system

Abstract

We provide the system. [Solution] A device and means for acquiring the user's biometric information, A device means for estimating the user's level of tension based on the aforementioned biological information, A device means for generating warning content according to the aforementioned level of tension, A device means for presenting the generated warning content to the user, A system that includes this.

Description

Technical Field

[0005] ,

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] [In recent years, with the frequent occurrence of natural disasters, many warning systems have been constructed. However, the problem is that excessive warnings often interfere with people's calm judgment and cause confusion. As a result, there is a risk of delaying evacuation actions or inducing incorrect judgments. There is a need for a system that can provide appropriate warnings according to the mental state of users.]<​​​​​[This invention provides a means for estimating the level of tension from heart rate, blood pressure, etc., using a device capable of acquiring the user's biometric information in real time. Furthermore, it adjusts the volume and tone of the warning sound according to the estimated level of tension, generating more appropriate warning content, thereby enabling the user to make calm judgments and take swift action. It also includes means for acquiring additional health data in cooperation with external health management services to improve the accuracy of the warning content.]

[0006] "User" refers to [an individual who uses this system and whose biometric information is collected].

[0007] "Biometric information" refers to data that indicates an individual's physical condition, such as heart rate and blood pressure, and is acquired in order to estimate the user's level of tension.

[0008] "Device" refers to [a general term for hardware and software used to acquire biometric information or issue warnings].

[0009] "Stress level" is an indicator that assesses the user's physical and mental stress levels, and is estimated based on biometric information.

[0010] "Warning content" refers to [messages or audio information sent to the user, with volume and tone adjusted according to the level of urgency].

[0011] "Health management services" refer to [external services or systems that provide health data and assist in understanding the user's health status]. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3]It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. 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), or Bluetooth (registered trademark).

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the 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.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0026] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] The system of the present invention is configured to acquire the user's biometric information and estimate their level of tension. This system includes a wearable device that can acquire biometric information such as heart rate and blood pressure in real time. The acquired biometric information is transmitted to a server via data communication.

[0034] The server analyzes the received biometric information and calculates the user's stress level. The stress level is estimated by considering fluctuations in biometric information and deviations from known baseline values. Based on the estimated stress level, the server determines the content of the warning. Specifically, it adjusts the volume of the warning sound or changes the tone of the voice message according to the stress level. The generated warning content is then sent back to the terminal and presented to the user.

[0035] Furthermore, the server integrates with external health management services, allowing it to acquire additional data about the user's health status. This information can be used to further optimize the content of warnings.

[0036] Specific example

[0037] Consider a scenario where, during a disaster, a device worn by a user detects an increase in heart rate from 90 to 120 beats per minute. This biometric information is immediately transmitted to a server.

[0038] The server analyzes the data and estimates from its fluctuations that the user is experiencing a high level of stress.

[0039] If the level of tension is high, the server will generate a message in a low volume and a calm tone that encourages avoidance behavior.

[0040] When this message is sent to a device, the device will play an appropriate audio warning to the user, thereby promoting calm evacuation actions.

[0041] In this way, the invention prevents confusion caused by excessive warnings and enables smooth evacuation, especially during disasters.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The device acquires the user's biometric information. Specifically, it collects biometric data at regular intervals using devices that measure heart rate and blood pressure.

[0045] Step 2:

[0046] The device sends the collected biometric information to the server. Using data communication capabilities, the biometric data is uploaded to the server securely and quickly.

[0047] Step 3:

[0048] The server analyzes the received biometric information and estimates the user's stress level based on those values. It then compares this to past baseline values ​​and standard data to determine the current stress level.

[0049] Step 4:

[0050] The server generates a warning based on the estimated level of tension. If the tension level is high, it lowers the volume and prepares a message in a gentle tone.

[0051] Step 5:

[0052] The server sends the generated warning message to the terminal. A retransmission-enabled communication method is used to prevent information loss.

[0053] Step 6:

[0054] The device plays an adapted warning message to the user. By delivering the voice message through the speaker or headphones, the user can receive the information accurately.

[0055] Step 7:

[0056] The server connects with external health management services to acquire additional health data. This allows for more refined estimations of warning content and stress levels, which can then be used to inform future decisions.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] In today's world, where user health management is increasingly important, there is a need to efficiently monitor users' stress levels and health conditions and provide appropriate and timely warnings. However, existing systems are insufficient for emergency response based on real-time biometric data, and there is a problem in that they cannot provide optimal warnings tailored to the user's situation.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes: a device for acquiring the user's biometric data; a computing device for analyzing the user's stress level based on the biometric data; and a speech synthesis device for generating a warning message corresponding to the stress level. This makes it possible to quickly generate and present an optimal warning that corresponds to the user's real-time health status.

[0062] A "user" is a person who uses the system and whose biometric data is acquired and analyzed.

[0063] "Biometric data" refers to information that indicates the user's physical condition, such as heart rate and blood pressure.

[0064] "Device" refers to the hardware and software installed to acquire and transmit users' biometric data.

[0065] "Stress level" is an indicator of the user's mental and physical state estimated based on fluctuations in biometric data.

[0066] A "calculating device" is a device that analyzes the level of tension based on received biological data and outputs the results.

[0067] A "speech synthesis device" is a device that generates voice messages using text data.

[0068] A "communication device" is a device used to transmit generated warning messages to users.

[0069] A "health status management service" is an external service that collects and utilizes users' health data.

[0070] A "warning message" is information that is generated and presented to the user based on their level of anxiety.

[0071] This invention is a system that acquires a user's biometric data in real time, analyzes their stress level based on the results, and generates and presents an appropriate warning message. It mainly uses a sensor device, a data transmission device, analysis software on a server, a speech synthesis engine, and a communication device.

[0072] Hardware and software configuration

[0073] The user wears a wearable device with a built-in heart rate sensor and blood pressure monitor. This device collects biometric data in real time and transmits it to the device via Bluetooth or Wi-Fi.

[0074] The device encrypts the received biometric data before transmitting it to the server. The data is securely managed by a database management system.

[0075] The server analyzes the level of stress using biometric data analysis software. This analysis method employs machine learning algorithms and data mining techniques. After estimating the level of stress, a warning message is generated by a speech synthesis engine. This message is sent from the server to the terminal and presented to the user via voice.

[0076] The server can also improve the accuracy of its analysis by integrating with external health management services, using more personalized data. It can retrieve data via APIs to make warnings more relevant.

[0077] Specific example

[0078] In the event of an emergency, the device worn by the user detects a sudden increase in heart rate. This data is sent to the server, as described above, and is determined to indicate a high level of stress. The server uses a generative AI model to generate a message in a calm voice encouraging the user to regulate their breathing. This message is sent to the device and used to encourage the user to remain calm.

[0079] Example of a prompt

[0080] "The user's heart rate is rapidly increasing. Create an emergency announcement in a calm tone and generate a message to help the user calm down."

[0081] This allows for responses tailored to the user's health condition, providing a safe and comfortable environment.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The user wears a wearable device with a built-in heart rate sensor and blood pressure monitor. This device acquires heart rate and blood pressure in real time and transmits the data to the terminal. The input is biometric data acquired from the user's body, and the output is real-time monitoring information using this data. The device takes measurements every second and transmits the data using Bluetooth.

[0085] Step 2:

[0086] The terminal collects biometric data received from the device at regular intervals (e.g., every 5 seconds) and sends it to the server using a secure protocol. The input is biometric data from the device, and the output is encrypted data that is securely sent to the server. In practice, as soon as the terminal receives the data, it applies an encryption algorithm to protect the data.

[0087] Step 3:

[0088] The server receives biometric data sent from the terminal and uses analysis software to estimate the level of stress. The input is encrypted biometric data, and the output is the estimated level of stress. The server executes an algorithm to analyze heart rate variability and calculates the level of stress by comparing it with past data.

[0089] Step 4:

[0090] The server generates an appropriate warning message using a generative AI model based on the estimated stress level. The input is the estimated stress level, and the output is a voice message corresponding to that stress level. In its specific operation, the server inputs the generated prompt sentence into the AI ​​model and uses a speech synthesis engine to output natural-sounding speech.

[0091] Step 5:

[0092] The terminal receives voice messages sent from the server and presents these messages to the user audibly. The input is voice message data from the server, and the output is an audible voice alert for the user. The terminal plays the message using its speaker and also uses a visual display to attract the user's attention.

[0093] Step 6:

[0094] The server integrates with external health management services to acquire additional health data and further improve the accuracy of the analysis. The input is supplementary health information obtained from the health management services, and the output is more personalized analysis results. The server integrates this data via APIs to optimize the content of warning messages.

[0095] (Application Example 1)

[0096] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0097] In recent years, the mental and physical stress experienced by workers in transportation operations has become a serious concern, negatively impacting safety and work efficiency. In particular, there are fears that increased stress levels could impair safe driving and lead to accidents. Given this situation, the goal is to improve safety and efficiency by utilizing workers' biometric information to assess their stress levels in real time and providing appropriate voice guidance.

[0098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0099] In this invention, the server includes: a device for acquiring the user's biometric information; a device for estimating the user's level of tension based on the biometric information; and a device for generating warning content corresponding to the level of tension. This enables real-time monitoring of the level of tension based on biometric information for those engaged in transportation work, and provides corresponding voice guidance, thereby improving safety and operational efficiency.

[0100] "User biometric information" refers to data that represents an individual's physiological state, such as heart rate and blood pressure.

[0101] A "stress level estimation device" is a device that analyzes acquired biometric information and evaluates its fluctuations and differences from baseline values ​​to quantify the user's mental stress level.

[0102] A "warning content generating device" is a device that has the function of creating audio or visual messages to alleviate tension based on the user's level of anxiety.

[0103] A "device that presents information to the user" refers to a device equipped with a display or speaker to inform the user of warning messages sent from the server.

[0104] "Persons engaged in transportation operations" refers to individuals who perform tasks related to the transportation of goods or people, and includes, for example, delivery personnel and drivers.

[0105] "Monitoring stress levels" means collecting biometric information in real time and continuously monitoring the mental state of stress based on that information.

[0106] "Voice guidance" refers to auditory information generated to alleviate employee anxiety and encourage calm behavior.

[0107] The system for carrying out this invention consists of three main components: a wearable device, a smartphone, and a server.

[0108] The user wears a wearable device that has the capability to measure heart rate and blood pressure in real time. Specifically, this biometric information is collected using sensors in a smartwatch. The biometric information acquired from the wearable device is transmitted to the user's smartphone via Bluetooth, and then sent to a server in the cloud via the internet.

[0109] The server uses a program to analyze the received biometric information. This analysis utilizes a Python script that monitors changes in numerical data and implements an algorithm to calculate stress levels.

[0110] Next, the server generates voice guidance tailored to the level of stress. The specific generation process utilizes natural language processing to create appropriate voice messages to reduce employee stress. Here, a generation AI model is used to enable flexible message generation that is not template-based.

[0111] The generated voice message is sent back to the smartphone and presented to the user through the smartphone application. The smartphone plays the voice message through its speaker, encouraging the user to drive calmly and stay calm.

[0112] For example, if a delivery driver becomes stressed during a delivery, the system will provide the user with a voice message such as, "Take a deep breath and relax." This voice message is generated by prompting the AI ​​model to "Generate an effective voice message for when a delivery driver is feeling stressed."

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The user wears a wearable device, which acquires biometric information such as heart rate and blood pressure in real time. Sensors on the device scan this data and transmit it to a smartphone via Bluetooth or Wi-Fi. The input is the biometric data measured by the wearable device, and the output is the data transmitted to the smartphone.

[0116] Step 2:

[0117] The smartphone, acting as the terminal, packages biometric information received from the wearable device into data packets for transmission to a server in the cloud. The input is the biometric data transmitted from the terminal, and the output is the data packets sent to the server. The terminal securely transmits the data using a communication protocol.

[0118] Step 3:

[0119] The server analyzes the received biometric information using an analysis program and calculates the level of tension. The input is the transmitted biometric data, and the output is a numerical value representing the user's tension level. The server estimates and quantifies the tension level based on fluctuations in the data and deviations from the average value.

[0120] Step 4:

[0121] The server uses a generative AI model to generate appropriate voice messages based on a numerical stress level. The input is the stress level and a prompt for the generative AI model, while the output is the generated voice message. The server's AI model performs non-template-based natural language processing to create flexible messages.

[0122] Step 5:

[0123] The server generates an audio message and prepares it to be sent to the smartphone (the terminal) for presentation to the user. The input is the audio message sent from the server, and the output is the transfer of the message data to the smartphone. The terminal's application prepares an interface for playing the received audio message.

[0124] Step 6:

[0125] The smartphone plays an audio message and presents it to the user. The input is the audio message received on the smartphone, and the output is the audio playback heard by the user. The device uses its speaker to deliver a message to the user that encourages calmness.

[0126] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0127] The present invention provides warnings that take into account the user's emotional state by combining an emotion engine with the acquisition of the user's biometric information to estimate their level of tension. This system has a mechanism to collect biometric information such as the user's heart rate and blood pressure via a terminal and transmit it to a server. The server not only estimates the user's level of tension from the received information, but can also analyze the collected data using the emotion engine to estimate the user's emotional state.

[0128] The emotion engine can analyze the user's facial expressions using facial recognition technology. The emotion data obtained from this analysis can be used to estimate the level of tension. The server then adjusts the content of warnings more appropriately based on the combination of the tension level estimated from biometric information and this emotion data.

[0129] As a concrete example, consider a scenario where a user is presumed to be in a state of anxiety during an earthquake. Suppose the emotion engine detects that the user's heart rate and facial expression indicate tension.

[0130] The device transfers this data to the server, which estimates the level of tension to be high and takes into account the information obtained from the emotion engine.

[0131] As a result, the server generates warning messages in a milder, more reassuring tone, and sets the volume to a lower level.

[0132] The device plays this message to the user, guiding them to a calm and reassuring state.

[0133] Thus, the present invention does not rely solely on biological information, but rather analyzes emotional states to provide warnings tailored to individual users, thereby enabling effective responses in emergencies such as disasters.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The device acquires the user's biometric information. It uses sensors to measure heart rate and blood pressure, and stores this information in a small data processing unit.

[0137] Step 2:

[0138] The device acquires a facial image of the user and collects this data for analysis by the emotion engine. Using facial recognition technology, it prepares to read emotional tendencies from facial expressions.

[0139] Step 3:

[0140] The device transmits biometric information and facial images acquired by the device to the server. A secure, encrypted communication protocol is used to prevent information leakage.

[0141] Step 4:

[0142] The server analyzes the received biometric information and estimates the user's level of stress from data such as heart rate and blood pressure. By comparing this to general reference values, it determines whether the level of stress is high or low.

[0143] Step 5:

[0144] The server analyzes facial image data using an emotion engine to estimate the user's emotional state from their facial expressions. It then classifies them into basic emotion categories (e.g., joy, anger, anxiety).

[0145] Step 6:

[0146] The server combines an estimate of stress levels based on biometric information with an analysis of the user's emotional state. This generates a warning message that is best suited to the user's situation.

[0147] Step 7:

[0148] The server generates a warning message and sends it to the terminal. The warning sound specifications and message content are adjusted before being sent to the terminal.

[0149] Step 8:

[0150] The device provides users with appropriate warnings. Information is delivered in a way that is easily understandable to the user, through voice messages and screen displays.

[0151] Step 9:

[0152] The server integrates with external health management services to acquire additional health data as needed. This data is then used to optimize future stress level estimations and warning content.

[0153] (Example 2)

[0154] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0155] In modern society, there is a demand for immediate and appropriate responses to individual stress and anxiety. However, conventional systems issue warnings based solely on physiological information, making it difficult to provide nuanced responses that take into account the user's emotional state. As a result, in emergencies, the system may unnecessarily stimulate the user or fail to prompt appropriate action.

[0156] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0157] In this invention, the server includes: a device for acquiring the user's physiological information; a device for estimating the user's state based on the physiological information; and a device for analyzing the user's facial expression data and estimating their emotional state. This makes it possible to generate and present appropriate warning content that takes both physiological indicators and emotional state into consideration.

[0158] "User" refers to the subject whose physiological information and emotional state are analyzed.

[0159] "Physiological information" refers to data that represents an individual's circulatory system indicators and physical condition.

[0160] "State" refers to the user's level of tension and stress based on the analysis of physiological information.

[0161] "Facial expression data" refers to information that captures the physical characteristics of the user's face.

[0162] "Emotional state" refers to the user's emotional response obtained from analyzing facial expression data.

[0163] "Warning content" refers to notification or instructional messages generated based on the user's state and emotional state.

[0164] "Device means" refers to hardware or software components incorporated to perform a specific function.

[0165] A "server" refers to a central processing unit that analyzes physiological information and emotional data, and generates warning messages based on the results.

[0166] Modes for carrying out the invention

[0167] This invention describes a system that analyzes a user's physiological information and emotional state and provides appropriate warnings in emergency situations. This system is constructed by combining multiple devices and technologies.

[0168] The device collects physiological information such as the user's heart rate and blood pressure in real time through wearable devices and sensors. These devices include, for example, smartwatches and medical monitoring devices.

[0169] The server receives physiological information transmitted from the terminal and uses analysis software to estimate the user's state. A physiological information analysis algorithm is used to estimate the state, evaluating tension and stress levels based on abnormal numerical fluctuations. Furthermore, the terminal captures the user's facial expressions with a camera and transmits them to the server. This facial expression data is analyzed by an emotion engine to estimate the user's emotional state.

[0170] The server integrates the estimated state and emotional state and generates appropriate warnings using a generative AI model. The generative AI model generates messages corresponding to the user's state by inputting prompts such as the following:

[0171] "The user's heart rate is higher than normal, and facial analysis indicates anxiety. Please suggest a calming message to this user in an emergency. The message should be designed to help the user remain calm."

[0172] The device displays generated warning messages both audibly and visually. Audible messages are played through the speaker, while visual instructions are shown on the display screen. This ensures users receive the most appropriate response for the situation, enabling effective emergency management.

[0173] As described above, the present invention comprehensively utilizes physiological information and emotional states to provide users with individually tailored information.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] The device measures the user's heart rate and blood pressure. The input is real-time physiological data obtained from a wearable device. This data is collected by sensors and digitized through digital signal processing. The physiological information output is used for the following analysis.

[0177] Step 2:

[0178] The device transmits the physiological information it measures to the server. The input is numerical data collected on the device, which is transferred to the server via an internet connection using a secure protocol. The output is the state in which the server has received this data.

[0179] Step 3:

[0180] The server analyzes the physiological information it receives. The input is physiological data such as heart rate and blood pressure. For data processing, a physiological information analysis algorithm is used to detect abnormal values ​​and patterns. The output is a numerical representation of the user's tension level and stress level.

[0181] Step 4:

[0182] The device captures the user's face with its camera. The input is real-time video data, which is converted into facial expression data using facial recognition technology. The output is digital data that reflects the user's facial expressions.

[0183] Step 5:

[0184] The server uses facial expression data to analyze emotional states. The input is facial expression data, which is classified into emotions such as surprise or anxiety using an emotion engine. Through data processing, the output is expressed as the user's emotional state.

[0185] Step 6:

[0186] The server integrates physiological and emotional states. Input consists of data on both tension levels and emotional states. The data integration process provides a comprehensive assessment of the user's state. The output is the integrated user state data.

[0187] Step 7:

[0188] The server generates warning content using a generation AI model. The input is integrated user state data, which is used to send prompts to the generation AI model for instructions. The output is a specific warning message.

[0189] Step 8:

[0190] The terminal notifies the user of a generated warning message. The input is the warning message sent from the server, which is presented to the user via voice or display. The output is the content of the warning received by the user, facilitating emergency response.

[0191] (Application Example 2)

[0192] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0193] In modern society, the rapid detection and response to suspicious individuals within buildings and facilities is crucial. However, conventional security systems lack the ability to recognize true threats in emergencies because they do not perform multifaceted analysis that includes biometric information and emotional states. Furthermore, the uniformity of warning messages prevents appropriate responses depending on the situation, which is a significant challenge.

[0194] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0195] In this invention, the server includes means for collecting the user's heart rate and facial expression data and estimating the level of tension based on biometric information; means for analyzing the user's emotional state using an emotion engine and generating warning content in combination with the level of tension; and means for presenting the generated warning at an appropriate time. This enables the rapid and accurate detection of suspicious individuals within a building and the provision of optimal warnings according to the situation.

[0196] "User" refers to a system user whose biometric information is acquired.

[0197] "Biometric information" refers to data that indicates a person's physical condition, such as heart rate and blood pressure.

[0198] "Stress level" refers to the degree of psychological tension estimated based on the user's biometric information.

[0199] "Emotional state" refers to information indicating the user's emotional state, obtained through facial expression analysis by an emotion engine.

[0200] "Warning content" refers to guidance or instructions generated based on the user's level of tension and emotional state.

[0201] "Facial expression analysis" is a technology that uses camera data to identify the emotional state of a user.

[0202] "Monitoring service" refers to a function that works in conjunction with an external security network to detect suspicious individuals.

[0203] To realize this invention, the system operates with the following configuration: The server uses a terminal consisting of a high-resolution camera and a heart rate sensor to collect the user's biometric information and facial expression data in real time. This information is transmitted to a security-based server, where an emotion engine using facial recognition technology and a vital data analysis module operate. The emotion engine utilizes the "OpenCV" and "emotionAI SDK" libraries to estimate the user's emotional state with high accuracy from the facial expression data.

[0204] The server calculates the user's level of tension based on biometric information such as heart rate and blood pressure, along with analyzed emotional states. Then, using a generative AI model, it constructs warning content tailored to each individual user and generates appropriate messages. For example, if suspicious behavior is detected, the system automatically adjusts the warning message output according to the situation to maintain the building's safety.

[0205] The hardware utilizes the "Axis P1375 Network Camera" and "POLAR H10" as network cameras and heart rate sensors. The software uses "Python Flask" to perform data processing with the server and conduct real-time analysis.

[0206] As a concrete example, if a camera detects an unusually tense facial expression and high heart rate while monitoring the interior of a building in the early morning, the server will analyze this and promptly notify security personnel. An example of a prompt message used here might be, "Analyze the visitor's facial expression image and heart rate data to assess their level of tension today. If there is a high probability of suspicious behavior, please tell me the appropriate way to notify security personnel." This system enables flexible security responses based on the situation.

[0207] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0208] Step 1:

[0209] The device collects the user's biometric information and facial expression data in real time using a high-resolution camera and heart rate sensor. Input consists of camera footage and heart rate data, which are converted into digital data and sent to a server.

[0210] Step 2:

[0211] The server stores biometric information and facial expression data received from the terminals in a database. Here, raw data is imported and organized by category. The output is a dataset ready for analysis.

[0212] Step 3:

[0213] The server uses "OpenCV" and "emotionAI SDK" to analyze stored facial expression data and estimate the user's emotional state. This process extracts facial features based on the input data and outputs emotion tags.

[0214] Step 4:

[0215] The server uses a vital data analysis module to calculate the user's stress level from heart rate and blood pressure data. The input is biometric data, and the output is a numerical stress level.

[0216] Step 5:

[0217] The server uses a generative AI model to generate warning messages based on acquired tension levels and emotional states. The input is the emotional state and tension level, and the output is an optimized warning message.

[0218] Step 6:

[0219] The server sends the generated warning message to the terminal and presents it to the user via audio and visual means. The input is the generated warning message, and the output is the notification content presented to the user.

[0220] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0221] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0222] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0223] [Second Embodiment]

[0224] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0225] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0226] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0227] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0228] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0229] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0230] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0231] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0232] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0233] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0234] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0235] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0236] The system of the present invention is configured to acquire the user's biometric information and estimate their level of tension. This system includes a wearable device that can acquire biometric information such as heart rate and blood pressure in real time. The acquired biometric information is transmitted to a server via data communication.

[0237] The server analyzes the received biometric information and calculates the user's stress level. The stress level is estimated by considering fluctuations in biometric information and deviations from known baseline values. Based on the estimated stress level, the server determines the content of the warning. Specifically, it adjusts the volume of the warning sound or changes the tone of the voice message according to the stress level. The generated warning content is then sent back to the terminal and presented to the user.

[0238] Furthermore, the server integrates with external health management services, allowing it to acquire additional data about the user's health status. This information can be used to further optimize the content of warnings.

[0239] Specific example

[0240] Consider a scenario where, during a disaster, a device worn by a user detects an increase in heart rate from 90 to 120 beats per minute. This biometric information is immediately transmitted to a server.

[0241] The server analyzes the data and estimates from its fluctuations that the user is experiencing a high level of stress.

[0242] If the level of tension is high, the server will generate a message in a low volume and a calm tone that encourages avoidance behavior.

[0243] When this message is sent to a device, the device will play an appropriate audio warning to the user, thereby promoting calm evacuation actions.

[0244] In this way, the invention prevents confusion caused by excessive warnings and enables smooth evacuation, especially during disasters.

[0245] The following describes the processing flow.

[0246] Step 1:

[0247] The device acquires the user's biometric information. Specifically, it collects biometric data at regular intervals using devices that measure heart rate and blood pressure.

[0248] Step 2:

[0249] The device sends the collected biometric information to the server. Using data communication capabilities, the biometric data is uploaded to the server securely and quickly.

[0250] Step 3:

[0251] The server analyzes the received biometric information and estimates the user's stress level based on those values. It then compares this to past baseline values ​​and standard data to determine the current stress level.

[0252] Step 4:

[0253] The server generates a warning based on the estimated level of tension. If the tension level is high, it lowers the volume and prepares a message in a gentle tone.

[0254] Step 5:

[0255] The server sends the generated warning message to the terminal. A retransmission-enabled communication method is used to prevent information loss.

[0256] Step 6:

[0257] The device plays an adapted warning message to the user. By delivering the voice message through the speaker or headphones, the user can receive the information accurately.

[0258] Step 7:

[0259] The server connects with external health management services to acquire additional health data. This allows for more refined estimations of warning content and stress levels, which can then be used to inform future decisions.

[0260] (Example 1)

[0261] Next, we will describe Example 1. 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."

[0262] In today's world, where user health management is increasingly important, there is a need to efficiently monitor users' stress levels and health conditions and provide appropriate and timely warnings. However, existing systems are insufficient for emergency response based on real-time biometric data, and there is a problem in that they cannot provide optimal warnings tailored to the user's situation.

[0263] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0264] In this invention, the server includes: a device for acquiring the user's biometric data; a computing device for analyzing the user's stress level based on the biometric data; and a speech synthesis device for generating a warning message corresponding to the stress level. This makes it possible to quickly generate and present an optimal warning that corresponds to the user's real-time health status.

[0265] A "user" is a person who uses the system and whose biometric data is acquired and analyzed.

[0266] "Biometric data" refers to information that indicates the user's physical condition, such as heart rate and blood pressure.

[0267] "Device" refers to the hardware and software installed to acquire and transmit users' biometric data.

[0268] "Stress level" is an indicator of the user's mental and physical state estimated based on fluctuations in biometric data.

[0269] A "calculating device" is a device that analyzes the level of tension based on received biological data and outputs the results.

[0270] A "speech synthesis device" is a device that generates voice messages using text data.

[0271] A "communication device" is a device used to transmit generated warning messages to users.

[0272] A "health status management service" is an external service that collects and utilizes users' health data.

[0273] A "warning message" is information that is generated and presented to the user based on their level of anxiety.

[0274] This invention is a system that acquires a user's biometric data in real time, analyzes their stress level based on the results, and generates and presents an appropriate warning message. It mainly uses a sensor device, a data transmission device, analysis software on a server, a speech synthesis engine, and a communication device.

[0275] Hardware and software configuration

[0276] The user wears a wearable device with a built-in heart rate sensor and blood pressure monitor. This device collects biometric data in real time and transmits it to the device via Bluetooth or Wi-Fi.

[0277] The device encrypts the received biometric data before transmitting it to the server. The data is securely managed by a database management system.

[0278] The server analyzes the level of stress using biometric data analysis software. This analysis method employs machine learning algorithms and data mining techniques. After estimating the level of stress, a warning message is generated by a speech synthesis engine. This message is sent from the server to the terminal and presented to the user via voice.

[0279] The server can also improve the accuracy of analysis by collaborating with an external health management service, using more personalized data. It obtains data through an API and makes the content of the warnings more appropriate.

[0280] Specific example

[0281] When an emergency occurs, the device worn by the user detects a sudden increase in heart rate. This data is sent to the server as described above, and it is determined that the tension level is high. The server uses a generative AI model to generate a "message prompting the user to adjust breathing in a calm voice". This message is sent to the terminal and used to prompt the user to act calmly.

[0282] Example of a prompt sentence

[0283] "Since the user's heart rate has increased rapidly, generate a message that creates an emergency announcement in a calm tone and induces the user to calm down."

[0284] This enables responses according to the user's health condition and provides a safe and comfortable environment.

[0285] The flow of the specific process in Example 1 will be described using FIG. 11.

[0286] Step 1:

[0287] The user is wearing a wearable device with a built-in heart rate sensor and blood pressure monitor. This device acquires the heart rate and blood pressure in real time and transmits the data to the terminal. The input is biometric data obtained from the user's body, and the output is real-time monitoring information using this data. The device measures every second and performs data transmission using Bluetooth.

[0288] Step 2:

[0289] The terminal collects biometric data received from the device at regular intervals (e.g., every 5 seconds) and sends it to the server using a secure protocol. The input is biometric data from the device, and the output is encrypted data that is securely sent to the server. In practice, as soon as the terminal receives the data, it applies an encryption algorithm to protect the data.

[0290] Step 3:

[0291] The server receives biometric data sent from the terminal and uses analysis software to estimate the level of stress. The input is encrypted biometric data, and the output is the estimated level of stress. The server executes an algorithm to analyze heart rate variability and calculates the level of stress by comparing it with past data.

[0292] Step 4:

[0293] The server generates an appropriate warning message using a generative AI model based on the estimated stress level. The input is the estimated stress level, and the output is a voice message corresponding to that stress level. In its specific operation, the server inputs the generated prompt sentence into the AI ​​model and uses a speech synthesis engine to output natural-sounding speech.

[0294] Step 5:

[0295] The terminal receives voice messages sent from the server and presents these messages to the user audibly. The input is voice message data from the server, and the output is an audible voice alert for the user. The terminal plays the message using its speaker and also uses a visual display to attract the user's attention.

[0296] Step 6:

[0297] The server integrates with external health management services to acquire additional health data and further improve the accuracy of the analysis. The input is supplementary health information obtained from the health management services, and the output is more personalized analysis results. The server integrates this data via APIs to optimize the content of warning messages.

[0298] (Application Example 1)

[0299] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0300] In recent years, the mental and physical stress experienced by workers in transportation operations has become a serious concern, negatively impacting safety and work efficiency. In particular, there are fears that increased stress levels could impair safe driving and lead to accidents. Given this situation, the goal is to improve safety and efficiency by utilizing workers' biometric information to assess their stress levels in real time and providing appropriate voice guidance.

[0301] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0302] In this invention, the server includes: a device for acquiring the user's biometric information; a device for estimating the user's level of tension based on the biometric information; and a device for generating warning content corresponding to the level of tension. This enables real-time monitoring of the level of tension based on biometric information for those engaged in transportation work, and provides corresponding voice guidance, thereby improving safety and operational efficiency.

[0303] "User biometric information" refers to data that represents an individual's physiological state, such as heart rate and blood pressure.

[0304] The "device for estimating tension" is a device that analyzes the acquired biological information and evaluates its fluctuations and differences from reference values to quantify the mental tension state of the user.

[0305] The "device for generating warning content" is a device that has the function of creating voice or visual messages for relaxing tension based on the tension level of the user.

[0306] The "device for presenting to the user" is a device equipped with a display and a speaker to inform the user of the warning content sent from the server.

[0307] The "person engaged in transportation business" refers to an individual who conducts business related to the transportation of goods or people, including, for example, delivery workers and drivers.

[0308] "Monitoring the tension level" means collecting biological information in real time and continuously monitoring the mental tension state based on that information.

[0309] "Voice guidance" is auditory information generated to relieve the tension of the person engaged in the work and encourage calm behavior.

[0310] The system for implementing this invention consists of three main components: a wearable device, a smartphone, and a server.

[0311] The user wears a wearable device, which has the function of measuring heart rate and blood pressure in real time. Specifically, these biological information are collected using the sensors of a smartwatch. The biological information obtained from the wearable device is transmitted to the user's smartphone via Bluetooth and then sent to a server on the cloud through the Internet.

[0312] The server uses a program to analyze the received biometric information. This analysis utilizes a Python script that monitors changes in numerical data and implements an algorithm to calculate stress levels.

[0313] Next, the server generates voice guidance tailored to the level of stress. The specific generation process utilizes natural language processing to create appropriate voice messages to reduce employee stress. Here, a generation AI model is used to enable flexible message generation that is not template-based.

[0314] The generated voice message is sent back to the smartphone and presented to the user through the smartphone application. The smartphone plays the voice message through its speaker, encouraging the user to drive calmly and stay calm.

[0315] For example, if a delivery driver becomes stressed during a delivery, the system will provide the user with a voice message such as, "Take a deep breath and relax." This voice message is generated by prompting the AI ​​model to "Generate an effective voice message for when a delivery driver is feeling stressed."

[0316] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0317] Step 1:

[0318] The user wears a wearable device, which acquires biometric information such as heart rate and blood pressure in real time. Sensors on the device scan this data and transmit it to a smartphone via Bluetooth or Wi-Fi. The input is the biometric data measured by the wearable device, and the output is the data transmitted to the smartphone.

[0319] Step 2:

[0320] The smartphone, acting as the terminal, packages biometric information received from the wearable device into data packets for transmission to a server in the cloud. The input is the biometric data transmitted from the terminal, and the output is the data packets sent to the server. The terminal securely transmits the data using a communication protocol.

[0321] Step 3:

[0322] The server analyzes the received biometric information using an analysis program and calculates the level of tension. The input is the transmitted biometric data, and the output is a numerical value representing the user's tension level. The server estimates and quantifies the tension level based on fluctuations in the data and deviations from the average value.

[0323] Step 4:

[0324] The server uses a generative AI model to generate appropriate voice messages based on a numerical stress level. The input is the stress level and a prompt for the generative AI model, while the output is the generated voice message. The server's AI model performs non-template-based natural language processing to create flexible messages.

[0325] Step 5:

[0326] The server generates an audio message and prepares it to be sent to the smartphone (the terminal) for presentation to the user. The input is the audio message sent from the server, and the output is the transfer of the message data to the smartphone. The terminal's application prepares an interface for playing the received audio message.

[0327] Step 6:

[0328] The smartphone plays an audio message and presents it to the user. The input is the audio message received on the smartphone, and the output is the audio playback heard by the user. The device uses its speaker to deliver a message to the user that encourages calmness.

[0329] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0330] The present invention provides warnings that take into account the user's emotional state by combining an emotion engine with the acquisition of the user's biometric information to estimate their level of tension. This system has a mechanism to collect biometric information such as the user's heart rate and blood pressure via a terminal and transmit it to a server. The server not only estimates the user's level of tension from the received information, but can also analyze the collected data using the emotion engine to estimate the user's emotional state.

[0331] The emotion engine can analyze the user's facial expressions using facial recognition technology. The emotion data obtained from this analysis can be used to estimate the level of tension. The server then adjusts the content of warnings more appropriately based on the combination of the tension level estimated from biometric information and this emotion data.

[0332] As a concrete example, consider a scenario where a user is presumed to be in a state of anxiety during an earthquake. Suppose the emotion engine detects that the user's heart rate and facial expression indicate tension.

[0333] The device transfers this data to the server, which estimates the level of tension to be high and takes into account the information obtained from the emotion engine.

[0334] As a result, the server generates warning messages in a milder, more reassuring tone, and sets the volume to a lower level.

[0335] The device plays this message to the user, guiding them to a calm and reassuring state.

[0336] Thus, the present invention does not rely solely on biological information, but rather analyzes emotional states to provide warnings tailored to individual users, thereby enabling effective responses in emergencies such as disasters.

[0337] The following describes the processing flow.

[0338] Step 1:

[0339] The device acquires the user's biometric information. It uses sensors to measure heart rate and blood pressure, and stores this information in a small data processing unit.

[0340] Step 2:

[0341] The device acquires a facial image of the user and collects this data for analysis by the emotion engine. Using facial recognition technology, it prepares to read emotional tendencies from facial expressions.

[0342] Step 3:

[0343] The device transmits biometric information and facial images acquired by the device to the server. A secure, encrypted communication protocol is used to prevent information leakage.

[0344] Step 4:

[0345] The server analyzes the received biometric information and estimates the user's level of stress from data such as heart rate and blood pressure. By comparing this to general reference values, it determines whether the level of stress is high or low.

[0346] Step 5:

[0347] The server analyzes facial image data using an emotion engine to estimate the user's emotional state from their facial expressions. It then classifies them into basic emotion categories (e.g., joy, anger, anxiety).

[0348] Step 6:

[0349] The server combines an estimate of stress levels based on biometric information with an analysis of the user's emotional state. This generates a warning message that is best suited to the user's situation.

[0350] Step 7:

[0351] The server generates a warning message and sends it to the terminal. The warning sound specifications and message content are adjusted before being sent to the terminal.

[0352] Step 8:

[0353] The device provides users with appropriate warnings. Information is delivered in a way that is easily understandable to the user, through voice messages and screen displays.

[0354] Step 9:

[0355] The server integrates with external health management services to acquire additional health data as needed. This data is then used to optimize future stress level estimations and warning content.

[0356] (Example 2)

[0357] Next, we will describe Example 2. 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".

[0358] In modern society, there is a demand for immediate and appropriate responses to individual stress and anxiety. However, conventional systems issue warnings based solely on physiological information, making it difficult to provide nuanced responses that take into account the user's emotional state. As a result, in emergencies, the system may unnecessarily stimulate the user or fail to prompt appropriate action.

[0359] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0360] In this invention, the server includes: a device for acquiring the user's physiological information; a device for estimating the user's state based on the physiological information; and a device for analyzing the user's facial expression data and estimating their emotional state. This makes it possible to generate and present appropriate warning content that takes both physiological indicators and emotional state into consideration.

[0361] "User" refers to the subject whose physiological information and emotional state are analyzed.

[0362] "Physiological information" refers to data that represents an individual's circulatory system indicators and physical condition.

[0363] "State" refers to the user's level of tension and stress based on the analysis of physiological information.

[0364] "Facial expression data" refers to information that captures the physical characteristics of the user's face.

[0365] "Emotional state" refers to the user's emotional response obtained from analyzing facial expression data.

[0366] "Warning content" refers to notification or instructional messages generated based on the user's state and emotional state.

[0367] "Device means" refers to hardware or software components incorporated to perform a specific function.

[0368] A "server" refers to a central processing unit that analyzes physiological information and emotional data, and generates warning messages based on the results.

[0369] Modes for carrying out the invention

[0370] This invention describes a system that analyzes a user's physiological information and emotional state and provides appropriate warnings in emergency situations. This system is constructed by combining multiple devices and technologies.

[0371] The device collects physiological information such as the user's heart rate and blood pressure in real time through wearable devices and sensors. These devices include, for example, smartwatches and medical monitoring devices.

[0372] The server receives physiological information transmitted from the terminal and uses analysis software to estimate the user's state. A physiological information analysis algorithm is used to estimate the state, evaluating tension and stress levels based on abnormal numerical fluctuations. Furthermore, the terminal captures the user's facial expressions with a camera and transmits them to the server. This facial expression data is analyzed by an emotion engine to estimate the user's emotional state.

[0373] The server integrates the estimated state and emotional state and generates appropriate warnings using a generative AI model. The generative AI model generates messages corresponding to the user's state by inputting prompts such as the following:

[0374] "The user's heart rate is higher than normal, and facial analysis indicates anxiety. Please suggest a calming message to this user in an emergency. The message should be designed to help the user remain calm."

[0375] The device displays generated warning messages both audibly and visually. Audible messages are played through the speaker, while visual instructions are shown on the display screen. This ensures users receive the most appropriate response for the situation, enabling effective emergency management.

[0376] As described above, the present invention comprehensively utilizes physiological information and emotional states to provide users with individually tailored information.

[0377] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0378] Step 1:

[0379] The device measures the user's heart rate and blood pressure. The input is real-time physiological data obtained from a wearable device. This data is collected by sensors and digitized through digital signal processing. The physiological information output is used for the following analysis.

[0380] Step 2:

[0381] The device transmits the physiological information it measures to the server. The input is numerical data collected on the device, which is transferred to the server via an internet connection using a secure protocol. The output is the state in which the server has received this data.

[0382] Step 3:

[0383] The server analyzes the physiological information it receives. The input is physiological data such as heart rate and blood pressure. For data processing, a physiological information analysis algorithm is used to detect abnormal values ​​and patterns. The output is a numerical representation of the user's tension level and stress level.

[0384] Step 4:

[0385] The device captures the user's face with its camera. The input is real-time video data, which is converted into facial expression data using facial recognition technology. The output is digital data that reflects the user's facial expressions.

[0386] Step 5:

[0387] The server uses facial expression data to analyze emotional states. The input is facial expression data, which is classified into emotions such as surprise or anxiety using an emotion engine. Through data processing, the output is expressed as the user's emotional state.

[0388] Step 6:

[0389] The server integrates physiological and emotional states. Input consists of data on both tension levels and emotional states. The data integration process provides a comprehensive assessment of the user's state. The output is the integrated user state data.

[0390] Step 7:

[0391] The server generates warning content using a generation AI model. The input is integrated user state data, which is used to send prompts to the generation AI model for instructions. The output is a specific warning message.

[0392] Step 8:

[0393] The terminal notifies the user of a generated warning message. The input is the warning message sent from the server, which is presented to the user via voice or display. The output is the content of the warning received by the user, facilitating emergency response.

[0394] (Application Example 2)

[0395] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0396] In modern society, the rapid detection and response to suspicious individuals within buildings and facilities is crucial. However, conventional security systems lack the ability to recognize true threats in emergencies because they do not perform multifaceted analysis that includes biometric information and emotional states. Furthermore, the uniformity of warning messages prevents appropriate responses depending on the situation, which is a significant challenge.

[0397] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0398] In this invention, the server includes means for collecting the user's heart rate and facial expression data and estimating the level of tension based on biometric information; means for analyzing the user's emotional state using an emotion engine and generating warning content in combination with the level of tension; and means for presenting the generated warning at an appropriate time. This enables the rapid and accurate detection of suspicious individuals within a building and the provision of optimal warnings according to the situation.

[0399] "User" refers to a system user whose biometric information is acquired.

[0400] "Biometric information" refers to data that indicates a person's physical condition, such as heart rate and blood pressure.

[0401] "Stress level" refers to the degree of psychological tension estimated based on the user's biometric information.

[0402] "Emotional state" refers to information indicating the user's emotional state, obtained through facial expression analysis by an emotion engine.

[0403] "Warning content" refers to guidance or instructions generated based on the user's level of tension and emotional state.

[0404] "Facial expression analysis" is a technology that uses camera data to identify the emotional state of a user.

[0405] "Monitoring service" refers to a function that works in conjunction with an external security network to detect suspicious individuals.

[0406] To realize this invention, the system operates with the following configuration: The server uses a terminal consisting of a high-resolution camera and a heart rate sensor to collect the user's biometric information and facial expression data in real time. This information is transmitted to a security-based server, where an emotion engine using facial recognition technology and a vital data analysis module operate. The emotion engine utilizes the "OpenCV" and "emotionAI SDK" libraries to estimate the user's emotional state with high accuracy from the facial expression data.

[0407] The server calculates the user's level of tension based on biometric information such as heart rate and blood pressure, along with analyzed emotional states. Then, using a generative AI model, it constructs warning content tailored to each individual user and generates appropriate messages. For example, if suspicious behavior is detected, the system automatically adjusts the warning message output according to the situation to maintain the building's safety.

[0408] The hardware utilizes the "Axis P1375 Network Camera" and "POLAR H10" as network cameras and heart rate sensors. The software uses "Python Flask" to perform data processing with the server and conduct real-time analysis.

[0409] As a concrete example, if a camera detects an unusually tense facial expression and high heart rate while monitoring the interior of a building in the early morning, the server will analyze this and promptly notify security personnel. An example of a prompt message used here might be, "Analyze the visitor's facial expression image and heart rate data to assess their level of tension today. If there is a high probability of suspicious behavior, please tell me the appropriate way to notify security personnel." This system enables flexible security responses based on the situation.

[0410] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0411] Step 1:

[0412] The device collects the user's biometric information and facial expression data in real time using a high-resolution camera and heart rate sensor. Input consists of camera footage and heart rate data, which are converted into digital data and sent to a server.

[0413] Step 2:

[0414] The server stores biometric information and facial expression data received from the terminals in a database. Here, raw data is imported and organized by category. The output is a dataset ready for analysis.

[0415] Step 3:

[0416] The server uses "OpenCV" and "emotionAI SDK" to analyze stored facial expression data and estimate the user's emotional state. This process extracts facial features based on the input data and outputs emotion tags.

[0417] Step 4:

[0418] The server uses a vital data analysis module to calculate the user's stress level from heart rate and blood pressure data. The input is biometric data, and the output is a numerical stress level.

[0419] Step 5:

[0420] The server uses a generative AI model to generate warning messages based on acquired tension levels and emotional states. The input is the emotional state and tension level, and the output is an optimized warning message.

[0421] Step 6:

[0422] The server sends the generated warning message to the terminal and presents it to the user via audio and visual means. The input is the generated warning message, and the output is the notification content presented to the user.

[0423] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0424] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0425] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0426] [Third Embodiment]

[0427] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0428] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0429] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0430] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0431] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0432] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0433] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0434] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0435] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0436] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0437] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0438] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0439] The system of the present invention is configured to acquire the user's biometric information and estimate their level of tension. This system includes a wearable device that can acquire biometric information such as heart rate and blood pressure in real time. The acquired biometric information is transmitted to a server via data communication.

[0440] The server analyzes the received biometric information and calculates the user's stress level. The stress level is estimated by considering fluctuations in biometric information and deviations from known baseline values. Based on the estimated stress level, the server determines the content of the warning. Specifically, it adjusts the volume of the warning sound or changes the tone of the voice message according to the stress level. The generated warning content is then sent back to the terminal and presented to the user.

[0441] Furthermore, the server integrates with external health management services, allowing it to acquire additional data about the user's health status. This information can be used to further optimize the content of warnings.

[0442] Specific example

[0443] Consider a scenario where, during a disaster, a device worn by a user detects an increase in heart rate from 90 to 120 beats per minute. This biometric information is immediately transmitted to a server.

[0444] The server analyzes the data and estimates from its fluctuations that the user is experiencing a high level of stress.

[0445] If the level of tension is high, the server will generate a message in a low volume and a calm tone that encourages avoidance behavior.

[0446] When this message is sent to a device, the device will play an appropriate audio warning to the user, thereby promoting calm evacuation actions.

[0447] In this way, the invention prevents confusion caused by excessive warnings and enables smooth evacuation, especially during disasters.

[0448] The following describes the processing flow.

[0449] Step 1:

[0450] The device acquires the user's biometric information. Specifically, it collects biometric data at regular intervals using devices that measure heart rate and blood pressure.

[0451] Step 2:

[0452] The device sends the collected biometric information to the server. Using data communication capabilities, the biometric data is uploaded to the server securely and quickly.

[0453] Step 3:

[0454] The server analyzes the received biometric information and estimates the user's stress level based on those values. It then compares this to past baseline values ​​and standard data to determine the current stress level.

[0455] Step 4:

[0456] The server generates a warning based on the estimated level of tension. If the tension level is high, it lowers the volume and prepares a message in a gentle tone.

[0457] Step 5:

[0458] The server sends the generated warning message to the terminal. A retransmission-enabled communication method is used to prevent information loss.

[0459] Step 6:

[0460] The device plays an adapted warning message to the user. By delivering the voice message through the speaker or headphones, the user can receive the information accurately.

[0461] Step 7:

[0462] The server connects with external health management services to acquire additional health data. This allows for more refined estimations of warning content and stress levels, which can then be used to inform future decisions.

[0463] (Example 1)

[0464] Next, we will describe Example 1. 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."

[0465] In today's world, where user health management is increasingly important, there is a need to efficiently monitor users' stress levels and health conditions and provide appropriate and timely warnings. However, existing systems are insufficient for emergency response based on real-time biometric data, and there is a problem in that they cannot provide optimal warnings tailored to the user's situation.

[0466] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0467] In this invention, the server includes: a device for acquiring the user's biometric data; a computing device for analyzing the user's stress level based on the biometric data; and a speech synthesis device for generating a warning message corresponding to the stress level. This makes it possible to quickly generate and present an optimal warning that corresponds to the user's real-time health status.

[0468] A "user" is a person who uses the system and whose biometric data is acquired and analyzed.

[0469] "Biometric data" refers to information that indicates the user's physical condition, such as heart rate and blood pressure.

[0470] "Device" refers to the hardware and software installed to acquire and transmit users' biometric data.

[0471] "Stress level" is an indicator of the user's mental and physical state estimated based on fluctuations in biometric data.

[0472] A "calculating device" is a device that analyzes the level of tension based on received biological data and outputs the results.

[0473] A "speech synthesis device" is a device that generates voice messages using text data.

[0474] A "communication device" is a device used to transmit generated warning messages to users.

[0475] A "health status management service" is an external service that collects and utilizes users' health data.

[0476] A "warning message" is information that is generated and presented to the user based on their level of anxiety.

[0477] This invention is a system that acquires a user's biometric data in real time, analyzes their stress level based on the results, and generates and presents an appropriate warning message. It mainly uses a sensor device, a data transmission device, analysis software on a server, a speech synthesis engine, and a communication device.

[0478] Hardware and software configuration

[0479] The user wears a wearable device with a built-in heart rate sensor and blood pressure monitor. This device collects biometric data in real time and transmits it to the device via Bluetooth or Wi-Fi.

[0480] The device encrypts the received biometric data before transmitting it to the server. The data is securely managed by a database management system.

[0481] The server analyzes the level of stress using biometric data analysis software. This analysis method employs machine learning algorithms and data mining techniques. After estimating the level of stress, a warning message is generated by a speech synthesis engine. This message is sent from the server to the terminal and presented to the user via voice.

[0482] The server can also improve the accuracy of its analysis by integrating with external health management services, using more personalized data. It can retrieve data via APIs to make warnings more relevant.

[0483] Specific example

[0484] In the event of an emergency, the device worn by the user detects a sudden increase in heart rate. This data is sent to the server, as described above, and is determined to indicate a high level of stress. The server uses a generative AI model to generate a message in a calm voice encouraging the user to regulate their breathing. This message is sent to the device and used to encourage the user to remain calm.

[0485] Example of a prompt

[0486] "The user's heart rate is rapidly increasing. Create an emergency announcement in a calm tone and generate a message to help the user calm down."

[0487] This allows for responses tailored to the user's health condition, providing a safe and comfortable environment.

[0488] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0489] Step 1:

[0490] The user wears a wearable device with a built-in heart rate sensor and blood pressure monitor. This device acquires heart rate and blood pressure in real time and transmits the data to the terminal. The input is biometric data acquired from the user's body, and the output is real-time monitoring information using this data. The device takes measurements every second and transmits the data using Bluetooth.

[0491] Step 2:

[0492] The terminal collects biometric data received from the device at regular intervals (e.g., every 5 seconds) and sends it to the server using a secure protocol. The input is biometric data from the device, and the output is encrypted data that is securely sent to the server. In practice, as soon as the terminal receives the data, it applies an encryption algorithm to protect the data.

[0493] Step 3:

[0494] The server receives biometric data sent from the terminal and uses analysis software to estimate the level of stress. The input is encrypted biometric data, and the output is the estimated level of stress. The server executes an algorithm to analyze heart rate variability and calculates the level of stress by comparing it with past data.

[0495] Step 4:

[0496] The server generates an appropriate warning message using a generative AI model based on the estimated stress level. The input is the estimated stress level, and the output is a voice message corresponding to that stress level. In its specific operation, the server inputs the generated prompt sentence into the AI ​​model and uses a speech synthesis engine to output natural-sounding speech.

[0497] Step 5:

[0498] The terminal receives voice messages sent from the server and presents these messages to the user audibly. The input is voice message data from the server, and the output is an audible voice alert for the user. The terminal plays the message using its speaker and also uses a visual display to attract the user's attention.

[0499] Step 6:

[0500] The server integrates with external health management services to acquire additional health data and further improve the accuracy of the analysis. The input is supplementary health information obtained from the health management services, and the output is more personalized analysis results. The server integrates this data via APIs to optimize the content of warning messages.

[0501] (Application Example 1)

[0502] Next, we will explain Application Example 1. In the following explanation, 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."

[0503] In recent years, the mental and physical stress experienced by workers in transportation operations has become a serious concern, negatively impacting safety and work efficiency. In particular, there are fears that increased stress levels could impair safe driving and lead to accidents. Given this situation, the goal is to improve safety and efficiency by utilizing workers' biometric information to assess their stress levels in real time and providing appropriate voice guidance.

[0504] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0505] In this invention, the server includes: a device for acquiring the user's biometric information; a device for estimating the user's level of tension based on the biometric information; and a device for generating warning content corresponding to the level of tension. This enables real-time monitoring of the level of tension based on biometric information for those engaged in transportation work, and provides corresponding voice guidance, thereby improving safety and operational efficiency.

[0506] "User biometric information" refers to data that represents an individual's physiological state, such as heart rate and blood pressure.

[0507] A "stress level estimation device" is a device that analyzes acquired biometric information and evaluates its fluctuations and differences from baseline values ​​to quantify the user's mental stress level.

[0508] A "warning content generating device" is a device that has the function of creating audio or visual messages to alleviate tension based on the user's level of anxiety.

[0509] A "device that presents information to the user" refers to a device equipped with a display or speaker to inform the user of warning messages sent from the server.

[0510] "Persons engaged in transportation operations" refers to individuals who perform tasks related to the transportation of goods or people, and includes, for example, delivery personnel and drivers.

[0511] "Monitoring stress levels" means collecting biometric information in real time and continuously monitoring the mental state of stress based on that information.

[0512] "Voice guidance" refers to auditory information generated to alleviate employee anxiety and encourage calm behavior.

[0513] The system for carrying out this invention consists of three main components: a wearable device, a smartphone, and a server.

[0514] The user wears a wearable device that has the capability to measure heart rate and blood pressure in real time. Specifically, this biometric information is collected using sensors in a smartwatch. The biometric information acquired from the wearable device is transmitted to the user's smartphone via Bluetooth, and then sent to a server in the cloud via the internet.

[0515] The server uses a program to analyze the received biometric information. This analysis utilizes a Python script that monitors changes in numerical data and implements an algorithm to calculate stress levels.

[0516] Next, the server generates voice guidance tailored to the level of stress. The specific generation process utilizes natural language processing to create appropriate voice messages to reduce employee stress. Here, a generation AI model is used to enable flexible message generation that is not template-based.

[0517] The generated voice message is sent back to the smartphone and presented to the user through the smartphone application. The smartphone plays the voice message through its speaker, encouraging the user to drive calmly and stay calm.

[0518] For example, if a delivery driver becomes stressed during a delivery, the system will provide the user with a voice message such as, "Take a deep breath and relax." This voice message is generated by prompting the AI ​​model to "Generate an effective voice message for when a delivery driver is feeling stressed."

[0519] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0520] Step 1:

[0521] The user wears a wearable device, which acquires biometric information such as heart rate and blood pressure in real time. Sensors on the device scan this data and transmit it to a smartphone via Bluetooth or Wi-Fi. The input is the biometric data measured by the wearable device, and the output is the data transmitted to the smartphone.

[0522] Step 2:

[0523] The smartphone, acting as the terminal, packages biometric information received from the wearable device into data packets for transmission to a server in the cloud. The input is the biometric data transmitted from the terminal, and the output is the data packets sent to the server. The terminal securely transmits the data using a communication protocol.

[0524] Step 3:

[0525] The server analyzes the received biometric information using an analysis program and calculates the level of tension. The input is the transmitted biometric data, and the output is a numerical value representing the user's tension level. The server estimates and quantifies the tension level based on fluctuations in the data and deviations from the average value.

[0526] Step 4:

[0527] The server uses a generative AI model to generate appropriate voice messages based on a numerical stress level. The input is the stress level and a prompt for the generative AI model, while the output is the generated voice message. The server's AI model performs non-template-based natural language processing to create flexible messages.

[0528] Step 5:

[0529] The server generates an audio message and prepares it to be sent to the smartphone (the terminal) for presentation to the user. The input is the audio message sent from the server, and the output is the transfer of the message data to the smartphone. The terminal's application prepares an interface for playing the received audio message.

[0530] Step 6:

[0531] The smartphone plays an audio message and presents it to the user. The input is the audio message received on the smartphone, and the output is the audio playback heard by the user. The device uses its speaker to deliver a message to the user that encourages calmness.

[0532] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0533] The present invention provides warnings that take into account the user's emotional state by combining an emotion engine with the acquisition of the user's biometric information to estimate their level of tension. This system has a mechanism to collect biometric information such as the user's heart rate and blood pressure via a terminal and transmit it to a server. The server not only estimates the user's level of tension from the received information, but can also analyze the collected data using the emotion engine to estimate the user's emotional state.

[0534] The emotion engine can analyze the user's facial expressions using facial recognition technology. The emotion data obtained from this analysis can be used to estimate the level of tension. The server then adjusts the content of warnings more appropriately based on the combination of the tension level estimated from biometric information and this emotion data.

[0535] As a concrete example, consider a scenario where a user is presumed to be in a state of anxiety during an earthquake. Suppose the emotion engine detects that the user's heart rate and facial expression indicate tension.

[0536] The device transfers this data to the server, which estimates the level of tension to be high and takes into account the information obtained from the emotion engine.

[0537] As a result, the server generates warning messages in a milder, more reassuring tone, and sets the volume to a lower level.

[0538] The device plays this message to the user, guiding them to a calm and reassuring state.

[0539] Thus, the present invention does not rely solely on biological information, but rather analyzes emotional states to provide warnings tailored to individual users, thereby enabling effective responses in emergencies such as disasters.

[0540] The following describes the processing flow.

[0541] Step 1:

[0542] The device acquires the user's biometric information. It uses sensors to measure heart rate and blood pressure, and stores this information in a small data processing unit.

[0543] Step 2:

[0544] The device acquires a facial image of the user and collects this data for analysis by the emotion engine. Using facial recognition technology, it prepares to read emotional tendencies from facial expressions.

[0545] Step 3:

[0546] The device transmits biometric information and facial images acquired by the device to the server. A secure, encrypted communication protocol is used to prevent information leakage.

[0547] Step 4:

[0548] The server analyzes the received biometric information and estimates the user's level of stress from data such as heart rate and blood pressure. By comparing this to general reference values, it determines whether the level of stress is high or low.

[0549] Step 5:

[0550] The server analyzes facial image data using an emotion engine to estimate the user's emotional state from their facial expressions. It then classifies them into basic emotion categories (e.g., joy, anger, anxiety).

[0551] Step 6:

[0552] The server combines an estimate of stress levels based on biometric information with an analysis of the user's emotional state. This generates a warning message that is best suited to the user's situation.

[0553] Step 7:

[0554] The server generates a warning message and sends it to the terminal. The warning sound specifications and message content are adjusted before being sent to the terminal.

[0555] Step 8:

[0556] The device provides users with appropriate warnings. Information is delivered in a way that is easily understandable to the user, through voice messages and screen displays.

[0557] Step 9:

[0558] The server integrates with external health management services to acquire additional health data as needed. This data is then used to optimize future stress level estimations and warning content.

[0559] (Example 2)

[0560] Next, we will describe Example 2. 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."

[0561] In modern society, there is a demand for immediate and appropriate responses to individual stress and anxiety. However, conventional systems issue warnings based solely on physiological information, making it difficult to provide nuanced responses that take into account the user's emotional state. As a result, in emergencies, the system may unnecessarily stimulate the user or fail to prompt appropriate action.

[0562] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0563] In this invention, the server includes: a device for acquiring the user's physiological information; a device for estimating the user's state based on the physiological information; and a device for analyzing the user's facial expression data and estimating their emotional state. This makes it possible to generate and present appropriate warning content that takes both physiological indicators and emotional state into consideration.

[0564] "User" refers to the subject whose physiological information and emotional state are analyzed.

[0565] "Physiological information" refers to data that represents an individual's circulatory system indicators and physical condition.

[0566] "State" refers to the user's level of tension and stress based on the analysis of physiological information.

[0567] "Facial expression data" refers to information that captures the physical characteristics of the user's face.

[0568] "Emotional state" refers to the user's emotional response obtained from analyzing facial expression data.

[0569] "Warning content" refers to notification or instructional messages generated based on the user's state and emotional state.

[0570] "Device means" refers to hardware or software components incorporated to perform a specific function.

[0571] A "server" refers to a central processing unit that analyzes physiological information and emotional data, and generates warning messages based on the results.

[0572] Modes for carrying out the invention

[0573] This invention describes a system that analyzes a user's physiological information and emotional state and provides appropriate warnings in emergency situations. This system is constructed by combining multiple devices and technologies.

[0574] The device collects physiological information such as the user's heart rate and blood pressure in real time through wearable devices and sensors. These devices include, for example, smartwatches and medical monitoring devices.

[0575] The server receives physiological information transmitted from the terminal and uses analysis software to estimate the user's state. A physiological information analysis algorithm is used to estimate the state, evaluating tension and stress levels based on abnormal numerical fluctuations. Furthermore, the terminal captures the user's facial expressions with a camera and transmits them to the server. This facial expression data is analyzed by an emotion engine to estimate the user's emotional state.

[0576] The server integrates the estimated state and emotional state and generates appropriate warnings using a generative AI model. The generative AI model generates messages corresponding to the user's state by inputting prompts such as the following:

[0577] "The user's heart rate is higher than normal, and facial analysis indicates anxiety. Please suggest a calming message to this user in an emergency. The message should be designed to help the user remain calm."

[0578] The device displays generated warning messages both audibly and visually. Audible messages are played through the speaker, while visual instructions are shown on the display screen. This ensures users receive the most appropriate response for the situation, enabling effective emergency management.

[0579] As described above, the present invention comprehensively utilizes physiological information and emotional states to provide users with individually tailored information.

[0580] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0581] Step 1:

[0582] The device measures the user's heart rate and blood pressure. The input is real-time physiological data obtained from a wearable device. This data is collected by sensors and digitized through digital signal processing. The physiological information output is used for the following analysis.

[0583] Step 2:

[0584] The device transmits the physiological information it measures to the server. The input is numerical data collected on the device, which is transferred to the server via an internet connection using a secure protocol. The output is the state in which the server has received this data.

[0585] Step 3:

[0586] The server analyzes the physiological information it receives. The input is physiological data such as heart rate and blood pressure. For data processing, a physiological information analysis algorithm is used to detect abnormal values ​​and patterns. The output is a numerical representation of the user's tension level and stress level.

[0587] Step 4:

[0588] The device captures the user's face with its camera. The input is real-time video data, which is converted into facial expression data using facial recognition technology. The output is digital data that reflects the user's facial expressions.

[0589] Step 5:

[0590] The server uses facial expression data to analyze emotional states. The input is facial expression data, which is classified into emotions such as surprise or anxiety using an emotion engine. Through data processing, the output is expressed as the user's emotional state.

[0591] Step 6:

[0592] The server integrates physiological and emotional states. Input consists of data on both tension levels and emotional states. The data integration process provides a comprehensive assessment of the user's state. The output is the integrated user state data.

[0593] Step 7:

[0594] The server generates warning content using a generation AI model. The input is integrated user state data, which is used to send prompts to the generation AI model for instructions. The output is a specific warning message.

[0595] Step 8:

[0596] The terminal notifies the user of a generated warning message. The input is the warning message sent from the server, which is presented to the user via voice or display. The output is the content of the warning received by the user, facilitating emergency response.

[0597] (Application Example 2)

[0598] Next, we will explain application example 2. In the following explanation, 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."

[0599] In modern society, the rapid detection and response to suspicious individuals within buildings and facilities is crucial. However, conventional security systems lack the ability to recognize true threats in emergencies because they do not perform multifaceted analysis that includes biometric information and emotional states. Furthermore, the uniformity of warning messages prevents appropriate responses depending on the situation, which is a significant challenge.

[0600] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0601] In this invention, the server includes means for collecting the user's heart rate and facial expression data and estimating the level of tension based on biometric information; means for analyzing the user's emotional state using an emotion engine and generating warning content in combination with the level of tension; and means for presenting the generated warning at an appropriate time. This enables the rapid and accurate detection of suspicious individuals within a building and the provision of optimal warnings according to the situation.

[0602] "User" refers to a system user whose biometric information is acquired.

[0603] "Biometric information" refers to data that indicates a person's physical condition, such as heart rate and blood pressure.

[0604] "Stress level" refers to the degree of psychological tension estimated based on the user's biometric information.

[0605] "Emotional state" refers to information indicating the user's emotional state, obtained through facial expression analysis by an emotion engine.

[0606] "Warning content" refers to guidance or instructions generated based on the user's level of tension and emotional state.

[0607] "Facial expression analysis" is a technology that uses camera data to identify the emotional state of a user.

[0608] "Monitoring service" refers to a function that works in conjunction with an external security network to detect suspicious individuals.

[0609] To realize this invention, the system operates with the following configuration: The server uses a terminal consisting of a high-resolution camera and a heart rate sensor to collect the user's biometric information and facial expression data in real time. This information is transmitted to a security-based server, where an emotion engine using facial recognition technology and a vital data analysis module operate. The emotion engine utilizes the "OpenCV" and "emotionAI SDK" libraries to estimate the user's emotional state with high accuracy from the facial expression data.

[0610] The server calculates the user's level of tension based on biometric information such as heart rate and blood pressure, along with analyzed emotional states. Then, using a generative AI model, it constructs warning content tailored to each individual user and generates appropriate messages. For example, if suspicious behavior is detected, the system automatically adjusts the warning message output according to the situation to maintain the building's safety.

[0611] The hardware utilizes the "Axis P1375 Network Camera" and "POLAR H10" as network cameras and heart rate sensors. The software uses "Python Flask" to perform data processing with the server and conduct real-time analysis.

[0612] As a concrete example, if a camera detects an unusually tense facial expression and high heart rate while monitoring the interior of a building in the early morning, the server will analyze this and promptly notify security personnel. An example of a prompt message used here might be, "Analyze the visitor's facial expression image and heart rate data to assess their level of tension today. If there is a high probability of suspicious behavior, please tell me the appropriate way to notify security personnel." This system enables flexible security responses based on the situation.

[0613] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0614] Step 1:

[0615] The device collects the user's biometric information and facial expression data in real time using a high-resolution camera and heart rate sensor. Input consists of camera footage and heart rate data, which are converted into digital data and sent to a server.

[0616] Step 2:

[0617] The server stores biometric information and facial expression data received from the terminals in a database. Here, raw data is imported and organized by category. The output is a dataset ready for analysis.

[0618] Step 3:

[0619] The server uses "OpenCV" and "emotionAI SDK" to analyze stored facial expression data and estimate the user's emotional state. This process extracts facial features based on the input data and outputs emotion tags.

[0620] Step 4:

[0621] The server uses a vital data analysis module to calculate the user's stress level from heart rate and blood pressure data. The input is biometric data, and the output is a numerical stress level.

[0622] Step 5:

[0623] The server uses a generative AI model to generate warning messages based on acquired tension levels and emotional states. The input is the emotional state and tension level, and the output is an optimized warning message.

[0624] Step 6:

[0625] The server sends the generated warning message to the terminal and presents it to the user via audio and visual means. The input is the generated warning message, and the output is the notification content presented to the user.

[0626] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0627] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0628] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0629] [Fourth Embodiment]

[0630] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0631] As shown in Figure 7, the 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.

[0632] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0633] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0634] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0635] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0636] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0637] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0638] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0639] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0640] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0641] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0642] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0643] The system of the present invention is configured to acquire the user's biometric information and estimate their level of tension. This system includes a wearable device that can acquire biometric information such as heart rate and blood pressure in real time. The acquired biometric information is transmitted to a server via data communication.

[0644] The server analyzes the received biometric information and calculates the user's stress level. The stress level is estimated by considering fluctuations in biometric information and deviations from known baseline values. Based on the estimated stress level, the server determines the content of the warning. Specifically, it adjusts the volume of the warning sound or changes the tone of the voice message according to the stress level. The generated warning content is then sent back to the terminal and presented to the user.

[0645] Furthermore, the server integrates with external health management services, allowing it to acquire additional data about the user's health status. This information can be used to further optimize the content of warnings.

[0646] Specific example

[0647] Consider a scenario where, during a disaster, a device worn by a user detects an increase in heart rate from 90 to 120 beats per minute. This biometric information is immediately transmitted to a server.

[0648] The server analyzes the data and estimates from its fluctuations that the user is experiencing a high level of stress.

[0649] If the level of tension is high, the server will generate a message in a low volume and a calm tone that encourages avoidance behavior.

[0650] When this message is sent to a device, the device will play an appropriate audio warning to the user, thereby promoting calm evacuation actions.

[0651] In this way, the invention prevents confusion caused by excessive warnings and enables smooth evacuation, especially during disasters.

[0652] The following describes the processing flow.

[0653] Step 1:

[0654] The device acquires the user's biometric information. Specifically, it collects biometric data at regular intervals using devices that measure heart rate and blood pressure.

[0655] Step 2:

[0656] The device sends the collected biometric information to the server. Using data communication capabilities, the biometric data is uploaded to the server securely and quickly.

[0657] Step 3:

[0658] The server analyzes the received biometric information and estimates the user's stress level based on those values. It then compares this to past baseline values ​​and standard data to determine the current stress level.

[0659] Step 4:

[0660] The server generates a warning based on the estimated level of tension. If the tension level is high, it lowers the volume and prepares a message in a gentle tone.

[0661] Step 5:

[0662] The server sends the generated warning message to the terminal. A retransmission-enabled communication method is used to prevent information loss.

[0663] Step 6:

[0664] The device plays an adapted warning message to the user. By delivering the voice message through the speaker or headphones, the user can receive the information accurately.

[0665] Step 7:

[0666] The server connects with external health management services to acquire additional health data. This allows for more refined estimations of warning content and stress levels, which can then be used to inform future decisions.

[0667] (Example 1)

[0668] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0669] In today's world, where user health management is increasingly important, there is a need to efficiently monitor users' stress levels and health conditions and provide appropriate and timely warnings. However, existing systems are insufficient for emergency response based on real-time biometric data, and there is a problem in that they cannot provide optimal warnings tailored to the user's situation.

[0670] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0671] In this invention, the server includes: a device for acquiring the user's biometric data; a computing device for analyzing the user's stress level based on the biometric data; and a speech synthesis device for generating a warning message corresponding to the stress level. This makes it possible to quickly generate and present an optimal warning that corresponds to the user's real-time health status.

[0672] A "user" is a person who uses the system and whose biometric data is acquired and analyzed.

[0673] "Biometric data" refers to information that indicates the user's physical condition, such as heart rate and blood pressure.

[0674] "Device" refers to the hardware and software installed to acquire and transmit users' biometric data.

[0675] "Stress level" is an indicator of the user's mental and physical state estimated based on fluctuations in biometric data.

[0676] A "calculating device" is a device that analyzes the level of tension based on received biological data and outputs the results.

[0677] A "speech synthesis device" is a device that generates voice messages using text data.

[0678] A "communication device" is a device used to transmit generated warning messages to users.

[0679] A "health status management service" is an external service that collects and utilizes users' health data.

[0680] A "warning message" is information that is generated and presented to the user based on their level of anxiety.

[0681] This invention is a system that acquires a user's biometric data in real time, analyzes their stress level based on the results, and generates and presents an appropriate warning message. It mainly uses a sensor device, a data transmission device, analysis software on a server, a speech synthesis engine, and a communication device.

[0682] Hardware and software configuration

[0683] The user wears a wearable device with a built-in heart rate sensor and blood pressure monitor. This device collects biometric data in real time and transmits it to the device via Bluetooth or Wi-Fi.

[0684] The device encrypts the received biometric data before transmitting it to the server. The data is securely managed by a database management system.

[0685] The server analyzes the level of stress using biometric data analysis software. This analysis method employs machine learning algorithms and data mining techniques. After estimating the level of stress, a warning message is generated by a speech synthesis engine. This message is sent from the server to the terminal and presented to the user via voice.

[0686] The server can also improve the accuracy of its analysis by integrating with external health management services, using more personalized data. It can retrieve data via APIs to make warnings more relevant.

[0687] Specific example

[0688] In the event of an emergency, the device worn by the user detects a sudden increase in heart rate. This data is sent to the server, as described above, and is determined to indicate a high level of stress. The server uses a generative AI model to generate a message in a calm voice encouraging the user to regulate their breathing. This message is sent to the device and used to encourage the user to remain calm.

[0689] Example of a prompt

[0690] "The user's heart rate is rapidly increasing. Create an emergency announcement in a calm tone and generate a message to help the user calm down."

[0691] This allows for responses tailored to the user's health condition, providing a safe and comfortable environment.

[0692] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0693] Step 1:

[0694] The user wears a wearable device with a built-in heart rate sensor and blood pressure monitor. This device acquires heart rate and blood pressure in real time and transmits the data to the terminal. The input is biometric data acquired from the user's body, and the output is real-time monitoring information using this data. The device takes measurements every second and transmits the data using Bluetooth.

[0695] Step 2:

[0696] The terminal collects biometric data received from the device at regular intervals (e.g., every 5 seconds) and sends it to the server using a secure protocol. The input is biometric data from the device, and the output is encrypted data that is securely sent to the server. In practice, as soon as the terminal receives the data, it applies an encryption algorithm to protect the data.

[0697] Step 3:

[0698] The server receives biometric data sent from the terminal and uses analysis software to estimate the level of stress. The input is encrypted biometric data, and the output is the estimated level of stress. The server executes an algorithm to analyze heart rate variability and calculates the level of stress by comparing it with past data.

[0699] Step 4:

[0700] The server generates an appropriate warning message using a generative AI model based on the estimated stress level. The input is the estimated stress level, and the output is a voice message corresponding to that stress level. In its specific operation, the server inputs the generated prompt sentence into the AI ​​model and uses a speech synthesis engine to output natural-sounding speech.

[0701] Step 5:

[0702] The terminal receives voice messages sent from the server and presents these messages to the user audibly. The input is voice message data from the server, and the output is an audible voice alert for the user. The terminal plays the message using its speaker and also uses a visual display to attract the user's attention.

[0703] Step 6:

[0704] The server integrates with external health management services to acquire additional health data and further improve the accuracy of the analysis. The input is supplementary health information obtained from the health management services, and the output is more personalized analysis results. The server integrates this data via APIs to optimize the content of warning messages.

[0705] (Application Example 1)

[0706] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0707] In recent years, the mental and physical stress experienced by workers in transportation operations has become a serious concern, negatively impacting safety and work efficiency. In particular, there are fears that increased stress levels could impair safe driving and lead to accidents. Given this situation, the goal is to improve safety and efficiency by utilizing workers' biometric information to assess their stress levels in real time and providing appropriate voice guidance.

[0708] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0709] In this invention, the server includes: a device for acquiring the user's biometric information; a device for estimating the user's level of tension based on the biometric information; and a device for generating warning content corresponding to the level of tension. This enables real-time monitoring of the level of tension based on biometric information for those engaged in transportation work, and provides corresponding voice guidance, thereby improving safety and operational efficiency.

[0710] "User biometric information" refers to data that represents an individual's physiological state, such as heart rate and blood pressure.

[0711] A "stress level estimation device" is a device that analyzes acquired biometric information and evaluates its fluctuations and differences from baseline values ​​to quantify the user's mental stress level.

[0712] A "warning content generating device" is a device that has the function of creating audio or visual messages to alleviate tension based on the user's level of anxiety.

[0713] A "device that presents information to the user" refers to a device equipped with a display or speaker to inform the user of warning messages sent from the server.

[0714] "Persons engaged in transportation operations" refers to individuals who perform tasks related to the transportation of goods or people, and includes, for example, delivery personnel and drivers.

[0715] "Monitoring stress levels" means collecting biometric information in real time and continuously monitoring the mental state of stress based on that information.

[0716] "Voice guidance" refers to auditory information generated to alleviate employee anxiety and encourage calm behavior.

[0717] The system for carrying out this invention consists of three main components: a wearable device, a smartphone, and a server.

[0718] The user wears a wearable device that has the capability to measure heart rate and blood pressure in real time. Specifically, this biometric information is collected using sensors in a smartwatch. The biometric information acquired from the wearable device is transmitted to the user's smartphone via Bluetooth, and then sent to a server in the cloud via the internet.

[0719] The server uses a program to analyze the received biometric information. This analysis utilizes a Python script that monitors changes in numerical data and implements an algorithm to calculate stress levels.

[0720] Next, the server generates voice guidance tailored to the level of stress. The specific generation process utilizes natural language processing to create appropriate voice messages to reduce employee stress. Here, a generation AI model is used to enable flexible message generation that is not template-based.

[0721] The generated voice message is sent back to the smartphone and presented to the user through the smartphone application. The smartphone plays the voice message through its speaker, encouraging the user to drive calmly and stay calm.

[0722] For example, if a delivery driver becomes stressed during a delivery, the system will provide the user with a voice message such as, "Take a deep breath and relax." This voice message is generated by prompting the AI ​​model to "Generate an effective voice message for when a delivery driver is feeling stressed."

[0723] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0724] Step 1:

[0725] The user wears a wearable device, which acquires biometric information such as heart rate and blood pressure in real time. Sensors on the device scan this data and transmit it to a smartphone via Bluetooth or Wi-Fi. The input is the biometric data measured by the wearable device, and the output is the data transmitted to the smartphone.

[0726] Step 2:

[0727] The smartphone, acting as the terminal, packages biometric information received from the wearable device into data packets for transmission to a server in the cloud. The input is the biometric data transmitted from the terminal, and the output is the data packets sent to the server. The terminal securely transmits the data using a communication protocol.

[0728] Step 3:

[0729] The server analyzes the received biometric information using an analysis program and calculates the level of tension. The input is the transmitted biometric data, and the output is a numerical value representing the user's tension level. The server estimates and quantifies the tension level based on fluctuations in the data and deviations from the average value.

[0730] Step 4:

[0731] The server uses a generative AI model to generate appropriate voice messages based on a numerical stress level. The input is the stress level and a prompt for the generative AI model, while the output is the generated voice message. The server's AI model performs non-template-based natural language processing to create flexible messages.

[0732] Step 5:

[0733] The server generates an audio message and prepares it to be sent to the smartphone (the terminal) for presentation to the user. The input is the audio message sent from the server, and the output is the transfer of the message data to the smartphone. The terminal's application prepares an interface for playing the received audio message.

[0734] Step 6:

[0735] The smartphone plays an audio message and presents it to the user. The input is the audio message received on the smartphone, and the output is the audio playback heard by the user. The device uses its speaker to deliver a message to the user that encourages calmness.

[0736] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0737] The present invention provides warnings that take into account the user's emotional state by combining an emotion engine with the acquisition of the user's biometric information to estimate their level of tension. This system has a mechanism to collect biometric information such as the user's heart rate and blood pressure via a terminal and transmit it to a server. The server not only estimates the user's level of tension from the received information, but can also analyze the collected data using the emotion engine to estimate the user's emotional state.

[0738] The emotion engine can analyze the user's facial expressions using facial recognition technology. The emotion data obtained from this analysis can be used to estimate the level of tension. The server then adjusts the content of warnings more appropriately based on the combination of the tension level estimated from biometric information and this emotion data.

[0739] As a concrete example, consider a scenario where a user is presumed to be in a state of anxiety during an earthquake. Suppose the emotion engine detects that the user's heart rate and facial expression indicate tension.

[0740] The device transfers this data to the server, which estimates the level of tension to be high and takes into account the information obtained from the emotion engine.

[0741] As a result, the server generates warning messages in a milder, more reassuring tone, and sets the volume to a lower level.

[0742] The device plays this message to the user, guiding them to a calm and reassuring state.

[0743] Thus, the present invention does not rely solely on biological information, but rather analyzes emotional states to provide warnings tailored to individual users, thereby enabling effective responses in emergencies such as disasters.

[0744] The following describes the processing flow.

[0745] Step 1:

[0746] The device acquires the user's biometric information. It uses sensors to measure heart rate and blood pressure, and stores this information in a small data processing unit.

[0747] Step 2:

[0748] The device acquires a facial image of the user and collects this data for analysis by the emotion engine. Using facial recognition technology, it prepares to read emotional tendencies from facial expressions.

[0749] Step 3:

[0750] The device transmits biometric information and facial images acquired by the device to the server. A secure, encrypted communication protocol is used to prevent information leakage.

[0751] Step 4:

[0752] The server analyzes the received biometric information and estimates the user's level of stress from data such as heart rate and blood pressure. By comparing this to general reference values, it determines whether the level of stress is high or low.

[0753] Step 5:

[0754] The server analyzes facial image data using an emotion engine to estimate the user's emotional state from their facial expressions. It then classifies them into basic emotion categories (e.g., joy, anger, anxiety).

[0755] Step 6:

[0756] The server combines an estimate of stress levels based on biometric information with an analysis of the user's emotional state. This generates a warning message that is best suited to the user's situation.

[0757] Step 7:

[0758] The server generates a warning message and sends it to the terminal. The warning sound specifications and message content are adjusted before being sent to the terminal.

[0759] Step 8:

[0760] The device provides users with appropriate warnings. Information is delivered in a way that is easily understandable to the user, through voice messages and screen displays.

[0761] Step 9:

[0762] The server integrates with external health management services to acquire additional health data as needed. This data is then used to optimize future stress level estimations and warning content.

[0763] (Example 2)

[0764] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0765] In modern society, there is a demand for immediate and appropriate responses to individual stress and anxiety. However, conventional systems issue warnings based solely on physiological information, making it difficult to provide nuanced responses that take into account the user's emotional state. As a result, in emergencies, the system may unnecessarily stimulate the user or fail to prompt appropriate action.

[0766] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0767] In this invention, the server includes: a device for acquiring the user's physiological information; a device for estimating the user's state based on the physiological information; and a device for analyzing the user's facial expression data and estimating their emotional state. This makes it possible to generate and present appropriate warning content that takes both physiological indicators and emotional state into consideration.

[0768] "User" refers to the subject whose physiological information and emotional state are analyzed.

[0769] "Physiological information" refers to data that represents an individual's circulatory system indicators and physical condition.

[0770] "State" refers to the user's level of tension and stress based on the analysis of physiological information.

[0771] "Facial expression data" refers to information that captures the physical characteristics of the user's face.

[0772] "Emotional state" refers to the user's emotional response obtained from analyzing facial expression data.

[0773] "Warning content" refers to notification or instructional messages generated based on the user's state and emotional state.

[0774] "Device means" refers to hardware or software components incorporated to perform a specific function.

[0775] A "server" refers to a central processing unit that analyzes physiological information and emotional data, and generates warning messages based on the results.

[0776] Modes for carrying out the invention

[0777] This invention describes a system that analyzes a user's physiological information and emotional state and provides appropriate warnings in emergency situations. This system is constructed by combining multiple devices and technologies.

[0778] The device collects physiological information such as the user's heart rate and blood pressure in real time through wearable devices and sensors. These devices include, for example, smartwatches and medical monitoring devices.

[0779] The server receives physiological information transmitted from the terminal and uses analysis software to estimate the user's state. A physiological information analysis algorithm is used to estimate the state, evaluating tension and stress levels based on abnormal numerical fluctuations. Furthermore, the terminal captures the user's facial expressions with a camera and transmits them to the server. This facial expression data is analyzed by an emotion engine to estimate the user's emotional state.

[0780] The server integrates the estimated state and emotional state and generates appropriate warnings using a generative AI model. The generative AI model generates messages corresponding to the user's state by inputting prompts such as the following:

[0781] "The user's heart rate is higher than normal, and facial analysis indicates anxiety. Please suggest a calming message to this user in an emergency. The message should be designed to help the user remain calm."

[0782] The device displays generated warning messages both audibly and visually. Audible messages are played through the speaker, while visual instructions are shown on the display screen. This ensures users receive the most appropriate response for the situation, enabling effective emergency management.

[0783] As described above, the present invention comprehensively utilizes physiological information and emotional states to provide users with individually tailored information.

[0784] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0785] Step 1:

[0786] The device measures the user's heart rate and blood pressure. The input is real-time physiological data obtained from a wearable device. This data is collected by sensors and digitized through digital signal processing. The physiological information output is used for the following analysis.

[0787] Step 2:

[0788] The device transmits the physiological information it measures to the server. The input is numerical data collected on the device, which is transferred to the server via an internet connection using a secure protocol. The output is the state in which the server has received this data.

[0789] Step 3:

[0790] The server analyzes the physiological information it receives. The input is physiological data such as heart rate and blood pressure. For data processing, a physiological information analysis algorithm is used to detect abnormal values ​​and patterns. The output is a numerical representation of the user's tension level and stress level.

[0791] Step 4:

[0792] The device captures the user's face with its camera. The input is real-time video data, which is converted into facial expression data using facial recognition technology. The output is digital data that reflects the user's facial expressions.

[0793] Step 5:

[0794] The server uses facial expression data to analyze emotional states. The input is facial expression data, which is classified into emotions such as surprise or anxiety using an emotion engine. Through data processing, the output is expressed as the user's emotional state.

[0795] Step 6:

[0796] The server integrates physiological and emotional states. Input consists of data on both tension levels and emotional states. The data integration process provides a comprehensive assessment of the user's state. The output is the integrated user state data.

[0797] Step 7:

[0798] The server generates warning content using a generation AI model. The input is integrated user state data, which is used to send prompts to the generation AI model for instructions. The output is a specific warning message.

[0799] Step 8:

[0800] The terminal notifies the user of a generated warning message. The input is the warning message sent from the server, which is presented to the user via voice or display. The output is the content of the warning received by the user, facilitating emergency response.

[0801] (Application Example 2)

[0802] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0803] In modern society, the rapid detection and response to suspicious individuals within buildings and facilities is crucial. However, conventional security systems lack the ability to recognize true threats in emergencies because they do not perform multifaceted analysis that includes biometric information and emotional states. Furthermore, the uniformity of warning messages prevents appropriate responses depending on the situation, which is a significant challenge.

[0804] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0805] In this invention, the server includes means for collecting the user's heart rate and facial expression data and estimating the level of tension based on biometric information; means for analyzing the user's emotional state using an emotion engine and generating warning content in combination with the level of tension; and means for presenting the generated warning at an appropriate time. This enables the rapid and accurate detection of suspicious individuals within a building and the provision of optimal warnings according to the situation.

[0806] "User" refers to a system user whose biometric information is acquired.

[0807] "Biometric information" refers to data that indicates a person's physical condition, such as heart rate and blood pressure.

[0808] "Stress level" refers to the degree of psychological tension estimated based on the user's biometric information.

[0809] "Emotional state" refers to information indicating the user's emotional state, obtained through facial expression analysis by an emotion engine.

[0810] "Warning content" refers to guidance or instructions generated based on the user's level of tension and emotional state.

[0811] "Facial expression analysis" is a technology that uses camera data to identify the emotional state of a user.

[0812] "Monitoring service" refers to a function that works in conjunction with an external security network to detect suspicious individuals.

[0813] To realize this invention, the system operates with the following configuration: The server uses a terminal consisting of a high-resolution camera and a heart rate sensor to collect the user's biometric information and facial expression data in real time. This information is transmitted to a security-based server, where an emotion engine using facial recognition technology and a vital data analysis module operate. The emotion engine utilizes the "OpenCV" and "emotionAI SDK" libraries to estimate the user's emotional state with high accuracy from the facial expression data.

[0814] The server calculates the user's level of tension based on biometric information such as heart rate and blood pressure, along with analyzed emotional states. Then, using a generative AI model, it constructs warning content tailored to each individual user and generates appropriate messages. For example, if suspicious behavior is detected, the system automatically adjusts the warning message output according to the situation to maintain the building's safety.

[0815] The hardware utilizes the "Axis P1375 Network Camera" and "POLAR H10" as network cameras and heart rate sensors. The software uses "Python Flask" to perform data processing with the server and conduct real-time analysis.

[0816] As a concrete example, if a camera detects an unusually tense facial expression and high heart rate while monitoring the interior of a building in the early morning, the server will analyze this and promptly notify security personnel. An example of a prompt message used here might be, "Analyze the visitor's facial expression image and heart rate data to assess their level of tension today. If there is a high probability of suspicious behavior, please tell me the appropriate way to notify security personnel." This system enables flexible security responses based on the situation.

[0817] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0818] Step 1:

[0819] The device collects the user's biometric information and facial expression data in real time using a high-resolution camera and heart rate sensor. Input consists of camera footage and heart rate data, which are converted into digital data and sent to a server.

[0820] Step 2:

[0821] The server stores biometric information and facial expression data received from the terminals in a database. Here, raw data is imported and organized by category. The output is a dataset ready for analysis.

[0822] Step 3:

[0823] The server uses "OpenCV" and "emotionAI SDK" to analyze stored facial expression data and estimate the user's emotional state. This process extracts facial features based on the input data and outputs emotion tags.

[0824] Step 4:

[0825] The server uses a vital data analysis module to calculate the user's stress level from heart rate and blood pressure data. The input is biometric data, and the output is a numerical stress level.

[0826] Step 5:

[0827] The server uses a generative AI model to generate warning messages based on acquired tension levels and emotional states. The input is the emotional state and tension level, and the output is an optimized warning message.

[0828] Step 6:

[0829] The server sends the generated warning message to the terminal and presents it to the user via audio and visual means. The input is the generated warning message, and the output is the notification content presented to the user.

[0830] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0831] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0832] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0833] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0834] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0835] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0836] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0837] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0838] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0839] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0840] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0841] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0842] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0843] 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.

[0844] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0845] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0846] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0847] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0848] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0849] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0850] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0851] The following is further disclosed regarding the embodiments described above.

[0852] (Claim 1)

[0853] [Device means for acquiring the user's biometric information,

[0854] [A device means for estimating the user's level of tension based on the aforementioned biological information,

[0855] [Device means for generating warning content according to the level of tension,

[0856] [Device means for presenting the generated warning content to the user,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] [The system according to claim 1, wherein the biological information includes heart rate and blood pressure.

[0860] (Claim 3)

[0861] The system according to claim 1, further comprising a device that cooperates with an external health management service to acquire additional health data.

[0862] "Example 1"

[0863] (Claim 1)

[0864] [Device and means for acquiring the user's biometric data,

[0865] [A computing device means that analyzes the user's level of tension based on the aforementioned biometric data,

[0866] [Voice synthesis device means for generating a warning message corresponding to the level of tension,

[0867] [A communication device means for transmitting the generated warning message to the user,

[0868] A system that includes this.

[0869] (Claim 2)

[0870] [The system according to claim 1, wherein the biometric data includes heart rate and blood pressure.

[0871] (Claim 3)

[0872] The system according to claim 1, further comprising a communication device that cooperates with an external health management service to acquire additional health information.

[0873] "Application Example 1"

[0874] (Claim 1)

[0875] [Device means for acquiring the user's biometric information,

[0876] [A device means for estimating the user's level of tension based on the aforementioned biological information,

[0877] [Device means for generating warning content according to the level of tension,

[0878] [Device means for presenting the generated warning content to the user,

[0879] [A device and means for monitoring the level of stress of personnel in transportation operations and generating appropriate voice guidance,

[0880] A system that includes this.

[0881] (Claim 2)

[0882] [The system according to claim 1, wherein the biological information includes heart rate and blood pressure, and further performs stress assessment according to the working environment of the worker.

[0883] (Claim 3)

[0884] The system according to claim 1, further comprising a device that collaborates with an external health management service to acquire additional health data and optimize voice messages to improve transportation safety.

[0885] "Example 2 of combining an emotion engine"

[0886] (Claim 1)

[0887] [Device means for acquiring the user's physiological information,

[0888] [Device means for estimating the user's state based on the physiological information,

[0889] [A device and means for analyzing the user's facial expression data and estimating their emotional state,

[0890] [Device means for generating warning content corresponding to the aforementioned state and emotional state,

[0891] [Device means for presenting the generated warning content to the user,

[0892] A system that includes this.

[0893] (Claim 2)

[0894] [The system according to claim 1, wherein the physiological information includes physiological indicators of the circulatory system.

[0895] (Claim 3)

[0896] The system according to claim 1, further comprising a device that cooperates with an external health management service to acquire additional health data.

[0897] "Application example 2 when combining with an emotional engine"

[0898] (Claim 1)

[0899] [Device means for acquiring the user's biometric information,

[0900] [A device means for estimating the user's level of tension based on the aforementioned biological information,

[0901] [In addition to the aforementioned level of tension, the device analyzes the user's emotional state,

[0902] [Device means for generating warning content according to the level of tension and emotional state,

[0903] [Device means for presenting the generated warning content to the user,

[0904] A system that includes this.

[0905] (Claim 2)

[0906] [The system according to claim 1, further comprising a device that estimates an emotional state by analyzing facial expressions, wherein the biological information includes heart rate and blood pressure.

[0907] (Claim 3)

[0908] The system according to claim 1, further comprising a device that works in conjunction with an external monitoring service to monitor visitor behavior and issue a warning if suspicious behavior is detected. [Explanation of Symbols]

[0909] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A device and means for acquiring the user's biometric information, A device means for estimating the user's level of tension based on the aforementioned biological information, A device means for generating warning content according to the aforementioned level of tension, A device means for presenting the generated warning content to the user, A system that includes this.

2. The system according to claim 1, wherein the aforementioned biological information includes heart rate and blood pressure.

3. The system according to claim 1, further comprising a device that collaborates with an external health management service to acquire additional health data.

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