System

Abstract

A system is provided.SOLUTION: Means for collecting health data, means for transmitting the collected health data to a server, means for analyzing the health data received by the server and generating a health profile, means for detecting an abnormality based on the generated health profile and notifying a user terminal when the abnormality is detected, means for generating a personalized preventive care plan based on the user's health data and feedback and providing it to the user terminal, and means for improving an AI model based on user feedback and new data; A system comprising: means for evolving a service; means for sharing health data with a medical professional to optimize a treatment plan; and means for notifying a user of feedback from the medical professional.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] This invention aims to improve the current situation in which elderly people living alone face challenges in managing their health, reducing loneliness, responding quickly to sudden illnesses or problems, and lacking sufficient communication with medical institutions. For elderly people to live a safe and fulfilling life, a system is needed that accurately collects and integrates daily health data, monitors it in real time, and detects abnormalities early and responds promptly. Personalized preventive care and communication with medical professionals are also essential. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means: a system including a means for collecting health data, a means for transmitting the collected health data to a server, a means for analyzing the health data received by the server and generating a health profile, a means for detecting abnormalities based on the generated health profile and notifying a user device when an abnormality is detected, a means for generating a personalized preventive care plan based on the user's health data and feedback and providing the plan to the user device, a means for improving an AI model based on user feedback and new data to evolve the service, a means for sharing health data with medical professionals to optimize treatment plans, and a means for notifying the user of feedback from medical professionals. This system enables elderly people living alone to manage their health, reduce loneliness, respond quickly, and connect with medical institutions.

[0006] "Health data" refers to various biometric data collected to indicate health status, such as heart rate, blood pressure, body temperature, and activity level.

[0007] The "server" is a central processing unit that collects, stores, and analyzes health data, and uses it to detect abnormalities and generate preventative care plans.

[0008] "User" refers to elderly people who use the health care system, and generally includes those who live alone.

[0009] A "user device" is a device used by a user, such as a smartphone or dedicated device, for collecting health data, sending notifications, and receiving feedback.

[0010] "Abnormality detection" is the process of detecting abnormalities when heart rate, blood pressure, etc. deviate from normal ranges based on collected health data.

[0011] A "preventive care plan" is a personalized plan for maintaining and improving health that combines nutrition, exercise, psychological support, and more, based on individual health data.

[0012] "Feedback" is information that is useful for improving and evolving the system, such as information from users about their daily living situation, the progress of care plans, and changes in their physical condition.

[0013] An "AI model" is an artificial intelligence algorithm that analyzes a user's health data, detects abnormalities, and generates preventative care plans.

[0014] A "healthcare professional" is a doctor or medical professional with specialized knowledge about the user's health condition and treatment plan.

[0015] A "treatment plan" is a specific plan for medical examination and treatment that is formulated by a medical professional based on the user's health condition. [Brief explanation of the drawings]

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

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

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

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

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

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

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

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

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

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

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0037] The present invention relates to a comprehensive system for supporting health management for elderly people living alone, and specific embodiments thereof will be described below.

[0038] Overall system overview

[0039] This system collects users' health data in real time, analyzes it, detects abnormalities, suggests preventive care, and shares the data with medical professionals all in one place. By exchanging data between the server, devices, and users, it provides an environment in which elderly people can live safe and fulfilling lives.

[0040] Health data collection and integration

[0041] The device collects health data such as heart rate, blood pressure, temperature, and activity levels from wearable devices and home medical devices, which connect to the device via Bluetooth or Wi-Fi.

[0042] The device periodically sends the collected data to the server. The data is sent using encrypted communication to ensure data security.

[0043] Health profile generation and anomaly detection

[0044] The server stores the received health data in a database and generates a health profile for each user, which includes the individual user's baseline values ​​and past health data.

[0045] The server uses AI models to analyze the data in real time and detect anomalies, such as generating an anomaly alert if the heart rate falls outside the resting range.

[0046] If an abnormality is detected, the server immediately sends a notification to the terminal.

[0047] Providing personalized preventative care

[0048] The server generates a preventative care plan based on the individual's health profile, which is based on an AI model and includes elements such as nutrition, exercise, and psychological support.

[0049] The device will present users with preventative care plans and support them in carrying them out, and will also include reminder and progress tracking features.

[0050] Self-learning and evolution

[0051] The server collects user feedback and analyzes it along with health data, allowing the AI ​​model to self-train and improve the accuracy of the service.

[0052] The server continues to update the system's functionality based on new medical knowledge and technological advances.

[0053] Medical collaboration and treatment optimization

[0054] With the user's consent, the server will share health data with medical professionals, allowing doctors to provide more accurate diagnoses and treatment plans.

[0055] The device notifies users of treatment plans received from medical professionals and provides the information they need to implement them, including medication schedules and follow-up appointment reminders.

[0056] Specific examples

[0057] Anomaly detection and rapid response

[0058] 1. The user puts on the wearable device when they wake up in the morning.

[0059] 2. The device collects heart rate data during the day and sends it to the server.

[0060] 3. The server analyzes the received data in real time and detects abnormal high heart rates.

[0061] 4. The server generates an anomaly detection alert and immediately sends it to the device.

[0062] 5. The device notifies the user of the abnormal alert and displays the message "Your heart rate is high. Please take a short rest." If necessary, it will also automatically notify emergency contacts.

[0063] Providing personalized preventative care

[0064] 1. The server analyzes the user's health profile and discovers that there are nutrient deficiencies.

[0065] 2. The server generates a meal plan for nutritional support.

[0066] 3. The device will notify the user of this meal plan and provide detailed recipes and a shopping list.

[0067] 4. The user follows the meal plan and inputs any changes in their physical condition into the device as feedback.

[0068] 5. The server will incorporate this feedback into future care plans.

[0069] Through these functions, the system comprehensively manages the user's health and provides an environment where elderly people living alone can live with peace of mind.

[0070] The processing flow will be explained below.

[0071] Program processing flow

[0072] Health data collection and integration

[0073] Step 1:

[0074] Users wear wearable devices or home medical devices that measure daily health data (heart rate, blood pressure, body temperature, activity level, etc.) in real time.

[0075] Step 2:

[0076] The device receives data from wearable devices and medical equipment via Bluetooth or WiFi, and this data is temporarily stored on the device.

[0077] Step 3:

[0078] The device sends the collected data to the server at regular intervals using encrypted communications, ensuring data security.

[0079] Health profile generation and anomaly detection

[0080] Step 4:

[0081] The server stores the health data received from the device in a database, where a health profile for each user is generated.

[0082] Step 5:

[0083] The server uses AI models to analyze the incoming data and apply anomaly detection algorithms, such as detecting abnormal heart rates or sudden fluctuations in blood pressure.

[0084] Step 6:

[0085] When an anomaly is detected, the server generates an anomaly alert, which includes the type of anomaly and specific numerical values.

[0086] Step 7:

[0087] The server immediately transmits the generated abnormality alert to the user terminal.

[0088] Step 8:

[0089] The device notifies the user of the abnormality alert, and the notification includes specific instructions (e.g., take a rest, contact a doctor).

[0090] Providing personalized preventative care

[0091] Step 9:

[0092] The server generates a preventative care plan based on the individual user's health profile, which includes nutrition, exercise, and psychological support.

[0093] Step 10:

[0094] The server transmits the generated preventive care plan to the user terminal.

[0095] Step 11:

[0096] The device then presents the received preventive care plan to the user, including reminders and help features.

[0097] Step 12:

[0098] The user incorporates the preventive care plan into their daily life and inputs the progress of the care plan and changes in their physical condition into the terminal.

[0099] Self-learning and evolution

[0100] Step 13:

[0101] The device periodically transmits feedback from the user to the server, including information such as diet history and exercise status.

[0102] Step 14:

[0103] The server updates the AI ​​model and performs self-learning based on the received feedback and health data.

[0104] Step 15:

[0105] The server uses the updated AI model to improve the accuracy of the next preventative care plan and anomaly detection algorithm.

[0106] Medical collaboration and treatment optimization

[0107] Step 16:

[0108] Users consent to data sharing with medical institutions through a settings screen within the application, and this consent is recorded within the system.

[0109] Step 17:

[0110] The server performs the function of securely sharing the user's health data with medical professionals, who then use it to develop a diagnosis and treatment plan.

[0111] Step 18:

[0112] The server receives feedback and treatment plans from medical professionals and transmits them to the user terminal.

[0113] Step 19:

[0114] The terminal notifies the user of the treatment plan and provides the information necessary to carry it out (e.g., medication schedule, follow-up appointment times).

[0115] Through these steps, the system provides comprehensive and personalized health management and support to elderly people living alone.

[0116] Example 1

[0117] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0118] Elderly people living alone require real-time monitoring of their health status and early detection of abnormalities. However, existing systems struggle to integrate functions such as health data collection and analysis, abnormality detection, provision of preventive care plans, and data sharing with medical professionals. Furthermore, the disparate nature of these functions makes them difficult to use. Furthermore, they lack the ability to evolve AI models based on user feedback and respond quickly in emergencies. Therefore, there is a need for a comprehensive system that provides support for the safe and secure lifestyles of elderly people living alone.

[0119] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0120] In this invention, the server includes: means for collecting health data; means for transmitting the collected health data to the server; means for analyzing the health data received by the server and generating a health profile; means for detecting abnormalities based on the generated health profile and notifying the user terminal when an abnormality is detected; means for generating a personalized preventive care plan based on the user's health data and feedback and providing it to the user terminal; means for improving the AI ​​model based on user feedback and new data and evolving the service; means for sharing the health data with medical professionals and optimizing the treatment plan; means for notifying the user of feedback from medical professionals; means for automatically notifying an emergency contact when an abnormality is detected; and means for providing a specific meal plan and shopping list based on the preventive care plan. This enables efficient and comprehensive health management of elderly people and supports their safe and secure lives.

[0121] "Health data" is information that indicates the physiological and health status of the user, such as heart rate, blood pressure, body temperature, and activity level.

[0122] A "means for collection" is a method or device for compiling a user's health data using a wearable device or home medical equipment.

[0123] "Transmitting means" refers to a method or device for encrypting the collected health data and sending it to the server.

[0124] The "analyzing means" is a method or device that processes the health data received by the server and evaluates the user's health status.

[0125] A "health profile" is a personalized physiological and health record generated from a user's health data.

[0126] A "detecting means" is a method or device for detecting abnormalities based on the generated health profile.

[0127] "Notification means" refers to a method or device for sending an alert to a user terminal or emergency contact when an abnormality is detected.

[0128] A "personalized preventative care plan" is a preventative or care plan created based on a user's individual health profile.

[0129] A "means for providing" is a method or device for informing a user of a preventive care plan and supporting its implementation.

[0130] "Means for improvement" refers to a method or device for updating the AI ​​model based on user feedback and new data to improve the service.

[0131] A "means for sharing" is a method or device for exchanging a user's health data with a medical professional to optimize a treatment plan.

[0132] "Means for automatically notifying" refers to a method or device for automatically notifying an emergency contact when an abnormality is detected.

[0133] A "means for providing a meal plan or shopping list" is a method or device that provides a user with meal suggestions and a list of ingredients based on a preventive care plan.

[0134] The present invention relates to a comprehensive system for supporting health management for elderly people living alone, and specific embodiments thereof will be described below.

[0135] Overall system overview

[0136] This system collects users' health data in real time, analyzes it, detects abnormalities, suggests preventive care, and shares the data with medical professionals. By exchanging data between the server, devices, and users, it provides an environment in which elderly people can live a safe and fulfilling life.

[0137] Health data collection and integration

[0138] The device collects health data such as heart rate, blood pressure, temperature, and activity levels from wearable devices and home medical devices, which connect to the device via Bluetooth or Wi-Fi.

[0139] Specific devices include Fitbit, Apple Watch, and common home blood pressure monitors and thermometers.

[0140] The collected data is collected using the device's API.

[0141] The device periodically sends the collected data to the server. The data is sent using SSL / TLS encryption to ensure data security.

[0142] Health profile generation and anomaly detection

[0143] The server stores the received health data in a database and generates a health profile for each user, which includes the individual user's baseline values ​​and past health data.

[0144] Database systems such as MySQL and MongoDB are used.

[0145] The server uses an AI model developed in Python to analyze the data in real time and detect anomalies, such as generating an anomaly alert if the heart rate deviates from the resting range.

[0146] If an abnormality is detected, the server immediately sends a notification to the terminal.

[0147] Providing personalized preventative care

[0148] The server generates a preventative care plan based on the individual's health profile, which is based on an AI model and includes elements such as nutrition, exercise, and psychological support.

[0149] The device will present users with preventative care plans and support them in carrying them out, and will also include reminder and progress tracking features.

[0150] Self-learning and evolution

[0151] The server collects user feedback and analyzes it along with health data, allowing the AI ​​model to self-train and improve the accuracy of the service.

[0152] The server continues to update the system's functionality based on new medical knowledge and technological advances.

[0153] Medical collaboration and treatment optimization

[0154] With the user's consent, the server will share health data with medical professionals, allowing doctors to provide more accurate diagnoses and treatment plans.

[0155] The device notifies users of treatment plans received from medical professionals and provides the information they need to implement them, including medication schedules and follow-up appointment reminders.

[0156] Specific examples

[0157] Anomaly detection and rapid response

[0158] 1. The user puts on the wearable device when they wake up in the morning.

[0159] 2. The device collects heart rate data during the day and sends it to the server.

[0160] 3. The server analyzes the received data in real time and detects abnormal high heart rates.

[0161] 4. The server generates an anomaly detection alert and immediately sends it to the device.

[0162] 5. The device notifies the user of the abnormal alert and displays the message "Your heart rate is high. Please take a short rest." If necessary, it will also automatically notify emergency contacts.

[0163] Providing personalized preventative care

[0164] 1. The server analyzes the user's health profile and discovers that there are nutrient deficiencies.

[0165] 2. The server generates a meal plan for nutritional support.

[0166] 3. The device will notify the user of this meal plan and provide detailed recipes and a shopping list.

[0167] 4. The user follows the meal plan and inputs any changes in their physical condition into the device as feedback.

[0168] 5. The server will incorporate this feedback into future care plans.

[0169] Through these functions, the system comprehensively manages the user's health and provides an environment where elderly people living alone can live with peace of mind.

[0170] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0171] Processing flow

[0172] Step 1: Collecting health data

[0173] A user puts on a wearable device (e.g., a general fitness tracker) when they wake up in the morning. They also use home medical devices (blood pressure monitor, thermometer).

[0174] The device collects data from wearable devices and medical equipment via Bluetooth or WiFi, such as heart rate and body temperature data.

[0175] Input: Sensor data from wearable devices and medical equipment

[0176] Output: Collected health data (heart rate, blood pressure, body temperature, activity level)

[0177] Step 2: Send and store data

[0178] The data collected by the device is periodically encrypted and sent securely to the server using the SSL / TLS protocol.

[0179] The server validates the data it receives and stores it in the database after verifying its validity.

[0180] Input: Collected health data

[0181] Output: Health data stored on the server

[0182] Step 3: Health profile generation and anomaly detection

[0183] The server generates a health profile for each user based on the health data stored in the database, including past data and baseline values.

[0184] Based on the generated profile, the server uses a generative AI model developed in Python to analyze the data in real time and detect abnormalities, such as when the heart rate is outside the resting range.

[0185] Input: Stored health data and generative AI model

[0186] Output: Health profile and abnormality detection results (alerts)

[0187] Step 4: Notification of abnormalities

[0188] If the server detects an abnormality, it immediately generates an anomaly detection alert and sends a notification to the terminal.

[0189] The device will notify the user of any abnormal alerts, for example, "Your heart rate is high. Please take a short rest."

[0190] Input: Anomaly detection result

[0191] Output: User notification

[0192] Step 5: Deliver personalized preventative care

[0193] The server generates a preventative care plan based on an individual's health profile, which includes nutrition, exercise, psychological support, and more.

[0194] The device presents the user with a preventive care plan, provides reminders, and tracks progress.

[0195] Input: Health profile and generative AI model

[0196] Output: Preventive Care Plan

[0197] Step 6: Gather feedback and improve the AI ​​model

[0198] The server collects feedback from the user about the running status and changes in physical condition.

[0199] The server analyzes this feedback and allows the generative AI model to self-train and improve accuracy.

[0200] Input: User feedback

[0201] Output: An improved generative AI model

[0202] Step 7: Care coordination and treatment optimization

[0203] The server shares health data with medical professionals based on the user's consent, for example by exchanging data using the FHIR protocol.

[0204] The device notifies the user of the treatment plan received from the medical professional and reminds them of medication schedules and follow-up appointments.

[0205] Input: Treatment plan from medical professional

[0206] Output: Notifications and reminders to the user

[0207] (Application example 1)

[0208] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0209] Elderly people living alone face challenges when managing their own health, including the difficulty of early detection of abnormalities and receiving appropriate preventive care. It is also difficult to quickly and appropriately connect with medical professionals. Furthermore, there is a lack of health support services linked to nearby stores and fitness centers that seniors can actually use.

[0210] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0211] In this invention, the server includes: means for collecting health data; means for transmitting the collected health data to the server; means for analyzing the health data received by the server and generating a health profile; means for detecting abnormalities based on the generated health profile and notifying the user terminal when an abnormality is detected; means for generating a personalized preventive care plan based on the user's health data and feedback and providing it to the user terminal; means for improving the AI ​​model based on user feedback and new data and evolving the service; means for sharing the health data with medical professionals and optimizing the treatment plan; means for notifying the user of feedback from medical professionals; means for providing health consultations or fitness support at affiliated physical stores based on the user's health data; and means for simultaneously notifying the user and related facilities when an abnormality is detected. This enables elderly people living alone to receive early detection of abnormalities and appropriate preventive care, and facilitates prompt and appropriate collaboration with medical professionals and physical stores.

[0212] "Health data" refers to numerical values ​​and information that indicate an individual's health status, such as heart rate, blood pressure, body temperature, and activity level.

[0213] "Means of collection" refers to the equipment and methods used to obtain a user's health data, such as using wearable devices or home medical equipment.

[0214] The "means of transmission" refers to the communication means for sending the collected health data to the server, and uses wireless communication technology such as Bluetooth or WiFi.

[0215] A "server" is a computer system that receives, analyzes, and stores collected health data.

[0216] The "analyzing means" refers to algorithms or software that evaluate the user's health status based on the received health data.

[0217] A "health profile" is a data set that compiles a user's health data history and baseline values, and is used to comprehensively assess an individual's health status.

[0218] An "anomaly detection method" is an algorithm or software that compares the health profile with newly collected data and identifies outliers.

[0219] "Means of notification" refers to methods or technologies for notifying a user terminal that an abnormality has been detected, such as a smartphone application.

[0220] "Personalized preventative care plans" refer to health management and improvement recommendations that are individually optimized based on the user's health data and feedback.

[0221] The "means for providing" refers to a method for displaying or notifying the personalized preventive care plan on the user terminal.

[0222] "AI model" refers to a machine learning or artificial intelligence model used to analyze health data and generate anomaly detection and preventative care plans.

[0223] A "healthcare professional" is a doctor or pharmacist who has the expertise to diagnose a user's health data and provide a treatment plan.

[0224] "Facilities" refers to affiliated physical stores and fitness centers, which are places that users can actually visit.

[0225] "Health consultation" means receiving health advice and information from experts such as pharmacists and nutritionists at a physical store.

[0226] "Fitness support" means receiving exercise guidance and advice from a trainer at a fitness center.

[0227] The system of the present invention is implemented by the following method and procedure. The system's core function is to collect a user's health data, analyze it in real time, detect abnormalities, and provide preventative care plans. It also includes collaborating with medical professionals based on the data to provide appropriate health consultations and fitness support to the user.

[0228] Data collection and transmission

[0229] The server collects health data from wearable devices and home medical devices (smartwatches, scales, blood pressure monitors, etc.) via Bluetooth or Wi-Fi. This periodically collects data such as heart rate, blood pressure, body temperature, and activity level. The collected data is encrypted by the smartphone and sent to the cloud server.

[0230] Analyzing data and generating health profiles

[0231] The server uses a cloud-based data analysis system (e.g., AWS, MySQL) to analyze the received data and generate a health profile for each user. This profile includes the user's baseline values ​​and past health data. An AI model (e.g., TensorFlow) is used to analyze the data in real time and detect outliers.

[0232] Anomaly detection and notification

[0233] If an abnormality is detected, the server immediately generates an abnormality alert and sends a notification to the user's smartphone. Furthermore, if the abnormality is serious, notifications are also sent to the user's partner physical stores (drugstores and fitness centers). This allows the elderly person to receive prompt and appropriate treatment.

[0234] Providing personalized preventative care plans

[0235] The server generates a personalized preventive care plan based on the user's health profile. This plan includes nutrition, exercise, and psychological support. The plan is then provided to the user via a smartphone application. The application also has reminder and progress tracking functions.

[0236] Health counseling and fitness support

[0237] Based on the user's health data, health consultations are provided at affiliated physical stores and fitness support is provided at fitness centers. If an abnormality is detected, a pharmacist at the drugstore or a fitness instructor will take appropriate action. This allows users to receive comprehensive support.

[0238] Examples of specific examples and prompts

[0239] Example 1: If high blood pressure is detected, the following notification is displayed on the user's smartphone:

[0240] Text format

[0241] Abnormalities detected. Your heart rate and blood pressure are out of normal range. Would you like to schedule a free consultation at your local drugstore?

[0242] Example 2: If an anomaly is detected during a workout at a fitness center, the following notification is sent to the instructor:

[0243] Text format

[0244] Your heart rate has deviated from the normal range during exercise. Would you like to contact your instructor?

[0245] The above is an embodiment of the present invention. This system enables elderly people living alone to receive early detection of abnormalities and appropriate preventive care, and also facilitates prompt and appropriate cooperation with medical professionals and brick-and-mortar stores.

[0246] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0247] Step 1:

[0248] Users wear a smartwatch or home medical device to collect health data. The smartwatch measures data such as heart rate, blood pressure, body temperature, and activity level, and transmits it to a smartphone via Bluetooth.

[0249] Input: User's health data (heart rate, blood pressure, body temperature, activity level, etc.)

[0250] Output: Health data collected on a smartphone

[0251] Step 2:

[0252] The device (smartphone) encrypts the collected health data and sends it to a cloud server, where the smartphone app periodically uploads the data to the server.

[0253] Input: Health data collected on a smartphone

[0254] Output: Encrypted data sent to the cloud server

[0255] Step 3:

[0256] The server analyzes the received health data and generates a health profile. Specifically, it stores the data in a database (MySQL) and performs anomaly detection analysis using an AI model (TensorFlow). It compares the data with past data, sets baseline values, and updates the health profile.

[0257] Input: Encrypted data sent to the cloud server

[0258] Output: Generated health profile

[0259] Step 4:

[0260] The server detects abnormalities based on the generated health profile. The AI ​​model analyzes the data in real time and generates an anomaly alert if it detects an abnormal value.

[0261] Input: Generated health profile

[0262] Output: Abnormal alert

[0263] Step 5:

[0264] If an abnormality is detected, the server sends a notification to the user's device, and simultaneously issues an abnormality alert and notifies partner facilities.

[0265] Input: Anomaly Alert

[0266] Output: Notification sent to user device and partner facility

[0267] Step 6:

[0268] The device will notify the user of any abnormalities and suggest specific measures to take if necessary, which in this case could include health consultations at a drugstore or support at a fitness center.

[0269] Input: Notification sent to user device

[0270] Output: Notify the user of the abnormality and suggest a solution

[0271] Step 7:

[0272] The server generates a personalized preventative care plan based on the user's health data and feedback. An AI model analyzes the user's condition and creates an individualized care plan.

[0273] Input: Health data and feedback

[0274] Output: A personalized preventative care plan

[0275] Step 8:

[0276] The device provides the user with the generated preventive care plan and supports implementation, and the app uses reminders and implementation tracking features to monitor the user's progress with the care plan.

[0277] Enter: personalized preventative care plans.

[0278] Output: Preventive care plans and support functions provided to users

[0279] Step 9:

[0280] The server improves the AI ​​model based on user feedback and new health data, evolving the service and improving the accuracy of future preventive care plans.

[0281] Input: Feedback and new health data

[0282] Output: Improved AI models and evolved services

[0283] Step 10:

[0284] The server shares health data with medical professionals to optimize treatment plans, and provides feedback from medical professionals to users to provide appropriate treatment and care.

[0285] Input: Health data and medical expert feedback

[0286] Output: Optimized treatment plan and notification to the user

[0287] The above processing steps realize a system that monitors the health status of elderly people in real time, detects abnormalities early, and enables appropriate responses.

[0288] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0289] The present invention relates to a comprehensive system for supporting health management for elderly people living alone, and by combining it with an emotion engine, manages not only physical health but also mental health. Specific embodiments of the system are described below.

[0290] Overall system overview

[0291] This system collects users' health and emotional data in real time, analyzes it, detects abnormalities, suggests preventive care, and shares the data with medical professionals. Through the exchange of data between the server, devices, and users, it provides an environment in which elderly people can live a safe and fulfilling life.

[0292] Collecting and integrating health and emotional data

[0293] The device collects health data such as heart rate, blood pressure, temperature, and activity levels from wearable devices and home medical devices, which connect to the device via Bluetooth or Wi-Fi.

[0294] The device uses a built-in emotion engine to collect emotion data from the user's facial expressions, voice, text messages, etc.

[0295] The terminal periodically transmits the health data and emotion data to the server using encrypted communication.

[0296] Health and emotional profile generation and anomaly detection

[0297] The server stores the health data and emotion data received from the terminals in a database and generates a health profile and emotion profile for each user.

[0298] The server uses AI models to analyze the incoming data and apply anomaly detection algorithms, for example, to detect abnormal heart rates or sudden fluctuations in emotional state.

[0299] When the server detects an abnormality, it generates an abnormality alert and immediately sends this alert to the user terminal.

[0300] The device will notify the user of abnormal alerts and display messages with specific instructions such as "Your heart rate is high. Please take a short rest" or "Your emotional state is unstable. We recommend you relax."

[0301] Providing personalized preventative care

[0302] The server generates a preventative care plan based on each individual's health and emotional profile, including nutrition, exercise, and psychological support.

[0303] The server transmits the generated preventive care plan to the user terminal.

[0304] The device will present users with preventative care plans and support them in carrying them out, and will also include reminder and progress tracking features.

[0305] Self-learning and evolution

[0306] The device collects feedback on the implementation of the preventive care plan and changes in emotional state and sends it to the server.

[0307] The server updates the AI ​​model and performs self-learning based on the collected feedback, health data, and emotional data.

[0308] The server continually updates the system's functionality based on new medical and psychological knowledge and technological advances.

[0309] Medical collaboration and treatment optimization

[0310] Users consent to data sharing with medical institutions through a settings screen within the application, and this consent is recorded within the system.

[0311] The server securely shares the user's health and emotional data with medical professionals, who can then use it to develop a diagnosis and treatment plan.

[0312] The server receives feedback and treatment plans from medical professionals and transmits them to the user terminal.

[0313] The terminal notifies the user of the treatment plan and provides the information necessary to carry it out (e.g., medication schedule, follow-up appointment times).

[0314] Specific examples

[0315] Real-time monitoring of emotional states and abnormal response

[0316] 1. After waking up in the morning, the user puts on the wearable device and launches the smartphone application.

[0317] 2. The device monitors the user's heart rate and emotional state in real time (for example, using facial recognition and voice analysis).

[0318] 3. The server analyzes the health and emotional data to detect abnormalities such as high heart rate or unstable emotional state.

[0319] 4. The server generates an anomaly detection alert and immediately notifies the user device.

[0320] 5. The device will notify the user of the abnormality detection alert and display a message such as "Your heart rate is high. Please take a short rest" or "Your emotional state is unstable. We recommend that you relax."

[0321] Providing personalized preventative care

[0322] 1. The server analyzes the user's health and emotional profile and discovers any nutrient deficiencies or psychological support needs.

[0323] 2. The server generates nutritional meal plans and relaxing mental exercise plans.

[0324] 3. The device will notify the user of these plans and provide specific instructions and reminders on how to implement them.

[0325] 4. The user carries out the preventive care plan and inputs changes in physical condition and emotions into the device as feedback.

[0326] 5. The server will incorporate this feedback into future care plans.

[0327] Through these functions, the system comprehensively manages the user's physical and mental health, providing an environment where elderly people living alone can live with peace of mind.

[0328] The processing flow will be explained below.

[0329] Program processing flow

[0330] Collecting and integrating health and emotional data

[0331] Step 1:

[0332] The user puts on a wearable device or home medical device and launches a smartphone app, which prepares to collect daily health and emotional data.

[0333] Step 2:

[0334] The device receives real-time health data such as heart rate, blood pressure, body temperature, and activity levels from wearable devices and medical equipment via Bluetooth and WiFi.

[0335] Step 3:

[0336] The device uses a camera and microphone to recognize the user's face and perform voice analysis to collect emotional data, including emotional analysis based on facial expressions and tone of voice.

[0337] Step 4:

[0338] The device periodically transmits the collected health and emotion data to a server using encrypted communications, ensuring data security.

[0339] Health and emotional profile generation and anomaly detection

[0340] Step 5:

[0341] The server stores the health data and emotion data received from the terminal in a database, and generates a health profile and emotion profile for each user.

[0342] Step 6:

[0343] The server uses AI models to analyze the incoming data and apply anomaly detection algorithms, for example, to detect abnormal heart rates or sudden fluctuations in emotional state.

[0344] Step 7:

[0345] When an anomaly is detected, the server generates an anomaly alert, which includes the type of anomaly and specific numerical values.

[0346] Step 8:

[0347] The server immediately transmits the generated abnormality alert to the user terminal.

[0348] Step 9:

[0349] The device will notify the user of abnormal alerts and display messages with specific instructions such as "Your heart rate is high. Please take a short rest" or "Your emotional state is unstable. We recommend you relax."

[0350] Providing personalized preventative care

[0351] Step 10:

[0352] The server generates a preventative care plan based on the individual user's health and emotional profile, which includes nutrition, exercise, and psychological support.

[0353] Step 11:

[0354] The server transmits the generated preventive care plan to the user terminal.

[0355] Step 12:

[0356] The device presents the user with a preventative care plan, including reminders and progress tracking.

[0357] Step 13:

[0358] The user incorporates the preventive care plan into their daily life and inputs their progress, changes in their physical condition, and changes in their emotional state into the terminal.

[0359] Self-learning and evolution

[0360] Step 14:

[0361] The device periodically transmits feedback from the user to the server, including information on dietary history, exercise status, and emotional changes.

[0362] Step 15:

[0363] The server updates the AI ​​model and performs self-learning based on the received feedback, health data, and emotional data.

[0364] Step 16:

[0365] The server uses the updated AI model to improve the accuracy of the next preventative care plan and anomaly detection algorithm.

[0366] Medical collaboration and treatment optimization

[0367] Step 17:

[0368] Users consent to data sharing with medical institutions through a settings screen within the application, and this consent is recorded within the system.

[0369] Step 18:

[0370] The server performs the function of securely sharing the user's health and emotional data with medical professionals, who then use it to develop a diagnosis and treatment plan.

[0371] Step 19:

[0372] The server receives feedback and treatment plans from medical professionals and transmits them to the user terminal.

[0373] Step 20:

[0374] The terminal notifies the user of the treatment plan and provides the information necessary to carry it out (e.g., medication schedule, follow-up appointment times).

[0375] Specific examples

[0376] Real-time monitoring of emotional states and abnormal response

[0377] Step 1:

[0378] After waking up in the morning, the user puts on the wearable device and launches the smartphone application.

[0379] Step 2:

[0380] The device receives heart rate data from the wearable device and simultaneously uses the camera and microphone to gather information about the user's emotional state, for example by analyzing emotions from facial expressions and tone of voice.

[0381] Step 3:

[0382] The server analyzes the received health and emotional data to detect abnormalities, such as if the heart rate is outside the normal range or if the user is in an unstable emotional state.

[0383] Step 4:

[0384] The server generates an anomaly detection alert and immediately notifies the user device.

[0385] Step 5:

[0386] The device receives the notification and displays specific instructions to the user, such as "Your heart rate is high. Please take a short rest" or "Your emotional state is unstable. We recommend you relax."

[0387] Providing personalized preventative care

[0388] Step 1:

[0389] The server analyzes the user's health and emotional profile and discovers deficiencies in certain nutrients or psychological support.

[0390] Step 2:

[0391] The server generates meal plans for optimal nutritional support and relaxation exercises for psychological support.

[0392] Step 3:

[0393] The device will notify the user of these plans and provide specific instructions on how to carry them out (e.g., recipes or exercise videos).

[0394] Step 4:

[0395] Users follow a preventative care plan and enter feedback into the device, including their diet, exercise routine, and emotional changes.

[0396] Step 5:

[0397] The server analyzes the collected feedback and incorporates it into the next preventative care plan.

[0398] Through these steps, the system provides comprehensive and personalized health and emotional care for seniors living alone.

[0399] Example 2

[0400] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0401] Currently, health and mental health management for older adults often relies on regular visits to medical institutions or self-management. However, these are difficult for older adults living alone, which requires the collection and analysis of real-time health and emotional data, early detection of abnormalities, and immediate response. Furthermore, there is a lack of personalized preventative care plans based on individual health and emotional conditions. This makes it difficult for older adults to maintain their health and ensure a high quality of life.

[0402] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting health data and emotional data in real time; means for encrypting the collected health data and emotional data and transmitting it to the server; means for storing the health data and emotional data received by the server and generating a health profile and an emotional profile; means for analyzing the health data and emotional data using an AI model to detect abnormalities; means for generating an abnormality alert and notifying the user terminal when an abnormality is detected; means for generating a personalized preventive care plan based on the user's health profile and emotional profile and providing it to the user terminal; means for supporting the execution of the preventive care plan through reminders and progress tracking functions; means for improving the AI ​​model based on user feedback and new data and evolving the service; means for sharing the health data and emotional data with medical professionals based on the user's consent to optimize the treatment plan; and means for notifying the user of feedback from medical professionals and the treatment plan. This enables comprehensive management of the health and mental health of elderly people, real-time abnormality detection, and early response. Additionally, providing personalized preventative care plans allows for more tailored care based on individual health and emotional states, improving the quality of life for seniors.

[0403] "Health data" refers to data that indicates the user's physical health condition, such as heart rate, blood pressure, body temperature, and activity level.

[0404] "Emotion data" is data that indicates the user's emotional state, obtained from facial expressions, voice, text messages, etc.

[0405] A "server" is a computer system that collects, stores, analyzes, and detects anomalies in data, provides care plans, collects feedback, and improves AI models.

[0406] A "terminal" is an electronic device that collects data from wearable devices and home medical equipment and transmits it to a server.

[0407] An "AI model" is a mathematical model that uses machine learning algorithms to analyze data, detect abnormalities, and generate personalized care plans.

[0408] A "health profile" is a data set that comprehensively indicates a user's health status and is generated based on the user's health data.

[0409] An "emotional profile" is a data set that comprehensively indicates a user's emotional state, generated based on the user's emotional data.

[0410] "Anomaly detection" is the process of analyzing collected health and emotional data to detect abnormal conditions that go beyond the normal range.

[0411] An "abnormality alert" is a warning message that notifies the user of a detected abnormal condition.

[0412] A "Preventive Care Plan" is a personalized health management and psychological support plan that is generated based on a user's health and emotional profile.

[0413] A "reminder" is a notification message that prompts the user to carry out a scheduled activity or care plan.

[0414] "Feedback" is the process by which users provide information about the progress of their preventive care plan and changes in their physical and emotional state.

[0415] A "health professional" is a professional with expertise related to the user's health and mental health, such as a doctor, nurse, or psychologist.

[0416] A "treatment plan" is a plan developed by a medical professional to resolve a user's health problem.

[0417] "Encryption" is a technology that converts data into a special code so that it cannot be deciphered by third parties.

[0418] MODE FOR CARRYING OUT THE INVENTION

[0419] The present invention relates to a comprehensive system for supporting health management for elderly people living alone. By combining an emotion engine, the system manages not only the user's physical health but also their mental health. Specific embodiments of the system are described below.

[0420] System Configuration

[0421] The system collects health and emotional data in real time, analyzes them, detects anomalies, provides preventive care, and shares data with medical professionals. The main components are the server, the device, and the user.

[0422] Health and emotional data collection

[0423] The device collects health data such as heart rate, blood pressure, temperature, and activity levels using wearable devices and home medical equipment, which connect to the device via Bluetooth or Wi-Fi.

[0424] The device uses a built-in emotion engine to collect emotion data from the user's facial expressions, voice, and text messages.

[0425] The device periodically encrypts and transmits the collected health and emotion data to a server.

[0426] Saving Data and Generating Profiles

[0427] The server stores the data received from the device in a database, for example, using MySQL.

[0428] The server generates health and emotional profiles for each user based on the stored data.

[0429] Data analysis and anomaly detection

[0430] The server uses a generative AI model (using, for example, TensorFlow or PyTorch) to analyze the incoming data in real time.

[0431] The server applies anomaly detection algorithms to detect abnormalities such as sudden fluctuations in heart rate or emotional state.

[0432] If an abnormality is detected, an abnormality alert is generated and notified immediately to the user terminal.

[0433] Providing preventative care

[0434] The server generates a personalized preventative care plan based on the health and emotional profiles.

[0435] The care plan includes nutritional guidance, exercise instruction, and psychological support.

[0436] The server transmits the generated preventive care plan to the user terminal.

[0437] The device notifies the user of the preventive care plan and provides specific implementation methods, reminders, and the ability to track implementation status.

[0438] Gathering feedback and self-learning systems

[0439] The device collects feedback on the progress of the preventive care plan and emotional state and sends it to the server.

[0440] The server updates the AI ​​model based on the collected feedback and performs self-learning.

[0441] Medical collaboration and treatment optimization

[0442] Users consent to sharing data with medical institutions through the application's settings screen, and this consent is recorded within the system.

[0443] The server securely shares the user's health and emotional data with medical professionals, who then use it to develop a diagnosis and treatment plan.

[0444] The server receives feedback and treatment plans from medical professionals and transmits them to the user terminal.

[0445] The terminal notifies the user of the treatment plan and provides the information necessary to carry it out (e.g., medication schedule, follow-up appointment times).

[0446] Specific examples

[0447] Real-time monitoring of emotional states and abnormal response

[0448] 1. After waking up in the morning, the user puts on the wearable device and launches the smartphone application.

[0449] 2. The device monitors the user's heart rate and emotional state in real time.

[0450] 3. The server analyzes the health and emotional data to detect abnormalities in heart rate and emotional instability.

[0451] 4. The server generates an anomaly detection alert and notifies the user device.

[0452] 5. The device displays an abnormal alert to the user, providing messages such as "Your heart rate is high. Please take a short rest" or "Your emotional state is unstable. We recommend that you relax."

[0453] Providing personalized preventative care

[0454] 1. The server analyzes the user's health and emotional profile and discovers any nutrient deficiencies or psychological support needs.

[0455] 2. The server generates nutritional meal plans and relaxing mental exercise plans.

[0456] 3. The device will notify the user of these plans and provide specific instructions and reminders on how to implement them.

[0457] 4. The user carries out the preventive care plan and inputs changes in physical condition and emotions into the device as feedback.

[0458] 5. The server will incorporate the feedback into future care plans.

[0459] Prompt Sentence Examples

[0460] "What are the steps to generate a personalized preventative care plan for seniors?"

[0461] "How can I collect sentiment data in real time?"

[0462] "Please tell me about the design of a system that notifies users of anomaly detection alerts."

[0463] In this way, this system comprehensively manages the user's physical and mental health, providing an environment where elderly people living alone can live with peace of mind.

[0464] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0465] Step 1: Data collection

[0466] The device collects health data such as heart rate, blood pressure, body temperature, and activity levels from wearable devices and home medical equipment via Bluetooth and WiFi.

[0467] Input: Real-time data from wearable devices and home medical equipment.

[0468] Output: Collected health data (heart rate, blood pressure, temperature, activity level, etc.).

[0469] The device uses a built-in emotion engine to collect emotion data from the user's facial expressions, voice, and text messages.

[0470] Input: User facial expressions, voice, and text messages.

[0471] Output: Collected emotion data.

[0472] Step 2: Send data

[0473] The device encrypts the collected health and emotion data and transmits it to a server on a regular basis.

[0474] Input: Collected health and emotion data.

[0475] Output: Data encrypted and sent to the server.

[0476] Step 3: Save data and create a profile

[0477] The server stores the health data and emotion data received from the terminal in a database.

[0478] Input: Encrypted data received from the terminal.

[0479] Output: Health and emotion data stored in a database.

[0480] The server generates a health profile and an emotional profile for each user based on the stored health and emotional data.

[0481] Input: Health and emotion data stored in a database.

[0482] Output: Generated health and emotion profiles.

[0483] Step 4: Data analysis and anomaly detection

[0484] The server uses AI models (using, for example, TensorFlow or PyTorch) to analyze health and emotional data and detect anomalies.

[0485] Input: Health profile and emotional profile.

[0486] Output: Parsed data and anomaly detection results.

[0487] The server applies anomaly detection algorithms to detect abnormalities in heart rate and sudden changes in emotions.

[0488] Specific behavior: Detects heart rate values ​​outside the normal range and sudden emotional fluctuations.

[0489] Output: Anomaly detection results.

[0490] Step 5: Anomaly alert generation and notification

[0491] If the server detects an abnormality, it generates an abnormality alert and immediately notifies the user terminal.

[0492] Input: Anomaly detection results.

[0493] Output: Generated anomaly alerts and notifications to user terminals.

[0494] The device will display an abnormality alert to the user and provide specific instructions, such as "Your heart rate is high. Please take a short rest" or "Your emotional state is unstable. We recommend you relax."

[0495] Specific behavior: Pop-up display of abnormality alert and audio notification.

[0496] Output: The notified message to the user.

[0497] Step 6: Generate a preventative care plan

[0498] The server generates a personalized preventative care plan based on the health and emotional profiles.

[0499] Input: Health profile, Emotion profile.

[0500] Output: Generated preventive care plan (including nutritional guidance, exercise instruction, and psychological support).

[0501] The server transmits the generated preventive care plan to the user terminal.

[0502] Specific actions: computation and plan generation.

[0503] Output: Preventive care plan sent to user device.

[0504] Step 7: Providing preventive care plans and supporting their implementation

[0505] The device notifies the user of the preventive care plan and provides specific implementation methods, reminders, and the ability to track implementation status.

[0506] Input: Submitted preventive care plan.

[0507] Output: Providing notification and tracking to the user.

[0508] Users act on preventative care plans and follow reminders to perform care practices.

[0509] Specific actions: confirmation of execution, reminder notification.

[0510] Step 8: Gather feedback and let the system self-train

[0511] The device collects feedback on the progress of the preventive care plan and emotional state and sends it to the server.

[0512] Input: Performance and emotional state feedback.

[0513] Output: Sending feedback to the server.

[0514] The server updates the AI ​​model based on the collected feedback and performs self-learning.

[0515] Specific operation: Model update, self-learning algorithm application.

[0516] Step 9: Care coordination and treatment optimization

[0517] Users consent to sharing data with medical institutions through the application's settings screen, and this consent is recorded within the system.

[0518] Input: Data sharing consent from user.

[0519] Output: Consent information recorded in the system.

[0520] The server securely shares the user's health and emotional data with medical professionals, who then use it to develop a diagnosis and treatment plan.

[0521] Specific actions: Data sharing, granting access to medical professionals.

[0522] Output: Data sent to medical professionals.

[0523] The server transmits feedback and treatment plans from medical professionals to the user terminal.

[0524] Input: Feedback and treatment plans from medical professionals.

[0525] Output: Send to user terminal.

[0526] The terminal notifies the user of the treatment plan and provides the information necessary to carry it out.

[0527] Specific operations: Notification of treatment plan, display of necessary information.

[0528] Output: The information provided to the user.

[0529] (Application example 2)

[0530] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0531] When elderly people live alone, it is often difficult to manage their health and mental health. In particular, if a sudden change in their health condition or emotional instability occurs while shopping in a brick-and-mortar store or during daily activities, there are a lack of safe ways to deal with it. It is also difficult to take appropriate action immediately when such an abnormality occurs. To address these issues, a comprehensive system is needed that aggregates health and emotional data in real time and provides immediate alerts and suggests specific actions when an abnormality is detected.

[0532] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0533] In this invention, the server includes: means for collecting health data; means for transmitting the collected health data to the server; means for analyzing the health data received by the server and generating a health profile; means for detecting abnormalities based on the generated health profile and notifying the user terminal when an abnormality is detected; means for generating a personalized preventive care plan based on the user's health data and feedback and providing the plan to the user terminal; means for improving the AI ​​model based on user feedback and new data to evolve the service; means for monitoring the user's health data and emotional data in real time in the physical store and for immediately issuing an alert and suggesting specific actions when an abnormality is detected; means for the user to input the progress of preventive care and emotional state as feedback into the terminal and for the system to adjust subsequent care plans based on the feedback; means for sharing health data with medical professionals and optimizing treatment plans; and means for notifying the user of feedback from medical professionals. This enables health and mental health management of elderly people to be performed in real time even in physical stores, enabling immediate action when an abnormality occurs.

[0534] Definitions of important terms included in the rewritten claims

[0535] "Health data" is information related to the user's physical condition, including heart rate, blood pressure, body temperature, activity level, and the like.

[0536] "Emotion data" is information that indicates the user's emotional state, and is data extracted from facial expressions, voice, text messages, and the like.

[0537] "Real-time monitoring" is the process of constantly monitoring a user's health and emotional data, and instantly detecting and responding to any abnormalities.

[0538] An "abnormality detection alert" is a warning message that is sent immediately when an abnormality occurs after analyzing the user's health and emotional data.

[0539] A "preventive care plan" is a personalized care program designed to maintain or improve health, based on a user's health and emotional profile.

[0540] "Feedback" is information provided by users about their progress in implementing preventative care plans and their emotional state, data that is used to improve and adjust the system.

[0541] "AI Model" refers to the artificial intelligence algorithms used to analyze incoming data, detect anomalies, and generate preventative care plans.

[0542] A "physical store" is a physical store, such as a supermarket or shop, where users can visit in person to receive services or products.

[0543] A "user terminal" is a smartphone, smart glasses, or other electronic device operated by a user, and is an interface for using various functions of the system.

[0544] A "healthcare professional" is a doctor, nurse, or other healthcare professional who provides a diagnosis or treatment plan based on a user's health data.

[0545] MODE FOR CARRYING OUT THE INVENTION

[0546] The present invention is a comprehensive system for managing the health and mental health of elderly people, and for detecting abnormalities and providing preventive care in real time based on the data. Specific embodiments of the system are described below.

[0547] Overall system overview

[0548] This system collects health and emotional data from user devices such as wearable devices, smartphones, and smart glasses, and then analyzes the data on a server, detects abnormalities, and provides preventive care.By monitoring the data in real time, the system immediately alerts users if an abnormality occurs and suggests appropriate actions to take.

[0549] Health and emotional data collection

[0550] The device collects data in the following ways:

[0551] Wearable devices collect health data such as heart rate, blood pressure, body temperature, and activity level. These devices connect to the device via Bluetooth or Wi-Fi.

[0552] It uses cameras in smart glasses and microphones in smartphones to collect emotional data from facial expressions, voice, text messages, and more.

[0553] Data transmission and storage

[0554] The device transmits the collected health and emotion data to a server using encrypted communication, which stores the received data in a database and generates a health profile and emotion profile for each user.

[0555] Data analysis and anomaly detection

[0556] The server analyzes the received health and emotion data and uses a generative AI model to detect abnormalities, such as an abnormally high heart rate or a sudden change in emotional state. If an abnormality is detected, the server generates an anomaly detection alert and immediately sends it to the user's device.

[0557] User Notification and Action Suggestions

[0558] When the device receives an anomaly detection alert, it notifies the user and suggests specific actions to take, such as displaying messages like "Your heart rate is high. Please take a short rest" or "Your emotional state is unstable. We recommend you relax."

[0559] Providing preventative care and gathering feedback

[0560] The server generates a preventive care plan based on each individual's health and emotional profile. This plan includes nutrition, exercise, and psychological support. The generated preventive care plan is sent to the user's terminal, which notifies the user of the preventive care plan and supports its implementation. The user inputs feedback on the implementation status of the preventive care plan and their emotional state into the terminal, and the data is sent to the server.

[0561] Medical collaboration and treatment optimization

[0562] The server shares the user's health and emotional data with medical professionals, who can then use it to diagnose and plan treatment. The server also receives feedback and treatment plans from medical professionals and sends them to the user's device.

[0563] Adding specific examples

[0564] Example 1:

[0565] If a user's heart rate suddenly spikes while shopping at a supermarket, their smartphone will display a message such as, "Your heart rate is high. Please take a short rest."

[0566] Example 2:

[0567] If the smart glasses sense that the user is in an emotionally unstable state, they will display instructions such as "Try some relaxation techniques."

[0568] Prompt Sentence Examples

[0569] "My heart rate spiked while I was grocery shopping. What action should I take?"

[0570] These functions make it possible to provide an environment where elderly people can live their daily lives with peace of mind, even if they live alone.

[0571] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0572] Program processing flow

[0573] Step 1:

[0574] Data collection and transmission

[0575] The device collects health data such as heart rate, blood pressure, body temperature, and activity level from the wearable device, and also uses the camera in the smart glasses and the microphone in the smartphone to collect emotional data from facial expressions, voice, text messages, and more.

[0576] Input: User health and emotion data.

[0577] Data processing / calculation: Data is transferred to the device via Bluetooth or Wi-Fi and encrypted.

[0578] Output: Encrypted health and emotion data.

[0579] Specific operation: The terminal connects to the device using Bluetooth or Wi-Fi communication to receive data, then encrypts the received data and sends it to the server.

[0580] Step 2:

[0581] Data analysis and profile generation

[0582] The server stores the received health and emotion data in a database, analyzes it, and generates health and emotion profiles using a generative AI model.

[0583] Input: Encrypted health and emotion data.

[0584] Data processing / calculation: The data is decrypted, stored in a database, and analyzed using an AI model.

[0585] Output: Health profile and emotional profile.

[0586] What it does: The server decrypts the data and stores it in a database, where it then uses an AI model to analyze the data and generate a profile.

[0587] Step 3:

[0588] Anomaly detection and notification

[0589] The server detects abnormalities based on the generated health and emotion profiles, and if an abnormality is detected, generates an anomaly detection alert and immediately notifies the user device.

[0590] Input: Health profile and emotional profile.

[0591] Data processing / calculation: Apply anomaly detection algorithms and generate alerts.

[0592] Output: Anomaly detection alert.

[0593] Specific operation: The server analyzes the profile and detects, for example, an abnormal increase in heart rate or a sudden change in emotion. If an abnormality is detected, an alert is generated and sent to the user's device.

[0594] Step 4:

[0595] User notification and suggested actions

[0596] When the device receives an anomaly detection alert, it notifies the user and suggests specific actions to take (e.g., taking a break or finding ways to relax).

[0597] Input: Anomaly detection alert.

[0598] Data processing / calculation: Generates alert messages and displays them on the screen.

[0599] Output: A notification message to the user.

[0600] Specific action: When the device receives an alert, it displays specific instructions on the user's screen, such as "Your heart rate is high. Please take a short rest."

[0601] Step 5:

[0602] Generate and deliver preventative care plans

[0603] The server generates a personalized preventive care plan based on the health profile and the emotional profile and provides it to the user terminal.

[0604] Input: Health and emotional profiles, existing feedback data.

[0605] Data processing / calculation: Create a preventative care plan based on the profile.

[0606] Output: A personalized preventative care plan.

[0607] What it does: The server analyzes the profile and generates a preventive care plan that includes nutritional advice, exercise plans, and psychological support tailored to each individual user.

[0608] Step 6:

[0609] Gathering feedback and improving the AI ​​model

[0610] The user inputs the progress of the preventive care plan and their emotional state as feedback into the terminal, and the data is sent to the server.

[0611] Input: Preventive care plan implementation results and emotional state feedback.

[0612] Data processing / calculation: collecting and storing feedback data, training AI models.

[0613] Output: An improved AI model.

[0614] Specific operation: The device periodically sends feedback data from the user to the server, and the server updates the AI ​​model based on that data.

[0615] Step 7:

[0616] Data collaboration with medical experts

[0617] The server shares the user's health and emotional data with medical professionals to assist in the development of diagnoses and treatment plans.

[0618] Input: Health and emotion data, requests from healthcare providers.

[0619] Data processing / computation: Collecting and sharing data, generating treatment plans.

[0620] Output: Treatment plan,feedback from medical professionals.

[0621] Specific operation: The server sends data to the medical professional using a secure communication method, receives feedback and treatment plans from the medical professional, and notifies the user.

[0622] Through this series of steps, the system can monitor the user's health and emotional data in real time, respond immediately to any abnormalities, and provide comprehensive support for the user's health management through personalized preventive care and medical coordination.

[0623] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0624] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0625] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0626] [Second embodiment]

[0627] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0628] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0629] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0630] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0631] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0632] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0633] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0634] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0635] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0636] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0637] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0638] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0639] The present invention relates to a comprehensive system for supporting health management for elderly people living alone, and specific embodiments thereof will be described below.

[0640] Overall system overview

[0641] This system collects users' health data in real time, analyzes it, detects abnormalities, suggests preventive care, and shares the data with medical professionals all in one place. By exchanging data between the server, devices, and users, it provides an environment in which elderly people can live safe and fulfilling lives.

[0642] Health data collection and integration

[0643] The device collects health data such as heart rate, blood pressure, temperature, and activity levels from wearable devices and home medical devices, which connect to the device via Bluetooth or Wi-Fi.

[0644] The device periodically sends the collected data to the server. The data is sent using encrypted communication to ensure data security.

[0645] Health profile generation and anomaly detection

[0646] The server stores the received health data in a database and generates a health profile for each user, which includes the individual user's baseline values ​​and past health data.

[0647] The server uses AI models to analyze the data in real time and detect anomalies, such as generating an anomaly alert if the heart rate falls outside the resting range.

[0648] If an abnormality is detected, the server immediately sends a notification to the terminal.

[0649] Providing personalized preventative care

[0650] The server generates a preventative care plan based on the individual's health profile, which is based on an AI model and includes elements such as nutrition, exercise, and psychological support.

[0651] The device will present users with preventative care plans and support them in carrying them out, and will also include reminder and progress tracking features.

[0652] Self-learning and evolution

[0653] The server collects user feedback and analyzes it along with health data, allowing the AI ​​model to self-train and improve the accuracy of the service.

[0654] The server continues to update the system's functionality based on new medical knowledge and technological advances.

[0655] Medical collaboration and treatment optimization

[0656] With the user's consent, the server will share health data with medical professionals, allowing doctors to provide more accurate diagnoses and treatment plans.

[0657] The device notifies users of treatment plans received from medical professionals and provides the information they need to implement them, including medication schedules and follow-up appointment reminders.

[0658] Specific examples

[0659] Anomaly detection and rapid response

[0660] 1. The user puts on the wearable device when they wake up in the morning.

[0661] 2. The device collects heart rate data during the day and sends it to the server.

[0662] 3. The server analyzes the received data in real time and detects abnormal high heart rates.

[0663] 4. The server generates an anomaly detection alert and immediately sends it to the device.

[0664] 5. The device notifies the user of the abnormal alert and displays the message "Your heart rate is high. Please take a short rest." If necessary, it will also automatically notify emergency contacts.

[0665] Providing personalized preventative care

[0666] 1. The server analyzes the user's health profile and discovers that there are nutrient deficiencies.

[0667] 2. The server generates a meal plan for nutritional support.

[0668] 3. The device will notify the user of this meal plan and provide detailed recipes and a shopping list.

[0669] 4. The user follows the meal plan and inputs any changes in their physical condition into the device as feedback.

[0670] 5. The server will incorporate this feedback into future care plans.

[0671] Through these functions, the system comprehensively manages the user's health and provides an environment where elderly people living alone can live with peace of mind.

[0672] The processing flow will be explained below.

[0673] Program processing flow

[0674] Health data collection and integration

[0675] Step 1:

[0676] Users wear wearable devices or home medical devices that measure daily health data (heart rate, blood pressure, body temperature, activity level, etc.) in real time.

[0677] Step 2:

[0678] The device receives data from wearable devices and medical equipment via Bluetooth or WiFi, and this data is temporarily stored on the device.

[0679] Step 3:

[0680] The device sends the collected data to the server at regular intervals using encrypted communications, ensuring data security.

[0681] Health profile generation and anomaly detection

[0682] Step 4:

[0683] The server stores the health data received from the device in a database, where a health profile for each user is generated.

[0684] Step 5:

[0685] The server uses AI models to analyze the incoming data and apply anomaly detection algorithms, such as detecting abnormal heart rates or sudden fluctuations in blood pressure.

[0686] Step 6:

[0687] When an anomaly is detected, the server generates an anomaly alert, which includes the type of anomaly and specific numerical values.

[0688] Step 7:

[0689] The server immediately transmits the generated abnormality alert to the user terminal.

[0690] Step 8:

[0691] The device notifies the user of the abnormality alert, and the notification includes specific instructions (e.g., take a rest, contact a doctor).

[0692] Providing personalized preventative care

[0693] Step 9:

[0694] The server generates a preventative care plan based on the individual user's health profile, which includes nutrition, exercise, and psychological support.

[0695] Step 10:

[0696] The server transmits the generated preventive care plan to the user terminal.

[0697] Step 11:

[0698] The device then presents the received preventive care plan to the user, including reminders and help features.

[0699] Step 12:

[0700] The user incorporates the preventive care plan into their daily life and inputs the progress of the care plan and changes in their physical condition into the terminal.

[0701] Self-learning and evolution

[0702] Step 13:

[0703] The device periodically transmits feedback from the user to the server, including information such as diet history and exercise status.

[0704] Step 14:

[0705] The server updates the AI ​​model and performs self-learning based on the received feedback and health data.

[0706] Step 15:

[0707] The server uses the updated AI model to improve the accuracy of the next preventative care plan and anomaly detection algorithm.

[0708] Medical collaboration and treatment optimization

[0709] Step 16:

[0710] Users consent to data sharing with medical institutions through a settings screen within the application, and this consent is recorded within the system.

[0711] Step 17:

[0712] The server performs the function of securely sharing the user's health data with medical professionals, who then use it to develop a diagnosis and treatment plan.

[0713] Step 18:

[0714] The server receives feedback and treatment plans from medical professionals and transmits them to the user terminal.

[0715] Step 19:

[0716] The terminal notifies the user of the treatment plan and provides the information necessary to carry it out (e.g., medication schedule, follow-up appointment times).

[0717] Through these steps, the system provides comprehensive and personalized health management and support to elderly people living alone.

[0718] Example 1

[0719] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0720] Elderly people living alone require real-time monitoring of their health status and early detection of abnormalities. However, existing systems struggle to integrate functions such as health data collection and analysis, abnormality detection, provision of preventive care plans, and data sharing with medical professionals. Furthermore, the disparate nature of these functions makes them difficult to use. Furthermore, they lack the ability to evolve AI models based on user feedback and respond quickly in emergencies. Therefore, there is a need for a comprehensive system that provides support for the safe and secure lifestyles of elderly people living alone.

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

[0722] In this invention, the server includes: means for collecting health data; means for transmitting the collected health data to the server; means for analyzing the health data received by the server and generating a health profile; means for detecting abnormalities based on the generated health profile and notifying the user terminal when an abnormality is detected; means for generating a personalized preventive care plan based on the user's health data and feedback and providing it to the user terminal; means for improving the AI ​​model based on user feedback and new data and evolving the service; means for sharing the health data with medical professionals and optimizing the treatment plan; means for notifying the user of feedback from medical professionals; means for automatically notifying an emergency contact when an abnormality is detected; and means for providing a specific meal plan and shopping list based on the preventive care plan. This enables efficient and comprehensive health management of elderly people and supports their safe and secure lives.

[0723] "Health data" is information that indicates the physiological and health status of the user, such as heart rate, blood pressure, body temperature, and activity level.

[0724] A "means for collection" is a method or device for compiling a user's health data using a wearable device or home medical equipment.

[0725] "Transmitting means" refers to a method or device for encrypting the collected health data and sending it to the server.

[0726] The "analyzing means" is a method or device that processes the health data received by the server and evaluates the user's health status.

[0727] A "health profile" is a personalized physiological and health record generated from a user's health data.

[0728] A "detecting means" is a method or device for detecting abnormalities based on the generated health profile.

[0729] "Notification means" refers to a method or device for sending an alert to a user terminal or emergency contact when an abnormality is detected.

[0730] A "personalized preventative care plan" is a preventative or care plan created based on a user's individual health profile.

[0731] A "means for providing" is a method or device for informing a user of a preventive care plan and supporting its implementation.

[0732] "Means for improvement" refers to a method or device for updating the AI ​​model based on user feedback and new data to improve the service.

[0733] A "means for sharing" is a method or device for exchanging a user's health data with a medical professional to optimize a treatment plan.

[0734] "Means for automatically notifying" refers to a method or device for automatically notifying an emergency contact when an abnormality is detected.

[0735] A "means for providing a meal plan or shopping list" is a method or device that provides a user with meal suggestions and a list of ingredients based on a preventive care plan.

[0736] The present invention relates to a comprehensive system for supporting health management for elderly people living alone, and specific embodiments thereof will be described below.

[0737] Overall system overview

[0738] This system collects users' health data in real time, analyzes it, detects abnormalities, suggests preventive care, and shares the data with medical professionals. By exchanging data between the server, devices, and users, it provides an environment in which elderly people can live a safe and fulfilling life.

[0739] Health data collection and integration

[0740] The device collects health data such as heart rate, blood pressure, temperature, and activity levels from wearable devices and home medical devices, which connect to the device via Bluetooth or Wi-Fi.

[0741] Specific devices include Fitbit, Apple Watch, and common home blood pressure monitors and thermometers.

[0742] The collected data is collected using the device's API.

[0743] The device periodically sends the collected data to the server. The data is sent using SSL / TLS encryption to ensure data security.

[0744] Health profile generation and anomaly detection

[0745] The server stores the received health data in a database and generates a health profile for each user, which includes the individual user's baseline values ​​and past health data.

[0746] Database systems such as MySQL and MongoDB are used.

[0747] The server uses an AI model developed in Python to analyze the data in real time and detect anomalies, such as generating an anomaly alert if the heart rate deviates from the resting range.

[0748] If an abnormality is detected, the server immediately sends a notification to the terminal.

[0749] Providing personalized preventative care

[0750] The server generates a preventative care plan based on the individual's health profile, which is based on an AI model and includes elements such as nutrition, exercise, and psychological support.

[0751] The device will present users with preventative care plans and support them in carrying them out, and will also include reminder and progress tracking features.

[0752] Self-learning and evolution

[0753] The server collects user feedback and analyzes it along with health data, allowing the AI ​​model to self-train and improve the accuracy of the service.

[0754] The server continues to update the system's functionality based on new medical knowledge and technological advances.

[0755] Medical collaboration and treatment optimization

[0756] With the user's consent, the server will share health data with medical professionals, allowing doctors to provide more accurate diagnoses and treatment plans.

[0757] The device notifies users of treatment plans received from medical professionals and provides the information they need to implement them, including medication schedules and follow-up appointment reminders.

[0758] Specific examples

[0759] Anomaly detection and rapid response

[0760] 1. The user puts on the wearable device when they wake up in the morning.

[0761] 2. The device collects heart rate data during the day and sends it to the server.

[0762] 3. The server analyzes the received data in real time and detects abnormal high heart rates.

[0763] 4. The server generates an anomaly detection alert and immediately sends it to the device.

[0764] 5. The device notifies the user of the abnormal alert and displays the message "Your heart rate is high. Please take a short rest." If necessary, it will also automatically notify emergency contacts.

[0765] Providing personalized preventative care

[0766] 1. The server analyzes the user's health profile and discovers that there are nutrient deficiencies.

[0767] 2. The server generates a meal plan for nutritional support.

[0768] 3. The device will notify the user of this meal plan and provide detailed recipes and a shopping list.

[0769] 4. The user follows the meal plan and inputs any changes in their physical condition into the device as feedback.

[0770] 5. The server will incorporate this feedback into future care plans.

[0771] Through these functions, the system comprehensively manages the user's health and provides an environment where elderly people living alone can live with peace of mind.

[0772] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0773] Processing flow

[0774] Step 1: Collecting health data

[0775] A user puts on a wearable device (e.g., a general fitness tracker) when they wake up in the morning. They also use home medical devices (blood pressure monitor, thermometer).

[0776] The device collects data from wearable devices and medical equipment via Bluetooth or WiFi, such as heart rate and body temperature data.

[0777] Input: Sensor data from wearable devices and medical equipment

[0778] Output: Collected health data (heart rate, blood pressure, body temperature, activity level)

[0779] Step 2: Send and store data

[0780] The data collected by the device is periodically encrypted and sent securely to the server using the SSL / TLS protocol.

[0781] The server validates the data it receives and stores it in the database after verifying its validity.

[0782] Input: Collected health data

[0783] Output: Health data stored on the server

[0784] Step 3: Health profile generation and anomaly detection

[0785] The server generates a health profile for each user based on the health data stored in the database, including past data and baseline values.

[0786] Based on the generated profile, the server uses a generative AI model developed in Python to analyze the data in real time and detect abnormalities, such as when the heart rate is outside the resting range.

[0787] Input: Stored health data and generative AI model

[0788] Output: Health profile and abnormality detection results (alerts)

[0789] Step 4: Notification of abnormalities

[0790] If the server detects an abnormality, it immediately generates an anomaly detection alert and sends a notification to the terminal.

[0791] The device will notify the user of any abnormal alerts, for example, "Your heart rate is high. Please take a short rest."

[0792] Input: Anomaly detection result

[0793] Output: User notification

[0794] Step 5: Deliver personalized preventative care

[0795] The server generates a preventative care plan based on an individual's health profile, which includes nutrition, exercise, psychological support, and more.

[0796] The device presents the user with a preventive care plan, provides reminders, and tracks progress.

[0797] Input: Health profile and generative AI model

[0798] Output: Preventive Care Plan

[0799] Step 6: Gather feedback and improve the AI ​​model

[0800] The server collects feedback from the user about the running status and changes in physical condition.

[0801] The server analyzes this feedback and allows the generative AI model to self-train and improve accuracy.

[0802] Input: User feedback

[0803] Output: An improved generative AI model

[0804] Step 7: Care coordination and treatment optimization

[0805] The server shares health data with medical professionals based on the user's consent, for example by exchanging data using the FHIR protocol.

[0806] The device notifies the user of the treatment plan received from the medical professional and reminds them of medication schedules and follow-up appointments.

[0807] Input: Treatment plan from medical professional

[0808] Output: Notifications and reminders to the user

[0809] (Application example 1)

[0810] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0811] Elderly people living alone face challenges when managing their own health, including the difficulty of early detection of abnormalities and receiving appropriate preventive care. It is also difficult to quickly and appropriately connect with medical professionals. Furthermore, there is a lack of health support services linked to nearby stores and fitness centers that seniors can actually use.

[0812] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0813] In this invention, the server includes: means for collecting health data; means for transmitting the collected health data to the server; means for analyzing the health data received by the server and generating a health profile; means for detecting abnormalities based on the generated health profile and notifying the user terminal when an abnormality is detected; means for generating a personalized preventive care plan based on the user's health data and feedback and providing it to the user terminal; means for improving the AI ​​model based on user feedback and new data and evolving the service; means for sharing the health data with medical professionals and optimizing the treatment plan; means for notifying the user of feedback from medical professionals; means for providing health consultations or fitness support at affiliated physical stores based on the user's health data; and means for simultaneously notifying the user and related facilities when an abnormality is detected. This enables elderly people living alone to receive early detection of abnormalities and appropriate preventive care, and facilitates prompt and appropriate collaboration with medical professionals and physical stores.

[0814] "Health data" refers to numerical values ​​and information that indicate an individual's health status, such as heart rate, blood pressure, body temperature, and activity level.

[0815] "Means of collection" refers to the equipment and methods used to obtain a user's health data, such as using wearable devices or home medical equipment.

[0816] The "means of transmission" refers to the communication means for sending the collected health data to the server, and uses wireless communication technology such as Bluetooth or WiFi.

[0817] A "server" is a computer system that receives, analyzes, and stores collected health data.

[0818] The "analyzing means" refers to algorithms or software that evaluate the user's health status based on the received health data.

[0819] A "health profile" is a data set that compiles a user's health data history and baseline values, and is used to comprehensively assess an individual's health status.

[0820] An "anomaly detection method" is an algorithm or software that compares the health profile with newly collected data and identifies outliers.

[0821] "Means of notification" refers to methods or technologies for notifying a user terminal that an abnormality has been detected, such as a smartphone application.

[0822] "Personalized preventative care plans" refer to health management and improvement recommendations that are individually optimized based on the user's health data and feedback.

[0823] The "means for providing" refers to a method for displaying or notifying the personalized preventive care plan on the user terminal.

[0824] "AI model" refers to a machine learning or artificial intelligence model used to analyze health data and generate anomaly detection and preventative care plans.

[0825] A "healthcare professional" is a doctor or pharmacist who has the expertise to diagnose a user's health data and provide a treatment plan.

[0826] "Facilities" refers to affiliated physical stores and fitness centers, which are places that users can actually visit.

[0827] "Health consultation" means receiving health advice and information from experts such as pharmacists and nutritionists at a physical store.

[0828] "Fitness support" means receiving exercise guidance and advice from a trainer at a fitness center.

[0829] The system of the present invention is implemented by the following method and procedure. The system's core function is to collect a user's health data, analyze it in real time, detect abnormalities, and provide preventative care plans. It also includes collaborating with medical professionals based on the data to provide appropriate health consultations and fitness support to the user.

[0830] Data collection and transmission

[0831] The server collects health data from wearable devices and home medical devices (smartwatches, scales, blood pressure monitors, etc.) via Bluetooth or Wi-Fi. This periodically collects data such as heart rate, blood pressure, body temperature, and activity level. The collected data is encrypted by the smartphone and sent to the cloud server.

[0832] Analyzing data and generating health profiles

[0833] The server uses a cloud-based data analysis system (e.g., AWS, MySQL) to analyze the received data and generate a health profile for each user. This profile includes the user's baseline values ​​and past health data. An AI model (e.g., TensorFlow) is used to analyze the data in real time and detect outliers.

[0834] Anomaly detection and notification

[0835] If an abnormality is detected, the server immediately generates an abnormality alert and sends a notification to the user's smartphone. Furthermore, if the abnormality is serious, notifications are also sent to the user's partner physical stores (drugstores and fitness centers). This allows the elderly person to receive prompt and appropriate treatment.

[0836] Providing personalized preventative care plans

[0837] The server generates a personalized preventive care plan based on the user's health profile. This plan includes nutrition, exercise, and psychological support. The plan is then provided to the user via a smartphone application. The application also has reminder and progress tracking functions.

[0838] Health counseling and fitness support

[0839] Based on the user's health data, health consultations are provided at affiliated physical stores and fitness support is provided at fitness centers. If an abnormality is detected, a pharmacist at the drugstore or a fitness instructor will take appropriate action. This allows users to receive comprehensive support.

[0840] Examples of specific examples and prompts

[0841] Example 1: If high blood pressure is detected, the following notification is displayed on the user's smartphone:

[0842] Text format

[0843] Abnormalities detected. Your heart rate and blood pressure are out of normal range. Would you like to schedule a free consultation at your local drugstore?

[0844] Example 2: If an anomaly is detected during a workout at a fitness center, the following notification is sent to the instructor:

[0845] Text format

[0846] Your heart rate has deviated from the normal range during exercise. Would you like to contact your instructor?

[0847] The above is an embodiment of the present invention. This system enables elderly people living alone to receive early detection of abnormalities and appropriate preventive care, and also facilitates prompt and appropriate cooperation with medical professionals and brick-and-mortar stores.

[0848] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0849] Step 1:

[0850] Users wear a smartwatch or home medical device to collect health data. The smartwatch measures data such as heart rate, blood pressure, body temperature, and activity level, and transmits it to a smartphone via Bluetooth.

[0851] Input: User's health data (heart rate, blood pressure, body temperature, activity level, etc.)

[0852] Output: Health data collected on a smartphone

[0853] Step 2:

[0854] The device (smartphone) encrypts the collected health data and sends it to a cloud server, where the smartphone app periodically uploads the data to the server.

[0855] Input: Health data collected on a smartphone

[0856] Output: Encrypted data sent to the cloud server

[0857] Step 3:

[0858] The server analyzes the received health data and generates a health profile. Specifically, it stores the data in a database (MySQL) and performs anomaly detection analysis using an AI model (TensorFlow). It compares the data with past data, sets baseline values, and updates the health profile.

[0859] Input: Encrypted data sent to the cloud server

[0860] Output: Generated health profile

[0861] Step 4:

[0862] The server detects abnormalities based on the generated health profile. The AI ​​model analyzes the data in real time and generates an anomaly alert if it detects an abnormal value.

[0863] Input: Generated health profile

[0864] Output: Abnormal alert

[0865] Step 5:

[0866] If an abnormality is detected, the server sends a notification to the user's device, and simultaneously issues an abnormality alert and notifies partner facilities.

[0867] Input: Anomaly Alert

[0868] Output: Notification sent to user device and partner facility

[0869] Step 6:

[0870] The device will notify the user of any abnormalities and suggest specific measures to take if necessary, which in this case could include health consultations at a drugstore or support at a fitness center.

[0871] Input: Notification sent to user device

[0872] Output: Notify the user of the abnormality and suggest a solution

[0873] Step 7:

[0874] The server generates a personalized preventative care plan based on the user's health data and feedback. An AI model analyzes the user's condition and creates an individualized care plan.

[0875] Input: Health data and feedback

[0876] Output: A personalized preventative care plan

[0877] Step 8:

[0878] The device provides the user with the generated preventive care plan and supports implementation, and the app uses reminders and implementation tracking features to monitor the user's progress with the care plan.

[0879] Enter: personalized preventative care plans.

[0880] Output: Preventive care plans and support functions provided to users

[0881] Step 9:

[0882] The server improves the AI ​​model based on user feedback and new health data, evolving the service and improving the accuracy of future preventive care plans.

[0883] Input: Feedback and new health data

[0884] Output: Improved AI models and evolved services

[0885] Step 10:

[0886] The server shares health data with medical professionals to optimize treatment plans, and provides feedback from medical professionals to users to provide appropriate treatment and care.

[0887] Input: Health data and medical expert feedback

[0888] Output: Optimized treatment plan and notification to the user

[0889] The above processing steps realize a system that monitors the health status of elderly people in real time, detects abnormalities early, and enables appropriate responses.

[0890] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0891] The present invention relates to a comprehensive system for supporting health management for elderly people living alone, and by combining it with an emotion engine, manages not only physical health but also mental health. Specific embodiments of the system are described below.

[0892] Overall system overview

[0893] This system collects users' health and emotional data in real time, analyzes it, detects abnormalities, suggests preventive care, and shares the data with medical professionals. Through the exchange of data between the server, devices, and users, it provides an environment in which elderly people can live a safe and fulfilling life.

[0894] Collecting and integrating health and emotional data

[0895] The device collects health data such as heart rate, blood pressure, temperature, and activity levels from wearable devices and home medical devices, which connect to the device via Bluetooth or Wi-Fi.

[0896] The device uses a built-in emotion engine to collect emotion data from the user's facial expressions, voice, text messages, etc.

[0897] The terminal periodically transmits the health data and emotion data to the server using encrypted communication.

[0898] Health and emotional profile generation and anomaly detection

[0899] The server stores the health data and emotion data received from the terminals in a database and generates a health profile and emotion profile for each user.

[0900] The server uses AI models to analyze the incoming data and apply anomaly detection algorithms, for example, to detect abnormal heart rates or sudden fluctuations in emotional state.

[0901] When the server detects an abnormality, it generates an abnormality alert and immediately sends this alert to the user terminal.

[0902] The device will notify the user of abnormal alerts and display messages with specific instructions such as "Your heart rate is high. Please take a short rest" or "Your emotional state is unstable. We recommend you relax."

[0903] Providing personalized preventative care

[0904] The server generates a preventative care plan based on each individual's health and emotional profile, including nutrition, exercise, and psychological support.

[0905] The server transmits the generated preventive care plan to the user terminal.

[0906] The device will present users with preventative care plans and support them in carrying them out, and will also include reminder and progress tracking features.

[0907] Self-learning and evolution

[0908] The device collects feedback on the implementation of the preventive care plan and changes in emotional state and sends it to the server.

[0909] The server updates the AI ​​model and performs self-learning based on the collected feedback, health data, and emotional data.

[0910] The server continually updates the system's functionality based on new medical and psychological knowledge and technological advances.

[0911] Medical collaboration and treatment optimization

[0912] Users consent to data sharing with medical institutions through a settings screen within the application, and this consent is recorded within the system.

[0913] The server securely shares the user's health and emotional data with medical professionals, who can then use it to develop a diagnosis and treatment plan.

[0914] The server receives feedback and treatment plans from medical professionals and transmits them to the user terminal.

[0915] The terminal notifies the user of the treatment plan and provides the information necessary to carry it out (e.g., medication schedule, follow-up appointment times).

[0916] Specific examples

[0917] Real-time monitoring of emotional states and abnormal response

[0918] 1. After waking up in the morning, the user puts on the wearable device and launches the smartphone application.

[0919] 2. The device monitors the user's heart rate and emotional state in real time (for example, using facial recognition and voice analysis).

[0920] 3. The server analyzes the health and emotional data to detect abnormalities such as high heart rate or unstable emotional state.

[0921] 4. The server generates an anomaly detection alert and immediately notifies the user device.

[0922] 5. The device will notify the user of the abnormality detection alert and display a message such as "Your heart rate is high. Please take a short rest" or "Your emotional state is unstable. We recommend that you relax."

[0923] Providing personalized preventative care

[0924] 1. The server analyzes the user's health and emotional profile and discovers any nutrient deficiencies or psychological support needs.

[0925] 2. The server generates nutritional meal plans and relaxing mental exercise plans.

[0926] 3. The device will notify the user of these plans and provide specific instructions and reminders on how to implement them.

[0927] 4. The user carries out the preventive care plan and inputs changes in physical condition and emotions into the device as feedback.

[0928] 5. The server will incorporate this feedback into future care plans.

[0929] Through these functions, the system comprehensively manages the user's physical and mental health, providing an environment where elderly people living alone can live with peace of mind.

[0930] The processing flow will be explained below.

[0931] Program processing flow

[0932] Collecting and integrating health and emotional data

[0933] Step 1:

[0934] The user puts on a wearable device or home medical device and launches a smartphone app, which prepares to collect daily health and emotional data.

[0935] Step 2:

[0936] The device receives real-time health data such as heart rate, blood pressure, body temperature, and activity levels from wearable devices and medical equipment via Bluetooth and WiFi.

[0937] Step 3:

[0938] The device uses a camera and microphone to recognize the user's face and perform voice analysis to collect emotional data, including emotional analysis based on facial expressions and tone of voice.

[0939] Step 4:

[0940] The device periodically transmits the collected health and emotion data to a server using encrypted communications, ensuring data security.

[0941] Health and emotional profile generation and anomaly detection

[0942] Step 5:

[0943] The server stores the health data and emotion data received from the terminal in a database, and generates a health profile and emotion profile for each user.

[0944] Step 6:

[0945] The server uses AI models to analyze the incoming data and apply anomaly detection algorithms, for example, to detect abnormal heart rates or sudden fluctuations in emotional state.

[0946] Step 7:

[0947] When an anomaly is detected, the server generates an anomaly alert, which includes the type of anomaly and specific numerical values.

[0948] Step 8:

[0949] The server immediately transmits the generated abnormality alert to the user terminal.

[0950] Step 9:

[0951] The device will notify the user of abnormal alerts and display messages with specific instructions such as "Your heart rate is high. Please take a short rest" or "Your emotional state is unstable. We recommend you relax."

[0952] Providing personalized preventative care

[0953] Step 10:

[0954] The server generates a preventative care plan based on the individual user's health and emotional profile, which includes nutrition, exercise, and psychological support.

[0955] Step 11:

[0956] The server transmits the generated preventive care plan to the user terminal.

[0957] Step 12:

[0958] The device presents the user with a preventative care plan, including reminders and progress tracking.

[0959] Step 13:

[0960] The user incorporates the preventive care plan into their daily life and inputs their progress, changes in their physical condition, and changes in their emotional state into the terminal.

[0961] Self-learning and evolution

[0962] Step 14:

[0963] The device periodically transmits feedback from the user to the server, including information on dietary history, exercise status, and emotional changes.

[0964] Step 15:

[0965] The server updates the AI ​​model and performs self-learning based on the received feedback, health data, and emotional data.

[0966] Step 16:

[0967] The server uses the updated AI model to improve the accuracy of the next preventative care plan and anomaly detection algorithm.

[0968] Medical collaboration and treatment optimization

[0969] Step 17:

[0970] Users consent to data sharing with medical institutions through a settings screen within the application, and this consent is recorded within the system.

[0971] Step 18:

[0972] The server performs the function of securely sharing the user's health and emotional data with medical professionals, who then use it to develop a diagnosis and treatment plan.

[0973] Step 19:

[0974] The server receives feedback and treatment plans from medical professionals and transmits them to the user terminal.

[0975] Step 20:

[0976] The terminal notifies the user of the treatment plan and provides the information necessary to carry it out (e.g., medication schedule, follow-up appointment times).

[0977] Specific examples

[0978] Real-time monitoring of emotional states and abnormal response

[0979] Step 1:

[0980] After waking up in the morning, the user puts on the wearable device and launches the smartphone application.

[0981] Step 2:

[0982] The device receives heart rate data from the wearable device and simultaneously uses the camera and microphone to gather information about the user's emotional state, for example by analyzing emotions from facial expressions and tone of voice.

[0983] Step 3:

[0984] The server analyzes the received health and emotional data to detect abnormalities, such as if the heart rate is outside the normal range or if the user is in an unstable emotional state.

[0985] Step 4:

[0986] The server generates an anomaly detection alert and immediately notifies the user device.

[0987] Step 5:

[0988] The device receives the notification and displays specific instructions to the user, such as "Your heart rate is high. Please take a short rest" or "Your emotional state is unstable. We recommend you relax."

[0989] Providing personalized preventative care

[0990] Step 1:

[0991] The server analyzes the user's health and emotional profile and discovers deficiencies in certain nutrients or psychological support.

[0992] Step 2:

[0993] The server generates meal plans for optimal nutritional support and relaxation exercises for psychological support.

[0994] Step 3:

[0995] The device will notify the user of these plans and provide specific instructions on how to carry them out (e.g., recipes or exercise videos).

[0996] Step 4:

[0997] Users follow a preventative care plan and enter feedback into the device, including their diet, exercise routine, and emotional changes.

[0998] Step 5:

[0999] The server analyzes the collected feedback and incorporates it into the next preventative care plan.

[1000] Through these steps, the system provides comprehensive and personalized health and emotional care for seniors living alone.

[1001] Example 2

[1002] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1003] Currently, health and mental health management for older adults often relies on regular visits to medical institutions or self-management. However, these are difficult for older adults living alone, which requires the collection and analysis of real-time health and emotional data, early detection of abnormalities, and immediate response. Furthermore, there is a lack of personalized preventative care plans based on individual health and emotional conditions. This makes it difficult for older adults to maintain their health and ensure a high quality of life.

[1004] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting health data and emotional data in real time; means for encrypting the collected health data and emotional data and transmitting it to the server; means for storing the health data and emotional data received by the server and generating a health profile and an emotional profile; means for analyzing the health data and emotional data using an AI model to detect abnormalities; means for generating an abnormality alert and notifying the user terminal when an abnormality is detected; means for generating a personalized preventive care plan based on the user's health profile and emotional profile and providing it to the user terminal; means for supporting the execution of the preventive care plan through reminders and progress tracking functions; means for improving the AI ​​model based on user feedback and new data and evolving the service; means for sharing the health data and emotional data with medical professionals based on the user's consent to optimize the treatment plan; and means for notifying the user of feedback from medical professionals and the treatment plan. This enables comprehensive management of the health and mental health of elderly people, real-time abnormality detection, and early response. Additionally, providing personalized preventative care plans allows for more tailored care based on individual health and emotional states, improving the quality of life for seniors.

[1005] "Health data" refers to data that indicates the user's physical health condition, such as heart rate, blood pressure, body temperature, and activity level.

[1006] "Emotion data" is data that indicates the user's emotional state, obtained from facial expressions, voice, text messages, etc.

[1007] A "server" is a computer system that collects, stores, analyzes, and detects anomalies in data, provides care plans, collects feedback, and improves AI models.

[1008] A "terminal" is an electronic device that collects data from wearable devices and home medical equipment and transmits it to a server.

[1009] An "AI model" is a mathematical model that uses machine learning algorithms to analyze data, detect abnormalities, and generate personalized care plans.

[1010] A "health profile" is a data set that comprehensively indicates a user's health status and is generated based on the user's health data.

[1011] An "emotional profile" is a data set that comprehensively indicates a user's emotional state, generated based on the user's emotional data.

[1012] "Anomaly detection" is the process of analyzing collected health and emotional data to detect abnormal conditions that go beyond the normal range.

[1013] An "abnormality alert" is a warning message that notifies the user of a detected abnormal condition.

[1014] A "Preventive Care Plan" is a personalized health management and psychological support plan that is generated based on a user's health and emotional profile.

[1015] A "reminder" is a notification message that prompts the user to carry out a scheduled activity or care plan.

[1016] "Feedback" is the process by which users provide information about the progress of their preventive care plan and changes in their physical and emotional state.

[1017] A "health professional" is a professional with expertise related to the user's health and mental health, such as a doctor, nurse, or psychologist.

[1018] A "treatment plan" is a plan developed by a medical professional to resolve a user's health problem.

[1019] "Encryption" is a technology that converts data into a special code so that it cannot be deciphered by third parties.

[1020] MODE FOR CARRYING OUT THE INVENTION

[1021] The present invention relates to a comprehensive system for supporting health management for elderly people living alone. By combining an emotion engine, the system manages not only the user's physical health but also their mental health. Specific embodiments of the system are described below.

[1022] System Configuration

[1023] The system collects health and emotional data in real time, analyzes them, detects anomalies, provides preventive care, and shares data with medical professionals. The main components are the server, the device, and the user.

[1024] Health and emotional data collection

[1025] The device collects health data such as heart rate, blood pressure, temperature, and activity levels using wearable devices and home medical equipment, which connect to the device via Bluetooth or Wi-Fi.

[1026] The device uses a built-in emotion engine to collect emotion data from the user's facial expressions, voice, and text messages.

[1027] The device periodically encrypts and transmits the collected health and emotion data to a server.

[1028] Saving Data and Generating Profiles

[1029] The server stores the data received from the device in a database, for example, using MySQL.

[1030] The server generates health and emotional profiles for each user based on the stored data.

[1031] Data analysis and anomaly detection

[1032] The server uses a generative AI model (using, for example, TensorFlow or PyTorch) to analyze the incoming data in real time.

[1033] The server applies anomaly detection algorithms to detect abnormalities such as sudden fluctuations in heart rate or emotional state.

[1034] If an abnormality is detected, an abnormality alert is generated and notified immediately to the user terminal.

[1035] Providing preventative care

[1036] The server generates a personalized preventative care plan based on the health and emotional profiles.

[1037] The care plan includes nutritional guidance, exercise instruction, and psychological support.

[1038] The server transmits the generated preventive care plan to the user terminal.

[1039] The device notifies the user of the preventive care plan and provides specific implementation methods, reminders, and the ability to track implementation status.

[1040] Gathering feedback and self-learning systems

[1041] The device collects feedback on the progress of the preventive care plan and emotional state and sends it to the server.

[1042] The server updates the AI ​​model based on the collected feedback and performs self-learning.

[1043] Medical collaboration and treatment optimization

[1044] Users consent to sharing data with medical institutions through the application's settings screen, and this consent is recorded within the system.

[1045] The server securely shares the user's health and emotional data with medical professionals, who then use it to develop a diagnosis and treatment plan.

[1046] The server receives feedback and treatment plans from medical professionals and transmits them to the user terminal.

[1047] The terminal notifies the user of the treatment plan and provides the information necessary to carry it out (e.g., medication schedule, follow-up appointment times).

[1048] Specific examples

[1049] Real-time monitoring of emotional states and abnormal response

[1050] 1. After waking up in the morning, the user puts on the wearable device and launches the smartphone application.

[1051] 2. The device monitors the user's heart rate and emotional state in real time.

[1052] 3. The server analyzes the health and emotional data to detect abnormalities in heart rate and emotional instability.

[1053] 4. The server generates an anomaly detection alert and notifies the user device.

[1054] 5. The device displays an abnormal alert to the user, providing messages such as "Your heart rate is high. Please take a short rest" or "Your emotional state is unstable. We recommend that you relax."

[1055] Providing personalized preventative care

[1056] 1. The server analyzes the user's health and emotional profile and discovers any nutrient deficiencies or psychological support needs.

[1057] 2. The server generates nutritional meal plans and relaxing mental exercise plans.

[1058] 3. The device will notify the user of these plans and provide specific instructions and reminders on how to implement them.

[1059] 4. The user carries out the preventive care plan and inputs changes in physical condition and emotions into the device as feedback.

[1060] 5. The server will incorporate the feedback into future care plans.

[1061] Prompt Sentence Examples

[1062] "What are the steps to generate a personalized preventative care plan for seniors?"

[1063] "How can I collect sentiment data in real time?"

[1064] "Please tell me about the design of a system that notifies users of anomaly detection alerts."

[1065] In this way, this system comprehensively manages the user's physical and mental health, providing an environment where elderly people living alone can live with peace of mind.

[1066] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1067] Step 1: Data collection

[1068] The device collects health data such as heart rate, blood pressure, body temperature, and activity levels from wearable devices and home medical equipment via Bluetooth and WiFi.

[1069] Input: Real-time data from wearable devices and home medical equipment.

[1070] Output: Collected health data (heart rate, blood pressure, temperature, activity level, etc.).

[1071] The device uses a built-in emotion engine to collect emotion data from the user's facial expressions, voice, and text messages.

[1072] Input: User facial expressions, voice, and text messages.

[1073] Output: Collected emotion data.

[1074] Step 2: Send data

[1075] The device encrypts the collected health and emotion data and transmits it to a server on a regular basis.

[1076] Input: Collected health and emotion data.

[1077] Output: Data encrypted and sent to the server.

[1078] Step 3: Save data and create a profile

[1079] The server stores the health data and emotion data received from the terminal in a database.

[1080] Input: Encrypted data received from the terminal.

[1081] Output: Health and emotion data stored in a database.

[1082] The server generates a health profile and an emotional profile for each user based on the stored health and emotional data.

[1083] Input: Health and emotion data stored in a database.

[1084] Output: Generated health and emotion profiles.

[1085] Step 4: Data analysis and anomaly detection

[1086] The server uses AI models (using, for example, TensorFlow or PyTorch) to analyze health and emotional data and detect anomalies.

[1087] Input: Health profile and emotional profile.

[1088] Output: Parsed data and anomaly detection results.

[1089] The server applies anomaly detection algorithms to detect abnormalities in heart rate and sudden changes in emotions.

[1090] Specific behavior: Detects heart rate values ​​outside the normal range and sudden emotional fluctuations.

[1091] Output: Anomaly detection results.

[1092] Step 5: Anomaly alert generation and notification

[1093] If the server detects an abnormality, it generates an abnormality alert and immediately notifies the user terminal.

[1094] Input: Anomaly detection results.

[1095] Output: Generated anomaly alerts and notifications to user terminals.

[1096] The device will display an abnormality alert to the user and provide specific instructions, such as "Your heart rate is high. Please take a short rest" or "Your emotional state is unstable. We recommend you relax."

[1097] Specific behavior: Pop-up display of abnormality alert and audio notification.

[1098] Output: The notified message to the user.

[1099] Step 6: Generate a preventative care plan

[1100] The server generates a personalized preventative care plan based on the health and emotional profiles.

[1101] Input: Health profile, Emotion profile.

[1102] Output: Generated preventive care plan (including nutritional guidance, exercise instruction, and psychological support).

[1103] The server transmits the generated preventive care plan to the user terminal.

[1104] Specific actions: computation and plan generation.

[1105] Output: Preventive care plan sent to user device.

[1106] Step 7: Providing preventive care plans and supporting their implementation

[1107] The device notifies the user of the preventive care plan and provides specific implementation methods, reminders, and the ability to track implementation status.

[1108] Input: Submitted preventive care plan.

[1109] Output: Providing notification and tracking to the user.

[1110] Users act on preventative care plans and follow reminders to perform care practices.

[1111] Specific actions: confirmation of execution, reminder notification.

[1112] Step 8: Gather feedback and let the system self-train

[1113] The device collects feedback on the progress of the preventive care plan and emotional state and sends it to the server.

[1114] Input: Performance and emotional state feedback.

[1115] Output: Sending feedback to the server.

[1116] The server updates the AI ​​model based on the collected feedback and performs self-learning.

[1117] Specific operation: Model update, self-learning algorithm application.

[1118] Step 9: Care coordination and treatment optimization

[1119] Users consent to sharing data with medical institutions through the application's settings screen, and this consent is recorded within the system.

[1120] Input: Data sharing consent from user.

[1121] Output: Consent information recorded in the system.

[1122] The server securely shares the user's health and emotional data with medical professionals, who then use it to develop a diagnosis and treatment plan.

[1123] Specific actions: Data sharing, granting access to medical professionals.

[1124] Output: Data sent to medical professionals.

[1125] The server transmits feedback and treatment plans from medical professionals to the user terminal.

[1126] Input: Feedback and treatment plans from medical professionals.

[1127] Output: Send to user terminal.

[1128] The terminal notifies the user of the treatment plan and provides the information necessary to carry it out.

[1129] Specific operations: Notification of treatment plan, display of necessary information.

[1130] Output: The information provided to the user.

[1131] (Application example 2)

[1132] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1133] When elderly people live alone, it is often difficult to manage their health and mental health. In particular, if a sudden change in their health condition or emotional instability occurs while shopping in a brick-and-mortar store or during daily activities, there are a lack of safe ways to deal with it. It is also difficult to take appropriate action immediately when such an abnormality occurs. To address these issues, a comprehensive system is needed that aggregates health and emotional data in real time and provides immediate alerts and suggests specific actions when an abnormality is detected.

[1134] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1135] In this invention, the server includes: means for collecting health data; means for transmitting the collected health data to the server; means for analyzing the health data received by the server and generating a health profile; means for detecting abnormalities based on the generated health profile and notifying the user terminal when an abnormality is detected; means for generating a personalized preventive care plan based on the user's health data and feedback and providing the plan to the user terminal; means for improving the AI ​​model based on user feedback and new data to evolve the service; means for monitoring the user's health data and emotional data in real time in the physical store and for immediately issuing an alert and suggesting specific actions when an abnormality is detected; means for the user to input the progress of preventive care and emotional state as feedback into the terminal and for the system to adjust subsequent care plans based on the feedback; means for sharing health data with medical professionals and optimizing treatment plans; and means for notifying the user of feedback from medical professionals. This enables health and mental health management of elderly people to be performed in real time even in physical stores, enabling immediate action when an abnormality occurs.

[1136] Definitions of important terms included in the rewritten claims

[1137] "Health data" is information related to the user's physical condition, including heart rate, blood pressure, body temperature, activity level, and the like.

[1138] "Emotion data" is information that indicates the user's emotional state, and is data extracted from facial expressions, voice, text messages, and the like.

[1139] "Real-time monitoring" is the process of constantly monitoring a user's health and emotional data, and instantly detecting and responding to any abnormalities.

[1140] An "abnormality detection alert" is a warning message that is sent immediately when an abnormality occurs after analyzing the user's health and emotional data.

[1141] A "preventive care plan" is a personalized care program designed to maintain or improve health, based on a user's health and emotional profile.

[1142] "Feedback" is information provided by users about their progress in implementing preventative care plans and their emotional state, data that is used to improve and adjust the system.

[1143] "AI Model" refers to the artificial intelligence algorithms used to analyze incoming data, detect anomalies, and generate preventative care plans.

[1144] A "physical store" is a physical store, such as a supermarket or shop, where users can visit in person to receive services or products.

[1145] A "user terminal" is a smartphone, smart glasses, or other electronic device operated by a user, and is an interface for using various functions of the system.

[1146] A "healthcare professional" is a doctor, nurse, or other healthcare professional who provides a diagnosis or treatment plan based on a user's health data.

[1147] MODE FOR CARRYING OUT THE INVENTION

[1148] The present invention is a comprehensive system for managing the health and mental health of elderly people, and for detecting abnormalities and providing preventive care in real time based on the data. Specific embodiments of the system are described below.

[1149] Overall system overview

[1150] This system collects health and emotional data from user devices such as wearable devices, smartphones, and smart glasses, and then analyzes the data on a server, detects abnormalities, and provides preventive care.By monitoring the data in real time, the system immediately alerts users if an abnormality occurs and suggests appropriate actions to take.

[1151] Health and emotional data collection

[1152] The device collects data in the following ways:

[1153] Wearable devices collect health data such as heart rate, blood pressure, body temperature, and activity level. These devices connect to the device via Bluetooth or Wi-Fi.

[1154] It uses cameras in smart glasses and microphones in smartphones to collect emotional data from facial expressions, voice, text messages, and more.

[1155] Data transmission and storage

[1156] The device transmits the collected health and emotion data to a server using encrypted communication, which stores the received data in a database and generates a health profile and emotion profile for each user.

[1157] Data analysis and anomaly detection

[1158] The server analyzes the received health and emotion data and uses a generative AI model to detect abnormalities, such as an abnormally high heart rate or a sudden change in emotional state. If an abnormality is detected, the server generates an anomaly detection alert and immediately sends it to the user's device.

[1159] User Notification and Action Suggestions

[1160] When the device receives an anomaly detection alert, it notifies the user and suggests specific actions to take, such as displaying messages like "Your heart rate is high. Please take a short rest" or "Your emotional state is unstable. We recommend you relax."

[1161] Providing preventative care and gathering feedback

[1162] The server generates a preventive care plan based on each individual's health and emotional profile. This plan includes nutrition, exercise, and psychological support. The generated preventive care plan is sent to the user's terminal, which notifies the user of the preventive care plan and supports its implementation. The user inputs feedback on the implementation status of the preventive care plan and their emotional state into the terminal, and the data is sent to the server.

[1163] Medical collaboration and treatment optimization

[1164] The server shares the user's health and emotional data with medical professionals, who can then use it to diagnose and plan treatment. The server also receives feedback and treatment plans from medical professionals and sends them to the user's device.

[1165] Adding specific examples

[1166] Example 1:

[1167] If a user's heart rate suddenly spikes while shopping at a supermarket, their smartphone will display a message such as, "Your heart rate is high. Please take a short rest."

[1168] Example 2:

[1169] If the smart glasses sense that the user is in an emotionally unstable state, they will display instructions such as "Try some relaxation techniques."

[1170] Prompt Sentence Examples

[1171] "My heart rate spiked while I was grocery shopping. What action should I take?"

[1172] These functions make it possible to provide an environment where elderly people can live their daily lives with peace of mind, even if they live alone.

[1173] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1174] Program processing flow

[1175] Step 1:

[1176] Data collection and transmission

[1177] The device collects health data such as heart rate, blood pressure, body temperature, and activity level from the wearable device, and also uses the camera in the smart glasses and the microphone in the smartphone to collect emotional data from facial expressions, voice, text messages, and more.

[1178] Input: User health and emotion data.

[1179] Data processing / calculation: Data is transferred to the device via Bluetooth or Wi-Fi and encrypted.

[1180] Output: Encrypted health and emotion data.

[1181] Specific operation: The terminal connects to the device using Bluetooth or Wi-Fi communication to receive data, then encrypts the received data and sends it to the server.

[1182] Step 2:

[1183] Data analysis and profile generation

[1184] The server stores the received health and emotion data in a database, analyzes it, and generates health and emotion profiles using a generative AI model.

[1185] Input: Encrypted health and emotion data.

[1186] Data processing / calculation: The data is decrypted, stored in a database, and analyzed using an AI model.

[1187] Output: Health profile and emotional profile.

[1188] What it does: The server decrypts the data and stores it in a database, where it then uses an AI model to analyze the data and generate a profile.

[1189] Step 3:

[1190] Anomaly detection and notification

[1191] The server detects abnormalities based on the generated health and emotion profiles, and if an abnormality is detected, generates an anomaly detection alert and immediately notifies the user device.

[1192] Input: Health profile and emotional profile.

[1193] Data processing / calculation: Apply anomaly detection algorithms and generate alerts.

[1194] Output: Anomaly detection alert.

[1195] Specific operation: The server analyzes the profile and detects, for example, an abnormal increase in heart rate or a sudden change in emotion. If an abnormality is detected, an alert is generated and sent to the user's device.

[1196] Step 4:

[1197] User notification and suggested actions

[1198] When the device receives an anomaly detection alert, it notifies the user and suggests specific actions to take (e.g., taking a break or finding ways to relax).

[1199] Input: Anomaly detection alert.

[1200] Data processing / calculation: Generates alert messages and displays them on the screen.

[1201] Output: A notification message to the user.

[1202] Specific action: When the device receives an alert, it displays specific instructions on the user's screen, such as "Your heart rate is high. Please take a short rest."

[1203] Step 5:

[1204] Generate and deliver preventative care plans

[1205] The server generates a personalized preventive care plan based on the health profile and the emotional profile and provides it to the user terminal.

[1206] Input: Health and emotional profiles, existing feedback data.

[1207] Data processing / calculation: Create a preventative care plan based on the profile.

[1208] Output: A personalized preventative care plan.

[1209] What it does: The server analyzes the profile and generates a preventive care plan that includes nutritional advice, exercise plans, and psychological support tailored to each individual user.

[1210] Step 6:

[1211] Gathering feedback and improving the AI ​​model

[1212] The user inputs the progress of the preventive care plan and their emotional state as feedback into the terminal, and the data is sent to the server.

[1213] Input: Preventive care plan implementation results and emotional state feedback.

[1214] Data processing / calculation: collecting and storing feedback data, training AI models.

[1215] Output: An improved AI model.

[1216] Specific operation: The device periodically sends feedback data from the user to the server, and the server updates the AI ​​model based on that data.

[1217] Step 7:

[1218] Data collaboration with medical experts

[1219] The server shares the user's health and emotional data with medical professionals to assist in the development of diagnoses and treatment plans.

[1220] Input: Health and emotion data, requests from healthcare providers.

[1221] Data processing / computation: Collecting and sharing data, generating treatment plans.

[1222] Output: Treatment plan,feedback from medical professionals.

[1223] Specific operation: The server sends data to the medical professional using a secure communication method, receives feedback and treatment plans from the medical professional, and notifies the user.

[1224] Through this series of steps, the system can monitor the user's health and emotional data in real time, respond immediately to any abnormalities, and provide comprehensive support for the user's health management through personalized preventive care and medical coordination.

[1225] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1226] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1227] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1228] [Third embodiment]

[1229] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1230] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1231] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1232] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1233] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1234] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1235] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1236] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1237] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1238] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1239] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1240] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1241] The present invention relates to a comprehensive system for supporting health management for elderly people living alone, and specific embodiments thereof will be described below.

[1242] Overall system overview

[1243] This system collects users' health data in real time, analyzes it, detects abnormalities, suggests preventive care, and shares the data with medical professionals all in one place. By exchanging data between the server, devices, and users, it provides an environment in which elderly people can live safe and fulfilling lives.

[1244] Health data collection and integration

[1245] The device collects health data such as heart rate, blood pressure, temperature, and activity levels from wearable devices and home medical devices, which connect to the device via Bluetooth or Wi-Fi.

[1246] The device periodically sends the collected data to the server. The data is sent using encrypted communication to ensure data security.

[1247] Health profile generation and anomaly detection

[1248] The server stores the received health data in a database and generates a health profile for each user, which includes the individual user's baseline values ​​and past health data.

[1249] The server uses AI models to analyze the data in real time and detect anomalies, such as generating an anomaly alert if the heart rate falls outside the resting range.

[1250] If an abnormality is detected, the server immediately sends a notification to the terminal.

[1251] Providing personalized preventative care

[1252] The server generates a preventative care plan based on the individual's health profile, which is based on an AI model and includes elements such as nutrition, exercise, and psychological support.

[1253] The device will present users with preventative care plans and support them in carrying them out, and will also include reminder and progress tracking features.

[1254] Self-learning and evolution

[1255] The server collects user feedback and analyzes it along with health data, allowing the AI ​​model to self-train and improve the accuracy of the service.

[1256] The server continues to update the system's functionality based on new medical knowledge and technological advances.

[1257] Medical collaboration and treatment optimization

[1258] With the user's consent, the server will share health data with medical professionals, allowing doctors to provide more accurate diagnoses and treatment plans.

[1259] The device notifies users of treatment plans received from medical professionals and provides the information they need to implement them, including medication schedules and follow-up appointment reminders.

[1260] Specific examples

[1261] Anomaly detection and rapid response

[1262] 1. The user puts on the wearable device when they wake up in the morning.

[1263] 2. The device collects heart rate data during the day and sends it to the server.

[1264] 3. The server analyzes the received data in real time and detects abnormal high heart rates.

[1265] 4. The server generates an anomaly detection alert and immediately sends it to the device.

[1266] 5. The device notifies the user of the abnormal alert and displays the message "Your heart rate is high. Please take a short rest." If necessary, it will also automatically notify emergency contacts.

[1267] Providing personalized preventative care

[1268] 1. The server analyzes the user's health profile and discovers that there are nutrient deficiencies.

[1269] 2. The server generates a meal plan for nutritional support.

[1270] 3. The device will notify the user of this meal plan and provide detailed recipes and a shopping list.

[1271] 4. The user follows the meal plan and inputs any changes in their physical condition into the device as feedback.

[1272] 5. The server will incorporate this feedback into future care plans.

[1273] Through these functions, the system comprehensively manages the user's health and provides an environment where elderly people living alone can live with peace of mind.

[1274] The processing flow will be explained below.

[1275] Program processing flow

[1276] Health data collection and integration

[1277] Step 1:

[1278] Users wear wearable devices or home medical devices that measure daily health data (heart rate, blood pressure, body temperature, activity level, etc.) in real time.

[1279] Step 2:

[1280] The device receives data from wearable devices and medical equipment via Bluetooth or WiFi, and this data is temporarily stored on the device.

[1281] Step 3:

[1282] The device sends the collected data to the server at regular intervals using encrypted communications, ensuring data security.

[1283] Health profile generation and anomaly detection

[1284] Step 4:

[1285] The server stores the health data received from the device in a database, where a health profile for each user is generated.

[1286] Step 5:

[1287] The server uses AI models to analyze the incoming data and apply anomaly detection algorithms, such as detecting abnormal heart rates or sudden fluctuations in blood pressure.

[1288] Step 6:

[1289] When an anomaly is detected, the server generates an anomaly alert, which includes the type of anomaly and specific numerical values.

[1290] Step 7:

[1291] The server immediately transmits the generated abnormality alert to the user terminal.

[1292] Step 8:

[1293] The device notifies the user of the abnormality alert, and the notification includes specific instructions (e.g., take a rest, contact a doctor).

[1294] Providing personalized preventative care

[1295] Step 9:

[1296] The server generates a preventative care plan based on the individual user's health profile, which includes nutrition, exercise, and psychological support.

[1297] Step 10:

[1298] The server transmits the generated preventive care plan to the user terminal.

[1299] Step 11:

[1300] The device then presents the received preventive care plan to the user, including reminders and help features.

[1301] Step 12:

[1302] The user incorporates the preventive care plan into their daily life and inputs the progress of the care plan and changes in their physical condition into the terminal.

[1303] Self-learning and evolution

[1304] Step 13:

[1305] The device periodically transmits feedback from the user to the server, including information such as diet history and exercise status.

[1306] Step 14:

[1307] The server updates the AI ​​model and performs self-learning based on the received feedback and health data.

[1308] Step 15:

[1309] The server uses the updated AI model to improve the accuracy of the next preventative care plan and anomaly detection algorithm.

[1310] Medical collaboration and treatment optimization

[1311] Step 16:

[1312] Users consent to data sharing with medical institutions through a settings screen within the application, and this consent is recorded within the system.

[1313] Step 17:

[1314] The server performs the function of securely sharing the user's health data with medical professionals, who then use it to develop a diagnosis and treatment plan.

[1315] Step 18:

[1316] The server receives feedback and treatment plans from medical professionals and transmits them to the user terminal.

[1317] Step 19:

[1318] The terminal notifies the user of the treatment plan and provides the information necessary to carry it out (e.g., medication schedule, follow-up appointment times).

[1319] Through these steps, the system provides comprehensive and personalized health management and support to elderly people living alone.

[1320] Example 1

[1321] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1322] Elderly people living alone require real-time monitoring of their health status and early detection of abnormalities. However, existing systems struggle to integrate functions such as health data collection and analysis, abnormality detection, provision of preventive care plans, and data sharing with medical professionals. Furthermore, the disparate nature of these functions makes them difficult to use. Furthermore, they lack the ability to evolve AI models based on user feedback and respond quickly in emergencies. Therefore, there is a need for a comprehensive system that provides support for the safe and secure lifestyles of elderly people living alone.

[1323] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1324] In this invention, the server includes: means for collecting health data; means for transmitting the collected health data to the server; means for analyzing the health data received by the server and generating a health profile; means for detecting abnormalities based on the generated health profile and notifying the user terminal when an abnormality is detected; means for generating a personalized preventive care plan based on the user's health data and feedback and providing it to the user terminal; means for improving the AI ​​model based on user feedback and new data and evolving the service; means for sharing the health data with medical professionals and optimizing the treatment plan; means for notifying the user of feedback from medical professionals; means for automatically notifying an emergency contact when an abnormality is detected; and means for providing a specific meal plan and shopping list based on the preventive care plan. This enables efficient and comprehensive health management of elderly people and supports their safe and secure lives.

[1325] "Health data" is information that indicates the physiological and health status of the user, such as heart rate, blood pressure, body temperature, and activity level.

[1326] A "means for collection" is a method or device for compiling a user's health data using a wearable device or home medical equipment.

[1327] "Transmitting means" refers to a method or device for encrypting the collected health data and sending it to the server.

[1328] The "analyzing means" is a method or device that processes the health data received by the server and evaluates the user's health status.

[1329] A "health profile" is a personalized physiological and health record generated from a user's health data.

[1330] A "detecting means" is a method or device for detecting abnormalities based on the generated health profile.

[1331] "Notification means" refers to a method or device for sending an alert to a user terminal or emergency contact when an abnormality is detected.

[1332] A "personalized preventative care plan" is a preventative or care plan created based on a user's individual health profile.

[1333] A "means for providing" is a method or device for informing a user of a preventive care plan and supporting its implementation.

[1334] "Means for improvement" refers to a method or device for updating the AI ​​model based on user feedback and new data to improve the service.

[1335] A "means for sharing" is a method or device for exchanging a user's health data with a medical professional to optimize a treatment plan.

[1336] "Means for automatically notifying" refers to a method or device for automatically notifying an emergency contact when an abnormality is detected.

[1337] A "means for providing a meal plan or shopping list" is a method or device that provides a user with meal suggestions and a list of ingredients based on a preventive care plan.

[1338] The present invention relates to a comprehensive system for supporting health management for elderly people living alone, and specific embodiments thereof will be described below.

[1339] Overall system overview

[1340] This system collects users' health data in real time, analyzes it, detects abnormalities, suggests preventive care, and shares the data with medical professionals. By exchanging data between the server, devices, and users, it provides an environment in which elderly people can live a safe and fulfilling life.

[1341] Health data collection and integration

[1342] The device collects health data such as heart rate, blood pressure, temperature, and activity levels from wearable devices and home medical devices, which connect to the device via Bluetooth or Wi-Fi.

[1343] Specific devices include Fitbit, Apple Watch, and common home blood pressure monitors and thermometers.

[1344] The collected data is collected using the device's API.

[1345] The device periodically sends the collected data to the server. The data is sent using SSL / TLS encryption to ensure data security.

[1346] Health profile generation and anomaly detection

[1347] The server stores the received health data in a database and generates a health profile for each user, which includes the individual user's baseline values ​​and past health data.

[1348] Database systems such as MySQL and MongoDB are used.

[1349] The server uses an AI model developed in Python to analyze the data in real time and detect anomalies, such as generating an anomaly alert if the heart rate deviates from the resting range.

[1350] If an abnormality is detected, the server immediately sends a notification to the terminal.

[1351] Providing personalized preventative care

[1352] The server generates a preventative care plan based on the individual's health profile, which is based on an AI model and includes elements such as nutrition, exercise, and psychological support.

[1353] The device will present users with preventative care plans and support them in carrying them out, and will also include reminder and progress tracking features.

[1354] Self-learning and evolution

[1355] The server collects user feedback and analyzes it along with health data, allowing the AI ​​model to self-train and improve the accuracy of the service.

[1356] The server continues to update the system's functionality based on new medical knowledge and technological advances.

[1357] Medical collaboration and treatment optimization

[1358] With the user's consent, the server will share health data with medical professionals, allowing doctors to provide more accurate diagnoses and treatment plans.

[1359] The device notifies users of treatment plans received from medical professionals and provides the information they need to implement them, including medication schedules and follow-up appointment reminders.

[1360] Specific examples

[1361] Anomaly detection and rapid response

[1362] 1. The user puts on the wearable device when they wake up in the morning.

[1363] 2. The device collects heart rate data during the day and sends it to the server.

[1364] 3. The server analyzes the received data in real time and detects abnormal high heart rates.

[1365] 4. The server generates an anomaly detection alert and immediately sends it to the device.

[1366] 5. The device notifies the user of the abnormal alert and displays the message "Your heart rate is high. Please take a short rest." If necessary, it will also automatically notify emergency contacts.

[1367] Providing personalized preventative care

[1368] 1. The server analyzes the user's health profile and discovers that there are nutrient deficiencies.

[1369] 2. The server generates a meal plan for nutritional support.

[1370] 3. The device will notify the user of this meal plan and provide detailed recipes and a shopping list.

[1371] 4. The user follows the meal plan and inputs any changes in their physical condition into the device as feedback.

[1372] 5. The server will incorporate this feedback into future care plans.

[1373] Through these functions, the system comprehensively manages the user's health and provides an environment where elderly people living alone can live with peace of mind.

[1374] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1375] Processing flow

[1376] Step 1: Collecting health data

[1377] A user puts on a wearable device (e.g., a general fitness tracker) when they wake up in the morning. They also use home medical devices (blood pressure monitor, thermometer).

[1378] The device collects data from wearable devices and medical equipment via Bluetooth or WiFi, such as heart rate and body temperature data.

[1379] Input: Sensor data from wearable devices and medical equipment

[1380] Output: Collected health data (heart rate, blood pressure, body temperature, activity level)

[1381] Step 2: Send and store data

[1382] The data collected by the device is periodically encrypted and sent securely to the server using the SSL / TLS protocol.

[1383] The server validates the data it receives and stores it in the database after verifying its validity.

[1384] Input: Collected health data

[1385] Output: Health data stored on the server

[1386] Step 3: Health profile generation and anomaly detection

[1387] The server generates a health profile for each user based on the health data stored in the database, including past data and baseline values.

[1388] Based on the generated profile, the server uses a generative AI model developed in Python to analyze the data in real time and detect abnormalities, such as when the heart rate is outside the resting range.

[1389] Input: Stored health data and generative AI model

[1390] Output: Health profile and abnormality detection results (alerts)

[1391] Step 4: Notification of abnormalities

[1392] If the server detects an abnormality, it immediately generates an anomaly detection alert and sends a notification to the terminal.

[1393] The device will notify the user of any abnormal alerts, for example, "Your heart rate is high. Please take a short rest."

[1394] Input: Anomaly detection result

[1395] Output: User notification

[1396] Step 5: Deliver personalized preventative care

[1397] The server generates a preventative care plan based on an individual's health profile, which includes nutrition, exercise, psychological support, and more.

[1398] The device presents the user with a preventive care plan, provides reminders, and tracks progress.

[1399] Input: Health profile and generative AI model

[1400] Output: Preventive Care Plan

[1401] Step 6: Gather feedback and improve the AI ​​model

[1402] The server collects feedback from the user about the running status and changes in physical condition.

[1403] The server analyzes this feedback and allows the generative AI model to self-train and improve accuracy.

[1404] Input: User feedback

[1405] Output: An improved generative AI model

[1406] Step 7: Care coordination and treatment optimization

[1407] The server shares health data with medical professionals based on the user's consent, for example by exchanging data using the FHIR protocol.

[1408] The device notifies the user of the treatment plan received from the medical professional and reminds them of medication schedules and follow-up appointments.

[1409] Input: Treatment plan from medical professional

[1410] Output: Notifications and reminders to the user

[1411] (Application example 1)

[1412] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1413] Elderly people living alone face challenges when managing their own health, including the difficulty of early detection of abnormalities and receiving appropriate preventive care. It is also difficult to quickly and appropriately connect with medical professionals. Furthermore, there is a lack of health support services linked to nearby stores and fitness centers that seniors can actually use.

[1414] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1415] In this invention, the server includes: means for collecting health data; means for transmitting the collected health data to the server; means for analyzing the health data received by the server and generating a health profile; means for detecting abnormalities based on the generated health profile and notifying the user terminal when an abnormality is detected; means for generating a personalized preventive care plan based on the user's health data and feedback and providing it to the user terminal; means for improving the AI ​​model based on user feedback and new data and evolving the service; means for sharing the health data with medical professionals and optimizing the treatment plan; means for notifying the user of feedback from medical professionals; means for providing health consultations or fitness support at affiliated physical stores based on the user's health data; and means for simultaneously notifying the user and related facilities when an abnormality is detected. This enables elderly people living alone to receive early detection of abnormalities and appropriate preventive care, and facilitates prompt and appropriate collaboration with medical professionals and physical stores.

[1416] "Health data" refers to numerical values ​​and information that indicate an individual's health status, such as heart rate, blood pressure, body temperature, and activity level.

[1417] "Means of collection" refers to the equipment and methods used to obtain a user's health data, such as using wearable devices or home medical equipment.

[1418] The "means of transmission" refers to the communication means for sending the collected health data to the server, and uses wireless communication technology such as Bluetooth or WiFi.

[1419] A "server" is a computer system that receives, analyzes, and stores collected health data.

[1420] The "analyzing means" refers to algorithms or software that evaluate the user's health status based on the received health data.

[1421] A "health profile" is a data set that compiles a user's health data history and baseline values, and is used to comprehensively assess an individual's health status.

[1422] An "anomaly detection method" is an algorithm or software that compares the health profile with newly collected data and identifies outliers.

[1423] "Means of notification" refers to methods or technologies for notifying a user terminal that an abnormality has been detected, such as a smartphone application.

[1424] "Personalized preventative care plans" refer to health management and improvement recommendations that are individually optimized based on the user's health data and feedback.

[1425] The "means for providing" refers to a method for displaying or notifying the personalized preventive care plan on the user terminal.

[1426] "AI model" refers to a machine learning or artificial intelligence model used to analyze health data and generate anomaly detection and preventative care plans.

[1427] A "healthcare professional" is a doctor or pharmacist who has the expertise to diagnose a user's health data and provide a treatment plan.

[1428] "Facilities" refers to affiliated physical stores and fitness centers, which are places that users can actually visit.

[1429] "Health consultation" means receiving health advice and information from experts such as pharmacists and nutritionists at a physical store.

[1430] "Fitness support" means receiving exercise guidance and advice from a trainer at a fitness center.

[1431] The system of the present invention is implemented by the following method and procedure. The system's core function is to collect a user's health data, analyze it in real time, detect abnormalities, and provide preventative care plans. It also includes collaborating with medical professionals based on the data to provide appropriate health consultations and fitness support to the user.

[1432] Data collection and transmission

[1433] The server collects health data from wearable devices and home medical devices (smartwatches, scales, blood pressure monitors, etc.) via Bluetooth or Wi-Fi. This periodically collects data such as heart rate, blood pressure, body temperature, and activity level. The collected data is encrypted by the smartphone and sent to the cloud server.

[1434] Analyzing data and generating health profiles

[1435] The server uses a cloud-based data analysis system (e.g., AWS, MySQL) to analyze the received data and generate a health profile for each user. This profile includes the user's baseline values ​​and past health data. An AI model (e.g., TensorFlow) is used to analyze the data in real time and detect outliers.

[1436] Anomaly detection and notification

[1437] If an abnormality is detected, the server immediately generates an abnormality alert and sends a notification to the user's smartphone. Furthermore, if the abnormality is serious, notifications are also sent to the user's partner physical stores (drugstores and fitness centers). This allows the elderly person to receive prompt and appropriate treatment.

[1438] Providing personalized preventative care plans

[1439] The server generates a personalized preventive care plan based on the user's health profile. This plan includes nutrition, exercise, and psychological support. The plan is then provided to the user via a smartphone application. The application also has reminder and progress tracking functions.

[1440] Health counseling and fitness support

[1441] Based on the user's health data, health consultations are provided at affiliated physical stores and fitness support is provided at fitness centers. If an abnormality is detected, a pharmacist at the drugstore or a fitness instructor will take appropriate action. This allows users to receive comprehensive support.

[1442] Examples of specific examples and prompts

[1443] Example 1: If high blood pressure is detected, the following notification is displayed on the user's smartphone:

[1444] Text format

[1445] Abnormalities detected. Your heart rate and blood pressure are out of normal range. Would you like to schedule a free consultation at your local drugstore?

[1446] Example 2: If an anomaly is detected during a workout at a fitness center, the following notification is sent to the instructor:

[1447] Text format

[1448] Your heart rate has deviated from the normal range during exercise. Would you like to contact your instructor?

[1449] The above is an embodiment of the present invention. This system enables elderly people living alone to receive early detection of abnormalities and appropriate preventive care, and also facilitates prompt and appropriate cooperation with medical professionals and brick-and-mortar stores.

[1450] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1451] Step 1:

[1452] Users wear a smartwatch or home medical device to collect health data. The smartwatch measures data such as heart rate, blood pressure, body temperature, and activity level, and transmits it to a smartphone via Bluetooth.

[1453] Input: User's health data (heart rate, blood pressure, body temperature, activity level, etc.)

[1454] Output: Health data collected on a smartphone

[1455] Step 2:

[1456] The device (smartphone) encrypts the collected health data and sends it to a cloud server, where the smartphone app periodically uploads the data to the server.

[1457] Input: Health data collected on a smartphone

[1458] Output: Encrypted data sent to the cloud server

[1459] Step 3:

[1460] The server analyzes the received health data and generates a health profile. Specifically, it stores the data in a database (MySQL) and performs anomaly detection analysis using an AI model (TensorFlow). It compares the data with past data, sets baseline values, and updates the health profile.

[1461] Input: Encrypted data sent to the cloud server

[1462] Output: Generated health profile

[1463] Step 4:

[1464] The server detects abnormalities based on the generated health profile. The AI ​​model analyzes the data in real time and generates an anomaly alert if it detects an abnormal value.

[1465] Input: Generated health profile

[1466] Output: Abnormal alert

[1467] Step 5:

[1468] If an abnormality is detected, the server sends a notification to the user's device, and simultaneously issues an abnormality alert and notifies partner facilities.

[1469] Input: Anomaly Alert

[1470] Output: Notification sent to user device and partner facility

[1471] Step 6:

[1472] The device will notify the user of any abnormalities and suggest specific measures to take if necessary, which in this case could include health consultations at a drugstore or support at a fitness center.

[1473] Input: Notification sent to user device

[1474] Output: Notify the user of the abnormality and suggest a solution

[1475] Step 7:

[1476] The server generates a personalized preventative care plan based on the user's health data and feedback. An AI model analyzes the user's condition and creates an individualized care plan.

[1477] Input: Health data and feedback

[1478] Output: A personalized preventative care plan

[1479] Step 8:

[1480] The device provides the user with the generated preventive care plan and supports implementation, and the app uses reminders and implementation tracking features to monitor the user's progress with the care plan.

[1481] Enter: personalized preventative care plans.

[1482] Output: Preventive care plans and support functions provided to users

[1483] Step 9:

[1484] The server improves the AI ​​model based on user feedback and new health data, evolving the service and improving the accuracy of future preventive care plans.

[1485] Input: Feedback and new health data

[1486] Output: Improved AI models and evolved services

[1487] Step 10:

[1488] The server shares health data with medical professionals to optimize treatment plans, and provides feedback from medical professionals to users to provide appropriate treatment and care.

[1489] Input: Health data and medical expert feedback

[1490] Output: Optimized treatment plan and notification to the user

[1491] The above processing steps realize a system that monitors the health status of elderly people in real time, detects abnormalities early, and enables appropriate responses.

[1492] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1493] The present invention relates to a comprehensive system for supporting health management for elderly people living alone, and by combining it with an emotion engine, manages not only physical health but also mental health. Specific embodiments of the system are described below.

[1494] Overall system overview

[1495] This system collects users' health and emotional data in real time, analyzes it, detects abnormalities, suggests preventive care, and shares the data with medical professionals. Through the exchange of data between the server, devices, and users, it provides an environment in which elderly people can live a safe and fulfilling life.

[1496] Collecting and integrating health and emotional data

[1497] The device collects health data such as heart rate, blood pressure, temperature, and activity levels from wearable devices and home medical devices, which connect to the device via Bluetooth or Wi-Fi.

[1498] The device uses a built-in emotion engine to collect emotion data from the user's facial expressions, voice, text messages, etc.

[1499] The terminal periodically transmits the health data and emotion data to the server using encrypted communication.

[1500] Health and emotional profile generation and anomaly detection

[1501] The server stores the health data and emotion data received from the terminals in a database and generates a health profile and emotion profile for each user.

[1502] The server uses AI models to analyze the incoming data and apply anomaly detection algorithms, for example, to detect abnormal heart rates or sudden fluctuations in emotional state.

[1503] When the server detects an abnormality, it generates an abnormality alert and immediately sends this alert to the user terminal.

[1504] The device will notify the user of abnormal alerts and display messages with specific instructions such as "Your heart rate is high. Please take a short rest" or "Your emotional state is unstable. We recommend you relax."

[1505] Providing personalized preventative care

[1506] The server generates a preventative care plan based on each individual's health and emotional profile, including nutrition, exercise, and psychological support.

[1507] The server transmits the generated preventive care plan to the user terminal.

[1508] The device will present users with preventative care plans and support them in carrying them out, and will also include reminder and progress tracking features.

[1509] Self-learning and evolution

[1510] The device collects feedback on the implementation of the preventive care plan and changes in emotional state and sends it to the server.

[1511] The server updates the AI ​​model and performs self-learning based on the collected feedback, health data, and emotional data.

[1512] The server continually updates the system's functionality based on new medical and psychological knowledge and technological advances.

[1513] Medical collaboration and treatment optimization

[1514] Users consent to data sharing with medical institutions through a settings screen within the application, and this consent is recorded within the system.

[1515] The server securely shares the user's health and emotional data with medical professionals, who can then use it to develop a diagnosis and treatment plan.

[1516] The server receives feedback and treatment plans from medical professionals and transmits them to the user terminal.

[1517] The terminal notifies the user of the treatment plan and provides the information necessary to carry it out (e.g., medication schedule, follow-up appointment times).

[1518] Specific examples

[1519] Real-time monitoring of emotional states and abnormal response

[1520] 1. After waking up in the morning, the user puts on the wearable device and launches the smartphone application.

[1521] 2. The device monitors the user's heart rate and emotional state in real time (for example, using facial recognition and voice analysis).

[1522] 3. The server analyzes the health and emotional data to detect abnormalities such as high heart rate or unstable emotional state.

[1523] 4. The server generates an anomaly detection alert and immediately notifies the user device.

[1524] 5. The device will notify the user of the abnormality detection alert and display a message such as "Your heart rate is high. Please take a short rest" or "Your emotional state is unstable. We recommend that you relax."

[1525] Providing personalized preventative care

[1526] 1. The server analyzes the user's health and emotional profile and discovers any nutrient deficiencies or psychological support needs.

[1527] 2. The server generates nutritional meal plans and relaxing mental exercise plans.

[1528] 3. The device will notify the user of these plans and provide specific instructions and reminders on how to implement them.

[1529] 4. The user carries out the preventive care plan and inputs changes in physical condition and emotions into the device as feedback.

[1530] 5. The server will incorporate this feedback into future care plans.

[1531] Through these functions, the system comprehensively manages the user's physical and mental health, providing an environment where elderly people living alone can live with peace of mind.

[1532] The processing flow will be explained below.

[1533] Program processing flow

[1534] Collecting and integrating health and emotional data

[1535] Step 1:

[1536] The user puts on a wearable device or home medical device and launches a smartphone app, which prepares to collect daily health and emotional data.

[1537] Step 2:

[1538] The device receives real-time health data such as heart rate, blood pressure, body temperature, and activity levels from wearable devices and medical equipment via Bluetooth and WiFi.

[1539] Step 3:

[1540] The device uses a camera and microphone to recognize the user's face and perform voice analysis to collect emotional data, including emotional analysis based on facial expressions and tone of voice.

[1541] Step 4:

[1542] The device periodically transmits the collected health and emotion data to a server using encrypted communications, ensuring data security.

[1543] Health and emotional profile generation and anomaly detection

[1544] Step 5:

[1545] The server stores the health data and emotion data received from the terminal in a database, and generates a health profile and emotion profile for each user.

[1546] Step 6:

[1547] The server uses AI models to analyze the incoming data and apply anomaly detection algorithms, for example, to detect abnormal heart rates or sudden fluctuations in emotional state.

[1548] Step 7:

[1549] When an anomaly is detected, the server generates an anomaly alert, which includes the type of anomaly and specific numerical values.

[1550] Step 8:

[1551] The server immediately transmits the generated abnormality alert to the user terminal.

[1552] Step 9:

[1553] The device will notify the user of abnormal alerts and display messages with specific instructions such as "Your heart rate is high. Please take a short rest" or "Your emotional state is unstable. We recommend you relax."

[1554] Providing personalized preventative care

[1555] Step 10:

[1556] The server generates a preventative care plan based on the individual user's health and emotional profile, which includes nutrition, exercise, and psychological support.

[1557] Step 11:

[1558] The server transmits the generated preventive care plan to the user terminal.

[1559] Step 12:

[1560] The device presents the user with a preventative care plan, including reminders and progress tracking.

[1561] Step 13:

[1562] The user incorporates the preventive care plan into their daily life and inputs their progress, changes in their physical condition, and changes in their emotional state into the terminal.

[1563] Self-learning and evolution

[1564] Step 14:

[1565] The device periodically transmits feedback from the user to the server, including information on dietary history, exercise status, and emotional changes.

[1566] Step 15:

[1567] The server updates the AI ​​model and performs self-learning based on the received feedback, health data, and emotional data.

[1568] Step 16:

[1569] The server uses the updated AI model to improve the accuracy of the next preventative care plan and anomaly detection algorithm.

[1570] Medical collaboration and treatment optimization

[1571] Step 17:

[1572] Users consent to data sharing with medical institutions through a settings screen within the application, and this consent is recorded within the system.

[1573] Step 18:

[1574] The server performs the function of securely sharing the user's health and emotional data with medical professionals, who then use it to develop a diagnosis and treatment plan.

[1575] Step 19:

[1576] The server receives feedback and treatment plans from medical professionals and transmits them to the user terminal.

[1577] Step 20:

[1578] The terminal notifies the user of the treatment plan and provides the information necessary to carry it out (e.g., medication schedule, follow-up appointment times).

[1579] Specific examples

[1580] Real-time monitoring of emotional states and abnormal response

[1581] Step 1:

[1582] After waking up in the morning, the user puts on the wearable device and launches the smartphone application.

[1583] Step 2:

[1584] The device receives heart rate data from the wearable device and simultaneously uses the camera and microphone to gather information about the user's emotional state, for example by analyzing emotions from facial expressions and tone of voice.

[1585] Step 3:

[1586] The server analyzes the received health and emotional data to detect abnormalities, such as if the heart rate is outside the normal range or if the user is in an unstable emotional state.

[1587] Step 4:

[1588] The server generates an anomaly detection alert and immediately notifies the user device.

[1589] Step 5:

[1590] The device receives the notification and displays specific instructions to the user, such as "Your heart rate is high. Please take a short rest" or "Your emotional state is unstable. We recommend you relax."

[1591] Providing personalized preventative care

[1592] Step 1:

[1593] The server analyzes the user's health and emotional profile and discovers deficiencies in certain nutrients or psychological support.

[1594] Step 2:

[1595] The server generates meal plans for optimal nutritional support and relaxation exercises for psychological support.

[1596] Step 3:

[1597] The device will notify the user of these plans and provide specific instructions on how to carry them out (e.g., recipes or exercise videos).

[1598] Step 4:

[1599] Users follow a preventative care plan and enter feedback into the device, including their diet, exercise routine, and emotional changes.

[1600] Step 5:

[1601] The server analyzes the collected feedback and incorporates it into the next preventative care plan.

[1602] Through these steps, the system provides comprehensive and personalized health and emotional care for seniors living alone.

[1603] Example 2

[1604] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1605] Currently, health and mental health management for older adults often relies on regular visits to medical institutions or self-management. However, these are difficult for older adults living alone, which requires the collection and analysis of real-time health and emotional data, early detection of abnormalities, and immediate response. Furthermore, there is a lack of personalized preventative care plans based on individual health and emotional conditions. This makes it difficult for older adults to maintain their health and ensure a high quality of life.

[1606] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting health data and emotional data in real time; means for encrypting the collected health data and emotional data and transmitting it to the server; means for storing the health data and emotional data received by the server and generating a health profile and an emotional profile; means for analyzing the health data and emotional data using an AI model to detect abnormalities; means for generating an abnormality alert and notifying the user terminal when an abnormality is detected; means for generating a personalized preventive care plan based on the user's health profile and emotional profile and providing it to the user terminal; means for supporting the execution of the preventive care plan through reminders and progress tracking functions; means for improving the AI ​​model based on user feedback and new data and evolving the service; means for sharing the health data and emotional data with medical professionals based on the user's consent to optimize the treatment plan; and means for notifying the user of feedback from medical professionals and the treatment plan. This enables comprehensive management of the health and mental health of elderly people, real-time abnormality detection, and early response. Additionally, providing personalized preventative care plans allows for more tailored care based on individual health and emotional states, improving the quality of life for seniors.

[1607] "Health data" refers to data that indicates the user's physical health condition, such as heart rate, blood pressure, body temperature, and activity level.

[1608] "Emotion data" is data that indicates the user's emotional state, obtained from facial expressions, voice, text messages, etc.

[1609] A "server" is a computer system that collects, stores, analyzes, and detects anomalies in data, provides care plans, collects feedback, and improves AI models.

[1610] A "terminal" is an electronic device that collects data from wearable devices and home medical equipment and transmits it to a server.

[1611] An "AI model" is a mathematical model that uses machine learning algorithms to analyze data, detect abnormalities, and generate personalized care plans.

[1612] A "health profile" is a data set that comprehensively indicates a user's health status and is generated based on the user's health data.

[1613] An "emotional profile" is a data set that comprehensively indicates a user's emotional state, generated based on the user's emotional data.

[1614] "Anomaly detection" is the process of analyzing collected health and emotional data to detect abnormal conditions that go beyond the normal range.

[1615] An "abnormality alert" is a warning message that notifies the user of a detected abnormal condition.

[1616] A "Preventive Care Plan" is a personalized health management and psychological support plan that is generated based on a user's health and emotional profile.

[1617] A "reminder" is a notification message that prompts the user to carry out a scheduled activity or care plan.

[1618] "Feedback" is the process by which users provide information about the progress of their preventive care plan and changes in their physical and emotional state.

[1619] A "health professional" is a professional with expertise related to the user's health and mental health, such as a doctor, nurse, or psychologist.

[1620] A "treatment plan" is a plan developed by a medical professional to resolve a user's health problem.

[1621] "Encryption" is a technology that converts data into a special code so that it cannot be deciphered by third parties.

[1622] MODE FOR CARRYING OUT THE INVENTION

[1623] The present invention relates to a comprehensive system for supporting health management for elderly people living alone. By combining an emotion engine, the system manages not only the user's physical health but also their mental health. Specific embodiments of the system are described below.

[1624] System Configuration

[1625] The system collects health and emotional data in real time, analyzes them, detects anomalies, provides preventive care, and shares data with medical professionals. The main components are the server, the device, and the user.

[1626] Health and emotional data collection

[1627] The device collects health data such as heart rate, blood pressure, temperature, and activity levels using wearable devices and home medical equipment, which connect to the device via Bluetooth or Wi-Fi.

[1628] The device uses a built-in emotion engine to collect emotion data from the user's facial expressions, voice, and text messages.

[1629] The device periodically encrypts and transmits the collected health and emotion data to a server.

[1630] Saving Data and Generating Profiles

[1631] The server stores the data received from the device in a database, for example, using MySQL.

[1632] The server generates health and emotional profiles for each user based on the stored data.

[1633] Data analysis and anomaly detection

[1634] The server uses a generative AI model (using, for example, TensorFlow or PyTorch) to analyze the incoming data in real time.

[1635] The server applies anomaly detection algorithms to detect abnormalities such as sudden fluctuations in heart rate or emotional state.

[1636] If an abnormality is detected, an abnormality alert is generated and notified immediately to the user terminal.

[1637] Providing preventative care

[1638] The server generates a personalized preventative care plan based on the health and emotional profiles.

[1639] The care plan includes nutritional guidance, exercise instruction, and psychological support.

[1640] The server transmits the generated preventive care plan to the user terminal.

[1641] The device notifies the user of the preventive care plan and provides specific implementation methods, reminders, and the ability to track implementation status.

[1642] Gathering feedback and self-learning systems

[1643] The device collects feedback on the progress of the preventive care plan and emotional state and sends it to the server.

[1644] The server updates the AI ​​model based on the collected feedback and performs self-learning.

[1645] Medical collaboration and treatment optimization

[1646] Users consent to sharing data with medical institutions through the application's settings screen, and this consent is recorded within the system.

[1647] The server securely shares the user's health and emotional data with medical professionals, who then use it to develop a diagnosis and treatment plan.

[1648] The server receives feedback and treatment plans from medical professionals and transmits them to the user terminal.

[1649] The terminal notifies the user of the treatment plan and provides the information necessary to carry it out (e.g., medication schedule, follow-up appointment times).

[1650] Specific examples

[1651] Real-time monitoring of emotional states and abnormal response

[1652] 1. After waking up in the morning, the user puts on the wearable device and launches the smartphone application.

[1653] 2. The device monitors the user's heart rate and emotional state in real time.

[1654] 3. The server analyzes the health and emotional data to detect abnormalities in heart rate and emotional instability.

[1655] 4. The server generates an anomaly detection alert and notifies the user device.

[1656] 5. The device displays an abnormal alert to the user, providing messages such as "Your heart rate is high. Please take a short rest" or "Your emotional state is unstable. We recommend that you relax."

[1657] Providing personalized preventative care

[1658] 1. The server analyzes the user's health and emotional profile and discovers any nutrient deficiencies or psychological support needs.

[1659] 2. The server generates nutritional meal plans and relaxing mental exercise plans.

[1660] 3. The device will notify the user of these plans and provide specific instructions and reminders on how to implement them.

[1661] 4. The user carries out the preventive care plan and inputs changes in physical condition and emotions into the device as feedback.

[1662] 5. The server will incorporate the feedback into future care plans.

[1663] Prompt Sentence Examples

[1664] "What are the steps to generate a personalized preventative care plan for seniors?"

[1665] "How can I collect sentiment data in real time?"

[1666] "Please tell me about the design of a system that notifies users of anomaly detection alerts."

[1667] In this way, this system comprehensively manages the user's physical and mental health, providing an environment where elderly people living alone can live with peace of mind.

[1668] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1669] Step 1: Data collection

[1670] The device collects health data such as heart rate, blood pressure, body temperature, and activity levels from wearable devices and home medical equipment via Bluetooth and WiFi.

[1671] Input: Real-time data from wearable devices and home medical equipment.

[1672] Output: Collected health data (heart rate, blood pressure, temperature, activity level, etc.).

[1673] The device uses a built-in emotion engine to collect emotion data from the user's facial expressions, voice, and text messages.

[1674] Input: User facial expressions, voice, and text messages.

[1675] Output: Collected emotion data.

[1676] Step 2: Send data

[1677] The device encrypts the collected health and emotion data and transmits it to a server on a regular basis.

[1678] Input: Collected health and emotion data.

[1679] Output: Data encrypted and sent to the server.

[1680] Step 3: Save data and create a profile

[1681] The server stores the health data and emotion data received from the terminal in a database.

[1682] Input: Encrypted data received from the terminal.

[1683] Output: Health and emotion data stored in a database.

[1684] The server generates a health profile and an emotional profile for each user based on the stored health and emotional data.

[1685] Input: Health and emotion data stored in a database.

[1686] Output: Generated health and emotion profiles.

[1687] Step 4: Data analysis and anomaly detection

[1688] The server uses AI models (using, for example, TensorFlow or PyTorch) to analyze health and emotional data and detect anomalies.

[1689] Input: Health profile and emotional profile.

[1690] Output: Parsed data and anomaly detection results.

[1691] The server applies anomaly detection algorithms to detect abnormalities in heart rate and sudden changes in emotions.

[1692] Specific behavior: Detects heart rate values ​​outside the normal range and sudden emotional fluctuations.

[1693] Output: Anomaly detection results.

[1694] Step 5: Anomaly alert generation and notification

[1695] If the server detects an abnormality, it generates an abnormality alert and immediately notifies the user terminal.

[1696] Input: Anomaly detection results.

[1697] Output: Generated anomaly alerts and notifications to user terminals.

[1698] The device will display an abnormality alert to the user and provide specific instructions, such as "Your heart rate is high. Please take a short rest" or "Your emotional state is unstable. We recommend you relax."

[1699] Specific behavior: Pop-up display of abnormality alert and audio notification.

[1700] Output: The notified message to the user.

[1701] Step 6: Generate a preventative care plan

[1702] The server generates a personalized preventative care plan based on the health and emotional profiles.

[1703] Input: Health profile, Emotion profile.

[1704] Output: Generated preventive care plan (including nutritional guidance, exercise instruction, and psychological support).

[1705] The server transmits the generated preventive care plan to the user terminal.

[1706] Specific actions: computation and plan generation.

[1707] Output: Preventive care plan sent to user device.

[1708] Step 7: Providing preventive care plans and supporting their implementation

[1709] The device notifies the user of the preventive care plan and provides specific implementation methods, reminders, and the ability to track implementation status.

[1710] Input: Submitted preventive care plan.

[1711] Output: Providing notification and tracking to the user.

[1712] Users act on preventative care plans and follow reminders to perform care practices.

[1713] Specific actions: confirmation of execution, reminder notification.

[1714] Step 8: Gather feedback and let the system self-train

[1715] The device collects feedback on the progress of the preventive care plan and emotional state and sends it to the server.

[1716] Input: Performance and emotional state feedback.

[1717] Output: Sending feedback to the server.

[1718] The server updates the AI ​​model based on the collected feedback and performs self-learning.

[1719] Specific operation: Model update, self-learning algorithm application.

[1720] Step 9: Care coordination and treatment optimization

[1721] Users consent to sharing data with medical institutions through the application's settings screen, and this consent is recorded within the system.

[1722] Input: Data sharing consent from user.

[1723] Output: Consent information recorded in the system.

[1724] The server securely shares the user's health and emotional data with medical professionals, who then use it to develop a diagnosis and treatment plan.

[1725] Specific actions: Data sharing, granting access to medical professionals.

[1726] Output: Data sent to medical professionals.

[1727] The server transmits feedback and treatment plans from medical professionals to the user terminal.

[1728] Input: Feedback and treatment plans from medical professionals.

[1729] Output: Send to user terminal.

[1730] The terminal notifies the user of the treatment plan and provides the information necessary to carry it out.

[1731] Specific operations: Notification of treatment plan, display of necessary information.

[1732] Output: The information provided to the user.

[1733] (Application example 2)

[1734] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1735] When elderly people live alone, it is often difficult to manage their health and mental health. In particular, if a sudden change in their health condition or emotional instability occurs while shopping in a brick-and-mortar store or during daily activities, there are a lack of safe ways to deal with it. It is also difficult to take appropriate action immediately when such an abnormality occurs. To address these issues, a comprehensive system is needed that aggregates health and emotional data in real time and provides immediate alerts and suggests specific actions when an abnormality is detected.

[1736] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1737] In this invention, the server includes: means for collecting health data; means for transmitting the collected health data to the server; means for analyzing the health data received by the server and generating a health profile; means for detecting abnormalities based on the generated health profile and notifying the user terminal when an abnormality is detected; means for generating a personalized preventive care plan based on the user's health data and feedback and providing the plan to the user terminal; means for improving the AI ​​model based on user feedback and new data to evolve the service; means for monitoring the user's health data and emotional data in real time in the physical store and for immediately issuing an alert and suggesting specific actions when an abnormality is detected; means for the user to input the progress of preventive care and emotional state as feedback into the terminal and for the system to adjust subsequent care plans based on the feedback; means for sharing health data with medical professionals and optimizing treatment plans; and means for notifying the user of feedback from medical professionals. This enables health and mental health management of elderly people to be performed in real time even in physical stores, enabling immediate action when an abnormality occurs.

[1738] Definitions of important terms included in the rewritten claims

[1739] "Health data" is information related to the user's physical condition, including heart rate, blood pressure, body temperature, activity level, and the like.

[1740] "Emotion data" is information that indicates the user's emotional state, and is data extracted from facial expressions, voice, text messages, and the like.

[1741] "Real-time monitoring" is the process of constantly monitoring a user's health and emotional data, and instantly detecting and responding to any abnormalities.

[1742] An "abnormality detection alert" is a warning message that is sent immediately when an abnormality occurs after analyzing the user's health and emotional data.

[1743] A "preventive care plan" is a personalized care program designed to maintain or improve health, based on a user's health and emotional profile.

[1744] "Feedback" is information provided by users about their progress in implementing preventative care plans and their emotional state, data that is used to improve and adjust the system.

[1745] "AI Model" refers to the artificial intelligence algorithms used to analyze incoming data, detect anomalies, and generate preventative care plans.

[1746] A "physical store" is a physical store, such as a supermarket or shop, where users can visit in person to receive services or products.

[1747] A "user terminal" is a smartphone, smart glasses, or other electronic device operated by a user, and is an interface for using various functions of the system.

[1748] A "healthcare professional" is a doctor, nurse, or other healthcare professional who provides a diagnosis or treatment plan based on a user's health data.

[1749] MODE FOR CARRYING OUT THE INVENTION

[1750] The present invention is a comprehensive system for managing the health and mental health of elderly people, and for detecting abnormalities and providing preventive care in real time based on the data. Specific embodiments of the system are described below.

[1751] Overall system overview

[1752] This system collects health and emotional data from user devices such as wearable devices, smartphones, and smart glasses, and then analyzes the data on a server, detects abnormalities, and provides preventive care.By monitoring the data in real time, the system immediately alerts users if an abnormality occurs and suggests appropriate actions to take.

[1753] Health and emotional data collection

[1754] The device collects data in the following ways:

[1755] Wearable devices collect health data such as heart rate, blood pressure, body temperature, and activity level. These devices connect to the device via Bluetooth or Wi-Fi.

[1756] It uses cameras in smart glasses and microphones in smartphones to collect emotional data from facial expressions, voice, text messages, and more.

[1757] Data transmission and storage

[1758] The device transmits the collected health and emotion data to a server using encrypted communication, which stores the received data in a database and generates a health profile and emotion profile for each user.

[1759] Data analysis and anomaly detection

[1760] The server analyzes the received health and emotion data and uses a generative AI model to detect abnormalities, such as an abnormally high heart rate or a sudden change in emotional state. If an abnormality is detected, the server generates an anomaly detection alert and immediately sends it to the user's device.

[1761] User Notification and Action Suggestions

[1762] When the device receives an anomaly detection alert, it notifies the user and suggests specific actions to take, such as displaying messages like "Your heart rate is high. Please take a short rest" or "Your emotional state is unstable. We recommend you relax."

[1763] Providing preventative care and gathering feedback

[1764] The server generates a preventive care plan based on each individual's health and emotional profile. This plan includes nutrition, exercise, and psychological support. The generated preventive care plan is sent to the user's terminal, which notifies the user of the preventive care plan and supports its implementation. The user inputs feedback on the implementation status of the preventive care plan and their emotional state into the terminal, and the data is sent to the server.

[1765] Medical collaboration and treatment optimization

[1766] The server shares the user's health and emotional data with medical professionals, who can then use it to diagnose and plan treatment. The server also receives feedback and treatment plans from medical professionals and sends them to the user's device.

[1767] Adding specific examples

[1768] Example 1:

[1769] If a user's heart rate suddenly spikes while shopping at a supermarket, their smartphone will display a message such as, "Your heart rate is high. Please take a short rest."

[1770] Example 2:

[1771] If the smart glasses sense that the user is in an emotionally unstable state, they will display instructions such as "Try some relaxation techniques."

[1772] Prompt Sentence Examples

[1773] "My heart rate spiked while I was grocery shopping. What action should I take?"

[1774] These functions make it possible to provide an environment where elderly people can live their daily lives with peace of mind, even if they live alone.

[1775] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1776] Program processing flow

[1777] Step 1:

[1778] Data collection and transmission

[1779] The device collects health data such as heart rate, blood pressure, body temperature, and activity level from the wearable device, and also uses the camera in the smart glasses and the microphone in the smartphone to collect emotional data from facial expressions, voice, text messages, and more.

[1780] Input: User health and emotion data.

[1781] Data processing / calculation: Data is transferred to the device via Bluetooth or Wi-Fi and encrypted.

[1782] Output: Encrypted health and emotion data.

[1783] Specific operation: The terminal connects to the device using Bluetooth or Wi-Fi communication to receive data, then encrypts the received data and sends it to the server.

[1784] Step 2:

[1785] Data analysis and profile generation

[1786] The server stores the received health and emotion data in a database, analyzes it, and generates health and emotion profiles using a generative AI model.

[1787] Input: Encrypted health and emotion data.

[1788] Data processing / calculation: The data is decrypted, stored in a database, and analyzed using an AI model.

[1789] Output: Health profile and emotional profile.

[1790] What it does: The server decrypts the data and stores it in a database, where it then uses an AI model to analyze the data and generate a profile.

[1791] Step 3:

[1792] Anomaly detection and notification

[1793] The server detects abnormalities based on the generated health and emotion profiles, and if an abnormality is detected, generates an anomaly detection alert and immediately notifies the user device.

[1794] Input: Health profile and emotional profile.

[1795] Data processing / calculation: Apply anomaly detection algorithms and generate alerts.

[1796] Output: Anomaly detection alert.

[1797] Specific operation: The server analyzes the profile and detects, for example, an abnormal increase in heart rate or a sudden change in emotion. If an abnormality is detected, an alert is generated and sent to the user's device.

[1798] Step 4:

[1799] User notification and suggested actions

[1800] When the device receives an anomaly detection alert, it notifies the user and suggests specific actions to take (e.g., taking a break or finding ways to relax).

[1801] Input: Anomaly detection alert.

[1802] Data processing / calculation: Generates alert messages and displays them on the screen.

[1803] Output: A notification message to the user.

[1804] Specific action: When the device receives an alert, it displays specific instructions on the user's screen, such as "Your heart rate is high. Please take a short rest."

[1805] Step 5:

[1806] Generate and deliver preventative care plans

[1807] The server generates a personalized preventive care plan based on the health profile and the emotional profile and provides it to the user terminal.

[1808] Input: Health and emotional profiles, existing feedback data.

[1809] Data processing / calculation: Create a preventative care plan based on the profile.

[1810] Output: A personalized preventative care plan.

[1811] What it does: The server analyzes the profile and generates a preventive care plan that includes nutritional advice, exercise plans, and psychological support tailored to each individual user.

[1812] Step 6:

[1813] Gathering feedback and improving the AI ​​model

[1814] The user inputs the progress of the preventive care plan and their emotional state as feedback into the terminal, and the data is sent to the server.

[1815] Input: Preventive care plan implementation results and emotional state feedback.

[1816] Data processing / calculation: collecting and storing feedback data, training AI models.

[1817] Output: An improved AI model.

[1818] Specific operation: The device periodically sends feedback data from the user to the server, and the server updates the AI ​​model based on that data.

[1819] Step 7:

[1820] Data collaboration with medical experts

[1821] The server shares the user's health and emotional data with medical professionals to assist in the development of diagnoses and treatment plans.

[1822] Input: Health and emotion data, requests from healthcare providers.

[1823] Data processing / computation: Collecting and sharing data, generating treatment plans.

[1824] Output: Treatment plan,feedback from medical professionals.

[1825] Specific operation: The server sends data to the medical professional using a secure communication method, receives feedback and treatment plans from the medical professional, and notifies the user.

[1826] Through this series of steps, the system can monitor the user's health and emotional data in real time, respond immediately to any abnormalities, and provide comprehensive support for the user's health management through personalized preventive care and medical coordination.

[1827] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1828] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1829] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1830] [Fourth embodiment]

[1831] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1832] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1833] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1834] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1835] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1836] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1837] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1838] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1839] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1840] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1841] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1842] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1843] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1844] The present invention relates to a comprehensive system for supporting health management for elderly people living alone, and specific embodiments thereof will be described below.

[1845] Overall system overview

[1846] This system collects users' health data in real time, analyzes it, detects abnormalities, suggests preventive care, and shares the data with medical professionals all in one place. By exchanging data between the server, devices, and users, it provides an environment in which elderly people can live safe and fulfilling lives.

[1847] Health data collection and integration

[1848] The device collects health data such as heart rate, blood pressure, temperature, and activity levels from wearable devices and home medical devices, which connect to the device via Bluetooth or Wi-Fi.

[1849] The device periodically sends the collected data to the server. The data is sent using encrypted communication to ensure data security.

[1850] Health profile generation and anomaly detection

[1851] The server stores the received health data in a database and generates a health profile for each user, which includes the individual user's baseline values ​​and past health data.

[1852] The server uses AI models to analyze the data in real time and detect anomalies, such as generating an anomaly alert if the heart rate falls outside the resting range.

[1853] If an abnormality is detected, the server immediately sends a notification to the terminal.

[1854] Providing personalized preventative care

[1855] The server generates a preventative care plan based on the individual's health profile, which is based on an AI model and includes elements such as nutrition, exercise, and psychological support.

[1856] The device will present users with preventative care plans and support them in carrying them out, and will also include reminder and progress tracking features.

[1857] Self-learning and evolution

[1858] The server collects user feedback and analyzes it along with health data, allowing the AI ​​model to self-train and improve the accuracy of the service.

[1859] The server continues to update the system's functionality based on new medical knowledge and technological advances.

[1860] Medical collaboration and treatment optimization

[1861] With the user's consent, the server will share health data with medical professionals, allowing doctors to provide more accurate diagnoses and treatment plans.

[1862] The device notifies users of treatment plans received from medical professionals and provides the information they need to implement them, including medication schedules and follow-up appointment reminders.

[1863] Specific examples

[1864] Anomaly detection and rapid response

[1865] 1. The user puts on the wearable device when they wake up in the morning.

[1866] 2. The device collects heart rate data during the day and sends it to the server.

[1867] 3. The server analyzes the received data in real time and detects abnormal high heart rates.

[1868] 4. The server generates an anomaly detection alert and immediately sends it to the device.

[1869] 5. The device notifies the user of the abnormal alert and displays the message "Your heart rate is high. Please take a short rest." If necessary, it will also automatically notify emergency contacts.

[1870] Providing personalized preventative care

[1871] 1. The server analyzes the user's health profile and discovers that there are nutrient deficiencies.

[1872] 2. The server generates a meal plan for nutritional support.

[1873] 3. The device will notify the user of this meal plan and provide detailed recipes and a shopping list.

[1874] 4. The user follows the meal plan and inputs any changes in their physical condition into the device as feedback.

[1875] 5. The server will incorporate this feedback into future care plans.

[1876] Through these functions, the system comprehensively manages the user's health and provides an environment where elderly people living alone can live with peace of mind.

[1877] The processing flow will be explained below.

[1878] Program processing flow

[1879] Health data collection and integration

[1880] Step 1:

[1881] Users wear wearable devices or home medical devices that measure daily health data (heart rate, blood pressure, body temperature, activity level, etc.) in real time.

[1882] Step 2:

[1883] The device receives data from wearable devices and medical equipment via Bluetooth or WiFi, and this data is temporarily stored on the device.

[1884] Step 3:

[1885] The device sends the collected data to the server at regular intervals using encrypted communications, ensuring data security.

[1886] Health profile generation and anomaly detection

[1887] Step 4:

[1888] The server stores the health data received from the device in a database, where a health profile for each user is generated.

[1889] Step 5:

[1890] The server uses AI models to analyze the incoming data and apply anomaly detection algorithms, such as detecting abnormal heart rates or sudden fluctuations in blood pressure.

[1891] Step 6:

[1892] When an anomaly is detected, the server generates an anomaly alert, which includes the type of anomaly and specific numerical values.

[1893] Step 7:

[1894] The server immediately transmits the generated abnormality alert to the user terminal.

[1895] Step 8:

[1896] The device notifies the user of the abnormality alert, and the notification includes specific instructions (e.g., take a rest, contact a doctor).

[1897] Providing personalized preventative care

[1898] Step 9:

[1899] The server generates a preventative care plan based on the individual user's health profile, which includes nutrition, exercise, and psychological support.

[1900] Step 10:

[1901] The server transmits the generated preventive care plan to the user terminal.

[1902] Step 11:

[1903] The device then presents the received preventive care plan to the user, including reminders and help features.

[1904] Step 12:

[1905] The user incorporates the preventive care plan into their daily life and inputs the progress of the care plan and changes in their physical condition into the terminal.

[1906] Self-learning and evolution

[1907] Step 13:

[1908] The device periodically transmits feedback from the user to the server, including information such as diet history and exercise status.

[1909] Step 14:

[1910] The server updates the AI ​​model and performs self-learning based on the received feedback and health data.

[1911] Step 15:

[1912] The server uses the updated AI model to improve the accuracy of the next preventative care plan and anomaly detection algorithm.

[1913] Medical collaboration and treatment optimization

[1914] Step 16:

[1915] Users consent to data sharing with medical institutions through a settings screen within the application, and this consent is recorded within the system.

[1916] Step 17:

[1917] The server performs the function of securely sharing the user's health data with medical professionals, who then use it to develop a diagnosis and treatment plan.

[1918] Step 18:

[1919] The server receives feedback and treatment plans from medical professionals and transmits them to the user terminal.

[1920] Step 19:

[1921] The terminal notifies the user of the treatment plan and provides the information necessary to carry it out (e.g., medication schedule, follow-up appointment times).

[1922] Through these steps, the system provides comprehensive and personalized health management and support to elderly people living alone.

[1923] Example 1

[1924] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1925] Elderly people living alone require real-time monitoring of their health status and early detection of abnormalities. However, existing systems struggle to integrate functions such as health data collection and analysis, abnormality detection, provision of preventive care plans, and data sharing with medical professionals. Furthermore, the disparate nature of these functions makes them difficult to use. Furthermore, they lack the ability to evolve AI models based on user feedback and respond quickly in emergencies. Therefore, there is a need for a comprehensive system that provides support for the safe and secure lifestyles of elderly people living alone.

[1926] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1927] In this invention, the server includes: means for collecting health data; means for transmitting the collected health data to the server; means for analyzing the health data received by the server and generating a health profile; means for detecting abnormalities based on the generated health profile and notifying the user terminal when an abnormality is detected; means for generating a personalized preventive care plan based on the user's health data and feedback and providing it to the user terminal; means for improving the AI ​​model based on user feedback and new data and evolving the service; means for sharing the health data with medical professionals and optimizing the treatment plan; means for notifying the user of feedback from medical professionals; means for automatically notifying an emergency contact when an abnormality is detected; and means for providing a specific meal plan and shopping list based on the preventive care plan. This enables efficient and comprehensive health management of elderly people and supports their safe and secure lives.

[1928] "Health data" is information that indicates the physiological and health status of the user, such as heart rate, blood pressure, body temperature, and activity level.

[1929] A "means for collection" is a method or device for compiling a user's health data using a wearable device or home medical equipment.

[1930] "Transmitting means" refers to a method or device for encrypting the collected health data and sending it to the server.

[1931] The "analyzing means" is a method or device that processes the health data received by the server and evaluates the user's health status.

[1932] A "health profile" is a personalized physiological and health record generated from a user's health data.

[1933] A "detecting means" is a method or device for detecting abnormalities based on the generated health profile.

[1934] "Notification means" refers to a method or device for sending an alert to a user terminal or emergency contact when an abnormality is detected.

[1935] A "personalized preventative care plan" is a preventative or care plan created based on a user's individual health profile.

[1936] A "means for providing" is a method or device for informing a user of a preventive care plan and supporting its implementation.

[1937] "Means for improvement" refers to a method or device for updating the AI ​​model based on user feedback and new data to improve the service.

[1938] A "means for sharing" is a method or device for exchanging a user's health data with a medical professional to optimize a treatment plan.

[1939] "Means for automatically notifying" refers to a method or device for automatically notifying an emergency contact when an abnormality is detected.

[1940] A "means for providing a meal plan or shopping list" is a method or device that provides a user with meal suggestions and a list of ingredients based on a preventive care plan.

[1941] The present invention relates to a comprehensive system for supporting health management for elderly people living alone, and specific embodiments thereof will be described below.

[1942] Overall system overview

[1943] This system collects users' health data in real time, analyzes it, detects abnormalities, suggests preventive care, and shares the data with medical professionals. By exchanging data between the server, devices, and users, it provides an environment in which elderly people can live a safe and fulfilling life.

[1944] Health data collection and integration

[1945] The device collects health data such as heart rate, blood pressure, temperature, and activity levels from wearable devices and home medical devices, which connect to the device via Bluetooth or Wi-Fi.

[1946] Specific devices include Fitbit, Apple Watch, and common home blood pressure monitors and thermometers.

[1947] The collected data is collected using the device's API.

[1948] The device periodically sends the collected data to the server. The data is sent using SSL / TLS encryption to ensure data security.

[1949] Health profile generation and anomaly detection

[1950] The server stores the received health data in a database and generates a health profile for each user, which includes the individual user's baseline values ​​and past health data.

[1951] Database systems such as MySQL and MongoDB are used.

[1952] The server uses an AI model developed in Python to analyze the data in real time and detect anomalies, such as generating an anomaly alert if the heart rate deviates from the resting range.

[1953] If an abnormality is detected, the server immediately sends a notification to the terminal.

[1954] Providing personalized preventative care

[1955] The server generates a preventative care plan based on the individual's health profile, which is based on an AI model and includes elements such as nutrition, exercise, and psychological support.

[1956] The device will present users with preventative care plans and support them in carrying them out, and will also include reminder and progress tracking features.

[1957] Self-learning and evolution

[1958] The server collects user feedback and analyzes it along with health data, allowing the AI ​​model to self-train and improve the accuracy of the service.

[1959] The server continues to update the system's functionality based on new medical knowledge and technological advances.

[1960] Medical collaboration and treatment optimization

[1961] With the user's consent, the server will share health data with medical professionals, allowing doctors to provide more accurate diagnoses and treatment plans.

[1962] The device notifies users of treatment plans received from medical professionals and provides the information they need to implement them, including medication schedules and follow-up appointment reminders.

[1963] Specific examples

[1964] Anomaly detection and rapid response

[1965] 1. The user puts on the wearable device when they wake up in the morning.

[1966] 2. The device collects heart rate data during the day and sends it to the server.

[1967] 3. The server analyzes the received data in real time and detects abnormal high heart rates.

[1968] 4. The server generates an anomaly detection alert and immediately sends it to the device.

[1969] 5. The device notifies the user of the abnormal alert and displays the message "Your heart rate is high. Please take a short rest." If necessary, it will also automatically notify emergency contacts.

[1970] Providing personalized preventative care

[1971] 1. The server analyzes the user's health profile and discovers that there are nutrient deficiencies.

[1972] 2. The server generates a meal plan for nutritional support.

[1973] 3. The device will notify the user of this meal plan and provide detailed recipes and a shopping list.

[1974] 4. The user follows the meal plan and inputs any changes in their physical condition into the device as feedback.

[1975] 5. The server will incorporate this feedback into future care plans.

[1976] Through these functions, the system comprehensively manages the user's health and provides an environment where elderly people living alone can live with peace of mind.

[1977] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1978] Processing flow

[1979] Step 1: Collecting health data

[1980] A user puts on a wearable device (e.g., a general fitness tracker) when they wake up in the morning. They also use home medical devices (blood pressure monitor, thermometer).

[1981] The device collects data from wearable devices and medical equipment via Bluetooth or WiFi, such as heart rate and body temperature data.

[1982] Input: Sensor data from wearable devices and medical equipment

[1983] Output: Collected health data (heart rate, blood pressure, body temperature, activity level)

[1984] Step 2: Send and store data

[1985] The data collected by the device is periodically encrypted and sent securely to the server using the SSL / TLS protocol.

[1986] The server validates the data it receives and stores it in the database after verifying its validity.

[1987] Input: Collected health data

[1988] Output: Health data stored on the server

[1989] Step 3: Health profile generation and anomaly detection

[1990] The server generates a health profile for each user based on the health data stored in the database, including past data and baseline values.

[1991] Based on the generated profile, the server uses a generative AI model developed in Python to analyze the data in real time and detect abnormalities, such as when the heart rate is outside the resting range.

[1992] Input: Stored health data and generative AI model

[1993] Output: Health profile and abnormality detection results (alerts)

[1994] Step 4: Notification of abnormalities

[1995] If the server detects an abnormality, it immediately generates an anomaly detection alert and sends a notification to the terminal.

[1996] The device will notify the user of any abnormal alerts, for example, "Your heart rate is high. Please take a short rest."

[1997] Input: Anomaly detection result

[1998] Output: User notification

[1999] Step 5: Deliver personalized preventative care

[2000] The server generates a preventative care plan based on an individual's health profile, which includes nutrition, exercise, psychological support, and more.

[2001] The device presents the user with a preventive care plan, provides reminders, and tracks progress.

[2002] Input: Health profile and generative AI model

[2003] Output: Preventive Care Plan

[2004] Step 6: Gather feedback and improve the AI ​​model

[2005] The server collects feedback from the user about the running status and changes in physical condition.

[2006] The server analyzes this feedback and allows the generative AI model to self-train and improve accuracy.

[2007] Input: User feedback

[2008] Output: An improved generative AI model

[2009] Step 7: Care coordination and treatment optimization

[2010] The server shares health data with medical professionals based on the user's consent, for example by exchanging data using the FHIR protocol.

[2011] The device notifies the user of the treatment plan received from the medical professional and reminds them of medication schedules and follow-up appointments.

[2012] Input: Treatment plan from medical professional

[2013] Output: Notifications and reminders to the user

[2014] (Application example 1)

[2015] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2016] Elderly people living alone face challenges when managing their own health, including the difficulty of early detection of abnormalities and receiving appropriate preventive care. It is also difficult to quickly and appropriately connect with medical professionals. Furthermore, there is a lack of health support services linked to nearby stores and fitness centers that seniors can actually use.

[2017] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2018] In this invention, the server includes: means for collecting health data; means for transmitting the collected health data to the server; means for analyzing the health data received by the server and generating a health profile; means for detecting abnormalities based on the generated health profile and notifying the user terminal when an abnormality is detected; means for generating a personalized preventive care plan based on the user's health data and feedback and providing it to the user terminal; means for improving the AI ​​model based on user feedback and new data and evolving the service; means for sharing the health data with medical professionals and optimizing the treatment plan; means for notifying the user of feedback from medical professionals; means for providing health consultations or fitness support at affiliated physical stores based on the user's health data; and means for simultaneously notifying the user and related facilities when an abnormality is detected. This enables elderly people living alone to receive early detection of abnormalities and appropriate preventive care, and facilitates prompt and appropriate collaboration with medical professionals and physical stores.

[2019] "Health data" refers to numerical values ​​and information that indicate an individual's health status, such as heart rate, blood pressure, body temperature, and activity level.

[2020] "Means of collection" refers to the equipment and methods used to obtain a user's health data, such as using wearable devices or home medical equipment.

[2021] The "means of transmission" refers to the communication means for sending the collected health data to the server, and uses wireless communication technology such as Bluetooth or WiFi.

[2022] A "server" is a computer system that receives, analyzes, and stores collected health data.

[2023] The "analyzing means" refers to algorithms or software that evaluate the user's health status based on the received health data.

[2024] A "health profile" is a data set that compiles a user's health data history and baseline values, and is used to comprehensively assess an individual's health status.

[2025] An "anomaly detection method" is an algorithm or software that compares the health profile with newly collected data and identifies outliers.

[2026] "Means of notification" refers to methods or technologies for notifying a user terminal that an abnormality has been detected, such as a smartphone application.

[2027] "Personalized preventative care plans" refer to health management and improvement recommendations that are individually optimized based on the user's health data and feedback.

[2028] The "means for providing" refers to a method for displaying or notifying the personalized preventive care plan on the user terminal.

[2029] "AI model" refers to a machine learning or artificial intelligence model used to analyze health data and generate anomaly detection and preventative care plans.

[2030] A "healthcare professional" is a doctor or pharmacist who has the expertise to diagnose a user's health data and provide a treatment plan.

[2031] "Facilities" refers to affiliated physical stores and fitness centers, which are places that users can actually visit.

[2032] "Health consultation" means receiving health advice and information from experts such as pharmacists and nutritionists at a physical store.

[2033] "Fitness support" means receiving exercise guidance and advice from a trainer at a fitness center.

[2034] The system of the present invention is implemented by the following method and procedure. The system's core function is to collect a user's health data, analyze it in real time, detect abnormalities, and provide preventative care plans. It also includes collaborating with medical professionals based on the data to provide appropriate health consultations and fitness support to the user.

[2035] Data collection and transmission

[2036] The server collects health data from wearable devices and home medical devices (smartwatches, scales, blood pressure monitors, etc.) via Bluetooth or Wi-Fi. This periodically collects data such as heart rate, blood pressure, body temperature, and activity level. The collected data is encrypted by the smartphone and sent to the cloud server.

[2037] Analyzing data and generating health profiles

[2038] The server uses a cloud-based data analysis system (e.g., AWS, MySQL) to analyze the received data and generate a health profile for each user. This profile includes the user's baseline values ​​and past health data. An AI model (e.g., TensorFlow) is used to analyze the data in real time and detect outliers.

[2039] Anomaly detection and notification

[2040] If an abnormality is detected, the server immediately generates an abnormality alert and sends a notification to the user's smartphone. Furthermore, if the abnormality is serious, notifications are also sent to the user's partner physical stores (drugstores and fitness centers). This allows the elderly person to receive prompt and appropriate treatment.

[2041] Providing personalized preventative care plans

[2042] The server generates a personalized preventive care plan based on the user's health profile. This plan includes nutrition, exercise, and psychological support. The plan is then provided to the user via a smartphone application. The application also has reminder and progress tracking functions.

[2043] Health counseling and fitness support

[2044] Based on the user's health data, health consultations are provided at affiliated physical stores and fitness support is provided at fitness centers. If an abnormality is detected, a pharmacist at the drugstore or a fitness instructor will take appropriate action. This allows users to receive comprehensive support.

[2045] Examples of specific examples and prompts

[2046] Example 1: If high blood pressure is detected, the following notification is displayed on the user's smartphone:

[2047] Text format

[2048] Abnormalities detected. Your heart rate and blood pressure are out of normal range. Would you like to schedule a free consultation at your local drugstore?

[2049] Example 2: If an anomaly is detected during a workout at a fitness center, the following notification is sent to the instructor:

[2050] Text format

[2051] Your heart rate has deviated from the normal range during exercise. Would you like to contact your instructor?

[2052] The above is an embodiment of the present invention. This system enables elderly people living alone to receive early detection of abnormalities and appropriate preventive care, and also facilitates prompt and appropriate cooperation with medical professionals and brick-and-mortar stores.

[2053] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2054] Step 1:

[2055] Users wear a smartwatch or home medical device to collect health data. The smartwatch measures data such as heart rate, blood pressure, body temperature, and activity level, and transmits it to a smartphone via Bluetooth.

[2056] Input: User's health data (heart rate, blood pressure, body temperature, activity level, etc.)

[2057] Output: Health data collected on a smartphone

[2058] Step 2:

[2059] The device (smartphone) encrypts the collected health data and sends it to a cloud server, where the smartphone app periodically uploads the data to the server.

[2060] Input: Health data collected on a smartphone

[2061] Output: Encrypted data sent to the cloud server

[2062] Step 3:

[2063] The server analyzes the received health data and generates a health profile. Specifically, it stores the data in a database (MySQL) and performs anomaly detection analysis using an AI model (TensorFlow). It compares the data with past data, sets baseline values, and updates the health profile.

[2064] Input: Encrypted data sent to the cloud server

[2065] Output: Generated health profile

[2066] Step 4:

[2067] The server detects abnormalities based on the generated health profile. The AI ​​model analyzes the data in real time and generates an anomaly alert if it detects an abnormal value.

[2068] Input: Generated health profile

[2069] Output: Abnormal alert

[2070] Step 5:

[2071] If an abnormality is detected, the server sends a notification to the user's device, and simultaneously issues an abnormality alert and notifies partner facilities.

[2072] Input: Anomaly Alert

[2073] Output: Notification sent to user device and partner facility

[2074] Step 6:

[2075] The device will notify the user of any abnormalities and suggest specific measures to take if necessary, which in this case could include health consultations at a drugstore or support at a fitness center.

[2076] Input: Notification sent to user device

[2077] Output: Notify the user of the abnormality and suggest a solution

[2078] Step 7:

[2079] The server generates a personalized preventative care plan based on the user's health data and feedback. An AI model analyzes the user's condition and creates an individualized care plan.

[2080] Input: Health data and feedback

[2081] Output: A personalized preventative care plan

[2082] Step 8:

[2083] The device provides the user with the generated preventive care plan and supports implementation, and the app uses reminders and implementation tracking features to monitor the user's progress with the care plan.

[2084] Enter: personalized preventative care plans.

[2085] Output: Preventive care plans and support functions provided to users

[2086] Step 9:

[2087] The server improves the AI ​​model based on user feedback and new health data, evolving the service and improving the accuracy of future preventive care plans.

[2088] Input: Feedback and new health data

[2089] Output: Improved AI models and evolved services

[2090] Step 10:

[2091] The server shares health data with medical professionals to optimize treatment plans, and provides feedback from medical professionals to users to provide appropriate treatment and care.

[2092] Input: Health data and medical expert feedback

[2093] Output: Optimized treatment plan and notification to the user

[2094] The above processing steps realize a system that monitors the health status of elderly people in real time, detects abnormalities early, and enables appropriate responses.

[2095] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2096] The present invention relates to a comprehensive system for supporting health management for elderly people living alone, and by combining it with an emotion engine, manages not only physical health but also mental health. Specific embodiments of the system are described below.

[2097] Overall system overview

[2098] This system collects users' health and emotional data in real time, analyzes it, detects abnormalities, suggests preventive care, and shares the data with medical professionals. Through the exchange of data between the server, devices, and users, it provides an environment in which elderly people can live a safe and fulfilling life.

[2099] Collecting and integrating health and emotional data

[2100] The device collects health data such as heart rate, blood pressure, temperature, and activity levels from wearable devices and home medical devices, which connect to the device via Bluetooth or Wi-Fi.

[2101] The device uses a built-in emotion engine to collect emotion data from the user's facial expressions, voice, text messages, etc.

[2102] The terminal periodically transmits the health data and emotion data to the server using encrypted communication.

[2103] Health and emotional profile generation and anomaly detection

[2104] The server stores the health data and emotion data received from the terminals in a database and generates a health profile and emotion profile for each user.

[2105] The server uses AI models to analyze the incoming data and apply anomaly detection algorithms, for example, to detect abnormal heart rates or sudden fluctuations in emotional state.

[2106] When the server detects an abnormality, it generates an abnormality alert and immediately sends this alert to the user terminal.

[2107] The device will notify the user of abnormal alerts and display messages with specific instructions such as "Your heart rate is high. Please take a short rest" or "Your emotional state is unstable. We recommend you relax."

[2108] Providing personalized preventative care

[2109] The server generates a preventative care plan based on each individual's health and emotional profile, including nutrition, exercise, and psychological support.

[2110] The server transmits the generated preventive care plan to the user terminal.

[2111] The device will present users with preventative care plans and support them in carrying them out, and will also include reminder and progress tracking features.

[2112] Self-learning and evolution

[2113] The device collects feedback on the implementation of the preventive care plan and changes in emotional state and sends it to the server.

[2114] The server updates the AI ​​model and performs self-learning based on the collected feedback, health data, and emotional data.

[2115] The server continually updates the system's functionality based on new medical and psychological knowledge and technological advances.

[2116] Medical collaboration and treatment optimization

[2117] Users consent to data sharing with medical institutions through a settings screen within the application, and this consent is recorded within the system.

[2118] The server securely shares the user's health and emotional data with medical professionals, who can then use it to develop a diagnosis and treatment plan.

[2119] The server receives feedback and treatment plans from medical professionals and transmits them to the user terminal.

[2120] The terminal notifies the user of the treatment plan and provides the information necessary to carry it out (e.g., medication schedule, follow-up appointment times).

[2121] Specific examples

[2122] Real-time monitoring of emotional states and abnormal response

[2123] 1. After waking up in the morning, the user puts on the wearable device and launches the smartphone application.

[2124] 2. The device monitors the user's heart rate and emotional state in real time (for example, using facial recognition and voice analysis).

[2125] 3. The server analyzes the health and emotional data to detect abnormalities such as high heart rate or unstable emotional state.

[2126] 4. The server generates an anomaly detection alert and immediately notifies the user device.

[2127] 5. The device will notify the user of the abnormality detection alert and display a message such as "Your heart rate is high. Please take a short rest" or "Your emotional state is unstable. We recommend that you relax."

[2128] Providing personalized preventative care

[2129] 1. The server analyzes the user's health and emotional profile and discovers any nutrient deficiencies or psychological support needs.

[2130] 2. The server generates nutritional meal plans and relaxing mental exercise plans.

[2131] 3. The device will notify the user of these plans and provide specific instructions and reminders on how to implement them.

[2132] 4. The user carries out the preventive care plan and inputs changes in physical condition and emotions into the device as feedback.

[2133] 5. The server will incorporate this feedback into future care plans.

[2134] Through these functions, the system comprehensively manages the user's physical and mental health, providing an environment where elderly people living alone can live with peace of mind.

[2135] The processing flow will be explained below.

[2136] Program processing flow

[2137] Collecting and integrating health and emotional data

[2138] Step 1:

[2139] The user puts on a wearable device or home medical device and launches a smartphone app, which prepares to collect daily health and emotional data.

[2140] Step 2:

[2141] The device receives real-time health data such as heart rate, blood pressure, body temperature, and activity levels from wearable devices and medical equipment via Bluetooth and WiFi.

[2142] Step 3:

[2143] The device uses a camera and microphone to recognize the user's face and perform voice analysis to collect emotional data, including emotional analysis based on facial expressions and tone of voice.

[2144] Step 4:

[2145] The device periodically transmits the collected health and emotion data to a server using encrypted communications, ensuring data security.

[2146] Health and emotional profile generation and anomaly detection

[2147] Step 5:

[2148] The server stores the health data and emotion data received from the terminal in a database, and generates a health profile and emotion profile for each user.

[2149] Step 6:

[2150] The server uses AI models to analyze the incoming data and apply anomaly detection algorithms, for example, to detect abnormal heart rates or sudden fluctuations in emotional state.

[2151] Step 7:

[2152] When an anomaly is detected, the server generates an anomaly alert, which includes the type of anomaly and specific numerical values.

[2153] Step 8:

[2154] The server immediately transmits the generated abnormality alert to the user terminal.

[2155] Step 9:

[2156] The device will notify the user of abnormal alerts and display messages with specific instructions such as "Your heart rate is high. Please take a short rest" or "Your emotional state is unstable. We recommend you relax."

[2157] Providing personalized preventative care

[2158] Step 10:

[2159] The server generates a preventative care plan based on the individual user's health and emotional profile, which includes nutrition, exercise, and psychological support.

[2160] Step 11:

[2161] The server transmits the generated preventive care plan to the user terminal.

[2162] Step 12:

[2163] The device presents the user with a preventative care plan, including reminders and progress tracking.

[2164] Step 13:

[2165] The user incorporates the preventive care plan into their daily life and inputs their progress, changes in their physical condition, and changes in their emotional state into the terminal.

[2166] Self-learning and evolution

[2167] Step 14:

[2168] The device periodically transmits feedback from the user to the server, including information on dietary history, exercise status, and emotional changes.

[2169] Step 15:

[2170] The server updates the AI ​​model and performs self-learning based on the received feedback, health data, and emotional data.

[2171] Step 16:

[2172] The server uses the updated AI model to improve the accuracy of the next preventative care plan and anomaly detection algorithm.

[2173] Medical collaboration and treatment optimization

[2174] Step 17:

[2175] Users consent to data sharing with medical institutions through a settings screen within the application, and this consent is recorded within the system.

[2176] Step 18:

[2177] The server performs the function of securely sharing the user's health and emotional data with medical professionals, who then use it to develop a diagnosis and treatment plan.

[2178] Step 19:

[2179] The server receives feedback and treatment plans from medical professionals and transmits them to the user terminal.

[2180] Step 20:

[2181] The terminal notifies the user of the treatment plan and provides the information necessary to carry it out (e.g., medication schedule, follow-up appointment times).

[2182] Specific examples

[2183] Real-time monitoring of emotional states and abnormal response

[2184] Step 1:

[2185] After waking up in the morning, the user puts on the wearable device and launches the smartphone application.

[2186] Step 2:

[2187] The device receives heart rate data from the wearable device and simultaneously uses the camera and microphone to gather information about the user's emotional state, for example by analyzing emotions from facial expressions and tone of voice.

[2188] Step 3:

[2189] The server analyzes the received health and emotional data to detect abnormalities, such as if the heart rate is outside the normal range or if the user is in an unstable emotional state.

[2190] Step 4:

[2191] The server generates an anomaly detection alert and immediately notifies the user device.

[2192] Step 5:

[2193] The device receives the notification and displays specific instructions to the user, such as "Your heart rate is high. Please take a short rest" or "Your emotional state is unstable. We recommend you relax."

[2194] Providing personalized preventative care

[2195] Step 1:

[2196] The server analyzes the user's health and emotional profile and discovers deficiencies in certain nutrients or psychological support.

[2197] Step 2:

[2198] The server generates meal plans for optimal nutritional support and relaxation exercises for psychological support.

[2199] Step 3:

[2200] The device will notify the user of these plans and provide specific instructions on how to carry them out (e.g., recipes or exercise videos).

[2201] Step 4:

[2202] Users follow a preventative care plan and enter feedback into the device, including their diet, exercise routine, and emotional changes.

[2203] Step 5:

[2204] The server analyzes the collected feedback and incorporates it into the next preventative care plan.

[2205] Through these steps, the system provides comprehensive and personalized health and emotional care for seniors living alone.

[2206] Example 2

[2207] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2208] Currently, health and mental health management for older adults often relies on regular visits to medical institutions or self-management. However, these are difficult for older adults living alone, which requires the collection and analysis of real-time health and emotional data, early detection of abnormalities, and immediate response. Furthermore, there is a lack of personalized preventative care plans based on individual health and emotional conditions. This makes it difficult for older adults to maintain their health and ensure a high quality of life.

[2209] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting health data and emotional data in real time; means for encrypting the collected health data and emotional data and transmitting it to the server; means for storing the health data and emotional data received by the server and generating a health profile and an emotional profile; means for analyzing the health data and emotional data using an AI model to detect abnormalities; means for generating an abnormality alert and notifying the user terminal when an abnormality is detected; means for generating a personalized preventive care plan based on the user's health profile and emotional profile and providing it to the user terminal; means for supporting the execution of the preventive care plan through reminders and progress tracking functions; means for improving the AI ​​model based on user feedback and new data and evolving the service; means for sharing the health data and emotional data with medical professionals based on the user's consent to optimize the treatment plan; and means for notifying the user of feedback from medical professionals and the treatment plan. This enables comprehensive management of the health and mental health of elderly people, real-time abnormality detection, and early response. Additionally, providing personalized preventative care plans allows for more tailored care based on individual health and emotional states, improving the quality of life for seniors.

[2210] "Health data" refers to data that indicates the user's physical health condition, such as heart rate, blood pressure, body temperature, and activity level.

[2211] "Emotion data" is data that indicates the user's emotional state, obtained from facial expressions, voice, text messages, etc.

[2212] A "server" is a computer system that collects, stores, analyzes, and detects anomalies in data, provides care plans, collects feedback, and improves AI models.

[2213] A "terminal" is an electronic device that collects data from wearable devices and home medical equipment and transmits it to a server.

[2214] An "AI model" is a mathematical model that uses machine learning algorithms to analyze data, detect abnormalities, and generate personalized care plans.

[2215] A "health profile" is a data set that comprehensively indicates a user's health status and is generated based on the user's health data.

[2216] An "emotional profile" is a data set that comprehensively indicates a user's emotional state, generated based on the user's emotional data.

[2217] "Anomaly detection" is the process of analyzing collected health and emotional data to detect abnormal conditions that go beyond the normal range.

[2218] An "abnormality alert" is a warning message that notifies the user of a detected abnormal condition.

[2219] A "Preventive Care Plan" is a personalized health management and psychological support plan that is generated based on a user's health and emotional profile.

[2220] A "reminder" is a notification message that prompts the user to carry out a scheduled activity or care plan.

[2221] "Feedback" is the process by which users provide information about the progress of their preventive care plan and changes in their physical and emotional state.

[2222] A "health professional" is a professional with expertise related to the user's health and mental health, such as a doctor, nurse, or psychologist.

[2223] A "treatment plan" is a plan developed by a medical professional to resolve a user's health problem.

[2224] "Encryption" is a technology that converts data into a special code so that it cannot be deciphered by third parties.

[2225] MODE FOR CARRYING OUT THE INVENTION

[2226] The present invention relates to a comprehensive system for supporting health management for elderly people living alone. By combining an emotion engine, the system manages not only the user's physical health but also their mental health. Specific embodiments of the system are described below.

[2227] System Configuration

[2228] The system collects health and emotional data in real time, analyzes them, detects anomalies, provides preventive care, and shares data with medical professionals. The main components are the server, the device, and the user.

[2229] Health and emotional data collection

[2230] ...

Claims

1. a means of collecting health data; means for transmitting the collected health data to a server; means for analyzing the received health data by the server and generating a health profile; means for detecting an abnormality based on the generated health profile and notifying a user terminal when an abnormality is detected; A means for generating a personalized preventative care plan based on the user's health data and feedback and providing the plan to the user device; A means to improve the AI ​​model and evolve the service based on user feedback and new data, A means to share health data with medical professionals to optimize treatment plans; a means for notifying the user of feedback from a medical professional; A system including:

2. 2. The system according to claim 1, further comprising means for monitoring the user's health data in real time and issuing an immediate warning if an abnormality is detected.

3. 2. The system according to claim 1, further comprising means for providing a personalized preventive care plan based on the user's health data and collecting feedback on the implementation status of the preventive care.

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