System

Abstract

To provide a system for enabling an individual to effectively monitor and manage a health condition without taking time and effort.SOLUTION: Acquiring image data of an individual using a camera, acquiring voice data of the individual using a voice input unit, encrypting the image data and the voice data, and transmitting the image data and the voice data to a server, analyzing the image data in the server to detect a clue to a facial expression, a body temperature, and a physical condition of the individual, analyzing the voice data in the server to estimate an emotion and a health condition of the individual, and evaluating the health condition of the individual in real time based on a result of the analysis in the server; The system includes a means for generating an appropriate health management advice, and a means for transmitting the generated health management advice to the terminal of the individual and notifying it by voice or text.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, it is important for individuals to constantly monitor and appropriately manage their own health. However, conventional health management methods are time-consuming and costly, making it difficult to receive expert advice on a daily basis. Furthermore, many health management applications require users to manually enter data, which places a significant burden on users. For this reason, there is a need for a system that allows individuals to easily monitor their health status in their natural living environment and receive appropriate advice in real time. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for acquiring image data of an individual using a camera, a means for acquiring voice data of the individual using a voice input means, and a means for encrypting the image data and voice data and transmitting them to a server. It also includes a means for analyzing the image data in the server to detect clues to the individual's facial expression, body temperature, and physical condition, a means for analyzing the voice data to estimate emotions and health status, and a means for evaluating the individual's health status in real time based on the analysis results and generating appropriate health management advice. Furthermore, the system provides a means for transmitting the generated health management advice to the individual's device and notifying them by voice or text, thereby enabling individuals to effectively monitor and manage their health status without hassle.

[0006] A "camera" is a device that converts light into an electrical signal and is used to capture image data of an individual.

[0007] "Voice input means" refers to a device that converts sound waves into electrical signals and is used to obtain an individual's voice data.

[0008] "Encryption" is a technology that converts data so that it cannot be read illegally, and is used to protect the confidentiality of data.

[0009] A "server" is a computer system that processes, stores, and distributes data over a network.

[0010] "Analysis" is the technique or process of processing acquired data for a specific purpose to discover its meaning and characteristics.

[0011] "Facial expression" refers to emotions or states expressed through the movement of facial muscles, and is information extracted from image data.

[0012] "Body temperature" refers to an individual's body temperature and is one of the health indicators estimated from image data.

[0013] "Physical condition" indicates the overall health condition of an individual, and is estimated from image data and voice data.

[0014] "Emotions" refer to psychological states such as joy, anger, sadness, and happiness, and are estimated from voice data and facial expression data.

[0015] "Health status" refers to the overall physical health of an individual, and is assessed in real time based on image and audio data.

[0016] "Health Management Advice" is a specific suggestion of action or precautions based on an individual's health status.

[0017] A "terminal" is a device (e.g., a smartphone or smart device) that is directly operated by a user and receives advice from a server.

[0018] "Real-time" means that data is processed and advice is provided almost immediately, without delay.

[0019] "Notification" is the process of directly communicating important information or advice to users.

[0020] A "natural living environment" refers to a situation in which the user does not feel any special operations or burden in their daily life. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0022] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0023] First, the terms used in the following description will be explained.

[0024] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0025] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0026] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0027] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0028] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0032] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0033] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0034] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0035] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0036] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0039] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0040] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0041] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0042] This invention relates to a system that uses a camera and voice input means to monitor an individual's health condition and provide appropriate health management advice. The entire system consists of the following steps: data acquisition, data transmission, data analysis, health condition estimation, and customized advice generation and notification. This system allows users to manage their health condition naturally in their daily lives.

[0043] Program processing

[0044] 1. Obtaining user data

[0045] Device: The camera is activated during a specified time period to capture images of the user's face and entire body. At the same time, voice data is also acquired using voice input. For example, if you speak to the smart mirror in the morning, the camera will capture the user's face and the microphone will ask questions about your health.

[0046] User: Answers a question, for example, "I slept well last night, but my throat is a bit sore."

[0047] 2. Data transmission

[0048] Terminal: Encrypts captured image and audio data. This encryption ensures data confidentiality.

[0049] Device: Sends encrypted data to the server using a secure communication protocol.

[0050] 3. Data Analysis

[0051] Server: The received image data is input into a machine learning model, and facial expressions, body temperature, and physical condition are analyzed from images of the user's face and entire body. Specifically, image analysis is used to detect changes in the user's stress level and body temperature.

[0052] Server: The received voice data is input into a speech recognition model and converted into text. Natural language processing is then used to extract keywords from the voice data that indicate the health condition (e.g., sore throat, whether or not the patient slept well).

[0053] 4. Health status estimation

[0054] Server: Integrates the analysis results of image and audio data with past health data and the user's health goals to estimate the user's health condition in real time. For example, if a person has a high stress level and a sore throat, it is estimated that they have the symptoms of a cold.

[0055] 5. Generating customized advice

[0056] Server: Generates customized advice based on the estimated health status and health goals. For example, if you have a sore throat, it generates specific advice such as, "Drink warm drinks and plenty of fluids today. We also recommend consulting a doctor if necessary."

[0057] 6. Notice to Users

[0058] Terminal: The advice received from the server is notified to the user by voice, and at the same time, the advice is displayed in text format on the application interface, allowing the user to confirm and act on the advice provided.

[0059] Specific examples

[0060] Morning Routine

[0061] User: Wake up in the morning and stand in front of the smart mirror.

[0062] Device: The camera captures the user's face and asks via voice input, "Good morning. How are you feeling?"

[0063] User: "I slept well last night, but my throat is a bit sore."

[0064] Terminal: The facial image is encrypted along with the audio data and sent to the server.

[0065] Server: Analyzes data and detects stress levels from facial expressions and sore throats from voice.

[0066] Server: Performs a comprehensive health assessment and determines if you have symptoms of a cold.

[0067] Server: Generate the advice "We recommend drinking warm fluids and staying hydrated. Consult a doctor if necessary."

[0068] On your device: Advice is given via voice and displayed as text in the application.

[0069] User: Act on the advice and start your day.

[0070] In this way, the present invention allows users to naturally monitor their health status in their daily lives and receive appropriate health management advice in real time. This is a system that allows users to maintain and improve their health without any special effort.

[0071] The processing flow will be explained below.

[0072] Step 1:

[0073] Terminal: The system activates the camera device at a specified time to capture the user's facial and full-body image data. For example, a smart mirror automatically activates in the morning and activates the camera when the user stands in front of it.

[0074] Step 2:

[0075] Terminal: A voice input means asks the user, "How are you feeling?" The voice questions are preset and can be customized.

[0076] Step 3:

[0077] User: Responds to the voice question with a specific response such as "My throat is a little sore." This allows the system to obtain the user's subjective physical condition information.

[0078] Step 4:

[0079] Terminal: The user's answers are recorded as voice data and encrypted along with the facial image. This encryption process ensures the confidentiality of the data.

[0080] Step 5:

[0081] Terminal: Sends encrypted image and audio data to a server via a secure communication protocol (e.g., HTTPS).

[0082] Step 6:

[0083] Server: The received image data is input into a machine learning model to analyze the user's facial expression, body temperature, and physical condition. For example, an image analysis algorithm evaluates stress levels from facial expressions and estimates body temperature from skin color.

[0084] Step 7:

[0085] Server: The voice data is input into a speech recognition model and converted into text data. Then, a natural language processing (NLP) model is used to extract keywords related to the health condition. For example, the phrase "sore throat" is detected and recorded in a database.

[0086] Step 8:

[0087] Server: Integrates the results of image and audio analysis, and estimates real-time health status by referencing past health data and the user's health goals. Specifically, it predicts that high stress levels and a sore throat are signs of a cold.

[0088] Step 9:

[0089] Server: Generates customized health management advice based on the estimated health status, for example, "Today, we recommend drinking plenty of warm drinks and staying hydrated."

[0090] Step 10:

[0091] Server: Sends the generated advice to the user's terminal in the form of voice data and text data.

[0092] Step 11:

[0093] Terminal: Advice received from the server is communicated to the user via voice notification, and at the same time, the advice is displayed in text format on the application interface.

[0094] Step 12:

[0095] User: Check the advice and take appropriate action based on it, for example, drinking a warm drink.

[0096] Example 1

[0097] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0098] Health management is becoming increasingly important in modern times, and many people want to be able to monitor their health status in real time and receive appropriate advice. However, conventional health management systems require users to use special devices and applications, making them difficult to use in everyday life. In addition, the data collection and analysis required to evaluate health status is often cumbersome and burdensome for users. This makes it difficult for users to use the systems continuously, resulting in problems such as inadequate health management.

[0099] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0100] In this invention, the server includes a means for analyzing image data to detect clues about an individual's facial expression, body temperature, and physical condition, a means for analyzing voice data to extract keywords that indicate the individual's health condition, and a means for including a generative AI model used to estimate the health condition, thereby enabling users to automatically monitor their health condition in their natural daily lives and receive appropriate health management advice in real time.

[0101] "Image data" refers to image information of an individual's face or entire body captured using a camera.

[0102] "Voice data" refers to voice information relating to an individual's speech and physical condition acquired using a voice input means.

[0103] "Analysis" refers to processing received image data and audio data using machine learning models and voice recognition models to extract information about an individual's facial expression, body temperature, physical condition, and health status.

[0104] The "server" is a computer system that analyzes the received data, estimates the individual's health condition based on the analysis results, and generates appropriate health management advice.

[0105] "Keywords indicating health status" are important words and phrases related to an individual's health status (e.g., sore throat, whether they slept well) extracted through voice data analysis.

[0106] "Generative AI model" means an artificial intelligence model (e.g., GPT-3) used to estimate health status and generate health management advice.

[0107] A "terminal" is a device that is equipped with a camera and voice input means, acquires personal data, transmits the encrypted data to a server, and notifies the user of generated advice by voice or text.

[0108] "Health Care Advice" is a specific recommendation generated by the server to improve or maintain an individual's health status.

[0109] "Encryption" is the process of converting captured image and audio data in a secure manner to maintain the confidentiality of the data.

[0110] "Real-time" refers to the rapid processing of data, from data acquisition and analysis to health status estimation and the generation and notification of advice.

[0111] This invention is a system for managing health conditions naturally in daily life and providing appropriate health management advice. Next, we will explain how to specifically implement this system.

[0112] The system includes a terminal equipped with a camera and voice input means for capturing images of the user's face and body, as well as voice data. The terminal activates the camera at a specified time to capture the user's image data. At the same time, the voice input means is used to capture the user's voice data. For example, when the user stands in front of the smart mirror in the morning, the camera captures the user's face and the microphone asks, "Did you sleep well last night?"

[0113] The acquired image and audio data is encrypted by the device using AES encryption technology, and the encrypted data is sent to the server via a secure communication protocol (e.g., HTTPS).

[0114] The server first decodes the received data. Then, it uses a machine learning model (e.g., a model integrating OpenCV and TensorFlow) to analyze the image data and detect clues about the user's facial expression, body temperature, and physical condition. Similarly, the server inputs the voice data into a speech recognition model (e.g., Google Speech-to-Text API) to convert it into text, and then uses natural language processing (NLP) techniques to extract keywords that indicate the user's health condition.

[0115] The server integrates the results of the image and audio analysis, and combines them with past health data and the user's health goals to estimate the user's health condition in real time. For example, if the user has a high stress level and a sore throat, it estimates that the user is at risk of developing a cold.

[0116] Based on the estimated health status, the server uses a generative AI model (e.g., GPT-3) to generate customized advice, such as "It's a good idea to drink warm drinks and drink plenty of fluids today. We also recommend that you consult a doctor if necessary."

[0117] Finally, the device notifies the user of the advice received from the server. The notification is made audibly using a speech synthesis function (e.g., Google Text-to-Speech) and also displayed in text format in the application interface. This allows the user to confirm and act on the advice provided.

[0118] Specific examples

[0119] Morning Routine Example

[0120] User: Wake up in the morning and stand in front of the smart mirror.

[0121] Device: The camera captures the user's face and asks via voice input, "Good morning. How are you feeling?"

[0122] User: "I slept well last night, but my throat is a bit sore."

[0123] Terminal: The facial image is encrypted along with the audio data and sent to the server.

[0124] Server: Analyzes data and detects stress levels from facial expressions and sore throats from voice.

[0125] Server: Performs a comprehensive health assessment and determines if you have symptoms of a cold.

[0126] Server: Generate the advice "We recommend drinking warm fluids and staying hydrated. Consult a doctor if necessary."

[0127] On your device: Advice is given via voice and displayed as text in the application.

[0128] User: Act on the advice and start your day.

[0129] Prompt Sentence Examples

[0130] "When you wake up in the morning and stand in front of a smart mirror and it asks you how you're feeling, how would you respond? For example, tell me if you slept well last night, or if you have a sore throat?"

[0131] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0132] Step 1: Data Acquisition

[0133] The device activates the camera during a specified time period and captures the user's facial image and full-body image. At the same time, it acquires the user's voice data using a voice input means. Specifically, the device automatically activates the camera and microphone at 6:00 a.m. and asks the user, "Good morning. How are you feeling?" The input at this stage is the user's facial image, full-body image, and voice data, and the output is the captured data.

[0134] Step 2: Encrypt and send data

[0135] The device encrypts the captured image and audio data using AES encryption technology. The encrypted data is sent to the server using a secure communication protocol (e.g., HTTPS). Specifically, the security module inside the device encrypts the data with AES and sends it to the server via HTTPS. The input at this stage is the captured data, and the output is encrypted data.

[0136] Step 3: Decrypt and analyze the data

[0137] The server first decrypts the received encrypted data. Next, it uses a machine learning model (e.g., a model integrating OpenCV and TensorFlow) to analyze the image data and detect clues to the user's facial expression, body temperature, and physical condition. The server also inputs the voice data into a speech recognition model (e.g., Google Speech-to-Text API) to convert it into text, and uses natural language processing (NLP) techniques to extract keywords that indicate the user's health condition. For example, the keyword "sore throat" is extracted. The input at this stage is the encrypted data, and the output is the analyzed facial expression data, body temperature data, physical condition clues, and textual voice data.

[0138] Step 4: Estimate health status

[0139] The server integrates the results of the image and audio data analysis with past health data and the user's health goals to estimate the user's health status in real time. Statistical analysis and generative AI models (e.g., GPT-3) are used to perform a comprehensive health assessment. For example, if a person has a high stress level and a sore throat, it is estimated that they have symptoms of a cold. The input to this stage is the analyzed data and past health data, and the output is an estimated health status.

[0140] Step 5: Generate customized advice

[0141] The server generates customized advice based on the estimated health status and the user's health goals. Specific advice is created using a generative AI model (e.g., GPT-3). For example, it might generate advice such as, "Drink warm drinks and plenty of fluids today. We also recommend consulting a doctor if necessary." The input of this stage is the estimated health status and the user's health goals, and the output is the generated health management advice.

[0142] Step 6: Advice Notification

[0143] The device uses a speech synthesis function (e.g., Google Text-to-Speech) to audibly notify the advice received from the server, and also displays it in text format on the application interface. For example, the device may notify the user, "Drink a warm drink today," and the same text will be displayed in the app. The input at this stage is the generated advice, and the output is the audio notification and the displayed text advice.

[0144] In this way, the system can automatically monitor health status in the natural course of daily life and provide appropriate health management advice in real time.

[0145] (Application example 1)

[0146] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0147] In modern society, personal health management is becoming increasingly important. However, it is not easy to regularly monitor one's health status and follow appropriate health management advice in the midst of busy daily lives. In particular, when visiting a physical store, there are few opportunities to monitor one's health status, which can lead to neglecting health management. In contrast, there is no system that allows individuals to manage their health status in their natural living environment and receive prompt and appropriate health management advice. To address this issue, the present invention aims to provide a health status monitoring system using a head-mounted display in a physical store.

[0148] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0149] In this invention, the server includes a means for encrypting and transmitting an individual's image data and voice data to the server, a means for analyzing the image data in the server to detect clues about the individual's facial expression, body temperature, and physical condition, and a means for analyzing the voice data in the server to estimate the individual's emotions and health condition. This makes it possible to evaluate an individual's health condition in real time even in a physical store and provide appropriate health management advice.

[0150] A "camera" is an electronic device for capturing image data.

[0151] "Voice input means" is a device for acquiring voice data.

[0152] A "server" is a device that processes and stores data on a computer network.

[0153] "Encryption" is the process of transforming information to protect the data.

[0154] "Facial expression" refers to an individual's emotional state as indicated by facial muscle movements.

[0155] "Body temperature" is the internal body temperature of an individual.

[0156] "Physical condition" refers to an individual's state of health and bodily function.

[0157] Emotions are an individual's mental reactions and feelings.

[0158] "Health status" is the overall state of an individual's physical and mental health.

[0159] A "head-mounted display" is a display device that is worn on the head.

[0160] A "brick and mortar store" is a physical store that offers goods and services in person.

[0161] A "personal device" is an electronic device for personal use.

[0162] "Natural living environment" is the normal environment in which an individual lives their daily life.

[0163] The system embodying this invention uses a camera and voice input means to monitor an individual's health condition and provide appropriate health management advice. This allows users to check their health condition in their natural living environment and take appropriate measures. The system includes the following components:

[0164] 1. Hardware

[0165] Camera: Used to capture image data of the user's face and entire body.

[0166] Voice input means: A device including a microphone for acquiring voice data from the user.

[0167] Head-mounted display (HMD): A display device worn by users in physical stores to monitor their health.

[0168] Personal device: An electronic device used by a user, such as a smartphone or tablet, used to receive and notify advice.

[0169] 2. Software

[0170] Image processing software: Uses libraries such as OpenCV to analyze image data and detect clues about the user's facial expression, body temperature, and physical condition.

[0171] Speech Recognition Software: Uses the SpeechRecognition library to convert voice data into text and estimate emotions and health status.

[0172] Encryption software: Encrypts image and audio data using the Fernet library.

[0173] Data transmission software: Uses the Requests library to send encrypted data to the server.

[0174] Server-side analysis software: Analyzes the received data, estimates the user's health status, and generates appropriate health management advice.

[0175] As an example, the following describes specific steps for a user to wear a head-mounted display in a physical store and respond to voice input.

[0176] 1. Obtaining user data:

[0177] Device: The camera is activated during a specified time period to capture images of the user's face and entire body. At the same time, voice data from the user is also acquired using a voice input method. For example, a user stands in front of a smart mirror installed at a specific location in a physical store and asks "Good morning. How are you feeling?" through the HMD.

[0178] User: Answers the question. "I'm feeling fine, but my eyes are a little dry."

[0179] 2. Data transmission:

[0180] Terminal: Encrypts captured image and audio data. This encryption ensures data confidentiality.

[0181] Device: Sends encrypted data to the server using a secure communication protocol.

[0182] 3. Data Analysis:

[0183] Server: The received image data is input into a machine learning model, which analyzes facial expressions, body temperature, and physical condition from images of the user's face and entire body.

[0184] Server: The received voice data is input into a voice recognition model and converted into text. Natural language processing is then used to extract keywords from the voice data that indicate health conditions.

[0185] 4. Health status estimation:

[0186] Server: Integrates the analysis results of image and audio data with past health data and the user's health goals to estimate the user's health condition in real time.

[0187] 5. Generate customized advice:

[0188] Server: Generates customized advice linked to health goals based on estimated health status.

[0189] 6. Notice to Users:

[0190] Terminal: The advice received from the server is notified to the user by voice, and at the same time, the advice is displayed in text format on the application interface.

[0191] Specific examples

[0192] For example, here are some prompts for a user wearing a head-mounted display in a physical store and responding to voice input:

[0193] Example prompt sentence:

[0194] User: My throat is a little sore. I slept well last night, but I still feel tired this morning.

[0195] This example allows the user to check their health status without much effort and take appropriate measures if necessary.

[0196] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0197] Step 1:

[0198] The device activates the camera during the specified time period and captures images of the user's face and entire body.

[0199] Input: Camera device

[0200] Output: User's face and full body image data

[0201] An image is acquired using a camera, and the captured image data is temporarily stored in storage.

[0202] Step 2:

[0203] The terminal acquires voice data from the user using a voice input means, and at this time asks the user simple questions about their health condition.

[0204] Input: Microphone, user speech

[0205] Output: User's voice data

[0206] The voice data of the user answering the questions is captured via a microphone and saved as an audio file.

[0207] Step 3:

[0208] The device encrypts the captured image and audio data using the Fernet library.

[0209] Input: Image data, audio data

[0210] Output: Encrypted image and audio data

[0211] Generate a Fernet key, encrypt image and audio data, and output the encrypted data.

[0212] Step 4:

[0213] The terminal transmits the encrypted image data and audio data to the server using a secure communication protocol (e.g., HTTPS).

[0214] Input: Encrypted image and audio data

[0215] Output: Message that data was sent successfully to the server

[0216] Send data to the server using a secure communication protocol and confirm successful transmission.

[0217] Step 5:

[0218] The server inputs the received image data into a machine learning model to detect clues about facial expressions, body temperature, and physical condition from images of the user's face and entire body.

[0219] Input: Encrypted image data

[0220] Output: Analysis results of the user's facial expression, body temperature, and physical condition

[0221] Machine learning models are used to analyze image data and detect fluctuations in facial expressions and body temperature.

[0222] Step 6:

[0223] The server inputs the received voice data into a voice recognition model, converts it into text, and then uses natural language processing to extract keywords from the voice data that indicate health conditions.

[0224] Input: Encrypted audio data

[0225] Output: Text data and keywords that indicate health conditions

[0226] A speech recognition model is used to convert the voice data into text, and natural language processing tools are used to extract keywords that indicate health conditions.

[0227] Step 7:

[0228] The server integrates the analysis results of the image data and audio data with past health data and the user's health goals to estimate the user's health condition in real time.

[0229] Input: Image data analysis results, audio data analysis results, past health data, health goals

[0230] Output: Estimated health status of the user

[0231] The analysis results are combined with past data and an algorithm is used to estimate the user's current health status.

[0232] Step 8:

[0233] The server generates customized advice linked to health goals based on the estimated health state.

[0234] Input: Health status estimation results

[0235] Output: Customized health advice

[0236] Based on the estimation results, an AI model is used to generate personalized advice.

[0237] Step 9:

[0238] The terminal notifies the user of the advice received from the server by voice, and at the same time displays the advice in text format on the application interface.

[0239] Input: customized health advice

[0240] Output: Audio and text notification of advice

[0241] The text advice is converted into speech using a speech synthesis tool and notified to the user, and is also displayed in the application.

[0242] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0243] The present invention relates to a health management system that incorporates an emotion engine that recognizes the user's emotions. This system uses a camera and voice input means to acquire personal image and voice data, encrypting the data and sending it to a server. The server then analyzes the image and voice data to detect facial expressions, body temperature, and clues to the user's physical condition. Furthermore, the emotion engine recognizes the user's emotions from the data, evaluates the user's health condition in real time based on the results, and generates appropriate health management advice.

[0244] Program processing

[0245] 1. Data Acquisition

[0246] Terminal: The system activates the camera device during a designated time period to capture the user's facial and full-body image data. It also acquires the user's voice data using a voice input method. For example, when using a smart mirror, the camera is activated when the user stands in front of it, and a voice prompt asks, "How are you feeling?"

[0247] User: Responds to the voice prompt with "I'm a little tired, but I feel good."

[0248] 2. Data Transmission

[0249] Terminal: Captured image and audio data is encrypted. Encryption ensures data confidentiality.

[0250] Terminal: Sends encrypted data to the server using a secure communication protocol.

[0251] 3. Data Analysis

[0252] Server: The received image data is input into a machine learning model, and facial expression analysis is performed to estimate the user's emotions, body temperature, and physical condition. For example, the facial expression analysis algorithm evaluates emotional states such as anger or joy.

[0253] Server: The voice data is input into a speech recognition model and converted into text data. The converted text data is analyzed using a natural language processing (NLP) model to extract emotional keywords and health status-related information.

[0254] 4. Analysis by Emotion Engine

[0255] Server: The emotion engine responsively recognizes the user's emotional state from the image data and audio data. For example, the emotion engine determines whether the user is happy or stressed from facial expressions and tone of voice.

[0256] 5. Health status estimation

[0257] Server: Evaluates the user's health condition in real time based on the results of image and audio analysis, the output of the emotion engine, past health data, and the user's health goals. For example, if emotion analysis detects that the user is feeling stressed, it generates questions and advice to find the cause.

[0258] 6. Generating customized advice

[0259] Server: Generates personalized health management advice based on the assessed health status, such as "Take a walk to relax today" or "Make sure to drink plenty of water."

[0260] 7. Sending Advice and Notifications

[0261] Server: Sends the generated advice to the user's device in the form of voice data and text data.

[0262] Terminal: Advice received from the server is communicated to the user via voice notification and simultaneously displayed in text format on the application interface.

[0263] Specific examples

[0264] Daily Activities

[0265] User: Stands in front of the smart mirror after returning home from work in the evening.

[0266] Terminal: The camera captures the user's face, and the voice input means asks, "How was your day today?"

[0267] User: "Today was stressful. I have a headache."

[0268] Terminal: Encrypts voice and facial image data and sends it to the server.

[0269] Server: Analyzes the data and the emotion engine detects stress levels and fatigue levels.

[0270] Server: Based on the health assessment results and emotional state, generate advice such as "To reduce stress today, it would be a good idea to take a 30-minute walk and take some deep breaths."

[0271] Device: Advice is given via voice notification and also displayed as text in the application.

[0272] User: Follow the advice and take action to relax.

[0273] This invention is a system that allows users to carry out multifaceted health management, including emotion analysis, in their natural daily lives, and effectively maintain and improve their health.

[0274] The processing flow will be explained below.

[0275] Step 1:

[0276] Terminal: The system activates the camera device at a specified time to capture the user's facial and full-body image data. For example, a smart mirror automatically activates in the morning and activates the camera when the user stands in front of it.

[0277] Step 2:

[0278] Terminal: A voice input means asks the user, "How are you feeling?" The voice questions are preset and can be customized.

[0279] Step 3:

[0280] User: Responds to the voice question with a specific response such as "My throat is a little sore." This allows the system to obtain the user's subjective physical condition information.

[0281] Step 4:

[0282] Terminal: The user's answers are recorded as voice data and encrypted along with the facial image. This encryption process ensures the confidentiality of the data.

[0283] Step 5:

[0284] Terminal: Sends encrypted image and audio data to a server via a secure communication protocol (e.g., HTTPS).

[0285] Step 6:

[0286] Server: The received image data is input into a machine learning model to analyze the user's facial expression, body temperature, and physical condition. For example, an image analysis algorithm evaluates stress levels from facial expressions and estimates body temperature from skin color.

[0287] Step 7:

[0288] Server: The voice data is input into a speech recognition model and converted into text data. Then, a natural language processing (NLP) model is used to extract keywords from the voice data that indicate the health condition (e.g., sore throat, whether or not the patient slept well).

[0289] Step 8:

[0290] Server: Recognizes the user's emotions from image and audio data using an emotion engine. For example, the emotion engine determines whether the user is in a stressful state based on facial expressions and tone of voice.

[0291] Step 9:

[0292] Server: Evaluates the user's health status in real time based on the analysis results, the output of the emotion engine, past health data, and the user's health goals. For example, if the user has high stress and a sore throat, it may be inferred to be a sign of a cold.

[0293] Step 10:

[0294] Server: Generates customized health management advice based on the estimated health status, for example, "Today, we recommend drinking plenty of warm drinks and staying hydrated."

[0295] Step 11:

[0296] Server: Sends the generated advice to the user's terminal in the form of voice data and text data.

[0297] Step 12:

[0298] Terminal: Advice received from the server is communicated to the user via voice notification, and at the same time, the advice is displayed in text format on the application interface.

[0299] Step 13:

[0300] User: Check the advice and take appropriate action based on it, for example, drinking a warm drink.

[0301] In this way, the present invention provides a system that allows users to naturally monitor their own health status in their daily lives and receive appropriate health management advice in real time, allowing users to maintain and improve their health without any special effort.

[0302] Example 2

[0303] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0304] Conventional health management systems have difficulty accurately recognizing a user's emotional state and assessing their health based on that. Furthermore, they lack a means to provide personalized health management advice in real time, preventing users from effectively managing their health.

[0305] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0306] In this invention, the server includes means for analyzing the image data and voice data to detect clues to the individual's facial expression, body temperature, and physical condition, means for analyzing the voice data to estimate the individual's emotions and health condition, and means for recognizing the individual's emotional state using an emotion engine based on the analysis results of the image data and voice data. This makes it possible to accurately recognize the individual's emotional state, evaluate the individual's health condition in real time based on that, and provide appropriate health management advice.

[0307] A "camera" is a device for capturing image data of an individual.

[0308] "Voice input means" refers to a device for acquiring individual voice data.

[0309] "Image data" refers to visual information of an individual's face and entire body captured through a camera.

[0310] "Voice data" refers to information about an individual's voice acquired through a voice input means.

[0311] "Encryption" is a technique that transforms information using a specific algorithm to keep the data confidential.

[0312] The "server" is a computer system that analyzes image data and audio data to detect an individual's facial expression, body temperature, and physical condition.

[0313] "Analysis" is the process of examining data in detail and extracting information.

[0314] "Facial expression" is data that indicates the movement of an individual's face and the state of their eyes, mouth, eyebrows, etc.

[0315] "Body temperature" is data indicating the temperature of an individual's body.

[0316] "Physical condition" is data that indicates an index of an individual's health condition.

[0317] "Emotions" are data that indicate an individual's inner feelings and mental state.

[0318] "Health status" is data that indicates the overall state of an individual's physical and mental health.

[0319] An "emotion engine" is an algorithm or system for recognizing an individual's emotional state from image and audio data.

[0320] "Health Management Advice" is information that provides appropriate guidance or recommendations to an individual based on their assessed health status.

[0321] A "terminal" is a device that notifies health management advice sent from a server by voice or text.

[0322] "Real-time" means that data processing and results are instantaneous, without delay.

[0323] The present invention relates to a health management system that incorporates an emotion engine that recognizes the user's emotions. This system uses a camera and voice input means to acquire personal image and voice data, encrypting the data and sending it to a server. The server then analyzes the image and voice data to detect facial expressions, body temperature, and clues to the user's physical condition. Furthermore, the emotion engine recognizes the user's emotions from the data, evaluates the user's health condition in real time based on the results, and generates appropriate health management advice.

[0324] The hardware and software required for the system to operate are as follows. The hardware used includes a "camera" that captures an individual's visual information, a "voice input means" that acquires audio information, a "server" that processes data, and a "terminal" that notifies information. The software includes a "machine learning model" (e.g., OpenCV, TensorFlow) that analyzes image and audio data, a "speech recognition model" (e.g., Google Speech-to-Text API) that converts audio into text, an "NLP model" (e.g., BERT) for natural language processing (NLP), and an "emotion engine" (e.g., Emotion API) that estimates emotional states.

[0325] Below is a concrete example of how the system actually works.

[0326] Example: Daily Activities

[0327] User: Stands in front of the smart mirror after returning home from work in the evening.

[0328] Terminal: The camera captures the user's face, and the voice input means asks, "How was your day today?"

[0329] User: "Today was stressful. I have a headache."

[0330] Terminal: Audio and facial image data are encrypted with AES-256 and sent to the server via HTTPS.

[0331] Server: The data is analyzed in real time, and the emotion engine detects stress and fatigue levels. The analysis usually takes just a few seconds.

[0332] Server: Based on the health assessment results and emotional state, generate advice such as "To reduce stress today, it would be good to take a 30-minute walk and take some deep breaths." A generative AI model (e.g., GPT-3) is used to generate advice.

[0333] Device: Advice is given via voice notification and also displayed as text in the application.

[0334] User: Follow the advice and take action to relax.

[0335] This invention is a system that allows users to carry out multifaceted health management, including emotion analysis, in their natural daily lives, and effectively maintain and improve their health.

[0336] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0337] Step 1:

[0338] Data Acquisition

[0339] Terminal: The camera device is activated during a specified time period to capture the user's facial image and full-body image data. It also acquires the user's voice data using a voice input method. For example, if the terminal is a smart mirror, the camera will automatically activate when the user stands in front of it, and a voice prompt will ask, "How are you feeling?"

[0340] Input: Image data of the user's face and entire body captured by a camera device, and voice data of the user acquired by a voice input means.

[0341] Output: Captured image and audio data.

[0342] Step 2:

[0343] Data transmission

[0344] Terminal: The captured image and audio data is encrypted using an encryption algorithm such as AES-256 and sent to the server using a secure communication protocol such as HTTPS.

[0345] Input: Acquired image and audio data.

[0346] Output: The encrypted image data and audio data are sent to the server.

[0347] Step 3:

[0348] Data analysis

[0349] Server: The received image data is input into a machine learning model (e.g., OpenCV or TensorFlow) to estimate the user's emotions, body temperature, and physical condition. The voice data is input into a voice recognition model (e.g., Google Speech-to-Text API) and converted into text data. The converted text data is analyzed using an NLP model (e.g., BERT) to extract emotional keywords and health-related information.

[0350] Input: Encrypted image and audio data.

[0351] Output: Analyzed facial expressions, body temperature, physical condition data, and text data, along with emotion keywords based on them.

[0352] Step 4:

[0353] Analysis by emotion engine

[0354] Server: Uses an emotion engine (e.g., Emotion API) to recognize the user's emotional state from image and audio data. Analyzes facial expressions and tone of voice to identify emotional states such as happiness, stress, and depression.

[0355] Input: Analyzed facial expression data, body temperature data, physical condition data, and voice data.

[0356] Output: The perceived emotional state of the user.

[0357] Step 5:

[0358] Health status estimation

[0359] Server: Evaluates the user's health status in real time based on the analysis results of image and audio data, the output of the emotion engine, past health data, and the user's health goals. The evaluation is performed using statistical methods and machine learning models (e.g., random forest, SVM).

[0360] Inputs: Analysis results, emotion engine output, historical health data, and health goals.

[0361] Output: Real-time assessed health status of the user.

[0362] Step 6:

[0363] Generating customized advice

[0364] Server: Generates personalized health management advice based on the assessed health status. Using a generative AI model (e.g., GPT-3), it generates specific advice appropriate for the user.

[0365] Input: Assessed health status.

[0366] Output: Customized health care advice.

[0367] Step 7:

[0368] Advice submission and notification

[0369] Server: Sends the generated advice to the terminal in the form of voice data and text data.

[0370] Terminal: The received advice is notified to the user by voice notification and is also displayed in text format in the application interface.

[0371] Enter: customized health care advice.

[0372] Output: Audio notification and text health advice.

[0373] (Application example 2)

[0374] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0375] In modern society, personal health management is considered important, but many systems require regular self-diagnosis and responses based on the results, making it difficult to receive real-time, individually customized advice in everyday life. Furthermore, there is a lack of systems in physical stores that analyze customers' emotions and health status and recommend optimal products and services based on that information. This can result in insufficient health management benefits or missed service opportunities, so it is necessary to solve these issues.

[0376] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for proposing optimal products and services in a physical store based on emotion analysis results, means for analyzing image and audio data and evaluating the user's health status, and means for generating and notifying individually customized health management advice. This enables customers to receive individually customized health management advice in real time in their daily lives. Furthermore, by proposing optimal products and services to customers in a physical store, it is possible to maximize service provision opportunities and improve health management effectiveness.

[0377] An "emotion engine" is a software algorithm that analyzes image and audio data to recognize the user's emotional state.

[0378] A "physical store" is a commercial facility located in a physical location where customers visit in person to purchase products or receive services.

[0379] "Image data" means digital data of an image or photograph of an individual's face or body captured using a camera device.

[0380] "Voice data" refers to digital data of an individual's voice captured using a microphone.

[0381] "Analysis" is the process of analyzing acquired image and audio data using machine learning models and algorithms to extract information.

[0382] "Health status" refers to an individual's physical and mental health, including factors such as body temperature, physical condition, and emotional state.

[0383] "Customized health management advice" refers to recommendations for maintaining or improving health that are specifically provided based on an individual user's health data and analysis results.

[0384] "Product and service suggestions" refers to information used to select and provide optimal products and services to users based on their emotions and health status obtained in physical stores.

[0385] "Transmission" is the process of communicating data from a terminal or device to a server and transferring information from a server to a terminal or device.

[0386] "Text" refers to information expressed as character data in a format that can be visually confirmed by the user.

[0387] "Encryption" is a technology that converts data into a format that cannot be deciphered by a third party in order to maintain the confidentiality of the data.

[0388] The present invention relates to a health management system that incorporates an emotion engine that recognizes a user's emotions. This system uses a camera and voice input means to acquire personal image and voice data, encrypting the data and sending it to a server. The server analyzes the image and voice data to detect facial expressions, body temperature, and clues to physical condition. Furthermore, the emotion engine recognizes the user's emotions from the data, and based on the results, evaluates the user's health condition in real time and generates appropriate health management advice. The system can also suggest optimal products and services in physical stores based on the user's health condition and emotions.

[0389] Hardware and software used

[0390] Hardware:

[0391] Smart mirror or head-mounted display (HMD): built-in camera and microphone

[0392] Small devices such as Raspberry Pi: devices for capturing and transmitting images and audio

[0393] software:

[0394] OpenCV: Image data acquisition and processing library

[0395] PyAudio: A library for working with audio input devices

[0396] Requests: A library for sending HTTP requests

[0397] Encryption Library: Data encryption processing

[0398] Emotion engine: For example, machine learning models such as Emotion API, Watson, Azure, etc.

[0399] Server side: a processing system for performing data analysis and proposal generation

[0400] System processing overview

[0401] 1. Data Acquisition

[0402] The device (smart mirror or HMD) activates its camera device during a designated time period to capture the user's facial and full-body image data. It also acquires the user's voice data using a voice input means. For example, when using a smart mirror, the camera is activated when the user stands in front of it, and a voice prompt asks, "How are you feeling?"

[0403] 2. Data Transmission

[0404] The device encrypts the captured image and audio data, ensuring confidentiality, and then transmits the encrypted data to a server using a secure communications protocol.

[0405] 3. Data Analysis

[0406] The server inputs the received image data into a machine learning model and performs facial expression analysis to estimate the user's emotions, body temperature, and physical condition. It also inputs the voice data into a voice recognition model and converts it into text data. The converted text data is then analyzed using a natural language processing (NLP) model to extract emotional keywords and health-related information.

[0407] 4. Analysis by Emotion Engine

[0408] The server uses an emotion engine to recognize the user's emotional state from the image data and audio data, for example, the emotion engine determines whether the user is in a happy or stressed state from facial expressions and tone of voice.

[0409] 5. Health status estimation and proposal generation

[0410] The server evaluates the user's health condition in real time based on the results of image and audio data analysis, the output of the emotion engine, past health data, and the user's health goals. Based on the user's emotional and health status, the server then suggests optimal products and services in physical stores. For example, a specific recommendation such as "This herbal tea is perfect for relaxation" may be generated.

[0411] 6. Notification of Advice and Suggestions

[0412] The generated advice and suggestions are sent to the user's device in the form of voice data and text data, and the device notifies the user of the advice received from the server by voice notification and simultaneously displays it in text format on the application interface.

[0413] Specific examples

[0414] Proposals in physical stores

[0415] User: Visits a cafe and stands in front of a smart mirror.

[0416] Device: Captures face and voice data.

[0417] Terminal: Asks "Welcome, how are you feeling today?"

[0418] User: "I'm a little tired, but I feel okay."

[0419] Server: Analyzes data using an emotion engine to detect signs of stress.

[0420] Server: "Would you like a relaxing herbal tea?"

[0421] On your device: Suggestions are announced via voice and text.

[0422] An example of a prompt to be fed to the generative AI model is as follows:

[0423] "Develop a program that analyzes customers' emotions and health status and makes health-oriented suggestions."

[0424] This allows users to manage their health from multiple angles, including emotion analysis, in their natural daily lives, and effectively maintain and improve their health. It also enables brick-and-mortar stores to provide optimal service to customers and improve customer satisfaction.

[0425] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0426] Step 1:

[0427] The terminal (smart mirror or head-mounted display) activates its camera device at a specified time and captures the user's facial image and full-body image data. It also acquires the user's voice data using a voice input means. Specifically, when the user stands in front of the mirror, the camera automatically activates and a voice prompt asks, "How are you feeling?" The user responds, "I'm a little tired, but I feel good." This causes the camera to capture the user's facial image and the microphone to acquire the voice data. The input is the user's facial image and voice data, and the output is the captured image file and voice file.

[0428] Step 2:

[0429] The terminal encrypts the captured image and audio data. Encryption ensures the confidentiality of the data. The specific encryption method used is an encryption algorithm such as AES (Advanced Encryption Standard). The input is the captured image file and audio file, and the output is an encrypted data file.

[0430] Step 3:

[0431] The terminal sends the encrypted data to the server using a secure communication protocol (e.g., HTTPS). Specifically, the terminal sends the encrypted data to the server in the form of an HTTP request, and the server stores the received data for processing. The input is the encrypted data file, and the output is the status of completion of transmission to the server.

[0432] Step 4:

[0433] The server inputs the received image data into a machine learning model and performs facial expression analysis to estimate the user's emotions, body temperature, and physical condition. Specifically, it uses an image analysis algorithm to read emotions from facial expressions and, if necessary, analyzes data from the body temperature sensor. The input is encrypted image data, and the output is data related to the user's emotional state and body temperature.

[0434] Step 5:

[0435] The server inputs the received voice data into a voice recognition model and converts it into text data. The converted text data is analyzed using a natural language processing model to extract emotional keywords and health-related information. Specifically, the system converts voice data into text, and analyzes that text to extract information about emotional states and health. The input is encrypted voice data, and the output is analyzed text data and emotional information.

[0436] Step 6:

[0437] The server uses an emotion engine to recognize the user's emotional state from image and audio data. Specifically, the emotion engine analyzes the image and audio data and determines whether the user is happy or stressed from their facial expressions and tone of voice. The input is the analyzed image and audio data, and the output is data related to the user's emotional state.

[0438] Step 7:

[0439] The server evaluates the user's health condition in real time based on the results of image and audio data analysis, the output of the emotion engine, past health data, and the user's health goals. Furthermore, based on these results, it proposes optimal products and services for physical stores. Specifically, the server comprehensively evaluates the user's analysis results and generates product recommendations that promote relaxation. The inputs are the analysis results data, past health data, and health goals, and the output is the generated health management advice and suggestions for physical stores.

[0440] Step 8:

[0441] The generated advice and suggestions are sent from the server to the user's device. The device notifies the user of the received advice via voice notification and simultaneously displays it in text format on the application interface. Specifically, the server sends the generated suggestions as an HTTP response, which the device receives and notifies the user. The input is the generated advice and suggestions, and the output is voice and text notifications to the user.

[0442] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0443] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0444] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0445] [Second embodiment]

[0446] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0447] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0448] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0449] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0450] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0451] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0452] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0453] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0454] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0455] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0456] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0457] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0458] This invention relates to a system that uses a camera and voice input means to monitor an individual's health condition and provide appropriate health management advice. The entire system consists of the following steps: data acquisition, data transmission, data analysis, health condition estimation, and customized advice generation and notification. This system allows users to manage their health condition naturally in their daily lives.

[0459] Program processing

[0460] 1. Obtaining user data

[0461] Device: The camera is activated during a specified time period to capture images of the user's face and entire body. At the same time, voice data is also acquired using voice input. For example, if you speak to the smart mirror in the morning, the camera will capture the user's face and the microphone will ask questions about your health.

[0462] User: Answers a question, for example, "I slept well last night, but my throat is a bit sore."

[0463] 2. Data transmission

[0464] Terminal: Encrypts captured image and audio data. This encryption ensures data confidentiality.

[0465] Device: Sends encrypted data to the server using a secure communication protocol.

[0466] 3. Data Analysis

[0467] Server: The received image data is input into a machine learning model, and facial expressions, body temperature, and physical condition are analyzed from images of the user's face and entire body. Specifically, image analysis is used to detect changes in the user's stress level and body temperature.

[0468] Server: The received voice data is input into a speech recognition model and converted into text. Natural language processing is then used to extract keywords from the voice data that indicate the health condition (e.g., sore throat, whether or not the patient slept well).

[0469] 4. Health status estimation

[0470] Server: Integrates the analysis results of image and audio data with past health data and the user's health goals to estimate the user's health condition in real time. For example, if a person has a high stress level and a sore throat, it is estimated that they have the symptoms of a cold.

[0471] 5. Generating customized advice

[0472] Server: Generates customized advice based on the estimated health status and health goals. For example, if you have a sore throat, it generates specific advice such as, "Drink warm drinks and plenty of fluids today. We also recommend consulting a doctor if necessary."

[0473] 6. Notice to Users

[0474] Terminal: The advice received from the server is notified to the user by voice, and at the same time, the advice is displayed in text format on the application interface, allowing the user to confirm and act on the advice provided.

[0475] Specific examples

[0476] Morning Routine

[0477] User: Wake up in the morning and stand in front of the smart mirror.

[0478] Device: The camera captures the user's face and asks via voice input, "Good morning. How are you feeling?"

[0479] User: "I slept well last night, but my throat is a bit sore."

[0480] Terminal: The facial image is encrypted along with the audio data and sent to the server.

[0481] Server: Analyzes data and detects stress levels from facial expressions and sore throats from voice.

[0482] Server: Performs a comprehensive health assessment and determines if you have symptoms of a cold.

[0483] Server: Generate the advice "We recommend drinking warm fluids and staying hydrated. Consult a doctor if necessary."

[0484] On your device: Advice is given via voice and displayed as text in the application.

[0485] User: Act on the advice and start your day.

[0486] In this way, the present invention allows users to naturally monitor their health status in their daily lives and receive appropriate health management advice in real time. This is a system that allows users to maintain and improve their health without any special effort.

[0487] The processing flow will be explained below.

[0488] Step 1:

[0489] Terminal: The system activates the camera device at a specified time to capture the user's facial and full-body image data. For example, a smart mirror automatically activates in the morning and activates the camera when the user stands in front of it.

[0490] Step 2:

[0491] Terminal: A voice input means asks the user, "How are you feeling?" The voice questions are preset and can be customized.

[0492] Step 3:

[0493] User: Responds to the voice question with a specific response such as "My throat is a little sore." This allows the system to obtain the user's subjective physical condition information.

[0494] Step 4:

[0495] Terminal: The user's answers are recorded as voice data and encrypted along with the facial image. This encryption process ensures the confidentiality of the data.

[0496] Step 5:

[0497] Terminal: Sends encrypted image and audio data to a server via a secure communication protocol (e.g., HTTPS).

[0498] Step 6:

[0499] Server: The received image data is input into a machine learning model to analyze the user's facial expression, body temperature, and physical condition. For example, an image analysis algorithm evaluates stress levels from facial expressions and estimates body temperature from skin color.

[0500] Step 7:

[0501] Server: The voice data is input into a speech recognition model and converted into text data. Then, a natural language processing (NLP) model is used to extract keywords related to the health condition. For example, the phrase "sore throat" is detected and recorded in a database.

[0502] Step 8:

[0503] Server: Integrates the results of image and audio analysis, and estimates real-time health status by referencing past health data and the user's health goals. Specifically, it predicts that high stress levels and a sore throat are signs of a cold.

[0504] Step 9:

[0505] Server: Generates customized health management advice based on the estimated health status, for example, "Today, we recommend drinking plenty of warm drinks and staying hydrated."

[0506] Step 10:

[0507] Server: Sends the generated advice to the user's terminal in the form of voice data and text data.

[0508] Step 11:

[0509] Terminal: Advice received from the server is communicated to the user via voice notification, and at the same time, the advice is displayed in text format on the application interface.

[0510] Step 12:

[0511] User: Check the advice and take appropriate action based on it, for example, drinking a warm drink.

[0512] Example 1

[0513] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0514] Health management is becoming increasingly important in modern times, and many people want to be able to monitor their health status in real time and receive appropriate advice. However, conventional health management systems require users to use special devices and applications, making them difficult to use in everyday life. In addition, the data collection and analysis required to evaluate health status is often cumbersome and burdensome for users. This makes it difficult for users to use the systems continuously, resulting in problems such as inadequate health management.

[0515] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0516] In this invention, the server includes a means for analyzing image data to detect clues about an individual's facial expression, body temperature, and physical condition, a means for analyzing voice data to extract keywords that indicate the individual's health condition, and a means for including a generative AI model used to estimate the health condition, thereby enabling users to automatically monitor their health condition in their natural daily lives and receive appropriate health management advice in real time.

[0517] "Image data" refers to image information of an individual's face or entire body captured using a camera.

[0518] "Voice data" refers to voice information relating to an individual's speech and physical condition acquired using a voice input means.

[0519] "Analysis" refers to processing received image data and audio data using machine learning models and voice recognition models to extract information about an individual's facial expression, body temperature, physical condition, and health status.

[0520] The "server" is a computer system that analyzes the received data, estimates the individual's health condition based on the analysis results, and generates appropriate health management advice.

[0521] "Keywords indicating health status" are important words and phrases related to an individual's health status (e.g., sore throat, whether they slept well) extracted through voice data analysis.

[0522] "Generative AI model" means an artificial intelligence model (e.g., GPT-3) used to estimate health status and generate health management advice.

[0523] A "terminal" is a device that is equipped with a camera and voice input means, acquires personal data, transmits the encrypted data to a server, and notifies the user of generated advice by voice or text.

[0524] "Health Care Advice" is a specific recommendation generated by the server to improve or maintain an individual's health status.

[0525] "Encryption" is the process of converting captured image and audio data in a secure manner to maintain the confidentiality of the data.

[0526] "Real-time" refers to the rapid processing of data, from data acquisition and analysis to health status estimation and the generation and notification of advice.

[0527] This invention is a system for managing health conditions naturally in daily life and providing appropriate health management advice. Next, we will explain how to specifically implement this system.

[0528] The system includes a terminal equipped with a camera and voice input means for capturing images of the user's face and body, as well as voice data. The terminal activates the camera at a specified time to capture the user's image data. At the same time, the voice input means is used to capture the user's voice data. For example, when the user stands in front of the smart mirror in the morning, the camera captures the user's face and the microphone asks, "Did you sleep well last night?"

[0529] The acquired image and audio data is encrypted by the device using AES encryption technology, and the encrypted data is sent to the server via a secure communication protocol (e.g., HTTPS).

[0530] The server first decodes the received data. Then, it uses a machine learning model (e.g., a model integrating OpenCV and TensorFlow) to analyze the image data and detect clues about the user's facial expression, body temperature, and physical condition. Similarly, the server inputs the voice data into a speech recognition model (e.g., Google Speech-to-Text API) to convert it into text, and then uses natural language processing (NLP) techniques to extract keywords that indicate the user's health condition.

[0531] The server integrates the results of the image and audio analysis, and combines them with past health data and the user's health goals to estimate the user's health condition in real time. For example, if the user has a high stress level and a sore throat, it estimates that the user is at risk of developing a cold.

[0532] Based on the estimated health status, the server uses a generative AI model (e.g., GPT-3) to generate customized advice, such as "It's a good idea to drink warm drinks and drink plenty of fluids today. We also recommend that you consult a doctor if necessary."

[0533] Finally, the device notifies the user of the advice received from the server. The notification is made audibly using a speech synthesis function (e.g., Google Text-to-Speech) and also displayed in text format in the application interface. This allows the user to confirm and act on the advice provided.

[0534] Specific examples

[0535] Morning Routine Example

[0536] User: Wake up in the morning and stand in front of the smart mirror.

[0537] Device: The camera captures the user's face and asks via voice input, "Good morning. How are you feeling?"

[0538] User: "I slept well last night, but my throat is a bit sore."

[0539] Terminal: The facial image is encrypted along with the audio data and sent to the server.

[0540] Server: Analyzes data and detects stress levels from facial expressions and sore throats from voice.

[0541] Server: Performs a comprehensive health assessment and determines if you have symptoms of a cold.

[0542] Server: Generate the advice "We recommend drinking warm fluids and staying hydrated. Consult a doctor if necessary."

[0543] On your device: Advice is given via voice and displayed as text in the application.

[0544] User: Act on the advice and start your day.

[0545] Prompt Sentence Examples

[0546] "When you wake up in the morning and stand in front of a smart mirror and it asks you how you're feeling, how would you respond? For example, tell me if you slept well last night, or if you have a sore throat?"

[0547] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0548] Step 1: Data Acquisition

[0549] The device activates the camera during a specified time period and captures the user's facial image and full-body image. At the same time, it acquires the user's voice data using a voice input means. Specifically, the device automatically activates the camera and microphone at 6:00 a.m. and asks the user, "Good morning. How are you feeling?" The input at this stage is the user's facial image, full-body image, and voice data, and the output is the captured data.

[0550] Step 2: Encrypt and send data

[0551] The device encrypts the captured image and audio data using AES encryption technology. The encrypted data is sent to the server using a secure communication protocol (e.g., HTTPS). Specifically, the security module inside the device encrypts the data with AES and sends it to the server via HTTPS. The input at this stage is the captured data, and the output is encrypted data.

[0552] Step 3: Decrypt and analyze the data

[0553] The server first decrypts the received encrypted data. Next, it uses a machine learning model (e.g., a model integrating OpenCV and TensorFlow) to analyze the image data and detect clues to the user's facial expression, body temperature, and physical condition. The server also inputs the voice data into a speech recognition model (e.g., Google Speech-to-Text API) to convert it into text, and uses natural language processing (NLP) techniques to extract keywords that indicate the user's health condition. For example, the keyword "sore throat" is extracted. The input at this stage is the encrypted data, and the output is the analyzed facial expression data, body temperature data, physical condition clues, and textual voice data.

[0554] Step 4: Estimate health status

[0555] The server integrates the results of the image and audio data analysis with past health data and the user's health goals to estimate the user's health status in real time. Statistical analysis and generative AI models (e.g., GPT-3) are used to perform a comprehensive health assessment. For example, if a person has a high stress level and a sore throat, it is estimated that they have symptoms of a cold. The input to this stage is the analyzed data and past health data, and the output is an estimated health status.

[0556] Step 5: Generate customized advice

[0557] The server generates customized advice based on the estimated health status and the user's health goals. Specific advice is created using a generative AI model (e.g., GPT-3). For example, it might generate advice such as, "Drink warm drinks and plenty of fluids today. We also recommend consulting a doctor if necessary." The input of this stage is the estimated health status and the user's health goals, and the output is the generated health management advice.

[0558] Step 6: Advice Notification

[0559] The device uses a speech synthesis function (e.g., Google Text-to-Speech) to audibly notify the advice received from the server, and also displays it in text format on the application interface. For example, the device may notify the user, "Drink a warm drink today," and the same text will be displayed in the app. The input at this stage is the generated advice, and the output is the audio notification and the displayed text advice.

[0560] In this way, the system can automatically monitor health status in the natural course of daily life and provide appropriate health management advice in real time.

[0561] (Application example 1)

[0562] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0563] In modern society, personal health management is becoming increasingly important. However, it is not easy to regularly monitor one's health status and follow appropriate health management advice in the midst of busy daily lives. In particular, when visiting a physical store, there are few opportunities to monitor one's health status, which can lead to neglecting health management. In contrast, there is no system that allows individuals to manage their health status in their natural living environment and receive prompt and appropriate health management advice. To address this issue, the present invention aims to provide a health status monitoring system using a head-mounted display in a physical store.

[0564] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0565] In this invention, the server includes a means for encrypting and transmitting an individual's image data and voice data to the server, a means for analyzing the image data in the server to detect clues about the individual's facial expression, body temperature, and physical condition, and a means for analyzing the voice data in the server to estimate the individual's emotions and health condition. This makes it possible to evaluate an individual's health condition in real time even in a physical store and provide appropriate health management advice.

[0566] A "camera" is an electronic device for capturing image data.

[0567] "Voice input means" is a device for acquiring voice data.

[0568] A "server" is a device that processes and stores data on a computer network.

[0569] "Encryption" is the process of transforming information to protect the data.

[0570] "Facial expression" refers to an individual's emotional state as indicated by facial muscle movements.

[0571] "Body temperature" is the internal body temperature of an individual.

[0572] "Physical condition" refers to an individual's state of health and bodily function.

[0573] Emotions are an individual's mental reactions and feelings.

[0574] "Health status" is the overall state of an individual's physical and mental health.

[0575] A "head-mounted display" is a display device that is worn on the head.

[0576] A "brick and mortar store" is a physical store that offers goods and services in person.

[0577] A "personal device" is an electronic device for personal use.

[0578] "Natural living environment" is the normal environment in which an individual lives their daily life.

[0579] The system embodying this invention uses a camera and voice input means to monitor an individual's health condition and provide appropriate health management advice. This allows users to check their health condition in their natural living environment and take appropriate measures. The system includes the following components:

[0580] 1. Hardware

[0581] Camera: Used to capture image data of the user's face and entire body.

[0582] Voice input means: A device including a microphone for acquiring voice data from the user.

[0583] Head-mounted display (HMD): A display device worn by users in physical stores to monitor their health.

[0584] Personal device: An electronic device used by a user, such as a smartphone or tablet, used to receive and notify advice.

[0585] 2. Software

[0586] Image processing software: Uses libraries such as OpenCV to analyze image data and detect clues about the user's facial expression, body temperature, and physical condition.

[0587] Speech Recognition Software: Uses the SpeechRecognition library to convert voice data into text and estimate emotions and health status.

[0588] Encryption software: Encrypts image and audio data using the Fernet library.

[0589] Data transmission software: Uses the Requests library to send encrypted data to the server.

[0590] Server-side analysis software: Analyzes the received data, estimates the user's health status, and generates appropriate health management advice.

[0591] As an example, the following describes specific steps for a user to wear a head-mounted display in a physical store and respond to voice input.

[0592] 1. Obtaining user data:

[0593] Device: The camera is activated during a specified time period to capture images of the user's face and entire body. At the same time, voice data from the user is also acquired using a voice input method. For example, a user stands in front of a smart mirror installed at a specific location in a physical store and asks "Good morning. How are you feeling?" through the HMD.

[0594] User: Answers the question. "I'm feeling fine, but my eyes are a little dry."

[0595] 2. Data transmission:

[0596] Terminal: Encrypts captured image and audio data. This encryption ensures data confidentiality.

[0597] Device: Sends encrypted data to the server using a secure communication protocol.

[0598] 3. Data Analysis:

[0599] Server: The received image data is input into a machine learning model, which analyzes facial expressions, body temperature, and physical condition from images of the user's face and entire body.

[0600] Server: The received voice data is input into a voice recognition model and converted into text. Natural language processing is then used to extract keywords from the voice data that indicate health conditions.

[0601] 4. Health status estimation:

[0602] Server: Integrates the analysis results of image and audio data with past health data and the user's health goals to estimate the user's health condition in real time.

[0603] 5. Generate customized advice:

[0604] Server: Generates customized advice linked to health goals based on estimated health status.

[0605] 6. Notice to Users:

[0606] Terminal: The advice received from the server is notified to the user by voice, and at the same time, the advice is displayed in text format on the application interface.

[0607] Specific examples

[0608] For example, here are some prompts for a user wearing a head-mounted display in a physical store and responding to voice input:

[0609] Example prompt sentence:

[0610] User: My throat is a little sore. I slept well last night, but I still feel tired this morning.

[0611] This example allows the user to check their health status without much effort and take appropriate measures if necessary.

[0612] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0613] Step 1:

[0614] The device activates the camera during the specified time period and captures images of the user's face and entire body.

[0615] Input: Camera device

[0616] Output: User's face and full body image data

[0617] An image is acquired using a camera, and the captured image data is temporarily stored in storage.

[0618] Step 2:

[0619] The terminal acquires voice data from the user using a voice input means, and at this time asks the user simple questions about their health condition.

[0620] Input: Microphone, user speech

[0621] Output: User's voice data

[0622] The voice data of the user answering the questions is captured via a microphone and saved as an audio file.

[0623] Step 3:

[0624] The device encrypts the captured image and audio data using the Fernet library.

[0625] Input: Image data, audio data

[0626] Output: Encrypted image and audio data

[0627] Generate a Fernet key, encrypt image and audio data, and output the encrypted data.

[0628] Step 4:

[0629] The terminal transmits the encrypted image data and audio data to the server using a secure communication protocol (e.g., HTTPS).

[0630] Input: Encrypted image and audio data

[0631] Output: Message that data was sent successfully to the server

[0632] Send data to the server using a secure communication protocol and confirm successful transmission.

[0633] Step 5:

[0634] The server inputs the received image data into a machine learning model to detect clues about facial expressions, body temperature, and physical condition from images of the user's face and entire body.

[0635] Input: Encrypted image data

[0636] Output: Analysis results of the user's facial expression, body temperature, and physical condition

[0637] Machine learning models are used to analyze image data and detect fluctuations in facial expressions and body temperature.

[0638] Step 6:

[0639] The server inputs the received voice data into a voice recognition model, converts it into text, and then uses natural language processing to extract keywords from the voice data that indicate health conditions.

[0640] Input: Encrypted audio data

[0641] Output: Text data and keywords that indicate health conditions

[0642] A speech recognition model is used to convert the voice data into text, and natural language processing tools are used to extract keywords that indicate health conditions.

[0643] Step 7:

[0644] The server integrates the analysis results of the image data and audio data with past health data and the user's health goals to estimate the user's health condition in real time.

[0645] Input: Image data analysis results, audio data analysis results, past health data, health goals

[0646] Output: Estimated health status of the user

[0647] The analysis results are combined with past data and an algorithm is used to estimate the user's current health status.

[0648] Step 8:

[0649] The server generates customized advice linked to health goals based on the estimated health state.

[0650] Input: Health status estimation results

[0651] Output: Customized health advice

[0652] Based on the estimation results, an AI model is used to generate personalized advice.

[0653] Step 9:

[0654] The terminal notifies the user of the advice received from the server by voice, and at the same time displays the advice in text format on the application interface.

[0655] Input: customized health advice

[0656] Output: Audio and text notification of advice

[0657] The text advice is converted into speech using a speech synthesis tool and notified to the user, and is also displayed in the application.

[0658] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0659] The present invention relates to a health management system that incorporates an emotion engine that recognizes the user's emotions. This system uses a camera and voice input means to acquire personal image and voice data, encrypting the data and sending it to a server. The server then analyzes the image and voice data to detect facial expressions, body temperature, and clues to the user's physical condition. Furthermore, the emotion engine recognizes the user's emotions from the data, evaluates the user's health condition in real time based on the results, and generates appropriate health management advice.

[0660] Program processing

[0661] 1. Data Acquisition

[0662] Terminal: The system activates the camera device during a designated time period to capture the user's facial and full-body image data. It also acquires the user's voice data using a voice input method. For example, when using a smart mirror, the camera is activated when the user stands in front of it, and a voice prompt asks, "How are you feeling?"

[0663] User: Responds to the voice prompt with "I'm a little tired, but I feel good."

[0664] 2. Data Transmission

[0665] Terminal: Captured image and audio data is encrypted. Encryption ensures data confidentiality.

[0666] Terminal: Sends encrypted data to the server using a secure communication protocol.

[0667] 3. Data Analysis

[0668] Server: The received image data is input into a machine learning model, and facial expression analysis is performed to estimate the user's emotions, body temperature, and physical condition. For example, the facial expression analysis algorithm evaluates emotional states such as anger or joy.

[0669] Server: The voice data is input into a speech recognition model and converted into text data. The converted text data is analyzed using a natural language processing (NLP) model to extract emotional keywords and health status-related information.

[0670] 4. Analysis by Emotion Engine

[0671] Server: The emotion engine responsively recognizes the user's emotional state from the image data and audio data. For example, the emotion engine determines whether the user is happy or stressed from facial expressions and tone of voice.

[0672] 5. Health status estimation

[0673] Server: Evaluates the user's health condition in real time based on the results of image and audio analysis, the output of the emotion engine, past health data, and the user's health goals. For example, if emotion analysis detects that the user is feeling stressed, it generates questions and advice to find the cause.

[0674] 6. Generating customized advice

[0675] Server: Generates personalized health management advice based on the assessed health status, such as "Take a walk to relax today" or "Make sure to drink plenty of water."

[0676] 7. Sending Advice and Notifications

[0677] Server: Sends the generated advice to the user's device in the form of voice data and text data.

[0678] Terminal: Advice received from the server is communicated to the user via voice notification and simultaneously displayed in text format on the application interface.

[0679] Specific examples

[0680] Daily Activities

[0681] User: Stands in front of the smart mirror after returning home from work in the evening.

[0682] Terminal: The camera captures the user's face, and the voice input means asks, "How was your day today?"

[0683] User: "Today was stressful. I have a headache."

[0684] Terminal: Encrypts voice and facial image data and sends it to the server.

[0685] Server: Analyzes the data and the emotion engine detects stress levels and fatigue levels.

[0686] Server: Based on the health assessment results and emotional state, generate advice such as "To reduce stress today, it would be a good idea to take a 30-minute walk and take some deep breaths."

[0687] Device: Advice is given via voice notification and also displayed as text in the application.

[0688] User: Follow the advice and take action to relax.

[0689] This invention is a system that allows users to carry out multifaceted health management, including emotion analysis, in their natural daily lives, and effectively maintain and improve their health.

[0690] The processing flow will be explained below.

[0691] Step 1:

[0692] Terminal: The system activates the camera device at a specified time to capture the user's facial and full-body image data. For example, a smart mirror automatically activates in the morning and activates the camera when the user stands in front of it.

[0693] Step 2:

[0694] Terminal: A voice input means asks the user, "How are you feeling?" The voice questions are preset and can be customized.

[0695] Step 3:

[0696] User: Responds to the voice question with a specific response such as "My throat is a little sore." This allows the system to obtain the user's subjective physical condition information.

[0697] Step 4:

[0698] Terminal: The user's answers are recorded as voice data and encrypted along with the facial image. This encryption process ensures the confidentiality of the data.

[0699] Step 5:

[0700] Terminal: Sends encrypted image and audio data to a server via a secure communication protocol (e.g., HTTPS).

[0701] Step 6:

[0702] Server: The received image data is input into a machine learning model to analyze the user's facial expression, body temperature, and physical condition. For example, an image analysis algorithm evaluates stress levels from facial expressions and estimates body temperature from skin color.

[0703] Step 7:

[0704] Server: The voice data is input into a speech recognition model and converted into text data. Then, a natural language processing (NLP) model is used to extract keywords from the voice data that indicate the health condition (e.g., sore throat, whether or not the patient slept well).

[0705] Step 8:

[0706] Server: Recognizes the user's emotions from image and audio data using an emotion engine. For example, the emotion engine determines whether the user is in a stressful state based on facial expressions and tone of voice.

[0707] Step 9:

[0708] Server: Evaluates the user's health status in real time based on the analysis results, the output of the emotion engine, past health data, and the user's health goals. For example, if the user has high stress and a sore throat, it may be inferred to be a sign of a cold.

[0709] Step 10:

[0710] Server: Generates customized health management advice based on the estimated health status, for example, "Today, we recommend drinking plenty of warm drinks and staying hydrated."

[0711] Step 11:

[0712] Server: Sends the generated advice to the user's terminal in the form of voice data and text data.

[0713] Step 12:

[0714] Terminal: Advice received from the server is communicated to the user via voice notification, and at the same time, the advice is displayed in text format on the application interface.

[0715] Step 13:

[0716] User: Check the advice and take appropriate action based on it, for example, drinking a warm drink.

[0717] In this way, the present invention provides a system that allows users to naturally monitor their own health status in their daily lives and receive appropriate health management advice in real time, allowing users to maintain and improve their health without any special effort.

[0718] Example 2

[0719] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0720] Conventional health management systems have difficulty accurately recognizing a user's emotional state and assessing their health based on that. Furthermore, they lack a means to provide personalized health management advice in real time, preventing users from effectively managing their health.

[0721] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0722] In this invention, the server includes means for analyzing the image data and voice data to detect clues to the individual's facial expression, body temperature, and physical condition, means for analyzing the voice data to estimate the individual's emotions and health condition, and means for recognizing the individual's emotional state using an emotion engine based on the analysis results of the image data and voice data. This makes it possible to accurately recognize the individual's emotional state, evaluate the individual's health condition in real time based on that, and provide appropriate health management advice.

[0723] A "camera" is a device for capturing image data of an individual.

[0724] "Voice input means" refers to a device for acquiring individual voice data.

[0725] "Image data" refers to visual information of an individual's face and entire body captured through a camera.

[0726] "Voice data" refers to information about an individual's voice acquired through a voice input means.

[0727] "Encryption" is a technique that transforms information using a specific algorithm to keep the data confidential.

[0728] The "server" is a computer system that analyzes image data and audio data to detect an individual's facial expression, body temperature, and physical condition.

[0729] "Analysis" is the process of examining data in detail and extracting information.

[0730] "Facial expression" is data that indicates the movement of an individual's face and the state of their eyes, mouth, eyebrows, etc.

[0731] "Body temperature" is data indicating the temperature of an individual's body.

[0732] "Physical condition" is data that indicates an index of an individual's health condition.

[0733] "Emotions" are data that indicate an individual's inner feelings and mental state.

[0734] "Health status" is data that indicates the overall state of an individual's physical and mental health.

[0735] An "emotion engine" is an algorithm or system for recognizing an individual's emotional state from image and audio data.

[0736] "Health Management Advice" is information that provides appropriate guidance or recommendations to an individual based on their assessed health status.

[0737] A "terminal" is a device that notifies health management advice sent from a server by voice or text.

[0738] "Real-time" means that data processing and results are instantaneous, without delay.

[0739] The present invention relates to a health management system that incorporates an emotion engine that recognizes the user's emotions. This system uses a camera and voice input means to acquire personal image and voice data, encrypting the data and sending it to a server. The server then analyzes the image and voice data to detect facial expressions, body temperature, and clues to the user's physical condition. Furthermore, the emotion engine recognizes the user's emotions from the data, evaluates the user's health condition in real time based on the results, and generates appropriate health management advice.

[0740] The hardware and software required for the system to operate are as follows. The hardware used includes a "camera" that captures an individual's visual information, a "voice input means" that acquires audio information, a "server" that processes data, and a "terminal" that notifies information. The software includes a "machine learning model" (e.g., OpenCV, TensorFlow) that analyzes image and audio data, a "speech recognition model" (e.g., Google Speech-to-Text API) that converts audio into text, an "NLP model" (e.g., BERT) for natural language processing (NLP), and an "emotion engine" (e.g., Emotion API) that estimates emotional states.

[0741] Below is a concrete example of how the system actually works.

[0742] Example: Daily Activities

[0743] User: Stands in front of the smart mirror after returning home from work in the evening.

[0744] Terminal: The camera captures the user's face, and the voice input means asks, "How was your day today?"

[0745] User: "Today was stressful. I have a headache."

[0746] Terminal: Audio and facial image data are encrypted with AES-256 and sent to the server via HTTPS.

[0747] Server: The data is analyzed in real time, and the emotion engine detects stress and fatigue levels. The analysis usually takes just a few seconds.

[0748] Server: Based on the health assessment results and emotional state, generate advice such as "To reduce stress today, it would be good to take a 30-minute walk and take some deep breaths." A generative AI model (e.g., GPT-3) is used to generate advice.

[0749] Device: Advice is given via voice notification and also displayed as text in the application.

[0750] User: Follow the advice and take action to relax.

[0751] This invention is a system that allows users to carry out multifaceted health management, including emotion analysis, in their natural daily lives, and effectively maintain and improve their health.

[0752] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0753] Step 1:

[0754] Data Acquisition

[0755] Terminal: The camera device is activated during a specified time period to capture the user's facial image and full-body image data. It also acquires the user's voice data using a voice input method. For example, if the terminal is a smart mirror, the camera will automatically activate when the user stands in front of it, and a voice prompt will ask, "How are you feeling?"

[0756] Input: Image data of the user's face and entire body captured by a camera device, and voice data of the user acquired by a voice input means.

[0757] Output: Captured image and audio data.

[0758] Step 2:

[0759] Data transmission

[0760] Terminal: The captured image and audio data is encrypted using an encryption algorithm such as AES-256 and sent to the server using a secure communication protocol such as HTTPS.

[0761] Input: Acquired image and audio data.

[0762] Output: The encrypted image data and audio data are sent to the server.

[0763] Step 3:

[0764] Data analysis

[0765] Server: The received image data is input into a machine learning model (e.g., OpenCV or TensorFlow) to estimate the user's emotions, body temperature, and physical condition. The voice data is input into a voice recognition model (e.g., Google Speech-to-Text API) and converted into text data. The converted text data is analyzed using an NLP model (e.g., BERT) to extract emotional keywords and health-related information.

[0766] Input: Encrypted image and audio data.

[0767] Output: Analyzed facial expressions, body temperature, physical condition data, and text data, along with emotion keywords based on them.

[0768] Step 4:

[0769] Analysis by emotion engine

[0770] Server: Uses an emotion engine (e.g., Emotion API) to recognize the user's emotional state from image and audio data. Analyzes facial expressions and tone of voice to identify emotional states such as happiness, stress, and depression.

[0771] Input: Analyzed facial expression data, body temperature data, physical condition data, and voice data.

[0772] Output: The perceived emotional state of the user.

[0773] Step 5:

[0774] Health status estimation

[0775] Server: Evaluates the user's health status in real time based on the analysis results of image and audio data, the output of the emotion engine, past health data, and the user's health goals. The evaluation is performed using statistical methods and machine learning models (e.g., random forest, SVM).

[0776] Inputs: Analysis results, emotion engine output, historical health data, and health goals.

[0777] Output: Real-time assessed health status of the user.

[0778] Step 6:

[0779] Generating customized advice

[0780] Server: Generates personalized health management advice based on the assessed health status. Using a generative AI model (e.g., GPT-3), it generates specific advice appropriate for the user.

[0781] Input: Assessed health status.

[0782] Output: Customized health care advice.

[0783] Step 7:

[0784] Advice submission and notification

[0785] Server: Sends the generated advice to the terminal in the form of voice data and text data.

[0786] Terminal: The received advice is notified to the user by voice notification and is also displayed in text format in the application interface.

[0787] Enter: customized health care advice.

[0788] Output: Audio notification and text health advice.

[0789] (Application example 2)

[0790] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0791] In modern society, personal health management is considered important, but many systems require regular self-diagnosis and responses based on the results, making it difficult to receive real-time, individually customized advice in everyday life. Furthermore, there is a lack of systems in physical stores that analyze customers' emotions and health status and recommend optimal products and services based on that information. This can result in insufficient health management benefits or missed service opportunities, so it is necessary to solve these issues.

[0792] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for proposing optimal products and services in a physical store based on emotion analysis results, means for analyzing image and audio data and evaluating the user's health status, and means for generating and notifying individually customized health management advice. This enables customers to receive individually customized health management advice in real time in their daily lives. Furthermore, by proposing optimal products and services to customers in a physical store, it is possible to maximize service provision opportunities and improve health management effectiveness.

[0793] An "emotion engine" is a software algorithm that analyzes image and audio data to recognize the user's emotional state.

[0794] A "physical store" is a commercial facility located in a physical location where customers visit in person to purchase products or receive services.

[0795] "Image data" means digital data of an image or photograph of an individual's face or body captured using a camera device.

[0796] "Voice data" refers to digital data of an individual's voice captured using a microphone.

[0797] "Analysis" is the process of analyzing acquired image and audio data using machine learning models and algorithms to extract information.

[0798] "Health status" refers to an individual's physical and mental health, including factors such as body temperature, physical condition, and emotional state.

[0799] "Customized health management advice" refers to recommendations for maintaining or improving health that are specifically provided based on an individual user's health data and analysis results.

[0800] "Product and service suggestions" refers to information used to select and provide optimal products and services to users based on their emotions and health status obtained in physical stores.

[0801] "Transmission" is the process of communicating data from a terminal or device to a server and transferring information from a server to a terminal or device.

[0802] "Text" refers to information expressed as character data in a format that can be visually confirmed by the user.

[0803] "Encryption" is a technology that converts data into a format that cannot be deciphered by a third party in order to maintain the confidentiality of the data.

[0804] The present invention relates to a health management system that incorporates an emotion engine that recognizes a user's emotions. This system uses a camera and voice input means to acquire personal image and voice data, encrypting the data and sending it to a server. The server analyzes the image and voice data to detect facial expressions, body temperature, and clues to physical condition. Furthermore, the emotion engine recognizes the user's emotions from the data, and based on the results, evaluates the user's health condition in real time and generates appropriate health management advice. The system can also suggest optimal products and services in physical stores based on the user's health condition and emotions.

[0805] Hardware and software used

[0806] Hardware:

[0807] Smart mirror or head-mounted display (HMD): built-in camera and microphone

[0808] Small devices such as Raspberry Pi: devices for capturing and transmitting images and audio

[0809] software:

[0810] OpenCV: Image data acquisition and processing library

[0811] PyAudio: A library for working with audio input devices

[0812] Requests: A library for sending HTTP requests

[0813] Encryption Library: Data encryption processing

[0814] Emotion engine: For example, machine learning models such as Emotion API, Watson, Azure, etc.

[0815] Server side: a processing system for performing data analysis and proposal generation

[0816] System processing overview

[0817] 1. Data Acquisition

[0818] The device (smart mirror or HMD) activates its camera device during a designated time period to capture the user's facial and full-body image data. It also acquires the user's voice data using a voice input means. For example, when using a smart mirror, the camera is activated when the user stands in front of it, and a voice prompt asks, "How are you feeling?"

[0819] 2. Data Transmission

[0820] The device encrypts the captured image and audio data, ensuring confidentiality, and then transmits the encrypted data to a server using a secure communications protocol.

[0821] 3. Data Analysis

[0822] The server inputs the received image data into a machine learning model and performs facial expression analysis to estimate the user's emotions, body temperature, and physical condition. It also inputs the voice data into a voice recognition model and converts it into text data. The converted text data is then analyzed using a natural language processing (NLP) model to extract emotional keywords and health-related information.

[0823] 4. Analysis by Emotion Engine

[0824] The server uses an emotion engine to recognize the user's emotional state from the image data and audio data, for example, the emotion engine determines whether the user is in a happy or stressed state from facial expressions and tone of voice.

[0825] 5. Health status estimation and proposal generation

[0826] The server evaluates the user's health condition in real time based on the results of image and audio data analysis, the output of the emotion engine, past health data, and the user's health goals. Based on the user's emotional and health status, the server then suggests optimal products and services in physical stores. For example, a specific recommendation such as "This herbal tea is perfect for relaxation" may be generated.

[0827] 6. Notification of Advice and Suggestions

[0828] The generated advice and suggestions are sent to the user's device in the form of voice data and text data, and the device notifies the user of the advice received from the server by voice notification and simultaneously displays it in text format on the application interface.

[0829] Specific examples

[0830] Proposals in physical stores

[0831] User: Visits a cafe and stands in front of a smart mirror.

[0832] Device: Captures face and voice data.

[0833] Terminal: Asks "Welcome, how are you feeling today?"

[0834] User: "I'm a little tired, but I feel okay."

[0835] Server: Analyzes data using an emotion engine to detect signs of stress.

[0836] Server: "Would you like a relaxing herbal tea?"

[0837] On your device: Suggestions are announced via voice and text.

[0838] An example of a prompt to be fed to the generative AI model is as follows:

[0839] "Develop a program that analyzes customers' emotions and health status and makes health-oriented suggestions."

[0840] This allows users to manage their health from multiple angles, including emotion analysis, in their natural daily lives, and effectively maintain and improve their health. It also enables brick-and-mortar stores to provide optimal service to customers and improve customer satisfaction.

[0841] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0842] Step 1:

[0843] The terminal (smart mirror or head-mounted display) activates its camera device at a specified time and captures the user's facial image and full-body image data. It also acquires the user's voice data using a voice input means. Specifically, when the user stands in front of the mirror, the camera automatically activates and a voice prompt asks, "How are you feeling?" The user responds, "I'm a little tired, but I feel good." This causes the camera to capture the user's facial image and the microphone to acquire the voice data. The input is the user's facial image and voice data, and the output is the captured image file and voice file.

[0844] Step 2:

[0845] The terminal encrypts the captured image and audio data. Encryption ensures the confidentiality of the data. The specific encryption method used is an encryption algorithm such as AES (Advanced Encryption Standard). The input is the captured image file and audio file, and the output is an encrypted data file.

[0846] Step 3:

[0847] The terminal sends the encrypted data to the server using a secure communication protocol (e.g., HTTPS). Specifically, the terminal sends the encrypted data to the server in the form of an HTTP request, and the server stores the received data for processing. The input is the encrypted data file, and the output is the status of completion of transmission to the server.

[0848] Step 4:

[0849] The server inputs the received image data into a machine learning model and performs facial expression analysis to estimate the user's emotions, body temperature, and physical condition. Specifically, it uses an image analysis algorithm to read emotions from facial expressions and, if necessary, analyzes data from the body temperature sensor. The input is encrypted image data, and the output is data related to the user's emotional state and body temperature.

[0850] Step 5:

[0851] The server inputs the received voice data into a voice recognition model and converts it into text data. The converted text data is analyzed using a natural language processing model to extract emotional keywords and health-related information. Specifically, the system converts voice data into text, and analyzes that text to extract information about emotional states and health. The input is encrypted voice data, and the output is analyzed text data and emotional information.

[0852] Step 6:

[0853] The server uses an emotion engine to recognize the user's emotional state from image and audio data. Specifically, the emotion engine analyzes the image and audio data and determines whether the user is happy or stressed from their facial expressions and tone of voice. The input is the analyzed image and audio data, and the output is data related to the user's emotional state.

[0854] Step 7:

[0855] The server evaluates the user's health condition in real time based on the results of image and audio data analysis, the output of the emotion engine, past health data, and the user's health goals. Furthermore, based on these results, it proposes optimal products and services for physical stores. Specifically, the server comprehensively evaluates the user's analysis results and generates product recommendations that promote relaxation. The inputs are the analysis results data, past health data, and health goals, and the output is the generated health management advice and suggestions for physical stores.

[0856] Step 8:

[0857] The generated advice and suggestions are sent from the server to the user's device. The device notifies the user of the received advice via voice notification and simultaneously displays it in text format on the application interface. Specifically, the server sends the generated suggestions as an HTTP response, which the device receives and notifies the user. The input is the generated advice and suggestions, and the output is voice and text notifications to the user.

[0858] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0859] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0860] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0861] [Third embodiment]

[0862] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0863] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0864] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0865] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0866] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0867] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0868] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0869] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0870] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0871] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0872] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0873] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0874] This invention relates to a system that uses a camera and voice input means to monitor an individual's health condition and provide appropriate health management advice. The entire system consists of the following steps: data acquisition, data transmission, data analysis, health condition estimation, and customized advice generation and notification. This system allows users to manage their health condition naturally in their daily lives.

[0875] Program processing

[0876] 1. Obtaining user data

[0877] Device: The camera is activated during a specified time period to capture images of the user's face and entire body. At the same time, voice data is also acquired using voice input. For example, if you speak to the smart mirror in the morning, the camera will capture the user's face and the microphone will ask questions about your health.

[0878] User: Answers a question, for example, "I slept well last night, but my throat is a bit sore."

[0879] 2. Data transmission

[0880] Terminal: Encrypts captured image and audio data. This encryption ensures data confidentiality.

[0881] Device: Sends encrypted data to the server using a secure communication protocol.

[0882] 3. Data Analysis

[0883] Server: The received image data is input into a machine learning model, and facial expressions, body temperature, and physical condition are analyzed from images of the user's face and entire body. Specifically, image analysis is used to detect changes in the user's stress level and body temperature.

[0884] Server: The received voice data is input into a speech recognition model and converted into text. Natural language processing is then used to extract keywords from the voice data that indicate the health condition (e.g., sore throat, whether or not the patient slept well).

[0885] 4. Health status estimation

[0886] Server: Integrates the analysis results of image and audio data with past health data and the user's health goals to estimate the user's health condition in real time. For example, if a person has a high stress level and a sore throat, it is estimated that they have the symptoms of a cold.

[0887] 5. Generating customized advice

[0888] Server: Generates customized advice based on the estimated health status and health goals. For example, if you have a sore throat, it generates specific advice such as, "Drink warm drinks and plenty of fluids today. We also recommend consulting a doctor if necessary."

[0889] 6. Notice to Users

[0890] Terminal: The advice received from the server is notified to the user by voice, and at the same time, the advice is displayed in text format on the application interface, allowing the user to confirm and act on the advice provided.

[0891] Specific examples

[0892] Morning Routine

[0893] User: Wake up in the morning and stand in front of the smart mirror.

[0894] Device: The camera captures the user's face and asks via voice input, "Good morning. How are you feeling?"

[0895] User: "I slept well last night, but my throat is a bit sore."

[0896] Terminal: The facial image is encrypted along with the audio data and sent to the server.

[0897] Server: Analyzes data and detects stress levels from facial expressions and sore throats from voice.

[0898] Server: Performs a comprehensive health assessment and determines if you have symptoms of a cold.

[0899] Server: Generate the advice "We recommend drinking warm fluids and staying hydrated. Consult a doctor if necessary."

[0900] On your device: Advice is given via voice and displayed as text in the application.

[0901] User: Act on the advice and start your day.

[0902] In this way, the present invention allows users to naturally monitor their health status in their daily lives and receive appropriate health management advice in real time. This is a system that allows users to maintain and improve their health without any special effort.

[0903] The processing flow will be explained below.

[0904] Step 1:

[0905] Terminal: The system activates the camera device at a specified time to capture the user's facial and full-body image data. For example, a smart mirror automatically activates in the morning and activates the camera when the user stands in front of it.

[0906] Step 2:

[0907] Terminal: A voice input means asks the user, "How are you feeling?" The voice questions are preset and can be customized.

[0908] Step 3:

[0909] User: Responds to the voice question with a specific response such as "My throat is a little sore." This allows the system to obtain the user's subjective physical condition information.

[0910] Step 4:

[0911] Terminal: The user's answers are recorded as voice data and encrypted along with the facial image. This encryption process ensures the confidentiality of the data.

[0912] Step 5:

[0913] Terminal: Sends encrypted image and audio data to a server via a secure communication protocol (e.g., HTTPS).

[0914] Step 6:

[0915] Server: The received image data is input into a machine learning model to analyze the user's facial expression, body temperature, and physical condition. For example, an image analysis algorithm evaluates stress levels from facial expressions and estimates body temperature from skin color.

[0916] Step 7:

[0917] Server: The voice data is input into a speech recognition model and converted into text data. Then, a natural language processing (NLP) model is used to extract keywords related to the health condition. For example, the phrase "sore throat" is detected and recorded in a database.

[0918] Step 8:

[0919] Server: Integrates the results of image and audio analysis, and estimates real-time health status by referencing past health data and the user's health goals. Specifically, it predicts that high stress levels and a sore throat are signs of a cold.

[0920] Step 9:

[0921] Server: Generates customized health management advice based on the estimated health status, for example, "Today, we recommend drinking plenty of warm drinks and staying hydrated."

[0922] Step 10:

[0923] Server: Sends the generated advice to the user's terminal in the form of voice data and text data.

[0924] Step 11:

[0925] Terminal: Advice received from the server is communicated to the user via voice notification, and at the same time, the advice is displayed in text format on the application interface.

[0926] Step 12:

[0927] User: Check the advice and take appropriate action based on it, for example, drinking a warm drink.

[0928] Example 1

[0929] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0930] Health management is becoming increasingly important in modern times, and many people want to be able to monitor their health status in real time and receive appropriate advice. However, conventional health management systems require users to use special devices and applications, making them difficult to use in everyday life. In addition, the data collection and analysis required to evaluate health status is often cumbersome and burdensome for users. This makes it difficult for users to use the systems continuously, resulting in problems such as inadequate health management.

[0931] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0932] In this invention, the server includes a means for analyzing image data to detect clues about an individual's facial expression, body temperature, and physical condition, a means for analyzing voice data to extract keywords that indicate the individual's health condition, and a means for including a generative AI model used to estimate the health condition, thereby enabling users to automatically monitor their health condition in their natural daily lives and receive appropriate health management advice in real time.

[0933] "Image data" refers to image information of an individual's face or entire body captured using a camera.

[0934] "Voice data" refers to voice information relating to an individual's speech and physical condition acquired using a voice input means.

[0935] "Analysis" refers to processing received image data and audio data using machine learning models and voice recognition models to extract information about an individual's facial expression, body temperature, physical condition, and health status.

[0936] The "server" is a computer system that analyzes the received data, estimates the individual's health condition based on the analysis results, and generates appropriate health management advice.

[0937] "Keywords indicating health status" are important words and phrases related to an individual's health status (e.g., sore throat, whether they slept well) extracted through voice data analysis.

[0938] "Generative AI model" means an artificial intelligence model (e.g., GPT-3) used to estimate health status and generate health management advice.

[0939] A "terminal" is a device that is equipped with a camera and voice input means, acquires personal data, transmits the encrypted data to a server, and notifies the user of generated advice by voice or text.

[0940] "Health Care Advice" is a specific recommendation generated by the server to improve or maintain an individual's health status.

[0941] "Encryption" is the process of converting captured image and audio data in a secure manner to maintain the confidentiality of the data.

[0942] "Real-time" refers to the rapid processing of data, from data acquisition and analysis to health status estimation and the generation and notification of advice.

[0943] This invention is a system for managing health conditions naturally in daily life and providing appropriate health management advice. Next, we will explain how to specifically implement this system.

[0944] The system includes a terminal equipped with a camera and voice input means for capturing images of the user's face and body, as well as voice data. The terminal activates the camera at a specified time to capture the user's image data. At the same time, the voice input means is used to capture the user's voice data. For example, when the user stands in front of the smart mirror in the morning, the camera captures the user's face and the microphone asks, "Did you sleep well last night?"

[0945] The acquired image and audio data is encrypted by the device using AES encryption technology, and the encrypted data is sent to the server via a secure communication protocol (e.g., HTTPS).

[0946] The server first decodes the received data. Then, it uses a machine learning model (e.g., a model integrating OpenCV and TensorFlow) to analyze the image data and detect clues about the user's facial expression, body temperature, and physical condition. Similarly, the server inputs the voice data into a speech recognition model (e.g., Google Speech-to-Text API) to convert it into text, and then uses natural language processing (NLP) techniques to extract keywords that indicate the user's health condition.

[0947] The server integrates the results of the image and audio analysis, and combines them with past health data and the user's health goals to estimate the user's health condition in real time. For example, if the user has a high stress level and a sore throat, it estimates that the user is at risk of developing a cold.

[0948] Based on the estimated health status, the server uses a generative AI model (e.g., GPT-3) to generate customized advice, such as "It's a good idea to drink warm drinks and drink plenty of fluids today. We also recommend that you consult a doctor if necessary."

[0949] Finally, the device notifies the user of the advice received from the server. The notification is made audibly using a speech synthesis function (e.g., Google Text-to-Speech) and also displayed in text format in the application interface. This allows the user to confirm and act on the advice provided.

[0950] Specific examples

[0951] Morning Routine Example

[0952] User: Wake up in the morning and stand in front of the smart mirror.

[0953] Device: The camera captures the user's face and asks via voice input, "Good morning. How are you feeling?"

[0954] User: "I slept well last night, but my throat is a bit sore."

[0955] Terminal: The facial image is encrypted along with the audio data and sent to the server.

[0956] Server: Analyzes data and detects stress levels from facial expressions and sore throats from voice.

[0957] Server: Performs a comprehensive health assessment and determines if you have symptoms of a cold.

[0958] Server: Generate the advice "We recommend drinking warm fluids and staying hydrated. Consult a doctor if necessary."

[0959] On your device: Advice is given via voice and displayed as text in the application.

[0960] User: Act on the advice and start your day.

[0961] Prompt Sentence Examples

[0962] "When you wake up in the morning and stand in front of a smart mirror and it asks you how you're feeling, how would you respond? For example, tell me if you slept well last night, or if you have a sore throat?"

[0963] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0964] Step 1: Data Acquisition

[0965] The device activates the camera during a specified time period and captures the user's facial image and full-body image. At the same time, it acquires the user's voice data using a voice input means. Specifically, the device automatically activates the camera and microphone at 6:00 a.m. and asks the user, "Good morning. How are you feeling?" The input at this stage is the user's facial image, full-body image, and voice data, and the output is the captured data.

[0966] Step 2: Encrypt and send data

[0967] The device encrypts the captured image and audio data using AES encryption technology. The encrypted data is sent to the server using a secure communication protocol (e.g., HTTPS). Specifically, the security module inside the device encrypts the data with AES and sends it to the server via HTTPS. The input at this stage is the captured data, and the output is encrypted data.

[0968] Step 3: Decrypt and analyze the data

[0969] The server first decrypts the received encrypted data. Next, it uses a machine learning model (e.g., a model integrating OpenCV and TensorFlow) to analyze the image data and detect clues to the user's facial expression, body temperature, and physical condition. The server also inputs the voice data into a speech recognition model (e.g., Google Speech-to-Text API) to convert it into text, and uses natural language processing (NLP) techniques to extract keywords that indicate the user's health condition. For example, the keyword "sore throat" is extracted. The input at this stage is the encrypted data, and the output is the analyzed facial expression data, body temperature data, physical condition clues, and textual voice data.

[0970] Step 4: Estimate health status

[0971] The server integrates the results of the image and audio data analysis with past health data and the user's health goals to estimate the user's health status in real time. Statistical analysis and generative AI models (e.g., GPT-3) are used to perform a comprehensive health assessment. For example, if a person has a high stress level and a sore throat, it is estimated that they have symptoms of a cold. The input to this stage is the analyzed data and past health data, and the output is an estimated health status.

[0972] Step 5: Generate customized advice

[0973] The server generates customized advice based on the estimated health status and the user's health goals. Specific advice is created using a generative AI model (e.g., GPT-3). For example, it might generate advice such as, "Drink warm drinks and plenty of fluids today. We also recommend consulting a doctor if necessary." The input of this stage is the estimated health status and the user's health goals, and the output is the generated health management advice.

[0974] Step 6: Advice Notification

[0975] The device uses a speech synthesis function (e.g., Google Text-to-Speech) to audibly notify the advice received from the server, and also displays it in text format on the application interface. For example, the device may notify the user, "Drink a warm drink today," and the same text will be displayed in the app. The input at this stage is the generated advice, and the output is the audio notification and the displayed text advice.

[0976] In this way, the system can automatically monitor health status in the natural course of daily life and provide appropriate health management advice in real time.

[0977] (Application example 1)

[0978] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0979] In modern society, personal health management is becoming increasingly important. However, it is not easy to regularly monitor one's health status and follow appropriate health management advice in the midst of busy daily lives. In particular, when visiting a physical store, there are few opportunities to monitor one's health status, which can lead to neglecting health management. In contrast, there is no system that allows individuals to manage their health status in their natural living environment and receive prompt and appropriate health management advice. To address this issue, the present invention aims to provide a health status monitoring system using a head-mounted display in a physical store.

[0980] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0981] In this invention, the server includes a means for encrypting and transmitting an individual's image data and voice data to the server, a means for analyzing the image data in the server to detect clues about the individual's facial expression, body temperature, and physical condition, and a means for analyzing the voice data in the server to estimate the individual's emotions and health condition. This makes it possible to evaluate an individual's health condition in real time even in a physical store and provide appropriate health management advice.

[0982] A "camera" is an electronic device for capturing image data.

[0983] "Voice input means" is a device for acquiring voice data.

[0984] A "server" is a device that processes and stores data on a computer network.

[0985] "Encryption" is the process of transforming information to protect the data.

[0986] "Facial expression" refers to an individual's emotional state as indicated by facial muscle movements.

[0987] "Body temperature" is the internal body temperature of an individual.

[0988] "Physical condition" refers to an individual's state of health and bodily function.

[0989] Emotions are an individual's mental reactions and feelings.

[0990] "Health status" is the overall state of an individual's physical and mental health.

[0991] A "head-mounted display" is a display device that is worn on the head.

[0992] A "brick and mortar store" is a physical store that offers goods and services in person.

[0993] A "personal device" is an electronic device for personal use.

[0994] "Natural living environment" is the normal environment in which an individual lives their daily life.

[0995] The system embodying this invention uses a camera and voice input means to monitor an individual's health condition and provide appropriate health management advice. This allows users to check their health condition in their natural living environment and take appropriate measures. The system includes the following components:

[0996] 1. Hardware

[0997] Camera: Used to capture image data of the user's face and entire body.

[0998] Voice input means: A device including a microphone for acquiring voice data from the user.

[0999] Head-mounted display (HMD): A display device worn by users in physical stores to monitor their health.

[1000] Personal device: An electronic device used by a user, such as a smartphone or tablet, used to receive and notify advice.

[1001] 2. Software

[1002] Image processing software: Uses libraries such as OpenCV to analyze image data and detect clues about the user's facial expression, body temperature, and physical condition.

[1003] Speech Recognition Software: Uses the SpeechRecognition library to convert voice data into text and estimate emotions and health status.

[1004] Encryption software: Encrypts image and audio data using the Fernet library.

[1005] Data transmission software: Uses the Requests library to send encrypted data to the server.

[1006] Server-side analysis software: Analyzes the received data, estimates the user's health status, and generates appropriate health management advice.

[1007] As an example, the following describes specific steps for a user to wear a head-mounted display in a physical store and respond to voice input.

[1008] 1. Obtaining user data:

[1009] Device: The camera is activated during a specified time period to capture images of the user's face and entire body. At the same time, voice data from the user is also acquired using a voice input method. For example, a user stands in front of a smart mirror installed at a specific location in a physical store and asks "Good morning. How are you feeling?" through the HMD.

[1010] User: Answers the question. "I'm feeling fine, but my eyes are a little dry."

[1011] 2. Data transmission:

[1012] Terminal: Encrypts captured image and audio data. This encryption ensures data confidentiality.

[1013] Device: Sends encrypted data to the server using a secure communication protocol.

[1014] 3. Data Analysis:

[1015] Server: The received image data is input into a machine learning model, which analyzes facial expressions, body temperature, and physical condition from images of the user's face and entire body.

[1016] Server: The received voice data is input into a voice recognition model and converted into text. Natural language processing is then used to extract keywords from the voice data that indicate health conditions.

[1017] 4. Health status estimation:

[1018] Server: Integrates the analysis results of image and audio data with past health data and the user's health goals to estimate the user's health condition in real time.

[1019] 5. Generate customized advice:

[1020] Server: Generates customized advice linked to health goals based on estimated health status.

[1021] 6. Notice to Users:

[1022] Terminal: The advice received from the server is notified to the user by voice, and at the same time, the advice is displayed in text format on the application interface.

[1023] Specific examples

[1024] For example, here are some prompts for a user wearing a head-mounted display in a physical store and responding to voice input:

[1025] Example prompt sentence:

[1026] User: My throat is a little sore. I slept well last night, but I still feel tired this morning.

[1027] This example allows the user to check their health status without much effort and take appropriate measures if necessary.

[1028] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1029] Step 1:

[1030] The device activates the camera during the specified time period and captures images of the user's face and entire body.

[1031] Input: Camera device

[1032] Output: User's face and full body image data

[1033] An image is acquired using a camera, and the captured image data is temporarily stored in storage.

[1034] Step 2:

[1035] The terminal acquires voice data from the user using a voice input means, and at this time asks the user simple questions about their health condition.

[1036] Input: Microphone, user speech

[1037] Output: User's voice data

[1038] The voice data of the user answering the questions is captured via a microphone and saved as an audio file.

[1039] Step 3:

[1040] The device encrypts the captured image and audio data using the Fernet library.

[1041] Input: Image data, audio data

[1042] Output: Encrypted image and audio data

[1043] Generate a Fernet key, encrypt image and audio data, and output the encrypted data.

[1044] Step 4:

[1045] The terminal transmits the encrypted image data and audio data to the server using a secure communication protocol (e.g., HTTPS).

[1046] Input: Encrypted image and audio data

[1047] Output: Message that data was sent successfully to the server

[1048] Send data to the server using a secure communication protocol and confirm successful transmission.

[1049] Step 5:

[1050] The server inputs the received image data into a machine learning model to detect clues about facial expressions, body temperature, and physical condition from images of the user's face and entire body.

[1051] Input: Encrypted image data

[1052] Output: Analysis results of the user's facial expression, body temperature, and physical condition

[1053] Machine learning models are used to analyze image data and detect fluctuations in facial expressions and body temperature.

[1054] Step 6:

[1055] The server inputs the received voice data into a voice recognition model, converts it into text, and then uses natural language processing to extract keywords from the voice data that indicate health conditions.

[1056] Input: Encrypted audio data

[1057] Output: Text data and keywords that indicate health conditions

[1058] A speech recognition model is used to convert the voice data into text, and natural language processing tools are used to extract keywords that indicate health conditions.

[1059] Step 7:

[1060] The server integrates the analysis results of the image data and audio data with past health data and the user's health goals to estimate the user's health condition in real time.

[1061] Input: Image data analysis results, audio data analysis results, past health data, health goals

[1062] Output: Estimated health status of the user

[1063] The analysis results are combined with past data and an algorithm is used to estimate the user's current health status.

[1064] Step 8:

[1065] The server generates customized advice linked to health goals based on the estimated health state.

[1066] Input: Health status estimation results

[1067] Output: Customized health advice

[1068] Based on the estimation results, an AI model is used to generate personalized advice.

[1069] Step 9:

[1070] The terminal notifies the user of the advice received from the server by voice, and at the same time displays the advice in text format on the application interface.

[1071] Input: customized health advice

[1072] Output: Audio and text notification of advice

[1073] The text advice is converted into speech using a speech synthesis tool and notified to the user, and is also displayed in the application.

[1074] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1075] The present invention relates to a health management system that incorporates an emotion engine that recognizes the user's emotions. This system uses a camera and voice input means to acquire personal image and voice data, encrypting the data and sending it to a server. The server then analyzes the image and voice data to detect facial expressions, body temperature, and clues to the user's physical condition. Furthermore, the emotion engine recognizes the user's emotions from the data, evaluates the user's health condition in real time based on the results, and generates appropriate health management advice.

[1076] Program processing

[1077] 1. Data Acquisition

[1078] Terminal: The system activates the camera device during a designated time period to capture the user's facial and full-body image data. It also acquires the user's voice data using a voice input method. For example, when using a smart mirror, the camera is activated when the user stands in front of it, and a voice prompt asks, "How are you feeling?"

[1079] User: Responds to the voice prompt with "I'm a little tired, but I feel good."

[1080] 2. Data Transmission

[1081] Terminal: Captured image and audio data is encrypted. Encryption ensures data confidentiality.

[1082] Terminal: Sends encrypted data to the server using a secure communication protocol.

[1083] 3. Data Analysis

[1084] Server: The received image data is input into a machine learning model, and facial expression analysis is performed to estimate the user's emotions, body temperature, and physical condition. For example, the facial expression analysis algorithm evaluates emotional states such as anger or joy.

[1085] Server: The voice data is input into a speech recognition model and converted into text data. The converted text data is analyzed using a natural language processing (NLP) model to extract emotional keywords and health status-related information.

[1086] 4. Analysis by Emotion Engine

[1087] Server: The emotion engine responsively recognizes the user's emotional state from the image data and audio data. For example, the emotion engine determines whether the user is happy or stressed from facial expressions and tone of voice.

[1088] 5. Health status estimation

[1089] Server: Evaluates the user's health condition in real time based on the results of image and audio analysis, the output of the emotion engine, past health data, and the user's health goals. For example, if emotion analysis detects that the user is feeling stressed, it generates questions and advice to find the cause.

[1090] 6. Generating customized advice

[1091] Server: Generates personalized health management advice based on the assessed health status, such as "Take a walk to relax today" or "Make sure to drink plenty of water."

[1092] 7. Sending Advice and Notifications

[1093] Server: Sends the generated advice to the user's device in the form of voice data and text data.

[1094] Terminal: Advice received from the server is communicated to the user via voice notification and simultaneously displayed in text format on the application interface.

[1095] Specific examples

[1096] Daily Activities

[1097] User: Stands in front of the smart mirror after returning home from work in the evening.

[1098] Terminal: The camera captures the user's face, and the voice input means asks, "How was your day today?"

[1099] User: "Today was stressful. I have a headache."

[1100] Terminal: Encrypts voice and facial image data and sends it to the server.

[1101] Server: Analyzes the data and the emotion engine detects stress levels and fatigue levels.

[1102] Server: Based on the health assessment results and emotional state, generate advice such as "To reduce stress today, it would be a good idea to take a 30-minute walk and take some deep breaths."

[1103] Device: Advice is given via voice notification and also displayed as text in the application.

[1104] User: Follow the advice and take action to relax.

[1105] This invention is a system that allows users to carry out multifaceted health management, including emotion analysis, in their natural daily lives, and effectively maintain and improve their health.

[1106] The processing flow will be explained below.

[1107] Step 1:

[1108] Terminal: The system activates the camera device at a specified time to capture the user's facial and full-body image data. For example, a smart mirror automatically activates in the morning and activates the camera when the user stands in front of it.

[1109] Step 2:

[1110] Terminal: A voice input means asks the user, "How are you feeling?" The voice questions are preset and can be customized.

[1111] Step 3:

[1112] User: Responds to the voice question with a specific response such as "My throat is a little sore." This allows the system to obtain the user's subjective physical condition information.

[1113] Step 4:

[1114] Terminal: The user's answers are recorded as voice data and encrypted along with the facial image. This encryption process ensures the confidentiality of the data.

[1115] Step 5:

[1116] Terminal: Sends encrypted image and audio data to a server via a secure communication protocol (e.g., HTTPS).

[1117] Step 6:

[1118] Server: The received image data is input into a machine learning model to analyze the user's facial expression, body temperature, and physical condition. For example, an image analysis algorithm evaluates stress levels from facial expressions and estimates body temperature from skin color.

[1119] Step 7:

[1120] Server: The voice data is input into a speech recognition model and converted into text data. Then, a natural language processing (NLP) model is used to extract keywords from the voice data that indicate the health condition (e.g., sore throat, whether or not the patient slept well).

[1121] Step 8:

[1122] Server: Recognizes the user's emotions from image and audio data using an emotion engine. For example, the emotion engine determines whether the user is in a stressful state based on facial expressions and tone of voice.

[1123] Step 9:

[1124] Server: Evaluates the user's health status in real time based on the analysis results, the output of the emotion engine, past health data, and the user's health goals. For example, if the user has high stress and a sore throat, it may be inferred to be a sign of a cold.

[1125] Step 10:

[1126] Server: Generates customized health management advice based on the estimated health status, for example, "Today, we recommend drinking plenty of warm drinks and staying hydrated."

[1127] Step 11:

[1128] Server: Sends the generated advice to the user's terminal in the form of voice data and text data.

[1129] Step 12:

[1130] Terminal: Advice received from the server is communicated to the user via voice notification, and at the same time, the advice is displayed in text format on the application interface.

[1131] Step 13:

[1132] User: Check the advice and take appropriate action based on it, for example, drinking a warm drink.

[1133] In this way, the present invention provides a system that allows users to naturally monitor their own health status in their daily lives and receive appropriate health management advice in real time, allowing users to maintain and improve their health without any special effort.

[1134] Example 2

[1135] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1136] Conventional health management systems have difficulty accurately recognizing a user's emotional state and assessing their health based on that. Furthermore, they lack a means to provide personalized health management advice in real time, preventing users from effectively managing their health.

[1137] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1138] In this invention, the server includes means for analyzing the image data and voice data to detect clues to the individual's facial expression, body temperature, and physical condition, means for analyzing the voice data to estimate the individual's emotions and health condition, and means for recognizing the individual's emotional state using an emotion engine based on the analysis results of the image data and voice data. This makes it possible to accurately recognize the individual's emotional state, evaluate the individual's health condition in real time based on that, and provide appropriate health management advice.

[1139] A "camera" is a device for capturing image data of an individual.

[1140] "Voice input means" refers to a device for acquiring individual voice data.

[1141] "Image data" refers to visual information of an individual's face and entire body captured through a camera.

[1142] "Voice data" refers to information about an individual's voice acquired through a voice input means.

[1143] "Encryption" is a technique that transforms information using a specific algorithm to keep the data confidential.

[1144] The "server" is a computer system that analyzes image data and audio data to detect an individual's facial expression, body temperature, and physical condition.

[1145] "Analysis" is the process of examining data in detail and extracting information.

[1146] "Facial expression" is data that indicates the movement of an individual's face and the state of their eyes, mouth, eyebrows, etc.

[1147] "Body temperature" is data indicating the temperature of an individual's body.

[1148] "Physical condition" is data that indicates an index of an individual's health condition.

[1149] "Emotions" are data that indicate an individual's inner feelings and mental state.

[1150] "Health status" is data that indicates the overall state of an individual's physical and mental health.

[1151] An "emotion engine" is an algorithm or system for recognizing an individual's emotional state from image and audio data.

[1152] "Health Management Advice" is information that provides appropriate guidance or recommendations to an individual based on their assessed health status.

[1153] A "terminal" is a device that notifies health management advice sent from a server by voice or text.

[1154] "Real-time" means that data processing and results are instantaneous, without delay.

[1155] The present invention relates to a health management system that incorporates an emotion engine that recognizes the user's emotions. This system uses a camera and voice input means to acquire personal image and voice data, encrypting the data and sending it to a server. The server then analyzes the image and voice data to detect facial expressions, body temperature, and clues to the user's physical condition. Furthermore, the emotion engine recognizes the user's emotions from the data, evaluates the user's health condition in real time based on the results, and generates appropriate health management advice.

[1156] The hardware and software required for the system to operate are as follows. The hardware used includes a "camera" that captures an individual's visual information, a "voice input means" that acquires audio information, a "server" that processes data, and a "terminal" that notifies information. The software includes a "machine learning model" (e.g., OpenCV, TensorFlow) that analyzes image and audio data, a "speech recognition model" (e.g., Google Speech-to-Text API) that converts audio into text, an "NLP model" (e.g., BERT) for natural language processing (NLP), and an "emotion engine" (e.g., Emotion API) that estimates emotional states.

[1157] Below is a concrete example of how the system actually works.

[1158] Example: Daily Activities

[1159] User: Stands in front of the smart mirror after returning home from work in the evening.

[1160] Terminal: The camera captures the user's face, and the voice input means asks, "How was your day today?"

[1161] User: "Today was stressful. I have a headache."

[1162] Terminal: Audio and facial image data are encrypted with AES-256 and sent to the server via HTTPS.

[1163] Server: The data is analyzed in real time, and the emotion engine detects stress and fatigue levels. The analysis usually takes just a few seconds.

[1164] Server: Based on the health assessment results and emotional state, generate advice such as "To reduce stress today, it would be good to take a 30-minute walk and take some deep breaths." A generative AI model (e.g., GPT-3) is used to generate advice.

[1165] Device: Advice is given via voice notification and also displayed as text in the application.

[1166] User: Follow the advice and take action to relax.

[1167] This invention is a system that allows users to carry out multifaceted health management, including emotion analysis, in their natural daily lives, and effectively maintain and improve their health.

[1168] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1169] Step 1:

[1170] Data Acquisition

[1171] Terminal: The camera device is activated during a specified time period to capture the user's facial image and full-body image data. It also acquires the user's voice data using a voice input method. For example, if the terminal is a smart mirror, the camera will automatically activate when the user stands in front of it, and a voice prompt will ask, "How are you feeling?"

[1172] Input: Image data of the user's face and entire body captured by a camera device, and voice data of the user acquired by a voice input means.

[1173] Output: Captured image and audio data.

[1174] Step 2:

[1175] Data transmission

[1176] Terminal: The captured image and audio data is encrypted using an encryption algorithm such as AES-256 and sent to the server using a secure communication protocol such as HTTPS.

[1177] Input: Acquired image and audio data.

[1178] Output: The encrypted image data and audio data are sent to the server.

[1179] Step 3:

[1180] Data analysis

[1181] Server: The received image data is input into a machine learning model (e.g., OpenCV or TensorFlow) to estimate the user's emotions, body temperature, and physical condition. The voice data is input into a voice recognition model (e.g., Google Speech-to-Text API) and converted into text data. The converted text data is analyzed using an NLP model (e.g., BERT) to extract emotional keywords and health-related information.

[1182] Input: Encrypted image and audio data.

[1183] Output: Analyzed facial expressions, body temperature, physical condition data, and text data, along with emotion keywords based on them.

[1184] Step 4:

[1185] Analysis by emotion engine

[1186] Server: Uses an emotion engine (e.g., Emotion API) to recognize the user's emotional state from image and audio data. Analyzes facial expressions and tone of voice to identify emotional states such as happiness, stress, and depression.

[1187] Input: Analyzed facial expression data, body temperature data, physical condition data, and voice data.

[1188] Output: The perceived emotional state of the user.

[1189] Step 5:

[1190] Health status estimation

[1191] Server: Evaluates the user's health status in real time based on the analysis results of image and audio data, the output of the emotion engine, past health data, and the user's health goals. The evaluation is performed using statistical methods and machine learning models (e.g., random forest, SVM).

[1192] Inputs: Analysis results, emotion engine output, historical health data, and health goals.

[1193] Output: Real-time assessed health status of the user.

[1194] Step 6:

[1195] Generating customized advice

[1196] Server: Generates personalized health management advice based on the assessed health status. Using a generative AI model (e.g., GPT-3), it generates specific advice appropriate for the user.

[1197] Input: Assessed health status.

[1198] Output: Customized health care advice.

[1199] Step 7:

[1200] Advice submission and notification

[1201] Server: Sends the generated advice to the terminal in the form of voice data and text data.

[1202] Terminal: The received advice is notified to the user by voice notification and is also displayed in text format in the application interface.

[1203] Enter: customized health care advice.

[1204] Output: Audio notification and text health advice.

[1205] (Application example 2)

[1206] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1207] In modern society, personal health management is considered important, but many systems require regular self-diagnosis and responses based on the results, making it difficult to receive real-time, individually customized advice in everyday life. Furthermore, there is a lack of systems in physical stores that analyze customers' emotions and health status and recommend optimal products and services based on that information. This can result in insufficient health management benefits or missed service opportunities, so it is necessary to solve these issues.

[1208] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for proposing optimal products and services in a physical store based on emotion analysis results, means for analyzing image and audio data and evaluating the user's health status, and means for generating and notifying individually customized health management advice. This enables customers to receive individually customized health management advice in real time in their daily lives. Furthermore, by proposing optimal products and services to customers in a physical store, it is possible to maximize service provision opportunities and improve health management effectiveness.

[1209] An "emotion engine" is a software algorithm that analyzes image and audio data to recognize the user's emotional state.

[1210] A "physical store" is a commercial facility located in a physical location where customers visit in person to purchase products or receive services.

[1211] "Image data" means digital data of an image or photograph of an individual's face or body captured using a camera device.

[1212] "Voice data" refers to digital data of an individual's voice captured using a microphone.

[1213] "Analysis" is the process of analyzing acquired image and audio data using machine learning models and algorithms to extract information.

[1214] "Health status" refers to an individual's physical and mental health, including factors such as body temperature, physical condition, and emotional state.

[1215] "Customized health management advice" refers to recommendations for maintaining or improving health that are specifically provided based on an individual user's health data and analysis results.

[1216] "Product and service suggestions" refers to information used to select and provide optimal products and services to users based on their emotions and health status obtained in physical stores.

[1217] "Transmission" is the process of communicating data from a terminal or device to a server and transferring information from a server to a terminal or device.

[1218] "Text" refers to information expressed as character data in a format that can be visually confirmed by the user.

[1219] "Encryption" is a technology that converts data into a format that cannot be deciphered by a third party in order to maintain the confidentiality of the data.

[1220] The present invention relates to a health management system that incorporates an emotion engine that recognizes a user's emotions. This system uses a camera and voice input means to acquire personal image and voice data, encrypting the data and sending it to a server. The server analyzes the image and voice data to detect facial expressions, body temperature, and clues to physical condition. Furthermore, the emotion engine recognizes the user's emotions from the data, and based on the results, evaluates the user's health condition in real time and generates appropriate health management advice. The system can also suggest optimal products and services in physical stores based on the user's health condition and emotions.

[1221] Hardware and software used

[1222] Hardware:

[1223] Smart mirror or head-mounted display (HMD): built-in camera and microphone

[1224] Small devices such as Raspberry Pi: devices for capturing and transmitting images and audio

[1225] software:

[1226] OpenCV: Image data acquisition and processing library

[1227] PyAudio: A library for working with audio input devices

[1228] Requests: A library for sending HTTP requests

[1229] Encryption Library: Data encryption processing

[1230] Emotion engine: For example, machine learning models such as Emotion API, Watson, Azure, etc.

[1231] Server side: a processing system for performing data analysis and proposal generation

[1232] System processing overview

[1233] 1. Data Acquisition

[1234] The device (smart mirror or HMD) activates its camera device during a designated time period to capture the user's facial and full-body image data. It also acquires the user's voice data using a voice input means. For example, when using a smart mirror, the camera is activated when the user stands in front of it, and a voice prompt asks, "How are you feeling?"

[1235] 2. Data Transmission

[1236] The device encrypts the captured image and audio data, ensuring confidentiality, and then transmits the encrypted data to a server using a secure communications protocol.

[1237] 3. Data Analysis

[1238] The server inputs the received image data into a machine learning model and performs facial expression analysis to estimate the user's emotions, body temperature, and physical condition. It also inputs the voice data into a voice recognition model and converts it into text data. The converted text data is then analyzed using a natural language processing (NLP) model to extract emotional keywords and health-related information.

[1239] 4. Analysis by Emotion Engine

[1240] The server uses an emotion engine to recognize the user's emotional state from the image data and audio data, for example, the emotion engine determines whether the user is in a happy or stressed state from facial expressions and tone of voice.

[1241] 5. Health status estimation and proposal generation

[1242] The server evaluates the user's health condition in real time based on the results of image and audio data analysis, the output of the emotion engine, past health data, and the user's health goals. Based on the user's emotional and health status, the server then suggests optimal products and services in physical stores. For example, a specific recommendation such as "This herbal tea is perfect for relaxation" may be generated.

[1243] 6. Notification of Advice and Suggestions

[1244] The generated advice and suggestions are sent to the user's device in the form of voice data and text data, and the device notifies the user of the advice received from the server by voice notification and simultaneously displays it in text format on the application interface.

[1245] Specific examples

[1246] Proposals in physical stores

[1247] User: Visits a cafe and stands in front of a smart mirror.

[1248] Device: Captures face and voice data.

[1249] Terminal: Asks "Welcome, how are you feeling today?"

[1250] User: "I'm a little tired, but I feel okay."

[1251] Server: Analyzes data using an emotion engine to detect signs of stress.

[1252] Server: "Would you like a relaxing herbal tea?"

[1253] On your device: Suggestions are announced via voice and text.

[1254] An example of a prompt to be fed to the generative AI model is as follows:

[1255] "Develop a program that analyzes customers' emotions and health status and makes health-oriented suggestions."

[1256] This allows users to manage their health from multiple angles, including emotion analysis, in their natural daily lives, and effectively maintain and improve their health. It also enables brick-and-mortar stores to provide optimal service to customers and improve customer satisfaction.

[1257] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1258] Step 1:

[1259] The terminal (smart mirror or head-mounted display) activates its camera device at a specified time and captures the user's facial image and full-body image data. It also acquires the user's voice data using a voice input means. Specifically, when the user stands in front of the mirror, the camera automatically activates and a voice prompt asks, "How are you feeling?" The user responds, "I'm a little tired, but I feel good." This causes the camera to capture the user's facial image and the microphone to acquire the voice data. The input is the user's facial image and voice data, and the output is the captured image file and voice file.

[1260] Step 2:

[1261] The terminal encrypts the captured image and audio data. Encryption ensures the confidentiality of the data. The specific encryption method used is an encryption algorithm such as AES (Advanced Encryption Standard). The input is the captured image file and audio file, and the output is an encrypted data file.

[1262] Step 3:

[1263] The terminal sends the encrypted data to the server using a secure communication protocol (e.g., HTTPS). Specifically, the terminal sends the encrypted data to the server in the form of an HTTP request, and the server stores the received data for processing. The input is the encrypted data file, and the output is the status of completion of transmission to the server.

[1264] Step 4:

[1265] The server inputs the received image data into a machine learning model and performs facial expression analysis to estimate the user's emotions, body temperature, and physical condition. Specifically, it uses an image analysis algorithm to read emotions from facial expressions and, if necessary, analyzes data from the body temperature sensor. The input is encrypted image data, and the output is data related to the user's emotional state and body temperature.

[1266] Step 5:

[1267] The server inputs the received voice data into a voice recognition model and converts it into text data. The converted text data is analyzed using a natural language processing model to extract emotional keywords and health-related information. Specifically, the system converts voice data into text, and analyzes that text to extract information about emotional states and health. The input is encrypted voice data, and the output is analyzed text data and emotional information.

[1268] Step 6:

[1269] The server uses an emotion engine to recognize the user's emotional state from image and audio data. Specifically, the emotion engine analyzes the image and audio data and determines whether the user is happy or stressed from their facial expressions and tone of voice. The input is the analyzed image and audio data, and the output is data related to the user's emotional state.

[1270] Step 7:

[1271] The server evaluates the user's health condition in real time based on the results of image and audio data analysis, the output of the emotion engine, past health data, and the user's health goals. Furthermore, based on these results, it proposes optimal products and services for physical stores. Specifically, the server comprehensively evaluates the user's analysis results and generates product recommendations that promote relaxation. The inputs are the analysis results data, past health data, and health goals, and the output is the generated health management advice and suggestions for physical stores.

[1272] Step 8:

[1273] The generated advice and suggestions are sent from the server to the user's device. The device notifies the user of the received advice via voice notification and simultaneously displays it in text format on the application interface. Specifically, the server sends the generated suggestions as an HTTP response, which the device receives and notifies the user. The input is the generated advice and suggestions, and the output is voice and text notifications to the user.

[1274] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1275] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1276] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1277] [Fourth embodiment]

[1278] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1279] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1280] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1281] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1282] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1283] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1284] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1285] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1286] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1287] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1288] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1289] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1290] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1291] This invention relates to a system that uses a camera and voice input means to monitor an individual's health condition and provide appropriate health management advice. The entire system consists of the following steps: data acquisition, data transmission, data analysis, health condition estimation, and customized advice generation and notification. This system allows users to manage their health condition naturally in their daily lives.

[1292] Program processing

[1293] 1. Obtaining user data

[1294] Device: The camera is activated during a specified time period to capture images of the user's face and entire body. At the same time, voice data is also acquired using voice input. For example, if you speak to the smart mirror in the morning, the camera will capture the user's face and the microphone will ask questions about your health.

[1295] User: Answers a question, for example, "I slept well last night, but my throat is a bit sore."

[1296] 2. Data transmission

[1297] Terminal: Encrypts captured image and audio data. This encryption ensures data confidentiality.

[1298] Device: Sends encrypted data to the server using a secure communication protocol.

[1299] 3. Data Analysis

[1300] Server: The received image data is input into a machine learning model, and facial expressions, body temperature, and physical condition are analyzed from images of the user's face and entire body. Specifically, image analysis is used to detect changes in the user's stress level and body temperature.

[1301] Server: The received voice data is input into a speech recognition model and converted into text. Natural language processing is then used to extract keywords from the voice data that indicate the health condition (e.g., sore throat, whether or not the patient slept well).

[1302] 4. Health status estimation

[1303] Server: Integrates the analysis results of image and audio data with past health data and the user's health goals to estimate the user's health condition in real time. For example, if a person has a high stress level and a sore throat, it is estimated that they have the symptoms of a cold.

[1304] 5. Generating customized advice

[1305] Server: Generates customized advice based on the estimated health status and health goals. For example, if you have a sore throat, it generates specific advice such as, "Drink warm drinks and plenty of fluids today. We also recommend consulting a doctor if necessary."

[1306] 6. Notice to Users

[1307] Terminal: The advice received from the server is notified to the user by voice, and at the same time, the advice is displayed in text format on the application interface, allowing the user to confirm and act on the advice provided.

[1308] Specific examples

[1309] Morning Routine

[1310] User: Wake up in the morning and stand in front of the smart mirror.

[1311] Device: The camera captures the user's face and asks via voice input, "Good morning. How are you feeling?"

[1312] User: "I slept well last night, but my throat is a bit sore."

[1313] Terminal: The facial image is encrypted along with the audio data and sent to the server.

[1314] Server: Analyzes data and detects stress levels from facial expressions and sore throats from voice.

[1315] Server: Performs a comprehensive health assessment and determines if you have symptoms of a cold.

[1316] Server: Generate the advice "We recommend drinking warm fluids and staying hydrated. Consult a doctor if necessary."

[1317] On your device: Advice is given via voice and displayed as text in the application.

[1318] User: Act on the advice and start your day.

[1319] In this way, the present invention allows users to naturally monitor their health status in their daily lives and receive appropriate health management advice in real time. This is a system that allows users to maintain and improve their health without any special effort.

[1320] The processing flow will be explained below.

[1321] Step 1:

[1322] Terminal: The system activates the camera device at a specified time to capture the user's facial and full-body image data. For example, a smart mirror automatically activates in the morning and activates the camera when the user stands in front of it.

[1323] Step 2:

[1324] Terminal: A voice input means asks the user, "How are you feeling?" The voice questions are preset and can be customized.

[1325] Step 3:

[1326] User: Responds to the voice question with a specific response such as "My throat is a little sore." This allows the system to obtain the user's subjective physical condition information.

[1327] Step 4:

[1328] Terminal: The user's answers are recorded as voice data and encrypted along with the facial image. This encryption process ensures the confidentiality of the data.

[1329] Step 5:

[1330] Terminal: Sends encrypted image and audio data to a server via a secure communication protocol (e.g., HTTPS).

[1331] Step 6:

[1332] Server: The received image data is input into a machine learning model to analyze the user's facial expression, body temperature, and physical condition. For example, an image analysis algorithm evaluates stress levels from facial expressions and estimates body temperature from skin color.

[1333] Step 7:

[1334] Server: The voice data is input into a speech recognition model and converted into text data. Then, a natural language processing (NLP) model is used to extract keywords related to the health condition. For example, the phrase "sore throat" is detected and recorded in a database.

[1335] Step 8:

[1336] Server: Integrates the results of image and audio analysis, and estimates real-time health status by referencing past health data and the user's health goals. Specifically, it predicts that high stress levels and a sore throat are signs of a cold.

[1337] Step 9:

[1338] Server: Generates customized health management advice based on the estimated health status, for example, "Today, we recommend drinking plenty of warm drinks and staying hydrated."

[1339] Step 10:

[1340] Server: Sends the generated advice to the user's terminal in the form of voice data and text data.

[1341] Step 11:

[1342] Terminal: Advice received from the server is communicated to the user via voice notification, and at the same time, the advice is displayed in text format on the application interface.

[1343] Step 12:

[1344] User: Check the advice and take appropriate action based on it, for example, drinking a warm drink.

[1345] Example 1

[1346] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1347] Health management is becoming increasingly important in modern times, and many people want to be able to monitor their health status in real time and receive appropriate advice. However, conventional health management systems require users to use special devices and applications, making them difficult to use in everyday life. In addition, the data collection and analysis required to evaluate health status is often cumbersome and burdensome for users. This makes it difficult for users to use the systems continuously, resulting in problems such as inadequate health management.

[1348] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1349] In this invention, the server includes a means for analyzing image data to detect clues about an individual's facial expression, body temperature, and physical condition, a means for analyzing voice data to extract keywords that indicate the individual's health condition, and a means for including a generative AI model used to estimate the health condition, thereby enabling users to automatically monitor their health condition in their natural daily lives and receive appropriate health management advice in real time.

[1350] "Image data" refers to image information of an individual's face or entire body captured using a camera.

[1351] "Voice data" refers to voice information relating to an individual's speech and physical condition acquired using a voice input means.

[1352] "Analysis" refers to processing received image data and audio data using machine learning models and voice recognition models to extract information about an individual's facial expression, body temperature, physical condition, and health status.

[1353] The "server" is a computer system that analyzes the received data, estimates the individual's health condition based on the analysis results, and generates appropriate health management advice.

[1354] "Keywords indicating health status" are important words and phrases related to an individual's health status (e.g., sore throat, whether they slept well) extracted through voice data analysis.

[1355] "Generative AI model" means an artificial intelligence model (e.g., GPT-3) used to estimate health status and generate health management advice.

[1356] A "terminal" is a device that is equipped with a camera and voice input means, acquires personal data, transmits the encrypted data to a server, and notifies the user of generated advice by voice or text.

[1357] "Health Care Advice" is a specific recommendation generated by the server to improve or maintain an individual's health status.

[1358] "Encryption" is the process of converting captured image and audio data in a secure manner to maintain the confidentiality of the data.

[1359] "Real-time" refers to the rapid processing of data, from data acquisition and analysis to health status estimation and the generation and notification of advice.

[1360] This invention is a system for managing health conditions naturally in daily life and providing appropriate health management advice. Next, we will explain how to specifically implement this system.

[1361] The system includes a terminal equipped with a camera and voice input means for capturing images of the user's face and body, as well as voice data. The terminal activates the camera at a specified time to capture the user's image data. At the same time, the voice input means is used to capture the user's voice data. For example, when the user stands in front of the smart mirror in the morning, the camera captures the user's face and the microphone asks, "Did you sleep well last night?"

[1362] The acquired image and audio data is encrypted by the device using AES encryption technology, and the encrypted data is sent to the server via a secure communication protocol (e.g., HTTPS).

[1363] The server first decodes the received data. Then, it uses a machine learning model (e.g., a model integrating OpenCV and TensorFlow) to analyze the image data and detect clues about the user's facial expression, body temperature, and physical condition. Similarly, the server inputs the voice data into a speech recognition model (e.g., Google Speech-to-Text API) to convert it into text, and then uses natural language processing (NLP) techniques to extract keywords that indicate the user's health condition.

[1364] The server integrates the results of the image and audio analysis, and combines them with past health data and the user's health goals to estimate the user's health condition in real time. For example, if the user has a high stress level and a sore throat, it estimates that the user is at risk of developing a cold.

[1365] Based on the estimated health status, the server uses a generative AI model (e.g., GPT-3) to generate customized advice, such as "It's a good idea to drink warm drinks and drink plenty of fluids today. We also recommend that you consult a doctor if necessary."

[1366] Finally, the device notifies the user of the advice received from the server. The notification is made audibly using a speech synthesis function (e.g., Google Text-to-Speech) and also displayed in text format in the application interface. This allows the user to confirm and act on the advice provided.

[1367] Specific examples

[1368] Morning Routine Example

[1369] User: Wake up in the morning and stand in front of the smart mirror.

[1370] Device: The camera captures the user's face and asks via voice input, "Good morning. How are you feeling?"

[1371] User: "I slept well last night, but my throat is a bit sore."

[1372] Terminal: The facial image is encrypted along with the audio data and sent to the server.

[1373] Server: Analyzes data and detects stress levels from facial expressions and sore throats from voice.

[1374] Server: Performs a comprehensive health assessment and determines if you have symptoms of a cold.

[1375] Server: Generate the advice "We recommend drinking warm fluids and staying hydrated. Consult a doctor if necessary."

[1376] On your device: Advice is given via voice and displayed as text in the application.

[1377] User: Act on the advice and start your day.

[1378] Prompt Sentence Examples

[1379] "When you wake up in the morning and stand in front of a smart mirror and it asks you how you're feeling, how would you respond? For example, tell me if you slept well last night, or if you have a sore throat?"

[1380] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1381] Step 1: Data Acquisition

[1382] The device activates the camera during a specified time period and captures the user's facial image and full-body image. At the same time, it acquires the user's voice data using a voice input means. Specifically, the device automatically activates the camera and microphone at 6:00 a.m. and asks the user, "Good morning. How are you feeling?" The input at this stage is the user's facial image, full-body image, and voice data, and the output is the captured data.

[1383] Step 2: Encrypt and send data

[1384] The device encrypts the captured image and audio data using AES encryption technology. The encrypted data is sent to the server using a secure communication protocol (e.g., HTTPS). Specifically, the security module inside the device encrypts the data with AES and sends it to the server via HTTPS. The input at this stage is the captured data, and the output is encrypted data.

[1385] Step 3: Decrypt and analyze the data

[1386] The server first decrypts the received encrypted data. Next, it uses a machine learning model (e.g., a model integrating OpenCV and TensorFlow) to analyze the image data and detect clues to the user's facial expression, body temperature, and physical condition. The server also inputs the voice data into a speech recognition model (e.g., Google Speech-to-Text API) to convert it into text, and uses natural language processing (NLP) techniques to extract keywords that indicate the user's health condition. For example, the keyword "sore throat" is extracted. The input at this stage is the encrypted data, and the output is the analyzed facial expression data, body temperature data, physical condition clues, and textual voice data.

[1387] Step 4: Estimate health status

[1388] The server integrates the results of the image and audio data analysis with past health data and the user's health goals to estimate the user's health status in real time. Statistical analysis and generative AI models (e.g., GPT-3) are used to perform a comprehensive health assessment. For example, if a person has a high stress level and a sore throat, it is estimated that they have symptoms of a cold. The input to this stage is the analyzed data and past health data, and the output is an estimated health status.

[1389] Step 5: Generate customized advice

[1390] The server generates customized advice based on the estimated health status and the user's health goals. Specific advice is created using a generative AI model (e.g., GPT-3). For example, it might generate advice such as, "Drink warm drinks and plenty of fluids today. We also recommend consulting a doctor if necessary." The input of this stage is the estimated health status and the user's health goals, and the output is the generated health management advice.

[1391] Step 6: Advice Notification

[1392] The device uses a speech synthesis function (e.g., Google Text-to-Speech) to audibly notify the advice received from the server, and also displays it in text format on the application interface. For example, the device may notify the user, "Drink a warm drink today," and the same text will be displayed in the app. The input at this stage is the generated advice, and the output is the audio notification and the displayed text advice.

[1393] In this way, the system can automatically monitor health status in the natural course of daily life and provide appropriate health management advice in real time.

[1394] (Application example 1)

[1395] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1396] In modern society, personal health management is becoming increasingly important. However, it is not easy to regularly monitor one's health status and follow appropriate health management advice in the midst of busy daily lives. In particular, when visiting a physical store, there are few opportunities to monitor one's health status, which can lead to neglecting health management. In contrast, there is no system that allows individuals to manage their health status in their natural living environment and receive prompt and appropriate health management advice. To address this issue, the present invention aims to provide a health status monitoring system using a head-mounted display in a physical store.

[1397] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1398] In this invention, the server includes a means for encrypting and transmitting an individual's image data and voice data to the server, a means for analyzing the image data in the server to detect clues about the individual's facial expression, body temperature, and physical condition, and a means for analyzing the voice data in the server to estimate the individual's emotions and health condition. This makes it possible to evaluate an individual's health condition in real time even in a physical store and provide appropriate health management advice.

[1399] A "camera" is an electronic device for capturing image data.

[1400] "Voice input means" is a device for acquiring voice data.

[1401] A "server" is a device that processes and stores data on a computer network.

[1402] "Encryption" is the process of transforming information to protect the data.

[1403] "Facial expression" refers to an individual's emotional state as indicated by facial muscle movements.

[1404] "Body temperature" is the internal body temperature of an individual.

[1405] "Physical condition" refers to an individual's state of health and bodily function.

[1406] Emotions are an individual's mental reactions and feelings.

[1407] "Health status" is the overall state of an individual's physical and mental health.

[1408] A "head-mounted display" is a display device that is worn on the head.

[1409] A "brick and mortar store" is a physical store that offers goods and services in person.

[1410] A "personal device" is an electronic device for personal use.

[1411] "Natural living environment" is the normal environment in which an individual lives their daily life.

[1412] The system embodying this invention uses a camera and voice input means to monitor an individual's health condition and provide appropriate health management advice. This allows users to check their health condition in their natural living environment and take appropriate measures. The system includes the following components:

[1413] 1. Hardware

[1414] Camera: Used to capture image data of the user's face and entire body.

[1415] Voice input means: A device including a microphone for acquiring voice data from the user.

[1416] Head-mounted display (HMD): A display device worn by users in physical stores to monitor their health.

[1417] Personal device: An electronic device used by a user, such as a smartphone or tablet, used to receive and notify advice.

[1418] 2. Software

[1419] Image processing software: Uses libraries such as OpenCV to analyze image data and detect clues about the user's facial expression, body temperature, and physical condition.

[1420] Speech Recognition Software: Uses the SpeechRecognition library to convert voice data into text and estimate emotions and health status.

[1421] Encryption software: Encrypts image and audio data using the Fernet library.

[1422] Data transmission software: Uses the Requests library to send encrypted data to the server.

[1423] Server-side analysis software: Analyzes the received data, estimates the user's health status, and generates appropriate health management advice.

[1424] As an example, the following describes specific steps for a user to wear a head-mounted display in a physical store and respond to voice input.

[1425] 1. Obtaining user data:

[1426] Device: The camera is activated during a specified time period to capture images of the user's face and entire body. At the same time, voice data from the user is also acquired using a voice input method. For example, a user stands in front of a smart mirror installed at a specific location in a physical store and asks "Good morning. How are you feeling?" through the HMD.

[1427] User: Answers the question. "I'm feeling fine, but my eyes are a little dry."

[1428] 2. Data transmission:

[1429] Terminal: Encrypts captured image and audio data. This encryption ensures data confidentiality.

[1430] Device: Sends encrypted data to the server using a secure communication protocol.

[1431] 3. Data Analysis:

[1432] Server: The received image data is input into a machine learning model, which analyzes facial expressions, body temperature, and physical condition from images of the user's face and entire body.

[1433] Server: The received voice data is input into a voice recognition model and converted into text. Natural language processing is then used to extract keywords from the voice data that indicate health conditions.

[1434] 4. Health status estimation:

[1435] Server: Integrates the analysis results of image and audio data with past health data and the user's health goals to estimate the user's health condition in real time.

[1436] 5. Generate customized advice:

[1437] Server: Generates customized advice linked to health goals based on estimated health status.

[1438] 6. Notice to Users:

[1439] Terminal: The advice received from the server is notified to the user by voice, and at the same time, the advice is displayed in text format on the application interface.

[1440] Specific examples

[1441] For example, here are some prompts for a user wearing a head-mounted display in a physical store and responding to voice input:

[1442] Example prompt sentence:

[1443] User: My throat is a little sore. I slept well last night, but I still feel tired this morning.

[1444] This example allows the user to check their health status without much effort and take appropriate measures if necessary.

[1445] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1446] Step 1:

[1447] The device activates the camera during the specified time period and captures images of the user's face and entire body.

[1448] Input: Camera device

[1449] Output: User's face and full body image data

[1450] An image is acquired using a camera, and the captured image data is temporarily stored in storage.

[1451] Step 2:

[1452] The terminal acquires voice data from the user using a voice input means, and at this time asks the user simple questions about their health condition.

[1453] Input: Microphone, user speech

[1454] Output: User's voice data

[1455] The voice data of the user answering the questions is captured via a microphone and saved as an audio file.

[1456] Step 3:

[1457] The device encrypts the captured image and audio data using the Fernet library.

[1458] Input: Image data, audio data

[1459] Output: Encrypted image and audio data

[1460] Generate a Fernet key, encrypt image and audio data, and output the encrypted data.

[1461] Step 4:

[1462] The terminal transmits the encrypted image data and audio data to the server using a secure communication protocol (e.g., HTTPS).

[1463] Input: Encrypted image and audio data

[1464] Output: Message that data was sent successfully to the server

[1465] Send data to the server using a secure communication protocol and confirm successful transmission.

[1466] Step 5:

[1467] The server inputs the received image data into a machine learning model to detect clues about facial expressions, body temperature, and physical condition from images of the user's face and entire body.

[1468] Input: Encrypted image data

[1469] Output: Analysis results of the user's facial expression, body temperature, and physical condition

[1470] Machine learning models are used to analyze image data and detect fluctuations in facial expressions and body temperature.

[1471] Step 6:

[1472] The server inputs the received voice data into a voice recognition model, converts it into text, and then uses natural language processing to extract keywords from the voice data that indicate health conditions.

[1473] Input: Encrypted audio data

[1474] Output: Text data and keywords that indicate health conditions

[1475] A speech recognition model is used to convert the voice data into text, and natural language processing tools are used to extract keywords that indicate health conditions.

[1476] Step 7:

[1477] The server integrates the analysis results of the image data and audio data with past health data and the user's health goals to estimate the user's health condition in real time.

[1478] Input: Image data analysis results, audio data analysis results, past health data, health goals

[1479] Output: Estimated health status of the user

[1480] The analysis results are combined with past data and an algorithm is used to estimate the user's current health status.

[1481] Step 8:

[1482] The server generates customized advice linked to health goals based on the estimated health state.

[1483] Input: Health status estimation results

[1484] Output: Customized health advice

[1485] Based on the estimation results, an AI model is used to generate personalized advice.

[1486] Step 9:

[1487] The terminal notifies the user of the advice received from the server by voice, and at the same time displays the advice in text format on the application interface.

[1488] Input: customized health advice

[1489] Output: Audio and text notification of advice

[1490] The text advice is converted into speech using a speech synthesis tool and notified to the user, and is also displayed in the application.

[1491] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1492] The present invention relates to a health management system that incorporates an emotion engine that recognizes the user's emotions. This system uses a camera and voice input means to acquire personal image and voice data, encrypting the data and sending it to a server. The server then analyzes the image and voice data to detect facial expressions, body temperature, and clues to the user's physical condition. Furthermore, the emotion engine recognizes the user's emotions from the data, evaluates the user's health condition in real time based on the results, and generates appropriate health management advice.

[1493] Program processing

[1494] 1. Data Acquisition

[1495] Terminal: The system activates the camera device during a designated time period to capture the user's facial and full-body image data. It also acquires the user's voice data using a voice input method. For example, when using a smart mirror, the camera is activated when the user stands in front of it, and a voice prompt asks, "How are you feeling?"

[1496] User: Responds to the voice prompt with "I'm a little tired, but I feel good."

[1497] 2. Data Transmission

[1498] Terminal: Captured image and audio data is encrypted. Encryption ensures data confidentiality.

[1499] Terminal: Sends encrypted data to the server using a secure communication protocol.

[1500] 3. Data Analysis

[1501] Server: The received image data is input into a machine learning model, and facial expression analysis is performed to estimate the user's emotions, body temperature, and physical condition. For example, the facial expression analysis algorithm evaluates emotional states such as anger or joy.

[1502] Server: The voice data is input into a speech recognition model and converted into text data. The converted text data is analyzed using a natural language processing (NLP) model to extract emotional keywords and health status-related information.

[1503] 4. Analysis by Emotion Engine

[1504] Server: The emotion engine responsively recognizes the user's emotional state from the image data and audio data. For example, the emotion engine determines whether the user is happy or stressed from facial expressions and tone of voice.

[1505] 5. Health status estimation

[1506] Server: Evaluates the user's health condition in real time based on the results of image and audio analysis, the output of the emotion engine, past health data, and the user's health goals. For example, if emotion analysis detects that the user is feeling stressed, it generates questions and advice to find the cause.

[1507] 6. Generating customized advice

[1508] Server: Generates personalized health management advice based on the assessed health status, such as "Take a walk to relax today" or "Make sure to drink plenty of water."

[1509] 7. Sending Advice and Notifications

[1510] Server: Sends the generated advice to the user's device in the form of voice data and text data.

[1511] Terminal: Advice received from the server is communicated to the user via voice notification and simultaneously displayed in text format on the application interface.

[1512] Specific examples

[1513] Daily Activities

[1514] User: Stands in front of the smart mirror after returning home from work in the evening.

[1515] Terminal: The camera captures the user's face, and the voice input means asks, "How was your day today?"

[1516] User: "Today was stressful. I have a headache."

[1517] Terminal: Encrypts voice and facial image data and sends it to the server.

[1518] Server: Analyzes the data and the emotion engine detects stress levels and fatigue levels.

[1519] Server: Based on the health assessment results and emotional state, generate advice such as "To reduce stress today, it would be a good idea to take a 30-minute walk and take some deep breaths."

[1520] Device: Advice is given via voice notification and also displayed as text in the application.

[1521] User: Follow the advice and take action to relax.

[1522] This invention is a system that allows users to carry out multifaceted health management, including emotion analysis, in their natural daily lives, and effectively maintain and improve their health.

[1523] The processing flow will be explained below.

[1524] Step 1:

[1525] Terminal: The system activates the camera device at a specified time to capture the user's facial and full-body image data. For example, a smart mirror automatically activates in the morning and activates the camera when the user stands in front of it.

[1526] Step 2:

[1527] Terminal: A voice input means asks the user, "How are you feeling?" The voice questions are preset and can be customized.

[1528] Step 3:

[1529] User: Responds to the voice question with a specific response such as "My throat is a little sore." This allows the system to obtain the user's subjective physical condition information.

[1530] Step 4:

[1531] Terminal: The user's answers are recorded as voice data and encrypted along with the facial image. This encryption process ensures the confidentiality of the data.

[1532] Step 5:

[1533] Terminal: Sends encrypted image and audio data to a server via a secure communication protocol (e.g., HTTPS).

[1534] Step 6:

[1535] Server: The received image data is input into a machine learning model to analyze the user's facial expression, body temperature, and physical condition. For example, an image analysis algorithm evaluates stress levels from facial expressions and estimates body temperature from skin color.

[1536] Step 7:

[1537] Server: The voice data is input into a speech recognition model and converted into text data. Then, a natural language processing (NLP) model is used to extract keywords from the voice data that indicate the health condition (e.g., sore throat, whether or not the patient slept well).

[1538] Step 8:

[1539] Server: Recognizes the user's emotions from image and audio data using an emotion engine. For example, the emotion engine determines whether the user is in a stressful state based on facial expressions and tone of voice.

[1540] Step 9:

[1541] Server: Evaluates the user's health status in real time based on the analysis results, the output of the emotion engine, past health data, and the user's health goals. For example, if the user has high stress and a sore throat, it may be inferred to be a sign of a cold.

[1542] Step 10:

[1543] Server: Generates customized health management advice based on the estimated health status, for example, "Today, we recommend drinking plenty of warm drinks and staying hydrated."

[1544] Step 11:

[1545] Server: Sends the generated advice to the user's terminal in the form of voice data and text data.

[1546] Step 12:

[1547] Terminal: Advice received from the server is communicated to the user via voice notification, and at the same time, the advice is displayed in text format on the application interface.

[1548] Step 13:

[1549] User: Check the advice and take appropriate action based on it, for example, drinking a warm drink.

[1550] In this way, the present invention provides a system that allows users to naturally monitor their own health status in their daily lives and receive appropriate health management advice in real time, allowing users to maintain and improve their health without any special effort.

[1551] Example 2

[1552] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1553] Conventional health management systems have difficulty accurately recognizing a user's emotional state and assessing their health based on that. Furthermore, they lack a means to provide personalized health management advice in real time, preventing users from effectively managing their health.

[1554] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1555] In this invention, the server includes means for analyzing the image data and voice data to detect clues to the individual's facial expression, body temperature, and physical condition, means for analyzing the voice data to estimate the individual's emotions and health condition, and means for recognizing the individual's emotional state using an emotion engine based on the analysis results of the image data and voice data. This makes it possible to accurately recognize the individual's emotional state, evaluate the individual's health condition in real time based on that, and provide appropriate health management advice.

[1556] A "camera" is a device for capturing image data of an individual.

[1557] "Voice input means" refers to a device for acquiring individual voice data.

[1558] "Image data" refers to visual information of an individual's face and entire body captured through a camera.

[1559] "Voice data" refers to information about an individual's voice acquired through a voice input means.

[1560] "Encryption" is a technique that transforms information using a specific algorithm to keep the data confidential.

[1561] The "server" is a computer system that analyzes image data and audio data to detect an individual's facial expression, body temperature, and physical condition.

[1562] "Analysis" is the process of examining data in detail and extracting information.

[1563] "Facial expression" is data that indicates the movement of an individual's face and the state of their eyes, mouth, eyebrows, etc.

[1564] "Body temperature" is data indicating the temperature of an individual's body.

[1565] "Physical condition" is data that indicates an index of an individual's health condition.

[1566] "Emotions" are data that indicate an individual's inner feelings and mental state.

[1567] "Health status" is data that indicates the overall state of an individual's physical and mental health.

[1568] An "emotion engine" is an algorithm or system for recognizing an individual's emotional state from image and audio data.

[1569] "Health Management Advice" is information that provides appropriate guidance or recommendations to an individual based on their assessed health status.

[1570] A "terminal" is a device that notifies health management advice sent from a server by voice or text.

[1571] "Real-time" means that data processing and results are instantaneous, without delay.

[1572] The present invention relates to a health management system that incorporates an emotion engine that recognizes the user's emotions. This system uses a camera and voice input means to acquire personal image and voice data, encrypting the data and sending it to a server. The server then analyzes the image and voice data to detect facial expressions, body temperature, and clues to the user's physical condition. Furthermore, the emotion engine recognizes the user's emotions from the data, evaluates the user's health condition in real time based on the results, and generates appropriate health management advice.

[1573] The hardware and software required for the system to operate are as follows. The hardware used includes a "camera" that captures an individual's visual information, a "voice input means" that acquires audio information, a "server" that processes data, and a "terminal" that notifies information. The software includes a "machine learning model" (e.g., OpenCV, TensorFlow) that analyzes image and audio data, a "speech recognition model" (e.g., Google Speech-to-Text API) that converts audio into text, an "NLP model" (e.g., BERT) for natural language processing (NLP), and an "emotion engine" (e.g., Emotion API) that estimates emotional states.

[1574] Below is a concrete example of how the system actually works.

[1575] Example: Daily Activities

[1576] User: Stands in front of the smart mirror after returning home from work in the evening.

[1577] Terminal: The camera captures the user's face, and the voice input means asks, "How was your day today?"

[1578] User: "Today was stressful. I have a headache."

[1579] Terminal: Audio and facial image data are encrypted with AES-256 and sent to the server via HTTPS.

[1580] Server: The data is analyzed in real time, and the emotion engine detects stress and fatigue levels. The analysis usually takes just a few seconds.

[1581] Server: Based on the health assessment results and emotional state, generate advice such as "To reduce stress today, it would be good to take a 30-minute walk and take some deep breaths." A generative AI model (e.g., GPT-3) is used to generate advice.

[1582] Device: Advice is given via voice notification and also displayed as text in the application.

[1583] User: Follow the advice and take action to relax.

[1584] This invention is a system that allows users to carry out multifaceted health management, including emotion analysis, in their natural daily lives, and effectively maintain and improve their health.

[1585] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1586] Step 1:

[1587] Data Acquisition

[1588] Terminal: The camera device is activated during a specified time period to capture the user's facial image and full-body image data. It also acquires the user's voice data using a voice input method. For example, if the terminal is a smart mirror, the camera will automatically activate when the user stands in front of it, and a voice prompt will ask, "How are you feeling?"

[1589] Input: Image data of the user's face and entire body captured by a camera device, and voice data of the user acquired by a voice input means.

[1590] Output: Captured image and audio data.

[1591] Step 2:

[1592] Data transmission

[1593] Terminal: The captured image and audio data is encrypted using an encryption algorithm such as AES-256 and sent to the server using a secure communication protocol such as HTTPS.

[1594] Input: Acquired image and audio data.

[1595] Output: The encrypted image data and audio data are sent to the server.

[1596] Step 3:

[1597] Data analysis

[1598] Server: The received image data is input into a machine learning model (e.g., OpenCV or TensorFlow) to estimate the user's emotions, body temperature, and physical condition. The voice data is input into a voice recognition model (e.g., Google Speech-to-Text API) and converted into text data. The converted text data is analyzed using an NLP model (e.g., BERT) to extract emotional keywords and health-related information.

[1599] Input: Encrypted image and audio data.

[1600] Output: Analyzed facial expressions, body temperature, physical condition data, and text data, along with emotion keywords based on them.

[1601] Step 4:

[1602] Analysis by emotion engine

[1603] Server: Uses an emotion engine (e.g., Emotion API) to recognize the user's emotional state from image and audio data. Analyzes facial expressions and tone of voice to identify emotional states such as happiness, stress, and depression.

[1604] Input: Analyzed facial expression data, body temperature data, physical condition data, and voice data.

[1605] Output: The perceived emotional state of the user.

[1606] Step 5:

[1607] Health status estimation

[1608] Server: Evaluates the user's health status in real time based on the analysis results of image and audio data, the output of the emotion engine, past health data, and the user's health goals. The evaluation is performed using statistical methods and machine learning models (e.g., random forest, SVM).

[1609] Inputs: Analysis results, emotion engine output, historical health data, and health goals.

[1610] Output: Real-time assessed health status of the user.

[1611] Step 6:

[1612] Generating customized advice

[1613] Server: Generates personalized health management advice based on the assessed health status. Using a generative AI model (e.g., GPT-3), it generates specific advice appropriate for the user.

[1614] Input: Assessed health status.

[1615] Output: Customized health care advice.

[1616] Step 7:

[1617] Advice submission and notification

[1618] Server: Sends the generated advice to the terminal in the form of voice data and text data.

[1619] Terminal: The received advice is notified to the user by voice notification and is also displayed in text format in the application interface.

[1620] Enter: customized health care advice.

[1621] Output: Audio notification and text health advice.

[1622] (Application example 2)

[1623] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1624] In modern society, personal health management is considered important, but many systems require regular self-diagnosis and responses based on the results, making it difficult to receive real-time, individually customized advice in everyday life. Furthermore, there is a lack of systems in physical stores that analyze customers' emotions and health status and recommend optimal products and services based on that information. This can result in insufficient health management benefits or missed service opportunities, so it is necessary to solve these issues.

[1625] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for proposing optimal products and services in a physical store based on emotion analysis results, means for analyzing image and audio data and evaluating the user's health status, and means for generating and notifying individually customized health management advice. This enables customers to receive individually customized health management advice in real time in their daily lives. Furthermore, by proposing optimal products and services to customers in a physical store, it is possible to maximize service provision opportunities and improve health management effectiveness.

[1626] An "emotion engine" is a software algorithm that analyzes image and audio data to recognize the user's emotional state.

[1627] A "physical store" is a commercial facility located in a physical location where customers visit in person to purchase products or receive services.

[1628] "Image data" means digital data of an image or photograph of an individual's face or body captured using a camera device.

[1629] "Voice data" refers to digital data of an individual's voice captured using a microphone.

[1630] "Analysis" is the process of analyzing acquired image and audio data using machine learning models and algorithms to extract information.

[1631] "Health status" refers to an individual's physical and mental health, including factors such as body temperature, physical condition, and emotional state.

[1632] "Customized health management advice" refers to recommendations for maintaining or improving health that are specifically provided based on an individual user's health data and analysis results.

[1633] "Product and service suggestions" refers to information used to select and provide optimal products and services to users based on their emotions and health status obtained in physical stores.

[1634] "Transmission" is the process of communicating data from a terminal or device to a server and transferring information from a server to a terminal or device.

[1635] "Text" refers to information expressed as character data in a format that can be visually confirmed by the user.

[1636] "Encryption" is a technology that converts data into a format that cannot be deciphered by a third party in order to maintain the confidentiality of the data.

[1637] The present invention relates to a health management system that incorporates an emotion engine that recognizes a user's emotions. This system uses a camera and voice input means to acquire personal image and voice data, encrypting the data and sending it to a server. The server analyzes the image and voice data to detect facial expressions, body temperature, and clues to physical condition. Furthermore, the emotion engine recognizes the user's emotions from the data, and based on the results, evaluates the user's health condition in real time and generates appropriate health management advice. The system can also suggest optimal products and services in physical stores based on the user's health condition and emotions.

[1638] Hardware and software used

[1639] Hardware:

[1640] Smart mirror or head-mounted display (HMD): built-in camera and microphone

[1641] Small devices such as Raspberry Pi: devices for capturing and transmitting images and audio

[1642] software:

[1643] OpenCV: Image data acquisition and processing library

[1644] PyAudio: A library for working with audio input devices

[1645] Requests: A library for sending HTTP requests

[1646] Encryption Library: Data encryption processing

[1647] Emotion engine: For example, machine learning models such as Emotion API, Watson, Azure, etc.

[1648] Server side: a processing system for performing data analysis and proposal generation

[1649] System processing overview

[1650] 1. Data Acquisition

[1651] The device (smart mirror or HMD) activates its camera device during a designated time period to capture the user's facial and full-body image data. It also acquires the user's voice data using a voice input means. For example, when using a smart mirror, the camera is activated when the user stands in front of it, and a voice prompt asks, "How are you feeling?"

[1652] 2. Data Transmission

[1653] The device encrypts the captured image and audio data, ensuring confidentiality, and then transmits the encrypted data to a server using a secure communications protocol.

[1654] 3. Data Analysis

[1655] The server inputs the received image data into a machine learning model and performs facial expression analysis to estimate the user's emotions, body temperature, and physical condition. It also inputs the voice data into a voice recognition model and converts it into text data. The converted text data is then analyzed using a natural language processing (NLP) model to extract emotional keywords and health-related information.

[1656] 4. Analysis by Emotion Engine

[1657] The server uses an emotion engine to recognize the user's emotional state from the image data and audio data, for example, the emotion engine determines whether the user is in a happy or stressed state from facial expressions and tone of voice.

[1658] 5. Health status estimation and proposal generation

[1659] The server evaluates the user's health condition in real time based on the results of image and audio data analysis, the output of the emotion engine, past health data, and the user's health goals. Based on the user's emotional and health status, the server then suggests optimal products and services in physical stores. For example, a specific recommendation such as "This herbal tea is perfect for relaxation" may be generated.

[1660] 6. Notification of Advice and Suggestions

[1661] The generated advice and suggestions are sent to the user's device in the form of voice data and text data, and the device notifies the user of the advice received from the server by voice notification and simultaneously displays it in text format on the application interface.

[1662] Specific examples

[1663] Proposals in physical stores

[1664] User: Visits a cafe and stands in front of a smart mirror.

[1665] Device: Captures face and voice data.

[1666] Terminal: Asks "Welcome, how are you feeling today?"

[1667] User: "I'm a little tired, but I feel okay."

[1668] Server: Analyzes data using an emotion engine to detect signs of stress.

[1669] Server: "Would you like a relaxing herbal tea?"

[1670] On your device: Suggestions are announced via voice and text.

[1671] An example of a prompt to be fed to the generative AI model is as follows:

[1672] "Develop a program that analyzes customers' emotions and health status and makes health-oriented suggestions."

[1673] This allows users to manage their health from multiple angles, including emotion analysis, in their natural daily lives, and effectively maintain and improve their health. It also enables brick-and-mortar stores to provide optimal service to customers and improve customer satisfaction.

[1674] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1675] Step 1:

[1676] The terminal (smart mirror or head-mounted display) activates its camera device at a specified time and captures the user's facial image and full-body image data. It also acquires the user's voice data using a voice input means. Specifically, when the user stands in front of the mirror, the camera automatically activates and a voice prompt asks, "How are you feeling?" The user responds, "I'm a little tired, but I feel good." This causes the camera to capture the user's facial image and the microphone to acquire the voice data. The input is the user's facial image and voice data, and the output is the captured image file and voice file.

[1677] Step 2:

[1678] The terminal encrypts the captured image and audio data. Encryption ensures the confidentiality of the data. The specific encryption method used is an encryption algorithm such as AES (Advanced Encryption Standard). The input is the captured image file and audio file, and the output is an encrypted data file.

[1679] Step 3:

[1680] The terminal sends the encrypted data to the server using a secure communication protocol (e.g., HTTPS). Specifically, the terminal sends the encrypted data to the server in the form of an HTTP request, and the server stores the received data for processing. The input is the encrypted data file, and the output is the status of completion of transmission to the server.

[1681] Step 4:

[1682] The server inputs the received image data into a machine learning model and performs facial expression analysis to estimate the user's emotions, body temperature, and physical condition. Specifically, it uses an image analysis algorithm to read emotions from facial expressions and, if necessary, analyzes data from the body temperature sensor. The input is encrypted image data, and the output is data related to the user's emotional state and body temperature.

[1683] Step 5:

[1684] The server inputs the received voice data into a voice recognition model and converts it into text data. The converted text data is analyzed using a natural language processing model to extract emotional keywords and health-related information. Specifically, the system converts voice data into text, and analyzes that text to extract information about emotional states and health. The input is encrypted voice data, and the output is analyzed text data and emotional information.

[1685] Step 6:

[1686] The server uses an emotion engine to recognize the user's emotional state from image and audio data. Specifically, the emotion engine analyzes the image and audio data and determines whether the user is happy or stressed from their facial expressions and tone of voice. The input is the analyzed image and audio data, and the output is data related to the user's emotional state.

[1687] Step 7:

[1688] The server evaluates the user's health condition in real time based on the results of image and audio data analysis, the output of the emotion engine, past health data, and the user's health goals. Furthermore, based on these results, it proposes optimal products and services for physical stores. Specifically, the server comprehensively evaluates the user's analysis results and generates product recommendations that promote relaxation. The inputs are the analysis results data, past health data, and health goals, and the output is the generated health management advice and suggestions for physical stores.

[1689] Step 8:

[1690] The generated advice and suggestions are sent from the server to the user's device. The device notifies the user of the received advice via voice notification and simultaneously displays it in text format on the application interface. Specifically, the server sends the generated suggestions as an HTTP response, which the device receives and notifies the user. The input is the generated advice and suggestions, and the output is voice and text notifications to the user.

[1691] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1692] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1693] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1694] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1695] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1696] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1697] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1698] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1699] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1700] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1701] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1702] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1703] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1704] 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.

[1705] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1706] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1707] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1708] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1709] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1710] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1711] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1712] The following is further disclosed regarding the above embodiment.

[1713] (Claim 1)

[1714] means for acquiring personal image data using a camera;

[1715] means for acquiring personal voice data using a voice input means;

[1716] means for encrypting the image data and audio data and transmitting the data to a server;

[1717] A means for analyzing the image data in the server and detecting clues about the individual's facial expression, body temperature, and physical condition;

[1718] a means for analyzing the voice data in a server and estimating the emotion and health condition of the individual;

[1719] a means for evaluating the health condition of an individual in real time based on the analysis results in the server and generating appropriate health management advice;

[1720] A means for transmitting the generated health management advice to a personal device and notifying the individual by voice or text;

[1721] A system including:

[1722] (Claim 2)

[1723] 2. The system according to claim 1, wherein the server integrates the analysis results with the individual's past health data and health goals to estimate the individual's health condition.

[1724] (Claim 3)

[1725] 2. The system according to claim 1, wherein the terminal includes means for notifying health management advice by voice, allowing an individual to manage their health condition in the course of their natural life.

[1726] "Example 1"

[1727] (Claim 1)

[1728] means for acquiring personal image data using a camera;

[1729] means for acquiring personal voice data using a voice input means;

[1730] means for encrypting the image data and audio data and transmitting the data to a server;

[1731] A means for analyzing the image data in the server and detecting clues about the individual's facial expression, body temperature, and physical condition;

[1732] a means for analyzing the voice data in a server and extracting keywords indicating the individual's health condition;

[1733] a means for estimating the health status of an individual based on the analysis results in the server;

[1734] A means including a generative AI model used in a server to estimate a health state;

[1735] A means for transmitting the generated health management advice to a personal device and notifying the individual by voice or text;

[1736] A system including:

[1737] (Claim 2)

[1738] 2. The system according to claim 1, wherein the server integrates the analysis results with the individual's past health data and health goals to estimate the individual's health condition.

[1739] (Claim 3)

[1740] 2. The system according to claim 1, wherein the terminal includes means for notifying health management advice by voice, allowing an individual to manage their health condition in the course of their natural life.

[1741] "Application Example 1"

[1742] (Claim 1)

[1743] means for acquiring personal image data using a camera;

[1744] means for acquiring personal voice data using a voice input means;

[1745] means for encrypting the image data and audio data and transmitting the data to a server;

[1746] A means for analyzing the image data in the server and detecting clues about the individual's facial expression, body temperature, and physical condition;

[1747] a means for analyzing the voice data in a server and estimating the emotion and health condition of the individual;

[1748] a means for evaluating the health condition of an individual in real time based on the analysis results in the server and generating appropriate health management advice;

[1749] A means for transmitting the generated health management advice to a personal device and notifying the individual by voice or text;

[1750] A means for monitoring and providing instructions regarding the health condition based on personal data acquired by a head-mounted display in a physical store using a home appliance-installed terminal;

[1751] A system including:

[1752] (Claim 2)

[1753] 2. The system according to claim 1, wherein the server integrates the analysis results with the individual's past health data and health goals to estimate the individual's health condition.

[1754] (Claim 3)

[1755] 2. The system according to claim 1, wherein the terminal includes means for notifying health management advice by voice, allowing an individual to manage their health condition in the course of their natural life.

[1756] "Example 2: Combining Emotion Engines"

[1757] (Claim 1)

[1758] means for acquiring personal image data using a camera;

[1759] means for acquiring personal voice data using a voice input means;

[1760] means for encrypting the image data and audio data and transmitting the data to a server;

[1761] A means for analyzing the image data in the server and detecting clues about the individual's facial expression, body temperature, and physical condition;

[1762] a means for analyzing the voice data in a server and estimating the emotion and health condition of the individual;

[1763] a means for recognizing an individual's emotional state using an emotion engine based on the analysis results of the image data and the voice data in a server;

[1764] a means for evaluating the individual's health condition in real time based on the analysis results, past health data, and health goals in the server, and generating appropriate health management advice;

[1765] A means for transmitting the generated health management advice to a personal device and notifying the individual by voice or text;

[1766] A system including:

[1767] (Claim 2)

[1768] 2. The system according to claim 1, further comprising means for estimating an individual's health condition by integrating the analysis results with past health data and health goals in the server.

[1769] (Claim 3)

[1770] 2. The system according to claim 1, further comprising means for notifying health management advice by voice in the terminal, thereby enabling an individual to manage their health condition in the course of their natural life.

[1771] "Application example 2 when combining emotion engines"

[1772] (Claim 1)

[1773] means for acquiring personal image data using a camera;

[1774] means for acquiring personal voice data using a voice input means;

[1775] means for encrypting the image data and audio data and transmitting the data to a server;

[1776] A means for analyzing the image data in the server and detecting clues about the individual's facial expression, body temperature, and physical condition;

[1777] a means for analyzing the voice data in a server and estimating the emotion and health condition of the individual;

[1778] means including an emotion engine for analyzing an emotional state of an individual at a server;

[1779] a means for evaluating the health condition of an individual in real time based on the analysis results in the server and generating appropriate health management advice;

[1780] A means for proposing optimal products and services in physical stores based on the analysis results;

[1781] A means for transmitting the generated health management advice to a personal device and notifying the individual by voice or text;

[1782] A system including:

[1783] (Claim 2)

[1784] The system described in claim 1, characterized in that the server integrates the analysis results with the individual's past health data and health goals to estimate the individual's health status, and based on the results, makes individually customized in-store service proposals.

[1785] (Claim 3)

[1786] The system according to claim 1, characterized in that the terminal includes a means for providing voice notification of health management advice or displaying suggestions for physical stores, allowing individuals to manage their health status and receive optimal services in the course of their natural lives. [Explanation of symbols]

[1787] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for acquiring personal image data using a camera; means for acquiring personal voice data using a voice input means; means for encrypting the image data and audio data and transmitting the data to a server; A means for analyzing the image data in the server and detecting clues about the individual's facial expression, body temperature, and physical condition; a means for analyzing the voice data in a server and estimating the emotion and health condition of the individual; a means for evaluating the health condition of an individual in real time based on the analysis results in the server and generating appropriate health management advice; A means for transmitting the generated health management advice to a personal device and notifying the individual by voice or text; A system including:

2. 2. The system according to claim 1, wherein the server integrates the analysis results with the individual's past health data and health goals to estimate the individual's health condition.

3. 2. The system according to claim 1, further comprising means for notifying health care advice by voice in said terminal, so that an individual can manage his / her health condition in the course of his / her natural life.

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