system

Abstract

Provide a system. A method for collecting biometric data of elderly people; means for transmitting the collected biometric data to a server; means for analyzing the biometric data received by the server; a means for detecting an anomaly based on the analysis results; a means for generating an alert when an anomaly is detected; means for generating an exercise program suitable for an elderly person; a means for providing the generated exercise program to the elderly person; means for collecting and transmitting activity data to a server; a means of collecting conversational data and generating empathetic responses; A means to provide empathetic conversations based on collected data, and A system including:

Description

[Technical Field]

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

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

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

[0004] In modern society, health management for the elderly is an important issue, and as part of this, daily monitoring of vital signs and providing appropriate care are required. However, it is difficult for elderly people to manage their health themselves, and it is difficult to understand their health status as they spend a lot of time at home. In addition, social isolation and the progression of dementia are major threats to the health of the elderly. Current technology has limited methods for effectively solving these problems, and a more comprehensive and personalized approach is needed. [Means for solving the problem]

[0005] To address these challenges, the present invention provides a system that includes a means for collecting biometric data from elderly people, a means for transmitting the collected data to a server, a means for analyzing the received data and detecting abnormalities, a means for generating an alert when an abnormality is detected, a means for generating and providing a customized exercise program suitable for the elderly, and a means for collecting activity data and conversation data and generating an empathetic response. This makes it possible to monitor the health status of elderly people in real time and respond quickly when an abnormality occurs. Furthermore, by providing empathetic conversations and appropriate exercise programs, it is possible to reduce social isolation and slow the progression of dementia.

[0006] "Biometric data" refers to data that indicates the physical condition of an elderly person, such as their heart rate, number of steps taken, activity level, and sleep patterns.

[0007] A "wearable device" is a device worn by a user to collect biometric data.

[0008] "Server" is a central computer system that receives, stores, and analyzes collected data, detects anomalies, and provides feedback.

[0009] "Analysis" is the process of using machine learning and other algorithms to assess the behavioral patterns and health status of older adults based on collected data.

[0010] An "anomaly" is a data point or pattern that deviates from normal patterns of behavior or health.

[0011] An "alert" is a notification that is generated when an abnormality is detected, and is a means of notifying necessary medical services or family members of the abnormality.

[0012] An "exercise program" is a customized exercise plan suitable for older adults, created based on their health status and individual needs.

[0013] "Empathic response" refers to conversation content and guidance that takes into consideration the feelings and circumstances of the elderly person, as a result of learning from the elderly person's past comments and behavioral tendencies.

[0014] "Monitoring" is the process of monitoring an elderly person's biometric data in real time and continuously checking for any abnormalities. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] This invention relates to a comprehensive health management system for the elderly. The system collects and analyzes biometric data, provides customized exercise programs, and generates and delivers empathetic conversations to monitor the health status of the elderly in real time, aiming to reduce social isolation and slow the progression of dementia.

[0037] 1. System Configuration

[0038] The system mainly consists of the following components:

[0039] Devices: Refers to wearable devices, smartphones, and tablets used daily by seniors. These devices collect biometric data and transmit it to a server. They also serve as an interface for providing exercise instructions and empathetic conversations via visual and audio displays.

[0040] Server: Installed in the cloud, it receives, analyzes, and stores data sent by the elderly. It also generates alerts for abnormalities, creates exercise programs, and generates empathetic responses.

[0041] 2. Data collection and transmission

[0042] The devices (wearable devices and in-home sensors) record elderly people's biometric data, such as heart rate, number of steps, activity level, and sleep patterns, in real time. The devices then transmit this data to a server at regular intervals.

[0043] 3. Data Analysis and Anomaly Detection

[0044] The server stores the received biometric data in a database. It then uses machine learning algorithms to analyze the data and model the elderly person's daily behavioral patterns. For example, if an elderly person typically wakes up at 7 a.m. and goes to bed at 10 p.m., but has recently started waking up late at night more frequently, the server will detect this as an anomaly and generate an alert.

[0045] 4. Providing customized exercise programs

[0046] Based on the analysis results, the server generates an exercise program suitable for the elderly. For example, if the analysis reveals that the elderly person has not been exercising recently, it will suggest light stretching and walking exercises. This program is sent to the device, and exercise instructions and videos are displayed on the screen to make it easy for the user to follow.

[0047] 5. Real-time monitoring and alerts

[0048] While the user is exercising, the device collects real-time heart rate and movement data and sends it to a server, which analyzes the data and immediately generates an alert if an abnormality is detected, notifying necessary medical services or family members.

[0049] 6. Empathetic conversation and guidance

[0050] The device converts everyday conversations into text data using voice recognition technology and sends it to a server. The server analyzes this conversation data and learns from past statements and behavioral trends. Based on the learning results, it generates empathetic responses and provides them to the elderly person via the device. For example, it can have a conversation like, "You woke up a little earlier than usual today. How are you feeling?"

[0051] Specific examples

[0052] Specifically, elderly people with high levels of care needs will use the following systems:

[0053] When the user wakes up in the morning, the wearable device records their heart rate and sleep duration.

[0054] The terminal sends this to the server.

[0055] The server analyzes this data to detect recent disruptions in sleep patterns.

[0056] Based on the results, the server adjusts the exercise program and sends a new exercise plan to the user's terminal.

[0057] The device suggests gentle, non-challenging exercises for the user and provides on-screen instructions and video guides.

[0058] While the user is exercising, the device transmits real-time heart rate and movement data to the server.

[0059] The server monitors the data and immediately generates an alert if there are any health abnormalities, notifying the user of the necessary medical services.

[0060] The device interacts with the elderly on a daily basis, engaging in empathetic conversations to reduce feelings of social isolation and provide psychological care.

[0061] The above is an embodiment of the present invention, which aims to comprehensively manage the health of elderly people, reduce their sense of social isolation, and slow the progression of dementia.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] The device collects real-time biometric data such as the elderly person's heart rate, number of steps taken, activity level, and sleep patterns. For example, a wearable device collects this data periodically throughout the day.

[0065] Step 2:

[0066] The device periodically transmits the collected biometric data to a server via Wi-Fi or Bluetooth, and the data is encrypted to protect privacy.

[0067] Step 3:

[0068] The server stores the received biometric data in a cloud database, where the data is organized by date and time.

[0069] Step 4:

[0070] The server analyzes the stored data and uses machine learning algorithms (e.g., clustering algorithms, time series analysis algorithms) to model the daily behavioral patterns of the elderly.

[0071] Step 5:

[0072] Based on the analysis results, the server detects abnormal values ​​or patterns that deviate from normal lifestyle patterns (e.g., a sudden increase in heart rate or a sudden decrease in sleep time).

[0073] Step 6:

[0074] If an abnormality is detected, the server generates an alert and notifies family members or medical services. For example, if an elderly person's heart rate increases significantly at night, an emergency notification will be sent.

[0075] Step 7:

[0076] Based on the analysis results, the server generates a customized exercise program for each senior, taking into account their individual health condition and past exercise history.

[0077] Step 8:

[0078] The server sends the generated exercise program to the terminal, which displays the received program and provides detailed exercise instructions and video guides to the user.

[0079] Step 9:

[0080] While the user is exercising, the device collects heart rate and movement data in real time and transmits it back to the server, allowing for constant monitoring of the user's condition during exercise.

[0081] Step 10:

[0082] The server reanalyzes the exercise data and checks for any abnormalities. If any abnormalities are detected, immediate action is taken.

[0083] Step 11:

[0084] The device uses voice recognition technology to convert the elderly person's everyday conversations into text data and sends it to a server.

[0085] Step 12:

[0086] The server analyzes the conversation data and uses natural language processing technology to learn the emotions and interests of the elderly.

[0087] Step 13:

[0088] The server generates an empathetic response based on the learning results and sends it to the device, which then provides the generated response to the user in voice or text format.

[0089] Step 14:

[0090] The empathetic response provided to the user provides psychological care and reduces feelings of social isolation, and this process is repeated on an ongoing basis to achieve comprehensive health management.

[0091] Example 1

[0092] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0093] Health management and reducing social isolation among the elderly are important issues in modern society. Effective collection and analysis of elderly biometric data and daily activity data is required for early detection of abnormalities and the provision of customized exercise programs. However, there is a lack of systems that can monitor this data in real time and respond appropriately. As a result, elderly people are at risk of not receiving appropriate care and their health condition worsening. Furthermore, a system that provides empathetic conversation is also needed to reduce social isolation.

[0094] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0095] In this invention, the server includes means for collecting biometric data of the elderly person, means for transmitting the collected biometric data to the server, means for storing the biometric data received by the server in a database, means for analyzing the stored biometric data, means for modeling the elderly person's daily behavioral patterns based on the analysis results, means for detecting abnormalities, means for generating an alert when an abnormality is detected, means for generating an exercise program suitable for the elderly person, and means for providing the generated exercise program to the elderly person, means for collecting activity data in real time and transmitting it to the server, means for analyzing the collected data in real time and detecting abnormalities, means for sending a notification to medical services or family members when an abnormality is detected, means for collecting conversation data and generating an empathetic response, and means for providing empathetic conversation based on the collected data. This enables comprehensive management of the elderly person's health status, detection of abnormalities in real time, and prompt response. Furthermore, providing empathetic conversation can reduce social isolation and slow the progression of dementia.

[0096] "Biometric data" refers to data that represents a person's physiological state, such as an elderly person's heart rate, number of steps, activity level, and sleep patterns.

[0097] The "server" is a computer system installed on the cloud that receives, analyzes, and stores data sent by elderly people.

[0098] A "database" is a data structure managed by a server for systematically storing received biometric data and activity data.

[0099] A "machine learning algorithm" is a computer program that analyzes data and models the daily behavioral patterns of older adults.

[0100] An "exercise program" is a plan that includes exercise and stretching menus suitable for seniors, and is created with the aim of improving their health.

[0101] An "alert" is a warning notification that is generated when an abnormality is detected, intended to notify medical services or family members.

[0102] "Activity data" is information collected during exercise, such as real-time heart rate and movement data.

[0103] "Empathetic responses" are sympathetic dialogues for elderly people that are generated based on past conversations and behavioral trends.

[0104] "Speech recognition technology" is a technology that allows a terminal to convert a user's everyday conversation into text.

[0105] The present invention relates to a comprehensive health management system for the elderly. The system collects and analyzes biometric data of the elderly, provides customized exercise programs, and generates and provides empathetic conversations to reduce social isolation and slow the progression of dementia.

[0106] System configuration

[0107] The system consists of the following components:

[0108] Devices: Refers to wearable devices, smartphones, and tablets used daily by seniors. These devices collect biometric data and transmit it to a server. They also serve as an interface for providing exercise instructions and empathetic conversations via visual and audio displays.

[0109] Server: Installed in the cloud, it receives, analyzes, and stores data sent by the elderly. It also generates alerts for anomaly detection, creates exercise programs, and generates empathetic responses. The server uses a database to store the received data and performs data analysis using machine learning algorithms.

[0110] Hardware and software used

[0111] Wearable devices: Collect biometric data such as heart rate, steps, activity levels, and sleep patterns.

[0112] Smartphone / Tablet: Provides communication means for sending data to the server and acts as an interface for exercise instructions and empathetic conversation.

[0113] Cloud server: Stores and analyzes data, generates exercise programs, generates alerts, and generates empathetic conversations.

[0114] Database: A data structure for organizing and storing data within a server.

[0115] Machine learning algorithms: Programs that analyze incoming data, model behavioral patterns, and detect anomalies.

[0116] Example of operation

[0117] Specifically, the system works as follows:

[0118] When an elderly person wakes up in the morning, the wearable device records their heart rate and sleep duration.

[0119] The terminal sends this to the server.

[0120] The server analyzes the data and detects any recent disruptions to sleep patterns.

[0121] Based on the results, the server adjusts the exercise program and sends a new exercise plan to the user's terminal.

[0122] The device suggests gentle, non-challenging exercises and provides on-screen instructions and video guides.

[0123] While the user is exercising, the device transmits real-time heart rate and movement data to the server.

[0124] The server monitors the data and immediately generates an alert if there are any health abnormalities, notifying the user of the necessary medical services.

[0125] The device interacts with seniors on a daily basis, reducing feelings of social isolation through empathetic conversation.

[0126] Examples of prompt statements

[0127] "We would like you to design and implement a system that generates appropriate exercise programs and provides empathetic conversations based on the analysis of biometric data collected from wearable devices, smartphones, and tablets used daily by the elderly. In particular, we would like it to include a function that collects data on heart rate, sleep patterns, etc. in real time to monitor the elderly's health and generate alerts when abnormalities are detected."

[0128] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0129] Step 1:

[0130] The terminal records the elderly person's biometric data, such as heart rate, number of steps, activity level, and sleep patterns, in real time. This data is obtained from a wearable device. Specifically, the terminal measures the subject's biometric indicators at regular intervals (e.g., every 10 minutes). The input is the elderly person's biometric data, and the output is the recorded biometric data.

[0131] Step 2:

[0132] The device transmits the recorded biometric data to the server at regular intervals (for example, once an hour). The transmitted data consists of heart rate, number of steps, activity level, sleep patterns, etc. The input is the recorded biometric data, and the output is the data to be transmitted to the server. Specifically, the device establishes a network connection with the server and transfers the data.

[0133] Step 3:

[0134] The server stores the received biometric data in a database. In the database, a table is created for each elderly person and the data is stored there. The input is the biometric data sent to the server, and the output is the data stored in the database. Specifically, the server checks the integrity of the data and inserts it into the database in the appropriate format.

[0135] Step 4:

[0136] The server uses machine learning algorithms to analyze the biometric data and model behavioral patterns. This analysis uses a time series analysis algorithm. The input is the biometric data stored in the database, and the output is a behavioral pattern model as a result of the analysis. Specifically, the server retrieves the data and applies the algorithm to analyze it.

[0137] Step 5:

[0138] The server detects anomalies based on the analysis results. This anomaly detection uses preset reference values ​​and behavioral pattern models. The input is the analysis results and reference values, and the output is a flag indicating whether or not an anomaly exists. Specifically, the server checks for the presence of an abnormal pattern based on the analysis results.

[0139] Step 6:

[0140] If an abnormality is detected, the server generates an alert and sends a notification to the necessary medical services or family members. The input is the abnormality detection flag, and the output is the generated alert and notification. The specific operations are to generate an alert statement and send a notification.

[0141] Step 7:

[0142] The server generates an exercise program based on the analysis results of the biometric data. This program is customized according to the user's activity level and health condition. The input is the analysis results, and the output is the exercise program. Specifically, the server selects an appropriate exercise menu and builds it into a program.

[0143] Step 8:

[0144] The device then provides the generated exercise program to the elderly. The input is the exercise program, and the output is displayed exercise instructions and video guides. Specifically, the device displays the exercise instructions on the screen and plays video guides as needed.

[0145] Step 9:

[0146] While the user is exercising, the device collects heart rate and movement data in real time. The input is the biometric data during exercise, and the output is the collected data. Specifically, the device performs high-frequency data sampling in real time.

[0147] Step 10:

[0148] The collected data is transmitted to the server in real time. The input is the collected real-time biometric data, and the output is the data to be sent to the server. Specifically, the device re-establishes a network connection with the server and transmits the data.

[0149] Step 11:

[0150] The server analyzes the received data in real time, and if an abnormality is detected, it immediately generates an alert and sends a notification. The input is the biometric data sent in real time, and the output is the alert and notification. Specifically, the server performs rapid data analysis, and if an abnormality is detected, it immediately sends a notification.

[0151] Step 12:

[0152] The device provides empathetic conversations with elderly people through everyday interactions. The input is conversation data with the user, and the output is a generated empathetic response. Specifically, the device uses voice recognition technology to send the conversation content to a server and provides the response received from the server to the user.

[0153] (Application example 1)

[0154] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0155] Health management for the elderly is a significant issue, and appropriate health monitoring and reduction of social isolation are required, especially for elderly people living alone or shopping in brick-and-mortar stores. Furthermore, elderly people are sensitive to specific health conditions, and customized exercise programs are needed. However, conventional systems have had difficulty integrating these into the elderly's daily lives. Therefore, there is a need to develop a system that allows elderly people to smoothly manage their health and receive appropriate exercise and psychological care.

[0156] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0157] In this invention, the server includes means for collecting biometric data of the elderly person, means for transmitting the collected biometric data to the server, means for analyzing the biometric data received by the server, means for detecting abnormalities based on the analysis results, means for generating an alert when an abnormality is detected, means for generating an exercise program suitable for the elderly person, means for providing the generated exercise program to the elderly person, means for collecting activity data and transmitting it to the server, means for collecting conversation data and generating empathetic responses, means for providing empathetic conversation based on the collected data, a robot that supports health management through dialogue with the elderly person in a physical store, and means for linking the robot with the elderly person's wearable device or smartphone. This allows the elderly person to receive health management in a physical store, and enables them to reduce their sense of social isolation and maintain their health properly through suggested exercise programs and empathetic conversation.

[0158] "Biometric data" refers to measurements of an older adult's physical condition and behavior, such as heart rate, number of steps taken, activity level, and sleep patterns.

[0159] "Means of collection" refers to devices and functions that collect biometric data from elderly people using wearable devices and in-home sensors.

[0160] A "server" is a computer system that receives, analyzes, and stores collected biometric data.

[0161] "Means for analysis" refers to the technology and algorithms used by the server to process the biometric data received and analyze the health status and behavioral patterns of the elderly person.

[0162] An "anomaly detection means" is a system that identifies health conditions that deviate from normal patterns in the analyzed data and reports the problem.

[0163] "Means for generating alerts" refers to the function of issuing a warning when an abnormality is detected and notifying relevant parties and medical services.

[0164] An "exercise program" is an exercise plan proposed based on the health status of the elderly person and the results of data analysis.

[0165] "Means for providing" refers to the methods and interfaces used to inform elderly people of the generated exercise program and enable them to carry it out.

[0166] "Activity Data" refers to information about your exercise, daily movements, and physical activity.

[0167] "Conversation data" refers to information collected as text or audio of conversations with elderly people.

[0168] "Empathic response" refers to feedback that provides appropriate responses to emotionally understand and support the conversations and actions of older people.

[0169] A "brick and mortar store" is a physical location, such as a retail store or service location, where seniors can visit and receive services in person.

[0170] A "robot" is an autonomous or remotely controlled mechanical device that assists elderly people in managing their health through dialogue within a physical store.

[0171] A "wearable device" is an electronic device that can be worn on the body and collects biometric data such as heart rate and activity level.

[0172] This invention relates to a comprehensive health management system for the elderly. This system collects and analyzes biometric data from the elderly, provides customized exercise programs, and generates and delivers empathetic conversations to monitor the elderly's health status in real time, aiming to reduce social isolation and slow the progression of dementia.

[0173] System configuration

[0174] The system mainly consists of the following elements:

[0175] 1. Devices: These devices are wearable devices, smartphones, and tablets used daily by seniors. These devices collect biometric data and transmit it to a server. They also serve as an interface for providing exercise instructions and empathetic conversations via visual and audio displays.

[0176] 2. Server: Installed on the cloud, it receives, analyzes, and stores data sent by the elderly. It also generates alerts for abnormality detection, creates exercise programs, and generates empathetic responses.

[0177] 3. Robot: A device that supports health management through dialogue with the elderly in physical stores. The robot connects to the elderly's wearable devices and smartphones to collect health data, display analysis results, provide exercise support, and engage in empathetic dialogue.

[0178] Data collection and transmission

[0179] The devices (wearable devices and in-home sensors) record elderly people's biometric data, such as heart rate, number of steps, activity level, and sleep patterns, in real time. The devices then transmit this data to a server at regular intervals.

[0180] Data Analysis and Anomaly Detection

[0181] The server stores the received biometric data in a database and analyzes it using machine learning algorithms to model the elderly person's daily behavioral patterns and generate alerts if any abnormalities are detected.

[0182] Providing customized exercise programs

[0183] Based on the analysis results, the server generates an exercise program suitable for the elderly. For example, if the analysis reveals that the elderly person has not been exercising recently, the server will suggest light stretching and walking exercises.

[0184] Real-time monitoring and alerts

[0185] While the user is exercising, the device collects real-time heart rate and movement data and sends it to a server, which analyzes the data and immediately generates an alert if an abnormality is detected, notifying necessary medical services or family members.

[0186] Empathetic conversation and guidance

[0187] The device converts everyday conversations into text data using voice recognition technology and sends it to a server. The server analyzes this conversation data and learns from past comments and behavioral trends. Based on the learning results, it generates empathetic responses and provides them to the elderly via the device.

[0188] Specific examples

[0189] For example, suppose a 70-year-old person visits a physical store and begins a conversation with a robot. The robot obtains data such as the user's heart rate and number of steps from the server in advance, and tells them, "You haven't been getting enough exercise lately, so I suggest you walk for 30 minutes today." While the user is exercising, the robot monitors their health data in real time and immediately issues an alert if there is an abnormality. The robot also provides emotional care to the user by engaging in empathetic conversation, such as asking, "Is there anything I can help you with today?"

[0190] Example prompts for generative AI models

[0191] Prompt: Create a scenario in which a healthcare assistant robot for the elderly visits a store and offers a customized exercise program based on the individual's heart rate and step count, and engages in empathetic conversation with the individual.

[0192] scenario:

[0193] When an elderly person visits the store, the robot greets them with "Hello, how are you?" The robot retrieves health data collected in real time from the server and analyzes it. It suggests, "It seems you haven't been getting enough exercise lately, so I suggest you take a 30-minute walk today. May I help you?" During the exercise, the robot encourages the person by saying, "Your heart rate is within the normal range. That's great!" and after the exercise, it engages in empathetic conversation by asking, "Is there anything I can help you with today?"

[0194] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0195] Step 1:

[0196] A user visits a physical store and begins interacting with a robot.

[0197] Input: A user enters a physical store and approaches the robot.

[0198] Processing: The robot recognizes the elderly user and provides a welcome message by displaying or speaking.

[0199] Output: The robot says "Hello, how are you?"

[0200] Step 2:

[0201] The device collects biometric data and transmits it to a server.

[0202] Input: The terminal (wearable device) collects biometric data such as heart rate, steps taken, and activity level.

[0203] Processing: The terminal transmits this data to the server at regular intervals.

[0204] Output: The latest biometric data is sent to the server and stored in a database.

[0205] Step 3:

[0206] The server analyzes the received biometric data.

[0207] Input: New biometric data sent to the server.

[0208] Processing: Using machine learning algorithms to analyze data and assess health and behavioral patterns.

[0209] Output: The analysis results will provide the elderly person's current health status and behavioral patterns.

[0210] Step 4:

[0211] Based on the analysis results, anomalies are detected and alerts are generated.

[0212] Input: Analysis result data.

[0213] Processing: If an abnormal pattern is detected, the server generates an alert and sends a notification to medical services and family members.

[0214] Output: An alert notification is sent to medical services and / or family members.

[0215] Step 5:

[0216] The server generates an exercise program suitable for the elderly.

[0217] Input: Analysis results, individual user health status data.

[0218] Processing: Based on these data, the server generates a customized exercise program for the elderly.

[0219] Output: A customized exercise program is generated and sent to the user's device.

[0220] Step 6:

[0221] The terminal provides the generated exercise program to the user.

[0222] Input: The generated exercise program.

[0223] Processing: The device displays exercise instructions and video guides on the screen and provides audio instructions.

[0224] Output: The user can see and hear the exercise program.

[0225] Step 7:

[0226] The user performs the exercise and the device collects data in real time.

[0227] Input: The exercise the user is performing, and movement data from the wearable device.

[0228] Processing: The device continues to record heart rate and movement data in real time and transmits it to the server.

[0229] Output: Real-time data is sent to a server and continuously monitored.

[0230] Step 8:

[0231] The server analyzes the real-time data and generates an empathetic response.

[0232] Inputs: Real-time data and historical conversation data.

[0233] Processing: Using machine learning algorithms to analyze this data and generate empathetic responses.

[0234] Output: The generated empathetic response is sent to the robot or device.

[0235] Step 9:

[0236] Robots provide interaction for the elderly through empathetic conversation.

[0237] Input: The generated empathic response.

[0238] Processing: The robot will respond to the user with empathetic comments such as, "Is there anything I can help you with today?"

[0239] Output: Elderly people can interact with the robot and receive psychological care.

[0240] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0241] The present invention relates to a system that comprehensively supports the health management of the elderly. It aims to monitor the overall health status of the elderly in real time and prevent social isolation and the progression of dementia by combining the collection, analysis, and abnormality detection of the elderly's biometric data, the provision of customized exercise programs, the generation of empathetic responses, and an emotion engine.

[0242] 1. System Configuration

[0243] The system consists of the following elements:

[0244] Devices: Wearable devices, smartphones, and tablets used daily by seniors. These devices collect biometric and emotional data and provide an interface to transmit it to a server.

[0245] Server: A cloud-based computer system that receives, analyzes, and stores biometric and emotional data, performing tasks such as anomaly detection, generating exercise programs, and providing empathetic responses.

[0246] Emotion engine: A system that analyzes the user's voice data and facial expression data to recognize their emotional state and transmits that data to a server.

[0247] 2. Data collection and transmission

[0248] The devices (wearable devices and in-home sensors) record biometric data such as the elderly person's heart rate, number of steps, activity level, and sleep patterns in real time, and also collect voice and facial expression data, which are then sent to a server.

[0249] 3. Data Analysis and Anomaly Detection

[0250] The server stores the received biometric and emotional data in a database. It then uses machine learning algorithms to analyze this data and model the user's daily behavioral patterns and emotional state. For example, if an elderly person normally speaks in a calm voice but has recently started to speak in a lower tone, the emotion engine will detect this and notify the server, which will recognize this as an anomaly and generate an alert.

[0251] 4. Providing customized exercise programs

[0252] Based on the analysis, the server generates an exercise program tailored to the individual, taking into account their individual health status, past exercise history, and emotional state. For example, if they are feeling depressed, it might suggest relaxation and gentle stretching exercises.

[0253] 5. Real-time monitoring and alerts

[0254] While the user is exercising, the device collects real-time heart rate, movement data, and emotional state and sends it to a server that analyzes the data and immediately generates an alert if an abnormality is detected, notifying necessary medical services or family members.

[0255] 6. Empathetic conversation and guidance

[0256] The device converts everyday conversations into text data using voice recognition technology and sends it to the server. The server analyzes this conversation data and uses natural language processing technology to learn the elderly person's emotions and interests. The emotion engine also integrates the emotional data obtained to generate empathetic responses tailored to the elderly person's emotional state. For example, it can respond in line with the user's emotions, such as, "You seem a little down today. Is there anything I can help you with?"

[0257] Specific examples

[0258] The following specific scenarios illustrate embodiments of the present invention:

[0259] When a user wakes up in the morning, the wearable device records their heart rate and sleep duration, and the emotion engine analyzes their voice tone and facial expressions to obtain emotional data.

[0260] The terminal transmits this data to the server.

[0261] The server analyzes this data to detect recent disruptions in sleep patterns and emotional states.

[0262] Based on the results, the server adjusts the exercise program and relaxation plan and sends the new exercise plan to the user's device.

[0263] The device suggests gentle, non-challenging exercises and relaxation exercises to the user, and displays on-screen instructions and video guides.

[0264] While the user is exercising, the device transmits real-time heart rate, movement data, and emotional state to the server.

[0265] The server monitors the data and immediately generates alerts if there are any abnormalities in health or emotional state, notifying necessary medical services or family members.

[0266] The device generates empathetic responses and provides dialogue through everyday conversations with the user, while at the same time providing more empathetic and appropriate responses by taking into account the emotional state recognized by the emotion engine.

[0267] As described above, the system of the present invention collects and analyzes biometric and emotional data of elderly people, and provides customized exercise programs and empathetic conversations, thereby realizing comprehensive health management.

[0268] The processing flow will be explained below.

[0269] Step 1:

[0270] The terminal (wearable device) collects biometric and emotional data such as heart rate, number of steps, activity level, sleep patterns, voice data, and facial expression data of the elderly in real time. For example, the wearable device collects this data periodically throughout the day.

[0271] Step 2:

[0272] The device transmits the collected biometric and emotional data to a server via Wi-Fi or Bluetooth, and the data is encrypted to protect privacy.

[0273] Step 3:

[0274] The server organizes and stores the received biometric and emotional data in a cloud database, where the data is categorized by date and time.

[0275] Step 4:

[0276] The server analyzes the stored data using machine learning algorithms (e.g., clustering algorithms and time series analysis algorithms) to model the elderly's daily behavioral patterns and emotional states.

[0277] Step 5:

[0278] Based on the analysis results, the server detects outliers or abnormal patterns (e.g., sudden increases in heart rate or sudden changes in facial expression) that deviate from normal behavioral patterns and emotional states.

[0279] Step 6:

[0280] If an anomaly is detected, the server generates an alert and notifies family members or medical services. For example, if an elderly person's emotional state suddenly worsens, an urgent notification will be sent.

[0281] Step 7:

[0282] The server generates a customized exercise program based on the elderly person's daily behavioral patterns and emotional state, taking into account their individual health status, past exercise history, and emotional state.

[0283] Step 8:

[0284] The server sends the generated exercise program to the device, which displays the received program and provides detailed exercise instructions and video guides.

[0285] Step 9:

[0286] When a user exercises, the device collects real-time heart rate, movement data, and emotional state and transmits this information to a server, allowing the user to monitor their condition during exercise.

[0287] Step 10:

[0288] The server reanalyzes the exercise and emotional state data to check for any abnormalities, and if any are detected, immediate action is taken.

[0289] Step 11:

[0290] The terminal uses voice recognition technology to convert the user's everyday conversation into text data and sends it to the server.

[0291] Step 12:

[0292] The server uses natural language processing technology to analyze the conversation data it receives and learns the emotions and interests of the elderly. It also integrates and analyzes voice tone and facial expression data obtained by the emotion engine.

[0293] Step 13:

[0294] The server generates an empathetic response based on the learning results and the user's emotional state and sends it to the device. For example, if the user is feeling depressed, it generates a response such as, "Try doing some exercises to relax today."

[0295] Step 14:

[0296] The device generates an empathetic response and provides it to the user via voice or text. The empathetic response provides psychological care and reduces feelings of social isolation. This process is repeated continuously to achieve comprehensive health management.

[0297] Example 2

[0298] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0299] Health management for the elderly is extremely important, but they have difficulty managing themselves and regular checkups at medical institutions are limited. This can lead to health conditions being overlooked and lack of continuous care. It can also increase the risk of social isolation and the progression of dementia. Furthermore, there is often a lack of provision of exercise programs tailored to health conditions and empathetic responses to emotions. A system to comprehensively solve these problems is needed.

[0300] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0301] In this invention, the server includes a means for analyzing biometric information, a means for detecting abnormalities, a means for generating an exercise program, a means for collecting dialogue information and generating empathetic responses, and a means for monitoring information in real time and detecting abnormalities. This enables comprehensive collection and analysis of biometric data and emotional data of elderly people, enabling real-time monitoring of their health status. Furthermore, by providing customized exercise programs and responding empathetically to their emotions, it is possible to reduce social isolation and slow the progression of dementia.

[0302] "Biometric information" refers to data that indicates the health status of elderly people, such as their heart rate, number of steps taken, activity level, and sleep patterns.

[0303] The "Central Processing Unit" is a cloud-based computer system that is responsible for analyzing, storing, and managing received biometric and emotional data.

[0304] An "exercise program" is a customized exercise and relaxation program provided to seniors based on their health status and past exercise history.

[0305] An "emotion engine" is a system that analyzes the user's voice data and facial expression data, recognizes their emotional state, and transmits the data to the central processing unit.

[0306] "Empathic responses" refer to words and actions that show empathy and are generated based on the elderly person's emotional state and the content of the conversation.

[0307] An "alert" is a notification or alert issued when the central processing unit detects an abnormality, and is used to notify necessary medical services or family members.

[0308] "Monitoring" refers to the process of monitoring the elderly's biometric and emotional data in real time to check for any abnormalities.

[0309] "Dialogue information" refers to data collected from everyday conversations and voice input with elderly people, and is analyzed by the emotion engine.

[0310] The present invention is a system that comprehensively supports health management for the elderly. This system is composed of a combination of collecting and analyzing biological information from the elderly, detecting abnormalities, providing customized exercise programs, generating empathetic responses, and an emotion engine. Detailed embodiments of this system are described below.

[0311] System configuration

[0312] Devices: These are wearable devices, smartphones, and tablets used daily by seniors. These devices record biometric information such as heart rate, number of steps, activity level, and sleep patterns in real time, and also have the ability to collect voice and facial expression data. The data is sent to a server using Bluetooth or Wi-Fi.

[0313] Server: A cloud-based computer system that receives, analyzes, and stores biometric and emotional data. The server analyzes the data using machine learning algorithms developed in Python (e.g., scikit-learn and TENSORFLOW®). The server also performs anomaly detection, generates exercise programs, and provides empathetic responses. Specifically, cloud services such as AWS® and Google® Cloud Platform can be used.

[0314] Emotion engine: A system that analyzes the user's voice data and facial expression data to recognize their emotional state. This sends data corresponding to the user's emotional state to the server. Furthermore, the emotion engine uses technologies such as Google Speech-to-Text, Google BERT, and Google Text-to-Speech.

[0315] Data collection and transmission

[0316] When a user uses a wearable device or smartphone, the device records real-time biometric information such as heart rate, steps taken, activity level, and sleep patterns, as well as voice and facial expression data. This data is then transmitted by the device to a central processing unit.

[0317] Data Analysis and Anomaly Detection

[0318] The server stores the received biometric and emotional data in a database. It then analyzes the data using Python machine learning libraries (e.g., scikit-learn and TensorFlow). If an abnormality is detected based on the analysis results, the server generates an alert and notifies the necessary medical services or family members.

[0319] Providing customized exercise programs

[0320] Based on the analysis results, the server generates an exercise program suitable for the elderly. This program is customized taking into account the elderly's health condition, past exercise history, emotional state, etc. The generated program is provided to the elderly via their device. For example, it may suggest an exercise program that includes relaxation and light stretching.

[0321] Real-time monitoring

[0322] When a user starts exercising, the device collects real-time heart rate, movement data, and emotional state and sends them to the server, which monitors the data and generates an immediate alert if any abnormalities are detected.

[0323] Empathetic conversation and guidance

[0324] The device uses voice recognition technology to convert everyday conversations with the user into text data and sends it to the server. The server then analyzes this conversation data using natural language processing technology. It also integrates emotional data obtained from the emotion engine to generate empathetic responses for the elderly. The generated responses are then conveyed to the user via the device using voice synthesis technology.

[0325] Specific examples

[0326] For example, when an elderly person wakes up in the morning, the wearable device records their heart rate and sleep duration. At the same time, the emotion engine analyzes the user's voice tone and facial expressions to obtain emotional data. This data is sent from the device to a server, which analyzes the data to detect recent disturbances in sleep patterns and emotional state. Based on the results, the server generates a customized exercise program and relaxation plan and sends it to the device. The device then displays instructions and video guides on the screen to encourage the user to follow the plan. During exercise, data is collected in real time, and if an abnormality is detected, an alert is generated immediately. The device also collects daily conversation data, which is analyzed by the server and used to provide empathetic support.

[0327] Prompt Sentence Examples

[0328] "Write Python code to record a user's heart rate, steps, activity, and sleep patterns and detect any anomalies."

[0329] "Design a system to send data acquired from a wearable device to a cloud server and analyze it."

[0330] "Propose an algorithm that generates a customized exercise program based on the emotional state of an elderly person."

[0331] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0332] Program processing flow

[0333] Step 1: Device preparation and data collection

[0334] The user puts on the wearable device or smartphone and launches the app.

[0335] Input: Wearable devices, smartphones

[0336] Output: Collected biometric information (e.g., heart rate, steps, activity level, sleep patterns)

[0337] The device uses sensors to record heart rate, steps, activity levels, and sleep patterns, and stores the data in the app.

[0338] Operation: Data measurement by sensor, data storage in app

[0339] The device collects voice data and facial expression data.

[0340] How it works: Records voice and facial expressions using the built-in microphone and camera

[0341] Step 2: Sending data

[0342] The terminal transmits the collected biometric and emotional data to a central processing unit.

[0343] Input: Collected biometric and emotional data

[0344] Output: Data sent to the central processing unit

[0345] How it works: Sends data using Bluetooth or Wi-Fi

[0346] Step 3: Data analysis

[0347] The server stores the received biometric information and emotion data in a cloud database.

[0348] Input: Transmitted biometric and emotional data

[0349] Output: Data stored in the database

[0350] How it works: Stores data using a database management system (e.g. AWS DynamoDB)

[0351] The server analyzes the data using Python machine learning libraries.

[0352] Input: Stored biometric and emotional data

[0353] Output: Analysis results (e.g., health status evaluation data, anomaly detection results)

[0354] Operation: Perform data analysis using scikit-learn and TensorFlow

[0355] Step 4: Detect anomalies

[0356] The server detects abnormalities based on the analysis results.

[0357] Input: Data analysis results

[0358] Output: Anomaly detection alert

[0359] How it works: Applies anomaly detection algorithms and generates alerts if anomalies are found

[0360] Step 5: Generate the exercise program

[0361] The server generates an exercise program suitable for the elderly.

[0362] Input: Health assessment data, past exercise history, emotional state

[0363] Output: Customized exercise program

[0364] Movement: Applying an algorithm to generate exercise programs that take into account health and emotional states

[0365] The server transmits the generated exercise program to the terminal.

[0366] Input: Customized exercise program

[0367] Output: Exercise program sent to the device

[0368] Operation: Send using a data transmission service (e.g. HTTP API)

[0369] Step 6: Real-time monitoring

[0370] The user begins exercising according to the recommended program.

[0371] Input: Customized exercise program

[0372] Output: Exercise performed

[0373] Movement: Follow the app's guide to complete the exercise

[0374] The device collects heart rate, movement data, and emotional state in real time and transmits this to a server.

[0375] Input: Data collected during exercise

[0376] Output: Real-time data sent to the server

[0377] Actions: Use sensors and voice recognition technology to collect data and transmit it in real time

[0378] Step 7: Empathetic conversation and guidance

[0379] The terminal uses voice recognition technology to convert everyday conversations with the user into text data and sends it to the server.

[0380] Input: Speech data of everyday conversation

[0381] Output: Text data sent to the server

[0382] How it works: Uses a speech recognition engine (e.g. Google Speech-to-Text)

[0383] The server analyzes this conversation data using natural language processing technology. It also integrates emotional data obtained from the emotion engine to generate an empathetic response. For example, "You seem to be feeling unwell today. Is there anything I can help you with?" The generated response is then conveyed to the user via the device using voice synthesis technology.

[0384] Input: Conversation data, emotion data

[0385] Output: Empathic response

[0386] How it works: Uses natural language processing technology (e.g., Google BERT) and speech synthesis technology (e.g., Google Text-to-Speech)

[0387] The above is a detailed processing flow of the system program. Through the specific operations at each step, the health management of elderly people can be effectively carried out.

[0388] (Application example 2)

[0389] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0390] Health management for the elderly requires real-time monitoring and appropriate responses, especially while on the move. However, current health management systems lack the functionality to respond to situations and environments while on the move, and do not provide sufficient support to ensure the safety of the elderly. They also lack the ability to respond empathetically to fluctuations in emotional states or provide exercise programs adapted to individual health conditions. This poses a challenge, making it difficult to immediately respond to deterioration in health or accidents while the elderly are on the move.

[0391] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting biometric data and emotional data of the elderly person, means for transmitting the collected biometric data and emotional data to the server, means for analyzing the biometric data and emotional data received by the server, means for detecting abnormalities based on the analysis results, means for generating an alert when an abnormality is detected, means for generating an exercise program suitable for the elderly person, means for providing the generated exercise program to the elderly person, means for collecting activity data and emotional data in real time and transmitting it to the server, means for generating an empathetic response based on the emotional data, means for providing empathetic conversation based on the collected data, and means for monitoring the health status of the elderly person while traveling in an autonomous vehicle and generating an appropriate response. This enables real-time monitoring of the elderly person's health status while traveling, empathetic responses according to their emotional state, and immediate response in the event of an abnormality.

[0392] "Biometric data" refers to data that indicates the physical condition of an elderly person, such as their heart rate, number of steps taken, activity level, and sleep patterns.

[0393] "Emotion data" is data that indicates the emotional state of the elderly person, obtained from their tone of voice and facial expressions.

[0394] The "server" is a cloud-based computer system that receives, analyzes, and stores collected biometric and emotional data, and performs anomaly detection and generates exercise programs.

[0395] "Analysis" is the process of using the received biometric and emotional data to model the elderly person's daily behavioral patterns and emotional state and detect abnormalities.

[0396] "Abnormal" refers to a situation in which the health condition of an elderly person is determined to be outside the normal range based on the analysis results.

[0397] An "alert" is a warning notification to seniors, their families, and medical services that is generated when an abnormality is detected.

[0398] An "exercise program" is a plan of exercise and relaxation tailored to the individual health condition of an elderly person based on analyzed biometric and emotional data.

[0399] "Activity data" refers to data related to the daily movements and behaviors of elderly people.

[0400] An "empathic response" is a system response that aims to understand the emotional state of the elderly person and respond appropriately.

[0401] An "autonomous vehicle" is a vehicle that can drive under its own control without the need for driver operation.

[0402] "Monitoring" is the process of monitoring biometric and emotional data in real time.

[0403] "Real-time" refers to the timing of data being collected and processed and analyzed immediately.

[0404] "Immediate response to abnormalities" is the process of quickly generating an alert and taking appropriate action when an abnormality in health status is detected.

[0405] The present invention is a system that comprehensively supports the health management of the elderly. It aims to monitor the overall health status of the elderly in real time by collecting biometric and emotional data of the elderly, analyzing it, detecting abnormalities, providing customized exercise programs, generating empathetic responses, and combining it with an emotion engine, thereby preventing social isolation and the progression of dementia.

[0406] System configuration

[0407] The system consists of the following elements:

[0408] Devices: Wearable devices, smartphones, tablets, and in-vehicle devices used daily by seniors. These devices collect biometric and emotional data and provide an interface to transmit it to a server.

[0409] Server: A cloud-based computer system that receives, analyzes, and stores biometric and emotional data, performing tasks such as anomaly detection, generating exercise programs, and providing empathetic responses.

[0410] Emotion engine: A system that analyzes the user's voice data and facial expression data to recognize their emotional state and transmits that data to a server.

[0411] Data collection and transmission

[0412] The devices used by the elderly (wearable devices and in-vehicle devices for autonomous vehicles) record biometric data such as heart rate, number of steps, activity level, and sleep patterns in real time. They also collect voice and facial expression data, which are then sent to a server where they are securely and efficiently stored.

[0413] Data Analysis and Anomaly Detection

[0414] The server stores the received biometric and emotional data in a database. It then uses machine learning algorithms to analyze this data and model the user's daily behavioral patterns and emotional state. For example, if an elderly person who normally speaks calmly has recently started to speak in a lower tone, the emotion engine will detect this and the server will recognize it as an anomaly and generate an alert.

[0415] Providing customized exercise programs

[0416] Based on the analysis, the server generates an appropriate exercise program for the elderly, taking into account their individual health and activity data, as well as their emotional state. For example, if they are feeling depressed, it might suggest relaxation and gentle stretching exercises.

[0417] Real-time monitoring and alerts

[0418] While the elderly exercise, the device collects real-time heart rate, movement data, and emotional state and sends it to a server that analyzes the data and immediately generates an alert if an abnormality is detected, notifying medical services and family members.

[0419] Empathetic conversation and guidance

[0420] The device converts everyday conversations into text data using voice recognition technology and sends it to the server. The server analyzes this conversation data and uses natural language processing technology to learn the emotions and interests of the elderly person. The emotion engine also integrates the emotional data obtained to generate empathetic responses tailored to the elderly person's emotional state. For example, it is possible to provide empathetic responses such as, "You seem a little tired today. Is there anything I can do to help you relax?"

[0421] Application to autonomous vehicles

[0422] The device installed in the self-driving vehicle monitors the elderly person's biometric and emotional data in real time while they are traveling. The collected data is sent to a server via the in-vehicle device, and appropriate responses or guidance are provided as needed. For example, if the heart rate increases while traveling, the display will say, "Try taking deep breaths."

[0423] Specific examples

[0424] 1. Situation: Elderly people use self-driving vehicles to go to the hospital.

[0425] 2. Data collection: In-car devices record heart rate and voice tones in real time.

[0426] 3. Data analysis: The server analyzes the data and detects anomalies.

[0427] 4. Response generation: Generate appropriate alerts and display relaxation suggestions on the vehicle's display. The voice system will instruct the driver to "take a deep breath."

[0428] Prompt Sentence Examples

[0429] "Analyze real-time data including the health status of seniors to detect abnormal heart rates and stress levels. Generate example responses within self-driving vehicles."

[0430] "Create an example of a customized exercise program for seniors while they are in an autonomous vehicle. Based on the user's heart rate and emotional data, suggest appropriate relaxation techniques."

[0431] As described above, the system of the present invention collects and analyzes biometric and emotional data of elderly people in real time, enabling safe and efficient health management even while on the move.

[0432] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0433] Step 1:

[0434] When a user gets into an autonomous vehicle, the terminal (in-vehicle device) automatically starts up. The terminal is equipped with various sensors (heart rate sensor, camera, microphone) to collect biometric and emotional data of the elderly person. The sensors begin to record the user's heart rate, voice tone, and facial expressions in real time. This data is collected as biometric and emotional data.

[0435] Input: Heart rate, voice tone, and facial expression data of elderly people

[0436] Output: Collected biometric and emotional data

[0437] Step 2:

[0438] The device transmits the collected biometric and emotional data to a cloud server. The data is transmitted securely using an encrypted protocol, ensuring that all data reaches the server in real time.

[0439] Input: Collected biometric and emotional data

[0440] Output: Transmitted biometric and emotional data (stored on the server)

[0441] Step 3:

[0442] The server analyzes the received biometric and emotional data. It uses a generative AI model to quickly analyze the data and model the elderly person's daily behavioral patterns and emotional state. Based on the analysis results, it determines whether any abnormalities have been detected.

[0443] Input: Transmitted biometric and emotional data

[0444] Output: Analysis results (normal or abnormal)

[0445] Step 4:

[0446] If the server detects an abnormality, it immediately generates an alert, which is then sent to medical services and the elderly person's family via SMS, email, app notification, etc.

[0447] Input: Analysis result (abnormal)

[0448] Output: Generated alert (notifying medical services and family)

[0449] Step 5:

[0450] Based on the analysis results, the server generates an exercise program tailored to the elderly, taking into account their biometric data, emotional data, and past exercise history. The generated program includes suggestions for relaxation techniques and light exercise.

[0451] Input: Analysis results (biometric data, emotional data)

[0452] Output: Generated exercise program

[0453] Step 6:

[0454] The generated exercise program is provided to the user via the device. Exercise instructions are displayed on the device's display and audio guidance is also provided. The user then performs exercises and relaxation exercises that can be easily performed in the car.

[0455] Input: Generated exercise program

[0456] Output: Providing an exercise program to the user

[0457] Step 7:

[0458] The device continuously collects heart rate, movement data, and emotional state during exercise, and transmits the collected data in real time to a server, which then analyzes the data and monitors the user's health.

[0459] Input: Biometric and emotional data during exercise

[0460] Output: Collected exercise data (sent to server)

[0461] Step 8:

[0462] The server evaluates the effectiveness of the exercise program based on the new data collected and adjusts the exercise program as needed.

[0463] Input: Collected exercise data

[0464] Output: Evaluation results and program adjustments

[0465] Step 9:

[0466] The device converts everyday conversations into text data using voice recognition technology and sends it to a server, which then uses natural language processing technology to learn the elderly's emotions and interests, and integrates the obtained emotional data into an emotion engine.

[0467] Input: Speech data of everyday conversation

[0468] Output: Text data (sent to server)

[0469] Step 10:

[0470] The server generates an empathetic response based on the emotional data. The generated response is provided to the user via the device, providing empathetic conversation such as, "You seem a little tired today. Is there anything I can help you relax?"

[0471] Input: Emotion data, text data

[0472] Output: Empathetic response (provided to user)

[0473] Through the above processing steps, the system of the present invention collects and analyzes biometric and emotional data of elderly people, realizing safe and effective health management even while on the move.

[0474] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0475] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0476] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0477] [Second embodiment]

[0478] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0479] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0480] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0481] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0482] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0483] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0484] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0485] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0486] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0487] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0488] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0489] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0490] This invention relates to a comprehensive health management system for the elderly. The system collects and analyzes biometric data, provides customized exercise programs, and generates and delivers empathetic conversations to monitor the health status of the elderly in real time, aiming to reduce social isolation and slow the progression of dementia.

[0491] 1. System Configuration

[0492] The system mainly consists of the following components:

[0493] Devices: Refers to wearable devices, smartphones, and tablets used daily by seniors. These devices collect biometric data and transmit it to a server. They also serve as an interface for providing exercise instructions and empathetic conversations via visual and audio displays.

[0494] Server: Installed in the cloud, it receives, analyzes, and stores data sent by the elderly. It also generates alerts for abnormalities, creates exercise programs, and generates empathetic responses.

[0495] 2. Data collection and transmission

[0496] The devices (wearable devices and in-home sensors) record elderly people's biometric data, such as heart rate, number of steps, activity level, and sleep patterns, in real time. The devices then transmit this data to a server at regular intervals.

[0497] 3. Data Analysis and Anomaly Detection

[0498] The server stores the received biometric data in a database. It then uses machine learning algorithms to analyze the data and model the elderly person's daily behavioral patterns. For example, if an elderly person typically wakes up at 7 a.m. and goes to bed at 10 p.m., but has recently started waking up late at night more frequently, the server will detect this as an anomaly and generate an alert.

[0499] 4. Providing customized exercise programs

[0500] Based on the analysis results, the server generates an exercise program suitable for the elderly. For example, if the analysis reveals that the elderly person has not been exercising recently, it will suggest light stretching and walking exercises. This program is sent to the device, and exercise instructions and videos are displayed on the screen to make it easy for the user to follow.

[0501] 5. Real-time monitoring and alerts

[0502] While the user is exercising, the device collects real-time heart rate and movement data and sends it to a server, which analyzes the data and immediately generates an alert if an abnormality is detected, notifying necessary medical services or family members.

[0503] 6. Empathetic conversation and guidance

[0504] The device converts everyday conversations into text data using voice recognition technology and sends it to a server. The server analyzes this conversation data and learns from past statements and behavioral trends. Based on the learning results, it generates empathetic responses and provides them to the elderly person via the device. For example, it can have a conversation like, "You woke up a little earlier than usual today. How are you feeling?"

[0505] Specific examples

[0506] Specifically, elderly people with high levels of care needs will use the following systems:

[0507] When the user wakes up in the morning, the wearable device records their heart rate and sleep duration.

[0508] The terminal sends this to the server.

[0509] The server analyzes this data to detect recent disruptions in sleep patterns.

[0510] Based on the results, the server adjusts the exercise program and sends a new exercise plan to the user's terminal.

[0511] The device suggests gentle, non-challenging exercises for the user and provides on-screen instructions and video guides.

[0512] While the user is exercising, the device transmits real-time heart rate and movement data to the server.

[0513] The server monitors the data and immediately generates an alert if there are any health abnormalities, notifying the user of the necessary medical services.

[0514] The device interacts with the elderly on a daily basis, engaging in empathetic conversations to reduce feelings of social isolation and provide psychological care.

[0515] The above is an embodiment of the present invention, which aims to comprehensively manage the health of elderly people, reduce their sense of social isolation, and slow the progression of dementia.

[0516] The processing flow will be explained below.

[0517] Step 1:

[0518] The device collects real-time biometric data such as the elderly person's heart rate, number of steps taken, activity level, and sleep patterns. For example, a wearable device collects this data periodically throughout the day.

[0519] Step 2:

[0520] The device periodically transmits the collected biometric data to a server via Wi-Fi or Bluetooth, and the data is encrypted to protect privacy.

[0521] Step 3:

[0522] The server stores the received biometric data in a cloud database, where the data is organized by date and time.

[0523] Step 4:

[0524] The server analyzes the stored data and uses machine learning algorithms (e.g., clustering algorithms, time series analysis algorithms) to model the daily behavioral patterns of the elderly.

[0525] Step 5:

[0526] Based on the analysis results, the server detects abnormal values ​​or patterns that deviate from normal lifestyle patterns (e.g., a sudden increase in heart rate or a sudden decrease in sleep time).

[0527] Step 6:

[0528] If an abnormality is detected, the server generates an alert and notifies family members or medical services. For example, if an elderly person's heart rate increases significantly at night, an emergency notification will be sent.

[0529] Step 7:

[0530] Based on the analysis results, the server generates a customized exercise program for each senior, taking into account their individual health condition and past exercise history.

[0531] Step 8:

[0532] The server sends the generated exercise program to the terminal, which displays the received program and provides detailed exercise instructions and video guides to the user.

[0533] Step 9:

[0534] While the user is exercising, the device collects heart rate and movement data in real time and transmits it back to the server, allowing for constant monitoring of the user's condition during exercise.

[0535] Step 10:

[0536] The server reanalyzes the exercise data and checks for any abnormalities. If any abnormalities are detected, immediate action is taken.

[0537] Step 11:

[0538] The device uses voice recognition technology to convert the elderly person's everyday conversations into text data and sends it to a server.

[0539] Step 12:

[0540] The server analyzes the conversation data and uses natural language processing technology to learn the emotions and interests of the elderly.

[0541] Step 13:

[0542] The server generates an empathetic response based on the learning results and sends it to the device, which then provides the generated response to the user in voice or text format.

[0543] Step 14:

[0544] The empathetic response provided to the user provides psychological care and reduces feelings of social isolation, and this process is repeated on an ongoing basis to achieve comprehensive health management.

[0545] Example 1

[0546] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0547] Health management and reducing social isolation among the elderly are important issues in modern society. Effective collection and analysis of elderly biometric data and daily activity data is required for early detection of abnormalities and the provision of customized exercise programs. However, there is a lack of systems that can monitor this data in real time and respond appropriately. As a result, elderly people are at risk of not receiving appropriate care and their health condition worsening. Furthermore, a system that provides empathetic conversation is also needed to reduce social isolation.

[0548] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0549] In this invention, the server includes means for collecting biometric data of the elderly person, means for transmitting the collected biometric data to the server, means for storing the biometric data received by the server in a database, means for analyzing the stored biometric data, means for modeling the elderly person's daily behavioral patterns based on the analysis results, means for detecting abnormalities, means for generating an alert when an abnormality is detected, means for generating an exercise program suitable for the elderly person, and means for providing the generated exercise program to the elderly person, means for collecting activity data in real time and transmitting it to the server, means for analyzing the collected data in real time and detecting abnormalities, means for sending a notification to medical services or family members when an abnormality is detected, means for collecting conversation data and generating an empathetic response, and means for providing empathetic conversation based on the collected data. This enables comprehensive management of the elderly person's health status, detection of abnormalities in real time, and prompt response. Furthermore, providing empathetic conversation can reduce social isolation and slow the progression of dementia.

[0550] "Biometric data" refers to data that represents a person's physiological state, such as an elderly person's heart rate, number of steps, activity level, and sleep patterns.

[0551] The "server" is a computer system installed on the cloud that receives, analyzes, and stores data sent by elderly people.

[0552] A "database" is a data structure managed by a server for systematically storing received biometric data and activity data.

[0553] A "machine learning algorithm" is a computer program that analyzes data and models the daily behavioral patterns of older adults.

[0554] An "exercise program" is a plan that includes exercise and stretching menus suitable for seniors, and is created with the aim of improving their health.

[0555] An "alert" is a warning notification that is generated when an abnormality is detected, intended to notify medical services or family members.

[0556] "Activity data" is information collected during exercise, such as real-time heart rate and movement data.

[0557] "Empathetic responses" are sympathetic dialogues for elderly people that are generated based on past conversations and behavioral trends.

[0558] "Speech recognition technology" is a technology that allows a terminal to convert a user's everyday conversation into text.

[0559] The present invention relates to a comprehensive health management system for the elderly. The system collects and analyzes biometric data of the elderly, provides customized exercise programs, and generates and provides empathetic conversations to reduce social isolation and slow the progression of dementia.

[0560] System configuration

[0561] The system consists of the following components:

[0562] Devices: Refers to wearable devices, smartphones, and tablets used daily by seniors. These devices collect biometric data and transmit it to a server. They also serve as an interface for providing exercise instructions and empathetic conversations via visual and audio displays.

[0563] Server: Installed in the cloud, it receives, analyzes, and stores data sent by the elderly. It also generates alerts for anomaly detection, creates exercise programs, and generates empathetic responses. The server uses a database to store the received data and performs data analysis using machine learning algorithms.

[0564] Hardware and software used

[0565] Wearable devices: Collect biometric data such as heart rate, steps, activity levels, and sleep patterns.

[0566] Smartphone / Tablet: Provides communication means for sending data to the server and acts as an interface for exercise instructions and empathetic conversation.

[0567] Cloud server: Stores and analyzes data, generates exercise programs, generates alerts, and generates empathetic conversations.

[0568] Database: A data structure for organizing and storing data within a server.

[0569] Machine learning algorithms: Programs that analyze incoming data, model behavioral patterns, and detect anomalies.

[0570] Example of operation

[0571] Specifically, the system works as follows:

[0572] When an elderly person wakes up in the morning, the wearable device records their heart rate and sleep duration.

[0573] The terminal sends this to the server.

[0574] The server analyzes the data and detects any recent disruptions to sleep patterns.

[0575] Based on the results, the server adjusts the exercise program and sends a new exercise plan to the user's terminal.

[0576] The device suggests gentle, non-challenging exercises and provides on-screen instructions and video guides.

[0577] While the user is exercising, the device transmits real-time heart rate and movement data to the server.

[0578] The server monitors the data and immediately generates an alert if there are any health abnormalities, notifying the user of the necessary medical services.

[0579] The device interacts with seniors on a daily basis, reducing feelings of social isolation through empathetic conversation.

[0580] Examples of prompt statements

[0581] "We would like you to design and implement a system that generates appropriate exercise programs and provides empathetic conversations based on the analysis of biometric data collected from wearable devices, smartphones, and tablets used daily by the elderly. In particular, we would like it to include a function that collects data on heart rate, sleep patterns, etc. in real time to monitor the elderly's health and generate alerts when abnormalities are detected."

[0582] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0583] Step 1:

[0584] The terminal records the elderly person's biometric data, such as heart rate, number of steps, activity level, and sleep patterns, in real time. This data is obtained from a wearable device. Specifically, the terminal measures the subject's biometric indicators at regular intervals (e.g., every 10 minutes). The input is the elderly person's biometric data, and the output is the recorded biometric data.

[0585] Step 2:

[0586] The device transmits the recorded biometric data to the server at regular intervals (for example, once an hour). The transmitted data consists of heart rate, number of steps, activity level, sleep patterns, etc. The input is the recorded biometric data, and the output is the data to be transmitted to the server. Specifically, the device establishes a network connection with the server and transfers the data.

[0587] Step 3:

[0588] The server stores the received biometric data in a database. In the database, a table is created for each elderly person and the data is stored there. The input is the biometric data sent to the server, and the output is the data stored in the database. Specifically, the server checks the integrity of the data and inserts it into the database in the appropriate format.

[0589] Step 4:

[0590] The server uses machine learning algorithms to analyze the biometric data and model behavioral patterns. This analysis uses a time series analysis algorithm. The input is the biometric data stored in the database, and the output is a behavioral pattern model as a result of the analysis. Specifically, the server retrieves the data and applies the algorithm to analyze it.

[0591] Step 5:

[0592] The server detects anomalies based on the analysis results. This anomaly detection uses preset reference values ​​and behavioral pattern models. The input is the analysis results and reference values, and the output is a flag indicating whether or not an anomaly exists. Specifically, the server checks for the presence of an abnormal pattern based on the analysis results.

[0593] Step 6:

[0594] If an abnormality is detected, the server generates an alert and sends a notification to the necessary medical services or family members. The input is the abnormality detection flag, and the output is the generated alert and notification. The specific operations are to generate an alert statement and send a notification.

[0595] Step 7:

[0596] The server generates an exercise program based on the analysis results of the biometric data. This program is customized according to the user's activity level and health condition. The input is the analysis results, and the output is the exercise program. Specifically, the server selects an appropriate exercise menu and builds it into a program.

[0597] Step 8:

[0598] The device then provides the generated exercise program to the elderly. The input is the exercise program, and the output is displayed exercise instructions and video guides. Specifically, the device displays the exercise instructions on the screen and plays video guides as needed.

[0599] Step 9:

[0600] While the user is exercising, the device collects heart rate and movement data in real time. The input is the biometric data during exercise, and the output is the collected data. Specifically, the device performs high-frequency data sampling in real time.

[0601] Step 10:

[0602] The collected data is transmitted to the server in real time. The input is the collected real-time biometric data, and the output is the data to be sent to the server. Specifically, the device re-establishes a network connection with the server and transmits the data.

[0603] Step 11:

[0604] The server analyzes the received data in real time, and if an abnormality is detected, it immediately generates an alert and sends a notification. The input is the biometric data sent in real time, and the output is the alert and notification. Specifically, the server performs rapid data analysis, and if an abnormality is detected, it immediately sends a notification.

[0605] Step 12:

[0606] The device provides empathetic conversations with elderly people through everyday interactions. The input is conversation data with the user, and the output is a generated empathetic response. Specifically, the device uses voice recognition technology to send the conversation content to a server and provides the response received from the server to the user.

[0607] (Application example 1)

[0608] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0609] Health management for the elderly is a significant issue, and appropriate health monitoring and reduction of social isolation are required, especially for elderly people living alone or shopping in brick-and-mortar stores. Furthermore, elderly people are sensitive to specific health conditions, and customized exercise programs are needed. However, conventional systems have had difficulty integrating these into the elderly's daily lives. Therefore, there is a need to develop a system that allows elderly people to smoothly manage their health and receive appropriate exercise and psychological care.

[0610] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0611] In this invention, the server includes means for collecting biometric data of the elderly person, means for transmitting the collected biometric data to the server, means for analyzing the biometric data received by the server, means for detecting abnormalities based on the analysis results, means for generating an alert when an abnormality is detected, means for generating an exercise program suitable for the elderly person, means for providing the generated exercise program to the elderly person, means for collecting activity data and transmitting it to the server, means for collecting conversation data and generating empathetic responses, means for providing empathetic conversation based on the collected data, a robot that supports health management through dialogue with the elderly person in a physical store, and means for linking the robot with the elderly person's wearable device or smartphone. This allows the elderly person to receive health management in a physical store, and enables them to reduce their sense of social isolation and maintain their health properly through suggested exercise programs and empathetic conversation.

[0612] "Biometric data" refers to measurements of an older adult's physical condition and behavior, such as heart rate, number of steps taken, activity level, and sleep patterns.

[0613] "Means of collection" refers to devices and functions that collect biometric data from elderly people using wearable devices and in-home sensors.

[0614] A "server" is a computer system that receives, analyzes, and stores collected biometric data.

[0615] "Means for analysis" refers to the technology and algorithms used by the server to process the biometric data received and analyze the health status and behavioral patterns of the elderly person.

[0616] An "anomaly detection means" is a system that identifies health conditions that deviate from normal patterns in the analyzed data and reports the problem.

[0617] "Means for generating alerts" refers to the function of issuing a warning when an abnormality is detected and notifying relevant parties and medical services.

[0618] An "exercise program" is an exercise plan proposed based on the health status of the elderly person and the results of data analysis.

[0619] "Means for providing" refers to the methods and interfaces used to inform elderly people of the generated exercise program and enable them to carry it out.

[0620] "Activity Data" refers to information about your exercise, daily movements, and physical activity.

[0621] "Conversation data" refers to information collected as text or audio of conversations with elderly people.

[0622] "Empathic response" refers to feedback that provides appropriate responses to emotionally understand and support the conversations and actions of older people.

[0623] A "brick and mortar store" is a physical location, such as a retail store or service location, where seniors can visit and receive services in person.

[0624] A "robot" is an autonomous or remotely controlled mechanical device that assists elderly people in managing their health through dialogue within a physical store.

[0625] A "wearable device" is an electronic device that can be worn on the body and collects biometric data such as heart rate and activity level.

[0626] This invention relates to a comprehensive health management system for the elderly. This system collects and analyzes biometric data from the elderly, provides customized exercise programs, and generates and delivers empathetic conversations to monitor the elderly's health status in real time, aiming to reduce social isolation and slow the progression of dementia.

[0627] System configuration

[0628] The system mainly consists of the following elements:

[0629] 1. Devices: These devices are wearable devices, smartphones, and tablets used daily by seniors. These devices collect biometric data and transmit it to a server. They also serve as an interface for providing exercise instructions and empathetic conversations via visual and audio displays.

[0630] 2. Server: Installed on the cloud, it receives, analyzes, and stores data sent by the elderly. It also generates alerts for abnormality detection, creates exercise programs, and generates empathetic responses.

[0631] 3. Robot: A device that supports health management through dialogue with the elderly in physical stores. The robot connects to the elderly's wearable devices and smartphones to collect health data, display analysis results, provide exercise support, and engage in empathetic dialogue.

[0632] Data collection and transmission

[0633] The devices (wearable devices and in-home sensors) record elderly people's biometric data, such as heart rate, number of steps, activity level, and sleep patterns, in real time. The devices then transmit this data to a server at regular intervals.

[0634] Data Analysis and Anomaly Detection

[0635] The server stores the received biometric data in a database and analyzes it using machine learning algorithms to model the elderly person's daily behavioral patterns and generate alerts if any abnormalities are detected.

[0636] Providing customized exercise programs

[0637] Based on the analysis results, the server generates an exercise program suitable for the elderly. For example, if the analysis reveals that the elderly person has not been exercising recently, the server will suggest light stretching and walking exercises.

[0638] Real-time monitoring and alerts

[0639] While the user is exercising, the device collects real-time heart rate and movement data and sends it to a server, which analyzes the data and immediately generates an alert if an abnormality is detected, notifying necessary medical services or family members.

[0640] Empathetic conversation and guidance

[0641] The device converts everyday conversations into text data using voice recognition technology and sends it to a server. The server analyzes this conversation data and learns from past comments and behavioral trends. Based on the learning results, it generates empathetic responses and provides them to the elderly via the device.

[0642] Specific examples

[0643] For example, suppose a 70-year-old person visits a physical store and begins a conversation with a robot. The robot obtains data such as the user's heart rate and number of steps from the server in advance, and tells them, "You haven't been getting enough exercise lately, so I suggest you walk for 30 minutes today." While the user is exercising, the robot monitors their health data in real time and immediately issues an alert if there is an abnormality. The robot also provides emotional care to the user by engaging in empathetic conversation, such as asking, "Is there anything I can help you with today?"

[0644] Example prompts for generative AI models

[0645] Prompt: Create a scenario in which a healthcare assistant robot for the elderly visits a store and offers a customized exercise program based on the individual's heart rate and step count, and engages in empathetic conversation with the individual.

[0646] scenario:

[0647] When an elderly person visits the store, the robot greets them with "Hello, how are you?" The robot retrieves health data collected in real time from the server and analyzes it. It suggests, "It seems you haven't been getting enough exercise lately, so I suggest you take a 30-minute walk today. May I help you?" During the exercise, the robot encourages the person by saying, "Your heart rate is within the normal range. That's great!" and after the exercise, it engages in empathetic conversation by asking, "Is there anything I can help you with today?"

[0648] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0649] Step 1:

[0650] A user visits a physical store and begins interacting with a robot.

[0651] Input: A user enters a physical store and approaches the robot.

[0652] Processing: The robot recognizes the elderly user and provides a welcome message by displaying or speaking.

[0653] Output: The robot says "Hello, how are you?"

[0654] Step 2:

[0655] The device collects biometric data and transmits it to a server.

[0656] Input: The terminal (wearable device) collects biometric data such as heart rate, steps taken, and activity level.

[0657] Processing: The terminal transmits this data to the server at regular intervals.

[0658] Output: The latest biometric data is sent to the server and stored in a database.

[0659] Step 3:

[0660] The server analyzes the received biometric data.

[0661] Input: New biometric data sent to the server.

[0662] Processing: Using machine learning algorithms to analyze data and assess health and behavioral patterns.

[0663] Output: The analysis results will provide the elderly person's current health status and behavioral patterns.

[0664] Step 4:

[0665] Based on the analysis results, anomalies are detected and alerts are generated.

[0666] Input: Analysis result data.

[0667] Processing: If an abnormal pattern is detected, the server generates an alert and sends a notification to medical services and family members.

[0668] Output: An alert notification is sent to medical services and / or family members.

[0669] Step 5:

[0670] The server generates an exercise program suitable for the elderly.

[0671] Input: Analysis results, individual user health status data.

[0672] Processing: Based on these data, the server generates a customized exercise program for the elderly.

[0673] Output: A customized exercise program is generated and sent to the user's device.

[0674] Step 6:

[0675] The terminal provides the generated exercise program to the user.

[0676] Input: The generated exercise program.

[0677] Processing: The device displays exercise instructions and video guides on the screen and provides audio instructions.

[0678] Output: The user can see and hear the exercise program.

[0679] Step 7:

[0680] The user performs the exercise and the device collects data in real time.

[0681] Input: The exercise the user is performing, and movement data from the wearable device.

[0682] Processing: The device continues to record heart rate and movement data in real time and transmits it to the server.

[0683] Output: Real-time data is sent to a server and continuously monitored.

[0684] Step 8:

[0685] The server analyzes the real-time data and generates an empathetic response.

[0686] Inputs: Real-time data and historical conversation data.

[0687] Processing: Using machine learning algorithms to analyze this data and generate empathetic responses.

[0688] Output: The generated empathetic response is sent to the robot or device.

[0689] Step 9:

[0690] Robots provide interaction for the elderly through empathetic conversation.

[0691] Input: The generated empathic response.

[0692] Processing: The robot will respond to the user with empathetic comments such as, "Is there anything I can help you with today?"

[0693] Output: Elderly people can interact with the robot and receive psychological care.

[0694] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0695] The present invention relates to a system that comprehensively supports the health management of the elderly. It aims to monitor the overall health status of the elderly in real time and prevent social isolation and the progression of dementia by combining the collection, analysis, and abnormality detection of the elderly's biometric data, the provision of customized exercise programs, the generation of empathetic responses, and an emotion engine.

[0696] 1. System Configuration

[0697] The system consists of the following elements:

[0698] Devices: Wearable devices, smartphones, and tablets used daily by seniors. These devices collect biometric and emotional data and provide an interface to transmit it to a server.

[0699] Server: A cloud-based computer system that receives, analyzes, and stores biometric and emotional data, performing tasks such as anomaly detection, generating exercise programs, and providing empathetic responses.

[0700] Emotion engine: A system that analyzes the user's voice data and facial expression data to recognize their emotional state and transmits that data to a server.

[0701] 2. Data collection and transmission

[0702] The devices (wearable devices and in-home sensors) record biometric data such as the elderly person's heart rate, number of steps, activity level, and sleep patterns in real time, and also collect voice and facial expression data, which are then sent to a server.

[0703] 3. Data Analysis and Anomaly Detection

[0704] The server stores the received biometric and emotional data in a database. It then uses machine learning algorithms to analyze this data and model the user's daily behavioral patterns and emotional state. For example, if an elderly person normally speaks in a calm voice but has recently started to speak in a lower tone, the emotion engine will detect this and notify the server, which will recognize this as an anomaly and generate an alert.

[0705] 4. Providing customized exercise programs

[0706] Based on the analysis, the server generates an exercise program tailored to the individual, taking into account their individual health status, past exercise history, and emotional state. For example, if they are feeling depressed, it might suggest relaxation and gentle stretching exercises.

[0707] 5. Real-time monitoring and alerts

[0708] While the user is exercising, the device collects real-time heart rate, movement data, and emotional state and sends it to a server that analyzes the data and immediately generates an alert if an abnormality is detected, notifying necessary medical services or family members.

[0709] 6. Empathetic conversation and guidance

[0710] The device converts everyday conversations into text data using voice recognition technology and sends it to the server. The server analyzes this conversation data and uses natural language processing technology to learn the elderly person's emotions and interests. The emotion engine also integrates the emotional data obtained to generate empathetic responses tailored to the elderly person's emotional state. For example, it can respond in line with the user's emotions, such as, "You seem a little down today. Is there anything I can help you with?"

[0711] Specific examples

[0712] The following specific scenarios illustrate embodiments of the present invention:

[0713] When a user wakes up in the morning, the wearable device records their heart rate and sleep duration, and the emotion engine analyzes their voice tone and facial expressions to obtain emotional data.

[0714] The terminal transmits this data to the server.

[0715] The server analyzes this data to detect recent disruptions in sleep patterns and emotional states.

[0716] Based on the results, the server adjusts the exercise program and relaxation plan and sends the new exercise plan to the user's device.

[0717] The device suggests gentle, non-challenging exercises and relaxation exercises to the user, and displays on-screen instructions and video guides.

[0718] While the user is exercising, the device transmits real-time heart rate, movement data, and emotional state to the server.

[0719] The server monitors the data and immediately generates alerts if there are any abnormalities in health or emotional state, notifying necessary medical services or family members.

[0720] The device generates empathetic responses and provides dialogue through everyday conversations with the user, while at the same time providing more empathetic and appropriate responses by taking into account the emotional state recognized by the emotion engine.

[0721] As described above, the system of the present invention collects and analyzes biometric and emotional data of elderly people, and provides customized exercise programs and empathetic conversations, thereby realizing comprehensive health management.

[0722] The processing flow will be explained below.

[0723] Step 1:

[0724] The terminal (wearable device) collects biometric and emotional data such as heart rate, number of steps, activity level, sleep patterns, voice data, and facial expression data of the elderly in real time. For example, the wearable device collects this data periodically throughout the day.

[0725] Step 2:

[0726] The device transmits the collected biometric and emotional data to a server via Wi-Fi or Bluetooth, and the data is encrypted to protect privacy.

[0727] Step 3:

[0728] The server organizes and stores the received biometric and emotional data in a cloud database, where the data is categorized by date and time.

[0729] Step 4:

[0730] The server analyzes the stored data using machine learning algorithms (e.g., clustering algorithms and time series analysis algorithms) to model the elderly's daily behavioral patterns and emotional states.

[0731] Step 5:

[0732] Based on the analysis results, the server detects outliers or abnormal patterns (e.g., sudden increases in heart rate or sudden changes in facial expression) that deviate from normal behavioral patterns and emotional states.

[0733] Step 6:

[0734] If an anomaly is detected, the server generates an alert and notifies family members or medical services. For example, if an elderly person's emotional state suddenly worsens, an urgent notification will be sent.

[0735] Step 7:

[0736] The server generates a customized exercise program based on the elderly person's daily behavioral patterns and emotional state, taking into account their individual health status, past exercise history, and emotional state.

[0737] Step 8:

[0738] The server sends the generated exercise program to the device, which displays the received program and provides detailed exercise instructions and video guides.

[0739] Step 9:

[0740] When a user exercises, the device collects real-time heart rate, movement data, and emotional state and transmits this information to a server, allowing the user to monitor their condition during exercise.

[0741] Step 10:

[0742] The server reanalyzes the exercise and emotional state data to check for any abnormalities, and if any are detected, immediate action is taken.

[0743] Step 11:

[0744] The terminal uses voice recognition technology to convert the user's everyday conversation into text data and sends it to the server.

[0745] Step 12:

[0746] The server uses natural language processing technology to analyze the conversation data it receives and learns the emotions and interests of the elderly. It also integrates and analyzes voice tone and facial expression data obtained by the emotion engine.

[0747] Step 13:

[0748] The server generates an empathetic response based on the learning results and the user's emotional state and sends it to the device. For example, if the user is feeling depressed, it generates a response such as, "Try doing some exercises to relax today."

[0749] Step 14:

[0750] The device generates an empathetic response and provides it to the user via voice or text. The empathetic response provides psychological care and reduces feelings of social isolation. This process is repeated continuously to achieve comprehensive health management.

[0751] Example 2

[0752] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0753] Health management for the elderly is extremely important, but they have difficulty managing themselves and regular checkups at medical institutions are limited. This can lead to health conditions being overlooked and lack of continuous care. It can also increase the risk of social isolation and the progression of dementia. Furthermore, there is often a lack of provision of exercise programs tailored to health conditions and empathetic responses to emotions. A system to comprehensively solve these problems is needed.

[0754] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0755] In this invention, the server includes a means for analyzing biometric information, a means for detecting abnormalities, a means for generating an exercise program, a means for collecting dialogue information and generating empathetic responses, and a means for monitoring information in real time and detecting abnormalities. This enables comprehensive collection and analysis of biometric data and emotional data of elderly people, enabling real-time monitoring of their health status. Furthermore, by providing customized exercise programs and responding empathetically to their emotions, it is possible to reduce social isolation and slow the progression of dementia.

[0756] "Biometric information" refers to data that indicates the health status of elderly people, such as their heart rate, number of steps taken, activity level, and sleep patterns.

[0757] The "Central Processing Unit" is a cloud-based computer system that is responsible for analyzing, storing, and managing received biometric and emotional data.

[0758] An "exercise program" is a customized exercise and relaxation program provided to seniors based on their health status and past exercise history.

[0759] An "emotion engine" is a system that analyzes the user's voice data and facial expression data, recognizes their emotional state, and transmits the data to the central processing unit.

[0760] "Empathic responses" refer to words and actions that show empathy and are generated based on the elderly person's emotional state and the content of the conversation.

[0761] An "alert" is a notification or alert issued when the central processing unit detects an abnormality, and is used to notify necessary medical services or family members.

[0762] "Monitoring" refers to the process of monitoring the elderly's biometric and emotional data in real time to check for any abnormalities.

[0763] "Dialogue information" refers to data collected from everyday conversations and voice input with elderly people, and is analyzed by the emotion engine.

[0764] The present invention is a system that comprehensively supports health management for the elderly. This system is composed of a combination of collecting and analyzing biological information from the elderly, detecting abnormalities, providing customized exercise programs, generating empathetic responses, and an emotion engine. Detailed embodiments of this system are described below.

[0765] System configuration

[0766] Devices: These are wearable devices, smartphones, and tablets used daily by seniors. These devices record biometric information such as heart rate, number of steps, activity level, and sleep patterns in real time, and also have the ability to collect voice and facial expression data. The data is sent to a server using Bluetooth or Wi-Fi.

[0767] Server: A cloud-based computer system that receives, analyzes, and stores biometric and emotional data. The server analyzes the data using machine learning algorithms developed in Python (e.g., scikit-learn and TensorFlow). The server also performs anomaly detection, generates exercise programs, and provides empathetic responses. Specifically, it can use cloud services such as AWS and Google Cloud Platform.

[0768] Emotion engine: A system that analyzes the user's voice data and facial expression data to recognize their emotional state. This sends data corresponding to the user's emotional state to the server. Furthermore, the emotion engine uses technologies such as Google Speech-to-Text, Google BERT, and Google Text-to-Speech.

[0769] Data collection and transmission

[0770] When a user uses a wearable device or smartphone, the device records real-time biometric information such as heart rate, steps taken, activity level, and sleep patterns, as well as voice and facial expression data. This data is then transmitted by the device to a central processing unit.

[0771] Data Analysis and Anomaly Detection

[0772] The server stores the received biometric and emotional data in a database. It then analyzes the data using Python machine learning libraries (e.g., scikit-learn and TensorFlow). If an abnormality is detected based on the analysis results, the server generates an alert and notifies the necessary medical services or family members.

[0773] Providing customized exercise programs

[0774] Based on the analysis results, the server generates an exercise program suitable for the elderly. This program is customized taking into account the elderly's health condition, past exercise history, emotional state, etc. The generated program is provided to the elderly via their device. For example, it may suggest an exercise program that includes relaxation and light stretching.

[0775] Real-time monitoring

[0776] When a user starts exercising, the device collects real-time heart rate, movement data, and emotional state and sends them to the server, which monitors the data and generates an immediate alert if any abnormalities are detected.

[0777] Empathetic conversation and guidance

[0778] The device uses voice recognition technology to convert everyday conversations with the user into text data and sends it to the server. The server then analyzes this conversation data using natural language processing technology. It also integrates emotional data obtained from the emotion engine to generate empathetic responses for the elderly. The generated responses are then conveyed to the user via the device using voice synthesis technology.

[0779] Specific examples

[0780] For example, when an elderly person wakes up in the morning, the wearable device records their heart rate and sleep duration. At the same time, the emotion engine analyzes the user's voice tone and facial expressions to obtain emotional data. This data is sent from the device to a server, which analyzes the data to detect recent disturbances in sleep patterns and emotional state. Based on the results, the server generates a customized exercise program and relaxation plan and sends it to the device. The device then displays instructions and video guides on the screen to encourage the user to follow the plan. During exercise, data is collected in real time, and if an abnormality is detected, an alert is generated immediately. The device also collects daily conversation data, which is analyzed by the server and used to provide empathetic support.

[0781] Prompt Sentence Examples

[0782] "Write Python code to record a user's heart rate, steps, activity, and sleep patterns and detect any anomalies."

[0783] "Design a system to send data acquired from a wearable device to a cloud server and analyze it."

[0784] "Propose an algorithm that generates a customized exercise program based on the emotional state of an elderly person."

[0785] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0786] Program processing flow

[0787] Step 1: Device preparation and data collection

[0788] The user puts on the wearable device or smartphone and launches the app.

[0789] Input: Wearable devices, smartphones

[0790] Output: Collected biometric information (e.g., heart rate, steps, activity level, sleep patterns)

[0791] The device uses sensors to record heart rate, steps, activity levels, and sleep patterns, and stores the data in the app.

[0792] Operation: Data measurement by sensor, data storage in app

[0793] The device collects voice data and facial expression data.

[0794] How it works: Records voice and facial expressions using the built-in microphone and camera

[0795] Step 2: Sending data

[0796] The terminal transmits the collected biometric and emotional data to a central processing unit.

[0797] Input: Collected biometric and emotional data

[0798] Output: Data sent to the central processing unit

[0799] How it works: Sends data using Bluetooth or Wi-Fi

[0800] Step 3: Data analysis

[0801] The server stores the received biometric information and emotion data in a cloud database.

[0802] Input: Transmitted biometric and emotional data

[0803] Output: Data stored in the database

[0804] How it works: Stores data using a database management system (e.g. AWS DynamoDB)

[0805] The server analyzes the data using Python machine learning libraries.

[0806] Input: Stored biometric and emotional data

[0807] Output: Analysis results (e.g., health status evaluation data, anomaly detection results)

[0808] Operation: Perform data analysis using scikit-learn and TensorFlow

[0809] Step 4: Detect anomalies

[0810] The server detects abnormalities based on the analysis results.

[0811] Input: Data analysis results

[0812] Output: Anomaly detection alert

[0813] How it works: Applies anomaly detection algorithms and generates alerts if anomalies are found

[0814] Step 5: Generate the exercise program

[0815] The server generates an exercise program suitable for the elderly.

[0816] Input: Health assessment data, past exercise history, emotional state

[0817] Output: Customized exercise program

[0818] Movement: Applying an algorithm to generate exercise programs that take into account health and emotional states

[0819] The server transmits the generated exercise program to the terminal.

[0820] Input: Customized exercise program

[0821] Output: Exercise program sent to the device

[0822] Operation: Send using a data transmission service (e.g. HTTP API)

[0823] Step 6: Real-time monitoring

[0824] The user begins exercising according to the recommended program.

[0825] Input: Customized exercise program

[0826] Output: Exercise performed

[0827] Movement: Follow the app's guide to complete the exercise

[0828] The device collects heart rate, movement data, and emotional state in real time and transmits this to a server.

[0829] Input: Data collected during exercise

[0830] Output: Real-time data sent to the server

[0831] Actions: Use sensors and voice recognition technology to collect data and transmit it in real time

[0832] Step 7: Empathetic conversation and guidance

[0833] The terminal uses voice recognition technology to convert everyday conversations with the user into text data and sends it to the server.

[0834] Input: Speech data of everyday conversation

[0835] Output: Text data sent to the server

[0836] How it works: Uses a speech recognition engine (e.g. Google Speech-to-Text)

[0837] The server analyzes this conversation data using natural language processing technology. It also integrates emotional data obtained from the emotion engine to generate an empathetic response. For example, "You seem to be feeling unwell today. Is there anything I can help you with?" The generated response is then conveyed to the user via the device using voice synthesis technology.

[0838] Input: Conversation data, emotion data

[0839] Output: Empathic response

[0840] How it works: Uses natural language processing technology (e.g., Google BERT) and speech synthesis technology (e.g., Google Text-to-Speech)

[0841] The above is a detailed processing flow of the system program. Through the specific operations at each step, the health management of elderly people can be effectively carried out.

[0842] (Application example 2)

[0843] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0844] Health management for the elderly requires real-time monitoring and appropriate responses, especially while on the move. However, current health management systems lack the functionality to respond to situations and environments while on the move, and do not provide sufficient support to ensure the safety of the elderly. They also lack the ability to respond empathetically to fluctuations in emotional states or provide exercise programs adapted to individual health conditions. This poses a challenge, making it difficult to immediately respond to deterioration in health or accidents while the elderly are on the move.

[0845] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting biometric data and emotional data of the elderly person, means for transmitting the collected biometric data and emotional data to the server, means for analyzing the biometric data and emotional data received by the server, means for detecting abnormalities based on the analysis results, means for generating an alert when an abnormality is detected, means for generating an exercise program suitable for the elderly person, means for providing the generated exercise program to the elderly person, means for collecting activity data and emotional data in real time and transmitting it to the server, means for generating an empathetic response based on the emotional data, means for providing empathetic conversation based on the collected data, and means for monitoring the health status of the elderly person while traveling in an autonomous vehicle and generating an appropriate response. This enables real-time monitoring of the elderly person's health status while traveling, empathetic responses according to their emotional state, and immediate response in the event of an abnormality.

[0846] "Biometric data" refers to data that indicates the physical condition of an elderly person, such as their heart rate, number of steps taken, activity level, and sleep patterns.

[0847] "Emotion data" is data that indicates the emotional state of the elderly person, obtained from their tone of voice and facial expressions.

[0848] The "server" is a cloud-based computer system that receives, analyzes, and stores collected biometric and emotional data, and performs anomaly detection and generates exercise programs.

[0849] "Analysis" is the process of using the received biometric and emotional data to model the elderly person's daily behavioral patterns and emotional state and detect abnormalities.

[0850] "Abnormal" refers to a situation in which the health condition of an elderly person is determined to be outside the normal range based on the analysis results.

[0851] An "alert" is a warning notification to seniors, their families, and medical services that is generated when an abnormality is detected.

[0852] An "exercise program" is a plan of exercise and relaxation tailored to the individual health condition of an elderly person based on analyzed biometric and emotional data.

[0853] "Activity data" refers to data related to the daily movements and behaviors of elderly people.

[0854] An "empathic response" is a system response that aims to understand the emotional state of the elderly person and respond appropriately.

[0855] An "autonomous vehicle" is a vehicle that can drive under its own control without the need for driver operation.

[0856] "Monitoring" is the process of monitoring biometric and emotional data in real time.

[0857] "Real-time" refers to the timing of data being collected and processed and analyzed immediately.

[0858] "Immediate response to abnormalities" is the process of quickly generating an alert and taking appropriate action when an abnormality in health status is detected.

[0859] The present invention is a system that comprehensively supports the health management of the elderly. It aims to monitor the overall health status of the elderly in real time by collecting biometric and emotional data of the elderly, analyzing it, detecting abnormalities, providing customized exercise programs, generating empathetic responses, and combining it with an emotion engine, thereby preventing social isolation and the progression of dementia.

[0860] System configuration

[0861] The system consists of the following elements:

[0862] Devices: Wearable devices, smartphones, tablets, and in-vehicle devices used daily by seniors. These devices collect biometric and emotional data and provide an interface to transmit it to a server.

[0863] Server: A cloud-based computer system that receives, analyzes, and stores biometric and emotional data, performing tasks such as anomaly detection, generating exercise programs, and providing empathetic responses.

[0864] Emotion engine: A system that analyzes the user's voice data and facial expression data to recognize their emotional state and transmits that data to a server.

[0865] Data collection and transmission

[0866] The devices used by the elderly (wearable devices and in-vehicle devices for autonomous vehicles) record biometric data such as heart rate, number of steps, activity level, and sleep patterns in real time. They also collect voice and facial expression data, which are then sent to a server where they are securely and efficiently stored.

[0867] Data Analysis and Anomaly Detection

[0868] The server stores the received biometric and emotional data in a database. It then uses machine learning algorithms to analyze this data and model the user's daily behavioral patterns and emotional state. For example, if an elderly person who normally speaks calmly has recently started to speak in a lower tone, the emotion engine will detect this and the server will recognize it as an anomaly and generate an alert.

[0869] Providing customized exercise programs

[0870] Based on the analysis, the server generates an appropriate exercise program for the elderly, taking into account their individual health and activity data, as well as their emotional state. For example, if they are feeling depressed, it might suggest relaxation and gentle stretching exercises.

[0871] Real-time monitoring and alerts

[0872] While the elderly exercise, the device collects real-time heart rate, movement data, and emotional state and sends it to a server that analyzes the data and immediately generates an alert if an abnormality is detected, notifying medical services and family members.

[0873] Empathetic conversation and guidance

[0874] The device converts everyday conversations into text data using voice recognition technology and sends it to the server. The server analyzes this conversation data and uses natural language processing technology to learn the emotions and interests of the elderly person. The emotion engine also integrates the emotional data obtained to generate empathetic responses tailored to the elderly person's emotional state. For example, it is possible to provide empathetic responses such as, "You seem a little tired today. Is there anything I can do to help you relax?"

[0875] Application to autonomous vehicles

[0876] The device installed in the self-driving vehicle monitors the elderly person's biometric and emotional data in real time while they are traveling. The collected data is sent to a server via the in-vehicle device, and appropriate responses or guidance are provided as needed. For example, if the heart rate increases while traveling, the display will say, "Try taking deep breaths."

[0877] Specific examples

[0878] 1. Situation: Elderly people use self-driving vehicles to go to the hospital.

[0879] 2. Data collection: In-car devices record heart rate and voice tones in real time.

[0880] 3. Data analysis: The server analyzes the data and detects anomalies.

[0881] 4. Response generation: Generate appropriate alerts and display relaxation suggestions on the vehicle's display. The voice system will instruct the driver to "take a deep breath."

[0882] Prompt Sentence Examples

[0883] "Analyze real-time data including the health status of seniors to detect abnormal heart rates and stress levels. Generate example responses within self-driving vehicles."

[0884] "Create an example of a customized exercise program for seniors while they are in an autonomous vehicle. Based on the user's heart rate and emotional data, suggest appropriate relaxation techniques."

[0885] As described above, the system of the present invention collects and analyzes biometric and emotional data of elderly people in real time, enabling safe and efficient health management even while on the move.

[0886] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0887] Step 1:

[0888] When a user gets into an autonomous vehicle, the terminal (in-vehicle device) automatically starts up. The terminal is equipped with various sensors (heart rate sensor, camera, microphone) to collect biometric and emotional data of the elderly person. The sensors begin to record the user's heart rate, voice tone, and facial expressions in real time. This data is collected as biometric and emotional data.

[0889] Input: Heart rate, voice tone, and facial expression data of elderly people

[0890] Output: Collected biometric and emotional data

[0891] Step 2:

[0892] The device transmits the collected biometric and emotional data to a cloud server. The data is transmitted securely using an encrypted protocol, ensuring that all data reaches the server in real time.

[0893] Input: Collected biometric and emotional data

[0894] Output: Transmitted biometric and emotional data (stored on the server)

[0895] Step 3:

[0896] The server analyzes the received biometric and emotional data. It uses a generative AI model to quickly analyze the data and model the elderly person's daily behavioral patterns and emotional state. Based on the analysis results, it determines whether any abnormalities have been detected.

[0897] Input: Transmitted biometric and emotional data

[0898] Output: Analysis results (normal or abnormal)

[0899] Step 4:

[0900] If the server detects an abnormality, it immediately generates an alert, which is then sent to medical services and the elderly person's family via SMS, email, app notification, etc.

[0901] Input: Analysis result (abnormal)

[0902] Output: Generated alert (notifying medical services and family)

[0903] Step 5:

[0904] Based on the analysis results, the server generates an exercise program tailored to the elderly, taking into account their biometric data, emotional data, and past exercise history. The generated program includes suggestions for relaxation techniques and light exercise.

[0905] Input: Analysis results (biometric data, emotional data)

[0906] Output: Generated exercise program

[0907] Step 6:

[0908] The generated exercise program is provided to the user via the device. Exercise instructions are displayed on the device's display and audio guidance is also provided. The user then performs exercises and relaxation exercises that can be easily performed in the car.

[0909] Input: Generated exercise program

[0910] Output: Providing an exercise program to the user

[0911] Step 7:

[0912] The device continuously collects heart rate, movement data, and emotional state during exercise, and transmits the collected data in real time to a server, which then analyzes the data and monitors the user's health.

[0913] Input: Biometric and emotional data during exercise

[0914] Output: Collected exercise data (sent to server)

[0915] Step 8:

[0916] The server evaluates the effectiveness of the exercise program based on the new data collected and adjusts the exercise program as needed.

[0917] Input: Collected exercise data

[0918] Output: Evaluation results and program adjustments

[0919] Step 9:

[0920] The device converts everyday conversations into text data using voice recognition technology and sends it to a server, which then uses natural language processing technology to learn the elderly's emotions and interests, and integrates the obtained emotional data into an emotion engine.

[0921] Input: Speech data of everyday conversation

[0922] Output: Text data (sent to server)

[0923] Step 10:

[0924] The server generates an empathetic response based on the emotional data. The generated response is provided to the user via the device, providing empathetic conversation such as, "You seem a little tired today. Is there anything I can help you relax?"

[0925] Input: Emotion data, text data

[0926] Output: Empathetic response (provided to user)

[0927] Through the above processing steps, the system of the present invention collects and analyzes biometric and emotional data of elderly people, realizing safe and effective health management even while on the move.

[0928] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0929] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0930] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0931] [Third embodiment]

[0932] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0933] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0934] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0935] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0936] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0937] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0938] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0939] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0940] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0941] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0942] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0943] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0944] This invention relates to a comprehensive health management system for the elderly. The system collects and analyzes biometric data, provides customized exercise programs, and generates and delivers empathetic conversations to monitor the health status of the elderly in real time, aiming to reduce social isolation and slow the progression of dementia.

[0945] 1. System Configuration

[0946] The system mainly consists of the following components:

[0947] Devices: Refers to wearable devices, smartphones, and tablets used daily by seniors. These devices collect biometric data and transmit it to a server. They also serve as an interface for providing exercise instructions and empathetic conversations via visual and audio displays.

[0948] Server: Installed in the cloud, it receives, analyzes, and stores data sent by the elderly. It also generates alerts for abnormalities, creates exercise programs, and generates empathetic responses.

[0949] 2. Data collection and transmission

[0950] The devices (wearable devices and in-home sensors) record elderly people's biometric data, such as heart rate, number of steps, activity level, and sleep patterns, in real time. The devices then transmit this data to a server at regular intervals.

[0951] 3. Data Analysis and Anomaly Detection

[0952] The server stores the received biometric data in a database. It then uses machine learning algorithms to analyze the data and model the elderly person's daily behavioral patterns. For example, if an elderly person typically wakes up at 7 a.m. and goes to bed at 10 p.m., but has recently started waking up late at night more frequently, the server will detect this as an anomaly and generate an alert.

[0953] 4. Providing customized exercise programs

[0954] Based on the analysis results, the server generates an exercise program suitable for the elderly. For example, if the analysis reveals that the elderly person has not been exercising recently, it will suggest light stretching and walking exercises. This program is sent to the device, and exercise instructions and videos are displayed on the screen to make it easy for the user to follow.

[0955] 5. Real-time monitoring and alerts

[0956] While the user is exercising, the device collects real-time heart rate and movement data and sends it to a server, which analyzes the data and immediately generates an alert if an abnormality is detected, notifying necessary medical services or family members.

[0957] 6. Empathetic conversation and guidance

[0958] The device converts everyday conversations into text data using voice recognition technology and sends it to a server. The server analyzes this conversation data and learns from past statements and behavioral trends. Based on the learning results, it generates empathetic responses and provides them to the elderly person via the device. For example, it can have a conversation like, "You woke up a little earlier than usual today. How are you feeling?"

[0959] Specific examples

[0960] Specifically, elderly people with high levels of care needs will use the following systems:

[0961] When the user wakes up in the morning, the wearable device records their heart rate and sleep duration.

[0962] The terminal sends this to the server.

[0963] The server analyzes this data to detect recent disruptions in sleep patterns.

[0964] Based on the results, the server adjusts the exercise program and sends a new exercise plan to the user's terminal.

[0965] The device suggests gentle, non-challenging exercises for the user and provides on-screen instructions and video guides.

[0966] While the user is exercising, the device transmits real-time heart rate and movement data to the server.

[0967] The server monitors the data and immediately generates an alert if there are any health abnormalities, notifying the user of the necessary medical services.

[0968] The device interacts with the elderly on a daily basis, engaging in empathetic conversations to reduce feelings of social isolation and provide psychological care.

[0969] The above is an embodiment of the present invention, which aims to comprehensively manage the health of elderly people, reduce their sense of social isolation, and slow the progression of dementia.

[0970] The processing flow will be explained below.

[0971] Step 1:

[0972] The device collects real-time biometric data such as the elderly person's heart rate, number of steps taken, activity level, and sleep patterns. For example, a wearable device collects this data periodically throughout the day.

[0973] Step 2:

[0974] The device periodically transmits the collected biometric data to a server via Wi-Fi or Bluetooth, and the data is encrypted to protect privacy.

[0975] Step 3:

[0976] The server stores the received biometric data in a cloud database, where the data is organized by date and time.

[0977] Step 4:

[0978] The server analyzes the stored data and uses machine learning algorithms (e.g., clustering algorithms, time series analysis algorithms) to model the daily behavioral patterns of the elderly.

[0979] Step 5:

[0980] Based on the analysis results, the server detects abnormal values ​​or patterns that deviate from normal lifestyle patterns (e.g., a sudden increase in heart rate or a sudden decrease in sleep time).

[0981] Step 6:

[0982] If an abnormality is detected, the server generates an alert and notifies family members or medical services. For example, if an elderly person's heart rate increases significantly at night, an emergency notification will be sent.

[0983] Step 7:

[0984] Based on the analysis results, the server generates a customized exercise program for each senior, taking into account their individual health condition and past exercise history.

[0985] Step 8:

[0986] The server sends the generated exercise program to the terminal, which displays the received program and provides detailed exercise instructions and video guides to the user.

[0987] Step 9:

[0988] While the user is exercising, the device collects heart rate and movement data in real time and transmits it back to the server, allowing for constant monitoring of the user's condition during exercise.

[0989] Step 10:

[0990] The server reanalyzes the exercise data and checks for any abnormalities. If any abnormalities are detected, immediate action is taken.

[0991] Step 11:

[0992] The device uses voice recognition technology to convert the elderly person's everyday conversations into text data and sends it to a server.

[0993] Step 12:

[0994] The server analyzes the conversation data and uses natural language processing technology to learn the emotions and interests of the elderly.

[0995] Step 13:

[0996] The server generates an empathetic response based on the learning results and sends it to the device, which then provides the generated response to the user in voice or text format.

[0997] Step 14:

[0998] The empathetic response provided to the user provides psychological care and reduces feelings of social isolation, and this process is repeated on an ongoing basis to achieve comprehensive health management.

[0999] Example 1

[1000] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1001] Health management and reducing social isolation among the elderly are important issues in modern society. Effective collection and analysis of elderly biometric data and daily activity data is required for early detection of abnormalities and the provision of customized exercise programs. However, there is a lack of systems that can monitor this data in real time and respond appropriately. As a result, elderly people are at risk of not receiving appropriate care and their health condition worsening. Furthermore, a system that provides empathetic conversation is also needed to reduce social isolation.

[1002] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1003] In this invention, the server includes means for collecting biometric data of the elderly person, means for transmitting the collected biometric data to the server, means for storing the biometric data received by the server in a database, means for analyzing the stored biometric data, means for modeling the elderly person's daily behavioral patterns based on the analysis results, means for detecting abnormalities, means for generating an alert when an abnormality is detected, means for generating an exercise program suitable for the elderly person, and means for providing the generated exercise program to the elderly person, means for collecting activity data in real time and transmitting it to the server, means for analyzing the collected data in real time and detecting abnormalities, means for sending a notification to medical services or family members when an abnormality is detected, means for collecting conversation data and generating an empathetic response, and means for providing empathetic conversation based on the collected data. This enables comprehensive management of the elderly person's health status, detection of abnormalities in real time, and prompt response. Furthermore, providing empathetic conversation can reduce social isolation and slow the progression of dementia.

[1004] "Biometric data" refers to data that represents a person's physiological state, such as an elderly person's heart rate, number of steps, activity level, and sleep patterns.

[1005] The "server" is a computer system installed on the cloud that receives, analyzes, and stores data sent by elderly people.

[1006] A "database" is a data structure managed by a server for systematically storing received biometric data and activity data.

[1007] A "machine learning algorithm" is a computer program that analyzes data and models the daily behavioral patterns of older adults.

[1008] An "exercise program" is a plan that includes exercise and stretching menus suitable for seniors, and is created with the aim of improving their health.

[1009] An "alert" is a warning notification that is generated when an abnormality is detected, intended to notify medical services or family members.

[1010] "Activity data" is information collected during exercise, such as real-time heart rate and movement data.

[1011] "Empathetic responses" are sympathetic dialogues for elderly people that are generated based on past conversations and behavioral trends.

[1012] "Speech recognition technology" is a technology that allows a terminal to convert a user's everyday conversation into text.

[1013] The present invention relates to a comprehensive health management system for the elderly. The system collects and analyzes biometric data of the elderly, provides customized exercise programs, and generates and provides empathetic conversations to reduce social isolation and slow the progression of dementia.

[1014] System configuration

[1015] The system consists of the following components:

[1016] Devices: Refers to wearable devices, smartphones, and tablets used daily by seniors. These devices collect biometric data and transmit it to a server. They also serve as an interface for providing exercise instructions and empathetic conversations via visual and audio displays.

[1017] Server: Installed in the cloud, it receives, analyzes, and stores data sent by the elderly. It also generates alerts for anomaly detection, creates exercise programs, and generates empathetic responses. The server uses a database to store the received data and performs data analysis using machine learning algorithms.

[1018] Hardware and software used

[1019] Wearable devices: Collect biometric data such as heart rate, steps, activity levels, and sleep patterns.

[1020] Smartphone / Tablet: Provides communication means for sending data to the server and acts as an interface for exercise instructions and empathetic conversation.

[1021] Cloud server: Stores and analyzes data, generates exercise programs, generates alerts, and generates empathetic conversations.

[1022] Database: A data structure for organizing and storing data within a server.

[1023] Machine learning algorithms: Programs that analyze incoming data, model behavioral patterns, and detect anomalies.

[1024] Example of operation

[1025] Specifically, the system works as follows:

[1026] When an elderly person wakes up in the morning, the wearable device records their heart rate and sleep duration.

[1027] The terminal sends this to the server.

[1028] The server analyzes the data and detects any recent disruptions to sleep patterns.

[1029] Based on the results, the server adjusts the exercise program and sends a new exercise plan to the user's terminal.

[1030] The device suggests gentle, non-challenging exercises and provides on-screen instructions and video guides.

[1031] While the user is exercising, the device transmits real-time heart rate and movement data to the server.

[1032] The server monitors the data and immediately generates an alert if there are any health abnormalities, notifying the user of the necessary medical services.

[1033] The device interacts with seniors on a daily basis, reducing feelings of social isolation through empathetic conversation.

[1034] Examples of prompt statements

[1035] "We would like you to design and implement a system that generates appropriate exercise programs and provides empathetic conversations based on the analysis of biometric data collected from wearable devices, smartphones, and tablets used daily by the elderly. In particular, we would like it to include a function that collects data on heart rate, sleep patterns, etc. in real time to monitor the elderly's health and generate alerts when abnormalities are detected."

[1036] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1037] Step 1:

[1038] The terminal records the elderly person's biometric data, such as heart rate, number of steps, activity level, and sleep patterns, in real time. This data is obtained from a wearable device. Specifically, the terminal measures the subject's biometric indicators at regular intervals (e.g., every 10 minutes). The input is the elderly person's biometric data, and the output is the recorded biometric data.

[1039] Step 2:

[1040] The device transmits the recorded biometric data to the server at regular intervals (for example, once an hour). The transmitted data consists of heart rate, number of steps, activity level, sleep patterns, etc. The input is the recorded biometric data, and the output is the data to be transmitted to the server. Specifically, the device establishes a network connection with the server and transfers the data.

[1041] Step 3:

[1042] The server stores the received biometric data in a database. In the database, a table is created for each elderly person and the data is stored there. The input is the biometric data sent to the server, and the output is the data stored in the database. Specifically, the server checks the integrity of the data and inserts it into the database in the appropriate format.

[1043] Step 4:

[1044] The server uses machine learning algorithms to analyze the biometric data and model behavioral patterns. This analysis uses a time series analysis algorithm. The input is the biometric data stored in the database, and the output is a behavioral pattern model as a result of the analysis. Specifically, the server retrieves the data and applies the algorithm to analyze it.

[1045] Step 5:

[1046] The server detects anomalies based on the analysis results. This anomaly detection uses preset reference values ​​and behavioral pattern models. The input is the analysis results and reference values, and the output is a flag indicating whether or not an anomaly exists. Specifically, the server checks for the presence of an abnormal pattern based on the analysis results.

[1047] Step 6:

[1048] If an abnormality is detected, the server generates an alert and sends a notification to the necessary medical services or family members. The input is the abnormality detection flag, and the output is the generated alert and notification. The specific operations are to generate an alert statement and send a notification.

[1049] Step 7:

[1050] The server generates an exercise program based on the analysis results of the biometric data. This program is customized according to the user's activity level and health condition. The input is the analysis results, and the output is the exercise program. Specifically, the server selects an appropriate exercise menu and builds it into a program.

[1051] Step 8:

[1052] The device then provides the generated exercise program to the elderly. The input is the exercise program, and the output is displayed exercise instructions and video guides. Specifically, the device displays the exercise instructions on the screen and plays video guides as needed.

[1053] Step 9:

[1054] While the user is exercising, the device collects heart rate and movement data in real time. The input is the biometric data during exercise, and the output is the collected data. Specifically, the device performs high-frequency data sampling in real time.

[1055] Step 10:

[1056] The collected data is transmitted to the server in real time. The input is the collected real-time biometric data, and the output is the data to be sent to the server. Specifically, the device re-establishes a network connection with the server and transmits the data.

[1057] Step 11:

[1058] The server analyzes the received data in real time, and if an abnormality is detected, it immediately generates an alert and sends a notification. The input is the biometric data sent in real time, and the output is the alert and notification. Specifically, the server performs rapid data analysis, and if an abnormality is detected, it immediately sends a notification.

[1059] Step 12:

[1060] The device provides empathetic conversations with elderly people through everyday interactions. The input is conversation data with the user, and the output is a generated empathetic response. Specifically, the device uses voice recognition technology to send the conversation content to a server and provides the response received from the server to the user.

[1061] (Application example 1)

[1062] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1063] Health management for the elderly is a significant issue, and appropriate health monitoring and reduction of social isolation are required, especially for elderly people living alone or shopping in brick-and-mortar stores. Furthermore, elderly people are sensitive to specific health conditions, and customized exercise programs are needed. However, conventional systems have had difficulty integrating these into the elderly's daily lives. Therefore, there is a need to develop a system that allows elderly people to smoothly manage their health and receive appropriate exercise and psychological care.

[1064] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1065] In this invention, the server includes means for collecting biometric data of the elderly person, means for transmitting the collected biometric data to the server, means for analyzing the biometric data received by the server, means for detecting abnormalities based on the analysis results, means for generating an alert when an abnormality is detected, means for generating an exercise program suitable for the elderly person, means for providing the generated exercise program to the elderly person, means for collecting activity data and transmitting it to the server, means for collecting conversation data and generating empathetic responses, means for providing empathetic conversation based on the collected data, a robot that supports health management through dialogue with the elderly person in a physical store, and means for linking the robot with the elderly person's wearable device or smartphone. This allows the elderly person to receive health management in a physical store, and enables them to reduce their sense of social isolation and maintain their health properly through suggested exercise programs and empathetic conversation.

[1066] "Biometric data" refers to measurements of an older adult's physical condition and behavior, such as heart rate, number of steps taken, activity level, and sleep patterns.

[1067] "Means of collection" refers to devices and functions that collect biometric data from elderly people using wearable devices and in-home sensors.

[1068] A "server" is a computer system that receives, analyzes, and stores collected biometric data.

[1069] "Means for analysis" refers to the technology and algorithms used by the server to process the biometric data received and analyze the health status and behavioral patterns of the elderly person.

[1070] An "anomaly detection means" is a system that identifies health conditions that deviate from normal patterns in the analyzed data and reports the problem.

[1071] "Means for generating alerts" refers to the function of issuing a warning when an abnormality is detected and notifying relevant parties and medical services.

[1072] An "exercise program" is an exercise plan proposed based on the health status of the elderly person and the results of data analysis.

[1073] "Means for providing" refers to the methods and interfaces used to inform elderly people of the generated exercise program and enable them to carry it out.

[1074] "Activity Data" refers to information about your exercise, daily movements, and physical activity.

[1075] "Conversation data" refers to information collected as text or audio of conversations with elderly people.

[1076] "Empathic response" refers to feedback that provides appropriate responses to emotionally understand and support the conversations and actions of older people.

[1077] A "brick and mortar store" is a physical location, such as a retail store or service location, where seniors can visit and receive services in person.

[1078] A "robot" is an autonomous or remotely controlled mechanical device that assists elderly people in managing their health through dialogue within a physical store.

[1079] A "wearable device" is an electronic device that can be worn on the body and collects biometric data such as heart rate and activity level.

[1080] This invention relates to a comprehensive health management system for the elderly. This system collects and analyzes biometric data from the elderly, provides customized exercise programs, and generates and delivers empathetic conversations to monitor the elderly's health status in real time, aiming to reduce social isolation and slow the progression of dementia.

[1081] System configuration

[1082] The system mainly consists of the following elements:

[1083] 1. Devices: These devices are wearable devices, smartphones, and tablets used daily by seniors. These devices collect biometric data and transmit it to a server. They also serve as an interface for providing exercise instructions and empathetic conversations via visual and audio displays.

[1084] 2. Server: Installed on the cloud, it receives, analyzes, and stores data sent by the elderly. It also generates alerts for abnormality detection, creates exercise programs, and generates empathetic responses.

[1085] 3. Robot: A device that supports health management through dialogue with the elderly in physical stores. The robot connects to the elderly's wearable devices and smartphones to collect health data, display analysis results, provide exercise support, and engage in empathetic dialogue.

[1086] Data collection and transmission

[1087] The devices (wearable devices and in-home sensors) record elderly people's biometric data, such as heart rate, number of steps, activity level, and sleep patterns, in real time. The devices then transmit this data to a server at regular intervals.

[1088] Data Analysis and Anomaly Detection

[1089] The server stores the received biometric data in a database and analyzes it using machine learning algorithms to model the elderly person's daily behavioral patterns and generate alerts if any abnormalities are detected.

[1090] Providing customized exercise programs

[1091] Based on the analysis results, the server generates an exercise program suitable for the elderly. For example, if the analysis reveals that the elderly person has not been exercising recently, the server will suggest light stretching and walking exercises.

[1092] Real-time monitoring and alerts

[1093] While the user is exercising, the device collects real-time heart rate and movement data and sends it to a server, which analyzes the data and immediately generates an alert if an abnormality is detected, notifying necessary medical services or family members.

[1094] Empathetic conversation and guidance

[1095] The device converts everyday conversations into text data using voice recognition technology and sends it to a server. The server analyzes this conversation data and learns from past comments and behavioral trends. Based on the learning results, it generates empathetic responses and provides them to the elderly via the device.

[1096] Specific examples

[1097] For example, suppose a 70-year-old person visits a physical store and begins a conversation with a robot. The robot obtains data such as the user's heart rate and number of steps from the server in advance, and tells them, "You haven't been getting enough exercise lately, so I suggest you walk for 30 minutes today." While the user is exercising, the robot monitors their health data in real time and immediately issues an alert if there is an abnormality. The robot also provides emotional care to the user by engaging in empathetic conversation, such as asking, "Is there anything I can help you with today?"

[1098] Example prompts for generative AI models

[1099] Prompt: Create a scenario in which a healthcare assistant robot for the elderly visits a store and offers a customized exercise program based on the individual's heart rate and step count, and engages in empathetic conversation with the individual.

[1100] scenario:

[1101] When an elderly person visits the store, the robot greets them with "Hello, how are you?" The robot retrieves health data collected in real time from the server and analyzes it. It suggests, "It seems you haven't been getting enough exercise lately, so I suggest you take a 30-minute walk today. May I help you?" During the exercise, the robot encourages the person by saying, "Your heart rate is within the normal range. That's great!" and after the exercise, it engages in empathetic conversation by asking, "Is there anything I can help you with today?"

[1102] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1103] Step 1:

[1104] A user visits a physical store and begins interacting with a robot.

[1105] Input: A user enters a physical store and approaches the robot.

[1106] Processing: The robot recognizes the elderly user and provides a welcome message by displaying or speaking.

[1107] Output: The robot says "Hello, how are you?"

[1108] Step 2:

[1109] The device collects biometric data and transmits it to a server.

[1110] Input: The terminal (wearable device) collects biometric data such as heart rate, steps taken, and activity level.

[1111] Processing: The terminal transmits this data to the server at regular intervals.

[1112] Output: The latest biometric data is sent to the server and stored in a database.

[1113] Step 3:

[1114] The server analyzes the received biometric data.

[1115] Input: New biometric data sent to the server.

[1116] Processing: Using machine learning algorithms to analyze data and assess health and behavioral patterns.

[1117] Output: The analysis results will provide the elderly person's current health status and behavioral patterns.

[1118] Step 4:

[1119] Based on the analysis results, anomalies are detected and alerts are generated.

[1120] Input: Analysis result data.

[1121] Processing: If an abnormal pattern is detected, the server generates an alert and sends a notification to medical services and family members.

[1122] Output: An alert notification is sent to medical services and / or family members.

[1123] Step 5:

[1124] The server generates an exercise program suitable for the elderly.

[1125] Input: Analysis results, individual user health status data.

[1126] Processing: Based on these data, the server generates a customized exercise program for the elderly.

[1127] Output: A customized exercise program is generated and sent to the user's device.

[1128] Step 6:

[1129] The terminal provides the generated exercise program to the user.

[1130] Input: The generated exercise program.

[1131] Processing: The device displays exercise instructions and video guides on the screen and provides audio instructions.

[1132] Output: The user can see and hear the exercise program.

[1133] Step 7:

[1134] The user performs the exercise and the device collects data in real time.

[1135] Input: The exercise the user is performing, and movement data from the wearable device.

[1136] Processing: The device continues to record heart rate and movement data in real time and transmits it to the server.

[1137] Output: Real-time data is sent to a server and continuously monitored.

[1138] Step 8:

[1139] The server analyzes the real-time data and generates an empathetic response.

[1140] Inputs: Real-time data and historical conversation data.

[1141] Processing: Using machine learning algorithms to analyze this data and generate empathetic responses.

[1142] Output: The generated empathetic response is sent to the robot or device.

[1143] Step 9:

[1144] Robots provide interaction for the elderly through empathetic conversation.

[1145] Input: The generated empathic response.

[1146] Processing: The robot will respond to the user with empathetic comments such as, "Is there anything I can help you with today?"

[1147] Output: Elderly people can interact with the robot and receive psychological care.

[1148] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1149] The present invention relates to a system that comprehensively supports the health management of the elderly. It aims to monitor the overall health status of the elderly in real time and prevent social isolation and the progression of dementia by combining the collection, analysis, and abnormality detection of the elderly's biometric data, the provision of customized exercise programs, the generation of empathetic responses, and an emotion engine.

[1150] 1. System Configuration

[1151] The system consists of the following elements:

[1152] Devices: Wearable devices, smartphones, and tablets used daily by seniors. These devices collect biometric and emotional data and provide an interface to transmit it to a server.

[1153] Server: A cloud-based computer system that receives, analyzes, and stores biometric and emotional data, performing tasks such as anomaly detection, generating exercise programs, and providing empathetic responses.

[1154] Emotion engine: A system that analyzes the user's voice data and facial expression data to recognize their emotional state and transmits that data to a server.

[1155] 2. Data collection and transmission

[1156] The devices (wearable devices and in-home sensors) record biometric data such as the elderly person's heart rate, number of steps, activity level, and sleep patterns in real time, and also collect voice and facial expression data, which are then sent to a server.

[1157] 3. Data Analysis and Anomaly Detection

[1158] The server stores the received biometric and emotional data in a database. It then uses machine learning algorithms to analyze this data and model the user's daily behavioral patterns and emotional state. For example, if an elderly person normally speaks in a calm voice but has recently started to speak in a lower tone, the emotion engine will detect this and notify the server, which will recognize this as an anomaly and generate an alert.

[1159] 4. Providing customized exercise programs

[1160] Based on the analysis, the server generates an exercise program tailored to the individual, taking into account their individual health status, past exercise history, and emotional state. For example, if they are feeling depressed, it might suggest relaxation and gentle stretching exercises.

[1161] 5. Real-time monitoring and alerts

[1162] While the user is exercising, the device collects real-time heart rate, movement data, and emotional state and sends it to a server that analyzes the data and immediately generates an alert if an abnormality is detected, notifying necessary medical services or family members.

[1163] 6. Empathetic conversation and guidance

[1164] The device converts everyday conversations into text data using voice recognition technology and sends it to the server. The server analyzes this conversation data and uses natural language processing technology to learn the elderly person's emotions and interests. The emotion engine also integrates the emotional data obtained to generate empathetic responses tailored to the elderly person's emotional state. For example, it can respond in line with the user's emotions, such as, "You seem a little down today. Is there anything I can help you with?"

[1165] Specific examples

[1166] The following specific scenarios illustrate embodiments of the present invention:

[1167] When a user wakes up in the morning, the wearable device records their heart rate and sleep duration, and the emotion engine analyzes their voice tone and facial expressions to obtain emotional data.

[1168] The terminal transmits this data to the server.

[1169] The server analyzes this data to detect recent disruptions in sleep patterns and emotional states.

[1170] Based on the results, the server adjusts the exercise program and relaxation plan and sends the new exercise plan to the user's device.

[1171] The device suggests gentle, non-challenging exercises and relaxation exercises to the user, and displays on-screen instructions and video guides.

[1172] While the user is exercising, the device transmits real-time heart rate, movement data, and emotional state to the server.

[1173] The server monitors the data and immediately generates alerts if there are any abnormalities in health or emotional state, notifying necessary medical services or family members.

[1174] The device generates empathetic responses and provides dialogue through everyday conversations with the user, while at the same time providing more empathetic and appropriate responses by taking into account the emotional state recognized by the emotion engine.

[1175] As described above, the system of the present invention collects and analyzes biometric and emotional data of elderly people, and provides customized exercise programs and empathetic conversations, thereby realizing comprehensive health management.

[1176] The processing flow will be explained below.

[1177] Step 1:

[1178] The terminal (wearable device) collects biometric and emotional data such as heart rate, number of steps, activity level, sleep patterns, voice data, and facial expression data of the elderly in real time. For example, the wearable device collects this data periodically throughout the day.

[1179] Step 2:

[1180] The device transmits the collected biometric and emotional data to a server via Wi-Fi or Bluetooth, and the data is encrypted to protect privacy.

[1181] Step 3:

[1182] The server organizes and stores the received biometric and emotional data in a cloud database, where the data is categorized by date and time.

[1183] Step 4:

[1184] The server analyzes the stored data using machine learning algorithms (e.g., clustering algorithms and time series analysis algorithms) to model the elderly's daily behavioral patterns and emotional states.

[1185] Step 5:

[1186] Based on the analysis results, the server detects outliers or abnormal patterns (e.g., sudden increases in heart rate or sudden changes in facial expression) that deviate from normal behavioral patterns and emotional states.

[1187] Step 6:

[1188] If an anomaly is detected, the server generates an alert and notifies family members or medical services. For example, if an elderly person's emotional state suddenly worsens, an urgent notification will be sent.

[1189] Step 7:

[1190] The server generates a customized exercise program based on the elderly person's daily behavioral patterns and emotional state, taking into account their individual health status, past exercise history, and emotional state.

[1191] Step 8:

[1192] The server sends the generated exercise program to the device, which displays the received program and provides detailed exercise instructions and video guides.

[1193] Step 9:

[1194] When a user exercises, the device collects real-time heart rate, movement data, and emotional state and transmits this information to a server, allowing the user to monitor their condition during exercise.

[1195] Step 10:

[1196] The server reanalyzes the exercise and emotional state data to check for any abnormalities, and if any are detected, immediate action is taken.

[1197] Step 11:

[1198] The terminal uses voice recognition technology to convert the user's everyday conversation into text data and sends it to the server.

[1199] Step 12:

[1200] The server uses natural language processing technology to analyze the conversation data it receives and learns the emotions and interests of the elderly. It also integrates and analyzes voice tone and facial expression data obtained by the emotion engine.

[1201] Step 13:

[1202] The server generates an empathetic response based on the learning results and the user's emotional state and sends it to the device. For example, if the user is feeling depressed, it generates a response such as, "Try doing some exercises to relax today."

[1203] Step 14:

[1204] The device generates an empathetic response and provides it to the user via voice or text. The empathetic response provides psychological care and reduces feelings of social isolation. This process is repeated continuously to achieve comprehensive health management.

[1205] Example 2

[1206] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1207] Health management for the elderly is extremely important, but they have difficulty managing themselves and regular checkups at medical institutions are limited. This can lead to health conditions being overlooked and lack of continuous care. It can also increase the risk of social isolation and the progression of dementia. Furthermore, there is often a lack of provision of exercise programs tailored to health conditions and empathetic responses to emotions. A system to comprehensively solve these problems is needed.

[1208] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1209] In this invention, the server includes a means for analyzing biometric information, a means for detecting abnormalities, a means for generating an exercise program, a means for collecting dialogue information and generating empathetic responses, and a means for monitoring information in real time and detecting abnormalities. This enables comprehensive collection and analysis of biometric data and emotional data of elderly people, enabling real-time monitoring of their health status. Furthermore, by providing customized exercise programs and responding empathetically to their emotions, it is possible to reduce social isolation and slow the progression of dementia.

[1210] "Biometric information" refers to data that indicates the health status of elderly people, such as their heart rate, number of steps taken, activity level, and sleep patterns.

[1211] The "Central Processing Unit" is a cloud-based computer system that is responsible for analyzing, storing, and managing received biometric and emotional data.

[1212] An "exercise program" is a customized exercise and relaxation program provided to seniors based on their health status and past exercise history.

[1213] An "emotion engine" is a system that analyzes the user's voice data and facial expression data, recognizes their emotional state, and transmits the data to the central processing unit.

[1214] "Empathic responses" refer to words and actions that show empathy and are generated based on the elderly person's emotional state and the content of the conversation.

[1215] An "alert" is a notification or alert issued when the central processing unit detects an abnormality, and is used to notify necessary medical services or family members.

[1216] "Monitoring" refers to the process of monitoring the elderly's biometric and emotional data in real time to check for any abnormalities.

[1217] "Dialogue information" refers to data collected from everyday conversations and voice input with elderly people, and is analyzed by the emotion engine.

[1218] The present invention is a system that comprehensively supports health management for the elderly. This system is composed of a combination of collecting and analyzing biological information from the elderly, detecting abnormalities, providing customized exercise programs, generating empathetic responses, and an emotion engine. Detailed embodiments of this system are described below.

[1219] System configuration

[1220] Devices: These are wearable devices, smartphones, and tablets used daily by seniors. These devices record biometric information such as heart rate, number of steps, activity level, and sleep patterns in real time, and also have the ability to collect voice and facial expression data. The data is sent to a server using Bluetooth or Wi-Fi.

[1221] Server: A cloud-based computer system that receives, analyzes, and stores biometric and emotional data. The server analyzes the data using machine learning algorithms developed in Python (e.g., scikit-learn and TensorFlow). The server also performs anomaly detection, generates exercise programs, and provides empathetic responses. Specifically, it can use cloud services such as AWS and Google Cloud Platform.

[1222] Emotion engine: A system that analyzes the user's voice data and facial expression data to recognize their emotional state. This sends data corresponding to the user's emotional state to the server. Furthermore, the emotion engine uses technologies such as Google Speech-to-Text, Google BERT, and Google Text-to-Speech.

[1223] Data collection and transmission

[1224] When a user uses a wearable device or smartphone, the device records real-time biometric information such as heart rate, steps taken, activity level, and sleep patterns, as well as voice and facial expression data. This data is then transmitted by the device to a central processing unit.

[1225] Data Analysis and Anomaly Detection

[1226] The server stores the received biometric and emotional data in a database. It then analyzes the data using Python machine learning libraries (e.g., scikit-learn and TensorFlow). If an abnormality is detected based on the analysis results, the server generates an alert and notifies the necessary medical services or family members.

[1227] Providing customized exercise programs

[1228] Based on the analysis results, the server generates an exercise program suitable for the elderly. This program is customized taking into account the elderly's health condition, past exercise history, emotional state, etc. The generated program is provided to the elderly via their device. For example, it may suggest an exercise program that includes relaxation and light stretching.

[1229] Real-time monitoring

[1230] When a user starts exercising, the device collects real-time heart rate, movement data, and emotional state and sends them to the server, which monitors the data and generates an immediate alert if any abnormalities are detected.

[1231] Empathetic conversation and guidance

[1232] The device uses voice recognition technology to convert everyday conversations with the user into text data and sends it to the server. The server then analyzes this conversation data using natural language processing technology. It also integrates emotional data obtained from the emotion engine to generate empathetic responses for the elderly. The generated responses are then conveyed to the user via the device using voice synthesis technology.

[1233] Specific examples

[1234] For example, when an elderly person wakes up in the morning, the wearable device records their heart rate and sleep duration. At the same time, the emotion engine analyzes the user's voice tone and facial expressions to obtain emotional data. This data is sent from the device to a server, which analyzes the data to detect recent disturbances in sleep patterns and emotional state. Based on the results, the server generates a customized exercise program and relaxation plan and sends it to the device. The device then displays instructions and video guides on the screen to encourage the user to follow the plan. During exercise, data is collected in real time, and if an abnormality is detected, an alert is generated immediately. The device also collects daily conversation data, which is analyzed by the server and used to provide empathetic support.

[1235] Prompt Sentence Examples

[1236] "Write Python code to record a user's heart rate, steps, activity, and sleep patterns and detect any anomalies."

[1237] "Design a system to send data acquired from a wearable device to a cloud server and analyze it."

[1238] "Propose an algorithm that generates a customized exercise program based on the emotional state of an elderly person."

[1239] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1240] Program processing flow

[1241] Step 1: Device preparation and data collection

[1242] The user puts on the wearable device or smartphone and launches the app.

[1243] Input: Wearable devices, smartphones

[1244] Output: Collected biometric information (e.g., heart rate, steps, activity level, sleep patterns)

[1245] The device uses sensors to record heart rate, steps, activity levels, and sleep patterns, and stores the data in the app.

[1246] Operation: Data measurement by sensor, data storage in app

[1247] The device collects voice data and facial expression data.

[1248] How it works: Records voice and facial expressions using the built-in microphone and camera

[1249] Step 2: Sending data

[1250] The terminal transmits the collected biometric and emotional data to a central processing unit.

[1251] Input: Collected biometric and emotional data

[1252] Output: Data sent to the central processing unit

[1253] How it works: Sends data using Bluetooth or Wi-Fi

[1254] Step 3: Data analysis

[1255] The server stores the received biometric information and emotion data in a cloud database.

[1256] Input: Transmitted biometric and emotional data

[1257] Output: Data stored in the database

[1258] How it works: Stores data using a database management system (e.g. AWS DynamoDB)

[1259] The server analyzes the data using Python machine learning libraries.

[1260] Input: Stored biometric and emotional data

[1261] Output: Analysis results (e.g., health status evaluation data, anomaly detection results)

[1262] Operation: Perform data analysis using scikit-learn and TensorFlow

[1263] Step 4: Detect anomalies

[1264] The server detects abnormalities based on the analysis results.

[1265] Input: Data analysis results

[1266] Output: Anomaly detection alert

[1267] How it works: Applies anomaly detection algorithms and generates alerts if anomalies are found

[1268] Step 5: Generate the exercise program

[1269] The server generates an exercise program suitable for the elderly.

[1270] Input: Health assessment data, past exercise history, emotional state

[1271] Output: Customized exercise program

[1272] Movement: Applying an algorithm to generate exercise programs that take into account health and emotional states

[1273] The server transmits the generated exercise program to the terminal.

[1274] Input: Customized exercise program

[1275] Output: Exercise program sent to the device

[1276] Operation: Send using a data transmission service (e.g. HTTP API)

[1277] Step 6: Real-time monitoring

[1278] The user begins exercising according to the recommended program.

[1279] Input: Customized exercise program

[1280] Output: Exercise performed

[1281] Movement: Follow the app's guide to complete the exercise

[1282] The device collects heart rate, movement data, and emotional state in real time and transmits this to a server.

[1283] Input: Data collected during exercise

[1284] Output: Real-time data sent to the server

[1285] Actions: Use sensors and voice recognition technology to collect data and transmit it in real time

[1286] Step 7: Empathetic conversation and guidance

[1287] The terminal uses voice recognition technology to convert everyday conversations with the user into text data and sends it to the server.

[1288] Input: Speech data of everyday conversation

[1289] Output: Text data sent to the server

[1290] How it works: Uses a speech recognition engine (e.g. Google Speech-to-Text)

[1291] The server analyzes this conversation data using natural language processing technology. It also integrates emotional data obtained from the emotion engine to generate an empathetic response. For example, "You seem to be feeling unwell today. Is there anything I can help you with?" The generated response is then conveyed to the user via the device using voice synthesis technology.

[1292] Input: Conversation data, emotion data

[1293] Output: Empathic response

[1294] How it works: Uses natural language processing technology (e.g., Google BERT) and speech synthesis technology (e.g., Google Text-to-Speech)

[1295] The above is a detailed processing flow of the system program. Through the specific operations at each step, the health management of elderly people can be effectively carried out.

[1296] (Application example 2)

[1297] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1298] Health management for the elderly requires real-time monitoring and appropriate responses, especially while on the move. However, current health management systems lack the functionality to respond to situations and environments while on the move, and do not provide sufficient support to ensure the safety of the elderly. They also lack the ability to respond empathetically to fluctuations in emotional states or provide exercise programs adapted to individual health conditions. This poses a challenge, making it difficult to immediately respond to deterioration in health or accidents while the elderly are on the move.

[1299] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting biometric data and emotional data of the elderly person, means for transmitting the collected biometric data and emotional data to the server, means for analyzing the biometric data and emotional data received by the server, means for detecting abnormalities based on the analysis results, means for generating an alert when an abnormality is detected, means for generating an exercise program suitable for the elderly person, means for providing the generated exercise program to the elderly person, means for collecting activity data and emotional data in real time and transmitting it to the server, means for generating an empathetic response based on the emotional data, means for providing empathetic conversation based on the collected data, and means for monitoring the health status of the elderly person while traveling in an autonomous vehicle and generating an appropriate response. This enables real-time monitoring of the elderly person's health status while traveling, empathetic responses according to their emotional state, and immediate response in the event of an abnormality.

[1300] "Biometric data" refers to data that indicates the physical condition of an elderly person, such as their heart rate, number of steps taken, activity level, and sleep patterns.

[1301] "Emotion data" is data that indicates the emotional state of the elderly person, obtained from their tone of voice and facial expressions.

[1302] The "server" is a cloud-based computer system that receives, analyzes, and stores collected biometric and emotional data, and performs anomaly detection and generates exercise programs.

[1303] "Analysis" is the process of using the received biometric and emotional data to model the elderly person's daily behavioral patterns and emotional state and detect abnormalities.

[1304] "Abnormal" refers to a situation in which the health condition of an elderly person is determined to be outside the normal range based on the analysis results.

[1305] An "alert" is a warning notification to seniors, their families, and medical services that is generated when an abnormality is detected.

[1306] An "exercise program" is a plan of exercise and relaxation tailored to the individual health condition of an elderly person based on analyzed biometric and emotional data.

[1307] "Activity data" refers to data related to the daily movements and behaviors of elderly people.

[1308] An "empathic response" is a system response that aims to understand the emotional state of the elderly person and respond appropriately.

[1309] An "autonomous vehicle" is a vehicle that can drive under its own control without the need for driver operation.

[1310] "Monitoring" is the process of monitoring biometric and emotional data in real time.

[1311] "Real-time" refers to the timing of data being collected and processed and analyzed immediately.

[1312] "Immediate response to abnormalities" is the process of quickly generating an alert and taking appropriate action when an abnormality in health status is detected.

[1313] The present invention is a system that comprehensively supports the health management of the elderly. It aims to monitor the overall health status of the elderly in real time by collecting biometric and emotional data of the elderly, analyzing it, detecting abnormalities, providing customized exercise programs, generating empathetic responses, and combining it with an emotion engine, thereby preventing social isolation and the progression of dementia.

[1314] System configuration

[1315] The system consists of the following elements:

[1316] Devices: Wearable devices, smartphones, tablets, and in-vehicle devices used daily by seniors. These devices collect biometric and emotional data and provide an interface to transmit it to a server.

[1317] Server: A cloud-based computer system that receives, analyzes, and stores biometric and emotional data, performing tasks such as anomaly detection, generating exercise programs, and providing empathetic responses.

[1318] Emotion engine: A system that analyzes the user's voice data and facial expression data to recognize their emotional state and transmits that data to a server.

[1319] Data collection and transmission

[1320] The devices used by the elderly (wearable devices and in-vehicle devices for autonomous vehicles) record biometric data such as heart rate, number of steps, activity level, and sleep patterns in real time. They also collect voice and facial expression data, which are then sent to a server where they are securely and efficiently stored.

[1321] Data Analysis and Anomaly Detection

[1322] The server stores the received biometric and emotional data in a database. It then uses machine learning algorithms to analyze this data and model the user's daily behavioral patterns and emotional state. For example, if an elderly person who normally speaks calmly has recently started to speak in a lower tone, the emotion engine will detect this and the server will recognize it as an anomaly and generate an alert.

[1323] Providing customized exercise programs

[1324] Based on the analysis, the server generates an appropriate exercise program for the elderly, taking into account their individual health and activity data, as well as their emotional state. For example, if they are feeling depressed, it might suggest relaxation and gentle stretching exercises.

[1325] Real-time monitoring and alerts

[1326] While the elderly exercise, the device collects real-time heart rate, movement data, and emotional state and sends it to a server that analyzes the data and immediately generates an alert if an abnormality is detected, notifying medical services and family members.

[1327] Empathetic conversation and guidance

[1328] The device converts everyday conversations into text data using voice recognition technology and sends it to the server. The server analyzes this conversation data and uses natural language processing technology to learn the emotions and interests of the elderly person. The emotion engine also integrates the emotional data obtained to generate empathetic responses tailored to the elderly person's emotional state. For example, it is possible to provide empathetic responses such as, "You seem a little tired today. Is there anything I can do to help you relax?"

[1329] Application to autonomous vehicles

[1330] The device installed in the self-driving vehicle monitors the elderly person's biometric and emotional data in real time while they are traveling. The collected data is sent to a server via the in-vehicle device, and appropriate responses or guidance are provided as needed. For example, if the heart rate increases while traveling, the display will say, "Try taking deep breaths."

[1331] Specific examples

[1332] 1. Situation: Elderly people use self-driving vehicles to go to the hospital.

[1333] 2. Data collection: In-car devices record heart rate and voice tones in real time.

[1334] 3. Data analysis: The server analyzes the data and detects anomalies.

[1335] 4. Response generation: Generate appropriate alerts and display relaxation suggestions on the vehicle's display. The voice system will instruct the driver to "take a deep breath."

[1336] Prompt Sentence Examples

[1337] "Analyze real-time data including the health status of seniors to detect abnormal heart rates and stress levels. Generate example responses within self-driving vehicles."

[1338] "Create an example of a customized exercise program for seniors while they are in an autonomous vehicle. Based on the user's heart rate and emotional data, suggest appropriate relaxation techniques."

[1339] As described above, the system of the present invention collects and analyzes biometric and emotional data of elderly people in real time, enabling safe and efficient health management even while on the move.

[1340] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1341] Step 1:

[1342] When a user gets into an autonomous vehicle, the terminal (in-vehicle device) automatically starts up. The terminal is equipped with various sensors (heart rate sensor, camera, microphone) to collect biometric and emotional data of the elderly person. The sensors begin to record the user's heart rate, voice tone, and facial expressions in real time. This data is collected as biometric and emotional data.

[1343] Input: Heart rate, voice tone, and facial expression data of elderly people

[1344] Output: Collected biometric and emotional data

[1345] Step 2:

[1346] The device transmits the collected biometric and emotional data to a cloud server. The data is transmitted securely using an encrypted protocol, ensuring that all data reaches the server in real time.

[1347] Input: Collected biometric and emotional data

[1348] Output: Transmitted biometric and emotional data (stored on the server)

[1349] Step 3:

[1350] The server analyzes the received biometric and emotional data. It uses a generative AI model to quickly analyze the data and model the elderly person's daily behavioral patterns and emotional state. Based on the analysis results, it determines whether any abnormalities have been detected.

[1351] Input: Transmitted biometric and emotional data

[1352] Output: Analysis results (normal or abnormal)

[1353] Step 4:

[1354] If the server detects an abnormality, it immediately generates an alert, which is then sent to medical services and the elderly person's family via SMS, email, app notification, etc.

[1355] Input: Analysis result (abnormal)

[1356] Output: Generated alert (notifying medical services and family)

[1357] Step 5:

[1358] Based on the analysis results, the server generates an exercise program tailored to the elderly, taking into account their biometric data, emotional data, and past exercise history. The generated program includes suggestions for relaxation techniques and light exercise.

[1359] Input: Analysis results (biometric data, emotional data)

[1360] Output: Generated exercise program

[1361] Step 6:

[1362] The generated exercise program is provided to the user via the device. Exercise instructions are displayed on the device's display and audio guidance is also provided. The user then performs exercises and relaxation exercises that can be easily performed in the car.

[1363] Input: Generated exercise program

[1364] Output: Providing an exercise program to the user

[1365] Step 7:

[1366] The device continuously collects heart rate, movement data, and emotional state during exercise, and transmits the collected data in real time to a server, which then analyzes the data and monitors the user's health.

[1367] Input: Biometric and emotional data during exercise

[1368] Output: Collected exercise data (sent to server)

[1369] Step 8:

[1370] The server evaluates the effectiveness of the exercise program based on the new data collected and adjusts the exercise program as needed.

[1371] Input: Collected exercise data

[1372] Output: Evaluation results and program adjustments

[1373] Step 9:

[1374] The device converts everyday conversations into text data using voice recognition technology and sends it to a server, which then uses natural language processing technology to learn the elderly's emotions and interests, and integrates the obtained emotional data into an emotion engine.

[1375] Input: Speech data of everyday conversation

[1376] Output: Text data (sent to server)

[1377] Step 10:

[1378] The server generates an empathetic response based on the emotional data. The generated response is provided to the user via the device, providing empathetic conversation such as, "You seem a little tired today. Is there anything I can help you relax?"

[1379] Input: Emotion data, text data

[1380] Output: Empathetic response (provided to user)

[1381] Through the above processing steps, the system of the present invention collects and analyzes biometric and emotional data of elderly people, realizing safe and effective health management even while on the move.

[1382] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1383] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1384] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1385] [Fourth embodiment]

[1386] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1387] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1388] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1389] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1390] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1391] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1392] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1393] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1394] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1395] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1396] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1397] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1398] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1399] This invention relates to a comprehensive health management system for the elderly. The system collects and analyzes biometric data, provides customized exercise programs, and generates and delivers empathetic conversations to monitor the health status of the elderly in real time, aiming to reduce social isolation and slow the progression of dementia.

[1400] 1. System Configuration

[1401] The system mainly consists of the following components:

[1402] Devices: Refers to wearable devices, smartphones, and tablets used daily by seniors. These devices collect biometric data and transmit it to a server. They also serve as an interface for providing exercise instructions and empathetic conversations via visual and audio displays.

[1403] Server: Installed in the cloud, it receives, analyzes, and stores data sent by the elderly. It also generates alerts for abnormalities, creates exercise programs, and generates empathetic responses.

[1404] 2. Data collection and transmission

[1405] The devices (wearable devices and in-home sensors) record elderly people's biometric data, such as heart rate, number of steps, activity level, and sleep patterns, in real time. The devices then transmit this data to a server at regular intervals.

[1406] 3. Data Analysis and Anomaly Detection

[1407] The server stores the received biometric data in a database. It then uses machine learning algorithms to analyze the data and model the elderly person's daily behavioral patterns. For example, if an elderly person typically wakes up at 7 a.m. and goes to bed at 10 p.m., but has recently started waking up late at night more frequently, the server will detect this as an anomaly and generate an alert.

[1408] 4. Providing customized exercise programs

[1409] Based on the analysis results, the server generates an exercise program suitable for the elderly. For example, if the analysis reveals that the elderly person has not been exercising recently, it will suggest light stretching and walking exercises. This program is sent to the device, and exercise instructions and videos are displayed on the screen to make it easy for the user to follow.

[1410] 5. Real-time monitoring and alerts

[1411] While the user is exercising, the device collects real-time heart rate and movement data and sends it to a server, which analyzes the data and immediately generates an alert if an abnormality is detected, notifying necessary medical services or family members.

[1412] 6. Empathetic conversation and guidance

[1413] The device converts everyday conversations into text data using voice recognition technology and sends it to a server. The server analyzes this conversation data and learns from past statements and behavioral trends. Based on the learning results, it generates empathetic responses and provides them to the elderly person via the device. For example, it can have a conversation like, "You woke up a little earlier than usual today. How are you feeling?"

[1414] Specific examples

[1415] Specifically, elderly people with high levels of care needs will use the following systems:

[1416] When the user wakes up in the morning, the wearable device records their heart rate and sleep duration.

[1417] The terminal sends this to the server.

[1418] The server analyzes this data to detect recent disruptions in sleep patterns.

[1419] Based on the results, the server adjusts the exercise program and sends a new exercise plan to the user's terminal.

[1420] The device suggests gentle, non-challenging exercises for the user and provides on-screen instructions and video guides.

[1421] While the user is exercising, the device transmits real-time heart rate and movement data to the server.

[1422] The server monitors the data and immediately generates an alert if there are any health abnormalities, notifying the user of the necessary medical services.

[1423] The device interacts with the elderly on a daily basis, engaging in empathetic conversations to reduce feelings of social isolation and provide psychological care.

[1424] The above is an embodiment of the present invention, which aims to comprehensively manage the health of elderly people, reduce their sense of social isolation, and slow the progression of dementia.

[1425] The processing flow will be explained below.

[1426] Step 1:

[1427] The device collects real-time biometric data such as the elderly person's heart rate, number of steps taken, activity level, and sleep patterns. For example, a wearable device collects this data periodically throughout the day.

[1428] Step 2:

[1429] The device periodically transmits the collected biometric data to a server via Wi-Fi or Bluetooth, and the data is encrypted to protect privacy.

[1430] Step 3:

[1431] The server stores the received biometric data in a cloud database, where the data is organized by date and time.

[1432] Step 4:

[1433] The server analyzes the stored data and uses machine learning algorithms (e.g., clustering algorithms, time series analysis algorithms) to model the daily behavioral patterns of the elderly.

[1434] Step 5:

[1435] Based on the analysis results, the server detects abnormal values ​​or patterns that deviate from normal lifestyle patterns (e.g., a sudden increase in heart rate or a sudden decrease in sleep time).

[1436] Step 6:

[1437] If an abnormality is detected, the server generates an alert and notifies family members or medical services. For example, if an elderly person's heart rate increases significantly at night, an emergency notification will be sent.

[1438] Step 7:

[1439] Based on the analysis results, the server generates a customized exercise program for each senior, taking into account their individual health condition and past exercise history.

[1440] Step 8:

[1441] The server sends the generated exercise program to the terminal, which displays the received program and provides detailed exercise instructions and video guides to the user.

[1442] Step 9:

[1443] While the user is exercising, the device collects heart rate and movement data in real time and transmits it back to the server, allowing for constant monitoring of the user's condition during exercise.

[1444] Step 10:

[1445] The server reanalyzes the exercise data and checks for any abnormalities. If any abnormalities are detected, immediate action is taken.

[1446] Step 11:

[1447] The device uses voice recognition technology to convert the elderly person's everyday conversations into text data and sends it to a server.

[1448] Step 12:

[1449] The server analyzes the conversation data and uses natural language processing technology to learn the emotions and interests of the elderly.

[1450] Step 13:

[1451] The server generates an empathetic response based on the learning results and sends it to the device, which then provides the generated response to the user in voice or text format.

[1452] Step 14:

[1453] The empathetic response provided to the user provides psychological care and reduces feelings of social isolation, and this process is repeated on an ongoing basis to achieve comprehensive health management.

[1454] Example 1

[1455] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1456] Health management and reducing social isolation among the elderly are important issues in modern society. Effective collection and analysis of elderly biometric data and daily activity data is required for early detection of abnormalities and the provision of customized exercise programs. However, there is a lack of systems that can monitor this data in real time and respond appropriately. As a result, elderly people are at risk of not receiving appropriate care and their health condition worsening. Furthermore, a system that provides empathetic conversation is also needed to reduce social isolation.

[1457] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1458] In this invention, the server includes means for collecting biometric data of the elderly person, means for transmitting the collected biometric data to the server, means for storing the biometric data received by the server in a database, means for analyzing the stored biometric data, means for modeling the elderly person's daily behavioral patterns based on the analysis results, means for detecting abnormalities, means for generating an alert when an abnormality is detected, means for generating an exercise program suitable for the elderly person, and means for providing the generated exercise program to the elderly person, means for collecting activity data in real time and transmitting it to the server, means for analyzing the collected data in real time and detecting abnormalities, means for sending a notification to medical services or family members when an abnormality is detected, means for collecting conversation data and generating an empathetic response, and means for providing empathetic conversation based on the collected data. This enables comprehensive management of the elderly person's health status, detection of abnormalities in real time, and prompt response. Furthermore, providing empathetic conversation can reduce social isolation and slow the progression of dementia.

[1459] "Biometric data" refers to data that represents a person's physiological state, such as an elderly person's heart rate, number of steps, activity level, and sleep patterns.

[1460] The "server" is a computer system installed on the cloud that receives, analyzes, and stores data sent by elderly people.

[1461] A "database" is a data structure managed by a server for systematically storing received biometric data and activity data.

[1462] A "machine learning algorithm" is a computer program that analyzes data and models the daily behavioral patterns of older adults.

[1463] An "exercise program" is a plan that includes exercise and stretching menus suitable for seniors, and is created with the aim of improving their health.

[1464] An "alert" is a warning notification that is generated when an abnormality is detected, intended to notify medical services or family members.

[1465] "Activity data" is information collected during exercise, such as real-time heart rate and movement data.

[1466] "Empathetic responses" are sympathetic dialogues for elderly people that are generated based on past conversations and behavioral trends.

[1467] "Speech recognition technology" is a technology that allows a terminal to convert a user's everyday conversation into text.

[1468] The present invention relates to a comprehensive health management system for the elderly. The system collects and analyzes biometric data of the elderly, provides customized exercise programs, and generates and provides empathetic conversations to reduce social isolation and slow the progression of dementia.

[1469] System configuration

[1470] The system consists of the following components:

[1471] Devices: Refers to wearable devices, smartphones, and tablets used daily by seniors. These devices collect biometric data and transmit it to a server. They also serve as an interface for providing exercise instructions and empathetic conversations via visual and audio displays.

[1472] Server: Installed in the cloud, it receives, analyzes, and stores data sent by the elderly. It also generates alerts for anomaly detection, creates exercise programs, and generates empathetic responses. The server uses a database to store the received data and performs data analysis using machine learning algorithms.

[1473] Hardware and software used

[1474] Wearable devices: Collect biometric data such as heart rate, steps, activity levels, and sleep patterns.

[1475] Smartphone / Tablet: Provides communication means for sending data to the server and acts as an interface for exercise instructions and empathetic conversation.

[1476] Cloud server: Stores and analyzes data, generates exercise programs, generates alerts, and generates empathetic conversations.

[1477] Database: A data structure for organizing and storing data within a server.

[1478] Machine learning algorithms: Programs that analyze incoming data, model behavioral patterns, and detect anomalies.

[1479] Example of operation

[1480] Specifically, the system works as follows:

[1481] When an elderly person wakes up in the morning, the wearable device records their heart rate and sleep duration.

[1482] The terminal sends this to the server.

[1483] The server analyzes the data and detects any recent disruptions to sleep patterns.

[1484] Based on the results, the server adjusts the exercise program and sends a new exercise plan to the user's terminal.

[1485] The device suggests gentle, non-challenging exercises and provides on-screen instructions and video guides.

[1486] While the user is exercising, the device transmits real-time heart rate and movement data to the server.

[1487] The server monitors the data and immediately generates an alert if there are any health abnormalities, notifying the user of the necessary medical services.

[1488] The device interacts with seniors on a daily basis, reducing feelings of social isolation through empathetic conversation.

[1489] Examples of prompt statements

[1490] "We would like you to design and implement a system that generates appropriate exercise programs and provides empathetic conversations based on the analysis of biometric data collected from wearable devices, smartphones, and tablets used daily by the elderly. In particular, we would like it to include a function that collects data on heart rate, sleep patterns, etc. in real time to monitor the elderly's health and generate alerts when abnormalities are detected."

[1491] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1492] Step 1:

[1493] The terminal records the elderly person's biometric data, such as heart rate, number of steps, activity level, and sleep patterns, in real time. This data is obtained from a wearable device. Specifically, the terminal measures the subject's biometric indicators at regular intervals (e.g., every 10 minutes). The input is the elderly person's biometric data, and the output is the recorded biometric data.

[1494] Step 2:

[1495] The device transmits the recorded biometric data to the server at regular intervals (for example, once an hour). The transmitted data consists of heart rate, number of steps, activity level, sleep patterns, etc. The input is the recorded biometric data, and the output is the data to be transmitted to the server. Specifically, the device establishes a network connection with the server and transfers the data.

[1496] Step 3:

[1497] The server stores the received biometric data in a database. In the database, a table is created for each elderly person and the data is stored there. The input is the biometric data sent to the server, and the output is the data stored in the database. Specifically, the server checks the integrity of the data and inserts it into the database in the appropriate format.

[1498] Step 4:

[1499] The server uses machine learning algorithms to analyze the biometric data and model behavioral patterns. This analysis uses a time series analysis algorithm. The input is the biometric data stored in the database, and the output is a behavioral pattern model as a result of the analysis. Specifically, the server retrieves the data and applies the algorithm to analyze it.

[1500] Step 5:

[1501] The server detects anomalies based on the analysis results. This anomaly detection uses preset reference values ​​and behavioral pattern models. The input is the analysis results and reference values, and the output is a flag indicating whether or not an anomaly exists. Specifically, the server checks for the presence of an abnormal pattern based on the analysis results.

[1502] Step 6:

[1503] If an abnormality is detected, the server generates an alert and sends a notification to the necessary medical services or family members. The input is the abnormality detection flag, and the output is the generated alert and notification. The specific operations are to generate an alert statement and send a notification.

[1504] Step 7:

[1505] The server generates an exercise program based on the analysis results of the biometric data. This program is customized according to the user's activity level and health condition. The input is the analysis results, and the output is the exercise program. Specifically, the server selects an appropriate exercise menu and builds it into a program.

[1506] Step 8:

[1507] The device then provides the generated exercise program to the elderly. The input is the exercise program, and the output is displayed exercise instructions and video guides. Specifically, the device displays the exercise instructions on the screen and plays video guides as needed.

[1508] Step 9:

[1509] While the user is exercising, the device collects heart rate and movement data in real time. The input is the biometric data during exercise, and the output is the collected data. Specifically, the device performs high-frequency data sampling in real time.

[1510] Step 10:

[1511] The collected data is transmitted to the server in real time. The input is the collected real-time biometric data, and the output is the data to be sent to the server. Specifically, the device re-establishes a network connection with the server and transmits the data.

[1512] Step 11:

[1513] The server analyzes the received data in real time, and if an abnormality is detected, it immediately generates an alert and sends a notification. The input is the biometric data sent in real time, and the output is the alert and notification. Specifically, the server performs rapid data analysis, and if an abnormality is detected, it immediately sends a notification.

[1514] Step 12:

[1515] The device provides empathetic conversations with elderly people through everyday interactions. The input is conversation data with the user, and the output is a generated empathetic response. Specifically, the device uses voice recognition technology to send the conversation content to a server and provides the response received from the server to the user.

[1516] (Application example 1)

[1517] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1518] Health management for the elderly is a significant issue, and appropriate health monitoring and reduction of social isolation are required, especially for elderly people living alone or shopping in brick-and-mortar stores. Furthermore, elderly people are sensitive to specific health conditions, and customized exercise programs are needed. However, conventional systems have had difficulty integrating these into the elderly's daily lives. Therefore, there is a need to develop a system that allows elderly people to smoothly manage their health and receive appropriate exercise and psychological care.

[1519] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1520] In this invention, the server includes means for collecting biometric data of the elderly person, means for transmitting the collected biometric data to the server, means for analyzing the biometric data received by the server, means for detecting abnormalities based on the analysis results, means for generating an alert when an abnormality is detected, means for generating an exercise program suitable for the elderly person, means for providing the generated exercise program to the elderly person, means for collecting activity data and transmitting it to the server, means for collecting conversation data and generating empathetic responses, means for providing empathetic conversation based on the collected data, a robot that supports health management through dialogue with the elderly person in a physical store, and means for linking the robot with the elderly person's wearable device or smartphone. This allows the elderly person to receive health management in a physical store, and enables them to reduce their sense of social isolation and maintain their health properly through suggested exercise programs and empathetic conversation.

[1521] "Biometric data" refers to measurements of an older adult's physical condition and behavior, such as heart rate, number of steps taken, activity level, and sleep patterns.

[1522] "Means of collection" refers to devices and functions that collect biometric data from elderly people using wearable devices and in-home sensors.

[1523] A "server" is a computer system that receives, analyzes, and stores collected biometric data.

[1524] "Means for analysis" refers to the technology and algorithms used by the server to process the biometric data received and analyze the health status and behavioral patterns of the elderly person.

[1525] An "anomaly detection means" is a system that identifies health conditions that deviate from normal patterns in the analyzed data and reports the problem.

[1526] "Means for generating alerts" refers to the function of issuing a warning when an abnormality is detected and notifying relevant parties and medical services.

[1527] An "exercise program" is an exercise plan proposed based on the health status of the elderly person and the results of data analysis.

[1528] "Means for providing" refers to the methods and interfaces used to inform elderly people of the generated exercise program and enable them to carry it out.

[1529] "Activity Data" refers to information about your exercise, daily movements, and physical activity.

[1530] "Conversation data" refers to information collected as text or audio of conversations with elderly people.

[1531] "Empathic response" refers to feedback that provides appropriate responses to emotionally understand and support the conversations and actions of older people.

[1532] A "brick and mortar store" is a physical location, such as a retail store or service location, where seniors can visit and receive services in person.

[1533] A "robot" is an autonomous or remotely controlled mechanical device that assists elderly people in managing their health through dialogue within a physical store.

[1534] A "wearable device" is an electronic device that can be worn on the body and collects biometric data such as heart rate and activity level.

[1535] This invention relates to a comprehensive health management system for the elderly. This system collects and analyzes biometric data from the elderly, provides customized exercise programs, and generates and delivers empathetic conversations to monitor the elderly's health status in real time, aiming to reduce social isolation and slow the progression of dementia.

[1536] System configuration

[1537] The system mainly consists of the following elements:

[1538] 1. Devices: These devices are wearable devices, smartphones, and tablets used daily by seniors. These devices collect biometric data and transmit it to a server. They also serve as an interface for providing exercise instructions and empathetic conversations via visual and audio displays.

[1539] 2. Server: Installed on the cloud, it receives, analyzes, and stores data sent by the elderly. It also generates alerts for abnormality detection, creates exercise programs, and generates empathetic responses.

[1540] 3. Robot: A device that supports health management through dialogue with the elderly in physical stores. The robot connects to the elderly's wearable devices and smartphones to collect health data, display analysis results, provide exercise support, and engage in empathetic dialogue.

[1541] Data collection and transmission

[1542] The devices (wearable devices and in-home sensors) record elderly people's biometric data, such as heart rate, number of steps, activity level, and sleep patterns, in real time. The devices then transmit this data to a server at regular intervals.

[1543] Data Analysis and Anomaly Detection

[1544] The server stores the received biometric data in a database and analyzes it using machine learning algorithms to model the elderly person's daily behavioral patterns and generate alerts if any abnormalities are detected.

[1545] Providing customized exercise programs

[1546] Based on the analysis results, the server generates an exercise program suitable for the elderly. For example, if the analysis reveals that the elderly person has not been exercising recently, the server will suggest light stretching and walking exercises.

[1547] Real-time monitoring and alerts

[1548] While the user is exercising, the device collects real-time heart rate and movement data and sends it to a server, which analyzes the data and immediately generates an alert if an abnormality is detected, notifying necessary medical services or family members.

[1549] Empathetic conversation and guidance

[1550] The device converts everyday conversations into text data using voice recognition technology and sends it to a server. The server analyzes this conversation data and learns from past comments and behavioral trends. Based on the learning results, it generates empathetic responses and provides them to the elderly via the device.

[1551] Specific examples

[1552] For example, suppose a 70-year-old person visits a physical store and begins a conversation with a robot. The robot obtains data such as the user's heart rate and number of steps from the server in advance, and tells them, "You haven't been getting enough exercise lately, so I suggest you walk for 30 minutes today." While the user is exercising, the robot monitors their health data in real time and immediately issues an alert if there is an abnormality. The robot also provides emotional care to the user by engaging in empathetic conversation, such as asking, "Is there anything I can help you with today?"

[1553] Example prompts for generative AI models

[1554] Prompt: Create a scenario in which a healthcare assistant robot for the elderly visits a store and offers a customized exercise program based on the individual's heart rate and step count, and engages in empathetic conversation with the individual.

[1555] scenario:

[1556] When an elderly person visits the store, the robot greets them with "Hello, how are you?" The robot retrieves health data collected in real time from the server and analyzes it. It suggests, "It seems you haven't been getting enough exercise lately, so I suggest you take a 30-minute walk today. May I help you?" During the exercise, the robot encourages the person by saying, "Your heart rate is within the normal range. That's great!" and after the exercise, it engages in empathetic conversation by asking, "Is there anything I can help you with today?"

[1557] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1558] Step 1:

[1559] A user visits a physical store and begins interacting with a robot.

[1560] Input: A user enters a physical store and approaches the robot.

[1561] Processing: The robot recognizes the elderly user and provides a welcome message by displaying or speaking.

[1562] Output: The robot says "Hello, how are you?"

[1563] Step 2:

[1564] The device collects biometric data and transmits it to a server.

[1565] Input: The terminal (wearable device) collects biometric data such as heart rate, steps taken, and activity level.

[1566] Processing: The terminal transmits this data to the server at regular intervals.

[1567] Output: The latest biometric data is sent to the server and stored in a database.

[1568] Step 3:

[1569] The server analyzes the received biometric data.

[1570] Input: New biometric data sent to the server.

[1571] Processing: Using machine learning algorithms to analyze data and assess health and behavioral patterns.

[1572] Output: The analysis results will provide the elderly person's current health status and behavioral patterns.

[1573] Step 4:

[1574] Based on the analysis results, anomalies are detected and alerts are generated.

[1575] Input: Analysis result data.

[1576] Processing: If an abnormal pattern is detected, the server generates an alert and sends a notification to medical services and family members.

[1577] Output: An alert notification is sent to medical services and / or family members.

[1578] Step 5:

[1579] The server generates an exercise program suitable for the elderly.

[1580] Input: Analysis results, individual user health status data.

[1581] Processing: Based on these data, the server generates a customized exercise program for the elderly.

[1582] Output: A customized exercise program is generated and sent to the user's device.

[1583] Step 6:

[1584] The terminal provides the generated exercise program to the user.

[1585] Input: The generated exercise program.

[1586] Processing: The device displays exercise instructions and video guides on the screen and provides audio instructions.

[1587] Output: The user can see and hear the exercise program.

[1588] Step 7:

[1589] The user performs the exercise and the device collects data in real time.

[1590] Input: The exercise the user is performing, and movement data from the wearable device.

[1591] Processing: The device continues to record heart rate and movement data in real time and transmits it to the server.

[1592] Output: Real-time data is sent to a server and continuously monitored.

[1593] Step 8:

[1594] The server analyzes the real-time data and generates an empathetic response.

[1595] Inputs: Real-time data and historical conversation data.

[1596] Processing: Using machine learning algorithms to analyze this data and generate empathetic responses.

[1597] Output: The generated empathetic response is sent to the robot or device.

[1598] Step 9:

[1599] Robots provide interaction for the elderly through empathetic conversation.

[1600] Input: The generated empathic response.

[1601] Processing: The robot will respond to the user with empathetic comments such as, "Is there anything I can help you with today?"

[1602] Output: Elderly people can interact with the robot and receive psychological care.

[1603] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1604] The present invention relates to a system that comprehensively supports the health management of the elderly. It aims to monitor the overall health status of the elderly in real time and prevent social isolation and the progression of dementia by combining the collection, analysis, and abnormality detection of the elderly's biometric data, the provision of customized exercise programs, the generation of empathetic responses, and an emotion engine.

[1605] 1. System Configuration

[1606] The system consists of the following elements:

[1607] Devices: Wearable devices, smartphones, and tablets used daily by seniors. These devices collect biometric and emotional data and provide an interface to transmit it to a server.

[1608] Server: A cloud-based computer system that receives, analyzes, and stores biometric and emotional data, performing tasks such as anomaly detection, generating exercise programs, and providing empathetic responses.

[1609] Emotion engine: A system that analyzes the user's voice data and facial expression data to recognize their emotional state and transmits that data to a server.

[1610] 2. Data collection and transmission

[1611] The devices (wearable devices and in-home sensors) record biometric data such as the elderly person's heart rate, number of steps, activity level, and sleep patterns in real time, and also collect voice and facial expression data, which are then sent to a server.

[1612] 3. Data Analysis and Anomaly Detection

[1613] The server stores the received biometric and emotional data in a database. It then uses machine learning algorithms to analyze this data and model the user's daily behavioral patterns and emotional state. For example, if an elderly person normally speaks in a calm voice but has recently started to speak in a lower tone, the emotion engine will detect this and notify the server, which will recognize this as an anomaly and generate an alert.

[1614] 4. Providing customized exercise programs

[1615] Based on the analysis, the server generates an exercise program tailored to the individual, taking into account their individual health status, past exercise history, and emotional state. For example, if they are feeling depressed, it might suggest relaxation and gentle stretching exercises.

[1616] 5. Real-time monitoring and alerts

[1617] While the user is exercising, the device collects real-time heart rate, movement data, and emotional state and sends it to a server that analyzes the data and immediately generates an alert if an abnormality is detected, notifying necessary medical services or family members.

[1618] 6. Empathetic conversation and guidance

[1619] The device converts everyday conversations into text data using voice recognition technology and sends it to the server. The server analyzes this conversation data and uses natural language processing technology to learn the elderly person's emotions and interests. The emotion engine also integrates the emotional data obtained to generate empathetic responses tailored to the elderly person's emotional state. For example, it can respond in line with the user's emotions, such as, "You seem a little down today. Is there anything I can help you with?"

[1620] Specific examples

[1621] The following specific scenarios illustrate embodiments of the present invention:

[1622] When a user wakes up in the morning, the wearable device records their heart rate and sleep duration, and the emotion engine analyzes their voice tone and facial expressions to obtain emotional data.

[1623] The terminal transmits this data to the server.

[1624] The server analyzes this data to detect recent disruptions in sleep patterns and emotional states.

[1625] Based on the results, the server adjusts the exercise program and relaxation plan and sends the new exercise plan to the user's device.

[1626] The device suggests gentle, non-challenging exercises and relaxation exercises to the user, and displays on-screen instructions and video guides.

[1627] While the user is exercising, the device transmits real-time heart rate, movement data, and emotional state to the server.

[1628] The server monitors the data and immediately generates alerts if there are any abnormalities in health or emotional state, notifying necessary medical services or family members.

[1629] The device generates empathetic responses and provides dialogue through everyday conversations with the user, while at the same time providing more empathetic and appropriate responses by taking into account the emotional state recognized by the emotion engine.

[1630] As described above, the system of the present invention collects and analyzes biometric and emotional data of elderly people, and provides customized exercise programs and empathetic conversations, thereby realizing comprehensive health management.

[1631] The processing flow will be explained below.

[1632] Step 1:

[1633] The terminal (wearable device) collects biometric and emotional data such as heart rate, number of steps, activity level, sleep patterns, voice data, and facial expression data of the elderly in real time. For example, the wearable device collects this data periodically throughout the day.

[1634] Step 2:

[1635] The device transmits the collected biometric and emotional data to a server via Wi-Fi or Bluetooth, and the data is encrypted to protect privacy.

[1636] Step 3:

[1637] The server organizes and stores the received biometric and emotional data in a cloud database, where the data is categorized by date and time.

[1638] Step 4:

[1639] The server analyzes the stored data using machine learning algorithms (e.g., clustering algorithms and time series analysis algorithms) to model the elderly's daily behavioral patterns and emotional states.

[1640] Step 5:

[1641] Based on the analysis results, the server detects outliers or abnormal patterns (e.g., sudden increases in heart rate or sudden changes in facial expression) that deviate from normal behavioral patterns and emotional states.

[1642] Step 6:

[1643] If an anomaly is detected, the server generates an alert and notifies family members or medical services. For example, if an elderly person's emotional state suddenly worsens, an urgent notification will be sent.

[1644] Step 7:

[1645] The server generates a customized exercise program based on the elderly person's daily behavioral patterns and emotional state, taking into account their individual health status, past exercise history, and emotional state.

[1646] Step 8:

[1647] The server sends the generated exercise program to the device, which displays the received program and provides detailed exercise instructions and video guides.

[1648] Step 9:

[1649] When a user exercises, the device collects real-time heart rate, movement data, and emotional state and transmits this information to a server, allowing the user to monitor their condition during exercise.

[1650] Step 10:

[1651] The server reanalyzes the exercise and emotional state data to check for any abnormalities, and if any are detected, immediate action is taken.

[1652] Step 11:

[1653] The terminal uses voice recognition technology to convert the user's everyday conversation into text data and sends it to the server.

[1654] Step 12:

[1655] The server uses natural language processing technology to analyze the conversation data it receives and learns the emotions and interests of the elderly. It also integrates and analyzes voice tone and facial expression data obtained by the emotion engine.

[1656] Step 13:

[1657] The server generates an empathetic response based on the learning results and the user's emotional state and sends it to the device. For example, if the user is feeling depressed, it generates a response such as, "Try doing some exercises to relax today."

[1658] Step 14:

[1659] The device generates an empathetic response and provides it to the user via voice or text. The empathetic response provides psychological care and reduces feelings of social isolation. This process is repeated continuously to achieve comprehensive health management.

[1660] Example 2

[1661] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1662] Health management for the elderly is extremely important, but they have difficulty managing themselves and regular checkups at medical institutions are limited. This can lead to health conditions being overlooked and lack of continuous care. It can also increase the risk of social isolation and the progression of dementia. Furthermore, there is often a lack of provision of exercise programs tailored to health conditions and empathetic responses to emotions. A system to comprehensively solve these problems is needed.

[1663] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1664] In this invention, the server includes a means for analyzing biometric information, a means for detecting abnormalities, a means for generating an exercise program, a means for collecting dialogue information and generating empathetic responses, and a means for monitoring information in real time and detecting abnormalities. This enables comprehensive collection and analysis of biometric data and emotional data of elderly people, enabling real-time monitoring of their health status. Furthermore, by providing customized exercise programs and responding empathetically to their emotions, it is possible to reduce social isolation and slow the progression of dementia.

[1665] "Biometric information" refers to data that indicates the health status of elderly people, such as their heart rate, number of steps taken, activity level, and sleep patterns.

[1666] The "Central Processing Unit" is a cloud-based computer system that is responsible for analyzing, storing, and managing received biometric and emotional data.

[1667] An "exercise program" is a customized exercise and relaxation program provided to seniors based on their health status and past exercise history.

[1668] An "emotion engine" is a system that analyzes the user's voice data and facial expression data, recognizes their emotional state, and transmits the data to the central processing unit.

[1669] "Empathic responses" refer to words and actions that show empathy and are generated based on the elderly person's emotional state and the content of the conversation.

[1670] An "alert" is a notification or alert issued when the central processing unit detects an abnormality, and is used to notify necessary medical services or family members.

[1671] "Monitoring" refers to the process of monitoring the elderly's biometric and emotional data in real time to check for any abnormalities.

[1672] "Dialogue information" refers to data collected from everyday conversations and voice input with elderly people, and is analyzed by the emotion engine.

[1673] The present invention is a system that comprehensively supports health management for the elderly. This system is composed of a combination of collecting and analyzing biological information from the elderly, detecting abnormalities, providing customized exercise programs, generating empathetic responses, and an emotion engine. Detailed embodiments of this system are described below.

[1674] System configuration

[1675] Devices: These are wearable devices, smartphones, and tablets used daily by seniors. These devices record biometric information such as heart rate, number of steps, activity level, and sleep patterns in real time, and also have the ability to collect voice and facial expression data. The data is sent to a server using Bluetooth or Wi-Fi.

[1676] Server: A cloud-based computer system that receives, analyzes, and stores biometric and emotional data. The server analyzes the data using machine learning algorithms developed in Python (e.g., scikit-learn and TensorFlow). The server also performs anomaly detection, generates exercise programs, and provides empathetic responses. Specifically, it can use cloud services such as AWS and Google Cloud Platform.

[1677] Emotion engine: A system that analyzes the user's voice data and facial expression data to recognize their emotional state. This sends data corresponding to the user's emotional state to the server. Furthermore, the emotion engine uses technologies such as Google Speech-to-Text, Google BERT, and Google Text-to-Speech.

[1678] Data collection and transmission

[1679] When a user uses a wearable device or smartphone, the device records real-time biometric information such as heart rate, steps taken, activity level, and sleep patterns, as well as voice and facial expression data. This data is then transmitted by the device to a central processing unit.

[1680] Data Analysis and Anomaly Detection

[1681] The server stores the received biometric and emotional data in a database. It then analyzes the data using Python machine learning libraries (e.g., scikit-learn and TensorFlow). If an abnormality is detected based on the analysis results, the server generates an alert and notifies the necessary medical services or family members.

[1682] Providing customized exercise programs

[1683] Based on the analysis results, the server generates an exercise program suitable for the elderly. This program is customized taking into account the elderly's health condition, past exercise history, emotional state, etc. The generated program is provided to the elderly via their device. For example, it may suggest an exercise program that includes relaxation and light stretching.

[1684] Real-time monitoring

[1685] When a user starts exercising, the device collects real-time heart rate, movement data, and emotional state and sends them to the server, which monitors the data and generates an immediate alert if any abnormalities are detected.

[1686] Empathetic conversation and guidance

[1687] The device uses voice recognition technology to convert everyday conversations with the user into text data and sends it to the server. The server then analyzes this conversation data using natural language processing technology. It also integrates emotional data obtained from the emotion engine to generate empathetic responses for the elderly. The generated responses are then conveyed to the user via the device using voice synthesis technology.

[1688] Specific examples

[1689] For example, when an elderly person wakes up in the morning, the wearable device records their heart rate and sleep duration. At the same time, the emotion engine analyzes the user's voice tone and facial expressions to obtain emotional data. This data is sent from the device to a server, which analyzes the data to detect recent disturbances in sleep patterns and emotional state. Based on the results, the server generates a customized exercise program and relaxation plan and sends it to the device. The device then displays instructions and video guides on the screen to encourage the user to follow the plan. During exercise, data is collected in real time, and if an abnormality is detected, an alert is generated immediately. The device also collects daily conversation data, which is analyzed by the server and used to provide empathetic support.

[1690] Prompt Sentence Examples

[1691] "Write Python code to record a user's heart rate, steps, activity, and sleep patterns and detect any anomalies."

[1692] "Design a system to send data acquired from a wearable device to a cloud server and analyze it."

[1693] "Propose an algorithm that generates a customized exercise program based on the emotional state of an elderly person."

[1694] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1695] Program processing flow

[1696] Step 1: Device preparation and data collection

[1697] The user puts on the wearable device or smartphone and launches the app.

[1698] Input: Wearable devices, smartphones

[1699] Output: Collected biometric information (e.g., heart rate, steps, activity level, sleep patterns)

[1700] The device uses sensors to record heart rate, steps, activity levels, and sleep patterns, and stores the data in the app.

[1701] Operation: Data measurement by sensor, data storage in app

[1702] The device collects voice data and facial expression data.

[1703] How it works: Records voice and facial expressions using the built-in microphone and camera

[1704] Step 2: Sending data

[1705] The terminal transmits the collected biometric and emotional data to a central processing unit.

[1706] Input: Collected biometric and emotional data

[1707] Output: Data sent to the central processing unit

[1708] How it works: Sends data using Bluetooth or Wi-Fi

[1709] Step 3: Data analysis

[1710] The server stores the received biometric information and emotion data in a cloud database.

[1711] Input: Transmitted biometric and emotional data

[1712] Output: Data stored in the database

[1713] How it works: Stores data using a database management system (e.g. AWS DynamoDB)

[1714] The server analyzes the data using Python machine learning libraries.

[1715] Input: Stored biometric and emotional data

[1716] Output: Analysis results (e.g., health status evaluation data, anomaly detection results)

[1717] Operation: Perform data analysis using scikit-learn and TensorFlow

[1718] Step 4: Detect anomalies

[1719] The server detects abnormalities based on the analysis results.

[1720] Input: Data analysis results

[1721] Output: Anomaly detection alert

[1722] How it works: Applies anomaly detection algorithms and generates alerts if anomalies are found

[1723] Step 5: Generate the exercise program

[1724] The server generates an exercise program suitable for the elderly.

[1725] Input: Health assessment data, past exercise history, emotional state

[1726] Output: Customized exercise program

[1727] Movement: Applying an algorithm to generate exercise programs that take into account health and emotional states

[1728] The server transmits the generated exercise program to the terminal.

[1729] Input: Customized exercise program

[1730] Output: Exercise program sent to the device

[1731] Operation: Send using a data transmission service (e.g. HTTP API)

[1732] Step 6: Real-time monitoring

[1733] The user begins exercising according to the recommended program.

[1734] Input: Customized exercise program

[1735] Output: Exercise performed

[1736] Movement: Follow the app's guide to complete the exercise

[1737] The device collects heart rate, movement data, and emotional state in real time and transmits this to a server.

[1738] Input: Data collected during exercise

[1739] Output: Real-time data sent to the server

[1740] Actions: Use sensors and voice recognition technology to collect data and transmit it in real time

[1741] Step 7: Empathetic conversation and guidance

[1742] The terminal uses voice recognition technology to convert everyday conversations with the user into text data and sends it to the server.

[1743] Input: Speech data of everyday conversation

[1744] Output: Text data sent to the server

[1745] How it works: Uses a speech recognition engine (e.g. Google Speech-to-Text)

[1746] The server analyzes this conversation data using natural language processing technology. It also integrates emotional data obtained from the emotion engine to generate an empathetic response. For example, "You seem to be feeling unwell today. Is there anything I can help you with?" The generated response is then conveyed to the user via the device using voice synthesis technology.

[1747] Input: Conversation data, emotion data

[1748] Output: Empathic response

[1749] How it works: Uses natural language processing technology (e.g., Google BERT) and speech synthesis technology (e.g., Google Text-to-Speech)

[1750] The above is a detailed processing flow of the system program. Through the specific operations at each step, the health management of elderly people can be effectively carried out.

[1751] (Application example 2)

[1752] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1753] Health management for the elderly requires real-time monitoring and appropriate responses, especially while on the move. However, current health management systems lack the functionality to respond to situations and environments while on the move, and do not provide sufficient support to ensure the safety of the elderly. They also lack the ability to respond empathetically to fluctuations in emotional states or provide exercise programs adapted to individual health conditions. This poses a challenge, making it difficult to immediately respond to deterioration in health or accidents while the elderly are on the move.

[1754] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting biometric data and emotional data of the elderly person, means for transmitting the collected biometric data and emotional data to the server, means for analyzing the biometric data and emotional data received by the server, means for detecting abnormalities based on the analysis results, means for generating an alert when an abnormality is detected, means for generating an exercise program suitable for the elderly person, means for providing the generated exercise program to the elderly person, means for collecting activity data and emotional data in real time and transmitting it to the server, means for generating an empathetic response based on the emotional data, means for providing empathetic conversation based on the collected data, and means for monitoring the health status of the elderly person while traveling in an autonomous vehicle and generating an appropriate response. This enables real-time monitoring of the elderly person's health status while traveling, empathetic responses according to their emotional state, and immediate response in the event of an abnormality.

[1755] "Biometric data" refers to data that indicates the physical condition of an elderly person, such as their heart rate, number of steps taken, activity level, and sleep patterns.

[1756] "Emotion data" is data that indicates the emotional state of the elderly person, obtained from their tone of voice and facial expressions.

[1757] The "server" is a cloud-based computer system that receives, analyzes, and stores collected biometric and emotional data, and performs anomaly detection and generates exercise programs.

[1758] "Analysis" is the process of using the received biometric and emotional data to model the elderly person's daily behavioral patterns and emotional state and detect abnormalities.

[1759] "Abnormal" refers to a situation in which the health condition of an elderly person is determined to be outside the normal range based on the analysis results.

[1760] An "alert" is a warning notification to seniors, their families, and medical services that is generated when an abnormality is detected.

[1761] An "exercise program" is a plan of exercise and relaxation tailored to the individual health condition of an elderly person based on analyzed biometric and emotional data.

[1762] "Activity data" refers to data related to the daily movements and behaviors of elderly people.

[1763] An "empathic response" is a system response that aims to understand the emotional state of the elderly person and respond appropriately.

[1764] An "autonomous vehicle" is a vehicle that can drive under its own control without the need for driver operation.

[1765] "Monitoring" is the process of monitoring biometric and emotional data in real time.

[1766] "Real-time" refers to the timing of data being collected and processed and analyzed immediately.

[1767] "Immediate response to abnormalities" is the process of quickly generating an alert and taking appropriate action when an abnormality in health status is detected.

[1768] The present invention is a system that comprehensively supports the health management of the elderly. It aims to monitor the overall health status of the elderly in real time by collecting biometric and emotional data of the elderly, analyzing it, detecting abnormalities, providing customized exercise programs, generating empathetic responses, and combining it with an emotion engine, thereby preventing social isolation and the progression of dementia.

[1769] System configuration

[1770] The system consists of the following elements:

[1771] Devices: Wearable devices, smartphones, tablets, and in-vehicle devices used daily by seniors. These devices collect biometric and emotional data and provide an interface to transmit it to a server.

[1772] Server: A cloud-based computer system that receives, analyzes, and stores biometric and emotional data, performing tasks such as anomaly detection, generating exercise programs, and providing empathetic responses.

[1773] Emotion engine: A system that analyzes the user's voice data and facial expression data to recognize their emotional state and transmits that data to a server.

[1774] Data collection and transmission

[1775] The devices used by the elderly (wearable devices and in-vehicle devices for autonomous vehicles) record biometric data such as heart rate, number of steps, activity level, and sleep patterns in real time. They also collect voice and facial expression data, which are then sent to a server where they are securely and efficiently stored.

[1776] Data Analysis and Anomaly Detection

[1777] The server stores the received biometric and emotional data in a database. It then uses machine learning algorithms to analyze this data and model the user's daily behavioral patterns and emotional state. For example, if an elderly person who normally speaks calmly has recently started to speak in a lower tone, the emotion engine will detect this and the server will recognize it as an anomaly and generate an alert.

[1778] Providing customized exercise programs

[1779] Based on the analysis, the server generates an appropriate exercise program for the elderly, taking into account their individual health and activity data, as well as their emotional state. For example, if they are feeling depressed, it might suggest relaxation and gentle stretching exercises.

[1780] Real-time monitoring and alerts

[1781] While the elderly exercise, the device collects real-time heart rate, movement data, and emotional state and sends it to a server that analyzes the data and immediately generates an alert if an abnormality is detected, notifying medical services and family members.

[1782] Empathetic conversation and guidance

[1783] The device converts everyday conversations into text data using voice recognition technology and sends it to the server. The server analyzes this conversation data and uses natural language processing technology to learn the emotions and interests of the elderly person. The emotion engine also integrates the emotional data obtained to generate empathetic responses tailored to the elderly person's emotional state. For example, it is possible to provide empathetic responses such as, "You seem a little tired today. Is there anything I can do to help you relax?"

[1784] Application to autonomous vehicles

[1785] The device installed in the self-driving vehicle monitors the elderly person's biometric and emotional data in real time while they are traveling. The collected data is sent to a server via the in-vehicle device, and appropriate responses or guidance are provided as needed. For example, if the heart rate increases while traveling, the display will say, "Try taking deep breaths."

[1786] Specific examples

[1787] 1. Situation: Elderly people use self-driving vehicles to go to the hospital.

[1788] 2. Data collection: In-car devices record heart rate and voice tones in real time.

[1789] 3. Data analysis: The server analyzes the data and detects anomalies.

[1790] 4. Response generation: Generate appropriate alerts and display relaxation suggestions on the vehicle's display. The voice system will instruct the driver to "take a deep breath."

[1791] Prompt Sentence Examples

[1792] "Analyze real-time data including the health status of seniors to detect abnormal heart rates and stress levels. Generate example responses within self-driving vehicles."

[1793] "Create an example of a customized exercise program for seniors while they are in an autonomous vehicle. Based on the user's heart rate and emotional data, suggest appropriate relaxation techniques."

[1794] As described above, the system of the present invention collects and analyzes biometric and emotional data of elderly people in real time, enabling safe and efficient health management even while on the move.

[1795] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1796] Step 1:

[1797] When a user gets into an autonomous vehicle, the terminal (in-vehicle device) automatically starts up. The terminal is equipped with various sensors (heart rate sensor, camera, microphone) to collect biometric and emotional data of the elderly person. The sensors begin to record the user's heart rate, voice tone, and facial expressions in real time. This data is collected as biometric and emotional data.

[1798] Input: Heart rate, voice tone, and facial expression data of elderly people

[1799] Output: Collected biometric and emotional data

[1800] Step 2:

[1801] The device transmits the collected biometric and emotional data to a cloud server. The data is transmitted securely using an encrypted protocol, ensuring that all data reaches the server in real time.

[1802] Input: Collected biometric and emotional data

[1803] Output: Transmitted biometric and emotional data (stored on the server)

[1804] Step 3:

[1805] The server analyzes the received biometric and emotional data. It uses a generative AI model to quickly analyze the data and model the elderly person's daily behavioral patterns and emotional state. Based on the analysis results, it determines whether any abnormalities have been detected.

[1806] Input: Transmitted biometric and emotional data

[1807] Output: Analysis results (normal or abnormal)

[1808] Step 4:

[1809] If the server detects an abnormality, it immediately generates an alert, which is then sent to medical services and the elderly person's family via SMS, email, app notification, etc.

[1810] Input: Analysis result (abnormal)

[1811] Output: Generated alert (notifying medical services and family)

[1812] Step 5:

[1813] Based on the analysis results, the server generates an exercise program tailored to the elderly, taking into account their biometric data, emotional data, and past exercise history. The generated program includes suggestions for relaxation techniques and light exercise.

[1814] Input: Analysis results (biometric data, emotional data)

[1815] Output: Generated exercise program

[1816] Step 6:

[1817] The generated exercise program is provided to the user via the device. Exercise instructions are displayed on the device's display and audio guidance is also provided. The user then performs exercises and relaxation exercises that can be easily performed in the car.

[1818] Input: Generated exercise program

[1819] Output: Providing an exercise program to the user

[1820] Step 7:

[1821] The device continuously collects heart rate, movement data, and emotional state during exercise, and transmits the collected data in real time to a server, which then analyzes the data and monitors the user's health.

[1822] Input: Biometric and emotional data during exercise

[1823] Output: Collected exercise data (sent to server)

[1824] Step 8:

[1825] The server evaluates the effectiveness of the exercise program based on the new data collected and adjusts the exercise program as needed.

[1826] Input: Collected exercise data

[1827] Output: Evaluation results and program adjustments

[1828] Step 9:

[1829] The device converts everyday conversations into text data using voice recognition technology and sends it to a server, which then uses natural language processing technology to learn the elderly's emotions and interests, and integrates the obtained emotional data into an emotion engine.

[1830] Input: Speech data of everyday conversation

[1831] Output: Text data (sent to server)

[1832] Step 10:

[1833] The server generates an empathetic response based on the emotional data. The generated response is provided to the user via the device, providing empathetic conversation such as, "You seem a little tired today. Is there anything I can help you relax?"

[1834] Input: Emotion data, text data

[1835] Output: Empathetic response (provided to user)

[1836] Through the above processing steps, the system of the present invention collects and analyzes biometric and emotional data of elderly people, realizing safe and effective health management even while on the move.

[1837] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1838] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and i...

Claims

1. a means of collecting biometric data of elderly people; means for transmitting the collected biometric data to a server; means for analyzing the biometric data received by the server; a means for detecting anomalies based on the analysis results; a means for generating an alert when an anomaly is detected; means for generating an exercise program suitable for an elderly person; a means for providing the generated exercise program to the elderly person; means for collecting and transmitting activity data to a server; a means of collecting conversational data and generating empathetic responses; A means to provide empathetic conversations based on collected data, and A system including:

2. The system of claim 1 , wherein the exercise program is customized based on the analyzed biometric data and the individual health condition of the elderly person.

3. The system of claim 1 , further comprising means for monitoring the elderly person's vital signs in real time and sending a notification to a medical service if an abnormality is detected.

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