System

Abstract

A system is provided.SOLUTION: A system for monitoring a health condition of a user, comprising: means for collecting information from a health care apparatus; means for storing and analyzing the collected information; means for generating and AI a learning material based on the analyzed information; means for creating a learning plan based on the generated learning material; means for presenting a learning task to the user based on the learning plan; and means for promoting cooperative learning with other users.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] Elderly people are prone to aging symptoms such as dementia due to declining cognitive function, and effective countermeasures are needed. Conventional learning and health management systems have difficulty providing personalized support that takes into account each user's health condition and cognitive ability, and tend to lack sustained motivation and interaction with others. Therefore, there is a need to provide systems that allow elderly people to maintain their health and improve their cognitive function while having fun. [Means for solving the problem]

[0005] According to the present invention, a system for monitoring a user's health status and generating personalized learning materials by analyzing collected data is provided. Specifically, the system includes a means for collecting data from a health management device, a server for storing and analyzing the collected data, a generation AI means for generating learning materials based on the analyzed data, a means for creating a learning plan based on the generated learning materials, a terminal means for presenting learning tasks to the user based on the learning plan, a gamification means for tracking the user's progress and maintaining motivation, and a means for promoting collaborative learning with other users. This system allows elderly people to maintain their health and cognitive function while having fun.

[0006] A "health management device" is a device that continuously monitors a user's health status and collects necessary data (e.g., heart rate, sleep patterns, etc.).

[0007] The "means for collecting data" is a system component that has the function of collecting user health data from health management devices and transmitting the data to a server.

[0008] "Server means" is a system component for receiving, storing and further analyzing collected health data.

[0009] The "generative AI means" is a system component that utilizes artificial intelligence technology to analyze users' health data and generate learning materials that are optimal for each individual user.

[0010] The "means for creating a study plan" is a system component for automatically creating an optimal study schedule and assignments based on the generated learning materials and the user's health condition.

[0011] "Terminal means" is a system component that has an interface for presenting learning tasks to the user and accepting input.

[0012] "Gamification tools" are system components that track users' learning progress and provide rewards such as badges and points to maintain motivation.

[0013] "Means for promoting collaborative learning" are system components that provide users with opportunities to learn collaboratively with other users and promote communication. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] This invention is a system for preventing cognitive decline and the progression of aging symptoms in the elderly while maintaining health in an enjoyable manner. This system works in conjunction with a health management device, generates learning materials optimized for each user, and provides a learning environment that incorporates collaborative learning with other users and gamification elements.

[0036] System Overview

[0037] The system consists of the following main components:

[0038] 1. Health management device

[0039] 2. Terminal

[0040] 3. Server

[0041] 4. Generation AI

[0042] 5. Study Plan

[0043] 6. Gamification

[0044] 7. Cooperative Learning

[0045] Overview of program processing

[0046] 1. Data Collection

[0047] Terminal

[0048] Health data such as heart rate and sleep patterns are periodically collected from a health management device worn by the user (e.g., a smartwatch). The data collected by the device is automatically sent to a server.

[0049] 2. Data Receipt and Analysis

[0050] server

[0051] The server stores and analyzes the data received from the device, and an analysis algorithm evaluates the user's health status and calculates health indicators such as stress level and sleep quality.

[0052] 3. Generating personalized learning materials

[0053] server

[0054] The server-based AI generates optimal learning materials based on the analyzed health indicators, and the materials are customized to the user's cognitive ability, past learning history, and interests.

[0055] 4. Create a study plan

[0056] server

[0057] Based on the generated learning materials, a learning plan is created based on the user's health condition and learning goals, including specific tasks and schedules.

[0058] 5. Presentation and implementation of learning tasks

[0059] Terminal

[0060] The device presents the user with daily tasks based on the learning plan, and as the learning progresses, the device receives feedback from the user and sends progress data to a server.

[0061] 6. Learning progress management and feedback

[0062] server

[0063] The server analyzes the progress data and awards badges and points according to the level of achievement, which helps to maintain the user's motivation and encourages them to continue learning.

[0064] 7. Promoting collaborative learning

[0065] Servers and Terminals

[0066] To promote collaborative learning with other users, the server generates information about collaborative tasks and challenges and sends it to the device, which displays the information and provides the user with an opportunity to collaborate with other users to solve the tasks.

[0067] Specific examples

[0068] Case of User A (65-year-old male)

[0069] Terminal

[0070] User A's smartwatch measures his / her heart rate and sleep patterns, and the device sends this data to a server every morning.

[0071] server

[0072] The server analyzes the received data and detects that User A's stress level is increasing and the quality of his or her sleep is declining. Based on this, the generative AI generates relaxation sessions and puzzle learning materials.

[0073] server

[0074] The server creates a study plan based on user A's health status, including three relaxation sessions per week and daily puzzle practice.

[0075] Terminal

[0076] The device presents learning tasks to User A and awards a badge each time it is completed. It also provides opportunities for collaborative learning with other users, allowing User A to solve the tasks together.

[0077] Server and Device

[0078] Data is continuously collected and the learning plan is optimized according to the health condition of User A. The system is also continuously improved based on feedback.

[0079] This system allows users to have fun while maintaining their health and improving their cognitive function.

[0080] The processing flow will be explained below.

[0081] Step 1:

[0082] Terminal

[0083] A health monitoring device worn by a user (e.g., a smartwatch) periodically measures health data such as heart rate and sleep patterns, and the measurement data is stored on the device.

[0084] Step 2:

[0085] Terminal

[0086] The stored health data is automatically sent to the server at regular intervals, and if the transmission is successful, the device proceeds to the next data collection cycle.

[0087] Step 3:

[0088] server

[0089] The data sent from the device is received and stored in a database, which identifies each user and prepares it for later analysis.

[0090] Step 4:

[0091] server

[0092] The received data is analyzed. An analytical algorithm is used to assess the user's health and calculate health indicators such as stress level and sleep quality. The results are updated in the user profile.

[0093] Step 5:

[0094] server

[0095] Based on the analysis results, the generative AI generates learning materials optimized for each user, taking into account the user's interests, cognitive ability, and past learning history.

[0096] Step 6:

[0097] server

[0098] Based on the generated learning materials, a learning plan is created, which includes specific learning tasks, implementation schedules, reminders, etc.

[0099] Step 7:

[0100] Terminal

[0101] According to the study plan sent from the server, daily study tasks are presented to the user. The tasks are displayed and the user can work on them.

[0102] Step 8:

[0103] User

[0104] Users work on the presented learning tasks, and when they complete the tasks, the device records their progress and sends it to the server.

[0105] Step 9:

[0106] server

[0107] The system analyzes the progress data sent by users, evaluates their achievement, and generates rewards such as badges and points according to their achievement, thereby increasing user motivation.

[0108] Step 10:

[0109] server

[0110] To provide opportunities for collaborative learning with other users, collaborative tasks and challenges are generated and sent to devices, encouraging users to learn collaboratively with others.

[0111] Step 11:

[0112] Terminal

[0113] Users will be notified of collaborative learning and challenges, allowing them to deepen their learning by solving collaborative challenges together with other users.

[0114] Step 12:

[0115] Servers and Terminals

[0116] The system continuously collects and analyzes data to optimize learning plans and materials based on the user's health condition and learning progress, and improves the system as a whole based on user feedback.

[0117] This series of steps allows seniors to have fun while maintaining their health and improving cognitive function.

[0118] Example 1

[0119] 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."

[0120] There is a need for systems that allow elderly people to maintain their health while having fun, while preventing cognitive decline and the progression of aging symptoms. However, conventional systems have difficulty in providing personalized responses based on each user's health condition, and lack mechanisms to maintain motivation or promote collaborative learning with other users. This often makes it difficult for users to continue managing their health and learning.

[0121] 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.

[0122] In this invention, the server includes a means for storing and analyzing data collected from the health management device, a means for calculating the user's health index using an analysis algorithm, and a generation AI means for generating learning materials based on the analyzed health index. This makes it possible to provide personalized learning materials according to the user's health condition, maintain motivation, and promote cooperative learning with other users.

[0123] A "health management device" is a device that monitors a user's health status and collects data such as heart rate and sleep patterns.

[0124] "Means for collecting data" refers to a method for obtaining health data such as heart rate and sleep patterns from health management devices.

[0125] The "means for transmitting data to the server" refers to a communication means for transferring collected health data to the server.

[0126] A "server" is a computer system that receives, stores, and analyzes collected data.

[0127] An "analysis algorithm" is a calculation method for analyzing collected health data and calculating the user's health indicators.

[0128] "Generative AI" is an artificial intelligence technology that generates optimal learning materials for users based on analyzed health indicators.

[0129] A "study plan" is a specific study schedule based on the generated study materials and adapted to the user's health condition and study goals.

[0130] A "terminal" is a device that presents learning tasks to a user and accepts operations.

[0131] "Progress data" is data that indicates the results of a user's learning tasks, and is sent from the terminal to the server.

[0132] "Gamification" is a reward system that uses badges and points to maintain user motivation and encourage continued learning.

[0133] "Collaborative learning" is a method in which multiple users work together to complete a learning task and achieve results.

[0134] The present invention is a system for preventing cognitive decline and the progression of aging symptoms in the elderly while maintaining health in an enjoyable manner. This system works in conjunction with a health management device, generates learning materials optimized for each user, and provides a learning environment that incorporates collaborative learning with other users and gamification elements. Specific embodiments of the system are described below.

[0135] The system consists of the following main components: health management device, terminal, server, generative AI, learning plan, gamification, and collaborative learning.

[0136] A user's health monitoring device (e.g., a smartwatch) periodically measures health data such as heart rate and sleep patterns. This data is automatically transmitted by the device to a server. The device transmits the data using a secure communication protocol (e.g., HTTPS) without requiring any user interaction.

[0137] The server stores the received health data in a database and uses an analytical algorithm to calculate health indicators, including stress levels and sleep quality. Based on the analysis results, a generative AI on the server generates learning materials optimal for the user. The materials are customized taking into account the user's cognitive ability, past learning history, and interests.

[0138] A study plan is created based on the generated learning materials. For example, a specific schedule may be set up, including three relaxation sessions per week and daily puzzle practice. This plan is saved on the server and presented to the user via their device.

[0139] The device notifies the user of daily learning tasks. For example, a notification such as "Please complete a 20-minute relaxation session today" is displayed in the morning. As the user completes the task, progress data is sent from the device to the server.

[0140] The server analyzes progress data and has a gamification function that automatically awards badges and points according to achievement, thereby maintaining user motivation. Progress reports are also generated periodically and displayed on the device.

[0141] To promote collaborative learning with other users, the server generates collaborative learning tasks and challenges and sends them to the device. For example, a task might be displayed such as, "Form a team of three people and take part in a one-week walking challenge." Users collaborate with other users to complete the task, and the device reports the results to the server.

[0142] As a specific example, consider a 65-year-old male user A. His smartwatch measures his heart rate and sleep patterns and sends the data to a server daily. The server analyzes the data and detects rising stress levels and declining sleep quality. A generative AI generates relaxation session and puzzle learning materials, and a learning plan is set up with three relaxation sessions per week and daily puzzle learning. Every morning, the device notifies user A of the learning tasks and awards a badge each time one is completed. Health data is continuously collected, and the learning plan is optimized according to user A's latest health condition.

[0143] Here are some example prompts that can be input to a generative AI model:

[0144] "Generate a study plan for User A, a 65-year-old male, who is in a state of high stress and low sleep quality, combining relaxation sessions and puzzle learning three times a week."

[0145] This system allows users to have fun while maintaining their health and improving their cognitive function.

[0146] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0147] System program processing steps

[0148] Step 1: Collecting health data

[0149] Terminal

[0150] The device periodically collects health data such as heart rate and sleep patterns from the user's health management device (e.g., smartwatch).

[0151] Input: Heart rate and sleep pattern data from smartwatch

[0152] Output: Collected health data

[0153] How it works: The device collects data from the smartwatch every hour and temporarily stores the collected data in its internal memory. For example, data points such as a heart rate of 70 BPM and 7 hours of sleep are collected.

[0154] Step 2: Sending data

[0155] Terminal

[0156] The device transmits the collected health data to a server in real time.

[0157] Input: Collected health data

[0158] Output: Data sent to the server

[0159] How it works: The device sends encrypted data using a secure communication protocol (e.g., HTTPS). No user interaction is required; everything happens automatically.

[0160] Step 3: Receiving and storing data

[0161] server

[0162] The server receives the health data sent from the device and stores it in a database.

[0163] Input: Health data sent from the device

[0164] Output: Data stored in the database

[0165] What it does: The server validates the data it receives and stores it in a database based on each user's unique identifier. It then merges it with past data to maintain a continuous record.

[0166] Step 4: Analyze the data

[0167] server

[0168] The server analyzes the stored health data using an analytical algorithm and calculates the user's health index.

[0169] Input: Stored health data

[0170] Output: Calculated health indicators (e.g., stress level, sleep quality)

[0171] How it works: The server uses data analysis software to calculate stress levels and sleep quality based on heart rate, sleep patterns, etc. For example, an increase in heart rate is considered to be an increase in stress.

[0172] Step 5: Generate personalized learning materials

[0173] server

[0174] The generation AI on the server generates learning materials that are optimal for the user based on the analyzed health indicators.

[0175] Input: Analyzed health indicators

[0176] Output: Personalized learning materials

[0177] How it works: Generative AI uses health indicators, the user's cognitive ability, past learning history, and interests as input data to generate learning materials. For example, if high stress is detected, relaxation sessions and puzzle learning materials will be generated.

[0178] Step 6: Create a study plan

[0179] server

[0180] Based on the generated teaching materials, a learning plan is created based on the user's health condition and learning goals.

[0181] Input: Generated learning materials, user's health status

[0182] Output: A specific study plan (e.g., three relaxation sessions per week)

[0183] What happens: The server uses a scheduling algorithm to plan an optimal schedule for the user, for example, setting relaxation sessions on Mondays, Wednesdays, and Fridays.

[0184] Step 7: Present and implement the learning task

[0185] Terminal

[0186] The device presents the user with daily assignments based on their learning plan.

[0187] Input:Study plan

[0188] Output: Notification to user, data on completed assignments

[0189] Specific operation: The device displays a notification every morning, prompting the user to "take a relaxation session today." The user conducts the session and enters their progress into the device.

[0190] Step 8: Progress monitoring and feedback

[0191] server

[0192] The server analyzes the progress data and awards badges and points based on the level of achievement.

[0193] Input: Progress data from the terminal

[0194] Output: Achievements, badges, points

[0195] What it does: The server updates the reward system based on the progress data, adds badges and points to the user's account, and periodically generates progress reports and sends them to the device.

[0196] Step 9: Facilitating collaborative learning

[0197] Servers and Terminals

[0198] To promote collaborative learning with other users, the server generates information on collaborative tasks and challenges and sends them to the terminals.

[0199] Input: Progress data, collaborative learning settings

[0200] Output: Collaborative tasks, progress by team

[0201] Specific operation: The server generates collaborative learning data and displays on the device, "Let's take part in a one-week walking challenge in teams of three." The user performs the challenge, and the device reports the results to the server.

[0202] Through each step, users can have fun while maintaining their health and improving their cognitive function.

[0203] (Application example 1)

[0204] 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."

[0205] In modern society, health management and maintaining / improving cognitive function for the elderly are important issues. In particular, effective methods are needed to prevent cognitive decline while maintaining health in a fun way for the elderly. It is also important to strengthen social connections and prevent isolation by providing an environment in physical stores where the elderly can learn and train in cooperation with other users. Furthermore, providing personalized health programs and learning materials to the elderly is thought to lead to sustainable health management and maintaining motivation.

[0206] 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.

[0207] In this invention, the server includes means for collecting data from a health management device to monitor the user's health status, means for storing and analyzing the collected data, means for generating learning materials based on the analyzed data, means for creating a learning plan based on the generated learning materials, means for presenting learning tasks to the user based on the learning plan, means for gamification to track the user's progress and maintain motivation, means for promoting collaborative learning with other users, means for analyzing data collected through the health management device and smart devices in the physical store and suggesting optimal learning materials and health programs to the user, and means for providing opportunities to complete learning tasks through collaboration with other users. This enables elderly people to maintain their health, improve their cognitive function, and strengthen their social connections while having fun in the physical store.

[0208] "User" refers to an individual who uses the system to manage their health or undertake learning tasks.

[0209] "Health status" refers to data that indicates the user's physical condition and health, such as heart rate, blood pressure, weight, body fat percentage, and sleep patterns.

[0210] A "health management device" is a device that can collect health data such as heart rate and sleep patterns, and examples include smartwatches and blood pressure monitors.

[0211] "Server" refers to a computer system connected to the Internet that stores and analyzes collected data.

[0212] "Generative AI" refers to artificial intelligence that automatically generates optimal learning materials for users based on collected and analyzed data.

[0213] A "study plan" refers to a plan based on the generated teaching materials that includes specific tasks and schedules tailored to the user's health condition and learning goals.

[0214] A "device" is a device that presents a user with tasks based on a learning plan and receives feedback from the user, including, for example, a smartphone or tablet.

[0215] "Gamification" refers to a method of keeping users motivated by tracking their progress and awarding badges and points based on their achievements.

[0216] "Collaborative learning" refers to learning methods and activities that involve working with other users to solve learning problems.

[0217] A "physical store" refers to a physical store that users can actually visit, where health management equipment and smart devices are installed.

[0218] A "smart device" is an electronic device that allows users to collect health data or check their learning plans, and examples include smartphones and tablets.

[0219] "Learning Materials" refers to various learning content provided to users, including question sets, quizzes, relaxation sessions, etc.

[0220] "Health Program" refers to the specific content and schedule of training, exercises, etc. aimed at improving the user's health.

[0221] "Study assignments" refer to specific tasks or problems that users should tackle based on their study plan.

[0222] This invention is a system for a brick-and-mortar store that supports health management and cognitive function improvement for the elderly. Specific embodiments for realizing this system will be described in detail below.

[0223] System Overview

[0224] The system consists of the following components to monitor users' health status and provide personalized learning materials:

[0225] 1. Health monitoring devices: Devices such as smartwatches collect data such as heart rate, blood pressure, weight, body fat percentage, and sleep patterns.

[0226] 2. Device: A device such as a smartphone or tablet presents learning tasks to users and receives feedback.

[0227] 3. Server: A cloud server for storing and analyzing data.

[0228] 4. Generative AI: Artificial intelligence that generates learning materials based on analyzed data.

[0229] 5. Learning plan: A specific schedule for the user's health management and learning using the generated learning materials.

[0230] 6. Gamification: Awarding badges and points to keep users motivated.

[0231] 7. Collaborative learning: A mechanism that provides opportunities for users to work with others to complete learning tasks.

[0232] Hardware and software used

[0233] Health management devices: smart watches, blood pressure monitors, body composition monitors

[0234] Devices: Smartphones (iOS, ANDROID (registered trademark)), tablets

[0235] Server: Cloud server (AWS (registered trademark), Google (registered trademark) Cloud Platform)

[0236] Generative AI: GPT-based models (e.g., GPT-3 (registered trademark), GPT-4 (registered trademark))

[0237] Data analysis algorithm: Python, TENSORFLOW (registered trademark)

[0238] Process Overview

[0239] Data collection

[0240] When a customer visits a physical store, their health data, such as heart rate, blood pressure, weight, body fat percentage, and sleep patterns, is collected from their health monitoring device, such as a smartwatch, and this data is sent to their smartphone via Bluetooth or Wi-Fi, and then sent to a cloud server.

[0241] Data reception and analysis

[0242] The server stores and analyzes the received data, and algorithms evaluate health indicators such as heart rate and weight fluctuations to calculate stress levels and sleep quality.

[0243] Generating personalized learning materials

[0244] Based on the analyzed health indicators, generative AI generates optimal learning materials for each user, customized to their cognitive abilities, past learning history, and interests.

[0245] Creating a study plan

[0246] Based on the generated learning materials, the server creates a learning plan based on the user's health condition and learning goals, including specific tasks and schedules.

[0247] Presenting and implementing learning tasks

[0248] The device presents the user with daily tasks based on the learning plan, and as the learning progresses, the device receives feedback from the user and sends progress data to a server.

[0249] Learning progress management and feedback

[0250] The server analyzes progress data and awards badges and points according to achievement, which helps maintain user motivation and encourages continued learning.

[0251] Promoting collaborative learning

[0252] To promote collaborative learning with other users, the server generates information about collaborative tasks and challenges and sends it to the device, which displays the information and provides the user with an opportunity to collaborate with other users to solve the tasks.

[0253] Specific examples

[0254] Case of User A (70-year-old woman)

[0255] Data collection: User A visits the store and collects data from their smartwatch. The smartphone then sends this data to the server.

[0256] Data analysis: The server analyzes User A's data and detects that his stress level is high and his sleep quality is poor.

[0257] Personalized learning material generation: Generative AI generates learning materials for relaxation sessions and kanji quizzes.

[0258] Creating a study plan: The server creates a study plan for User A that includes two relaxation sessions per week and daily kanji quizzes.

[0259] Completing learning tasks: The device presents learning tasks to User A and awards points each time they are completed.

[0260] Feedback and progress management: The server analyzes your progress data and awards badges to motivate you.

[0261] Promoting collaborative learning: Collaborative learning with other users is provided, and User A solves problems collaboratively.

[0262] Example prompts to input to the generative AI model

[0263] User A's health data:

[0264] Heart rate: 75 bpm, sleep time: 5 hours

[0265] Prompt the generative AI model:

[0266] This user has high stress levels and poor sleep quality. Generate learning materials to improve this user's cognitive function and maintain their health. Suggest easy kanji quizzes and relaxation sessions based on their past learning history.

[0267] This system allows seniors to maintain their health and improve their cognitive function while having fun in a physical store.

[0268] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0269] Step 1:

[0270] Data collection

[0271] When a user visits a physical store, health management devices (smartwatches, blood pressure monitors, body composition scales, etc.) collect health data such as heart rate, blood pressure, weight, body fat percentage, sleep patterns, etc. This data is sent to a smartphone via Bluetooth or Wi-Fi, and from there to a cloud server.

[0272] Input: Health data obtained from health monitoring devices.

[0273] Output: Health data sent to your smartphone.

[0274] Step 2:

[0275] Data reception and analysis

[0276] The server stores the health data received from the smartphone and uses analytical algorithms to evaluate health indicators such as heart rate and weight fluctuations, and calculate health conditions such as stress levels and sleep quality.

[0277] Input: Health data sent from your smartphone.

[0278] Output: Analyzed health indicators and health status data.

[0279] Step 3:

[0280] Generating personalized learning materials

[0281] Based on the analyzed data, the generative AI generates learning materials that are best suited to the user, such as relaxation sessions if stress levels are high, or kanji quizzes to maintain cognitive function.

[0282] Input: Analyzed health indicators and health status data.

[0283] Output: Personalized learning materials.

[0284] Step 4:

[0285] Creating a study plan

[0286] Based on the generated learning materials, the server creates a learning plan based on the user's health condition and learning goals, including specific tasks and schedules.

[0287] Input: Personalized learning materials and health status data.

[0288] Output: A personalized study plan.

[0289] Step 5:

[0290] Presenting and implementing learning tasks

[0291] The device presents the user with daily tasks based on the learning plan. As the user completes the tasks, the device receives feedback and sends progress data to the server.

[0292] Enter: personalized study plans.

[0293] Output: User's progress on the learning task.

[0294] Step 6:

[0295] Learning progress management and feedback

[0296] The server analyzes the progress data and awards badges and points according to the level of achievement, thereby maintaining the user's motivation.

[0297] Input: User's progress data on learning tasks.

[0298] Output: Parsed progress data and rewards (badges, points).

[0299] Step 7:

[0300] Promoting collaborative learning

[0301] To promote collaborative learning with other users, the server generates information about collaborative tasks and challenges and sends it to the device, which displays the information and provides the user with an opportunity to collaborate with other users to solve the tasks.

[0302] Input: System-wide training data and collaborative learning candidates.

[0303] Output: Information on collaborative tasks and notifications for collaborative learning.

[0304] 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.

[0305] This invention is a system for preventing cognitive decline and the progression of aging symptoms in the elderly while maintaining health in an enjoyable way. This system works in conjunction with a health management device to generate learning materials optimized for each user and provide a learning environment incorporating collaborative learning with other users and gamification elements. Furthermore, by combining it with an emotion engine that detects the user's emotional state, it aims to maintain the user's motivation and optimize their learning plan.

[0306] System Overview

[0307] The system consists of the following main components:

[0308] 1. Health management device

[0309] 2. Terminal

[0310] 3. Server

[0311] 4. Generation AI

[0312] 5. Study Plan

[0313] 6. Gamification

[0314] 7. Cooperative Learning

[0315] 8. Emotion Engine

[0316] Overview of program processing

[0317] 1. Data Collection

[0318] Terminal

[0319] A health management device worn by the user (e.g., a smartwatch) periodically measures health data such as heart rate and sleep patterns. In addition, an emotion engine analyzes the user's facial expressions and voice data to collect emotional data. The measurement data and emotional data are stored on the device.

[0320] 2. Data Receipt and Analysis

[0321] server

[0322] The data sent from the device is received and stored in a database. The stored data is identified for each user and prepared for later analysis. The analysis algorithm evaluates the user's health and emotional state and calculates stress levels, sleep quality, and emotional indicators. These calculation results are updated in the user profile.

[0323] 3. Generating personalized learning materials

[0324] server

[0325] Based on the analysis results, the generative AI generates learning materials optimized for each user, taking into account health data, emotional data, user interests, cognitive ability, and past learning history.

[0326] 4. Create a study plan

[0327] server

[0328] Based on the generated learning materials, a learning plan is created based on the user's health and emotional state, as well as their learning goals, including specific tasks, implementation schedules, and emotional reminders.

[0329] 5. Presentation and implementation of learning tasks

[0330] Terminal

[0331] The system presents daily learning tasks to users according to a learning plan sent from the server. The tasks are displayed and the user can work on them. Based on the user's emotional state, positive feedback and motivational messages are displayed at appropriate times.

[0332] 6. Learning progress management and feedback

[0333] server

[0334] The system analyzes progress and emotional data sent by users to evaluate their achievements, generates rewards such as badges and points based on their achievements, and motivates users. The emotional engine also provides appropriate feedback based on the user's current emotional state.

[0335] 7. Promoting collaborative learning

[0336] Servers and Terminals

[0337] To promote collaborative learning with other users, the server generates information about joint tasks and challenges and sends it to the device. The device displays this information, providing users with opportunities to collaborate with others to solve the tasks. Furthermore, the system optimizes collaborative learning by taking into account emotional data, promoting smooth communication.

[0338] Specific examples

[0339] Case of User A (65-year-old male)

[0340] Terminal

[0341] User A's smartwatch measures his / her heart rate and sleep patterns, while the device's camera and microphone analyze his / her facial expressions and voice to collect emotional data, which is then sent to a server every morning.

[0342] server

[0343] The server analyzes the received data and detects that User A's stress level is increasing, his sleep quality is declining, and his recent emotional state is unstable. Based on this, the generative AI generates relaxation sessions and puzzle learning materials.

[0344] server

[0345] The server creates a study plan based on user A's health and emotional state, including three relaxation sessions per week and daily puzzle practice.

[0346] Terminal

[0347] The device presents learning tasks to User A and awards a badge each time the task is completed. It also takes into account User A's emotional data and displays appropriate feedback and motivational messages. Furthermore, it provides opportunities for collaborative learning with other users, allowing User A to solve tasks together and receive emotional support.

[0348] Server and Device

[0349] It continuously collects data and optimizes the learning plan according to the health and emotional state of User A. It also improves the entire system based on user feedback.

[0350] The system allows users to have fun while maintaining their health and taking care of both their cognitive and emotional functions.

[0351] The processing flow will be explained below.

[0352] Step 1:

[0353] Terminal

[0354] A health management device worn by a user (e.g., a smartwatch) periodically measures health data such as heart rate and sleep patterns. In addition, the device's camera and microphone analyze the user's facial expressions and voice to collect emotional data. The collected health and emotional data is temporarily stored on the device.

[0355] Step 2:

[0356] Terminal

[0357] The collected health and emotion data is automatically transmitted to the server at regular intervals, and if the transmission is successful, the device proceeds to the next data collection cycle.

[0358] Step 3:

[0359] server

[0360] The data sent from the device is received and stored in a database, which identifies the user and prepares it for later analysis.

[0361] Step 4:

[0362] server

[0363] The received data is analyzed. Analysis algorithms are used to assess the user's health status (e.g., stress level, sleep quality) and emotional state (e.g., stress, happiness). The assessment results are updated in the user profile.

[0364] Step 5:

[0365] server

[0366] Based on the analysis results, the generative AI generates learning materials optimized for each user, taking into account health data, emotional data, user interests, cognitive ability, and past learning history.

[0367] Step 6:

[0368] server

[0369] Based on the generated learning materials, a study plan is created, which includes specific study tasks, implementation schedules, and emotional state-based reminders.

[0370] Step 7:

[0371] Terminal

[0372] The system presents daily learning tasks to users according to a learning plan sent from the server. The tasks are displayed and the user can work on them. Based on the user's emotional state, positive feedback and motivational messages are displayed at appropriate times.

[0373] Step 8:

[0374] User

[0375] Users work on the presented learning tasks, and when they complete the tasks, the device records their progress and sends it to the server.

[0376] Step 9:

[0377] server

[0378] The system analyzes progress and emotional data sent by users to evaluate their achievements, generates rewards such as badges and points based on their achievements, and motivates users. The emotional engine also provides appropriate feedback based on the user's current emotional state.

[0379] Step 10:

[0380] server

[0381] To provide opportunities for collaborative learning with other users, collaborative tasks and challenges are generated and sent to devices, encouraging users to learn collaboratively with others.

[0382] Step 11:

[0383] Terminal

[0384] Users will be notified of collaborative learning and challenges, allowing them to deepen their learning by solving collaborative challenges together with other users.

[0385] Step 12:

[0386] Server and Device

[0387] The system continuously collects and analyzes data to optimize learning plans and materials based on the user's health, emotional state, and learning progress, and also improves the system as a whole based on user feedback.

[0388] This series of steps allows users to have fun while maintaining their health and taking care of their cognitive and emotional well-being.

[0389] Example 2

[0390] 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."

[0391] Maintaining health while having fun while preventing cognitive decline and the progression of aging symptoms in the elderly is a social challenge. However, conventional health management and learning systems have difficulty providing personalized services that adapt to the health and emotional state of each individual user, and have not adequately maintained user motivation or provided emotional care. This has led to problems such as insufficient learning effects and health maintenance.

[0392] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0393] In this invention, the server includes means for collecting data from a device that collects physiological data, information processing means for saving and analyzing the collected data, artificial intelligence means for generating personalized learning materials based on the analyzed data, means for creating an individualized learning plan based on the generated learning materials, terminal means for presenting learning tasks to the user based on the learning plan, means including game elements for tracking the user's progress and maintaining motivation, means for promoting collaborative learning with other users, and engine means for detecting and analyzing the user's emotional state. This makes it possible to provide personalized learning materials and learning plans adapted to the user's health and emotional state, maintaining health while having fun, and caring for both cognitive function and emotions.

[0394] "Physiological data" refers to data about a user's biological body, such as their heart rate, sleep patterns, and activity levels.

[0395] "Device" refers to any health management equipment or terminal worn or used by a user.

[0396] "Information processing device" refers to a computer system for storing and analyzing collected data.

[0397] "Artificial intelligence" refers to algorithms and programs that analyze data and generate learning materials optimized for users.

[0398] "Individualized Learning Plan" refers to a plan that defines specific learning content and schedules based on the user's individual health and emotional state.

[0399] "Device" refers to an electronic device through which a user receives and completes learning assignments.

[0400] "Gamification" refers to reward systems such as badges and points used to motivate users.

[0401] "Collaborative learning" refers to a learning method in which multiple users work together to solve a learning task.

[0402] An "engine" refers to a program or hardware that detects and analyzes a user's emotional state.

[0403] This invention is a system for preventing cognitive decline and the progression of aging symptoms in the elderly while maintaining health in an enjoyable way. This system works in conjunction with a health management device to generate learning materials optimized for each user and provide a learning environment incorporating collaborative learning with other users and gamification elements. Furthermore, it combines an emotion engine that detects the user's emotional state to maintain the user's motivation and optimize the learning plan.

[0404] System configuration

[0405] This system is broadly composed of the following major hardware and software components:

[0406] Health management devices (e.g., smart watches)

[0407] Device (e.g. smartphone, tablet)

[0408] Server (cloud server)

[0409] Generation AI

[0410] Emotion Engine

[0411] Data collection

[0412] Terminal

[0413] The health management device worn by the user periodically collects physiological data such as heart rate and sleep patterns. Furthermore, the emotion engine analyzes the user's facial expressions and voice data to collect emotional data. This data is temporarily stored on the device.

[0414] Data reception and analysis

[0415] server

[0416] The device sends the collected data to a server, which stores it in a secure database. The stored data is uniquely identified for each user. Processing algorithms are used to analyze the data and calculate the user's health status (heart rate, sleep quality, etc.) and emotional state (happiness, sadness, etc.). This ensures that the user's health and emotional profile is always up to date.

[0417] Generating personalized learning materials

[0418] server

[0419] Based on the analysis results, the generative AI generates learning materials optimized for the user. Specifically, it generates learning materials based on the following information:

[0420] Health data (e.g., heart rate 85, poor sleep quality)

[0421] Emotional data (e.g., recent unstable emotional state)

[0422] User interests (e.g. puzzles, relaxation)

[0423] Cognitive ability

[0424] Past learning history (e.g., three sessions per week are valid)

[0425] Creating a study plan

[0426] server

[0427] Based on the generated learning materials, a learning plan is created based on the user's health and emotional state, including:

[0428] Specific learning tasks (e.g., relaxation sessions, puzzle learning)

[0429] Implementation Schedule

[0430] Reminders based on your emotional state (e.g., "Start relaxing now")

[0431] Presenting and implementing learning tasks

[0432] Terminal

[0433] According to the learning plan, the device notifies and displays daily learning tasks to the user, allowing them to immediately tackle the tasks. In addition, the device evaluates learning progress and emotional state in real time, providing positive feedback and motivational messages at appropriate times.

[0434] Learning progress management and feedback

[0435] server

[0436] The system analyzes progress and emotional data submitted by users to evaluate their learning achievement. Based on the evaluation, rewards such as badges and points are generated and notified to the user. In addition, the emotional engine provides appropriate feedback to maintain motivation for the next learning task.

[0437] Promoting collaborative learning

[0438] Servers and Terminals

[0439] The server generates information on collaborative tasks and challenges to promote collaborative learning with other users and sends it to the device, providing opportunities for users to solve tasks in collaboration with other users and providing appropriate feedback that takes into account emotional data.

[0440] Specific examples

[0441] For example, if a 65-year-old male user wears a smartwatch, the device will collect his heart rate and sleep patterns, and will also analyze his facial expressions and voice using a camera and microphone, and this data will be sent to a server in the morning.

[0442] The server analyzes the received data and detects increases in stress levels and decreases in sleep quality. Based on this, the generative AI generates relaxation sessions and puzzle learning materials. The server then creates an appropriate learning plan, and the device periodically presents learning tasks to the user.

[0443] Specific examples of prompts include:

[0444] Develop learning materials for cognitive maintenance and health management in older adults. Consider the following user information:

[0445] Gender: Male

[0446] Age: 65

[0447] Health data: Heart rate 85, poor sleep quality, high stress level

[0448] Emotional data: Recent unstable emotional state

[0449] Interests: Puzzles, relaxation

[0450] Past learning history: 3 sessions per week are valid

[0451] Generate personalized puzzle learning and relaxation session materials for your users.

[0452] The present invention allows users to have fun while maintaining their health and taking care of both their cognitive and emotional well-being.

[0453] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0454] Step 1: Data collection

[0455] Terminal

[0456] Input: Health monitoring device worn by the user (e.g., smartwatch) and its built-in camera and microphone.

[0457] How it works: The health management device periodically measures physiological data such as heart rate and sleep patterns. Using a camera and microphone, the emotion engine also analyzes the user's facial expressions and voice in real time to collect emotional data.

[0458] Data processing: The collected physiological data is temporarily stored on the device, and the emotion engine converts facial expressions and voice data into emotion indicators.

[0459] Output: Temporarily stored physiological and emotional indicator data.

[0460] Step 2: Receiving and analyzing data

[0461] server

[0462] Input: User physiological and emotional data sent from the device.

[0463] How it works: A server receives the data and stores it in a secure database. Processing algorithms analyze the data for each user and assess their health status, including stress levels, sleep quality, and emotional indicators.

[0464] Data calculation: Analyzes incoming data to calculate health indicators such as stress levels and sleep quality, and converts emotional data into an emotional profile.

[0465] Output: The updated user profile.

[0466] Step 3: Generate personalized learning materials

[0467] server

[0468] Input: Analyzed user profile (health indicators, emotional profile, user interests, cognitive abilities, and past learning history).

[0469] How it works: Generative AI generates optimal learning materials based on the user's health and emotional data.

[0470] Data processing: The generative AI model creates appropriate learning materials based on the prompt. For example, if the user is stressed, it will generate relaxation sessions or puzzle learning materials.

[0471] Output: Personalized learning materials.

[0472] Step 4: Create a study plan

[0473] server

[0474] Input: personalized learning materials and user profile.

[0475] How it works: The server creates a study plan based on the user's health and emotional state. This study plan includes specific tasks, a schedule, and reminders.

[0476] Data calculation: Based on the content of the study materials, specific tasks and schedules, such as three relaxation sessions per week or daily puzzle study, are calculated and reminders are set.

[0477] Output: Learning plan.

[0478] Step 5: Present and implement the learning task

[0479] Terminal

[0480] Input: lesson plan.

[0481] How it works: The device follows a study plan and displays daily learning tasks to the user. As the user works on the tasks, the device monitors their emotional state in real time and displays positive feedback and motivational messages at appropriate times.

[0482] Data processing: Learning progress and emotional data are collected in real time and temporarily stored on the device.

[0483] Output: The training data and the latest emotion data.

[0484] Step 6: Progress monitoring and feedback

[0485] server

[0486] Input: Progress and emotion data sent from the device.

[0487] How it works: The server analyzes progress data and emotion data to evaluate learning achievement. It generates rewards such as badges and points according to achievement and notifies the user. The emotion engine also provides appropriate feedback to maintain motivation for the next learning task.

[0488] Data computation: Applying algorithms to evaluate learning achievement and generate rewards.

[0489] Output: Feedback, badges, points, and other rewards.

[0490] Step 7: Facilitating collaborative learning

[0491] Servers and Terminals

[0492] Input: User profile, collaborative learning data with other users.

[0493] Operation: The server generates information about collaborative tasks and challenges to promote collaborative learning with other users and sends it to the device. The device notifies the user of this information and provides opportunities for collaborative learning.

[0494] Data processing: Optimize the collaborative learning task based on collaborative learning data and participating users' emotional data.

[0495] Output: Detailed information and feedback on collaborative learning.

[0496] In this way, the system of the present invention can provide a personalized learning environment based on the user's health and emotional state.

[0497] (Application example 2)

[0498] 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."

[0499] Elderly people need to maintain their health while having fun, while preventing cognitive decline and the progression of aging symptoms. Furthermore, in order to provide learning materials optimized for each individual user and create an effective learning environment, it is important to properly understand the user's health and emotional state and respond accordingly. However, current systems do not adequately integrate these elements, making it difficult to maintain user motivation and optimize effective learning plans.

[0500] 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.

[0501] In this invention, the server includes means for collecting data from the health management device, means for saving and analyzing the collected data, generation AI means for generating learning materials based on the analyzed data, means for creating a learning plan based on the generated learning materials, terminal means for presenting learning tasks to the user, gamification means for tracking the user's progress and maintaining their motivation, means for promoting collaborative learning with other users, an emotion engine for detecting and evaluating the user's emotional state, and means for maintaining the user's motivation and optimizing the learning plan based on the results of the emotion engine. This makes it possible to provide an individually optimized learning plan based on the user's health and emotional state, allowing the user to maintain their health while having fun and improving their cognitive ability.

[0502] A "health management device" is a device that periodically measures and collects health data such as a user's heart rate and sleep patterns.

[0503] The "server" is a system that contains a database and analytical algorithms for storing and analyzing collected data and assessing the user's health and emotional state.

[0504] "Generative AI" refers to artificial intelligence that generates learning materials optimized for users based on analyzed data.

[0505] "Terminal" refers to a device that presents learning tasks to users and tracks their progress, such as a smartphone or tablet.

[0506] "Gamification" is a technique that incorporates game elements to maintain user motivation and provides rewards such as badges and points.

[0507] "Collaborative learning" is a learning method in which users cooperate with other users to solve learning problems and promote smooth communication.

[0508] The "emotion engine" is a system that detects and evaluates a user's emotional state by analyzing their facial expressions and voice.

[0509] A "study plan" is a specific study schedule and assignments created based on the generated study materials and in accordance with the user's health and emotional state, as well as their learning goals.

[0510] The system of this invention aims to help users maintain their health and improve their cognitive abilities while having fun by effectively monitoring their health and emotional state and providing personalized educational materials based on that information. The main components of this system include:

[0511] 1. Health management device

[0512] A health management device is a device that measures a user's health data, such as heart rate and sleep patterns. A smartwatch is used as an example. The user wears this device all the time to collect daily health data.

[0513] 2. Terminal

[0514] The terminal is a device that presents learning tasks to users and tracks their progress, such as a smartphone or tablet. The terminal also receives data from the health management device and sends it to the server.

[0515] 3. Server

[0516] The server is a system that receives data sent from the terminal, stores it in a database, and analyzes it. The server performs the following processes:

[0517] Health and emotional data analysis

[0518] Update user profiles based on analysis results

[0519] Generating personalized learning materials using generative AI

[0520] Create and manage your study plan

[0521] 4. Generation AI

[0522] Generative AI is an artificial intelligence that generates optimized learning materials based on a user's health and emotional data, interests, cognitive ability, and past learning history. Generative AI uses deep learning algorithms to generate highly accurate learning materials.

[0523] 5. Gamification

[0524] Gamification is a technique that incorporates game elements to keep users motivated and provides rewards such as badges and points.

[0525] 6. Cooperative Learning

[0526] Collaborative learning is a learning method that allows users to cooperate with other users to solve learning problems and promote smooth communication. Through joint tasks and challenges, users support each other emotionally and intellectually.

[0527] 7. Emotion Engine

[0528] The emotion engine is a system that detects and evaluates the user's emotional state by analyzing their facial expressions and voice, which can determine the user's motivation and the optimal timing for reminders.

[0529] Examples:

[0530] Case of User A (65-year-old male)

[0531] health management device

[0532] User A wears a smartwatch to measure his / her heart rate and sleep patterns, and the data from the smartwatch is automatically transferred to the device every day.

[0533] Terminal

[0534] User A's smartphone receives data from the health management device and transmits it to the server. It also presents User A with a study plan and study tasks from the server.

[0535] server

[0536] The server analyzes the data sent from the device and detects that User A's stress level is increasing, his sleep quality is declining, and his recent emotional state is unstable. Based on this, the generative AI generates relaxation sessions and puzzle learning materials.

[0537] Generation AI

[0538] The generative AI generates optimized relaxation sessions and puzzle learning materials based on user A's health and emotional state.

[0539] Learning Plan

[0540] The server creates a study plan that includes three relaxation sessions per week and daily puzzles.

[0541] Gamification

[0542] A badge is awarded to User A every time he or she completes a task, helping the user to stay motivated and progress in their studies.

[0543] cooperative learning

[0544] The server provides opportunities for collaborative learning with other users, allowing user A to jointly solve problems and receive emotional support.

[0545] Emotion Engine

[0546] The emotion engine detects user A's emotional state and provides feedback and messages at the right time to increase motivation.

[0547] These elements work together to enable users to maintain their health and improve their cognitive function while having fun. A specific generative AI model could be a deep learning model such as GPT-3. An example of a prompt would be, "Generate relaxation session materials based on heart rate and sleep data."

[0548] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0549] Step 1:

[0550] The terminal collects and stores data from the health management device.

[0551] Input: Health data such as the user's heart rate and sleep patterns sent from health monitoring devices.

[0552] Output: The acquired health data is saved on the device.

[0553] The terminal periodically acquires data from the health management device and stores it in its internal memory, which is used in subsequent processing steps.

[0554] Step 2:

[0555] The device uses an emotion engine to collect user emotion data.

[0556] Input: Facial expression data captured by the device's camera, voice data captured by the microphone.

[0557] Output: The analyzed emotion data is saved on the device.

[0558] The device uses a camera and microphone to collect the user's facial expressions and voice, which are then analyzed by an emotion engine, resulting in an emotional index such as positive or negative.

[0559] Step 3:

[0560] The device transmits the collected health and emotion data to a server.

[0561] Input: Health data and emotional data stored on the device.

[0562] Output: The transmitted data is saved on the server.

[0563] The device periodically uploads this data to the server, which stores the received data in a database.

[0564] Step 4:

[0565] The server analyzes the received data and assesses the user's health and emotional state.

[0566] Input: Health data and emotion data stored on the server.

[0567] Output: Evaluated health status indicators, emotional status indicators.

[0568] The server uses analytical algorithms to calculate stress levels, sleep quality, and emotional indicators, and updates them into the user profile.

[0569] Step 5:

[0570] The server uses generative AI to generate personalized teaching materials based on the analysis results.

[0571] Input: Assessed health status indicators, emotional state indicators, user interests and past learning history.

[0572] Output: Optimized learning materials.

[0573] Based on the evaluation results and the user's profile information, the generative AI generates learning materials tailored to the user, such as relaxation sessions or puzzle learning materials.

[0574] Step 6:

[0575] The server creates a learning plan based on the generated learning materials.

[0576] Input: Optimized learning materials, assessed health status indicators and emotional state indicators.

[0577] Output: Personalized study plan.

[0578] The server creates a learning plan based on the learning materials and the user's status, including specific learning tasks and their implementation schedule.

[0579] Step 7:

[0580] The device presents learning tasks to the user.

[0581] Input: The lesson plan sent from the server.

[0582] Output: The learning task displayed on the screen.

[0583] The device presents users with tasks at specific times each day according to their learning plan, and when they complete the tasks, they are awarded badges and points.

[0584] Step 8:

[0585] The device tracks the user's progress and feeds the data back to the server.

[0586] Input: User's learning task completion status, emotional data.

[0587] Output: Progress and emotion data sent to the server.

[0588] Each time the user completes a task, the device collects progress data and emotional data at that time and sends it to the server.

[0589] Step 9:

[0590] The server provides gamification elements based on the feedback.

[0591] Input: Progress and emotion data sent from the device.

[0592] Output: Rewards (badges, points) to the user, feedback message.

[0593] The server rewards the user according to their progress, generates feedback messages based on their emotional state at appropriate times, and sends them to the device.

[0594] Step 10:

[0595] The server generates collaborative tasks to promote collaborative learning with other users and provides them through the terminals.

[0596] Input: Data of multiple users, collaborative assignment template.

[0597] Output: Collaborative learning tasks, contact information between users.

[0598] The server analyzes data from multiple users, generates tasks optimized for effective collaborative learning, and provides them to users via their devices.

[0599] Through these steps, users can maintain their health and improve their cognitive function while having fun. The system utilizes a deep learning model, known as a "generative AI model," to provide an optimal learning experience for each individual user. For example, it can generate specific learning materials using a prompt such as, "Please generate relaxation session materials based on heart rate and sleep data."

[0600] 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.

[0601] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0602] 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.

[0603] [Second embodiment]

[0604] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0605] 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.

[0606] 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).

[0607] 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.

[0608] 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.

[0609] 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).

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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."

[0616] This invention is a system for preventing cognitive decline and the progression of aging symptoms in the elderly while maintaining health in an enjoyable manner. This system works in conjunction with a health management device, generates learning materials optimized for each user, and provides a learning environment that incorporates collaborative learning with other users and gamification elements.

[0617] System Overview

[0618] The system consists of the following main components:

[0619] 1. Health management device

[0620] 2. Terminal

[0621] 3. Server

[0622] 4. Generation AI

[0623] 5. Study Plan

[0624] 6. Gamification

[0625] 7. Cooperative Learning

[0626] Overview of program processing

[0627] 1. Data Collection

[0628] Terminal

[0629] Health data such as heart rate and sleep patterns are periodically collected from a health management device worn by the user (e.g., a smartwatch). The data collected by the device is automatically sent to a server.

[0630] 2. Data Receipt and Analysis

[0631] server

[0632] The server stores and analyzes the data received from the device, and an analysis algorithm evaluates the user's health status and calculates health indicators such as stress level and sleep quality.

[0633] 3. Generating personalized learning materials

[0634] server

[0635] The server-based AI generates optimal learning materials based on the analyzed health indicators, and the materials are customized to the user's cognitive ability, past learning history, and interests.

[0636] 4. Create a study plan

[0637] server

[0638] Based on the generated learning materials, a learning plan is created based on the user's health condition and learning goals, including specific tasks and schedules.

[0639] 5. Presentation and implementation of learning tasks

[0640] Terminal

[0641] The device presents the user with daily tasks based on the learning plan, and as the learning progresses, the device receives feedback from the user and sends progress data to a server.

[0642] 6. Learning progress management and feedback

[0643] server

[0644] The server analyzes the progress data and awards badges and points according to the level of achievement, which helps to maintain the user's motivation and encourages them to continue learning.

[0645] 7. Promoting collaborative learning

[0646] Servers and Terminals

[0647] To promote collaborative learning with other users, the server generates information about collaborative tasks and challenges and sends it to the device, which displays the information and provides the user with an opportunity to collaborate with other users to solve the tasks.

[0648] Specific examples

[0649] Case of User A (65-year-old male)

[0650] Terminal

[0651] User A's smartwatch measures his / her heart rate and sleep patterns, and the device sends this data to a server every morning.

[0652] server

[0653] The server analyzes the received data and detects that User A's stress level is increasing and the quality of his or her sleep is declining. Based on this, the generative AI generates relaxation sessions and puzzle learning materials.

[0654] server

[0655] The server creates a study plan based on user A's health status, including three relaxation sessions per week and daily puzzle practice.

[0656] Terminal

[0657] The device presents learning tasks to User A and awards a badge each time it is completed. It also provides opportunities for collaborative learning with other users, allowing User A to solve the tasks together.

[0658] Server and Device

[0659] Data is continuously collected and the learning plan is optimized according to the health condition of User A. The system is also continuously improved based on feedback.

[0660] This system allows users to have fun while maintaining their health and improving their cognitive function.

[0661] The processing flow will be explained below.

[0662] Step 1:

[0663] Terminal

[0664] A health monitoring device worn by a user (e.g., a smartwatch) periodically measures health data such as heart rate and sleep patterns, and the measurement data is stored on the device.

[0665] Step 2:

[0666] Terminal

[0667] The stored health data is automatically sent to the server at regular intervals, and if the transmission is successful, the device proceeds to the next data collection cycle.

[0668] Step 3:

[0669] server

[0670] The data sent from the device is received and stored in a database, which identifies each user and prepares it for later analysis.

[0671] Step 4:

[0672] server

[0673] The received data is analyzed. An analytical algorithm is used to assess the user's health and calculate health indicators such as stress level and sleep quality. The results are updated in the user profile.

[0674] Step 5:

[0675] server

[0676] Based on the analysis results, the generative AI generates learning materials optimized for each user, taking into account the user's interests, cognitive ability, and past learning history.

[0677] Step 6:

[0678] server

[0679] Based on the generated learning materials, a learning plan is created, which includes specific learning tasks, implementation schedules, reminders, etc.

[0680] Step 7:

[0681] Terminal

[0682] According to the study plan sent from the server, daily study tasks are presented to the user. The tasks are displayed and the user can work on them.

[0683] Step 8:

[0684] User

[0685] Users work on the presented learning tasks, and when they complete the tasks, the device records their progress and sends it to the server.

[0686] Step 9:

[0687] server

[0688] The system analyzes the progress data sent by users, evaluates their achievement, and generates rewards such as badges and points according to their achievement, thereby increasing user motivation.

[0689] Step 10:

[0690] server

[0691] To provide opportunities for collaborative learning with other users, collaborative tasks and challenges are generated and sent to devices, encouraging users to learn collaboratively with others.

[0692] Step 11:

[0693] Terminal

[0694] Users will be notified of collaborative learning and challenges, allowing them to deepen their learning by solving collaborative challenges together with other users.

[0695] Step 12:

[0696] Servers and Terminals

[0697] The system continuously collects and analyzes data to optimize learning plans and materials based on the user's health condition and learning progress, and improves the system as a whole based on user feedback.

[0698] This series of steps allows seniors to have fun while maintaining their health and improving cognitive function.

[0699] Example 1

[0700] 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."

[0701] There is a need for systems that allow elderly people to maintain their health while having fun, while preventing cognitive decline and the progression of aging symptoms. However, conventional systems have difficulty in providing personalized responses based on each user's health condition, and lack mechanisms to maintain motivation or promote collaborative learning with other users. This often makes it difficult for users to continue managing their health and learning.

[0702] 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.

[0703] In this invention, the server includes a means for storing and analyzing data collected from the health management device, a means for calculating the user's health index using an analysis algorithm, and a generation AI means for generating learning materials based on the analyzed health index. This makes it possible to provide personalized learning materials according to the user's health condition, maintain motivation, and promote cooperative learning with other users.

[0704] A "health management device" is a device that monitors a user's health status and collects data such as heart rate and sleep patterns.

[0705] "Means for collecting data" refers to a method for obtaining health data such as heart rate and sleep patterns from health management devices.

[0706] The "means for transmitting data to the server" refers to a communication means for transferring collected health data to the server.

[0707] A "server" is a computer system that receives, stores, and analyzes collected data.

[0708] An "analysis algorithm" is a calculation method for analyzing collected health data and calculating the user's health indicators.

[0709] "Generative AI" is an artificial intelligence technology that generates optimal learning materials for users based on analyzed health indicators.

[0710] A "study plan" is a specific study schedule based on the generated study materials and adapted to the user's health condition and study goals.

[0711] A "terminal" is a device that presents learning tasks to a user and accepts operations.

[0712] "Progress data" is data that indicates the results of a user's learning tasks, and is sent from the terminal to the server.

[0713] "Gamification" is a reward system that uses badges and points to maintain user motivation and encourage continued learning.

[0714] "Collaborative learning" is a method in which multiple users work together to complete a learning task and achieve results.

[0715] The present invention is a system for preventing cognitive decline and the progression of aging symptoms in the elderly while maintaining health in an enjoyable manner. This system works in conjunction with a health management device, generates learning materials optimized for each user, and provides a learning environment that incorporates collaborative learning with other users and gamification elements. Specific embodiments of the system are described below.

[0716] The system consists of the following main components: health management device, terminal, server, generative AI, learning plan, gamification, and collaborative learning.

[0717] A user's health monitoring device (e.g., a smartwatch) periodically measures health data such as heart rate and sleep patterns. This data is automatically transmitted by the device to a server. The device transmits the data using a secure communication protocol (e.g., HTTPS) without requiring any user interaction.

[0718] The server stores the received health data in a database and uses an analytical algorithm to calculate health indicators, including stress levels and sleep quality. Based on the analysis results, a generative AI on the server generates learning materials optimal for the user. The materials are customized taking into account the user's cognitive ability, past learning history, and interests.

[0719] A study plan is created based on the generated learning materials. For example, a specific schedule may be set up, including three relaxation sessions per week and daily puzzle practice. This plan is saved on the server and presented to the user via their device.

[0720] The device notifies the user of daily learning tasks. For example, a notification such as "Please complete a 20-minute relaxation session today" is displayed in the morning. As the user completes the task, progress data is sent from the device to the server.

[0721] The server analyzes progress data and has a gamification function that automatically awards badges and points according to achievement, thereby maintaining user motivation. Progress reports are also generated periodically and displayed on the device.

[0722] To promote collaborative learning with other users, the server generates collaborative learning tasks and challenges and sends them to the device. For example, a task might be displayed such as, "Form a team of three people and take part in a one-week walking challenge." Users collaborate with other users to complete the task, and the device reports the results to the server.

[0723] As a specific example, consider a 65-year-old male user A. His smartwatch measures his heart rate and sleep patterns and sends the data to a server daily. The server analyzes the data and detects rising stress levels and declining sleep quality. A generative AI generates relaxation session and puzzle learning materials, and a learning plan is set up with three relaxation sessions per week and daily puzzle learning. Every morning, the device notifies user A of the learning tasks and awards a badge each time one is completed. Health data is continuously collected, and the learning plan is optimized according to user A's latest health condition.

[0724] Here are some example prompts that can be input to a generative AI model:

[0725] "Generate a study plan for User A, a 65-year-old male, who is in a state of high stress and low sleep quality, combining relaxation sessions and puzzle learning three times a week."

[0726] This system allows users to have fun while maintaining their health and improving their cognitive function.

[0727] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0728] System program processing steps

[0729] Step 1: Collecting health data

[0730] Terminal

[0731] The device periodically collects health data such as heart rate and sleep patterns from the user's health management device (e.g., smartwatch).

[0732] Input: Heart rate and sleep pattern data from smartwatch

[0733] Output: Collected health data

[0734] How it works: The device collects data from the smartwatch every hour and temporarily stores the collected data in its internal memory. For example, data points such as a heart rate of 70 BPM and 7 hours of sleep are collected.

[0735] Step 2: Sending data

[0736] Terminal

[0737] The device transmits the collected health data to a server in real time.

[0738] Input: Collected health data

[0739] Output: Data sent to the server

[0740] How it works: The device sends encrypted data using a secure communication protocol (e.g., HTTPS). No user interaction is required; everything happens automatically.

[0741] Step 3: Receiving and storing data

[0742] server

[0743] The server receives the health data sent from the device and stores it in a database.

[0744] Input: Health data sent from the device

[0745] Output: Data stored in the database

[0746] What it does: The server validates the data it receives and stores it in a database based on each user's unique identifier. It then merges it with past data to maintain a continuous record.

[0747] Step 4: Analyze the data

[0748] server

[0749] The server analyzes the stored health data using an analytical algorithm and calculates the user's health index.

[0750] Input: Stored health data

[0751] Output: Calculated health indicators (e.g., stress level, sleep quality)

[0752] How it works: The server uses data analysis software to calculate stress levels and sleep quality based on heart rate, sleep patterns, etc. For example, an increase in heart rate is considered to be an increase in stress.

[0753] Step 5: Generate personalized learning materials

[0754] server

[0755] The generation AI on the server generates learning materials that are optimal for the user based on the analyzed health indicators.

[0756] Input: Analyzed health indicators

[0757] Output: Personalized learning materials

[0758] How it works: Generative AI uses health indicators, the user's cognitive ability, past learning history, and interests as input data to generate learning materials. For example, if high stress is detected, relaxation sessions and puzzle learning materials will be generated.

[0759] Step 6: Create a study plan

[0760] server

[0761] Based on the generated teaching materials, a learning plan is created based on the user's health condition and learning goals.

[0762] Input: Generated learning materials, user's health status

[0763] Output: A specific study plan (e.g., three relaxation sessions per week)

[0764] What happens: The server uses a scheduling algorithm to plan an optimal schedule for the user, for example, setting relaxation sessions on Mondays, Wednesdays, and Fridays.

[0765] Step 7: Present and implement the learning task

[0766] Terminal

[0767] The device presents the user with daily assignments based on their learning plan.

[0768] Input:Study plan

[0769] Output: Notification to user, data on completed assignments

[0770] Specific operation: The device displays a notification every morning, prompting the user to "take a relaxation session today." The user conducts the session and enters their progress into the device.

[0771] Step 8: Progress monitoring and feedback

[0772] server

[0773] The server analyzes the progress data and awards badges and points based on the level of achievement.

[0774] Input: Progress data from the terminal

[0775] Output: Achievements, badges, points

[0776] What it does: The server updates the reward system based on the progress data, adds badges and points to the user's account, and periodically generates progress reports and sends them to the device.

[0777] Step 9: Facilitating collaborative learning

[0778] Servers and Terminals

[0779] To promote collaborative learning with other users, the server generates information on collaborative tasks and challenges and sends them to the terminals.

[0780] Input: Progress data, collaborative learning settings

[0781] Output: Collaborative tasks, progress by team

[0782] Specific operation: The server generates collaborative learning data and displays on the device, "Let's take part in a one-week walking challenge in teams of three." The user performs the challenge, and the device reports the results to the server.

[0783] Through each step, users can have fun while maintaining their health and improving their cognitive function.

[0784] (Application example 1)

[0785] 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."

[0786] In modern society, health management and maintaining / improving cognitive function for the elderly are important issues. In particular, effective methods are needed to prevent cognitive decline while maintaining health in a fun way for the elderly. It is also important to strengthen social connections and prevent isolation by providing an environment in physical stores where the elderly can learn and train in cooperation with other users. Furthermore, providing personalized health programs and learning materials to the elderly is thought to lead to sustainable health management and maintaining motivation.

[0787] 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.

[0788] In this invention, the server includes means for collecting data from a health management device to monitor the user's health status, means for storing and analyzing the collected data, means for generating learning materials based on the analyzed data, means for creating a learning plan based on the generated learning materials, means for presenting learning tasks to the user based on the learning plan, means for gamification to track the user's progress and maintain motivation, means for promoting collaborative learning with other users, means for analyzing data collected through the health management device and smart devices in the physical store and suggesting optimal learning materials and health programs to the user, and means for providing opportunities to complete learning tasks through collaboration with other users. This enables elderly people to maintain their health, improve their cognitive function, and strengthen their social connections while having fun in the physical store.

[0789] "User" refers to an individual who uses the system to manage their health or undertake learning tasks.

[0790] "Health status" refers to data that indicates the user's physical condition and health, such as heart rate, blood pressure, weight, body fat percentage, and sleep patterns.

[0791] A "health management device" is a device that can collect health data such as heart rate and sleep patterns, and examples include smartwatches and blood pressure monitors.

[0792] "Server" refers to a computer system connected to the Internet that stores and analyzes collected data.

[0793] "Generative AI" refers to artificial intelligence that automatically generates optimal learning materials for users based on collected and analyzed data.

[0794] A "study plan" refers to a plan based on the generated teaching materials that includes specific tasks and schedules tailored to the user's health condition and learning goals.

[0795] A "device" is a device that presents a user with tasks based on a learning plan and receives feedback from the user, including, for example, a smartphone or tablet.

[0796] "Gamification" refers to a method of keeping users motivated by tracking their progress and awarding badges and points based on their achievements.

[0797] "Collaborative learning" refers to learning methods and activities that involve working with other users to solve learning problems.

[0798] A "physical store" refers to a physical store that users can actually visit, where health management equipment and smart devices are installed.

[0799] A "smart device" is an electronic device that allows users to collect health data or check their learning plans, and examples include smartphones and tablets.

[0800] "Learning Materials" refers to various learning content provided to users, including question sets, quizzes, relaxation sessions, etc.

[0801] "Health Program" refers to the specific content and schedule of training, exercises, etc. aimed at improving the user's health.

[0802] "Study assignments" refer to specific tasks or problems that users should tackle based on their study plan.

[0803] This invention is a system for a brick-and-mortar store that supports health management and cognitive function improvement for the elderly. Specific embodiments for realizing this system will be described in detail below.

[0804] System Overview

[0805] The system consists of the following components to monitor users' health status and provide personalized learning materials:

[0806] 1. Health monitoring devices: Devices such as smartwatches collect data such as heart rate, blood pressure, weight, body fat percentage, and sleep patterns.

[0807] 2. Device: A device such as a smartphone or tablet presents learning tasks to users and receives feedback.

[0808] 3. Server: A cloud server for storing and analyzing data.

[0809] 4. Generative AI: Artificial intelligence that generates learning materials based on analyzed data.

[0810] 5. Learning plan: A specific schedule for the user's health management and learning using the generated learning materials.

[0811] 6. Gamification: Awarding badges and points to keep users motivated.

[0812] 7. Collaborative learning: A mechanism that provides opportunities for users to work with others to complete learning tasks.

[0813] Hardware and software used

[0814] Health management devices: smart watches, blood pressure monitors, body composition monitors

[0815] Devices: smartphones (iOS, Android), tablets

[0816] Server: Cloud server (AWS, Google Cloud Platform)

[0817] Generative AI: GPT-based models (e.g., GPT-3, GPT-4)

[0818] Data analysis algorithms: Python, TensorFlow

[0819] Process Overview

[0820] Data collection

[0821] When a customer visits a physical store, their health data, such as heart rate, blood pressure, weight, body fat percentage, and sleep patterns, is collected from their health monitoring device, such as a smartwatch, and this data is sent to their smartphone via Bluetooth or Wi-Fi, and then sent to a cloud server.

[0822] Data reception and analysis

[0823] The server stores and analyzes the received data, and algorithms evaluate health indicators such as heart rate and weight fluctuations to calculate stress levels and sleep quality.

[0824] Generating personalized learning materials

[0825] Based on the analyzed health indicators, generative AI generates optimal learning materials for each user, customized to their cognitive abilities, past learning history, and interests.

[0826] Creating a study plan

[0827] Based on the generated learning materials, the server creates a learning plan based on the user's health condition and learning goals, including specific tasks and schedules.

[0828] Presenting and implementing learning tasks

[0829] The device presents the user with daily tasks based on the learning plan, and as the learning progresses, the device receives feedback from the user and sends progress data to a server.

[0830] Learning progress management and feedback

[0831] The server analyzes progress data and awards badges and points according to achievement, which helps maintain user motivation and encourages continued learning.

[0832] Promoting collaborative learning

[0833] To promote collaborative learning with other users, the server generates information about collaborative tasks and challenges and sends it to the device, which displays the information and provides the user with an opportunity to collaborate with other users to solve the tasks.

[0834] Specific examples

[0835] Case of User A (70-year-old woman)

[0836] Data collection: User A visits the store and collects data from their smartwatch. The smartphone then sends this data to the server.

[0837] Data analysis: The server analyzes User A's data and detects that his stress level is high and his sleep quality is poor.

[0838] Personalized learning material generation: Generative AI generates learning materials for relaxation sessions and kanji quizzes.

[0839] Creating a study plan: The server creates a study plan for User A that includes two relaxation sessions per week and daily kanji quizzes.

[0840] Completing learning tasks: The device presents learning tasks to User A and awards points each time they are completed.

[0841] Feedback and progress management: The server analyzes your progress data and awards badges to motivate you.

[0842] Promoting collaborative learning: Collaborative learning with other users is provided, and User A solves problems collaboratively.

[0843] Example prompts to input to the generative AI model

[0844] User A's health data:

[0845] Heart rate: 75 bpm, sleep time: 5 hours

[0846] Prompt the generative AI model:

[0847] This user has high stress levels and poor sleep quality. Generate learning materials to improve this user's cognitive function and maintain their health. Suggest easy kanji quizzes and relaxation sessions based on their past learning history.

[0848] This system allows seniors to maintain their health and improve their cognitive function while having fun in a physical store.

[0849] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0850] Step 1:

[0851] Data collection

[0852] When a user visits a physical store, health management devices (smartwatches, blood pressure monitors, body composition scales, etc.) collect health data such as heart rate, blood pressure, weight, body fat percentage, sleep patterns, etc. This data is sent to a smartphone via Bluetooth or Wi-Fi, and from there to a cloud server.

[0853] Input: Health data obtained from health monitoring devices.

[0854] Output: Health data sent to your smartphone.

[0855] Step 2:

[0856] Data reception and analysis

[0857] The server stores the health data received from the smartphone and uses analytical algorithms to evaluate health indicators such as heart rate and weight fluctuations, and calculate health conditions such as stress levels and sleep quality.

[0858] Input: Health data sent from your smartphone.

[0859] Output: Analyzed health indicators and health status data.

[0860] Step 3:

[0861] Generating personalized learning materials

[0862] Based on the analyzed data, the generative AI generates learning materials that are best suited to the user, such as relaxation sessions if stress levels are high, or kanji quizzes to maintain cognitive function.

[0863] Input: Analyzed health indicators and health status data.

[0864] Output: Personalized learning materials.

[0865] Step 4:

[0866] Creating a study plan

[0867] Based on the generated learning materials, the server creates a learning plan based on the user's health condition and learning goals, including specific tasks and schedules.

[0868] Input: Personalized learning materials and health status data.

[0869] Output: A personalized study plan.

[0870] Step 5:

[0871] Presenting and implementing learning tasks

[0872] The device presents the user with daily tasks based on the learning plan. As the user completes the tasks, the device receives feedback and sends progress data to the server.

[0873] Enter: personalized study plans.

[0874] Output: User's progress on the learning task.

[0875] Step 6:

[0876] Learning progress management and feedback

[0877] The server analyzes the progress data and awards badges and points according to the level of achievement, thereby maintaining the user's motivation.

[0878] Input: User's progress data on learning tasks.

[0879] Output: Parsed progress data and rewards (badges, points).

[0880] Step 7:

[0881] Promoting collaborative learning

[0882] To promote collaborative learning with other users, the server generates information about collaborative tasks and challenges and sends it to the device, which displays the information and provides the user with an opportunity to collaborate with other users to solve the tasks.

[0883] Input: System-wide training data and collaborative learning candidates.

[0884] Output: Information on collaborative tasks and notifications for collaborative learning.

[0885] 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.

[0886] This invention is a system for preventing cognitive decline and the progression of aging symptoms in the elderly while maintaining health in an enjoyable way. This system works in conjunction with a health management device to generate learning materials optimized for each user and provide a learning environment incorporating collaborative learning with other users and gamification elements. Furthermore, by combining it with an emotion engine that detects the user's emotional state, it aims to maintain the user's motivation and optimize their learning plan.

[0887] System Overview

[0888] The system consists of the following main components:

[0889] 1. Health management device

[0890] 2. Terminal

[0891] 3. Server

[0892] 4. Generation AI

[0893] 5. Study Plan

[0894] 6. Gamification

[0895] 7. Cooperative Learning

[0896] 8. Emotion Engine

[0897] Overview of program processing

[0898] 1. Data Collection

[0899] Terminal

[0900] A health management device worn by the user (e.g., a smartwatch) periodically measures health data such as heart rate and sleep patterns. In addition, an emotion engine analyzes the user's facial expressions and voice data to collect emotional data. The measurement data and emotional data are stored on the device.

[0901] 2. Data Receipt and Analysis

[0902] server

[0903] The data sent from the device is received and stored in a database. The stored data is identified for each user and prepared for later analysis. The analysis algorithm evaluates the user's health and emotional state and calculates stress levels, sleep quality, and emotional indicators. These calculation results are updated in the user profile.

[0904] 3. Generating personalized learning materials

[0905] server

[0906] Based on the analysis results, the generative AI generates learning materials optimized for each user, taking into account health data, emotional data, user interests, cognitive ability, and past learning history.

[0907] 4. Create a study plan

[0908] server

[0909] Based on the generated learning materials, a learning plan is created based on the user's health and emotional state, as well as their learning goals, including specific tasks, implementation schedules, and emotional reminders.

[0910] 5. Presentation and implementation of learning tasks

[0911] Terminal

[0912] The system presents daily learning tasks to users according to a learning plan sent from the server. The tasks are displayed and the user can work on them. Based on the user's emotional state, positive feedback and motivational messages are displayed at appropriate times.

[0913] 6. Learning progress management and feedback

[0914] server

[0915] The system analyzes progress and emotional data sent by users to evaluate their achievements, generates rewards such as badges and points based on their achievements, and motivates users. The emotional engine also provides appropriate feedback based on the user's current emotional state.

[0916] 7. Promoting collaborative learning

[0917] Servers and Terminals

[0918] To promote collaborative learning with other users, the server generates information about joint tasks and challenges and sends it to the device. The device displays this information, providing users with opportunities to collaborate with others to solve the tasks. Furthermore, the system optimizes collaborative learning by taking into account emotional data, promoting smooth communication.

[0919] Specific examples

[0920] Case of User A (65-year-old male)

[0921] Terminal

[0922] User A's smartwatch measures his / her heart rate and sleep patterns, while the device's camera and microphone analyze his / her facial expressions and voice to collect emotional data, which is then sent to a server every morning.

[0923] server

[0924] The server analyzes the received data and detects that User A's stress level is increasing, his sleep quality is declining, and his recent emotional state is unstable. Based on this, the generative AI generates relaxation sessions and puzzle learning materials.

[0925] server

[0926] The server creates a study plan based on user A's health and emotional state, including three relaxation sessions per week and daily puzzle practice.

[0927] Terminal

[0928] The device presents learning tasks to User A and awards a badge each time the task is completed. It also takes into account User A's emotional data and displays appropriate feedback and motivational messages. Furthermore, it provides opportunities for collaborative learning with other users, allowing User A to solve tasks together and receive emotional support.

[0929] Server and Device

[0930] It continuously collects data and optimizes the learning plan according to the health and emotional state of User A. It also improves the entire system based on user feedback.

[0931] The system allows users to have fun while maintaining their health and taking care of both their cognitive and emotional functions.

[0932] The processing flow will be explained below.

[0933] Step 1:

[0934] Terminal

[0935] A health management device worn by a user (e.g., a smartwatch) periodically measures health data such as heart rate and sleep patterns. In addition, the device's camera and microphone analyze the user's facial expressions and voice to collect emotional data. The collected health and emotional data is temporarily stored on the device.

[0936] Step 2:

[0937] Terminal

[0938] The collected health and emotion data is automatically transmitted to the server at regular intervals, and if the transmission is successful, the device proceeds to the next data collection cycle.

[0939] Step 3:

[0940] server

[0941] The data sent from the device is received and stored in a database, which identifies the user and prepares it for later analysis.

[0942] Step 4:

[0943] server

[0944] The received data is analyzed. Analysis algorithms are used to assess the user's health status (e.g., stress level, sleep quality) and emotional state (e.g., stress, happiness). The assessment results are updated in the user profile.

[0945] Step 5:

[0946] server

[0947] Based on the analysis results, the generative AI generates learning materials optimized for each user, taking into account health data, emotional data, user interests, cognitive ability, and past learning history.

[0948] Step 6:

[0949] server

[0950] Based on the generated learning materials, a study plan is created, which includes specific study tasks, implementation schedules, and emotional state-based reminders.

[0951] Step 7:

[0952] Terminal

[0953] The system presents daily learning tasks to users according to a learning plan sent from the server. The tasks are displayed and the user can work on them. Based on the user's emotional state, positive feedback and motivational messages are displayed at appropriate times.

[0954] Step 8:

[0955] User

[0956] Users work on the presented learning tasks, and when they complete the tasks, the device records their progress and sends it to the server.

[0957] Step 9:

[0958] server

[0959] The system analyzes progress and emotional data sent by users to evaluate their achievements, generates rewards such as badges and points based on their achievements, and motivates users. The emotional engine also provides appropriate feedback based on the user's current emotional state.

[0960] Step 10:

[0961] server

[0962] To provide opportunities for collaborative learning with other users, collaborative tasks and challenges are generated and sent to devices, encouraging users to learn collaboratively with others.

[0963] Step 11:

[0964] Terminal

[0965] Users will be notified of collaborative learning and challenges, allowing them to deepen their learning by solving collaborative challenges together with other users.

[0966] Step 12:

[0967] Server and Device

[0968] The system continuously collects and analyzes data to optimize learning plans and materials based on the user's health, emotional state, and learning progress, and also improves the system as a whole based on user feedback.

[0969] This series of steps allows users to have fun while maintaining their health and taking care of their cognitive and emotional well-being.

[0970] Example 2

[0971] 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."

[0972] Maintaining health while having fun while preventing cognitive decline and the progression of aging symptoms in the elderly is a social challenge. However, conventional health management and learning systems have difficulty providing personalized services that adapt to the health and emotional state of each individual user, and have not adequately maintained user motivation or provided emotional care. This has led to problems such as insufficient learning effects and health maintenance.

[0973] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0974] In this invention, the server includes means for collecting data from a device that collects physiological data, information processing means for saving and analyzing the collected data, artificial intelligence means for generating personalized learning materials based on the analyzed data, means for creating an individualized learning plan based on the generated learning materials, terminal means for presenting learning tasks to the user based on the learning plan, means including game elements for tracking the user's progress and maintaining motivation, means for promoting collaborative learning with other users, and engine means for detecting and analyzing the user's emotional state. This makes it possible to provide personalized learning materials and learning plans adapted to the user's health and emotional state, maintaining health while having fun, and caring for both cognitive function and emotions.

[0975] "Physiological data" refers to data about a user's biological body, such as their heart rate, sleep patterns, and activity levels.

[0976] "Device" refers to any health management equipment or terminal worn or used by a user.

[0977] "Information processing device" refers to a computer system for storing and analyzing collected data.

[0978] "Artificial intelligence" refers to algorithms and programs that analyze data and generate learning materials optimized for users.

[0979] "Individualized Learning Plan" refers to a plan that defines specific learning content and schedules based on the user's individual health and emotional state.

[0980] "Device" refers to an electronic device through which a user receives and completes learning assignments.

[0981] "Gamification" refers to reward systems such as badges and points used to motivate users.

[0982] "Collaborative learning" refers to a learning method in which multiple users work together to solve a learning task.

[0983] An "engine" refers to a program or hardware that detects and analyzes a user's emotional state.

[0984] This invention is a system for preventing cognitive decline and the progression of aging symptoms in the elderly while maintaining health in an enjoyable way. This system works in conjunction with a health management device to generate learning materials optimized for each user and provide a learning environment incorporating collaborative learning with other users and gamification elements. Furthermore, it combines an emotion engine that detects the user's emotional state to maintain the user's motivation and optimize the learning plan.

[0985] System configuration

[0986] This system is broadly composed of the following major hardware and software components:

[0987] Health management devices (e.g., smart watches)

[0988] Device (e.g. smartphone, tablet)

[0989] Server (cloud server)

[0990] Generation AI

[0991] Emotion Engine

[0992] Data collection

[0993] Terminal

[0994] The health management device worn by the user periodically collects physiological data such as heart rate and sleep patterns. Furthermore, the emotion engine analyzes the user's facial expressions and voice data to collect emotional data. This data is temporarily stored on the device.

[0995] Data reception and analysis

[0996] server

[0997] The device sends the collected data to a server, which stores it in a secure database. The stored data is uniquely identified for each user. Processing algorithms are used to analyze the data and calculate the user's health status (heart rate, sleep quality, etc.) and emotional state (happiness, sadness, etc.). This ensures that the user's health and emotional profile is always up to date.

[0998] Generating personalized learning materials

[0999] server

[1000] Based on the analysis results, the generative AI generates learning materials optimized for the user. Specifically, it generates learning materials based on the following information:

[1001] Health data (e.g., heart rate 85, poor sleep quality)

[1002] Emotional data (e.g., recent unstable emotional state)

[1003] User interests (e.g. puzzles, relaxation)

[1004] Cognitive ability

[1005] Past learning history (e.g., three sessions per week are valid)

[1006] Creating a study plan

[1007] server

[1008] Based on the generated learning materials, a learning plan is created based on the user's health and emotional state, including:

[1009] Specific learning tasks (e.g., relaxation sessions, puzzle learning)

[1010] Implementation Schedule

[1011] Reminders based on your emotional state (e.g., "Start relaxing now")

[1012] Presenting and implementing learning tasks

[1013] Terminal

[1014] According to the learning plan, the device notifies and displays daily learning tasks to the user, allowing them to immediately tackle the tasks. In addition, the device evaluates learning progress and emotional state in real time, providing positive feedback and motivational messages at appropriate times.

[1015] Learning progress management and feedback

[1016] server

[1017] The system analyzes progress and emotional data submitted by users to evaluate their learning achievement. Based on the evaluation, rewards such as badges and points are generated and notified to the user. In addition, the emotional engine provides appropriate feedback to maintain motivation for the next learning task.

[1018] Promoting collaborative learning

[1019] Servers and Terminals

[1020] The server generates information on collaborative tasks and challenges to promote collaborative learning with other users and sends it to the device, providing opportunities for users to solve tasks in collaboration with other users and providing appropriate feedback that takes into account emotional data.

[1021] Specific examples

[1022] For example, if a 65-year-old male user wears a smartwatch, the device will collect his heart rate and sleep patterns, and will also analyze his facial expressions and voice using a camera and microphone, and this data will be sent to a server in the morning.

[1023] The server analyzes the received data and detects increases in stress levels and decreases in sleep quality. Based on this, the generative AI generates relaxation sessions and puzzle learning materials. The server then creates an appropriate learning plan, and the device periodically presents learning tasks to the user.

[1024] Specific examples of prompts include:

[1025] Develop learning materials for cognitive maintenance and health management in older adults. Consider the following user information:

[1026] Gender: Male

[1027] Age: 65

[1028] Health data: Heart rate 85, poor sleep quality, high stress level

[1029] Emotional data: Recent unstable emotional state

[1030] Interests: Puzzles, relaxation

[1031] Past learning history: 3 sessions per week are valid

[1032] Generate personalized puzzle learning and relaxation session materials for your users.

[1033] The present invention allows users to have fun while maintaining their health and taking care of both their cognitive and emotional well-being.

[1034] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1035] Step 1: Data collection

[1036] Terminal

[1037] Input: Health monitoring device worn by the user (e.g., smartwatch) and its built-in camera and microphone.

[1038] How it works: The health management device periodically measures physiological data such as heart rate and sleep patterns. Using a camera and microphone, the emotion engine also analyzes the user's facial expressions and voice in real time to collect emotional data.

[1039] Data processing: The collected physiological data is temporarily stored on the device, and the emotion engine converts facial expressions and voice data into emotion indicators.

[1040] Output: Temporarily stored physiological and emotional indicator data.

[1041] Step 2: Receiving and analyzing data

[1042] server

[1043] Input: User physiological and emotional data sent from the device.

[1044] How it works: A server receives the data and stores it in a secure database. Processing algorithms analyze the data for each user and assess their health status, including stress levels, sleep quality, and emotional indicators.

[1045] Data calculation: Analyzes incoming data to calculate health indicators such as stress levels and sleep quality, and converts emotional data into an emotional profile.

[1046] Output: The updated user profile.

[1047] Step 3: Generate personalized learning materials

[1048] server

[1049] Input: Analyzed user profile (health indicators, emotional profile, user interests, cognitive abilities, and past learning history).

[1050] How it works: Generative AI generates optimal learning materials based on the user's health and emotional data.

[1051] Data processing: The generative AI model creates appropriate learning materials based on the prompt. For example, if the user is stressed, it will generate relaxation sessions or puzzle learning materials.

[1052] Output: Personalized learning materials.

[1053] Step 4: Create a study plan

[1054] server

[1055] Input: personalized learning materials and user profile.

[1056] How it works: The server creates a study plan based on the user's health and emotional state. This study plan includes specific tasks, a schedule, and reminders.

[1057] Data calculation: Based on the content of the study materials, specific tasks and schedules, such as three relaxation sessions per week or daily puzzle study, are calculated and reminders are set.

[1058] Output: Learning plan.

[1059] Step 5: Present and implement the learning task

[1060] Terminal

[1061] Input: lesson plan.

[1062] How it works: The device follows a study plan and displays daily learning tasks to the user. As the user works on the tasks, the device monitors their emotional state in real time and displays positive feedback and motivational messages at appropriate times.

[1063] Data processing: Learning progress and emotional data are collected in real time and temporarily stored on the device.

[1064] Output: The training data and the latest emotion data.

[1065] Step 6: Progress monitoring and feedback

[1066] server

[1067] Input: Progress and emotion data sent from the device.

[1068] How it works: The server analyzes progress data and emotion data to evaluate learning achievement. It generates rewards such as badges and points according to achievement and notifies the user. The emotion engine also provides appropriate feedback to maintain motivation for the next learning task.

[1069] Data computation: Applying algorithms to evaluate learning achievement and generate rewards.

[1070] Output: Feedback, badges, points, and other rewards.

[1071] Step 7: Facilitating collaborative learning

[1072] Servers and Terminals

[1073] Input: User profile, collaborative learning data with other users.

[1074] Operation: The server generates information about collaborative tasks and challenges to promote collaborative learning with other users and sends it to the device. The device notifies the user of this information and provides opportunities for collaborative learning.

[1075] Data processing: Optimize the collaborative learning task based on collaborative learning data and participating users' emotional data.

[1076] Output: Detailed information and feedback on collaborative learning.

[1077] In this way, the system of the present invention can provide a personalized learning environment based on the user's health and emotional state.

[1078] (Application example 2)

[1079] 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."

[1080] Elderly people need to maintain their health while having fun, while preventing cognitive decline and the progression of aging symptoms. Furthermore, in order to provide learning materials optimized for each individual user and create an effective learning environment, it is important to properly understand the user's health and emotional state and respond accordingly. However, current systems do not adequately integrate these elements, making it difficult to maintain user motivation and optimize effective learning plans.

[1081] 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.

[1082] In this invention, the server includes means for collecting data from the health management device, means for saving and analyzing the collected data, generation AI means for generating learning materials based on the analyzed data, means for creating a learning plan based on the generated learning materials, terminal means for presenting learning tasks to the user, gamification means for tracking the user's progress and maintaining their motivation, means for promoting collaborative learning with other users, an emotion engine for detecting and evaluating the user's emotional state, and means for maintaining the user's motivation and optimizing the learning plan based on the results of the emotion engine. This makes it possible to provide an individually optimized learning plan based on the user's health and emotional state, allowing the user to maintain their health while having fun and improving their cognitive ability.

[1083] A "health management device" is a device that periodically measures and collects health data such as a user's heart rate and sleep patterns.

[1084] The "server" is a system that contains a database and analytical algorithms for storing and analyzing collected data and assessing the user's health and emotional state.

[1085] "Generative AI" refers to artificial intelligence that generates learning materials optimized for users based on analyzed data.

[1086] "Terminal" refers to a device that presents learning tasks to users and tracks their progress, such as a smartphone or tablet.

[1087] "Gamification" is a technique that incorporates game elements to maintain user motivation and provides rewards such as badges and points.

[1088] "Collaborative learning" is a learning method in which users cooperate with other users to solve learning problems and promote smooth communication.

[1089] The "emotion engine" is a system that detects and evaluates a user's emotional state by analyzing their facial expressions and voice.

[1090] A "study plan" is a specific study schedule and assignments created based on the generated study materials and in accordance with the user's health and emotional state, as well as their learning goals.

[1091] The system of this invention aims to help users maintain their health and improve their cognitive abilities while having fun by effectively monitoring their health and emotional state and providing personalized educational materials based on that information. The main components of this system include:

[1092] 1. Health management device

[1093] A health management device is a device that measures a user's health data, such as heart rate and sleep patterns. A smartwatch is used as an example. The user wears this device all the time to collect daily health data.

[1094] 2. Terminal

[1095] The terminal is a device that presents learning tasks to users and tracks their progress, such as a smartphone or tablet. The terminal also receives data from the health management device and sends it to the server.

[1096] 3. Server

[1097] The server is a system that receives data sent from the terminal, stores it in a database, and analyzes it. The server performs the following processes:

[1098] Health and emotional data analysis

[1099] Update user profiles based on analysis results

[1100] Generating personalized learning materials using generative AI

[1101] Create and manage your study plan

[1102] 4. Generation AI

[1103] Generative AI is an artificial intelligence that generates optimized learning materials based on a user's health and emotional data, interests, cognitive ability, and past learning history. Generative AI uses deep learning algorithms to generate highly accurate learning materials.

[1104] 5. Gamification

[1105] Gamification is a technique that incorporates game elements to keep users motivated and provides rewards such as badges and points.

[1106] 6. Cooperative Learning

[1107] Collaborative learning is a learning method that allows users to cooperate with other users to solve learning problems and promote smooth communication. Through joint tasks and challenges, users support each other emotionally and intellectually.

[1108] 7. Emotion Engine

[1109] The emotion engine is a system that detects and evaluates the user's emotional state by analyzing their facial expressions and voice, which can determine the user's motivation and the optimal timing for reminders.

[1110] Examples:

[1111] Case of User A (65-year-old male)

[1112] health management device

[1113] User A wears a smartwatch to measure his / her heart rate and sleep patterns, and the data from the smartwatch is automatically transferred to the device every day.

[1114] Terminal

[1115] User A's smartphone receives data from the health management device and transmits it to the server. It also presents User A with a study plan and study tasks from the server.

[1116] server

[1117] The server analyzes the data sent from the device and detects that User A's stress level is increasing, his sleep quality is declining, and his recent emotional state is unstable. Based on this, the generative AI generates relaxation sessions and puzzle learning materials.

[1118] Generation AI

[1119] The generative AI generates optimized relaxation sessions and puzzle learning materials based on user A's health and emotional state.

[1120] Learning Plan

[1121] The server creates a study plan that includes three relaxation sessions per week and daily puzzles.

[1122] Gamification

[1123] A badge is awarded to User A every time he or she completes a task, helping the user to stay motivated and progress in their studies.

[1124] cooperative learning

[1125] The server provides opportunities for collaborative learning with other users, allowing user A to jointly solve problems and receive emotional support.

[1126] Emotion Engine

[1127] The emotion engine detects user A's emotional state and provides feedback and messages at the right time to increase motivation.

[1128] These elements work together to enable users to maintain their health and improve their cognitive function while having fun. A specific generative AI model could be a deep learning model such as GPT-3. An example of a prompt would be, "Generate relaxation session materials based on heart rate and sleep data."

[1129] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1130] Step 1:

[1131] The terminal collects and stores data from the health management device.

[1132] Input: Health data such as the user's heart rate and sleep patterns sent from health monitoring devices.

[1133] Output: The acquired health data is saved on the device.

[1134] The terminal periodically acquires data from the health management device and stores it in its internal memory, which is used in subsequent processing steps.

[1135] Step 2:

[1136] The device uses an emotion engine to collect user emotion data.

[1137] Input: Facial expression data captured by the device's camera, voice data captured by the microphone.

[1138] Output: The analyzed emotion data is saved on the device.

[1139] The device uses a camera and microphone to collect the user's facial expressions and voice, which are then analyzed by an emotion engine, resulting in an emotional index such as positive or negative.

[1140] Step 3:

[1141] The device transmits the collected health and emotion data to a server.

[1142] Input: Health data and emotional data stored on the device.

[1143] Output: The transmitted data is saved on the server.

[1144] The device periodically uploads this data to the server, which stores the received data in a database.

[1145] Step 4:

[1146] The server analyzes the received data and assesses the user's health and emotional state.

[1147] Input: Health data and emotion data stored on the server.

[1148] Output: Evaluated health status indicators, emotional status indicators.

[1149] The server uses analytical algorithms to calculate stress levels, sleep quality, and emotional indicators, and updates them into the user profile.

[1150] Step 5:

[1151] The server uses generative AI to generate personalized teaching materials based on the analysis results.

[1152] Input: Assessed health status indicators, emotional state indicators, user interests and past learning history.

[1153] Output: Optimized learning materials.

[1154] Based on the evaluation results and the user's profile information, the generative AI generates learning materials tailored to the user, such as relaxation sessions or puzzle learning materials.

[1155] Step 6:

[1156] The server creates a learning plan based on the generated learning materials.

[1157] Input: Optimized learning materials, assessed health status indicators and emotional state indicators.

[1158] Output: Personalized study plan.

[1159] The server creates a learning plan based on the learning materials and the user's status, including specific learning tasks and their implementation schedule.

[1160] Step 7:

[1161] The device presents learning tasks to the user.

[1162] Input: The lesson plan sent from the server.

[1163] Output: The learning task displayed on the screen.

[1164] The device presents users with tasks at specific times each day according to their learning plan, and when they complete the tasks, they are awarded badges and points.

[1165] Step 8:

[1166] The device tracks the user's progress and feeds the data back to the server.

[1167] Input: User's learning task completion status, emotional data.

[1168] Output: Progress and emotion data sent to the server.

[1169] Each time the user completes a task, the device collects progress data and emotional data at that time and sends it to the server.

[1170] Step 9:

[1171] The server provides gamification elements based on the feedback.

[1172] Input: Progress and emotion data sent from the device.

[1173] Output: Rewards (badges, points) to the user, feedback message.

[1174] The server rewards the user according to their progress, generates feedback messages based on their emotional state at appropriate times, and sends them to the device.

[1175] Step 10:

[1176] The server generates collaborative tasks to promote collaborative learning with other users and provides them through the terminals.

[1177] Input: Data of multiple users, collaborative assignment template.

[1178] Output: Collaborative learning tasks, contact information between users.

[1179] The server analyzes data from multiple users, generates tasks optimized for effective collaborative learning, and provides them to users via their devices.

[1180] Through these steps, users can maintain their health and improve their cognitive function while having fun. The system utilizes a deep learning model, known as a "generative AI model," to provide an optimal learning experience for each individual user. For example, it can generate specific learning materials using a prompt such as, "Please generate relaxation session materials based on heart rate and sleep data."

[1181] 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.

[1182] 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.

[1183] 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.

[1184] [Third embodiment]

[1185] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1186] 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.

[1187] 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).

[1188] 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.

[1189] 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.

[1190] 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).

[1191] 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.

[1192] 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.

[1193] 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.

[1194] 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.

[1195] 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.

[1196] 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."

[1197] This invention is a system for preventing cognitive decline and the progression of aging symptoms in the elderly while maintaining health in an enjoyable manner. This system works in conjunction with a health management device, generates learning materials optimized for each user, and provides a learning environment that incorporates collaborative learning with other users and gamification elements.

[1198] System Overview

[1199] The system consists of the following main components:

[1200] 1. Health management device

[1201] 2. Terminal

[1202] 3. Server

[1203] 4. Generation AI

[1204] 5. Study Plan

[1205] 6. Gamification

[1206] 7. Cooperative Learning

[1207] Overview of program processing

[1208] 1. Data Collection

[1209] Terminal

[1210] Health data such as heart rate and sleep patterns are periodically collected from a health management device worn by the user (e.g., a smartwatch). The data collected by the device is automatically sent to a server.

[1211] 2. Data Receipt and Analysis

[1212] server

[1213] The server stores and analyzes the data received from the device, and an analysis algorithm evaluates the user's health status and calculates health indicators such as stress level and sleep quality.

[1214] 3. Generating personalized learning materials

[1215] server

[1216] The server-based AI generates optimal learning materials based on the analyzed health indicators, and the materials are customized to the user's cognitive ability, past learning history, and interests.

[1217] 4. Create a study plan

[1218] server

[1219] Based on the generated learning materials, a learning plan is created based on the user's health condition and learning goals, including specific tasks and schedules.

[1220] 5. Presentation and implementation of learning tasks

[1221] Terminal

[1222] The device presents the user with daily tasks based on the learning plan, and as the learning progresses, the device receives feedback from the user and sends progress data to a server.

[1223] 6. Learning progress management and feedback

[1224] server

[1225] The server analyzes the progress data and awards badges and points according to the level of achievement, which helps to maintain the user's motivation and encourages them to continue learning.

[1226] 7. Promoting collaborative learning

[1227] Servers and Terminals

[1228] To promote collaborative learning with other users, the server generates information about collaborative tasks and challenges and sends it to the device, which displays the information and provides the user with an opportunity to collaborate with other users to solve the tasks.

[1229] Specific examples

[1230] Case of User A (65-year-old male)

[1231] Terminal

[1232] User A's smartwatch measures his / her heart rate and sleep patterns, and the device sends this data to a server every morning.

[1233] server

[1234] The server analyzes the received data and detects that User A's stress level is increasing and the quality of his or her sleep is declining. Based on this, the generative AI generates relaxation sessions and puzzle learning materials.

[1235] server

[1236] The server creates a study plan based on user A's health status, including three relaxation sessions per week and daily puzzle practice.

[1237] Terminal

[1238] The device presents learning tasks to User A and awards a badge each time it is completed. It also provides opportunities for collaborative learning with other users, allowing User A to solve the tasks together.

[1239] Server and Device

[1240] Data is continuously collected and the learning plan is optimized according to the health condition of User A. The system is also continuously improved based on feedback.

[1241] This system allows users to have fun while maintaining their health and improving their cognitive function.

[1242] The processing flow will be explained below.

[1243] Step 1:

[1244] Terminal

[1245] A health monitoring device worn by a user (e.g., a smartwatch) periodically measures health data such as heart rate and sleep patterns, and the measurement data is stored on the device.

[1246] Step 2:

[1247] Terminal

[1248] The stored health data is automatically sent to the server at regular intervals, and if the transmission is successful, the device proceeds to the next data collection cycle.

[1249] Step 3:

[1250] server

[1251] The data sent from the device is received and stored in a database, which identifies each user and prepares it for later analysis.

[1252] Step 4:

[1253] server

[1254] The received data is analyzed. An analytical algorithm is used to assess the user's health and calculate health indicators such as stress level and sleep quality. The results are updated in the user profile.

[1255] Step 5:

[1256] server

[1257] Based on the analysis results, the generative AI generates learning materials optimized for each user, taking into account the user's interests, cognitive ability, and past learning history.

[1258] Step 6:

[1259] server

[1260] Based on the generated learning materials, a learning plan is created, which includes specific learning tasks, implementation schedules, reminders, etc.

[1261] Step 7:

[1262] Terminal

[1263] According to the study plan sent from the server, daily study tasks are presented to the user. The tasks are displayed and the user can work on them.

[1264] Step 8:

[1265] User

[1266] Users work on the presented learning tasks, and when they complete the tasks, the device records their progress and sends it to the server.

[1267] Step 9:

[1268] server

[1269] The system analyzes the progress data sent by users, evaluates their achievement, and generates rewards such as badges and points according to their achievement, thereby increasing user motivation.

[1270] Step 10:

[1271] server

[1272] To provide opportunities for collaborative learning with other users, collaborative tasks and challenges are generated and sent to devices, encouraging users to learn collaboratively with others.

[1273] Step 11:

[1274] Terminal

[1275] Users will be notified of collaborative learning and challenges, allowing them to deepen their learning by solving collaborative challenges together with other users.

[1276] Step 12:

[1277] Servers and Terminals

[1278] The system continuously collects and analyzes data to optimize learning plans and materials based on the user's health condition and learning progress, and improves the system as a whole based on user feedback.

[1279] This series of steps allows seniors to have fun while maintaining their health and improving cognitive function.

[1280] Example 1

[1281] 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."

[1282] There is a need for systems that allow elderly people to maintain their health while having fun, while preventing cognitive decline and the progression of aging symptoms. However, conventional systems have difficulty in providing personalized responses based on each user's health condition, and lack mechanisms to maintain motivation or promote collaborative learning with other users. This often makes it difficult for users to continue managing their health and learning.

[1283] 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.

[1284] In this invention, the server includes a means for storing and analyzing data collected from the health management device, a means for calculating the user's health index using an analysis algorithm, and a generation AI means for generating learning materials based on the analyzed health index. This makes it possible to provide personalized learning materials according to the user's health condition, maintain motivation, and promote cooperative learning with other users.

[1285] A "health management device" is a device that monitors a user's health status and collects data such as heart rate and sleep patterns.

[1286] "Means for collecting data" refers to a method for obtaining health data such as heart rate and sleep patterns from health management devices.

[1287] The "means for transmitting data to the server" refers to a communication means for transferring collected health data to the server.

[1288] A "server" is a computer system that receives, stores, and analyzes collected data.

[1289] An "analysis algorithm" is a calculation method for analyzing collected health data and calculating the user's health indicators.

[1290] "Generative AI" is an artificial intelligence technology that generates optimal learning materials for users based on analyzed health indicators.

[1291] A "study plan" is a specific study schedule based on the generated study materials and adapted to the user's health condition and study goals.

[1292] A "terminal" is a device that presents learning tasks to a user and accepts operations.

[1293] "Progress data" is data that indicates the results of a user's learning tasks, and is sent from the terminal to the server.

[1294] "Gamification" is a reward system that uses badges and points to maintain user motivation and encourage continued learning.

[1295] "Collaborative learning" is a method in which multiple users work together to complete a learning task and achieve results.

[1296] The present invention is a system for preventing cognitive decline and the progression of aging symptoms in the elderly while maintaining health in an enjoyable manner. This system works in conjunction with a health management device, generates learning materials optimized for each user, and provides a learning environment that incorporates collaborative learning with other users and gamification elements. Specific embodiments of the system are described below.

[1297] The system consists of the following main components: health management device, terminal, server, generative AI, learning plan, gamification, and collaborative learning.

[1298] A user's health monitoring device (e.g., a smartwatch) periodically measures health data such as heart rate and sleep patterns. This data is automatically transmitted by the device to a server. The device transmits the data using a secure communication protocol (e.g., HTTPS) without requiring any user interaction.

[1299] The server stores the received health data in a database and uses an analytical algorithm to calculate health indicators, including stress levels and sleep quality. Based on the analysis results, a generative AI on the server generates learning materials optimal for the user. The materials are customized taking into account the user's cognitive ability, past learning history, and interests.

[1300] A study plan is created based on the generated learning materials. For example, a specific schedule may be set up, including three relaxation sessions per week and daily puzzle practice. This plan is saved on the server and presented to the user via their device.

[1301] The device notifies the user of daily learning tasks. For example, a notification such as "Please complete a 20-minute relaxation session today" is displayed in the morning. As the user completes the task, progress data is sent from the device to the server.

[1302] The server analyzes progress data and has a gamification function that automatically awards badges and points according to achievement, thereby maintaining user motivation. Progress reports are also generated periodically and displayed on the device.

[1303] To promote collaborative learning with other users, the server generates collaborative learning tasks and challenges and sends them to the device. For example, a task might be displayed such as, "Form a team of three people and take part in a one-week walking challenge." Users collaborate with other users to complete the task, and the device reports the results to the server.

[1304] As a specific example, consider a 65-year-old male user A. His smartwatch measures his heart rate and sleep patterns and sends the data to a server daily. The server analyzes the data and detects rising stress levels and declining sleep quality. A generative AI generates relaxation session and puzzle learning materials, and a learning plan is set up with three relaxation sessions per week and daily puzzle learning. Every morning, the device notifies user A of the learning tasks and awards a badge each time one is completed. Health data is continuously collected, and the learning plan is optimized according to user A's latest health condition.

[1305] Here are some example prompts that can be input to a generative AI model:

[1306] "Generate a study plan for User A, a 65-year-old male, who is in a state of high stress and low sleep quality, combining relaxation sessions and puzzle learning three times a week."

[1307] This system allows users to have fun while maintaining their health and improving their cognitive function.

[1308] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1309] System program processing steps

[1310] Step 1: Collecting health data

[1311] Terminal

[1312] The device periodically collects health data such as heart rate and sleep patterns from the user's health management device (e.g., smartwatch).

[1313] Input: Heart rate and sleep pattern data from smartwatch

[1314] Output: Collected health data

[1315] How it works: The device collects data from the smartwatch every hour and temporarily stores the collected data in its internal memory. For example, data points such as a heart rate of 70 BPM and 7 hours of sleep are collected.

[1316] Step 2: Sending data

[1317] Terminal

[1318] The device transmits the collected health data to a server in real time.

[1319] Input: Collected health data

[1320] Output: Data sent to the server

[1321] How it works: The device sends encrypted data using a secure communication protocol (e.g., HTTPS). No user interaction is required; everything happens automatically.

[1322] Step 3: Receiving and storing data

[1323] server

[1324] The server receives the health data sent from the device and stores it in a database.

[1325] Input: Health data sent from the device

[1326] Output: Data stored in the database

[1327] What it does: The server validates the data it receives and stores it in a database based on each user's unique identifier. It then merges it with past data to maintain a continuous record.

[1328] Step 4: Analyze the data

[1329] server

[1330] The server analyzes the stored health data using an analytical algorithm and calculates the user's health index.

[1331] Input: Stored health data

[1332] Output: Calculated health indicators (e.g., stress level, sleep quality)

[1333] How it works: The server uses data analysis software to calculate stress levels and sleep quality based on heart rate, sleep patterns, etc. For example, an increase in heart rate is considered to be an increase in stress.

[1334] Step 5: Generate personalized learning materials

[1335] server

[1336] The generation AI on the server generates learning materials that are optimal for the user based on the analyzed health indicators.

[1337] Input: Analyzed health indicators

[1338] Output: Personalized learning materials

[1339] How it works: Generative AI uses health indicators, the user's cognitive ability, past learning history, and interests as input data to generate learning materials. For example, if high stress is detected, relaxation sessions and puzzle learning materials will be generated.

[1340] Step 6: Create a study plan

[1341] server

[1342] Based on the generated teaching materials, a learning plan is created based on the user's health condition and learning goals.

[1343] Input: Generated learning materials, user's health status

[1344] Output: A specific study plan (e.g., three relaxation sessions per week)

[1345] What happens: The server uses a scheduling algorithm to plan an optimal schedule for the user, for example, setting relaxation sessions on Mondays, Wednesdays, and Fridays.

[1346] Step 7: Present and implement the learning task

[1347] Terminal

[1348] The device presents the user with daily assignments based on their learning plan.

[1349] Input:Study plan

[1350] Output: Notification to user, data on completed assignments

[1351] Specific operation: The device displays a notification every morning, prompting the user to "take a relaxation session today." The user conducts the session and enters their progress into the device.

[1352] Step 8: Progress monitoring and feedback

[1353] server

[1354] The server analyzes the progress data and awards badges and points based on the level of achievement.

[1355] Input: Progress data from the terminal

[1356] Output: Achievements, badges, points

[1357] What it does: The server updates the reward system based on the progress data, adds badges and points to the user's account, and periodically generates progress reports and sends them to the device.

[1358] Step 9: Facilitating collaborative learning

[1359] Servers and Terminals

[1360] To promote collaborative learning with other users, the server generates information on collaborative tasks and challenges and sends them to the terminals.

[1361] Input: Progress data, collaborative learning settings

[1362] Output: Collaborative tasks, progress by team

[1363] Specific operation: The server generates collaborative learning data and displays on the device, "Let's take part in a one-week walking challenge in teams of three." The user performs the challenge, and the device reports the results to the server.

[1364] Through each step, users can have fun while maintaining their health and improving their cognitive function.

[1365] (Application example 1)

[1366] 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."

[1367] In modern society, health management and maintaining / improving cognitive function for the elderly are important issues. In particular, effective methods are needed to prevent cognitive decline while maintaining health in a fun way for the elderly. It is also important to strengthen social connections and prevent isolation by providing an environment in physical stores where the elderly can learn and train in cooperation with other users. Furthermore, providing personalized health programs and learning materials to the elderly is thought to lead to sustainable health management and maintaining motivation.

[1368] 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.

[1369] In this invention, the server includes means for collecting data from a health management device to monitor the user's health status, means for storing and analyzing the collected data, means for generating learning materials based on the analyzed data, means for creating a learning plan based on the generated learning materials, means for presenting learning tasks to the user based on the learning plan, means for gamification to track the user's progress and maintain motivation, means for promoting collaborative learning with other users, means for analyzing data collected through the health management device and smart devices in the physical store and suggesting optimal learning materials and health programs to the user, and means for providing opportunities to complete learning tasks through collaboration with other users. This enables elderly people to maintain their health, improve their cognitive function, and strengthen their social connections while having fun in the physical store.

[1370] "User" refers to an individual who uses the system to manage their health or undertake learning tasks.

[1371] "Health status" refers to data that indicates the user's physical condition and health, such as heart rate, blood pressure, weight, body fat percentage, and sleep patterns.

[1372] A "health management device" is a device that can collect health data such as heart rate and sleep patterns, and examples include smartwatches and blood pressure monitors.

[1373] "Server" refers to a computer system connected to the Internet that stores and analyzes collected data.

[1374] "Generative AI" refers to artificial intelligence that automatically generates optimal learning materials for users based on collected and analyzed data.

[1375] A "study plan" refers to a plan based on the generated teaching materials that includes specific tasks and schedules tailored to the user's health condition and learning goals.

[1376] A "device" is a device that presents a user with tasks based on a learning plan and receives feedback from the user, including, for example, a smartphone or tablet.

[1377] "Gamification" refers to a method of keeping users motivated by tracking their progress and awarding badges and points based on their achievements.

[1378] "Collaborative learning" refers to learning methods and activities that involve working with other users to solve learning problems.

[1379] A "physical store" refers to a physical store that users can actually visit, where health management equipment and smart devices are installed.

[1380] A "smart device" is an electronic device that allows users to collect health data or check their learning plans, and examples include smartphones and tablets.

[1381] "Learning Materials" refers to various learning content provided to users, including question sets, quizzes, relaxation sessions, etc.

[1382] "Health Program" refers to the specific content and schedule of training, exercises, etc. aimed at improving the user's health.

[1383] "Study assignments" refer to specific tasks or problems that users should tackle based on their study plan.

[1384] This invention is a system for a brick-and-mortar store that supports health management and cognitive function improvement for the elderly. Specific embodiments for realizing this system will be described in detail below.

[1385] System Overview

[1386] The system consists of the following components to monitor users' health status and provide personalized learning materials:

[1387] 1. Health monitoring devices: Devices such as smartwatches collect data such as heart rate, blood pressure, weight, body fat percentage, and sleep patterns.

[1388] 2. Device: A device such as a smartphone or tablet presents learning tasks to users and receives feedback.

[1389] 3. Server: A cloud server for storing and analyzing data.

[1390] 4. Generative AI: Artificial intelligence that generates learning materials based on analyzed data.

[1391] 5. Learning plan: A specific schedule for the user's health management and learning using the generated learning materials.

[1392] 6. Gamification: Awarding badges and points to keep users motivated.

[1393] 7. Collaborative learning: A mechanism that provides opportunities for users to work with others to complete learning tasks.

[1394] Hardware and software used

[1395] Health management devices: smart watches, blood pressure monitors, body composition monitors

[1396] Devices: smartphones (iOS, Android), tablets

[1397] Server: Cloud server (AWS, Google Cloud Platform)

[1398] Generative AI: GPT-based models (e.g., GPT-3, GPT-4)

[1399] Data analysis algorithms: Python, TensorFlow

[1400] Process Overview

[1401] Data collection

[1402] When a customer visits a physical store, their health data, such as heart rate, blood pressure, weight, body fat percentage, and sleep patterns, is collected from their health monitoring device, such as a smartwatch, and this data is sent to their smartphone via Bluetooth or Wi-Fi, and then sent to a cloud server.

[1403] Data reception and analysis

[1404] The server stores and analyzes the received data, and algorithms evaluate health indicators such as heart rate and weight fluctuations to calculate stress levels and sleep quality.

[1405] Generating personalized learning materials

[1406] Based on the analyzed health indicators, generative AI generates optimal learning materials for each user, customized to their cognitive abilities, past learning history, and interests.

[1407] Creating a study plan

[1408] Based on the generated learning materials, the server creates a learning plan based on the user's health condition and learning goals, including specific tasks and schedules.

[1409] Presenting and implementing learning tasks

[1410] The device presents the user with daily tasks based on the learning plan, and as the learning progresses, the device receives feedback from the user and sends progress data to a server.

[1411] Learning progress management and feedback

[1412] The server analyzes progress data and awards badges and points according to achievement, which helps maintain user motivation and encourages continued learning.

[1413] Promoting collaborative learning

[1414] To promote collaborative learning with other users, the server generates information about collaborative tasks and challenges and sends it to the device, which displays the information and provides the user with an opportunity to collaborate with other users to solve the tasks.

[1415] Specific examples

[1416] Case of User A (70-year-old woman)

[1417] Data collection: User A visits the store and collects data from their smartwatch. The smartphone then sends this data to the server.

[1418] Data analysis: The server analyzes User A's data and detects that his stress level is high and his sleep quality is poor.

[1419] Personalized learning material generation: Generative AI generates learning materials for relaxation sessions and kanji quizzes.

[1420] Creating a study plan: The server creates a study plan for User A that includes two relaxation sessions per week and daily kanji quizzes.

[1421] Completing learning tasks: The device presents learning tasks to User A and awards points each time they are completed.

[1422] Feedback and progress management: The server analyzes your progress data and awards badges to motivate you.

[1423] Promoting collaborative learning: Collaborative learning with other users is provided, and User A solves problems collaboratively.

[1424] Example prompts to input to the generative AI model

[1425] User A's health data:

[1426] Heart rate: 75 bpm, sleep time: 5 hours

[1427] Prompt the generative AI model:

[1428] This user has high stress levels and poor sleep quality. Generate learning materials to improve this user's cognitive function and maintain their health. Suggest easy kanji quizzes and relaxation sessions based on their past learning history.

[1429] This system allows seniors to maintain their health and improve their cognitive function while having fun in a physical store.

[1430] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1431] Step 1:

[1432] Data collection

[1433] When a user visits a physical store, health management devices (smartwatches, blood pressure monitors, body composition scales, etc.) collect health data such as heart rate, blood pressure, weight, body fat percentage, sleep patterns, etc. This data is sent to a smartphone via Bluetooth or Wi-Fi, and from there to a cloud server.

[1434] Input: Health data obtained from health monitoring devices.

[1435] Output: Health data sent to your smartphone.

[1436] Step 2:

[1437] Data reception and analysis

[1438] The server stores the health data received from the smartphone and uses analytical algorithms to evaluate health indicators such as heart rate and weight fluctuations, and calculate health conditions such as stress levels and sleep quality.

[1439] Input: Health data sent from your smartphone.

[1440] Output: Analyzed health indicators and health status data.

[1441] Step 3:

[1442] Generating personalized learning materials

[1443] Based on the analyzed data, the generative AI generates learning materials that are best suited to the user, such as relaxation sessions if stress levels are high, or kanji quizzes to maintain cognitive function.

[1444] Input: Analyzed health indicators and health status data.

[1445] Output: Personalized learning materials.

[1446] Step 4:

[1447] Creating a study plan

[1448] Based on the generated learning materials, the server creates a learning plan based on the user's health condition and learning goals, including specific tasks and schedules.

[1449] Input: Personalized learning materials and health status data.

[1450] Output: A personalized study plan.

[1451] Step 5:

[1452] Presenting and implementing learning tasks

[1453] The device presents the user with daily tasks based on the learning plan. As the user completes the tasks, the device receives feedback and sends progress data to the server.

[1454] Enter: personalized study plans.

[1455] Output: User's progress on the learning task.

[1456] Step 6:

[1457] Learning progress management and feedback

[1458] The server analyzes the progress data and awards badges and points according to the level of achievement, thereby maintaining the user's motivation.

[1459] Input: User's progress data on learning tasks.

[1460] Output: Parsed progress data and rewards (badges, points).

[1461] Step 7:

[1462] Promoting collaborative learning

[1463] To promote collaborative learning with other users, the server generates information about collaborative tasks and challenges and sends it to the device, which displays the information and provides the user with an opportunity to collaborate with other users to solve the tasks.

[1464] Input: System-wide training data and collaborative learning candidates.

[1465] Output: Information on collaborative tasks and notifications for collaborative learning.

[1466] 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.

[1467] This invention is a system for preventing cognitive decline and the progression of aging symptoms in the elderly while maintaining health in an enjoyable way. This system works in conjunction with a health management device to generate learning materials optimized for each user and provide a learning environment incorporating collaborative learning with other users and gamification elements. Furthermore, by combining it with an emotion engine that detects the user's emotional state, it aims to maintain the user's motivation and optimize their learning plan.

[1468] System Overview

[1469] The system consists of the following main components:

[1470] 1. Health management device

[1471] 2. Terminal

[1472] 3. Server

[1473] 4. Generation AI

[1474] 5. Study Plan

[1475] 6. Gamification

[1476] 7. Cooperative Learning

[1477] 8. Emotion Engine

[1478] Overview of program processing

[1479] 1. Data Collection

[1480] Terminal

[1481] A health management device worn by the user (e.g., a smartwatch) periodically measures health data such as heart rate and sleep patterns. In addition, an emotion engine analyzes the user's facial expressions and voice data to collect emotional data. The measurement data and emotional data are stored on the device.

[1482] 2. Data Receipt and Analysis

[1483] server

[1484] The data sent from the device is received and stored in a database. The stored data is identified for each user and prepared for later analysis. The analysis algorithm evaluates the user's health and emotional state and calculates stress levels, sleep quality, and emotional indicators. These calculation results are updated in the user profile.

[1485] 3. Generating personalized learning materials

[1486] server

[1487] Based on the analysis results, the generative AI generates learning materials optimized for each user, taking into account health data, emotional data, user interests, cognitive ability, and past learning history.

[1488] 4. Create a study plan

[1489] server

[1490] Based on the generated learning materials, a learning plan is created based on the user's health and emotional state, as well as their learning goals, including specific tasks, implementation schedules, and emotional reminders.

[1491] 5. Presentation and implementation of learning tasks

[1492] Terminal

[1493] The system presents daily learning tasks to users according to a learning plan sent from the server. The tasks are displayed and the user can work on them. Based on the user's emotional state, positive feedback and motivational messages are displayed at appropriate times.

[1494] 6. Learning progress management and feedback

[1495] server

[1496] The system analyzes progress and emotional data sent by users to evaluate their achievements, generates rewards such as badges and points based on their achievements, and motivates users. The emotional engine also provides appropriate feedback based on the user's current emotional state.

[1497] 7. Promoting collaborative learning

[1498] Servers and Terminals

[1499] To promote collaborative learning with other users, the server generates information about joint tasks and challenges and sends it to the device. The device displays this information, providing users with opportunities to collaborate with others to solve the tasks. Furthermore, the system optimizes collaborative learning by taking into account emotional data, promoting smooth communication.

[1500] Specific examples

[1501] Case of User A (65-year-old male)

[1502] Terminal

[1503] User A's smartwatch measures his / her heart rate and sleep patterns, while the device's camera and microphone analyze his / her facial expressions and voice to collect emotional data, which is then sent to a server every morning.

[1504] server

[1505] The server analyzes the received data and detects that User A's stress level is increasing, his sleep quality is declining, and his recent emotional state is unstable. Based on this, the generative AI generates relaxation sessions and puzzle learning materials.

[1506] server

[1507] The server creates a study plan based on user A's health and emotional state, including three relaxation sessions per week and daily puzzle practice.

[1508] Terminal

[1509] The device presents learning tasks to User A and awards a badge each time the task is completed. It also takes into account User A's emotional data and displays appropriate feedback and motivational messages. Furthermore, it provides opportunities for collaborative learning with other users, allowing User A to solve tasks together and receive emotional support.

[1510] Server and Device

[1511] It continuously collects data and optimizes the learning plan according to the health and emotional state of User A. It also improves the entire system based on user feedback.

[1512] The system allows users to have fun while maintaining their health and taking care of both their cognitive and emotional functions.

[1513] The processing flow will be explained below.

[1514] Step 1:

[1515] Terminal

[1516] A health management device worn by a user (e.g., a smartwatch) periodically measures health data such as heart rate and sleep patterns. In addition, the device's camera and microphone analyze the user's facial expressions and voice to collect emotional data. The collected health and emotional data is temporarily stored on the device.

[1517] Step 2:

[1518] Terminal

[1519] The collected health and emotion data is automatically transmitted to the server at regular intervals, and if the transmission is successful, the device proceeds to the next data collection cycle.

[1520] Step 3:

[1521] server

[1522] The data sent from the device is received and stored in a database, which identifies the user and prepares it for later analysis.

[1523] Step 4:

[1524] server

[1525] The received data is analyzed. Analysis algorithms are used to assess the user's health status (e.g., stress level, sleep quality) and emotional state (e.g., stress, happiness). The assessment results are updated in the user profile.

[1526] Step 5:

[1527] server

[1528] Based on the analysis results, the generative AI generates learning materials optimized for each user, taking into account health data, emotional data, user interests, cognitive ability, and past learning history.

[1529] Step 6:

[1530] server

[1531] Based on the generated learning materials, a study plan is created, which includes specific study tasks, implementation schedules, and emotional state-based reminders.

[1532] Step 7:

[1533] Terminal

[1534] The system presents daily learning tasks to users according to a learning plan sent from the server. The tasks are displayed and the user can work on them. Based on the user's emotional state, positive feedback and motivational messages are displayed at appropriate times.

[1535] Step 8:

[1536] User

[1537] Users work on the presented learning tasks, and when they complete the tasks, the device records their progress and sends it to the server.

[1538] Step 9:

[1539] server

[1540] The system analyzes progress and emotional data sent by users to evaluate their achievements, generates rewards such as badges and points based on their achievements, and motivates users. The emotional engine also provides appropriate feedback based on the user's current emotional state.

[1541] Step 10:

[1542] server

[1543] To provide opportunities for collaborative learning with other users, collaborative tasks and challenges are generated and sent to devices, encouraging users to learn collaboratively with others.

[1544] Step 11:

[1545] Terminal

[1546] Users will be notified of collaborative learning and challenges, allowing them to deepen their learning by solving collaborative challenges together with other users.

[1547] Step 12:

[1548] Server and Device

[1549] The system continuously collects and analyzes data to optimize learning plans and materials based on the user's health, emotional state, and learning progress, and also improves the system as a whole based on user feedback.

[1550] This series of steps allows users to have fun while maintaining their health and taking care of their cognitive and emotional well-being.

[1551] Example 2

[1552] 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."

[1553] Maintaining health while having fun while preventing cognitive decline and the progression of aging symptoms in the elderly is a social challenge. However, conventional health management and learning systems have difficulty providing personalized services that adapt to the health and emotional state of each individual user, and have not adequately maintained user motivation or provided emotional care. This has led to problems such as insufficient learning effects and health maintenance.

[1554] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1555] In this invention, the server includes means for collecting data from a device that collects physiological data, information processing means for saving and analyzing the collected data, artificial intelligence means for generating personalized learning materials based on the analyzed data, means for creating an individualized learning plan based on the generated learning materials, terminal means for presenting learning tasks to the user based on the learning plan, means including game elements for tracking the user's progress and maintaining motivation, means for promoting collaborative learning with other users, and engine means for detecting and analyzing the user's emotional state. This makes it possible to provide personalized learning materials and learning plans adapted to the user's health and emotional state, maintaining health while having fun, and caring for both cognitive function and emotions.

[1556] "Physiological data" refers to data about a user's biological body, such as their heart rate, sleep patterns, and activity levels.

[1557] "Device" refers to any health management equipment or terminal worn or used by a user.

[1558] "Information processing device" refers to a computer system for storing and analyzing collected data.

[1559] "Artificial intelligence" refers to algorithms and programs that analyze data and generate learning materials optimized for users.

[1560] "Individualized Learning Plan" refers to a plan that defines specific learning content and schedules based on the user's individual health and emotional state.

[1561] "Device" refers to an electronic device through which a user receives and completes learning assignments.

[1562] "Gamification" refers to reward systems such as badges and points used to motivate users.

[1563] "Collaborative learning" refers to a learning method in which multiple users work together to solve a learning task.

[1564] An "engine" refers to a program or hardware that detects and analyzes a user's emotional state.

[1565] This invention is a system for preventing cognitive decline and the progression of aging symptoms in the elderly while maintaining health in an enjoyable way. This system works in conjunction with a health management device to generate learning materials optimized for each user and provide a learning environment incorporating collaborative learning with other users and gamification elements. Furthermore, it combines an emotion engine that detects the user's emotional state to maintain the user's motivation and optimize the learning plan.

[1566] System configuration

[1567] This system is broadly composed of the following major hardware and software components:

[1568] Health management devices (e.g., smart watches)

[1569] Device (e.g. smartphone, tablet)

[1570] Server (cloud server)

[1571] Generation AI

[1572] Emotion Engine

[1573] Data collection

[1574] Terminal

[1575] The health management device worn by the user periodically collects physiological data such as heart rate and sleep patterns. Furthermore, the emotion engine analyzes the user's facial expressions and voice data to collect emotional data. This data is temporarily stored on the device.

[1576] Data reception and analysis

[1577] server

[1578] The device sends the collected data to a server, which stores it in a secure database. The stored data is uniquely identified for each user. Processing algorithms are used to analyze the data and calculate the user's health status (heart rate, sleep quality, etc.) and emotional state (happiness, sadness, etc.). This ensures that the user's health and emotional profile is always up to date.

[1579] Generating personalized learning materials

[1580] server

[1581] Based on the analysis results, the generative AI generates learning materials optimized for the user. Specifically, it generates learning materials based on the following information:

[1582] Health data (e.g., heart rate 85, poor sleep quality)

[1583] Emotional data (e.g., recent unstable emotional state)

[1584] User interests (e.g. puzzles, relaxation)

[1585] Cognitive ability

[1586] Past learning history (e.g., three sessions per week are valid)

[1587] Creating a study plan

[1588] server

[1589] Based on the generated learning materials, a learning plan is created based on the user's health and emotional state, including:

[1590] Specific learning tasks (e.g., relaxation sessions, puzzle learning)

[1591] Implementation Schedule

[1592] Reminders based on your emotional state (e.g., "Start relaxing now")

[1593] Presenting and implementing learning tasks

[1594] Terminal

[1595] According to the learning plan, the device notifies and displays daily learning tasks to the user, allowing them to immediately tackle the tasks. In addition, the device evaluates learning progress and emotional state in real time, providing positive feedback and motivational messages at appropriate times.

[1596] Learning progress management and feedback

[1597] server

[1598] The system analyzes progress and emotional data submitted by users to evaluate their learning achievement. Based on the evaluation, rewards such as badges and points are generated and notified to the user. In addition, the emotional engine provides appropriate feedback to maintain motivation for the next learning task.

[1599] Promoting collaborative learning

[1600] Servers and Terminals

[1601] The server generates information on collaborative tasks and challenges to promote collaborative learning with other users and sends it to the device, providing opportunities for users to solve tasks in collaboration with other users and providing appropriate feedback that takes into account emotional data.

[1602] Specific examples

[1603] For example, if a 65-year-old male user wears a smartwatch, the device will collect his heart rate and sleep patterns, and will also analyze his facial expressions and voice using a camera and microphone, and this data will be sent to a server in the morning.

[1604] The server analyzes the received data and detects increases in stress levels and decreases in sleep quality. Based on this, the generative AI generates relaxation sessions and puzzle learning materials. The server then creates an appropriate learning plan, and the device periodically presents learning tasks to the user.

[1605] Specific examples of prompts include:

[1606] Develop learning materials for cognitive maintenance and health management in older adults. Consider the following user information:

[1607] Gender: Male

[1608] Age: 65

[1609] Health data: Heart rate 85, poor sleep quality, high stress level

[1610] Emotional data: Recent unstable emotional state

[1611] Interests: Puzzles, relaxation

[1612] Past learning history: 3 sessions per week are valid

[1613] Generate personalized puzzle learning and relaxation session materials for your users.

[1614] The present invention allows users to have fun while maintaining their health and taking care of both their cognitive and emotional well-being.

[1615] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1616] Step 1: Data collection

[1617] Terminal

[1618] Input: Health monitoring device worn by the user (e.g., smartwatch) and its built-in camera and microphone.

[1619] How it works: The health management device periodically measures physiological data such as heart rate and sleep patterns. Using a camera and microphone, the emotion engine also analyzes the user's facial expressions and voice in real time to collect emotional data.

[1620] Data processing: The collected physiological data is temporarily stored on the device, and the emotion engine converts facial expressions and voice data into emotion indicators.

[1621] Output: Temporarily stored physiological and emotional indicator data.

[1622] Step 2: Receiving and analyzing data

[1623] server

[1624] Input: User physiological and emotional data sent from the device.

[1625] How it works: A server receives the data and stores it in a secure database. Processing algorithms analyze the data for each user and assess their health status, including stress levels, sleep quality, and emotional indicators.

[1626] Data calculation: Analyzes incoming data to calculate health indicators such as stress levels and sleep quality, and converts emotional data into an emotional profile.

[1627] Output: The updated user profile.

[1628] Step 3: Generate personalized learning materials

[1629] server

[1630] Input: Analyzed user profile (health indicators, emotional profile, user interests, cognitive abilities, and past learning history).

[1631] How it works: Generative AI generates optimal learning materials based on the user's health and emotional data.

[1632] Data processing: The generative AI model creates appropriate learning materials based on the prompt. For example, if the user is stressed, it will generate relaxation sessions or puzzle learning materials.

[1633] Output: Personalized learning materials.

[1634] Step 4: Create a study plan

[1635] server

[1636] Input: personalized learning materials and user profile.

[1637] How it works: The server creates a study plan based on the user's health and emotional state. This study plan includes specific tasks, a schedule, and reminders.

[1638] Data calculation: Based on the content of the study materials, specific tasks and schedules, such as three relaxation sessions per week or daily puzzle study, are calculated and reminders are set.

[1639] Output: Learning plan.

[1640] Step 5: Present and implement the learning task

[1641] Terminal

[1642] Input: lesson plan.

[1643] How it works: The device follows a study plan and displays daily learning tasks to the user. As the user works on the tasks, the device monitors their emotional state in real time and displays positive feedback and motivational messages at appropriate times.

[1644] Data processing: Learning progress and emotional data are collected in real time and temporarily stored on the device.

[1645] Output: The training data and the latest emotion data.

[1646] Step 6: Progress monitoring and feedback

[1647] server

[1648] Input: Progress and emotion data sent from the device.

[1649] How it works: The server analyzes progress data and emotion data to evaluate learning achievement. It generates rewards such as badges and points according to achievement and notifies the user. The emotion engine also provides appropriate feedback to maintain motivation for the next learning task.

[1650] Data computation: Applying algorithms to evaluate learning achievement and generate rewards.

[1651] Output: Feedback, badges, points, and other rewards.

[1652] Step 7: Facilitating collaborative learning

[1653] Servers and Terminals

[1654] Input: User profile, collaborative learning data with other users.

[1655] Operation: The server generates information about collaborative tasks and challenges to promote collaborative learning with other users and sends it to the device. The device notifies the user of this information and provides opportunities for collaborative learning.

[1656] Data processing: Optimize the collaborative learning task based on collaborative learning data and participating users' emotional data.

[1657] Output: Detailed information and feedback on collaborative learning.

[1658] In this way, the system of the present invention can provide a personalized learning environment based on the user's health and emotional state.

[1659] (Application example 2)

[1660] 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."

[1661] Elderly people need to maintain their health while having fun, while preventing cognitive decline and the progression of aging symptoms. Furthermore, in order to provide learning materials optimized for each individual user and create an effective learning environment, it is important to properly understand the user's health and emotional state and respond accordingly. However, current systems do not adequately integrate these elements, making it difficult to maintain user motivation and optimize effective learning plans.

[1662] 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.

[1663] In this invention, the server includes means for collecting data from the health management device, means for saving and analyzing the collected data, generation AI means for generating learning materials based on the analyzed data, means for creating a learning plan based on the generated learning materials, terminal means for presenting learning tasks to the user, gamification means for tracking the user's progress and maintaining their motivation, means for promoting collaborative learning with other users, an emotion engine for detecting and evaluating the user's emotional state, and means for maintaining the user's motivation and optimizing the learning plan based on the results of the emotion engine. This makes it possible to provide an individually optimized learning plan based on the user's health and emotional state, allowing the user to maintain their health while having fun and improving their cognitive ability.

[1664] A "health management device" is a device that periodically measures and collects health data such as a user's heart rate and sleep patterns.

[1665] The "server" is a system that contains a database and analytical algorithms for storing and analyzing collected data and assessing the user's health and emotional state.

[1666] "Generative AI" refers to artificial intelligence that generates learning materials optimized for users based on analyzed data.

[1667] "Terminal" refers to a device that presents learning tasks to users and tracks their progress, such as a smartphone or tablet.

[1668] "Gamification" is a technique that incorporates game elements to maintain user motivation and provides rewards such as badges and points.

[1669] "Collaborative learning" is a learning method in which users cooperate with other users to solve learning problems and promote smooth communication.

[1670] The "emotion engine" is a system that detects and evaluates a user's emotional state by analyzing their facial expressions and voice.

[1671] A "study plan" is a specific study schedule and assignments created based on the generated study materials and in accordance with the user's health and emotional state, as well as their learning goals.

[1672] The system of this invention aims to help users maintain their health and improve their cognitive abilities while having fun by effectively monitoring their health and emotional state and providing personalized educational materials based on that information. The main components of this system include:

[1673] 1. Health management device

[1674] A health management device is a device that measures a user's health data, such as heart rate and sleep patterns. A smartwatch is used as an example. The user wears this device all the time to collect daily health data.

[1675] 2. Terminal

[1676] The terminal is a device that presents learning tasks to users and tracks their progress, such as a smartphone or tablet. The terminal also receives data from the health management device and sends it to the server.

[1677] 3. Server

[1678] The server is a system that receives data sent from the terminal, stores it in a database, and analyzes it. The server performs the following processes:

[1679] Health and emotional data analysis

[1680] Update user profiles based on analysis results

[1681] Generating personalized learning materials using generative AI

[1682] Create and manage your study plan

[1683] 4. Generation AI

[1684] Generative AI is an artificial intelligence that generates optimized learning materials based on a user's health and emotional data, interests, cognitive ability, and past learning history. Generative AI uses deep learning algorithms to generate highly accurate learning materials.

[1685] 5. Gamification

[1686] Gamification is a technique that incorporates game elements to keep users motivated and provides rewards such as badges and points.

[1687] 6. Cooperative Learning

[1688] Collaborative learning is a learning method that allows users to cooperate with other users to solve learning problems and promote smooth communication. Through joint tasks and challenges, users support each other emotionally and intellectually.

[1689] 7. Emotion Engine

[1690] The emotion engine is a system that detects and evaluates the user's emotional state by analyzing their facial expressions and voice, which can determine the user's motivation and the optimal timing for reminders.

[1691] Examples:

[1692] Case of User A (65-year-old male)

[1693] health management device

[1694] User A wears a smartwatch to measure his / her heart rate and sleep patterns, and the data from the smartwatch is automatically transferred to the device every day.

[1695] Terminal

[1696] User A's smartphone receives data from the health management device and transmits it to the server. It also presents User A with a study plan and study tasks from the server.

[1697] server

[1698] The server analyzes the data sent from the device and detects that User A's stress level is increasing, his sleep quality is declining, and his recent emotional state is unstable. Based on this, the generative AI generates relaxation sessions and puzzle learning materials.

[1699] Generation AI

[1700] The generative AI generates optimized relaxation sessions and puzzle learning materials based on user A's health and emotional state.

[1701] Learning Plan

[1702] The server creates a study plan that includes three relaxation sessions per week and daily puzzles.

[1703] Gamification

[1704] A badge is awarded to User A every time he or she completes a task, helping the user to stay motivated and progress in their studies.

[1705] cooperative learning

[1706] The server provides opportunities for collaborative learning with other users, allowing user A to jointly solve problems and receive emotional support.

[1707] Emotion Engine

[1708] The emotion engine detects user A's emotional state and provides feedback and messages at the right time to increase motivation.

[1709] These elements work together to enable users to maintain their health and improve their cognitive function while having fun. A specific generative AI model could be a deep learning model such as GPT-3. An example of a prompt would be, "Generate relaxation session materials based on heart rate and sleep data."

[1710] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1711] Step 1:

[1712] The terminal collects and stores data from the health management device.

[1713] Input: Health data such as the user's heart rate and sleep patterns sent from health monitoring devices.

[1714] Output: The acquired health data is saved on the device.

[1715] The terminal periodically acquires data from the health management device and stores it in its internal memory, which is used in subsequent processing steps.

[1716] Step 2:

[1717] The device uses an emotion engine to collect user emotion data.

[1718] Input: Facial expression data captured by the device's camera, voice data captured by the microphone.

[1719] Output: The analyzed emotion data is saved on the device.

[1720] The device uses a camera and microphone to collect the user's facial expressions and voice, which are then analyzed by an emotion engine, resulting in an emotional index such as positive or negative.

[1721] Step 3:

[1722] The device transmits the collected health and emotion data to a server.

[1723] Input: Health data and emotional data stored on the device.

[1724] Output: The transmitted data is saved on the server.

[1725] The device periodically uploads this data to the server, which stores the received data in a database.

[1726] Step 4:

[1727] The server analyzes the received data and assesses the user's health and emotional state.

[1728] Input: Health data and emotion data stored on the server.

[1729] Output: Evaluated health status indicators, emotional status indicators.

[1730] The server uses analytical algorithms to calculate stress levels, sleep quality, and emotional indicators, and updates them into the user profile.

[1731] Step 5:

[1732] The server uses generative AI to generate personalized teaching materials based on the analysis results.

[1733] Input: Assessed health status indicators, emotional state indicators, user interests and past learning history.

[1734] Output: Optimized learning materials.

[1735] Based on the evaluation results and the user's profile information, the generative AI generates learning materials tailored to the user, such as relaxation sessions or puzzle learning materials.

[1736] Step 6:

[1737] The server creates a learning plan based on the generated learning materials.

[1738] Input: Optimized learning materials, assessed health status indicators and emotional state indicators.

[1739] Output: Personalized study plan.

[1740] The server creates a learning plan based on the learning materials and the user's status, including specific learning tasks and their implementation schedule.

[1741] Step 7:

[1742] The device presents learning tasks to the user.

[1743] Input: The lesson plan sent from the server.

[1744] Output: The learning task displayed on the screen.

[1745] The device presents users with tasks at specific times each day according to their learning plan, and when they complete the tasks, they are awarded badges and points.

[1746] Step 8:

[1747] The device tracks the user's progress and feeds the data back to the server.

[1748] Input: User's learning task completion status, emotional data.

[1749] Output: Progress and emotion data sent to the server.

[1750] Each time the user completes a task, the device collects progress data and emotional data at that time and sends it to the server.

[1751] Step 9:

[1752] The server provides gamification elements based on the feedback.

[1753] Input: Progress and emotion data sent from the device.

[1754] Output: Rewards (badges, points) to the user, feedback message.

[1755] The server rewards the user according to their progress, generates feedback messages based on their emotional state at appropriate times, and sends them to the device.

[1756] Step 10:

[1757] The server generates collaborative tasks to promote collaborative learning with other users and provides them through the terminals.

[1758] Input: Data of multiple users, collaborative assignment template.

[1759] Output: Collaborative learning tasks, contact information between users.

[1760] The server analyzes data from multiple users, generates tasks optimized for effective collaborative learning, and provides them to users via their devices.

[1761] Through these steps, users can maintain their health and improve their cognitive function while having fun. The system utilizes a deep learning model, known as a "generative AI model," to provide an optimal learning experience for each individual user. For example, it can generate specific learning materials using a prompt such as, "Please generate relaxation session materials based on heart rate and sleep data."

[1762] 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.

[1763] 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.

[1764] 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.

[1765] [Fourth embodiment]

[1766] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1767] 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.

[1768] 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).

[1769] 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.

[1770] 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.

[1771] 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).

[1772] 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.

[1773] 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.

[1774] 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.

[1775] 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.

[1776] 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.

[1777] 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.

[1778] 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."

[1779] This invention is a system for preventing cognitive decline and the progression of aging symptoms in the elderly while maintaining health in an enjoyable manner. This system works in conjunction with a health management device, generates learning materials optimized for each user, and provides a learning environment that incorporates collaborative learning with other users and gamification elements.

[1780] System Overview

[1781] The system consists of the following main components:

[1782] 1. Health management device

[1783] 2. Terminal

[1784] 3. Server

[1785] 4. Generation AI

[1786] 5. Study Plan

[1787] 6. Gamification

[1788] 7. Cooperative Learning

[1789] Overview of program processing

[1790] 1. Data Collection

[1791] Terminal

[1792] Health data such as heart rate and sleep patterns are periodically collected from a health management device worn by the user (e.g., a smartwatch). The data collected by the device is automatically sent to a server.

[1793] 2. Data Receipt and Analysis

[1794] server

[1795] The server stores and analyzes the data received from the device, and an analysis algorithm evaluates the user's health status and calculates health indicators such as stress level and sleep quality.

[1796] 3. Generating personalized learning materials

[1797] server

[1798] The server-based AI generates optimal learning materials based on the analyzed health indicators, and the materials are customized to the user's cognitive ability, past learning history, and interests.

[1799] 4. Create a study plan

[1800] server

[1801] Based on the generated learning materials, a learning plan is created based on the user's health condition and learning goals, including specific tasks and schedules.

[1802] 5. Presentation and implementation of learning tasks

[1803] Terminal

[1804] The device presents the user with daily tasks based on the learning plan, and as the learning progresses, the device receives feedback from the user and sends progress data to a server.

[1805] 6. Learning progress management and feedback

[1806] server

[1807] The server analyzes the progress data and awards badges and points according to the level of achievement, which helps to maintain the user's motivation and encourages them to continue learning.

[1808] 7. Promoting collaborative learning

[1809] Servers and Terminals

[1810] To promote collaborative learning with other users, the server generates information about collaborative tasks and challenges and sends it to the device, which displays the information and provides the user with an opportunity to collaborate with other users to solve the tasks.

[1811] Specific examples

[1812] Case of User A (65-year-old male)

[1813] Terminal

[1814] User A's smartwatch measures his / her heart rate and sleep patterns, and the device sends this data to a server every morning.

[1815] server

[1816] The server analyzes the received data and detects that User A's stress level is increasing and the quality of his or her sleep is declining. Based on this, the generative AI generates relaxation sessions and puzzle learning materials.

[1817] server

[1818] The server creates a study plan based on user A's health status, including three relaxation sessions per week and daily puzzle practice.

[1819] Terminal

[1820] The device presents learning tasks to User A and awards a badge each time it is completed. It also provides opportunities for collaborative learning with other users, allowing User A to solve the tasks together.

[1821] Server and Device

[1822] Data is continuously collected and the learning plan is optimized according to the health condition of User A. The system is also continuously improved based on feedback.

[1823] This system allows users to have fun while maintaining their health and improving their cognitive function.

[1824] The processing flow will be explained below.

[1825] Step 1:

[1826] Terminal

[1827] A health monitoring device worn by a user (e.g., a smartwatch) periodically measures health data such as heart rate and sleep patterns, and the measurement data is stored on the device.

[1828] Step 2:

[1829] Terminal

[1830] The stored health data is automatically sent to the server at regular intervals, and if the transmission is successful, the device proceeds to the next data collection cycle.

[1831] Step 3:

[1832] server

[1833] The data sent from the device is received and stored in a database, which identifies each user and prepares it for later analysis.

[1834] Step 4:

[1835] server

[1836] The received data is analyzed. An analytical algorithm is used to assess the user's health and calculate health indicators such as stress level and sleep quality. The results are updated in the user profile.

[1837] Step 5:

[1838] server

[1839] Based on the analysis results, the generative AI generates learning materials optimized for each user, taking into account the user's interests, cognitive ability, and past learning history.

[1840] Step 6:

[1841] server

[1842] Based on the generated learning materials, a learning plan is created, which includes specific learning tasks, implementation schedules, reminders, etc.

[1843] Step 7:

[1844] Terminal

[1845] According to the study plan sent from the server, daily study tasks are presented to the user. The tasks are displayed and the user can work on them.

[1846] Step 8:

[1847] User

[1848] Users work on the presented learning tasks, and when they complete the tasks, the device records their progress and sends it to the server.

[1849] Step 9:

[1850] server

[1851] The system analyzes the progress data sent by users, evaluates their achievement, and generates rewards such as badges and points according to their achievement, thereby increasing user motivation.

[1852] Step 10:

[1853] server

[1854] To provide opportunities for collaborative learning with other users, collaborative tasks and challenges are generated and sent to devices, encouraging users to learn collaboratively with others.

[1855] Step 11:

[1856] Terminal

[1857] Users will be notified of collaborative learning and challenges, allowing them to deepen their learning by solving collaborative challenges together with other users.

[1858] Step 12:

[1859] Servers and Terminals

[1860] The system continuously collects and analyzes data to optimize learning plans and materials based on the user's health condition and learning progress, and improves the system as a whole based on user feedback.

[1861] This series of steps allows seniors to have fun while maintaining their health and improving cognitive function.

[1862] Example 1

[1863] 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."

[1864] There is a need for systems that allow elderly people to maintain their health while having fun, while preventing cognitive decline and the progression of aging symptoms. However, conventional systems have difficulty in providing personalized responses based on each user's health condition, and lack mechanisms to maintain motivation or promote collaborative learning with other users. This often makes it difficult for users to continue managing their health and learning.

[1865] 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.

[1866] In this invention, the server includes a means for storing and analyzing data collected from the health management device, a means for calculating the user's health index using an analysis algorithm, and a generation AI means for generating learning materials based on the analyzed health index. This makes it possible to provide personalized learning materials according to the user's health condition, maintain motivation, and promote cooperative learning with other users.

[1867] A "health management device" is a device that monitors a user's health status and collects data such as heart rate and sleep patterns.

[1868] "Means for collecting data" refers to a method for obtaining health data such as heart rate and sleep patterns from health management devices.

[1869] The "means for transmitting data to the server" refers to a communication means for transferring collected health data to the server.

[1870] A "server" is a computer system that receives, stores, and analyzes collected data.

[1871] An "analysis algorithm" is a calculation method for analyzing collected health data and calculating the user's health indicators.

[1872] "Generative AI" is an artificial intelligence technology that generates optimal learning materials for users based on analyzed health indicators.

[1873] A "study plan" is a specific study schedule based on the generated study materials and adapted to the user's health condition and study goals.

[1874] A "terminal" is a device that presents learning tasks to a user and accepts operations.

[1875] "Progress data" is data that indicates the results of a user's learning tasks, and is sent from the terminal to the server.

[1876] "Gamification" is a reward system that uses badges and points to maintain user motivation and encourage continued learning.

[1877] "Collaborative learning" is a method in which multiple users work together to complete a learning task and achieve results.

[1878] The present invention is a system for preventing cognitive decline and the progression of aging symptoms in the elderly while maintaining health in an enjoyable manner. This system works in conjunction with a health management device, generates learning materials optimized for each user, and provides a learning environment that incorporates collaborative learning with other users and gamification elements. Specific embodiments of the system are described below.

[1879] The system consists of the following main components: health management device, terminal, server, generative AI, learning plan, gamification, and collaborative learning.

[1880] A user's health monitoring device (e.g., a smartwatch) periodically measures health data such as heart rate and sleep patterns. This data is automatically transmitted by the device to a server. The device transmits the data using a secure communication protocol (e.g., HTTPS) without requiring any user interaction.

[1881] The server stores the received health data in a database and uses an analytical algorithm to calculate health indicators, including stress levels and sleep quality. Based on the analysis results, a generative AI on the server generates learning materials optimal for the user. The materials are customized taking into account the user's cognitive ability, past learning history, and interests.

[1882] A study plan is created based on the generated learning materials. For example, a specific schedule may be set up, including three relaxation sessions per week and daily puzzle practice. This plan is saved on the server and presented to the user via their device.

[1883] The device notifies the user of daily learning tasks. For example, a notification such as "Please complete a 20-minute relaxation session today" is displayed in the morning. As the user completes the task, progress data is sent from the device to the server.

[1884] The server analyzes progress data and has a gamification function that automatically awards badges and points according to achievement, thereby maintaining user motivation. Progress reports are also generated periodically and displayed on the device.

[1885] To promote collaborative learning with other users, the server generates collaborative learning tasks and challenges and sends them to the device. For example, a task might be displayed such as, "Form a team of three people and take part in a one-week walking challenge." Users collaborate with other users to complete the task, and the device reports the results to the server.

[1886] As a specific example, consider a 65-year-old male user A. His smartwatch measures his heart rate and sleep patterns and sends the data to a server daily. The server analyzes the data and detects rising stress levels and declining sleep quality. A generative AI generates relaxation session and puzzle learning materials, and a learning plan is set up with three relaxation sessions per week and daily puzzle learning. Every morning, the device notifies user A of the learning tasks and awards a badge each time one is completed. Health data is continuously collected, and the learning plan is optimized according to user A's latest health condition.

[1887] Here are some example prompts that can be input to a generative AI model:

[1888] "Generate a study plan for User A, a 65-year-old male, who is in a state of high stress and low sleep quality, combining relaxation sessions and puzzle learning three times a week."

[1889] This system allows users to have fun while maintaining their health and improving their cognitive function.

[1890] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1891] System program processing steps

[1892] Step 1: Collecting health data

[1893] Terminal

[1894] The device periodically collects health data such as heart rate and sleep patterns from the user's health management device (e.g., smartwatch).

[1895] Input: Heart rate and sleep pattern data from smartwatch

[1896] Output: Collected health data

[1897] How it works: The device collects data from the smartwatch every hour and temporarily stores the collected data in its internal memory. For example, data points such as a heart rate of 70 BPM and 7 hours of sleep are collected.

[1898] Step 2: Sending data

[1899] Terminal

[1900] The device transmits the collected health data to a server in real time.

[1901] Input: Collected health data

[1902] Output: Data sent to the server

[1903] How it works: The device sends encrypted data using a secure communication protocol (e.g., HTTPS). No user interaction is required; everything happens automatically.

[1904] Step 3: Receiving and storing data

[1905] server

[1906] The server receives the health data sent from the device and stores it in a database.

[1907] Input: Health data sent from the device

[1908] Output: Data stored in the database

[1909] What it does: The server validates the data it receives and stores it in a database based on each user's unique identifier. It then merges it with past data to maintain a continuous record.

[1910] Step 4: Analyze the data

[1911] server

[1912] The server analyzes the stored health data using an analytical algorithm and calculates the user's health index.

[1913] Input: Stored health data

[1914] Output: Calculated health indicators (e.g., stress level, sleep quality)

[1915] How it works: The server uses data analysis software to calculate stress levels and sleep quality based on heart rate, sleep patterns, etc. For example, an increase in heart rate is considered to be an increase in stress.

[1916] Step 5: Generate personalized learning materials

[1917] server

[1918] The generation AI on the server generates learning materials that are optimal for the user based on the analyzed health indicators.

[1919] Input: Analyzed health indicators

[1920] Output: Personalized learning materials

[1921] How it works: Generative AI uses health indicators, the user's cognitive ability, past learning history, and interests as input data to generate learning materials. For example, if high stress is detected, relaxation sessions and puzzle learning materials will be generated.

[1922] Step 6: Create a study plan

[1923] server

[1924] Based on the generated teaching materials, a learning plan is created based on the user's health condition and learning goals.

[1925] Input: Generated learning materials, user's health status

[1926] Output: A specific study plan (e.g., three relaxation sessions per week)

[1927] What happens: The server uses a scheduling algorithm to plan an optimal schedule for the user, for example, setting relaxation sessions on Mondays, Wednesdays, and Fridays.

[1928] Step 7: Present and implement the learning task

[1929] Terminal

[1930] The device presents the user with daily assignments based on their learning plan.

[1931] Input:Study plan

[1932] Output: Notification to user, data on completed assignments

[1933] Specific operation: The device displays a notification every morning, prompting the user to "take a relaxation session today." The user conducts the session and enters their progress into the device.

[1934] Step 8: Progress monitoring and feedback

[1935] server

[1936] The server analyzes the progress data and awards badges and points based on the level of achievement.

[1937] Input: Progress data from the terminal

[1938] Output: Achievements, badges, points

[1939] What it does: The server updates the reward system based on the progress data, adds badges and points to the user's account, and periodically generates progress reports and sends them to the device.

[1940] Step 9: Facilitating collaborative learning

[1941] Servers and Terminals

[1942] To promote collaborative learning with other users, the server generates information on collaborative tasks and challenges and sends them to the terminals.

[1943] Input: Progress data, collaborative learning settings

[1944] Output: Collaborative tasks, progress by team

[1945] Specific operation: The server generates collaborative learning data and displays on the device, "Let's take part in a one-week walking challenge in teams of three." The user performs the challenge, and the device reports the results to the server.

[1946] Through each step, users can have fun while maintaining their health and improving their cognitive function.

[1947] (Application example 1)

[1948] 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."

[1949] In modern society, health management and maintaining / improving cognitive function for the elderly are important issues. In particular, effective methods are needed to prevent cognitive decline while maintaining health in a fun way for the elderly. It is also important to strengthen social connections and prevent isolation by providing an environment in physical stores where the elderly can learn and train in cooperation with other users. Furthermore, providing personalized health programs and learning materials to the elderly is thought to lead to sustainable health management and maintaining motivation.

[1950] 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.

[1951] In this invention, the server includes means for collecting data from a health management device to monitor the user's health status, means for storing and analyzing the collected data, means for generating learning materials based on the analyzed data, means for creating a learning plan based on the generated learning materials, means for presenting learning tasks to the user based on the learning plan, means for gamification to track the user's progress and maintain motivation, means for promoting collaborative learning with other users, means for analyzing data collected through the health management device and smart devices in the physical store and suggesting optimal learning materials and health programs to the user, and means for providing opportunities to complete learning tasks through collaboration with other users. This enables elderly people to maintain their health, improve their cognitive function, and strengthen their social connections while having fun in the physical store.

[1952] "User" refers to an individual who uses the system to manage their health or undertake learning tasks.

[1953] "Health status" refers to data that indicates the user's physical condition and health, such as heart rate, blood pressure, weight, body fat percentage, and sleep patterns.

[1954] A "health management device" is a device that can collect health data such as heart rate and sleep patterns, and examples include smartwatches and blood pressure monitors.

[1955] "Server" refers to a computer system connected to the Internet that stores and analyzes collected data.

[1956] "Generative AI" refers to artificial intelligence that automatically generates optimal learning materials for users based on collected and analyzed data.

[1957] A "study plan" refers to a plan based on the generated teaching materials that includes specific tasks and schedules tailored to the user's health condition and learning goals.

[1958] A "device" is a device that presents a user with tasks based on a learning plan and receives feedback from the user, including, for example, a smartphone or tablet.

[1959] "Gamification" refers to a method of keeping users motivated by tracking their progress and awarding badges and points based on their achievements.

[1960] "Collaborative learning" refers to learning methods and activities that involve working with other users to solve learning problems.

[1961] A "physical store" refers to a physical store that users can actually visit, where health management equipment and smart devices are installed.

[1962] A "smart device" is an electronic device that allows users to collect health data or check their learning plans, and examples include smartphones and tablets.

[1963] "Learning Materials" refers to various learning content provided to users, including question sets, quizzes, relaxation sessions, etc.

[1964] "Health Program" refers to the specific content and schedule of training, exercises, etc. aimed at improving the user's health.

[1965] "Study assignments" refer to specific tasks or problems that users should tackle based on their study plan.

[1966] This invention is a system for a brick-and-mortar store that supports health management and cognitive function improvement for the elderly. Specific embodiments for realizing this system will be described in detail below.

[1967] System Overview

[1968] The system consists of the following components to monitor users' health status and provide personalized learning materials:

[1969] 1. Health monitoring devices: Devices such as smartwatches collect data such as heart rate, blood pressure, weight, body fat percentage, and sleep patterns.

[1970] 2. Device: A device such as a smartphone or tablet presents learning tasks to users and receives feedback.

[1971] 3. Server: A cloud server for storing and analyzing data.

[1972] 4. Generative AI: Artificial intelligence that generates learning materials based on analyzed data.

[1973] 5. Learning plan: A specific schedule for the user's health management and learning using the generated learning materials.

[1974] 6. Gamification: Awarding badges and points to keep users motivated.

[1975] 7. Collaborative learning: A mechanism that provides opportunities for users to work with others to complete learning tasks.

[1976] Hardware and software used

[1977] Health management devices: smart watches, blood pressure monitors, body composition monitors

[1978] Devices: smartphones (iOS, Android), tablets

[1979] Server: Cloud server (AWS, Google Cloud Platform)

[1980] Generative AI: GPT-based models (e.g., GPT-3, GPT-4)

[1981] Data analysis algorithms: Python, TensorFlow

[1982] Process Overview

[1983] Data collection

[1984] When a customer visits a physical store, their health data, such as heart rate, blood pressure, weight, body fat percentage, and sleep patterns, is collected from their health monitoring device, such as a smartwatch, and this data is sent to their smartphone via Bluetooth or Wi-Fi, and then sent to a cloud server.

[1985] Data reception and analysis

[1986] The server stores and analyzes the received data, and algorithms evaluate health indicators such as heart rate and weight fluctuations to calculate stress levels and sleep quality.

[1987] Generating personalized learning materials

[1988] Based on the analyzed health indicators, generative AI generates optimal learning materials for each user, customized to their cognitive abilities, past learning history, and interests.

[1989] Creating a study plan

[1990] Based on the generated learning materials, the server creates a learning plan based on the user's health condition and learning goals, including specific tasks and schedules.

[1991] Presenting and implementing learning tasks

[1992] The device presents the user with daily tasks based on the learning plan, and as the learning progresses, the device receives feedback from the user and sends progress data to a server.

[1993] Learning progress management and feedback

[1994] The server analyzes progress data and awards badges and points according to achievement, which helps maintain user motivation and encourages continued learning.

[1995] Promoting collaborative learning

[1996] To promote collaborative learning with other users, the server generates information about collaborative tasks and challenges and sends it to the device, which displays the information and provides the user with an opportunity to collaborate with other users to solve the tasks.

[1997] Specific examples

[1998] Case of User A (70-year-old woman)

[1999] Data collection: User A visits the store and collects data from their smartwatch. The smartphone then sends this data to the server.

[2000] Data analysis: The server analyzes User A's data and detects that his stress level is high and his sleep quality is poor.

[2001] Personalized learning material generation: Generative AI generates learning materials for relaxation sessions and kanji quizzes.

[2002] Creating a study plan: The server creates a study plan for User A that includes two relaxation sessions per week and daily kanji quizzes.

[2003] Completing learning tasks: The device presents learning tasks to User A and awards points each time they are completed.

[2004] Feedback and progress management: The server analyzes your progress data and awards badges to motivate you.

[2005] Promoting collaborative learning: Collaborative learning with other users is provided, and User A solves problems collaboratively.

[2006] Example prompts to input to the generative AI model

[2007] User A's health data:

[2008] Heart rate: 75 bpm, sleep time: 5 hours

[2009] Prompt the generative AI model:

[2010] This user has high stress levels and poor sleep quality. Generate learning materials to improve this user's cognitive function and maintain their health. Suggest easy kanji quizzes and relaxation sessions based on their past learning history.

[2011] This system allows seniors to maintain their health and improve their cognitive function while having fun in a physical store.

[2012] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2013] Step 1:

[2014] Data collection

[2015] When a user visits a physical store, health management devices (smartwatches, blood pressure monitors, body composition scales, etc.) collect health data such as heart rate, blood pressure, weight, body fat percentage, sleep patterns, etc. This data is sent to a smartphone via Bluetooth or Wi-Fi, and from there to a cloud server.

[2016] Input: Health data obtained from health monitoring devices.

[2017] Output: Health data sent to your smartphone.

[2018] Step 2:

[2019] Data reception and analysis

[2020] The server stores the health data received from the smartphone and uses analytical algorithms to evaluate health indicators such as heart rate and weight fluctuations, and calculate health conditions such as stress levels and sleep quality.

[2021] Input: Health data sent from your smartphone.

[2022] Output: Analyzed health indicators and health status data.

[2023] Step 3:

[2024] Generating personalized learning materials

[2025] Based on the analyzed data, the generative AI generates learning materials that are best suited to the user, such as relaxation sessions if stress levels are high, or kanji quizzes to maintain cognitive function.

[2026] Input: Analyzed health indicators and health status data.

[2027] Output: Personalized learning materials.

[2028] Step 4:

[2029] Creating a study plan

[2030] Based on the generated learning materials, the server creates a learning plan based on the user's health condition and learning goals, including specific tasks and schedules.

[2031] Input: Personalized learning materials and health status data.

[2032] Output: A personalized study plan.

[2033] Step 5:

[2034] Presenting and implementing learning tasks

[2035] The device presents the user with daily tasks based on the learning plan. As the user completes the tasks, the device receives feedback and sends progress data to the server.

[2036] Enter: personalized study plans.

[2037] Output: User's progress on the learning task.

[2038] Step 6:

[2039] Learning progress management and feedback

[2040] The server analyzes the progress data and awards badges and points according to the level of achievement, thereby maintaining the user's motivation.

[2041] Input: User's progress data on learning tasks.

[2042] Output: Parsed progress data and rewards (badges, points).

[2043] Step 7:

[2044] Promoting collaborative learning

[2045] To promote collaborative learning with other users, the server generates information about collaborative tasks and challenges and sends it to the device, which displays the information and provides the user with an opportunity to collaborate with other users to solve the tasks.

[2046] Input: System-wide training data and collaborative learning candidates.

[2047] Output: Information on collaborative tasks and notifications for collaborative learning.

[2048] 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.

[2049] This invention is a system for preventing cognitive decline and the progression of aging symptoms in the elderly while maintaining health in an enjoyable way. This system works in conjunction with a health management device to generate learning materials optimized for each user and provide a learning environment incorporating collaborative learning with other users and gamification elements. Furthermore, by combining it with an emotion engine that detects the user's emotional state, it aims to maintain the user's motivation and optimize their learning plan.

[2050] System Overview

[2051] The system consists of the following main components:

[2052] 1. Health management device

[2053] 2. Terminal

[2054] 3. Server

[2055] 4. Generation AI

[2056] 5. Study Plan

[2057] 6. Gamification

[2058] 7. Cooperative Learning

[2059] 8. Emotion Engine

[2060] Overview of program processing

[2061] 1. Data Collection

[2062] Terminal

[2063] A health management device worn by the user (e.g., a smartwatch) periodically measures health data such as heart rate and sleep patterns. In addition, an emotion engine analyzes the user's facial expressions and voice data to collect emotional data. The measurement data and emotional data are stored on the device.

[2064] 2. Data Receipt and Analysis

[2065] server

[2066] The data sent from the device is received and stored in a database. The stored data is identified for each user and prepared for later analysis. The analysis algorithm evaluates the user's health and emotional state and calculates stress levels, sleep quality, and emotional indicators. These calculation results are updated in the user profile.

[2067] 3. Generating personalized learning materials

[2068] server

[2069] Based on the analysis results, the generative AI generates learning materials optimized for each user, taking into account health data, emotional data, user interests, cognitive ability, and past learning history.

[2070] 4. Create a study plan

[2071] server

[2072] Based on the generated learning materials, a learning plan is created based on the user's health and emotional state, as well as their learning goals, including specific tasks, implementation schedules, and emotional reminders.

[2073] 5. Presentation and implementation of learning tasks

[2074] Terminal

[2075] The system presents daily learning tasks to users according to a learning plan sent from the server. The tasks are displayed and the user can work on them. Based on the user's emotional state, positive feedback and motivational messages are displayed at appropriate times.

[2076] 6. Learning progress management and feedback

[2077] server

[2078] The system analyzes progress and emotional data sent by users to evaluate their achievements, generates rewards such as badges and points based on their achievements, and motivates users. The emotional engine also provides appropriate feedback based on the user's current emotional state.

[2079] 7. Promoting collaborative learning

[2080] Servers and Terminals

[2081] To promote collaborative learning with other users, the server generates information about joint tasks and challenges and sends it to the device. The device displays this information, providing users with opportunities to collaborate with others to solve the tasks. Furthermore, the system optimizes collaborative learning by taking into account emotional data, promoting smooth communication.

[2082] Specific examples

[2083] Case of User A (65-year-old male)

[2084] Terminal

[2085] User A's smartwatch measures his / her heart rate and sleep patterns, while the device's camera and microphone analyze his / her facial expressions and voice to collect emotional data, which is then sent to a server every morning.

[2086] server

[2087] The server analyzes the received data and detects that User A's stress level is increasing, his sleep quality is declining, and his recent emotional state is unstable. Based on this, the generative AI generates relaxation sessions and puzzle learning materials.

[2088] server

[2089] The server creates a study plan based on user A's health and emotional state, including three relaxation sessions per week and daily puzzle practice.

[2090] Terminal

[2091] The device presents learning tasks to User A and awards a badge each time the task is completed. It also takes into account User A's emotional data and displays appropriate feedback and motivational messages. Furthermore, it provides opportunities for collaborative learning with other users, allowing User A to solve tasks together and receive emotional support.

[2092] Server and Device

[2093] It continuously collects data and optimizes the learning plan according to the health and emotional state of User A. It also improves the entire system based on user feedback.

[2094] The system allows users to have fun while maintaining their health and taking care of both their cognitive and emotional functions.

[2095] The processing flow will be explained below.

[2096] Step 1:

[2097] Terminal

[2098] A health management device worn by a user (e.g., a smartwatch) periodically measures health data such as heart rate and sleep patterns. In addition, the device's camera and microphone analyze the user's facial expressions and voice to collect emotional data. The collected health and emotional data is temporarily stored on the device.

[2099] Step 2:

[2100] Terminal

[2101] The collected health and emotion data is automatically transmitted to the server at regular intervals, and if the transmission is successful, the device proceeds to the next data collection cycle.

[2102] Step 3:

[2103] server

[2104] The data sent from the device is received and stored in a database, which identifies the user and prepares it for later analysis.

[2105] Step 4:

[2106] server

[2107] The received data is analyzed. Analysis algorithms are used to assess the user's health status (e.g., stress level, sleep quality) and emotional state (e.g., stress, happiness). The assessment results are updated in the user profile.

[2108] Step 5:

[2109] server

[2110] Based on the analysis results, the generative AI generates learning materials optimized for each user, taking into account health data, emotional data, user interests, cognitive ability, and past learning history.

[2111] Step 6:

[2112] server

[2113] Based on the generated learning materials, a study plan is created, which includes specific study tasks, implementation schedules, and emotional state-based reminders.

[2114] Step 7:

[2115] Terminal

[2116] The system presents daily learning tasks to users according to a learning plan sent from the server. The tasks are displayed and the user can work on them. Based on the user's emotional state, positive feedback and motivational messages are displayed at appropriate times.

[2117] Step 8:

[2118] User

[2119] Users work on the presented learning tasks, and when they complete the tasks, the device records their progress and sends it to the server.

[2120] Step 9:

[2121] server

[2122] The system analyzes progress and emotional data sent by users to evaluate their achievements, generates rewards such as badges and points based on their achievements, and motivates users. The emotional engine also provides appropriate feedback based on the user's current emotional state.

[2123] Step 10:

[2124] server

[2125] To provide opportunities for collaborative learning with other users, collaborative tasks and challenges are generated and sent to devices, encouraging users to learn collaboratively with others.

[2126] Step 11:

[2127] Terminal

[2128] Users will be notified of collaborative learning and challenges, allowing them to deepen their learning by solving collaborative challenges together with other users.

[2129] Step 12:

[2130] Server and Device

[2131] The system continuously collects and analyzes data to optimize learning plans and materials based on the user's health, emotional state, and learning progress, and also improves the system as a whole based on user feedback.

[2132] This series of steps allows users to have fun while maintaining their health and taking care of their cognitive and emotional well-being.

[2133] Example 2

[2134] 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."

[2135] Maintaining health while having fun while preventing cognitive decline and the progression of aging symptoms in the elderly is a social challenge. However, conventional health management and learning systems have difficulty providing personalized services that adapt to the health and emotional state of each individual user, and have not adequately maintained user motivation or provided emotional care. This has led to problems such as insufficient learning effects and health maintenance.

[2136] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2137] In this invention, the server includes means for collecting data from a device that collects physiological data, information processing means for saving and analyzing the collected data, artificial intelligence means for generating personalized learning materials based on the analyzed data, means for creating an individualized learning plan based on the generated learning materials, terminal means for presenting learning tasks to the user based on the learning plan, means including game elements for tracking the user's progress and maintaining motivation, means for promoting collaborative learning with other users, and engine means for detecting and analyzing the user's emotional state. This makes it possible to provide personalized learning materials and learning plans adapted to the user's health and emotional state, maintaining health while having fun, and caring for both cognitive function and emotions.

[2138] "Physiological data" refers to data about a user's biological body, such as their heart rate, sleep patterns, and activity levels.

[2139] "Device" refers to any health management equipment or terminal worn or used by a user.

[2140] "Information processing device" refers to a computer system for storing and analyzing collected data.

[2141] "Artificial intelligence" refers to algorithms and programs that analyze data and generate learning materials optimized for users.

[2142] "Individualized Learning Plan" refers to a plan that defines specific learning content and schedules based on the user's individual health and emotional state.

[2143] "Device" refers to an electronic device through which a user receives and completes learning assignments.

[2144] "Gamification" refers to reward systems such as badges and points used to motivate users.

[2145] "Collaborative learning" refers to a learning method in which multiple users work together to solve a learning task.

[2146] An "engine" refers to a program or hardware that detects and analyzes a user's emotional state.

[2147] This invention is a system for preventing cognitive decline and the progression of aging symptoms in the elderly while maintaining health in an enjoyable way. This system works in conjunction with a health management device to generate learning materials optimized for each user and provide a learning environment incorporating collaborative learning with other users and gamification elements. Furthermore, it combines an emotion engine that detects the user's emotional state to maintain the user's motivation and optimize the learning plan.

[2148] System configuration

[2149] This system is broadly composed of the following major hardware and software components:

[2150] Health management devices (e.g., smart watches)

[2151] Device (e.g. smartphone, tablet)

[2152] Server (cloud server)

[2153] Generation AI

[2154] Emotion Engine

[2155] Data collection

[2156] Terminal

[2157] The health management device worn by the user periodically collects physiological data such as heart rate and sleep patterns. Furthermore, the emotion engine analyzes the user's facial expressions and voice data to collect emotional data. This data is temporarily stored on the device.

[2158] Data reception and analysis

[2159] server

[2160] The device sends the collected data to a server, which stores it in a secure database. The stored data is uniquely identified for each user. Processing algorithms are used to analyze the data and calculate the user's health status (heart rate, sleep quality, etc.) and emotional state (happiness, sadness, etc.). This ensures that the user's health and emotional profile is always up to date.

[2161] Generating personalized learning materials

[2162] server

[2163] Based on the analysis results, the generative AI generates learning materials optimized for the user. Specifically, it generates learning materials based on the following information:

[2164] Health data (e.g., heart rate 85, poor sleep quality)

[2165] Emotional data (e.g., recent unstable emotional state)

[2166] User interests (e.g. puzzles, relaxation)

[2167] Cognitive ability

[2168] Past learning history (e.g., three sessions per week are valid)

[2169] Creating a study plan

[2170] server

[2171] Based on the generated learning materials, a learning plan is created based on the user's health and emotional state, including:

[2172] Specific learning tasks (e.g., relaxation sessions, puzzle learning)

[2173] Implementation Schedule

[2174] Reminders based on your emotional state (e.g., "Start relaxing now")

[2175] Presenting and implementing learning tasks

[2176] Terminal

[2177] According to the learning plan, the device notifies and displays daily learning tasks to the user, allowing them to immediately tackle the tasks. In addition, the device evaluates learning progress and emotional state in real time, providing positive feedback and motivational messages at appropriate times.

[2178] Learning progress management and feedback

[2179] server

[2180] The system analyzes progress and emotional data submitted by users to evaluate their learning achievement. Based on the evaluation, rewards such as badges and points are generated and notified to the user. In addition, the emotional engine provides appropriate feedback to maintain motivation for the next learning task.

[2181] Promoting collaborative learning

[2182] Servers and Terminals

[2183] The server generates information on collaborative tasks and challenges to promote collaborative learning with other users and sends it to the device, providing opportunities for users to solve tasks in collaboration with other users and providing appropriate feedback that takes into account emotional data.

[2184] Specific examples

[2185] For example, if a 65-year-old male user wears a smartwatch, the device will collect his heart rate and sleep patterns, and will also analyze his facial expressions and voice using a camera and microphone, and this data will be sent to a server in the morning.

[2186] The server analyzes the received data and detects increases in stress levels and decreases in sleep quality. Based on this, the generative AI generates relaxation sessions and puzzle learning materials. The server then creates an appropriate learning plan, and the device periodically presents learning tasks to the user.

[2187] Specific examples of prompts include:

[2188] Develop learning materials for cognitive maintenance and health management in older adults. Consider the following user information:

[2189] Gender: Male

[2190] Age: 65

[2191] Health data: Heart rate 85, poor sleep quality, high stress level

[2192] Emotional data: Recent unstable emotional state

[2193] Interests: Puzzles, relaxation

[2194] Past learning history: 3 sessions per week are valid

[2195] Generate personalized puzzle learning and relaxation session materials for your users.

[2196] The present invention allows users to have fun while maintaining their health and taking care of both their cognitive and emotional well-being.

[2197] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2198] Step 1: Data collection

[2199] Terminal

[2200] Input: Health monitoring device worn by the user (e.g., smartwatch) and its built-in camera and microphone.

[2201] How it works: The health management device periodically measures physiological data such as heart rate and sleep patterns. Using a camera and microphone, the emotion engine also analyzes the user's facial expressions and voice in real time to collect emotional data.

[2202] Data processing: The collected physiological data is temporarily stored on the device, and the emotion engine converts facial expressions and voice data into emotion indicators.

[2203] Output: Temporarily stored physiological and emotional indicator data.

[2204] Step 2: Receiving and analyzing data

[2205] server

[2206] Input: User physiological and emotional data sent from the device.

[2207] How it works: A server receives the data and stores it in a secure database. Processing algorithms analyze the data for each user and assess their health status, including stress levels, sleep quality, and emotional indicators.

[2208] Data calculation: Analyzes incoming data to calculate health indicators such as stress levels and sleep quality, and converts emotional data into an emotional profile.

[2209] Output: The updated user profile.

[2210] Step 3: Generate personalized learning materials

[2211] server

[2212] Input: Analyzed user profile (health indicators, emotional profile, user interests, cognitive abilities, and past learning history).

[2213] How it works: Generative AI generates optimal learning materials based on the user's health and emotional data.

[2214] Data processing: The generative AI model creates appropriate learning materials based on the prompt. For example, if the user is stressed, it will generate relaxation sessions or puzzle learning materials.

[2215] Output: Personalized learning materials.

[2216] Step 4: Create a study plan

[2217] server

[2218] Input: personalized learning materials and user profile.

[2219] How it works: The server creates a study plan based on the user's health and emotional state. This study plan includes specific tasks, a schedule, and reminders.

[2220] Data calculation: Based on the content of the study materials, specific tasks and schedules, such as three relaxation sessions per week or daily puzzle study, are calculated and reminders are set.

[2221] Output: Learning plan.

[2222] Step 5: Present and implement the learning task

[2223] Terminal

[2224] Input: lesson plan.

[2225] How it works: The device follows a study plan and displays daily learning tasks to the user. As the user works on the tasks, the device monitors their emotional state in real time and displays positive feedback and motivational messages at appropriate times.

[2226] Data processing: Learning progress and emotional data are collected in real time and temporarily stored on the device.

[2227] Output: The training data and the latest emotion data.

[2228] Step 6: Progress monitoring and feedback

[2229] server

[2230] Input: Progress and emotion data sent from the device.

[2231] How it works: The server analyzes progress data and emotion data to evaluate learning achievement. It generates rewards such as badges and points according to achievement and notifies the user. The emotion engine also provides appropriate feedback to maintain motivation for the next learning task.

[2232] Data computation: Applying algorithms to evaluate learning achievement and generate rewards.

[2233] Output: Feedback, badges, points, and other rewards.

[2234] Step 7: Facilitating collaborative learning

[2235] Servers and Terminals

[2236] Input: User profile, collaborative learning data with other users.

[2237] Operation: The server generates information about collaborative tasks and challenges to promote collaborative learning with other users and sends it to the device. The device notifies the user of this information and provides opportunities for collaborative learning.

[2238] Data processing: Optimize the collaborative learning task based on collaborative learning data and participating users' emotional data.

[2239] Output: Detailed information and feedback on collaborative learning.

[2240] In this way, the system of the present invention can provide a personalized learning environment based on the user's health and emotional state.

[2241] (Application example 2)

[2242] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2243] Elderly people need to maintain their health while having fun, while preventing cognitive decline and the progression of aging symptoms. Furthermore, in order to provide learning materials optimized for each individual user and create an effective learning environment, it is important to properly understand the user's health and emotional state and respond accordingly. However, current systems do not adequately integrate these elements, making it difficult to maintain user motivation and optimize effective learning plans.

[2244] 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.

[2245] In this invention, the server includes means for collecting data from the health management device, means for saving and analyzing the collected data, generation AI means for generating learning materials based on the analyzed data, means for creating a learning plan based on the generated learning materials, terminal means for presenting learning tasks to the user, gamification means for tracking the user's progress and maintaining their motivation, means for promoting collaborative learning with other users, an emotion engine for detecting and evaluating the user's emotional state, and means for maintaining the user's motivation and optimizing the learning plan based on the results of the emotion engine. This makes it possible to provide an individually optimized learning plan based on the user's health and emotional state, allowing the user to maintain their health while having fun and improving their cognitive ability.

[2246] A "health management device" is a device that periodically measures and collects health data such as a user's heart rate and sleep patterns.

[2247] The "server" is a system that contains a database and analytical algorithms for storing and analyzing collected data and assessing the user's health and emotional state.

[2248] "Generative AI" refers to artificial intelligence that generates learning materials optimized for users based on analyzed data.

[2249] "Terminal" refers to a device that presents learning tasks to users and tracks their progress, such as a smartphone or tablet.

[2250] "Gamification" is a technique that incorporates game elements to maintain user motivation and provides rewards such as badges and points.

[2251] "Collaborative learning" is a learning method in which users cooperate with other users to solve learning problems and promote smooth communication.

[2252] The "emotion engine" is a system that detects and evaluates a user's emotional state by analyzing their facial expressions and voice.

[2253] A "study plan" is a specific study schedule and assignments created based on the generated study materials and in accordance with the user's health and emotional state, as well as their learning goals.

[2254] The system of this invention aims to help users maintain their health and improve their cognitive abilities while having fun by effectively monitoring their health and emotional state and providing personalized educational materials based on that information. The main components of this system include:

[2255] 1. Health management device

[2256] A health management device is a device that measures a user's health data, such as heart rate and sleep patterns. A smartwatch is used as an example. The user wears this device all the time to collect daily health data.

[2257] 2. Terminal

[2258] The terminal is a device that presents learning tasks to users and tracks their progress, such as a smartphone or tablet. The terminal also receives data from the health management device and sends it to the server.

[2259] 3. Server

[2260] The server is a system that receives data sent from the terminal, stores it in a database, and analyzes it. The server performs the following processes:

[2261] Health and emotional data analysis

[2262] Update user profiles based on analysis results

[2263] Generating personalized learning materials using generative AI

[2264] Create and manage your study plan

[2265] 4. Generation AI

[2266] Generative AI is an artificial intelligence that generates optimized learning materials based on a user's health and emotional data, interests, cognitive ability, and past learning history. Generative AI uses deep learning algorithms to generate highly accurate learning materials.

[2267] 5. Gamification

[2268] Gamification is a technique that incorporates game elements to keep users motivated and provides rewards such as badges and points.

[2269] 6. Cooperative Learning

[2270] Collaborative learning is a learning method that allows users to cooperate with other users to solve learning problems and promote smooth communication. Through joint tasks and challenges, users support each other emotionally and intellectually.

[2271] 7. Emotion Engine

[2272] The emotion engine is a system that detects and evaluates the user's emotional state by analyzing their facial expressions and voice, which can determine the user's motivation and the optimal timing for reminders.

[2273] Examples:

[2274] Case of User A (65-year-old male)

[2275] health management device

[2276] User A wears a smartwatch to measure his / her heart rate and sleep patterns, and the data from the smartwatch is automatically transferred to the device every day.

[2277] Terminal

[2278] User A's smartphone receives data from the health management device and transmits it to the server. It also presents User A with a study plan and study tasks from the server.

[2279] server

[2280] The server analyzes the data sent from the device and detects that User A's stress level is increasing, his sleep quality is declining, and his recent emotional state is unstable. Based on this, the generative AI generates relaxation sessions and puzzle learning materials.

[2281] Generation AI

[2282] The generative AI generates optimized relaxation sessions and puzzle learning materials based on user A's health and emotional state.

[2283] Learning Plan

[2284] The server creates a study plan that includes three relaxation sessions per week and daily puzzles.

[2285] Gamification

[2286] A badge is awarded to User A every time he or she completes a task, helping the user to stay motivated and progress in their studies.

[2287] cooperative learning

[2288] The server provides opportunities for collaborative learning with other users, allowing user A to jointly solve problems and receive emotional support.

[2289] Emotion Engine

[2290] The emotion engine detects user A's emotional state and provides feedback and messages at the right time to increase motivation.

[2291] These elements work together to enable users to maintain their health and improve their cognitive function while having fun. A specific generative AI model could be a deep learning model such as GPT-3. An example of a prompt would be, "Generate relaxation session materials based on heart rate and sleep data."

[2292] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2293] Step 1:

[2294] The terminal collects and stores data from the health management device.

[2295] Input: Health data such as the user's heart rate and sleep patterns sent from health monitoring devices.

[2296] Output: The acquired health data is saved on the device.

[2297] The terminal periodically acquires data from the health management device and stores it in its internal memory, which is used in subsequent processing steps.

[2298] Step 2:

[2299] The device uses an emotion engine to collect user emotion data.

[2300] Input: Facial expression data captured by the device's camera, voice data captured by the microphone.

[2301] Output: The analyzed emotion data is saved on the device.

[2302] The device uses a camera and microphone to collect the user's facial expressions and voice, which are then analyzed by an emotion engine, resulting in an emotional index such as positive or negative.

[2303] Step 3:

[2304] The device transmits the collected health and emotion data to a server.

[2305] Input: Health data and emotional data stored on the device.

[2306] Output: The transmitted data is saved on the server.

[2307] The device periodically uploads this data to the server, which stores the received data in a database.

[2308] Step 4:

[2309] The server analyzes the received data and assesses the user's health and emotional state.

[2310] Input: Health data and emotion data stored on the server.

[2311] Output: Evaluated health status indicators, emotional status indicators.

[2312] The server uses analytical algorithms to calculate stress levels, sleep quality, and emotional indicators, and updates them into the user profile.

[2313] Step 5:

[2314] The server uses generative AI to generate personalized teaching materials based on the analysis results.

[2315] Input: Assessed health status indicators, emotional state indicators, user interests and past learning history.

[2316] Output: Optimized learning materials.

[2317] Based on the evaluation results and the user's profile information, the generative AI generates learning materials tailored to the user, such as relaxation sessions or puzzle learning materials.

[2318] Step 6:

[2319] The server creates a learning plan based on the generated learning materials.

[2320] Input: Optimized learning materials, assessed health status indicators and emotional state indicators.

[2321] Output: Personalized study plan.

[2322] The server creates a learning plan based on the learning materials and the user's status, including specific learning tasks and their implementation schedule.

[2323] Step 7:

[2324] The device presents learning tasks to the user.

[2325] Input: The lesson plan sent from the server.

[2326] Output: The learning task displayed on the screen.

[2327] The device presents users with tasks at specific times each day according to their learning plan, and when they complete the tasks, they are awarded badges and points.

[2328] Step 8:

[2329] The device tracks the user's progress and feeds the data back to the server.

[2330] Input: User's learning task completion status, emotional data.

[2331] Output: Progress and emotion data sent to the server.

[2332] Each time the user completes a task, the device collects progress data and emotional data at that time and sends it to the server.

[2333] Step 9:

[2334] The server provides gamification elements based on the feedback.

[2335] Input: Progress and emotion data sent from the device.

[2336] Output: Rewards (badges, points) to the user, feedback message.

[2337] The server rewards the user according to their progress, generates feedback messages based on their emotional state at appropriate times, and sends them to the device.

[2338] Step 10:

[2339] The server generates collaborative tasks to promote collaborative learning with other users and provides them through the terminals.

[2340] Input: Data of multiple users, collaborative assignment template.

[2341] Output: Collaborative learning tasks, contact information between users.

[2342] The server analyzes data from multiple users, generates tasks optimized for effective collaborative learning, and provides them to users via their devices.

[2343] Through these steps, users can maintain their health and improve their cognitive function while having fun. The system utilizes a deep learning model, known as a "generative AI model," to provide an optimal learning experience for each individual user. For example, it can generate specific learning materials using a prompt such as, "Please generate relaxation session materials based on heart rate and sleep data."

[2344] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2345] 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.

[2346] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2347] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2348] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is a...

Claims

1. To monitor the user's health, means for collecting data from a health management device; a server means for storing and analyzing the collected data; A generation AI means for generating learning materials based on the analyzed data; a means for creating a lesson plan based on the generated teaching materials; a terminal means for presenting learning tasks to a user based on the learning plan; Gamification measures to track users' progress and keep them motivated; a means to facilitate collaborative learning with others; A system including:

2. Analyze the user's health status data, means for generating personalized educational materials responsive to a user's health condition; The system of claim 1 .

3. means for optimizing a learning plan based on the analyzed health status data of the user; The system of claim 1 .

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