System

Abstract

A system is provided.SOLUTION: A system comprising: means for recognizing a voice input by a user to activate the system; means for authenticating the user using a face authentication algorithm; means for collecting facial expression and voice data of the user after the authentication; means for comparing the collected data with past data to detect an abnormality; and means for transmitting a notification to an administrator when the abnormality is detected.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 an aging society, the number of elderly people living alone is increasing. This makes it difficult for their families and caregivers to properly manage and monitor their health. In particular, there is a need for a method to check daily changes in facial expressions and voice in real time and respond quickly when abnormalities occur. [Means for solving the problem]

[0005] The above problem is solved by a system equipped with a means for activating the system via user voice input, a means for user authentication using a facial recognition algorithm, a means for collecting facial expression and voice data, a means for detecting abnormalities by comparing the collected data with past data, and a means for sending a notification to an administrator when an abnormality is detected. This system also includes a means for advising the user on how to deal with an abnormality when it is detected, and uses a photo frame-type terminal for authentication and data collection. This allows for real-time monitoring of the user's health condition, enabling rapid response in the event of an abnormality.

[0006] "Means for recognizing voice input and activating the system" refers to technology that detects the voice emitted by the user and uses it as a trigger to start the system.

[0007] "Means for authenticating users using facial recognition algorithms" refers to technology that analyzes a user's facial image captured by a camera and identifies the user based on specific features.

[0008] "Means for collecting user facial expression and voice data after authentication" refers to technology that acquires the user's facial movements, tone of voice, etc., and stores this data after the authentication process is completed.

[0009] "Means for detecting abnormalities by comparing collected data with past data" refers to a technology that compares currently collected facial and voice data of users with an existing database to determine whether or not there are any abnormalities.

[0010] "Means for sending a notification to an administrator when an abnormality is detected" refers to a technology that transmits information about an abnormality to an administrator in real time when the system detects an abnormality.

[0011] "Means for providing advice on how to deal with the problem" refers to a technique that provides appropriate countermeasures and advice to the user when an abnormality is detected.

[0012] A "photo frame terminal" looks like a normal photo frame at first glance, but is a multifunctional electronic device equipped with a camera, microphone, display, etc. [Brief explanation of the drawings]

[0013] [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

[0014] 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.

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

[0016] 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).

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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."

[0021] [First embodiment]

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

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0028] 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.

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

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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."

[0034] This invention provides "Vital Frame," a system that uses a photo frame-type device to support health management for elderly people and others living alone. This system monitors the user's health status by combining user voice input, face authentication, facial expression recognition, and voice analysis, and notifies the administrator if an abnormality is detected. In addition, if an abnormality occurs, the system provides the user with appropriate advice on how to deal with it.

[0035] System Overview

[0036] The device wakes up when the user says "good morning" every morning.

[0037] The device captures the user's voice through a microphone and triggers the system by detecting the phrase "good morning" using a voice recognition algorithm.

[0038] Explanation by language generation:

[0039] When the user says "Good morning," the device recognizes the voice and starts up the system.

[0040] A device equipped with a means to identify users through facial recognition.

[0041] The device captures the user's face with its camera and uses a facial recognition algorithm to identify the user. This process allows the device to identify the user and proceed to the next step.

[0042] Explanation by language generation:

[0043] The device captures the user's face with a camera and runs a facial recognition algorithm based on the image to identify the user.

[0044] After authentication, the device asks AI questions and collects facial and voice data from the user.

[0045] For example, it asks a question like, "Did you sleep well last night?", and the user responds. At that time, the facial recognition algorithm analyzes the user's facial expressions, and the voice analysis algorithm analyzes the intonation and sound patterns of the voice.

[0046] Explanation by language generation:

[0047] The device asks, "Did you sleep well last night?" and when the user answers "yes," it collects their facial expressions and voice.

[0048] The collected data is sent to a server and an algorithm is run to compare it with past data and detect anomalies.

[0049] The collected facial expression and voice data is sent from the device to a server, which compares it with past data to detect any abnormalities. If an abnormality is detected, an administrator is notified.

[0050] Explanation by language generation:

[0051] The device sends data to the server, which compares it with past data to detect any anomalies. If an anomaly is detected, the administrator is notified.

[0052] If an error occurs, the system provides advice to the user on how to deal with the problem.

[0053] When an abnormality is detected, the device will provide the user with advice on how to deal with the problem via voice messages, etc.

[0054] Explanation by language generation:

[0055] The device advises the user, "You seem to be feeling unwell today. Please take it easy and rest."

[0056] A device equipped with a means of everyday communication.

[0057] Under normal circumstances, the device will hold daily conversations with the user and support their mental health. For example, when the user asks, "What's the weather like today?", the device will respond, "It's sunny today. It would be nice to go for a walk."

[0058] Explanation by language generation:

[0059] When a user asks, "What's the weather like today?" the device will respond with, "It's sunny today," interacting with the user through everyday conversations.

[0060] ---

[0061] In this way, the Vital Frame system monitors the user's health condition daily and has the ability to respond quickly in the event of an abnormality. By combining the above functions, it is possible to ensure the safety and health of family members living far away.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] The user speaks "Good morning" into the photo frame device. The device captures the voice through its microphone, and the voice recognition function detects the word "Good morning." If the voice is recognized correctly, the system will start up.

[0065] Step 2:

[0066] The device uses a camera to capture the user's face. A facial recognition algorithm analyzes this image and identifies the user. If the user is successfully authenticated, the device proceeds to the next step.

[0067] Step 3:

[0068] The device will start asking AI questions, such as, "Did you sleep well last night?" As the user responds, the device will simultaneously capture their facial expressions with the camera and their voice with the microphone.

[0069] Step 4:

[0070] The device uses facial expression recognition algorithms to analyze the captured facial expression data, and voice analysis algorithms to analyze the tone and intonation of the voice, thereby conducting a detailed analysis of the collected data.

[0071] Step 5:

[0072] The facial expression data and voice data analyzed by the device are sent to the server, which automatically stores the received data in a database.

[0073] Step 6:

[0074] The server compares newly received data with past data to determine whether there are any anomalies. If necessary, it runs an anomaly detection algorithm to check for abnormal trends in the data.

[0075] Step 7:

[0076] If an abnormality is detected, the server will send a notification to the administrator (family member, caregiver, etc.) via SMS or email, informing them of the nature of the abnormality and the need for action.

[0077] Step 8:

[0078] If the device detects an abnormality, it will provide the user with voice advice on how to deal with the situation. For example, it might say, "You seem to be feeling unwell today. Please take it easy and rest."

[0079] Step 9:

[0080] If no abnormalities are found, the device returns to its normal state and provides everyday conversation functions. When the user asks, "What's the weather like today?", the device responds, "It's sunny today," and continues the conversation.

[0081] Step 10:

[0082] The device periodically recaptures the user's facial expressions and voice and sends the data to the server, allowing the user's health to be monitored based on the latest data.

[0083] Here are the detailed processing steps of the "Vital Frame" system:

[0084] Example 1

[0085] 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."

[0086] It is often difficult for elderly people and users living alone to properly manage their own health. In particular, it is necessary to understand their daily health status and respond quickly if any abnormalities are detected. At the same time, mental health support is also important, and there is a need to maintain users' mental health through daily communication. Few existing systems comprehensively meet these requirements, and an effective solution is needed.

[0087] 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.

[0088] In this invention, the server includes a means for recognizing a user's voice input and activating the system, a means for authenticating the user using a facial recognition algorithm, a means for collecting the user's facial expression and voice data after authentication, a means for comparing the collected data with past data to detect abnormalities, a means for sending a notification to an administrator when an abnormality is detected, and a means for advising the user on how to deal with the abnormality when an abnormality is detected. This allows for comprehensive monitoring of the user's health status, and enables prompt notification and action when an abnormality is detected. It also supports mental health through everyday conversation, contributing to maintaining the user's mental health.

[0089] "Voice input" refers to the voice information that the user speaks through a microphone, and is the means by which the system recognizes and analyzes that voice information.

[0090] "System startup" refers to the system starting to operate as a trigger for the user's voice input.

[0091] A "facial recognition algorithm" refers to a computational method or software used to analyze a user's facial image and identify the individual.

[0092] "Authentication" refers to verifying a user's identity and granting them permission to use a system.

[0093] "Facial Expression Data" refers to the results of analyzing specific facial expressions of a user captured through a camera.

[0094] "Voice data" refers to information collected from a user's voice and converted into an analyzable format.

[0095] "Past data" refers to facial expression and voice data of users that has been collected and saved up to now, and is the information that serves as the reference when detecting anomalies.

[0096] "Anomaly detection" refers to comparing collected data with past data to discover unusual conditions or problems.

[0097] "Administrator" refers to the person or organization that monitors and manages the system and is the person who will be notified if an abnormality is detected.

[0098] "Notification" refers to warning information sent to an administrator when an abnormality is detected.

[0099] "Advice on how to deal with the problem" refers to specific instructions on what to do or warnings given to the user when an abnormality is detected.

[0100] "Supporting mental health" refers to providing assistance to maintain and promote the user's mental health through everyday conversation.

[0101] A "display device" refers to a device that has a display as a user interface and performs functions such as facial recognition and data collection.

[0102] This invention functions as a system to support health management for elderly people and users living alone. This system uses a photo frame-type device to monitor the user's health status by combining voice input, face authentication, facial expression recognition, and voice analysis, and notifies a manager if an abnormality is detected. In addition, if an abnormality occurs, the system provides the user with appropriate advice on how to deal with it.

[0103] System configuration

[0104] This system uses the following hardware and software:

[0105] Device: A photo frame-type device equipped with a microphone, camera, and display.

[0106] Speech recognition software: Google Cloud Speech-to-Text.

[0107] Facial recognition algorithm: Amazon Rekognition is used.

[0108] Facial expression recognition algorithm: Uses Microsoft Azure Face API.

[0109] Speech analysis software: IBM Watson Speech to Text.

[0110] Server: TensorFlow is used as a machine learning model for anomaly detection.

[0111] Processing flow

[0112] 1. Boot the system:

[0113] The user speaks "Good morning" to the device. The device's microphone picks up the voice and uses Google Cloud Speech-to-Text for speech recognition. If "Good morning" is recognized, the system wakes up.

[0114] 2. User face recognition:

[0115] The device captures the user's face with its camera and uses Amazon Rekognition to perform facial recognition on the image, thereby identifying the user.

[0116] 3. AI-powered questioning and data collection:

[0117] After authentication, the device asks the user, "Did you sleep well last night?" In response to the user's answer, the camera captures their facial expression and the microphone collects their audio. The facial expression is analyzed using the Microsoft Azure Face API, and the audio is analyzed using IBM Watson Speech to Text.

[0118] 4. Data transmission and analysis:

[0119] The collected data is sent from the device to the server via a secure protocol (HTTPS). The server uses TensorFlow to compare the data with past data and detect anomalies. If an anomaly is detected, the server sends a notification to the administrator (by email or SMS).

[0120] 5. Advice on what to do in case of an abnormality:

[0121] When an abnormality is detected, the server sends a notification to the device, which then provides the user with pre-defined advice in the form of a voice message (e.g., "You seem to be feeling unwell today. Please take it easy and rest.").

[0122] 6. Daily communication:

[0123] During normal times, the device will hold daily conversations with the user to support their mental health. When the user asks, "What's the weather like today?", the device will retrieve information from a weather information API (e.g., OpenWeatherMap) and provide a voice response using Google Text-to-Speech.

[0124] Examples of specific examples and prompts

[0125] Examples:

[0126] The user says "Good morning."

[0127] The system starts when the device's microphone picks up the voice and the voice recognition software recognizes "good morning."

[0128] The user answers "Yes" to the question "Did you sleep well last night?"

[0129] The server detects an abnormality and notifies the administrator by email.

[0130] In the event of an abnormality, the device will advise, "You seem to be feeling unwell today. Please take it easy and rest."

[0131] When a user asks, in everyday conversation, "What's the weather like today?", the device will respond with, "It's sunny today."

[0132] Example prompt sentence:

[0133] When I say "Good morning," how does my device use my voice?

[0134] Please explain the steps to identify users using a facial recognition algorithm.

[0135] Describe the process for collecting data. How will the collected data be used?

[0136] Please explain the process for analyzing the collected data. How will you be notified if anomalies are found?

[0137] Please explain the process for advising on how to deal with an anomaly after it is detected.

[0138] Please provide details of your normal communication functions.

[0139] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0140] Step 1:

[0141] System wake-up via user voice input

[0142] Input: The user speaks "Good morning" into the device.

[0143] What happens: The user says "Good morning" to the device. The microphone captures the speech and sends it to the speech recognition software (Google Cloud Speech-to-Text).

[0144] Data processing / calculation: Speech recognition software converts the voice data into text data and verifies that the text is "Good morning."

[0145] Output: The system wakes up and sets a flag to proceed to the next processing step.

[0146] Step 2:

[0147] User facial recognition

[0148] Input: The system startup flag is set and the device's camera is activated.

[0149] What happens: The device camera captures the user's face, and sends the image to a facial recognition algorithm (Amazon Rekognition).

[0150] Data processing / computation: Facial recognition algorithms analyze facial images to obtain the user's identity.

[0151] Output: The user is authenticated and the authentication result is returned to the device.

[0152] Step 3:

[0153] AI-powered questioning and data collection

[0154] Input: Face recognition is successful and the user is recognized.

[0155] Specific behavior: The device asks the user, "Did you sleep well last night?" The user answers "Yes" or "No."

[0156] Data processing / calculation: The camera captures the user's facial expressions and the microphone collects their voice. The data is then analyzed using a facial recognition algorithm (Microsoft Azure Face API) and voice analysis software (IBM Watson Speech to Text).

[0157] Output: Analyzed facial expression and voice data are generated and sent to the next step.

[0158] Step 4:

[0159] Data transmission and analysis

[0160] Input: Analyzed facial expression data and voice data are saved on the device.

[0161] Specific operation: The device sends the collected data to the server via a secure communication protocol (HTTPS).

[0162] Data processing / calculation: The server analyzes the received data and compares it with past data. Anomaly detection algorithms (TensorFlow) are used to detect abnormalities in health status.

[0163] Output: If an abnormality is detected, the server will notify the administrator via email or SMS.

[0164] Step 5:

[0165] Advice on what to do when something goes wrong

[0166] Input: The server detects an anomaly and sends a notification to the device.

[0167] Specific operation: The device receives an abnormality notification and provides the user with pre-defined advice on how to deal with the problem via voice message.

[0168] Data processing / calculation: A specific method of dealing with the abnormality is selected.

[0169] Output: The device provides the user with a spoken advice saying, "You seem to be feeling unwell today. Please take it easy and rest."

[0170] Step 6:

[0171] Daily communication

[0172] Input: Under normal circumstances when the system is operating normally.

[0173] Specific behavior: The user asks the device, "What's the weather like today?"

[0174] Data processing / calculation: The device sends a request to the weather information API (OpenWeatherMap) to obtain the current day's weather information. A response is generated using speech synthesis software (Google Text-to-Speech).

[0175] Output: The device responds by saying, "It's a sunny day today. It would be nice to go for a walk."

[0176] (Application example 1)

[0177] 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."

[0178] For elderly people living alone, it is important to continuously monitor their daily health status and detect abnormalities early. However, existing health management systems have had difficulty in real-time monitoring unless the user periodically inputs data. Furthermore, there were issues with systems lacking the functionality to quickly advise on appropriate countermeasures when abnormalities were detected, and lacking in daily psychological support.

[0179] 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.

[0180] In this invention, the server includes a means for recognizing a user's voice input and activating the system, a means for authenticating the user using a facial recognition algorithm, a means for collecting the user's facial expression and voice data after authentication, a means for comparing the collected data with past data to detect abnormalities, a means for sending a notification to an administrator when an abnormality is detected, a means for providing the user with audio advice on how to deal with the abnormality when an abnormality is detected, a means for interacting with the user through everyday conversation and supporting a healthy mental state, a means for responding to the user's questions using a generative artificial intelligence model and providing everyday conversation using prompt sentences, and a smart display or smartphone-type terminal for authentication and data collection. This makes it possible to monitor the health status of elderly people living alone on a daily basis in real time, and to provide quick and accurate advice in the event of an abnormality, thereby providing safety and security.

[0181] "User voice input" refers to a method in which a user inputs information into a system by speaking.

[0182] The "means for starting the system" is a function that causes the system to start operating in response to the user's voice input.

[0183] A "facial recognition algorithm" is a technology that analyzes a user's facial image captured by a camera and compares it with registered data.

[0184] "Means for authenticating users" refers to a function that identifies users using a facial recognition algorithm.

[0185] "Means for collecting facial and voice data" refers to a function that captures and analyzes the user's facial expressions and voice.

[0186] "Means for detecting abnormalities by comparing past data" refers to a technique for comparing collected data with past data to find abnormalities.

[0187] "Means for sending notification to administrator" is a function that sends an alert to a person in charge of management when an abnormality is detected.

[0188] "A means for providing voice advice on how to deal with the problem" is a function that provides voice instructions on how to deal with the problem when an abnormality is detected.

[0189] "Means of interacting with users through everyday conversation" is a function that allows users to have everyday discussions.

[0190] "Means to support a healthy mental state" are technologies that allow users to continue interacting in order to maintain their mental health.

[0191] A "generative artificial intelligence model" is an algorithm for generating natural conversations based on large amounts of data.

[0192] A "prompt sentence" is an instruction sentence that an artificial intelligence uses to generate an appropriate response.

[0193] A "smart display or smartphone-type terminal" is a display or mobile phone with advanced functions, and is a device that can input and output various types of data.

[0194] System Overview

[0195] This invention is a system that realizes "CareGuard," a health management system for the elderly. CareGuard monitors the user's health status by combining the user's voice input, face recognition, facial expression recognition, and voice analysis. If an abnormality is detected, the system sends a notification to the administrator and also has the function of providing the user with voice advice on how to respond appropriately. It also interacts with the user through everyday conversations to support a healthy mental state.

[0196] Hardware and Software Configuration

[0197] The CareGuard system consists of a smart display or smartphone-type device and a cloud server. The hardware configuration includes a camera, microphone, and display. The system uses the following software components:

[0198] 1. Face recognition algorithm (e.g. OpenCV library)

[0199] 2. Speech recognition algorithms (e.g., SpeechRecognition library)

[0200] 3. Facial expression recognition algorithms (e.g., dlib library)

[0201] 4. Generative AI models (e.g., GPT-3)

[0202] 5. Prompt Sentence Management System

[0203] Detailed system processing explanation

[0204] 1. System startup

[0205] When a user speaks "good morning," a microphone captures the speech and a speech recognition algorithm detects the phrase, waking up the system.

[0206] 2. Facial Recognition

[0207] The system uses a camera to capture the user's face and identifies them through a facial recognition algorithm.

[0208] 3. Interview and data collection

[0209] After facial recognition, the AI ​​assistant will ask questions such as, "Did you sleep well last night?", using facial expression recognition and voice analysis algorithms to collect the user's facial and voice data.

[0210] 4. Data analysis and anomaly detection

[0211] The collected data is sent to a cloud server and compared with past data. Response times, tone of voice, subtle changes in facial expressions, etc. are analyzed to detect anomalies.

[0212] 5. Notifications and advice on how to deal with the issue

[0213] If an abnormality is detected, the server sends a notification to an administrator (e.g., family member or caregiver), and at the same time, gives the user audio advice on how to deal with the situation (e.g., "You seem to be feeling unwell today. Please take it easy and rest.").

[0214] 6. Daily conversation and emotional support

[0215] Even if there are no abnormalities, the AI ​​assistant will interact with the user through everyday conversations, such as saying, "The weather is sunny today. It would be nice to go for a walk." To do this, a generative AI model is used to generate natural conversations using prompt sentences.

[0216] Examples of prompt statements

[0217] Here are some example prompts to input to a generative AI model:

[0218] "Every morning, the user speaks "Good morning." The system then activates the system after analyzing the speech recognition."

[0219] "Identify users by recognizing their faces using a smartphone camera."

[0220] "The AI ​​assistant collects voice and facial expression data in response to questions."

[0221] "If an abnormality is detected, a notification will be sent to family members or caregivers."

[0222] This explanation will help you understand how the CareGuard system works in practice, as it is designed to provide peace of mind and security for seniors living alone.

[0223] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0224] Step 1:

[0225] Wake up the system with voice input

[0226] The user speaks "Good morning" (input). The device's microphone captures the user's voice, and the speech recognition algorithm (SpeechRecognition library) analyzes the voice (data processing). If the analysis result matches "Good morning," the system wakes up and proceeds to the next step (output).

[0227] Step 2:

[0228] User recognition through facial recognition

[0229] The device camera captures the user's face (input). The face recognition algorithm (OpenCV library) analyzes the captured image and compares it with the registered user's face data (data calculation). If the user is recognized, the process proceeds to the next step (output).

[0230] Step 3:

[0231] AI assistant asks questions and collects data

[0232] The device asks the authenticated user, "Did you sleep well last night?" (input). The user responds verbally, and their facial expressions and voice are captured (input). The facial expression recognition algorithm (dlib) and voice analysis algorithm (SpeechRecognition library) analyze the user's facial expressions and voice and collect the data (data processing). The collected data is sent to a cloud server (output).

[0233] Step 4:

[0234] Data analysis and anomaly detection

[0235] The server compares the facial and voice data sent to the cloud with past data (input). The AI ​​algorithm analyzes this data and determines whether there are any abnormalities (data calculation). If an abnormality is detected, the system proceeds to the next step (output).

[0236] Step 5:

[0237] Sending notifications

[0238] If an abnormality is detected, the server sends a notification to an administrator (e.g., family member or caregiver) (output). Specifically, it sends an alert to the administrator's email address, which includes the details of the abnormality and recommended actions to take (input: abnormal data).

[0239] Step 6:

[0240] Advice for users on what to do

[0241] When an abnormality is detected, the device will give the user voice advice on how to deal with the situation (input). For example, it will give a voice message such as "You seem to be feeling unwell today. Please take it easy and rest" (output).

[0242] Step 7:

[0243] Daily conversation and emotional support

[0244] Even if no abnormalities are detected, the device will engage in everyday conversation with the user (input). A generative AI model (such as GPT-3) will generate appropriate answers to questions from the user (data calculation) and provide natural conversation using prompt sentences (output). For example, if the user asks, "What's the weather like today?" the device will respond, "It's sunny today. It would be nice to go for a walk."

[0245] ---

[0246] This enables the CareGuard system to effectively support users' health management, respond quickly if an abnormality is detected, and also provide daily mental support.

[0247] 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.

[0248] This invention provides "Vital Frame," a system that uses a photo frame-type device to support health and emotional management for elderly people and others living alone. This system combines user voice input, face authentication, facial expression recognition, voice analysis, and an emotion engine to grasp the user's emotional state and comprehensively monitor their health and psychological state.

[0249] System Overview

[0250] User-initiated voice activation

[0251] The system is started by the user saying "Good morning" every morning. The device captures the voice through the microphone and detects the word "Good morning" using its voice recognition function. When this voice is recognized, the system starts up.

[0252] Explanation by language generation:

[0253] When the user says "Good morning," the device recognizes the voice and starts up the system.

[0254] User identification by facial recognition

[0255] After booting, the device uses its camera to capture the user's face, which allows a facial recognition algorithm to analyze the image and identify the user. Once the user is identified, the system proceeds to the next processing step.

[0256] Explanation by language generation:

[0257] The device captures the user's face with its camera, runs a facial recognition algorithm, and recognizes the user.

[0258] Collection of facial expression and voice data after authentication

[0259] After authentication, the device asks AI questions and collects facial and voice data from the user. For example, it might ask, "Did you sleep well last night?" When the user responds, the device simultaneously captures their facial expression with the camera and their voice with the microphone.

[0260] Explanation by language generation:

[0261] The device asks, "Did you sleep well last night?" and when the user answers "yes," it collects their facial expressions and voice.

[0262] Emotion recognition by emotion engine

[0263] The device inputs the collected facial and voice data into an emotion engine to analyze the user's emotional state, for example, identifying emotions such as joy, sadness, and surprise, and then integrates the results with other data to provide a comprehensive assessment of the user's health.

[0264] Explanation by language generation:

[0265] The device runs the user's facial expressions and voice data through an emotion engine to identify the user's emotions.

[0266] Sending data to the server and detecting anomalies

[0267] The device transmits the facial, voice, and emotion data it captures to a server, which then runs an algorithm to compare the new data with past data and detect anomalies. If an anomaly is detected, a notification is sent to an administrator.

[0268] Explanation by language generation:

[0269] The device sends data to the server, and if the server detects an abnormality, it notifies the administrator.

[0270] Advice on what to do when something goes wrong

[0271] If an abnormality is detected, the device will provide the user with voice advice on how to deal with the situation, such as "You seem to be feeling unwell today. Please take it easy and rest."

[0272] Explanation by language generation:

[0273] The device advises the user, "You seem to be feeling unwell today. Please take it easy and rest."

[0274] Daily communication

[0275] Under normal circumstances, the device will hold daily conversations with the user, providing support for not only their health but also their psychological state. Using the analysis results of the emotion engine, the device will have appropriate conversations based on the user's mood.

[0276] Explanation by language generation:

[0277] When a user asks, "What's the weather like today?", the device responds with, "It's sunny today," and responds based on the results of the emotion engine.

[0278] This allows the Vital Frame system to comprehensively monitor the user's health and emotional state and respond quickly in the event of an abnormality. By combining it with the emotion engine, more precise health management and psychological support are possible.

[0279] The processing flow will be explained below.

[0280] Step 1:

[0281] The user speaks "Good morning" into the photo frame device. The device captures the voice through its microphone, and the voice recognition function detects the word "Good morning." If the voice is recognized correctly, the system will start up.

[0282] Step 2:

[0283] The device uses a camera to capture the user's face. A facial recognition algorithm analyzes this image and identifies the user. If the user is successfully authenticated, the device proceeds to the next step.

[0284] Step 3:

[0285] The device will start asking AI questions, such as, "Did you sleep well last night?" As the user responds, the device will simultaneously capture their facial expressions with the camera and their voice with the microphone.

[0286] Step 4:

[0287] The device uses facial expression recognition algorithms to analyze the captured facial expression data, and voice analysis algorithms to analyze the tone and intonation of the voice, thereby conducting a detailed analysis of the collected data.

[0288] Step 5:

[0289] The facial expression and voice data analyzed by the device are input into an emotion engine to analyze the user's emotional state. Based on the collected data, the emotion engine identifies emotions such as joy, sadness, and surprise.

[0290] Step 6:

[0291] The device transmits the analyzed facial expression data, voice data, and emotion data to the server, which automatically stores the received data in a database.

[0292] Step 7:

[0293] The server compares newly received data with past data to determine whether there are any anomalies. If necessary, it runs an anomaly detection algorithm to check for abnormal trends in the data.

[0294] Step 8:

[0295] If an abnormality is detected, the server will send a notification to the administrator (family member, caregiver, etc.) via SMS or email, informing them of the nature of the abnormality and the need for action.

[0296] Step 9:

[0297] If the device detects an abnormality, it will provide the user with voice advice on how to deal with the situation. For example, it might say, "You seem to be feeling unwell today. Please take it easy and rest."

[0298] Step 10:

[0299] If no abnormalities are found, the device returns to its normal state and provides everyday conversation functions. When the user asks, "What's the weather like today?", the device responds, "It's sunny today," and continues the conversation. The device uses an emotion engine to respond according to the user's emotions.

[0300] Step 11:

[0301] The device periodically recaptures the user's facial expressions and voice and sends the data to the server, allowing the device to monitor the user's health and emotional state based on the most up-to-date data.

[0302] These are the specific processing steps in the "Vital Frame" system.

[0303] Example 2

[0304] 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."

[0305] The goal is to comprehensively monitor the health and emotional state of elderly people and users living alone, and quickly detect and respond to any abnormalities. Conventional systems have had difficulty efficiently and effectively collecting and analyzing this information and taking appropriate action.

[0306] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for recognizing a voice input by a user and activating the system, means for authenticating the user using a face recognition algorithm, means for collecting facial expression and voice data of the user after authentication, means for inputting the collected data into an emotion engine and analyzing the emotional state, means for comparing data including the analysis results with past data to detect abnormalities, and means for sending a notification to an administrator when an abnormality is detected. This makes it possible to comprehensively monitor the user's health state and emotional state and to respond quickly when an abnormality occurs.

[0307] "User voice input" is the process by which the system captures the user's voice through a microphone and recognizes that voice.

[0308] "Means for starting the system" refers to a function for starting the operation of the system by a trigger such as voice input.

[0309] A "face recognition algorithm" is an algorithm that analyzes facial images captured by a camera and determines the user's identity.

[0310] "Means for authenticating users" refers to a function for identifying who a user is, using a facial recognition algorithm or the like.

[0311] "Means for collecting user's facial expression and voice data" refers to a function for capturing the user's facial expression and voice information using a camera or microphone and collecting this data.

[0312] An "emotion engine" is software or algorithm that analyzes collected facial and voice data to identify a user's emotional state.

[0313] The "means for analyzing emotional state" is a function for analyzing the user's emotions using an emotion engine and identifying their state.

[0314] The "means for detecting abnormalities by comparing past data" is a function for comparing newly collected data with past data and identifying abnormal conditions that are different from normal.

[0315] The "means for sending a notification to an administrator when an abnormality is detected" is a function for sending a notification to an administrator when an abnormality is detected.

[0316] The "means for providing advice on how to deal with the problem" is a function that provides voice instructions to the user on how to deal with the problem when an abnormality is detected.

[0317] A "photo frame terminal" is a device shaped like a photo frame, equipped with functions such as a camera, microphone, and display.

[0318] "Abnormal" refers to a deviation from normal health or emotional state.

[0319] An "administrator" is a person or organization responsible for monitoring the health and emotional state of users in the system and intervening when necessary.

[0320] This invention provides a system called "Vital Frame" that supports health and emotional management for elderly people and users living alone. The system uses a photo frame-type device to grasp the user's emotional state and comprehensively monitor their health and psychological state by combining voice input, face authentication, facial expression recognition, voice analysis, and an emotion engine.

[0321] Hardware and software used

[0322] Microphone: Used to capture the user's voice.

[0323] Camera: Used to capture the user's face and facial expressions.

[0324] Google Speech-to-Text API: Speech recognition software that converts voice input into text.

[0325] Amazon Rekognition: Image analysis software that provides facial recognition algorithms.

[0326] Microsoft Azure Emotion API: An emotion recognition engine that analyzes the user's emotional state.

[0327] Server: A computer that stores and analyzes data.

[0328] Photo frame type terminal: A device that integrates the above hardware and software.

[0329] 1. User voice input

[0330] Every morning, the user inputs the voice by saying "Good morning." The device's microphone captures this voice and uses the Google Speech-to-Text API to recognize the word "Good morning." Once this voice is recognized, the system wakes up.

[0331] Specific examples

[0332] The user says "Good morning." The audio is captured by the device's microphone and converted to text using the Google Speech-to-Text API.

[0333] Prompt Sentence Examples

[0334] Convert the following Japanese audio to text: "Good morning"

[0335] 2. User identification through facial recognition

[0336] Once the system is up and running, the device's camera automatically captures the user's face and runs a facial recognition algorithm using Amazon Rekognition, which determines the user's identity.

[0337] Specific examples

[0338] The camera captures the user's face and uses Amazon Rekognition to recognize it.

[0339] 3. Collecting facial and voice data after authentication

[0340] Once facial recognition is complete, the device will ask an AI question, such as, "Did you sleep well last night?" When the user responds, the camera captures their facial expression and the microphone captures their voice.

[0341] Specific examples

[0342] The device asks, "Did you sleep well last night?" and captures your facial expressions and voice as you answer "yes."

[0343] 4. Emotion Recognition by Emotion Engine

[0344] The collected facial and voice data is fed into an emotion engine using the Microsoft Azure Emotion API, which analyzes this data and identifies the user's emotional state.

[0345] Specific examples

[0346] The captured data is analyzed using the Microsoft Azure Emotion API to identify the user's emotions such as happiness, sadness, surprise, etc.

[0347] Prompt Sentence Examples

[0348] Analyze the following facial expression and audio data to identify the emotion: "Image data: <Image file path> Audio data: <Audio file path>"

[0349] 5. Sending data to the server and detecting anomalies

[0350] The device sends the collected data to a server, where an algorithm compares it with past data to detect anomalies, and if an anomaly is detected, a notification is sent to the administrator.

[0351] 6. Advice on what to do in case of an abnormality

[0352] If the device detects an abnormality, it will give the user voice advice on how to deal with the situation, such as "You seem to be feeling unwell today. Please take it easy and rest."

[0353] 7. Daily communication

[0354] During normal times, the device will hold daily conversations with the user and provide appropriate responses based on the results of the emotion engine. By supporting not only the user's health but also their psychological state, the device aims to improve their quality of life.

[0355] Specific examples

[0356] When a user asks, "What's the weather like today?", the device responds with, "It's sunny today," and then responds according to the user's emotional state.

[0357] Through these processes, the Vital Frame system can comprehensively monitor the user's health and emotional state and respond quickly to any abnormalities. By combining this with the emotion engine, more precise health management and psychological support are possible.

[0358] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0359] Step 1:

[0360] When a user says "Good morning," the device's microphone captures the voice. The device sends the voice input ("Good morning") to the Google Speech-to-Text API and receives text data (recognized as "Good morning"). The device confirms that this text data is "Good morning" and wakes up the system.

[0361] Step 2:

[0362] When the device starts up, the camera automatically captures the user's face. The captured facial image data is sent to Amazon Rekognition, which runs a facial recognition algorithm to identify the user. Based on the recognition result (user ID), the system loads the system settings for that user.

[0363] Step 3:

[0364] Once the user authentication is complete, the device will begin asking questions automatically using AI. Specifically, the device will ask aloud, "Did you sleep well last night?" When the user responds, the device's camera will capture the user's facial expression and the microphone will capture their voice. This data (facial image data and voice data) will be sent to the emotion engine.

[0365] Step 4:

[0366] The device inputs the collected facial image data and voice data into the Microsoft Azure Emotion API, which analyzes the user's emotional state. For example, emotions such as joy, sadness, and surprise are output. The analysis results are then integrated with other health data.

[0367] Step 5:

[0368] The device sends analyzed emotional data and other health data to a server, which compares the received data with past data and runs algorithms to detect anomalies. If an anomaly is detected, a real-time notification is sent to the administrator.

[0369] Step 6:

[0370] When the server reports an abnormality, the device will advise the user on how to deal with the situation. For example, the system might give a voice instruction such as, "You seem to be feeling unwell today. Please take it easy and rest." This interaction allows the user to take appropriate action.

[0371] Step 7:

[0372] Under normal circumstances, the device will engage in everyday conversations with the user. When the user asks, "What's the weather like today?", the device will provide weather information. The device will then use the results of its emotion engine to generate an appropriate answer based on the user's emotional state. For example, the system might respond, "It's sunny today. It feels good to go outside."

[0373] In this way, the "Vital Frame" system can comprehensively monitor the user's health and emotional state, quickly detect and respond to abnormalities, and thereby improve the user's quality of life.

[0374] (Application example 2)

[0375] 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."

[0376] When elderly people live alone, they often lack adequate health care and psychological support. As a result, poor health care and feelings of loneliness can lead to serious problems. In particular, poor dietary choices and a lack of psychological support can lead to a deterioration in overall health and emotional state. The present invention aims to solve these problems and provide a system that allows elderly people to live alone in a healthier and more secure way.

[0377] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0378] In this invention, the server includes means for recognizing voice input and activating the system, means for authenticating the user using a face recognition algorithm, means for collecting the user's facial expression and voice data after authentication, means for detecting abnormalities by comparing the collected data with past data, means for sending a notification to an administrator when an abnormality is detected, means for analyzing the user's emotional state when an abnormality is detected, and means for making dietary suggestions based on the emotional state. This makes it possible to comprehensively manage the user's health and emotional state and provide appropriate dietary suggestions and psychological support.

[0379] The "means for starting the system" refers to a means having a function for recognizing a user's voice input and starting the operation of the system.

[0380] A "face recognition algorithm" is a technology that has the function of analyzing image data captured using a camera and identifying a user's face.

[0381] The "means for collecting facial expression and voice data" refers to a means having the function of collecting the user's facial expression with a camera and voice with a microphone.

[0382] "Means for detecting anomalies" refers to means that have algorithms or functions that compare collected data with past data and identify anomalies.

[0383] The "means for sending a notification to an administrator" is a means having a function for sending a notification to a pre-registered administrator when an abnormality is detected.

[0384] "Means for analyzing the emotional state of a user" refers to means having algorithms or techniques for analyzing the emotional state of a user based on collected facial expression and voice data.

[0385] The "means for making meal suggestions" is a means having a function of suggesting appropriate foods and meal menus to the user based on the analyzed emotional state.

[0386] This invention provides "Vital Meal," an application example of a system that supports health and emotional management for elderly people living alone, using a smartphone and a head-mounted display. "Vital Meal" combines voice input, face recognition, facial expression recognition, voice analysis, and an emotion engine to comprehensively manage the user's health and emotional state, and based on that, makes meal suggestions and provides psychological support.

[0387] The system works as follows:

[0388] System configuration

[0389] 1. Voice input recognition method

[0390] The system wakes up when the user speaks. This process uses a microphone and the speech_recognition library. For example, if the user says "Good morning," the system wakes up.

[0391] 2. Facial Recognition Methods

[0392] After the system boots up, it uses a camera to capture the user's face and then uses a facial recognition algorithm to identify the user. This process uses the OpenCV library, for example, to detect the user's face and compare it with the registered facial data for authentication.

[0393] 3. Facial and vocal data collection methods

[0394] After authentication, the AI ​​asks questions and collects facial and voice data from the user. For example, it asks, "Did you sleep well last night?" and collects data from the camera and microphone when the user answers "yes."

[0395] 4. Anomaly Detection Methods

[0396] The collected data is input into an emotion engine to analyze the user's emotional state. It then checks whether any abnormalities are detected. If any abnormalities are detected, an algorithm is activated to compare them with past data.

[0397] 5. Means of notification

[0398] If an abnormality is detected, a notification is sent to the administrator. For example, if an abnormal emotional state is detected, an alert is sent to the administrator's smartphone.

[0399] 6. Emotional state analysis method

[0400] If an anomaly is detected, the emotion engine is used to analyze the detailed emotional state. For example, if the user is analyzed as being "tired," the reason and degree of fatigue are further analyzed.

[0401] 7. Meal suggestion tools

[0402] Based on the analyzed emotional state, the system will suggest appropriate foods and meal plans to the user. For example, if it determines that an energy replenishment is needed, it will suggest a combination of banana and yogurt.

[0403] Hardware and software used

[0404] Hardware: smartphone, head-mounted display, microphone, camera

[0405] Software: speech_recognition library, OpenCV library, emotion engine (proprietary algorithm or third-party API), food suggestion engine (proprietary algorithm or third-party API)

[0406] Specific examples

[0407] "What would you recommend for dinner tonight?"

[0408] "Suggest an energizing meal"

[0409] "Tell me some low-calorie lunch recipes"

[0410] This system allows users to receive individually tailored health management and psychological support, improving the quality of life for elderly people living alone.

[0411] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0412] Step 1:

[0413] Device startup and voice recognition

[0414] The device captures the user's voice input with a microphone and converts the voice to text using the speech_recognition library. For example, if a user says "Good morning," the system recognizes the voice. This voice data is input, and the speech recognition engine outputs the text data "Good morning."

[0415] Step 2:

[0416] User identification by facial recognition

[0417] After the system boots up, the device will use the camera to capture the user's face. The image will be analyzed using the OpenCV library, and the face recognition algorithm will identify the user. This face image will be the input data, and the face recognition algorithm will output a user ID. For example, if the user's face matches, the user ID will be recognized by the system.

[0418] Step 3:

[0419] Facial and vocal data collection

[0420] After authenticating the user, the device asks questions such as "Did you sleep well last night?" While the user answers, the camera captures their facial expressions and the microphone collects their voice. The collected facial images and voice data become input data, and this data is used for subsequent processing. For example, it records whether the user is smiling when answering "yes."

[0421] Step 4:

[0422] Sentiment analysis and anomaly detection

[0423] The collected data is sent to an emotion engine, which analyzes the user's emotional state based on facial expressions and voice. The emotion analysis algorithm takes these data as input and outputs the user's emotional state (e.g., "happy," "sad," "tired," etc.). The system then compares it with past data to detect anomalies. For example, if the user is determined to be "tired," it checks whether the state is abnormal.

[0424] Step 5:

[0425] Administrator Notification

[0426] If an abnormality is detected, the device will send a notification to the administrator via the server. The abnormal state data will be sent as input, and a notification message will be displayed on the administrator's smartphone or PC. For example, if the user's emotional state is detected as "sadness" for a long period of time, an alert will be sent to the administrator.

[0427] Step 6:

[0428] Meal suggestions based on emotional state

[0429] Based on the data analyzed by the emotion engine, the system makes meal suggestions suitable for the user. The meal suggestion engine receives the results of emotion analysis as input and outputs the optimal meal menu. For example, if the user feels "tired," it will suggest foods to replenish energy (bananas, yogurt, etc.).

[0430] Step 7:

[0431] Daily communication and psychological support

[0432] The device provides psychological support to users through everyday conversations. If a user asks, "What's the weather like today?", the system will respond with, "It's sunny today," and then engage in appropriate dialogue accordingly. This allows communication tailored to the user's mood, providing psychological support.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] [Second embodiment]

[0437] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0438] 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.

[0439] 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).

[0440] 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.

[0441] 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.

[0442] 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).

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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."

[0449] This invention provides "Vital Frame," a system that uses a photo frame-type device to support health management for elderly people and others living alone. This system monitors the user's health status by combining user voice input, face authentication, facial expression recognition, and voice analysis, and notifies the administrator if an abnormality is detected. In addition, if an abnormality occurs, the system provides the user with appropriate advice on how to deal with it.

[0450] System Overview

[0451] The device wakes up when the user says "good morning" every morning.

[0452] The device captures the user's voice through a microphone and triggers the system by detecting the phrase "good morning" using a voice recognition algorithm.

[0453] Explanation by language generation:

[0454] When the user says "Good morning," the device recognizes the voice and starts up the system.

[0455] A device equipped with a means to identify users through facial recognition.

[0456] The device captures the user's face with its camera and uses a facial recognition algorithm to identify the user. This process allows the device to identify the user and proceed to the next step.

[0457] Explanation by language generation:

[0458] The device captures the user's face with a camera and runs a facial recognition algorithm based on the image to identify the user.

[0459] After authentication, the device asks AI questions and collects facial and voice data from the user.

[0460] For example, it asks a question like, "Did you sleep well last night?", and the user responds. At that time, the facial recognition algorithm analyzes the user's facial expressions, and the voice analysis algorithm analyzes the intonation and sound patterns of the voice.

[0461] Explanation by language generation:

[0462] The device asks, "Did you sleep well last night?" and when the user answers "yes," it collects their facial expressions and voice.

[0463] The collected data is sent to a server and an algorithm is run to compare it with past data and detect anomalies.

[0464] The collected facial expression and voice data is sent from the device to a server, which compares it with past data to detect any abnormalities. If an abnormality is detected, an administrator is notified.

[0465] Explanation by language generation:

[0466] The device sends data to the server, which compares it with past data to detect any anomalies. If an anomaly is detected, the administrator is notified.

[0467] If an error occurs, the system provides advice to the user on how to deal with the problem.

[0468] When an abnormality is detected, the device will provide the user with advice on how to deal with the problem via voice messages, etc.

[0469] Explanation by language generation:

[0470] The device advises the user, "You seem to be feeling unwell today. Please take it easy and rest."

[0471] A device equipped with a means of everyday communication.

[0472] Under normal circumstances, the device will hold daily conversations with the user and support their mental health. For example, when the user asks, "What's the weather like today?", the device will respond, "It's sunny today. It would be nice to go for a walk."

[0473] Explanation by language generation:

[0474] When a user asks, "What's the weather like today?" the device will respond with, "It's sunny today," interacting with the user through everyday conversations.

[0475] ---

[0476] In this way, the Vital Frame system monitors the user's health condition daily and has the ability to respond quickly in the event of an abnormality. By combining the above functions, it is possible to ensure the safety and health of family members living far away.

[0477] The processing flow will be explained below.

[0478] Step 1:

[0479] The user speaks "Good morning" into the photo frame device. The device captures the voice through its microphone, and the voice recognition function detects the word "Good morning." If the voice is recognized correctly, the system will start up.

[0480] Step 2:

[0481] The device uses a camera to capture the user's face. A facial recognition algorithm analyzes this image and identifies the user. If the user is successfully authenticated, the device proceeds to the next step.

[0482] Step 3:

[0483] The device will start asking AI questions, such as, "Did you sleep well last night?" As the user responds, the device will simultaneously capture their facial expressions with the camera and their voice with the microphone.

[0484] Step 4:

[0485] The device uses facial expression recognition algorithms to analyze the captured facial expression data, and voice analysis algorithms to analyze the tone and intonation of the voice, thereby conducting a detailed analysis of the collected data.

[0486] Step 5:

[0487] The facial expression data and voice data analyzed by the device are sent to the server, which automatically stores the received data in a database.

[0488] Step 6:

[0489] The server compares newly received data with past data to determine whether there are any anomalies. If necessary, it runs an anomaly detection algorithm to check for abnormal trends in the data.

[0490] Step 7:

[0491] If an abnormality is detected, the server will send a notification to the administrator (family member, caregiver, etc.) via SMS or email, informing them of the nature of the abnormality and the need for action.

[0492] Step 8:

[0493] If the device detects an abnormality, it will provide the user with voice advice on how to deal with the situation. For example, it might say, "You seem to be feeling unwell today. Please take it easy and rest."

[0494] Step 9:

[0495] If no abnormalities are found, the device returns to its normal state and provides everyday conversation functions. When the user asks, "What's the weather like today?", the device responds, "It's sunny today," and continues the conversation.

[0496] Step 10:

[0497] The device periodically recaptures the user's facial expressions and voice and sends the data to the server, allowing the user's health to be monitored based on the latest data.

[0498] Here are the detailed processing steps of the "Vital Frame" system:

[0499] Example 1

[0500] 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."

[0501] It is often difficult for elderly people and users living alone to properly manage their own health. In particular, it is necessary to understand their daily health status and respond quickly if any abnormalities are detected. At the same time, mental health support is also important, and there is a need to maintain users' mental health through daily communication. Few existing systems comprehensively meet these requirements, and an effective solution is needed.

[0502] 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.

[0503] In this invention, the server includes a means for recognizing a user's voice input and activating the system, a means for authenticating the user using a facial recognition algorithm, a means for collecting the user's facial expression and voice data after authentication, a means for comparing the collected data with past data to detect abnormalities, a means for sending a notification to an administrator when an abnormality is detected, and a means for advising the user on how to deal with the abnormality when an abnormality is detected. This allows for comprehensive monitoring of the user's health status, and enables prompt notification and action when an abnormality is detected. It also supports mental health through everyday conversation, contributing to maintaining the user's mental health.

[0504] "Voice input" refers to the voice information that the user speaks through a microphone, and is the means by which the system recognizes and analyzes that voice information.

[0505] "System startup" refers to the system starting to operate as a trigger for the user's voice input.

[0506] A "facial recognition algorithm" refers to a computational method or software used to analyze a user's facial image and identify the individual.

[0507] "Authentication" refers to verifying a user's identity and granting them permission to use a system.

[0508] "Facial Expression Data" refers to the results of analyzing specific facial expressions of a user captured through a camera.

[0509] "Voice data" refers to information collected from a user's voice and converted into an analyzable format.

[0510] "Past data" refers to facial expression and voice data of users that has been collected and saved up to now, and is the information that serves as the reference when detecting anomalies.

[0511] "Anomaly detection" refers to comparing collected data with past data to discover unusual conditions or problems.

[0512] "Administrator" refers to the person or organization that monitors and manages the system and is the person who will be notified if an abnormality is detected.

[0513] "Notification" refers to warning information sent to an administrator when an abnormality is detected.

[0514] "Advice on how to deal with the problem" refers to specific instructions on what to do or warnings given to the user when an abnormality is detected.

[0515] "Supporting mental health" refers to providing assistance to maintain and promote the user's mental health through everyday conversation.

[0516] A "display device" refers to a device that has a display as a user interface and performs functions such as facial recognition and data collection.

[0517] This invention functions as a system to support health management for elderly people and users living alone. This system uses a photo frame-type device to monitor the user's health status by combining voice input, face authentication, facial expression recognition, and voice analysis, and notifies a manager if an abnormality is detected. In addition, if an abnormality occurs, the system provides the user with appropriate advice on how to deal with it.

[0518] System configuration

[0519] This system uses the following hardware and software:

[0520] Device: A photo frame-type device equipped with a microphone, camera, and display.

[0521] Speech recognition software: Google Cloud Speech-to-Text.

[0522] Facial recognition algorithm: Amazon Rekognition is used.

[0523] Facial expression recognition algorithm: Uses Microsoft Azure Face API.

[0524] Speech analysis software: IBM Watson Speech to Text.

[0525] Server: TensorFlow is used as a machine learning model for anomaly detection.

[0526] Processing flow

[0527] 1. Boot the system:

[0528] The user speaks "Good morning" to the device. The device's microphone picks up the voice and uses Google Cloud Speech-to-Text for speech recognition. If "Good morning" is recognized, the system wakes up.

[0529] 2. User face recognition:

[0530] The device captures the user's face with its camera and uses Amazon Rekognition to perform facial recognition on the image, thereby identifying the user.

[0531] 3. AI-powered questioning and data collection:

[0532] After authentication, the device asks the user, "Did you sleep well last night?" In response to the user's answer, the camera captures their facial expression and the microphone collects their audio. The facial expression is analyzed using the Microsoft Azure Face API, and the audio is analyzed using IBM Watson Speech to Text.

[0533] 4. Data transmission and analysis:

[0534] The collected data is sent from the device to the server via a secure protocol (HTTPS). The server uses TensorFlow to compare the data with past data and detect anomalies. If an anomaly is detected, the server sends a notification to the administrator (by email or SMS).

[0535] 5. Advice on what to do in case of an abnormality:

[0536] When an abnormality is detected, the server sends a notification to the device, which then provides the user with pre-defined advice in the form of a voice message (e.g., "You seem to be feeling unwell today. Please take it easy and rest.").

[0537] 6. Daily communication:

[0538] During normal times, the device will hold daily conversations with the user to support their mental health. When the user asks, "What's the weather like today?", the device will retrieve information from a weather information API (e.g., OpenWeatherMap) and provide a voice response using Google Text-to-Speech.

[0539] Examples of specific examples and prompts

[0540] Examples:

[0541] The user says "Good morning."

[0542] The system starts when the device's microphone picks up the voice and the voice recognition software recognizes "good morning."

[0543] The user answers "Yes" to the question "Did you sleep well last night?"

[0544] The server detects an abnormality and notifies the administrator by email.

[0545] In the event of an abnormality, the device will advise, "You seem to be feeling unwell today. Please take it easy and rest."

[0546] When a user asks, in everyday conversation, "What's the weather like today?", the device will respond with, "It's sunny today."

[0547] Example prompt sentence:

[0548] When I say "Good morning," how does my device use my voice?

[0549] Please explain the steps to identify users using a facial recognition algorithm.

[0550] Describe the process for collecting data. How will the collected data be used?

[0551] Please explain the process for analyzing the collected data. How will you be notified if anomalies are found?

[0552] Please explain the process for advising on how to deal with an anomaly after it is detected.

[0553] Please provide details of your normal communication functions.

[0554] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0555] Step 1:

[0556] System wake-up via user voice input

[0557] Input: The user speaks "Good morning" into the device.

[0558] What happens: The user says "Good morning" to the device. The microphone captures the speech and sends it to the speech recognition software (Google Cloud Speech-to-Text).

[0559] Data processing / calculation: Speech recognition software converts the voice data into text data and verifies that the text is "Good morning."

[0560] Output: The system wakes up and sets a flag to proceed to the next processing step.

[0561] Step 2:

[0562] User facial recognition

[0563] Input: The system startup flag is set and the device's camera is activated.

[0564] What happens: The device camera captures the user's face, and sends the image to a facial recognition algorithm (Amazon Rekognition).

[0565] Data processing / computation: Facial recognition algorithms analyze facial images to obtain the user's identity.

[0566] Output: The user is authenticated and the authentication result is returned to the device.

[0567] Step 3:

[0568] AI-powered questioning and data collection

[0569] Input: Face recognition is successful and the user is recognized.

[0570] Specific behavior: The device asks the user, "Did you sleep well last night?" The user answers "Yes" or "No."

[0571] Data processing / calculation: The camera captures the user's facial expressions and the microphone collects their voice. The data is then analyzed using a facial recognition algorithm (Microsoft Azure Face API) and voice analysis software (IBM Watson Speech to Text).

[0572] Output: Analyzed facial expression and voice data are generated and sent to the next step.

[0573] Step 4:

[0574] Data transmission and analysis

[0575] Input: Analyzed facial expression data and voice data are saved on the device.

[0576] Specific operation: The device sends the collected data to the server via a secure communication protocol (HTTPS).

[0577] Data processing / calculation: The server analyzes the received data and compares it with past data. Anomaly detection algorithms (TensorFlow) are used to detect abnormalities in health status.

[0578] Output: If an abnormality is detected, the server will notify the administrator via email or SMS.

[0579] Step 5:

[0580] Advice on what to do when something goes wrong

[0581] Input: The server detects an anomaly and sends a notification to the device.

[0582] Specific operation: The device receives an abnormality notification and provides the user with pre-defined advice on how to deal with the problem via voice message.

[0583] Data processing / calculation: A specific method of dealing with the abnormality is selected.

[0584] Output: The device provides the user with a spoken advice saying, "You seem to be feeling unwell today. Please take it easy and rest."

[0585] Step 6:

[0586] Daily communication

[0587] Input: Under normal circumstances when the system is operating normally.

[0588] Specific behavior: The user asks the device, "What's the weather like today?"

[0589] Data processing / calculation: The device sends a request to the weather information API (OpenWeatherMap) to obtain the current day's weather information. A response is generated using speech synthesis software (Google Text-to-Speech).

[0590] Output: The device responds by saying, "It's a sunny day today. It would be nice to go for a walk."

[0591] (Application example 1)

[0592] 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."

[0593] For elderly people living alone, it is important to continuously monitor their daily health status and detect abnormalities early. However, existing health management systems have had difficulty in real-time monitoring unless the user periodically inputs data. Furthermore, there were issues with systems lacking the functionality to quickly advise on appropriate countermeasures when abnormalities were detected, and lacking in daily psychological support.

[0594] 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.

[0595] In this invention, the server includes a means for recognizing a user's voice input and activating the system, a means for authenticating the user using a facial recognition algorithm, a means for collecting the user's facial expression and voice data after authentication, a means for comparing the collected data with past data to detect abnormalities, a means for sending a notification to an administrator when an abnormality is detected, a means for providing the user with audio advice on how to deal with the abnormality when an abnormality is detected, a means for interacting with the user through everyday conversation and supporting a healthy mental state, a means for responding to the user's questions using a generative artificial intelligence model and providing everyday conversation using prompt sentences, and a smart display or smartphone-type terminal for authentication and data collection. This makes it possible to monitor the health status of elderly people living alone on a daily basis in real time, and to provide quick and accurate advice in the event of an abnormality, thereby providing safety and security.

[0596] "User voice input" refers to a method in which a user inputs information into a system by speaking.

[0597] The "means for starting the system" is a function that causes the system to start operating in response to the user's voice input.

[0598] A "facial recognition algorithm" is a technology that analyzes a user's facial image captured by a camera and compares it with registered data.

[0599] "Means for authenticating users" refers to a function that identifies users using a facial recognition algorithm.

[0600] "Means for collecting facial and voice data" refers to a function that captures and analyzes the user's facial expressions and voice.

[0601] "Means for detecting abnormalities by comparing past data" refers to a technique for comparing collected data with past data to find abnormalities.

[0602] "Means for sending notification to administrator" is a function that sends an alert to a person in charge of management when an abnormality is detected.

[0603] "A means for providing voice advice on how to deal with the problem" is a function that provides voice instructions on how to deal with the problem when an abnormality is detected.

[0604] "Means of interacting with users through everyday conversation" is a function that allows users to have everyday discussions.

[0605] "Means to support a healthy mental state" are technologies that allow users to continue interacting in order to maintain their mental health.

[0606] A "generative artificial intelligence model" is an algorithm for generating natural conversations based on large amounts of data.

[0607] A "prompt sentence" is an instruction sentence that an artificial intelligence uses to generate an appropriate response.

[0608] A "smart display or smartphone-type terminal" is a display or mobile phone with advanced functions, and is a device that can input and output various types of data.

[0609] System Overview

[0610] This invention is a system that realizes "CareGuard," a health management system for the elderly. CareGuard monitors the user's health status by combining the user's voice input, face recognition, facial expression recognition, and voice analysis. If an abnormality is detected, the system sends a notification to the administrator and also has the function of providing the user with voice advice on how to respond appropriately. It also interacts with the user through everyday conversations to support a healthy mental state.

[0611] Hardware and Software Configuration

[0612] The CareGuard system consists of a smart display or smartphone-type device and a cloud server. The hardware configuration includes a camera, microphone, and display. The system uses the following software components:

[0613] 1. Face recognition algorithm (e.g. OpenCV library)

[0614] 2. Speech recognition algorithms (e.g., SpeechRecognition library)

[0615] 3. Facial expression recognition algorithms (e.g., dlib library)

[0616] 4. Generative AI models (e.g., GPT-3)

[0617] 5. Prompt Sentence Management System

[0618] Detailed system processing explanation

[0619] 1. System startup

[0620] When a user speaks "good morning," a microphone captures the speech and a speech recognition algorithm detects the phrase, waking up the system.

[0621] 2. Facial Recognition

[0622] The system uses a camera to capture the user's face and identifies them through a facial recognition algorithm.

[0623] 3. Interview and data collection

[0624] After facial recognition, the AI ​​assistant will ask questions such as, "Did you sleep well last night?", using facial expression recognition and voice analysis algorithms to collect the user's facial and voice data.

[0625] 4. Data analysis and anomaly detection

[0626] The collected data is sent to a cloud server and compared with past data. Response times, tone of voice, subtle changes in facial expressions, etc. are analyzed to detect anomalies.

[0627] 5. Notifications and advice on how to deal with the issue

[0628] If an abnormality is detected, the server sends a notification to an administrator (e.g., family member or caregiver), and at the same time, gives the user audio advice on how to deal with the situation (e.g., "You seem to be feeling unwell today. Please take it easy and rest.").

[0629] 6. Daily conversation and emotional support

[0630] Even if there are no abnormalities, the AI ​​assistant will interact with the user through everyday conversations, such as saying, "The weather is sunny today. It would be nice to go for a walk." To do this, a generative AI model is used to generate natural conversations using prompt sentences.

[0631] Examples of prompt statements

[0632] Here are some example prompts to input to a generative AI model:

[0633] "Every morning, the user speaks "Good morning." The system then activates the system after analyzing the speech recognition."

[0634] "Identify users by recognizing their faces using a smartphone camera."

[0635] "The AI ​​assistant collects voice and facial expression data in response to questions."

[0636] "If an abnormality is detected, a notification will be sent to family members or caregivers."

[0637] This explanation will help you understand how the CareGuard system works in practice, as it is designed to provide peace of mind and security for seniors living alone.

[0638] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0639] Step 1:

[0640] Wake up the system with voice input

[0641] The user speaks "Good morning" (input). The device's microphone captures the user's voice, and the speech recognition algorithm (SpeechRecognition library) analyzes the voice (data processing). If the analysis result matches "Good morning," the system wakes up and proceeds to the next step (output).

[0642] Step 2:

[0643] User recognition through facial recognition

[0644] The device camera captures the user's face (input). The face recognition algorithm (OpenCV library) analyzes the captured image and compares it with the registered user's face data (data calculation). If the user is recognized, the process proceeds to the next step (output).

[0645] Step 3:

[0646] AI assistant asks questions and collects data

[0647] The device asks the authenticated user, "Did you sleep well last night?" (input). The user responds verbally, and their facial expressions and voice are captured (input). The facial expression recognition algorithm (dlib) and voice analysis algorithm (SpeechRecognition library) analyze the user's facial expressions and voice and collect the data (data processing). The collected data is sent to a cloud server (output).

[0648] Step 4:

[0649] Data analysis and anomaly detection

[0650] The server compares the facial and voice data sent to the cloud with past data (input). The AI ​​algorithm analyzes this data and determines whether there are any abnormalities (data calculation). If an abnormality is detected, the system proceeds to the next step (output).

[0651] Step 5:

[0652] Sending notifications

[0653] If an abnormality is detected, the server sends a notification to an administrator (e.g., family member or caregiver) (output). Specifically, it sends an alert to the administrator's email address, which includes the details of the abnormality and recommended actions to take (input: abnormal data).

[0654] Step 6:

[0655] Advice for users on what to do

[0656] When an abnormality is detected, the device will give the user voice advice on how to deal with the situation (input). For example, it will give a voice message such as "You seem to be feeling unwell today. Please take it easy and rest" (output).

[0657] Step 7:

[0658] Daily conversation and emotional support

[0659] Even if no abnormalities are detected, the device will engage in everyday conversation with the user (input). A generative AI model (such as GPT-3) will generate appropriate answers to questions from the user (data calculation) and provide natural conversation using prompt sentences (output). For example, if the user asks, "What's the weather like today?" the device will respond, "It's sunny today. It would be nice to go for a walk."

[0660] ---

[0661] This enables the CareGuard system to effectively support users' health management, respond quickly if an abnormality is detected, and also provide daily mental support.

[0662] 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.

[0663] This invention provides "Vital Frame," a system that uses a photo frame-type device to support health and emotional management for elderly people and others living alone. This system combines user voice input, face authentication, facial expression recognition, voice analysis, and an emotion engine to grasp the user's emotional state and comprehensively monitor their health and psychological state.

[0664] System Overview

[0665] User-initiated voice activation

[0666] The system is started by the user saying "Good morning" every morning. The device captures the voice through the microphone and detects the word "Good morning" using its voice recognition function. When this voice is recognized, the system starts up.

[0667] Explanation by language generation:

[0668] When the user says "Good morning," the device recognizes the voice and starts up the system.

[0669] User identification by facial recognition

[0670] After booting, the device uses its camera to capture the user's face, which allows a facial recognition algorithm to analyze the image and identify the user. Once the user is identified, the system proceeds to the next processing step.

[0671] Explanation by language generation:

[0672] The device captures the user's face with its camera, runs a facial recognition algorithm, and recognizes the user.

[0673] Collection of facial expression and voice data after authentication

[0674] After authentication, the device asks AI questions and collects facial and voice data from the user. For example, it might ask, "Did you sleep well last night?" When the user responds, the device simultaneously captures their facial expression with the camera and their voice with the microphone.

[0675] Explanation by language generation:

[0676] The device asks, "Did you sleep well last night?" and when the user answers "yes," it collects their facial expressions and voice.

[0677] Emotion recognition by emotion engine

[0678] The device inputs the collected facial and voice data into an emotion engine to analyze the user's emotional state, for example, identifying emotions such as joy, sadness, and surprise, and then integrates the results with other data to provide a comprehensive assessment of the user's health.

[0679] Explanation by language generation:

[0680] The device runs the user's facial expressions and voice data through an emotion engine to identify the user's emotions.

[0681] Sending data to the server and detecting anomalies

[0682] The device transmits the facial, voice, and emotion data it captures to a server, which then runs an algorithm to compare the new data with past data and detect anomalies. If an anomaly is detected, a notification is sent to an administrator.

[0683] Explanation by language generation:

[0684] The device sends data to the server, and if the server detects an abnormality, it notifies the administrator.

[0685] Advice on what to do when something goes wrong

[0686] If an abnormality is detected, the device will provide the user with voice advice on how to deal with the situation, such as "You seem to be feeling unwell today. Please take it easy and rest."

[0687] Explanation by language generation:

[0688] The device advises the user, "You seem to be feeling unwell today. Please take it easy and rest."

[0689] Daily communication

[0690] Under normal circumstances, the device will hold daily conversations with the user, providing support for not only their health but also their psychological state. Using the analysis results of the emotion engine, the device will have appropriate conversations based on the user's mood.

[0691] Explanation by language generation:

[0692] When a user asks, "What's the weather like today?", the device responds with, "It's sunny today," and responds based on the results of the emotion engine.

[0693] This allows the Vital Frame system to comprehensively monitor the user's health and emotional state and respond quickly in the event of an abnormality. By combining it with the emotion engine, more precise health management and psychological support are possible.

[0694] The processing flow will be explained below.

[0695] Step 1:

[0696] The user speaks "Good morning" into the photo frame device. The device captures the voice through its microphone, and the voice recognition function detects the word "Good morning." If the voice is recognized correctly, the system will start up.

[0697] Step 2:

[0698] The device uses a camera to capture the user's face. A facial recognition algorithm analyzes this image and identifies the user. If the user is successfully authenticated, the device proceeds to the next step.

[0699] Step 3:

[0700] The device will start asking AI questions, such as, "Did you sleep well last night?" As the user responds, the device will simultaneously capture their facial expressions with the camera and their voice with the microphone.

[0701] Step 4:

[0702] The device uses facial expression recognition algorithms to analyze the captured facial expression data, and voice analysis algorithms to analyze the tone and intonation of the voice, thereby conducting a detailed analysis of the collected data.

[0703] Step 5:

[0704] The facial expression and voice data analyzed by the device are input into an emotion engine to analyze the user's emotional state. Based on the collected data, the emotion engine identifies emotions such as joy, sadness, and surprise.

[0705] Step 6:

[0706] The device transmits the analyzed facial expression data, voice data, and emotion data to the server, which automatically stores the received data in a database.

[0707] Step 7:

[0708] The server compares newly received data with past data to determine whether there are any anomalies. If necessary, it runs an anomaly detection algorithm to check for abnormal trends in the data.

[0709] Step 8:

[0710] If an abnormality is detected, the server will send a notification to the administrator (family member, caregiver, etc.) via SMS or email, informing them of the nature of the abnormality and the need for action.

[0711] Step 9:

[0712] If the device detects an abnormality, it will provide the user with voice advice on how to deal with the situation. For example, it might say, "You seem to be feeling unwell today. Please take it easy and rest."

[0713] Step 10:

[0714] If no abnormalities are found, the device returns to its normal state and provides everyday conversation functions. When the user asks, "What's the weather like today?", the device responds, "It's sunny today," and continues the conversation. The device uses an emotion engine to respond according to the user's emotions.

[0715] Step 11:

[0716] The device periodically recaptures the user's facial expressions and voice and sends the data to the server, allowing the device to monitor the user's health and emotional state based on the most up-to-date data.

[0717] These are the specific processing steps in the "Vital Frame" system.

[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] The goal is to comprehensively monitor the health and emotional state of elderly people and users living alone, and quickly detect and respond to any abnormalities. Conventional systems have had difficulty efficiently and effectively collecting and analyzing this information and taking appropriate action.

[0721] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for recognizing a voice input by a user and activating the system, means for authenticating the user using a face recognition algorithm, means for collecting facial expression and voice data of the user after authentication, means for inputting the collected data into an emotion engine and analyzing the emotional state, means for comparing data including the analysis results with past data to detect abnormalities, and means for sending a notification to an administrator when an abnormality is detected. This makes it possible to comprehensively monitor the user's health state and emotional state and to respond quickly when an abnormality occurs.

[0722] "User voice input" is the process by which the system captures the user's voice through a microphone and recognizes that voice.

[0723] "Means for starting the system" refers to a function for starting the operation of the system by a trigger such as voice input.

[0724] A "face recognition algorithm" is an algorithm that analyzes facial images captured by a camera and determines the user's identity.

[0725] "Means for authenticating users" refers to a function for identifying who a user is, using a facial recognition algorithm or the like.

[0726] "Means for collecting user's facial expression and voice data" refers to a function for capturing the user's facial expression and voice information using a camera or microphone and collecting this data.

[0727] An "emotion engine" is software or algorithm that analyzes collected facial and voice data to identify a user's emotional state.

[0728] The "means for analyzing emotional state" is a function for analyzing the user's emotions using an emotion engine and identifying their state.

[0729] The "means for detecting abnormalities by comparing past data" is a function for comparing newly collected data with past data and identifying abnormal conditions that are different from normal.

[0730] The "means for sending a notification to an administrator when an abnormality is detected" is a function for sending a notification to an administrator when an abnormality is detected.

[0731] The "means for providing advice on how to deal with the problem" is a function that provides voice instructions to the user on how to deal with the problem when an abnormality is detected.

[0732] A "photo frame terminal" is a device shaped like a photo frame, equipped with functions such as a camera, microphone, and display.

[0733] "Abnormal" refers to a deviation from normal health or emotional state.

[0734] An "administrator" is a person or organization responsible for monitoring the health and emotional state of users in the system and intervening when necessary.

[0735] This invention provides a system called "Vital Frame" that supports health and emotional management for elderly people and users living alone. The system uses a photo frame-type device to grasp the user's emotional state and comprehensively monitor their health and psychological state by combining voice input, face authentication, facial expression recognition, voice analysis, and an emotion engine.

[0736] Hardware and software used

[0737] Microphone: Used to capture the user's voice.

[0738] Camera: Used to capture the user's face and facial expressions.

[0739] Google Speech-to-Text API: Speech recognition software that converts voice input into text.

[0740] Amazon Rekognition: Image analysis software that provides facial recognition algorithms.

[0741] Microsoft Azure Emotion API: An emotion recognition engine that analyzes the user's emotional state.

[0742] Server: A computer that stores and analyzes data.

[0743] Photo frame type terminal: A device that integrates the above hardware and software.

[0744] 1. User voice input

[0745] Every morning, the user inputs the voice by saying "Good morning." The device's microphone captures this voice and uses the Google Speech-to-Text API to recognize the word "Good morning." Once this voice is recognized, the system wakes up.

[0746] Specific examples

[0747] The user says "Good morning." The audio is captured by the device's microphone and converted to text using the Google Speech-to-Text API.

[0748] Prompt Sentence Examples

[0749] Convert the following Japanese audio to text: "Good morning"

[0750] 2. User identification through facial recognition

[0751] Once the system is up and running, the device's camera automatically captures the user's face and runs a facial recognition algorithm using Amazon Rekognition, which determines the user's identity.

[0752] Specific examples

[0753] The camera captures the user's face and uses Amazon Rekognition to recognize it.

[0754] 3. Collecting facial and voice data after authentication

[0755] Once facial recognition is complete, the device will ask an AI question, such as, "Did you sleep well last night?" When the user responds, the camera captures their facial expression and the microphone captures their voice.

[0756] Specific examples

[0757] The device asks, "Did you sleep well last night?" and captures your facial expressions and voice as you answer "yes."

[0758] 4. Emotion Recognition by Emotion Engine

[0759] The collected facial and voice data is fed into an emotion engine using the Microsoft Azure Emotion API, which analyzes this data and identifies the user's emotional state.

[0760] Specific examples

[0761] The captured data is analyzed using the Microsoft Azure Emotion API to identify the user's emotions such as happiness, sadness, surprise, etc.

[0762] Prompt Sentence Examples

[0763] Analyze the following facial expression and audio data to identify the emotion: "Image data: <Image file path> Audio data: <Audio file path>"

[0764] 5. Sending data to the server and detecting anomalies

[0765] The device sends the collected data to a server, where an algorithm compares it with past data to detect anomalies, and if an anomaly is detected, a notification is sent to the administrator.

[0766] 6. Advice on what to do in case of an abnormality

[0767] If the device detects an abnormality, it will give the user voice advice on how to deal with the situation, such as "You seem to be feeling unwell today. Please take it easy and rest."

[0768] 7. Daily communication

[0769] During normal times, the device will hold daily conversations with the user and provide appropriate responses based on the results of the emotion engine. By supporting not only the user's health but also their psychological state, the device aims to improve their quality of life.

[0770] Specific examples

[0771] When a user asks, "What's the weather like today?", the device responds with, "It's sunny today," and then responds according to the user's emotional state.

[0772] Through these processes, the Vital Frame system can comprehensively monitor the user's health and emotional state and respond quickly to any abnormalities. By combining this with the emotion engine, more precise health management and psychological support are possible.

[0773] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0774] Step 1:

[0775] When a user says "Good morning," the device's microphone captures the voice. The device sends the voice input ("Good morning") to the Google Speech-to-Text API and receives text data (recognized as "Good morning"). The device confirms that this text data is "Good morning" and wakes up the system.

[0776] Step 2:

[0777] When the device starts up, the camera automatically captures the user's face. The captured facial image data is sent to Amazon Rekognition, which runs a facial recognition algorithm to identify the user. Based on the recognition result (user ID), the system loads the system settings for that user.

[0778] Step 3:

[0779] Once the user authentication is complete, the device will begin asking questions automatically using AI. Specifically, the device will ask aloud, "Did you sleep well last night?" When the user responds, the device's camera will capture the user's facial expression and the microphone will capture their voice. This data (facial image data and voice data) will be sent to the emotion engine.

[0780] Step 4:

[0781] The device inputs the collected facial image data and voice data into the Microsoft Azure Emotion API, which analyzes the user's emotional state. For example, emotions such as joy, sadness, and surprise are output. The analysis results are then integrated with other health data.

[0782] Step 5:

[0783] The device sends analyzed emotional data and other health data to a server, which compares the received data with past data and runs algorithms to detect anomalies. If an anomaly is detected, a real-time notification is sent to the administrator.

[0784] Step 6:

[0785] When the server reports an abnormality, the device will advise the user on how to deal with the situation. For example, the system might give a voice instruction such as, "You seem to be feeling unwell today. Please take it easy and rest." This interaction allows the user to take appropriate action.

[0786] Step 7:

[0787] Under normal circumstances, the device will engage in everyday conversations with the user. When the user asks, "What's the weather like today?", the device will provide weather information. The device will then use the results of its emotion engine to generate an appropriate answer based on the user's emotional state. For example, the system might respond, "It's sunny today. It feels good to go outside."

[0788] In this way, the "Vital Frame" system can comprehensively monitor the user's health and emotional state, quickly detect and respond to abnormalities, and thereby improve the user's quality of life.

[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] When elderly people live alone, they often lack adequate health care and psychological support. As a result, poor health care and feelings of loneliness can lead to serious problems. In particular, poor dietary choices and a lack of psychological support can lead to a deterioration in overall health and emotional state. The present invention aims to solve these problems and provide a system that allows elderly people to live alone in a healthier and more secure way.

[0792] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0793] In this invention, the server includes means for recognizing voice input and activating the system, means for authenticating the user using a face recognition algorithm, means for collecting the user's facial expression and voice data after authentication, means for detecting abnormalities by comparing the collected data with past data, means for sending a notification to an administrator when an abnormality is detected, means for analyzing the user's emotional state when an abnormality is detected, and means for making dietary suggestions based on the emotional state. This makes it possible to comprehensively manage the user's health and emotional state and provide appropriate dietary suggestions and psychological support.

[0794] The "means for starting the system" refers to a means having a function for recognizing a user's voice input and starting the operation of the system.

[0795] A "face recognition algorithm" is a technology that has the function of analyzing image data captured using a camera and identifying a user's face.

[0796] The "means for collecting facial expression and voice data" refers to a means having the function of collecting the user's facial expression with a camera and voice with a microphone.

[0797] "Means for detecting anomalies" refers to means that have algorithms or functions that compare collected data with past data and identify anomalies.

[0798] The "means for sending a notification to an administrator" is a means having a function for sending a notification to a pre-registered administrator when an abnormality is detected.

[0799] "Means for analyzing the emotional state of a user" refers to means having algorithms or techniques for analyzing the emotional state of a user based on collected facial expression and voice data.

[0800] The "means for making meal suggestions" is a means having a function of suggesting appropriate foods and meal menus to the user based on the analyzed emotional state.

[0801] This invention provides "Vital Meal," an application example of a system that supports health and emotional management for elderly people living alone, using a smartphone and a head-mounted display. "Vital Meal" combines voice input, face recognition, facial expression recognition, voice analysis, and an emotion engine to comprehensively manage the user's health and emotional state, and based on that, makes meal suggestions and provides psychological support.

[0802] The system works as follows:

[0803] System configuration

[0804] 1. Voice input recognition method

[0805] The system wakes up when the user speaks. This process uses a microphone and the speech_recognition library. For example, if the user says "Good morning," the system wakes up.

[0806] 2. Facial Recognition Methods

[0807] After the system boots up, it uses a camera to capture the user's face and then uses a facial recognition algorithm to identify the user. This process uses the OpenCV library, for example, to detect the user's face and compare it with the registered facial data for authentication.

[0808] 3. Facial and vocal data collection methods

[0809] After authentication, the AI ​​asks questions and collects facial and voice data from the user. For example, it asks, "Did you sleep well last night?" and collects data from the camera and microphone when the user answers "yes."

[0810] 4. Anomaly Detection Methods

[0811] The collected data is input into an emotion engine to analyze the user's emotional state. It then checks whether any abnormalities are detected. If any abnormalities are detected, an algorithm is activated to compare them with past data.

[0812] 5. Means of notification

[0813] If an abnormality is detected, a notification is sent to the administrator. For example, if an abnormal emotional state is detected, an alert is sent to the administrator's smartphone.

[0814] 6. Emotional state analysis method

[0815] If an anomaly is detected, the emotion engine is used to analyze the detailed emotional state. For example, if the user is analyzed as being "tired," the reason and degree of fatigue are further analyzed.

[0816] 7. Meal suggestion tools

[0817] Based on the analyzed emotional state, the system will suggest appropriate foods and meal plans to the user. For example, if it determines that an energy replenishment is needed, it will suggest a combination of banana and yogurt.

[0818] Hardware and software used

[0819] Hardware: smartphone, head-mounted display, microphone, camera

[0820] Software: speech_recognition library, OpenCV library, emotion engine (proprietary algorithm or third-party API), food suggestion engine (proprietary algorithm or third-party API)

[0821] Specific examples

[0822] "What would you recommend for dinner tonight?"

[0823] "Suggest an energizing meal"

[0824] "Tell me some low-calorie lunch recipes"

[0825] This system allows users to receive individually tailored health management and psychological support, improving the quality of life for elderly people living alone.

[0826] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0827] Step 1:

[0828] Device startup and voice recognition

[0829] The device captures the user's voice input with a microphone and converts the voice to text using the speech_recognition library. For example, if a user says "Good morning," the system recognizes the voice. This voice data is input, and the speech recognition engine outputs the text data "Good morning."

[0830] Step 2:

[0831] User identification by facial recognition

[0832] After the system boots up, the device will use the camera to capture the user's face. The image will be analyzed using the OpenCV library, and the face recognition algorithm will identify the user. This face image will be the input data, and the face recognition algorithm will output a user ID. For example, if the user's face matches, the user ID will be recognized by the system.

[0833] Step 3:

[0834] Facial and vocal data collection

[0835] After authenticating the user, the device asks questions such as "Did you sleep well last night?" While the user answers, the camera captures their facial expressions and the microphone collects their voice. The collected facial images and voice data become input data, and this data is used for subsequent processing. For example, it records whether the user is smiling when answering "yes."

[0836] Step 4:

[0837] Sentiment analysis and anomaly detection

[0838] The collected data is sent to an emotion engine, which analyzes the user's emotional state based on facial expressions and voice. The emotion analysis algorithm takes these data as input and outputs the user's emotional state (e.g., "happy," "sad," "tired," etc.). The system then compares it with past data to detect anomalies. For example, if the user is determined to be "tired," it checks whether the state is abnormal.

[0839] Step 5:

[0840] Administrator Notification

[0841] If an abnormality is detected, the device will send a notification to the administrator via the server. The abnormal state data will be sent as input, and a notification message will be displayed on the administrator's smartphone or PC. For example, if the user's emotional state is detected as "sadness" for a long period of time, an alert will be sent to the administrator.

[0842] Step 6:

[0843] Meal suggestions based on emotional state

[0844] Based on the data analyzed by the emotion engine, the system makes meal suggestions suitable for the user. The meal suggestion engine receives the results of emotion analysis as input and outputs the optimal meal menu. For example, if the user feels "tired," it will suggest foods to replenish energy (bananas, yogurt, etc.).

[0845] Step 7:

[0846] Daily communication and psychological support

[0847] The device provides psychological support to users through everyday conversations. If a user asks, "What's the weather like today?", the system will respond with, "It's sunny today," and then engage in appropriate dialogue accordingly. This allows communication tailored to the user's mood, providing psychological support.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] [Third embodiment]

[0852] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0853] 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.

[0854] 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).

[0855] 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.

[0856] 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.

[0857] 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).

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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."

[0864] This invention provides "Vital Frame," a system that uses a photo frame-type device to support health management for elderly people and others living alone. This system monitors the user's health status by combining user voice input, face authentication, facial expression recognition, and voice analysis, and notifies the administrator if an abnormality is detected. In addition, if an abnormality occurs, the system provides the user with appropriate advice on how to deal with it.

[0865] System Overview

[0866] The device wakes up when the user says "good morning" every morning.

[0867] The device captures the user's voice through a microphone and triggers the system by detecting the phrase "good morning" using a voice recognition algorithm.

[0868] Explanation by language generation:

[0869] When the user says "Good morning," the device recognizes the voice and starts up the system.

[0870] A device equipped with a means to identify users through facial recognition.

[0871] The device captures the user's face with its camera and uses a facial recognition algorithm to identify the user. This process allows the device to identify the user and proceed to the next step.

[0872] Explanation by language generation:

[0873] The device captures the user's face with a camera and runs a facial recognition algorithm based on the image to identify the user.

[0874] After authentication, the device asks AI questions and collects facial and voice data from the user.

[0875] For example, it asks a question like, "Did you sleep well last night?", and the user responds. At that time, the facial recognition algorithm analyzes the user's facial expressions, and the voice analysis algorithm analyzes the intonation and sound patterns of the voice.

[0876] Explanation by language generation:

[0877] The device asks, "Did you sleep well last night?" and when the user answers "yes," it collects their facial expressions and voice.

[0878] The collected data is sent to a server and an algorithm is run to compare it with past data and detect anomalies.

[0879] The collected facial expression and voice data is sent from the device to a server, which compares it with past data to detect any abnormalities. If an abnormality is detected, an administrator is notified.

[0880] Explanation by language generation:

[0881] The device sends data to the server, which compares it with past data to detect any anomalies. If an anomaly is detected, the administrator is notified.

[0882] If an error occurs, the system provides advice to the user on how to deal with the problem.

[0883] When an abnormality is detected, the device will provide the user with advice on how to deal with the problem via voice messages, etc.

[0884] Explanation by language generation:

[0885] The device advises the user, "You seem to be feeling unwell today. Please take it easy and rest."

[0886] A device equipped with a means of everyday communication.

[0887] Under normal circumstances, the device will hold daily conversations with the user and support their mental health. For example, when the user asks, "What's the weather like today?", the device will respond, "It's sunny today. It would be nice to go for a walk."

[0888] Explanation by language generation:

[0889] When a user asks, "What's the weather like today?" the device will respond with, "It's sunny today," interacting with the user through everyday conversations.

[0890] ---

[0891] In this way, the Vital Frame system monitors the user's health condition daily and has the ability to respond quickly in the event of an abnormality. By combining the above functions, it is possible to ensure the safety and health of family members living far away.

[0892] The processing flow will be explained below.

[0893] Step 1:

[0894] The user speaks "Good morning" into the photo frame device. The device captures the voice through its microphone, and the voice recognition function detects the word "Good morning." If the voice is recognized correctly, the system will start up.

[0895] Step 2:

[0896] The device uses a camera to capture the user's face. A facial recognition algorithm analyzes this image and identifies the user. If the user is successfully authenticated, the device proceeds to the next step.

[0897] Step 3:

[0898] The device will start asking AI questions, such as, "Did you sleep well last night?" As the user responds, the device will simultaneously capture their facial expressions with the camera and their voice with the microphone.

[0899] Step 4:

[0900] The device uses facial expression recognition algorithms to analyze the captured facial expression data, and voice analysis algorithms to analyze the tone and intonation of the voice, thereby conducting a detailed analysis of the collected data.

[0901] Step 5:

[0902] The facial expression data and voice data analyzed by the device are sent to the server, which automatically stores the received data in a database.

[0903] Step 6:

[0904] The server compares newly received data with past data to determine whether there are any anomalies. If necessary, it runs an anomaly detection algorithm to check for abnormal trends in the data.

[0905] Step 7:

[0906] If an abnormality is detected, the server will send a notification to the administrator (family member, caregiver, etc.) via SMS or email, informing them of the nature of the abnormality and the need for action.

[0907] Step 8:

[0908] If the device detects an abnormality, it will provide the user with voice advice on how to deal with the situation. For example, it might say, "You seem to be feeling unwell today. Please take it easy and rest."

[0909] Step 9:

[0910] If no abnormalities are found, the device returns to its normal state and provides everyday conversation functions. When the user asks, "What's the weather like today?", the device responds, "It's sunny today," and continues the conversation.

[0911] Step 10:

[0912] The device periodically recaptures the user's facial expressions and voice and sends the data to the server, allowing the user's health to be monitored based on the latest data.

[0913] Here are the detailed processing steps of the "Vital Frame" system:

[0914] Example 1

[0915] 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."

[0916] It is often difficult for elderly people and users living alone to properly manage their own health. In particular, it is necessary to understand their daily health status and respond quickly if any abnormalities are detected. At the same time, mental health support is also important, and there is a need to maintain users' mental health through daily communication. Few existing systems comprehensively meet these requirements, and an effective solution is needed.

[0917] 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.

[0918] In this invention, the server includes a means for recognizing a user's voice input and activating the system, a means for authenticating the user using a facial recognition algorithm, a means for collecting the user's facial expression and voice data after authentication, a means for comparing the collected data with past data to detect abnormalities, a means for sending a notification to an administrator when an abnormality is detected, and a means for advising the user on how to deal with the abnormality when an abnormality is detected. This allows for comprehensive monitoring of the user's health status, and enables prompt notification and action when an abnormality is detected. It also supports mental health through everyday conversation, contributing to maintaining the user's mental health.

[0919] "Voice input" refers to the voice information that the user speaks through a microphone, and is the means by which the system recognizes and analyzes that voice information.

[0920] "System startup" refers to the system starting to operate as a trigger for the user's voice input.

[0921] A "facial recognition algorithm" refers to a computational method or software used to analyze a user's facial image and identify the individual.

[0922] "Authentication" refers to verifying a user's identity and granting them permission to use a system.

[0923] "Facial Expression Data" refers to the results of analyzing specific facial expressions of a user captured through a camera.

[0924] "Voice data" refers to information collected from a user's voice and converted into an analyzable format.

[0925] "Past data" refers to facial expression and voice data of users that has been collected and saved up to now, and is the information that serves as the reference when detecting anomalies.

[0926] "Anomaly detection" refers to comparing collected data with past data to discover unusual conditions or problems.

[0927] "Administrator" refers to the person or organization that monitors and manages the system and is the person who will be notified if an abnormality is detected.

[0928] "Notification" refers to warning information sent to an administrator when an abnormality is detected.

[0929] "Advice on how to deal with the problem" refers to specific instructions on what to do or warnings given to the user when an abnormality is detected.

[0930] "Supporting mental health" refers to providing assistance to maintain and promote the user's mental health through everyday conversation.

[0931] A "display device" refers to a device that has a display as a user interface and performs functions such as facial recognition and data collection.

[0932] This invention functions as a system to support health management for elderly people and users living alone. This system uses a photo frame-type device to monitor the user's health status by combining voice input, face authentication, facial expression recognition, and voice analysis, and notifies a manager if an abnormality is detected. In addition, if an abnormality occurs, the system provides the user with appropriate advice on how to deal with it.

[0933] System configuration

[0934] This system uses the following hardware and software:

[0935] Device: A photo frame-type device equipped with a microphone, camera, and display.

[0936] Speech recognition software: Google Cloud Speech-to-Text.

[0937] Facial recognition algorithm: Amazon Rekognition is used.

[0938] Facial expression recognition algorithm: Uses Microsoft Azure Face API.

[0939] Speech analysis software: IBM Watson Speech to Text.

[0940] Server: TensorFlow is used as a machine learning model for anomaly detection.

[0941] Processing flow

[0942] 1. Boot the system:

[0943] The user speaks "Good morning" to the device. The device's microphone picks up the voice and uses Google Cloud Speech-to-Text for speech recognition. If "Good morning" is recognized, the system wakes up.

[0944] 2. User face recognition:

[0945] The device captures the user's face with its camera and uses Amazon Rekognition to perform facial recognition on the image, thereby identifying the user.

[0946] 3. AI-powered questioning and data collection:

[0947] After authentication, the device asks the user, "Did you sleep well last night?" In response to the user's answer, the camera captures their facial expression and the microphone collects their audio. The facial expression is analyzed using the Microsoft Azure Face API, and the audio is analyzed using IBM Watson Speech to Text.

[0948] 4. Data transmission and analysis:

[0949] The collected data is sent from the device to the server via a secure protocol (HTTPS). The server uses TensorFlow to compare the data with past data and detect anomalies. If an anomaly is detected, the server sends a notification to the administrator (by email or SMS).

[0950] 5. Advice on what to do in case of an abnormality:

[0951] When an abnormality is detected, the server sends a notification to the device, which then provides the user with pre-defined advice in the form of a voice message (e.g., "You seem to be feeling unwell today. Please take it easy and rest.").

[0952] 6. Daily communication:

[0953] During normal times, the device will hold daily conversations with the user to support their mental health. When the user asks, "What's the weather like today?", the device will retrieve information from a weather information API (e.g., OpenWeatherMap) and provide a voice response using Google Text-to-Speech.

[0954] Examples of specific examples and prompts

[0955] Examples:

[0956] The user says "Good morning."

[0957] The system starts when the device's microphone picks up the voice and the voice recognition software recognizes "good morning."

[0958] The user answers "Yes" to the question "Did you sleep well last night?"

[0959] The server detects an abnormality and notifies the administrator by email.

[0960] In the event of an abnormality, the device will advise, "You seem to be feeling unwell today. Please take it easy and rest."

[0961] When a user asks, in everyday conversation, "What's the weather like today?", the device will respond with, "It's sunny today."

[0962] Example prompt sentence:

[0963] When I say "Good morning," how does my device use my voice?

[0964] Please explain the steps to identify users using a facial recognition algorithm.

[0965] Describe the process for collecting data. How will the collected data be used?

[0966] Please explain the process for analyzing the collected data. How will you be notified if anomalies are found?

[0967] Please explain the process for advising on how to deal with an anomaly after it is detected.

[0968] Please provide details of your normal communication functions.

[0969] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0970] Step 1:

[0971] System wake-up via user voice input

[0972] Input: The user speaks "Good morning" into the device.

[0973] What happens: The user says "Good morning" to the device. The microphone captures the speech and sends it to the speech recognition software (Google Cloud Speech-to-Text).

[0974] Data processing / calculation: Speech recognition software converts the voice data into text data and verifies that the text is "Good morning."

[0975] Output: The system wakes up and sets a flag to proceed to the next processing step.

[0976] Step 2:

[0977] User facial recognition

[0978] Input: The system startup flag is set and the device's camera is activated.

[0979] What happens: The device camera captures the user's face, and sends the image to a facial recognition algorithm (Amazon Rekognition).

[0980] Data processing / computation: Facial recognition algorithms analyze facial images to obtain the user's identity.

[0981] Output: The user is authenticated and the authentication result is returned to the device.

[0982] Step 3:

[0983] AI-powered questioning and data collection

[0984] Input: Face recognition is successful and the user is recognized.

[0985] Specific behavior: The device asks the user, "Did you sleep well last night?" The user answers "Yes" or "No."

[0986] Data processing / calculation: The camera captures the user's facial expressions and the microphone collects their voice. The data is then analyzed using a facial recognition algorithm (Microsoft Azure Face API) and voice analysis software (IBM Watson Speech to Text).

[0987] Output: Analyzed facial expression and voice data are generated and sent to the next step.

[0988] Step 4:

[0989] Data transmission and analysis

[0990] Input: Analyzed facial expression data and voice data are saved on the device.

[0991] Specific operation: The device sends the collected data to the server via a secure communication protocol (HTTPS).

[0992] Data processing / calculation: The server analyzes the received data and compares it with past data. Anomaly detection algorithms (TensorFlow) are used to detect abnormalities in health status.

[0993] Output: If an abnormality is detected, the server will notify the administrator via email or SMS.

[0994] Step 5:

[0995] Advice on what to do when something goes wrong

[0996] Input: The server detects an anomaly and sends a notification to the device.

[0997] Specific operation: The device receives an abnormality notification and provides the user with pre-defined advice on how to deal with the problem via voice message.

[0998] Data processing / calculation: A specific method of dealing with the abnormality is selected.

[0999] Output: The device provides the user with a spoken advice saying, "You seem to be feeling unwell today. Please take it easy and rest."

[1000] Step 6:

[1001] Daily communication

[1002] Input: Under normal circumstances when the system is operating normally.

[1003] Specific behavior: The user asks the device, "What's the weather like today?"

[1004] Data processing / calculation: The device sends a request to the weather information API (OpenWeatherMap) to obtain the current day's weather information. A response is generated using speech synthesis software (Google Text-to-Speech).

[1005] Output: The device responds by saying, "It's a sunny day today. It would be nice to go for a walk."

[1006] (Application example 1)

[1007] 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."

[1008] For elderly people living alone, it is important to continuously monitor their daily health status and detect abnormalities early. However, existing health management systems have had difficulty in real-time monitoring unless the user periodically inputs data. Furthermore, there were issues with systems lacking the functionality to quickly advise on appropriate countermeasures when abnormalities were detected, and lacking in daily psychological support.

[1009] 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.

[1010] In this invention, the server includes a means for recognizing a user's voice input and activating the system, a means for authenticating the user using a facial recognition algorithm, a means for collecting the user's facial expression and voice data after authentication, a means for comparing the collected data with past data to detect abnormalities, a means for sending a notification to an administrator when an abnormality is detected, a means for providing the user with audio advice on how to deal with the abnormality when an abnormality is detected, a means for interacting with the user through everyday conversation and supporting a healthy mental state, a means for responding to the user's questions using a generative artificial intelligence model and providing everyday conversation using prompt sentences, and a smart display or smartphone-type terminal for authentication and data collection. This makes it possible to monitor the health status of elderly people living alone on a daily basis in real time, and to provide quick and accurate advice in the event of an abnormality, thereby providing safety and security.

[1011] "User voice input" refers to a method in which a user inputs information into a system by speaking.

[1012] The "means for starting the system" is a function that causes the system to start operating in response to the user's voice input.

[1013] A "facial recognition algorithm" is a technology that analyzes a user's facial image captured by a camera and compares it with registered data.

[1014] "Means for authenticating users" refers to a function that identifies users using a facial recognition algorithm.

[1015] "Means for collecting facial and voice data" refers to a function that captures and analyzes the user's facial expressions and voice.

[1016] "Means for detecting abnormalities by comparing past data" refers to a technique for comparing collected data with past data to find abnormalities.

[1017] "Means for sending notification to administrator" is a function that sends an alert to a person in charge of management when an abnormality is detected.

[1018] "A means for providing voice advice on how to deal with the problem" is a function that provides voice instructions on how to deal with the problem when an abnormality is detected.

[1019] "Means of interacting with users through everyday conversation" is a function that allows users to have everyday discussions.

[1020] "Means to support a healthy mental state" are technologies that allow users to continue interacting in order to maintain their mental health.

[1021] A "generative artificial intelligence model" is an algorithm for generating natural conversations based on large amounts of data.

[1022] A "prompt sentence" is an instruction sentence that an artificial intelligence uses to generate an appropriate response.

[1023] A "smart display or smartphone-type terminal" is a display or mobile phone with advanced functions, and is a device that can input and output various types of data.

[1024] System Overview

[1025] This invention is a system that realizes "CareGuard," a health management system for the elderly. CareGuard monitors the user's health status by combining the user's voice input, face recognition, facial expression recognition, and voice analysis. If an abnormality is detected, the system sends a notification to the administrator and also has the function of providing the user with voice advice on how to respond appropriately. It also interacts with the user through everyday conversations to support a healthy mental state.

[1026] Hardware and Software Configuration

[1027] The CareGuard system consists of a smart display or smartphone-type device and a cloud server. The hardware configuration includes a camera, microphone, and display. The system uses the following software components:

[1028] 1. Face recognition algorithm (e.g. OpenCV library)

[1029] 2. Speech recognition algorithms (e.g., SpeechRecognition library)

[1030] 3. Facial expression recognition algorithms (e.g., dlib library)

[1031] 4. Generative AI models (e.g., GPT-3)

[1032] 5. Prompt Sentence Management System

[1033] Detailed system processing explanation

[1034] 1. System startup

[1035] When a user speaks "good morning," a microphone captures the speech and a speech recognition algorithm detects the phrase, waking up the system.

[1036] 2. Facial Recognition

[1037] The system uses a camera to capture the user's face and identifies them through a facial recognition algorithm.

[1038] 3. Interview and data collection

[1039] After facial recognition, the AI ​​assistant will ask questions such as, "Did you sleep well last night?", using facial expression recognition and voice analysis algorithms to collect the user's facial and voice data.

[1040] 4. Data analysis and anomaly detection

[1041] The collected data is sent to a cloud server and compared with past data. Response times, tone of voice, subtle changes in facial expressions, etc. are analyzed to detect anomalies.

[1042] 5. Notifications and advice on how to deal with the issue

[1043] If an abnormality is detected, the server sends a notification to an administrator (e.g., family member or caregiver), and at the same time, gives the user audio advice on how to deal with the situation (e.g., "You seem to be feeling unwell today. Please take it easy and rest.").

[1044] 6. Daily conversation and emotional support

[1045] Even if there are no abnormalities, the AI ​​assistant will interact with the user through everyday conversations, such as saying, "The weather is sunny today. It would be nice to go for a walk." To do this, a generative AI model is used to generate natural conversations using prompt sentences.

[1046] Examples of prompt statements

[1047] Here are some example prompts to input to a generative AI model:

[1048] "Every morning, the user speaks "Good morning." The system then activates the system after analyzing the speech recognition."

[1049] "Identify users by recognizing their faces using a smartphone camera."

[1050] "The AI ​​assistant collects voice and facial expression data in response to questions."

[1051] "If an abnormality is detected, a notification will be sent to family members or caregivers."

[1052] This explanation will help you understand how the CareGuard system works in practice, as it is designed to provide peace of mind and security for seniors living alone.

[1053] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1054] Step 1:

[1055] Wake up the system with voice input

[1056] The user speaks "Good morning" (input). The device's microphone captures the user's voice, and the speech recognition algorithm (SpeechRecognition library) analyzes the voice (data processing). If the analysis result matches "Good morning," the system wakes up and proceeds to the next step (output).

[1057] Step 2:

[1058] User recognition through facial recognition

[1059] The device camera captures the user's face (input). The face recognition algorithm (OpenCV library) analyzes the captured image and compares it with the registered user's face data (data calculation). If the user is recognized, the process proceeds to the next step (output).

[1060] Step 3:

[1061] AI assistant asks questions and collects data

[1062] The device asks the authenticated user, "Did you sleep well last night?" (input). The user responds verbally, and their facial expressions and voice are captured (input). The facial expression recognition algorithm (dlib) and voice analysis algorithm (SpeechRecognition library) analyze the user's facial expressions and voice and collect the data (data processing). The collected data is sent to a cloud server (output).

[1063] Step 4:

[1064] Data analysis and anomaly detection

[1065] The server compares the facial and voice data sent to the cloud with past data (input). The AI ​​algorithm analyzes this data and determines whether there are any abnormalities (data calculation). If an abnormality is detected, the system proceeds to the next step (output).

[1066] Step 5:

[1067] Sending notifications

[1068] If an abnormality is detected, the server sends a notification to an administrator (e.g., family member or caregiver) (output). Specifically, it sends an alert to the administrator's email address, which includes the details of the abnormality and recommended actions to take (input: abnormal data).

[1069] Step 6:

[1070] Advice for users on what to do

[1071] When an abnormality is detected, the device will give the user voice advice on how to deal with the situation (input). For example, it will give a voice message such as "You seem to be feeling unwell today. Please take it easy and rest" (output).

[1072] Step 7:

[1073] Daily conversation and emotional support

[1074] Even if no abnormalities are detected, the device will engage in everyday conversation with the user (input). A generative AI model (such as GPT-3) will generate appropriate answers to questions from the user (data calculation) and provide natural conversation using prompt sentences (output). For example, if the user asks, "What's the weather like today?" the device will respond, "It's sunny today. It would be nice to go for a walk."

[1075] ---

[1076] This enables the CareGuard system to effectively support users' health management, respond quickly if an abnormality is detected, and also provide daily mental support.

[1077] 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.

[1078] This invention provides "Vital Frame," a system that uses a photo frame-type device to support health and emotional management for elderly people and others living alone. This system combines user voice input, face authentication, facial expression recognition, voice analysis, and an emotion engine to grasp the user's emotional state and comprehensively monitor their health and psychological state.

[1079] System Overview

[1080] User-initiated voice activation

[1081] The system is started by the user saying "Good morning" every morning. The device captures the voice through the microphone and detects the word "Good morning" using its voice recognition function. When this voice is recognized, the system starts up.

[1082] Explanation by language generation:

[1083] When the user says "Good morning," the device recognizes the voice and starts up the system.

[1084] User identification by facial recognition

[1085] After booting, the device uses its camera to capture the user's face, which allows a facial recognition algorithm to analyze the image and identify the user. Once the user is identified, the system proceeds to the next processing step.

[1086] Explanation by language generation:

[1087] The device captures the user's face with its camera, runs a facial recognition algorithm, and recognizes the user.

[1088] Collection of facial expression and voice data after authentication

[1089] After authentication, the device asks AI questions and collects facial and voice data from the user. For example, it might ask, "Did you sleep well last night?" When the user responds, the device simultaneously captures their facial expression with the camera and their voice with the microphone.

[1090] Explanation by language generation:

[1091] The device asks, "Did you sleep well last night?" and when the user answers "yes," it collects their facial expressions and voice.

[1092] Emotion recognition by emotion engine

[1093] The device inputs the collected facial and voice data into an emotion engine to analyze the user's emotional state, for example, identifying emotions such as joy, sadness, and surprise, and then integrates the results with other data to provide a comprehensive assessment of the user's health.

[1094] Explanation by language generation:

[1095] The device runs the user's facial expressions and voice data through an emotion engine to identify the user's emotions.

[1096] Sending data to the server and detecting anomalies

[1097] The device transmits the facial, voice, and emotion data it captures to a server, which then runs an algorithm to compare the new data with past data and detect anomalies. If an anomaly is detected, a notification is sent to an administrator.

[1098] Explanation by language generation:

[1099] The device sends data to the server, and if the server detects an abnormality, it notifies the administrator.

[1100] Advice on what to do when something goes wrong

[1101] If an abnormality is detected, the device will provide the user with voice advice on how to deal with the situation, such as "You seem to be feeling unwell today. Please take it easy and rest."

[1102] Explanation by language generation:

[1103] The device advises the user, "You seem to be feeling unwell today. Please take it easy and rest."

[1104] Daily communication

[1105] Under normal circumstances, the device will hold daily conversations with the user, providing support for not only their health but also their psychological state. Using the analysis results of the emotion engine, the device will have appropriate conversations based on the user's mood.

[1106] Explanation by language generation:

[1107] When a user asks, "What's the weather like today?", the device responds with, "It's sunny today," and responds based on the results of the emotion engine.

[1108] This allows the Vital Frame system to comprehensively monitor the user's health and emotional state and respond quickly in the event of an abnormality. By combining it with the emotion engine, more precise health management and psychological support are possible.

[1109] The processing flow will be explained below.

[1110] Step 1:

[1111] The user speaks "Good morning" into the photo frame device. The device captures the voice through its microphone, and the voice recognition function detects the word "Good morning." If the voice is recognized correctly, the system will start up.

[1112] Step 2:

[1113] The device uses a camera to capture the user's face. A facial recognition algorithm analyzes this image and identifies the user. If the user is successfully authenticated, the device proceeds to the next step.

[1114] Step 3:

[1115] The device will start asking AI questions, such as, "Did you sleep well last night?" As the user responds, the device will simultaneously capture their facial expressions with the camera and their voice with the microphone.

[1116] Step 4:

[1117] The device uses facial expression recognition algorithms to analyze the captured facial expression data, and voice analysis algorithms to analyze the tone and intonation of the voice, thereby conducting a detailed analysis of the collected data.

[1118] Step 5:

[1119] The facial expression and voice data analyzed by the device are input into an emotion engine to analyze the user's emotional state. Based on the collected data, the emotion engine identifies emotions such as joy, sadness, and surprise.

[1120] Step 6:

[1121] The device transmits the analyzed facial expression data, voice data, and emotion data to the server, which automatically stores the received data in a database.

[1122] Step 7:

[1123] The server compares newly received data with past data to determine whether there are any anomalies. If necessary, it runs an anomaly detection algorithm to check for abnormal trends in the data.

[1124] Step 8:

[1125] If an abnormality is detected, the server will send a notification to the administrator (family member, caregiver, etc.) via SMS or email, informing them of the nature of the abnormality and the need for action.

[1126] Step 9:

[1127] If the device detects an abnormality, it will provide the user with voice advice on how to deal with the situation. For example, it might say, "You seem to be feeling unwell today. Please take it easy and rest."

[1128] Step 10:

[1129] If no abnormalities are found, the device returns to its normal state and provides everyday conversation functions. When the user asks, "What's the weather like today?", the device responds, "It's sunny today," and continues the conversation. The device uses an emotion engine to respond according to the user's emotions.

[1130] Step 11:

[1131] The device periodically recaptures the user's facial expressions and voice and sends the data to the server, allowing the device to monitor the user's health and emotional state based on the most up-to-date data.

[1132] These are the specific processing steps in the "Vital Frame" system.

[1133] Example 2

[1134] 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."

[1135] The goal is to comprehensively monitor the health and emotional state of elderly people and users living alone, and quickly detect and respond to any abnormalities. Conventional systems have had difficulty efficiently and effectively collecting and analyzing this information and taking appropriate action.

[1136] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for recognizing a voice input by a user and activating the system, means for authenticating the user using a face recognition algorithm, means for collecting facial expression and voice data of the user after authentication, means for inputting the collected data into an emotion engine and analyzing the emotional state, means for comparing data including the analysis results with past data to detect abnormalities, and means for sending a notification to an administrator when an abnormality is detected. This makes it possible to comprehensively monitor the user's health state and emotional state and to respond quickly when an abnormality occurs.

[1137] "User voice input" is the process by which the system captures the user's voice through a microphone and recognizes that voice.

[1138] "Means for starting the system" refers to a function for starting the operation of the system by a trigger such as voice input.

[1139] A "face recognition algorithm" is an algorithm that analyzes facial images captured by a camera and determines the user's identity.

[1140] "Means for authenticating users" refers to a function for identifying who a user is, using a facial recognition algorithm or the like.

[1141] "Means for collecting user's facial expression and voice data" refers to a function for capturing the user's facial expression and voice information using a camera or microphone and collecting this data.

[1142] An "emotion engine" is software or algorithm that analyzes collected facial and voice data to identify a user's emotional state.

[1143] The "means for analyzing emotional state" is a function for analyzing the user's emotions using an emotion engine and identifying their state.

[1144] The "means for detecting abnormalities by comparing past data" is a function for comparing newly collected data with past data and identifying abnormal conditions that are different from normal.

[1145] The "means for sending a notification to an administrator when an abnormality is detected" is a function for sending a notification to an administrator when an abnormality is detected.

[1146] The "means for providing advice on how to deal with the problem" is a function that provides voice instructions to the user on how to deal with the problem when an abnormality is detected.

[1147] A "photo frame terminal" is a device shaped like a photo frame, equipped with functions such as a camera, microphone, and display.

[1148] "Abnormal" refers to a deviation from normal health or emotional state.

[1149] An "administrator" is a person or organization responsible for monitoring the health and emotional state of users in the system and intervening when necessary.

[1150] This invention provides a system called "Vital Frame" that supports health and emotional management for elderly people and users living alone. The system uses a photo frame-type device to grasp the user's emotional state and comprehensively monitor their health and psychological state by combining voice input, face authentication, facial expression recognition, voice analysis, and an emotion engine.

[1151] Hardware and software used

[1152] Microphone: Used to capture the user's voice.

[1153] Camera: Used to capture the user's face and facial expressions.

[1154] Google Speech-to-Text API: Speech recognition software that converts voice input into text.

[1155] Amazon Rekognition: Image analysis software that provides facial recognition algorithms.

[1156] Microsoft Azure Emotion API: An emotion recognition engine that analyzes the user's emotional state.

[1157] Server: A computer that stores and analyzes data.

[1158] Photo frame type terminal: A device that integrates the above hardware and software.

[1159] 1. User voice input

[1160] Every morning, the user inputs the voice by saying "Good morning." The device's microphone captures this voice and uses the Google Speech-to-Text API to recognize the word "Good morning." Once this voice is recognized, the system wakes up.

[1161] Specific examples

[1162] The user says "Good morning." The audio is captured by the device's microphone and converted to text using the Google Speech-to-Text API.

[1163] Prompt Sentence Examples

[1164] Convert the following Japanese audio to text: "Good morning"

[1165] 2. User identification through facial recognition

[1166] Once the system is up and running, the device's camera automatically captures the user's face and runs a facial recognition algorithm using Amazon Rekognition, which determines the user's identity.

[1167] Specific examples

[1168] The camera captures the user's face and uses Amazon Rekognition to recognize it.

[1169] 3. Collecting facial and voice data after authentication

[1170] Once facial recognition is complete, the device will ask an AI question, such as, "Did you sleep well last night?" When the user responds, the camera captures their facial expression and the microphone captures their voice.

[1171] Specific examples

[1172] The device asks, "Did you sleep well last night?" and captures your facial expressions and voice as you answer "yes."

[1173] 4. Emotion Recognition by Emotion Engine

[1174] The collected facial and voice data is fed into an emotion engine using the Microsoft Azure Emotion API, which analyzes this data and identifies the user's emotional state.

[1175] Specific examples

[1176] The captured data is analyzed using the Microsoft Azure Emotion API to identify the user's emotions such as happiness, sadness, surprise, etc.

[1177] Prompt Sentence Examples

[1178] Analyze the following facial expression and audio data to identify the emotion: "Image data: <Image file path> Audio data: <Audio file path>"

[1179] 5. Sending data to the server and detecting anomalies

[1180] The device sends the collected data to a server, where an algorithm compares it with past data to detect anomalies, and if an anomaly is detected, a notification is sent to the administrator.

[1181] 6. Advice on what to do in case of an abnormality

[1182] If the device detects an abnormality, it will give the user voice advice on how to deal with the situation, such as "You seem to be feeling unwell today. Please take it easy and rest."

[1183] 7. Daily communication

[1184] During normal times, the device will hold daily conversations with the user and provide appropriate responses based on the results of the emotion engine. By supporting not only the user's health but also their psychological state, the device aims to improve their quality of life.

[1185] Specific examples

[1186] When a user asks, "What's the weather like today?", the device responds with, "It's sunny today," and then responds according to the user's emotional state.

[1187] Through these processes, the Vital Frame system can comprehensively monitor the user's health and emotional state and respond quickly to any abnormalities. By combining this with the emotion engine, more precise health management and psychological support are possible.

[1188] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1189] Step 1:

[1190] When a user says "Good morning," the device's microphone captures the voice. The device sends the voice input ("Good morning") to the Google Speech-to-Text API and receives text data (recognized as "Good morning"). The device confirms that this text data is "Good morning" and wakes up the system.

[1191] Step 2:

[1192] When the device starts up, the camera automatically captures the user's face. The captured facial image data is sent to Amazon Rekognition, which runs a facial recognition algorithm to identify the user. Based on the recognition result (user ID), the system loads the system settings for that user.

[1193] Step 3:

[1194] Once the user authentication is complete, the device will begin asking questions automatically using AI. Specifically, the device will ask aloud, "Did you sleep well last night?" When the user responds, the device's camera will capture the user's facial expression and the microphone will capture their voice. This data (facial image data and voice data) will be sent to the emotion engine.

[1195] Step 4:

[1196] The device inputs the collected facial image data and voice data into the Microsoft Azure Emotion API, which analyzes the user's emotional state. For example, emotions such as joy, sadness, and surprise are output. The analysis results are then integrated with other health data.

[1197] Step 5:

[1198] The device sends analyzed emotional data and other health data to a server, which compares the received data with past data and runs algorithms to detect anomalies. If an anomaly is detected, a real-time notification is sent to the administrator.

[1199] Step 6:

[1200] When the server reports an abnormality, the device will advise the user on how to deal with the situation. For example, the system might give a voice instruction such as, "You seem to be feeling unwell today. Please take it easy and rest." This interaction allows the user to take appropriate action.

[1201] Step 7:

[1202] Under normal circumstances, the device will engage in everyday conversations with the user. When the user asks, "What's the weather like today?", the device will provide weather information. The device will then use the results of its emotion engine to generate an appropriate answer based on the user's emotional state. For example, the system might respond, "It's sunny today. It feels good to go outside."

[1203] In this way, the "Vital Frame" system can comprehensively monitor the user's health and emotional state, quickly detect and respond to abnormalities, and thereby improve the user's quality of life.

[1204] (Application example 2)

[1205] 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."

[1206] When elderly people live alone, they often lack adequate health care and psychological support. As a result, poor health care and feelings of loneliness can lead to serious problems. In particular, poor dietary choices and a lack of psychological support can lead to a deterioration in overall health and emotional state. The present invention aims to solve these problems and provide a system that allows elderly people to live alone in a healthier and more secure way.

[1207] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1208] In this invention, the server includes means for recognizing voice input and activating the system, means for authenticating the user using a face recognition algorithm, means for collecting the user's facial expression and voice data after authentication, means for detecting abnormalities by comparing the collected data with past data, means for sending a notification to an administrator when an abnormality is detected, means for analyzing the user's emotional state when an abnormality is detected, and means for making dietary suggestions based on the emotional state. This makes it possible to comprehensively manage the user's health and emotional state and provide appropriate dietary suggestions and psychological support.

[1209] The "means for starting the system" refers to a means having a function for recognizing a user's voice input and starting the operation of the system.

[1210] A "face recognition algorithm" is a technology that has the function of analyzing image data captured using a camera and identifying a user's face.

[1211] The "means for collecting facial expression and voice data" refers to a means having the function of collecting the user's facial expression with a camera and voice with a microphone.

[1212] "Means for detecting anomalies" refers to means that have algorithms or functions that compare collected data with past data and identify anomalies.

[1213] The "means for sending a notification to an administrator" is a means having a function for sending a notification to a pre-registered administrator when an abnormality is detected.

[1214] "Means for analyzing the emotional state of a user" refers to means having algorithms or techniques for analyzing the emotional state of a user based on collected facial expression and voice data.

[1215] The "means for making meal suggestions" is a means having a function of suggesting appropriate foods and meal menus to the user based on the analyzed emotional state.

[1216] This invention provides "Vital Meal," an application example of a system that supports health and emotional management for elderly people living alone, using a smartphone and a head-mounted display. "Vital Meal" combines voice input, face recognition, facial expression recognition, voice analysis, and an emotion engine to comprehensively manage the user's health and emotional state, and based on that, makes meal suggestions and provides psychological support.

[1217] The system works as follows:

[1218] System configuration

[1219] 1. Voice input recognition method

[1220] The system wakes up when the user speaks. This process uses a microphone and the speech_recognition library. For example, if the user says "Good morning," the system wakes up.

[1221] 2. Facial Recognition Methods

[1222] After the system boots up, it uses a camera to capture the user's face and then uses a facial recognition algorithm to identify the user. This process uses the OpenCV library, for example, to detect the user's face and compare it with the registered facial data for authentication.

[1223] 3. Facial and vocal data collection methods

[1224] After authentication, the AI ​​asks questions and collects facial and voice data from the user. For example, it asks, "Did you sleep well last night?" and collects data from the camera and microphone when the user answers "yes."

[1225] 4. Anomaly Detection Methods

[1226] The collected data is input into an emotion engine to analyze the user's emotional state. It then checks whether any abnormalities are detected. If any abnormalities are detected, an algorithm is activated to compare them with past data.

[1227] 5. Means of notification

[1228] If an abnormality is detected, a notification is sent to the administrator. For example, if an abnormal emotional state is detected, an alert is sent to the administrator's smartphone.

[1229] 6. Emotional state analysis method

[1230] If an anomaly is detected, the emotion engine is used to analyze the detailed emotional state. For example, if the user is analyzed as being "tired," the reason and degree of fatigue are further analyzed.

[1231] 7. Meal suggestion tools

[1232] Based on the analyzed emotional state, the system will suggest appropriate foods and meal plans to the user. For example, if it determines that an energy replenishment is needed, it will suggest a combination of banana and yogurt.

[1233] Hardware and software used

[1234] Hardware: smartphone, head-mounted display, microphone, camera

[1235] Software: speech_recognition library, OpenCV library, emotion engine (proprietary algorithm or third-party API), food suggestion engine (proprietary algorithm or third-party API)

[1236] Specific examples

[1237] "What would you recommend for dinner tonight?"

[1238] "Suggest an energizing meal"

[1239] "Tell me some low-calorie lunch recipes"

[1240] This system allows users to receive individually tailored health management and psychological support, improving the quality of life for elderly people living alone.

[1241] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1242] Step 1:

[1243] Device startup and voice recognition

[1244] The device captures the user's voice input with a microphone and converts the voice to text using the speech_recognition library. For example, if a user says "Good morning," the system recognizes the voice. This voice data is input, and the speech recognition engine outputs the text data "Good morning."

[1245] Step 2:

[1246] User identification by facial recognition

[1247] After the system boots up, the device will use the camera to capture the user's face. The image will be analyzed using the OpenCV library, and the face recognition algorithm will identify the user. This face image will be the input data, and the face recognition algorithm will output a user ID. For example, if the user's face matches, the user ID will be recognized by the system.

[1248] Step 3:

[1249] Facial and vocal data collection

[1250] After authenticating the user, the device asks questions such as "Did you sleep well last night?" While the user answers, the camera captures their facial expressions and the microphone collects their voice. The collected facial images and voice data become input data, and this data is used for subsequent processing. For example, it records whether the user is smiling when answering "yes."

[1251] Step 4:

[1252] Sentiment analysis and anomaly detection

[1253] The collected data is sent to an emotion engine, which analyzes the user's emotional state based on facial expressions and voice. The emotion analysis algorithm takes these data as input and outputs the user's emotional state (e.g., "happy," "sad," "tired," etc.). The system then compares it with past data to detect anomalies. For example, if the user is determined to be "tired," it checks whether the state is abnormal.

[1254] Step 5:

[1255] Administrator Notification

[1256] If an abnormality is detected, the device will send a notification to the administrator via the server. The abnormal state data will be sent as input, and a notification message will be displayed on the administrator's smartphone or PC. For example, if the user's emotional state is detected as "sadness" for a long period of time, an alert will be sent to the administrator.

[1257] Step 6:

[1258] Meal suggestions based on emotional state

[1259] Based on the data analyzed by the emotion engine, the system makes meal suggestions suitable for the user. The meal suggestion engine receives the results of emotion analysis as input and outputs the optimal meal menu. For example, if the user feels "tired," it will suggest foods to replenish energy (bananas, yogurt, etc.).

[1260] Step 7:

[1261] Daily communication and psychological support

[1262] The device provides psychological support to users through everyday conversations. If a user asks, "What's the weather like today?", the system will respond with, "It's sunny today," and then engage in appropriate dialogue accordingly. This allows communication tailored to the user's mood, providing psychological support.

[1263] 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.

[1264] 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.

[1265] 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.

[1266] [Fourth embodiment]

[1267] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1268] 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.

[1269] 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).

[1270] 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.

[1271] 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.

[1272] 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).

[1273] 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.

[1274] 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.

[1275] 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.

[1276] 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.

[1277] 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.

[1278] 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.

[1279] 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."

[1280] This invention provides "Vital Frame," a system that uses a photo frame-type device to support health management for elderly people and others living alone. This system monitors the user's health status by combining user voice input, face authentication, facial expression recognition, and voice analysis, and notifies the administrator if an abnormality is detected. In addition, if an abnormality occurs, the system provides the user with appropriate advice on how to deal with it.

[1281] System Overview

[1282] The device wakes up when the user says "good morning" every morning.

[1283] The device captures the user's voice through a microphone and triggers the system by detecting the phrase "good morning" using a voice recognition algorithm.

[1284] Explanation by language generation:

[1285] When the user says "Good morning," the device recognizes the voice and starts up the system.

[1286] A device equipped with a means to identify users through facial recognition.

[1287] The device captures the user's face with its camera and uses a facial recognition algorithm to identify the user. This process allows the device to identify the user and proceed to the next step.

[1288] Explanation by language generation:

[1289] The device captures the user's face with a camera and runs a facial recognition algorithm based on the image to identify the user.

[1290] After authentication, the device asks AI questions and collects facial and voice data from the user.

[1291] For example, it asks a question like, "Did you sleep well last night?", and the user responds. At that time, the facial recognition algorithm analyzes the user's facial expressions, and the voice analysis algorithm analyzes the intonation and sound patterns of the voice.

[1292] Explanation by language generation:

[1293] The device asks, "Did you sleep well last night?" and when the user answers "yes," it collects their facial expressions and voice.

[1294] The collected data is sent to a server and an algorithm is run to compare it with past data and detect anomalies.

[1295] The collected facial expression and voice data is sent from the device to a server, which compares it with past data to detect any abnormalities. If an abnormality is detected, an administrator is notified.

[1296] Explanation by language generation:

[1297] The device sends data to the server, which compares it with past data to detect any anomalies. If an anomaly is detected, the administrator is notified.

[1298] If an error occurs, the system provides advice to the user on how to deal with the problem.

[1299] When an abnormality is detected, the device will provide the user with advice on how to deal with the problem via voice messages, etc.

[1300] Explanation by language generation:

[1301] The device advises the user, "You seem to be feeling unwell today. Please take it easy and rest."

[1302] A device equipped with a means of everyday communication.

[1303] Under normal circumstances, the device will hold daily conversations with the user and support their mental health. For example, when the user asks, "What's the weather like today?", the device will respond, "It's sunny today. It would be nice to go for a walk."

[1304] Explanation by language generation:

[1305] When a user asks, "What's the weather like today?" the device will respond with, "It's sunny today," interacting with the user through everyday conversations.

[1306] ---

[1307] In this way, the Vital Frame system monitors the user's health condition daily and has the ability to respond quickly in the event of an abnormality. By combining the above functions, it is possible to ensure the safety and health of family members living far away.

[1308] The processing flow will be explained below.

[1309] Step 1:

[1310] The user speaks "Good morning" into the photo frame device. The device captures the voice through its microphone, and the voice recognition function detects the word "Good morning." If the voice is recognized correctly, the system will start up.

[1311] Step 2:

[1312] The device uses a camera to capture the user's face. A facial recognition algorithm analyzes this image and identifies the user. If the user is successfully authenticated, the device proceeds to the next step.

[1313] Step 3:

[1314] The device will start asking AI questions, such as, "Did you sleep well last night?" As the user responds, the device will simultaneously capture their facial expressions with the camera and their voice with the microphone.

[1315] Step 4:

[1316] The device uses facial expression recognition algorithms to analyze the captured facial expression data, and voice analysis algorithms to analyze the tone and intonation of the voice, thereby conducting a detailed analysis of the collected data.

[1317] Step 5:

[1318] The facial expression data and voice data analyzed by the device are sent to the server, which automatically stores the received data in a database.

[1319] Step 6:

[1320] The server compares newly received data with past data to determine whether there are any anomalies. If necessary, it runs an anomaly detection algorithm to check for abnormal trends in the data.

[1321] Step 7:

[1322] If an abnormality is detected, the server will send a notification to the administrator (family member, caregiver, etc.) via SMS or email, informing them of the nature of the abnormality and the need for action.

[1323] Step 8:

[1324] If the device detects an abnormality, it will provide the user with voice advice on how to deal with the situation. For example, it might say, "You seem to be feeling unwell today. Please take it easy and rest."

[1325] Step 9:

[1326] If no abnormalities are found, the device returns to its normal state and provides everyday conversation functions. When the user asks, "What's the weather like today?", the device responds, "It's sunny today," and continues the conversation.

[1327] Step 10:

[1328] The device periodically recaptures the user's facial expressions and voice and sends the data to the server, allowing the user's health to be monitored based on the latest data.

[1329] Here are the detailed processing steps of the "Vital Frame" system:

[1330] Example 1

[1331] 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."

[1332] It is often difficult for elderly people and users living alone to properly manage their own health. In particular, it is necessary to understand their daily health status and respond quickly if any abnormalities are detected. At the same time, mental health support is also important, and there is a need to maintain users' mental health through daily communication. Few existing systems comprehensively meet these requirements, and an effective solution is needed.

[1333] 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.

[1334] In this invention, the server includes a means for recognizing a user's voice input and activating the system, a means for authenticating the user using a facial recognition algorithm, a means for collecting the user's facial expression and voice data after authentication, a means for comparing the collected data with past data to detect abnormalities, a means for sending a notification to an administrator when an abnormality is detected, and a means for advising the user on how to deal with the abnormality when an abnormality is detected. This allows for comprehensive monitoring of the user's health status, and enables prompt notification and action when an abnormality is detected. It also supports mental health through everyday conversation, contributing to maintaining the user's mental health.

[1335] "Voice input" refers to the voice information that the user speaks through a microphone, and is the means by which the system recognizes and analyzes that voice information.

[1336] "System startup" refers to the system starting to operate as a trigger for the user's voice input.

[1337] A "facial recognition algorithm" refers to a computational method or software used to analyze a user's facial image and identify the individual.

[1338] "Authentication" refers to verifying a user's identity and granting them permission to use a system.

[1339] "Facial Expression Data" refers to the results of analyzing specific facial expressions of a user captured through a camera.

[1340] "Voice data" refers to information collected from a user's voice and converted into an analyzable format.

[1341] "Past data" refers to facial expression and voice data of users that has been collected and saved up to now, and is the information that serves as the reference when detecting anomalies.

[1342] "Anomaly detection" refers to comparing collected data with past data to discover unusual conditions or problems.

[1343] "Administrator" refers to the person or organization that monitors and manages the system and is the person who will be notified if an abnormality is detected.

[1344] "Notification" refers to warning information sent to an administrator when an abnormality is detected.

[1345] "Advice on how to deal with the problem" refers to specific instructions on what to do or warnings given to the user when an abnormality is detected.

[1346] "Supporting mental health" refers to providing assistance to maintain and promote the user's mental health through everyday conversation.

[1347] A "display device" refers to a device that has a display as a user interface and performs functions such as facial recognition and data collection.

[1348] This invention functions as a system to support health management for elderly people and users living alone. This system uses a photo frame-type device to monitor the user's health status by combining voice input, face authentication, facial expression recognition, and voice analysis, and notifies a manager if an abnormality is detected. In addition, if an abnormality occurs, the system provides the user with appropriate advice on how to deal with it.

[1349] System configuration

[1350] This system uses the following hardware and software:

[1351] Device: A photo frame-type device equipped with a microphone, camera, and display.

[1352] Speech recognition software: Google Cloud Speech-to-Text.

[1353] Facial recognition algorithm: Amazon Rekognition is used.

[1354] Facial expression recognition algorithm: Uses Microsoft Azure Face API.

[1355] Speech analysis software: IBM Watson Speech to Text.

[1356] Server: TensorFlow is used as a machine learning model for anomaly detection.

[1357] Processing flow

[1358] 1. Boot the system:

[1359] The user speaks "Good morning" to the device. The device's microphone picks up the voice and uses Google Cloud Speech-to-Text for speech recognition. If "Good morning" is recognized, the system wakes up.

[1360] 2. User face recognition:

[1361] The device captures the user's face with its camera and uses Amazon Rekognition to perform facial recognition on the image, thereby identifying the user.

[1362] 3. AI-powered questioning and data collection:

[1363] After authentication, the device asks the user, "Did you sleep well last night?" In response to the user's answer, the camera captures their facial expression and the microphone collects their audio. The facial expression is analyzed using the Microsoft Azure Face API, and the audio is analyzed using IBM Watson Speech to Text.

[1364] 4. Data transmission and analysis:

[1365] The collected data is sent from the device to the server via a secure protocol (HTTPS). The server uses TensorFlow to compare the data with past data and detect anomalies. If an anomaly is detected, the server sends a notification to the administrator (by email or SMS).

[1366] 5. Advice on what to do in case of an abnormality:

[1367] When an abnormality is detected, the server sends a notification to the device, which then provides the user with pre-defined advice in the form of a voice message (e.g., "You seem to be feeling unwell today. Please take it easy and rest.").

[1368] 6. Daily communication:

[1369] During normal times, the device will hold daily conversations with the user to support their mental health. When the user asks, "What's the weather like today?", the device will retrieve information from a weather information API (e.g., OpenWeatherMap) and provide a voice response using Google Text-to-Speech.

[1370] Examples of specific examples and prompts

[1371] Examples:

[1372] The user says "Good morning."

[1373] The system starts when the device's microphone picks up the voice and the voice recognition software recognizes "good morning."

[1374] The user answers "Yes" to the question "Did you sleep well last night?"

[1375] The server detects an abnormality and notifies the administrator by email.

[1376] In the event of an abnormality, the device will advise, "You seem to be feeling unwell today. Please take it easy and rest."

[1377] When a user asks, in everyday conversation, "What's the weather like today?", the device will respond with, "It's sunny today."

[1378] Example prompt sentence:

[1379] When I say "Good morning," how does my device use my voice?

[1380] Please explain the steps to identify users using a facial recognition algorithm.

[1381] Describe the process for collecting data. How will the collected data be used?

[1382] Please explain the process for analyzing the collected data. How will you be notified if anomalies are found?

[1383] Please explain the process for advising on how to deal with an anomaly after it is detected.

[1384] Please provide details of your normal communication functions.

[1385] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1386] Step 1:

[1387] System wake-up via user voice input

[1388] Input: The user speaks "Good morning" into the device.

[1389] What happens: The user says "Good morning" to the device. The microphone captures the speech and sends it to the speech recognition software (Google Cloud Speech-to-Text).

[1390] Data processing / calculation: Speech recognition software converts the voice data into text data and verifies that the text is "Good morning."

[1391] Output: The system wakes up and sets a flag to proceed to the next processing step.

[1392] Step 2:

[1393] User facial recognition

[1394] Input: The system startup flag is set and the device's camera is activated.

[1395] What happens: The device camera captures the user's face, and sends the image to a facial recognition algorithm (Amazon Rekognition).

[1396] Data processing / computation: Facial recognition algorithms analyze facial images to obtain the user's identity.

[1397] Output: The user is authenticated and the authentication result is returned to the device.

[1398] Step 3:

[1399] AI-powered questioning and data collection

[1400] Input: Face recognition is successful and the user is recognized.

[1401] Specific behavior: The device asks the user, "Did you sleep well last night?" The user answers "Yes" or "No."

[1402] Data processing / calculation: The camera captures the user's facial expressions and the microphone collects their voice. The data is then analyzed using a facial recognition algorithm (Microsoft Azure Face API) and voice analysis software (IBM Watson Speech to Text).

[1403] Output: Analyzed facial expression and voice data are generated and sent to the next step.

[1404] Step 4:

[1405] Data transmission and analysis

[1406] Input: Analyzed facial expression data and voice data are saved on the device.

[1407] Specific operation: The device sends the collected data to the server via a secure communication protocol (HTTPS).

[1408] Data processing / calculation: The server analyzes the received data and compares it with past data. Anomaly detection algorithms (TensorFlow) are used to detect abnormalities in health status.

[1409] Output: If an abnormality is detected, the server will notify the administrator via email or SMS.

[1410] Step 5:

[1411] Advice on what to do when something goes wrong

[1412] Input: The server detects an anomaly and sends a notification to the device.

[1413] Specific operation: The device receives an abnormality notification and provides the user with pre-defined advice on how to deal with the problem via voice message.

[1414] Data processing / calculation: A specific method of dealing with the abnormality is selected.

[1415] Output: The device provides the user with a spoken advice saying, "You seem to be feeling unwell today. Please take it easy and rest."

[1416] Step 6:

[1417] Daily communication

[1418] Input: Under normal circumstances when the system is operating normally.

[1419] Specific behavior: The user asks the device, "What's the weather like today?"

[1420] Data processing / calculation: The device sends a request to the weather information API (OpenWeatherMap) to obtain the current day's weather information. A response is generated using speech synthesis software (Google Text-to-Speech).

[1421] Output: The device responds by saying, "It's a sunny day today. It would be nice to go for a walk."

[1422] (Application example 1)

[1423] 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."

[1424] For elderly people living alone, it is important to continuously monitor their daily health status and detect abnormalities early. However, existing health management systems have had difficulty in real-time monitoring unless the user periodically inputs data. Furthermore, there were issues with systems lacking the functionality to quickly advise on appropriate countermeasures when abnormalities were detected, and lacking in daily psychological support.

[1425] 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.

[1426] In this invention, the server includes a means for recognizing a user's voice input and activating the system, a means for authenticating the user using a facial recognition algorithm, a means for collecting the user's facial expression and voice data after authentication, a means for comparing the collected data with past data to detect abnormalities, a means for sending a notification to an administrator when an abnormality is detected, a means for providing the user with audio advice on how to deal with the abnormality when an abnormality is detected, a means for interacting with the user through everyday conversation and supporting a healthy mental state, a means for responding to the user's questions using a generative artificial intelligence model and providing everyday conversation using prompt sentences, and a smart display or smartphone-type terminal for authentication and data collection. This makes it possible to monitor the health status of elderly people living alone on a daily basis in real time, and to provide quick and accurate advice in the event of an abnormality, thereby providing safety and security.

[1427] "User voice input" refers to a method in which a user inputs information into a system by speaking.

[1428] The "means for starting the system" is a function that causes the system to start operating in response to the user's voice input.

[1429] A "facial recognition algorithm" is a technology that analyzes a user's facial image captured by a camera and compares it with registered data.

[1430] "Means for authenticating users" refers to a function that identifies users using a facial recognition algorithm.

[1431] "Means for collecting facial and voice data" refers to a function that captures and analyzes the user's facial expressions and voice.

[1432] "Means for detecting abnormalities by comparing past data" refers to a technique for comparing collected data with past data to find abnormalities.

[1433] "Means for sending notification to administrator" is a function that sends an alert to a person in charge of management when an abnormality is detected.

[1434] "A means for providing voice advice on how to deal with the problem" is a function that provides voice instructions on how to deal with the problem when an abnormality is detected.

[1435] "Means of interacting with users through everyday conversation" is a function that allows users to have everyday discussions.

[1436] "Means to support a healthy mental state" are technologies that allow users to continue interacting in order to maintain their mental health.

[1437] A "generative artificial intelligence model" is an algorithm for generating natural conversations based on large amounts of data.

[1438] A "prompt sentence" is an instruction sentence that an artificial intelligence uses to generate an appropriate response.

[1439] A "smart display or smartphone-type terminal" is a display or mobile phone with advanced functions, and is a device that can input and output various types of data.

[1440] System Overview

[1441] This invention is a system that realizes "CareGuard," a health management system for the elderly. CareGuard monitors the user's health status by combining the user's voice input, face recognition, facial expression recognition, and voice analysis. If an abnormality is detected, the system sends a notification to the administrator and also has the function of providing the user with voice advice on how to respond appropriately. It also interacts with the user through everyday conversations to support a healthy mental state.

[1442] Hardware and Software Configuration

[1443] The CareGuard system consists of a smart display or smartphone-type device and a cloud server. The hardware configuration includes a camera, microphone, and display. The system uses the following software components:

[1444] 1. Face recognition algorithm (e.g. OpenCV library)

[1445] 2. Speech recognition algorithms (e.g., SpeechRecognition library)

[1446] 3. Facial expression recognition algorithms (e.g., dlib library)

[1447] 4. Generative AI models (e.g., GPT-3)

[1448] 5. Prompt Sentence Management System

[1449] Detailed system processing explanation

[1450] 1. System startup

[1451] When a user speaks "good morning," a microphone captures the speech and a speech recognition algorithm detects the phrase, waking up the system.

[1452] 2. Facial Recognition

[1453] The system uses a camera to capture the user's face and identifies them through a facial recognition algorithm.

[1454] 3. Interview and data collection

[1455] After facial recognition, the AI ​​assistant will ask questions such as, "Did you sleep well last night?", using facial expression recognition and voice analysis algorithms to collect the user's facial and voice data.

[1456] 4. Data analysis and anomaly detection

[1457] The collected data is sent to a cloud server and compared with past data. Response times, tone of voice, subtle changes in facial expressions, etc. are analyzed to detect anomalies.

[1458] 5. Notifications and advice on how to deal with the issue

[1459] If an abnormality is detected, the server sends a notification to an administrator (e.g., family member or caregiver), and at the same time, gives the user audio advice on how to deal with the situation (e.g., "You seem to be feeling unwell today. Please take it easy and rest.").

[1460] 6. Daily conversation and emotional support

[1461] Even if there are no abnormalities, the AI ​​assistant will interact with the user through everyday conversations, such as saying, "The weather is sunny today. It would be nice to go for a walk." To do this, a generative AI model is used to generate natural conversations using prompt sentences.

[1462] Examples of prompt statements

[1463] Here are some example prompts to input to a generative AI model:

[1464] "Every morning, the user speaks "Good morning." The system then activates the system after analyzing the speech recognition."

[1465] "Identify users by recognizing their faces using a smartphone camera."

[1466] "The AI ​​assistant collects voice and facial expression data in response to questions."

[1467] "If an abnormality is detected, a notification will be sent to family members or caregivers."

[1468] This explanation will help you understand how the CareGuard system works in practice, as it is designed to provide peace of mind and security for seniors living alone.

[1469] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1470] Step 1:

[1471] Wake up the system with voice input

[1472] The user speaks "Good morning" (input). The device's microphone captures the user's voice, and the speech recognition algorithm (SpeechRecognition library) analyzes the voice (data processing). If the analysis result matches "Good morning," the system wakes up and proceeds to the next step (output).

[1473] Step 2:

[1474] User recognition through facial recognition

[1475] The device camera captures the user's face (input). The face recognition algorithm (OpenCV library) analyzes the captured image and compares it with the registered user's face data (data calculation). If the user is recognized, the process proceeds to the next step (output).

[1476] Step 3:

[1477] AI assistant asks questions and collects data

[1478] The device asks the authenticated user, "Did you sleep well last night?" (input). The user responds verbally, and their facial expressions and voice are captured (input). The facial expression recognition algorithm (dlib) and voice analysis algorithm (SpeechRecognition library) analyze the user's facial expressions and voice and collect the data (data processing). The collected data is sent to a cloud server (output).

[1479] Step 4:

[1480] Data analysis and anomaly detection

[1481] The server compares the facial and voice data sent to the cloud with past data (input). The AI ​​algorithm analyzes this data and determines whether there are any abnormalities (data calculation). If an abnormality is detected, the system proceeds to the next step (output).

[1482] Step 5:

[1483] Sending notifications

[1484] If an abnormality is detected, the server sends a notification to an administrator (e.g., family member or caregiver) (output). Specifically, it sends an alert to the administrator's email address, which includes the details of the abnormality and recommended actions to take (input: abnormal data).

[1485] Step 6:

[1486] Advice for users on what to do

[1487] When an abnormality is detected, the device will give the user voice advice on how to deal with the situation (input). For example, it will give a voice message such as "You seem to be feeling unwell today. Please take it easy and rest" (output).

[1488] Step 7:

[1489] Daily conversation and emotional support

[1490] Even if no abnormalities are detected, the device will engage in everyday conversation with the user (input). A generative AI model (such as GPT-3) will generate appropriate answers to questions from the user (data calculation) and provide natural conversation using prompt sentences (output). For example, if the user asks, "What's the weather like today?" the device will respond, "It's sunny today. It would be nice to go for a walk."

[1491] ---

[1492] This enables the CareGuard system to effectively support users' health management, respond quickly if an abnormality is detected, and also provide daily mental support.

[1493] 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.

[1494] This invention provides "Vital Frame," a system that uses a photo frame-type device to support health and emotional management for elderly people and others living alone. This system combines user voice input, face authentication, facial expression recognition, voice analysis, and an emotion engine to grasp the user's emotional state and comprehensively monitor their health and psychological state.

[1495] System Overview

[1496] User-initiated voice activation

[1497] The system is started by the user saying "Good morning" every morning. The device captures the voice through the microphone and detects the word "Good morning" using its voice recognition function. When this voice is recognized, the system starts up.

[1498] Explanation by language generation:

[1499] When the user says "Good morning," the device recognizes the voice and starts up the system.

[1500] User identification by facial recognition

[1501] After booting, the device uses its camera to capture the user's face, which allows a facial recognition algorithm to analyze the image and identify the user. Once the user is identified, the system proceeds to the next processing step.

[1502] Explanation by language generation:

[1503] The device captures the user's face with its camera, runs a facial recognition algorithm, and recognizes the user.

[1504] Collection of facial expression and voice data after authentication

[1505] After authentication, the device asks AI questions and collects facial and voice data from the user. For example, it might ask, "Did you sleep well last night?" When the user responds, the device simultaneously captures their facial expression with the camera and their voice with the microphone.

[1506] Explanation by language generation:

[1507] The device asks, "Did you sleep well last night?" and when the user answers "yes," it collects their facial expressions and voice.

[1508] Emotion recognition by emotion engine

[1509] The device inputs the collected facial and voice data into an emotion engine to analyze the user's emotional state, for example, identifying emotions such as joy, sadness, and surprise, and then integrates the results with other data to provide a comprehensive assessment of the user's health.

[1510] Explanation by language generation:

[1511] The device runs the user's facial expressions and voice data through an emotion engine to identify the user's emotions.

[1512] Sending data to the server and detecting anomalies

[1513] The device transmits the facial, voice, and emotion data it captures to a server, which then runs an algorithm to compare the new data with past data and detect anomalies. If an anomaly is detected, a notification is sent to an administrator.

[1514] Explanation by language generation:

[1515] The device sends data to the server, and if the server detects an abnormality, it notifies the administrator.

[1516] Advice on what to do when something goes wrong

[1517] If an abnormality is detected, the device will provide the user with voice advice on how to deal with the situation, such as "You seem to be feeling unwell today. Please take it easy and rest."

[1518] Explanation by language generation:

[1519] The device advises the user, "You seem to be feeling unwell today. Please take it easy and rest."

[1520] Daily communication

[1521] Under normal circumstances, the device will hold daily conversations with the user, providing support for not only their health but also their psychological state. Using the analysis results of the emotion engine, the device will have appropriate conversations based on the user's mood.

[1522] Explanation by language generation:

[1523] When a user asks, "What's the weather like today?", the device responds with, "It's sunny today," and responds based on the results of the emotion engine.

[1524] This allows the Vital Frame system to comprehensively monitor the user's health and emotional state and respond quickly in the event of an abnormality. By combining it with the emotion engine, more precise health management and psychological support are possible.

[1525] The processing flow will be explained below.

[1526] Step 1:

[1527] The user speaks "Good morning" into the photo frame device. The device captures the voice through its microphone, and the voice recognition function detects the word "Good morning." If the voice is recognized correctly, the system will start up.

[1528] Step 2:

[1529] The device uses a camera to capture the user's face. A facial recognition algorithm analyzes this image and identifies the user. If the user is successfully authenticated, the device proceeds to the next step.

[1530] Step 3:

[1531] The device will start asking AI questions, such as, "Did you sleep well last night?" As the user responds, the device will simultaneously capture their facial expressions with the camera and their voice with the microphone.

[1532] Step 4:

[1533] The device uses facial expression recognition algorithms to analyze the captured facial expression data, and voice analysis algorithms to analyze the tone and intonation of the voice, thereby conducting a detailed analysis of the collected data.

[1534] Step 5:

[1535] The facial expression and voice data analyzed by the device are input into an emotion engine to analyze the user's emotional state. Based on the collected data, the emotion engine identifies emotions such as joy, sadness, and surprise.

[1536] Step 6:

[1537] The device transmits the analyzed facial expression data, voice data, and emotion data to the server, which automatically stores the received data in a database.

[1538] Step 7:

[1539] The server compares newly received data with past data to determine whether there are any anomalies. If necessary, it runs an anomaly detection algorithm to check for abnormal trends in the data.

[1540] Step 8:

[1541] If an abnormality is detected, the server will send a notification to the administrator (family member, caregiver, etc.) via SMS or email, informing them of the nature of the abnormality and the need for action.

[1542] Step 9:

[1543] If the device detects an abnormality, it will provide the user with voice advice on how to deal with the situation. For example, it might say, "You seem to be feeling unwell today. Please take it easy and rest."

[1544] Step 10:

[1545] If no abnormalities are found, the device returns to its normal state and provides everyday conversation functions. When the user asks, "What's the weather like today?", the device responds, "It's sunny today," and continues the conversation. The device uses an emotion engine to respond according to the user's emotions.

[1546] Step 11:

[1547] The device periodically recaptures the user's facial expressions and voice and sends the data to the server, allowing the device to monitor the user's health and emotional state based on the most up-to-date data.

[1548] These are the specific processing steps in the "Vital Frame" system.

[1549] Example 2

[1550] 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."

[1551] The goal is to comprehensively monitor the health and emotional state of elderly people and users living alone, and quickly detect and respond to any abnormalities. Conventional systems have had difficulty efficiently and effectively collecting and analyzing this information and taking appropriate action.

[1552] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for recognizing a voice input by a user and activating the system, means for authenticating the user using a face recognition algorithm, means for collecting facial expression and voice data of the user after authentication, means for inputting the collected data into an emotion engine and analyzing the emotional state, means for comparing data including the analysis results with past data to detect abnormalities, and means for sending a notification to an administrator when an abnormality is detected. This makes it possible to comprehensively monitor the user's health state and emotional state and to respond quickly when an abnormality occurs.

[1553] "User voice input" is the process by which the system captures the user's voice through a microphone and recognizes that voice.

[1554] "Means for starting the system" refers to a function for starting the operation of the system by a trigger such as voice input.

[1555] A "face recognition algorithm" is an algorithm that analyzes facial images captured by a camera and determines the user's identity.

[1556] "Means for authenticating users" refers to a function for identifying who a user is, using a facial recognition algorithm or the like.

[1557] "Means for collecting user's facial expression and voice data" refers to a function for capturing the user's facial expression and voice information using a camera or microphone and collecting this data.

[1558] An "emotion engine" is software or algorithm that analyzes collected facial and voice data to identify a user's emotional state.

[1559] The "means for analyzing emotional state" is a function for analyzing the user's emotions using an emotion engine and identifying their state.

[1560] The "means for detecting abnormalities by comparing past data" is a function for comparing newly collected data with past data and identifying abnormal conditions that are different from normal.

[1561] The "means for sending a notification to an administrator when an abnormality is detected" is a function for sending a notification to an administrator when an abnormality is detected.

[1562] The "means for providing advice on how to deal with the problem" is a function that provides voice instructions to the user on how to deal with the problem when an abnormality is detected.

[1563] A "photo frame terminal" is a device shaped like a photo frame, equipped with functions such as a camera, microphone, and display.

[1564] "Abnormal" refers to a deviation from normal health or emotional state.

[1565] An "administrator" is a person or organization responsible for monitoring the health and emotional state of users in the system and intervening when necessary.

[1566] This invention provides a system called "Vital Frame" that supports health and emotional management for elderly people and users living alone. The system uses a photo frame-type device to grasp the user's emotional state and comprehensively monitor their health and psychological state by combining voice input, face authentication, facial expression recognition, voice analysis, and an emotion engine.

[1567] Hardware and software used

[1568] Microphone: Used to capture the user's voice.

[1569] Camera: Used to capture the user's face and facial expressions.

[1570] Google Speech-to-Text API: Speech recognition software that converts voice input into text.

[1571] Amazon Rekognition: Image analysis software that provides facial recognition algorithms.

[1572] Microsoft Azure Emotion API: An emotion recognition engine that analyzes the user's emotional state.

[1573] Server: A computer that stores and analyzes data.

[1574] Photo frame type terminal: A device that integrates the above hardware and software.

[1575] 1. User voice input

[1576] Every morning, the user inputs the voice by saying "Good morning." The device's microphone captures this voice and uses the Google Speech-to-Text API to recognize the word "Good morning." Once this voice is recognized, the system wakes up.

[1577] Specific examples

[1578] The user says "Good morning." The audio is captured by the device's microphone and converted to text using the Google Speech-to-Text API.

[1579] Prompt Sentence Examples

[1580] Convert the following Japanese audio to text: "Good morning"

[1581] 2. User identification through facial recognition

[1582] Once the system is up and running, the device's camera automatically captures the user's face and runs a facial recognition algorithm using Amazon Rekognition, which determines the user's identity.

[1583] Specific examples

[1584] The camera captures the user's face and uses Amazon Rekognition to recognize it.

[1585] 3. Collecting facial and voice data after authentication

[1586] Once facial recognition is complete, the device will ask an AI question, such as, "Did you sleep well last night?" When the user responds, the camera captures their facial expression and the microphone captures their voice.

[1587] Specific examples

[1588] The device asks, "Did you sleep well last night?" and captures your facial expressions and voice as you answer "yes."

[1589] 4. Emotion Recognition by Emotion Engine

[1590] The collected facial and voice data is fed into an emotion engine using the Microsoft Azure Emotion API, which analyzes this data and identifies the user's emotional state.

[1591] Specific examples

[1592] The captured data is analyzed using the Microsoft Azure Emotion API to identify the user's emotions such as happiness, sadness, surprise, etc.

[1593] Prompt Sentence Examples

[1594] Analyze the following facial expression and audio data to identify the emotion: "Image data: <Image file path> Audio data: <Audio file path>"

[1595] 5. Sending data to the server and detecting anomalies

[1596] The device sends the collected data to a server, where an algorithm compares it with past data to detect anomalies, and if an anomaly is detected, a notification is sent to the administrator.

[1597] 6. Advice on what to do in case of an abnormality

[1598] If the device detects an abnormality, it will give the user voice advice on how to deal with the situation, such as "You seem to be feeling unwell today. Please take it easy and rest."

[1599] 7. Daily communication

[1600] During normal times, the device will hold daily conversations with the user and provide appropriate responses based on the results of the emotion engine. By supporting not only the user's health but also their psychological state, the device aims to improve their quality of life.

[1601] Specific examples

[1602] When a user asks, "What's the weather like today?", the device responds with, "It's sunny today," and then responds according to the user's emotional state.

[1603] Through these processes, the Vital Frame system can comprehensively monitor the user's health and emotional state and respond quickly to any abnormalities. By combining this with the emotion engine, more precise health management and psychological support are possible.

[1604] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1605] Step 1:

[1606] When a user says "Good morning," the device's microphone captures the voice. The device sends the voice input ("Good morning") to the Google Speech-to-Text API and receives text data (recognized as "Good morning"). The device confirms that this text data is "Good morning" and wakes up the system.

[1607] Step 2:

[1608] When the device starts up, the camera automatically captures the user's face. The captured facial image data is sent to Amazon Rekognition, which runs a facial recognition algorithm to identify the user. Based on the recognition result (user ID), the system loads the system settings for that user.

[1609] Step 3:

[1610] Once the user authentication is complete, the device will begin asking questions automatically using AI. Specifically, the device will ask aloud, "Did you sleep well last night?" When the user responds, the device's camera will capture the user's facial expression and the microphone will capture their voice. This data (facial image data and voice data) will be sent to the emotion engine.

[1611] Step 4:

[1612] The device inputs the collected facial image data and voice data into the Microsoft Azure Emotion API, which analyzes the user's emotional state. For example, emotions such as joy, sadness, and surprise are output. The analysis results are then integrated with other health data.

[1613] Step 5:

[1614] The device sends analyzed emotional data and other health data to a server, which compares the received data with past data and runs algorithms to detect anomalies. If an anomaly is detected, a real-time notification is sent to the administrator.

[1615] Step 6:

[1616] When the server reports an abnormality, the device will advise the user on how to deal with the situation. For example, the system might give a voice instruction such as, "You seem to be feeling unwell today. Please take it easy and rest." This interaction allows the user to take appropriate action.

[1617] Step 7:

[1618] Under normal circumstances, the device will engage in everyday conversations with the user. When the user asks, "What's the weather like today?", the device will provide weather information. The device will then use the results of its emotion engine to generate an appropriate answer based on the user's emotional state. For example, the system might respond, "It's sunny today. It feels good to go outside."

[1619] In this way, the "Vital Frame" system can comprehensively monitor the user's health and emotional state, quickly detect and respond to abnormalities, and thereby improve the user's quality of life.

[1620] (Application example 2)

[1621] 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."

[1622] When elderly people live alone, they often lack adequate health care and psychological support. As a result, poor health care and feelings of loneliness can lead to serious problems. In particular, poor dietary choices and a lack of psychological support can lead to a deterioration in overall health and emotional state. The present invention aims to solve these problems and provide a system that allows elderly people to live alone in a healthier and more secure way.

[1623] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1624] In this invention, the server includes means for recognizing voice input and activating the system, means for authenticating the user using a face recognition algorithm, means for collecting the user's facial expression and voice data after authentication, means for detecting abnormalities by comparing the collected data with past data, means for sending a notification to an administrator when an abnormality is detected, means for analyzing the user's emotional state when an abnormality is detected, and means for making dietary suggestions based on the emotional state. This makes it possible to comprehensively manage the user's health and emotional state and provide appropriate dietary suggestions and psychological support.

[1625] The "means for starting the system" refers to a means having a function for recognizing a user's voice input and starting the operation of the system.

[1626] A "face recognition algorithm" is a technology that has the function of analyzing image data captured using a camera and identifying a user's face.

[1627] The "means for collecting facial expression and voice data" refers to a means having the function of collecting the user's facial expression with a camera and voice with a microphone.

[1628] "Means for detecting anomalies" refers to means that have algorithms or functions that compare collected data with past data and identify anomalies.

[1629] The "means for sending a notification to an administrator" is a means having a function for sending a notification to a pre-registered administrator when an abnormality is detected.

[1630] "Means for analyzing the emotional state of a user" refers to means having algorithms or techniques for analyzing the emotional state of a user based on collected facial expression and voice data.

[1631] The "means for making meal suggestions" is a means having a function of suggesting appropriate foods and meal menus to the user based on the analyzed emotional state.

[1632] This invention provides "Vital Meal," an application example of a system that supports health and emotional management for elderly people living alone, using a smartphone and a head-mounted display. "Vital Meal" combines voice input, face recognition, facial expression recognition, voice analysis, and an emotion engine to comprehensively manage the user's health and emotional state, and based on that, makes meal suggestions and provides psychological support.

[1633] The system works as follows:

[1634] System configuration

[1635] 1. Voice input recognition method

[1636] The system wakes up when the user speaks. This process uses a microphone and the speech_recognition library. For example, if the user says "Good morning," the system wakes up.

[1637] 2. Facial Recognition Methods

[1638] After the system boots up, it uses a camera to capture the user's face and then uses a facial recognition algorithm to identify the user. This process uses the OpenCV library, for example, to detect the user's face and compare it with the registered facial data for authentication.

[1639] 3. Facial and vocal data collection methods

[1640] After authentication, the AI ​​asks questions and collects facial and voice data from the user. For example, it asks, "Did you sleep well last night?" and collects data from the camera and microphone when the user answers "yes."

[1641] 4. Anomaly Detection Methods

[1642] The collected data is input into an emotion engine to analyze the user's emotional state. It then checks whether any abnormalities are detected. If any abnormalities are detected, an algorithm is activated to compare them with past data.

[1643] 5. Means of notification

[1644] If an abnormality is detected, a notification is sent to the administrator. For example, if an abnormal emotional state is detected, an alert is sent to the administrator's smartphone.

[1645] 6. Emotional state analysis method

[1646] If an anomaly is detected, the emotion engine is used to analyze the detailed emotional state. For example, if the user is analyzed as being "tired," the reason and degree of fatigue are further analyzed.

[1647] 7. Meal suggestion tools

[1648] Based on the analyzed emotional state, the system will suggest appropriate foods and meal plans to the user. For example, if it determines that an energy replenishment is needed, it will suggest a combination of banana and yogurt.

[1649] Hardware and software used

[1650] Hardware: smartphone, head-mounted display, microphone, camera

[1651] Software: speech_recognition library, OpenCV library, emotion engine (proprietary algorithm or third-party API), food suggestion engine (proprietary algorithm or third-party API)

[1652] Specific examples

[1653] "What would you recommend for dinner tonight?"

[1654] "Suggest an energizing meal"

[1655] "Tell me some low-calorie lunch recipes"

[1656] This system allows users to receive individually tailored health management and psychological support, improving the quality of life for elderly people living alone.

[1657] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1658] Step 1:

[1659] Device startup and voice recognition

[1660] The device captures the user's voice input with a microphone and converts the voice to text using the speech_recognition library. For example, if a user says "Good morning," the system recognizes the voice. This voice data is input, and the speech recognition engine outputs the text data "Good morning."

[1661] Step 2:

[1662] User identification by facial recognition

[1663] After the system boots up, the device will use the camera to capture the user's face. The image will be analyzed using the OpenCV library, and the face recognition algorithm will identify the user. This face image will be the input data, and the face recognition algorithm will output a user ID. For example, if the user's face matches, the user ID will be recognized by the system.

[1664] Step 3:

[1665] Facial and vocal data collection

[1666] After authenticating the user, the device asks questions such as "Did you sleep well last night?" While the user answers, the camera captures their facial expressions and the microphone collects their voice. The collected facial images and voice data become input data, and this data is used for subsequent processing. For example, it records whether the user is smiling when answering "yes."

[1667] Step 4:

[1668] Sentiment analysis and anomaly detection

[1669] The collected data is sent to an emotion engine, which analyzes the user's emotional state based on facial expressions and voice. The emotion analysis algorithm takes these data as input and outputs the user's emotional state (e.g., "happy," "sad," "tired," etc.). The system then compares it with past data to detect anomalies. For example, if the user is determined to be "tired," it checks whether the state is abnormal.

[1670] Step 5:

[1671] Administrator Notification

[1672] If an abnormality is detected, the device will send a notification to the administrator via the server. The abnormal state data will be sent as input, and a notification message will be displayed on the administrator's smartphone or PC. For example, if the user's emotional state is detected as "sadness" for a long period of time, an alert will be sent to the administrator.

[1673] Step 6:

[1674] Meal suggestions based on emotional state

[1675] Based on the data analyzed by the emotion engine, the system makes meal suggestions suitable for the user. The meal suggestion engine receives the results of emotion analysis as input and outputs the optimal meal menu. For example, if the user feels "tired," it will suggest foods to replenish energy (bananas, yogurt, etc.).

[1676] Step 7:

[1677] Daily communication and psychological support

[1678] The device provides psychological support to users through everyday conversations. If a user asks, "What's the weather like today?", the system will respond with, "It's sunny today," and then engage in appropriate dialogue accordingly. This allows communication tailored to the user's mood, providing psychological support.

[1679] 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.

[1680] 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.

[1681] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1682] 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.

[1683] 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.

[1684] 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.

[1685] 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).

[1686] 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.

[1687] 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."

[1688] 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.

[1689] 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).

[1690] 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.

[1691] 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.

[1692] 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.

[1693] 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.

[1694] 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.

[1695] 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.

[1696] 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.

[1697] 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.

[1698] 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.

[1699] 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.

[1700] The following is further disclosed regarding the above embodiment.

[1701] (Claim 1)

[1702] means for recognizing a user's voice input to activate the system;

[1703] a means for authenticating a user using a facial recognition algorithm;

[1704] means for collecting facial and voice data of the user after authentication;

[1705] A means of comparing collected data with past data to detect anomalies;

[1706] The system includes a means for sending a notification to an administrator when an anomaly is detected.

[1707] (Claim 2)

[1708] 2. The system according to claim 1, further comprising means for giving advice to the user on how to deal with an abnormality when the abnormality is detected.

[1709] (Claim 3)

[1710] 10. The system of claim 1, further comprising a photo frame type terminal for authentication and data collection.

[1711] "Example 1"

[1712] (Claim 1)

[1713] means for recognizing a user's voice input to activate the system;

[1714] a means for authenticating a user using a facial recognition algorithm;

[1715] means for collecting facial and voice data of the user after authentication;

[1716] A means of comparing collected data with past data to detect anomalies;

[1717] a means for sending a notification to an administrator when an anomaly is detected;

[1718] and means for advising the user on how to deal with an abnormality when the abnormality is detected.

[1719] (Claim 2)

[1720] The system of claim 1, further comprising means for conducting daily conversations with the user to support mental health.

[1721] (Claim 3)

[1722] 10. The system of claim 1, further comprising a display terminal for authentication and data collection.

[1723] "Application Example 1"

[1724] (Claim 1)

[1725] means for recognizing a user's voice input to activate the system;

[1726] a means for authenticating a user using a facial recognition algorithm;

[1727] means for collecting facial and voice data of the user after authentication;

[1728] A means of comparing collected data with past data to detect anomalies;

[1729] a means for sending a notification to an administrator when an anomaly is detected;

[1730] When an abnormality is detected, a means of providing audio advice to the user on how to deal with the problem,

[1731] A system that includes a means of interacting with users through everyday conversation and supporting a healthy mental state.

[1732] (Claim 2)

[1733] 10. The system of claim 1, further comprising means for utilizing a generative artificial intelligence model to respond to a user's questions and provide everyday conversation using prompt sentences.

[1734] (Claim 3)

[1735] 10. The system of claim 1, further comprising a smart display or smartphone-type terminal for authentication and data collection.

[1736] "Example 2: Combining Emotion Engines"

[1737] (Claim 1)

[1738] means for recognizing a user's voice input to activate the system;

[1739] a means for authenticating a user using a facial recognition algorithm;

[1740] means for collecting facial and voice data of the user after authentication;

[1741] A means for inputting the collected data into an emotion engine to analyze the emotional state;

[1742] A means for detecting anomalies by comparing data containing analysis results with past data;

[1743] The system includes a means for sending a notification to an administrator when an anomaly is detected.

[1744] (Claim 2)

[1745] 2. The system according to claim 1, further comprising means for giving advice to the user on how to deal with an abnormality when the abnormality is detected.

[1746] (Claim 3)

[1747] 10. The system of claim 1, further comprising a photo frame type terminal for performing authentication, data collection and sentiment analysis.

[1748] "Application example 2 when combining emotion engines"

[1749] (Claim 1)

[1750] means for recognizing a user's voice input to activate the system;

[1751] a means for authenticating a user using a facial recognition algorithm;

[1752] means for collecting facial and voice data of the user after authentication;

[1753] A means of comparing collected data with past data to detect anomalies;

[1754] a means for sending a notification to an administrator when an anomaly is detected;

[1755] means for analyzing the emotional state of the user when an anomaly is detected;

[1756] A system including a means for providing meal suggestions based on emotional state.

[1757] (Claim 2)

[1758] 2. The system according to claim 1, further comprising means for giving advice to the user on how to deal with an abnormality when the abnormality is detected.

[1759] (Claim 3)

[1760] 10. The system of claim 1, further comprising a photo frame type terminal for authentication and data collection. [Explanation of symbols]

[1761] 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 recognizing a user's voice input to activate the system; a means for authenticating a user using a facial recognition algorithm; means for collecting facial and voice data of the user after authentication; A means of comparing collected data with past data to detect anomalies; The system includes a means for sending a notification to an administrator when an anomaly is detected.

2. 2. The system according to claim 1, further comprising means for giving advice to a user on how to deal with an abnormality when the abnormality is detected.

3. 2. The system according to claim 1, further comprising a photo frame type terminal for authentication and data collection.

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