system

Abstract

We provide the system. [Solution] A data collection means for acquiring audio information, visual information, and instrument data, and integrating these data, A data processing means that analyzes the data acquired by the aforementioned data collection means and evaluates the user's health status, A response generation means that provides personalized advice to the user based on the results of the data processing means, A notification system that issues warnings when it detects a user's health condition or an emergency, The aforementioned notification means remotely tracks the user's actions and health status and provides a notification function when an abnormality is detected. A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the progress of an aging society, the shortage of caregiving personnel and the increasing burden on caregivers are recognized as serious problems. Also, ensuring safety for the elderly to live independently is an important issue. In particular, there is a demand for a system to appropriately respond to falls and sudden changes in the health condition of the elderly, but current technologies lack an efficient and effective solution for comprehensively achieving this. Against such a background, the need for a system that can grasp the health condition of the elderly in real time and provide individualized care is increasing.

Means for Solving the Problems

[0005] This invention provides means for acquiring voice data, image data, and sensor data, and integrates and analyzes this data to evaluate the user's health status in real time. Furthermore, it includes feedback generation means that provide personalized advice to the user based on the analysis results. In addition, if an abnormality in the user's health status or an emergency is detected, an emergency notification is issued quickly to provide necessary information to caregivers and family members. In this way, it is possible to support the independent living of the elderly and reduce the burden of caregiving.

[0006] "Voice data" refers to data that records user speech information in digital format.

[0007] "Image data" refers to data that digitally records visual information, including information about the user's posture and facial expressions.

[0008] "Sensor data" refers to data that measures and records information about living organisms and the environment, such as heart rate and body temperature, in digital format.

[0009] A "data acquisition means" is a mechanism for collecting audio data, image data, and sensor data and inputting them into a system.

[0010] A "data analysis tool" is a mechanism for analyzing acquired data and evaluating the user's health status.

[0011] A "feedback generation mechanism" is a system for generating advice and instructions for the user based on the results of data analysis.

[0012] A "notification mechanism" is a system that provides alerts and information to caregivers and family members when a user's health condition or an emergency occurs. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

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

[0016] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

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

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is a system intended for monitoring and caring for the elderly. It integrates voice, image, and sensor data to evaluate the user's condition in real time and provides advice or emergency notifications as needed. One of the main components in this system's embodiment is a program that acquires and processes data. Its operation will be described below.

[0035] server

[0036] The server receives audio, image, and sensor data transmitted from the terminal and processes each data individually. For audio data, it first converts it to text using speech recognition technology, and then uses natural language processing to determine the user's needs and condition. For image data, computer vision algorithms are applied to analyze the user's posture and movements to check for abnormalities. Sensor data is analyzed, for example, to detect trends in heart rate and body temperature, and abnormal numerical changes are detected. Finally, the results of these data analyses are integrated to generate feedback for the user. If the user's condition is abnormal, an alert is immediately issued to notify family members or caregivers.

[0037] terminal

[0038] The device is responsible for continuously collecting voice, image, and sensor data near the user, utilizing voice commands and environmental sensors, and transmitting it to the server. The device is equipped with a microphone and camera, constantly monitoring the user's situation. It also uses speech synthesis technology to convey feedback from the server to the user. For example, it provides advice in natural language, such as "It's time to drink some water" or "You should take a short rest."

[0039] User

[0040] Users interact with the system through their devices in their daily lives. For example, if a user says, "I'm not feeling very well," the device immediately sends that audio to the server, and appropriate feedback is provided. Furthermore, in emergencies, the device autonomously sends data to the server without the user having to take any action, and necessary measures are taken.

[0041] As a concrete example, suppose a user suddenly falls while moving around their home. In this case, the device detects the abnormal posture and immediately sends a message to the server indicating the fall. Based on this information, the server sends an emergency notification to family members or caregivers stating that a fall has occurred, prompting them to take necessary action. In this way, swift and efficient care becomes possible.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The device collects audio data, image data, and sensor data in real time using microphones, cameras, and various sensors located around the user. Audio data captures the user's speech, image data records the user's posture and environment via the camera, and sensor data collects biometric information such as heart rate and body temperature.

[0045] Step 2:

[0046] The terminal transmits collected audio, images, and sensor data to the server via wireless communication. During this process, the data is converted and compressed into the appropriate format before transmission, ensuring efficient data transfer.

[0047] Step 3:

[0048] The server inputs the audio data sent from the terminal into the speech recognition engine and converts the audio into text format. The resulting text is then passed to the natural language processing unit, which interprets the user's intent and emotions.

[0049] Step 4:

[0050] The server analyzes image data using computer vision algorithms to estimate the user's posture and detect anomalies in the environment. For example, if there is a possibility of falling, the system will identify signs of it.

[0051] Step 5:

[0052] The server analyzes sensor data to detect abnormal heart rate, body temperature changes, and other abnormalities. This allows for the detection of changes in health status.

[0053] Step 6:

[0054] The server integrates the analysis results from steps 3-5 and generates feedback for the user. For example, if an anomaly is detected, a warning message is generated.

[0055] Step 7:

[0056] The server sends the generated feedback to the terminal, presenting it to the user via speech synthesis or display. The terminal then plays instructions such as "Please take a short break" or "Please stay hydrated" in voice.

[0057] Step 8:

[0058] The server will also send emergency notifications to family members or caregivers via email or SMS, as needed. For example, notifications will be sent in the event of an emergency such as a fall.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] In monitoring and caring for the elderly, accurately understanding their health status in real time is crucial. However, conventional technologies often struggle to effectively integrate voice, visual, and physical sensor data, resulting in insufficient comprehensive health assessments. Furthermore, systems capable of responding quickly to abnormalities are limited. This leads to a problem where the safety of elderly individuals living independently is not adequately ensured.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes information gathering means for acquiring and integrating voice data, visual materials, and physical sensor information; information analysis means for analyzing the information collected by the information gathering means and evaluating the individual's health status; and response generation means for providing personalized advice based on the results of the information analysis means. This enables a multifaceted evaluation of the health status of elderly people, allowing for personalized advice and rapid emergency response.

[0064] "Audio data" refers to recordings of human voices that have been physically collected, and it is possible to analyze this data to extract semantic information.

[0065] "Visual data" refers to visual information such as images and videos acquired using cameras or other imaging devices, and is used to analyze this data to recognize the actions and states of a subject.

[0066] "Physical sensor information" refers to data collected by sensors that measure environmental and biological information such as temperature, heart rate, and movement, and is used to evaluate an individual's health status and environmental changes.

[0067] "Information gathering means" refers to technical means for acquiring and integrating audio data, visual materials, and physical sensor information.

[0068] "Information analysis means" refers to technical means for processing and analyzing collected data to evaluate an individual's health status and physical condition.

[0069] "Response generation means" refers to technical means for generating and providing appropriate advice or instructions to an individual based on the results of analysis.

[0070] "Warning measures" refer to technical means for detecting an individual's health condition or an emergency situation and promptly notifying them.

[0071] This invention is a comprehensive system for monitoring the elderly, integrating voice data, visual information, and physical sensor data to analyze the user's health status and provide timely advice and emergency notifications. Specific embodiments of this system are described below.

[0072] terminal

[0073] The terminal is responsible for collecting audio, visual, and physical sensor information necessary for the user's daily life. The terminal is equipped with a microphone, camera, heart rate monitor, and body temperature sensor, and collects data in real time. This allows for continuous monitoring of the user's voice and movement patterns, as well as vital signs. For example, if the terminal detects that the user has fallen in the bathroom, it can immediately notify the server of the abnormal situation.

[0074] server

[0075] The server plays a central role in receiving and analyzing data transmitted from the terminal. Audio data is converted into text using speech recognition technology (e.g., general speech recognition software), and natural language processing technology is used to evaluate the user's health status. For visual data, machine vision technology is used to analyze the user's posture and movements and detect abnormalities. For physical sensor information, fluctuations in heart rate and body temperature are monitored, and alerts are generated if unusual values ​​are observed. For example, if a sudden increase in heart rate is detected, it is immediately determined that there is an abnormality in the user's health status. All of these analysis results are integrated to generate feedback for the user.

[0076] Based on the feedback it generates, the server uses speech synthesis technology to deliver appropriate advice to the user via the device. It also notifies family members and caregivers as needed to facilitate a quick response. In emergencies, an automatic alert is issued, ensuring a rapid response is possible.

[0077] User

[0078] Through interaction with the device, users can live their daily lives with peace of mind. For example, if a user says, "I'm not feeling well today," the device immediately sends that information to the server and provides feedback. This helps them avoid unnecessary actions and obtain information useful for health management.

[0079] An example of a prompt message might be, "Please explain the operating principle of a system that detects falls in the elderly." This system allows users to improve their safety in daily life, and enables family members and caregivers to monitor their loved ones with peace of mind.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1: Data Collection

[0082] The device continuously collects audio, visual, and physical sensor information from the user's environment. Specifically, it records audio using a built-in microphone and captures images and videos with a camera. It also acquires biometric information through a heart rate monitor and body temperature sensor. The input for this step is the user's voice and environmental conditions, while the output is a collection of data from various sensors.

[0083] Step 2: Data transmission

[0084] The terminal transmits the collected data to the server using a secure communication protocol (e.g., HTTPS). Voice data, image data, and sensor data are transmitted periodically or triggered. The input in this step is the raw data obtained in the data collection step, and the output is the structured data sent to the server.

[0085] Step 3: Audio Data Processing

[0086] The server converts received audio data into text using speech recognition technology. Then, natural language processing is used to extract information about the user's intentions and health status from the text. The input is an audio file sent from the terminal, and the output is semantic information of the user's utterances in text format. Specifically, if the user says "I need rest," appropriate advice will be prepared.

[0087] Step 4: Image Data Processing

[0088] The server processes image data using machine vision algorithms to analyze the user's posture and movements. This allows for the detection of falls and abnormal movements. The input for this step is an image file sent from the terminal, and the output is the classification result of the user's posture. For example, if the user is in an unnatural posture, the moment is identified and an alert is generated.

[0089] Step 5: Sensor Data Processing

[0090] The server analyzes sensor data such as heart rate and body temperature to detect abnormal values. Digital signal processing technology is used to identify patterns of abnormal fluctuations. The input for this step is biosensor data transmitted from the terminal, and the output is an assessment of the health status. If the heart rate falls outside the normal range, that information is highlighted.

[0091] Step 6: Data Integration and Feedback Generation

[0092] The server integrates the analyzed data to perform a comprehensive health assessment. Based on this, it generates personalized feedback for the user. Inputs are the results of voice, visual, and sensor data analysis, and output is the integrated health assessment and advice in text format. For example, it might generate advice such as, "Please drink plenty of fluids."

[0093] Step 7: Notification and Advice Provision

[0094] The server uses speech synthesis technology to send the generated advice to the terminal and notifies the user. If an emergency is detected, an alert is also sent to family members or caregivers. The input for this step is the advice obtained in the feedback generation step, and the output is the notification sent to the user. The user receives a voice message saying, "You need rest."

[0095] (Application Example 1)

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

[0097] When elderly people live alone, they face the challenge of being able to respond quickly to changes in their health or emergencies. Furthermore, there is a need to ensure the safety of elderly people from a distance and to respond immediately in case of emergencies, thus addressing the needs of caregivers and family members.

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

[0099] In this invention, the server includes data collection means for acquiring and integrating audio information, visual information, and instrument data; data processing means for analyzing the data acquired by the data collection means and evaluating the user's health status; response generation means for providing personalized advice to the user based on the results of the data processing means; notification means for issuing warnings when an abnormality in the user's health status or in an emergency is detected; and means for remotely tracking the user's movements and health status and providing a notification function when an abnormality is detected. This enables caregivers and family members in remote locations to monitor the elderly person's condition in real time and intervene quickly when necessary.

[0100] "Audio information" refers to data obtained from the words and sounds spoken by users, and is used for communication and state analysis.

[0101] "Visual information" refers to image and video data acquired through cameras and image sensors, and is used to analyze the user's posture and movements.

[0102] "Measurement device data" refers to data such as heart rate, body temperature, and movement obtained using measuring devices, and serves as basic data for evaluating the user's health status.

[0103] A "data collection method" is a system that efficiently acquires and integrates audio information, visual information, and instrument data.

[0104] "Data processing means" refers to technologies used to analyze collected data and evaluate the health status and behavior of users.

[0105] A "response generation means" is a system that creates and provides appropriate advice and instructions to users based on the results of data processing.

[0106] A "reporting mechanism" is a system that sends a warning to relevant parties when an abnormality occurs with a user.

[0107] "Means of providing notification functions" refers to technology that sends real-time information about the user's condition to caregivers or family members who are in a remote location, prompting them to take prompt action.

[0108] To implement this invention, a system is used that appropriately combines three main elements: a server, a terminal, and a user.

[0109] server:

[0110] The server receives audio, visual, and instrument data and processes each type of data using specific technologies. Audio information is converted to text using speech recognition technology, and the user's needs and condition are determined through natural language processing. Visual information is analyzed using computer vision technology to check for abnormalities in the user's posture and movements. Instrument data, such as heart rate and body temperature, is monitored, and abnormal fluctuations in these values ​​are detected. This data is integrated to ultimately generate feedback for the user. In addition, if an abnormality is detected, family members or caregivers are quickly notified through notification systems.

[0111] Terminal:

[0112] The terminal is installed near the user and plays a role in continuously collecting data using voice commands and environmental measuring instruments, and transmitting it to the server. The terminal is equipped with a high-sensitivity microphone and camera, which allows for detailed monitoring of the user's situation. Furthermore, it uses speech synthesis technology to convey feedback from the server in voice and provide appropriate advice to the user.

[0113] User:

[0114] Users interact with the system through their devices in everyday situations. For example, if a user falls while moving around the house, the device automatically sends this abnormal posture information to the server, which immediately notifies family members or caregivers that a fall has occurred. This enables a quick response.

[0115] Example of a prompt:

[0116] "Imagine a monitoring system for the elderly. Design an application that uses voice, images, and sensor data to monitor their condition in real time and notify if there is an abnormality. How would you build the program?"

[0117] In this way, by implementing the invention, it is possible to comprehensively monitor the user's health condition and provide prompt support when necessary.

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The device captures the user's voice using a microphone and sends the captured audio data directly to the server. The input audio data is then converted to text using speech recognition technology on the server. This prepares the device for processing the audio as text.

[0121] Step 2:

[0122] The server receives visual information transmitted from the terminal. Image data captured by the camera is analyzed by the server using computer vision technology. The user's posture and movements are determined from the input image data. This allows the server to evaluate whether any abnormal movements (such as falls) are present.

[0123] Step 3:

[0124] The terminal continuously measures the user's instrument data (e.g., heart rate, body temperature) and transmits it to the server. The server analyzes this data and detects abnormal values. It compares the input instrument data with reference values ​​to identify any abnormal changes.

[0125] Step 4:

[0126] The server integrates the processing results of audio, visual, and instrumental data. This involves using data fusion technology to perform a comprehensive evaluation of each processing result. Based on the integrated results, the server assesses the user's health status and generates feedback as needed.

[0127] Step 5:

[0128] If an anomaly is detected, the server will send a warning to family members or caregivers through notification channels. This includes immediate notification via email or messaging to encourage a quick response.

[0129] Step 6:

[0130] The device uses speech synthesis technology to convey feedback provided by the server to the user. Advice such as "It's time to drink some water" or "You should take a short break" is output in voice. Through this process, the user can receive appropriate notifications from the system.

[0131] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0132] This invention aims to provide more accurate advice and support to an elderly person by adding a function to recognize emotions in a system that monitors the health status of the elderly and provides individualized care. This system acquires and analyzes voice, image, and various sensor data to evaluate the user's condition in real time and also grasps their emotional state through an emotion engine.

[0133] server

[0134] The server receives audio data, image data, and sensor data transmitted from the terminal. After reception, the audio data is converted to text using speech recognition technology, and the user's intent is interpreted using natural language processing. Furthermore, the tone and speed of the user's voice are analyzed from the audio data, and an emotion engine is executed to recognize emotions. For image data, computer vision technology is used to analyze the user's facial expressions and posture to estimate emotions. Sensor data is used to assess the user's current health status. Based on the integrated analysis of the data obtained in this way, personalized feedback is generated for the user. If it is an emergency, the server sends an alert to family members or caregivers.

[0135] terminal

[0136] The terminal is positioned near the user and collects voice, image, and sensor data in real time, transmitting it to a server. Equipped with a microphone and camera, the terminal collects data to accurately interpret emotions. When receiving feedback from the server, it uses speech synthesis to communicate it to the user verbally. This allows the user to receive information not only visually but also aurally. For example, it can provide specific advice such as, "You seem a little upset; is there anything I can do to help?"

[0137] User

[0138] Users interact with the system in their daily lives when they feel something is off or when they need advice. For example, if a user says, "I'm not feeling very well today," the emotion engine analyzes the tone of their voice and detects signs of sadness or fatigue. Then, appropriate support is provided.

[0139] For example, if a user is feeling lonely, the device can transmit this information to the server, which can then use an emotion engine to provide necessary support or contact family members. This enables efficient care that takes emotions into consideration.

[0140] The following describes the processing flow.

[0141] Step 1:

[0142] The device acquires voice, images, and sensor data in real time near the user. It uses a microphone to collect user speech and a camera to record changes in facial expressions and posture. It also acquires biometric information such as heart rate and movement from sensors.

[0143] Step 2:

[0144] The device transmits collected audio data, image data, and sensor data to the server. The data is converted to an appropriate format and sent to the server via the communication path.

[0145] Step 3:

[0146] The server processes the received audio data through a speech recognition engine and converts it into text. Furthermore, it uses natural language processing to interpret the user's intent and emotions. In this process, it utilizes an emotion engine to analyze the tone and speed of the voice and estimate the user's emotions.

[0147] Step 4:

[0148] The server analyzes image data using computer vision algorithms. It evaluates the user's facial expressions and posture, and detects signs of emotion using an emotion engine.

[0149] Step 5:

[0150] The server analyzes sensor data to assess the user's health status. It analyzes heart rate, body temperature, and movement patterns to check if they are outside the normal range.

[0151] Step 6:

[0152] The server integrates the analysis results of voice, image, and sensor data to assess the user's overall health and emotional state.

[0153] Step 7:

[0154] The server generates feedback based on the user's status, creating emotional and health-related advice, or, if necessary, messages of well-being and encouragement.

[0155] Step 8:

[0156] The server sends the generated feedback to the terminal. The terminal then conveys the feedback to the user via speech synthesis, providing messages such as, "You seem to be feeling a little unwell today. Do you need any support?"

[0157] Step 9:

[0158] The server will send notifications to family members or caregivers as needed when it detects abnormalities or significant emotional changes. This will create a continuous support system.

[0159] (Example 2)

[0160] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0161] In medical care and support for the elderly, there is a need to accurately understand the physiological and emotional state of patients in real time and provide individualized advice. However, conventional systems can be slow to respond to emotional changes or emergencies, so a system capable of faster and more accurate responses is needed.

[0162] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0163] In this invention, the server includes information acquisition means for collecting and integrating acoustic information, visual information, and composite sensor information; information analysis means for analyzing the collected information to evaluate the user's physiological and emotional state; and response generation means for generating personalized advice based on the analysis results. This makes it possible to monitor the user's state in real time and respond quickly and appropriately.

[0164] "Acoustic information" refers to information collected from the user's acoustics and used for analysis.

[0165] "Visual information" refers to image data used to capture the user's posture and facial expressions.

[0166] "Combined sensor information" refers to information obtained from multiple sensors used to measure the user's physiological state, such as heart rate and body temperature.

[0167] "Information acquisition means" refers to a device or mechanism for collecting and integrating acoustic information, visual information, and composite sensor information.

[0168] "Information analysis means" refers to a device or mechanism for analyzing collected information and evaluating the physiological and emotional state of the user.

[0169] "Response generation means" refers to a device or mechanism for creating and providing personalized advice to a user based on evaluation results obtained by information analysis means.

[0170] A "notification device" is a device or mechanism that makes an emergency call to a pre-set contact when it detects a user's physiological state or an emergency abnormality.

[0171] This invention is a health monitoring system for the elderly that collects acoustic, visual, and combined sensor information in real time and analyzes it to provide users with appropriate advice and support.

[0172] The server first receives acoustic information transmitted from the terminal. This acoustic information is converted into text information using acoustic recognition technology. General-purpose acoustic conversion software is used for this process. Then, natural language processing technology is applied to the text information to understand the intent of the user's speech. An emotion recognition engine is used to analyze the tone and speed of the sound, and the emotional state is estimated using a specific algorithm.

[0173] For visual information, the system utilizes image processing technology built into the device. The camera constantly captures the user's facial expressions and posture, and transmits this data to the server. The server uses this visual information to recognize the user's posture and facial expressions and determine their overall emotional state.

[0174] Furthermore, the system continuously monitors the user's physiological indicators, such as heart rate and body temperature, using combined sensor data. This data is also transmitted to a server and analyzed as part of a comprehensive health assessment.

[0175] Based on the results of all this information analysis, the server generates text-based or voice-based advice optimized for the user. In this process, a generative AI model can be used to provide more natural-sounding feedback.

[0176] The device uses speech synthesis technology to convey feedback received from the server to the user. A concrete example of this system is when the device detects that a user is feeling lonely, and the server quickly generates the necessary support and notifies the family. An example of a prompt sentence to be input into the generating AI model is, "What kind of data is collected and what kind of feedback is generated when an elderly person is feeling anxious?"

[0177] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0178] Step 1:

[0179] The terminal is placed near the user and uses a microphone to collect acoustic information in real time. It receives acoustic data as input and transmits it to a server. Specifically, it records everyday conversations and emotionally charged voices and prepares them for transmission in digital format.

[0180] Step 2:

[0181] The device uses a camera to collect visual information. It acquires image data as input and sends it to the server. Specifically, it captures the user's facial expressions and body movements to generate images that capture changes in emotions and posture.

[0182] Step 3:

[0183] The device collects physiological data using a combined sensor that measures heart rate and body temperature. It acquires biometric data as input and transmits it to a server. Specifically, it records multiple indicators of the user's health status as digital data.

[0184] Step 4:

[0185] The server converts acoustic information received from the terminal into text using acoustic recognition technology. It uses acoustic data as input and generates character data as output. Specifically, it analyzes the amplitude and frequency of the sound and converts it into an appropriate string of characters.

[0186] Step 5:

[0187] The server analyzes text data using a natural language processing engine to understand the user's intentions and emotions. It uses text data as input and outputs evaluation results of emotions and intentions. Specifically, it performs evaluation by analyzing context and identifying emotion vocabulary and patterns.

[0188] Step 6:

[0189] The server analyzes image data using computer vision technology to evaluate the user's facial expressions and posture. It receives image data as input and generates feedback on posture and facial expressions as output. Specifically, it identifies facial features and body poses and estimates emotional states.

[0190] Step 7:

[0191] The server analyzes physiological data from sensors to assess health status. It uses biometric data as input and outputs health status assessment results. Specifically, it processes numerical data using statistical methods and compares it to normal ranges.

[0192] Step 8:

[0193] The server integrates all analysis results and generates personalized advice using a response generation mechanism. This utilizes a generative AI model. The analysis results are the input, and the advice is generated as text or audio data. Specifically, it constructs appropriate advice based on the results, creating a series of contextual responses.

[0194] Step 9:

[0195] The terminal uses speech synthesis technology to convey feedback sent from the server to the user. It receives advice data as input and generates speech as output. Specifically, it converts text into speech and plays it back through the speaker to inform the user.

[0196] (Application Example 2)

[0197] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0198] In managing the health of the elderly, it is essential to respond appropriately to changes in their emotions. However, conventional systems have difficulty accurately assessing emotional states, making it impossible to provide personalized support. Furthermore, there has been a challenge in analyzing users' emotions and health status in real time and providing rapid feedback. This invention aims to solve these problems and provide comprehensive health management that includes the emotional state of the elderly.

[0199] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0200] In this invention, the server includes data collection means for acquiring and integrating voice information, image information, and detection data; data analysis means for evaluating the user's health status; and emotion analysis means for analyzing the user's emotional state and providing support that takes the user's emotions into consideration. This enables real-time analysis of the emotions and health status of elderly people, allowing for personalized support and rapid feedback.

[0201] "Audio information" refers to all data collected as audio, including the characteristics of words and sounds emitted by the user.

[0202] "Image information" refers to visual data acquired using devices such as cameras, and includes the user's facial expressions, posture, and surrounding environment.

[0203] "Detection data" refers to all information acquired from sensors, including physical conditions such as temperature, body movement, and heart rate.

[0204] "Data collection means" refers to a method or apparatus for acquiring audio information, image information, and detection data, and for integrating and processing them.

[0205] "Data analysis means" refers to methods or techniques for evaluating a user's health status based on collected data.

[0206] "Emotional analysis means" refers to methods or technologies for analyzing a user's emotions and providing emotion-based support.

[0207] "Feedback generation means" refers to a method or technology for generating and providing personalized information and advice to users based on the results of data analysis.

[0208] The system for realizing this invention is configured with both a server and a terminal working together. The server plays a central role in acquiring and integrating voice information, image information, and detection data. This utilizes voice recognition technology (e.g., TENSORFLOW® or Dialogflow) and computer vision technology for image analysis (OpenCV or TensorFlow). The server analyzes the acquired data and evaluates the user's health and emotional state in real time through sentiment analysis. Based on the analyzed information, it can provide personalized information to the user using feedback generation means and issue emergency notifications as needed.

[0209] The terminal, equipped with a microphone and camera, plays a role in collecting data such as the user's voice and posture. It can utilize mobile devices such as smartphones or smart glasses, thus achieving portability. It collects voice data from the user and sends it to a server for analysis. Furthermore, it reports feedback from the server to the user using speech synthesis technology (such as Google® Text-to-Speech).

[0210] Users can receive daily monitoring using their devices and get the support and advice they need from the system. For example, if an elderly person comments, "I'm not feeling very well today," the system will perform sentiment analysis based on the tone of their voice and provide appropriate search results and support.

[0211] One specific use case is the ability to generate a prompt advising the user to take a break if a negative tone of voice is detected. An example of a prompt might be, "Your tone of voice is negative. Is something bothering you, or do you need a break?"

[0212] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0213] Step 1:

[0214] The device collects audio and image information from the user's surroundings using a microphone and camera. At this stage, audio is input as raw analog data, and images are collected in still image and video formats. Processing is then performed to convert this data digitally and prepare it for transmission to the server.

[0215] Step 2:

[0216] The server converts audio data sent from the terminal into text using speech recognition technology. First, filtering and noise reduction are performed, and then a speech recognition algorithm (e.g., TensorFlow) converts the analog audio into a digital string. This is provided as the output of the speech analysis.

[0217] Step 3:

[0218] Upon receiving image information, the server uses computer vision technology (e.g., OpenCV) to analyze the user's facial expressions and posture. The image data is analyzed at the pixel level to extract features that indicate the user's emotions. This results in an estimated emotion being obtained as the output of the image analysis.

[0219] Step 4:

[0220] The server integrates speech recognition results and image analysis results to assess the user's current health and emotional state. Using NLP techniques, it identifies emotions and intentions from text data and performs context-aware analysis. This generates a unified output regarding the user's state.

[0221] Step 5:

[0222] Based on the generated output, the server uses feedback generation mechanisms to construct advice tailored to the user. It generates appropriate support messages that correspond to the user's emotions and state obtained in the previous step. For example, if the user's tone of voice is negative, it will create advice such as, "Let's take a short break."

[0223] Step 6:

[0224] If the generated feedback indicates urgency, the server uses notification methods to send an alert to the designated emergency contact. This ensures that family members and caregivers are quickly informed in situations requiring immediate intervention.

[0225] Step 7:

[0226] The terminal delivers feedback sent from the server to the user as voice messages using speech synthesis technology. Through voice output, it communicates the server's analysis results to the user and prompts them to take action as needed. This allows users to receive system instructions not only visually but also aurally.

[0227] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0228] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0229] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0230] [Second Embodiment]

[0231] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0232] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0233] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0234] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0235] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0236] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0237] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0238] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0239] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0240] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0241] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0242] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0243] This invention is a system intended for monitoring and caring for the elderly. It integrates voice, image, and sensor data to evaluate the user's condition in real time and provides advice or emergency notifications as needed. One of the main components in this system's embodiment is a program that acquires and processes data. Its operation will be described below.

[0244] server

[0245] The server receives audio, image, and sensor data transmitted from the terminal and processes each data individually. For audio data, it first converts it to text using speech recognition technology, and then uses natural language processing to determine the user's needs and condition. For image data, computer vision algorithms are applied to analyze the user's posture and movements to check for abnormalities. Sensor data is analyzed, for example, to detect trends in heart rate and body temperature, and abnormal numerical changes are detected. Finally, the results of these data analyses are integrated to generate feedback for the user. If the user's condition is abnormal, an alert is immediately issued to notify family members or caregivers.

[0246] terminal

[0247] The device is responsible for continuously collecting voice, image, and sensor data near the user, utilizing voice commands and environmental sensors, and transmitting it to the server. The device is equipped with a microphone and camera, constantly monitoring the user's situation. It also uses speech synthesis technology to convey feedback from the server to the user. For example, it provides advice in natural language, such as "It's time to drink some water" or "You should take a short rest."

[0248] User

[0249] Users interact with the system through their devices in their daily lives. For example, if a user says, "I'm not feeling very well," the device immediately sends that audio to the server, and appropriate feedback is provided. Furthermore, in emergencies, the device autonomously sends data to the server without the user having to take any action, and necessary measures are taken.

[0250] As a concrete example, suppose a user suddenly falls while moving around their home. In this case, the device detects the abnormal posture and immediately sends a message to the server indicating the fall. Based on this information, the server sends an emergency notification to family members or caregivers stating that a fall has occurred, prompting them to take necessary action. In this way, swift and efficient care becomes possible.

[0251] The following describes the processing flow.

[0252] Step 1:

[0253] The device collects audio data, image data, and sensor data in real time using microphones, cameras, and various sensors located around the user. Audio data captures the user's speech, image data records the user's posture and environment via the camera, and sensor data collects biometric information such as heart rate and body temperature.

[0254] Step 2:

[0255] The terminal transmits collected audio, images, and sensor data to the server via wireless communication. During this process, the data is converted and compressed into the appropriate format before transmission, ensuring efficient data transfer.

[0256] Step 3:

[0257] The server inputs the audio data sent from the terminal into the speech recognition engine and converts the audio into text format. The resulting text is then passed to the natural language processing unit, which interprets the user's intent and emotions.

[0258] Step 4:

[0259] The server analyzes image data using computer vision algorithms to estimate the user's posture and detect anomalies in the environment. For example, if there is a possibility of falling, the system will identify signs of it.

[0260] Step 5:

[0261] The server analyzes sensor data to detect abnormal heart rate, body temperature changes, and other abnormalities. This allows for the detection of changes in health status.

[0262] Step 6:

[0263] The server integrates the analysis results from steps 3-5 and generates feedback for the user. For example, if an anomaly is detected, a warning message is generated.

[0264] Step 7:

[0265] The server sends the generated feedback to the terminal, presenting it to the user via speech synthesis or display. The terminal then plays instructions such as "Please take a short break" or "Please stay hydrated" in voice.

[0266] Step 8:

[0267] The server will also send emergency notifications to family members or caregivers via email or SMS, as needed. For example, notifications will be sent in the event of an emergency such as a fall.

[0268] (Example 1)

[0269] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0270] In monitoring and caring for the elderly, accurately understanding their health status in real time is crucial. However, conventional technologies often struggle to effectively integrate voice, visual, and physical sensor data, resulting in insufficient comprehensive health assessments. Furthermore, systems capable of responding quickly to abnormalities are limited. This leads to a problem where the safety of elderly individuals living independently is not adequately ensured.

[0271] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0272] In this invention, the server includes information gathering means for acquiring and integrating voice data, visual materials, and physical sensor information; information analysis means for analyzing the information collected by the information gathering means and evaluating the individual's health status; and response generation means for providing personalized advice based on the results of the information analysis means. This enables a multifaceted evaluation of the health status of elderly people, allowing for personalized advice and rapid emergency response.

[0273] "Audio data" refers to recordings of human voices that have been physically collected, and it is possible to analyze this data to extract semantic information.

[0274] "Visual data" refers to visual information such as images and videos acquired using cameras or other imaging devices, and is used to analyze this data to recognize the actions and states of a subject.

[0275] "Physical sensor information" refers to data collected by sensors that measure environmental and biological information such as temperature, heart rate, and movement, and is used to evaluate an individual's health status and environmental changes.

[0276] "Information gathering means" refers to technical means for acquiring and integrating audio data, visual materials, and physical sensor information.

[0277] "Information analysis means" refers to technical means for processing and analyzing collected data to evaluate an individual's health status and physical condition.

[0278] "Response generation means" refers to technical means for generating and providing appropriate advice or instructions to an individual based on the results of analysis.

[0279] "Warning measures" refer to technical means for detecting an individual's health condition or an emergency situation and promptly notifying them.

[0280] This invention is an integrated system for monitoring the elderly, which integrates voice data, visual materials, and physical sensor information to analyze the user's health status and provide appropriate advice and emergency notifications as needed. The specific embodiments of the system will be described below.

[0281] Terminal

[0282] The terminal is responsible for collecting voice, visual, and physical sensor information necessary for the user's daily life. The terminal is equipped with a microphone, camera, heart rate monitor, body temperature sensor, etc., and collects data in real time. As a result, the user's voice, movement patterns, and vital signs are continuously monitored. For example, when the terminal detects that the user has "fallen in the bathroom", it can immediately notify the server of the abnormal situation.

[0283] Server

[0284] The server plays a central role in receiving and analyzing the data sent from the terminal. The voice data is converted into character information using voice recognition technology (e.g., general voice recognition software), and the user's health status is evaluated using natural language processing technology. For visual materials, the user's posture and movements are analyzed using machine vision technology to detect abnormalities. For physical sensor information, the fluctuations in heart rate and body temperature are monitored, and an alert is generated when unusual values are observed. For example, when a sudden increase in heart rate is detected, it is immediately determined that there is an abnormality in the health status. All these analysis results are integrated to generate feedback for the user.

[0285] Based on the generated feedback, the server uses voice synthesis technology to convey appropriate advice to the user via the terminal. Also, notifications are sent to family members and caregivers as needed to facilitate prompt response. In case of an emergency, an alert is automatically sent, ensuring a system is in place for quick response.

[0286] User

[0287] Through interaction with the device, users can live their daily lives with peace of mind. For example, if a user says, "I'm not feeling well today," the device immediately sends that information to the server and provides feedback. This helps them avoid unnecessary actions and obtain information useful for health management.

[0288] An example of a prompt message might be, "Please explain the operating principle of a system that detects falls in the elderly." This system allows users to improve their safety in daily life, and enables family members and caregivers to monitor their loved ones with peace of mind.

[0289] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0290] Step 1: Data Collection

[0291] The device continuously collects audio, visual, and physical sensor information from the user's environment. Specifically, it records audio using a built-in microphone and captures images and videos with a camera. It also acquires biometric information through a heart rate monitor and body temperature sensor. The input for this step is the user's voice and environmental conditions, while the output is a collection of data from various sensors.

[0292] Step 2: Data transmission

[0293] The terminal transmits the collected data to the server using a secure communication protocol (e.g., HTTPS). Voice data, image data, and sensor data are transmitted periodically or triggered. The input in this step is the raw data obtained in the data collection step, and the output is the structured data sent to the server.

[0294] Step 3: Audio Data Processing

[0295] The server converts received audio data into text using speech recognition technology. Then, natural language processing is used to extract information about the user's intentions and health status from the text. The input is an audio file sent from the terminal, and the output is semantic information of the user's utterances in text format. Specifically, if the user says "I need rest," appropriate advice will be prepared.

[0296] Step 4: Image Data Processing

[0297] The server processes image data using machine vision algorithms to analyze the user's posture and movements. This allows for the detection of falls and abnormal movements. The input for this step is an image file sent from the terminal, and the output is the classification result of the user's posture. For example, if the user is in an unnatural posture, the moment is identified and an alert is generated.

[0298] Step 5: Sensor Data Processing

[0299] The server analyzes sensor data such as heart rate and body temperature to detect abnormal values. Digital signal processing technology is used to identify patterns of abnormal fluctuations. The input for this step is biosensor data transmitted from the terminal, and the output is an assessment of the health status. If the heart rate falls outside the normal range, that information is highlighted.

[0300] Step 6: Data Integration and Feedback Generation

[0301] The server integrates the analyzed data to perform a comprehensive health assessment. Based on this, it generates personalized feedback for the user. Inputs are the results of voice, visual, and sensor data analysis, and output is the integrated health assessment and advice in text format. For example, it might generate advice such as, "Please drink plenty of fluids."

[0302] Step 7: Notification and Advice Provision

[0303] The server uses speech synthesis technology to send the generated advice to the terminal and notify the user. Also, when an emergency is detected, an alert is sent to the family members and caregivers. The input for this step is the advice obtained in the feedback generation step, and the output is the content of the notification to the user. The user will receive an audio message saying "You need a break."

[0304] (Application Example 1)

[0305] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0306] When an elderly person lives alone, there is a problem that it is difficult to quickly respond to changes in health conditions and emergencies. Also, there is a need to ensure the safety of the elderly from a remote location by caregivers and family members, and to respond immediately in case of an abnormality.

[0307] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0308] In this invention, the server includes data collection means for acquiring voice information, visual information, and measuring instrument data and integrating these data, data processing means for analyzing the data acquired by the data collection means and evaluating the health status of the user, response generation means for providing individualized advice to the user based on the results of the data processing means, notification means for issuing a warning when an abnormality in the user's health status or an emergency is detected, and means for remotely tracking the user's actions and health status by the notification means and providing a notification function when an abnormality is detected. As a result, caregivers and family members who are in a remote location can monitor the status of the elderly in real time and intervene promptly when necessary.

[0309] "Voice information" is data obtained from the words and sounds spoken by the user, and is used for communication and status analysis.

[0310] "Visual information" refers to image and video data acquired through cameras and image sensors, and is used to analyze the user's posture and movements.

[0311] "Measurement device data" refers to data such as heart rate, body temperature, and movement obtained using measuring devices, and serves as basic data for evaluating the user's health status.

[0312] A "data collection method" is a system that efficiently acquires and integrates audio information, visual information, and instrument data.

[0313] "Data processing means" refers to technologies used to analyze collected data and evaluate the health status and behavior of users.

[0314] A "response generation means" is a system that creates and provides appropriate advice and instructions to users based on the results of data processing.

[0315] A "reporting mechanism" is a system that sends a warning to relevant parties when an abnormality occurs with a user.

[0316] "Means of providing notification functions" refers to technology that sends real-time information about the user's condition to caregivers or family members who are in a remote location, prompting them to take prompt action.

[0317] To implement this invention, a system is used that appropriately combines three main elements: a server, a terminal, and a user.

[0318] server:

[0319] The server receives audio, visual, and instrument data and processes each type of data using specific technologies. Audio information is converted to text using speech recognition technology, and the user's needs and condition are determined through natural language processing. Visual information is analyzed using computer vision technology to check for abnormalities in the user's posture and movements. Instrument data, such as heart rate and body temperature, is monitored, and abnormal fluctuations in these values ​​are detected. This data is integrated to ultimately generate feedback for the user. In addition, if an abnormality is detected, family members or caregivers are quickly notified through notification systems.

[0320] Terminal:

[0321] The terminal is installed near the user and plays a role in continuously collecting data using voice commands and environmental measuring instruments, and transmitting it to the server. The terminal is equipped with a high-sensitivity microphone and camera, which allows for detailed monitoring of the user's situation. Furthermore, it uses speech synthesis technology to convey feedback from the server in voice and provide appropriate advice to the user.

[0322] User:

[0323] Users interact with the system through their devices in everyday situations. For example, if a user falls while moving around the house, the device automatically sends this abnormal posture information to the server, which immediately notifies family members or caregivers that a fall has occurred. This enables a quick response.

[0324] Example of a prompt:

[0325] "Imagine a monitoring system for the elderly. Design an application that uses voice, images, and sensor data to monitor their condition in real time and notify if there is an abnormality. How would you build the program?"

[0326] In this way, by implementing the invention, it is possible to comprehensively monitor the user's health condition and provide prompt support when necessary.

[0327] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0328] Step 1:

[0329] The device captures the user's voice using a microphone and sends the captured audio data directly to the server. The input audio data is then converted to text using speech recognition technology on the server. This prepares the device for processing the audio as text.

[0330] Step 2:

[0331] The server receives visual information transmitted from the terminal. Image data captured by the camera is analyzed by the server using computer vision technology. The user's posture and movements are determined from the input image data. This allows the server to evaluate whether any abnormal movements (such as falls) are present.

[0332] Step 3:

[0333] The terminal continuously measures the user's instrument data (e.g., heart rate, body temperature) and transmits it to the server. The server analyzes this data and detects abnormal values. It compares the input instrument data with reference values ​​to identify any abnormal changes.

[0334] Step 4:

[0335] The server integrates the processing results of audio, visual, and instrumental data. This involves using data fusion technology to perform a comprehensive evaluation of each processing result. Based on the integrated results, the server assesses the user's health status and generates feedback as needed.

[0336] Step 5:

[0337] If an anomaly is detected, the server will send a warning to family members or caregivers through notification channels. This includes immediate notification via email or messaging to encourage a quick response.

[0338] Step 6:

[0339] The device uses speech synthesis technology to convey feedback provided by the server to the user. Advice such as "It's time to drink some water" or "You should take a short break" is output in voice. Through this process, the user can receive appropriate notifications from the system.

[0340] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0341] This invention aims to provide more accurate advice and support to an elderly person by adding a function to recognize emotions in a system that monitors the health status of the elderly and provides individualized care. This system acquires and analyzes voice, image, and various sensor data to evaluate the user's condition in real time and also grasps their emotional state through an emotion engine.

[0342] server

[0343] The server receives audio data, image data, and sensor data transmitted from the terminal. After reception, the audio data is converted to text using speech recognition technology, and the user's intent is interpreted using natural language processing. Furthermore, the tone and speed of the user's voice are analyzed from the audio data, and an emotion engine is executed to recognize emotions. For image data, computer vision technology is used to analyze the user's facial expressions and posture to estimate emotions. Sensor data is used to assess the user's current health status. Based on the integrated analysis of the data obtained in this way, personalized feedback is generated for the user. If it is an emergency, the server sends an alert to family members or caregivers.

[0344] terminal

[0345] The terminal is positioned near the user and collects voice, image, and sensor data in real time, transmitting it to a server. Equipped with a microphone and camera, the terminal collects data to accurately interpret emotions. When receiving feedback from the server, it uses speech synthesis to communicate it to the user verbally. This allows the user to receive information not only visually but also aurally. For example, it can provide specific advice such as, "You seem a little upset; is there anything I can do to help?"

[0346] User

[0347] Users interact with the system in their daily lives when they feel something is off or when they need advice. For example, if a user says, "I'm not feeling very well today," the emotion engine analyzes the tone of their voice and detects signs of sadness or fatigue. Then, appropriate support is provided.

[0348] For example, if a user is feeling lonely, the device can transmit this information to the server, which can then use an emotion engine to provide necessary support or contact family members. This enables efficient care that takes emotions into consideration.

[0349] The following describes the processing flow.

[0350] Step 1:

[0351] The device acquires voice, images, and sensor data in real time near the user. It uses a microphone to collect user speech and a camera to record changes in facial expressions and posture. It also acquires biometric information such as heart rate and movement from sensors.

[0352] Step 2:

[0353] The device transmits collected audio data, image data, and sensor data to the server. The data is converted to an appropriate format and sent to the server via the communication path.

[0354] Step 3:

[0355] The server processes the received audio data through a speech recognition engine and converts it into text. Furthermore, it uses natural language processing to interpret the user's intent and emotions. In this process, it utilizes an emotion engine to analyze the tone and speed of the voice and estimate the user's emotions.

[0356] Step 4:

[0357] The server analyzes image data using computer vision algorithms. It evaluates the user's facial expressions and posture, and detects signs of emotion using an emotion engine.

[0358] Step 5:

[0359] The server analyzes sensor data to assess the user's health status. It analyzes heart rate, body temperature, and movement patterns to check if they are outside the normal range.

[0360] Step 6:

[0361] The server integrates the analysis results of voice, image, and sensor data to assess the user's overall health and emotional state.

[0362] Step 7:

[0363] The server generates feedback based on the user's status, creating emotional and health-related advice, or, if necessary, messages of well-being and encouragement.

[0364] Step 8:

[0365] The server sends the generated feedback to the terminal. The terminal then conveys the feedback to the user via speech synthesis, providing messages such as, "You seem to be feeling a little unwell today. Do you need any support?"

[0366] Step 9:

[0367] The server will send notifications to family members or caregivers as needed when it detects abnormalities or significant emotional changes. This will create a continuous support system.

[0368] (Example 2)

[0369] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0370] In medical care and support for the elderly, there is a need to accurately understand the physiological and emotional state of patients in real time and provide individualized advice. However, conventional systems can be slow to respond to emotional changes or emergencies, so a system capable of faster and more accurate responses is needed.

[0371] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0372] In this invention, the server includes information acquisition means for collecting and integrating acoustic information, visual information, and composite sensor information; information analysis means for analyzing the collected information to evaluate the user's physiological and emotional state; and response generation means for generating personalized advice based on the analysis results. This makes it possible to monitor the user's state in real time and respond quickly and appropriately.

[0373] "Acoustic information" refers to information collected from the user's acoustics and used for analysis.

[0374] "Visual information" refers to image data used to capture the user's posture and facial expressions.

[0375] "Combined sensor information" refers to information obtained from multiple sensors used to measure the user's physiological state, such as heart rate and body temperature.

[0376] "Information acquisition means" refers to a device or mechanism for collecting and integrating acoustic information, visual information, and composite sensor information.

[0377] "Information analysis means" refers to a device or mechanism for analyzing collected information and evaluating the physiological and emotional state of the user.

[0378] "Response generation means" refers to a device or mechanism for creating and providing personalized advice to a user based on evaluation results obtained by information analysis means.

[0379] A "notification device" is a device or mechanism that makes an emergency call to a pre-set contact when it detects a user's physiological state or an emergency abnormality.

[0380] This invention is a health monitoring system for the elderly that collects acoustic, visual, and combined sensor information in real time and analyzes it to provide users with appropriate advice and support.

[0381] The server first receives acoustic information transmitted from the terminal. This acoustic information is converted into text information using acoustic recognition technology. General-purpose acoustic conversion software is used for this process. Then, natural language processing technology is applied to the text information to understand the intent of the user's speech. An emotion recognition engine is used to analyze the tone and speed of the sound, and the emotional state is estimated using a specific algorithm.

[0382] For visual information, the system utilizes image processing technology built into the device. The camera constantly captures the user's facial expressions and posture, and transmits this data to the server. The server uses this visual information to recognize the user's posture and facial expressions and determine their overall emotional state.

[0383] Furthermore, the system continuously monitors the user's physiological indicators, such as heart rate and body temperature, using combined sensor data. This data is also transmitted to a server and analyzed as part of a comprehensive health assessment.

[0384] Based on the results of all this information analysis, the server generates text-based or voice-based advice optimized for the user. In this process, a generative AI model can be used to provide more natural-sounding feedback.

[0385] The device uses speech synthesis technology to convey feedback received from the server to the user. A concrete example of this system is when the device detects that a user is feeling lonely, and the server quickly generates the necessary support and notifies the family. An example of a prompt sentence to be input into the generating AI model is, "What kind of data is collected and what kind of feedback is generated when an elderly person is feeling anxious?"

[0386] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0387] Step 1:

[0388] The terminal is placed near the user and uses a microphone to collect acoustic information in real time. It receives acoustic data as input and transmits it to a server. Specifically, it records everyday conversations and emotionally charged voices and prepares them for transmission in digital format.

[0389] Step 2:

[0390] The device uses a camera to collect visual information. It acquires image data as input and sends it to the server. Specifically, it captures the user's facial expressions and body movements to generate images that capture changes in emotions and posture.

[0391] Step 3:

[0392] The device collects physiological data using a combined sensor that measures heart rate and body temperature. It acquires biometric data as input and transmits it to a server. Specifically, it records multiple indicators of the user's health status as digital data.

[0393] Step 4:

[0394] The server converts acoustic information received from the terminal into text using acoustic recognition technology. It uses acoustic data as input and generates character data as output. Specifically, it analyzes the amplitude and frequency of the sound and converts it into an appropriate string of characters.

[0395] Step 5:

[0396] The server analyzes text data using a natural language processing engine to understand the user's intentions and emotions. It uses text data as input and outputs evaluation results of emotions and intentions. Specifically, it performs evaluation by analyzing context and identifying emotion vocabulary and patterns.

[0397] Step 6:

[0398] The server analyzes image data using computer vision technology to evaluate the user's facial expressions and posture. It receives image data as input and generates feedback on posture and facial expressions as output. Specifically, it identifies facial features and body poses and estimates emotional states.

[0399] Step 7:

[0400] The server analyzes physiological data from sensors to assess health status. It uses biometric data as input and outputs health status assessment results. Specifically, it processes numerical data using statistical methods and compares it to normal ranges.

[0401] Step 8:

[0402] The server integrates all analysis results and generates personalized advice using a response generation mechanism. This utilizes a generative AI model. The analysis results are the input, and the advice is generated as text or audio data. Specifically, it constructs appropriate advice based on the results, creating a series of contextual responses.

[0403] Step 9:

[0404] The terminal uses speech synthesis technology to convey feedback sent from the server to the user. It receives advice data as input and generates speech as output. Specifically, it converts text into speech and plays it back through the speaker to inform the user.

[0405] (Application Example 2)

[0406] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0407] In managing the health of the elderly, it is essential to respond appropriately to changes in their emotions. However, conventional systems have difficulty accurately assessing emotional states, making it impossible to provide personalized support. Furthermore, there has been a challenge in analyzing users' emotions and health status in real time and providing rapid feedback. This invention aims to solve these problems and provide comprehensive health management that includes the emotional state of the elderly.

[0408] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0409] In this invention, the server includes data collection means for acquiring and integrating voice information, image information, and detection data; data analysis means for evaluating the user's health status; and emotion analysis means for analyzing the user's emotional state and providing support that takes the user's emotions into consideration. This enables real-time analysis of the emotions and health status of elderly people, allowing for personalized support and rapid feedback.

[0410] "Audio information" refers to all data collected as audio, including the characteristics of words and sounds emitted by the user.

[0411] "Image information" refers to visual data acquired using devices such as cameras, and includes the user's facial expressions, posture, and surrounding environment.

[0412] "Detection data" refers to all information acquired from sensors, including physical conditions such as temperature, body movement, and heart rate.

[0413] "Data collection means" refers to a method or apparatus for acquiring audio information, image information, and detection data, and for integrating and processing them.

[0414] "Data analysis means" refers to methods or techniques for evaluating a user's health status based on collected data.

[0415] "Emotional analysis means" refers to methods or technologies for analyzing a user's emotions and providing emotion-based support.

[0416] "Feedback generation means" refers to a method or technology for generating and providing personalized information and advice to users based on the results of data analysis.

[0417] The system for realizing this invention is configured with both a server and a terminal working together. The server plays a central role in acquiring and integrating voice information, image information, and detection data. This utilizes voice recognition technology (e.g., TensorFlow or Dialogflow) and computer vision technology for image analysis (OpenCV or TensorFlow). The server analyzes the acquired data and evaluates the user's health and emotional state in real time through sentiment analysis. Based on the analyzed information, it can provide personalized information to the user using feedback generation means and issue emergency notifications as needed.

[0418] The terminal, equipped with a microphone and camera, plays a role in collecting data such as the user's voice and posture. It can utilize mobile devices such as smartphones or smart glasses, thus achieving portability. It collects voice data from the user and sends it to a server for analysis. Furthermore, it reports feedback from the server to the user using speech synthesis technology (such as Google Text-to-Speech).

[0419] Users can receive daily monitoring using their devices and get the support and advice they need from the system. For example, if an elderly person comments, "I'm not feeling very well today," the system will perform sentiment analysis based on the tone of their voice and provide appropriate search results and support.

[0420] One specific use case is the ability to generate a prompt advising the user to take a break if a negative tone of voice is detected. An example of a prompt might be, "Your tone of voice is negative. Is something bothering you, or do you need a break?"

[0421] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0422] Step 1:

[0423] The device collects audio and image information from the user's surroundings using a microphone and camera. At this stage, audio is input as raw analog data, and images are collected in still image and video formats. Processing is then performed to convert this data digitally and prepare it for transmission to the server.

[0424] Step 2:

[0425] The server converts audio data sent from the terminal into text using speech recognition technology. First, filtering and noise reduction are performed, and then a speech recognition algorithm (e.g., TensorFlow) converts the analog audio into a digital string. This is provided as the output of the speech analysis.

[0426] Step 3:

[0427] Upon receiving image information, the server uses computer vision technology (e.g., OpenCV) to analyze the user's facial expressions and posture. The image data is analyzed at the pixel level to extract features that indicate the user's emotions. This results in an estimated emotion being obtained as the output of the image analysis.

[0428] Step 4:

[0429] The server integrates speech recognition results and image analysis results to assess the user's current health and emotional state. Using NLP techniques, it identifies emotions and intentions from text data and performs context-aware analysis. This generates a unified output regarding the user's state.

[0430] Step 5:

[0431] Based on the generated output, the server uses feedback generation mechanisms to construct advice tailored to the user. It generates appropriate support messages that correspond to the user's emotions and state obtained in the previous step. For example, if the user's tone of voice is negative, it will create advice such as, "Let's take a short break."

[0432] Step 6:

[0433] If the generated feedback indicates urgency, the server uses notification methods to send an alert to the designated emergency contact. This ensures that family members and caregivers are quickly informed in situations requiring immediate intervention.

[0434] Step 7:

[0435] The terminal delivers feedback sent from the server to the user as voice messages using speech synthesis technology. Through voice output, it communicates the server's analysis results to the user and prompts them to take action as needed. This allows users to receive system instructions not only visually but also aurally.

[0436] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0437] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0438] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0439] [Third Embodiment]

[0440] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0441] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0442] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0443] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0444] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0445] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0446] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0447] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0448] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0449] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0450] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0451] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0452] This invention is a system intended for monitoring and caring for the elderly. It integrates voice, image, and sensor data to evaluate the user's condition in real time and provides advice or emergency notifications as needed. One of the main components in this system's embodiment is a program that acquires and processes data. Its operation will be described below.

[0453] server

[0454] The server receives audio, image, and sensor data transmitted from the terminal and processes each data individually. For audio data, it first converts it to text using speech recognition technology, and then uses natural language processing to determine the user's needs and condition. For image data, computer vision algorithms are applied to analyze the user's posture and movements to check for abnormalities. Sensor data is analyzed, for example, to detect trends in heart rate and body temperature, and abnormal numerical changes are detected. Finally, the results of these data analyses are integrated to generate feedback for the user. If the user's condition is abnormal, an alert is immediately issued to notify family members or caregivers.

[0455] terminal

[0456] The device is responsible for continuously collecting voice, image, and sensor data near the user, utilizing voice commands and environmental sensors, and transmitting it to the server. The device is equipped with a microphone and camera, constantly monitoring the user's situation. It also uses speech synthesis technology to convey feedback from the server to the user. For example, it provides advice in natural language, such as "It's time to drink some water" or "You should take a short rest."

[0457] User

[0458] Users interact with the system through their devices in their daily lives. For example, if a user says, "I'm not feeling very well," the device immediately sends that audio to the server, and appropriate feedback is provided. Furthermore, in emergencies, the device autonomously sends data to the server without the user having to take any action, and necessary measures are taken.

[0459] As a concrete example, suppose a user suddenly falls while moving around their home. In this case, the device detects the abnormal posture and immediately sends a message to the server indicating the fall. Based on this information, the server sends an emergency notification to family members or caregivers stating that a fall has occurred, prompting them to take necessary action. In this way, swift and efficient care becomes possible.

[0460] The following describes the processing flow.

[0461] Step 1:

[0462] The device collects audio data, image data, and sensor data in real time using microphones, cameras, and various sensors located around the user. Audio data captures the user's speech, image data records the user's posture and environment via the camera, and sensor data collects biometric information such as heart rate and body temperature.

[0463] Step 2:

[0464] The terminal transmits collected audio, images, and sensor data to the server via wireless communication. During this process, the data is converted and compressed into the appropriate format before transmission, ensuring efficient data transfer.

[0465] Step 3:

[0466] The server inputs the audio data sent from the terminal into the speech recognition engine and converts the audio into text format. The resulting text is then passed to the natural language processing unit, which interprets the user's intent and emotions.

[0467] Step 4:

[0468] The server analyzes image data using computer vision algorithms to estimate the user's posture and detect anomalies in the environment. For example, if there is a possibility of falling, the system will identify signs of it.

[0469] Step 5:

[0470] The server analyzes sensor data to detect abnormal heart rate, body temperature changes, and other abnormalities. This allows for the detection of changes in health status.

[0471] Step 6:

[0472] The server integrates the analysis results from steps 3-5 and generates feedback for the user. For example, if an anomaly is detected, a warning message is generated.

[0473] Step 7:

[0474] The server sends the generated feedback to the terminal, presenting it to the user via speech synthesis or display. The terminal then plays instructions such as "Please take a short break" or "Please stay hydrated" in voice.

[0475] Step 8:

[0476] The server will also send emergency notifications to family members or caregivers via email or SMS, as needed. For example, notifications will be sent in the event of an emergency such as a fall.

[0477] (Example 1)

[0478] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0479] In monitoring and caring for the elderly, accurately understanding their health status in real time is crucial. However, conventional technologies often struggle to effectively integrate voice, visual, and physical sensor data, resulting in insufficient comprehensive health assessments. Furthermore, systems capable of responding quickly to abnormalities are limited. This leads to a problem where the safety of elderly individuals living independently is not adequately ensured.

[0480] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0481] In this invention, the server includes information gathering means for acquiring and integrating voice data, visual materials, and physical sensor information; information analysis means for analyzing the information collected by the information gathering means and evaluating the individual's health status; and response generation means for providing personalized advice based on the results of the information analysis means. This enables a multifaceted evaluation of the health status of elderly people, allowing for personalized advice and rapid emergency response.

[0482] "Audio data" refers to recordings of human voices that have been physically collected, and it is possible to analyze this data to extract semantic information.

[0483] "Visual data" refers to visual information such as images and videos acquired using cameras or other imaging devices, and is used to analyze this data to recognize the actions and states of a subject.

[0484] "Physical sensor information" refers to data collected by sensors that measure environmental and biological information such as temperature, heart rate, and movement, and is used to evaluate an individual's health status and environmental changes.

[0485] "Information gathering means" refers to technical means for acquiring and integrating audio data, visual materials, and physical sensor information.

[0486] "Information analysis means" refers to technical means for processing and analyzing collected data to evaluate an individual's health status and physical condition.

[0487] "Response generation means" refers to technical means for generating and providing appropriate advice or instructions to an individual based on the results of analysis.

[0488] "Warning measures" refer to technical means for detecting an individual's health condition or an emergency situation and promptly notifying them.

[0489] This invention is a comprehensive system for monitoring the elderly, integrating voice data, visual information, and physical sensor data to analyze the user's health status and provide timely advice and emergency notifications. Specific embodiments of this system are described below.

[0490] terminal

[0491] The terminal is responsible for collecting audio, visual, and physical sensor information necessary for the user's daily life. The terminal is equipped with a microphone, camera, heart rate monitor, and body temperature sensor, and collects data in real time. This allows for continuous monitoring of the user's voice and movement patterns, as well as vital signs. For example, if the terminal detects that the user has fallen in the bathroom, it can immediately notify the server of the abnormal situation.

[0492] server

[0493] The server plays a central role in receiving and analyzing data transmitted from the terminal. Audio data is converted into text using speech recognition technology (e.g., general speech recognition software), and natural language processing technology is used to evaluate the user's health status. For visual data, machine vision technology is used to analyze the user's posture and movements and detect abnormalities. For physical sensor information, fluctuations in heart rate and body temperature are monitored, and alerts are generated if unusual values ​​are observed. For example, if a sudden increase in heart rate is detected, it is immediately determined that there is an abnormality in the user's health status. All of these analysis results are integrated to generate feedback for the user.

[0494] Based on the feedback it generates, the server uses speech synthesis technology to deliver appropriate advice to the user via the device. It also notifies family members and caregivers as needed to facilitate a quick response. In emergencies, an automatic alert is issued, ensuring a rapid response is possible.

[0495] User

[0496] Through interaction with the device, users can live their daily lives with peace of mind. For example, if a user says, "I'm not feeling well today," the device immediately sends that information to the server and provides feedback. This helps them avoid unnecessary actions and obtain information useful for health management.

[0497] An example of a prompt message might be, "Please explain the operating principle of a system that detects falls in the elderly." This system allows users to improve their safety in daily life, and enables family members and caregivers to monitor their loved ones with peace of mind.

[0498] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0499] Step 1: Data Collection

[0500] The device continuously collects audio, visual, and physical sensor information from the user's environment. Specifically, it records audio using a built-in microphone and captures images and videos with a camera. It also acquires biometric information through a heart rate monitor and body temperature sensor. The input for this step is the user's voice and environmental conditions, while the output is a collection of data from various sensors.

[0501] Step 2: Data transmission

[0502] The terminal transmits the collected data to the server using a secure communication protocol (e.g., HTTPS). Voice data, image data, and sensor data are transmitted periodically or triggered. The input in this step is the raw data obtained in the data collection step, and the output is the structured data sent to the server.

[0503] Step 3: Audio Data Processing

[0504] The server converts received audio data into text using speech recognition technology. Then, natural language processing is used to extract information about the user's intentions and health status from the text. The input is an audio file sent from the terminal, and the output is semantic information of the user's utterances in text format. Specifically, if the user says "I need rest," appropriate advice will be prepared.

[0505] Step 4: Image Data Processing

[0506] The server processes image data using machine vision algorithms to analyze the user's posture and movements. This allows for the detection of falls and abnormal movements. The input for this step is an image file sent from the terminal, and the output is the classification result of the user's posture. For example, if the user is in an unnatural posture, the moment is identified and an alert is generated.

[0507] Step 5: Sensor Data Processing

[0508] The server analyzes sensor data such as heart rate and body temperature to detect abnormal values. Digital signal processing technology is used to identify patterns of abnormal fluctuations. The input for this step is biosensor data transmitted from the terminal, and the output is an assessment of the health status. If the heart rate falls outside the normal range, that information is highlighted.

[0509] Step 6: Data Integration and Feedback Generation

[0510] The server integrates the analyzed data to perform a comprehensive health assessment. Based on this, it generates personalized feedback for the user. Inputs are the results of voice, visual, and sensor data analysis, and output is the integrated health assessment and advice in text format. For example, it might generate advice such as, "Please drink plenty of fluids."

[0511] Step 7: Notification and Advice Provision

[0512] The server uses speech synthesis technology to send the generated advice to the terminal and notifies the user. If an emergency is detected, an alert is also sent to family members or caregivers. The input for this step is the advice obtained in the feedback generation step, and the output is the notification sent to the user. The user receives a voice message saying, "You need rest."

[0513] (Application Example 1)

[0514] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0515] When elderly people live alone, they face the challenge of being able to respond quickly to changes in their health or emergencies. Furthermore, there is a need to ensure the safety of elderly people from a distance and to respond immediately in case of emergencies, thus addressing the needs of caregivers and family members.

[0516] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0517] In this invention, the server includes data collection means for acquiring and integrating audio information, visual information, and instrument data; data processing means for analyzing the data acquired by the data collection means and evaluating the user's health status; response generation means for providing personalized advice to the user based on the results of the data processing means; notification means for issuing warnings when an abnormality in the user's health status or in an emergency is detected; and means for remotely tracking the user's movements and health status and providing a notification function when an abnormality is detected. This enables caregivers and family members in remote locations to monitor the elderly person's condition in real time and intervene quickly when necessary.

[0518] "Audio information" refers to data obtained from the words and sounds spoken by users, and is used for communication and state analysis.

[0519] "Visual information" refers to image and video data acquired through cameras and image sensors, and is used to analyze the user's posture and movements.

[0520] "Measurement device data" refers to data such as heart rate, body temperature, and movement obtained using measuring devices, and serves as basic data for evaluating the user's health status.

[0521] A "data collection method" is a system that efficiently acquires and integrates audio information, visual information, and instrument data.

[0522] "Data processing means" refers to technologies used to analyze collected data and evaluate the health status and behavior of users.

[0523] A "response generation means" is a system that creates and provides appropriate advice and instructions to users based on the results of data processing.

[0524] A "reporting mechanism" is a system that sends a warning to relevant parties when an abnormality occurs with a user.

[0525] "Means of providing notification functions" refers to technology that sends real-time information about the user's condition to caregivers or family members who are in a remote location, prompting them to take prompt action.

[0526] To implement this invention, a system is used that appropriately combines three main elements: a server, a terminal, and a user.

[0527] server:

[0528] The server receives audio, visual, and instrument data and processes each type of data using specific technologies. Audio information is converted to text using speech recognition technology, and the user's needs and condition are determined through natural language processing. Visual information is analyzed using computer vision technology to check for abnormalities in the user's posture and movements. Instrument data, such as heart rate and body temperature, is monitored, and abnormal fluctuations in these values ​​are detected. This data is integrated to ultimately generate feedback for the user. In addition, if an abnormality is detected, family members or caregivers are quickly notified through notification systems.

[0529] Terminal:

[0530] The terminal is installed near the user and plays a role in continuously collecting data using voice commands and environmental measuring instruments, and transmitting it to the server. The terminal is equipped with a high-sensitivity microphone and camera, which allows for detailed monitoring of the user's situation. Furthermore, it uses speech synthesis technology to convey feedback from the server in voice and provide appropriate advice to the user.

[0531] User:

[0532] Users interact with the system through their devices in everyday situations. For example, if a user falls while moving around the house, the device automatically sends this abnormal posture information to the server, which immediately notifies family members or caregivers that a fall has occurred. This enables a quick response.

[0533] Example of a prompt:

[0534] "Imagine a monitoring system for the elderly. Design an application that uses voice, images, and sensor data to monitor their condition in real time and notify if there is an abnormality. How would you build the program?"

[0535] In this way, by implementing the invention, it is possible to comprehensively monitor the user's health condition and provide prompt support when necessary.

[0536] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0537] Step 1:

[0538] The device captures the user's voice using a microphone and sends the captured audio data directly to the server. The input audio data is then converted to text using speech recognition technology on the server. This prepares the device for processing the audio as text.

[0539] Step 2:

[0540] The server receives visual information transmitted from the terminal. Image data captured by the camera is analyzed by the server using computer vision technology. The user's posture and movements are determined from the input image data. This allows the server to evaluate whether any abnormal movements (such as falls) are present.

[0541] Step 3:

[0542] The terminal continuously measures the user's instrument data (e.g., heart rate, body temperature) and transmits it to the server. The server analyzes this data and detects abnormal values. It compares the input instrument data with reference values ​​to identify any abnormal changes.

[0543] Step 4:

[0544] The server integrates the processing results of audio, visual, and instrumental data. This involves using data fusion technology to perform a comprehensive evaluation of each processing result. Based on the integrated results, the server assesses the user's health status and generates feedback as needed.

[0545] Step 5:

[0546] If an anomaly is detected, the server will send a warning to family members or caregivers through notification channels. This includes immediate notification via email or messaging to encourage a quick response.

[0547] Step 6:

[0548] The device uses speech synthesis technology to convey feedback provided by the server to the user. Advice such as "It's time to drink some water" or "You should take a short break" is output in voice. Through this process, the user can receive appropriate notifications from the system.

[0549] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0550] This invention aims to provide more accurate advice and support to an elderly person by adding a function to recognize emotions in a system that monitors the health status of the elderly and provides individualized care. This system acquires and analyzes voice, image, and various sensor data to evaluate the user's condition in real time and also grasps their emotional state through an emotion engine.

[0551] server

[0552] The server receives audio data, image data, and sensor data transmitted from the terminal. After reception, the audio data is converted to text using speech recognition technology, and the user's intent is interpreted using natural language processing. Furthermore, the tone and speed of the user's voice are analyzed from the audio data, and an emotion engine is executed to recognize emotions. For image data, computer vision technology is used to analyze the user's facial expressions and posture to estimate emotions. Sensor data is used to assess the user's current health status. Based on the integrated analysis of the data obtained in this way, personalized feedback is generated for the user. If it is an emergency, the server sends an alert to family members or caregivers.

[0553] terminal

[0554] The terminal is positioned near the user and collects voice, image, and sensor data in real time, transmitting it to a server. Equipped with a microphone and camera, the terminal collects data to accurately interpret emotions. When receiving feedback from the server, it uses speech synthesis to communicate it to the user verbally. This allows the user to receive information not only visually but also aurally. For example, it can provide specific advice such as, "You seem a little upset; is there anything I can do to help?"

[0555] User

[0556] Users interact with the system in their daily lives when they feel something is off or when they need advice. For example, if a user says, "I'm not feeling very well today," the emotion engine analyzes the tone of their voice and detects signs of sadness or fatigue. Then, appropriate support is provided.

[0557] For example, if a user is feeling lonely, the device can transmit this information to the server, which can then use an emotion engine to provide necessary support or contact family members. This enables efficient care that takes emotions into consideration.

[0558] The following describes the processing flow.

[0559] Step 1:

[0560] The device acquires voice, images, and sensor data in real time near the user. It uses a microphone to collect user speech and a camera to record changes in facial expressions and posture. It also acquires biometric information such as heart rate and movement from sensors.

[0561] Step 2:

[0562] The device transmits collected audio data, image data, and sensor data to the server. The data is converted to an appropriate format and sent to the server via the communication path.

[0563] Step 3:

[0564] The server processes the received audio data through a speech recognition engine and converts it into text. Furthermore, it uses natural language processing to interpret the user's intent and emotions. In this process, it utilizes an emotion engine to analyze the tone and speed of the voice and estimate the user's emotions.

[0565] Step 4:

[0566] The server analyzes image data using computer vision algorithms. It evaluates the user's facial expressions and posture, and detects signs of emotion using an emotion engine.

[0567] Step 5:

[0568] The server analyzes sensor data to assess the user's health status. It analyzes heart rate, body temperature, and movement patterns to check if they are outside the normal range.

[0569] Step 6:

[0570] The server integrates the analysis results of voice, image, and sensor data to assess the user's overall health and emotional state.

[0571] Step 7:

[0572] The server generates feedback based on the user's status, creating emotional and health-related advice, or, if necessary, messages of well-being and encouragement.

[0573] Step 8:

[0574] The server sends the generated feedback to the terminal. The terminal then conveys the feedback to the user via speech synthesis, providing messages such as, "You seem to be feeling a little unwell today. Do you need any support?"

[0575] Step 9:

[0576] The server will send notifications to family members or caregivers as needed when it detects abnormalities or significant emotional changes. This will create a continuous support system.

[0577] (Example 2)

[0578] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0579] In medical care and support for the elderly, there is a need to accurately understand the physiological and emotional state of patients in real time and provide individualized advice. However, conventional systems can be slow to respond to emotional changes or emergencies, so a system capable of faster and more accurate responses is needed.

[0580] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0581] In this invention, the server includes information acquisition means for collecting and integrating acoustic information, visual information, and composite sensor information; information analysis means for analyzing the collected information to evaluate the user's physiological and emotional state; and response generation means for generating personalized advice based on the analysis results. This makes it possible to monitor the user's state in real time and respond quickly and appropriately.

[0582] "Acoustic information" refers to information collected from the user's acoustics and used for analysis.

[0583] "Visual information" refers to image data used to capture the user's posture and facial expressions.

[0584] "Combined sensor information" refers to information obtained from multiple sensors used to measure the user's physiological state, such as heart rate and body temperature.

[0585] "Information acquisition means" refers to a device or mechanism for collecting and integrating acoustic information, visual information, and composite sensor information.

[0586] "Information analysis means" refers to a device or mechanism for analyzing collected information and evaluating the physiological and emotional state of the user.

[0587] "Response generation means" refers to a device or mechanism for creating and providing personalized advice to a user based on evaluation results obtained by information analysis means.

[0588] A "notification device" is a device or mechanism that makes an emergency call to a pre-set contact when it detects a user's physiological state or an emergency abnormality.

[0589] This invention is a health monitoring system for the elderly that collects acoustic, visual, and combined sensor information in real time and analyzes it to provide users with appropriate advice and support.

[0590] The server first receives acoustic information transmitted from the terminal. This acoustic information is converted into text information using acoustic recognition technology. General-purpose acoustic conversion software is used for this process. Then, natural language processing technology is applied to the text information to understand the intent of the user's speech. An emotion recognition engine is used to analyze the tone and speed of the sound, and the emotional state is estimated using a specific algorithm.

[0591] For visual information, the system utilizes image processing technology built into the device. The camera constantly captures the user's facial expressions and posture, and transmits this data to the server. The server uses this visual information to recognize the user's posture and facial expressions and determine their overall emotional state.

[0592] Furthermore, the system continuously monitors the user's physiological indicators, such as heart rate and body temperature, using combined sensor data. This data is also transmitted to a server and analyzed as part of a comprehensive health assessment.

[0593] Based on the results of all this information analysis, the server generates text-based or voice-based advice optimized for the user. In this process, a generative AI model can be used to provide more natural-sounding feedback.

[0594] The device uses speech synthesis technology to convey feedback received from the server to the user. A concrete example of this system is when the device detects that a user is feeling lonely, and the server quickly generates the necessary support and notifies the family. An example of a prompt sentence to be input into the generating AI model is, "What kind of data is collected and what kind of feedback is generated when an elderly person is feeling anxious?"

[0595] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0596] Step 1:

[0597] The terminal is placed near the user and uses a microphone to collect acoustic information in real time. It receives acoustic data as input and transmits it to a server. Specifically, it records everyday conversations and emotionally charged voices and prepares them for transmission in digital format.

[0598] Step 2:

[0599] The device uses a camera to collect visual information. It acquires image data as input and sends it to the server. Specifically, it captures the user's facial expressions and body movements to generate images that capture changes in emotions and posture.

[0600] Step 3:

[0601] The device collects physiological data using a combined sensor that measures heart rate and body temperature. It acquires biometric data as input and transmits it to a server. Specifically, it records multiple indicators of the user's health status as digital data.

[0602] Step 4:

[0603] The server converts acoustic information received from the terminal into text using acoustic recognition technology. It uses acoustic data as input and generates character data as output. Specifically, it analyzes the amplitude and frequency of the sound and converts it into an appropriate string of characters.

[0604] Step 5:

[0605] The server analyzes text data using a natural language processing engine to understand the user's intentions and emotions. It uses text data as input and outputs evaluation results of emotions and intentions. Specifically, it performs evaluation by analyzing context and identifying emotion vocabulary and patterns.

[0606] Step 6:

[0607] The server analyzes image data using computer vision technology to evaluate the user's facial expressions and posture. It receives image data as input and generates feedback on posture and facial expressions as output. Specifically, it identifies facial features and body poses and estimates emotional states.

[0608] Step 7:

[0609] The server analyzes physiological data from sensors to assess health status. It uses biometric data as input and outputs health status assessment results. Specifically, it processes numerical data using statistical methods and compares it to normal ranges.

[0610] Step 8:

[0611] The server integrates all analysis results and generates personalized advice using a response generation mechanism. This utilizes a generative AI model. The analysis results are the input, and the advice is generated as text or audio data. Specifically, it constructs appropriate advice based on the results, creating a series of contextual responses.

[0612] Step 9:

[0613] The terminal uses speech synthesis technology to convey feedback sent from the server to the user. It receives advice data as input and generates speech as output. Specifically, it converts text into speech and plays it back through the speaker to inform the user.

[0614] (Application Example 2)

[0615] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0616] In managing the health of the elderly, it is essential to respond appropriately to changes in their emotions. However, conventional systems have difficulty accurately assessing emotional states, making it impossible to provide personalized support. Furthermore, there has been a challenge in analyzing users' emotions and health status in real time and providing rapid feedback. This invention aims to solve these problems and provide comprehensive health management that includes the emotional state of the elderly.

[0617] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0618] In this invention, the server includes data collection means for acquiring and integrating voice information, image information, and detection data; data analysis means for evaluating the user's health status; and emotion analysis means for analyzing the user's emotional state and providing support that takes the user's emotions into consideration. This enables real-time analysis of the emotions and health status of elderly people, allowing for personalized support and rapid feedback.

[0619] "Audio information" refers to all data collected as audio, including the characteristics of words and sounds emitted by the user.

[0620] "Image information" refers to visual data acquired using devices such as cameras, and includes the user's facial expressions, posture, and surrounding environment.

[0621] "Detection data" refers to all information acquired from sensors, including physical conditions such as temperature, body movement, and heart rate.

[0622] "Data collection means" refers to a method or apparatus for acquiring audio information, image information, and detection data, and for integrating and processing them.

[0623] "Data analysis means" refers to methods or techniques for evaluating a user's health status based on collected data.

[0624] "Emotional analysis means" refers to methods or technologies for analyzing a user's emotions and providing emotion-based support.

[0625] "Feedback generation means" refers to a method or technology for generating and providing personalized information and advice to users based on the results of data analysis.

[0626] The system for realizing this invention is configured with both a server and a terminal working together. The server plays a central role in acquiring and integrating voice information, image information, and detection data. This utilizes voice recognition technology (e.g., TensorFlow or Dialogflow) and computer vision technology for image analysis (OpenCV or TensorFlow). The server analyzes the acquired data and evaluates the user's health and emotional state in real time through sentiment analysis. Based on the analyzed information, it can provide personalized information to the user using feedback generation means and issue emergency notifications as needed.

[0627] The terminal, equipped with a microphone and camera, plays a role in collecting data such as the user's voice and posture. It can utilize mobile devices such as smartphones or smart glasses, thus achieving portability. It collects voice data from the user and sends it to a server for analysis. Furthermore, it reports feedback from the server to the user using speech synthesis technology (such as Google Text-to-Speech).

[0628] Users can receive daily monitoring using their devices and get the support and advice they need from the system. For example, if an elderly person comments, "I'm not feeling very well today," the system will perform sentiment analysis based on the tone of their voice and provide appropriate search results and support.

[0629] One specific use case is the ability to generate a prompt advising the user to take a break if a negative tone of voice is detected. An example of a prompt might be, "Your tone of voice is negative. Is something bothering you, or do you need a break?"

[0630] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0631] Step 1:

[0632] The device collects audio and image information from the user's surroundings using a microphone and camera. At this stage, audio is input as raw analog data, and images are collected in still image and video formats. Processing is then performed to convert this data digitally and prepare it for transmission to the server.

[0633] Step 2:

[0634] The server converts audio data sent from the terminal into text using speech recognition technology. First, filtering and noise reduction are performed, and then a speech recognition algorithm (e.g., TensorFlow) converts the analog audio into a digital string. This is provided as the output of the speech analysis.

[0635] Step 3:

[0636] Upon receiving image information, the server uses computer vision technology (e.g., OpenCV) to analyze the user's facial expressions and posture. The image data is analyzed at the pixel level to extract features that indicate the user's emotions. This results in an estimated emotion being obtained as the output of the image analysis.

[0637] Step 4:

[0638] The server integrates speech recognition results and image analysis results to assess the user's current health and emotional state. Using NLP techniques, it identifies emotions and intentions from text data and performs context-aware analysis. This generates a unified output regarding the user's state.

[0639] Step 5:

[0640] Based on the generated output, the server uses feedback generation mechanisms to construct advice tailored to the user. It generates appropriate support messages that correspond to the user's emotions and state obtained in the previous step. For example, if the user's tone of voice is negative, it will create advice such as, "Let's take a short break."

[0641] Step 6:

[0642] If the generated feedback indicates urgency, the server uses notification methods to send an alert to the designated emergency contact. This ensures that family members and caregivers are quickly informed in situations requiring immediate intervention.

[0643] Step 7:

[0644] The terminal delivers feedback sent from the server to the user as voice messages using speech synthesis technology. Through voice output, it communicates the server's analysis results to the user and prompts them to take action as needed. This allows users to receive system instructions not only visually but also aurally.

[0645] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0646] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0647] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0648] [Fourth Embodiment]

[0649] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0650] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0651] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0652] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0653] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0654] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0655] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0656] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0657] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0658] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0659] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0660] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0661] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0662] This invention is a system intended for monitoring and caring for the elderly. It integrates voice, image, and sensor data to evaluate the user's condition in real time and provides advice or emergency notifications as needed. One of the main components in this system's embodiment is a program that acquires and processes data. Its operation will be described below.

[0663] server

[0664] The server receives audio, image, and sensor data transmitted from the terminal and processes each data individually. For audio data, it first converts it to text using speech recognition technology, and then uses natural language processing to determine the user's needs and condition. For image data, computer vision algorithms are applied to analyze the user's posture and movements to check for abnormalities. Sensor data is analyzed, for example, to detect trends in heart rate and body temperature, and abnormal numerical changes are detected. Finally, the results of these data analyses are integrated to generate feedback for the user. If the user's condition is abnormal, an alert is immediately issued to notify family members or caregivers.

[0665] terminal

[0666] The device is responsible for continuously collecting voice, image, and sensor data near the user, utilizing voice commands and environmental sensors, and transmitting it to the server. The device is equipped with a microphone and camera, constantly monitoring the user's situation. It also uses speech synthesis technology to convey feedback from the server to the user. For example, it provides advice in natural language, such as "It's time to drink some water" or "You should take a short rest."

[0667] User

[0668] Users interact with the system through their devices in their daily lives. For example, if a user says, "I'm not feeling very well," the device immediately sends that audio to the server, and appropriate feedback is provided. Furthermore, in emergencies, the device autonomously sends data to the server without the user having to take any action, and necessary measures are taken.

[0669] As a concrete example, suppose a user suddenly falls while moving around their home. In this case, the device detects the abnormal posture and immediately sends a message to the server indicating the fall. Based on this information, the server sends an emergency notification to family members or caregivers stating that a fall has occurred, prompting them to take necessary action. In this way, swift and efficient care becomes possible.

[0670] The following describes the processing flow.

[0671] Step 1:

[0672] The device collects audio data, image data, and sensor data in real time using microphones, cameras, and various sensors located around the user. Audio data captures the user's speech, image data records the user's posture and environment via the camera, and sensor data collects biometric information such as heart rate and body temperature.

[0673] Step 2:

[0674] The terminal transmits collected audio, images, and sensor data to the server via wireless communication. During this process, the data is converted and compressed into the appropriate format before transmission, ensuring efficient data transfer.

[0675] Step 3:

[0676] The server inputs the audio data sent from the terminal into the speech recognition engine and converts the audio into text format. The resulting text is then passed to the natural language processing unit, which interprets the user's intent and emotions.

[0677] Step 4:

[0678] The server analyzes image data using computer vision algorithms to estimate the user's posture and detect anomalies in the environment. For example, if there is a possibility of falling, the system will identify signs of it.

[0679] Step 5:

[0680] The server analyzes sensor data to detect abnormal heart rate, body temperature changes, and other abnormalities. This allows for the detection of changes in health status.

[0681] Step 6:

[0682] The server integrates the analysis results from steps 3-5 and generates feedback for the user. For example, if an anomaly is detected, a warning message is generated.

[0683] Step 7:

[0684] The server sends the generated feedback to the terminal, presenting it to the user via speech synthesis or display. The terminal then plays instructions such as "Please take a short break" or "Please stay hydrated" in voice.

[0685] Step 8:

[0686] The server will also send emergency notifications to family members or caregivers via email or SMS, as needed. For example, notifications will be sent in the event of an emergency such as a fall.

[0687] (Example 1)

[0688] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0689] In monitoring and caring for the elderly, accurately understanding their health status in real time is crucial. However, conventional technologies often struggle to effectively integrate voice, visual, and physical sensor data, resulting in insufficient comprehensive health assessments. Furthermore, systems capable of responding quickly to abnormalities are limited. This leads to a problem where the safety of elderly individuals living independently is not adequately ensured.

[0690] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0691] In this invention, the server includes information gathering means for acquiring and integrating voice data, visual materials, and physical sensor information; information analysis means for analyzing the information collected by the information gathering means and evaluating the individual's health status; and response generation means for providing personalized advice based on the results of the information analysis means. This enables a multifaceted evaluation of the health status of elderly people, allowing for personalized advice and rapid emergency response.

[0692] "Audio data" refers to recordings of human voices that have been physically collected, and it is possible to analyze this data to extract semantic information.

[0693] "Visual data" refers to visual information such as images and videos acquired using cameras or other imaging devices, and is used to analyze this data to recognize the actions and states of a subject.

[0694] "Physical sensor information" refers to data collected by sensors that measure environmental and biological information such as temperature, heart rate, and movement, and is used to evaluate an individual's health status and environmental changes.

[0695] "Information gathering means" refers to technical means for acquiring and integrating audio data, visual materials, and physical sensor information.

[0696] "Information analysis means" refers to technical means for processing and analyzing collected data to evaluate an individual's health status and physical condition.

[0697] "Response generation means" refers to technical means for generating and providing appropriate advice or instructions to an individual based on the results of analysis.

[0698] "Warning measures" refer to technical means for detecting an individual's health condition or an emergency situation and promptly notifying them.

[0699] This invention is a comprehensive system for monitoring the elderly, integrating voice data, visual information, and physical sensor data to analyze the user's health status and provide timely advice and emergency notifications. Specific embodiments of this system are described below.

[0700] terminal

[0701] The terminal is responsible for collecting audio, visual, and physical sensor information necessary for the user's daily life. The terminal is equipped with a microphone, camera, heart rate monitor, and body temperature sensor, and collects data in real time. This allows for continuous monitoring of the user's voice and movement patterns, as well as vital signs. For example, if the terminal detects that the user has fallen in the bathroom, it can immediately notify the server of the abnormal situation.

[0702] server

[0703] The server plays a central role in receiving and analyzing data transmitted from the terminal. Audio data is converted into text using speech recognition technology (e.g., general speech recognition software), and natural language processing technology is used to evaluate the user's health status. For visual data, machine vision technology is used to analyze the user's posture and movements and detect abnormalities. For physical sensor information, fluctuations in heart rate and body temperature are monitored, and alerts are generated if unusual values ​​are observed. For example, if a sudden increase in heart rate is detected, it is immediately determined that there is an abnormality in the user's health status. All of these analysis results are integrated to generate feedback for the user.

[0704] Based on the feedback it generates, the server uses speech synthesis technology to deliver appropriate advice to the user via the device. It also notifies family members and caregivers as needed to facilitate a quick response. In emergencies, an automatic alert is issued, ensuring a rapid response is possible.

[0705] User

[0706] Through interaction with the device, users can live their daily lives with peace of mind. For example, if a user says, "I'm not feeling well today," the device immediately sends that information to the server and provides feedback. This helps them avoid unnecessary actions and obtain information useful for health management.

[0707] An example of a prompt message might be, "Please explain the operating principle of a system that detects falls in the elderly." This system allows users to improve their safety in daily life, and enables family members and caregivers to monitor their loved ones with peace of mind.

[0708] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0709] Step 1: Data Collection

[0710] The device continuously collects audio, visual, and physical sensor information from the user's environment. Specifically, it records audio using a built-in microphone and captures images and videos with a camera. It also acquires biometric information through a heart rate monitor and body temperature sensor. The input for this step is the user's voice and environmental conditions, while the output is a collection of data from various sensors.

[0711] Step 2: Data transmission

[0712] The terminal transmits the collected data to the server using a secure communication protocol (e.g., HTTPS). Voice data, image data, and sensor data are transmitted periodically or triggered. The input in this step is the raw data obtained in the data collection step, and the output is the structured data sent to the server.

[0713] Step 3: Audio Data Processing

[0714] The server converts received audio data into text using speech recognition technology. Then, natural language processing is used to extract information about the user's intentions and health status from the text. The input is an audio file sent from the terminal, and the output is semantic information of the user's utterances in text format. Specifically, if the user says "I need rest," appropriate advice will be prepared.

[0715] Step 4: Image Data Processing

[0716] The server processes image data using machine vision algorithms to analyze the user's posture and movements. This allows for the detection of falls and abnormal movements. The input for this step is an image file sent from the terminal, and the output is the classification result of the user's posture. For example, if the user is in an unnatural posture, the moment is identified and an alert is generated.

[0717] Step 5: Sensor Data Processing

[0718] The server analyzes sensor data such as heart rate and body temperature to detect abnormal values. Digital signal processing technology is used to identify patterns of abnormal fluctuations. The input for this step is biosensor data transmitted from the terminal, and the output is an assessment of the health status. If the heart rate falls outside the normal range, that information is highlighted.

[0719] Step 6: Data Integration and Feedback Generation

[0720] The server integrates the analyzed data to perform a comprehensive health assessment. Based on this, it generates personalized feedback for the user. Inputs are the results of voice, visual, and sensor data analysis, and output is the integrated health assessment and advice in text format. For example, it might generate advice such as, "Please drink plenty of fluids."

[0721] Step 7: Notification and Advice Provision

[0722] The server uses speech synthesis technology to send the generated advice to the terminal and notifies the user. If an emergency is detected, an alert is also sent to family members or caregivers. The input for this step is the advice obtained in the feedback generation step, and the output is the notification sent to the user. The user receives a voice message saying, "You need rest."

[0723] (Application Example 1)

[0724] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0725] When elderly people live alone, they face the challenge of being able to respond quickly to changes in their health or emergencies. Furthermore, there is a need to ensure the safety of elderly people from a distance and to respond immediately in case of emergencies, thus addressing the needs of caregivers and family members.

[0726] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0727] In this invention, the server includes data collection means for acquiring and integrating audio information, visual information, and instrument data; data processing means for analyzing the data acquired by the data collection means and evaluating the user's health status; response generation means for providing personalized advice to the user based on the results of the data processing means; notification means for issuing warnings when an abnormality in the user's health status or in an emergency is detected; and means for remotely tracking the user's movements and health status and providing a notification function when an abnormality is detected. This enables caregivers and family members in remote locations to monitor the elderly person's condition in real time and intervene quickly when necessary.

[0728] "Audio information" refers to data obtained from the words and sounds spoken by users, and is used for communication and state analysis.

[0729] "Visual information" refers to image and video data acquired through cameras and image sensors, and is used to analyze the user's posture and movements.

[0730] "Measurement device data" refers to data such as heart rate, body temperature, and movement obtained using measuring devices, and serves as basic data for evaluating the user's health status.

[0731] A "data collection method" is a system that efficiently acquires and integrates audio information, visual information, and instrument data.

[0732] "Data processing means" refers to technologies used to analyze collected data and evaluate the health status and behavior of users.

[0733] A "response generation means" is a system that creates and provides appropriate advice and instructions to users based on the results of data processing.

[0734] A "reporting mechanism" is a system that sends a warning to relevant parties when an abnormality occurs with a user.

[0735] "Means of providing notification functions" refers to technology that sends real-time information about the user's condition to caregivers or family members who are in a remote location, prompting them to take prompt action.

[0736] To implement this invention, a system is used that appropriately combines three main elements: a server, a terminal, and a user.

[0737] server:

[0738] The server receives audio, visual, and instrument data and processes each type of data using specific technologies. Audio information is converted to text using speech recognition technology, and the user's needs and condition are determined through natural language processing. Visual information is analyzed using computer vision technology to check for abnormalities in the user's posture and movements. Instrument data, such as heart rate and body temperature, is monitored, and abnormal fluctuations in these values ​​are detected. This data is integrated to ultimately generate feedback for the user. In addition, if an abnormality is detected, family members or caregivers are quickly notified through notification systems.

[0739] Terminal:

[0740] The terminal is installed near the user and plays a role in continuously collecting data using voice commands and environmental measuring instruments, and transmitting it to the server. The terminal is equipped with a high-sensitivity microphone and camera, which allows for detailed monitoring of the user's situation. Furthermore, it uses speech synthesis technology to convey feedback from the server in voice and provide appropriate advice to the user.

[0741] User:

[0742] Users interact with the system through their devices in everyday situations. For example, if a user falls while moving around the house, the device automatically sends this abnormal posture information to the server, which immediately notifies family members or caregivers that a fall has occurred. This enables a quick response.

[0743] Example of a prompt:

[0744] "Imagine a monitoring system for the elderly. Design an application that uses voice, images, and sensor data to monitor their condition in real time and notify if there is an abnormality. How would you build the program?"

[0745] In this way, by implementing the invention, it is possible to comprehensively monitor the user's health condition and provide prompt support when necessary.

[0746] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0747] Step 1:

[0748] The device captures the user's voice using a microphone and sends the captured audio data directly to the server. The input audio data is then converted to text using speech recognition technology on the server. This prepares the device for processing the audio as text.

[0749] Step 2:

[0750] The server receives visual information transmitted from the terminal. Image data captured by the camera is analyzed by the server using computer vision technology. The user's posture and movements are determined from the input image data. This allows the server to evaluate whether any abnormal movements (such as falls) are present.

[0751] Step 3:

[0752] The terminal continuously measures the user's instrument data (e.g., heart rate, body temperature) and transmits it to the server. The server analyzes this data and detects abnormal values. It compares the input instrument data with reference values ​​to identify any abnormal changes.

[0753] Step 4:

[0754] The server integrates the processing results of audio, visual, and instrumental data. This involves using data fusion technology to perform a comprehensive evaluation of each processing result. Based on the integrated results, the server assesses the user's health status and generates feedback as needed.

[0755] Step 5:

[0756] If an anomaly is detected, the server will send a warning to family members or caregivers through notification channels. This includes immediate notification via email or messaging to encourage a quick response.

[0757] Step 6:

[0758] The device uses speech synthesis technology to convey feedback provided by the server to the user. Advice such as "It's time to drink some water" or "You should take a short break" is output in voice. Through this process, the user can receive appropriate notifications from the system.

[0759] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0760] This invention aims to provide more accurate advice and support to an elderly person by adding a function to recognize emotions in a system that monitors the health status of the elderly and provides individualized care. This system acquires and analyzes voice, image, and various sensor data to evaluate the user's condition in real time and also grasps their emotional state through an emotion engine.

[0761] server

[0762] The server receives audio data, image data, and sensor data transmitted from the terminal. After reception, the audio data is converted to text using speech recognition technology, and the user's intent is interpreted using natural language processing. Furthermore, the tone and speed of the user's voice are analyzed from the audio data, and an emotion engine is executed to recognize emotions. For image data, computer vision technology is used to analyze the user's facial expressions and posture to estimate emotions. Sensor data is used to assess the user's current health status. Based on the integrated analysis of the data obtained in this way, personalized feedback is generated for the user. If it is an emergency, the server sends an alert to family members or caregivers.

[0763] terminal

[0764] The terminal is positioned near the user and collects voice, image, and sensor data in real time, transmitting it to a server. Equipped with a microphone and camera, the terminal collects data to accurately interpret emotions. When receiving feedback from the server, it uses speech synthesis to communicate it to the user verbally. This allows the user to receive information not only visually but also aurally. For example, it can provide specific advice such as, "You seem a little upset; is there anything I can do to help?"

[0765] User

[0766] Users interact with the system in their daily lives when they feel something is off or when they need advice. For example, if a user says, "I'm not feeling very well today," the emotion engine analyzes the tone of their voice and detects signs of sadness or fatigue. Then, appropriate support is provided.

[0767] For example, if a user is feeling lonely, the device can transmit this information to the server, which can then use an emotion engine to provide necessary support or contact family members. This enables efficient care that takes emotions into consideration.

[0768] The following describes the processing flow.

[0769] Step 1:

[0770] The device acquires voice, images, and sensor data in real time near the user. It uses a microphone to collect user speech and a camera to record changes in facial expressions and posture. It also acquires biometric information such as heart rate and movement from sensors.

[0771] Step 2:

[0772] The device transmits collected audio data, image data, and sensor data to the server. The data is converted to an appropriate format and sent to the server via the communication path.

[0773] Step 3:

[0774] The server processes the received audio data through a speech recognition engine and converts it into text. Furthermore, it uses natural language processing to interpret the user's intent and emotions. In this process, it utilizes an emotion engine to analyze the tone and speed of the voice and estimate the user's emotions.

[0775] Step 4:

[0776] The server analyzes image data using computer vision algorithms. It evaluates the user's facial expressions and posture, and detects signs of emotion using an emotion engine.

[0777] Step 5:

[0778] The server analyzes sensor data to assess the user's health status. It analyzes heart rate, body temperature, and movement patterns to check if they are outside the normal range.

[0779] Step 6:

[0780] The server integrates the analysis results of voice, image, and sensor data to assess the user's overall health and emotional state.

[0781] Step 7:

[0782] The server generates feedback based on the user's status, creating emotional and health-related advice, or, if necessary, messages of well-being and encouragement.

[0783] Step 8:

[0784] The server sends the generated feedback to the terminal. The terminal then conveys the feedback to the user via speech synthesis, providing messages such as, "You seem to be feeling a little unwell today. Do you need any support?"

[0785] Step 9:

[0786] The server will send notifications to family members or caregivers as needed when it detects abnormalities or significant emotional changes. This will create a continuous support system.

[0787] (Example 2)

[0788] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0789] In medical care and support for the elderly, there is a need to accurately understand the physiological and emotional state of patients in real time and provide individualized advice. However, conventional systems can be slow to respond to emotional changes or emergencies, so a system capable of faster and more accurate responses is needed.

[0790] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0791] In this invention, the server includes information acquisition means for collecting and integrating acoustic information, visual information, and composite sensor information; information analysis means for analyzing the collected information to evaluate the user's physiological and emotional state; and response generation means for generating personalized advice based on the analysis results. This makes it possible to monitor the user's state in real time and respond quickly and appropriately.

[0792] "Acoustic information" refers to information collected from the user's acoustics and used for analysis.

[0793] "Visual information" refers to image data used to capture the user's posture and facial expressions.

[0794] "Combined sensor information" refers to information obtained from multiple sensors used to measure the user's physiological state, such as heart rate and body temperature.

[0795] "Information acquisition means" refers to a device or mechanism for collecting and integrating acoustic information, visual information, and composite sensor information.

[0796] "Information analysis means" refers to a device or mechanism for analyzing collected information and evaluating the physiological and emotional state of the user.

[0797] "Response generation means" refers to a device or mechanism for creating and providing personalized advice to a user based on evaluation results obtained by information analysis means.

[0798] A "notification device" is a device or mechanism that makes an emergency call to a pre-set contact when it detects a user's physiological state or an emergency abnormality.

[0799] This invention is a health monitoring system for the elderly that collects acoustic, visual, and combined sensor information in real time and analyzes it to provide users with appropriate advice and support.

[0800] The server first receives acoustic information transmitted from the terminal. This acoustic information is converted into text information using acoustic recognition technology. General-purpose acoustic conversion software is used for this process. Then, natural language processing technology is applied to the text information to understand the intent of the user's speech. An emotion recognition engine is used to analyze the tone and speed of the sound, and the emotional state is estimated using a specific algorithm.

[0801] For visual information, the system utilizes image processing technology built into the device. The camera constantly captures the user's facial expressions and posture, and transmits this data to the server. The server uses this visual information to recognize the user's posture and facial expressions and determine their overall emotional state.

[0802] Furthermore, the system continuously monitors the user's physiological indicators, such as heart rate and body temperature, using combined sensor data. This data is also transmitted to a server and analyzed as part of a comprehensive health assessment.

[0803] Based on the results of all this information analysis, the server generates text-based or voice-based advice optimized for the user. In this process, a generative AI model can be used to provide more natural-sounding feedback.

[0804] The device uses speech synthesis technology to convey feedback received from the server to the user. A concrete example of this system is when the device detects that a user is feeling lonely, and the server quickly generates the necessary support and notifies the family. An example of a prompt sentence to be input into the generating AI model is, "What kind of data is collected and what kind of feedback is generated when an elderly person is feeling anxious?"

[0805] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0806] Step 1:

[0807] The terminal is placed near the user and uses a microphone to collect acoustic information in real time. It receives acoustic data as input and transmits it to a server. Specifically, it records everyday conversations and emotionally charged voices and prepares them for transmission in digital format.

[0808] Step 2:

[0809] The device uses a camera to collect visual information. It acquires image data as input and sends it to the server. Specifically, it captures the user's facial expressions and body movements to generate images that capture changes in emotions and posture.

[0810] Step 3:

[0811] The device collects physiological data using a combined sensor that measures heart rate and body temperature. It acquires biometric data as input and transmits it to a server. Specifically, it records multiple indicators of the user's health status as digital data.

[0812] Step 4:

[0813] The server converts acoustic information received from the terminal into text using acoustic recognition technology. It uses acoustic data as input and generates character data as output. Specifically, it analyzes the amplitude and frequency of the sound and converts it into an appropriate string of characters.

[0814] Step 5:

[0815] The server analyzes text data using a natural language processing engine to understand the user's intentions and emotions. It uses text data as input and outputs evaluation results of emotions and intentions. Specifically, it performs evaluation by analyzing context and identifying emotion vocabulary and patterns.

[0816] Step 6:

[0817] The server analyzes image data using computer vision technology to evaluate the user's facial expressions and posture. It receives image data as input and generates feedback on posture and facial expressions as output. Specifically, it identifies facial features and body poses and estimates emotional states.

[0818] Step 7:

[0819] The server analyzes physiological data from sensors to assess health status. It uses biometric data as input and outputs health status assessment results. Specifically, it processes numerical data using statistical methods and compares it to normal ranges.

[0820] Step 8:

[0821] The server integrates all analysis results and generates personalized advice using a response generation mechanism. This utilizes a generative AI model. The analysis results are the input, and the advice is generated as text or audio data. Specifically, it constructs appropriate advice based on the results, creating a series of contextual responses.

[0822] Step 9:

[0823] The terminal uses speech synthesis technology to convey feedback sent from the server to the user. It receives advice data as input and generates speech as output. Specifically, it converts text into speech and plays it back through the speaker to inform the user.

[0824] (Application Example 2)

[0825] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0826] In managing the health of the elderly, it is essential to respond appropriately to changes in their emotions. However, conventional systems have difficulty accurately assessing emotional states, making it impossible to provide personalized support. Furthermore, there has been a challenge in analyzing users' emotions and health status in real time and providing rapid feedback. This invention aims to solve these problems and provide comprehensive health management that includes the emotional state of the elderly.

[0827] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0828] In this invention, the server includes data collection means for acquiring and integrating voice information, image information, and detection data; data analysis means for evaluating the user's health status; and emotion analysis means for analyzing the user's emotional state and providing support that takes the user's emotions into consideration. This enables real-time analysis of the emotions and health status of elderly people, allowing for personalized support and rapid feedback.

[0829] "Audio information" refers to all data collected as audio, including the characteristics of words and sounds emitted by the user.

[0830] "Image information" refers to visual data acquired using devices such as cameras, and includes the user's facial expressions, posture, and surrounding environment.

[0831] "Detection data" refers to all information acquired from sensors, including physical conditions such as temperature, body movement, and heart rate.

[0832] "Data collection means" refers to a method or apparatus for acquiring audio information, image information, and detection data, and for integrating and processing them.

[0833] "Data analysis means" refers to methods or techniques for evaluating a user's health status based on collected data.

[0834] "Emotional analysis means" refers to methods or technologies for analyzing a user's emotions and providing emotion-based support.

[0835] "Feedback generation means" refers to a method or technology for generating and providing personalized information and advice to users based on the results of data analysis.

[0836] The system for realizing this invention is configured with both a server and a terminal working together. The server plays a central role in acquiring and integrating voice information, image information, and detection data. This utilizes voice recognition technology (e.g., TensorFlow or Dialogflow) and computer vision technology for image analysis (OpenCV or TensorFlow). The server analyzes the acquired data and evaluates the user's health and emotional state in real time through sentiment analysis. Based on the analyzed information, it can provide personalized information to the user using feedback generation means and issue emergency notifications as needed.

[0837] The terminal, equipped with a microphone and camera, plays a role in collecting data such as the user's voice and posture. It can utilize mobile devices such as smartphones or smart glasses, thus achieving portability. It collects voice data from the user and sends it to a server for analysis. Furthermore, it reports feedback from the server to the user using speech synthesis technology (such as Google Text-to-Speech).

[0838] Users can receive daily monitoring using their devices and get the support and advice they need from the system. For example, if an elderly person comments, "I'm not feeling very well today," the system will perform sentiment analysis based on the tone of their voice and provide appropriate search results and support.

[0839] One specific use case is the ability to generate a prompt advising the user to take a break if a negative tone of voice is detected. An example of a prompt might be, "Your tone of voice is negative. Is something bothering you, or do you need a break?"

[0840] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0841] Step 1:

[0842] The device collects audio and image information from the user's surroundings using a microphone and camera. At this stage, audio is input as raw analog data, and images are collected in still image and video formats. Processing is then performed to convert this data digitally and prepare it for transmission to the server.

[0843] Step 2:

[0844] The server converts audio data sent from the terminal into text using speech recognition technology. First, filtering and noise reduction are performed, and then a speech recognition algorithm (e.g., TensorFlow) converts the analog audio into a digital string. This is provided as the output of the speech analysis.

[0845] Step 3:

[0846] Upon receiving image information, the server uses computer vision technology (e.g., OpenCV) to analyze the user's facial expressions and posture. The image data is analyzed at the pixel level to extract features that indicate the user's emotions. This results in an estimated emotion being obtained as the output of the image analysis.

[0847] Step 4:

[0848] The server integrates speech recognition results and image analysis results to assess the user's current health and emotional state. Using NLP techniques, it identifies emotions and intentions from text data and performs context-aware analysis. This generates a unified output regarding the user's state.

[0849] Step 5:

[0850] Based on the generated output, the server uses feedback generation mechanisms to construct advice tailored to the user. It generates appropriate support messages that correspond to the user's emotions and state obtained in the previous step. For example, if the user's tone of voice is negative, it will create advice such as, "Let's take a short break."

[0851] Step 6:

[0852] If the generated feedback indicates urgency, the server uses notification methods to send an alert to the designated emergency contact. This ensures that family members and caregivers are quickly informed in situations requiring immediate intervention.

[0853] Step 7:

[0854] The terminal delivers feedback sent from the server to the user as voice messages using speech synthesis technology. Through voice output, it communicates the server's analysis results to the user and prompts them to take action as needed. This allows users to receive system instructions not only visually but also aurally.

[0855] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0856] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0857] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0858] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0859] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0860] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0861] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0862] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0863] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0864] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0865] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0866] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0867] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0868] 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.

[0869] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0870] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0871] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0872] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0873] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0874] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0875] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0876] The following is further disclosed regarding the embodiments described above.

[0877] (Claim 1)

[0878] A data acquisition means for acquiring audio data, image data, and sensor data, and integrating these data,

[0879] A data analysis means that analyzes the data acquired by the aforementioned data acquisition means and evaluates the user's health status,

[0880] A feedback generation means that provides personalized advice to the user based on the results of the data analysis means,

[0881] A system that includes a notification mechanism to send emergency notifications when it detects a user's health condition or an emergency.

[0882] (Claim 2)

[0883] The system according to claim 1, wherein the data acquisition means includes means for converting the user's voice into text using speech recognition technology.

[0884] (Claim 3)

[0885] The system according to claim 1, wherein the data analysis means includes means for recognizing the user's posture from image data using computer vision technology.

[0886] "Example 1"

[0887] (Claim 1)

[0888] Information gathering means for acquiring and integrating audio data, visual materials, and physical sensor information,

[0889] Information analysis means for analyzing information collected by the aforementioned information collection means and evaluating an individual's health status,

[0890] Based on the results of the information analysis means, a response generation means provides advice tailored to the individual,

[0891] A system that includes a warning mechanism to issue emergency notifications when it detects an abnormality in an individual's health condition or an emergency situation.

[0892] (Claim 2)

[0893] The system according to claim 1, wherein the information gathering means includes means for converting an individual's voice into text information using speech recognition technology.

[0894] (Claim 3)

[0895] The system according to claim 1, wherein the information analysis means includes means for recognizing an individual's posture from visual material using machine vision technology.

[0896] "Application Example 1"

[0897] (Claim 1)

[0898] A data collection means for acquiring audio information, visual information, and instrument data, and integrating these data,

[0899] A data processing means that analyzes the data acquired by the aforementioned data collection means and evaluates the user's health status,

[0900] A response generation means that provides personalized advice to the user based on the results of the data processing means,

[0901] A notification system that issues warnings when it detects a user's health condition or an emergency,

[0902] The aforementioned notification means remotely tracks the user's actions and health status and provides a notification function when an abnormality is detected.

[0903] A system that includes this.

[0904] (Claim 2)

[0905] The system according to claim 1, wherein the data collection means includes means for converting a user's voice into a document using speech recognition technology.

[0906] (Claim 3)

[0907] The system according to claim 1, wherein the data processing means includes means for recognizing the user's state from visual information using image recognition technology.

[0908] "Example 2 of combining an emotion engine"

[0909] (Claim 1)

[0910] Information acquisition means for collecting acoustic information, visual information, and composite sensor information, and integrating this information,

[0911] Information analysis means for analyzing information collected by the aforementioned information acquisition means and evaluating the user's physiological and emotional state,

[0912] A response generation means that presents personalized advice to the user based on the results of the information analysis means,

[0913] A system that includes a notification mechanism to send an emergency alert when it detects a user's physiological state or an emergency abnormality.

[0914] (Claim 2)

[0915] The system according to claim 1, wherein the information acquisition means includes means for converting the user's voice into text information using acoustic recognition technology.

[0916] (Claim 3)

[0917] The system according to claim 1, wherein the information analysis means includes means for recognizing the user's posture from visual information using image processing technology.

[0918] "Application example 2 when combining with an emotional engine"

[0919] (Claim 1)

[0920] A data collection means that acquires audio information, image information, and detection data, and integrates this information,

[0921] A data analysis means that analyzes the information obtained by the aforementioned data collection means and evaluates the user's health status,

[0922] A feedback generation means that provides personalized information to the user based on the results of the data analysis means,

[0923] A notification system that sends emergency notifications when it detects a user's health condition or an emergency situation,

[0924] An emotion analysis tool that analyzes the emotional state of users and provides support that takes the user's emotions into consideration,

[0925] A means for analyzing audio and image data input from mobile devices and smart glasses in real time,

[0926] A system that includes this.

[0927] (Claim 2)

[0928] The system according to claim 1, wherein the data collection means includes means for converting a user's voice into a string of characters using speech recognition technology.

[0929] (Claim 3)

[0930] The system according to claim 1, wherein the data analysis means includes means for recognizing the user's posture from image information using image analysis technology. [Explanation of symbols]

[0931] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A data collection means for acquiring audio information, visual information, and instrument data, and integrating these data, A data processing means that analyzes the data acquired by the aforementioned data collection means and evaluates the user's health status, A response generation means that provides personalized advice to the user based on the results of the data processing means, A notification system that issues warnings when it detects a user's health condition or an emergency, The aforementioned notification means remotely tracks the user's actions and health status and provides a notification function when an abnormality is detected. A system that includes this.

2. The system according to claim 1, wherein the data collection means includes means for converting a user's voice into a document using speech recognition technology.

3. The system according to claim 1, wherein the data processing means includes means for recognizing the user's state from visual information using image recognition technology.

Images