Continuously collected biometric, audio, and image data

[0339] Output: Real-time data collected

[0340] Step 3: Sending data

[0341] The device sends the collected data at regular intervals (for example, every minute) to a server. This data includes pulse data, body temperature data, sweat data, voice data, and, if necessary, image and video data. The communication method is that the smartwatch sends the data to a smartphone via Bluetooth, and the smartphone then sends the data to the server via the Internet.

[0342] Input: Real-time data collected

[0343] Output: Data sent to the server

[0344] Step 4: Analyze the data

[0345] The server analyzes the received data in real time. Specifically, it calculates and evaluates fluctuations from baseline values ​​in pulse rate, body temperature, and sweat data. The emotion engine also analyzes audio, image, and video data, and uses natural language processing (NLP) technology to detect keywords, tones, and facial expressions that indicate anger.

[0346] Input: Data sent to the server

[0347] Output: Analysis result (user's emotional state)

[0348] Step 5: Anger detection

[0349] The server comprehensively evaluates the analysis results and determines whether the user is in an angry state. For example, if the pulse rate is 30% or more higher than normal, the body temperature is 0.5 degrees higher than normal, and keywords indicating anger are detected in the voice data, the server determines that the user is in an angry state.

[0350] Input: Analysis results

[0351] Output: Angry state determination result

[0352] Step 6: Generate and send notifications

[0353] The server generates an appropriate notification message to ease the user's anger. For example, it generates a message such as "Take a deep breath and calm down" or a joke such as "If you think the train is crowded, you're here to see me today!" The notification message generated based on the analysis results of the emotion engine is sent to the device.

[0354] Input: Angry state result

[0355] Output: Information message

[0356] Step 7: Notify users

[0357] The device receives the notification message and presents it to the user. The smartwatch vibrates and displays the message "Take a deep breath and calm down." The smartphone plays the generated joke aloud, allowing the user to calm down immediately.

[0358] Input: Notification message

[0359] Output: Notification displayed to the user

[0360] Through these processing steps, the system can detect user anger early and respond appropriately, thereby preventing trouble in public places and providing a safe and comfortable environment for users and those around them.

[0361] (Application example 2)

[0362] 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."

[0363] Previously, there were limitations to how to manage the stress and anger experienced by operators working in factories, which affected work efficiency and safety. Furthermore, there was a lack of means to monitor the emotional state of operators in real time and take appropriate measures, making it difficult to provide appropriate alerts early on. As a result, this can have a negative impact on the mental and physical health of operators.

[0364] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0365] In this invention, the server includes means for acquiring pulse data, means for acquiring body temperature data, means for acquiring sweat data, means for acquiring voice data, means for analyzing the acquired pulse data, body temperature data, sweat data, and voice data, means for determining whether the user is in an angry state based on the analysis results, means for sending a notification to the user when it is determined that the user is in an angry state, means for generating a notification appropriate to the work environment when the user is working, and means for displaying or voicing the generated notification on the automatic operation device. This makes it possible to early detect stress or anger experienced by operators working in a factory and provide appropriate alerts and advice.

[0366] "Pulse data" refers to data obtained by measuring and recording the user's heart rate.

[0367] "Body temperature data" refers to data obtained by measuring and recording the user's body temperature.

[0368] "Sweat data" refers to data obtained by measuring and recording the humidity and electrical conductivity of the user's skin surface.

[0369] "Voice data" refers to data that is recorded and saved of the voice uttered by the user.

[0370] "Means for acquiring" refers to devices or sensors for collecting pulse data, body temperature data, sweat data, or voice data.

[0371] The "analyzing means" refers to algorithms and software for processing and analyzing the various data acquired (pulse data, body temperature data, sweat data, and voice data).

[0372] The "means for determining" refers to logic or criteria for determining whether the user is in an angry state based on the results of data analysis.

[0373] A "means for sending a notification" is a procedure or device for transmitting a notification message to a user.

[0374] The "means for generating notifications suited to the working environment" is a system that takes into account the work that the user is doing and creates notifications suited to that situation.

[0375] An "automated operation device" is a device or robot used in a factory that has the function of displaying or vocalizing notifications to an operator.

[0376] This invention is an anger management system that monitors the stress and anger of factory workers in real time and provides notifications at appropriate times. The system collects and analyzes the operator's biometric data (pulse, body temperature, sweat) and voice data to determine whether the operator is in an angry state and notifies them as necessary.

[0377] System Configuration

[0378] The system includes the following components:

[0379] Hardware

[0380] 1. Terminals (in-factory robots, mobile terminals)

[0381] How pulse data is acquired: A sensor built into the robot measures the operator's pulse in real time.

[0382] Body temperature data acquisition method: A temperature sensor built into the robot measures the operator's body temperature in real time.

[0383] Method of acquiring sweating data: Sensors built into the robot detect the operator's sweating status in real time.

[0384] How voice data is acquired: The robot's microphone records the operator's voice and saves it as an audio file.

[0385] software

[0386] 2. Server

[0387] Data analysis means: The server receives the acquired pulse data, body temperature data, sweat data and voice data and analyzes them.

[0388] Anger detection method: Based on the analysis results, determine whether the operator is in an angry state.

[0389] Emotion engine: Analyzes voice data, pulse data, body temperature data, and sweat data to identify the operator's emotions in detail.

[0390] Notification generating means: Generates an appropriate notification message if it is determined that the operator is in an angry state.

[0391] Notification sending means: Sends the generated notification message to the robot or mobile device.

[0392] Program processing description

[0393] Initial Setup

[0394] The device registers the operator's basic data (normal pulse, body temperature, sweat level), and the operator can select the type of notification (voice, text, flashing light) according to their preference.

[0395] Data collection

[0396] The terminal periodically (for example, every minute) collects the operator's pulse, body temperature, and sweat data. At the same time, the terminal's microphone records the operator's voice in real time and saves it as audio data.

[0397] Data transmission

[0398] The data collected by the device is sent to the server at regular intervals (for example, every minute). This data includes pulse data, body temperature data, sweat data, and voice data.

[0399] Data analysis

[0400] The server analyzes the transmitted data in real time. Specifically, it calculates and evaluates fluctuations in pulse rate, body temperature, and sweat data from normal baseline values. The emotion engine analyzes the voice data and uses natural language processing (NLP) to detect keywords and tones that indicate anger.

[0401] Anger detection

[0402] The server comprehensively evaluates the analysis results and determines whether the operator is in an angry state. For example, if the pulse rate is 30% or more higher than normal, the body temperature is 0.5 degrees higher, and the voice data contains keywords indicating anger, the operator is determined to be in an angry state.

[0403] Generate and send notifications

[0404] If the server detects that the operator is angry, it generates an appropriate notification message. Based on the analysis results of the emotion engine, it generates a notification that matches the operator's emotions. For example, it generates a message such as "Take a short break" or advice such as "Take a deep breath and relax." The generated message is sent to the robot or mobile device.

[0405] Specific examples

[0406] If an operator in a factory starts to get irritated while working, their pulse rate increases, and their voice tone becomes rough:

[0407] Sensors: The robot's built-in sensors detect increased pulse rate, body temperature, and increased sweating.

[0408] Audio data: The microphone detects words like "Damn!" and "Why doesn't this work?"

[0409] Analysis: The server interprets this as an angry state and immediately generates the message "Take a break."

[0410] Notification: The robot will give you voice advice saying, "Take a short break."

[0411] Prompt Sentence Examples

[0412] "An operator is becoming angry while working. Please analyze their current pulse, body temperature, and sweat data and generate an appropriate notification message."

[0413] This system can improve work efficiency and safety by detecting stress and anger experienced by operators working in factories at an early stage and providing appropriate alerts and advice.

[0414] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0415] Step 1:

[0416] The terminal registers the operator's basic data.

[0417] Input: The operator's normal pulse rate, body temperature, and sweat level.

[0418] Operation: The terminal's sensors measure the operator's initial data and register it in the system.

[0419] Output: The initial base data is saved in the system.

[0420] Step 2:

[0421] The user selects the type of notification.

[0422] Input: Operator notification preference (voice, text, flashing light).

[0423] How it works: The operator selects the type of notification using the terminal interface.

[0424] Output: The selected notification type is set in the system.

[0425] Step 3:

[0426] The terminal periodically collects the operator's biometric data.

[0427] Input: Operator pulse, temperature, and sweat data.

[0428] How it works: The device's built-in sensors measure biometric data every minute.

[0429] Output: The measured biometric data is stored on the device.

[0430] Step 4:

[0431] The terminal collects the operator's voice data.

[0432] Input: Operator's voice (speech content, tone).

[0433] How it works: The device's microphone records the operator's voice in real time and saves it as an audio file.

[0434] Output: The recorded audio file will be saved on your device.

[0435] Step 5:

[0436] The terminal transmits the collected data to the server.

[0437] Input: Collected biometric and audio data.

[0438] How it works: The device sends data packets to the server every minute.

[0439] Output: Biometric and audio data transmitted to the server.

[0440] Step 6:

[0441] The server analyzes the data.

[0442] Input: Biometric and voice data sent to the server.

[0443] How it works: The server's data analysis means evaluates fluctuations in pulse, body temperature, and sweat data by comparing them with baseline values. The emotion engine also analyzes voice data using NLP to detect keywords and tones that indicate anger.

[0444] Output: Obtain analysis results (e.g., pulse rate increase of 30% or more, body temperature increase of 0.5 degrees or more, increased sweating, anger keywords in the voice).

[0445] Step 7:

[0446] The server determines the anger state.

[0447] Input: Analysis results.

[0448] How it works: The server aggregates and evaluates the analysis results to determine whether the operator is in an angry state.

[0449] Output: Operator's situation assessment result (whether angry or not).

[0450] Step 8:

[0451] The server generates an appropriate notification message.

[0452] Input: Operator's situation assessment result (anger state).

[0453] Action: Based on the analysis results, the emotion engine generates an appropriate notification message (e.g., take a short break, take a deep breath, and relax).

[0454] Output: The generated notification message.

[0455] Step 9:

[0456] The server sends a notification message to the terminal.

[0457] Input: The generated notification message.

[0458] Operation: The server sends a notification message to the terminal as a packet.

[0459] Output: The device receives the notification message.

[0460] Step 10:

[0461] The terminal presents the notification message to the operator.

[0462] Input: Notification message.

[0463] Operation: The device will present a notification message to the operator in the manner configured (voice, text, flashing light).

[0464] Output: A notification message is presented to the operator, encouraging the user to exercise restraint.

[0465] 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.

[0466] 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.

[0467] 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.

[0468] [Second embodiment]

[0469] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0470] 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.

[0471] 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).

[0472] 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.

[0473] 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.

[0474] 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).

[0475] 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.

[0476] 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.

[0477] 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.

[0478] 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.

[0479] 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.

[0480] 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."

[0481] This invention relates to an anger management AI system that prevents trouble in crowded public places, especially on trains, etc. This system is characterized by sending notifications and messages urging self-control at appropriate times when the user becomes angry.

[0482] System Configuration

[0483] The system includes the following components:

[0484] 1. Devices (smartwatches, smartphones)

[0485] How pulse data is acquired: A sensor built into the device measures the user's pulse in real time.

[0486] Body temperature data acquisition method: A temperature sensor built into the device measures the user's body temperature in real time.

[0487] Method for acquiring sweat data: A sensor built into the device detects the user's sweating status in real time.

[0488] Means for acquiring voice data: The device's microphone records the user's voice and saves it as an audio file.

[0489] 2. Server

[0490] Data analysis means: The server receives the acquired pulse data, body temperature data, sweat data and voice data and analyzes them.

[0491] Anger detection means: Based on the analysis results, it is determined whether the user is in an angry state.

[0492] Notification generating means: When it is determined that the user is in an angry state, a message for sending a notification to the user is generated.

[0493] Notification sending means: Sends the generated notification message to the smartwatch or smartphone.

[0494] Explanation of program processing

[0495] 1. Initial Setup

[0496] The device registers the user's basic data (normal pulse, body temperature, sweat level) as the initial setting, and the user can select the type of notification (vibration, voice, text) according to their preference.

[0497] 2. Data collection

[0498] The device periodically collects pulse, body temperature, and sweat data. Audio data is also recorded in real time using a microphone and sent to a server.

[0499] 3. Data Analysis

[0500] The server receives the transmitted data and analyzes it, assessing how much the pulse rate has deviated from normal, whether body temperature has risen, and whether sweating has increased. It also converts the voice data into text and uses natural language processing (NLP) to detect keywords and tones that indicate anger.

[0501] 4. Anger detection

[0502] The server determines whether the user is in an angry state based on the analysis results. For example, if the pulse rate is 30% higher than normal, the body temperature is 0.5 degrees higher, and the voice data contains keywords indicating anger, the server determines that the user is in an angry state.

[0503] 5. Generate and send notifications

[0504] If the server detects the user's anger, it generates an appropriate notification message, such as "Take a deep breath and calm down" or a joke like "If you thought the train was crowded, you're here to see me today!" The generated message is sent to the smartwatch or smartphone.

[0505] The device receives the generated notification and presents it to the user: the smartwatch vibrates and displays a message, and the smartphone plays a message or joke aloud.

[0506] Specific examples

[0507] For example, suppose a user becomes irritated on a train, their pulse rate and body temperature rise, they start sweating, and they say things like, "Stop messing around!" or "Why is it so crowded?" In this case, the device collects this data and sends it to the server. The server analyzes the data and, if it determines that the user is angry, it generates an appropriate message and sends it to the device. Finally, the device tries to calm the user down by telling them to "take a deep breath and calm down" or telling them a joke.

[0508] By using this system, it is possible to prevent trouble in public places and provide a safe and comfortable environment for users and those around them.

[0509] The processing flow will be explained below.

[0510] Step 1:

[0511] The device registers the user's basic data (normal pulse, body temperature, sweat level) as the initial setting, and the user also selects the type of notification (vibration, voice, text) according to their preference.

[0512] Step 2:

[0513] The device periodically (for example, every minute) collects the user's pulse, body temperature, and sweat data. At the same time, the device's microphone records the user's voice in real time and saves it as audio data.

[0514] Step 3:

[0515] The terminal transmits the collected pulse data, body temperature data, sweat data, and voice data to a server at regular intervals (for example, every minute).

[0516] Step 4:

[0517] The server analyzes the received data in real time. Specifically, it analyzes pulse data and calculates variations from normal baseline values. It also calculates variations from normal baseline values ​​for body temperature data and analyzes sweat data.

[0518] Step 5:

[0519] The server converts the speech data into text and uses natural language processing (NLP) to detect keywords and tones that indicate anger, such as phrases like "Don't mess with me!" or "Why is it so busy?"

[0520] Step 6:

[0521] The server comprehensively evaluates the analysis results and determines whether the user is in an angry state. If the pulse rate is 30% or more higher than normal, the body temperature is 0.5 degrees higher, and the voice data contains keywords related to anger, the server determines that the user is in an angry state.

[0522] Step 7:

[0523] If the server determines that the user is angry, it generates an appropriate notification message, such as "Take a deep breath and calm down" or a joke like "If you thought the train was crowded, you're here to see me today!"

[0524] Step 8:

[0525] The server sends the generated notification message to the device, which then sends the notification to the user's smartwatch and smartphone.

[0526] Step 9:

[0527] The device will present the received notification to the user: the smartwatch will vibrate and display the message "Take a deep breath and calm down," and the smartphone will play a joke.

[0528] Step 10:

[0529] The user receives a notification and can take action, for example, by taking a deep breath or listening to a joke to calm down.

[0530] Step 11:

[0531] The server again collects and analyzes the data to observe the user's reaction. If it determines that the user's anger continues, it will send additional notifications or advice, such as a reminder like "You're almost at the station!"

[0532] These steps will help detect user anger early and take appropriate measures to prevent trouble in public places.

[0533] Example 1

[0534] 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."

[0535] Anger-related troubles in public places, especially on trains, are a major social problem. Conventional technologies lack the means to monitor a user's emotional state in real time and send notifications at the appropriate time, making it difficult to prevent trouble before it occurs. Furthermore, there was no technology that efficiently analyzed a user's physiological and voice data and quickly detected anger based on the analysis results.

[0536] 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.

[0537] In this invention, the server includes means for acquiring pulse data, means for acquiring body temperature data, means for acquiring sweat data, means for acquiring voice data, means for registering basic data as initial settings, means for analyzing the acquired pulse data, body temperature data, sweat data, and voice data, means for converting the voice data into text and analyzing it using natural language processing, means for determining whether the user is in an angry state based on the analysis result, and means for sending a notification to the user when it is determined that the user is in an angry state. This makes it possible to monitor the user's emotional state in real time, quickly detect an anger state, and send a notification at an appropriate time.

[0538] "Pulse data" is data measured using the user's heart rate and heart rhythm as indicators.

[0539] "Body temperature data" is data obtained by measuring the user's body temperature.

[0540] "Sweat data" is data obtained by measuring the amount of sweat on the user's skin surface using a sensor.

[0541] "Voice data" refers to sound data of a user's voice collected using a recording device.

[0542] "Basic data" is reference data that indicates the user's normal state, such as normal pulse rate, body temperature, and sweat level.

[0543] "Analysis" is a computational process for determining the user's state and emotions based on various acquired data.

[0544] "Natural language processing" is a technology that converts voice data into text and analyzes that text to understand emotions and intent.

[0545] The "angry state" is a state in which the user is emotionally aroused and feels angry.

[0546] A "notification" is a means of conveying some information or message to a user.

[0547] "Server" refers to a computer system that handles processes such as data analysis, decision-making, and notification generation and transmission.

[0548] A "terminal" is a device that collects data from users and communicates with servers, and primarily includes smartwatches and smartphones.

[0549] This invention relates to an anger management AI system that prevents trouble in crowded public places, especially on trains, etc. This system is characterized by sending notifications and messages urging self-control at appropriate times when the user becomes angry.

[0550] System Configuration

[0551] The system includes the following components:

[0552] 1. Devices (smartwatches, smartphones)

[0553] How pulse data is acquired: A sensor built into the device measures the user's pulse in real time.

[0554] Body temperature data acquisition method: A temperature sensor built into the device measures the user's body temperature in real time.

[0555] Method for acquiring sweat data: A sensor built into the device detects the user's sweating status in real time.

[0556] Means for acquiring voice data: The device's microphone records the user's voice and saves it as an audio file.

[0557] A means for registering basic data as initial settings: The user registers their normal pulse rate, body temperature, and sweat level as initial settings.

[0558] 2. Server

[0559] Data analysis means: The server receives the acquired pulse data, body temperature data, sweat data and voice data and analyzes them.

[0560] Speech-to-text conversion and natural language processing means: Convert speech data into text and use natural language processing (NLP) to analyze keywords and tones that indicate anger.

[0561] Anger detection means: Based on the analysis results, it is determined whether the user is in an angry state.

[0562] Notification generating means: When it is determined that the user is in an angry state, a message for sending a notification to the user is generated.

[0563] Notification sending means: Sends the generated notification message to the smartwatch or smartphone.

[0564] Specific Examples

[0565] For example, imagine a user getting irritated on a train, their pulse rate and body temperature rising, sweating increasing, and uttering phrases like "Stop messing around!" or "Why is it so crowded?" At this time, the device collects this data and sends it to the server. The server analyzes the data and, if it determines that the user is angry, generates an appropriate message and sends it to the device. Finally, the device tries to calm the user down by telling them to "take a deep breath and calm down" or telling them a joke.

[0566] Prompt Sentence Examples

[0567] Below is an example of input to a generative AI model to illustrate this system.

[0568] "Please explain an anger management AI system for controlling anger in public places. This system collects and analyzes the user's biometric and voice data in real time. If an abnormality is detected, it sends a message to the user urging them to control themselves. Please explain in detail what data is collected, how it is analyzed, and what kind of notification is sent."

[0569] By using this system, it is possible to prevent trouble in public places and provide a safe and comfortable environment for users and those around them.

[0570] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0571] Step 1:

[0572] Initial Setup

[0573] When the user starts the system for the first time, they input their basic data (normal pulse, body temperature, and sweat level) using a smartphone app. This basic data is then saved on the device. The user can also select the type of notification (vibration, voice, or text). The input is the user's normal physiological data and notification preferences, and the output is the settings saved on the device.

[0574] Specific behavior:

[0575] 1. The user launches the smartphone app.

[0576] 2. The user enters pulse rate, body temperature, and sweat level according to the screen where basic data is entered.

[0577] 3. On the screen where the user selects the type of notification, select "Vibrate," "Voice," or "Text."

[0578] Step 2:

[0579] Data collection

[0580] The device collects physiological data (pulse, body temperature, sweat) and voice data at regular intervals. The sensor measures this data in real time and temporarily stores it in internal memory. The data is then sent to a server at regular intervals. The input is the physiological data and voice data acquired in real time, and the output is the data sent to the server.

[0581] Specific behavior:

[0582] 1. The device measures pulse rate, body temperature, and sweat data every minute.

[0583] 2. The device's microphone records the user's voice in real time and saves it as an audio file.

[0584] 3. The device sends the collected data to a server via the Internet every five minutes.

[0585] Step 3:

[0586] Data analysis

[0587] The server receives the transmitted data and begins analysis. First, pulse data is evaluated for fluctuations over time to detect deviations from normal baseline values. Next, body temperature data is monitored for temperature fluctuations and compared to baseline values. Sweat data is analyzed in a similar manner to evaluate increases in sweating. Finally, the voice data is converted into text and analyzed using natural language processing (NLP) for keywords and tones that indicate anger. The input is the various physiological data and voice data transmitted to the server, and the output is the analysis results.

[0588] Specific behavior:

[0589] 1. The server analyzes the pulse data and evaluates variations from baseline.

[0590] 2. The server analyzes the body temperature data and determines the degree of temperature rise.

[0591] 3. The server analyzes the sweat data and evaluates the increase in sweat rate.

[0592] 4. The server converts the audio data into text and uses NLP to detect keywords and tones that indicate anger.

[0593] Step 4:

[0594] Anger detection

[0595] The server determines whether the user is in an angry state based on the analysis results. For example, if the pulse rate is 30% higher than normal, the body temperature is 0.5 degrees higher, and the voice data contains anger-related keywords such as "Don't mess with me" or "Why," the server determines the user is in an angry state. The input is the analysis results, and the output is the anger state determination result.

[0596] Specific behavior:

[0597] 1. The server comprehensively evaluates the analysis results of pulse, body temperature, and sweat data.

[0598] 2. The server uses the results of the voice data analysis to determine the emotional state.

[0599] 3. The server determines whether the user's emotional state is angry.

[0600] Step 5:

[0601] Notification generation and delivery

[0602] If the server detects the user's anger, it generates an appropriate notification message. For example, it generates a joke such as "Take a deep breath and calm down" or "Everyone's here to see me today!" This notification message is sent to the smartwatch or smartphone. The input is the anger state judgment result, and the output is the generated notification message.

[0603] Specific behavior:

[0604] 1. The server generates an appropriate notification message for the user.

[0605] 2. The server sends the generated notification message to the terminal.

[0606] 3. The terminal receives the notification message and presents it to the user.

[0607] 4. The smartwatch will vibrate and display a message, and the smartphone will play the message aloud.

[0608] Through the above steps, the anger management AI system of the present invention can monitor the user's emotional state in real time and encourage self-control at the appropriate time, thereby preventing trouble in public places.

[0609] (Application example 1)

[0610] 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."

[0611] In modern brick-and-mortar stores, when customers feel stressed or angry, it often negatively impacts the atmosphere in the store and other customers. However, it is difficult for on-site staff to sense the situation in real time and respond appropriately. Therefore, a method is needed to detect customers' emotional states in real time and respond appropriately.

[0612] 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.

[0613] In this invention, the server includes means for acquiring pulse data, means for acquiring body temperature data, means for acquiring sweat data, means for acquiring voice data, means for analyzing the acquired pulse data, body temperature data, sweat data, and voice data, means for determining whether the user is in an angry state based on the analysis results, means for sending a notification to the user when it is determined that the user is in an angry state, means for sending a notification to the user's smart electronic device in a physical store, and message generation means using a generative AI model to ensure that the notification includes a specific message and specific suggestions such as promoting relaxation or offering a special discount. This makes it possible to automatically notify customers in a physical store of appropriate messages and suggestions in real time when they feel stressed or angry.

[0614] "Pulse data" is data indicating the user's heart rate, and refers to the number of times the user's heart beats measured in real time.

[0615] "Body temperature data" is data indicating the user's body temperature, and refers to the internal and external temperatures of the user's body measured in real time.

[0616] "Sweat data" is data indicating the amount of sweat produced by the user, and refers to the amount of sweat secreted from the user's skin measured in real time.

[0617] "Voice data" refers to data indicating the content of a user's speech, and refers to the characteristics of the user's words and voice collected in real time.

[0618] The "analysis means" is a means for analyzing the various acquired data and evaluating the emotional state of the user based on the data.

[0619] The "determination means" is a means for determining whether the user is in an angry state based on the analysis result obtained by the analysis means.

[0620] The "notification means" is a means for conveying an appropriate message to the user when it is determined that the user is in an angry state.

[0621] "Smart electronic devices" refer to portable devices that users use on a daily basis, such as smartwatches and smartphones.

[0622] A "generative AI model" is a model that uses artificial intelligence to generate specific messages and suggestions, allowing for personalized messages to be generated for users.

[0623] The "message generation means" is a means of generating appropriate messages and suggestions according to the user's state, and uses a generative AI model.

[0624] The present invention is a system for detecting stress and anger in real time in a brick-and-mortar store and responding appropriately using the user's smart electronic devices, a cloud server, and a generative AI model.

[0625] System Configuration

[0626] The system includes the following components:

[0627] 1. Smart Electronics

[0628] This includes smartwatches and smartphones.

[0629] How to obtain pulse data: The user's heart rate is obtained in real time using the sensors in the smartwatch.

[0630] Body temperature data acquisition method: The user's body temperature is acquired in real time using a temperature sensor in a smartwatch or smartphone.

[0631] Means of acquiring sweat data: The amount of sweat a user generates is acquired in real time using sensors in smart electronic devices.

[0632] Method of acquiring voice data: The smartphone's microphone is used to collect the user's speech in real time.

[0633] 2. Server

[0634] Data analysis means: The server receives and analyzes pulse, body temperature, sweat, and voice data sent from smart electronic devices.

[0635] Anger detection means: Based on the analysis results, it is determined whether the user is in an angry state.

[0636] Notification generator: If the user is determined to be in an angry state, an appropriate message is generated using a generative AI model.

[0637] Notification sending method: Send the generated message to your smartwatch or smartphone.

[0638] Hardware and software used

[0639] Hardware:

[0640] Smartwatch (e.g. Samsung Galaxy Watch)

[0641] Smartphone (e.g. iPhone 13)

[0642] Cloud server (e.g. AWS EC2 instance)

[0643] software:

[0644] TensorFlow: Used to implement an anger detection model based on pulse, body temperature, and sweat data.

[0645] Twilio: Used to send notification messages to users' smart electronic devices.

[0646] OpenAI GPT-3.5: Used to generate response messages using a generative AI model.

[0647] Specific example explanation

[0648] For example, if a customer in a physical store becomes stressed due to a long wait or crowding, the system collects the customer's pulse, body temperature, and sweat rate in real time, and also records what they say with a microphone. This data is sent to a server for analysis. If the server determines that the customer is angry based on the analysis results, it uses a generative AI model (OpenAI GPT-3.5) to generate a message such as "Take a deep breath and calm down" or a suggestion message offering a special discount coupon.

[0649] Example prompt for a generative AI model:

[0650] "Generate a soothing message for a person who is likely angry or stressed:"

[0651] Finally, this generated message is sent via Twilio to a smartwatch or smartphone, for example, a notification will appear on the customer's smartwatch saying, "Enjoy a relaxed shopping experience with our special coupons," which will calm the customer and maintain a good atmosphere in the store.

[0652] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0653] Step 1:

[0654] The device collects the user's pulse, body temperature, sweat, and voice data in real time. Specifically, sensors within the smartwatch measure the pulse and body temperature and detect the amount of sweat. The smartphone's microphone also collects surrounding sounds and sends this data to a cloud server. The input is various vital data and voice data, and the output is data sent to the server.

[0655] Step 2:

[0656] The server receives the data acquired by the data analysis means and analyzes each data. Specifically, it uses TensorFlow to analyze pulse, body temperature, and sweat data and evaluates how much these data deviate from baseline values. It also converts voice data into text and uses natural language processing (NLP) to detect emotional tone and keywords that indicate anger. The input is the data sent from the device, and the output is the analysis results.

[0657] Step 3:

[0658] The server determines whether the user is in an angry state based on the analysis results. Specific criteria include pulse rate 30% or more higher than normal, body temperature 0.5 degrees higher, increased sweating, and the voice data containing keywords indicating anger. The input is the analysis results, and the output is the anger state determination.

[0659] Step 4:

[0660] If the server determines that the user is in an angry state, it uses a generative AI model (OpenAI GPT-3.5) to generate an appropriate message. Specifically, the prompt "Generate a soothing message for a person who is likely angry or stressed:" is input into the generative AI model, which outputs messages such as "Take a deep breath and calm down" or "Use a special coupon and enjoy a relaxing shopping experience." The input is the determination of the user's angry state, and the output is the generated message.

[0661] Step 5:

[0662] The server sends the generated message to the user's device as a notification. Specifically, it uses Twilio to send the message to a smartwatch or smartphone. The user's smartwatch vibrates and displays the generated message. The smartphone plays the message aloud. The input is the generated message, and the output is a notification to the user's device.

[0663] Step 6:

[0664] The user receives a notification from the device and checks the message displayed on the screen or the voice guidance, which allows the user to take a deep breath or use a special coupon to reduce stress or anger. The input is the notification to the device, and the output is the user's reaction.

[0665] 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.

[0666] This invention relates to an anger management AI system that prevents trouble in crowded public places, especially on trains, etc. This system is characterized by sending notifications and messages urging self-control at appropriate times when the user becomes angry, and its effectiveness is enhanced by combining it with an emotion engine.

[0667] System Configuration

[0668] The system includes the following components:

[0669] 1. Devices (smartwatches, smartphones)

[0670] How pulse data is acquired: A sensor built into the device measures the user's pulse in real time.

[0671] Body temperature data acquisition method: A temperature sensor built into the device measures the user's body temperature in real time.

[0672] Method for acquiring sweat data: A sensor built into the device detects the user's sweating status in real time.

[0673] Means for acquiring voice data: The device's microphone records the user's voice and saves it as an audio file.

[0674] Means for acquiring image data and video data (if necessary): The smartphone camera captures the user's facial expressions and movements and sends the data to the server.

[0675] 2. Server

[0676] Data analysis means: The server receives the acquired pulse data, body temperature data, sweat data, voice data, image data and video data and analyzes them.

[0677] Anger detection: Based on the analysis results, it is determined whether the user is in an angry state. The emotion engine plays an important role here.

[0678] Emotion engine: Analyzes voice data, pulse data, body temperature data, sweat data, as well as image and video data to identify the user's emotions in detail.

[0679] Notification generation means: If the user is determined to be in an angry state, a message is generated to notify the user. Based on the analysis results of the emotion engine, a notification is generated that matches the user's emotions.

[0680] Notification sending means: Sends the generated notification message to the smartwatch or smartphone.

[0681] Explanation of program processing

[0682] 1. Initial Setup

[0683] The device registers the user's basic data (normal pulse, body temperature, sweat level) as the initial setting, and the user can select the type of notification (vibration, voice, text) according to their preference.

[0684] 2. Data collection

[0685] The device periodically (for example, every minute) collects the user's pulse, body temperature, and sweat data. At the same time, the device's microphone records the user's voice in real time and saves it as audio data. Furthermore, the smartphone's camera captures the user's facial expressions and movements as needed and saves them as image and video data.

[0686] 3. Data transmission

[0687] The collected data is sent to the server at regular intervals (for example, every minute). This data includes pulse data, body temperature data, sweat data, voice data, image data, and video data.

[0688] 4. Data Analysis

[0689] The server analyzes the transmitted data in real time. Specifically, it calculates and evaluates fluctuations in pulse, body temperature, and sweat data from their normal baseline values. The emotion engine analyzes the audio, image, and video data and uses natural language processing (NLP) to detect keywords and tones that indicate anger, as well as the user's facial expressions and actions.

[0690] 5. Anger detection

[0691] The server comprehensively evaluates the analysis results and determines whether the user is in an angry state. If the pulse rate is 30% or more higher than normal, the body temperature is 0.5 degrees or more higher, and the voice data or image data contains keywords or facial expressions that indicate anger, the user is determined to be in an angry state.

[0692] 6. Generating and Sending Notifications

[0693] If the server detects that the user is angry, it generates an appropriate notification message. Based on the analysis results of the emotion engine, it generates a notification that matches the user's emotions. For example, it generates a message such as "Take a deep breath and calm down" or a joke such as "If you think the train is crowded, everyone came to see me today!" The generated message is sent to the smartwatch or smartphone.

[0694] The device receives the generated notification and presents it to the user: the smartwatch vibrates and displays the message "Take a deep breath and calm down," and the smartphone plays a joke aloud.

[0695] Specific examples

[0696] For example, suppose a user becomes irritated on a train, their pulse rate and body temperature rise, they start sweating, they say things like "Stop messing around!" or "Why is it so crowded?", and their expression becomes grim. In this case, the device collects this data and sends it to the server. The server analyzes this data and, if it determines that the user is angry using its emotion engine, it generates an appropriate message and sends it to the device. Finally, the device tries to calm the user down by telling them to "take a deep breath and calm down" or telling a joke.

[0697] This system can detect user anger early and take appropriate measures to prevent trouble in public places, providing a safe and comfortable environment for users and those around them.

[0698] The processing flow will be explained below.

[0699] Step 1:

[0700] The device registers the user's basic data (normal pulse, body temperature, sweat level) as the initial setting, and the user also selects the type of notification (vibration, voice, text) according to their preference.

[0701] Step 2:

[0702] The device periodically (for example, every minute) collects the user's pulse, body temperature, and sweat data. At the same time, the device's microphone records the user's voice in real time and saves it as audio data. If necessary, the smartphone camera captures the user's facial expressions and movements, collecting image and video data.

[0703] Step 3:

[0704] The terminal transmits the collected pulse data, body temperature data, sweat data, voice data, image data, and video data to a server at regular intervals (for example, every minute).

[0705] Step 4:

[0706] The server analyzes the received data in real time. It analyzes pulse data to calculate variations from normal baseline values, as well as body temperature and sweat data.

[0707] Step 5:

[0708] The server converts the voice data into text and uses natural language processing (NLP) to detect keywords and tones that indicate anger, while simultaneously analyzing image and video data to identify emotions from the user's facial expressions and movements.

[0709] Step 6:

[0710] The emotion engine comprehensively evaluates the analysis results and determines the user's emotional state. If the pulse rate is 30% or more higher than normal, the body temperature is 0.5 degrees higher, and the voice and image data contain keywords or facial expressions that indicate anger, the user is determined to be in an angry state.

[0711] Step 7:

[0712] If the server detects that the user is angry, it generates an appropriate notification message based on the analysis results of the emotion engine. For example, it generates a message like "Take a deep breath and calm down" or a joke like "I thought the train was crowded, but everyone came to see me today!"

[0713] Step 8:

[0714] The server sends the generated notification message to the device, which then sends the notification to the user's smartwatch and smartphone.

[0715] Step 9:

[0716] The device receives the notification and presents it to the user accordingly: the smartwatch vibrates and displays the message "Take a deep breath and calm down," and the smartphone plays a joke aloud.

[0717] Step 10:

[0718] The user receives a notification and can take action, for example, by taking a deep breath or listening to a joke to calm down.

[0719] Step 11:

[0720] The server again collects and analyzes the data to observe the user's reaction. If it determines that the user's anger continues, it will send additional notifications or advice, such as a reminder like "You're almost at the station!"

[0721] These steps will help detect user anger early and take appropriate measures to prevent trouble in public places.

[0722] Example 2

[0723] 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."

[0724] In modern public places, especially on trains, users often become angry due to crowding and stress. This can cause trouble and have a negative impact on those around them, so there is a need for a system that can detect users' anger early and encourage appropriate responses.

[0725] 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.

[0726] In this invention, the server includes means for acquiring pulse data, means for acquiring body temperature data, means for acquiring sweat data, means for acquiring voice data, means for acquiring image data and video data, means for analyzing the acquired pulse data, body temperature data, sweat data, voice data, image data and video data, means for determining whether the user is in an angry state based on the analysis results, and means for sending a notification to the user when it is determined that the user is in an angry state. This makes it possible to monitor the user's emotional state in detail and in real time and send notifications at appropriate times.

[0727] "Pulse data" is biological information that indicates the user's heart rate.

[0728] "Body temperature data" is biological information indicating the user's body temperature.

[0729] "Sweat data" is biological information that indicates the amount of sweat of the user and the associated humidity of the skin.

[0730] "Voice data" is digital information that records the user's speech or voice.

[0731] "Image data" is digital information of a still image capturing the user's facial expression and posture.

[0732] "Video data" is digital information of moving images of the user's facial expressions and movements.

[0733] "Emotion engine" is a general term for algorithms and software that analyzes audio data, image data, video data, and various biometric data to determine the user's emotional state.

[0734] "Notification" means a message, alert, or information sent to a user to prompt the user to take action.

[0735] A "personal digital assistant" is a portable electronic device capable of data communication, such as a smartphone or tablet.

[0736] A "mobile device" is an electronic device worn on the wrist, such as a smartwatch.

[0737] This invention relates to an anger management AI system that prevents trouble in crowded public places, especially on trains, etc. This system is characterized by sending notifications and messages urging self-control at appropriate times when the user becomes angry, and its effectiveness is enhanced by combining it with an emotion engine.

[0738] System Configuration

[0739] The system includes the following components:

[0740] 1. Devices (smartwatches, smartphones)

[0741] How pulse data is acquired: A sensor built into the device measures the user's pulse in real time.

[0742] Body temperature data acquisition method: A temperature sensor built into the device measures the user's body temperature in real time.

[0743] Method for acquiring sweat data: A sensor built into the device detects the user's sweating status in real time.

[0744] Means for acquiring voice data: The device's microphone records the user's voice and saves it as an audio file.

[0745] Means for acquiring image data and video data (if necessary): The smartphone camera captures the user's facial expressions and movements and sends the data to the server.

[0746] 2. Server

[0747] Data analysis means: The server receives the acquired pulse data, body temperature data, sweat data, voice data, image data and video data and analyzes them.

[0748] Anger detection: Based on the analysis results, it is determined whether the user is in an angry state. The emotion engine plays an important role here.

[0749] Emotion engine: Analyzes voice data, pulse data, body temperature data, sweat data, as well as image and video data to identify the user's emotions in detail.

[0750] Notification generation means: If the user is determined to be in an angry state, a message is generated to notify the user. Based on the analysis results of the emotion engine, a notification is generated that matches the user's emotions.

[0751] Notification sending means: Sends the generated notification message to the smartwatch or smartphone.

[0752] Detailed Description of the Invention

[0753] Data collection

[0754] The device collects the user's pulse, body temperature, and sweat data every minute. For example, when a user is riding a train, the smartwatch's sensor measures the user's pulse, and the body temperature sensor measures the user's skin temperature. The microphone constantly monitors and records the user's speech in real time. If necessary, the smartphone's camera records the user's facial expressions and movements.

[0755] Data transmission and analysis

[0756] The device periodically transmits the collected data to a server. The server analyzes the received data in real time and evaluates fluctuations from baseline in pulse rate, body temperature, and sweat data. The emotion engine analyzes audio, image, and video data and uses natural language processing (NLP) technology to identify keywords and tones that indicate anger, as well as the user's facial expressions and actions.

[0757] Anger detection and notification generation

[0758] The server comprehensively evaluates the analysis results, and if it determines that the user is angry, it generates an appropriate notification message and sends it to the device. Based on the analysis results of the emotion engine, it generates a notification that matches the user's emotions. For example, it generates a message such as "Take a deep breath and calm down" or a joke such as "If you think the train is crowded, everyone came to see me today!" These messages are sent to the smartwatch or smartphone.

[0759] User Notification

[0760] The device receives the notification and presents it to the user. The smartwatch vibrates and displays the message "Take a deep breath and calm down," while the smartphone plays a joke. The goal is for the user to receive the notification and calm down immediately.

[0761] Specific examples

[0762] For example, suppose a user becomes irritated on a train, their pulse rate and body temperature rise, they start sweating, they say things like "Stop messing around!" or "Why is it so crowded?", and their expression becomes grim. In this case, the device collects this data and sends it to the server. The server analyzes this data and, if it determines that the user is angry using its emotion engine, it generates an appropriate message and sends it to the device. Finally, the device tries to calm the user down by telling them to "take a deep breath and calm down" or telling a joke.

[0763] Prompt Sentence Examples

[0764] Here are some examples of prompts to input to a generative AI model:

[0765] If the user is frustrated and expressing anger, generate a conversational message to defuse the situation. For example, if the user's pulse rate is 30% higher than normal, their body temperature is more than half a degree higher, and their voice recording contains the words "It's so busy!", generate a message or joke to calm the user.

[0766] This system can prevent trouble in public places and provide a safe and comfortable environment for users and those around them.

[0767] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0768] Step 1: Initial Setup

[0769] When a user uses the system for the first time, the device collects the user's basic data. The user wears the smartwatch and remains still for a few minutes to measure their normal pulse, body temperature, and sweat rate. Once the basic data is collected, the smartphone app opens and the user selects the type of notification (vibration, voice, text). This information is stored on the server as initial setting data.

[0770] Input: User's biometric information, notification settings

[0771] Output: Initial setting data

[0772] Step 2: Collect data

[0773] The device collects the user's pulse, body temperature, and sweat data every minute. For example, while riding a train, the smartwatch's sensor measures the user's pulse, and the body temperature sensor measures the user's skin temperature. The device's built-in microphone also records the user's voice in real time. If necessary, the smartphone's camera captures the user's facial expressions and movements.

[0774] Input: Continuously collected biometric, audio, and image data

[0775] Output: Real-time data collected

[0776] Step 3: Sending data

[0777] The device sends the collected data at regular intervals (for example, every minute) to a server. This data includes pulse data, body temperature data, sweat data, voice data, and, if necessary, image and video data. The communication method is that the smartwatch sends the data to a smartphone via Bluetooth, and the smartphone then sends the data to the server via the Internet.

[0778] Input: Real-time data collected

[0779] Output: Data sent to the server

[0780] Step 4: Analyze the data

[0781] The server analyzes the received data in real time. Specifically, it calculates and evaluates fluctuations from baseline values ​​in pulse rate, body temperature, and sweat data. The emotion engine also analyzes audio, image, and video data, and uses natural language processing (NLP) technology to detect keywords, tones, and facial expressions that indicate anger.

[0782] Input: Data sent to the server

[0783] Output: Analysis result (user's emotional state)

[0784] Step 5: Anger detection

[0785] The server comprehensively evaluates the analysis results and determines whether the user is in an angry state. For example, if the pulse rate is 30% or more higher than normal, the body temperature is 0.5 degrees higher than normal, and keywords indicating anger are detected in the voice data, the server determines that the user is in an angry state.

[0786] Input: Analysis results

[0787] Output: Angry state determination result

[0788] Step 6: Generate and send notifications

[0789] The server generates an appropriate notification message to ease the user's anger. For example, it generates a message such as "Take a deep breath and calm down" or a joke such as "If you think the train is crowded, you're here to see me today!" The notification message generated based on the analysis results of the emotion engine is sent to the device.

[0790] Input: Angry state result

[0791] Output: Information message

[0792] Step 7: Notify users

[0793] The device receives the notification message and presents it to the user. The smartwatch vibrates and displays the message "Take a deep breath and calm down." The smartphone plays the generated joke aloud, allowing the user to calm down immediately.

[0794] Input: Notification message

[0795] Output: Notification displayed to the user

[0796] Through these processing steps, the system can detect user anger early and respond appropriately, thereby preventing trouble in public places and providing a safe and comfortable environment for users and those around them.

[0797] (Application example 2)

[0798] 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."

[0799] Previously, there were limitations to how to manage the stress and anger experienced by operators working in factories, which affected work efficiency and safety. Furthermore, there was a lack of means to monitor the emotional state of operators in real time and take appropriate measures, making it difficult to provide appropriate alerts early on. As a result, this can have a negative impact on the mental and physical health of operators.

[0800] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0801] In this invention, the server includes means for acquiring pulse data, means for acquiring body temperature data, means for acquiring sweat data, means for acquiring voice data, means for analyzing the acquired pulse data, body temperature data, sweat data, and voice data, means for determining whether the user is in an angry state based on the analysis results, means for sending a notification to the user when it is determined that the user is in an angry state, means for generating a notification appropriate to the work environment when the user is working, and means for displaying or voicing the generated notification on the automatic operation device. This makes it possible to early detect stress or anger experienced by operators working in a factory and provide appropriate alerts and advice.

[0802] "Pulse data" refers to data obtained by measuring and recording the user's heart rate.

[0803] "Body temperature data" refers to data obtained by measuring and recording the user's body temperature.

[0804] "Sweat data" refers to data obtained by measuring and recording the humidity and electrical conductivity of the user's skin surface.

[0805] "Voice data" refers to data that is recorded and saved of the voice uttered by the user.

[0806] "Means for acquiring" refers to devices or sensors for collecting pulse data, body temperature data, sweat data, or voice data.

[0807] The "analyzing means" refers to algorithms and software for processing and analyzing the various data acquired (pulse data, body temperature data, sweat data, and voice data).

[0808] The "means for determining" refers to logic or criteria for determining whether the user is in an angry state based on the results of data analysis.

[0809] A "means for sending a notification" is a procedure or device for transmitting a notification message to a user.

[0810] The "means for generating notifications suited to the working environment" is a system that takes into account the work that the user is doing and creates notifications suited to that situation.

[0811] An "automated operation device" is a device or robot used in a factory that has the function of displaying or vocalizing notifications to an operator.

[0812] This invention is an anger management system that monitors the stress and anger of factory workers in real time and provides notifications at appropriate times. The system collects and analyzes the operator's biometric data (pulse, body temperature, sweat) and voice data to determine whether the operator is in an angry state and notifies them as necessary.

[0813] System Configuration

[0814] The system includes the following components:

[0815] Hardware

[0816] 1. Terminals (in-factory robots, mobile terminals)

[0817] How pulse data is acquired: A sensor built into the robot measures the operator's pulse in real time.

[0818] Body temperature data acquisition method: A temperature sensor built into the robot measures the operator's body temperature in real time.

[0819] Method of acquiring sweating data: Sensors built into the robot detect the operator's sweating status in real time.

[0820] How voice data is acquired: The robot's microphone records the operator's voice and saves it as an audio file.

[0821] software

[0822] 2. Server

[0823] Data analysis means: The server receives the acquired pulse data, body temperature data, sweat data and voice data and analyzes them.

[0824] Anger detection method: Based on the analysis results, determine whether the operator is in an angry state.

[0825] Emotion engine: Analyzes voice data, pulse data, body temperature data, and sweat data to identify the operator's emotions in detail.

[0826] Notification generating means: Generates an appropriate notification message if it is determined that the operator is in an angry state.

[0827] Notification sending means: Sends the generated notification message to the robot or mobile device.

[0828] Program processing description

[0829] Initial Setup

[0830] The device registers the operator's basic data (normal pulse, body temperature, sweat level), and the operator can select the type of notification (voice, text, flashing light) according to their preference.

[0831] Data collection

[0832] The terminal periodically (for example, every minute) collects the operator's pulse, body temperature, and sweat data. At the same time, the terminal's microphone records the operator's voice in real time and saves it as audio data.

[0833] Data transmission

[0834] The data collected by the device is sent to the server at regular intervals (for example, every minute). This data includes pulse data, body temperature data, sweat data, and voice data.

[0835] Data analysis

[0836] The server analyzes the transmitted data in real time. Specifically, it calculates and evaluates fluctuations in pulse rate, body temperature, and sweat data from normal baseline values. The emotion engine analyzes the voice data and uses natural language processing (NLP) to detect keywords and tones that indicate anger.

[0837] Anger detection

[0838] The server comprehensively evaluates the analysis results and determines whether the operator is in an angry state. For example, if the pulse rate is 30% or more higher than normal, the body temperature is 0.5 degrees higher, and the voice data contains keywords indicating anger, the operator is determined to be in an angry state.

[0839] Generate and send notifications

[0840] If the server detects that the operator is angry, it generates an appropriate notification message. Based on the analysis results of the emotion engine, it generates a notification that matches the operator's emotions. For example, it generates a message such as "Take a short break" or advice such as "Take a deep breath and relax." The generated message is sent to the robot or mobile device.

[0841] Specific examples

[0842] If an operator in a factory starts to get irritated while working, their pulse rate increases, and their voice tone becomes rough:

[0843] Sensors: The robot's built-in sensors detect increased pulse rate, body temperature, and increased sweating.

[0844] Audio data: The microphone detects words like "Damn!" and "Why doesn't this work?"

[0845] Analysis: The server interprets this as an angry state and immediately generates the message "Take a break."

[0846] Notification: The robot will give you voice advice saying, "Take a short break."

[0847] Prompt Sentence Examples

[0848] "An operator is becoming angry while working. Please analyze their current pulse, body temperature, and sweat data and generate an appropriate notification message."

[0849] This system can improve work efficiency and safety by detecting stress and anger experienced by operators working in factories at an early stage and providing appropriate alerts and advice.

[0850] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0851] Step 1:

[0852] The terminal registers the operator's basic data.

[0853] Input: The operator's normal pulse rate, body temperature, and sweat level.

[0854] Operation: The terminal's sensors measure the operator's initial data and register it in the system.

[0855] Output: The initial base data is saved in the system.

[0856] Step 2:

[0857] The user selects the type of notification.

[0858] Input: Operator notification preference (voice, text, flashing light).

[0859] How it works: The operator selects the type of notification using the terminal interface.

[0860] Output: The selected notification type is set in the system.

[0861] Step 3:

[0862] The terminal periodically collects the operator's biometric data.

[0863] Input: Operator pulse, temperature, and sweat data.

[0864] How it works: The device's built-in sensors measure biometric data every minute.

[0865] Output: The measured biometric data is stored on the device.

[0866] Step 4:

[0867] The terminal collects the operator's voice data.

[0868] Input: Operator's voice (speech content, tone).

[0869] How it works: The device's microphone records the operator's voice in real time and saves it as an audio file.

[0870] Output: The recorded audio file will be saved on your device.

[0871] Step 5:

[0872] The terminal transmits the collected data to the server.

[0873] Input: Collected biometric and audio data.

[0874] How it works: The device sends data packets to the server every minute.

[0875] Output: Biometric and audio data transmitted to the server.

[0876] Step 6:

[0877] The server analyzes the data.

[0878] Input: Biometric and voice data sent to the server.

[0879] How it works: The server's data analysis means evaluates fluctuations in pulse, body temperature, and sweat data by comparing them with baseline values. The emotion engine also analyzes voice data using NLP to detect keywords and tones that indicate anger.

[0880] Output: Obtain analysis results (e.g., pulse rate increase of 30% or more, body temperature increase of 0.5 degrees or more, increased sweating, anger keywords in the voice).

[0881] Step 7:

[0882] The server determines the anger state.

[0883] Input: Analysis results.

[0884] How it works: The server aggregates and evaluates the analysis results to determine whether the operator is in an angry state.

[0885] Output: Operator's situation assessment result (whether angry or not).

[0886] Step 8:

[0887] The server generates an appropriate notification message.

[0888] Input: Operator's situation assessment result (anger state).

[0889] Action: Based on the analysis results, the emotion engine generates an appropriate notification message (e.g., take a short break, take a deep breath, and relax).

[0890] Output: The generated notification message.

[0891] Step 9:

[0892] The server sends a notification message to the terminal.

[0893] Input: The generated notification message.

[0894] Operation: The server sends a notification message to the terminal as a packet.

[0895] Output: The device receives the notification message.

[0896] Step 10:

[0897] The terminal presents the notification message to the operator.

[0898] Input: Notification message.

[0899] Operation: The device will present a notification message to the operator in the manner configured (voice, text, flashing light).

[0900] Output: A notification message is presented to the operator, encouraging the user to exercise restraint.

[0901] 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.

[0902] 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.

[0903] 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.

[0904] [Third embodiment]

[0905] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0906] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0907] 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).

[0908] 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.

[0909] 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.

[0910] 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).

[0911] 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.

[0912] 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.

[0913] 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.

[0914] 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.

[0915] 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.

[0916] 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."

[0917] This invention relates to an anger management AI system that prevents trouble in crowded public places, especially on trains, etc. This system is characterized by sending notifications and messages urging self-control at appropriate times when the user becomes angry.

[0918] System Configuration

[0919] The system includes the following components:

[0920] 1. Devices (smartwatches, smartphones)

[0921] How pulse data is acquired: A sensor built into the device measures the user's pulse in real time.

[0922] Body temperature data acquisition method: A temperature sensor built into the device measures the user's body temperature in real time.

[0923] Method for acquiring sweat data: A sensor built into the device detects the user's sweating status in real time.

[0924] Means for acquiring voice data: The device's microphone records the user's voice and saves it as an audio file.

[0925] 2. Server

[0926] Data analysis means: The server receives the acquired pulse data, body temperature data, sweat data and voice data and analyzes them.

[0927] Anger detection means: Based on the analysis results, it is determined whether the user is in an angry state.

[0928] Notification generating means: When it is determined that the user is in an angry state, a message for sending a notification to the user is generated.

[0929] Notification sending means: Sends the generated notification message to the smartwatch or smartphone.

[0930] Explanation of program processing

[0931] 1. Initial Setup

[0932] The device registers the user's basic data (normal pulse, body temperature, sweat level) as the initial setting, and the user can select the type of notification (vibration, voice, text) according to their preference.

[0933] 2. Data collection

[0934] The device periodically collects pulse, body temperature, and sweat data. Audio data is also recorded in real time using a microphone and sent to a server.

[0935] 3. Data Analysis

[0936] The server receives the transmitted data and analyzes it, assessing how much the pulse rate has deviated from normal, whether body temperature has risen, and whether sweating has increased. It also converts the voice data into text and uses natural language processing (NLP) to detect keywords and tones that indicate anger.

[0937] 4. Anger detection

[0938] The server determines whether the user is in an angry state based on the analysis results. For example, if the pulse rate is 30% higher than normal, the body temperature is 0.5 degrees higher, and the voice data contains keywords indicating anger, the server determines that the user is in an angry state.

[0939] 5. Generate and send notifications

[0940] If the server detects the user's anger, it generates an appropriate notification message, such as "Take a deep breath and calm down" or a joke like "If you thought the train was crowded, you're here to see me today!" The generated message is sent to the smartwatch or smartphone.

[0941] The device receives the generated notification and presents it to the user: the smartwatch vibrates and displays a message, and the smartphone plays a message or joke aloud.

[0942] Specific examples

[0943] For example, suppose a user becomes irritated on a train, their pulse rate and body temperature rise, they start sweating, and they say things like, "Stop messing around!" or "Why is it so crowded?" In this case, the device collects this data and sends it to the server. The server analyzes the data and, if it determines that the user is angry, it generates an appropriate message and sends it to the device. Finally, the device tries to calm the user down by telling them to "take a deep breath and calm down" or telling them a joke.

[0944] By using this system, it is possible to prevent trouble in public places and provide a safe and comfortable environment for users and those around them.

[0945] The processing flow will be explained below.

[0946] Step 1:

[0947] The device registers the user's basic data (normal pulse, body temperature, sweat level) as the initial setting, and the user also selects the type of notification (vibration, voice, text) according to their preference.

[0948] Step 2:

[0949] The device periodically (for example, every minute) collects the user's pulse, body temperature, and sweat data. At the same time, the device's microphone records the user's voice in real time and saves it as audio data.

[0950] Step 3:

[0951] The terminal transmits the collected pulse data, body temperature data, sweat data, and voice data to a server at regular intervals (for example, every minute).

[0952] Step 4:

[0953] The server analyzes the received data in real time. Specifically, it analyzes pulse data and calculates variations from normal baseline values. It also calculates variations from normal baseline values ​​for body temperature data and analyzes sweat data.

[0954] Step 5:

[0955] The server converts the speech data into text and uses natural language processing (NLP) to detect keywords and tones that indicate anger, such as phrases like "Don't mess with me!" or "Why is it so busy?"

[0956] Step 6:

[0957] The server comprehensively evaluates the analysis results and determines whether the user is in an angry state. If the pulse rate is 30% or more higher than normal, the body temperature is 0.5 degrees higher, and the voice data contains keywords related to anger, the server determines that the user is in an angry state.

[0958] Step 7:

[0959] If the server determines that the user is angry, it generates an appropriate notification message, such as "Take a deep breath and calm down" or a joke like "If you thought the train was crowded, you're here to see me today!"

[0960] Step 8:

[0961] The server sends the generated notification message to the device, which then sends the notification to the user's smartwatch and smartphone.

[0962] Step 9:

[0963] The device will present the received notification to the user: the smartwatch will vibrate and display the message "Take a deep breath and calm down," and the smartphone will play a joke.

[0964] Step 10:

[0965] The user receives a notification and can take action, for example, by taking a deep breath or listening to a joke to calm down.

[0966] Step 11:

[0967] The server again collects and analyzes the data to observe the user's reaction. If it determines that the user's anger continues, it will send additional notifications or advice, such as a reminder like "You're almost at the station!"

[0968] These steps will help detect user anger early and take appropriate measures to prevent trouble in public places.

[0969] Example 1

[0970] 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."

[0971] Anger-related troubles in public places, especially on trains, are a major social problem. Conventional technologies lack the means to monitor a user's emotional state in real time and send notifications at the appropriate time, making it difficult to prevent trouble before it occurs. Furthermore, there was no technology that efficiently analyzed a user's physiological and voice data and quickly detected anger based on the analysis results.

[0972] 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.

[0973] In this invention, the server includes means for acquiring pulse data, means for acquiring body temperature data, means for acquiring sweat data, means for acquiring voice data, means for registering basic data as initial settings, means for analyzing the acquired pulse data, body temperature data, sweat data, and voice data, means for converting the voice data into text and analyzing it using natural language processing, means for determining whether the user is in an angry state based on the analysis result, and means for sending a notification to the user when it is determined that the user is in an angry state. This makes it possible to monitor the user's emotional state in real time, quickly detect an anger state, and send a notification at an appropriate time.

[0974] "Pulse data" is data measured using the user's heart rate and heart rhythm as indicators.

[0975] "Body temperature data" is data obtained by measuring the user's body temperature.

[0976] "Sweat data" is data obtained by measuring the amount of sweat on the user's skin surface using a sensor.

[0977] "Voice data" refers to sound data of a user's voice collected using a recording device.

[0978] "Basic data" is reference data that indicates the user's normal state, such as normal pulse rate, body temperature, and sweat level.

[0979] "Analysis" is a computational process for determining the user's state and emotions based on various acquired data.

[0980] "Natural language processing" is a technology that converts voice data into text and analyzes that text to understand emotions and intent.

[0981] The "angry state" is a state in which the user is emotionally aroused and feels angry.

[0982] A "notification" is a means of conveying some information or message to a user.

[0983] "Server" refers to a computer system that handles processes such as data analysis, decision-making, and notification generation and transmission.

[0984] A "terminal" is a device that collects data from users and communicates with servers, and primarily includes smartwatches and smartphones.

[0985] This invention relates to an anger management AI system that prevents trouble in crowded public places, especially on trains, etc. This system is characterized by sending notifications and messages urging self-control at appropriate times when the user becomes angry.

[0986] System Configuration

[0987] The system includes the following components:

[0988] 1. Devices (smartwatches, smartphones)

[0989] How pulse data is acquired: A sensor built into the device measures the user's pulse in real time.

[0990] Body temperature data acquisition method: A temperature sensor built into the device measures the user's body temperature in real time.

[0991] Method for acquiring sweat data: A sensor built into the device detects the user's sweating status in real time.

[0992] Means for acquiring voice data: The device's microphone records the user's voice and saves it as an audio file.

[0993] A means for registering basic data as initial settings: The user registers their normal pulse rate, body temperature, and sweat level as initial settings.

[0994] 2. Server

[0995] Data analysis means: The server receives the acquired pulse data, body temperature data, sweat data and voice data and analyzes them.

[0996] Speech-to-text conversion and natural language processing means: Convert speech data into text and use natural language processing (NLP) to analyze keywords and tones that indicate anger.

[0997] Anger detection means: Based on the analysis results, it is determined whether the user is in an angry state.

[0998] Notification generating means: When it is determined that the user is in an angry state, a message for sending a notification to the user is generated.

[0999] Notification sending means: Sends the generated notification message to the smartwatch or smartphone.

[1000] Specific Examples

[1001] For example, imagine a user getting irritated on a train, their pulse rate and body temperature rising, sweating increasing, and uttering phrases like "Stop messing around!" or "Why is it so crowded?" At this time, the device collects this data and sends it to the server. The server analyzes the data and, if it determines that the user is angry, generates an appropriate message and sends it to the device. Finally, the device tries to calm the user down by telling them to "take a deep breath and calm down" or telling them a joke.

[1002] Prompt Sentence Examples

[1003] Below is an example of input to a generative AI model to illustrate this system.

[1004] "Please explain an anger management AI system for controlling anger in public places. This system collects and analyzes the user's biometric and voice data in real time. If an abnormality is detected, it sends a message to the user urging them to control themselves. Please explain in detail what data is collected, how it is analyzed, and what kind of notification is sent."

[1005] By using this system, it is possible to prevent trouble in public places and provide a safe and comfortable environment for users and those around them.

[1006] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1007] Step 1:

[1008] Initial Setup

[1009] When the user starts the system for the first time, they input their basic data (normal pulse, body temperature, and sweat level) using a smartphone app. This basic data is then saved on the device. The user can also select the type of notification (vibration, voice, or text). The input is the user's normal physiological data and notification preferences, and the output is the settings saved on the device.

[1010] Specific behavior:

[1011] 1. The user launches the smartphone app.

[1012] 2. The user enters pulse rate, body temperature, and sweat level according to the screen where basic data is entered.

[1013] 3. On the screen where the user selects the type of notification, select "Vibrate," "Voice," or "Text."

[1014] Step 2:

[1015] Data collection

[1016] The device collects physiological data (pulse, body temperature, sweat) and voice data at regular intervals. The sensor measures this data in real time and temporarily stores it in internal memory. The data is then sent to a server at regular intervals. The input is the physiological data and voice data acquired in real time, and the output is the data sent to the server.

[1017] Specific behavior:

[1018] 1. The device measures pulse rate, body temperature, and sweat data every minute.

[1019] 2. The device's microphone records the user's voice in real time and saves it as an audio file.

[1020] 3. The device sends the collected data to a server via the Internet every five minutes.

[1021] Step 3:

[1022] Data analysis

[1023] The server receives the transmitted data and begins analysis. First, pulse data is evaluated for fluctuations over time to detect deviations from normal baseline values. Next, body temperature data is monitored for temperature fluctuations and compared to baseline values. Sweat data is analyzed in a similar manner to evaluate increases in sweating. Finally, the voice data is converted into text and analyzed using natural language processing (NLP) for keywords and tones that indicate anger. The input is the various physiological data and voice data transmitted to the server, and the output is the analysis results.

[1024] Specific behavior:

[1025] 1. The server analyzes the pulse data and evaluates variations from baseline.

[1026] 2. The server analyzes the body temperature data and determines the degree of temperature rise.

[1027] 3. The server analyzes the sweat data and evaluates the increase in sweat rate.

[1028] 4. The server converts the audio data into text and uses NLP to detect keywords and tones that indicate anger.

[1029] Step 4:

[1030] Anger detection

[1031] The server determines whether the user is in an angry state based on the analysis results. For example, if the pulse rate is 30% higher than normal, the body temperature is 0.5 degrees higher, and the voice data contains anger-related keywords such as "Don't mess with me" or "Why," the server determines the user is in an angry state. The input is the analysis results, and the output is the anger state determination result.

[1032] Specific behavior:

[1033] 1. The server comprehensively evaluates the analysis results of pulse, body temperature, and sweat data.

[1034] 2. The server uses the results of the voice data analysis to determine the emotional state.

[1035] 3. The server determines whether the user's emotional state is angry.

[1036] Step 5:

[1037] Notification generation and delivery

[1038] If the server detects the user's anger, it generates an appropriate notification message. For example, it generates a joke such as "Take a deep breath and calm down" or "Everyone's here to see me today!" This notification message is sent to the smartwatch or smartphone. The input is the anger state judgment result, and the output is the generated notification message.

[1039] Specific behavior:

[1040] 1. The server generates an appropriate notification message for the user.

[1041] 2. The server sends the generated notification message to the terminal.

[1042] 3. The terminal receives the notification message and presents it to the user.

[1043] 4. The smartwatch will vibrate and display a message, and the smartphone will play the message aloud.

[1044] Through the above steps, the anger management AI system of the present invention can monitor the user's emotional state in real time and encourage self-control at the appropriate time, thereby preventing trouble in public places.

[1045] (Application example 1)

[1046] 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."

[1047] In modern brick-and-mortar stores, when customers feel stressed or angry, it often negatively impacts the atmosphere in the store and other customers. However, it is difficult for on-site staff to sense the situation in real time and respond appropriately. Therefore, a method is needed to detect customers' emotional states in real time and respond appropriately.

[1048] 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.

[1049] In this invention, the server includes means for acquiring pulse data, means for acquiring body temperature data, means for acquiring sweat data, means for acquiring voice data, means for analyzing the acquired pulse data, body temperature data, sweat data, and voice data, means for determining whether the user is in an angry state based on the analysis results, means for sending a notification to the user when it is determined that the user is in an angry state, means for sending a notification to the user's smart electronic device in a physical store, and message generation means using a generative AI model to ensure that the notification includes a specific message and specific suggestions such as promoting relaxation or offering a special discount. This makes it possible to automatically notify customers in a physical store of appropriate messages and suggestions in real time when they feel stressed or angry.

[1050] "Pulse data" is data indicating the user's heart rate, and refers to the number of times the user's heart beats measured in real time.

[1051] "Body temperature data" is data indicating the user's body temperature, and refers to the internal and external temperatures of the user's body measured in real time.

[1052] "Sweat data" is data indicating the amount of sweat produced by the user, and refers to the amount of sweat secreted from the user's skin measured in real time.

[1053] "Voice data" refers to data indicating the content of a user's speech, and refers to the characteristics of the user's words and voice collected in real time.

[1054] The "analysis means" is a means for analyzing the various acquired data and evaluating the emotional state of the user based on the data.

[1055] The "determination means" is a means for determining whether the user is in an angry state based on the analysis result obtained by the analysis means.

[1056] The "notification means" is a means for conveying an appropriate message to the user when it is determined that the user is in an angry state.

[1057] "Smart electronic devices" refer to portable devices that users use on a daily basis, such as smartwatches and smartphones.

[1058] A "generative AI model" is a model that uses artificial intelligence to generate specific messages and suggestions, allowing for personalized messages to be generated for users.

[1059] The "message generation means" is a means of generating appropriate messages and suggestions according to the user's state, and uses a generative AI model.

[1060] The present invention is a system for detecting stress and anger in real time in a brick-and-mortar store and responding appropriately using the user's smart electronic devices, a cloud server, and a generative AI model.

[1061] System Configuration

[1062] The system includes the following components:

[1063] 1. Smart Electronics

[1064] This includes smartwatches and smartphones.

[1065] How to obtain pulse data: The user's heart rate is obtained in real time using the sensors in the smartwatch.

[1066] Body temperature data acquisition method: The user's body temperature is acquired in real time using a temperature sensor in a smartwatch or smartphone.

[1067] Means of acquiring sweat data: The amount of sweat a user generates is acquired in real time using sensors in smart electronic devices.

[1068] Method of acquiring voice data: The smartphone's microphone is used to collect the user's speech in real time.

[1069] 2. Server

[1070] Data analysis means: The server receives and analyzes pulse, body temperature, sweat, and voice data sent from smart electronic devices.

[1071] Anger detection means: Based on the analysis results, it is determined whether the user is in an angry state.

[1072] Notification generator: If the user is determined to be in an angry state, an appropriate message is generated using a generative AI model.

[1073] Notification sending method: Send the generated message to your smartwatch or smartphone.

[1074] Hardware and software used

[1075] Hardware:

[1076] Smartwatch (e.g. Samsung Galaxy Watch)

[1077] Smartphone (e.g. iPhone 13)

[1078] Cloud server (e.g. AWS EC2 instance)

[1079] software:

[1080] TensorFlow: Used to implement an anger detection model based on pulse, body temperature, and sweat data.

[1081] Twilio: Used to send notification messages to users' smart electronic devices.

[1082] OpenAI GPT-3.5: Used to generate response messages using a generative AI model.

[1083] Specific example explanation

[1084] For example, if a customer in a physical store becomes stressed due to a long wait or crowding, the system collects the customer's pulse, body temperature, and sweat rate in real time, and also records what they say with a microphone. This data is sent to a server for analysis. If the server determines that the customer is angry based on the analysis results, it uses a generative AI model (OpenAI GPT-3.5) to generate a message such as "Take a deep breath and calm down" or a suggestion message offering a special discount coupon.

[1085] Example prompt for a generative AI model:

[1086] "Generate a soothing message for a person who is likely angry or stressed:"

[1087] Finally, this generated message is sent via Twilio to a smartwatch or smartphone, for example, a notification will appear on the customer's smartwatch saying, "Enjoy a relaxed shopping experience with our special coupons," which will calm the customer and maintain a good atmosphere in the store.

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

[1089] Step 1:

[1090] The device collects the user's pulse, body temperature, sweat, and voice data in real time. Specifically, sensors within the smartwatch measure the pulse and body temperature and detect the amount of sweat. The smartphone's microphone also collects surrounding sounds and sends this data to a cloud server. The input is various vital data and voice data, and the output is data sent to the server.

[1091] Step 2:

[1092] The server receives the data acquired by the data analysis means and analyzes each data. Specifically, it uses TensorFlow to analyze pulse, body temperature, and sweat data and evaluates how much these data deviate from baseline values. It also converts voice data into text and uses natural language processing (NLP) to detect emotional tone and keywords that indicate anger. The input is the data sent from the device, and the output is the analysis results.

[1093] Step 3:

[1094] The server determines whether the user is in an angry state based on the analysis results. Specific criteria include pulse rate 30% or more higher than normal, body temperature 0.5 degrees higher, increased sweating, and the voice data containing keywords indicating anger. The input is the analysis results, and the output is the anger state determination.

[1095] Step 4:

[1096] If the server determines that the user is in an angry state, it uses a generative AI model (OpenAI GPT-3.5) to generate an appropriate message. Specifically, the prompt "Generate a soothing message for a person who is likely angry or stressed:" is input into the generative AI model, which outputs messages such as "Take a deep breath and calm down" or "Use a special coupon and enjoy a relaxing shopping experience." The input is the determination of the user's angry state, and the output is the generated message.

[1097] Step 5:

[1098] The server sends the generated message to the user's device as a notification. Specifically, it uses Twilio to send the message to a smartwatch or smartphone. The user's smartwatch vibrates and displays the generated message. The smartphone plays the message aloud. The input is the generated message, and the output is a notification to the user's device.

[1099] Step 6:

[1100] The user receives a notification from the device and checks the message displayed on the screen or the voice guidance, which allows the user to take a deep breath or use a special coupon to reduce stress or anger. The input is the notification to the device, and the output is the user's reaction.

[1101] 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.

[1102] This invention relates to an anger management AI system that prevents trouble in crowded public places, especially on trains, etc. This system is characterized by sending notifications and messages urging self-control at appropriate times when the user becomes angry, and its effectiveness is enhanced by combining it with an emotion engine.

[1103] System Configuration

[1104] The system includes the following components:

[1105] 1. Devices (smartwatches, smartphones)

[1106] How pulse data is acquired: A sensor built into the device measures the user's pulse in real time.

[1107] Body temperature data acquisition method: A temperature sensor built into the device measures the user's body temperature in real time.

[1108] Method for acquiring sweat data: A sensor built into the device detects the user's sweating status in real time.

[1109] Means for acquiring voice data: The device's microphone records the user's voice and saves it as an audio file.

[1110] Means for acquiring image data and video data (if necessary): The smartphone camera captures the user's facial expressions and movements and sends the data to the server.

[1111] 2. Server

[1112] Data analysis means: The server receives the acquired pulse data, body temperature data, sweat data, voice data, image data and video data and analyzes them.

[1113] Anger detection: Based on the analysis results, it is determined whether the user is in an angry state. The emotion engine plays an important role here.

[1114] Emotion engine: Analyzes voice data, pulse data, body temperature data, sweat data, as well as image and video data to identify the user's emotions in detail.

[1115] Notification generation means: If the user is determined to be in an angry state, a message is generated to notify the user. Based on the analysis results of the emotion engine, a notification is generated that matches the user's emotions.

[1116] Notification sending means: Sends the generated notification message to the smartwatch or smartphone.

[1117] Explanation of program processing

[1118] 1. Initial Setup

[1119] The device registers the user's basic data (normal pulse, body temperature, sweat level) as the initial setting, and the user can select the type of notification (vibration, voice, text) according to their preference.

[1120] 2. Data collection

[1121] The device periodically (for example, every minute) collects the user's pulse, body temperature, and sweat data. At the same time, the device's microphone records the user's voice in real time and saves it as audio data. Furthermore, the smartphone's camera captures the user's facial expressions and movements as needed and saves them as image and video data.

[1122] 3. Data transmission

[1123] The collected data is sent to the server at regular intervals (for example, every minute). This data includes pulse data, body temperature data, sweat data, voice data, image data, and video data.

[1124] 4. Data Analysis

[1125] The server analyzes the transmitted data in real time. Specifically, it calculates and evaluates fluctuations in pulse, body temperature, and sweat data from their normal baseline values. The emotion engine analyzes the audio, image, and video data and uses natural language processing (NLP) to detect keywords and tones that indicate anger, as well as the user's facial expressions and actions.

[1126] 5. Anger detection

[1127] The server comprehensively evaluates the analysis results and determines whether the user is in an angry state. If the pulse rate is 30% or more higher than normal, the body temperature is 0.5 degrees or more higher, and the voice data or image data contains keywords or facial expressions that indicate anger, the user is determined to be in an angry state.

[1128] 6. Generating and Sending Notifications

[1129] If the server detects that the user is angry, it generates an appropriate notification message. Based on the analysis results of the emotion engine, it generates a notification that matches the user's emotions. For example, it generates a message such as "Take a deep breath and calm down" or a joke such as "If you think the train is crowded, everyone came to see me today!" The generated message is sent to the smartwatch or smartphone.

[1130] The device receives the generated notification and presents it to the user: the smartwatch vibrates and displays the message "Take a deep breath and calm down," and the smartphone plays a joke aloud.

[1131] Specific examples

[1132] For example, suppose a user becomes irritated on a train, their pulse rate and body temperature rise, they start sweating, they say things like "Stop messing around!" or "Why is it so crowded?", and their expression becomes grim. In this case, the device collects this data and sends it to the server. The server analyzes this data and, if it determines that the user is angry using its emotion engine, it generates an appropriate message and sends it to the device. Finally, the device tries to calm the user down by telling them to "take a deep breath and calm down" or telling a joke.

[1133] This system can detect user anger early and take appropriate measures to prevent trouble in public places, providing a safe and comfortable environment for users and those around them.

[1134] The processing flow will be explained below.

[1135] Step 1:

[1136] The device registers the user's basic data (normal pulse, body temperature, sweat level) as the initial setting, and the user also selects the type of notification (vibration, voice, text) according to their preference.

[1137] Step 2:

[1138] The device periodically (for example, every minute) collects the user's pulse, body temperature, and sweat data. At the same time, the device's microphone records the user's voice in real time and saves it as audio data. If necessary, the smartphone camera captures the user's facial expressions and movements, collecting image and video data.

[1139] Step 3:

[1140] The terminal transmits the collected pulse data, body temperature data, sweat data, voice data, image data, and video data to a server at regular intervals (for example, every minute).

[1141] Step 4:

[1142] The server analyzes the received data in real time. It analyzes pulse data to calculate variations from normal baseline values, as well as body temperature and sweat data.

[1143] Step 5:

[1144] The server converts the voice data into text and uses natural language processing (NLP) to detect keywords and tones that indicate anger, while simultaneously analyzing image and video data to identify emotions from the user's facial expressions and movements.

[1145] Step 6:

[1146] The emotion engine comprehensively evaluates the analysis results and determines the user's emotional state. If the pulse rate is 30% or more higher than normal, the body temperature is 0.5 degrees higher, and the voice and image data contain keywords or facial expressions that indicate anger, the user is determined to be in an angry state.

[1147] Step 7:

[1148] If the server detects that the user is angry, it generates an appropriate notification message based on the analysis results of the emotion engine. For example, it generates a message like "Take a deep breath and calm down" or a joke like "I thought the train was crowded, but everyone came to see me today!"

[1149] Step 8:

[1150] The server sends the generated notification message to the device, which then sends the notification to the user's smartwatch and smartphone.

[1151] Step 9:

[1152] The device receives the notification and presents it to the user accordingly: the smartwatch vibrates and displays the message "Take a deep breath and calm down," and the smartphone plays a joke aloud.

[1153] Step 10:

[1154] The user receives a notification and can take action, for example, by taking a deep breath or listening to a joke to calm down.

[1155] Step 11:

[1156] The server again collects and analyzes the data to observe the user's reaction. If it determines that the user's anger continues, it will send additional notifications or advice, such as a reminder like "You're almost at the station!"

[1157] These steps will help detect user anger early and take appropriate measures to prevent trouble in public places.

[1158] Example 2

[1159] 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."

[1160] In modern public places, especially on trains, users often become angry due to crowding and stress. This can cause trouble and have a negative impact on those around them, so there is a need for a system that can detect users' anger early and encourage appropriate responses.

[1161] 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.

[1162] In this invention, the server includes means for acquiring pulse data, means for acquiring body temperature data, means for acquiring sweat data, means for acquiring voice data, means for acquiring image data and video data, means for analyzing the acquired pulse data, body temperature data, sweat data, voice data, image data and video data, means for determining whether the user is in an angry state based on the analysis results, and means for sending a notification to the user when it is determined that the user is in an angry state. This makes it possible to monitor the user's emotional state in detail and in real time and send notifications at appropriate times.

[1163] "Pulse data" is biological information that indicates the user's heart rate.

[1164] "Body temperature data" is biological information indicating the user's body temperature.

[1165] "Sweat data" is biological information that indicates the amount of sweat of the user and the associated humidity of the skin.

[1166] "Voice data" is digital information that records the user's speech or voice.

[1167] "Image data" is digital information of a still image capturing the user's facial expression and posture.

[1168] "Video data" is digital information of moving images of the user's facial expressions and movements.

[1169] "Emotion engine" is a general term for algorithms and software that analyzes audio data, image data, video data, and various biometric data to determine the user's emotional state.

[1170] "Notification" means a message, alert, or information sent to a user to prompt the user to take action.

[1171] A "personal digital assistant" is a portable electronic device capable of data communication, such as a smartphone or tablet.

[1172] A "mobile device" is an electronic device worn on the wrist, such as a smartwatch.

[1173] This invention relates to an anger management AI system that prevents trouble in crowded public places, especially on trains, etc. This system is characterized by sending notifications and messages urging self-control at appropriate times when the user becomes angry, and its effectiveness is enhanced by combining it with an emotion engine.

[1174] System Configuration

[1175] The system includes the following components:

[1176] 1. Devices (smartwatches, smartphones)

[1177] How pulse data is acquired: A sensor built into the device measures the user's pulse in real time.

[1178] Body temperature data acquisition method: A temperature sensor built into the device measures the user's body temperature in real time.

[1179] Method for acquiring sweat data: A sensor built into the device detects the user's sweating status in real time.

[1180] Means for acquiring voice data: The device's microphone records the user's voice and saves it as an audio file.

[1181] Means for acquiring image data and video data (if necessary): The smartphone camera captures the user's facial expressions and movements and sends the data to the server.

[1182] 2. Server

[1183] Data analysis means: The server receives the acquired pulse data, body temperature data, sweat data, voice data, image data and video data and analyzes them.

[1184] Anger detection: Based on the analysis results, it is determined whether the user is in an angry state. The emotion engine plays an important role here.

[1185] Emotion engine: Analyzes voice data, pulse data, body temperature data, sweat data, as well as image and video data to identify the user's emotions in detail.

[1186] Notification generation means: If the user is determined to be in an angry state, a message is generated to notify the user. Based on the analysis results of the emotion engine, a notification is generated that matches the user's emotions.

[1187] Notification sending means: Sends the generated notification message to the smartwatch or smartphone.

[1188] Detailed Description of the Invention

[1189] Data collection

[1190] The device collects the user's pulse, body temperature, and sweat data every minute. For example, when a user is riding a train, the smartwatch's sensor measures the user's pulse, and the body temperature sensor measures the user's skin temperature. The microphone constantly monitors and records the user's speech in real time. If necessary, the smartphone's camera records the user's facial expressions and movements.

[1191] Data transmission and analysis

[1192] The device periodically transmits the collected data to a server. The server analyzes the received data in real time and evaluates fluctuations from baseline in pulse rate, body temperature, and sweat data. The emotion engine analyzes audio, image, and video data and uses natural language processing (NLP) technology to identify keywords and tones that indicate anger, as well as the user's facial expressions and actions.

[1193] Anger detection and notification generation

[1194] The server comprehensively evaluates the analysis results, and if it determines that the user is angry, it generates an appropriate notification message and sends it to the device. Based on the analysis results of the emotion engine, it generates a notification that matches the user's emotions. For example, it generates a message such as "Take a deep breath and calm down" or a joke such as "If you think the train is crowded, everyone came to see me today!" These messages are sent to the smartwatch or smartphone.

[1195] User Notification

[1196] The device receives the notification and presents it to the user. The smartwatch vibrates and displays the message "Take a deep breath and calm down," while the smartphone plays a joke. The goal is for the user to receive the notification and calm down immediately.

[1197] Specific examples

[1198] For example, suppose a user becomes irritated on a train, their pulse rate and body temperature rise, they start sweating, they say things like "Stop messing around!" or "Why is it so crowded?", and their expression becomes grim. In this case, the device collects this data and sends it to the server. The server analyzes this data and, if it determines that the user is angry using its emotion engine, it generates an appropriate message and sends it to the device. Finally, the device tries to calm the user down by telling them to "take a deep breath and calm down" or telling a joke.

[1199] Prompt Sentence Examples

[1200] Here are some examples of prompts to input to a generative AI model:

[1201] If the user is frustrated and expressing anger, generate a conversational message to defuse the situation. For example, if the user's pulse rate is 30% higher than normal, their body temperature is more than half a degree higher, and their voice recording contains the words "It's so busy!", generate a message or joke to calm the user.

[1202] This system can prevent trouble in public places and provide a safe and comfortable environment for users and those around them.

[1203] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1204] Step 1: Initial Setup

[1205] When a user uses the system for the first time, the device collects the user's basic data. The user wears the smartwatch and remains still for a few minutes to measure their normal pulse, body temperature, and sweat rate. Once the basic data is collected, the smartphone app opens and the user selects the type of notification (vibration, voice, text). This information is stored on the server as initial setting data.

[1206] Input: User's biometric information, notification settings

[1207] Output: Initial setting data

[1208] Step 2: Collect data

[1209] The device collects the user's pulse, body temperature, and sweat data every minute. For example, while riding a train, the smartwatch's sensor measures the user's pulse, and the body temperature sensor measures the user's skin temperature. The device's built-in microphone also records the user's voice in real time. If necessary, the smartphone's camera captures the user's facial expressions and movements.

[1210] Input: Continuously collected biometric, audio, and image data

[1211] Output: Real-time data collected

[1212] Step 3: Sending data

[1213] The device sends the collected data at regular intervals (for example, every minute) to a server. This data includes pulse data, body temperature data, sweat data, voice data, and, if necessary, image and video data. The communication method is that the smartwatch sends the data to a smartphone via Bluetooth, and the smartphone then sends the data to the server via the Internet.

[1214] Input: Real-time data collected

[1215] Output: Data sent to the server

[1216] Step 4: Analyze the data

[1217] The server analyzes the received data in real time. Specifically, it calculates and evaluates fluctuations from baseline values ​​in pulse rate, body temperature, and sweat data. The emotion engine also analyzes audio, image, and video data, and uses natural language processing (NLP) technology to detect keywords, tones, and facial expressions that indicate anger.

[1218] Input: Data sent to the server

[1219] Output: Analysis result (user's emotional state)

[1220] Step 5: Anger detection

[1221] The server comprehensively evaluates the analysis results and determines whether the user is in an angry state. For example, if the pulse rate is 30% or more higher than normal, the body temperature is 0.5 degrees higher than normal, and keywords indicating anger are detected in the voice data, the server determines that the user is in an angry state.

[1222] Input: Analysis results

[1223] Output: Angry state determination result

[1224] Step 6: Generate and send notifications

[1225] The server generates an appropriate notification message to ease the user's anger. For example, it generates a message such as "Take a deep breath and calm down" or a joke such as "If you think the train is crowded, you're here to see me today!" The notification message generated based on the analysis results of the emotion engine is sent to the device.

[1226] Input: Angry state result

[1227] Output: Information message

[1228] Step 7: Notify users

[1229] The device receives the notification message and presents it to the user. The smartwatch vibrates and displays the message "Take a deep breath and calm down." The smartphone plays the generated joke aloud, allowing the user to calm down immediately.

[1230] Input: Notification message

[1231] Output: Notification displayed to the user

[1232] Through these processing steps, the system can detect user anger early and respond appropriately, thereby preventing trouble in public places and providing a safe and comfortable environment for users and those around them.

[1233] (Application example 2)

[1234] 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."

[1235] Previously, there were limitations to how to manage the stress and anger experienced by operators working in factories, which affected work efficiency and safety. Furthermore, there was a lack of means to monitor the emotional state of operators in real time and take appropriate measures, making it difficult to provide appropriate alerts early on. As a result, this can have a negative impact on the mental and physical health of operators.

[1236] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1237] In this invention, the server includes means for acquiring pulse data, means for acquiring body temperature data, means for acquiring sweat data, means for acquiring voice data, means for analyzing the acquired pulse data, body temperature data, sweat data, and voice data, means for determining whether the user is in an angry state based on the analysis results, means for sending a notification to the user when it is determined that the user is in an angry state, means for generating a notification appropriate to the work environment when the user is working, and means for displaying or voicing the generated notification on the automatic operation device. This makes it possible to early detect stress or anger experienced by operators working in a factory and provide appropriate alerts and advice.

[1238] "Pulse data" refers to data obtained by measuring and recording the user's heart rate.

[1239] "Body temperature data" refers to data obtained by measuring and recording the user's body temperature.

[1240] "Sweat data" refers to data obtained by measuring and recording the humidity and electrical conductivity of the user's skin surface.

[1241] "Voice data" refers to data that is recorded and saved of the voice uttered by the user.

[1242] "Means for acquiring" refers to devices or sensors for collecting pulse data, body temperature data, sweat data, or voice data.

[1243] The "analyzing means" refers to algorithms and software for processing and analyzing the various data acquired (pulse data, body temperature data, sweat data, and voice data).

[1244] The "means for determining" refers to logic or criteria for determining whether the user is in an angry state based on the results of data analysis.

[1245] A "means for sending a notification" is a procedure or device for transmitting a notification message to a user.

[1246] The "means for generating notifications suited to the working environment" is a system that takes into account the work that the user is doing and creates notifications suited to that situation.

[1247] An "automated operation device" is a device or robot used in a factory that has the function of displaying or vocalizing notifications to an operator.

[1248] This invention is an anger management system that monitors the stress and anger of factory workers in real time and provides notifications at appropriate times. The system collects and analyzes the operator's biometric data (pulse, body temperature, sweat) and voice data to determine whether the operator is in an angry state and notifies them as necessary.

[1249] System Configuration

[1250] The system includes the following components:

[1251] Hardware

[1252] 1. Terminals (in-factory robots, mobile terminals)

[1253] How pulse data is acquired: A sensor built into the robot measures the operator's pulse in real time.

[1254] Body temperature data acquisition method: A temperature sensor built into the robot measures the operator's body temperature in real time.

[1255] Method of acquiring sweating data: Sensors built into the robot detect the operator's sweating status in real time.

[1256] How voice data is acquired: The robot's microphone records the operator's voice and saves it as an audio file.

[1257] software

[1258] 2. Server

[1259] Data analysis means: The server receives the acquired pulse data, body temperature data, sweat data and voice data and analyzes them.

[1260] Anger detection method: Based on the analysis results, determine whether the operator is in an angry state.

[1261] Emotion engine: Analyzes voice data, pulse data, body temperature data, and sweat data to identify the operator's emotions in detail.

[1262] Notification generating means: Generates an appropriate notification message if it is determined that the operator is in an angry state.

[1263] Notification sending means: Sends the generated notification message to the robot or mobile device.

[1264] Program processing description

[1265] Initial Setup

[1266] The device registers the operator's basic data (normal pulse, body temperature, sweat level), and the operator can select the type of notification (voice, text, flashing light) according to their preference.

[1267] Data collection

[1268] The terminal periodically (for example, every minute) collects the operator's pulse, body temperature, and sweat data. At the same time, the terminal's microphone records the operator's voice in real time and saves it as audio data.

[1269] Data transmission

[1270] The data collected by the device is sent to the server at regular intervals (for example, every minute). This data includes pulse data, body temperature data, sweat data, and voice data.

[1271] Data analysis

[1272] The server analyzes the transmitted data in real time. Specifically, it calculates and evaluates fluctuations in pulse rate, body temperature, and sweat data from normal baseline values. The emotion engine analyzes the voice data and uses natural language processing (NLP) to detect keywords and tones that indicate anger.

[1273] Anger detection

[1274] The server comprehensively evaluates the analysis results and determines whether the operator is in an angry state. For example, if the pulse rate is 30% or more higher than normal, the body temperature is 0.5 degrees higher, and the voice data contains keywords indicating anger, the operator is determined to be in an angry state.

[1275] Generate and send notifications

[1276] If the server detects that the operator is angry, it generates an appropriate notification message. Based on the analysis results of the emotion engine, it generates a notification that matches the operator's emotions. For example, it generates a message such as "Take a short break" or advice such as "Take a deep breath and relax." The generated message is sent to the robot or mobile device.

[1277] Specific examples

[1278] If an operator in a factory starts to get irritated while working, their pulse rate increases, and their voice tone becomes rough:

[1279] Sensors: The robot's built-in sensors detect increased pulse rate, body temperature, and increased sweating.

[1280] Audio data: The microphone detects words like "Damn!" and "Why doesn't this work?"

[1281] Analysis: The server interprets this as an angry state and immediately generates the message "Take a break."

[1282] Notification: The robot will give you voice advice saying, "Take a short break."

[1283] Prompt Sentence Examples

[1284] "An operator is becoming angry while working. Please analyze their current pulse, body temperature, and sweat data and generate an appropriate notification message."

[1285] This system can improve work efficiency and safety by detecting stress and anger experienced by operators working in factories at an early stage and providing appropriate alerts and advice.

[1286] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1287] Step 1:

[1288] The terminal registers the operator's basic data.

[1289] Input: The operator's normal pulse rate, body temperature, and sweat level.

[1290] Operation: The terminal's sensors measure the operator's initial data and register it in the system.

[1291] Output: The initial base data is saved in the system.

[1292] Step 2:

[1293] The user selects the type of notification.

[1294] Input: Operator notification preference (voice, text, flashing light).

[1295] How it works: The operator selects the type of notification using the terminal interface.

[1296] Output: The selected notification type is set in the system.

[1297] Step 3:

[1298] The terminal periodically collects the operator's biometric data.

[1299] Input: Operator pulse, temperature, and sweat data.

[1300] How it works: The device's built-in sensors measure biometric data every minute.

[1301] Output: The measured biometric data is stored on the device.

[1302] Step 4:

[1303] The terminal collects the operator's voice data.

[1304] Input: Operator's voice (speech content, tone).

[1305] How it works: The device's microphone records the operator's voice in real time and saves it as an audio file.

[1306] Output: The recorded audio file will be saved on your device.

[1307] Step 5:

[1308] The terminal transmits the collected data to the server.

[1309] Input: Collected biometric and audio data.

[1310] How it works: The device sends data packets to the server every minute.

[1311] Output: Biometric and audio data transmitted to the server.

[1312] Step 6:

[1313] The server analyzes the data.

[1314] Input: Biometric and voice data sent to the server.

[1315] How it works: The server's data analysis means evaluates fluctuations in pulse, body temperature, and sweat data by comparing them with baseline values. The emotion engine also analyzes voice data using NLP to detect keywords and tones that indicate anger.

[1316] Output: Obtain analysis results (e.g., pulse rate increase of 30% or more, body temperature increase of 0.5 degrees or more, increased sweating, anger keywords in the voice).

[1317] Step 7:

[1318] The server determines the anger state.

[1319] Input: Analysis results.

[1320] How it works: The server aggregates and evaluates the analysis results to determine whether the operator is in an angry state.

[1321] Output: Operator's situation assessment result (whether angry or not).

[1322] Step 8:

[1323] The server generates an appropriate notification message.

[1324] Input: Operator's situation assessment result (anger state).

[1325] Action: Based on the analysis results, the emotion engine generates an appropriate notification message (e.g., take a short break, take a deep breath, and relax).

[1326] Output: The generated notification message.

[1327] Step 9:

[1328] The server sends a notification message to the terminal.

[1329] Input: The generated notification message.

[1330] Operation: The server sends a notification message to the terminal as a packet.

[1331] Output: The device receives the notification message.

[1332] Step 10:

[1333] The terminal presents the notification message to the operator.

[1334] Input: Notification message.

[1335] Operation: The device will present a notification message to the operator in the manner configured (voice, text, flashing light).

[1336] Output: A notification message is presented to the operator, encouraging the user to exercise restraint.

[1337] 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.

[1338] 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.

[1339] 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.

[1340] [Fourth embodiment]

[1341] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1342] 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.

[1343] 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).

[1344] 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.

[1345] 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.

[1346] 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).

[1347] 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.

[1348] 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.

[1349] 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.

[1350] 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.

[1351] 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.

[1352] 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.

[1353] 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."

[1354] This invention relates to an anger management AI system that prevents trouble in crowded public places, especially on trains, etc. This system is characterized by sending notifications and messages urging self-control at appropriate times when the user becomes angry.

[1355] System Configuration

[1356] The system includes the following components:

[1357] 1. Devices (smartwatches, smartphones)

[1358] How pulse data is acquired: A sensor built into the device measures the user's pulse in real time.

[1359] Body temperature data acquisition method: A temperature sensor built into the device measures the user's body temperature in real time.

[1360] Method for acquiring sweat data: A sensor built into the device detects the user's sweating status in real time.

[1361] Means for acquiring voice data: The device's microphone records the user's voice and saves it as an audio file.

[1362] 2. Server

[1363] Data analysis means: The server receives the acquired pulse data, body temperature data, sweat data and voice data and analyzes them.

[1364] Anger detection means: Based on the analysis results, it is determined whether the user is in an angry state.

[1365] Notification generating means: When it is determined that the user is in an angry state, a message for sending a notification to the user is generated.

[1366] Notification sending means: Sends the generated notification message to the smartwatch or smartphone.

[1367] Explanation of program processing

[1368] 1. Initial Setup

[1369] The device registers the user's basic data (normal pulse, body temperature, sweat level) as the initial setting, and the user can select the type of notification (vibration, voice, text) according to their preference.

[1370] 2. Data collection

[1371] The device periodically collects pulse, body temperature, and sweat data. Audio data is also recorded in real time using a microphone and sent to a server.

[1372] 3. Data Analysis

[1373] The server receives the transmitted data and analyzes it, assessing how much the pulse rate has deviated from normal, whether body temperature has risen, and whether sweating has increased. It also converts the voice data into text and uses natural language processing (NLP) to detect keywords and tones that indicate anger.

[1374] 4. Anger detection

[1375] The server determines whether the user is in an angry state based on the analysis results. For example, if the pulse rate is 30% higher than normal, the body temperature is 0.5 degrees higher, and the voice data contains keywords indicating anger, the server determines that the user is in an angry state.

[1376] 5. Generate and send notifications

[1377] If the server detects the user's anger, it generates an appropriate notification message, such as "Take a deep breath and calm down" or a joke like "If you thought the train was crowded, you're here to see me today!" The generated message is sent to the smartwatch or smartphone.

[1378] The device receives the generated notification and presents it to the user: the smartwatch vibrates and displays a message, and the smartphone plays a message or joke aloud.

[1379] Specific examples

[1380] For example, suppose a user becomes irritated on a train, their pulse rate and body temperature rise, they start sweating, and they say things like, "Stop messing around!" or "Why is it so crowded?" In this case, the device collects this data and sends it to the server. The server analyzes the data and, if it determines that the user is angry, it generates an appropriate message and sends it to the device. Finally, the device tries to calm the user down by telling them to "take a deep breath and calm down" or telling them a joke.

[1381] By using this system, it is possible to prevent trouble in public places and provide a safe and comfortable environment for users and those around them.

[1382] The processing flow will be explained below.

[1383] Step 1:

[1384] The device registers the user's basic data (normal pulse, body temperature, sweat level) as the initial setting, and the user also selects the type of notification (vibration, voice, text) according to their preference.

[1385] Step 2:

[1386] The device periodically (for example, every minute) collects the user's pulse, body temperature, and sweat data. At the same time, the device's microphone records the user's voice in real time and saves it as audio data.

[1387] Step 3:

[1388] The terminal transmits the collected pulse data, body temperature data, sweat data, and voice data to a server at regular intervals (for example, every minute).

[1389] Step 4:

[1390] The server analyzes the received data in real time. Specifically, it analyzes pulse data and calculates variations from normal baseline values. It also calculates variations from normal baseline values ​​for body temperature data and analyzes sweat data.

[1391] Step 5:

[1392] The server converts the speech data into text and uses natural language processing (NLP) to detect keywords and tones that indicate anger, such as phrases like "Don't mess with me!" or "Why is it so busy?"

[1393] Step 6:

[1394] The server comprehensively evaluates the analysis results and determines whether the user is in an angry state. If the pulse rate is 30% or more higher than normal, the body temperature is 0.5 degrees higher, and the voice data contains keywords related to anger, the server determines that the user is in an angry state.

[1395] Step 7:

[1396] If the server determines that the user is angry, it generates an appropriate notification message, such as "Take a deep breath and calm down" or a joke like "If you thought the train was crowded, you're here to see me today!"

[1397] Step 8:

[1398] The server sends the generated notification message to the device, which then sends the notification to the user's smartwatch and smartphone.

[1399] Step 9:

[1400] The device will present the received notification to the user: the smartwatch will vibrate and display the message "Take a deep breath and calm down," and the smartphone will play a joke.

[1401] Step 10:

[1402] The user receives a notification and can take action, for example, by taking a deep breath or listening to a joke to calm down.

[1403] Step 11:

[1404] The server again collects and analyzes the data to observe the user's reaction. If it determines that the user's anger continues, it will send additional notifications or advice, such as a reminder like "You're almost at the station!"

[1405] These steps will help detect user anger early and take appropriate measures to prevent trouble in public places.

[1406] Example 1

[1407] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1408] Anger-related troubles in public places, especially on trains, are a major social problem. Conventional technologies lack the means to monitor a user's emotional state in real time and send notifications at the appropriate time, making it difficult to prevent trouble before it occurs. Furthermore, there was no technology that efficiently analyzed a user's physiological and voice data and quickly detected anger based on the analysis results.

[1409] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1410] In this invention, the server includes means for acquiring pulse data, means for acquiring body temperature data, means for acquiring sweat data, means for acquiring voice data, means for registering basic data as initial settings, means for analyzing the acquired pulse data, body temperature data, sweat data, and voice data, means for converting the voice data into text and analyzing it using natural language processing, means for determining whether the user is in an angry state based on the analysis result, and means for sending a notification to the user when it is determined that the user is in an angry state. This makes it possible to monitor the user's emotional state in real time, quickly detect an anger state, and send a notification at an appropriate time.

[1411] "Pulse data" is data measured using the user's heart rate and heart rhythm as indicators.

[1412] "Body temperature data" is data obtained by measuring the user's body temperature.

[1413] "Sweat data" is data obtained by measuring the amount of sweat on the user's skin surface using a sensor.

[1414] "Voice data" refers to sound data of a user's voice collected using a recording device.

[1415] "Basic data" is reference data that indicates the user's normal state, such as normal pulse rate, body temperature, and sweat level.

[1416] "Analysis" is a computational process for determining the user's state and emotions based on various acquired data.

[1417] "Natural language processing" is a technology that converts voice data into text and analyzes that text to understand emotions and intent.

[1418] The "angry state" is a state in which the user is emotionally aroused and feels angry.

[1419] A "notification" is a means of conveying some information or message to a user.

[1420] "Server" refers to a computer system that handles processes such as data analysis, decision-making, and notification generation and transmission.

[1421] A "terminal" is a device that collects data from users and communicates with servers, and primarily includes smartwatches and smartphones.

[1422] This invention relates to an anger management AI system that prevents trouble in crowded public places, especially on trains, etc. This system is characterized by sending notifications and messages urging self-control at appropriate times when the user becomes angry.

[1423] System Configuration

[1424] The system includes the following components:

[1425] 1. Devices (smartwatches, smartphones)

[1426] How pulse data is acquired: A sensor built into the device measures the user's pulse in real time.

[1427] Body temperature data acquisition method: A temperature sensor built into the device measures the user's body temperature in real time.

[1428] Method for acquiring sweat data: A sensor built into the device detects the user's sweating status in real time.

[1429] Means for acquiring voice data: The device's microphone records the user's voice and saves it as an audio file.

[1430] A means for registering basic data as initial settings: The user registers their normal pulse rate, body temperature, and sweat level as initial settings.

[1431] 2. Server

[1432] Data analysis means: The server receives the acquired pulse data, body temperature data, sweat data and voice data and analyzes them.

[1433] Speech-to-text conversion and natural language processing means: Convert speech data into text and use natural language processing (NLP) to analyze keywords and tones that indicate anger.

[1434] Anger detection means: Based on the analysis results, it is determined whether the user is in an angry state.

[1435] Notification generating means: When it is determined that the user is in an angry state, a message for sending a notification to the user is generated.

[1436] Notification sending means: Sends the generated notification message to the smartwatch or smartphone.

[1437] Specific Examples

[1438] For example, imagine a user getting irritated on a train, their pulse rate and body temperature rising, sweating increasing, and uttering phrases like "Stop messing around!" or "Why is it so crowded?" At this time, the device collects this data and sends it to the server. The server analyzes the data and, if it determines that the user is angry, generates an appropriate message and sends it to the device. Finally, the device tries to calm the user down by telling them to "take a deep breath and calm down" or telling them a joke.

[1439] Prompt Sentence Examples

[1440] Below is an example of input to a generative AI model to illustrate this system.

[1441] "Please explain an anger management AI system for controlling anger in public places. This system collects and analyzes the user's biometric and voice data in real time. If an abnormality is detected, it sends a message to the user urging them to control themselves. Please explain in detail what data is collected, how it is analyzed, and what kind of notification is sent."

[1442] By using this system, it is possible to prevent trouble in public places and provide a safe and comfortable environment for users and those around them.

[1443] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1444] Step 1:

[1445] Initial Setup

[1446] When the user starts the system for the first time, they input their basic data (normal pulse, body temperature, and sweat level) using a smartphone app. This basic data is then saved on the device. The user can also select the type of notification (vibration, voice, or text). The input is the user's normal physiological data and notification preferences, and the output is the settings saved on the device.

[1447] Specific behavior:

[1448] 1. The user launches the smartphone app.

[1449] 2. The user enters pulse rate, body temperature, and sweat level according to the screen where basic data is entered.

[1450] 3. On the screen where the user selects the type of notification, select "Vibrate," "Voice," or "Text."

[1451] Step 2:

[1452] Data collection

[1453] The device collects physiological data (pulse, body temperature, sweat) and voice data at regular intervals. The sensor measures this data in real time and temporarily stores it in internal memory. The data is then sent to a server at regular intervals. The input is the physiological data and voice data acquired in real time, and the output is the data sent to the server.

[1454] Specific behavior:

[1455] 1. The device measures pulse rate, body temperature, and sweat data every minute.

[1456] 2. The device's microphone records the user's voice in real time and saves it as an audio file.

[1457] 3. The device sends the collected data to a server via the Internet every five minutes.

[1458] Step 3:

[1459] Data analysis

[1460] The server receives the transmitted data and begins analysis. First, pulse data is evaluated for fluctuations over time to detect deviations from normal baseline values. Next, body temperature data is monitored for temperature fluctuations and compared to baseline values. Sweat data is analyzed in a similar manner to evaluate increases in sweating. Finally, the voice data is converted into text and analyzed using natural language processing (NLP) for keywords and tones that indicate anger. The input is the various physiological data and voice data transmitted to the server, and the output is the analysis results.

[1461] Specific behavior:

[1462] 1. The server analyzes the pulse data and evaluates variations from baseline.

[1463] 2. The server analyzes the body temperature data and determines the degree of temperature rise.

[1464] 3. The server analyzes the sweat data and evaluates the increase in sweat rate.

[1465] 4. The server converts the audio data into text and uses NLP to detect keywords and tones that indicate anger.

[1466] Step 4:

[1467] Anger detection

[1468] The server determines whether the user is in an angry state based on the analysis results. For example, if the pulse rate is 30% higher than normal, the body temperature is 0.5 degrees higher, and the voice data contains anger-related keywords such as "Don't mess with me" or "Why," the server determines the user is in an angry state. The input is the analysis results, and the output is the anger state determination result.

[1469] Specific behavior:

[1470] 1. The server comprehensively evaluates the analysis results of pulse, body temperature, and sweat data.

[1471] 2. The server uses the results of the voice data analysis to determine the emotional state.

[1472] 3. The server determines whether the user's emotional state is angry.

[1473] Step 5:

[1474] Notification generation and delivery

[1475] If the server detects the user's anger, it generates an appropriate notification message. For example, it generates a joke such as "Take a deep breath and calm down" or "Everyone's here to see me today!" This notification message is sent to the smartwatch or smartphone. The input is the anger state judgment result, and the output is the generated notification message.

[1476] Specific behavior:

[1477] 1. The server generates an appropriate notification message for the user.

[1478] 2. The server sends the generated notification message to the terminal.

[1479] 3. The terminal receives the notification message and presents it to the user.

[1480] 4. The smartwatch will vibrate and display a message, and the smartphone will play the message aloud.

[1481] Through the above steps, the anger management AI system of the present invention can monitor the user's emotional state in real time and encourage self-control at the appropriate time, thereby preventing trouble in public places.

[1482] (Application example 1)

[1483] 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."

[1484] In modern brick-and-mortar stores, when customers feel stressed or angry, it often negatively impacts the atmosphere in the store and other customers. However, it is difficult for on-site staff to sense the situation in real time and respond appropriately. Therefore, a method is needed to detect customers' emotional states in real time and respond appropriately.

[1485] 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.

[1486] In this invention, the server includes means for acquiring pulse data, means for acquiring body temperature data, means for acquiring sweat data, means for acquiring voice data, means for analyzing the acquired pulse data, body temperature data, sweat data, and voice data, means for determining whether the user is in an angry state based on the analysis results, means for sending a notification to the user when it is determined that the user is in an angry state, means for sending a notification to the user's smart electronic device in a physical store, and message generation means using a generative AI model to ensure that the notification includes a specific message and specific suggestions such as promoting relaxation or offering a special discount. This makes it possible to automatically notify customers in a physical store of appropriate messages and suggestions in real time when they feel stressed or angry.

[1487] "Pulse data" is data indicating the user's heart rate, and refers to the number of times the user's heart beats measured in real time.

[1488] "Body temperature data" is data indicating the user's body temperature, and refers to the internal and external temperatures of the user's body measured in real time.

[1489] "Sweat data" is data indicating the amount of sweat produced by the user, and refers to the amount of sweat secreted from the user's skin measured in real time.

[1490] "Voice data" refers to data indicating the content of a user's speech, and refers to the characteristics of the user's words and voice collected in real time.

[1491] The "analysis means" is a means for analyzing the various acquired data and evaluating the emotional state of the user based on the data.

[1492] The "determination means" is a means for determining whether the user is in an angry state based on the analysis result obtained by the analysis means.

[1493] The "notification means" is a means for conveying an appropriate message to the user when it is determined that the user is in an angry state.

[1494] "Smart electronic devices" refer to portable devices that users use on a daily basis, such as smartwatches and smartphones.

[1495] A "generative AI model" is a model that uses artificial intelligence to generate specific messages and suggestions, allowing for personalized messages to be generated for users.

[1496] The "message generation means" is a means of generating appropriate messages and suggestions according to the user's state, and uses a generative AI model.

[1497] The present invention is a system for detecting stress and anger in real time in a brick-and-mortar store and responding appropriately using the user's smart electronic devices, a cloud server, and a generative AI model.

[1498] System Configuration

[1499] The system includes the following components:

[1500] 1. Smart Electronics

[1501] This includes smartwatches and smartphones.

[1502] How to obtain pulse data: The user's heart rate is obtained in real time using the sensors in the smartwatch.

[1503] Body temperature data acquisition method: The user's body temperature is acquired in real time using a temperature sensor in a smartwatch or smartphone.

[1504] Means of acquiring sweat data: The amount of sweat a user generates is acquired in real time using sensors in smart electronic devices.

[1505] Method of acquiring voice data: The smartphone's microphone is used to collect the user's speech in real time.

[1506] 2. Server

[1507] Data analysis means: The server receives and analyzes pulse, body temperature, sweat, and voice data sent from smart electronic devices.

[1508] Anger detection means: Based on the analysis results, it is determined whether the user is in an angry state.

[1509] Notification generator: If the user is determined to be in an angry state, an appropriate message is generated using a generative AI model.

[1510] Notification sending method: Send the generated message to your smartwatch or smartphone.

[1511] Hardware and software used

[1512] Hardware:

[1513] Smartwatch (e.g. Samsung Galaxy Watch)

[1514] Smartphone (e.g. iPhone 13)

[1515] Cloud server (e.g. AWS EC2 instance)

[1516] software:

[1517] TensorFlow: Used to implement an anger detection model based on pulse, body temperature, and sweat data.

[1518] Twilio: Used to send notification messages to users' smart electronic devices.

[1519] OpenAI GPT-3.5: Used to generate response messages using a generative AI model.

[1520] Specific example explanation

[1521] For example, if a customer in a physical store becomes stressed due to a long wait or crowding, the system collects the customer's pulse, body temperature, and sweat rate in real time, and also records what they say with a microphone. This data is sent to a server for analysis. If the server determines that the customer is angry based on the analysis results, it uses a generative AI model (OpenAI GPT-3.5) to generate a message such as "Take a deep breath and calm down" or a suggestion message offering a special discount coupon.

[1522] Example prompt for a generative AI model:

[1523] "Generate a soothing message for a person who is likely angry or stressed:"

[1524] Finally, this generated message is sent via Twilio to a smartwatch or smartphone, for example, a notification will appear on the customer's smartwatch saying, "Enjoy a relaxed shopping experience with our special coupons," which will calm the customer and maintain a good atmosphere in the store.

[1525] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1526] Step 1:

[1527] The device collects the user's pulse, body temperature, sweat, and voice data in real time. Specifically, sensors within the smartwatch measure the pulse and body temperature and detect the amount of sweat. The smartphone's microphone also collects surrounding sounds and sends this data to a cloud server. The input is various vital data and voice data, and the output is data sent to the server.

[1528] Step 2:

[1529] The server receives the data acquired by the data analysis means and analyzes each data. Specifically, it uses TensorFlow to analyze pulse, body temperature, and sweat data and evaluates how much these data deviate from baseline values. It also converts voice data into text and uses natural language processing (NLP) to detect emotional tone and keywords that indicate anger. The input is the data sent from the device, and the output is the analysis results.

[1530] Step 3:

[1531] The server determines whether the user is in an angry state based on the analysis results. Specific criteria include pulse rate 30% or more higher than normal, body temperature 0.5 degrees higher, increased sweating, and the voice data containing keywords indicating anger. The input is the analysis results, and the output is the anger state determination.

[1532] Step 4:

[1533] If the server determines that the user is in an angry state, it uses a generative AI model (OpenAI GPT-3.5) to generate an appropriate message. Specifically, the prompt "Generate a soothing message for a person who is likely angry or stressed:" is input into the generative AI model, which outputs messages such as "Take a deep breath and calm down" or "Use a special coupon and enjoy a relaxing shopping experience." The input is the determination of the user's angry state, and the output is the generated message.

[1534] Step 5:

[1535] The server sends the generated message to the user's device as a notification. Specifically, it uses Twilio to send the message to a smartwatch or smartphone. The user's smartwatch vibrates and displays the generated message. The smartphone plays the message aloud. The input is the generated message, and the output is a notification to the user's device.

[1536] Step 6:

[1537] The user receives a notification from the device and checks the message displayed on the screen or the voice guidance, which allows the user to take a deep breath or use a special coupon to reduce stress or anger. The input is the notification to the device, and the output is the user's reaction.

[1538] 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.

[1539] This invention relates to an anger management AI system that prevents trouble in crowded public places, especially on trains, etc. This system is characterized by sending notifications and messages urging self-control at appropriate times when the user becomes angry, and its effectiveness is enhanced by combining it with an emotion engine.

[1540] System Configuration

[1541] The system includes the following components:

[1542] 1. Devices (smartwatches, smartphones)

[1543] How pulse data is acquired: A sensor built into the device measures the user's pulse in real time.

[1544] Body temperature data acquisition method: A temperature sensor built into the device measures the user's body temperature in real time.

[1545] Method for acquiring sweat data: A sensor built into the device detects the user's sweating status in real time.

[1546] Means for acquiring voice data: The device's microphone records the user's voice and saves it as an audio file.

[1547] Means for acquiring image data and video data (if necessary): The smartphone camera captures the user's facial expressions and movements and sends the data to the server.

[1548] 2. Server

[1549] Data analysis means: The server receives the acquired pulse data, body temperature data, sweat data, voice data, image data and video data and analyzes them.

[1550] Anger detection: Based on the analysis results, it is determined whether the user is in an angry state. The emotion engine plays an important role here.

[1551] Emotion engine: Analyzes voice data, pulse data, body temperature data, sweat data, as well as image and video data to identify the user's emotions in detail.

[1552] Notification generation means: If the user is determined to be in an angry state, a message is generated to notify the user. Based on the analysis results of the emotion engine, a notification is generated that matches the user's emotions.

[1553] Notification sending means: Sends the generated notification message to the smartwatch or smartphone.

[1554] Explanation of program processing

[1555] 1. Initial Setup

[1556] The device registers the user's basic data (normal pulse, body temperature, sweat level) as the initial setting, and the user can select the type of notification (vibration, voice, text) according to their preference.

[1557] 2. Data collection

[1558] The device periodically (for example, every minute) collects the user's pulse, body temperature, and sweat data. At the same time, the device's microphone records the user's voice in real time and saves it as audio data. Furthermore, the smartphone's camera captures the user's facial expressions and movements as needed and saves them as image and video data.

[1559] 3. Data transmission

[1560] The collected data is sent to the server at regular intervals (for example, every minute). This data includes pulse data, body temperature data, sweat data, voice data, image data, and video data.

[1561] 4. Data Analysis

[1562] The server analyzes the transmitted data in real time. Specifically, it calculates and evaluates fluctuations in pulse, body temperature, and sweat data from their normal baseline values. The emotion engine analyzes the audio, image, and video data and uses natural language processing (NLP) to detect keywords and tones that indicate anger, as well as the user's facial expressions and actions.

[1563] 5. Anger detection

[1564] The server comprehensively evaluates the analysis results and determines whether the user is in an angry state. If the pulse rate is 30% or more higher than normal, the body temperature is 0.5 degrees or more higher, and the voice data or image data contains keywords or facial expressions that indicate anger, the user is determined to be in an angry state.

[1565] 6. Generating and Sending Notifications

[1566] If the server detects that the user is angry, it generates an appropriate notification message. Based on the analysis results of the emotion engine, it generates a notification that matches the user's emotions. For example, it generates a message such as "Take a deep breath and calm down" or a joke such as "If you think the train is crowded, everyone came to see me today!" The generated message is sent to the smartwatch or smartphone.

[1567] The device receives the generated notification and presents it to the user: the smartwatch vibrates and displays the message "Take a deep breath and calm down," and the smartphone plays a joke aloud.

[1568] Specific examples

[1569] For example, suppose a user becomes irritated on a train, their pulse rate and body temperature rise, they start sweating, they say things like "Stop messing around!" or "Why is it so crowded?", and their expression becomes grim. In this case, the device collects this data and sends it to the server. The server analyzes this data and, if it determines that the user is angry using its emotion engine, it generates an appropriate message and sends it to the device. Finally, the device tries to calm the user down by telling them to "take a deep breath and calm down" or telling a joke.

[1570] This system can detect user anger early and take appropriate measures to prevent trouble in public places, providing a safe and comfortable environment for users and those around them.

[1571] The processing flow will be explained below.

[1572] Step 1:

[1573] The device registers the user's basic data (normal pulse, body temperature, sweat level) as the initial setting, and the user also selects the type of notification (vibration, voice, text) according to their preference.

[1574] Step 2:

[1575] The device periodically (for example, every minute) collects the user's pulse, body temperature, and sweat data. At the same time, the device's microphone records the user's voice in real time and saves it as audio data. If necessary, the smartphone camera captures the user's facial expressions and movements, collecting image and video data.

[1576] Step 3:

[1577] The terminal transmits the collected pulse data, body temperature data, sweat data, voice data, image data, and video data to a server at regular intervals (for example, every minute).

[1578] Step 4:

[1579] The server analyzes the received data in real time. It analyzes pulse data to calculate variations from normal baseline values, as well as body temperature and sweat data.

[1580] Step 5:

[1581] The server converts the voice data into text and uses natural language processing (NLP) to detect keywords and tones that indicate anger, while simultaneously analyzing image and video data to identify emotions from the user's facial expressions and movements.

[1582] Step 6:

[1583] The emotion engine comprehensively evaluates the analysis results and determines the user's emotional state. If the pulse rate is 30% or more higher than normal, the body temperature is 0.5 degrees higher, and the voice and image data contain keywords or facial expressions that indicate anger, the user is determined to be in an angry state.

[1584] Step 7:

[1585] If the server detects that the user is angry, it generates an appropriate notification message based on the analysis results of the emotion engine. For example, it generates a message like "Take a deep breath and calm down" or a joke like "I thought the train was crowded, but everyone came to see me today!"

[1586] Step 8:

[1587] The server sends the generated notification message to the device, which then sends the notification to the user's smartwatch and smartphone.

[1588] Step 9:

[1589] The device receives the notification and presents it to the user accordingly: the smartwatch vibrates and displays the message "Take a deep breath and calm down," and the smartphone plays a joke aloud.

[1590] Step 10:

[1591] The user receives a notification and can take action, for example, by taking a deep breath or listening to a joke to calm down.

[1592] Step 11:

[1593] The server again collects and analyzes the data to observe the user's reaction. If it determines that the user's anger continues, it will send additional notifications or advice, such as a reminder like "You're almost at the station!"

[1594] These steps will help detect user anger early and take appropriate measures to prevent trouble in public places.

[1595] Example 2

[1596] 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."

[1597] In modern public places, especially on trains, users often become angry due to crowding and stress. This can cause trouble and have a negative impact on those around them, so there is a need for a system that can detect users' anger early and encourage appropriate responses.

[1598] 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.

[1599] In this invention, the server includes means for acquiring pulse data, means for acquiring body temperature data, means for acquiring sweat data, means for acquiring voice data, means for acquiring image data and video data, means for analyzing the acquired pulse data, body temperature data, sweat data, voice data, image data and video data, means for determining whether the user is in an angry state based on the analysis results, and means for sending a notification to the user when it is determined that the user is in an angry state. This makes it possible to monitor the user's emotional state in detail and in real time and send notifications at appropriate times.

[1600] "Pulse data" is biological information that indicates the user's heart rate.

[1601] "Body temperature data" is biological information indicating the user's body temperature.

[1602] "Sweat data" is biological information that indicates the amount of sweat of the user and the associated humidity of the skin.

[1603] "Voice data" is digital information that records the user's speech or voice.

[1604] "Image data" is digital information of a still image capturing the user's facial expression and posture.

[1605] "Video data" is digital information of moving images of the user's facial expressions and movements.

[1606] "Emotion engine" is a general term for algorithms and software that analyzes audio data, image data, video data, and various biometric data to determine the user's emotional state.

[1607] "Notification" means a message, alert, or information sent to a user to prompt the user to take action.

[1608] A "personal digital assistant" is a portable electronic device capable of data communication, such as a smartphone or tablet.

[1609] A "mobile device" is an electronic device worn on the wrist, such as a smartwatch.

[1610] This invention relates to an anger management AI system that prevents trouble in crowded public places, especially on trains, etc. This system is characterized by sending notifications and messages urging self-control at appropriate times when the user becomes angry, and its effectiveness is enhanced by combining it with an emotion engine.

[1611] System Configuration

[1612] The system includes the following components:

[1613] 1. Devices (smartwatches, smartphones)

[1614] How pulse data is acquired: A sensor built into the device measures the user's pulse in real time.

[1615] Body temperature data acquisition method: A temperature sensor built into the device measures the user's body temperature in real time.

[1616] Method for acquiring sweat data: A sensor built into the device detects the user's sweating status in real time.

[1617] Means for acquiring voice data: The device's microphone records the user's voice and saves it as an audio file.

[1618] Means for acquiring image data and video data (if necessary): The smartphone camera captures the user's facial expressions and movements and sends the data to the server.

[1619] 2. Server

[1620] Data analysis means: The server receives the acquired pulse data, body temperature data, sweat data, voice data, image data and video data and analyzes them.

[1621] Anger detection: Based on the analysis results, it is determined whether the user is in an angry state. The emotion engine plays an important role here.

[1622] Emotion engine: Analyzes voice data, pulse data, body temperature data, sweat data, as well as image and video data to identify the user's emotions in detail.

[1623] Notification generation means: If the user is determined to be in an angry state, a message is generated to notify the user. Based on the analysis results of the emotion engine, a notification is generated that matches the user's emotions.

[1624] Notification sending means: Sends the generated notification message to the smartwatch or smartphone.

[1625] Detailed Description of the Invention

[1626] Data collection

[1627] The device collects the user's pulse, body temperature, and sweat data every minute. For example, when a user is riding a train, the smartwatch's sensor measures the user's pulse, and the body temperature sensor measures the user's skin temperature. The microphone constantly monitors and records the user's speech in real time. If necessary, the smartphone's camera records the user's facial expressions and movements.

[1628] Data transmission and analysis

[1629] The device periodically transmits the collected data to a server. The server analyzes the received data in real time and evaluates fluctuations from baseline in pulse rate, body temperature, and sweat data. The emotion engine analyzes audio, image, and video data and uses natural language processing (NLP) technology to identify keywords and tones that indicate anger, as well as the user's facial expressions and actions.

[1630] Anger detection and notification generation

[1631] The server comprehensively evaluates the analysis results, and if it determines that the user is angry, it generates an appropriate notification message and sends it to the device. Based on the analysis results of the emotion engine, it generates a notification that matches the user's emotions. For example, it generates a message such as "Take a deep breath and calm down" or a joke such as "If you think the train is crowded, everyone came to see me today!" These messages are sent to the smartwatch or smartphone.

[1632] User Notification

[1633] The device receives the notification and presents it to the user. The smartwatch vibrates and displays the message "Take a deep breath and calm down," while the smartphone plays a joke. The goal is for the user to receive the notification and calm down immediately.

[1634] Specific examples

[1635] For example, suppose a user becomes irritated on a train, their pulse rate and body temperature rise, they start sweating, they say things like "Stop messing around!" or "Why is it so crowded?", and their expression becomes grim. In this case, the device collects this data and sends it to the server. The server analyzes this data and, if it determines that the user is angry using its emotion engine, it generates an appropriate message and sends it to the device. Finally, the device tries to calm the user down by telling them to "take a deep breath and calm down" or telling a joke.

[1636] Prompt Sentence Examples

[1637] Here are some examples of prompts to input to a generative AI model:

[1638] If the user is frustrated and expressing anger, generate a conversational message to defuse the situation. For example, if the user's pulse rate is 30% higher than normal, their body temperature is more than half a degree higher, and their voice recording contains the words "It's so busy!", generate a message or joke to calm the user.

[1639] This system can prevent trouble in public places and provide a safe and comfortable environment for users and those around them.

[1640] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1641] Step 1: Initial Setup

[1642] When a user uses the system for the first time, the device collects the user's basic data. The user wears the smartwatch and remains still for a few minutes to measure their normal pulse, body temperature, and sweat rate. Once the basic data is collected, the smartphone app opens and the user selects the type of notification (vibration, voice, text). This information is stored on the server as initial setting data.

[1643] Input: User's biometric information, notification settings

[1644] Output: Initial setting data

[1645] Step 2: Collect data

[1646] The device collects the user's pulse, body temperature, and sweat data every minute. For example, while riding a train, the smartwatch's sensor measures the user's pulse, and the body temperature sensor measures the user's skin temperature. The device's built-in microphone also records the user's voice in real time. If necessary, the smartphone's camera captures the user's facial expressions and movements.

[1647] Input: Continuously collected biometric, audio, and image data

[1648] Output: Real-time data collected

[1649] Step 3: Sending data

[1650] The device sends the collected data at regular intervals (for example, every minute) to a server. This data includes pulse data, body temperature data, sweat data, voice data, and, if necessary, image and video data. The communication method is that the smartwatch sends the data to a smartphone via Bluetooth, and the smartphone then sends the data to the server via the Internet.

[1651] Input: Real-time data collected

[1652] Output: Data sent to the server

[1653] Step 4: Analyze the data

[1654] The server analyzes the received data in real time. Specifically, it calculates and evaluates fluctuations from baseline values ​​in pulse rate, body temperature, and sweat data. The emotion engine also analyzes audio, image, and video data, and uses natural language processing (NLP) technology to detect keywords, tones, and facial expressions that indicate anger.

[1655] Input: Data sent to the server

[1656] Output: Analysis result (user's emotional state)

[1657] Step 5: Anger detection

[1658] The server comprehensively evaluates the analysis results and determines whether the user is in an angry state. For example, if the pulse rate is 30% or more higher than normal, the body temperature is 0.5 degrees higher than normal, and keywords indicating anger are detected in the voice data, the server determines that the user is in an angry state.

[1659] Input: Analysis results

[1660] Output: Angry state determination result

[1661] Step 6: Generate and send notifications

[1662] The server generates an appropriate notification message to ease the user's anger. For example, it generates a message such as "Take a deep breath and calm down" or a joke such as "If you think the train is crowded, you're here to see me today!" The notification message generated based on the analysis results of the emotion engine is sent to the device.

[1663] Input: Angry state result

[1664] Output: Information message

[1665] Step 7: Notify users

[1666] The device receives the notification message and presents it to the user. The smartwatch vibrates and displays the message "Take a deep breath and calm down." The smartphone plays the generated joke aloud, allowing the user to calm down immediately.

[1667] Input: Notification message

[1668] Output: Notification displayed to the user

[1669] Through these processing steps, the system can detect user anger early and respond appropriately, thereby preventing trouble in public places and providing a safe and comfortable environment for users and those around them.

[1670] (Application example 2)

[1671] 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."

[1672] Previously, there were limitations to how to manage the stress and anger experienced by operators working in factories, which affected work efficiency and safety. Furthermore, there was a lack of means to monitor the emotional state of operators in real time and take appropriate measures, making it difficult to provide appropriate alerts early on. As a result, this can have a negative impact on the mental and physical health of operators.

[1673] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1674] In this invention, the server includes means for acquiring pulse data, means for acquiring body temperature data, means for acquiring sweat data, means for acquiring voice data, means for analyzing the acquired pulse data, body temperature data, sweat data, and voice data, means for determining whether the user is in an angry state based on the analysis results, means for sending a notification to the user when it is determined that the user is in an angry state, means for generating a notification appropriate to the work environment when the user is working, and means for displaying or voicing the generated notification on the automatic operation device. This makes it possible to early detect stress or anger experienced by operators working in a factory and provide appropriate alerts and advice.

[1675] "Pulse data" refers to data obtained by measuring and recording the user's heart rate.

[1676] "Body temperature data" refers to data obtained by measuring and recording the user's body temperature.

[1677] "Sweat data" refers to data obtained by measuring and recording the humidity and electrical conductivity of the user's skin surface.

[1678] "Voice data" refers to data that is recorded and saved of the voice uttered by the user.

[1679] "Means for acquiring" refers to devices or sensors for collecting pulse data, body temperature data, sweat data, or voice data.

[1680] The "analyzing means" refers to algorithms and software for processing and analyzing the various data acquired (pulse data, body temperature data, sweat data, and voice data).

[1681] The "means for determining" refers to logic or criteria for determining whether the user is in an angry state based on the results of data analysis.

[1682] A "means for sending a notification" is a procedure or device for transmitting a notification message to a user.

[1683] The "means for generating notifications suited to the working environment" is a system that takes into account the work that the user is doing and creates notifications suited to that situation.

[1684] An "automated operation device" is a device or robot used in a factory that has the function of displaying or vocalizing notifications to an operator.

[1685] This invention is an anger management system that monitors the stress and anger of factory workers in real time and provides notifications at appropriate times. The system collects and analyzes the operator's biometric data (pulse, body temperature, sweat) and voice data to determine whether the operator is in an angry state and notifies them as necessary.

[1686] System Configuration

[1687] The system includes the following components:

[1688] Hardware

[1689] 1. Terminals (in-factory robots, mobile terminals)

[1690] How pulse data is acquired: A sensor built into the robot measures the operator's pulse in real time.

[1691] Body temperature data acquisition method: A temperature sensor built into the robot measures the operator's body temperature in real time.

[1692] Method of acquiring sweating data: Sensors built into the robot detect the operator's sweating status in real time.

[1693] How voice data is acquired: The robot's microphone records the operator's voice and saves it as an audio file.

[1694] software

[1695] 2. Server

[1696] Data analysis means: The server receives the acquired pulse data, body temperature data, sweat data and voice data and analyzes them.

[1697] Anger detection method: Based on the analysis results, determine whether the operator is in an angry state.

[1698] Emotion engine: Analyzes voice data, pulse data, body temperature data, and sweat data to identify the operator's emotions in detail.

[1699] Notification generating means: Generates an appropriate notification message if it is determined that the operator is in an angry state.

[1700] Notification sending means: Sends the generated notification message to the robot or mobile device.

[1701] Program processing description

[1702] Initial Setup

[1703] The device registers the operator's basic data (normal pulse, body temperature, sweat level), and the operator can select the type of notification (voice, text, flashing light) according to their preference.

[1704] Data collection

[1705] The terminal periodically (for example, every minute) collects the operator's pulse, body temperature, and sweat data. At the same time, the terminal's microphone records the operator's voice in real time and saves it as audio data.

[1706] Data transmission

[1707] The data collected by the device is sent to the server at regular intervals (for example, every minute). This data includes pulse data, body temperature data, sweat data, and voice data.

[1708] Data analysis

[1709] The server analyzes the transmitted data in real time. Specifically, it calculates and evaluates fluctuations in pulse rate, body temperature, and sweat data from normal baseline values. The emotion engine analyzes the voice data and uses natural language processing (NLP) to detect keywords and tones that indicate anger.

[1710] Anger detection

[1711] The server comprehensively evaluates the analysis results and determines whether the operator is in an angry state. For example, if the pulse rate is 30% or more higher than normal, the body temperature is 0.5 degrees higher, and the voice data contains keywords indicating anger, the operator is determined to be in an angry state.

[1712] Generate and send notifications

[1713] If the server detects that the operator is angry, it generates an appropriate notification message. Based on the analysis results of the emotion engine, it generates a notification that matches the operator's emotions. For example, it generates a message such as "Take a short break" or advice such as "Take a deep breath and relax." The generated message is sent to the robot or mobile device.

[1714] Specific examples

[1715] If an operator in a factory starts to get irritated while working, their pulse rate increases, and their voice tone becomes rough:

[1716] Sensors: The robot's built-in sensors detect increased pulse rate, body temperature, and increased sweating.

[1717] Audio data: The microphone detects words like "Damn!" and "Why doesn't this work?"

[1718] Analysis: The server interprets this as an angry state and immediately generates the message "Take a break."

[1719] Notification: The robot will give you voice advice saying, "Take a short break."

[1720] Prompt Sentence Examples

[1721] "An operator is becoming angry while working. Please analyze their current pulse, body temperature, and sweat data and generate an appropriate notification message."

[1722] This system can improve work efficiency and safety by detecting stress and anger experienced by operators working in factories at an early stage and providing appropriate alerts and advice.

[1723] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1724] Step 1:

[1725] The terminal registers the operator's basic data.

[1726] Input: The operator's normal pulse rate, body temperature, and sweat level.

[1727] Operation: The terminal's sensors measure the operator's initial data and register it in the system.

[1728] Output: The initial base data is saved in the system.

[1729] Step 2:

[1730] The user selects the type of notification.

[1731] Input: Operator notification preference (voice, text, flashing light).

[1732] How it works: The operator selects the type of notification using the terminal interface.

[1733] Output: The selected notification type is set in the system.

[1734] Step 3:

[1735] The terminal periodically collects the operator's biometric data.

[1736] Input: Operator pulse, temperature, and sweat data.

[1737] How it works: The device's built-in sensors measure biometric data every minute.

[1738] Output: The measured biometric data is stored on the device.

[1739] Step 4:

[1740] The terminal collects the operator's voice data.

[1741] Input: Operator's voice (speech content, tone).

[1742] How it works: The device's microphone records the operator's voice in real time and saves it as an audio file.

[1743] Output: The recorded audio file will be saved on your device.

[1744] Step 5:

[1745] The terminal transmits the collected data to the server.

[1746] Input: Collected biometric and audio data.

[1747] How it works: The device sends data packets to the server every minute.

[1748] Output: Biometric and audio data transmitted to the server.

[1749] Step 6:

[1750] The server analyzes the data.

[1751] Input: Biometric and voice data sent to the server.

[1752] How it works: The server's data analysis means evaluates fluctuations in pulse, body temperature, and sweat data by comparing them with baseline values. The emotion engine also analyzes voice data using NLP to detect keywords and tones that indicate anger.

[1753] Output: Obtain analysis results (e.g., pulse rate increase of 30% or more, body temperature increase of 0.5 degrees or more, increased sweating, anger keywords in the voice).

[1754] Step 7:

[1755] The server determines the anger state.

[1756] Input: Analysis results.

[1757] How it works: The server aggregates and evaluates the analysis results to determine whether the operator is in an angry state.

[1758] Output: Operator's situation assessment result (whether angry or not).

[1759] Step 8:

[1760] The server generates an appropriate notification message.

[1761] Input: Operator's situation assessment result (anger state).

[1762] Action: Based on the analysis results, the emotion engine generates an appropriate notification message (e.g., take a short break, take a deep breath, and relax).

[1763] Output: The generated notification message.

[1764] Step 9:

[1765] The server sends a notification message to the terminal.

[1766] Input: The generated notification message.

[1767] Operation: The server sends a notification message to the terminal as a packet.

[1768] Output: The device receives the notification message.

[1769] Step 10:

[1770] The terminal presents the notification message to the operator.

[1771] Input: Notification message.

[1772] Operation: The device will present a notification message to the operator in the manner configured (voice, text, flashing light).

[1773] Output: A notification message is presented to the operator, encouraging the user to exercise restraint.

[1774] 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.

[1775] 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.

[1776] 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.

[1777] 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.

[1778] 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.

[1779] 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.

[1780] 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).

[1781] 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.

[1782] 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."

[1783] 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.

[1784] 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).

[1785] 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.

[1786] 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.

[1787] 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.

[1788] 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.

[1789] 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.

[1790] 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.

[1791] 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.

[1792] 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.

[1793] 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.

[1794] 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.

[1795] The following is further disclosed regarding the above embodiment.

[1796] (Claim 1)

[1797] means for acquiring pulse data;

[1798] a means for acquiring body temperature data;

[1799] a means for acquiring sweat data;

[1800] means for acquiring audio data;

[1801] means for analyzing the acquired pulse data, body temperature data, sweat data, and voice data;

[1802] A means for determining whether the user is in an angry state based on the analysis result;

[1803] means for sending a notification to the user when the user is determined to be in an angry state;

[1804] A system including:

[1805] (Claim 2)

[1806] 10. The system of claim 1, further comprising means for transmitting a message generated based on the analysis result.

[1807] (Claim 3)

[1808] 10. The system of claim 1, further comprising means for transmitting notifications to at least a smartwatch and a smartphone.

[1809] "Example 1"

[1810] (Claim 1)

[1811] means for acquiring pulse data;

[1812] a means for acquiring body temperature data;

[1813] a means for acquiring sweat data;

[1814] means for acquiring audio data;

[1815] A means for registering basic data as initial settings;

[1816] means for analyzing the acquired pulse data, body temperature data, sweat data, and voice data;

[1817] means for converting the voice data into text and analyzing it using natural language processing;

[1818] A means for determining whether the user is in an angry state based on the analysis result;

[1819] means for sending a notification to the user when the user is determined to be in an angry state;

[1820] A system including:

[1821] (Claim 2)

[1822] 10. The system of claim 1, further comprising means for transmitting a message generated based on the analysis result.

[1823] (Claim 3)

[1824] 10. The system of claim 1, further comprising means for transmitting the notification to at least a personal digital assistant and a mobile terminal.

[1825] "Application Example 1"

[1826] (Claim 1)

[1827] means for acquiring pulse data;

[1828] a means for acquiring body temperature data;

[1829] a means for acquiring sweat data;

[1830] means for acquiring audio data;

[1831] means for analyzing the acquired pulse data, body temperature data, sweat data, and voice data;

[1832] A means for determining whether the user is in an angry state based on the analysis result;

[1833] means for sending a notification to the user when the user is determined to be in an angry state;

[1834] means for transmitting a notification to the user's smart electronic device at the physical store;

[1835] a message generation means using a generative AI model to generate notifications containing specific messages and specific offers, such as a relaxation promotion or a special discount offer;

[1836] A system including:

[1837] (Claim 2)

[1838] 10. The system of claim 1, further comprising means for transmitting a message generated based on the analysis result.

[1839] (Claim 3)

[1840] 10. The system of claim 1, further comprising means for transmitting notifications to at least a smartwatch and a smartphone.

[1841] "Example 2: Combining Emotion Engines"

[1842] (Claim 1)

[1843] means for acquiring pulse data;

[1844] a means for acquiring body temperature data;

[1845] a means for acquiring sweat data;

[1846] means for acquiring audio data;

[1847] means for acquiring image and video data;

[1848] means for analyzing the acquired pulse data, body temperature data, sweat data, voice data, image data and video data;

[1849] A means for determining whether the user is in an angry state based on the analysis result;

[1850] means for sending a notification to the user when the user is determined to be in an angry state;

[1851] A system including:

[1852] (Claim 2)

[1853] 10. The system according to claim 1, further comprising means for transmitting a message using an emotion engine generated based on the analysis result.

[1854] (Claim 3)

[1855] 10. The system of claim 1, wherein the notification includes at least a personal digital assistant and means for sending the notification to the personal digital assistant.

[1856] "Application example 2 when combining emotion engines"

[1857] (Claim 1)

[1858] means for acquiring pulse data;

[1859] a means for acquiring body temperature data;

[1860] a means for acquiring sweat data;

[1861] means for acquiring audio data;

[1862] means for analyzing the acquired pulse data, body temperature data, sweat data, and voice data;

[1863] A means for determining whether the user is in an angry state based on the analysis result;

[1864] means for sending a notification to the user when the user is determined to be in an angry state;

[1865] means for generating notifications appropriate to the user's working environment when the user is working;

[1866] means for displaying or vocalizing the generated notification on an automated operating device;

[1867] A system including:

[1868] (Claim 2)

[1869] 10. The system of claim 1, further comprising means for transmitting a message generated based on the analysis result.

[1870] (Claim 3)

[1871] 10. The system of claim 1, including means for transmitting notifications to at least an automated device and a mobile device. [Explanation of symbols]

[1872] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for acquiring pulse data; a means for acquiring body temperature data; a means for acquiring sweat data; means for acquiring audio data; means for analyzing the acquired pulse data, body temperature data, sweat data, and voice data; A means for determining whether the user is in an angry state based on the analysis result; means for sending a notification to the user when the user is determined to be in an angry state; A system including:

2. The system of claim 1 further comprising means for transmitting a message generated based on the analysis result.

3. The system of claim 1 , further comprising means for transmitting notifications to at least a smartwatch and a smartphone.

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