system

Abstract

Provide a system. 【Solution means】 Input means for acquiring biometric information of the elderly, Evaluation means for evaluating a health risk based on the biometric information, Plan generation means for generating an individual exercise plan and diet plan based on the health risk, Output means for presenting the exercise plan and diet plan, Communication means for notifying a medical institution when the health risk is high, Information processing means for managing the plan on a mobile terminal, Display means for visually presenting the biometric information and analysis results, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the health management of the elderly, there is a problem that it is difficult to provide appropriate exercise and diet proposals based on individual health conditions in real time. Also, there is a problem that it is difficult for family members living at a distance to grasp the health condition of the elderly and make prompt cooperation with medical institutions as needed.

Means for Solving the Problems

[0005] This invention solves these problems by providing means for inputting biometric information of elderly people in real time, means for evaluating health risks based on the input information, means for generating individual exercise and meal plans based on the evaluation results, means for outputting these plans in voice and visual formats, and communication means for notifying medical institutions in the event of a high risk. This makes health management for the elderly more effective and facilitates collaboration with family members living far away and medical institutions.

[0006] The term "elderly" generally refers to people aged 65 and over, who are particularly in need of health management and support for daily living.

[0007] "Biometric information" refers to data that indicates an individual's physical condition, such as heart rate, body temperature, and blood pressure, and is used to assess health status.

[0008] "Health risk" refers to an indicator that quantifies the likelihood of developing a disease or health disorder based on an individual's health status and lifestyle.

[0009] An "exercise plan" is a program that defines appropriate exercise content and frequency, based on an individual's health condition and physical fitness level.

[0010] A "meal plan" is a specific guideline regarding the content and amount of food consumed, created according to an individual's nutritional status and health goals.

[0011] An "input means" is a component used to collect necessary data using devices or sensors.

[0012] An "evaluation tool" is a function that analyzes collected data and uses that information to determine the risk to a person's health status.

[0013] A "plan generation means" is a component that has the function of forming an exercise and diet plan that is individually suited to the user based on the evaluation results.

[0014] The "output means" is a device or method for presenting the generated plan or information to the user visually or aurally.

[0015] The "communication means" is a component that enables data transmission and reception and coordinates with external medical institutions and related parties.

Brief Description of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] The system according to the present invention is an automated platform for comprehensively managing the health status of elderly individuals. This system aims to acquire biometric information of elderly individuals in real time and assess their health risks.

[0038] Data acquisition and storage

[0039] Devices: Wearable devices continuously monitor vital data such as heart rate, blood pressure, and body temperature of elderly individuals. Additionally, smart speakers record meal content and timing via voice input.

[0040] Server: Frequently receives the above data and securely stores it in a cloud database. Each data point is saved with a timestamp and used for future analysis.

[0041] Health risk analysis

[0042] Server: Using biometric information stored in the database, an AI algorithm analyzes the data. The AI ​​assesses individual health risks, for example, detecting early signs of heart disease from abnormal heart rate or elevated blood pressure.

[0043] Terminal: Based on the analysis results, it notifies the user of warnings and advice, and encourages them to seek medical attention if necessary.

[0044] Providing exercise and meal plans

[0045] Server: Based on health risks, the AI ​​generates individually customized exercise and meal plans. For example, if there is a risk of diabetes, it will suggest a low-carbohydrate diet and light exercise such as walking.

[0046] Terminal: The generated plan is explained to the user via voice or displayed on the screen. The plan can also be adjusted to take into account the user's lifestyle and preferences.

[0047] Collaboration with medical institutions

[0048] Server: If a high health risk is identified, an automatic notification is sent to pre-registered healthcare providers and family members. This notification includes recommended actions and healthcare provider information.

[0049] User: You can perform actions as needed and make appointments to visit medical institutions.

[0050] This system aims to improve the quality of life for the elderly by providing 24-hour health monitoring and individualized health management. Furthermore, these features enable family members living remotely to appropriately support the health of their elderly relatives.

[0051] The following describes the processing flow.

[0052] Step 1:

[0053] Devices: Wearable devices worn by elderly individuals measure vital data such as heart rate, blood pressure, and body temperature in real time. Smart speakers record the contents of meals spoken by the user using voice recognition technology.

[0054] Step 2:

[0055] Server: Receives vital data and meal information transmitted from terminals in real time, organizes and stores it in a cloud database. During this process, it checks for missing data and prompts the user to retrieve any missing data.

[0056] Step 3:

[0057] Server: Based on stored data, it utilizes advanced machine learning algorithms to assess individual health risks. It predicts the risk of heart disease, for example, from abnormal heart rate patterns and rising blood pressure trends.

[0058] Step 4:

[0059] Device: If the analysis determines that there is a high health risk, an alert will be sent to the user immediately. The alert will be delivered via audio and visual display, and will explain the health condition that requires attention.

[0060] Step 5:

[0061] Server: Automatically generates personalized exercise and meal plans based on the user's health status. For example, for elderly people who are sedentary, it suggests simple stretches that can be done at home and a balanced meal plan.

[0062] Step 6:

[0063] Terminal: Notifies the user of the created plan and presents options for changing the plan or adjusting the schedule. Users can request adjustments using voice commands.

[0064] Step 7:

[0065] Server: If a health risk is particularly high, the server will automatically contact pre-registered family members or medical institutions. The contact will include information about the current health status and recommended actions.

[0066] Step 8:

[0067] User: The device allows users to perform daily activities according to the proposed plan and request support as needed. User operation is simple and supported by voice and touch interfaces.

[0068] (Example 1)

[0069] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0070] Currently, managing the health of the elderly requires real-time monitoring of their condition and the provision of appropriate preventive measures. However, conventional systems collect biometric information intermittently, which can lead to overlooking health risks or providing inappropriate advice. Furthermore, mechanisms for elderly individuals to appropriately collaborate with medical institutions are not yet fully established. A more comprehensive and automated health management system is needed to address these challenges.

[0071] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0072] In this invention, the server includes information input means, storage means, analysis means, plan generation means, information output means, and communication means. This enables the acquisition and analysis of biological information of elderly individuals in real time, allowing for appropriate health risk assessment, the proposal of exercise and diet plans based on that assessment, and rapid coordination with medical institutions in high-risk situations.

[0073] "Information input means" refers to a device or method that acquires biometric information of elderly individuals in real time and provides that information to a system.

[0074] A "memory device" is a digital medium or recording medium for safely and efficiently storing acquired biometric information.

[0075] "Analysis means" refers to computational processing or software that uses acquired biometric information to evaluate health risks using an AI algorithm.

[0076] "Plan generation means" refers to a process or program for generating individual exercise plans and meal plans based on analysis results.

[0077] "Information output means" refers to a method or device for providing generated plan information to the user through audio or visual means.

[0078] "Communication means" refers to a network or communication technology used to notify appropriate organizations or individuals when there is a high health risk.

[0079] This invention is an automated platform for comprehensively managing the health status of elderly individuals. This platform collects health data from elderly individuals' daily lives in real time, analyzes it using AI, and assesses health risks as needed, thereby providing higher quality health management.

[0080] Data acquisition and storage

[0081] The device uses wearable devices to collect biometric information such as heart rate, blood pressure, and body temperature from elderly individuals in real time. Furthermore, it uses a smart speaker to receive voice input regarding meal content and time, and converts it into text data.

[0082] The server receives data transmitted using an encrypted protocol and stores it in a cloud-based database. This data is stored with a timestamp and serves as a baseline for future analysis.

[0083] Health risk assessment and notification

[0084] The server assesses health risks by analyzing stored biometric data using AI algorithms. The AI ​​model, for example, detects abnormal patterns in heart rate and blood pressure and predicts the likelihood of heart disease.

[0085] Based on the analysis results, the device will provide warnings and advice to the user via voice or display. If a high risk is identified, it will also automatically send notifications to healthcare providers and family members.

[0086] Providing exercise and meal plans

[0087] The server generates individually customized exercise and meal plans based on health risk assessments. This includes features such as suggesting low-carbohydrate diets and light exercise plans for elderly individuals at risk of diabetes.

[0088] The device provides the user with an audio explanation of the generated plan and also displays its contents on the screen. The user can then adjust the plan to suit their preferences and daily life.

[0089] As a concrete example, suppose a 70-year-old user is using a wearable device to monitor their daily health. If the device detects an abnormal heart rate after the user ate a high-fat meal the previous night, the server analyzes the data and notifies the user of their risk of heart disease visually and audibly. The device then suggests a plan for light exercise and a low-fat diet. If the user agrees to the risk assessment, an automatic notification function to a medical institution is activated.

[0090] Examples of prompts to input into a generative AI model:

[0091] "Please analyze the impact of last night's high-fat meal on my heart rate and suggest appropriate health advice."

[0092] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0093] Step 1:

[0094] The device continuously acquires the heart rate, blood pressure, and body temperature of elderly individuals via a wearable device. The input is sensor data, and the output is this biometric information transferred to the device's application. The data is transmitted to the device using Bluetooth communication.

[0095] Step 2:

[0096] The device converts voice data acquired from a smart speaker into text data. The input is voice-based information about meals and their times, and the output is saved as a text-based meal record. Speech recognition technology is used for this process.

[0097] Step 3:

[0098] The device encrypts the acquired biometric information and dietary data and sends it to the server using a secure protocol. The input is the encrypted dataset, and the output is the completion of data transmission to the server. HTTPS is used for data transfer.

[0099] Step 4:

[0100] The server stores the received data in a cloud database. The input is the transmitted encrypted data, and the output is the completion of writing to the database. A timestamp is added to the data to prepare it for later analysis.

[0101] Step 5:

[0102] The server analyzes biometric information stored in a cloud database using AI algorithms. The input is past and present biometric data, and the output is an assessment of health risks. The AI ​​analyzes data patterns and detects anomalies.

[0103] Step 6:

[0104] The server evaluates individual health risks based on the analysis results and generates a risk score. The input is the AI ​​analysis results, and the output is the risk assessment score. It checks for abnormal increases in heart rate, blood pressure, etc.

[0105] Step 7:

[0106] The terminal receives instructions from the server and presents warnings and advice to the user via voice or visual means. Input is the server's risk assessment data, and output is a warning notification to the user. If necessary, it prompts the user to seek medical attention.

[0107] Step 8:

[0108] The server automatically sends notifications to pre-registered healthcare providers and family members if the user's health risk is high. The input is the high-risk assessment result, and the output is the completion of sending the notification. This notification includes countermeasures and contact information.

[0109] Step 9:

[0110] The server generates individualized exercise and diet plans based on health risks. The input is health status assessment data, and the output is a customized plan. The AI ​​derives a plan tailored to each user.

[0111] Step 10:

[0112] The terminal presents the user with a generated exercise and meal plan. The input is the server-generated plan, and the output is either a display on the screen or audio guidance. The user can adjust the plan as needed.

[0113] (Application Example 1)

[0114] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0115] Comprehensively and in real time managing the health status of the elderly is a major challenge, especially for family members and caregivers living remotely. Traditional systems lack data visualization and appropriate communication methods in emergencies, making it difficult to respond quickly to health risks in the elderly. Furthermore, there are few tools that allow for easy management and tracking of individually optimized exercise and diet plans, making it difficult to implement them in daily life.

[0116] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0117] In this invention, the server includes an input means for acquiring biometric information of elderly individuals, an evaluation means for evaluating health risks based on the biometric information, and a plan generation means for generating individual exercise and meal plans based on the health risks. This enables efficient health management in the daily lives of elderly individuals and allows for quick and appropriate responses in emergencies. Furthermore, by enabling information management via mobile terminals, users or caregivers can monitor the health status of elderly individuals in real time and support appropriate decision-making.

[0118] "Input means" refers to devices or methods for continuously acquiring biometric information from elderly individuals, and involves collecting vital data such as heart rate, blood pressure, and body temperature using wearable devices and sensors.

[0119] "Evaluation methods" refer to processes and technologies for analyzing and evaluating health risks based on acquired biometric information, and involve using artificial intelligence and algorithms to detect abnormal values ​​and signs of risk.

[0120] A "plan generation method" refers to a method or system for creating individual exercise and diet plans based on health risk assessments, proposing an optimal plan tailored to each individual's health condition.

[0121] "Output means" refers to a method for presenting the generated exercise plan, meal plan, and health status information to the user, and includes visual displays and audio guidance.

[0122] "Communication methods" refer to technologies used to notify medical institutions or designated contacts in cases of high health risk, enabling rapid emergency contact.

[0123] "Information processing means" refers to systems and processes for managing biometric information and analysis results on mobile devices, and which are used for data storage, organization, and display.

[0124] "Display means" refers to devices or interfaces that visually present biometric information and health analysis results, and that communicate data to users in an easy-to-understand manner.

[0125] The system for implementing the present invention is a platform that includes multiple hardware and software elements. This description will focus primarily on the mobile terminal and the server.

[0126] server:

[0127] The server plays a central role in receiving, storing, and analyzing biometric data. It collects data from wearable devices and smart speakers via the cloud and securely stores it in a database. The stored data is analyzed in real time using AI models (e.g., TENSORFLOW®) to assess individual health risks. For example, if an elderly person's daily heart rate data indicates signs of heart disease, the server immediately determines the risk level and triggers the next action.

[0128] Terminal:

[0129] Mobile devices serve as both information displays and interfaces. Generated exercise and meal plans are presented to the elderly and their caregivers visually or audibly. Using a React Native application, users can visually monitor their health status and receive the generated plans. Furthermore, in emergencies, the device can quickly send notifications to medical institutions. This functionality enables a rapid response in emergencies.

[0130] User:

[0131] Users receive health-based information through their devices and implement suggested exercise and diet plans. For example, if they receive a notification that "three walks per week are recommended," they will incorporate that plan into their daily life. Users can also use their devices to manage contact with medical institutions when necessary.

[0132] For example, if AI analysis determines that daily exercise contributes to a stable heart rate, it's possible to use that result to prompt, "Please suggest a suitable morning walking plan for a 65-year-old man." This generates an individually optimized exercise plan, which is then presented to the user.

[0133] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0134] Step 1:

[0135] The server acquires biometric and lifestyle data in real time from wearable devices and smart speakers. Input data includes heart rate, blood pressure, body temperature, and dietary information. This data is then temporarily stored in cloud storage. A time-stamped dataset is generated as output.

[0136] Step 2:

[0137] The server inputs data stored in the cloud into an AI model (TensorFlow, for example) to assess health risks. The AI ​​model analyzes patterns such as abnormal heart rate and elevated blood pressure. As a result, it outputs a risk score for each data point.

[0138] Step 3:

[0139] The server creates individualized exercise and diet plans based on the generated risk score. Using an AI model, it generates plans such as, "You appear to be inactive, so we recommend three 30-minute walks per week." This results in the output of an optimal exercise and diet plan.

[0140] Step 4:

[0141] The device presents the plan received from the server to the user visually or audibly. The displayed plan includes specific exercise and dietary suggestions and is designed to be implemented by the user in their daily life. The user receives the plan content as output.

[0142] Step 5:

[0143] Users implement the plan generated using their device. They input daily health data and implementation results, feeding this information back to the server. The input data is used for future analysis and plan improvement.

[0144] Step 6:

[0145] If the device detects a high health risk, it will quickly send a notification to a medical institution or pre-configured contacts. This allows for immediate response in emergencies. The output will include a notification that the message has been sent and a list of the next actions to take.

[0146] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0147] The system according to the present invention is designed to provide advanced health management for the elderly, and by incorporating an emotion analysis function, it enables more personalized health management. This system acquires the biological information and emotional state of elderly individuals in real time and performs comprehensive health risk assessment and adjustment.

[0148] Data acquisition and emotion recognition

[0149] Device: A wearable device collects vital data, including heart rate and body temperature. Furthermore, a device equipped with a camera and microphone analyzes the user's emotions from their facial expressions and voice. For example, the camera captures the user's face and extracts emotions from their facial expressions.

[0150] Server: Integrates vital data and emotional data sent from terminals and stores them in a cloud-based database.

[0151] Integrated assessment of health risks and emotions

[0152] Server: Applies machine learning algorithms to comprehensively assess health risks based on vital and emotional data. For example, it examines the correlation between stress levels and heart rate to more accurately determine the risk of cardiovascular disease.

[0153] Terminal: Provides real-time feedback of analysis results to the user and issues alerts when necessary.

[0154] Plan generation and dynamic adjustment

[0155] Server: Based on evaluation results, it generates exercise and meal plans tailored to the user's emotional state. For example, for a user feeling anxious, it suggests light exercise with a relaxing effect and a meal menu that takes their emotions into consideration for the day.

[0156] Device: Present the plan to the user via voice and display, emphasizing an emotionally sensitive approach.

[0157] Notification and support for healthcare institutions

[0158] Server: Automatically notifies pre-registered healthcare providers and family members if there is a high health risk or a significant deterioration in emotional state. The notification includes recommended actions and healthcare provider information.

[0159] User: Through the device, users can select actions based on the proposed plan and decide to visit a medical institution if necessary. Users can also request additional support through voice guidance.

[0160] This system provides comprehensive health management that considers not only the physical aspects of health but also mental health, improving the quality of life for the elderly. It also enables rapid responses to emotional changes and facilitates smooth collaboration with family and medical institutions.

[0161] The following describes the processing flow.

[0162] Step 1:

[0163] Device: A wearable device acquires vital data such as the elderly person's heart rate and body temperature in real time and sends this data to the cloud. At the same time, a camera captures the user's facial expressions and a microphone records voice to acquire emotional data.

[0164] Step 2:

[0165] Server: Receives biometric and emotional data transmitted from terminals and stores it in a database. Preprocessing is performed on the data during storage to impute missing values ​​and ensure data integrity.

[0166] Step 3:

[0167] Server: Uses advanced machine learning algorithms to analyze stored vital and emotional data. The goal of the analysis is to evaluate the relationship between health risks and emotional states and to determine overall health status.

[0168] Step 4:

[0169] Device: Based on the analysis results, the device immediately notifies the user of any health risks. The notification includes specific risk factors and recommended countermeasures.

[0170] Step 5:

[0171] Server: Automatically generates exercise and meal plans tailored to the user's health and emotional state. When generating these plans, it considers options that are effective in reducing stress and stabilizing emotions.

[0172] Step 6:

[0173] Device: Presents the user with generated exercise and meal plans. The presentation is done via both voice and display, and the user can adjust the plan as needed.

[0174] Step 7:

[0175] Server: Automatically notifies healthcare providers and family members of the situation if there is a high health risk or if emotional state deteriorates significantly. The notification includes necessary actions and recommendations for seeking medical attention.

[0176] Step 8:

[0177] User: Utilize the device to incorporate the suggested plan into daily life. Receive additional advice via voice guidance when needed, making it easier to choose data-driven actions.

[0178] (Example 2)

[0179] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0180] In managing the health of the elderly, there is a need to go beyond simply acquiring biometric information and consider the user's mental health to achieve more comprehensive and individualized health management. However, conventional systems lack mechanisms to analyze emotional information in real time and incorporate it into health risk assessments, making it difficult to manage both physical and mental health in an integrated manner.

[0181] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0182] In this invention, the server includes an input means for acquiring biometric information of elderly individuals, an emotion analysis means for analyzing emotional information, and an integrated evaluation means for assessing health risks. This enables the integration of biometric and emotional information to provide more accurate health risk assessments and personalized health management plans.

[0183] "Input means" refers to devices and technologies for collecting biometric and emotional information of elderly people in real time.

[0184] "Evaluation means" refers to a system or algorithm for analyzing and evaluating a user's health risks based on collected biometric and emotional information.

[0185] A "plan generation method" is a technology for generating exercise and diet plans tailored to each elderly person based on the results of a health risk assessment.

[0186] "Output means" refers to methods or devices for presenting the generated health management plan to the user in an easily understandable manner.

[0187] "Communication means" refers to a system or device for notifying medical institutions or family members of information when an assessed health risk is high.

[0188] "Emotional analysis methods" refer to technologies and algorithms that analyze voice and facial expressions obtained from a device to identify the user's emotional state.

[0189] An "integrated evaluation method" is a system or algorithm that combines biometric information and emotional information to more accurately assess a user's health risks.

[0190] A description of embodiments for carrying out the present invention will be provided.

[0191] This system manages the health of the elderly from both a physical and mental perspective, and is realized by combining wearable devices with the latest analytical technology. Here, the wearable device acquires the user's biometric information, such as heart rate and body temperature, in real time. In addition, a terminal with emotion recognition capabilities analyzes the user's facial expressions and voice to extract emotional information. These two types of data are transmitted to and stored on a cloud server.

[0192] The server utilizes machine learning algorithms to comprehensively evaluate biometric and emotional information. This enables the accurate identification of a user's health risks. For example, if elevated stress and heart rate fluctuations are detected simultaneously, the server assesses the risk of developing heart disease and, if necessary, promptly notifies a medical institution.

[0193] Furthermore, the server generates exercise and meal plans that take into account the user's emotional state, based on the health risk assessment results. The terminal presents these plans to the user in an easy-to-understand manner via voice and display. This allows the user to incorporate necessary actions into their daily life.

[0194] A concrete example of this behavior is when a user experiencing anxiety follows the device's suggestions and performs light exercises to relax. The device also provides feedback to help the user adjust the plan applied to them according to daily changes.

[0195] An example of a prompt to input into a generative AI model is: "Please describe a system for the integrated management of the physical and mental health of older adults. Specifically, please explain how emotions and biometric information are used to assess health risks."

[0196] In this way, the present invention provides comprehensive support for elderly people to lead healthy and fulfilling lives.

[0197] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0198] Step 1:

[0199] The device acquires biometric information such as heart rate and body temperature in real time via the user's wearable device. It captures facial expressions and voice tone using a camera and microphone, and extracts emotional information using an emotion analysis algorithm. This data is sent as input to a cloud server.

[0200] Step 2:

[0201] The server stores the received biometric and emotional information in a database. During this process, timestamps are used to organize the data chronologically, creating a user health history. The input data is normalized and compressed for later analysis before being stored.

[0202] Step 3:

[0203] The server applies machine learning algorithms to comprehensively analyze stored biometric and emotional data. Factors such as heart rate variability and stress levels are considered to assess health risks. The output calculates potential health risks and their severity.

[0204] Step 4:

[0205] Based on the assessed health risks, the server generates exercise and meal plans tailored to the user's emotional state. Specifically, if relaxation is needed, simple yoga instructions and nutritious meal menus are suggested. This plan is then sent to the terminal as system output.

[0206] Step 5:

[0207] The terminal notifies the user of the plan received from the server. It explains the plan's contents and implementation methods using voice and display. It also issues alerts for health risks that require attention. Based on this information, the user can take the suggested actions.

[0208] Step 6:

[0209] The server automatically sends notifications to pre-registered medical institutions and family members if it detects a health risk exceeding a certain threshold. The notification includes information about the risk and recommended medical actions to encourage prompt response.

[0210] (Application Example 2)

[0211] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0212] Health management for the elderly requires a comprehensive approach that considers not only physical but also emotional aspects. However, conventional systems have difficulty integrating and analyzing biometric information and emotional states, limiting their ability to provide individually appropriate exercise and dietary plans. Furthermore, they struggle to respond quickly to sudden increases in health risks or deterioration of emotional state.

[0213] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0214] In this invention, the server includes information acquisition means for acquiring biometric data and emotional state data in real time, analysis means for comprehensively evaluating health risks, and plan generation means for generating and dynamically adjusting individual exercise and dietary plans. This enables personalized health management that considers both health and emotions, as well as rapid risk response.

[0215] "Biometric data" refers to information collected to understand the health status of elderly individuals, such as their heart rate and body temperature.

[0216] "Emotional state data" refers to data that represents emotions, analyzed from the facial expressions and voice tone of elderly individuals.

[0217] "Information acquisition means" refers to various devices and technologies used to acquire biometric data and emotional state data in real time.

[0218] "Analysis methods" refer to algorithms and machine learning models used to assess health risks using biometric data and emotional state data.

[0219] The "plan generation means" is a function that generates individual exercise and diet plans based on the results of health risk assessments and emotional states, and dynamically adjusts them as needed.

[0220] "Output means" refers to an interface for providing users with generated exercise and meal plans, using audio and visual information.

[0221] "Communication methods" refer to technologies that automatically notify external organizations when there is a high health risk.

[0222] This invention is a system for comprehensively managing the health and emotional state of elderly individuals. The main components of the system are information acquisition means, analysis means, plan generation means, output means, and communication means.

[0223] First, the information acquisition method involves collecting biometric and emotional state data of elderly individuals in real time using wearable devices, cameras, and microphones. This data includes heart rate, body temperature, facial expressions, and voice. This allows for an accurate understanding of the user's health status and emotions.

[0224] The server stores collected biometric and emotional state data in the cloud and performs analysis using specific algorithms. Machine learning models, such as TensorFlow, are used to comprehensively assess health risks from this data. This analysis enables predictions regarding conditions such as heart disease and stress levels.

[0225] Based on the results obtained by the analysis, the server uses a plan generation tool to create individual exercise and meal plans. These plans are dynamically adjusted according to the user's current emotional state and provided to the user as specific suggestions. For example, if the user is feeling anxious, suggestions for light exercise with a relaxing effect and meal menus that take their emotions into consideration will be provided.

[0226] The output method presents the plan to the user via smartphones or other devices, using audio and visual information. This allows the user to intuitively understand and execute the plan.

[0227] The communication system notifies pre-registered external organizations and family members if health risks increase or emotional state deteriorates significantly. This notification includes recommended actions and information on healthcare facilities to facilitate prompt action.

[0228] For example, if a morning health check detects an elevated heart rate and an anxious expression, the server suggests a meditation program and plays corresponding music. This allows the user to continue their daily life with peace of mind.

[0229] An example of a prompt message for a generative AI model might be: "A 70-year-old woman has a higher-than-usual heart rate this morning and looks anxious. Based on this data, suggest actions that can help her relax. Options include light stretching and morning relaxation music."

[0230] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0231] Step 1:

[0232] The device collects biometric and emotional state data of elderly individuals in real time using wearable devices, smartphone cameras, and microphones. Inputs include heart rate, body temperature, facial expressions, and voice, which are acquired from sensors, cameras, and microphones. Outputs are sent to a server as raw data.

[0233] Step 2:

[0234] The server stores the received raw data in the cloud and performs data preprocessing. This preprocessing includes noise reduction and normalization, converting the data into a format suitable for analysis. The input is raw data sent from the terminal, and the output is a preprocessed dataset.

[0235] Step 3:

[0236] The server uses a pre-processed dataset and a machine learning model to analyze health risks and emotional states. This analysis employs a generative AI model to assess cardiovascular disease risk and stress levels. The input is pre-processed data, and the output is an estimate of health risk and emotional state.

[0237] Step 4:

[0238] The server uses a plan generation mechanism to create individual exercise and meal plans based on the analysis results, and dynamically adjusts them. The plan content is optimized according to the user's emotional state. The input is the health risk assessment results and emotional state estimates, and the output is specific exercise and meal suggestions.

[0239] Step 5:

[0240] The device presents the generated exercise and meal plans to the user through audio and visual information. Relaxation-inducing content may also be played at this stage. The input is the plan content sent from the server, and the output is designed to enhance the user's understanding and motivate them to follow the plan.

[0241] Step 6:

[0242] The server uses communication methods to notify registered external organizations and family members if health risks increase or emotional states deteriorate. The notification includes recommended actions. Input is the analysis results, and output is a notification message to encourage prompt action.

[0243] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0244] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0245] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0246] [Second Embodiment]

[0247] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0248] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0249] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0250] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0251] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0252] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0253] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0254] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0255] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0256] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0257] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0258] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0259] The system according to the present invention is an automated platform for comprehensively managing the health status of elderly individuals. This system aims to acquire biometric information of elderly individuals in real time and assess their health risks.

[0260] Data acquisition and storage

[0261] Devices: Wearable devices continuously monitor vital data such as heart rate, blood pressure, and body temperature of elderly individuals. Additionally, smart speakers record meal content and timing via voice input.

[0262] Server: Frequently receives the above data and securely stores it in a cloud database. Each data point is saved with a timestamp and used for future analysis.

[0263] Health risk analysis

[0264] Server: Using biometric information stored in the database, an AI algorithm analyzes the data. The AI ​​assesses individual health risks, for example, detecting early signs of heart disease from abnormal heart rate or elevated blood pressure.

[0265] Terminal: Based on the analysis results, it notifies the user of warnings and advice, and encourages them to seek medical attention if necessary.

[0266] Providing exercise and meal plans

[0267] Server: Based on health risks, the AI ​​generates individually customized exercise and meal plans. For example, if there is a risk of diabetes, it will suggest a low-carbohydrate diet and light exercise such as walking.

[0268] Terminal: The generated plan is explained to the user via voice or displayed on the screen. The plan can also be adjusted to take into account the user's lifestyle and preferences.

[0269] Collaboration with medical institutions

[0270] Server: If a high health risk is identified, an automatic notification is sent to pre-registered healthcare providers and family members. This notification includes recommended actions and healthcare provider information.

[0271] User: You can perform actions as needed and make appointments to visit medical institutions.

[0272] This system aims to improve the quality of life for the elderly by providing 24-hour health monitoring and individualized health management. Furthermore, these features enable family members living remotely to appropriately support the health of their elderly relatives.

[0273] The following describes the processing flow.

[0274] Step 1:

[0275] Devices: Wearable devices worn by elderly individuals measure vital data such as heart rate, blood pressure, and body temperature in real time. Smart speakers record the contents of meals spoken by the user using voice recognition technology.

[0276] Step 2:

[0277] Server: Receives vital data and meal information transmitted from terminals in real time, organizes and stores it in a cloud database. During this process, it checks for missing data and prompts the user to retrieve any missing data.

[0278] Step 3:

[0279] Server: Based on stored data, it utilizes advanced machine learning algorithms to assess individual health risks. It predicts the risk of heart disease, for example, from abnormal heart rate patterns and rising blood pressure trends.

[0280] Step 4:

[0281] Device: If the analysis determines that there is a high health risk, an alert will be sent to the user immediately. The alert will be delivered via audio and visual display, and will explain the health condition that requires attention.

[0282] Step 5:

[0283] Server: Based on the user's health condition, it automatically generates personalized exercise plans and diet plans. For example, for elderly people with insufficient exercise, it proposes simple stretches that can be done at home and a balanced diet menu.

[0284] Step 6:

[0285] Terminal: It notifies the user of the created plan and presents options for plan changes and schedule adjustments. The user can request adjustments via voice commands.

[0286] Step 7:

[0287] Server: When the health risk is particularly high, it automatically contacts pre-registered family members or medical institutions. The contact includes information about the current health condition and recommended countermeasures.

[0288] Step 8:

[0289] User: Using the terminal, the user can carry out daily activities according to the proposed plan and request support if necessary. The user's operations are simple and are supported by an interface using voice or touch.

[0290] (Example 1)

[0291] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0292] Currently, in the health management of the elderly, real-time status monitoring and appropriate preventive measures are required. However, in conventional systems, the collection of biometric information is intermittent, which may result in overlooking health risks and leading to inappropriate advice. Also, the mechanism for the elderly to cooperate appropriately with medical institutions is not fully established. A more comprehensive and automated health management system to solve these problems is desired.

[0293] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0294] In this invention, the server includes information input means, storage means, analysis means, plan generation means, information output means, and communication means. This enables the acquisition and analysis of biological information of elderly individuals in real time, allowing for appropriate health risk assessment, the proposal of exercise and diet plans based on that assessment, and rapid coordination with medical institutions in high-risk situations.

[0295] "Information input means" refers to a device or method that acquires biometric information of elderly individuals in real time and provides that information to a system.

[0296] A "memory device" is a digital medium or recording medium for safely and efficiently storing acquired biometric information.

[0297] "Analysis means" refers to computational processing or software that uses acquired biometric information to evaluate health risks using an AI algorithm.

[0298] "Plan generation means" refers to a process or program for generating individual exercise plans and meal plans based on analysis results.

[0299] "Information output means" refers to a method or device for providing generated plan information to the user through audio or visual means.

[0300] "Communication means" refers to a network or communication technology used to notify appropriate organizations or individuals when there is a high health risk.

[0301] This invention is an automated platform for comprehensively managing the health status of elderly individuals. This platform collects health data from elderly individuals' daily lives in real time, analyzes it using AI, and assesses health risks as needed, thereby providing higher quality health management.

[0302] Data acquisition and storage

[0303] The terminal uses a wearable device to collect biometric information such as the heart rate, blood pressure, and body temperature of the elderly in real time. Furthermore, using a smart speaker, it receives the diet content and time as voice input and converts it into text data.

[0304] The server receives the data transmitted using an encryption protocol and stores it in a cloud-based database. This data is stored together with a timestamp and functions as a baseline for future analysis.

[0305] Health risk assessment and notification

[0306] The server evaluates the health risk by analyzing the stored biometric information using an AI algorithm. The AI model detects abnormal patterns in, for example, the heart rate and blood pressure and predicts the possibility of heart disease.

[0307] Based on the analysis results, the terminal presents warnings and advice to the user through voice or display. Also, when a high risk is confirmed, it automatically sends notifications to medical institutions and family members.

[0308] Provision of exercise and diet plans

[0309] Based on the health risk assessment, the server generates individually customized exercise and diet plans. This includes, for example, the function of proposing a low-carb diet and a light exercise plan for the elderly at risk of diabetes.

[0310] The terminal explains the generated plan to the user by voice and further displays its content on the display. The user can adjust it to a plan that suits their wishes and daily life.

[0311] As a concrete example, suppose a 70-year-old user is using a wearable device to monitor their daily health. If the device detects an abnormal heart rate after the user ate a high-fat meal the previous night, the server analyzes the data and notifies the user of their risk of heart disease visually and audibly. The device then suggests a plan for light exercise and a low-fat diet. If the user agrees to the risk assessment, an automatic notification function to a medical institution is activated.

[0312] Examples of prompts to input into a generative AI model:

[0313] "Please analyze the impact of last night's high-fat meal on my heart rate and suggest appropriate health advice."

[0314] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0315] Step 1:

[0316] The device continuously acquires the heart rate, blood pressure, and body temperature of elderly individuals via a wearable device. The input is sensor data, and the output is this biometric information transferred to the device's application. The data is transmitted to the device using Bluetooth communication.

[0317] Step 2:

[0318] The device converts voice data acquired from a smart speaker into text data. The input is voice-based information about meals and their times, and the output is saved as a text-based meal record. Speech recognition technology is used for this process.

[0319] Step 3:

[0320] The device encrypts the acquired biometric information and dietary data and sends it to the server using a secure protocol. The input is the encrypted dataset, and the output is the completion of data transmission to the server. HTTPS is used for data transfer.

[0321] Step 4:

[0322] The server stores the received data in a cloud database. The input is the transmitted encrypted data, and the output is the completion of writing to the database. A timestamp is added to the data to prepare it for later analysis.

[0323] Step 5:

[0324] The server analyzes biometric information stored in a cloud database using AI algorithms. The input is past and present biometric data, and the output is an assessment of health risks. The AI ​​analyzes data patterns and detects anomalies.

[0325] Step 6:

[0326] The server evaluates individual health risks based on the analysis results and generates a risk score. The input is the AI ​​analysis results, and the output is the risk assessment score. It checks for abnormal increases in heart rate, blood pressure, etc.

[0327] Step 7:

[0328] The terminal receives instructions from the server and presents warnings and advice to the user via voice or visual means. Input is the server's risk assessment data, and output is a warning notification to the user. If necessary, it prompts the user to seek medical attention.

[0329] Step 8:

[0330] The server automatically sends notifications to pre-registered healthcare providers and family members if the user's health risk is high. The input is the high-risk assessment result, and the output is the completion of sending the notification. This notification includes countermeasures and contact information.

[0331] Step 9:

[0332] The server generates individualized exercise and diet plans based on health risks. The input is health status assessment data, and the output is a customized plan. The AI ​​derives a plan tailored to each user.

[0333] Step 10:

[0334] The terminal presents the user with a generated exercise and meal plan. The input is the server-generated plan, and the output is either a display on the screen or audio guidance. The user can adjust the plan as needed.

[0335] (Application Example 1)

[0336] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0337] Comprehensively and in real time managing the health status of the elderly is a major challenge, especially for family members and caregivers living remotely. Traditional systems lack data visualization and appropriate communication methods in emergencies, making it difficult to respond quickly to health risks in the elderly. Furthermore, there are few tools that allow for easy management and tracking of individually optimized exercise and diet plans, making it difficult to implement them in daily life.

[0338] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0339] In this invention, the server includes an input means for acquiring biometric information of elderly individuals, an evaluation means for evaluating health risks based on the biometric information, and a plan generation means for generating individual exercise and meal plans based on the health risks. This enables efficient health management in the daily lives of elderly individuals and allows for quick and appropriate responses in emergencies. Furthermore, by enabling information management via mobile terminals, users or caregivers can monitor the health status of elderly individuals in real time and support appropriate decision-making.

[0340] "Input means" refers to devices or methods for continuously acquiring biometric information from elderly individuals, and involves collecting vital data such as heart rate, blood pressure, and body temperature using wearable devices and sensors.

[0341] "Evaluation methods" refer to processes and technologies for analyzing and evaluating health risks based on acquired biometric information, and involve using artificial intelligence and algorithms to detect abnormal values ​​and signs of risk.

[0342] A "plan generation method" refers to a method or system for creating individual exercise and diet plans based on health risk assessments, proposing an optimal plan tailored to each individual's health condition.

[0343] "Output means" refers to a method for presenting the generated exercise plan, meal plan, and health status information to the user, and includes visual displays and audio guidance.

[0344] "Communication methods" refer to technologies used to notify medical institutions or designated contacts in cases of high health risk, enabling rapid emergency contact.

[0345] "Information processing means" refers to systems and processes for managing biometric information and analysis results on mobile devices, and which are used for data storage, organization, and display.

[0346] "Display means" refers to devices or interfaces that visually present biometric information and health analysis results, and that communicate data to users in an easy-to-understand manner.

[0347] The system for implementing the present invention is a platform that includes multiple hardware and software elements. This description will focus primarily on the mobile terminal and the server.

[0348] server:

[0349] The server plays a central role in receiving, storing, and analyzing biometric data. It collects data from wearable devices and smart speakers via the cloud and securely stores it in a database. The stored data is analyzed in real time using AI models (e.g., TensorFlow) to assess individual health risks. For example, if an elderly person's daily heart rate data indicates signs of heart disease, the server immediately determines the risk level and triggers the next action.

[0350] Terminal:

[0351] Mobile devices serve as both information displays and interfaces. Generated exercise and meal plans are presented to the elderly and their caregivers visually or audibly. Using a React Native application, users can visually monitor their health status and receive the generated plans. Furthermore, in emergencies, the device can quickly send notifications to medical institutions. This functionality enables a rapid response in emergencies.

[0352] User:

[0353] Users receive health-based information through their devices and implement suggested exercise and diet plans. For example, if they receive a notification that "three walks per week are recommended," they will incorporate that plan into their daily life. Users can also use their devices to manage contact with medical institutions when necessary.

[0354] For example, if AI analysis determines that daily exercise contributes to a stable heart rate, it's possible to use that result to prompt, "Please suggest a suitable morning walking plan for a 65-year-old man." This generates an individually optimized exercise plan, which is then presented to the user.

[0355] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0356] Step 1:

[0357] The server acquires biometric and lifestyle data in real time from wearable devices and smart speakers. Input data includes heart rate, blood pressure, body temperature, and dietary information. This data is then temporarily stored in cloud storage. A time-stamped dataset is generated as output.

[0358] Step 2:

[0359] The server inputs data stored in the cloud into an AI model (TensorFlow, for example) to assess health risks. The AI ​​model analyzes patterns such as abnormal heart rate and elevated blood pressure. As a result, it outputs a risk score for each data point.

[0360] Step 3:

[0361] The server creates individualized exercise and diet plans based on the generated risk score. Using an AI model, it generates plans such as, "You appear to be inactive, so we recommend three 30-minute walks per week." This results in the output of an optimal exercise and diet plan.

[0362] Step 4:

[0363] The device presents the plan received from the server to the user visually or audibly. The displayed plan includes specific exercise and dietary suggestions and is designed to be implemented by the user in their daily life. The user receives the plan content as output.

[0364] Step 5:

[0365] Users implement the plan generated using their device. They input daily health data and implementation results, feeding this information back to the server. The input data is used for future analysis and plan improvement.

[0366] Step 6:

[0367] If the device detects a high health risk, it will quickly send a notification to a medical institution or pre-configured contacts. This allows for immediate response in emergencies. The output will include a notification that the message has been sent and a list of the next actions to take.

[0368] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0369] The system according to the present invention is designed to provide advanced health management for the elderly, and by incorporating an emotion analysis function, it enables more personalized health management. This system acquires the biological information and emotional state of elderly individuals in real time and performs comprehensive health risk assessment and adjustment.

[0370] Data acquisition and emotion recognition

[0371] Device: A wearable device collects vital data, including heart rate and body temperature. Furthermore, a device equipped with a camera and microphone analyzes the user's emotions from their facial expressions and voice. For example, the camera captures the user's face and extracts emotions from their facial expressions.

[0372] Server: Integrates vital data and emotional data sent from terminals and stores them in a cloud-based database.

[0373] Integrated assessment of health risks and emotions

[0374] Server: Applies machine learning algorithms to comprehensively assess health risks based on vital and emotional data. For example, it examines the correlation between stress levels and heart rate to more accurately determine the risk of cardiovascular disease.

[0375] Terminal: Provides real-time feedback of analysis results to the user and issues alerts when necessary.

[0376] Plan generation and dynamic adjustment

[0377] Server: Based on evaluation results, it generates exercise and meal plans tailored to the user's emotional state. For example, for a user feeling anxious, it suggests light exercise with a relaxing effect and a meal menu that takes their emotions into consideration for the day.

[0378] Device: Present the plan to the user via voice and display, emphasizing an emotionally sensitive approach.

[0379] Notification and support for healthcare institutions

[0380] Server: Automatically notifies pre-registered healthcare providers and family members if there is a high health risk or a significant deterioration in emotional state. The notification includes recommended actions and healthcare provider information.

[0381] User: Through the device, users can select actions based on the proposed plan and decide to visit a medical institution if necessary. Users can also request additional support through voice guidance.

[0382] This system provides comprehensive health management that considers not only the physical aspects of health but also mental health, improving the quality of life for the elderly. It also enables rapid responses to emotional changes and facilitates smooth collaboration with family and medical institutions.

[0383] The following describes the processing flow.

[0384] Step 1:

[0385] Device: A wearable device acquires vital data such as the elderly person's heart rate and body temperature in real time and sends this data to the cloud. At the same time, a camera captures the user's facial expressions and a microphone records voice to acquire emotional data.

[0386] Step 2:

[0387] Server: Receives biometric and emotional data transmitted from terminals and stores it in a database. Preprocessing is performed on the data during storage to impute missing values ​​and ensure data integrity.

[0388] Step 3:

[0389] Server: Uses advanced machine learning algorithms to analyze stored vital and emotional data. The goal of the analysis is to evaluate the relationship between health risks and emotional states and to determine overall health status.

[0390] Step 4:

[0391] Device: Based on the analysis results, the device immediately notifies the user of any health risks. The notification includes specific risk factors and recommended countermeasures.

[0392] Step 5:

[0393] Server: Automatically generates exercise and meal plans tailored to the user's health and emotional state. When generating these plans, it considers options that are effective in reducing stress and stabilizing emotions.

[0394] Step 6:

[0395] Device: Presents the user with generated exercise and meal plans. The presentation is done via both voice and display, and the user can adjust the plan as needed.

[0396] Step 7:

[0397] Server: Automatically notifies healthcare providers and family members of the situation if there is a high health risk or if emotional state deteriorates significantly. The notification includes necessary actions and recommendations for seeking medical attention.

[0398] Step 8:

[0399] User: Utilize the device to incorporate the suggested plan into daily life. Receive additional advice via voice guidance when needed, making it easier to choose data-driven actions.

[0400] (Example 2)

[0401] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0402] In managing the health of the elderly, there is a need to go beyond simply acquiring biometric information and consider the user's mental health to achieve more comprehensive and individualized health management. However, conventional systems lack mechanisms to analyze emotional information in real time and incorporate it into health risk assessments, making it difficult to manage both physical and mental health in an integrated manner.

[0403] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0404] In this invention, the server includes an input means for acquiring biometric information of elderly individuals, an emotion analysis means for analyzing emotional information, and an integrated evaluation means for assessing health risks. This enables the integration of biometric and emotional information to provide more accurate health risk assessments and personalized health management plans.

[0405] "Input means" refers to devices and technologies for collecting biometric and emotional information of elderly people in real time.

[0406] "Evaluation means" refers to a system or algorithm for analyzing and evaluating a user's health risks based on collected biometric and emotional information.

[0407] A "plan generation method" is a technology for generating exercise and diet plans tailored to each elderly person based on the results of a health risk assessment.

[0408] "Output means" refers to methods or devices for presenting the generated health management plan to the user in an easily understandable manner.

[0409] "Communication means" refers to a system or device for notifying medical institutions or family members of information when an assessed health risk is high.

[0410] "Emotional analysis methods" refer to technologies and algorithms that analyze voice and facial expressions obtained from a device to identify the user's emotional state.

[0411] An "integrated evaluation method" is a system or algorithm that combines biometric information and emotional information to more accurately assess a user's health risks.

[0412] A description of embodiments for carrying out the present invention will be provided.

[0413] This system manages the health of the elderly from both a physical and mental perspective, and is realized by combining wearable devices with the latest analytical technology. Here, the wearable device acquires the user's biometric information, such as heart rate and body temperature, in real time. In addition, a terminal with emotion recognition capabilities analyzes the user's facial expressions and voice to extract emotional information. These two types of data are transmitted to and stored on a cloud server.

[0414] The server utilizes machine learning algorithms to comprehensively evaluate biometric and emotional information. This enables the accurate identification of a user's health risks. For example, if elevated stress and heart rate fluctuations are detected simultaneously, the server assesses the risk of developing heart disease and, if necessary, promptly notifies a medical institution.

[0415] Furthermore, the server generates exercise and meal plans that take into account the user's emotional state, based on the health risk assessment results. The terminal presents these plans to the user in an easy-to-understand manner via voice and display. This allows the user to incorporate necessary actions into their daily life.

[0416] A concrete example of this behavior is when a user experiencing anxiety follows the device's suggestions and performs light exercises to relax. The device also provides feedback to help the user adjust the plan applied to them according to daily changes.

[0417] An example of a prompt to input into a generative AI model is: "Please describe a system for the integrated management of the physical and mental health of older adults. Specifically, please explain how emotions and biometric information are used to assess health risks."

[0418] In this way, the present invention provides comprehensive support for elderly people to lead healthy and fulfilling lives.

[0419] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0420] Step 1:

[0421] The device acquires biometric information such as heart rate and body temperature in real time via the user's wearable device. It captures facial expressions and voice tone using a camera and microphone, and extracts emotional information using an emotion analysis algorithm. This data is sent as input to a cloud server.

[0422] Step 2:

[0423] The server stores the received biometric and emotional information in a database. During this process, timestamps are used to organize the data chronologically, creating a user health history. The input data is normalized and compressed for later analysis before being stored.

[0424] Step 3:

[0425] The server applies machine learning algorithms to comprehensively analyze stored biometric and emotional data. Factors such as heart rate variability and stress levels are considered to assess health risks. The output calculates potential health risks and their severity.

[0426] Step 4:

[0427] Based on the assessed health risks, the server generates exercise and meal plans tailored to the user's emotional state. Specifically, if relaxation is needed, simple yoga instructions and nutritious meal menus are suggested. This plan is then sent to the terminal as system output.

[0428] Step 5:

[0429] The terminal notifies the user of the plan received from the server. It explains the plan's contents and implementation methods using voice and display. It also issues alerts for health risks that require attention. Based on this information, the user can take the suggested actions.

[0430] Step 6:

[0431] The server automatically sends notifications to pre-registered medical institutions and family members if it detects a health risk exceeding a certain threshold. The notification includes information about the risk and recommended medical actions to encourage prompt response.

[0432] (Application Example 2)

[0433] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0434] Health management for the elderly requires a comprehensive approach that considers not only physical but also emotional aspects. However, conventional systems have difficulty integrating and analyzing biometric information and emotional states, limiting their ability to provide individually appropriate exercise and dietary plans. Furthermore, they struggle to respond quickly to sudden increases in health risks or deterioration of emotional state.

[0435] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0436] In this invention, the server includes information acquisition means for acquiring biometric data and emotional state data in real time, analysis means for comprehensively evaluating health risks, and plan generation means for generating and dynamically adjusting individual exercise and dietary plans. This enables personalized health management that considers both health and emotions, as well as rapid risk response.

[0437] "Biometric data" refers to information collected to understand the health status of elderly individuals, such as their heart rate and body temperature.

[0438] "Emotional state data" refers to data that represents emotions, analyzed from the facial expressions and voice tone of elderly individuals.

[0439] "Information acquisition means" refers to various devices and technologies used to acquire biometric data and emotional state data in real time.

[0440] "Analysis methods" refer to algorithms and machine learning models used to assess health risks using biometric data and emotional state data.

[0441] The "plan generation means" is a function that generates individual exercise and diet plans based on the results of health risk assessments and emotional states, and dynamically adjusts them as needed.

[0442] "Output means" refers to an interface for providing users with generated exercise and meal plans, using audio and visual information.

[0443] "Communication methods" refer to technologies that automatically notify external organizations when there is a high health risk.

[0444] This invention is a system for comprehensively managing the health and emotional state of elderly individuals. The main components of the system are information acquisition means, analysis means, plan generation means, output means, and communication means.

[0445] First, the information acquisition method involves collecting biometric and emotional state data of elderly individuals in real time using wearable devices, cameras, and microphones. This data includes heart rate, body temperature, facial expressions, and voice. This allows for an accurate understanding of the user's health status and emotions.

[0446] The server stores collected biometric and emotional state data in the cloud and performs analysis using specific algorithms. Machine learning models, such as TensorFlow, are used to comprehensively assess health risks from this data. This analysis enables predictions regarding conditions such as heart disease and stress levels.

[0447] Based on the results obtained by the analysis, the server uses a plan generation tool to create individual exercise and meal plans. These plans are dynamically adjusted according to the user's current emotional state and provided to the user as specific suggestions. For example, if the user is feeling anxious, suggestions for light exercise with a relaxing effect and meal menus that take their emotions into consideration will be provided.

[0448] The output method presents the plan to the user via smartphones or other devices, using audio and visual information. This allows the user to intuitively understand and execute the plan.

[0449] The communication system notifies pre-registered external organizations and family members if health risks increase or emotional state deteriorates significantly. This notification includes recommended actions and information on healthcare facilities to facilitate prompt action.

[0450] For example, if a morning health check detects an elevated heart rate and an anxious expression, the server suggests a meditation program and plays corresponding music. This allows the user to continue their daily life with peace of mind.

[0451] An example of a prompt message for a generative AI model might be: "A 70-year-old woman has a higher-than-usual heart rate this morning and looks anxious. Based on this data, suggest actions that can help her relax. Options include light stretching and morning relaxation music."

[0452] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0453] Step 1:

[0454] The device collects biometric and emotional state data of elderly individuals in real time using wearable devices, smartphone cameras, and microphones. Inputs include heart rate, body temperature, facial expressions, and voice, which are acquired from sensors, cameras, and microphones. Outputs are sent to a server as raw data.

[0455] Step 2:

[0456] The server stores the received raw data in the cloud and performs data preprocessing. This preprocessing includes noise reduction and normalization, converting the data into a format suitable for analysis. The input is raw data sent from the terminal, and the output is a preprocessed dataset.

[0457] Step 3:

[0458] The server uses a pre-processed dataset and a machine learning model to analyze health risks and emotional states. This analysis employs a generative AI model to assess cardiovascular disease risk and stress levels. The input is pre-processed data, and the output is an estimate of health risk and emotional state.

[0459] Step 4:

[0460] The server uses a plan generation mechanism to create individual exercise and meal plans based on the analysis results, and dynamically adjusts them. The plan content is optimized according to the user's emotional state. The input is the health risk assessment results and emotional state estimates, and the output is specific exercise and meal suggestions.

[0461] Step 5:

[0462] The device presents the generated exercise and meal plans to the user through audio and visual information. Relaxation-inducing content may also be played at this stage. The input is the plan content sent from the server, and the output is designed to enhance the user's understanding and motivate them to follow the plan.

[0463] Step 6:

[0464] The server uses communication methods to notify registered external organizations and family members if health risks increase or emotional states deteriorate. The notification includes recommended actions. Input is the analysis results, and output is a notification message to encourage prompt action.

[0465] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0466] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0467] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0468] [Third Embodiment]

[0469] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0470] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0471] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0472] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0473] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0474] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0475] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0476] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0477] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0478] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0479] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0480] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0481] The system according to the present invention is an automated platform for comprehensively managing the health status of elderly individuals. This system aims to acquire biometric information of elderly individuals in real time and assess their health risks.

[0482] Data acquisition and storage

[0483] Devices: Wearable devices continuously monitor vital data such as heart rate, blood pressure, and body temperature of elderly individuals. Additionally, smart speakers record meal content and timing via voice input.

[0484] Server: Frequently receives the above data and securely stores it in a cloud database. Each data point is saved with a timestamp and used for future analysis.

[0485] Health risk analysis

[0486] Server: Using biometric information stored in the database, an AI algorithm analyzes the data. The AI ​​assesses individual health risks, for example, detecting early signs of heart disease from abnormal heart rate or elevated blood pressure.

[0487] Terminal: Based on the analysis results, it notifies the user of warnings and advice, and encourages them to seek medical attention if necessary.

[0488] Providing exercise and meal plans

[0489] Server: Based on health risks, the AI ​​generates individually customized exercise and meal plans. For example, if there is a risk of diabetes, it will suggest a low-carbohydrate diet and light exercise such as walking.

[0490] Terminal: The generated plan is explained to the user via voice or displayed on the screen. The plan can also be adjusted to take into account the user's lifestyle and preferences.

[0491] Collaboration with medical institutions

[0492] Server: If a high health risk is identified, an automatic notification is sent to pre-registered healthcare providers and family members. This notification includes recommended actions and healthcare provider information.

[0493] User: You can perform actions as needed and make appointments to visit medical institutions.

[0494] This system aims to improve the quality of life for the elderly by providing 24-hour health monitoring and individualized health management. Furthermore, these features enable family members living remotely to appropriately support the health of their elderly relatives.

[0495] The following describes the processing flow.

[0496] Step 1:

[0497] Devices: Wearable devices worn by elderly individuals measure vital data such as heart rate, blood pressure, and body temperature in real time. Smart speakers record the contents of meals spoken by the user using voice recognition technology.

[0498] Step 2:

[0499] Server: Receives vital data and meal information transmitted from terminals in real time, organizes and stores it in a cloud database. During this process, it checks for missing data and prompts the user to retrieve any missing data.

[0500] Step 3:

[0501] Server: Based on stored data, it utilizes advanced machine learning algorithms to assess individual health risks. It predicts the risk of heart disease, for example, from abnormal heart rate patterns and rising blood pressure trends.

[0502] Step 4:

[0503] Device: If the analysis determines that there is a high health risk, an alert will be sent to the user immediately. The alert will be delivered via audio and visual display, and will explain the health condition that requires attention.

[0504] Step 5:

[0505] Server: Automatically generates personalized exercise and meal plans based on the user's health status. For example, for elderly people who are sedentary, it suggests simple stretches that can be done at home and a balanced meal plan.

[0506] Step 6:

[0507] Terminal: Notifies the user of the created plan and presents options for changing the plan or adjusting the schedule. Users can request adjustments using voice commands.

[0508] Step 7:

[0509] Server: If a health risk is particularly high, the server will automatically contact pre-registered family members or medical institutions. The contact will include information about the current health status and recommended actions.

[0510] Step 8:

[0511] User: The device allows users to perform daily activities according to the proposed plan and request support as needed. User operation is simple and supported by voice and touch interfaces.

[0512] (Example 1)

[0513] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0514] Currently, managing the health of the elderly requires real-time monitoring of their condition and the provision of appropriate preventive measures. However, conventional systems collect biometric information intermittently, which can lead to overlooking health risks or providing inappropriate advice. Furthermore, mechanisms for elderly individuals to appropriately collaborate with medical institutions are not yet fully established. A more comprehensive and automated health management system is needed to address these challenges.

[0515] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0516] In this invention, the server includes information input means, storage means, analysis means, plan generation means, information output means, and communication means. This enables the acquisition and analysis of biological information of elderly individuals in real time, allowing for appropriate health risk assessment, the proposal of exercise and diet plans based on that assessment, and rapid coordination with medical institutions in high-risk situations.

[0517] "Information input means" refers to a device or method that acquires biometric information of elderly individuals in real time and provides that information to a system.

[0518] A "memory device" is a digital medium or recording medium for safely and efficiently storing acquired biometric information.

[0519] "Analysis means" refers to computational processing or software that uses acquired biometric information to evaluate health risks using an AI algorithm.

[0520] "Plan generation means" refers to a process or program for generating individual exercise plans and meal plans based on analysis results.

[0521] "Information output means" refers to a method or device for providing generated plan information to the user through audio or visual means.

[0522] "Communication means" refers to a network or communication technology used to notify appropriate organizations or individuals when there is a high health risk.

[0523] This invention is an automated platform for comprehensively managing the health status of elderly individuals. This platform collects health data from elderly individuals' daily lives in real time, analyzes it using AI, and assesses health risks as needed, thereby providing higher quality health management.

[0524] Data acquisition and storage

[0525] The device uses wearable devices to collect biometric information such as heart rate, blood pressure, and body temperature from elderly individuals in real time. Furthermore, it uses a smart speaker to receive voice input regarding meal content and time, and converts it into text data.

[0526] The server receives data transmitted using an encrypted protocol and stores it in a cloud-based database. This data is stored with a timestamp and serves as a baseline for future analysis.

[0527] Health risk assessment and notification

[0528] The server assesses health risks by analyzing stored biometric data using AI algorithms. The AI ​​model, for example, detects abnormal patterns in heart rate and blood pressure and predicts the likelihood of heart disease.

[0529] Based on the analysis results, the device will provide warnings and advice to the user via voice or display. If a high risk is identified, it will also automatically send notifications to healthcare providers and family members.

[0530] Providing exercise and meal plans

[0531] The server generates individually customized exercise and meal plans based on health risk assessments. This includes features such as suggesting low-carbohydrate diets and light exercise plans for elderly individuals at risk of diabetes.

[0532] The device provides the user with an audio explanation of the generated plan and also displays its contents on the screen. The user can then adjust the plan to suit their preferences and daily life.

[0533] As a concrete example, suppose a 70-year-old user is using a wearable device to monitor their daily health. If the device detects an abnormal heart rate after the user ate a high-fat meal the previous night, the server analyzes the data and notifies the user of their risk of heart disease visually and audibly. The device then suggests a plan for light exercise and a low-fat diet. If the user agrees to the risk assessment, an automatic notification function to a medical institution is activated.

[0534] Examples of prompts to input into a generative AI model:

[0535] "Please analyze the impact of last night's high-fat meal on my heart rate and suggest appropriate health advice."

[0536] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0537] Step 1:

[0538] The device continuously acquires the heart rate, blood pressure, and body temperature of elderly individuals via a wearable device. The input is sensor data, and the output is this biometric information transferred to the device's application. The data is transmitted to the device using Bluetooth communication.

[0539] Step 2:

[0540] The device converts voice data acquired from a smart speaker into text data. The input is voice-based information about meals and their times, and the output is saved as a text-based meal record. Speech recognition technology is used for this process.

[0541] Step 3:

[0542] The device encrypts the acquired biometric information and dietary data and sends it to the server using a secure protocol. The input is the encrypted dataset, and the output is the completion of data transmission to the server. HTTPS is used for data transfer.

[0543] Step 4:

[0544] The server stores the received data in a cloud database. The input is the transmitted encrypted data, and the output is the completion of writing to the database. A timestamp is added to the data to prepare it for later analysis.

[0545] Step 5:

[0546] The server analyzes biometric information stored in a cloud database using AI algorithms. The input is past and present biometric data, and the output is an assessment of health risks. The AI ​​analyzes data patterns and detects anomalies.

[0547] Step 6:

[0548] The server evaluates individual health risks based on the analysis results and generates a risk score. The input is the AI ​​analysis results, and the output is the risk assessment score. It checks for abnormal increases in heart rate, blood pressure, etc.

[0549] Step 7:

[0550] The terminal receives instructions from the server and presents warnings and advice to the user via voice or visual means. Input is the server's risk assessment data, and output is a warning notification to the user. If necessary, it prompts the user to seek medical attention.

[0551] Step 8:

[0552] The server automatically sends notifications to pre-registered healthcare providers and family members if the user's health risk is high. The input is the high-risk assessment result, and the output is the completion of sending the notification. This notification includes countermeasures and contact information.

[0553] Step 9:

[0554] The server generates individualized exercise and diet plans based on health risks. The input is health status assessment data, and the output is a customized plan. The AI ​​derives a plan tailored to each user.

[0555] Step 10:

[0556] The terminal presents the user with a generated exercise and meal plan. The input is the server-generated plan, and the output is either a display on the screen or audio guidance. The user can adjust the plan as needed.

[0557] (Application Example 1)

[0558] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0559] Comprehensively and in real time managing the health status of the elderly is a major challenge, especially for family members and caregivers living remotely. Traditional systems lack data visualization and appropriate communication methods in emergencies, making it difficult to respond quickly to health risks in the elderly. Furthermore, there are few tools that allow for easy management and tracking of individually optimized exercise and diet plans, making it difficult to implement them in daily life.

[0560] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0561] In this invention, the server includes an input means for acquiring biometric information of elderly individuals, an evaluation means for evaluating health risks based on the biometric information, and a plan generation means for generating individual exercise and meal plans based on the health risks. This enables efficient health management in the daily lives of elderly individuals and allows for quick and appropriate responses in emergencies. Furthermore, by enabling information management via mobile terminals, users or caregivers can monitor the health status of elderly individuals in real time and support appropriate decision-making.

[0562] "Input means" refers to devices or methods for continuously acquiring biometric information from elderly individuals, and involves collecting vital data such as heart rate, blood pressure, and body temperature using wearable devices and sensors.

[0563] "Evaluation methods" refer to processes and technologies for analyzing and evaluating health risks based on acquired biometric information, and involve using artificial intelligence and algorithms to detect abnormal values ​​and signs of risk.

[0564] A "plan generation method" refers to a method or system for creating individual exercise and diet plans based on health risk assessments, proposing an optimal plan tailored to each individual's health condition.

[0565] "Output means" refers to a method for presenting the generated exercise plan, meal plan, and health status information to the user, and includes visual displays and audio guidance.

[0566] "Communication methods" refer to technologies used to notify medical institutions or designated contacts in cases of high health risk, enabling rapid emergency contact.

[0567] "Information processing means" refers to systems and processes for managing biometric information and analysis results on mobile devices, and which are used for data storage, organization, and display.

[0568] "Display means" refers to devices or interfaces that visually present biometric information and health analysis results, and that communicate data to users in an easy-to-understand manner.

[0569] The system for implementing the present invention is a platform that includes multiple hardware and software elements. This description will focus primarily on the mobile terminal and the server.

[0570] server:

[0571] The server plays a central role in receiving, storing, and analyzing biometric data. It collects data from wearable devices and smart speakers via the cloud and securely stores it in a database. The stored data is analyzed in real time using AI models (e.g., TensorFlow) to assess individual health risks. For example, if an elderly person's daily heart rate data indicates signs of heart disease, the server immediately determines the risk level and triggers the next action.

[0572] Terminal:

[0573] Mobile devices serve as both information displays and interfaces. Generated exercise and meal plans are presented to the elderly and their caregivers visually or audibly. Using a React Native application, users can visually monitor their health status and receive the generated plans. Furthermore, in emergencies, the device can quickly send notifications to medical institutions. This functionality enables a rapid response in emergencies.

[0574] User:

[0575] Users receive health-based information through their devices and implement suggested exercise and diet plans. For example, if they receive a notification that "three walks per week are recommended," they will incorporate that plan into their daily life. Users can also use their devices to manage contact with medical institutions when necessary.

[0576] For example, if AI analysis determines that daily exercise contributes to a stable heart rate, it's possible to use that result to prompt, "Please suggest a suitable morning walking plan for a 65-year-old man." This generates an individually optimized exercise plan, which is then presented to the user.

[0577] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0578] Step 1:

[0579] The server acquires biometric and lifestyle data in real time from wearable devices and smart speakers. Input data includes heart rate, blood pressure, body temperature, and dietary information. This data is then temporarily stored in cloud storage. A time-stamped dataset is generated as output.

[0580] Step 2:

[0581] The server inputs data stored in the cloud into an AI model (TensorFlow, for example) to assess health risks. The AI ​​model analyzes patterns such as abnormal heart rate and elevated blood pressure. As a result, it outputs a risk score for each data point.

[0582] Step 3:

[0583] The server creates individualized exercise and diet plans based on the generated risk score. Using an AI model, it generates plans such as, "You appear to be inactive, so we recommend three 30-minute walks per week." This results in the output of an optimal exercise and diet plan.

[0584] Step 4:

[0585] The device presents the plan received from the server to the user visually or audibly. The displayed plan includes specific exercise and dietary suggestions and is designed to be implemented by the user in their daily life. The user receives the plan content as output.

[0586] Step 5:

[0587] Users implement the plan generated using their device. They input daily health data and implementation results, feeding this information back to the server. The input data is used for future analysis and plan improvement.

[0588] Step 6:

[0589] If the device detects a high health risk, it will quickly send a notification to a medical institution or pre-configured contacts. This allows for immediate response in emergencies. The output will include a notification that the message has been sent and a list of the next actions to take.

[0590] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0591] The system according to the present invention is designed to provide advanced health management for the elderly, and by incorporating an emotion analysis function, it enables more personalized health management. This system acquires the biological information and emotional state of elderly individuals in real time and performs comprehensive health risk assessment and adjustment.

[0592] Data acquisition and emotion recognition

[0593] Device: A wearable device collects vital data, including heart rate and body temperature. Furthermore, a device equipped with a camera and microphone analyzes the user's emotions from their facial expressions and voice. For example, the camera captures the user's face and extracts emotions from their facial expressions.

[0594] Server: Integrates vital data and emotional data sent from terminals and stores them in a cloud-based database.

[0595] Integrated assessment of health risks and emotions

[0596] Server: Applies machine learning algorithms to comprehensively assess health risks based on vital and emotional data. For example, it examines the correlation between stress levels and heart rate to more accurately determine the risk of cardiovascular disease.

[0597] Terminal: Provides real-time feedback of analysis results to the user and issues alerts when necessary.

[0598] Plan generation and dynamic adjustment

[0599] Server: Based on evaluation results, it generates exercise and meal plans tailored to the user's emotional state. For example, for a user feeling anxious, it suggests light exercise with a relaxing effect and a meal menu that takes their emotions into consideration for the day.

[0600] Device: Present the plan to the user via voice and display, emphasizing an emotionally sensitive approach.

[0601] Notification and support for healthcare institutions

[0602] Server: Automatically notifies pre-registered healthcare providers and family members if there is a high health risk or a significant deterioration in emotional state. The notification includes recommended actions and healthcare provider information.

[0603] User: Through the device, users can select actions based on the proposed plan and decide to visit a medical institution if necessary. Users can also request additional support through voice guidance.

[0604] This system provides comprehensive health management that considers not only the physical aspects of health but also mental health, improving the quality of life for the elderly. It also enables rapid responses to emotional changes and facilitates smooth collaboration with family and medical institutions.

[0605] The following describes the processing flow.

[0606] Step 1:

[0607] Device: A wearable device acquires vital data such as the elderly person's heart rate and body temperature in real time and sends this data to the cloud. At the same time, a camera captures the user's facial expressions and a microphone records voice to acquire emotional data.

[0608] Step 2:

[0609] Server: Receives biometric and emotional data transmitted from terminals and stores it in a database. Preprocessing is performed on the data during storage to impute missing values ​​and ensure data integrity.

[0610] Step 3:

[0611] Server: Uses advanced machine learning algorithms to analyze stored vital and emotional data. The goal of the analysis is to evaluate the relationship between health risks and emotional states and to determine overall health status.

[0612] Step 4:

[0613] Device: Based on the analysis results, the device immediately notifies the user of any health risks. The notification includes specific risk factors and recommended countermeasures.

[0614] Step 5:

[0615] Server: Automatically generates exercise and meal plans tailored to the user's health and emotional state. When generating these plans, it considers options that are effective in reducing stress and stabilizing emotions.

[0616] Step 6:

[0617] Device: Presents the user with generated exercise and meal plans. The presentation is done via both voice and display, and the user can adjust the plan as needed.

[0618] Step 7:

[0619] Server: Automatically notifies healthcare providers and family members of the situation if there is a high health risk or if emotional state deteriorates significantly. The notification includes necessary actions and recommendations for seeking medical attention.

[0620] Step 8:

[0621] User: Utilize the device to incorporate the suggested plan into daily life. Receive additional advice via voice guidance when needed, making it easier to choose data-driven actions.

[0622] (Example 2)

[0623] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0624] In managing the health of the elderly, there is a need to go beyond simply acquiring biometric information and consider the user's mental health to achieve more comprehensive and individualized health management. However, conventional systems lack mechanisms to analyze emotional information in real time and incorporate it into health risk assessments, making it difficult to manage both physical and mental health in an integrated manner.

[0625] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0626] In this invention, the server includes an input means for acquiring biometric information of elderly individuals, an emotion analysis means for analyzing emotional information, and an integrated evaluation means for assessing health risks. This enables the integration of biometric and emotional information to provide more accurate health risk assessments and personalized health management plans.

[0627] "Input means" refers to devices and technologies for collecting biometric and emotional information of elderly people in real time.

[0628] "Evaluation means" refers to a system or algorithm for analyzing and evaluating a user's health risks based on collected biometric and emotional information.

[0629] A "plan generation method" is a technology for generating exercise and diet plans tailored to each elderly person based on the results of a health risk assessment.

[0630] "Output means" refers to methods or devices for presenting the generated health management plan to the user in an easily understandable manner.

[0631] "Communication means" refers to a system or device for notifying medical institutions or family members of information when an assessed health risk is high.

[0632] "Emotional analysis methods" refer to technologies and algorithms that analyze voice and facial expressions obtained from a device to identify the user's emotional state.

[0633] An "integrated evaluation method" is a system or algorithm that combines biometric information and emotional information to more accurately assess a user's health risks.

[0634] A description of embodiments for carrying out the present invention will be provided.

[0635] This system manages the health of the elderly from both a physical and mental perspective, and is realized by combining wearable devices with the latest analytical technology. Here, the wearable device acquires the user's biometric information, such as heart rate and body temperature, in real time. In addition, a terminal with emotion recognition capabilities analyzes the user's facial expressions and voice to extract emotional information. These two types of data are transmitted to and stored on a cloud server.

[0636] The server utilizes machine learning algorithms to comprehensively evaluate biometric and emotional information. This enables the accurate identification of a user's health risks. For example, if elevated stress and heart rate fluctuations are detected simultaneously, the server assesses the risk of developing heart disease and, if necessary, promptly notifies a medical institution.

[0637] Furthermore, the server generates exercise and meal plans that take into account the user's emotional state, based on the health risk assessment results. The terminal presents these plans to the user in an easy-to-understand manner via voice and display. This allows the user to incorporate necessary actions into their daily life.

[0638] A concrete example of this behavior is when a user experiencing anxiety follows the device's suggestions and performs light exercises to relax. The device also provides feedback to help the user adjust the plan applied to them according to daily changes.

[0639] An example of a prompt to input into a generative AI model is: "Please describe a system for the integrated management of the physical and mental health of older adults. Specifically, please explain how emotions and biometric information are used to assess health risks."

[0640] In this way, the present invention provides comprehensive support for elderly people to lead healthy and fulfilling lives.

[0641] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0642] Step 1:

[0643] The device acquires biometric information such as heart rate and body temperature in real time via the user's wearable device. It captures facial expressions and voice tone using a camera and microphone, and extracts emotional information using an emotion analysis algorithm. This data is sent as input to a cloud server.

[0644] Step 2:

[0645] The server stores the received biometric and emotional information in a database. During this process, timestamps are used to organize the data chronologically, creating a user health history. The input data is normalized and compressed for later analysis before being stored.

[0646] Step 3:

[0647] The server applies machine learning algorithms to comprehensively analyze stored biometric and emotional data. Factors such as heart rate variability and stress levels are considered to assess health risks. The output calculates potential health risks and their severity.

[0648] Step 4:

[0649] Based on the assessed health risks, the server generates exercise and meal plans tailored to the user's emotional state. Specifically, if relaxation is needed, simple yoga instructions and nutritious meal menus are suggested. This plan is then sent to the terminal as system output.

[0650] Step 5:

[0651] The terminal notifies the user of the plan received from the server. It explains the plan's contents and implementation methods using voice and display. It also issues alerts for health risks that require attention. Based on this information, the user can take the suggested actions.

[0652] Step 6:

[0653] The server automatically sends notifications to pre-registered medical institutions and family members if it detects a health risk exceeding a certain threshold. The notification includes information about the risk and recommended medical actions to encourage prompt response.

[0654] (Application Example 2)

[0655] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0656] Health management for the elderly requires a comprehensive approach that considers not only physical but also emotional aspects. However, conventional systems have difficulty integrating and analyzing biometric information and emotional states, limiting their ability to provide individually appropriate exercise and dietary plans. Furthermore, they struggle to respond quickly to sudden increases in health risks or deterioration of emotional state.

[0657] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0658] In this invention, the server includes information acquisition means for acquiring biometric data and emotional state data in real time, analysis means for comprehensively evaluating health risks, and plan generation means for generating and dynamically adjusting individual exercise and dietary plans. This enables personalized health management that considers both health and emotions, as well as rapid risk response.

[0659] "Biometric data" refers to information collected to understand the health status of elderly individuals, such as their heart rate and body temperature.

[0660] "Emotional state data" refers to data that represents emotions, analyzed from the facial expressions and voice tone of elderly individuals.

[0661] "Information acquisition means" refers to various devices and technologies used to acquire biometric data and emotional state data in real time.

[0662] "Analysis methods" refer to algorithms and machine learning models used to assess health risks using biometric data and emotional state data.

[0663] The "plan generation means" is a function that generates individual exercise and diet plans based on the results of health risk assessments and emotional states, and dynamically adjusts them as needed.

[0664] "Output means" refers to an interface for providing users with generated exercise and meal plans, using audio and visual information.

[0665] "Communication methods" refer to technologies that automatically notify external organizations when there is a high health risk.

[0666] This invention is a system for comprehensively managing the health and emotional state of elderly individuals. The main components of the system are information acquisition means, analysis means, plan generation means, output means, and communication means.

[0667] First, the information acquisition method involves collecting biometric and emotional state data of elderly individuals in real time using wearable devices, cameras, and microphones. This data includes heart rate, body temperature, facial expressions, and voice. This allows for an accurate understanding of the user's health status and emotions.

[0668] The server stores collected biometric and emotional state data in the cloud and performs analysis using specific algorithms. Machine learning models, such as TensorFlow, are used to comprehensively assess health risks from this data. This analysis enables predictions regarding conditions such as heart disease and stress levels.

[0669] Based on the results obtained by the analysis, the server uses a plan generation tool to create individual exercise and meal plans. These plans are dynamically adjusted according to the user's current emotional state and provided to the user as specific suggestions. For example, if the user is feeling anxious, suggestions for light exercise with a relaxing effect and meal menus that take their emotions into consideration will be provided.

[0670] The output method presents the plan to the user via smartphones or other devices, using audio and visual information. This allows the user to intuitively understand and execute the plan.

[0671] The communication system notifies pre-registered external organizations and family members if health risks increase or emotional state deteriorates significantly. This notification includes recommended actions and information on healthcare facilities to facilitate prompt action.

[0672] For example, if a morning health check detects an elevated heart rate and an anxious expression, the server suggests a meditation program and plays corresponding music. This allows the user to continue their daily life with peace of mind.

[0673] An example of a prompt message for a generative AI model might be: "A 70-year-old woman has a higher-than-usual heart rate this morning and looks anxious. Based on this data, suggest actions that can help her relax. Options include light stretching and morning relaxation music."

[0674] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0675] Step 1:

[0676] The device collects biometric and emotional state data of elderly individuals in real time using wearable devices, smartphone cameras, and microphones. Inputs include heart rate, body temperature, facial expressions, and voice, which are acquired from sensors, cameras, and microphones. Outputs are sent to a server as raw data.

[0677] Step 2:

[0678] The server stores the received raw data in the cloud and performs data preprocessing. This preprocessing includes noise reduction and normalization, converting the data into a format suitable for analysis. The input is raw data sent from the terminal, and the output is a preprocessed dataset.

[0679] Step 3:

[0680] The server uses a pre-processed dataset and a machine learning model to analyze health risks and emotional states. This analysis employs a generative AI model to assess cardiovascular disease risk and stress levels. The input is pre-processed data, and the output is an estimate of health risk and emotional state.

[0681] Step 4:

[0682] The server uses a plan generation mechanism to create individual exercise and meal plans based on the analysis results, and dynamically adjusts them. The plan content is optimized according to the user's emotional state. The input is the health risk assessment results and emotional state estimates, and the output is specific exercise and meal suggestions.

[0683] Step 5:

[0684] The device presents the generated exercise and meal plans to the user through audio and visual information. Relaxation-inducing content may also be played at this stage. The input is the plan content sent from the server, and the output is designed to enhance the user's understanding and motivate them to follow the plan.

[0685] Step 6:

[0686] The server uses communication methods to notify registered external organizations and family members if health risks increase or emotional states deteriorate. The notification includes recommended actions. Input is the analysis results, and output is a notification message to encourage prompt action.

[0687] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0688] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0689] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0690] [Fourth Embodiment]

[0691] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0692] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0693] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0694] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0695] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0696] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0697] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0698] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0699] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0700] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0701] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0702] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0703] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0704] The system according to the present invention is an automated platform for comprehensively managing the health status of elderly individuals. This system aims to acquire biometric information of elderly individuals in real time and assess their health risks.

[0705] Data acquisition and storage

[0706] Devices: Wearable devices continuously monitor vital data such as heart rate, blood pressure, and body temperature of elderly individuals. Additionally, smart speakers record meal content and timing via voice input.

[0707] Server: Frequently receives the above data and securely stores it in a cloud database. Each data point is saved with a timestamp and used for future analysis.

[0708] Health risk analysis

[0709] Server: Using biometric information stored in the database, an AI algorithm analyzes the data. The AI ​​assesses individual health risks, for example, detecting early signs of heart disease from abnormal heart rate or elevated blood pressure.

[0710] Terminal: Based on the analysis results, it notifies the user of warnings and advice, and encourages them to seek medical attention if necessary.

[0711] Providing exercise and meal plans

[0712] Server: Based on health risks, the AI ​​generates individually customized exercise and meal plans. For example, if there is a risk of diabetes, it will suggest a low-carbohydrate diet and light exercise such as walking.

[0713] Terminal: The generated plan is explained to the user via voice or displayed on the screen. The plan can also be adjusted to take into account the user's lifestyle and preferences.

[0714] Collaboration with medical institutions

[0715] Server: If a high health risk is identified, an automatic notification is sent to pre-registered healthcare providers and family members. This notification includes recommended actions and healthcare provider information.

[0716] User: You can perform actions as needed and make appointments to visit medical institutions.

[0717] This system aims to improve the quality of life for the elderly by providing 24-hour health monitoring and individualized health management. Furthermore, these features enable family members living remotely to appropriately support the health of their elderly relatives.

[0718] The following describes the processing flow.

[0719] Step 1:

[0720] Devices: Wearable devices worn by elderly individuals measure vital data such as heart rate, blood pressure, and body temperature in real time. Smart speakers record the contents of meals spoken by the user using voice recognition technology.

[0721] Step 2:

[0722] Server: Receives vital data and meal information transmitted from terminals in real time, organizes and stores it in a cloud database. During this process, it checks for missing data and prompts the user to retrieve any missing data.

[0723] Step 3:

[0724] Server: Based on stored data, it utilizes advanced machine learning algorithms to assess individual health risks. It predicts the risk of heart disease, for example, from abnormal heart rate patterns and rising blood pressure trends.

[0725] Step 4:

[0726] Device: If the analysis determines that there is a high health risk, an alert will be sent to the user immediately. The alert will be delivered via audio and visual display, and will explain the health condition that requires attention.

[0727] Step 5:

[0728] Server: Automatically generates personalized exercise and meal plans based on the user's health status. For example, for elderly people who are sedentary, it suggests simple stretches that can be done at home and a balanced meal plan.

[0729] Step 6:

[0730] Terminal: Notifies the user of the created plan and presents options for changing the plan or adjusting the schedule. Users can request adjustments using voice commands.

[0731] Step 7:

[0732] Server: If a health risk is particularly high, the server will automatically contact pre-registered family members or medical institutions. The contact will include information about the current health status and recommended actions.

[0733] Step 8:

[0734] User: The device allows users to perform daily activities according to the proposed plan and request support as needed. User operation is simple and supported by voice and touch interfaces.

[0735] (Example 1)

[0736] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0737] Currently, managing the health of the elderly requires real-time monitoring of their condition and the provision of appropriate preventive measures. However, conventional systems collect biometric information intermittently, which can lead to overlooking health risks or providing inappropriate advice. Furthermore, mechanisms for elderly individuals to appropriately collaborate with medical institutions are not yet fully established. A more comprehensive and automated health management system is needed to address these challenges.

[0738] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0739] In this invention, the server includes information input means, storage means, analysis means, plan generation means, information output means, and communication means. This enables the acquisition and analysis of biological information of elderly individuals in real time, allowing for appropriate health risk assessment, the proposal of exercise and diet plans based on that assessment, and rapid coordination with medical institutions in high-risk situations.

[0740] "Information input means" refers to a device or method that acquires biometric information of elderly individuals in real time and provides that information to a system.

[0741] A "memory device" is a digital medium or recording medium for safely and efficiently storing acquired biometric information.

[0742] "Analysis means" refers to computational processing or software that uses acquired biometric information to evaluate health risks using an AI algorithm.

[0743] "Plan generation means" refers to a process or program for generating individual exercise plans and meal plans based on analysis results.

[0744] "Information output means" refers to a method or device for providing generated plan information to the user through audio or visual means.

[0745] "Communication means" refers to a network or communication technology used to notify appropriate organizations or individuals when there is a high health risk.

[0746] This invention is an automated platform for comprehensively managing the health status of elderly individuals. This platform collects health data from elderly individuals' daily lives in real time, analyzes it using AI, and assesses health risks as needed, thereby providing higher quality health management.

[0747] Data acquisition and storage

[0748] The device uses wearable devices to collect biometric information such as heart rate, blood pressure, and body temperature from elderly individuals in real time. Furthermore, it uses a smart speaker to receive voice input regarding meal content and time, and converts it into text data.

[0749] The server receives data transmitted using an encrypted protocol and stores it in a cloud-based database. This data is stored with a timestamp and serves as a baseline for future analysis.

[0750] Health risk assessment and notification

[0751] The server assesses health risks by analyzing stored biometric data using AI algorithms. The AI ​​model, for example, detects abnormal patterns in heart rate and blood pressure and predicts the likelihood of heart disease.

[0752] Based on the analysis results, the device will provide warnings and advice to the user via voice or display. If a high risk is identified, it will also automatically send notifications to healthcare providers and family members.

[0753] Providing exercise and meal plans

[0754] The server generates individually customized exercise and meal plans based on health risk assessments. This includes features such as suggesting low-carbohydrate diets and light exercise plans for elderly individuals at risk of diabetes.

[0755] The device provides the user with an audio explanation of the generated plan and also displays its contents on the screen. The user can then adjust the plan to suit their preferences and daily life.

[0756] As a concrete example, suppose a 70-year-old user is using a wearable device to monitor their daily health. If the device detects an abnormal heart rate after the user ate a high-fat meal the previous night, the server analyzes the data and notifies the user of their risk of heart disease visually and audibly. The device then suggests a plan for light exercise and a low-fat diet. If the user agrees to the risk assessment, an automatic notification function to a medical institution is activated.

[0757] Examples of prompts to input into a generative AI model:

[0758] "Please analyze the impact of last night's high-fat meal on my heart rate and suggest appropriate health advice."

[0759] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0760] Step 1:

[0761] The device continuously acquires the heart rate, blood pressure, and body temperature of elderly individuals via a wearable device. The input is sensor data, and the output is this biometric information transferred to the device's application. The data is transmitted to the device using Bluetooth communication.

[0762] Step 2:

[0763] The device converts voice data acquired from a smart speaker into text data. The input is voice-based information about meals and their times, and the output is saved as a text-based meal record. Speech recognition technology is used for this process.

[0764] Step 3:

[0765] The device encrypts the acquired biometric information and dietary data and sends it to the server using a secure protocol. The input is the encrypted dataset, and the output is the completion of data transmission to the server. HTTPS is used for data transfer.

[0766] Step 4:

[0767] The server stores the received data in a cloud database. The input is the transmitted encrypted data, and the output is the completion of writing to the database. A timestamp is added to the data to prepare it for later analysis.

[0768] Step 5:

[0769] The server analyzes biometric information stored in a cloud database using AI algorithms. The input is past and present biometric data, and the output is an assessment of health risks. The AI ​​analyzes data patterns and detects anomalies.

[0770] Step 6:

[0771] The server evaluates individual health risks based on the analysis results and generates a risk score. The input is the AI ​​analysis results, and the output is the risk assessment score. It checks for abnormal increases in heart rate, blood pressure, etc.

[0772] Step 7:

[0773] The terminal receives instructions from the server and presents warnings and advice to the user via voice or visual means. Input is the server's risk assessment data, and output is a warning notification to the user. If necessary, it prompts the user to seek medical attention.

[0774] Step 8:

[0775] The server automatically sends notifications to pre-registered healthcare providers and family members if the user's health risk is high. The input is the high-risk assessment result, and the output is the completion of sending the notification. This notification includes countermeasures and contact information.

[0776] Step 9:

[0777] The server generates individualized exercise and diet plans based on health risks. The input is health status assessment data, and the output is a customized plan. The AI ​​derives a plan tailored to each user.

[0778] Step 10:

[0779] The terminal presents the user with a generated exercise and meal plan. The input is the server-generated plan, and the output is either a display on the screen or audio guidance. The user can adjust the plan as needed.

[0780] (Application Example 1)

[0781] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0782] Comprehensively and in real time managing the health status of the elderly is a major challenge, especially for family members and caregivers living remotely. Traditional systems lack data visualization and appropriate communication methods in emergencies, making it difficult to respond quickly to health risks in the elderly. Furthermore, there are few tools that allow for easy management and tracking of individually optimized exercise and diet plans, making it difficult to implement them in daily life.

[0783] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0784] In this invention, the server includes an input means for acquiring biometric information of elderly individuals, an evaluation means for evaluating health risks based on the biometric information, and a plan generation means for generating individual exercise and meal plans based on the health risks. This enables efficient health management in the daily lives of elderly individuals and allows for quick and appropriate responses in emergencies. Furthermore, by enabling information management via mobile terminals, users or caregivers can monitor the health status of elderly individuals in real time and support appropriate decision-making.

[0785] "Input means" refers to devices or methods for continuously acquiring biometric information from elderly individuals, and involves collecting vital data such as heart rate, blood pressure, and body temperature using wearable devices and sensors.

[0786] "Evaluation methods" refer to processes and technologies for analyzing and evaluating health risks based on acquired biometric information, and involve using artificial intelligence and algorithms to detect abnormal values ​​and signs of risk.

[0787] A "plan generation method" refers to a method or system for creating individual exercise and diet plans based on health risk assessments, proposing an optimal plan tailored to each individual's health condition.

[0788] "Output means" refers to a method for presenting the generated exercise plan, meal plan, and health status information to the user, and includes visual displays and audio guidance.

[0789] "Communication methods" refer to technologies used to notify medical institutions or designated contacts in cases of high health risk, enabling rapid emergency contact.

[0790] "Information processing means" refers to systems and processes for managing biometric information and analysis results on mobile devices, and which are used for data storage, organization, and display.

[0791] "Display means" refers to devices or interfaces that visually present biometric information and health analysis results, and that communicate data to users in an easy-to-understand manner.

[0792] The system for implementing the present invention is a platform that includes multiple hardware and software elements. This description will focus primarily on the mobile terminal and the server.

[0793] server:

[0794] The server plays a central role in receiving, storing, and analyzing biometric data. It collects data from wearable devices and smart speakers via the cloud and securely stores it in a database. The stored data is analyzed in real time using AI models (e.g., TensorFlow) to assess individual health risks. For example, if an elderly person's daily heart rate data indicates signs of heart disease, the server immediately determines the risk level and triggers the next action.

[0795] Terminal:

[0796] Mobile devices serve as both information displays and interfaces. Generated exercise and meal plans are presented to the elderly and their caregivers visually or audibly. Using a React Native application, users can visually monitor their health status and receive the generated plans. Furthermore, in emergencies, the device can quickly send notifications to medical institutions. This functionality enables a rapid response in emergencies.

[0797] User:

[0798] Users receive health-based information through their devices and implement suggested exercise and diet plans. For example, if they receive a notification that "three walks per week are recommended," they will incorporate that plan into their daily life. Users can also use their devices to manage contact with medical institutions when necessary.

[0799] For example, if AI analysis determines that daily exercise contributes to a stable heart rate, it's possible to use that result to prompt, "Please suggest a suitable morning walking plan for a 65-year-old man." This generates an individually optimized exercise plan, which is then presented to the user.

[0800] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0801] Step 1:

[0802] The server acquires biometric and lifestyle data in real time from wearable devices and smart speakers. Input data includes heart rate, blood pressure, body temperature, and dietary information. This data is then temporarily stored in cloud storage. A time-stamped dataset is generated as output.

[0803] Step 2:

[0804] The server inputs data stored in the cloud into an AI model (TensorFlow, for example) to assess health risks. The AI ​​model analyzes patterns such as abnormal heart rate and elevated blood pressure. As a result, it outputs a risk score for each data point.

[0805] Step 3:

[0806] The server creates individualized exercise and diet plans based on the generated risk score. Using an AI model, it generates plans such as, "You appear to be inactive, so we recommend three 30-minute walks per week." This results in the output of an optimal exercise and diet plan.

[0807] Step 4:

[0808] The device presents the plan received from the server to the user visually or audibly. The displayed plan includes specific exercise and dietary suggestions and is designed to be implemented by the user in their daily life. The user receives the plan content as output.

[0809] Step 5:

[0810] Users implement the plan generated using their device. They input daily health data and implementation results, feeding this information back to the server. The input data is used for future analysis and plan improvement.

[0811] Step 6:

[0812] If the device detects a high health risk, it will quickly send a notification to a medical institution or pre-configured contacts. This allows for immediate response in emergencies. The output will include a notification that the message has been sent and a list of the next actions to take.

[0813] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0814] The system according to the present invention is designed to provide advanced health management for the elderly, and by incorporating an emotion analysis function, it enables more personalized health management. This system acquires the biological information and emotional state of elderly individuals in real time and performs comprehensive health risk assessment and adjustment.

[0815] Data acquisition and emotion recognition

[0816] Device: A wearable device collects vital data, including heart rate and body temperature. Furthermore, a device equipped with a camera and microphone analyzes the user's emotions from their facial expressions and voice. For example, the camera captures the user's face and extracts emotions from their facial expressions.

[0817] Server: Integrates vital data and emotional data sent from terminals and stores them in a cloud-based database.

[0818] Integrated assessment of health risks and emotions

[0819] Server: Applies machine learning algorithms to comprehensively assess health risks based on vital and emotional data. For example, it examines the correlation between stress levels and heart rate to more accurately determine the risk of cardiovascular disease.

[0820] Terminal: Provides real-time feedback of analysis results to the user and issues alerts when necessary.

[0821] Plan generation and dynamic adjustment

[0822] Server: Based on evaluation results, it generates exercise and meal plans tailored to the user's emotional state. For example, for a user feeling anxious, it suggests light exercise with a relaxing effect and a meal menu that takes their emotions into consideration for the day.

[0823] Device: Present the plan to the user via voice and display, emphasizing an emotionally sensitive approach.

[0824] Notification and support for healthcare institutions

[0825] Server: Automatically notifies pre-registered healthcare providers and family members if there is a high health risk or a significant deterioration in emotional state. The notification includes recommended actions and healthcare provider information.

[0826] User: Through the device, users can select actions based on the proposed plan and decide to visit a medical institution if necessary. Users can also request additional support through voice guidance.

[0827] This system provides comprehensive health management that considers not only the physical aspects of health but also mental health, improving the quality of life for the elderly. It also enables rapid responses to emotional changes and facilitates smooth collaboration with family and medical institutions.

[0828] The following describes the processing flow.

[0829] Step 1:

[0830] Device: A wearable device acquires vital data such as the elderly person's heart rate and body temperature in real time and sends this data to the cloud. At the same time, a camera captures the user's facial expressions and a microphone records voice to acquire emotional data.

[0831] Step 2:

[0832] Server: Receives biometric and emotional data transmitted from terminals and stores it in a database. Preprocessing is performed on the data during storage to impute missing values ​​and ensure data integrity.

[0833] Step 3:

[0834] Server: Uses advanced machine learning algorithms to analyze stored vital and emotional data. The goal of the analysis is to evaluate the relationship between health risks and emotional states and to determine overall health status.

[0835] Step 4:

[0836] Device: Based on the analysis results, the device immediately notifies the user of any health risks. The notification includes specific risk factors and recommended countermeasures.

[0837] Step 5:

[0838] Server: Automatically generates exercise and meal plans tailored to the user's health and emotional state. When generating these plans, it considers options that are effective in reducing stress and stabilizing emotions.

[0839] Step 6:

[0840] Device: Presents the user with generated exercise and meal plans. The presentation is done via both voice and display, and the user can adjust the plan as needed.

[0841] Step 7:

[0842] Server: Automatically notifies healthcare providers and family members of the situation if there is a high health risk or if emotional state deteriorates significantly. The notification includes necessary actions and recommendations for seeking medical attention.

[0843] Step 8:

[0844] User: Utilize the device to incorporate the suggested plan into daily life. Receive additional advice via voice guidance when needed, making it easier to choose data-driven actions.

[0845] (Example 2)

[0846] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0847] In managing the health of the elderly, there is a need to go beyond simply acquiring biometric information and consider the user's mental health to achieve more comprehensive and individualized health management. However, conventional systems lack mechanisms to analyze emotional information in real time and incorporate it into health risk assessments, making it difficult to manage both physical and mental health in an integrated manner.

[0848] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0849] In this invention, the server includes an input means for acquiring biometric information of elderly individuals, an emotion analysis means for analyzing emotional information, and an integrated evaluation means for assessing health risks. This enables the integration of biometric and emotional information to provide more accurate health risk assessments and personalized health management plans.

[0850] "Input means" refers to devices and technologies for collecting biometric and emotional information of elderly people in real time.

[0851] "Evaluation means" refers to a system or algorithm for analyzing and evaluating a user's health risks based on collected biometric and emotional information.

[0852] A "plan generation method" is a technology for generating exercise and diet plans tailored to each elderly person based on the results of a health risk assessment.

[0853] "Output means" refers to methods or devices for presenting the generated health management plan to the user in an easily understandable manner.

[0854] "Communication means" refers to a system or device for notifying medical institutions or family members of information when an assessed health risk is high.

[0855] "Emotional analysis methods" refer to technologies and algorithms that analyze voice and facial expressions obtained from a device to identify the user's emotional state.

[0856] An "integrated evaluation method" is a system or algorithm that combines biometric information and emotional information to more accurately assess a user's health risks.

[0857] A description of embodiments for carrying out the present invention will be provided.

[0858] This system manages the health of the elderly from both a physical and mental perspective, and is realized by combining wearable devices with the latest analytical technology. Here, the wearable device acquires the user's biometric information, such as heart rate and body temperature, in real time. In addition, a terminal with emotion recognition capabilities analyzes the user's facial expressions and voice to extract emotional information. These two types of data are transmitted to and stored on a cloud server.

[0859] The server utilizes machine learning algorithms to comprehensively evaluate biometric and emotional information. This enables the accurate identification of a user's health risks. For example, if elevated stress and heart rate fluctuations are detected simultaneously, the server assesses the risk of developing heart disease and, if necessary, promptly notifies a medical institution.

[0860] Furthermore, the server generates exercise and meal plans that take into account the user's emotional state, based on the health risk assessment results. The terminal presents these plans to the user in an easy-to-understand manner via voice and display. This allows the user to incorporate necessary actions into their daily life.

[0861] A concrete example of this behavior is when a user experiencing anxiety follows the device's suggestions and performs light exercises to relax. The device also provides feedback to help the user adjust the plan applied to them according to daily changes.

[0862] An example of a prompt to input into a generative AI model is: "Please describe a system for the integrated management of the physical and mental health of older adults. Specifically, please explain how emotions and biometric information are used to assess health risks."

[0863] In this way, the present invention provides comprehensive support for elderly people to lead healthy and fulfilling lives.

[0864] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0865] Step 1:

[0866] The device acquires biometric information such as heart rate and body temperature in real time via the user's wearable device. It captures facial expressions and voice tone using a camera and microphone, and extracts emotional information using an emotion analysis algorithm. This data is sent as input to a cloud server.

[0867] Step 2:

[0868] The server stores the received biometric and emotional information in a database. During this process, timestamps are used to organize the data chronologically, creating a user health history. The input data is normalized and compressed for later analysis before being stored.

[0869] Step 3:

[0870] The server applies machine learning algorithms to comprehensively analyze stored biometric and emotional data. Factors such as heart rate variability and stress levels are considered to assess health risks. The output calculates potential health risks and their severity.

[0871] Step 4:

[0872] Based on the assessed health risks, the server generates exercise and meal plans tailored to the user's emotional state. Specifically, if relaxation is needed, simple yoga instructions and nutritious meal menus are suggested. This plan is then sent to the terminal as system output.

[0873] Step 5:

[0874] The terminal notifies the user of the plan received from the server. It explains the plan's contents and implementation methods using voice and display. It also issues alerts for health risks that require attention. Based on this information, the user can take the suggested actions.

[0875] Step 6:

[0876] The server automatically sends notifications to pre-registered medical institutions and family members if it detects a health risk exceeding a certain threshold. The notification includes information about the risk and recommended medical actions to encourage prompt response.

[0877] (Application Example 2)

[0878] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0879] Health management for the elderly requires a comprehensive approach that considers not only physical but also emotional aspects. However, conventional systems have difficulty integrating and analyzing biometric information and emotional states, limiting their ability to provide individually appropriate exercise and dietary plans. Furthermore, they struggle to respond quickly to sudden increases in health risks or deterioration of emotional state.

[0880] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0881] In this invention, the server includes information acquisition means for acquiring biometric data and emotional state data in real time, analysis means for comprehensively evaluating health risks, and plan generation means for generating and dynamically adjusting individual exercise and dietary plans. This enables personalized health management that considers both health and emotions, as well as rapid risk response.

[0882] "Biometric data" refers to information collected to understand the health status of elderly individuals, such as their heart rate and body temperature.

[0883] "Emotional state data" refers to data that represents emotions, analyzed from the facial expressions and voice tone of elderly individuals.

[0884] "Information acquisition means" refers to various devices and technologies used to acquire biometric data and emotional state data in real time.

[0885] "Analysis methods" refer to algorithms and machine learning models used to assess health risks using biometric data and emotional state data.

[0886] The "plan generation means" is a function that generates individual exercise and diet plans based on the results of health risk assessments and emotional states, and dynamically adjusts them as needed.

[0887] "Output means" refers to an interface for providing users with generated exercise and meal plans, using audio and visual information.

[0888] "Communication methods" refer to technologies that automatically notify external organizations when there is a high health risk.

[0889] This invention is a system for comprehensively managing the health and emotional state of elderly individuals. The main components of the system are information acquisition means, analysis means, plan generation means, output means, and communication means.

[0890] First, the information acquisition method involves collecting biometric and emotional state data of elderly individuals in real time using wearable devices, cameras, and microphones. This data includes heart rate, body temperature, facial expressions, and voice. This allows for an accurate understanding of the user's health status and emotions.

[0891] The server stores collected biometric and emotional state data in the cloud and performs analysis using specific algorithms. Machine learning models, such as TensorFlow, are used to comprehensively assess health risks from this data. This analysis enables predictions regarding conditions such as heart disease and stress levels.

[0892] Based on the results obtained by the analysis, the server uses a plan generation tool to create individual exercise and meal plans. These plans are dynamically adjusted according to the user's current emotional state and provided to the user as specific suggestions. For example, if the user is feeling anxious, suggestions for light exercise with a relaxing effect and meal menus that take their emotions into consideration will be provided.

[0893] The output method presents the plan to the user via smartphones or other devices, using audio and visual information. This allows the user to intuitively understand and execute the plan.

[0894] The communication system notifies pre-registered external organizations and family members if health risks increase or emotional state deteriorates significantly. This notification includes recommended actions and information on healthcare facilities to facilitate prompt action.

[0895] For example, if a morning health check detects an elevated heart rate and an anxious expression, the server suggests a meditation program and plays corresponding music. This allows the user to continue their daily life with peace of mind.

[0896] An example of a prompt message for a generative AI model might be: "A 70-year-old woman has a higher-than-usual heart rate this morning and looks anxious. Based on this data, suggest actions that can help her relax. Options include light stretching and morning relaxation music."

[0897] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0898] Step 1:

[0899] The device collects biometric and emotional state data of elderly individuals in real time using wearable devices, smartphone cameras, and microphones. Inputs include heart rate, body temperature, facial expressions, and voice, which are acquired from sensors, cameras, and microphones. Outputs are sent to a server as raw data.

[0900] Step 2:

[0901] The server stores the received raw data in the cloud and performs data preprocessing. This preprocessing includes noise reduction and normalization, converting the data into a format suitable for analysis. The input is raw data sent from the terminal, and the output is a preprocessed dataset.

[0902] Step 3:

[0903] The server uses a pre-processed dataset and a machine learning model to analyze health risks and emotional states. This analysis employs a generative AI model to assess cardiovascular disease risk and stress levels. The input is pre-processed data, and the output is an estimate of health risk and emotional state.

[0904] Step 4:

[0905] The server uses a plan generation mechanism to create individual exercise and meal plans based on the analysis results, and dynamically adjusts them. The plan content is optimized according to the user's emotional state. The input is the health risk assessment results and emotional state estimates, and the output is specific exercise and meal suggestions.

[0906] Step 5:

[0907] The device presents the generated exercise and meal plans to the user through audio and visual information. Relaxation-inducing content may also be played at this stage. The input is the plan content sent from the server, and the output is designed to enhance the user's understanding and motivate them to follow the plan.

[0908] Step 6:

[0909] The server uses communication methods to notify registered external organizations and family members if health risks increase or emotional states deteriorate. The notification includes recommended actions. Input is the analysis results, and output is a notification message to encourage prompt action.

[0910] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0911] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0912] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0913] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0914] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0915] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0916] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0917] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0918] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0919] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0920] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0921] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0922] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0923] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0924] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0925] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0926] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0927] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0928] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0929] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0930] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0931] The following is further disclosed regarding the embodiments described above.

[0932] (Claim 1)

[0933] An input method for acquiring biometric information of elderly people,

[0934] An evaluation means for evaluating health risks based on the aforementioned biological information,

[0935] A plan generation means for generating individual exercise plans and meal plans based on the aforementioned health risks,

[0936] Output means for presenting the aforementioned exercise plan and meal plan,

[0937] A system including a means of communication for notifying medical institutions in cases where the aforementioned health risk is high.

[0938] (Claim 2)

[0939] The system according to claim 1, characterized in that the input means includes means for acquiring vital information in real time.

[0940] (Claim 3)

[0941] The system according to claim 1, characterized in that the output means presents the plan using both audio and visual information.

[0942] "Example 1"

[0943] (Claim 1)

[0944] A means of inputting information to acquire biometric information of elderly people,

[0945] A storage means for storing the aforementioned biometric information in a cloud database,

[0946] An analytical means for evaluating health risks using an AI algorithm based on the aforementioned biometric information,

[0947] A plan generation means for generating individual exercise plans and meal plans based on the aforementioned health risks,

[0948] Information output means for presenting the exercise plan and meal plan as audio or visual information,

[0949] A system including a means of communication for notifying pre-registered organizations in cases where the aforementioned health risk is high.

[0950] (Claim 2)

[0951] The system according to claim 1, characterized in that the information input means includes means for acquiring vital information of elderly persons in real time.

[0952] (Claim 3)

[0953] The system according to claim 1, characterized in that the information output means presents the plan using audio and visual information, and allows the user to adjust the plan.

[0954] "Application Example 1"

[0955] (Claim 1)

[0956] An input method for acquiring biometric information of elderly people,

[0957] An evaluation means for evaluating health risks based on the aforementioned biological information,

[0958] A plan generation means for generating individual exercise plans and meal plans based on the aforementioned health risks,

[0959] Output means for presenting the aforementioned exercise plan and meal plan,

[0960] A means of communication for notifying medical institutions in cases where the aforementioned health risk is high,

[0961] Information processing means for managing the aforementioned plan on a mobile device,

[0962] A display means for visually presenting the aforementioned biological information and analysis results,

[0963] A system that includes this.

[0964] (Claim 2)

[0965] The system according to claim 1, characterized in that the input means includes means for acquiring vital information in real time.

[0966] (Claim 3)

[0967] The system according to claim 1, characterized in that the output means presents the plan using both audio and visual information, and the display means visualizes the health status of the elderly person.

[0968] "Example 2 of combining an emotion engine"

[0969] (Claim 1)

[0970] An input method for acquiring biometric information of elderly people,

[0971] An evaluation means for evaluating health risks based on the aforementioned biological information,

[0972] A plan generation means for generating individual exercise plans and meal plans based on the aforementioned health risks,

[0973] Output means for presenting the aforementioned exercise plan and meal plan,

[0974] A means of communication for notifying medical institutions in cases where the aforementioned health risk is high,

[0975] A means of analyzing the emotional information of the elderly,

[0976] An integrated evaluation means for more accurately assessing health risks by integrating the aforementioned emotional information and biological information,

[0977] A system that includes this.

[0978] (Claim 2)

[0979] The system according to claim 1, characterized in that the input means includes means for acquiring vital information and emotional information in real time.

[0980] (Claim 3)

[0981] The system according to claim 1, characterized in that the output means presents the plan using both audio and visual information and provides positive feedback that takes emotional information into consideration.

[0982] "Application example 2 when combining with an emotional engine"

[0983] (Claim 1)

[0984] A means of acquiring information for obtaining biometric data and emotional state data of elderly people in real time,

[0985] An analytical means for comprehensively evaluating health risks based on the aforementioned biometric data and emotional state data,

[0986] A plan generation means for generating individual exercise and meal plans based on the aforementioned health risks and emotional states, and for dynamically adjusting them,

[0987] Output means for presenting the aforementioned exercise plan and meal plan using audio and visual information, and for suggesting alternative plans including relaxation methods as needed,

[0988] A system including means of communication for notifying pre-registered external organizations when the aforementioned health risks are high or when emotional state deteriorates significantly.

[0989] (Claim 2)

[0990] The system according to claim 1, characterized in that the information acquisition means has an emotion analysis function and analyzes the facial expressions and voice of elderly people using a camera and a microphone.

[0991] (Claim 3)

[0992] The system according to claim 1, characterized in that the output means provides emotionally sensitive feedback to the user via a smart device and notifies family members or caregivers. [Explanation of symbols]

[0993] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An input method for acquiring biometric information of elderly people, An evaluation means for evaluating health risks based on the aforementioned biological information, A plan generation means for generating individual exercise plans and meal plans based on the aforementioned health risks, Output means for presenting the aforementioned exercise plan and meal plan, A means of communication for notifying medical institutions in cases where the aforementioned health risk is high, Information processing means for managing the aforementioned plan on a mobile device, A display means for visually presenting the aforementioned biological information and analysis results, A system that includes this.

2. The system according to claim 1, characterized in that the input means includes means for acquiring vital information in real time.

3. The system according to claim 1, characterized in that the output means presents the plan using both audio and visual information, and the display means visualizes the health status of the elderly person.

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