system

Abstract

We provide the system. [Solution] A means of collecting individual facial information on a daily basis using a device that acquires facial recognition data, A means for transmitting acquired facial information to an analysis device via a data transmission device, The analysis device extracts feature points from transmitted facial information to evaluate an individual's health status, and detects abnormalities by comparing them with normal values. A means of informing an individual of the nature of the abnormality and medical institution information using a notification device when an abnormality is detected, A means of providing medical institution information via an information terminal accessible to an individual, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 modern society, although the importance of health management is increasing, many people have difficulty ensuring the time to receive regular health check-ups. Especially for individuals with busy lives and people living in areas with limited access to medical institutions, early detection of diseases is not easy. As a result, there is a problem that changes in health conditions may be overlooked and symptoms may progress.]

Means for Solving the Problems

[0005] [This invention provides a system that automatically collects an individual's facial information on a daily basis using facial recognition technology and monitors their health status by analyzing this information. This system includes a process of extracting feature points and detecting anomalies. If an anomaly is detected, the individual is notified and provided with information on the nearest medical institution. This allows users to easily understand their health status in their daily lives and seek medical attention early if necessary.]

[0006] "Facial recognition data" refers to image information used to analyze the facial features of an individual, and is data processed by a recognition algorithm.

[0007] "Device" refers to a machine or system designed to perform specific functions such as acquiring, transmitting, analyzing, and notifying about facial recognition data.

[0008] "Personal facial information" refers to data that includes facial feature points and details necessary to identify an individual person.

[0009] "Feature points" refer to specific location and shape information extracted from facial image data, used for analysis and comparison.

[0010] An "anomaly" refers to a change or indication that falls outside the expected range when compared to normal data from the past.

[0011] A "notification device" refers to a device or system used to transmit information to a user, including email and dedicated apps.

[0012] "Medical institution information" refers to information necessary for users to receive medical services, such as the location, contact information, and operating hours of hospitals and clinics. [Brief explanation of the drawing]

[0013] [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]It is a conceptual diagram showing an example of the main 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

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the labeled 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.

[0017] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention relates to a health monitoring system using facial recognition technology. The system aims to acquire an individual's facial information daily and evaluate their health status in real time. The following describes specific embodiments of the system.

[0035] First, the device takes a picture of the user's face with its camera. This face capture can be done automatically at a set time each day. The captured facial image data is then processed for feature point extraction. At this stage, data such as the color around the eyes, skin texture, and facial contours are important.

[0036] Next, the acquired data is sent from the terminal to the server. This transmission is encrypted for security reasons, and the system is designed to prevent data leakage during communication. The transmitted data is then processed on the server for feature point analysis.

[0037] The server uses AI-based analysis algorithms to extract key health indicators from the data and compare them to historical data. This comparison can detect changes that fall outside the normal range, i.e., "abnormalities." For example, this might apply if skin color suddenly deteriorates.

[0038] If an anomaly is detected, the server will notify the user. This notification is sent via a dedicated application on a smartphone or PC and is automatically delivered to the user. The notification includes specific details of the anomaly and information about the nearest medical facility. The medical facility information includes location, opening hours, and whether appointments are available, enabling a quick response.

[0039] After receiving a notification, users can refer to the provided medical information and, if necessary, seek a diagnosis from a specialist. This system enables individual users to monitor their own health status on a daily basis, detect abnormalities early, and receive prompt treatment.

[0040] Thus, a health management system using facial recognition offers significant convenience and contributes to health, especially for users who have difficulty accessing regular medical facilities.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The device captures the user's face with its camera and acquires facial image data. The lighting conditions and angle during shooting are optimized to ensure high-quality images that accurately reflect the user's facial features.

[0044] Step 2:

[0045] The device encodes the facial image data it acquires into a predetermined format and prepares it for data transmission. For privacy protection, the facial data is encrypted at this stage.

[0046] Step 3:

[0047] The terminal sends encoded facial image data to the server. This transmission is performed in real time or in batches at regular intervals.

[0048] Step 4:

[0049] The server decodes the received facial image data and uses an AI model to analyze facial feature points. Patterns of eye opening and closing, changes in skin tone, and other features are extracted at this stage.

[0050] Step 5:

[0051] The server compares the extracted feature points with historical data to detect anomalies. If a change outside the predefined normal range is detected, it is flagged as "abnormal."

[0052] Step 6:

[0053] If the server detects an anomaly, it will notify the user. An alert message will be sent via a dedicated app installed on a smartphone or computer.

[0054] Step 7:

[0055] The server uses the user's location information to collect information on nearby medical facilities. It then creates a list of the most suitable facilities from a database of partner medical institutions.

[0056] Step 8:

[0057] The user should review the notification received and contact a nearby medical institution if necessary. Based on the information provided, it is recommended that they make an appointment and seek medical attention in a timely manner.

[0058] (Example 1)

[0059] 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."

[0060] [In a health monitoring system using facial recognition, the aim is to enable easy monitoring of individual health status in daily life, allowing for early detection of abnormalities and prompt response. Furthermore, it aims to support individual health management by suggesting optimal medical services in cases where access to medical facilities is difficult.]

[0061] 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.

[0062] In this invention, the server includes: means for periodically collecting facial data of a person using a visual information acquisition unit; means for transmitting the collected facial data to a central processing unit using a communication unit; means for the central processing unit to calculate health indicators from the received facial data and detect abnormal conditions by comparing them with reference values; and means for communicating details of the abnormal condition and information about healthcare providers to the person via a notification unit when an abnormal condition is confirmed. This enables individual users to monitor their health status on a daily basis, allowing for early detection and rapid response to abnormalities.

[0063] A "visual information acquisition unit" is a device or function for periodically and automatically collecting facial data of individuals.

[0064] The "communication unit" is a device that has the function of securely and quickly transmitting collected facial data to the central processing unit.

[0065] A "central processing unit" is a device that calculates health indicators from received facial data and determines whether or not there is an abnormal condition by comparing them with reference values.

[0066] A "notification unit" is a device used to inform a person of the details of an abnormal condition and related healthcare provider information when such a condition is detected.

[0067] An "abnormal state" refers to a condition in which the results of health indicator calculations fall outside the standard range, and early detection and intervention are recommended.

[0068] "Healthcare provider information" refers to information necessary for a person to receive appropriate medical services if an abnormality is detected.

[0069] This invention is a health monitoring system based on facial recognition technology, which is primarily operated by a terminal, a server, and a user.

[0070] First, the device periodically acquires the user's facial data using its built-in camera. A general-purpose device with a camera, such as a smartphone or personal computer, is suitable for this process. Image processing is performed using image analysis libraries such as OpenCV to extract facial feature points. These feature points include information such as the color around the eyes, skin texture, and facial contours.

[0071] Next, the device securely transmits the extracted facial data to the server. This transmission uses the HTTPS protocol and SSL / TLS to guarantee data confidentiality. Before transmission, the data is encrypted using AES-256.

[0072] The server analyzes the received facial data and calculates health indicators. This analysis utilizes AI-based models and frameworks such as TENSORFLOW® and PyTorch. Based on the analysis results, if the health status deviates from the standard range, an abnormality is determined. In particular, rapid changes in complexion and swelling are detected as abnormal conditions.

[0073] Subsequently, if an anomaly is detected, the server will notify the user. This notification will be provided in real time via a smartphone or PC application. The notification will include details of the abnormal condition and information on the nearest healthcare provider. This information will include location, contact details, and appointment availability, enabling a quick response.

[0074] As a concrete example, a user working from home uses the system every morning to perform a health check. One day, the system detects a slight fading of skin color compared to past data and issues a notification recommending rest. Another example of a prompt message to input into the generating AI model is an instruction such as, "Analyze the following health data and generate a notification message if an abnormality is detected." This allows the AI ​​model to output an easily understandable abnormality notification to the user.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The device captures the user's face using its built-in camera and acquires facial data. The input is the image captured by the camera, and the output is facial image data automatically captured at a specific time. This image data is stored in high resolution for analysis of facial features.

[0078] Step 2:

[0079] The device extracts important feature points from the acquired facial image data. The input is the facial image data obtained in step 1, and the output is feature point data that quantifies the color around the eyes, skin texture, facial contours, etc. This process uses OpenCV for image analysis, applying specific filters to clarify skin texture and contours.

[0080] Step 3:

[0081] The terminal receives the extracted feature point data as input, encrypts it, and sends the data to the server. The output is encrypted transmitted data. This transmission is encrypted using AES-256 and secure using the HTTPS protocol.

[0082] Step 4:

[0083] The server receives encrypted data sent from the terminal, decrypts it, and obtains feature point data. The input is the encrypted data sent from the terminal, and the output is the decrypted feature point data. The decrypted data is then formatted in preparation for analysis by an AI algorithm.

[0084] Step 5:

[0085] The server uses an AI-based analysis model to analyze feature point data as input. The output is an assessment of health status. This analysis uses a model built with TensorFlow to determine whether current health indicators exceed the reference range by comparing them with historical data.

[0086] Step 6:

[0087] The server notifies the user if it detects an abnormal condition as a result of its analysis. The input is the health status assessment result, and the output is notification information for the user. This notification includes the specific details of the abnormality, as well as information on the nearest healthcare provider. The notification is delivered via push notification through a dedicated application.

[0088] Step 7:

[0089] The user receives a notification and checks its contents through the application. The input is the notification information received from the server, and the output is the user's action, specifically contacting a healthcare provider or making an appointment. Based on the information provided, the user selects the appropriate healthcare provider and proceeds with the necessary actions.

[0090] (Application Example 1)

[0091] 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."

[0092] It is essential to establish a system that allows for real-time monitoring of workers' health within the workplace and enables managers to respond promptly in the event of an abnormality. Without such a health management system, there is a risk of decreased productivity and safety problems due to the deterioration of workers' health.

[0093] 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.

[0094] In this invention, the server includes means for collecting facial information of individual workers on a daily basis using a device for acquiring facial recognition data, means for using a data transmission device for transmitting data to an analysis device, and means for the analysis device to evaluate the worker's health status from the facial information and notify the manager when an abnormality is detected. This enables rapid detection of abnormalities in the health status of workers in the workplace, allowing for appropriate manager intervention and improvement of the work environment.

[0095] "Facial recognition data" refers to individual feature point information extracted from facial images, which is used to identify individuals and assess their health status.

[0096] "Worker" refers to all employees and staff who perform duties in a specific work environment.

[0097] "Daily data collection" refers to the process of acquiring data at a fixed time each day, and continuously accumulating that data.

[0098] An "analysis device" is a general term for computers and software used to process received facial information data and perform health status assessments and anomaly detection.

[0099] "An anomaly" refers to a specific change in the collected facial recognition data that deviates from a normal state of health.

[0100] "Notifying the administrator" means sending an alert or necessary information to the person with administrative privileges when an anomaly is detected.

[0101] "Administrator information terminals" refer to electronic devices and software used by administrators to manage the health of workers and respond to abnormal situations.

[0102] "Suggestions for improving the work environment" refers to specific advice on environmental adjustments and work process changes aimed at improving workers' health and work efficiency.

[0103] This invention relates to a system that aims to monitor the health status of workers in real time and notify managers if any abnormalities are detected. This system is realized by periodically acquiring and analyzing the facial information of workers using facial recognition technology.

[0104] The server collects workers' facial data at a fixed time each day using terminals equipped with smart cameras and facial recognition applications. These terminals photograph each worker's face and record it digitally as feature point data. The obtained facial data is encrypted and securely transmitted to the server via the internet.

[0105] The server utilizes AI analysis algorithms such as TensorFlow and OpenCV, based on Python, to process transmitted data in real time. This analysis evaluates major changes in health status by comparing it with historical data and detects anomalies. If an anomaly is detected, the server sends an alert to the administrator's information terminal. After receiving the notification, the administrator's information terminal provides a means to view the specific details of the anomaly and suggestions for improving the work environment.

[0106] For example, if a worker in a factory is identified as having a pale complexion, the system immediately notifies the manager. The manager can then use a notification terminal to have the worker take a break and arrange for medical services if necessary. This system contributes to improving workplace safety and maintaining work efficiency.

[0107] An example of an input prompt for a generative AI model might be: "Your workplace uses facial recognition technology for daily health checks. Please explain in detail how this technology works and how it ensures employee safety."

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The terminal captures the worker's face with a smart camera. The input is the face image acquired by the camera, and the output is the facial information (feature point data) of a specific worker. Processing is performed to extract important feature points such as eye color and skin texture from the face image.

[0111] Step 2:

[0112] The terminal encrypts the extracted feature point data and sends it to the server using a secure communication path. The input is feature point data, and the output is encrypted data. In this step, an encryption algorithm is used to ensure the security of the communication.

[0113] Step 3:

[0114] The server receives encrypted data and decrypts it. The input is encrypted facial information data, and the output is feature point data in a format suitable for analysis. Decrypting the data makes it ready for the server to analyze.

[0115] Step 4:

[0116] The server uses an AI analysis algorithm to analyze incoming data and evaluate the worker's health status. The input is decoded feature point data, and the output is the health status evaluation result. The server uses TensorFlow and OpenCV to compare facial information with past data and detect anomalies.

[0117] Step 5:

[0118] If a server abnormality is detected, a notification is sent to the administrator's information terminal. The input is the health status evaluation result (presence or absence of abnormality), and the output is notification information for the administrator. When the server detects an abnormality in a worker, a function is executed to send an alert to the administrator that includes the specific details of the abnormality.

[0119] Step 6:

[0120] The administrator receives the notification and takes action. The input is an anomaly notification sent from the server, and the output is instructions for action regarding worker health management and work adjustments. The administrator can check the details of the anomaly presented via the information terminal and issue instructions for appropriate countermeasures.

[0121] 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.

[0122] This invention is a system for monitoring health and emotional states by combining facial recognition technology with an emotion engine. This system enables the evaluation of both the user's physical and psychological state, providing more comprehensive health management. The following describes specific embodiments for carrying out this invention.

[0123] First, the device captures the user's face with its camera and obtains facial information. This information is used to extract feature points necessary for evaluating health status, and includes things like eye opening and skin texture. Furthermore, this facial information is also used as data to analyze emotional state.

[0124] Next, the device sends this facial information to the server. The transmitted data is encrypted using a secure protocol, thus ensuring privacy protection.

[0125] On the server, an AI-based analysis system analyzes health characteristics from facial information, while an emotion engine detects emotional states. These emotional states include happiness, anger, sadness, surprise, and so on. This analysis assesses not only physical health but also whether the user is experiencing stress or psychological problems.

[0126] For example, if the server detects signs of stress from a user's facial information, it will suggest stress reduction measures based on an assessment of their health status. These measures may include suggesting listening to soothing music or taking breaks to relax.

[0127] When an anomaly is detected, the server sends a notification to the user. The notification includes the nature of the anomaly, recommended actions, and information on medical facilities. If a psychological disturbance is detected, information on counseling services and mental health resources will also be provided.

[0128] This system allows users to easily understand their own health and emotional state in their daily lives and take timely measures as needed. It is designed to provide an environment that monitors both physical and mental health and offers appropriate support, especially in today's stressful society.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The device captures the user's face with its camera. It adjusts the focus to ensure the face is clearly visible and captures a clear facial image under appropriate lighting, including natural light.

[0132] Step 2:

[0133] The device compresses the facial image data it acquires and encrypts it as needed. This ensures data confidentiality while preparing it for transmission to the server.

[0134] Step 3:

[0135] The device sends the prepared facial image data to the server. The data is transferred using a high-speed and secure communication protocol.

[0136] Step 4:

[0137] The server analyzes the facial image data it receives. First, it uses an AI algorithm to extract feature points necessary for evaluating health status. It analyzes specific health indicators such as changes around the eyes and changes in skin tone.

[0138] Step 5:

[0139] The server uses an emotion engine to analyze the user's emotional state from facial information. It reads common emotional states such as happiness, anger, and surprise from facial expressions and aggregates that information.

[0140] Step 6:

[0141] The server detects anomalies based on the results of health and emotional status. If there is a significant change in health indicators, or if a persistent stress state is identified through emotional analysis, it is recorded as an anomaly.

[0142] Step 7:

[0143] If the server detects an anomaly, it will notify the user. The notification will include details of the anomaly, recommended actions, and information on relevant medical institutions and counseling services.

[0144] Step 8:

[0145] Users can review notifications and access the suggested medical facilities and mental health resources as needed. Based on the information provided, users can choose appropriate actions and use them to manage their own health.

[0146] (Example 2)

[0147] 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".

[0148] In modern society, it is becoming increasingly important for individuals to appropriately understand and manage their own physical and emotional states. However, traditional methods make it difficult to comprehensively monitor physical and psychological health, and there is a lack of adequate support, especially in situations where a rapid response to stress or emotional fluctuations is required. Therefore, there is a growing need for a system that accurately assesses health and emotional states in daily life and prompts appropriate action when problems arise.

[0149] 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.

[0150] In this invention, the server includes means for routinely collecting facial features using an imaging device that acquires individual facial information; means for encrypting and transmitting the acquired facial information to an analysis system using an information transmission device; and means for extracting features for evaluating an individual's health and emotional state from the transmitted facial information using an analysis system with an AI model, and detecting abnormalities by comparing them with the normal range. This makes it possible for individuals to comprehensively understand their own health and emotional state in their daily lives and to take appropriate measures immediately if there are any abnormalities.

[0151] An "imaging device" is a device used to acquire an individual's facial information and has the capability to collect high-resolution image data.

[0152] An "information transmission device" is a device that transmits acquired facial information to an analysis system and is equipped with a function to encrypt and protect the data.

[0153] The "analysis system" is a device that uses an AI model to analyze transmitted facial information and extract features for evaluating health and emotional states.

[0154] An "AI model" is an algorithm that uses machine learning techniques to analyze facial information and evaluate health and emotional states.

[0155] "Features" are data points extracted from facial information and are used as indicators to evaluate health status and emotional state.

[0156] The "normal range" is a baseline value used to determine whether a person's health and emotional state falls within a certain range based on the characteristics they have been assessed.

[0157] An "abnormal" result refers to a state where the evaluation of the transmitted facial information deviates from the normal range, indicating a potential problem in health or emotional state.

[0158] A "notification device" is a device that informs an individual of information when an abnormality is detected, and its role is to deliver information about the abnormality and suggestions for health management.

[0159] A "communication terminal" is a device accessible to an individual that can receive information such as the provision of mental health resources.

[0160] "Mental health resources" refer to information and resources such as education, support, and services provided to help individuals achieve their mental health.

[0161] This invention is a system for monitoring an individual's health and emotional state, utilizing facial recognition technology and an AI model. This system is implemented in the following manner:

[0162] First, the device uses a high-resolution camera to acquire personal facial information. The camera is equipped with an optical sensor to capture clear facial features, and the acquired image data is digitized and stored. This data acquisition is performed automatically and routinely, designed to reduce the burden on the user.

[0163] Next, the device transmits this facial information to the server using encryption technology. This process employs security protocols to ensure secure data transfer. For example, TLS (Transport Layer Security) is used to protect the data.

[0164] The server runs an AI model to analyze the received facial information. Specifically, it uses an open-source machine learning library (e.g., TensorFlow) to extract features from the image data to evaluate health and emotional states. This AI model calculates data from changes in facial expressions and facial features, and evaluates an individual's stress level and emotional fluctuations in real time.

[0165] Based on the analysis results, the server generates information to support the user's health management. For example, if stress is detected, it may suggest listening to music as a relaxation measure or provide information on nearby support facilities. Notifications are automatically sent to the user's communication device and can be quickly checked by the user via push notifications or email.

[0166] Examples of specific prompt messages include the following:

[0167] "Analyze the user's emotions and propose stress reduction measures."

[0168] "Please extract characteristic features of health status and assess psychological health status."

[0169] This system aims to contribute to improving physical and psychological health by capturing changes in an individual's health and emotions in their daily life and providing support as needed.

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] The device captures the user's face with a camera and acquires high-resolution image data. This input data contains fundamental information necessary to represent facial features, including facial contours, eye movements, and skin texture. In this acquisition process, an optical sensor captures the image and stores it as digital data.

[0173] Step 2:

[0174] The device encrypts the acquired facial image data and sends it to the server using a secure protocol. This input data is encrypted using protocols such as TLS for transmission. Privacy is protected because the encrypted data arrives securely at the server.

[0175] Step 3:

[0176] The server analyzes the received facial data using an AI model. This process utilizes machine learning algorithms to analyze the data, extracting features that evaluate the user's health and emotional state. Specifically, libraries such as TensorFlow are used to perform calculations for estimating health indicators and emotional changes.

[0177] Step 4:

[0178] The server generates insights for stress reduction and health improvement based on the analysis results. Specifically, it compares the obtained characteristics with historical data and, if anomalies or stress are detected, provides the user with suggestions for relaxation methods and information on mental health support. This output is generated by retrieving appropriate information from the server's resource database.

[0179] Step 5:

[0180] The server sends generated insights and notifications to the user's communication device. These notifications utilize push notifications and email functions, delivering personalized messages to the user. Based on this output, the user can check their health status and take necessary actions.

[0181] (Application Example 2)

[0182] 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 device 14 will be referred to as the "terminal."

[0183] In modern society, there is a need to comprehensively monitor an individual's health and emotional state and provide appropriate health management. However, conventional systems are limited to evaluating physical health, making it difficult to assess emotional states and provide customized services based on them. Furthermore, in retail settings, providing real-time services tailored to the customer's situation is challenging. To solve these problems, a comprehensive health and emotional monitoring system using facial information is necessary.

[0184] 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.

[0185] In this invention, the server includes means for collecting an individual's facial information on a daily basis using a device for acquiring facial recognition data; means for transmitting the acquired facial information to an analysis device using a data transmission device; and means for extracting feature points from the transmitted facial information to evaluate the individual's health and emotional state, and detecting abnormalities by comparing them with normal values ​​using the analysis device. This enables comprehensive management of an individual's health and emotional state, and allows for appropriate action suggestions and service customization when abnormalities are detected.

[0186] "Facial recognition data" refers to information extracted from an individual's face to identify that individual.

[0187] "Emotional state" refers to an individual's mental state and includes emotions such as happiness, anger, sadness, and surprise.

[0188] "Health status" refers to a standard used to assess an individual's physical or mental well-being, and is used to detect abnormalities by comparing them to normal values.

[0189] An "analysis device" refers to a device that analyzes acquired facial information, extracts characteristic points of health and emotional state, and evaluates them.

[0190] A "notification device" is a device that, when an abnormality is detected, informs the individual of the nature of the abnormality, recommended actions, and information about medical institutions.

[0191] An "information terminal" is a device accessible to an individual and used to provide information about medical institutions and recommended actions.

[0192] A "store" is a physical location for providing goods or services to customers.

[0193] "Service provision" refers to taking appropriate action based on the customer's emotional and health condition, using information obtained from analytical devices.

[0194] This system uses a terminal with a built-in camera to acquire facial recognition data. The terminal collects individual facial information daily and sends this information to a server for analysis. For security reasons, the data is transmitted using an encrypted protocol (e.g., HTTPS).

[0195] Upon receiving the collected facial information, the server uses an AI-based analysis system to evaluate the health and emotional state of the individual. Specifically, it extracts facial feature points and compares them to known normal values ​​to detect abnormalities. It also uses an emotion engine to identify the emotional state. The software used includes facial recognition libraries (e.g., OpenCV) and emotion analysis engines (e.g., Emotion API).

[0196] If an anomaly is detected, the server will provide the user with information about the anomaly, recommended actions, and medical facilities via a notification device. Users can access these notifications using their information terminals and decide on actions based on the information provided.

[0197] For example, within a store, staff can use smart glasses to understand customers' emotions and health status in real time and provide service accordingly. This functionality enables the provision of more appropriate services to customers, improving the in-store experience.

[0198] An example of a prompt to input into the generation AI model would be: "Generate an appropriate message to display to a store employee when a specific customer in the store is experiencing stress."

[0199] This system enables comprehensive management of an individual's health and emotional state, providing effective and adaptive services. As a result, it can improve the user experience and create a better health management environment.

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] The device captures an individual's face with its camera and collects facial recognition data. The input is real-time video data acquired through the camera, and the output is facial information including facial feature points. This information is processed using a facial recognition library (e.g., OpenCV).

[0203] Step 2:

[0204] The device transmits the acquired facial information to the server using a secure protocol (e.g., HTTPS). The input is the facial information collected by the device, and the output is encrypted facial information that arrives securely on the server.

[0205] Step 3:

[0206] The server analyzes the received facial information using an analysis device. The input is encrypted facial recognition data, and the output is an evaluation of health and emotional status. The data includes analysis of facial feature points, comparison with normal values, and identification of emotions by an emotion engine.

[0207] Step 4:

[0208] If an anomaly is detected based on the analysis results, the server sends a notification to the user via a notification device. The input is the evaluation results of health and emotional state, and the output is a notification message that includes the nature of the anomaly, recommended actions, and information on medical facilities.

[0209] Step 5:

[0210] Users receive notifications through their information terminals and decide on actions based on the information provided. The input is the message received from the notification device, and the output is the specific corrective action the user takes.

[0211] Step 6:

[0212] Within the store, a server uses an AI model to generate information for store staff to display regarding the customer's emotional and health status. The input is emotional and health data based on the customer's facial information, and the output is an appropriate customer service message for the staff to read. Prompts can be used to generate these customer service messages.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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".

[0229] This invention relates to a health monitoring system using facial recognition technology. The system aims to acquire an individual's facial information daily and evaluate their health status in real time. The following describes specific embodiments of the system.

[0230] First, the device takes a picture of the user's face with its camera. This face capture can be done automatically at a set time each day. The captured facial image data is then processed for feature point extraction. At this stage, data such as the color around the eyes, skin texture, and facial contours are important.

[0231] Next, the acquired data is sent from the terminal to the server. This transmission is encrypted for security reasons, and the system is designed to prevent data leakage during communication. The transmitted data is then processed on the server for feature point analysis.

[0232] The server uses AI-based analysis algorithms to extract key health indicators from the data and compare them to historical data. This comparison can detect changes that fall outside the normal range, i.e., "abnormalities." For example, this might apply if skin color suddenly deteriorates.

[0233] If an anomaly is detected, the server will notify the user. This notification is sent via a dedicated application on a smartphone or PC and is automatically delivered to the user. The notification includes specific details of the anomaly and information about the nearest medical facility. The medical facility information includes location, opening hours, and whether appointments are available, enabling a quick response.

[0234] After receiving a notification, users can refer to the provided medical information and, if necessary, seek a diagnosis from a specialist. This system enables individual users to monitor their own health status on a daily basis, detect abnormalities early, and receive prompt treatment.

[0235] Thus, a health management system using facial recognition offers significant convenience and contributes to health, especially for users who have difficulty accessing regular medical facilities.

[0236] The following describes the processing flow.

[0237] Step 1:

[0238] The device captures the user's face with its camera and acquires facial image data. The lighting conditions and angle during shooting are optimized to ensure high-quality images that accurately reflect the user's facial features.

[0239] Step 2:

[0240] The device encodes the facial image data it acquires into a predetermined format and prepares it for data transmission. For privacy protection, the facial data is encrypted at this stage.

[0241] Step 3:

[0242] The terminal sends encoded facial image data to the server. This transmission is performed in real time or in batches at regular intervals.

[0243] Step 4:

[0244] The server decodes the received facial image data and uses an AI model to analyze facial feature points. Patterns of eye opening and closing, changes in skin tone, and other features are extracted at this stage.

[0245] Step 5:

[0246] The server compares the extracted feature points with historical data to detect anomalies. If a change outside the predefined normal range is detected, it is flagged as "abnormal."

[0247] Step 6:

[0248] If the server detects an anomaly, it will notify the user. An alert message will be sent via a dedicated app installed on a smartphone or computer.

[0249] Step 7:

[0250] The server uses the user's location information to collect information on nearby medical facilities. It then creates a list of the most suitable facilities from a database of partner medical institutions.

[0251] Step 8:

[0252] The user should review the notification received and contact a nearby medical institution if necessary. Based on the information provided, it is recommended that they make an appointment and seek medical attention in a timely manner.

[0253] (Example 1)

[0254] 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."

[0255] [In a health monitoring system using facial recognition, the aim is to enable easy monitoring of individual health status in daily life, allowing for early detection of abnormalities and prompt response. Furthermore, it aims to support individual health management by suggesting optimal medical services in cases where access to medical facilities is difficult.]

[0256] 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.

[0257] In this invention, the server includes: means for periodically collecting facial data of a person using a visual information acquisition unit; means for transmitting the collected facial data to a central processing unit using a communication unit; means for the central processing unit to calculate health indicators from the received facial data and detect abnormal conditions by comparing them with reference values; and means for communicating details of the abnormal condition and information about healthcare providers to the person via a notification unit when an abnormal condition is confirmed. This enables individual users to monitor their health status on a daily basis, allowing for early detection and rapid response to abnormalities.

[0258] A "visual information acquisition unit" is a device or function for periodically and automatically collecting facial data of individuals.

[0259] The "communication unit" is a device that has the function of securely and quickly transmitting collected facial data to the central processing unit.

[0260] A "central processing unit" is a device that calculates health indicators from received facial data and determines whether or not there is an abnormal condition by comparing them with reference values.

[0261] A "notification unit" is a device used to inform a person of the details of an abnormal condition and related healthcare provider information when such a condition is detected.

[0262] An "abnormal state" refers to a condition in which the results of health indicator calculations fall outside the standard range, and early detection and intervention are recommended.

[0263] "Healthcare provider information" refers to information necessary for a person to receive appropriate medical services if an abnormality is detected.

[0264] This invention is a health monitoring system based on facial recognition technology, which is primarily operated by a terminal, a server, and a user.

[0265] First, the device periodically acquires the user's facial data using its built-in camera. A general-purpose device with a camera, such as a smartphone or personal computer, is suitable for this process. Image processing is performed using image analysis libraries such as OpenCV to extract facial feature points. These feature points include information such as the color around the eyes, skin texture, and facial contours.

[0266] Next, the device securely transmits the extracted facial data to the server. This transmission uses the HTTPS protocol and SSL / TLS to guarantee data confidentiality. Before transmission, the data is encrypted using AES-256.

[0267] The server analyzes the received facial data and calculates health indicators. This analysis utilizes AI-based models and frameworks such as TensorFlow and PyTorch. Based on the analysis results, if the health status deviates from the standard range, an abnormality is determined. In particular, rapid changes in complexion and swelling are detected as abnormal conditions.

[0268] Subsequently, if an anomaly is detected, the server will notify the user. This notification will be provided in real time via a smartphone or PC application. The notification will include details of the abnormal condition and information on the nearest healthcare provider. This information will include location, contact details, and appointment availability, enabling a quick response.

[0269] As a concrete example, a user working from home uses the system every morning to perform a health check. One day, the system detects a slight fading of skin color compared to past data and issues a notification recommending rest. Another example of a prompt message to input into the generating AI model is an instruction such as, "Analyze the following health data and generate a notification message if an abnormality is detected." This allows the AI ​​model to output an easily understandable abnormality notification to the user.

[0270] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0271] Step 1:

[0272] The device captures the user's face using its built-in camera and acquires facial data. The input is the image captured by the camera, and the output is facial image data automatically captured at a specific time. This image data is stored in high resolution for analysis of facial features.

[0273] Step 2:

[0274] The device extracts important feature points from the acquired facial image data. The input is the facial image data obtained in step 1, and the output is feature point data that quantifies the color around the eyes, skin texture, facial contours, etc. This process uses OpenCV for image analysis, applying specific filters to clarify skin texture and contours.

[0275] Step 3:

[0276] The terminal takes the extracted feature point data as input, encrypts it, and sends the data to the server. The output is the encrypted transmission data. This transmission is encrypted with AES-256 to ensure security using the HTTPS protocol.

[0277] Step 4:

[0278] The server receives the encrypted data sent from the terminal, decrypts it to obtain the feature point data. The input is the encrypted data sent from the terminal, and the output is the feature point data after decryption. The decrypted data is formatted for analysis by the AI algorithm.

[0279] Step 5:

[0280] The server analyzes the feature point data as input using an AI-based analysis model. The output is the evaluation result of the health status. A model built with TensorFlow is used for this analysis to determine whether the current health indicators exceed the standard range compared to past data.

[0281] Step 6:

[0282] If the server detects an abnormal state as a result of the analysis, it notifies the user. The input is the evaluation result of the health status, and the output is the notification information to the user. This notification includes the specific details of the abnormality along with the information of the nearest medical provider. The notification is pushed through a dedicated application.

[0283] Step 7:

[0284] The user receives the notification and checks the content through the application. The input is the notification information received from the server, and the output is the user's action, specifically contacting or making a reservation with a medical provider. The user selects an appropriate medical institution based on the provided information and proceeds with the response.

[0285] (Application Example 1)

[0286] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0287] There is a need to establish a system that monitors the health status of workers in real time in the workplace and enables the administrator to respond promptly when an abnormality occurs. Without such a health management mechanism, there may be problems such as a decrease in labor productivity and safety due to the deterioration of workers' health.

[0288] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0289] In this invention, the server includes means for collecting daily face information of individual workers using a device for acquiring face recognition data, means for using a data transmission device for transmitting data to an analysis device, and means for evaluating the health status of workers from the face information by the analysis device and notifying the administrator when an abnormality is detected. As a result, it becomes possible to quickly detect an abnormality in the health status of workers in the workplace and enable appropriate administrator intervention and improvement of the working environment.

[0290] "Face recognition data" is individual feature point information extracted from a face image and is used for identifying an individual and evaluating the health status.

[0291] "Worker" refers to all employees and staff who perform tasks in a specific working environment.

[0292] "Collect daily" represents a process of acquiring data at a fixed time every day and continuously accumulating data.

[0293] "Analysis device" is a general term for a computer or software that processes the received face information data and performs health status evaluation and abnormality detection.

[0294] "An anomaly" refers to a specific change in the collected facial recognition data that deviates from a normal state of health.

[0295] "Notifying the administrator" means sending an alert or necessary information to the person with administrative privileges when an anomaly is detected.

[0296] "Administrator information terminals" refer to electronic devices and software used by administrators to manage the health of workers and respond to abnormal situations.

[0297] "Suggestions for improving the work environment" refers to specific advice on environmental adjustments and work process changes aimed at improving workers' health and work efficiency.

[0298] This invention relates to a system that aims to monitor the health status of workers in real time and notify managers if any abnormalities are detected. This system is realized by periodically acquiring and analyzing the facial information of workers using facial recognition technology.

[0299] The server collects workers' facial data at a fixed time each day using terminals equipped with smart cameras and facial recognition applications. These terminals photograph each worker's face and record it digitally as feature point data. The obtained facial data is encrypted and securely transmitted to the server via the internet.

[0300] The server utilizes AI analysis algorithms such as TensorFlow and OpenCV, based on Python, to process transmitted data in real time. This analysis evaluates major changes in health status by comparing it with historical data and detects anomalies. If an anomaly is detected, the server sends an alert to the administrator's information terminal. After receiving the notification, the administrator's information terminal provides a means to view the specific details of the anomaly and suggestions for improving the work environment.

[0301] For example, when a worker with a poor complexion is recognized in a factory, the system immediately notifies the administrator of this information. The administrator makes the worker take a break through the notification terminal and arranges medical services if necessary. This system contributes to improving the safety of the working environment and maintaining work efficiency.

[0302] As an example of an input prompt sentence for the generative AI model, something like "In your workplace, you conduct daily health checks using face recognition technology. Please specifically explain how this technology operates and how it ensures the safety of employees." can be considered.

[0303] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0304] Step 1:

[0305] The terminal captures the face of the worker with a smart camera. The input is the face image acquired by the camera, and the output is the face information (feature point data) of a specific worker. Processing is performed to extract important feature points such as the color of the eyes and the texture of the skin from the face image.

[0306] Step 2:

[0307] The terminal encrypts the extracted feature point data and transmits it to the server using a secure communication path. The input is the feature point data, and the output is the encrypted data. In this step, an encryption algorithm is used to ensure the security of the communication.

[0308] Step 3:

[0309] The server receives the encrypted data and decrypts it. The input is the encrypted face information data, and the output is the feature point data in a format suitable for analysis. By decrypting the data, the server can put the received data in a state where it can be analyzed.

[0310] Step 4:

[0311] The server uses an AI analysis algorithm to analyze incoming data and evaluate the worker's health status. The input is decoded feature point data, and the output is the health status evaluation result. The server uses TensorFlow and OpenCV to compare facial information with past data and detect anomalies.

[0312] Step 5:

[0313] If a server abnormality is detected, a notification is sent to the administrator's information terminal. The input is the health status evaluation result (presence or absence of abnormality), and the output is notification information for the administrator. When the server detects an abnormality in a worker, a function is executed to send an alert to the administrator that includes the specific details of the abnormality.

[0314] Step 6:

[0315] The administrator receives the notification and takes action. The input is an anomaly notification sent from the server, and the output is instructions for action regarding worker health management and work adjustments. The administrator can check the details of the anomaly presented via the information terminal and issue instructions for appropriate countermeasures.

[0316] 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.

[0317] This invention is a system for monitoring health and emotional states by combining facial recognition technology with an emotion engine. This system enables the evaluation of both the user's physical and psychological state, providing more comprehensive health management. The following describes specific embodiments for carrying out this invention.

[0318] First, the device captures the user's face with its camera and obtains facial information. This information is used to extract feature points necessary for evaluating health status, and includes things like eye opening and skin texture. Furthermore, this facial information is also used as data to analyze emotional state.

[0319] Next, the device sends this facial information to the server. The transmitted data is encrypted using a secure protocol, thus ensuring privacy protection.

[0320] On the server, an AI-based analysis system analyzes health characteristics from facial information, while an emotion engine detects emotional states. These emotional states include happiness, anger, sadness, surprise, and so on. This analysis assesses not only physical health but also whether the user is experiencing stress or psychological problems.

[0321] For example, if the server detects signs of stress from a user's facial information, it will suggest stress reduction measures based on an assessment of their health status. These measures may include suggesting listening to soothing music or taking breaks to relax.

[0322] When an anomaly is detected, the server sends a notification to the user. The notification includes the nature of the anomaly, recommended actions, and information on medical facilities. If a psychological disturbance is detected, information on counseling services and mental health resources will also be provided.

[0323] This system allows users to easily understand their own health and emotional state in their daily lives and take timely measures as needed. It is designed to provide an environment that monitors both physical and mental health and offers appropriate support, especially in today's stressful society.

[0324] The following describes the processing flow.

[0325] Step 1:

[0326] The device captures the user's face with its camera. It adjusts the focus to ensure the face is clearly visible and captures a clear facial image under appropriate lighting, including natural light.

[0327] Step 2:

[0328] The device compresses the facial image data it acquires and encrypts it as needed. This ensures data confidentiality while preparing it for transmission to the server.

[0329] Step 3:

[0330] The device sends the prepared facial image data to the server. The data is transferred using a high-speed and secure communication protocol.

[0331] Step 4:

[0332] The server analyzes the facial image data it receives. First, it uses an AI algorithm to extract feature points necessary for evaluating health status. It analyzes specific health indicators such as changes around the eyes and changes in skin tone.

[0333] Step 5:

[0334] The server uses an emotion engine to analyze the user's emotional state from facial information. It reads common emotional states such as happiness, anger, and surprise from facial expressions and aggregates that information.

[0335] Step 6:

[0336] The server detects anomalies based on the results of health and emotional status. If there is a significant change in health indicators, or if a persistent stress state is identified through emotional analysis, it is recorded as an anomaly.

[0337] Step 7:

[0338] If the server detects an anomaly, it will notify the user. The notification will include details of the anomaly, recommended actions, and information on relevant medical institutions and counseling services.

[0339] Step 8:

[0340] Users can review notifications and access the suggested medical facilities and mental health resources as needed. Based on the information provided, users can choose appropriate actions and use them to manage their own health.

[0341] (Example 2)

[0342] 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".

[0343] In modern society, it is becoming increasingly important for individuals to appropriately understand and manage their own physical and emotional states. However, traditional methods make it difficult to comprehensively monitor physical and psychological health, and there is a lack of adequate support, especially in situations where a rapid response to stress or emotional fluctuations is required. Therefore, there is a growing need for a system that accurately assesses health and emotional states in daily life and prompts appropriate action when problems arise.

[0344] 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.

[0345] In this invention, the server includes means for routinely collecting facial features using an imaging device that acquires individual facial information; means for encrypting and transmitting the acquired facial information to an analysis system using an information transmission device; and means for extracting features for evaluating an individual's health and emotional state from the transmitted facial information using an analysis system with an AI model, and detecting abnormalities by comparing them with the normal range. This makes it possible for individuals to comprehensively understand their own health and emotional state in their daily lives and to take appropriate measures immediately if there are any abnormalities.

[0346] An "imaging device" is a device used to acquire an individual's facial information and has the capability to collect high-resolution image data.

[0347] An "information transmission device" is a device that transmits acquired facial information to an analysis system and is equipped with a function to encrypt and protect the data.

[0348] The "analysis system" is a device that uses an AI model to analyze transmitted facial information and extract features for evaluating health and emotional states.

[0349] An "AI model" is an algorithm that uses machine learning techniques to analyze facial information and evaluate health and emotional states.

[0350] "Features" are data points extracted from facial information and are used as indicators to evaluate health status and emotional state.

[0351] The "normal range" is a baseline value used to determine whether a person's health and emotional state falls within a certain range based on the characteristics they have been assessed.

[0352] An "abnormal" result refers to a state where the evaluation of the transmitted facial information deviates from the normal range, indicating a potential problem in health or emotional state.

[0353] A "notification device" is a device that informs an individual of information when an abnormality is detected, and its role is to deliver information about the abnormality and suggestions for health management.

[0354] A "communication terminal" is a device accessible to an individual that can receive information such as the provision of mental health resources.

[0355] "Mental health resources" refer to information and resources such as education, support, and services provided to help individuals achieve their mental health.

[0356] This invention is a system for monitoring an individual's health and emotional state, utilizing facial recognition technology and an AI model. This system is implemented in the following manner:

[0357] First, the device uses a high-resolution camera to acquire personal facial information. The camera is equipped with an optical sensor to capture clear facial features, and the acquired image data is digitized and stored. This data acquisition is performed automatically and routinely, designed to reduce the burden on the user.

[0358] Next, the device transmits this facial information to the server using encryption technology. This process employs security protocols to ensure secure data transfer. For example, TLS (Transport Layer Security) is used to protect the data.

[0359] The server runs an AI model to analyze the received facial information. Specifically, it uses an open-source machine learning library (e.g., TensorFlow) to extract features from the image data to evaluate health and emotional states. This AI model calculates data from changes in facial expressions and facial features, and evaluates an individual's stress level and emotional fluctuations in real time.

[0360] Based on the analysis results, the server generates information to support the user's health management. For example, if stress is detected, it may suggest listening to music as a relaxation measure or provide information on nearby support facilities. Notifications are automatically sent to the user's communication device and can be quickly checked by the user via push notifications or email.

[0361] Examples of specific prompt messages include the following:

[0362] "Analyze the user's emotions and propose stress reduction measures."

[0363] "Please extract characteristic features of health status and assess psychological health status."

[0364] This system aims to contribute to improving physical and psychological health by capturing changes in an individual's health and emotions in their daily life and providing support as needed.

[0365] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0366] Step 1:

[0367] The device captures the user's face with a camera and acquires high-resolution image data. This input data contains fundamental information necessary to represent facial features, including facial contours, eye movements, and skin texture. In this acquisition process, an optical sensor captures the image and stores it as digital data.

[0368] Step 2:

[0369] The device encrypts the acquired facial image data and sends it to the server using a secure protocol. This input data is encrypted using protocols such as TLS for transmission. Privacy is protected because the encrypted data arrives securely at the server.

[0370] Step 3:

[0371] The server analyzes the received facial data using an AI model. This process utilizes machine learning algorithms to analyze the data, extracting features that evaluate the user's health and emotional state. Specifically, libraries such as TensorFlow are used to perform calculations for estimating health indicators and emotional changes.

[0372] Step 4:

[0373] The server generates insights for stress reduction and health improvement based on the analysis results. Specifically, it compares the obtained characteristics with historical data and, if anomalies or stress are detected, provides the user with suggestions for relaxation methods and information on mental health support. This output is generated by retrieving appropriate information from the server's resource database.

[0374] Step 5:

[0375] The server sends generated insights and notifications to the user's communication device. These notifications utilize push notifications and email functions, delivering personalized messages to the user. Based on this output, the user can check their health status and take necessary actions.

[0376] (Application Example 2)

[0377] 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."

[0378] In modern society, there is a need to comprehensively monitor an individual's health and emotional state and provide appropriate health management. However, conventional systems are limited to evaluating physical health, making it difficult to assess emotional states and provide customized services based on them. Furthermore, in retail settings, providing real-time services tailored to the customer's situation is challenging. To solve these problems, a comprehensive health and emotional monitoring system using facial information is necessary.

[0379] 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.

[0380] In this invention, the server includes means for collecting an individual's facial information on a daily basis using a device for acquiring facial recognition data; means for transmitting the acquired facial information to an analysis device using a data transmission device; and means for extracting feature points from the transmitted facial information to evaluate the individual's health and emotional state, and detecting abnormalities by comparing them with normal values ​​using the analysis device. This enables comprehensive management of an individual's health and emotional state, and allows for appropriate action suggestions and service customization when abnormalities are detected.

[0381] "Facial recognition data" refers to information extracted from an individual's face to identify that individual.

[0382] "Emotional state" refers to an individual's mental state and includes emotions such as happiness, anger, sadness, and surprise.

[0383] "Health status" refers to a standard used to assess an individual's physical or mental well-being, and is used to detect abnormalities by comparing them to normal values.

[0384] An "analysis device" refers to a device that analyzes acquired facial information, extracts characteristic points of health and emotional state, and evaluates them.

[0385] A "notification device" is a device that, when an abnormality is detected, informs the individual of the nature of the abnormality, recommended actions, and information about medical institutions.

[0386] An "information terminal" is a device accessible to an individual and used to provide information about medical institutions and recommended actions.

[0387] A "store" is a physical location for providing goods or services to customers.

[0388] "Service provision" refers to taking appropriate action based on the customer's emotional and health condition, using information obtained from analytical devices.

[0389] This system uses a terminal with a built-in camera to acquire facial recognition data. The terminal collects individual facial information daily and sends this information to a server for analysis. For security reasons, the data is transmitted using an encrypted protocol (e.g., HTTPS).

[0390] Upon receiving the collected facial information, the server uses an AI-based analysis system to evaluate the health and emotional state of the individual. Specifically, it extracts facial feature points and compares them to known normal values ​​to detect abnormalities. It also uses an emotion engine to identify the emotional state. The software used includes facial recognition libraries (e.g., OpenCV) and emotion analysis engines (e.g., Emotion API).

[0391] If an anomaly is detected, the server will provide the user with information about the anomaly, recommended actions, and medical facilities via a notification device. Users can access these notifications using their information terminals and decide on actions based on the information provided.

[0392] For example, within a store, staff can use smart glasses to understand customers' emotions and health status in real time and provide service accordingly. This functionality enables the provision of more appropriate services to customers, improving the in-store experience.

[0393] An example of a prompt to input into the generation AI model would be: "Generate an appropriate message to display to a store employee when a specific customer in the store is experiencing stress."

[0394] This system enables comprehensive management of an individual's health and emotional state, providing effective and adaptive services. As a result, it can improve the user experience and create a better health management environment.

[0395] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0396] Step 1:

[0397] The device captures an individual's face with its camera and collects facial recognition data. The input is real-time video data acquired through the camera, and the output is facial information including facial feature points. This information is processed using a facial recognition library (e.g., OpenCV).

[0398] Step 2:

[0399] The device transmits the acquired facial information to the server using a secure protocol (e.g., HTTPS). The input is the facial information collected by the device, and the output is encrypted facial information that arrives securely on the server.

[0400] Step 3:

[0401] The server analyzes the received facial information using an analysis device. The input is encrypted facial recognition data, and the output is an evaluation of health and emotional status. The data includes analysis of facial feature points, comparison with normal values, and identification of emotions by an emotion engine.

[0402] Step 4:

[0403] If an anomaly is detected based on the analysis results, the server sends a notification to the user via a notification device. The input is the evaluation results of health and emotional state, and the output is a notification message that includes the nature of the anomaly, recommended actions, and information on medical facilities.

[0404] Step 5:

[0405] Users receive notifications through their information terminals and decide on actions based on the information provided. The input is the message received from the notification device, and the output is the specific corrective action the user takes.

[0406] Step 6:

[0407] Within the store, a server uses an AI model to generate information for store staff to display regarding the customer's emotional and health status. The input is emotional and health data based on the customer's facial information, and the output is an appropriate customer service message for the staff to read. Prompts can be used to generate these customer service messages.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] [Third Embodiment]

[0412] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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".

[0424] This invention relates to a health monitoring system using facial recognition technology. The system aims to acquire an individual's facial information daily and evaluate their health status in real time. The following describes specific embodiments of the system.

[0425] First, the device takes a picture of the user's face with its camera. This face capture can be done automatically at a set time each day. The captured facial image data is then processed for feature point extraction. At this stage, data such as the color around the eyes, skin texture, and facial contours are important.

[0426] Next, the acquired data is sent from the terminal to the server. This transmission is encrypted for security reasons, and the system is designed to prevent data leakage during communication. The transmitted data is then processed on the server for feature point analysis.

[0427] The server uses AI-based analysis algorithms to extract key health indicators from the data and compare them to historical data. This comparison can detect changes that fall outside the normal range, i.e., "abnormalities." For example, this might apply if skin color suddenly deteriorates.

[0428] If an anomaly is detected, the server will notify the user. This notification is sent via a dedicated application on a smartphone or PC and is automatically delivered to the user. The notification includes specific details of the anomaly and information about the nearest medical facility. The medical facility information includes location, opening hours, and whether appointments are available, enabling a quick response.

[0429] After receiving a notification, users can refer to the provided medical information and, if necessary, seek a diagnosis from a specialist. This system enables individual users to monitor their own health status on a daily basis, detect abnormalities early, and receive prompt treatment.

[0430] Thus, a health management system using facial recognition offers significant convenience and contributes to health, especially for users who have difficulty accessing regular medical facilities.

[0431] The following describes the processing flow.

[0432] Step 1:

[0433] The device captures the user's face with its camera and acquires facial image data. The lighting conditions and angle during shooting are optimized to ensure high-quality images that accurately reflect the user's facial features.

[0434] Step 2:

[0435] The device encodes the facial image data it acquires into a predetermined format and prepares it for data transmission. For privacy protection, the facial data is encrypted at this stage.

[0436] Step 3:

[0437] The terminal sends encoded facial image data to the server. This transmission is performed in real time or in batches at regular intervals.

[0438] Step 4:

[0439] The server decodes the received facial image data and uses an AI model to analyze facial feature points. Patterns of eye opening and closing, changes in skin tone, and other features are extracted at this stage.

[0440] Step 5:

[0441] The server compares the extracted feature points with historical data to detect anomalies. If a change outside the predefined normal range is detected, it is flagged as "abnormal."

[0442] Step 6:

[0443] If the server detects an anomaly, it will notify the user. An alert message will be sent via a dedicated app installed on a smartphone or computer.

[0444] Step 7:

[0445] The server uses the user's location information to collect information on nearby medical facilities. It then creates a list of the most suitable facilities from a database of partner medical institutions.

[0446] Step 8:

[0447] The user should review the notification received and contact a nearby medical institution if necessary. Based on the information provided, it is recommended that they make an appointment and seek medical attention in a timely manner.

[0448] (Example 1)

[0449] 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."

[0450] [In a health monitoring system using facial recognition, the aim is to enable easy monitoring of individual health status in daily life, allowing for early detection of abnormalities and prompt response. Furthermore, it aims to support individual health management by suggesting optimal medical services in cases where access to medical facilities is difficult.]

[0451] 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.

[0452] In this invention, the server includes: means for periodically collecting facial data of a person using a visual information acquisition unit; means for transmitting the collected facial data to a central processing unit using a communication unit; means for the central processing unit to calculate health indicators from the received facial data and detect abnormal conditions by comparing them with reference values; and means for communicating details of the abnormal condition and information about healthcare providers to the person via a notification unit when an abnormal condition is confirmed. This enables individual users to monitor their health status on a daily basis, allowing for early detection and rapid response to abnormalities.

[0453] A "visual information acquisition unit" is a device or function for periodically and automatically collecting facial data of individuals.

[0454] The "communication unit" is a device that has the function of securely and quickly transmitting collected facial data to the central processing unit.

[0455] A "central processing unit" is a device that calculates health indicators from received facial data and determines whether or not there is an abnormal condition by comparing them with reference values.

[0456] A "notification unit" is a device used to inform a person of the details of an abnormal condition and related healthcare provider information when such a condition is detected.

[0457] An "abnormal state" refers to a condition in which the results of health indicator calculations fall outside the standard range, and early detection and intervention are recommended.

[0458] "Healthcare provider information" refers to information necessary for a person to receive appropriate medical services if an abnormality is detected.

[0459] This invention is a health monitoring system based on facial recognition technology, which is primarily operated by a terminal, a server, and a user.

[0460] First, the device periodically acquires the user's facial data using its built-in camera. A general-purpose device with a camera, such as a smartphone or personal computer, is suitable for this process. Image processing is performed using image analysis libraries such as OpenCV to extract facial feature points. These feature points include information such as the color around the eyes, skin texture, and facial contours.

[0461] Next, the device securely transmits the extracted facial data to the server. This transmission uses the HTTPS protocol and SSL / TLS to guarantee data confidentiality. Before transmission, the data is encrypted using AES-256.

[0462] The server analyzes the received facial data and calculates health indicators. This analysis utilizes AI-based models and frameworks such as TensorFlow and PyTorch. Based on the analysis results, if the health status deviates from the standard range, an abnormality is determined. In particular, rapid changes in complexion and swelling are detected as abnormal conditions.

[0463] Subsequently, if an anomaly is detected, the server will notify the user. This notification will be provided in real time via a smartphone or PC application. The notification will include details of the abnormal condition and information on the nearest healthcare provider. This information will include location, contact details, and appointment availability, enabling a quick response.

[0464] As a concrete example, a user working from home uses the system every morning to perform a health check. One day, the system detects a slight fading of skin color compared to past data and issues a notification recommending rest. Another example of a prompt message to input into the generating AI model is an instruction such as, "Analyze the following health data and generate a notification message if an abnormality is detected." This allows the AI ​​model to output an easily understandable abnormality notification to the user.

[0465] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0466] Step 1:

[0467] The device captures the user's face using its built-in camera and acquires facial data. The input is the image captured by the camera, and the output is facial image data automatically captured at a specific time. This image data is stored in high resolution for analysis of facial features.

[0468] Step 2:

[0469] The device extracts important feature points from the acquired facial image data. The input is the facial image data obtained in step 1, and the output is feature point data that quantifies the color around the eyes, skin texture, facial contours, etc. This process uses OpenCV for image analysis, applying specific filters to clarify skin texture and contours.

[0470] Step 3:

[0471] The terminal receives the extracted feature point data as input, encrypts it, and sends the data to the server. The output is encrypted transmitted data. This transmission is encrypted using AES-256 and secure using the HTTPS protocol.

[0472] Step 4:

[0473] The server receives encrypted data sent from the terminal, decrypts it, and obtains feature point data. The input is the encrypted data sent from the terminal, and the output is the decrypted feature point data. The decrypted data is then formatted in preparation for analysis by an AI algorithm.

[0474] Step 5:

[0475] The server uses an AI-based analysis model to analyze feature point data as input. The output is an assessment of health status. This analysis uses a model built with TensorFlow to determine whether current health indicators exceed the reference range by comparing them with historical data.

[0476] Step 6:

[0477] The server notifies the user if it detects an abnormal condition as a result of its analysis. The input is the health status assessment result, and the output is notification information for the user. This notification includes the specific details of the abnormality, as well as information on the nearest healthcare provider. The notification is delivered via push notification through a dedicated application.

[0478] Step 7:

[0479] The user receives a notification and checks its contents through the application. The input is the notification information received from the server, and the output is the user's action, specifically contacting a healthcare provider or making an appointment. Based on the information provided, the user selects the appropriate healthcare provider and proceeds with the necessary actions.

[0480] (Application Example 1)

[0481] 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."

[0482] It is essential to establish a system that allows for real-time monitoring of workers' health within the workplace and enables managers to respond promptly in the event of an abnormality. Without such a health management system, there is a risk of decreased productivity and safety problems due to the deterioration of workers' health.

[0483] 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.

[0484] In this invention, the server includes means for collecting facial information of individual workers on a daily basis using a device for acquiring facial recognition data, means for using a data transmission device for transmitting data to an analysis device, and means for the analysis device to evaluate the worker's health status from the facial information and notify the manager when an abnormality is detected. This enables rapid detection of abnormalities in the health status of workers in the workplace, allowing for appropriate manager intervention and improvement of the work environment.

[0485] "Facial recognition data" refers to individual feature point information extracted from facial images, which is used to identify individuals and assess their health status.

[0486] "Worker" refers to all employees and staff who perform duties in a specific work environment.

[0487] "Daily data collection" refers to the process of acquiring data at a fixed time each day, and continuously accumulating that data.

[0488] An "analysis device" is a general term for computers and software used to process received facial information data and perform health status assessments and anomaly detection.

[0489] "An anomaly" refers to a specific change in the collected facial recognition data that deviates from a normal state of health.

[0490] "Notifying the administrator" means sending an alert or necessary information to the person with administrative privileges when an anomaly is detected.

[0491] "Administrator information terminals" refer to electronic devices and software used by administrators to manage the health of workers and respond to abnormal situations.

[0492] "Suggestions for improving the work environment" refers to specific advice on environmental adjustments and work process changes aimed at improving workers' health and work efficiency.

[0493] This invention relates to a system that aims to monitor the health status of workers in real time and notify managers if any abnormalities are detected. This system is realized by periodically acquiring and analyzing the facial information of workers using facial recognition technology.

[0494] The server collects workers' facial data at a fixed time each day using terminals equipped with smart cameras and facial recognition applications. These terminals photograph each worker's face and record it digitally as feature point data. The obtained facial data is encrypted and securely transmitted to the server via the internet.

[0495] The server utilizes AI analysis algorithms such as TensorFlow and OpenCV, based on Python, to process transmitted data in real time. This analysis evaluates major changes in health status by comparing it with historical data and detects anomalies. If an anomaly is detected, the server sends an alert to the administrator's information terminal. After receiving the notification, the administrator's information terminal provides a means to view the specific details of the anomaly and suggestions for improving the work environment.

[0496] For example, if a worker in a factory is identified as having a pale complexion, the system immediately notifies the manager. The manager can then use a notification terminal to have the worker take a break and arrange for medical services if necessary. This system contributes to improving workplace safety and maintaining work efficiency.

[0497] An example of an input prompt for a generative AI model might be: "Your workplace uses facial recognition technology for daily health checks. Please explain in detail how this technology works and how it ensures employee safety."

[0498] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0499] Step 1:

[0500] The terminal captures the worker's face with a smart camera. The input is the face image acquired by the camera, and the output is the facial information (feature point data) of a specific worker. Processing is performed to extract important feature points such as eye color and skin texture from the face image.

[0501] Step 2:

[0502] The terminal encrypts the extracted feature point data and sends it to the server using a secure communication path. The input is feature point data, and the output is encrypted data. In this step, an encryption algorithm is used to ensure the security of the communication.

[0503] Step 3:

[0504] The server receives encrypted data and decrypts it. The input is encrypted facial information data, and the output is feature point data in a format suitable for analysis. Decrypting the data makes it ready for the server to analyze.

[0505] Step 4:

[0506] The server uses an AI analysis algorithm to analyze incoming data and evaluate the worker's health status. The input is decoded feature point data, and the output is the health status evaluation result. The server uses TensorFlow and OpenCV to compare facial information with past data and detect anomalies.

[0507] Step 5:

[0508] If a server abnormality is detected, a notification is sent to the administrator's information terminal. The input is the health status evaluation result (presence or absence of abnormality), and the output is notification information for the administrator. When the server detects an abnormality in a worker, a function is executed to send an alert to the administrator that includes the specific details of the abnormality.

[0509] Step 6:

[0510] The administrator receives the notification and takes action. The input is an anomaly notification sent from the server, and the output is instructions for action regarding worker health management and work adjustments. The administrator can check the details of the anomaly presented via the information terminal and issue instructions for appropriate countermeasures.

[0511] 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.

[0512] This invention is a system for monitoring health and emotional states by combining facial recognition technology with an emotion engine. This system enables the evaluation of both the user's physical and psychological state, providing more comprehensive health management. The following describes specific embodiments for carrying out this invention.

[0513] First, the device captures the user's face with its camera and obtains facial information. This information is used to extract feature points necessary for evaluating health status, and includes things like eye opening and skin texture. Furthermore, this facial information is also used as data to analyze emotional state.

[0514] Next, the device sends this facial information to the server. The transmitted data is encrypted using a secure protocol, thus ensuring privacy protection.

[0515] On the server, an AI-based analysis system analyzes health characteristics from facial information, while an emotion engine detects emotional states. These emotional states include happiness, anger, sadness, surprise, and so on. This analysis assesses not only physical health but also whether the user is experiencing stress or psychological problems.

[0516] For example, if the server detects signs of stress from a user's facial information, it will suggest stress reduction measures based on an assessment of their health status. These measures may include suggesting listening to soothing music or taking breaks to relax.

[0517] When an anomaly is detected, the server sends a notification to the user. The notification includes the nature of the anomaly, recommended actions, and information on medical facilities. If a psychological disturbance is detected, information on counseling services and mental health resources will also be provided.

[0518] This system allows users to easily understand their own health and emotional state in their daily lives and take timely measures as needed. It is designed to provide an environment that monitors both physical and mental health and offers appropriate support, especially in today's stressful society.

[0519] The following describes the processing flow.

[0520] Step 1:

[0521] The device captures the user's face with its camera. It adjusts the focus to ensure the face is clearly visible and captures a clear facial image under appropriate lighting, including natural light.

[0522] Step 2:

[0523] The device compresses the facial image data it acquires and encrypts it as needed. This ensures data confidentiality while preparing it for transmission to the server.

[0524] Step 3:

[0525] The device sends the prepared facial image data to the server. The data is transferred using a high-speed and secure communication protocol.

[0526] Step 4:

[0527] The server analyzes the facial image data it receives. First, it uses an AI algorithm to extract feature points necessary for evaluating health status. It analyzes specific health indicators such as changes around the eyes and changes in skin tone.

[0528] Step 5:

[0529] The server uses an emotion engine to analyze the user's emotional state from facial information. It reads common emotional states such as happiness, anger, and surprise from facial expressions and aggregates that information.

[0530] Step 6:

[0531] The server detects anomalies based on the results of health and emotional status. If there is a significant change in health indicators, or if a persistent stress state is identified through emotional analysis, it is recorded as an anomaly.

[0532] Step 7:

[0533] If the server detects an anomaly, it will notify the user. The notification will include details of the anomaly, recommended actions, and information on relevant medical institutions and counseling services.

[0534] Step 8:

[0535] Users can review notifications and access the suggested medical facilities and mental health resources as needed. Based on the information provided, users can choose appropriate actions and use them to manage their own health.

[0536] (Example 2)

[0537] 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."

[0538] In modern society, it is becoming increasingly important for individuals to appropriately understand and manage their own physical and emotional states. However, traditional methods make it difficult to comprehensively monitor physical and psychological health, and there is a lack of adequate support, especially in situations where a rapid response to stress or emotional fluctuations is required. Therefore, there is a growing need for a system that accurately assesses health and emotional states in daily life and prompts appropriate action when problems arise.

[0539] 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.

[0540] In this invention, the server includes means for routinely collecting facial features using an imaging device that acquires individual facial information; means for encrypting and transmitting the acquired facial information to an analysis system using an information transmission device; and means for extracting features for evaluating an individual's health and emotional state from the transmitted facial information using an analysis system with an AI model, and detecting abnormalities by comparing them with the normal range. This makes it possible for individuals to comprehensively understand their own health and emotional state in their daily lives and to take appropriate measures immediately if there are any abnormalities.

[0541] An "imaging device" is a device used to acquire an individual's facial information and has the capability to collect high-resolution image data.

[0542] An "information transmission device" is a device that transmits acquired facial information to an analysis system and is equipped with a function to encrypt and protect the data.

[0543] The "analysis system" is a device that uses an AI model to analyze transmitted facial information and extract features for evaluating health and emotional states.

[0544] An "AI model" is an algorithm that uses machine learning techniques to analyze facial information and evaluate health and emotional states.

[0545] "Features" are data points extracted from facial information and are used as indicators to evaluate health status and emotional state.

[0546] The "normal range" is a baseline value used to determine whether a person's health and emotional state falls within a certain range based on the characteristics they have been assessed.

[0547] An "abnormal" result refers to a state where the evaluation of the transmitted facial information deviates from the normal range, indicating a potential problem in health or emotional state.

[0548] A "notification device" is a device that informs an individual of information when an abnormality is detected, and its role is to deliver information about the abnormality and suggestions for health management.

[0549] A "communication terminal" is a device accessible to an individual that can receive information such as the provision of mental health resources.

[0550] "Mental health resources" refer to information and resources such as education, support, and services provided to help individuals achieve their mental health.

[0551] This invention is a system for monitoring an individual's health and emotional state, utilizing facial recognition technology and an AI model. This system is implemented in the following manner:

[0552] First, the device uses a high-resolution camera to acquire personal facial information. The camera is equipped with an optical sensor to capture clear facial features, and the acquired image data is digitized and stored. This data acquisition is performed automatically and routinely, designed to reduce the burden on the user.

[0553] Next, the device transmits this facial information to the server using encryption technology. This process employs security protocols to ensure secure data transfer. For example, TLS (Transport Layer Security) is used to protect the data.

[0554] The server runs an AI model to analyze the received facial information. Specifically, it uses an open-source machine learning library (e.g., TensorFlow) to extract features from the image data to evaluate health and emotional states. This AI model calculates data from changes in facial expressions and facial features, and evaluates an individual's stress level and emotional fluctuations in real time.

[0555] Based on the analysis results, the server generates information to support the user's health management. For example, if stress is detected, it may suggest listening to music as a relaxation measure or provide information on nearby support facilities. Notifications are automatically sent to the user's communication device and can be quickly checked by the user via push notifications or email.

[0556] Examples of specific prompt messages include the following:

[0557] "Analyze the user's emotions and propose stress reduction measures."

[0558] "Please extract characteristic features of health status and assess psychological health status."

[0559] This system aims to contribute to improving physical and psychological health by capturing changes in an individual's health and emotions in their daily life and providing support as needed.

[0560] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0561] Step 1:

[0562] The device captures the user's face with a camera and acquires high-resolution image data. This input data contains fundamental information necessary to represent facial features, including facial contours, eye movements, and skin texture. In this acquisition process, an optical sensor captures the image and stores it as digital data.

[0563] Step 2:

[0564] The device encrypts the acquired facial image data and sends it to the server using a secure protocol. This input data is encrypted using protocols such as TLS for transmission. Privacy is protected because the encrypted data arrives securely at the server.

[0565] Step 3:

[0566] The server analyzes the received facial data using an AI model. This process utilizes machine learning algorithms to analyze the data, extracting features that evaluate the user's health and emotional state. Specifically, libraries such as TensorFlow are used to perform calculations for estimating health indicators and emotional changes.

[0567] Step 4:

[0568] The server generates insights for stress reduction and health improvement based on the analysis results. Specifically, it compares the obtained characteristics with historical data and, if anomalies or stress are detected, provides the user with suggestions for relaxation methods and information on mental health support. This output is generated by retrieving appropriate information from the server's resource database.

[0569] Step 5:

[0570] The server sends generated insights and notifications to the user's communication device. These notifications utilize push notifications and email functions, delivering personalized messages to the user. Based on this output, the user can check their health status and take necessary actions.

[0571] (Application Example 2)

[0572] 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."

[0573] In modern society, there is a need to comprehensively monitor an individual's health and emotional state and provide appropriate health management. However, conventional systems are limited to evaluating physical health, making it difficult to assess emotional states and provide customized services based on them. Furthermore, in retail settings, providing real-time services tailored to the customer's situation is challenging. To solve these problems, a comprehensive health and emotional monitoring system using facial information is necessary.

[0574] 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.

[0575] In this invention, the server includes means for collecting an individual's facial information on a daily basis using a device for acquiring facial recognition data; means for transmitting the acquired facial information to an analysis device using a data transmission device; and means for extracting feature points from the transmitted facial information to evaluate the individual's health and emotional state, and detecting abnormalities by comparing them with normal values ​​using the analysis device. This enables comprehensive management of an individual's health and emotional state, and allows for appropriate action suggestions and service customization when abnormalities are detected.

[0576] "Facial recognition data" refers to information extracted from an individual's face to identify that individual.

[0577] "Emotional state" refers to an individual's mental state and includes emotions such as happiness, anger, sadness, and surprise.

[0578] "Health status" refers to a standard used to assess an individual's physical or mental well-being, and is used to detect abnormalities by comparing them to normal values.

[0579] An "analysis device" refers to a device that analyzes acquired facial information, extracts characteristic points of health and emotional state, and evaluates them.

[0580] A "notification device" is a device that, when an abnormality is detected, informs the individual of the nature of the abnormality, recommended actions, and information about medical institutions.

[0581] An "information terminal" is a device accessible to an individual and used to provide information about medical institutions and recommended actions.

[0582] A "store" is a physical location for providing goods or services to customers.

[0583] "Service provision" refers to taking appropriate action based on the customer's emotional and health condition, using information obtained from analytical devices.

[0584] This system uses a terminal with a built-in camera to acquire facial recognition data. The terminal collects individual facial information daily and sends this information to a server for analysis. For security reasons, the data is transmitted using an encrypted protocol (e.g., HTTPS).

[0585] Upon receiving the collected facial information, the server uses an AI-based analysis system to evaluate the health and emotional state of the individual. Specifically, it extracts facial feature points and compares them to known normal values ​​to detect abnormalities. It also uses an emotion engine to identify the emotional state. The software used includes facial recognition libraries (e.g., OpenCV) and emotion analysis engines (e.g., Emotion API).

[0586] If an anomaly is detected, the server will provide the user with information about the anomaly, recommended actions, and medical facilities via a notification device. Users can access these notifications using their information terminals and decide on actions based on the information provided.

[0587] For example, within a store, staff can use smart glasses to understand customers' emotions and health status in real time and provide service accordingly. This functionality enables the provision of more appropriate services to customers, improving the in-store experience.

[0588] An example of a prompt to input into the generation AI model would be: "Generate an appropriate message to display to a store employee when a specific customer in the store is experiencing stress."

[0589] This system enables comprehensive management of an individual's health and emotional state, providing effective and adaptive services. As a result, it can improve the user experience and create a better health management environment.

[0590] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0591] Step 1:

[0592] The device captures an individual's face with its camera and collects facial recognition data. The input is real-time video data acquired through the camera, and the output is facial information including facial feature points. This information is processed using a facial recognition library (e.g., OpenCV).

[0593] Step 2:

[0594] The device transmits the acquired facial information to the server using a secure protocol (e.g., HTTPS). The input is the facial information collected by the device, and the output is encrypted facial information that arrives securely on the server.

[0595] Step 3:

[0596] The server analyzes the received facial information using an analysis device. The input is encrypted facial recognition data, and the output is an evaluation of health and emotional status. The data includes analysis of facial feature points, comparison with normal values, and identification of emotions by an emotion engine.

[0597] Step 4:

[0598] If an anomaly is detected based on the analysis results, the server sends a notification to the user via a notification device. The input is the evaluation results of health and emotional state, and the output is a notification message that includes the nature of the anomaly, recommended actions, and information on medical facilities.

[0599] Step 5:

[0600] Users receive notifications through their information terminals and decide on actions based on the information provided. The input is the message received from the notification device, and the output is the specific corrective action the user takes.

[0601] Step 6:

[0602] Within the store, a server uses an AI model to generate information for store staff to display regarding the customer's emotional and health status. The input is emotional and health data based on the customer's facial information, and the output is an appropriate customer service message for the staff to read. Prompts can be used to generate these customer service messages.

[0603] 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.

[0604] 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.

[0605] 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.

[0606] [Fourth Embodiment]

[0607] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0608] 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.

[0609] 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).

[0610] 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.

[0611] 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.

[0612] 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).

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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".

[0620] This invention relates to a health monitoring system using facial recognition technology. The system aims to acquire an individual's facial information daily and evaluate their health status in real time. The following describes specific embodiments of the system.

[0621] First, the device takes a picture of the user's face with its camera. This face capture can be done automatically at a set time each day. The captured facial image data is then processed for feature point extraction. At this stage, data such as the color around the eyes, skin texture, and facial contours are important.

[0622] Next, the acquired data is sent from the terminal to the server. This transmission is encrypted for security reasons, and the system is designed to prevent data leakage during communication. The transmitted data is then processed on the server for feature point analysis.

[0623] The server uses AI-based analysis algorithms to extract key health indicators from the data and compare them to historical data. This comparison can detect changes that fall outside the normal range, i.e., "abnormalities." For example, this might apply if skin color suddenly deteriorates.

[0624] If an anomaly is detected, the server will notify the user. This notification is sent via a dedicated application on a smartphone or PC and is automatically delivered to the user. The notification includes specific details of the anomaly and information about the nearest medical facility. The medical facility information includes location, opening hours, and whether appointments are available, enabling a quick response.

[0625] After receiving a notification, users can refer to the provided medical information and, if necessary, seek a diagnosis from a specialist. This system enables individual users to monitor their own health status on a daily basis, detect abnormalities early, and receive prompt treatment.

[0626] Thus, a health management system using facial recognition offers significant convenience and contributes to health, especially for users who have difficulty accessing regular medical facilities.

[0627] The following describes the processing flow.

[0628] Step 1:

[0629] The device captures the user's face with its camera and acquires facial image data. The lighting conditions and angle during shooting are optimized to ensure high-quality images that accurately reflect the user's facial features.

[0630] Step 2:

[0631] The device encodes the facial image data it acquires into a predetermined format and prepares it for data transmission. For privacy protection, the facial data is encrypted at this stage.

[0632] Step 3:

[0633] The terminal sends encoded facial image data to the server. This transmission is performed in real time or in batches at regular intervals.

[0634] Step 4:

[0635] The server decodes the received facial image data and uses an AI model to analyze facial feature points. Patterns of eye opening and closing, changes in skin tone, and other features are extracted at this stage.

[0636] Step 5:

[0637] The server compares the extracted feature points with historical data to detect anomalies. If a change outside the predefined normal range is detected, it is flagged as "abnormal."

[0638] Step 6:

[0639] If the server detects an anomaly, it will notify the user. An alert message will be sent via a dedicated app installed on a smartphone or computer.

[0640] Step 7:

[0641] The server uses the user's location information to collect information on nearby medical facilities. It then creates a list of the most suitable facilities from a database of partner medical institutions.

[0642] Step 8:

[0643] The user should review the notification received and contact a nearby medical institution if necessary. Based on the information provided, it is recommended that they make an appointment and seek medical attention in a timely manner.

[0644] (Example 1)

[0645] 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".

[0646] [In a health monitoring system using facial recognition, the aim is to enable easy monitoring of individual health status in daily life, allowing for early detection of abnormalities and prompt response. Furthermore, it aims to support individual health management by suggesting optimal medical services in cases where access to medical facilities is difficult.]

[0647] 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.

[0648] In this invention, the server includes: means for periodically collecting facial data of a person using a visual information acquisition unit; means for transmitting the collected facial data to a central processing unit using a communication unit; means for the central processing unit to calculate health indicators from the received facial data and detect abnormal conditions by comparing them with reference values; and means for communicating details of the abnormal condition and information about healthcare providers to the person via a notification unit when an abnormal condition is confirmed. This enables individual users to monitor their health status on a daily basis, allowing for early detection and rapid response to abnormalities.

[0649] A "visual information acquisition unit" is a device or function for periodically and automatically collecting facial data of individuals.

[0650] The "communication unit" is a device that has the function of securely and quickly transmitting collected facial data to the central processing unit.

[0651] A "central processing unit" is a device that calculates health indicators from received facial data and determines whether or not there is an abnormal condition by comparing them with reference values.

[0652] A "notification unit" is a device used to inform a person of the details of an abnormal condition and related healthcare provider information when such a condition is detected.

[0653] An "abnormal state" refers to a condition in which the results of health indicator calculations fall outside the standard range, and early detection and intervention are recommended.

[0654] "Healthcare provider information" refers to information necessary for a person to receive appropriate medical services if an abnormality is detected.

[0655] This invention is a health monitoring system based on facial recognition technology, which is primarily operated by a terminal, a server, and a user.

[0656] First, the device periodically acquires the user's facial data using its built-in camera. A general-purpose device with a camera, such as a smartphone or personal computer, is suitable for this process. Image processing is performed using image analysis libraries such as OpenCV to extract facial feature points. These feature points include information such as the color around the eyes, skin texture, and facial contours.

[0657] Next, the device securely transmits the extracted facial data to the server. This transmission uses the HTTPS protocol and SSL / TLS to guarantee data confidentiality. Before transmission, the data is encrypted using AES-256.

[0658] The server analyzes the received facial data and calculates health indicators. This analysis utilizes AI-based models and frameworks such as TensorFlow and PyTorch. Based on the analysis results, if the health status deviates from the standard range, an abnormality is determined. In particular, rapid changes in complexion and swelling are detected as abnormal conditions.

[0659] Subsequently, if an anomaly is detected, the server will notify the user. This notification will be provided in real time via a smartphone or PC application. The notification will include details of the abnormal condition and information on the nearest healthcare provider. This information will include location, contact details, and appointment availability, enabling a quick response.

[0660] As a concrete example, a user working from home uses the system every morning to perform a health check. One day, the system detects a slight fading of skin color compared to past data and issues a notification recommending rest. Another example of a prompt message to input into the generating AI model is an instruction such as, "Analyze the following health data and generate a notification message if an abnormality is detected." This allows the AI ​​model to output an easily understandable abnormality notification to the user.

[0661] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0662] Step 1:

[0663] The device captures the user's face using its built-in camera and acquires facial data. The input is the image captured by the camera, and the output is facial image data automatically captured at a specific time. This image data is stored in high resolution for analysis of facial features.

[0664] Step 2:

[0665] The device extracts important feature points from the acquired facial image data. The input is the facial image data obtained in step 1, and the output is feature point data that quantifies the color around the eyes, skin texture, facial contours, etc. This process uses OpenCV for image analysis, applying specific filters to clarify skin texture and contours.

[0666] Step 3:

[0667] The terminal receives the extracted feature point data as input, encrypts it, and sends the data to the server. The output is encrypted transmitted data. This transmission is encrypted using AES-256 and secure using the HTTPS protocol.

[0668] Step 4:

[0669] The server receives encrypted data sent from the terminal, decrypts it, and obtains feature point data. The input is the encrypted data sent from the terminal, and the output is the decrypted feature point data. The decrypted data is then formatted in preparation for analysis by an AI algorithm.

[0670] Step 5:

[0671] The server uses an AI-based analysis model to analyze feature point data as input. The output is an assessment of health status. This analysis uses a model built with TensorFlow to determine whether current health indicators exceed the reference range by comparing them with historical data.

[0672] Step 6:

[0673] The server notifies the user if it detects an abnormal condition as a result of its analysis. The input is the health status assessment result, and the output is notification information for the user. This notification includes the specific details of the abnormality, as well as information on the nearest healthcare provider. The notification is delivered via push notification through a dedicated application.

[0674] Step 7:

[0675] The user receives a notification and checks its contents through the application. The input is the notification information received from the server, and the output is the user's action, specifically contacting a healthcare provider or making an appointment. Based on the information provided, the user selects the appropriate healthcare provider and proceeds with the necessary actions.

[0676] (Application Example 1)

[0677] 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".

[0678] It is essential to establish a system that allows for real-time monitoring of workers' health within the workplace and enables managers to respond promptly in the event of an abnormality. Without such a health management system, there is a risk of decreased productivity and safety problems due to the deterioration of workers' health.

[0679] 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.

[0680] In this invention, the server includes means for collecting facial information of individual workers on a daily basis using a device for acquiring facial recognition data, means for using a data transmission device for transmitting data to an analysis device, and means for the analysis device to evaluate the worker's health status from the facial information and notify the manager when an abnormality is detected. This enables rapid detection of abnormalities in the health status of workers in the workplace, allowing for appropriate manager intervention and improvement of the work environment.

[0681] "Facial recognition data" refers to individual feature point information extracted from facial images, which is used to identify individuals and assess their health status.

[0682] "Worker" refers to all employees and staff who perform duties in a specific work environment.

[0683] "Daily data collection" refers to the process of acquiring data at a fixed time each day, and continuously accumulating that data.

[0684] An "analysis device" is a general term for computers and software used to process received facial information data and perform health status assessments and anomaly detection.

[0685] "An anomaly" refers to a specific change in the collected facial recognition data that deviates from a normal state of health.

[0686] "Notifying the administrator" means sending an alert or necessary information to the person with administrative privileges when an anomaly is detected.

[0687] "Administrator information terminals" refer to electronic devices and software used by administrators to manage the health of workers and respond to abnormal situations.

[0688] "Suggestions for improving the work environment" refers to specific advice on environmental adjustments and work process changes aimed at improving workers' health and work efficiency.

[0689] This invention relates to a system that aims to monitor the health status of workers in real time and notify managers if any abnormalities are detected. This system is realized by periodically acquiring and analyzing the facial information of workers using facial recognition technology.

[0690] The server collects workers' facial data at a fixed time each day using terminals equipped with smart cameras and facial recognition applications. These terminals photograph each worker's face and record it digitally as feature point data. The obtained facial data is encrypted and securely transmitted to the server via the internet.

[0691] The server utilizes AI analysis algorithms such as TensorFlow and OpenCV, based on Python, to process transmitted data in real time. This analysis evaluates major changes in health status by comparing it with historical data and detects anomalies. If an anomaly is detected, the server sends an alert to the administrator's information terminal. After receiving the notification, the administrator's information terminal provides a means to view the specific details of the anomaly and suggestions for improving the work environment.

[0692] For example, if a worker in a factory is identified as having a pale complexion, the system immediately notifies the manager. The manager can then use a notification terminal to have the worker take a break and arrange for medical services if necessary. This system contributes to improving workplace safety and maintaining work efficiency.

[0693] An example of an input prompt for a generative AI model might be: "Your workplace uses facial recognition technology for daily health checks. Please explain in detail how this technology works and how it ensures employee safety."

[0694] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0695] Step 1:

[0696] The terminal captures the worker's face with a smart camera. The input is the face image acquired by the camera, and the output is the facial information (feature point data) of a specific worker. Processing is performed to extract important feature points such as eye color and skin texture from the face image.

[0697] Step 2:

[0698] The terminal encrypts the extracted feature point data and sends it to the server using a secure communication path. The input is feature point data, and the output is encrypted data. In this step, an encryption algorithm is used to ensure the security of the communication.

[0699] Step 3:

[0700] The server receives encrypted data and decrypts it. The input is encrypted facial information data, and the output is feature point data in a format suitable for analysis. Decrypting the data makes it ready for the server to analyze.

[0701] Step 4:

[0702] The server uses an AI analysis algorithm to analyze incoming data and evaluate the worker's health status. The input is decoded feature point data, and the output is the health status evaluation result. The server uses TensorFlow and OpenCV to compare facial information with past data and detect anomalies.

[0703] Step 5:

[0704] If a server abnormality is detected, a notification is sent to the administrator's information terminal. The input is the health status evaluation result (presence or absence of abnormality), and the output is notification information for the administrator. When the server detects an abnormality in a worker, a function is executed to send an alert to the administrator that includes the specific details of the abnormality.

[0705] Step 6:

[0706] The administrator receives the notification and takes action. The input is an anomaly notification sent from the server, and the output is instructions for action regarding worker health management and work adjustments. The administrator can check the details of the anomaly presented via the information terminal and issue instructions for appropriate countermeasures.

[0707] 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.

[0708] This invention is a system for monitoring health and emotional states by combining facial recognition technology with an emotion engine. This system enables the evaluation of both the user's physical and psychological state, providing more comprehensive health management. The following describes specific embodiments for carrying out this invention.

[0709] First, the device captures the user's face with its camera and obtains facial information. This information is used to extract feature points necessary for evaluating health status, and includes things like eye opening and skin texture. Furthermore, this facial information is also used as data to analyze emotional state.

[0710] Next, the device sends this facial information to the server. The transmitted data is encrypted using a secure protocol, thus ensuring privacy protection.

[0711] On the server, an AI-based analysis system analyzes health characteristics from facial information, while an emotion engine detects emotional states. These emotional states include happiness, anger, sadness, surprise, and so on. This analysis assesses not only physical health but also whether the user is experiencing stress or psychological problems.

[0712] For example, if the server detects signs of stress from a user's facial information, it will suggest stress reduction measures based on an assessment of their health status. These measures may include suggesting listening to soothing music or taking breaks to relax.

[0713] When an anomaly is detected, the server sends a notification to the user. The notification includes the nature of the anomaly, recommended actions, and information on medical facilities. If a psychological disturbance is detected, information on counseling services and mental health resources will also be provided.

[0714] This system allows users to easily understand their own health and emotional state in their daily lives and take timely measures as needed. It is designed to provide an environment that monitors both physical and mental health and offers appropriate support, especially in today's stressful society.

[0715] The following describes the processing flow.

[0716] Step 1:

[0717] The device captures the user's face with its camera. It adjusts the focus to ensure the face is clearly visible and captures a clear facial image under appropriate lighting, including natural light.

[0718] Step 2:

[0719] The device compresses the facial image data it acquires and encrypts it as needed. This ensures data confidentiality while preparing it for transmission to the server.

[0720] Step 3:

[0721] The device sends the prepared facial image data to the server. The data is transferred using a high-speed and secure communication protocol.

[0722] Step 4:

[0723] The server analyzes the facial image data it receives. First, it uses an AI algorithm to extract feature points necessary for evaluating health status. It analyzes specific health indicators such as changes around the eyes and changes in skin tone.

[0724] Step 5:

[0725] The server uses an emotion engine to analyze the user's emotional state from facial information. It reads common emotional states such as happiness, anger, and surprise from facial expressions and aggregates that information.

[0726] Step 6:

[0727] The server detects anomalies based on the results of health and emotional status. If there is a significant change in health indicators, or if a persistent stress state is identified through emotional analysis, it is recorded as an anomaly.

[0728] Step 7:

[0729] If the server detects an anomaly, it will notify the user. The notification will include details of the anomaly, recommended actions, and information on relevant medical institutions and counseling services.

[0730] Step 8:

[0731] Users can review notifications and access the suggested medical facilities and mental health resources as needed. Based on the information provided, users can choose appropriate actions and use them to manage their own health.

[0732] (Example 2)

[0733] 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".

[0734] In modern society, it is becoming increasingly important for individuals to appropriately understand and manage their own physical and emotional states. However, traditional methods make it difficult to comprehensively monitor physical and psychological health, and there is a lack of adequate support, especially in situations where a rapid response to stress or emotional fluctuations is required. Therefore, there is a growing need for a system that accurately assesses health and emotional states in daily life and prompts appropriate action when problems arise.

[0735] 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.

[0736] In this invention, the server includes means for routinely collecting facial features using an imaging device that acquires individual facial information; means for encrypting and transmitting the acquired facial information to an analysis system using an information transmission device; and means for extracting features for evaluating an individual's health and emotional state from the transmitted facial information using an analysis system with an AI model, and detecting abnormalities by comparing them with the normal range. This makes it possible for individuals to comprehensively understand their own health and emotional state in their daily lives and to take appropriate measures immediately if there are any abnormalities.

[0737] An "imaging device" is a device used to acquire an individual's facial information and has the capability to collect high-resolution image data.

[0738] An "information transmission device" is a device that transmits acquired facial information to an analysis system and is equipped with a function to encrypt and protect the data.

[0739] The "analysis system" is a device that uses an AI model to analyze transmitted facial information and extract features for evaluating health and emotional states.

[0740] An "AI model" is an algorithm that uses machine learning techniques to analyze facial information and evaluate health and emotional states.

[0741] "Features" are data points extracted from facial information and are used as indicators to evaluate health status and emotional state.

[0742] The "normal range" is a baseline value used to determine whether a person's health and emotional state falls within a certain range based on the characteristics they have been assessed.

[0743] An "abnormal" result refers to a state where the evaluation of the transmitted facial information deviates from the normal range, indicating a potential problem in health or emotional state.

[0744] A "notification device" is a device that informs an individual of information when an abnormality is detected, and its role is to deliver information about the abnormality and suggestions for health management.

[0745] A "communication terminal" is a device accessible to an individual that can receive information such as the provision of mental health resources.

[0746] "Mental health resources" refer to information and resources such as education, support, and services provided to help individuals achieve their mental health.

[0747] This invention is a system for monitoring an individual's health and emotional state, utilizing facial recognition technology and an AI model. This system is implemented in the following manner:

[0748] First, the device uses a high-resolution camera to acquire personal facial information. The camera is equipped with an optical sensor to capture clear facial features, and the acquired image data is digitized and stored. This data acquisition is performed automatically and routinely, designed to reduce the burden on the user.

[0749] Next, the device transmits this facial information to the server using encryption technology. This process employs security protocols to ensure secure data transfer. For example, TLS (Transport Layer Security) is used to protect the data.

[0750] The server runs an AI model to analyze the received facial information. Specifically, it uses an open-source machine learning library (e.g., TensorFlow) to extract features from the image data to evaluate health and emotional states. This AI model calculates data from changes in facial expressions and facial features, and evaluates an individual's stress level and emotional fluctuations in real time.

[0751] Based on the analysis results, the server generates information to support the user's health management. For example, if stress is detected, it may suggest listening to music as a relaxation measure or provide information on nearby support facilities. Notifications are automatically sent to the user's communication device and can be quickly checked by the user via push notifications or email.

[0752] Examples of specific prompt messages include the following:

[0753] "Analyze the user's emotions and propose stress reduction measures."

[0754] "Please extract characteristic features of health status and assess psychological health status."

[0755] This system aims to contribute to improving physical and psychological health by capturing changes in an individual's health and emotions in their daily life and providing support as needed.

[0756] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0757] Step 1:

[0758] The device captures the user's face with a camera and acquires high-resolution image data. This input data contains fundamental information necessary to represent facial features, including facial contours, eye movements, and skin texture. In this acquisition process, an optical sensor captures the image and stores it as digital data.

[0759] Step 2:

[0760] The device encrypts the acquired facial image data and sends it to the server using a secure protocol. This input data is encrypted using protocols such as TLS for transmission. Privacy is protected because the encrypted data arrives securely at the server.

[0761] Step 3:

[0762] The server analyzes the received facial data using an AI model. This process utilizes machine learning algorithms to analyze the data, extracting features that evaluate the user's health and emotional state. Specifically, libraries such as TensorFlow are used to perform calculations for estimating health indicators and emotional changes.

[0763] Step 4:

[0764] The server generates insights for stress reduction and health improvement based on the analysis results. Specifically, it compares the obtained characteristics with historical data and, if anomalies or stress are detected, provides the user with suggestions for relaxation methods and information on mental health support. This output is generated by retrieving appropriate information from the server's resource database.

[0765] Step 5:

[0766] The server sends generated insights and notifications to the user's communication device. These notifications utilize push notifications and email functions, delivering personalized messages to the user. Based on this output, the user can check their health status and take necessary actions.

[0767] (Application Example 2)

[0768] 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".

[0769] In modern society, there is a need to comprehensively monitor an individual's health and emotional state and provide appropriate health management. However, conventional systems are limited to evaluating physical health, making it difficult to assess emotional states and provide customized services based on them. Furthermore, in retail settings, providing real-time services tailored to the customer's situation is challenging. To solve these problems, a comprehensive health and emotional monitoring system using facial information is necessary.

[0770] 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.

[0771] In this invention, the server includes means for collecting an individual's facial information on a daily basis using a device for acquiring facial recognition data; means for transmitting the acquired facial information to an analysis device using a data transmission device; and means for extracting feature points from the transmitted facial information to evaluate the individual's health and emotional state, and detecting abnormalities by comparing them with normal values ​​using the analysis device. This enables comprehensive management of an individual's health and emotional state, and allows for appropriate action suggestions and service customization when abnormalities are detected.

[0772] "Facial recognition data" refers to information extracted from an individual's face to identify that individual.

[0773] "Emotional state" refers to an individual's mental state and includes emotions such as happiness, anger, sadness, and surprise.

[0774] "Health status" refers to a standard used to assess an individual's physical or mental well-being, and is used to detect abnormalities by comparing them to normal values.

[0775] An "analysis device" refers to a device that analyzes acquired facial information, extracts characteristic points of health and emotional state, and evaluates them.

[0776] A "notification device" is a device that, when an abnormality is detected, informs the individual of the nature of the abnormality, recommended actions, and information about medical institutions.

[0777] An "information terminal" is a device accessible to an individual and used to provide information about medical institutions and recommended actions.

[0778] A "store" is a physical location for providing goods or services to customers.

[0779] "Service provision" refers to taking appropriate action based on the customer's emotional and health condition, using information obtained from analytical devices.

[0780] This system uses a terminal with a built-in camera to acquire facial recognition data. The terminal collects individual facial information daily and sends this information to a server for analysis. For security reasons, the data is transmitted using an encrypted protocol (e.g., HTTPS).

[0781] Upon receiving the collected facial information, the server uses an AI-based analysis system to evaluate the health and emotional state of the individual. Specifically, it extracts facial feature points and compares them to known normal values ​​to detect abnormalities. It also uses an emotion engine to identify the emotional state. The software used includes facial recognition libraries (e.g., OpenCV) and emotion analysis engines (e.g., Emotion API).

[0782] If an anomaly is detected, the server will provide the user with information about the anomaly, recommended actions, and medical facilities via a notification device. Users can access these notifications using their information terminals and decide on actions based on the information provided.

[0783] For example, within a store, staff can use smart glasses to understand customers' emotions and health status in real time and provide service accordingly. This functionality enables the provision of more appropriate services to customers, improving the in-store experience.

[0784] An example of a prompt to input into the generation AI model would be: "Generate an appropriate message to display to a store employee when a specific customer in the store is experiencing stress."

[0785] This system enables comprehensive management of an individual's health and emotional state, providing effective and adaptive services. As a result, it can improve the user experience and create a better health management environment.

[0786] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0787] Step 1:

[0788] The device captures an individual's face with its camera and collects facial recognition data. The input is real-time video data acquired through the camera, and the output is facial information including facial feature points. This information is processed using a facial recognition library (e.g., OpenCV).

[0789] Step 2:

[0790] The device transmits the acquired facial information to the server using a secure protocol (e.g., HTTPS). The input is the facial information collected by the device, and the output is encrypted facial information that arrives securely on the server.

[0791] Step 3:

[0792] The server analyzes the received facial information using an analysis device. The input is encrypted facial recognition data, and the output is an evaluation of health and emotional status. The data includes analysis of facial feature points, comparison with normal values, and identification of emotions by an emotion engine.

[0793] Step 4:

[0794] If an anomaly is detected based on the analysis results, the server sends a notification to the user via a notification device. The input is the evaluation results of health and emotional state, and the output is a notification message that includes the nature of the anomaly, recommended actions, and information on medical facilities.

[0795] Step 5:

[0796] Users receive notifications through their information terminals and decide on actions based on the information provided. The input is the message received from the notification device, and the output is the specific corrective action the user takes.

[0797] Step 6:

[0798] Within the store, a server uses an AI model to generate information for store staff to display regarding the customer's emotional and health status. The input is emotional and health data based on the customer's facial information, and the output is an appropriate customer service message for the staff to read. Prompts can be used to generate these customer service messages.

[0799] 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.

[0800] 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.

[0801] 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 robot 414.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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."

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] The following is further disclosed regarding the embodiments described above.

[0821] (Claim 1)

[0822] [A means of collecting individual facial information on a daily basis using a device that acquires facial recognition data,

[0823] [Means for transmitting acquired facial information to an analysis device via a data transmission device,

[0824] [The analysis device extracts feature points from transmitted facial information to evaluate an individual's health status, and detects abnormalities by comparing them with normal values.]

[0825] [A means of notifying the individual of the nature of the abnormality and medical institution information using a notification device when an abnormality is detected,

[0826] [Means of providing medical institution information via information terminals accessible to individuals,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] [The system according to claim 1, wherein the analysis device identifies a change pattern by comparing it with past facial information data and evaluates the degree of progression of the abnormality.

[0830] (Claim 3)

[0831] The system according to claim 1, wherein the notification device proposes the selection of the most suitable medical institution based on the nature of the abnormality.

[0832] "Example 1"

[0833] (Claim 1)

[0834] [Method for periodically collecting facial data of a person by a visual information acquisition unit,

[0835] [Means for transmitting collected facial data to a central processing unit using a communication unit,

[0836] [In the central processing unit, means for calculating health indicators from received facial data and detecting abnormal conditions by comparing them with reference values,

[0837] [Means for communicating details of the abnormal condition and information about the healthcare provider to the person via the notification department when an abnormal condition is confirmed,

[0838] [Means for making healthcare provider information accessible to individuals using information processing equipment available to them,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] [The system according to claim 1, in which a central processing unit compares previously acquired facial data with current data to clarify the trend of change and determines the progression of an abnormal state.

[0842] (Claim 3)

[0843] [The system according to claim 1, in which the notification department proposes the selection of the most suitable healthcare provider based on the content of the abnormal condition.

[0844] "Application Example 1"

[0845] (Claim 1)

[0846] [A means of collecting facial information of individual workers on a daily basis using a device that acquires facial recognition data,

[0847] [Means for transmitting acquired facial information to an analysis device via a data transmission device,

[0848] [An analysis device extracts feature points from transmitted facial information to evaluate the health status of the worker, and a means for detecting abnormalities by comparing them with normal values.]

[0849] [If an anomaly is detected, a means of notifying the administrator of the details of the anomaly using a notification device,

[0850] [Means of supporting worker health management via administrator information terminals,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] [The system according to claim 1, in which the analysis device identifies patterns of changes in health status by comparing them with past facial information data and evaluates the degree of progression of the abnormality.

[0854] (Claim 3)

[0855] [The system according to claim 1, wherein the notification device makes suggestions for improving the work environment based on the nature of the abnormality.

[0856] "Example 2 of combining an emotion engine"

[0857] (Claim 1)

[0858] [An imaging device that acquires personal facial information, a means of routinely collecting facial features,

[0859] [Means for transmitting acquired facial information to an analysis system in encrypted form using an information transmission device,

[0860] [An analysis system using an AI model extracts features from transmitted facial information to evaluate an individual's health and emotional state, and detects abnormalities by comparing them with the normal range.]

[0861] [Means for identifying emotional states and generating stress reduction measures,

[0862] [When an abnormality is detected, a means of informing the individual of the abnormality and health management information using a notification device,

[0863] [Means of providing mental health resources via communication terminals accessible to individuals,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] [The system according to claim 1, wherein the analysis system detects change patterns by comparing them with accumulated data of past facial information and evaluates the progression of the abnormality.

[0867] (Claim 3)

[0868] [The system according to claim 1, wherein the notification device presents the optimal selection of mental health support based on the analysis results.

[0869] "Application example 2 of combining emotional engines"

[0870] (Claim 1)

[0871] [A means of collecting individual facial information on a daily basis using a device that acquires facial recognition data,

[0872] [Means for transmitting acquired facial information to an analysis device via a data transmission device,

[0873] [The analysis device extracts feature points from transmitted facial information to evaluate an individual's health and emotional state, and detects abnormalities by comparing them with normal values.]

[0874] [When an abnormality is detected, a notification device is used to inform the individual of the nature of the abnormality, recommended actions, and information on relevant medical institutions.]

[0875] [Means of providing information on medical institutions and recommended actions via information terminals accessible to individuals,

[0876] [A means of displaying the emotional and health status of customers in real time in stores and providing store staff with information to provide optimal service,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, wherein the analysis device identifies change patterns by comparing them with past facial information data and evaluates the degree of progression of abnormalities and changes in emotional state.

[0880] (Claim 3)

[0881] [The system according to claim 1, wherein the notification device proposes the selection of the most suitable medical institution and a method of providing services based on the abnormal content and emotional state. [Explanation of Symbols]

[0882] 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 means of collecting individual facial information on a daily basis using a device that acquires facial recognition data, A means for transmitting acquired facial information to an analysis device via a data transmission device, The analysis device extracts feature points from transmitted facial information to evaluate an individual's health status, and detects abnormalities by comparing them with normal values. A means of informing an individual of the nature of the abnormality and medical institution information using a notification device when an abnormality is detected, A means of providing medical institution information via an information terminal accessible to an individual, A system that includes this.

2. The system according to claim 1, wherein the analysis device identifies a change pattern by comparing it with past facial information data and evaluates the degree of progression of the abnormality.

3. The system according to claim 1, wherein the notification device proposes the selection of the most suitable medical institution based on the nature of the abnormality.

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