System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting personal health data; means for transmitting the health data to a cloud server; means for analyzing the health data at the cloud server to identify a user's behavior pattern; means for generating personalized health improvement advice or guidance based on the behavior pattern; and means for transmitting the advice or guidance to a user's terminal for display.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] Personal health management is becoming increasingly important in modern society, but many people find it difficult to effectively collect and analyze their own health data and receive appropriate advice based on that data. Current health management systems struggle to provide personalized advice based on individual users' characteristics and goals, and lack the means to sustain motivation for self-improvement and health improvement. Therefore, there is a need for systems that can accurately understand individuals' behavioral patterns and lifestyle habits and provide optimal advice and guidance. [Means for solving the problem]

[0005] The present invention provides a system that collects personal health data, transmits the data to a cloud server for analysis, and generates personalized health improvement advice and guidance based on the user's behavioral patterns. Specifically, the system includes a means for collecting personal health data, a means for transmitting the health data to a cloud server, a means for analyzing the health data on the cloud server and identifying the user's behavioral patterns, a means for generating personalized health improvement advice and guidance based on the behavioral patterns, and a means for transmitting the advice and guidance to the user's device for display. This system allows users to understand their health status in real time and receive optimal advice based on scientific evidence. Furthermore, by providing a means for users to manually input health data and a means for the cloud server to acquire and analyze data via API integration with external cloud services, more detailed and multifaceted health management is possible.

[0006] "Health data" refers to numerical information that indicates an individual's health status, such as heart rate, number of steps, calories burned, and sleep data.

[0007] A "cloud server" is a remote server that stores, processes, and manages data over the Internet.

[0008] "Behavioral patterns" are data that indicate the tendency of a series of actions and habits in the user's daily life.

[0009] "Advice" or "guidance" is instructions or suggestions aimed at improving a user's health or modifying their behavior.

[0010] A "terminal" is an electronic device used by a user, such as a smartphone, tablet, or personal computer.

[0011] "Means of collection" refers to methods or apparatus for obtaining personal health data using devices or sensors.

[0012] "Means of transmission" refers to the communication technology or method used to transfer collected data to the cloud server.

[0013] "Means of analysis" are algorithms or programs that process collected data and extract meaningful information.

[0014] "Means of identification" refers to the process of clearly identifying a user's behavioral patterns and health status from the analysis results.

[0015] The "means for generating" is a mechanism for creating appropriate advice and guidance based on identified behavioral patterns and health conditions.

[0016] The "display means" refers to an interface or device for visually presenting the generated advice or guidance to the user.

[0017] "External cloud services" are internet-based data storage and processing services provided by other companies or providers.

[0018] "API integration" refers to the technical procedures for enabling data exchange and function calls between different systems and services. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] System Overview

[0041] This invention is a system that collects and analyzes personal health data and provides personalized advice and guidance for improving health based on that data. This system supports users in improving their health and modifying their behavior by understanding their health status in real time and providing optimal advice based on scientific evidence.

[0042] System configuration

[0043] 1. Data Collection Phase

[0044] The device connects to the user's wearable device (smartwatch or fitness tracker) and collects real-time health data such as heart rate, steps, calories burned, and sleep data. Users can also manually enter data such as dietary habits and emotional state through the application.

[0045] 2. Data transmission phase

[0046] The device periodically transmits the collected health data to a cloud server over the internet using a secure protocol.

[0047] 3. Data analysis phase

[0048] The server stores the received health data on a cloud server and performs preprocessing (missing value completion, noise removal). It then analyzes the data using machine learning algorithms to identify the user's behavioral patterns. It also connects with external cloud services (e.g., calendar services, task management services) via API, integrating and analyzing the acquired data.

[0049] 4. Advice Generation Phase

[0050] The server generates personalized health improvement advice and guidance based on the identified behavioral patterns, customized to the user's specific goals (e.g., weight loss, muscle gain).

[0051] 5. Notification and display phase

[0052] The server sends the generated advice to the user's device, which then visually displays it to the user. The user can then view the advice in real time through the application and use it to influence their next actions.

[0053] Specific examples

[0054] Example 1: User A is dieting

[0055] User A has a goal of walking 10,000 steps every day.

[0056] The device acquires step count data from the smartwatch and transmits it to the server.

[0057] The server analyzes the weekly step count data and generates advice to User A, such as "Your average step count this week is 9,500 steps. You're almost there!"

[0058] The server works in conjunction with the calendar service to suggest, "It would be a good idea to add some walking time to a day next week when you have fewer meetings."

[0059] The terminal notifies this information to user A in real time.

[0060] Example 2: User B needs stress management

[0061] User B has recently become more prone to stress.

[0062] The terminal acquires the heart rate data of user B and transmits it to the server.

[0063] The server analyzes that an increased heart rate is related to lack of sleep and generates advice to User B saying, "You've been getting less than eight hours of sleep recently. Try doing some relaxing yoga or meditation."

[0064] The server suggests yoga and meditation tutorial videos, and the device notifies User B.

[0065] In this way, the system of the present invention provides a personalized health improvement plan for each user, supporting health management while maintaining the user's motivation.

[0066] The processing flow will be explained below.

[0067] Step 1:

[0068] The device connects to the user's wearable device (smartwatch, fitness tracker, etc.) via Bluetooth or Wi-Fi and collects real-time health data (heart rate, steps, calories burned, sleep data, etc.).

[0069] Step 2:

[0070] Users manually enter information such as what they eat and their emotional state through an application on their smartphone or tablet, which also collects this data.

[0071] Step 3:

[0072] The device periodically transmits the collected health data and manually entered data to a cloud server using a secure communication protocol (such as HTTPS).

[0073] Step 4:

[0074] The server stores all received data in a database, where it checks the data for consistency and performs missing value imputation and noise removal as necessary.

[0075] Step 5:

[0076] The server analyzes the data using machine learning algorithms, clustering algorithms to identify user behavior patterns, and classification algorithms to classify specific behaviors (e.g., healthy behaviors, unhealthy behaviors).

[0077] Step 6:

[0078] The server generates personalized health improvement advice and guidance based on the user's behavioral patterns and lifestyle habits. Advice is customized to the user's specific goals (e.g., dieting, muscle building).

[0079] Step 7:

[0080] The server transmits the generated advice to the user's terminal, which visually displays the advice to the user.

[0081] Step 8:

[0082] The application allows users to review advice and take action, and if necessary, they can enter more data or set new goals.

[0083] Step 9:

[0084] The server obtains data through API integration with external cloud services (e.g., calendar services, task management services) and integrates the user's schedule and task information into the analysis, allowing it to provide more detailed and personalized advice.

[0085] Step 10:

[0086] Users can share their health data and advice with family and friends as needed. The device supports this sharing function and shares data within a specified range.

[0087] Example 1

[0088] 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."

[0089] There is a demand for effective and efficient support for users' health improvement and behavior modification by understanding their health status in real time and providing optimal health improvement advice based on scientific evidence. However, conventional systems have insufficient data collection, analysis, and advice provision, making it difficult to provide accurate advice customized for each user.

[0090] 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.

[0091] In this invention, the server includes means for collecting health data, means for transmitting the health data to a cloud server, means for preprocessing the health data (missing value imputation, noise removal) on the cloud server, means for analyzing the health data on the cloud server and identifying individual behavioral patterns, means for generating personalized health improvement advice and guidance based on the behavioral patterns, means for transmitting the generated advice and guidance to the user's terminal and visually displaying it, means for collecting data in real time from the wearable device during the health data collection phase, means for the user to manually input dietary details and emotional state through an application, and means for encrypting and transmitting the collected data. This makes it possible to grasp the user's health condition in detail and provide an individual health improvement plan quickly and effectively.

[0092] "Health Data" refers to information about an individual's health and behavior, such as an individual's heart rate, steps taken, calories burned, sleep data, diet, and emotional state.

[0093] "Cloud server" refers to a server accessible via the Internet that is a remote computing resource used for data storage, preprocessing, analysis, advice generation, and so on.

[0094] "Preprocessing" refers to data cleansing operations such as missing value imputation and noise removal that are performed on collected health data.

[0095] "Behavioral patterns" refer to trends or regularities in a series of behaviors identified by analyzing a user's health data.

[0096] "Advice and guidance" refers to personalized health improvement instructions and advice generated based on a user's behavioral patterns.

[0097] "User device" refers to a device used by an individual, such as a smartphone, tablet, or PC, which displays advice and guidance sent from the cloud server.

[0098] "Wearable devices" refers to electronic devices worn by individuals, such as smartwatches and fitness trackers, that collect real-time health data.

[0099] "Encryption" refers to the process of protecting collected data using techniques such as symmetric key encryption or public key encryption to ensure that only authorized individuals can access the data.

[0100] A "preprocessing module" refers to a software component that runs on a cloud server and performs missing value completion and noise removal in data.

[0101] A "machine learning model" refers to a program that implements an algorithm to use collected data to identify user behavior patterns and predict future behavior.

[0102] A "natural language generation model" refers to a program that uses artificial intelligence technology to generate advice and guidance for users in natural-sounding sentences.

[0103] "API integration" refers to an interface that allows different software systems to communicate with each other and exchange data.

[0104] The present invention is a system for collecting and analyzing personal health data and providing personalized advice and guidance for improving health. The following describes in detail an embodiment of the present invention.

[0105] System Overview

[0106] The system of the present invention supports users in improving their health and modifying their behavior by understanding their personal health status in real time and providing optimal advice based on scientific evidence. This system consists of the following main components:

[0107] System configuration

[0108] 1. Data Collection

[0109] The terminal collects real-time health data (heart rate, steps, calories burned, sleep data, etc.) from wearable devices (e.g., smart watches, fitness trackers) using the wearable device's dedicated API (e.g., Apple HealthKit, GOOGLE FI™ API).

[0110] Users can manually enter their dietary habits, emotional state, and more through the mobile application.

[0111] 2. Data Transmission

[0112] The device encrypts the collected data (e.g., AES encryption) and periodically transmits it to the cloud server via a secure communication protocol (e.g., HTTPS).

[0113] 3. Data Preprocessing

[0114] The server stores the received data in temporary storage (e.g., AWS (registered trademark) S3 bucket) on the cloud server.

[0115] The server uses preprocessing modules to impute missing values ​​(e.g., mean imputation) and remove noise (e.g., moving average filter) from the data.

[0116] 4. Data Analysis

[0117] The server inputs the preprocessed data into a machine learning model (e.g., random forest, neural network).

[0118] The server uses Python libraries (e.g., scikit-learn, TENSORFLOW®) to identify user behavior patterns.

[0119] The server connects with external cloud services (e.g., Google (registered trademark) Calendar, task management services) via API, obtains daily schedules and task information, and integrates it into the analysis.

[0120] 5. Advice Generation

[0121] The server generates personalized health improvement advice and guidance based on the analysis results of the machine learning model.

[0122] The server uses a natural language generation model (e.g., GPT-4®) to generate advice tailored to the user's specific goals (e.g., lose weight, gain muscle).

[0123] 6. Advice Notice

[0124] The server transmits the generated advice to the user's terminal.

[0125] The terminal displays the received advice to the user in real time through the notification function of the application.

[0126] Users can use the application's dashboard function to view details of the advice and apply it to their daily actions.

[0127] Specific examples

[0128] Example 1: User A is dieting

[0129] User A has a goal of walking 10,000 steps every day.

[0130] The device collects step count data from the smartwatch and sends the data to the server every night at midnight.

[0131] The server accumulates one week's worth of step count data, and the preprocessing module fills in missing values ​​in the data.

[0132] The server uses a machine learning model to analyze User A's step count data and generate advice such as, "Your average step count this week is 9,500. You're almost there!"

[0133] The server connects to the calendar service via an API and suggests, "It would be a good idea to add some walking time on a day next week when you have fewer meetings," and notifies the device of this.

[0134] Prompt Sentence Examples

[0135] User A's health goal is to take 10,000 steps per day. Their average step count this week is 9,500. Please suggest the next action appropriate for User A. This requires integration with a calendar service.

[0136] Example 2: User B needs stress management

[0137] User B has recently become more prone to stress.

[0138] The device collects heart rate data and transmits it to a server in real time when stress levels rise.

[0139] The server analyzes the heart rate data and performs noise removal using a pre-processing module.

[0140] The server analyzes the increased heart rate and sleep data, finds a correlation, and generates advice such as, "You've been getting less than eight hours of sleep recently. Try some relaxing yoga or meditation."

[0141] The server suggests yoga and meditation tutorial videos through an external video service API and notifies the device of this.

[0142] Prompt Sentence Examples

[0143] User B has been experiencing increased stress recently. Analyze their heart rate and sleep data and provide appropriate stress management advice, including suggestions for yoga and meditation.

[0144] This enables the system to provide each user with a personalized health improvement plan, supporting effective health management while maintaining user motivation.

[0145] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0146] Step 1: Data collection

[0147] The device collects real-time health data from wearable devices, such as heart rate, steps, calories burned, and sleep data, using the wearable device's dedicated API.

[0148] Input: Sensor data from wearable devices.

[0149] Output: Health data stored on the device.

[0150] Specific operation: The terminal communicates with the wearable device periodically (for example, every minute) to obtain and store the latest data.

[0151] Step 2: Manual data entry

[0152] Users manually enter health data, such as diet and emotional state, through a mobile application.

[0153] Input: Data entered by the user, such as dietary details and emotional state.

[0154] Output: Manually entered data stored in the application.

[0155] Specific operation: The user enters data using the application's input form, and the device saves it.

[0156] Step 3: Send data

[0157] The terminal encrypts the collected data (real-time data and manually entered data) and transmits it to the cloud server using a secure communication protocol (e.g., HTTPS).

[0158] Input: All health data stored on the device.

[0159] Output: Encrypted data sent to the cloud server.

[0160] Specific operation: The terminal compiles data on a fixed schedule (for example, every hour), encrypts it using AES, and sends it to the cloud server.

[0161] Step 4: Data Preprocessing

[0162] The server stores the data sent to the cloud in temporary storage (e.g., AWS S3 bucket) and uses preprocessing modules to perform missing value imputation and noise removal.

[0163] Input: Raw data stored on cloud servers.

[0164] Output: Preprocessed, missing value imputed, and denoised data.

[0165] Specific operation: The server uses a preprocessing module (for example, Python's Pandas library) to filter the data and impute missing values.

[0166] Step 5: Data analysis

[0167] The server inputs the preprocessed data into a machine learning model (e.g., random forest, neural network) to identify user behavior patterns. It also obtains data through API integration with external cloud services (e.g., calendar services, task management services) and performs integrated analysis.

[0168] Input: Preprocessed data, data obtained from external cloud services.

[0169] Output: Behavioral patterns as a result of the analysis.

[0170] Specific operation: The server analyzes the data using machine learning algorithms (e.g., scikit-learn, TensorFlow), and based on the identified behavioral patterns, further integrates the data with external services using APIs.

[0171] Step 6: Advice Generation

[0172] The server generates personalized health improvement advice based on the analysis results of the machine learning model. This advice is generated as sentences using a natural language generation model (e.g., GPT-4).

[0173] Input: Behavioral patterns as analysis results, user health goals.

[0174] Output: The generated health advice.

[0175] Specific operation: The server uses a natural language generation model to generate advice in natural language based on specific behavioral patterns.

[0176] Step 7: Advice Notification

[0177] The server transmits the generated advice to the user's terminal, which displays it in real time.

[0178] Input: Generated health advice.

[0179] Output: Advice displayed on the user's terminal.

[0180] Specific operation: The server encrypts the advice and delivers it to the device, which then visually displays it to the user using the application's notification function.

[0181] This clearly defines the specific operations at each step and the input and output data, and provides a detailed explanation of the processing flow of the entire system.

[0182] (Application example 1)

[0183] 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."

[0184] In modern society, personal health management is an important issue. Effective training and nutritional management, especially at fitness gyms, require real-time, personalized support. However, current systems struggle to effectively collect and analyze users' health data and provide specific advice based on it. Furthermore, there are insufficient means for fitness gym instructors to appropriately share data and provide effective support to users. Therefore, an effective data collection, analysis, and advice-providing system is needed to help users achieve their goals and achieve sustainable health improvements.

[0185] 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.

[0186] In this invention, the server includes means for collecting personal health data, means for transmitting the health data to a cloud server, means for analyzing the health data in the cloud server and identifying the user's behavioral patterns, means for generating personalized health improvement advice and guidance based on the behavioral patterns, means for transmitting the advice and guidance to the user's device and displaying it, and means for providing training plans and nutritional advice at the fitness gym. This allows users and instructors at the fitness gym to share data in real time and implement personalized health improvement plans.

[0187] "Personal health data" refers to information such as heart rate, steps, calories burned, sleep data, and manually entered dietary information and emotional state.

[0188] A "cloud server" is a server system that can transmit, collect, store, and analyze data via the Internet.

[0189] "Behavioral patterns" refer to trends in daily behavior and health conditions identified based on a user's health data.

[0190] "Health improvement advice and guidance" refers to specific courses of action or suggestions provided to improve a user's health.

[0191] "Terminal" refers to a device used by a user, such as a smartphone, tablet, or smartwatch.

[0192] A "fitness gym" refers to a facility where professional instructors provide training and nutritional management guidance.

[0193] "Training Plan" refers to a customized exercise schedule and exercise instructions based on a user's health data.

[0194] "Nutrition Advice" refers to guidance on appropriate dietary and nutritional intake provided to a user based on their health goals.

[0195] "Shared" refers to a state in which multiple users or services can access and use the same data.

[0196] This invention is a health management system that allows users of fitness gyms to receive training plans and nutrition advice in real time. The system collects personal health data, analyzes that data on a cloud server, and provides health improvement advice based on the user's individual behavioral patterns.

[0197] System configuration

[0198] Health Data Collection and Transmission

[0199] Users use wearable devices such as smartwatches to collect health data such as heart rate, number of steps, calories burned, and sleep data. This data is sent to a cloud server via a device such as a smartphone. This data is sent using a secure communication protocol such as HTTPS.

[0200] Analyzing the data

[0201] The cloud server stores the received health data and performs preprocessing (missing value completion, noise removal). The data is then analyzed using machine learning libraries such as scikit-learn to identify the user's behavioral patterns. Furthermore, the cloud server also processes the user's manually entered dietary details and emotional state, conducting a comprehensive data analysis. The analysis results are shared with the fitness gym instructor in real time.

[0202] Advice generation and notification

[0203] The cloud server generates personalized health improvement advice and guidance based on the identified behavioral patterns. This advice is customized to the user's specific goals (e.g., weight loss, muscle building). The generated advice is then sent to the user's device and displayed visually. The user can receive this advice via their smartphone and incorporate it into their next actions.

[0204] Specific examples

[0205] For users who are dieting

[0206] If a user is aiming to lose weight, the system analyzes their daily step count data and calorie consumption data and generates advice such as, "Today's step count is 7,000. You have 3,000 steps left to reach your goal." It also provides specific instructions on calories burned, such as, "Today you have burned 500 kilocalories. Burn an additional 200 kilocalories."

[0207] For users who need stress management

[0208] If the user is diagnosed with stress due to elevated heart rate or insufficient sleep, the system will generate advice such as, "Your heart rate has been high recently and you've been getting less than eight hours of sleep. Try yoga or meditation to relax."

[0209] Prompt Sentence Examples

[0210] Based on the user's step count and calorie consumption data, evaluate the progress towards the target number of steps and calorie consumption and provide specific advice to the user.

[0211] Generate specific advice for stress management based on the user's heart rate and sleep data.

[0212] In this way, the system of the present invention provides fitness gym users with personalized health improvement plans, supporting health management while maintaining the users' motivation.

[0213] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0214] Step 1:

[0215] A user uses a wearable device to collect health data.

[0216] Input: Health data such as heart rate, steps, calories burned, and sleep data.

[0217] Output: Collected health data.

[0218] Specific operation: Smartwatches and fitness trackers measure health data from the user's body in real time and transmit it to devices such as smartphones.

[0219] Step 2:

[0220] The health data collected by the device is sent to a cloud server.

[0221] Input: Collected health data.

[0222] Output: Health data sent to cloud server.

[0223] Specific operation: The device sends the collected health data to a cloud server via the Internet using a secure protocol (e.g., HTTPS).

[0224] Step 3:

[0225] The server accumulates the received health data and performs preprocessing.

[0226] Input: Health data sent to cloud server.

[0227] Output: Preprocessed health data.

[0228] Specific operation: The server performs data preprocessing such as imputing missing values ​​and removing noise, etc. This is where processing is done to ensure data consistency and accuracy.

[0229] Step 4:

[0230] The server analyzes the pre-processed health data to identify the user's behavioral patterns.

[0231] Input: Preprocessed health data.

[0232] Output: User behavior patterns.

[0233] How it works: The server uses a machine learning library (e.g., scikit-learn) to extract features from the data and analyze it for specific behavioral patterns. At this point, the machine learning model predicts the behavioral patterns based on the training data.

[0234] Step 5:

[0235] The server generates health improvement advice and guidance based on the identified behavioral patterns.

[0236] Input: User behavior patterns.

[0237] Output: Health improvement advice and guidance.

[0238] How it works: The server generates advice and guidance customized to the user's goals (e.g., weight loss, muscle gain). Using generative AI models and prompts, personalized advice is created for each individual user.

[0239] Step 6:

[0240] The server transmits the generated advice and guidance to the user's terminal.

[0241] Enter: health improvement advice and guidance.

[0242] Output: Advice and guidance sent to the user's device.

[0243] Specific operation: The server sends advice and guidance to the user's smartphone via the Internet.

[0244] Step 7:

[0245] The terminal displays the advice and guidance sent to the user.

[0246] Input: Advice and guidance sent to the user's device.

[0247] Output: Advice or guidance that is displayed to the user.

[0248] Specific operation: The smartphone application notifies the user of the received advice and visually displays it to them, allowing them to decide their next course of action.

[0249] 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.

[0250] System Overview

[0251] This system collects and analyzes personal health and emotional data, and provides individualized advice and guidance for improving health based on the collected data. In particular, by combining it with an emotion engine, it achieves highly personalized guidance that takes into account the user's emotional state.

[0252] System configuration

[0253] 1. Data Collection Phase

[0254] The device connects to the user's wearable device (smartwatch, fitness tracker, etc.) and collects real-time health data such as heart rate, steps, calories burned, and sleep data. Furthermore, the user can manually input their diet and emotional state through the application. Additionally, the emotion engine automatically recognizes emotions from the user's facial expressions, voice tone, text input, etc., and generates emotion data.

[0255] 2. Data transmission phase

[0256] The device periodically transmits the collected health and emotion data to a cloud server over the internet using a secure protocol (e.g., HTTPS).

[0257] 3. Data analysis phase

[0258] The server stores all received data in a database. When saving, it checks the data's integrity and performs missing value imputation and noise removal as necessary. It then analyzes the data using machine learning algorithms to identify the user's behavioral patterns and emotional state. It also connects with external cloud services (e.g., calendar services, task management services) via API, integrating and analyzing the acquired data.

[0259] 4. Advice Generation Phase

[0260] The server generates personalized health improvement advice and guidance based on the identified behavioral patterns and emotional state. Specifically, by incorporating the user's emotional data, it suggests relaxation methods based on stress levels and activities to maintain positive emotions.

[0261] 5. Notification and display phase

[0262] The server sends the generated advice and guidance to the user's device, which then visually displays it to the user. The user can then check the advice in real time through the application and use it to influence their next actions.

[0263] Specific examples

[0264] Example 1: User C whose main purpose is stress management

[0265] User C is experiencing increasing stress at work.

[0266] The terminal acquires the heart rate data from the smartwatch and transmits it to the server.

[0267] The emotion engine detects user C's stress level from his / her daily facial expressions and tone of voice.

[0268] The server analyzes the increased heart rate and the emotion engine data and generates advice to User C, saying, "Your stress level has been increasing recently. Try a short meditation to relax."

[0269] The device suggests yoga and meditation tutorial videos and notifies User C.

[0270] Example 2: User D wants to maintain a positive lifestyle

[0271] User D wants to maintain a healthy and positive lifestyle.

[0272] The device acquires activity data from User D's fitness tracker and sends it to the server.

[0273] The emotion engine collects emotion data from user D's daily text input and speech and identifies the prevalence of positive emotions.

[0274] The server analyzes this data comprehensively and generates advice such as, "Your recent activity has been going well. To keep up this pace, why not try a new jogging course this weekend?"

[0275] The terminal notifies User D of the proposal.

[0276] The system of the present invention provides each user with a personalized health improvement plan based on health data and emotional data, and supports health management while maintaining the user's motivation.

[0277] The processing flow will be explained below.

[0278] Step 1:

[0279] The device connects to the user's wearable device (smartwatch, fitness tracker, etc.) via Bluetooth or Wi-Fi and collects real-time health data (heart rate, steps, calories burned, sleep data, etc.).

[0280] Step 2:

[0281] Users manually enter their diet and emotional state through an application on a smartphone or tablet, which also collects this data.

[0282] Step 3:

[0283] The emotion engine automatically recognizes emotions from the user's facial expressions, voice tone, text input, etc., and generates emotion data.

[0284] Step 4:

[0285] The device periodically transmits the collected health and emotion data to a cloud server using a secure communication protocol (such as HTTPS).

[0286] Step 5:

[0287] The server stores all received data in a database, where it checks the data for consistency and performs missing value imputation and noise removal as necessary.

[0288] Step 6:

[0289] The server analyzes the data using machine learning algorithms, clustering algorithms to identify user behavior patterns, and classification algorithms to classify specific behaviors (e.g., healthy behaviors, unhealthy behaviors).

[0290] Step 7:

[0291] The server obtains data via API integration with external cloud services (e.g., calendar services, task management services) and integrates user schedules and task information into the analysis.

[0292] Step 8:

[0293] The server generates personalized health improvement advice and guidance based on the identified behavioral patterns and emotional state. Specifically, by incorporating the user's emotional data, it suggests relaxation methods based on stress levels and activities to maintain positive emotions.

[0294] Step 9:

[0295] The server transmits the generated advice and guidance to the user's terminal, which visually displays the advice to the user.

[0296] Step 10:

[0297] The application allows users to review advice and take action, and if necessary, they can enter more data or set new goals.

[0298] Example 2

[0299] 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."

[0300] Conventional health management systems have the ability to collect and analyze personal health data, but the technology to combine this with emotional data to provide personalized advice has not been sufficiently developed. As a result, it has been difficult to provide comprehensive health management that takes into account the psychological state of each individual user. Another issue is that insufficient integration with external data services means it is not possible to comprehensively understand users' behavioral patterns and lifestyle rhythms.

[0301] 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.

[0302] In this invention, the server includes a means for collecting personal health information, an emotion engine for analyzing the personal emotional information, a means for the emotion engine to detect the individual's emotional state, and a means for generating individualized health improvement advice and guidance based on the emotional information. This enables highly personalized guidance that takes the user's emotional state into consideration. Furthermore, because the server includes a means for acquiring data via API integration with external data services, it is possible to comprehensively understand the user's behavioral patterns and lifestyle rhythms and provide more accurate advice.

[0303] "Health information" refers to numerical values ​​and data that indicate an individual's health status, such as heart rate, number of steps, calories burned, and sleep data.

[0304] An "emotion engine" is software or algorithms that automatically recognize an individual's emotional state by analyzing their facial expressions, voice tone, text input, etc.

[0305] A "remote server" is a computer system located in a remote location via the Internet for storing, analyzing, and processing data.

[0306] "API integration" refers to the use of an interface that allows different software and services to communicate data with each other and share functions.

[0307] "Personalized health improvement advice" refers to advice and recommendations for improving health that are customized for each individual based on personal health and emotional information.

[0308] "Behavioral patterns" are data that show a series of actions and habits in an individual's daily life, and are an important indicator for health management.

[0309] overview

[0310] This invention is a system that collects and analyzes personal health and emotional information, and provides individualized health improvement advice and guidance based on that information. In particular, by combining this system with an emotion engine, this system realizes highly personalized guidance that takes into account the user's emotional state.

[0311] System configuration

[0312] 1. Data Collection Phase

[0313] The device connects to the user's wearable device (e.g., smartwatch) and collects real-time health information such as heart rate, steps, calories burned, and sleep data. Users can manually enter their diet and emotional state through the application. In addition, the emotion engine analyzes the user's facial expressions and voice tone collected through the device's camera and microphone to automatically generate emotional information.

[0314] 2. Data transmission phase

[0315] The device periodically transmits the collected health and emotional information to a cloud server via the Internet using a secure protocol (e.g., HTTPS).

[0316] 3. Data analysis phase

[0317] The server stores the received data in a database. When saving, it checks the data's integrity and performs missing value completion and noise removal. Next, it analyzes the data using machine learning algorithms to identify the user's behavioral patterns and emotional state. It also connects via API to external cloud services (e.g., calendar services, task management services), integrating the acquired data for analysis.

[0318] 4. Advice Generation Phase

[0319] The server generates personalized health improvement advice and guidance based on the identified behavioral patterns and emotional state. Specifically, it takes into account the user's emotional information and suggests relaxation methods according to stress levels and activities to maintain positive emotions.

[0320] 5. Notification and display phase

[0321] The server sends the generated advice and guidance to the user's device, which visually displays it to the user, who can then view the advice in real time through the application.

[0322] Specific examples

[0323] Example 1: User A whose main purpose is stress management

[0324] User A is experiencing increasing stress at work.

[0325] The terminal acquires the heart rate data from the smartwatch and transmits it to the server.

[0326] The emotion engine detects user A's stress level from his / her daily facial expressions and tone of voice.

[0327] The server analyzes the increased heart rate and data from the emotion engine and generates advice such as, "Your stress levels have been increasing recently. Try a short meditation to relax."

[0328] The device suggests yoga and meditation tutorial videos and notifies User A.

[0329] Example 2: User B wants to maintain a positive lifestyle

[0330] User B wants to maintain a healthy and positive lifestyle.

[0331] The device acquires activity data from User B's fitness tracker and sends it to the server.

[0332] The emotion engine collects emotion data from User B's daily text inputs and speech and identifies that there are many positive emotions.

[0333] The server comprehensively analyzes the data and generates advice such as, "You've been doing well lately. To keep up the pace, why not try a new jogging route this weekend?"

[0334] The terminal notifies User B of the proposed content.

[0335] Prompt Sentence Examples

[0336] Prompt 1:

[0337] "If the user is feeling stressed, generate advice on what relaxation methods to suggest. Data includes increased heart rate and negative emotion data."

[0338] Prompt 2:

[0339] "Generate specific activity suggestions to motivate users to maintain positive emotions and continue their activity habits. Recent activity data has been very positive."

[0340] The above is the details of the "Mode for Carrying Out the Invention" of the present invention. This system makes it possible to provide a personalized health improvement plan to each user and support health management.

[0341] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0342] Step 1: Connect your device

[0343] The terminal connects to the wearable device (e.g., smart watch) worn by the user using Bluetooth or Wi-Fi. The specific procedure involves launching the terminal application and executing the pairing process with the device. The input is information about the user's wearable device, and the output is the established connection status.

[0344] Step 2: Gathering health information

[0345] The terminal collects real-time health information from the wearable device, such as heart rate, steps, calories burned, and sleep data. This involves retrieving data via the API provided by the device. The input is sensor data from the wearable device, and the output is the collected health information.

[0346] Step 3: Manual Entry

[0347] The user manually inputs their health information and emotional state through the application interface. Specifically, they input their dietary information and emotional state in text or selection format. The input is the data manually entered by the user, and the output is the manually entered data.

[0348] Step 4: Recognizing emotional information

[0349] The device's emotion engine analyzes the user's facial expressions and voice tone collected from the camera and microphone to generate emotion information. The emotion engine performs the analysis using machine learning algorithms. The inputs are video data and audio data, and the output is analyzed emotion information.

[0350] Step 5: Send data to the cloud

[0351] The device periodically transmits the collected health and emotional information to a cloud server. The transmission is secure using the HTTPS protocol. The input is the collected health and emotional information, and the output is the data stored on the remote server.

[0352] Step 6: Save your data

[0353] The server stores the received data in a database. When storing, it unifies the data format and checks its consistency. The input is the transmitted data, and the output is the data stored in the database.

[0354] Step 7: Data integrity check and correction

[0355] The server detects missing values ​​and noise in the data, and performs interpolation and noise removal. Specific algorithms include linear interpolation and moving average filters. The input is the stored data, and the output is the corrected data.

[0356] Step 8: Analyze behavioral patterns

[0357] The server uses machine learning algorithms to identify user behavioral patterns, such as analyzing time-series data to extract the user's exercise habits and sleep patterns. The input is the data whose integrity has been verified, and the output is the analyzed behavioral patterns.

[0358] Step 9: Integrate with external cloud services

[0359] The server connects to an external cloud service (e.g., a calendar service, task management service) via API to obtain or provide the required data. The input is the API key and query for the external service, and the output is the obtained additional data.

[0360] Step 10: Generating Advice

[0361] The server generates personalized health improvement advice based on the user's behavioral patterns and emotional state. Based on the analysis results, it suggests specific actions, such as "do a short meditation." The inputs are behavioral patterns and emotional information, and the output is customized advice.

[0362] Step 11: Submitting Advice

[0363] The server transmits the generated advice to the user's terminal using a secure communication protocol, with the generated advice as input and the advice displayed on the terminal as output.

[0364] Step 12: Viewing Advice

[0365] The terminal visually displays the received advice to the user. The user opens the application and checks the provided advice. The input is the received advice, and the output is the visually displayed advice.

[0366] Prompt Sentence Examples

[0367] Prompt 1:

[0368] "If the user is feeling stressed, generate advice on what relaxation methods to suggest. Data includes increased heart rate and negative emotion data."

[0369] Prompt 2:

[0370] "Generate specific activity suggestions to motivate users to maintain positive emotions and continue their activity habits. Recent activity data has been very positive."

[0371] (Application example 2)

[0372] 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."

[0373] In modern society, a variety of health issues affect individuals. In particular, it is difficult to provide appropriate health improvement guidance to individuals, and uniform advice that does not take into account each individual's emotional state makes effective health management difficult. In particular, there is a need for a method to provide real-time, personalized health improvement recommendations to users at physical gyms and other physical facilities.

[0374] 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.

[0375] In this invention, the server includes means for collecting biometric information and emotional information of an individual, means for transmitting the biometric information and emotional information to a cloud device, means for analyzing the biometric information and emotional information in the cloud device and identifying the user's behavioral patterns and emotional state, means for generating individual health improvement recommendations and instructions based on the behavioral patterns and emotional state, and means for transmitting and displaying the recommendations and instructions to the user's terminal, thereby enabling personalized health improvement recommendations that take into account the user's real-time emotional state.

[0376] "Personal biometric information" refers to data related to the user's health condition, such as the user's heart rate, number of steps, calories burned, and sleep data.

[0377] "Emotional information" refers to data about a user's emotional state, obtained from the user's facial expressions, voice tone, text input, etc.

[0378] "Cloud device" refers to a remote server connected via the Internet for storing, processing, and analyzing data.

[0379] "Behavioral patterns" refer to a series of actions and activity trends in a user's daily life that are identified through analysis and learning.

[0380] "Personalized health improvement recommendations and instructions" refers to specific suggestions and instructions for promoting and improving health that are generated specifically for a user based on the user's identified behavioral patterns and emotional state.

[0381] "User's device" refers to an electronic device used by a user, such as a smartphone, tablet, or personal computer.

[0382] "API integration" refers to the process of exchanging information with external cloud services using an application programming interface.

[0383] This invention is a system that collects and analyzes an individual's biometric and emotional information and provides personalized health recommendations in real time. Specifically, the invention is implemented using the following hardware and software:

[0384] Hardware and Software

[0385] 1. Wearable devices: Smartwatches and fitness trackers are used to collect real-time biometric information such as heart rate, steps taken, calories burned, and sleep.

[0386] 2. Smartphone or tablet: A device held by the user that acts as a central device for collecting, displaying, and transmitting health and emotional information.

[0387] 3. Cloud server: A remote server for storing and analyzing collected data and generating health improvement recommendations, such as AWS (Amazon Web Services) or Google Cloud Platform.

[0388] 4. Camera and microphone: The camera and microphone built into the smartphone are used to analyze emotional information from the user's facial expressions and tone of voice.

[0389] Data processing and calculation

[0390] 1. Data Collection:

[0391] Biometric information is collected from the user's wearable device.

[0392] Use a camera or microphone to capture emotional information, for example, by analyzing facial expressions using OpenCV and voice tone with a speech recognition library.

[0393] The collected data is sent from the smartphone to a cloud server using the HTTPS protocol.

[0394] 2. Data processing on cloud servers:

[0395] The collected biometric and emotional information is stored on a cloud server.

[0396] When data is stored in a database (e.g., MySQL or MongoDB), the integrity of the data is checked.

[0397] Cleanse the data by imputing missing values ​​and removing noise.

[0398] Machine learning algorithms (e.g., TensorFlow and PyTorch) are used to analyze the data and identify behavioral patterns and emotional states.

[0399] 3. Generate health improvement recommendations:

[0400] Generate personalized health improvement recommendations and instructions based on identified behavioral patterns and emotional states.

[0401] For example, if it determines that the user is feeling stressed, it will recommend that they "do some relaxation exercises."

[0402] 4. Recommendation Notification and Display:

[0403] The generated recommendations and instructions are then sent back to the user's device.

[0404] A notification will appear on your smartphone prompting you to take the next step.

[0405] Specific examples

[0406] Let us take the example of user A using this system while training at a sports gym.

[0407] 1. The user wears a smartwatch to collect biometric information such as heart rate and calories burned.

[0408] 2. Turn on the camera and microphone on your smartphone to capture facial expressions and audio during training.

[0409] 3. The collected data is sent to a cloud server.

[0410] 4. The cloud server analyzes the data and identifies the user's fatigue and stress levels.

[0411] 5. A notification appears on the user's smartphone with a recommendation such as, "Your fatigue is increasing. Try stretching for 5 minutes to warm down."

[0412] Prompt Sentence Examples

[0413] "Please compile today's training data. Create a report that includes exercise history, heart rate variability, and emotional changes (from facial expressions and voice tone) and come up with the next recommendation for the user."

[0414] The specific embodiments for carrying out the present invention have been described above.

[0415] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0416] Step 1:

[0417] The device collects biometric information (heart rate, steps, calories burned, sleep data, etc.) from wearable devices (smartwatches and fitness trackers).

[0418] Input: Biometric data from wearable devices

[0419] Output: Collected biometric data

[0420] Specific operation: The terminal connects to the wearable device via Bluetooth or Wi-Fi and periodically acquires biometric information.

[0421] Step 2:

[0422] The device uses a camera and microphone to capture emotional information from the user's facial expressions and voice tone.

[0423] Input: Camera video and audio data

[0424] Output: Analyzed emotion information data

[0425] Specific operation: The device recognizes the user's emotional state by analyzing facial expressions from camera footage using OpenCV and analyzing voice tones using a voice recognition library.

[0426] Step 3:

[0427] The terminal transmits the collected biometric information and emotional information to a cloud server.

[0428] Input: Biometric data and emotional data

[0429] Output: Comprehensive data sent to the cloud server

[0430] Specific operation: The device securely uploads biometric and emotional information to the cloud server using the HTTPS protocol.

[0431] Step 4:

[0432] The server stores the biometric information and emotion information stored in the cloud server in a database and checks the consistency of the data.

[0433] Input: Comprehensive data sent to the cloud server

[0434] Output: Consistent data stored in the database

[0435] Specific operation: The server stores the received data in MySQL or MongoDB, and cleanses the data by filling in missing data and removing noise.

[0436] Step 5:

[0437] The server uses machine learning algorithms to analyze the biometric and emotional information to identify the user's behavioral patterns and emotional state.

[0438] Input: Consistent data stored in a database

[0439] Output: Analyzed behavioral patterns and emotional states

[0440] Specific operation: The server uses TensorFlow and PyTorch to run data analysis models and identify the user's behavioral patterns and emotional state.

[0441] Step 6:

[0442] The server generates personalized health improvement recommendations and instructions based on the identified behavioral patterns and emotional state.

[0443] Input: Analyzed behavioral patterns and emotional states

[0444] Output: Generated health improvement recommendations and instructions

[0445] Specific Actions: The server uses the generative AI model to generate recommendations and specific instructions for health improvement that are optimized for each user.

[0446] Step 7:

[0447] The server transmits the generated health improvement recommendations and instructions to the terminal, which notifies and displays them to the user.

[0448] Input: Generated health improvement recommendations and instructions

[0449] Output: Notification displayed on the device

[0450] Specific operation: The server sends recommendations and instructions in JSON format to the device, which interprets them and displays them on the user interface, which the user can view on their smartphone screen.

[0451] 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.

[0452] 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.

[0453] 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.

[0454] [Second embodiment]

[0455] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0456] 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.

[0457] 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).

[0458] 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.

[0459] 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.

[0460] 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).

[0461] 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.

[0462] 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.

[0463] 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.

[0464] 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.

[0465] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0466] 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."

[0467] System Overview

[0468] This invention is a system that collects and analyzes personal health data and provides personalized advice and guidance for improving health based on that data. This system supports users in improving their health and modifying their behavior by understanding their health status in real time and providing optimal advice based on scientific evidence.

[0469] System configuration

[0470] 1. Data Collection Phase

[0471] The device connects to the user's wearable device (smartwatch or fitness tracker) and collects real-time health data such as heart rate, steps, calories burned, and sleep data. Users can also manually enter data such as dietary habits and emotional state through the application.

[0472] 2. Data transmission phase

[0473] The device periodically transmits the collected health data to a cloud server over the internet using a secure protocol.

[0474] 3. Data analysis phase

[0475] The server stores the received health data on a cloud server and performs preprocessing (missing value completion, noise removal). It then analyzes the data using machine learning algorithms to identify the user's behavioral patterns. It also connects with external cloud services (e.g., calendar services, task management services) via API, integrating and analyzing the acquired data.

[0476] 4. Advice Generation Phase

[0477] The server generates personalized health improvement advice and guidance based on the identified behavioral patterns, customized to the user's specific goals (e.g., weight loss, muscle gain).

[0478] 5. Notification and display phase

[0479] The server sends the generated advice to the user's device, which then visually displays it to the user. The user can then view the advice in real time through the application and use it to influence their next actions.

[0480] Specific examples

[0481] Example 1: User A is dieting

[0482] User A has a goal of walking 10,000 steps every day.

[0483] The device acquires step count data from the smartwatch and transmits it to the server.

[0484] The server analyzes the weekly step count data and generates advice to User A, such as "Your average step count this week is 9,500 steps. You're almost there!"

[0485] The server works in conjunction with the calendar service to suggest, "It would be a good idea to add some walking time to a day next week when you have fewer meetings."

[0486] The terminal notifies this information to user A in real time.

[0487] Example 2: User B needs stress management

[0488] User B has recently become more prone to stress.

[0489] The terminal acquires the heart rate data of user B and transmits it to the server.

[0490] The server analyzes that an increased heart rate is related to lack of sleep and generates advice to User B saying, "You've been getting less than eight hours of sleep recently. Try doing some relaxing yoga or meditation."

[0491] The server suggests yoga and meditation tutorial videos, and the device notifies User B.

[0492] In this way, the system of the present invention provides a personalized health improvement plan for each user, supporting health management while maintaining the user's motivation.

[0493] The processing flow will be explained below.

[0494] Step 1:

[0495] The device connects to the user's wearable device (smartwatch, fitness tracker, etc.) via Bluetooth or Wi-Fi and collects real-time health data (heart rate, steps, calories burned, sleep data, etc.).

[0496] Step 2:

[0497] Users manually enter information such as what they eat and their emotional state through an application on their smartphone or tablet, which also collects this data.

[0498] Step 3:

[0499] The device periodically transmits the collected health data and manually entered data to a cloud server using a secure communication protocol (such as HTTPS).

[0500] Step 4:

[0501] The server stores all received data in a database, where it checks the data for consistency and performs missing value imputation and noise removal as necessary.

[0502] Step 5:

[0503] The server analyzes the data using machine learning algorithms, clustering algorithms to identify user behavior patterns, and classification algorithms to classify specific behaviors (e.g., healthy behaviors, unhealthy behaviors).

[0504] Step 6:

[0505] The server generates personalized health improvement advice and guidance based on the user's behavioral patterns and lifestyle habits. Advice is customized to the user's specific goals (e.g., dieting, muscle building).

[0506] Step 7:

[0507] The server transmits the generated advice to the user's terminal, which visually displays the advice to the user.

[0508] Step 8:

[0509] The application allows users to review advice and take action, and if necessary, they can enter more data or set new goals.

[0510] Step 9:

[0511] The server obtains data through API integration with external cloud services (e.g., calendar services, task management services) and integrates the user's schedule and task information into the analysis, allowing it to provide more detailed and personalized advice.

[0512] Step 10:

[0513] Users can share their health data and advice with family and friends as needed. The device supports this sharing function and shares data within a specified range.

[0514] Example 1

[0515] 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."

[0516] There is a demand for effective and efficient support for users' health improvement and behavior modification by understanding their health status in real time and providing optimal health improvement advice based on scientific evidence. However, conventional systems have insufficient data collection, analysis, and advice provision, making it difficult to provide accurate advice customized for each user.

[0517] 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.

[0518] In this invention, the server includes means for collecting health data, means for transmitting the health data to a cloud server, means for preprocessing the health data (missing value imputation, noise removal) on the cloud server, means for analyzing the health data on the cloud server and identifying individual behavioral patterns, means for generating personalized health improvement advice and guidance based on the behavioral patterns, means for transmitting the generated advice and guidance to the user's terminal and visually displaying it, means for collecting data in real time from the wearable device during the health data collection phase, means for the user to manually input dietary details and emotional state through an application, and means for encrypting and transmitting the collected data. This makes it possible to grasp the user's health condition in detail and provide an individual health improvement plan quickly and effectively.

[0519] "Health Data" refers to information about an individual's health and behavior, such as an individual's heart rate, steps taken, calories burned, sleep data, diet, and emotional state.

[0520] "Cloud server" refers to a server accessible via the Internet that is a remote computing resource used for data storage, preprocessing, analysis, advice generation, and so on.

[0521] "Preprocessing" refers to data cleansing operations such as missing value imputation and noise removal that are performed on collected health data.

[0522] "Behavioral patterns" refer to trends or regularities in a series of behaviors identified by analyzing a user's health data.

[0523] "Advice and guidance" refers to personalized health improvement instructions and advice generated based on a user's behavioral patterns.

[0524] "User device" refers to a device used by an individual, such as a smartphone, tablet, or PC, which displays advice and guidance sent from the cloud server.

[0525] "Wearable devices" refers to electronic devices worn by individuals, such as smartwatches and fitness trackers, that collect real-time health data.

[0526] "Encryption" refers to the process of protecting collected data using techniques such as symmetric key encryption or public key encryption to ensure that only authorized individuals can access the data.

[0527] A "preprocessing module" refers to a software component that runs on a cloud server and performs missing value completion and noise removal in data.

[0528] A "machine learning model" refers to a program that implements an algorithm to use collected data to identify user behavior patterns and predict future behavior.

[0529] A "natural language generation model" refers to a program that uses artificial intelligence technology to generate advice and guidance for users in natural-sounding sentences.

[0530] "API integration" refers to an interface that allows different software systems to communicate with each other and exchange data.

[0531] The present invention is a system for collecting and analyzing personal health data and providing personalized advice and guidance for improving health. The following describes in detail an embodiment of the present invention.

[0532] System Overview

[0533] The system of the present invention supports users in improving their health and modifying their behavior by understanding their personal health status in real time and providing optimal advice based on scientific evidence. This system consists of the following main components:

[0534] System configuration

[0535] 1. Data Collection

[0536] The terminal collects real-time health data (heart rate, steps, calories burned, sleep data, etc.) from wearable devices (e.g., smart watches, fitness trackers) using the wearable device's dedicated API (e.g., Apple HealthKit, GOOGLE FIT (registered trademark) API).

[0537] Users can manually enter their dietary habits, emotional state, and more through the mobile application.

[0538] 2. Data Transmission

[0539] The device encrypts the collected data (e.g., AES encryption) and periodically transmits it to the cloud server via a secure communication protocol (e.g., HTTPS).

[0540] 3. Data Preprocessing

[0541] The server stores the received data in temporary storage (e.g., AWS S3 bucket) on the cloud server.

[0542] The server uses preprocessing modules to impute missing values ​​(e.g., mean imputation) and remove noise (e.g., moving average filter) from the data.

[0543] 4. Data Analysis

[0544] The server inputs the preprocessed data into a machine learning model (e.g., random forest, neural network).

[0545] The server uses Python libraries (e.g., scikit-learn, TensorFlow) to identify user behavior patterns.

[0546] The server connects with external cloud services (e.g., Google Calendar, task management services) via API to obtain daily schedules and task information and integrate it into the analysis.

[0547] 5. Advice Generation

[0548] The server generates personalized health improvement advice and guidance based on the analysis results of the machine learning model.

[0549] The server uses a natural language generation model (e.g., GPT-4) to generate advice tailored to the user's specific goals (e.g., losing weight, gaining muscle).

[0550] 6. Advice Notice

[0551] The server transmits the generated advice to the user's terminal.

[0552] The terminal displays the received advice to the user in real time through the notification function of the application.

[0553] Users can use the application's dashboard function to view details of the advice and apply it to their daily actions.

[0554] Specific examples

[0555] Example 1: User A is dieting

[0556] User A has a goal of walking 10,000 steps every day.

[0557] The device collects step count data from the smartwatch and sends the data to the server every night at midnight.

[0558] The server accumulates one week's worth of step count data, and the preprocessing module fills in missing values ​​in the data.

[0559] The server uses a machine learning model to analyze User A's step count data and generate advice such as, "Your average step count this week is 9,500. You're almost there!"

[0560] The server connects to the calendar service via an API and suggests, "It would be a good idea to add some walking time on a day next week when you have fewer meetings," and notifies the device of this.

[0561] Prompt Sentence Examples

[0562] User A's health goal is to take 10,000 steps per day. Their average step count this week is 9,500. Please suggest the next action appropriate for User A. This requires integration with a calendar service.

[0563] Example 2: User B needs stress management

[0564] User B has recently become more prone to stress.

[0565] The device collects heart rate data and transmits it to a server in real time when stress levels rise.

[0566] The server analyzes the heart rate data and performs noise removal using a pre-processing module.

[0567] The server analyzes the increased heart rate and sleep data, finds a correlation, and generates advice such as, "You've been getting less than eight hours of sleep recently. Try some relaxing yoga or meditation."

[0568] The server suggests yoga and meditation tutorial videos through an external video service API and notifies the device of this.

[0569] Prompt Sentence Examples

[0570] User B has been experiencing increased stress recently. Analyze their heart rate and sleep data and provide appropriate stress management advice, including suggestions for yoga and meditation.

[0571] This enables the system to provide each user with a personalized health improvement plan, supporting effective health management while maintaining user motivation.

[0572] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0573] Step 1: Data collection

[0574] The device collects real-time health data from wearable devices, such as heart rate, steps, calories burned, and sleep data, using the wearable device's dedicated API.

[0575] Input: Sensor data from wearable devices.

[0576] Output: Health data stored on the device.

[0577] Specific operation: The terminal communicates with the wearable device periodically (for example, every minute) to obtain and store the latest data.

[0578] Step 2: Manual data entry

[0579] Users manually enter health data, such as diet and emotional state, through a mobile application.

[0580] Input: Data entered by the user, such as dietary details and emotional state.

[0581] Output: Manually entered data stored in the application.

[0582] Specific operation: The user enters data using the application's input form, and the device saves it.

[0583] Step 3: Send data

[0584] The terminal encrypts the collected data (real-time data and manually entered data) and transmits it to the cloud server using a secure communication protocol (e.g., HTTPS).

[0585] Input: All health data stored on the device.

[0586] Output: Encrypted data sent to the cloud server.

[0587] Specific operation: The terminal compiles data on a fixed schedule (for example, every hour), encrypts it using AES, and sends it to the cloud server.

[0588] Step 4: Data Preprocessing

[0589] The server stores the data sent to the cloud in temporary storage (e.g., AWS S3 bucket) and uses preprocessing modules to perform missing value imputation and noise removal.

[0590] Input: Raw data stored on cloud servers.

[0591] Output: Preprocessed, missing value imputed, and denoised data.

[0592] Specific operation: The server uses a preprocessing module (for example, Python's Pandas library) to filter the data and impute missing values.

[0593] Step 5: Data analysis

[0594] The server inputs the preprocessed data into a machine learning model (e.g., random forest, neural network) to identify user behavior patterns. It also obtains data through API integration with external cloud services (e.g., calendar services, task management services) and performs integrated analysis.

[0595] Input: Preprocessed data, data obtained from external cloud services.

[0596] Output: Behavioral patterns as a result of the analysis.

[0597] Specific operation: The server analyzes the data using machine learning algorithms (e.g., scikit-learn, TensorFlow), and based on the identified behavioral patterns, further integrates the data with external services using APIs.

[0598] Step 6: Advice Generation

[0599] The server generates personalized health improvement advice based on the analysis results of the machine learning model. This advice is generated as sentences using a natural language generation model (e.g., GPT-4).

[0600] Input: Behavioral patterns as analysis results, user health goals.

[0601] Output: The generated health advice.

[0602] Specific operation: The server uses a natural language generation model to generate advice in natural language based on specific behavioral patterns.

[0603] Step 7: Advice Notification

[0604] The server transmits the generated advice to the user's terminal, which displays it in real time.

[0605] Input: Generated health advice.

[0606] Output: Advice displayed on the user's terminal.

[0607] Specific operation: The server encrypts the advice and delivers it to the device, which then visually displays it to the user using the application's notification function.

[0608] This clearly defines the specific operations at each step and the input and output data, and provides a detailed explanation of the processing flow of the entire system.

[0609] (Application example 1)

[0610] 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."

[0611] In modern society, personal health management is an important issue. Effective training and nutritional management, especially at fitness gyms, require real-time, personalized support. However, current systems struggle to effectively collect and analyze users' health data and provide specific advice based on it. Furthermore, there are insufficient means for fitness gym instructors to appropriately share data and provide effective support to users. Therefore, an effective data collection, analysis, and advice-providing system is needed to help users achieve their goals and achieve sustainable health improvements.

[0612] 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.

[0613] In this invention, the server includes means for collecting personal health data, means for transmitting the health data to a cloud server, means for analyzing the health data in the cloud server and identifying the user's behavioral patterns, means for generating personalized health improvement advice and guidance based on the behavioral patterns, means for transmitting the advice and guidance to the user's device and displaying it, and means for providing training plans and nutritional advice at the fitness gym. This allows users and instructors at the fitness gym to share data in real time and implement personalized health improvement plans.

[0614] "Personal health data" refers to information such as heart rate, steps, calories burned, sleep data, and manually entered dietary information and emotional state.

[0615] A "cloud server" is a server system that can transmit, collect, store, and analyze data via the Internet.

[0616] "Behavioral patterns" refer to trends in daily behavior and health conditions identified based on a user's health data.

[0617] "Health improvement advice and guidance" refers to specific courses of action or suggestions provided to improve a user's health.

[0618] "Terminal" refers to a device used by a user, such as a smartphone, tablet, or smartwatch.

[0619] A "fitness gym" refers to a facility where professional instructors provide training and nutritional management guidance.

[0620] "Training Plan" refers to a customized exercise schedule and exercise instructions based on a user's health data.

[0621] "Nutrition Advice" refers to guidance on appropriate dietary and nutritional intake provided to a user based on their health goals.

[0622] "Shared" refers to a state in which multiple users or services can access and use the same data.

[0623] This invention is a health management system that allows users of fitness gyms to receive training plans and nutrition advice in real time. The system collects personal health data, analyzes that data on a cloud server, and provides health improvement advice based on the user's individual behavioral patterns.

[0624] System configuration

[0625] Health Data Collection and Transmission

[0626] Users use wearable devices such as smartwatches to collect health data such as heart rate, number of steps, calories burned, and sleep data. This data is sent to a cloud server via a device such as a smartphone. This data is sent using a secure communication protocol such as HTTPS.

[0627] Analyzing the data

[0628] The cloud server stores the received health data and performs preprocessing (missing value completion, noise removal). The data is then analyzed using machine learning libraries such as scikit-learn to identify the user's behavioral patterns. Furthermore, the cloud server also processes the user's manually entered dietary details and emotional state, conducting a comprehensive data analysis. The analysis results are shared with the fitness gym instructor in real time.

[0629] Advice generation and notification

[0630] The cloud server generates personalized health improvement advice and guidance based on the identified behavioral patterns. This advice is customized to the user's specific goals (e.g., weight loss, muscle building). The generated advice is then sent to the user's device and displayed visually. The user can receive this advice via their smartphone and incorporate it into their next actions.

[0631] Specific examples

[0632] For users who are dieting

[0633] If a user is aiming to lose weight, the system analyzes their daily step count data and calorie consumption data and generates advice such as, "Today's step count is 7,000. You have 3,000 steps left to reach your goal." It also provides specific instructions on calories burned, such as, "Today you have burned 500 kilocalories. Burn an additional 200 kilocalories."

[0634] For users who need stress management

[0635] If the user is diagnosed with stress due to elevated heart rate or insufficient sleep, the system will generate advice such as, "Your heart rate has been high recently and you've been getting less than eight hours of sleep. Try yoga or meditation to relax."

[0636] Prompt Sentence Examples

[0637] Based on the user's step count and calorie consumption data, evaluate the progress towards the target number of steps and calorie consumption and provide specific advice to the user.

[0638] Generate specific advice for stress management based on the user's heart rate and sleep data.

[0639] In this way, the system of the present invention provides fitness gym users with personalized health improvement plans, supporting health management while maintaining the users' motivation.

[0640] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0641] Step 1:

[0642] A user uses a wearable device to collect health data.

[0643] Input: Health data such as heart rate, steps, calories burned, and sleep data.

[0644] Output: Collected health data.

[0645] Specific operation: Smartwatches and fitness trackers measure health data from the user's body in real time and transmit it to devices such as smartphones.

[0646] Step 2:

[0647] The health data collected by the device is sent to a cloud server.

[0648] Input: Collected health data.

[0649] Output: Health data sent to cloud server.

[0650] Specific operation: The device sends the collected health data to a cloud server via the Internet using a secure protocol (e.g., HTTPS).

[0651] Step 3:

[0652] The server accumulates the received health data and performs preprocessing.

[0653] Input: Health data sent to cloud server.

[0654] Output: Preprocessed health data.

[0655] Specific operation: The server performs data preprocessing such as imputing missing values ​​and removing noise, etc. This is where processing is done to ensure data consistency and accuracy.

[0656] Step 4:

[0657] The server analyzes the pre-processed health data to identify the user's behavioral patterns.

[0658] Input: Preprocessed health data.

[0659] Output: User behavior patterns.

[0660] How it works: The server uses a machine learning library (e.g., scikit-learn) to extract features from the data and analyze it for specific behavioral patterns. At this point, the machine learning model predicts the behavioral patterns based on the training data.

[0661] Step 5:

[0662] The server generates health improvement advice and guidance based on the identified behavioral patterns.

[0663] Input: User behavior patterns.

[0664] Output: Health improvement advice and guidance.

[0665] How it works: The server generates advice and guidance customized to the user's goals (e.g., weight loss, muscle gain). Using generative AI models and prompts, personalized advice is created for each individual user.

[0666] Step 6:

[0667] The server transmits the generated advice and guidance to the user's terminal.

[0668] Enter: health improvement advice and guidance.

[0669] Output: Advice and guidance sent to the user's device.

[0670] Specific operation: The server sends advice and guidance to the user's smartphone via the Internet.

[0671] Step 7:

[0672] The terminal displays the advice and guidance sent to the user.

[0673] Input: Advice and guidance sent to the user's device.

[0674] Output: Advice or guidance that is displayed to the user.

[0675] Specific operation: The smartphone application notifies the user of the received advice and visually displays it to them, allowing them to decide their next course of action.

[0676] 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.

[0677] System Overview

[0678] This system collects and analyzes personal health and emotional data, and provides individualized advice and guidance for improving health based on the collected data. In particular, by combining it with an emotion engine, it achieves highly personalized guidance that takes into account the user's emotional state.

[0679] System configuration

[0680] 1. Data Collection Phase

[0681] The device connects to the user's wearable device (smartwatch, fitness tracker, etc.) and collects real-time health data such as heart rate, steps, calories burned, and sleep data. Furthermore, the user can manually input their diet and emotional state through the application. Additionally, the emotion engine automatically recognizes emotions from the user's facial expressions, voice tone, text input, etc., and generates emotion data.

[0682] 2. Data transmission phase

[0683] The device periodically transmits the collected health and emotion data to a cloud server over the internet using a secure protocol (e.g., HTTPS).

[0684] 3. Data analysis phase

[0685] The server stores all received data in a database. When saving, it checks the data's integrity and performs missing value imputation and noise removal as necessary. It then analyzes the data using machine learning algorithms to identify the user's behavioral patterns and emotional state. It also connects with external cloud services (e.g., calendar services, task management services) via API, integrating and analyzing the acquired data.

[0686] 4. Advice Generation Phase

[0687] The server generates personalized health improvement advice and guidance based on the identified behavioral patterns and emotional state. Specifically, by incorporating the user's emotional data, it suggests relaxation methods based on stress levels and activities to maintain positive emotions.

[0688] 5. Notification and display phase

[0689] The server sends the generated advice and guidance to the user's device, which then visually displays it to the user. The user can then check the advice in real time through the application and use it to influence their next actions.

[0690] Specific examples

[0691] Example 1: User C whose main purpose is stress management

[0692] User C is experiencing increasing stress at work.

[0693] The terminal acquires the heart rate data from the smartwatch and transmits it to the server.

[0694] The emotion engine detects user C's stress level from his / her daily facial expressions and tone of voice.

[0695] The server analyzes the increased heart rate and the emotion engine data and generates advice to User C, saying, "Your stress level has been increasing recently. Try a short meditation to relax."

[0696] The device suggests yoga and meditation tutorial videos and notifies User C.

[0697] Example 2: User D wants to maintain a positive lifestyle

[0698] User D wants to maintain a healthy and positive lifestyle.

[0699] The device acquires activity data from User D's fitness tracker and sends it to the server.

[0700] The emotion engine collects emotion data from user D's daily text input and speech and identifies the prevalence of positive emotions.

[0701] The server analyzes this data comprehensively and generates advice such as, "Your recent activity has been going well. To keep up this pace, why not try a new jogging course this weekend?"

[0702] The terminal notifies User D of the proposal.

[0703] The system of the present invention provides each user with a personalized health improvement plan based on health data and emotional data, and supports health management while maintaining the user's motivation.

[0704] The processing flow will be explained below.

[0705] Step 1:

[0706] The device connects to the user's wearable device (smartwatch, fitness tracker, etc.) via Bluetooth or Wi-Fi and collects real-time health data (heart rate, steps, calories burned, sleep data, etc.).

[0707] Step 2:

[0708] Users manually enter their diet and emotional state through an application on a smartphone or tablet, which also collects this data.

[0709] Step 3:

[0710] The emotion engine automatically recognizes emotions from the user's facial expressions, voice tone, text input, etc., and generates emotion data.

[0711] Step 4:

[0712] The device periodically transmits the collected health and emotion data to a cloud server using a secure communication protocol (such as HTTPS).

[0713] Step 5:

[0714] The server stores all received data in a database, where it checks the data for consistency and performs missing value imputation and noise removal as necessary.

[0715] Step 6:

[0716] The server analyzes the data using machine learning algorithms, clustering algorithms to identify user behavior patterns, and classification algorithms to classify specific behaviors (e.g., healthy behaviors, unhealthy behaviors).

[0717] Step 7:

[0718] The server obtains data via API integration with external cloud services (e.g., calendar services, task management services) and integrates user schedules and task information into the analysis.

[0719] Step 8:

[0720] The server generates personalized health improvement advice and guidance based on the identified behavioral patterns and emotional state. Specifically, by incorporating the user's emotional data, it suggests relaxation methods based on stress levels and activities to maintain positive emotions.

[0721] Step 9:

[0722] The server transmits the generated advice and guidance to the user's terminal, which visually displays the advice to the user.

[0723] Step 10:

[0724] The application allows users to review advice and take action, and if necessary, they can enter more data or set new goals.

[0725] Example 2

[0726] 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."

[0727] Conventional health management systems have the ability to collect and analyze personal health data, but the technology to combine this with emotional data to provide personalized advice has not been sufficiently developed. As a result, it has been difficult to provide comprehensive health management that takes into account the psychological state of each individual user. Another issue is that insufficient integration with external data services means it is not possible to comprehensively understand users' behavioral patterns and lifestyle rhythms.

[0728] 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.

[0729] In this invention, the server includes a means for collecting personal health information, an emotion engine for analyzing the personal emotional information, a means for the emotion engine to detect the individual's emotional state, and a means for generating individualized health improvement advice and guidance based on the emotional information. This enables highly personalized guidance that takes the user's emotional state into consideration. Furthermore, because the server includes a means for acquiring data via API integration with external data services, it is possible to comprehensively understand the user's behavioral patterns and lifestyle rhythms and provide more accurate advice.

[0730] "Health information" refers to numerical values ​​and data that indicate an individual's health status, such as heart rate, number of steps, calories burned, and sleep data.

[0731] An "emotion engine" is software or algorithms that automatically recognize an individual's emotional state by analyzing their facial expressions, voice tone, text input, etc.

[0732] A "remote server" is a computer system located in a remote location via the Internet for storing, analyzing, and processing data.

[0733] "API integration" refers to the use of an interface that allows different software and services to communicate data with each other and share functions.

[0734] "Personalized health improvement advice" refers to advice and recommendations for improving health that are customized for each individual based on personal health and emotional information.

[0735] "Behavioral patterns" are data that show a series of actions and habits in an individual's daily life, and are an important indicator for health management.

[0736] overview

[0737] This invention is a system that collects and analyzes personal health and emotional information, and provides individualized health improvement advice and guidance based on that information. In particular, by combining this system with an emotion engine, this system realizes highly personalized guidance that takes into account the user's emotional state.

[0738] System configuration

[0739] 1. Data Collection Phase

[0740] The device connects to the user's wearable device (e.g., smartwatch) and collects real-time health information such as heart rate, steps, calories burned, and sleep data. Users can manually enter their diet and emotional state through the application. In addition, the emotion engine analyzes the user's facial expressions and voice tone collected through the device's camera and microphone to automatically generate emotional information.

[0741] 2. Data transmission phase

[0742] The device periodically transmits the collected health and emotional information to a cloud server via the Internet using a secure protocol (e.g., HTTPS).

[0743] 3. Data analysis phase

[0744] The server stores the received data in a database. When saving, it checks the data's integrity and performs missing value completion and noise removal. Next, it analyzes the data using machine learning algorithms to identify the user's behavioral patterns and emotional state. It also connects via API to external cloud services (e.g., calendar services, task management services), integrating the acquired data for analysis.

[0745] 4. Advice Generation Phase

[0746] The server generates personalized health improvement advice and guidance based on the identified behavioral patterns and emotional state. Specifically, it takes into account the user's emotional information and suggests relaxation methods according to stress levels and activities to maintain positive emotions.

[0747] 5. Notification and display phase

[0748] The server sends the generated advice and guidance to the user's device, which visually displays it to the user, who can then view the advice in real time through the application.

[0749] Specific examples

[0750] Example 1: User A whose main purpose is stress management

[0751] User A is experiencing increasing stress at work.

[0752] The terminal acquires the heart rate data from the smartwatch and transmits it to the server.

[0753] The emotion engine detects user A's stress level from his / her daily facial expressions and tone of voice.

[0754] The server analyzes the increased heart rate and data from the emotion engine and generates advice such as, "Your stress levels have been increasing recently. Try a short meditation to relax."

[0755] The device suggests yoga and meditation tutorial videos and notifies User A.

[0756] Example 2: User B wants to maintain a positive lifestyle

[0757] User B wants to maintain a healthy and positive lifestyle.

[0758] The device acquires activity data from User B's fitness tracker and sends it to the server.

[0759] The emotion engine collects emotion data from User B's daily text inputs and speech and identifies that there are many positive emotions.

[0760] The server comprehensively analyzes the data and generates advice such as, "You've been doing well lately. To keep up the pace, why not try a new jogging route this weekend?"

[0761] The terminal notifies User B of the proposed content.

[0762] Prompt Sentence Examples

[0763] Prompt 1:

[0764] "If the user is feeling stressed, generate advice on what relaxation methods to suggest. Data includes increased heart rate and negative emotion data."

[0765] Prompt 2:

[0766] "Generate specific activity suggestions to motivate users to maintain positive emotions and continue their activity habits. Recent activity data has been very positive."

[0767] The above is the details of the "Mode for Carrying Out the Invention" of the present invention. This system makes it possible to provide a personalized health improvement plan to each user and support health management.

[0768] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0769] Step 1: Connect your device

[0770] The terminal connects to the wearable device (e.g., smart watch) worn by the user using Bluetooth or Wi-Fi. The specific procedure involves launching the terminal application and executing the pairing process with the device. The input is information about the user's wearable device, and the output is the established connection status.

[0771] Step 2: Gathering health information

[0772] The terminal collects real-time health information from the wearable device, such as heart rate, steps, calories burned, and sleep data. This involves retrieving data via the API provided by the device. The input is sensor data from the wearable device, and the output is the collected health information.

[0773] Step 3: Manual Entry

[0774] The user manually inputs their health information and emotional state through the application interface. Specifically, they input their dietary information and emotional state in text or selection format. The input is the data manually entered by the user, and the output is the manually entered data.

[0775] Step 4: Recognizing emotional information

[0776] The device's emotion engine analyzes the user's facial expressions and voice tone collected from the camera and microphone to generate emotion information. The emotion engine performs the analysis using machine learning algorithms. The inputs are video data and audio data, and the output is analyzed emotion information.

[0777] Step 5: Send data to the cloud

[0778] The device periodically transmits the collected health and emotional information to a cloud server. The transmission is secure using the HTTPS protocol. The input is the collected health and emotional information, and the output is the data stored on the remote server.

[0779] Step 6: Save your data

[0780] The server stores the received data in a database. When storing, it unifies the data format and checks its consistency. The input is the transmitted data, and the output is the data stored in the database.

[0781] Step 7: Data integrity check and correction

[0782] The server detects missing values ​​and noise in the data, and performs interpolation and noise removal. Specific algorithms include linear interpolation and moving average filters. The input is the stored data, and the output is the corrected data.

[0783] Step 8: Analyze behavioral patterns

[0784] The server uses machine learning algorithms to identify user behavioral patterns, such as analyzing time-series data to extract the user's exercise habits and sleep patterns. The input is the data whose integrity has been verified, and the output is the analyzed behavioral patterns.

[0785] Step 9: Integrate with external cloud services

[0786] The server connects to an external cloud service (e.g., a calendar service, task management service) via API to obtain or provide the required data. The input is the API key and query for the external service, and the output is the obtained additional data.

[0787] Step 10: Generating Advice

[0788] The server generates personalized health improvement advice based on the user's behavioral patterns and emotional state. Based on the analysis results, it suggests specific actions, such as "do a short meditation." The inputs are behavioral patterns and emotional information, and the output is customized advice.

[0789] Step 11: Submitting Advice

[0790] The server transmits the generated advice to the user's terminal using a secure communication protocol, with the generated advice as input and the advice displayed on the terminal as output.

[0791] Step 12: Viewing Advice

[0792] The terminal visually displays the received advice to the user. The user opens the application and checks the provided advice. The input is the received advice, and the output is the visually displayed advice.

[0793] Prompt Sentence Examples

[0794] Prompt 1:

[0795] "If the user is feeling stressed, generate advice on what relaxation methods to suggest. Data includes increased heart rate and negative emotion data."

[0796] Prompt 2:

[0797] "Generate specific activity suggestions to motivate users to maintain positive emotions and continue their activity habits. Recent activity data has been very positive."

[0798] (Application example 2)

[0799] 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."

[0800] In modern society, a variety of health issues affect individuals. In particular, it is difficult to provide appropriate health improvement guidance to individuals, and uniform advice that does not take into account each individual's emotional state makes effective health management difficult. In particular, there is a need for a method to provide real-time, personalized health improvement recommendations to users at physical gyms and other physical facilities.

[0801] 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.

[0802] In this invention, the server includes means for collecting biometric information and emotional information of an individual, means for transmitting the biometric information and emotional information to a cloud device, means for analyzing the biometric information and emotional information in the cloud device and identifying the user's behavioral patterns and emotional state, means for generating individual health improvement recommendations and instructions based on the behavioral patterns and emotional state, and means for transmitting and displaying the recommendations and instructions to the user's terminal, thereby enabling personalized health improvement recommendations that take into account the user's real-time emotional state.

[0803] "Personal biometric information" refers to data related to the user's health condition, such as the user's heart rate, number of steps, calories burned, and sleep data.

[0804] "Emotional information" refers to data about a user's emotional state, obtained from the user's facial expressions, voice tone, text input, etc.

[0805] "Cloud device" refers to a remote server connected via the Internet for storing, processing, and analyzing data.

[0806] "Behavioral patterns" refer to a series of actions and activity trends in a user's daily life that are identified through analysis and learning.

[0807] "Personalized health improvement recommendations and instructions" refers to specific suggestions and instructions for promoting and improving health that are generated specifically for a user based on the user's identified behavioral patterns and emotional state.

[0808] "User's device" refers to an electronic device used by a user, such as a smartphone, tablet, or personal computer.

[0809] "API integration" refers to the process of exchanging information with external cloud services using an application programming interface.

[0810] This invention is a system that collects and analyzes an individual's biometric and emotional information and provides personalized health recommendations in real time. Specifically, the invention is implemented using the following hardware and software:

[0811] Hardware and Software

[0812] 1. Wearable devices: Smartwatches and fitness trackers are used to collect real-time biometric information such as heart rate, steps taken, calories burned, and sleep.

[0813] 2. Smartphone or tablet: A device held by the user that acts as a central device for collecting, displaying, and transmitting health and emotional information.

[0814] 3. Cloud server: A remote server for storing and analyzing collected data and generating health improvement recommendations, such as AWS (Amazon Web Services) or Google Cloud Platform.

[0815] 4. Camera and microphone: The camera and microphone built into the smartphone are used to analyze emotional information from the user's facial expressions and tone of voice.

[0816] Data processing and calculation

[0817] 1. Data Collection:

[0818] Biometric information is collected from the user's wearable device.

[0819] Use a camera or microphone to capture emotional information, for example, by analyzing facial expressions using OpenCV and voice tone with a speech recognition library.

[0820] The collected data is sent from the smartphone to a cloud server using the HTTPS protocol.

[0821] 2. Data processing on cloud servers:

[0822] The collected biometric and emotional information is stored on a cloud server.

[0823] When data is saved to a database (e.g., MySQL or MongoDB), the integrity of the data is checked.

[0824] Cleanse the data by imputing missing values ​​and removing noise.

[0825] Machine learning algorithms (e.g., TensorFlow and PyTorch) are used to analyze the data and identify behavioral patterns and emotional states.

[0826] 3. Generate health improvement recommendations:

[0827] Generate personalized health improvement recommendations and instructions based on identified behavioral patterns and emotional states.

[0828] For example, if it determines that the user is feeling stressed, it will recommend that they "do some relaxation exercises."

[0829] 4. Recommendation Notification and Display:

[0830] The generated recommendations and instructions are then sent back to the user's device.

[0831] A notification will appear on your smartphone prompting you to take the next step.

[0832] Specific examples

[0833] Let us take the example of user A using this system while training at a sports gym.

[0834] 1. The user wears a smartwatch to collect biometric information such as heart rate and calories burned.

[0835] 2. Turn on the camera and microphone on your smartphone to capture facial expressions and audio during training.

[0836] 3. The collected data is sent to a cloud server.

[0837] 4. The cloud server analyzes the data and identifies the user's fatigue and stress levels.

[0838] 5. A notification appears on the user's smartphone with a recommendation such as, "Your fatigue is increasing. Try stretching for 5 minutes to warm down."

[0839] Prompt Sentence Examples

[0840] "Please compile today's training data. Create a report that includes exercise history, heart rate variability, and emotional changes (from facial expressions and voice tone) and come up with the next recommendation for the user."

[0841] The specific embodiments for carrying out the present invention have been described above.

[0842] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0843] Step 1:

[0844] The device collects biometric information (heart rate, steps, calories burned, sleep data, etc.) from wearable devices (smartwatches and fitness trackers).

[0845] Input: Biometric data from wearable devices

[0846] Output: Collected biometric data

[0847] Specific operation: The terminal connects to the wearable device via Bluetooth or Wi-Fi and periodically acquires biometric information.

[0848] Step 2:

[0849] The device uses a camera and microphone to capture emotional information from the user's facial expressions and voice tone.

[0850] Input: Camera video and audio data

[0851] Output: Analyzed emotion information data

[0852] Specific operation: The device recognizes the user's emotional state by analyzing facial expressions from camera footage using OpenCV and analyzing voice tones using a voice recognition library.

[0853] Step 3:

[0854] The terminal transmits the collected biometric information and emotional information to a cloud server.

[0855] Input: Biometric data and emotional data

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

[0857] Specific operation: The device securely uploads biometric and emotional information to the cloud server using the HTTPS protocol.

[0858] Step 4:

[0859] The server stores the biometric information and emotion information stored in the cloud server in a database and checks the consistency of the data.

[0860] Input: Comprehensive data sent to the cloud server

[0861] Output: Consistent data stored in the database

[0862] Specific operation: The server stores the received data in MySQL or MongoDB, and cleanses the data by filling in missing data and removing noise.

[0863] Step 5:

[0864] The server uses machine learning algorithms to analyze the biometric and emotional information to identify the user's behavioral patterns and emotional state.

[0865] Input: Consistent data stored in a database

[0866] Output: Analyzed behavioral patterns and emotional states

[0867] Specific operation: The server uses TensorFlow and PyTorch to run data analysis models and identify the user's behavioral patterns and emotional state.

[0868] Step 6:

[0869] The server generates personalized health improvement recommendations and instructions based on the identified behavioral patterns and emotional state.

[0870] Input: Analyzed behavioral patterns and emotional states

[0871] Output: Generated health improvement recommendations and instructions

[0872] Specific Actions: The server uses the generative AI model to generate recommendations and specific instructions for health improvement that are optimized for each user.

[0873] Step 7:

[0874] The server transmits the generated health improvement recommendations and instructions to the terminal, which notifies and displays them to the user.

[0875] Input: Generated health improvement recommendations and instructions

[0876] Output: Notification displayed on the device

[0877] Specific operation: The server sends recommendations and instructions in JSON format to the device, which interprets them and displays them on the user interface, which the user can view on their smartphone screen.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] [Third embodiment]

[0882] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0883] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0884] 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).

[0885] 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.

[0886] 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.

[0887] 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).

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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."

[0894] System Overview

[0895] This invention is a system that collects and analyzes personal health data and provides personalized advice and guidance for improving health based on that data. This system supports users in improving their health and modifying their behavior by understanding their health status in real time and providing optimal advice based on scientific evidence.

[0896] System configuration

[0897] 1. Data Collection Phase

[0898] The device connects to the user's wearable device (smartwatch or fitness tracker) and collects real-time health data such as heart rate, steps, calories burned, and sleep data. Users can also manually enter data such as dietary habits and emotional state through the application.

[0899] 2. Data transmission phase

[0900] The device periodically transmits the collected health data to a cloud server over the internet using a secure protocol.

[0901] 3. Data analysis phase

[0902] The server stores the received health data on a cloud server and performs preprocessing (missing value completion, noise removal). It then analyzes the data using machine learning algorithms to identify the user's behavioral patterns. It also connects with external cloud services (e.g., calendar services, task management services) via API, integrating and analyzing the acquired data.

[0903] 4. Advice Generation Phase

[0904] The server generates personalized health improvement advice and guidance based on the identified behavioral patterns, customized to the user's specific goals (e.g., weight loss, muscle gain).

[0905] 5. Notification and display phase

[0906] The server sends the generated advice to the user's device, which then visually displays it to the user. The user can then view the advice in real time through the application and use it to influence their next actions.

[0907] Specific examples

[0908] Example 1: User A is dieting

[0909] User A has a goal of walking 10,000 steps every day.

[0910] The device acquires step count data from the smartwatch and transmits it to the server.

[0911] The server analyzes the weekly step count data and generates advice to User A, such as "Your average step count this week is 9,500 steps. You're almost there!"

[0912] The server works in conjunction with the calendar service to suggest, "It would be a good idea to add some walking time to a day next week when you have fewer meetings."

[0913] The terminal notifies this information to user A in real time.

[0914] Example 2: User B needs stress management

[0915] User B has recently become more prone to stress.

[0916] The terminal acquires the heart rate data of user B and transmits it to the server.

[0917] The server analyzes that an increased heart rate is related to lack of sleep and generates advice to User B saying, "You've been getting less than eight hours of sleep recently. Try doing some relaxing yoga or meditation."

[0918] The server suggests yoga and meditation tutorial videos, and the device notifies User B.

[0919] In this way, the system of the present invention provides a personalized health improvement plan for each user, supporting health management while maintaining the user's motivation.

[0920] The processing flow will be explained below.

[0921] Step 1:

[0922] The device connects to the user's wearable device (smartwatch, fitness tracker, etc.) via Bluetooth or Wi-Fi and collects real-time health data (heart rate, steps, calories burned, sleep data, etc.).

[0923] Step 2:

[0924] Users manually enter information such as what they eat and their emotional state through an application on their smartphone or tablet, which also collects this data.

[0925] Step 3:

[0926] The device periodically transmits the collected health data and manually entered data to a cloud server using a secure communication protocol (such as HTTPS).

[0927] Step 4:

[0928] The server stores all received data in a database, where it checks the data for consistency and performs missing value imputation and noise removal as necessary.

[0929] Step 5:

[0930] The server analyzes the data using machine learning algorithms, clustering algorithms to identify user behavior patterns, and classification algorithms to classify specific behaviors (e.g., healthy behaviors, unhealthy behaviors).

[0931] Step 6:

[0932] The server generates personalized health improvement advice and guidance based on the user's behavioral patterns and lifestyle habits. Advice is customized to the user's specific goals (e.g., dieting, muscle building).

[0933] Step 7:

[0934] The server transmits the generated advice to the user's terminal, which visually displays the advice to the user.

[0935] Step 8:

[0936] The application allows users to review advice and take action, and if necessary, they can enter more data or set new goals.

[0937] Step 9:

[0938] The server obtains data through API integration with external cloud services (e.g., calendar services, task management services) and integrates the user's schedule and task information into the analysis, allowing it to provide more detailed and personalized advice.

[0939] Step 10:

[0940] Users can share their health data and advice with family and friends as needed. The device supports this sharing function and shares data within a specified range.

[0941] Example 1

[0942] 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."

[0943] There is a demand for effective and efficient support for users' health improvement and behavior modification by understanding their health status in real time and providing optimal health improvement advice based on scientific evidence. However, conventional systems have insufficient data collection, analysis, and advice provision, making it difficult to provide accurate advice customized for each user.

[0944] 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.

[0945] In this invention, the server includes means for collecting health data, means for transmitting the health data to a cloud server, means for preprocessing the health data (missing value imputation, noise removal) on the cloud server, means for analyzing the health data on the cloud server and identifying individual behavioral patterns, means for generating personalized health improvement advice and guidance based on the behavioral patterns, means for transmitting the generated advice and guidance to the user's terminal and visually displaying it, means for collecting data in real time from the wearable device during the health data collection phase, means for the user to manually input dietary details and emotional state through an application, and means for encrypting and transmitting the collected data. This makes it possible to grasp the user's health condition in detail and provide an individual health improvement plan quickly and effectively.

[0946] "Health Data" refers to information about an individual's health and behavior, such as an individual's heart rate, steps taken, calories burned, sleep data, diet, and emotional state.

[0947] "Cloud server" refers to a server accessible via the Internet that is a remote computing resource used for data storage, preprocessing, analysis, advice generation, and so on.

[0948] "Preprocessing" refers to data cleansing operations such as missing value imputation and noise removal that are performed on collected health data.

[0949] "Behavioral patterns" refer to trends or regularities in a series of behaviors identified by analyzing a user's health data.

[0950] "Advice and guidance" refers to personalized health improvement instructions and advice generated based on a user's behavioral patterns.

[0951] "User device" refers to a device used by an individual, such as a smartphone, tablet, or PC, which displays advice and guidance sent from the cloud server.

[0952] "Wearable devices" refers to electronic devices worn by individuals, such as smartwatches and fitness trackers, that collect real-time health data.

[0953] "Encryption" refers to the process of protecting collected data using techniques such as symmetric key encryption or public key encryption to ensure that only authorized individuals can access the data.

[0954] A "preprocessing module" refers to a software component that runs on a cloud server and performs missing value completion and noise removal in data.

[0955] A "machine learning model" refers to a program that implements an algorithm to use collected data to identify user behavior patterns and predict future behavior.

[0956] A "natural language generation model" refers to a program that uses artificial intelligence technology to generate advice and guidance for users in natural-sounding sentences.

[0957] "API integration" refers to an interface that allows different software systems to communicate with each other and exchange data.

[0958] The present invention is a system for collecting and analyzing personal health data and providing personalized advice and guidance for improving health. The following describes in detail an embodiment of the present invention.

[0959] System Overview

[0960] The system of the present invention supports users in improving their health and modifying their behavior by understanding their personal health status in real time and providing optimal advice based on scientific evidence. This system consists of the following main components:

[0961] System configuration

[0962] 1. Data Collection

[0963] The terminal collects real-time health data (heart rate, steps, calories burned, sleep data, etc.) from wearable devices (e.g., smartwatches, fitness trackers) using the wearable device's dedicated API (e.g., Apple HealthKit, Google Fit API).

[0964] Users can manually enter their dietary habits, emotional state, and more through the mobile application.

[0965] 2. Data Transmission

[0966] The device encrypts the collected data (e.g., AES encryption) and periodically transmits it to the cloud server via a secure communication protocol (e.g., HTTPS).

[0967] 3. Data Preprocessing

[0968] The server stores the received data in temporary storage (e.g., AWS S3 bucket) on the cloud server.

[0969] The server uses preprocessing modules to impute missing values ​​(e.g., mean imputation) and remove noise (e.g., moving average filter) from the data.

[0970] 4. Data Analysis

[0971] The server inputs the preprocessed data into a machine learning model (e.g., random forest, neural network).

[0972] The server uses Python libraries (e.g., scikit-learn, TensorFlow) to identify user behavior patterns.

[0973] The server connects with external cloud services (e.g., Google Calendar, task management services) via API to obtain daily schedules and task information and integrate it into the analysis.

[0974] 5. Advice Generation

[0975] The server generates personalized health improvement advice and guidance based on the analysis results of the machine learning model.

[0976] The server uses a natural language generation model (e.g., GPT-4) to generate advice tailored to the user's specific goals (e.g., losing weight, gaining muscle).

[0977] 6. Advice Notice

[0978] The server transmits the generated advice to the user's terminal.

[0979] The terminal displays the received advice to the user in real time through the notification function of the application.

[0980] Users can use the application's dashboard function to view details of the advice and apply it to their daily actions.

[0981] Specific examples

[0982] Example 1: User A is dieting

[0983] User A has a goal of walking 10,000 steps every day.

[0984] The device collects step count data from the smartwatch and sends the data to the server every night at midnight.

[0985] The server accumulates one week's worth of step count data, and the preprocessing module fills in missing values ​​in the data.

[0986] The server uses a machine learning model to analyze User A's step count data and generate advice such as, "Your average step count this week is 9,500. You're almost there!"

[0987] The server connects to the calendar service via an API and suggests, "It would be a good idea to add some walking time on a day next week when you have fewer meetings," and notifies the device of this.

[0988] Prompt Sentence Examples

[0989] User A's health goal is to take 10,000 steps per day. Their average step count this week is 9,500. Please suggest the next action appropriate for User A. This requires integration with a calendar service.

[0990] Example 2: User B needs stress management

[0991] User B has recently become more prone to stress.

[0992] The device collects heart rate data and transmits it to a server in real time when stress levels rise.

[0993] The server analyzes the heart rate data and performs noise removal using a pre-processing module.

[0994] The server analyzes the increased heart rate and sleep data, finds a correlation, and generates advice such as, "You've been getting less than eight hours of sleep recently. Try some relaxing yoga or meditation."

[0995] The server suggests yoga and meditation tutorial videos through an external video service API and notifies the device of this.

[0996] Prompt Sentence Examples

[0997] User B has been experiencing increased stress recently. Analyze their heart rate and sleep data and provide appropriate stress management advice, including suggestions for yoga and meditation.

[0998] This enables the system to provide each user with a personalized health improvement plan, supporting effective health management while maintaining user motivation.

[0999] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1000] Step 1: Data collection

[1001] The device collects real-time health data from wearable devices, such as heart rate, steps, calories burned, and sleep data, using the wearable device's dedicated API.

[1002] Input: Sensor data from wearable devices.

[1003] Output: Health data stored on the device.

[1004] Specific operation: The terminal communicates with the wearable device periodically (for example, every minute) to obtain and store the latest data.

[1005] Step 2: Manual data entry

[1006] Users manually enter health data, such as diet and emotional state, through a mobile application.

[1007] Input: Data entered by the user, such as dietary details and emotional state.

[1008] Output: Manually entered data stored in the application.

[1009] Specific operation: The user enters data using the application's input form, and the device saves it.

[1010] Step 3: Send data

[1011] The terminal encrypts the collected data (real-time data and manually entered data) and transmits it to the cloud server using a secure communication protocol (e.g., HTTPS).

[1012] Input: All health data stored on the device.

[1013] Output: Encrypted data sent to the cloud server.

[1014] Specific operation: The terminal compiles data on a fixed schedule (for example, every hour), encrypts it using AES, and sends it to the cloud server.

[1015] Step 4: Data Preprocessing

[1016] The server stores the data sent to the cloud in temporary storage (e.g., AWS S3 bucket) and uses preprocessing modules to perform missing value imputation and noise removal.

[1017] Input: Raw data stored on cloud servers.

[1018] Output: Preprocessed, missing value imputed, and denoised data.

[1019] Specific operation: The server uses a preprocessing module (for example, Python's Pandas library) to filter the data and impute missing values.

[1020] Step 5: Data analysis

[1021] The server inputs the preprocessed data into a machine learning model (e.g., random forest, neural network) to identify user behavior patterns. It also obtains data through API integration with external cloud services (e.g., calendar services, task management services) and performs integrated analysis.

[1022] Input: Preprocessed data, data obtained from external cloud services.

[1023] Output: Behavioral patterns as a result of the analysis.

[1024] Specific operation: The server analyzes the data using machine learning algorithms (e.g., scikit-learn, TensorFlow), and based on the identified behavioral patterns, further integrates the data with external services using APIs.

[1025] Step 6: Advice Generation

[1026] The server generates personalized health improvement advice based on the analysis results of the machine learning model. This advice is generated as sentences using a natural language generation model (e.g., GPT-4).

[1027] Input: Behavioral patterns as analysis results, user health goals.

[1028] Output: The generated health advice.

[1029] Specific operation: The server uses a natural language generation model to generate advice in natural language based on specific behavioral patterns.

[1030] Step 7: Advice Notification

[1031] The server transmits the generated advice to the user's terminal, which displays it in real time.

[1032] Input: Generated health advice.

[1033] Output: Advice displayed on the user's terminal.

[1034] Specific operation: The server encrypts the advice and delivers it to the device, which then visually displays it to the user using the application's notification function.

[1035] This clearly defines the specific operations at each step and the input and output data, and provides a detailed explanation of the processing flow of the entire system.

[1036] (Application example 1)

[1037] 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."

[1038] In modern society, personal health management is an important issue. Effective training and nutritional management, especially at fitness gyms, require real-time, personalized support. However, current systems struggle to effectively collect and analyze users' health data and provide specific advice based on it. Furthermore, there are insufficient means for fitness gym instructors to appropriately share data and provide effective support to users. Therefore, an effective data collection, analysis, and advice-providing system is needed to help users achieve their goals and achieve sustainable health improvements.

[1039] 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.

[1040] In this invention, the server includes means for collecting personal health data, means for transmitting the health data to a cloud server, means for analyzing the health data in the cloud server and identifying the user's behavioral patterns, means for generating personalized health improvement advice and guidance based on the behavioral patterns, means for transmitting the advice and guidance to the user's device and displaying it, and means for providing training plans and nutritional advice at the fitness gym. This allows users and instructors at the fitness gym to share data in real time and implement personalized health improvement plans.

[1041] "Personal health data" refers to information such as heart rate, steps, calories burned, sleep data, and manually entered dietary information and emotional state.

[1042] A "cloud server" is a server system that can transmit, collect, store, and analyze data via the Internet.

[1043] "Behavioral patterns" refer to trends in daily behavior and health conditions identified based on a user's health data.

[1044] "Health improvement advice and guidance" refers to specific courses of action or suggestions provided to improve a user's health.

[1045] "Terminal" refers to a device used by a user, such as a smartphone, tablet, or smartwatch.

[1046] A "fitness gym" refers to a facility where professional instructors provide training and nutritional management guidance.

[1047] "Training Plan" refers to a customized exercise schedule and exercise instructions based on a user's health data.

[1048] "Nutrition Advice" refers to guidance on appropriate dietary and nutritional intake provided to a user based on their health goals.

[1049] "Shared" refers to a state in which multiple users or services can access and use the same data.

[1050] This invention is a health management system that allows users of fitness gyms to receive training plans and nutrition advice in real time. The system collects personal health data, analyzes that data on a cloud server, and provides health improvement advice based on the user's individual behavioral patterns.

[1051] System configuration

[1052] Health Data Collection and Transmission

[1053] Users use wearable devices such as smartwatches to collect health data such as heart rate, number of steps, calories burned, and sleep data. This data is sent to a cloud server via a device such as a smartphone. This data is sent using a secure communication protocol such as HTTPS.

[1054] Analyzing the data

[1055] The cloud server stores the received health data and performs preprocessing (missing value completion, noise removal). The data is then analyzed using machine learning libraries such as scikit-learn to identify the user's behavioral patterns. Furthermore, the cloud server also processes the user's manually entered dietary details and emotional state, conducting a comprehensive data analysis. The analysis results are shared with the fitness gym instructor in real time.

[1056] Advice generation and notification

[1057] The cloud server generates personalized health improvement advice and guidance based on the identified behavioral patterns. This advice is customized to the user's specific goals (e.g., weight loss, muscle building). The generated advice is then sent to the user's device and displayed visually. The user can receive this advice via their smartphone and incorporate it into their next actions.

[1058] Specific examples

[1059] For users who are dieting

[1060] If a user is aiming to lose weight, the system analyzes their daily step count data and calorie consumption data and generates advice such as, "Today's step count is 7,000. You have 3,000 steps left to reach your goal." It also provides specific instructions on calories burned, such as, "Today you have burned 500 kilocalories. Burn an additional 200 kilocalories."

[1061] For users who need stress management

[1062] If the user is diagnosed with stress due to elevated heart rate or insufficient sleep, the system will generate advice such as, "Your heart rate has been high recently and you've been getting less than eight hours of sleep. Try yoga or meditation to relax."

[1063] Prompt Sentence Examples

[1064] Based on the user's step count and calorie consumption data, evaluate the progress towards the target number of steps and calorie consumption and provide specific advice to the user.

[1065] Generate specific advice for stress management based on the user's heart rate and sleep data.

[1066] In this way, the system of the present invention provides fitness gym users with personalized health improvement plans, supporting health management while maintaining the users' motivation.

[1067] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1068] Step 1:

[1069] A user uses a wearable device to collect health data.

[1070] Input: Health data such as heart rate, steps, calories burned, and sleep data.

[1071] Output: Collected health data.

[1072] Specific operation: Smartwatches and fitness trackers measure health data from the user's body in real time and transmit it to devices such as smartphones.

[1073] Step 2:

[1074] The health data collected by the device is sent to a cloud server.

[1075] Input: Collected health data.

[1076] Output: Health data sent to cloud server.

[1077] Specific operation: The device sends the collected health data to a cloud server via the Internet using a secure protocol (e.g., HTTPS).

[1078] Step 3:

[1079] The server accumulates the received health data and performs preprocessing.

[1080] Input: Health data sent to cloud server.

[1081] Output: Preprocessed health data.

[1082] Specific operation: The server performs data preprocessing such as imputing missing values ​​and removing noise, etc. This is where processing is done to ensure data consistency and accuracy.

[1083] Step 4:

[1084] The server analyzes the pre-processed health data to identify the user's behavioral patterns.

[1085] Input: Preprocessed health data.

[1086] Output: User behavior patterns.

[1087] How it works: The server uses a machine learning library (e.g., scikit-learn) to extract features from the data and analyze it for specific behavioral patterns. At this point, the machine learning model predicts the behavioral patterns based on the training data.

[1088] Step 5:

[1089] The server generates health improvement advice and guidance based on the identified behavioral patterns.

[1090] Input: User behavior patterns.

[1091] Output: Health improvement advice and guidance.

[1092] How it works: The server generates advice and guidance customized to the user's goals (e.g., weight loss, muscle gain). Using generative AI models and prompts, personalized advice is created for each individual user.

[1093] Step 6:

[1094] The server transmits the generated advice and guidance to the user's terminal.

[1095] Enter: health improvement advice and guidance.

[1096] Output: Advice and guidance sent to the user's device.

[1097] Specific operation: The server sends advice and guidance to the user's smartphone via the Internet.

[1098] Step 7:

[1099] The terminal displays the advice and guidance sent to the user.

[1100] Input: Advice and guidance sent to the user's device.

[1101] Output: Advice or guidance that is displayed to the user.

[1102] Specific operation: The smartphone application notifies the user of the received advice and visually displays it to them, allowing them to decide their next course of action.

[1103] 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.

[1104] System Overview

[1105] This system collects and analyzes personal health and emotional data, and provides individualized advice and guidance for improving health based on the collected data. In particular, by combining it with an emotion engine, it achieves highly personalized guidance that takes into account the user's emotional state.

[1106] System configuration

[1107] 1. Data Collection Phase

[1108] The device connects to the user's wearable device (smartwatch, fitness tracker, etc.) and collects real-time health data such as heart rate, steps, calories burned, and sleep data. Furthermore, the user can manually input their diet and emotional state through the application. Additionally, the emotion engine automatically recognizes emotions from the user's facial expressions, voice tone, text input, etc., and generates emotion data.

[1109] 2. Data transmission phase

[1110] The device periodically transmits the collected health and emotion data to a cloud server over the internet using a secure protocol (e.g., HTTPS).

[1111] 3. Data analysis phase

[1112] The server stores all received data in a database. When saving, it checks the data's integrity and performs missing value imputation and noise removal as necessary. It then analyzes the data using machine learning algorithms to identify the user's behavioral patterns and emotional state. It also connects with external cloud services (e.g., calendar services, task management services) via API, integrating and analyzing the acquired data.

[1113] 4. Advice Generation Phase

[1114] The server generates personalized health improvement advice and guidance based on the identified behavioral patterns and emotional state. Specifically, by incorporating the user's emotional data, it suggests relaxation methods based on stress levels and activities to maintain positive emotions.

[1115] 5. Notification and display phase

[1116] The server sends the generated advice and guidance to the user's device, which then visually displays it to the user. The user can then check the advice in real time through the application and use it to influence their next actions.

[1117] Specific examples

[1118] Example 1: User C whose main purpose is stress management

[1119] User C is experiencing increasing stress at work.

[1120] The terminal acquires the heart rate data from the smartwatch and transmits it to the server.

[1121] The emotion engine detects user C's stress level from his / her daily facial expressions and tone of voice.

[1122] The server analyzes the increased heart rate and the emotion engine data and generates advice to User C, saying, "Your stress level has been increasing recently. Try a short meditation to relax."

[1123] The device suggests yoga and meditation tutorial videos and notifies User C.

[1124] Example 2: User D wants to maintain a positive lifestyle

[1125] User D wants to maintain a healthy and positive lifestyle.

[1126] The device acquires activity data from User D's fitness tracker and sends it to the server.

[1127] The emotion engine collects emotion data from user D's daily text input and speech and identifies the prevalence of positive emotions.

[1128] The server analyzes this data comprehensively and generates advice such as, "Your recent activity has been going well. To keep up this pace, why not try a new jogging course this weekend?"

[1129] The terminal notifies User D of the proposal.

[1130] The system of the present invention provides each user with a personalized health improvement plan based on health data and emotional data, and supports health management while maintaining the user's motivation.

[1131] The processing flow will be explained below.

[1132] Step 1:

[1133] The device connects to the user's wearable device (smartwatch, fitness tracker, etc.) via Bluetooth or Wi-Fi and collects real-time health data (heart rate, steps, calories burned, sleep data, etc.).

[1134] Step 2:

[1135] Users manually enter their diet and emotional state through an application on a smartphone or tablet, which also collects this data.

[1136] Step 3:

[1137] The emotion engine automatically recognizes emotions from the user's facial expressions, voice tone, text input, etc., and generates emotion data.

[1138] Step 4:

[1139] The device periodically transmits the collected health and emotion data to a cloud server using a secure communication protocol (such as HTTPS).

[1140] Step 5:

[1141] The server stores all received data in a database, where it checks the data for consistency and performs missing value imputation and noise removal as necessary.

[1142] Step 6:

[1143] The server analyzes the data using machine learning algorithms, clustering algorithms to identify user behavior patterns, and classification algorithms to classify specific behaviors (e.g., healthy behaviors, unhealthy behaviors).

[1144] Step 7:

[1145] The server obtains data via API integration with external cloud services (e.g., calendar services, task management services) and integrates user schedules and task information into the analysis.

[1146] Step 8:

[1147] The server generates personalized health improvement advice and guidance based on the identified behavioral patterns and emotional state. Specifically, by incorporating the user's emotional data, it suggests relaxation methods based on stress levels and activities to maintain positive emotions.

[1148] Step 9:

[1149] The server transmits the generated advice and guidance to the user's terminal, which visually displays the advice to the user.

[1150] Step 10:

[1151] The application allows users to review advice and take action, and if necessary, they can enter more data or set new goals.

[1152] Example 2

[1153] 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."

[1154] Conventional health management systems have the ability to collect and analyze personal health data, but the technology to combine this with emotional data to provide personalized advice has not been sufficiently developed. As a result, it has been difficult to provide comprehensive health management that takes into account the psychological state of each individual user. Another issue is that insufficient integration with external data services means it is not possible to comprehensively understand users' behavioral patterns and lifestyle rhythms.

[1155] 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.

[1156] In this invention, the server includes a means for collecting personal health information, an emotion engine for analyzing the personal emotional information, a means for the emotion engine to detect the individual's emotional state, and a means for generating individualized health improvement advice and guidance based on the emotional information. This enables highly personalized guidance that takes the user's emotional state into consideration. Furthermore, because the server includes a means for acquiring data via API integration with external data services, it is possible to comprehensively understand the user's behavioral patterns and lifestyle rhythms and provide more accurate advice.

[1157] "Health information" refers to numerical values ​​and data that indicate an individual's health status, such as heart rate, number of steps, calories burned, and sleep data.

[1158] An "emotion engine" is software or algorithms that automatically recognize an individual's emotional state by analyzing their facial expressions, voice tone, text input, etc.

[1159] A "remote server" is a computer system located in a remote location via the Internet for storing, analyzing, and processing data.

[1160] "API integration" refers to the use of an interface that allows different software and services to communicate data with each other and share functions.

[1161] "Personalized health improvement advice" refers to advice and recommendations for improving health that are customized for each individual based on personal health and emotional information.

[1162] "Behavioral patterns" are data that show a series of actions and habits in an individual's daily life, and are an important indicator for health management.

[1163] overview

[1164] This invention is a system that collects and analyzes personal health and emotional information, and provides individualized health improvement advice and guidance based on that information. In particular, by combining this system with an emotion engine, this system realizes highly personalized guidance that takes into account the user's emotional state.

[1165] System configuration

[1166] 1. Data Collection Phase

[1167] The device connects to the user's wearable device (e.g., smartwatch) and collects real-time health information such as heart rate, steps, calories burned, and sleep data. Users can manually enter their diet and emotional state through the application. In addition, the emotion engine analyzes the user's facial expressions and voice tone collected through the device's camera and microphone to automatically generate emotional information.

[1168] 2. Data transmission phase

[1169] The device periodically transmits the collected health and emotional information to a cloud server via the Internet using a secure protocol (e.g., HTTPS).

[1170] 3. Data analysis phase

[1171] The server stores the received data in a database. When saving, it checks the data's integrity and performs missing value completion and noise removal. Next, it analyzes the data using machine learning algorithms to identify the user's behavioral patterns and emotional state. It also connects via API to external cloud services (e.g., calendar services, task management services), integrating the acquired data for analysis.

[1172] 4. Advice Generation Phase

[1173] The server generates personalized health improvement advice and guidance based on the identified behavioral patterns and emotional state. Specifically, it takes into account the user's emotional information and suggests relaxation methods according to stress levels and activities to maintain positive emotions.

[1174] 5. Notification and display phase

[1175] The server sends the generated advice and guidance to the user's device, which visually displays it to the user, who can then view the advice in real time through the application.

[1176] Specific examples

[1177] Example 1: User A whose main purpose is stress management

[1178] User A is experiencing increasing stress at work.

[1179] The terminal acquires the heart rate data from the smartwatch and transmits it to the server.

[1180] The emotion engine detects user A's stress level from his / her daily facial expressions and tone of voice.

[1181] The server analyzes the increased heart rate and data from the emotion engine and generates advice such as, "Your stress levels have been increasing recently. Try a short meditation to relax."

[1182] The device suggests yoga and meditation tutorial videos and notifies User A.

[1183] Example 2: User B wants to maintain a positive lifestyle

[1184] User B wants to maintain a healthy and positive lifestyle.

[1185] The device acquires activity data from User B's fitness tracker and sends it to the server.

[1186] The emotion engine collects emotion data from User B's daily text inputs and speech and identifies that there are many positive emotions.

[1187] The server comprehensively analyzes the data and generates advice such as, "You've been doing well lately. To keep up the pace, why not try a new jogging route this weekend?"

[1188] The terminal notifies User B of the proposed content.

[1189] Prompt Sentence Examples

[1190] Prompt 1:

[1191] "If the user is feeling stressed, generate advice on what relaxation methods to suggest. Data includes increased heart rate and negative emotion data."

[1192] Prompt 2:

[1193] "Generate specific activity suggestions to motivate users to maintain positive emotions and continue their activity habits. Recent activity data has been very positive."

[1194] The above is the details of the "Mode for Carrying Out the Invention" of the present invention. This system makes it possible to provide a personalized health improvement plan to each user and support health management.

[1195] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1196] Step 1: Connect your device

[1197] The terminal connects to the wearable device (e.g., smart watch) worn by the user using Bluetooth or Wi-Fi. The specific procedure involves launching the terminal application and executing the pairing process with the device. The input is information about the user's wearable device, and the output is the established connection status.

[1198] Step 2: Gathering health information

[1199] The terminal collects real-time health information from the wearable device, such as heart rate, steps, calories burned, and sleep data. This involves retrieving data via the API provided by the device. The input is sensor data from the wearable device, and the output is the collected health information.

[1200] Step 3: Manual Entry

[1201] The user manually inputs their health information and emotional state through the application interface. Specifically, they input their dietary information and emotional state in text or selection format. The input is the data manually entered by the user, and the output is the manually entered data.

[1202] Step 4: Recognizing emotional information

[1203] The device's emotion engine analyzes the user's facial expressions and voice tone collected from the camera and microphone to generate emotion information. The emotion engine performs the analysis using machine learning algorithms. The inputs are video data and audio data, and the output is analyzed emotion information.

[1204] Step 5: Send data to the cloud

[1205] The device periodically transmits the collected health and emotional information to a cloud server. The transmission is secure using the HTTPS protocol. The input is the collected health and emotional information, and the output is the data stored on the remote server.

[1206] Step 6: Save your data

[1207] The server stores the received data in a database. When storing, it unifies the data format and checks its consistency. The input is the transmitted data, and the output is the data stored in the database.

[1208] Step 7: Data integrity check and correction

[1209] The server detects missing values ​​and noise in the data, and performs interpolation and noise removal. Specific algorithms include linear interpolation and moving average filters. The input is the stored data, and the output is the corrected data.

[1210] Step 8: Analyze behavioral patterns

[1211] The server uses machine learning algorithms to identify user behavioral patterns, such as analyzing time-series data to extract the user's exercise habits and sleep patterns. The input is the data whose integrity has been verified, and the output is the analyzed behavioral patterns.

[1212] Step 9: Integrate with external cloud services

[1213] The server connects to an external cloud service (e.g., a calendar service, task management service) via API to obtain or provide the required data. The input is the API key and query for the external service, and the output is the obtained additional data.

[1214] Step 10: Generating Advice

[1215] The server generates personalized health improvement advice based on the user's behavioral patterns and emotional state. Based on the analysis results, it suggests specific actions, such as "do a short meditation." The inputs are behavioral patterns and emotional information, and the output is customized advice.

[1216] Step 11: Submitting Advice

[1217] The server transmits the generated advice to the user's terminal using a secure communication protocol, with the generated advice as input and the advice displayed on the terminal as output.

[1218] Step 12: Viewing Advice

[1219] The terminal visually displays the received advice to the user. The user opens the application and checks the provided advice. The input is the received advice, and the output is the visually displayed advice.

[1220] Prompt Sentence Examples

[1221] Prompt 1:

[1222] "If the user is feeling stressed, generate advice on what relaxation methods to suggest. Data includes increased heart rate and negative emotion data."

[1223] Prompt 2:

[1224] "Generate specific activity suggestions to motivate users to maintain positive emotions and continue their activity habits. Recent activity data has been very positive."

[1225] (Application example 2)

[1226] 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."

[1227] In modern society, a variety of health issues affect individuals. In particular, it is difficult to provide appropriate health improvement guidance to individuals, and uniform advice that does not take into account each individual's emotional state makes effective health management difficult. In particular, there is a need for a method to provide real-time, personalized health improvement recommendations to users at physical gyms and other physical facilities.

[1228] 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.

[1229] In this invention, the server includes means for collecting biometric information and emotional information of an individual, means for transmitting the biometric information and emotional information to a cloud device, means for analyzing the biometric information and emotional information in the cloud device and identifying the user's behavioral patterns and emotional state, means for generating individual health improvement recommendations and instructions based on the behavioral patterns and emotional state, and means for transmitting and displaying the recommendations and instructions to the user's terminal, thereby enabling personalized health improvement recommendations that take into account the user's real-time emotional state.

[1230] "Personal biometric information" refers to data related to the user's health condition, such as the user's heart rate, number of steps, calories burned, and sleep data.

[1231] "Emotional information" refers to data about a user's emotional state, obtained from the user's facial expressions, voice tone, text input, etc.

[1232] "Cloud device" refers to a remote server connected via the Internet for storing, processing, and analyzing data.

[1233] "Behavioral patterns" refer to a series of actions and activity trends in a user's daily life that are identified through analysis and learning.

[1234] "Personalized health improvement recommendations and instructions" refers to specific suggestions and instructions for promoting and improving health that are generated specifically for a user based on the user's identified behavioral patterns and emotional state.

[1235] "User's device" refers to an electronic device used by a user, such as a smartphone, tablet, or personal computer.

[1236] "API integration" refers to the process of exchanging information with external cloud services using an application programming interface.

[1237] This invention is a system that collects and analyzes an individual's biometric and emotional information and provides personalized health recommendations in real time. Specifically, the invention is implemented using the following hardware and software:

[1238] Hardware and Software

[1239] 1. Wearable devices: Smartwatches and fitness trackers are used to collect real-time biometric information such as heart rate, steps taken, calories burned, and sleep.

[1240] 2. Smartphone or tablet: A device held by the user that acts as a central device for collecting, displaying, and transmitting health and emotional information.

[1241] 3. Cloud server: A remote server for storing and analyzing collected data and generating health improvement recommendations, such as AWS (Amazon Web Services) or Google Cloud Platform.

[1242] 4. Camera and microphone: The camera and microphone built into the smartphone are used to analyze emotional information from the user's facial expressions and tone of voice.

[1243] Data processing and calculation

[1244] 1. Data Collection:

[1245] Biometric information is collected from the user's wearable device.

[1246] Use a camera or microphone to capture emotional information, for example, by analyzing facial expressions using OpenCV and voice tone with a speech recognition library.

[1247] The collected data is sent from the smartphone to a cloud server using the HTTPS protocol.

[1248] 2. Data processing on cloud servers:

[1249] The collected biometric and emotional information is stored on a cloud server.

[1250] When data is saved to a database (e.g., MySQL or MongoDB), the integrity of the data is checked.

[1251] Cleanse the data by imputing missing values ​​and removing noise.

[1252] Machine learning algorithms (e.g., TensorFlow and PyTorch) are used to analyze the data and identify behavioral patterns and emotional states.

[1253] 3. Generate health improvement recommendations:

[1254] Generate personalized health improvement recommendations and instructions based on identified behavioral patterns and emotional states.

[1255] For example, if it determines that the user is feeling stressed, it will recommend that they "do some relaxation exercises."

[1256] 4. Recommendation Notification and Display:

[1257] The generated recommendations and instructions are then sent back to the user's device.

[1258] A notification will appear on your smartphone prompting you to take the next step.

[1259] Specific examples

[1260] Let us take the example of user A using this system while training at a sports gym.

[1261] 1. The user wears a smartwatch to collect biometric information such as heart rate and calories burned.

[1262] 2. Turn on the camera and microphone on your smartphone to capture facial expressions and audio during training.

[1263] 3. The collected data is sent to a cloud server.

[1264] 4. The cloud server analyzes the data and identifies the user's fatigue and stress levels.

[1265] 5. A notification appears on the user's smartphone with a recommendation such as, "Your fatigue is increasing. Try stretching for 5 minutes to warm down."

[1266] Prompt Sentence Examples

[1267] "Please compile today's training data. Create a report that includes exercise history, heart rate variability, and emotional changes (from facial expressions and voice tone) and come up with the next recommendation for the user."

[1268] The specific embodiments for carrying out the present invention have been described above.

[1269] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1270] Step 1:

[1271] The device collects biometric information (heart rate, steps, calories burned, sleep data, etc.) from wearable devices (smartwatches and fitness trackers).

[1272] Input: Biometric data from wearable devices

[1273] Output: Collected biometric data

[1274] Specific operation: The terminal connects to the wearable device via Bluetooth or Wi-Fi and periodically acquires biometric information.

[1275] Step 2:

[1276] The device uses a camera and microphone to capture emotional information from the user's facial expressions and voice tone.

[1277] Input: Camera video and audio data

[1278] Output: Analyzed emotion information data

[1279] Specific operation: The device recognizes the user's emotional state by analyzing facial expressions from camera footage using OpenCV and analyzing voice tones using a voice recognition library.

[1280] Step 3:

[1281] The terminal transmits the collected biometric information and emotional information to a cloud server.

[1282] Input: Biometric data and emotional data

[1283] Output: Comprehensive data sent to the cloud server

[1284] Specific operation: The device securely uploads biometric and emotional information to the cloud server using the HTTPS protocol.

[1285] Step 4:

[1286] The server stores the biometric information and emotion information stored in the cloud server in a database and checks the consistency of the data.

[1287] Input: Comprehensive data sent to the cloud server

[1288] Output: Consistent data stored in the database

[1289] Specific operation: The server stores the received data in MySQL or MongoDB, and cleanses the data by filling in missing data and removing noise.

[1290] Step 5:

[1291] The server uses machine learning algorithms to analyze the biometric and emotional information to identify the user's behavioral patterns and emotional state.

[1292] Input: Consistent data stored in a database

[1293] Output: Analyzed behavioral patterns and emotional states

[1294] Specific operation: The server uses TensorFlow and PyTorch to run data analysis models and identify the user's behavioral patterns and emotional state.

[1295] Step 6:

[1296] The server generates personalized health improvement recommendations and instructions based on the identified behavioral patterns and emotional state.

[1297] Input: Analyzed behavioral patterns and emotional states

[1298] Output: Generated health improvement recommendations and instructions

[1299] Specific Actions: The server uses the generative AI model to generate recommendations and specific instructions for health improvement that are optimized for each user.

[1300] Step 7:

[1301] The server transmits the generated health improvement recommendations and instructions to the terminal, which notifies and displays them to the user.

[1302] Input: Generated health improvement recommendations and instructions

[1303] Output: Notification displayed on the device

[1304] Specific operation: The server sends recommendations and instructions in JSON format to the device, which interprets them and displays them on the user interface, which the user can view on their smartphone screen.

[1305] 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.

[1306] 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.

[1307] 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.

[1308] [Fourth embodiment]

[1309] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1310] 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.

[1311] 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).

[1312] 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.

[1313] 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.

[1314] 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).

[1315] 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.

[1316] 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.

[1317] 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.

[1318] 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.

[1319] 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.

[1320] 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.

[1321] 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."

[1322] System Overview

[1323] This invention is a system that collects and analyzes personal health data and provides personalized advice and guidance for improving health based on that data. This system supports users in improving their health and modifying their behavior by understanding their health status in real time and providing optimal advice based on scientific evidence.

[1324] System configuration

[1325] 1. Data Collection Phase

[1326] The device connects to the user's wearable device (smartwatch or fitness tracker) and collects real-time health data such as heart rate, steps, calories burned, and sleep data. Users can also manually enter data such as dietary habits and emotional state through the application.

[1327] 2. Data transmission phase

[1328] The device periodically transmits the collected health data to a cloud server over the internet using a secure protocol.

[1329] 3. Data analysis phase

[1330] The server stores the received health data on a cloud server and performs preprocessing (missing value completion, noise removal). It then analyzes the data using machine learning algorithms to identify the user's behavioral patterns. It also connects with external cloud services (e.g., calendar services, task management services) via API, integrating and analyzing the acquired data.

[1331] 4. Advice Generation Phase

[1332] The server generates personalized health improvement advice and guidance based on the identified behavioral patterns, customized to the user's specific goals (e.g., weight loss, muscle gain).

[1333] 5. Notification and display phase

[1334] The server sends the generated advice to the user's device, which then visually displays it to the user. The user can then view the advice in real time through the application and use it to influence their next actions.

[1335] Specific examples

[1336] Example 1: User A is dieting

[1337] User A has a goal of walking 10,000 steps every day.

[1338] The device acquires step count data from the smartwatch and transmits it to the server.

[1339] The server analyzes the weekly step count data and generates advice to User A, such as "Your average step count this week is 9,500 steps. You're almost there!"

[1340] The server works in conjunction with the calendar service to suggest, "It would be a good idea to add some walking time to a day next week when you have fewer meetings."

[1341] The terminal notifies this information to user A in real time.

[1342] Example 2: User B needs stress management

[1343] User B has recently become more prone to stress.

[1344] The terminal acquires the heart rate data of user B and transmits it to the server.

[1345] The server analyzes that an increased heart rate is related to lack of sleep and generates advice to User B saying, "You've been getting less than eight hours of sleep recently. Try doing some relaxing yoga or meditation."

[1346] The server suggests yoga and meditation tutorial videos, and the device notifies User B.

[1347] In this way, the system of the present invention provides a personalized health improvement plan for each user, supporting health management while maintaining the user's motivation.

[1348] The processing flow will be explained below.

[1349] Step 1:

[1350] The device connects to the user's wearable device (smartwatch, fitness tracker, etc.) via Bluetooth or Wi-Fi and collects real-time health data (heart rate, steps, calories burned, sleep data, etc.).

[1351] Step 2:

[1352] Users manually enter information such as what they eat and their emotional state through an application on their smartphone or tablet, which also collects this data.

[1353] Step 3:

[1354] The device periodically transmits the collected health data and manually entered data to a cloud server using a secure communication protocol (such as HTTPS).

[1355] Step 4:

[1356] The server stores all received data in a database, where it checks the data for consistency and performs missing value imputation and noise removal as necessary.

[1357] Step 5:

[1358] The server analyzes the data using machine learning algorithms, clustering algorithms to identify user behavior patterns, and classification algorithms to classify specific behaviors (e.g., healthy behaviors, unhealthy behaviors).

[1359] Step 6:

[1360] The server generates personalized health improvement advice and guidance based on the user's behavioral patterns and lifestyle habits. Advice is customized to the user's specific goals (e.g., dieting, muscle building).

[1361] Step 7:

[1362] The server transmits the generated advice to the user's terminal, which visually displays the advice to the user.

[1363] Step 8:

[1364] The application allows users to review advice and take action, and if necessary, they can enter more data or set new goals.

[1365] Step 9:

[1366] The server obtains data through API integration with external cloud services (e.g., calendar services, task management services) and integrates the user's schedule and task information into the analysis, allowing it to provide more detailed and personalized advice.

[1367] Step 10:

[1368] Users can share their health data and advice with family and friends as needed. The device supports this sharing function and shares data within a specified range.

[1369] Example 1

[1370] 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."

[1371] There is a demand for effective and efficient support for users' health improvement and behavior modification by understanding their health status in real time and providing optimal health improvement advice based on scientific evidence. However, conventional systems have insufficient data collection, analysis, and advice provision, making it difficult to provide accurate advice customized for each user.

[1372] 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.

[1373] In this invention, the server includes means for collecting health data, means for transmitting the health data to a cloud server, means for preprocessing the health data (missing value imputation, noise removal) on the cloud server, means for analyzing the health data on the cloud server and identifying individual behavioral patterns, means for generating personalized health improvement advice and guidance based on the behavioral patterns, means for transmitting the generated advice and guidance to the user's terminal and visually displaying it, means for collecting data in real time from the wearable device during the health data collection phase, means for the user to manually input dietary details and emotional state through an application, and means for encrypting and transmitting the collected data. This makes it possible to grasp the user's health condition in detail and provide an individual health improvement plan quickly and effectively.

[1374] "Health Data" refers to information about an individual's health and behavior, such as an individual's heart rate, steps taken, calories burned, sleep data, diet, and emotional state.

[1375] "Cloud server" refers to a server accessible via the Internet that is a remote computing resource used for data storage, preprocessing, analysis, advice generation, and so on.

[1376] "Preprocessing" refers to data cleansing operations such as missing value imputation and noise removal that are performed on collected health data.

[1377] "Behavioral patterns" refer to trends or regularities in a series of behaviors identified by analyzing a user's health data.

[1378] "Advice and guidance" refers to personalized health improvement instructions and advice generated based on a user's behavioral patterns.

[1379] "User device" refers to a device used by an individual, such as a smartphone, tablet, or PC, which displays advice and guidance sent from the cloud server.

[1380] "Wearable devices" refers to electronic devices worn by individuals, such as smartwatches and fitness trackers, that collect real-time health data.

[1381] "Encryption" refers to the process of protecting collected data using techniques such as symmetric key encryption or public key encryption to ensure that only authorized individuals can access the data.

[1382] A "preprocessing module" refers to a software component that runs on a cloud server and performs missing value completion and noise removal in data.

[1383] A "machine learning model" refers to a program that implements an algorithm to use collected data to identify user behavior patterns and predict future behavior.

[1384] A "natural language generation model" refers to a program that uses artificial intelligence technology to generate advice and guidance for users in natural-sounding sentences.

[1385] "API integration" refers to an interface that allows different software systems to communicate with each other and exchange data.

[1386] The present invention is a system for collecting and analyzing personal health data and providing personalized advice and guidance for improving health. The following describes in detail an embodiment of the present invention.

[1387] System Overview

[1388] The system of the present invention supports users in improving their health and modifying their behavior by understanding their personal health status in real time and providing optimal advice based on scientific evidence. This system consists of the following main components:

[1389] System configuration

[1390] 1. Data Collection

[1391] The terminal collects real-time health data (heart rate, steps, calories burned, sleep data, etc.) from wearable devices (e.g., smartwatches, fitness trackers) using the wearable device's dedicated API (e.g., Apple HealthKit, Google Fit API).

[1392] Users can manually enter their dietary habits, emotional state, and more through the mobile application.

[1393] 2. Data Transmission

[1394] The device encrypts the collected data (e.g., AES encryption) and periodically transmits it to the cloud server via a secure communication protocol (e.g., HTTPS).

[1395] 3. Data Preprocessing

[1396] The server stores the received data in temporary storage (e.g., AWS S3 bucket) on the cloud server.

[1397] The server uses preprocessing modules to impute missing values ​​(e.g., mean imputation) and remove noise (e.g., moving average filter) from the data.

[1398] 4. Data Analysis

[1399] The server inputs the preprocessed data into a machine learning model (e.g., random forest, neural network).

[1400] The server uses Python libraries (e.g., scikit-learn, TensorFlow) to identify user behavior patterns.

[1401] The server connects with external cloud services (e.g., Google Calendar, task management services) via API to obtain daily schedules and task information and integrate it into the analysis.

[1402] 5. Advice Generation

[1403] The server generates personalized health improvement advice and guidance based on the analysis results of the machine learning model.

[1404] The server uses a natural language generation model (e.g., GPT-4) to generate advice tailored to the user's specific goals (e.g., losing weight, gaining muscle).

[1405] 6. Advice Notice

[1406] The server transmits the generated advice to the user's terminal.

[1407] The terminal displays the received advice to the user in real time through the notification function of the application.

[1408] Users can use the application's dashboard function to view details of the advice and apply it to their daily actions.

[1409] Specific examples

[1410] Example 1: User A is dieting

[1411] User A has a goal of walking 10,000 steps every day.

[1412] The device collects step count data from the smartwatch and sends the data to the server every night at midnight.

[1413] The server accumulates one week's worth of step count data, and the preprocessing module fills in missing values ​​in the data.

[1414] The server uses a machine learning model to analyze User A's step count data and generate advice such as, "Your average step count this week is 9,500. You're almost there!"

[1415] The server connects to the calendar service via an API and suggests, "It would be a good idea to add some walking time on a day next week when you have fewer meetings," and notifies the device of this.

[1416] Prompt Sentence Examples

[1417] User A's health goal is to take 10,000 steps per day. Their average step count this week is 9,500. Please suggest the next action appropriate for User A. This requires integration with a calendar service.

[1418] Example 2: User B needs stress management

[1419] User B has recently become more prone to stress.

[1420] The device collects heart rate data and transmits it to a server in real time when stress levels rise.

[1421] The server analyzes the heart rate data and performs noise removal using a pre-processing module.

[1422] The server analyzes the increased heart rate and sleep data, finds a correlation, and generates advice such as, "You've been getting less than eight hours of sleep recently. Try some relaxing yoga or meditation."

[1423] The server suggests yoga and meditation tutorial videos through an external video service API and notifies the device of this.

[1424] Prompt Sentence Examples

[1425] User B has been experiencing increased stress recently. Analyze their heart rate and sleep data and provide appropriate stress management advice, including suggestions for yoga and meditation.

[1426] This enables the system to provide each user with a personalized health improvement plan, supporting effective health management while maintaining user motivation.

[1427] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1428] Step 1: Data collection

[1429] The device collects real-time health data from wearable devices, such as heart rate, steps, calories burned, and sleep data, using the wearable device's dedicated API.

[1430] Input: Sensor data from wearable devices.

[1431] Output: Health data stored on the device.

[1432] Specific operation: The terminal communicates with the wearable device periodically (for example, every minute) to obtain and store the latest data.

[1433] Step 2: Manual data entry

[1434] Users manually enter health data, such as diet and emotional state, through a mobile application.

[1435] Input: Data entered by the user, such as dietary details and emotional state.

[1436] Output: Manually entered data stored in the application.

[1437] Specific operation: The user enters data using the application's input form, and the device saves it.

[1438] Step 3: Send data

[1439] The terminal encrypts the collected data (real-time data and manually entered data) and transmits it to the cloud server using a secure communication protocol (e.g., HTTPS).

[1440] Input: All health data stored on the device.

[1441] Output: Encrypted data sent to the cloud server.

[1442] Specific operation: The terminal compiles data on a fixed schedule (for example, every hour), encrypts it using AES, and sends it to the cloud server.

[1443] Step 4: Data Preprocessing

[1444] The server stores the data sent to the cloud in temporary storage (e.g., AWS S3 bucket) and uses preprocessing modules to perform missing value imputation and noise removal.

[1445] Input: Raw data stored on cloud servers.

[1446] Output: Preprocessed, missing value imputed, and denoised data.

[1447] Specific operation: The server uses a preprocessing module (for example, Python's Pandas library) to filter the data and impute missing values.

[1448] Step 5: Data analysis

[1449] The server inputs the preprocessed data into a machine learning model (e.g., random forest, neural network) to identify user behavior patterns. It also obtains data through API integration with external cloud services (e.g., calendar services, task management services) and performs integrated analysis.

[1450] Input: Preprocessed data, data obtained from external cloud services.

[1451] Output: Behavioral patterns as a result of the analysis.

[1452] Specific operation: The server analyzes the data using machine learning algorithms (e.g., scikit-learn, TensorFlow), and based on the identified behavioral patterns, further integrates the data with external services using APIs.

[1453] Step 6: Advice Generation

[1454] The server generates personalized health improvement advice based on the analysis results of the machine learning model. This advice is generated as sentences using a natural language generation model (e.g., GPT-4).

[1455] Input: Behavioral patterns as analysis results, user health goals.

[1456] Output: The generated health advice.

[1457] Specific operation: The server uses a natural language generation model to generate advice in natural language based on specific behavioral patterns.

[1458] Step 7: Advice Notification

[1459] The server transmits the generated advice to the user's terminal, which displays it in real time.

[1460] Input: Generated health advice.

[1461] Output: Advice displayed on the user's terminal.

[1462] Specific operation: The server encrypts the advice and delivers it to the device, which then visually displays it to the user using the application's notification function.

[1463] This clearly defines the specific operations at each step and the input and output data, and provides a detailed explanation of the processing flow of the entire system.

[1464] (Application example 1)

[1465] 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."

[1466] In modern society, personal health management is an important issue. Effective training and nutritional management, especially at fitness gyms, require real-time, personalized support. However, current systems struggle to effectively collect and analyze users' health data and provide specific advice based on it. Furthermore, there are insufficient means for fitness gym instructors to appropriately share data and provide effective support to users. Therefore, an effective data collection, analysis, and advice-providing system is needed to help users achieve their goals and achieve sustainable health improvements.

[1467] 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.

[1468] In this invention, the server includes means for collecting personal health data, means for transmitting the health data to a cloud server, means for analyzing the health data in the cloud server and identifying the user's behavioral patterns, means for generating personalized health improvement advice and guidance based on the behavioral patterns, means for transmitting the advice and guidance to the user's device and displaying it, and means for providing training plans and nutritional advice at the fitness gym. This allows users and instructors at the fitness gym to share data in real time and implement personalized health improvement plans.

[1469] "Personal health data" refers to information such as heart rate, steps, calories burned, sleep data, and manually entered dietary information and emotional state.

[1470] A "cloud server" is a server system that can transmit, collect, store, and analyze data via the Internet.

[1471] "Behavioral patterns" refer to trends in daily behavior and health conditions identified based on a user's health data.

[1472] "Health improvement advice and guidance" refers to specific courses of action or suggestions provided to improve a user's health.

[1473] "Terminal" refers to a device used by a user, such as a smartphone, tablet, or smartwatch.

[1474] A "fitness gym" refers to a facility where professional instructors provide training and nutritional management guidance.

[1475] "Training Plan" refers to a customized exercise schedule and exercise instructions based on a user's health data.

[1476] "Nutrition Advice" refers to guidance on appropriate dietary and nutritional intake provided to a user based on their health goals.

[1477] "Shared" refers to a state in which multiple users or services can access and use the same data.

[1478] This invention is a health management system that allows users of fitness gyms to receive training plans and nutrition advice in real time. The system collects personal health data, analyzes that data on a cloud server, and provides health improvement advice based on the user's individual behavioral patterns.

[1479] System configuration

[1480] Health Data Collection and Transmission

[1481] Users use wearable devices such as smartwatches to collect health data such as heart rate, number of steps, calories burned, and sleep data. This data is sent to a cloud server via a device such as a smartphone. This data is sent using a secure communication protocol such as HTTPS.

[1482] Analyzing the data

[1483] The cloud server stores the received health data and performs preprocessing (missing value completion, noise removal). The data is then analyzed using machine learning libraries such as scikit-learn to identify the user's behavioral patterns. Furthermore, the cloud server also processes the user's manually entered dietary details and emotional state, conducting a comprehensive data analysis. The analysis results are shared with the fitness gym instructor in real time.

[1484] Advice generation and notification

[1485] The cloud server generates personalized health improvement advice and guidance based on the identified behavioral patterns. This advice is customized to the user's specific goals (e.g., weight loss, muscle building). The generated advice is then sent to the user's device and displayed visually. The user can receive this advice via their smartphone and incorporate it into their next actions.

[1486] Specific examples

[1487] For users who are dieting

[1488] If a user is aiming to lose weight, the system analyzes their daily step count data and calorie consumption data and generates advice such as, "Today's step count is 7,000. You have 3,000 steps left to reach your goal." It also provides specific instructions on calories burned, such as, "Today you have burned 500 kilocalories. Burn an additional 200 kilocalories."

[1489] For users who need stress management

[1490] If the user is diagnosed with stress due to elevated heart rate or insufficient sleep, the system will generate advice such as, "Your heart rate has been high recently and you've been getting less than eight hours of sleep. Try yoga or meditation to relax."

[1491] Prompt Sentence Examples

[1492] Based on the user's step count and calorie consumption data, evaluate the progress towards the target number of steps and calorie consumption and provide specific advice to the user.

[1493] Generate specific advice for stress management based on the user's heart rate and sleep data.

[1494] In this way, the system of the present invention provides fitness gym users with personalized health improvement plans, supporting health management while maintaining the users' motivation.

[1495] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1496] Step 1:

[1497] A user uses a wearable device to collect health data.

[1498] Input: Health data such as heart rate, steps, calories burned, and sleep data.

[1499] Output: Collected health data.

[1500] Specific operation: Smartwatches and fitness trackers measure health data from the user's body in real time and transmit it to devices such as smartphones.

[1501] Step 2:

[1502] The health data collected by the device is sent to a cloud server.

[1503] Input: Collected health data.

[1504] Output: Health data sent to cloud server.

[1505] Specific operation: The device sends the collected health data to a cloud server via the Internet using a secure protocol (e.g., HTTPS).

[1506] Step 3:

[1507] The server accumulates the received health data and performs preprocessing.

[1508] Input: Health data sent to cloud server.

[1509] Output: Preprocessed health data.

[1510] Specific operation: The server performs data preprocessing such as imputing missing values ​​and removing noise, etc. This is where processing is done to ensure data consistency and accuracy.

[1511] Step 4:

[1512] The server analyzes the pre-processed health data to identify the user's behavioral patterns.

[1513] Input: Preprocessed health data.

[1514] Output: User behavior patterns.

[1515] How it works: The server uses a machine learning library (e.g., scikit-learn) to extract features from the data and analyze it for specific behavioral patterns. At this point, the machine learning model predicts the behavioral patterns based on the training data.

[1516] Step 5:

[1517] The server generates health improvement advice and guidance based on the identified behavioral patterns.

[1518] Input: User behavior patterns.

[1519] Output: Health improvement advice and guidance.

[1520] How it works: The server generates advice and guidance customized to the user's goals (e.g., weight loss, muscle gain). Using generative AI models and prompts, personalized advice is created for each individual user.

[1521] Step 6:

[1522] The server transmits the generated advice and guidance to the user's terminal.

[1523] Enter: health improvement advice and guidance.

[1524] Output: Advice and guidance sent to the user's device.

[1525] Specific operation: The server sends advice and guidance to the user's smartphone via the Internet.

[1526] Step 7:

[1527] The terminal displays the advice and guidance sent to the user.

[1528] Input: Advice and guidance sent to the user's device.

[1529] Output: Advice or guidance that is displayed to the user.

[1530] Specific operation: The smartphone application notifies the user of the received advice and visually displays it to them, allowing them to decide their next course of action.

[1531] 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.

[1532] System Overview

[1533] This system collects and analyzes personal health and emotional data, and provides individualized advice and guidance for improving health based on the collected data. In particular, by combining it with an emotion engine, it achieves highly personalized guidance that takes into account the user's emotional state.

[1534] System configuration

[1535] 1. Data Collection Phase

[1536] The device connects to the user's wearable device (smartwatch, fitness tracker, etc.) and collects real-time health data such as heart rate, steps, calories burned, and sleep data. Furthermore, the user can manually input their diet and emotional state through the application. Additionally, the emotion engine automatically recognizes emotions from the user's facial expressions, voice tone, text input, etc., and generates emotion data.

[1537] 2. Data transmission phase

[1538] The device periodically transmits the collected health and emotion data to a cloud server over the internet using a secure protocol (e.g., HTTPS).

[1539] 3. Data analysis phase

[1540] The server stores all received data in a database. When saving, it checks the data's integrity and performs missing value imputation and noise removal as necessary. It then analyzes the data using machine learning algorithms to identify the user's behavioral patterns and emotional state. It also connects with external cloud services (e.g., calendar services, task management services) via API, integrating and analyzing the acquired data.

[1541] 4. Advice Generation Phase

[1542] The server generates personalized health improvement advice and guidance based on the identified behavioral patterns and emotional state. Specifically, by incorporating the user's emotional data, it suggests relaxation methods based on stress levels and activities to maintain positive emotions.

[1543] 5. Notification and display phase

[1544] The server sends the generated advice and guidance to the user's device, which then visually displays it to the user. The user can then check the advice in real time through the application and use it to influence their next actions.

[1545] Specific examples

[1546] Example 1: User C whose main purpose is stress management

[1547] User C is experiencing increasing stress at work.

[1548] The terminal acquires the heart rate data from the smartwatch and transmits it to the server.

[1549] The emotion engine detects user C's stress level from his / her daily facial expressions and tone of voice.

[1550] The server analyzes the increased heart rate and the emotion engine data and generates advice to User C, saying, "Your stress level has been increasing recently. Try a short meditation to relax."

[1551] The device suggests yoga and meditation tutorial videos and notifies User C.

[1552] Example 2: User D wants to maintain a positive lifestyle

[1553] User D wants to maintain a healthy and positive lifestyle.

[1554] The device acquires activity data from User D's fitness tracker and sends it to the server.

[1555] The emotion engine collects emotion data from user D's daily text input and speech and identifies the prevalence of positive emotions.

[1556] The server analyzes this data comprehensively and generates advice such as, "Your recent activity has been going well. To keep up this pace, why not try a new jogging course this weekend?"

[1557] The terminal notifies User D of the proposal.

[1558] The system of the present invention provides each user with a personalized health improvement plan based on health data and emotional data, and supports health management while maintaining the user's motivation.

[1559] The processing flow will be explained below.

[1560] Step 1:

[1561] The device connects to the user's wearable device (smartwatch, fitness tracker, etc.) via Bluetooth or Wi-Fi and collects real-time health data (heart rate, steps, calories burned, sleep data, etc.).

[1562] Step 2:

[1563] Users manually enter their diet and emotional state through an application on a smartphone or tablet, which also collects this data.

[1564] Step 3:

[1565] The emotion engine automatically recognizes emotions from the user's facial expressions, voice tone, text input, etc., and generates emotion data.

[1566] Step 4:

[1567] The device periodically transmits the collected health and emotion data to a cloud server using a secure communication protocol (such as HTTPS).

[1568] Step 5:

[1569] The server stores all received data in a database, where it checks the data for consistency and performs missing value imputation and noise removal as necessary.

[1570] Step 6:

[1571] The server analyzes the data using machine learning algorithms, clustering algorithms to identify user behavior patterns, and classification algorithms to classify specific behaviors (e.g., healthy behaviors, unhealthy behaviors).

[1572] Step 7:

[1573] The server obtains data via API integration with external cloud services (e.g., calendar services, task management services) and integrates user schedules and task information into the analysis.

[1574] Step 8:

[1575] The server generates personalized health improvement advice and guidance based on the identified behavioral patterns and emotional state. Specifically, by incorporating the user's emotional data, it suggests relaxation methods based on stress levels and activities to maintain positive emotions.

[1576] Step 9:

[1577] The server transmits the generated advice and guidance to the user's terminal, which visually displays the advice to the user.

[1578] Step 10:

[1579] The application allows users to review advice and take action, and if necessary, they can enter more data or set new goals.

[1580] Example 2

[1581] 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."

[1582] Conventional health management systems have the ability to collect and analyze personal health data, but the technology to combine this with emotional data to provide personalized advice has not been sufficiently developed. As a result, it has been difficult to provide comprehensive health management that takes into account the psychological state of each individual user. Another issue is that insufficient integration with external data services means it is not possible to comprehensively understand users' behavioral patterns and lifestyle rhythms.

[1583] 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.

[1584] In this invention, the server includes a means for collecting personal health information, an emotion engine for analyzing the personal emotional information, a means for the emotion engine to detect the individual's emotional state, and a means for generating individualized health improvement advice and guidance based on the emotional information. This enables highly personalized guidance that takes the user's emotional state into consideration. Furthermore, because the server includes a means for acquiring data via API integration with external data services, it is possible to comprehensively understand the user's behavioral patterns and lifestyle rhythms and provide more accurate advice.

[1585] "Health information" refers to numerical values ​​and data that indicate an individual's health status, such as heart rate, number of steps, calories burned, and sleep data.

[1586] An "emotion engine" is software or algorithms that automatically recognize an individual's emotional state by analyzing their facial expressions, voice tone, text input, etc.

[1587] A "remote server" is a computer system located in a remote location via the Internet for storing, analyzing, and processing data.

[1588] "API integration" refers to the use of an interface that allows different software and services to communicate data with each other and share functions.

[1589] "Personalized health improvement advice" refers to advice and recommendations for improving health that are customized for each individual based on personal health and emotional information.

[1590] "Behavioral patterns" are data that show a series of actions and habits in an individual's daily life, and are an important indicator for health management.

[1591] overview

[1592] This invention is a system that collects and analyzes personal health and emotional information, and provides individualized health improvement advice and guidance based on that information. In particular, by combining this system with an emotion engine, this system realizes highly personalized guidance that takes into account the user's emotional state.

[1593] System configuration

[1594] 1. Data Collection Phase

[1595] The device connects to the user's wearable device (e.g., smartwatch) and collects real-time health information such as heart rate, steps, calories burned, and sleep data. Users can manually enter their diet and emotional state through the application. In addition, the emotion engine analyzes the user's facial expressions and voice tone collected through the device's camera and microphone to automatically generate emotional information.

[1596] 2. Data transmission phase

[1597] The device periodically transmits the collected health and emotional information to a cloud server via the Internet using a secure protocol (e.g., HTTPS).

[1598] 3. Data analysis phase

[1599] The server stores the received data in a database. When saving, it checks the data's integrity and performs missing value completion and noise removal. Next, it analyzes the data using machine learning algorithms to identify the user's behavioral patterns and emotional state. It also connects via API to external cloud services (e.g., calendar services, task management services), integrating the acquired data for analysis.

[1600] 4. Advice Generation Phase

[1601] The server generates personalized health improvement advice and guidance based on the identified behavioral patterns and emotional state. Specifically, it takes into account the user's emotional information and suggests relaxation methods according to stress levels and activities to maintain positive emotions.

[1602] 5. Notification and display phase

[1603] The server sends the generated advice and guidance to the user's device, which visually displays it to the user, who can then view the advice in real time through the application.

[1604] Specific examples

[1605] Example 1: User A whose main purpose is stress management

[1606] User A is experiencing increasing stress at work.

[1607] The terminal acquires the heart rate data from the smartwatch and transmits it to the server.

[1608] The emotion engine detects user A's stress level from his / her daily facial expressions and tone of voice.

[1609] The server analyzes the increased heart rate and data from the emotion engine and generates advice such as, "Your stress levels have been increasing recently. Try a short meditation to relax."

[1610] The device suggests yoga and meditation tutorial videos and notifies User A.

[1611] Example 2: User B wants to maintain a positive lifestyle

[1612] User B wants to maintain a healthy and positive lifestyle.

[1613] The device acquires activity data from User B's fitness tracker and sends it to the server.

[1614] The emotion engine collects emotion data from User B's daily text inputs and speech and identifies that there are many positive emotions.

[1615] The server comprehensively analyzes the data and generates advice such as, "You've been doing well lately. To keep up the pace, why not try a new jogging route this weekend?"

[1616] The terminal notifies User B of the proposed content.

[1617] Prompt Sentence Examples

[1618] Prompt 1:

[1619] "If the user is feeling stressed, generate advice on what relaxation methods to suggest. Data includes increased heart rate and negative emotion data."

[1620] Prompt 2:

[1621] "Generate specific activity suggestions to motivate users to maintain positive emotions and continue their activity habits. Recent activity data has been very positive."

[1622] The above is the details of the "Mode for Carrying Out the Invention" of the present invention. This system makes it possible to provide a personalized health improvement plan to each user and support health management.

[1623] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1624] Step 1: Connect your device

[1625] The terminal connects to the wearable device (e.g., smart watch) worn by the user using Bluetooth or Wi-Fi. The specific procedure involves launching the terminal application and executing the pairing process with the device. The input is information about the user's wearable device, and the output is the established connection status.

[1626] Step 2: Gathering health information

[1627] The terminal collects real-time health information from the wearable device, such as heart rate, steps, calories burned, and sleep data. This involves retrieving data via the API provided by the device. The input is sensor data from the wearable device, and the output is the collected health information.

[1628] Step 3: Manual Entry

[1629] The user manually inputs their health information and emotional state through the application interface. Specifically, they input their dietary information and emotional state in text or selection format. The input is the data manually entered by the user, and the output is the manually entered data.

[1630] Step 4: Recognizing emotional information

[1631] The device's emotion engine analyzes the user's facial expressions and voice tone collected from the camera and microphone to generate emotion information. The emotion engine performs the analysis using machine learning algorithms. The inputs are video data and audio data, and the output is analyzed emotion information.

[1632] Step 5: Send data to the cloud

[1633] The device periodically transmits the collected health and emotional information to a cloud server. The transmission is secure using the HTTPS protocol. The input is the collected health and emotional information, and the output is the data stored on the remote server.

[1634] Step 6: Save your data

[1635] The server stores the received data in a database. When storing, it unifies the data format and checks its consistency. The input is the transmitted data, and the output is the data stored in the database.

[1636] Step 7: Data integrity check and correction

[1637] The server detects missing values ​​and noise in the data, and performs interpolation and noise removal. Specific algorithms include linear interpolation and moving average filters. The input is the stored data, and the output is the corrected data.

[1638] Step 8: Analyze behavioral patterns

[1639] The server uses machine learning algorithms to identify user behavioral patterns, such as analyzing time-series data to extract the user's exercise habits and sleep patterns. The input is the data whose integrity has been verified, and the output is the analyzed behavioral patterns.

[1640] Step 9: Integrate with external cloud services

[1641] The server connects to an external cloud service (e.g., a calendar service, task management service) via API to obtain or provide the required data. The input is the API key and query for the external service, and the output is the obtained additional data.

[1642] Step 10: Generating Advice

[1643] The server generates personalized health improvement advice based on the user's behavioral patterns and emotional state. Based on the analysis results, it suggests specific actions, such as "do a short meditation." The inputs are behavioral patterns and emotional information, and the output is customized advice.

[1644] Step 11: Submitting Advice

[1645] The server transmits the generated advice to the user's terminal using a secure communication protocol, with the generated advice as input and the advice displayed on the terminal as output.

[1646] Step 12: Viewing Advice

[1647] The terminal visually displays the received advice to the user. The user opens the application and checks the provided advice. The input is the received advice, and the output is the visually displayed advice.

[1648] Prompt Sentence Examples

[1649] Prompt 1:

[1650] "If the user is feeling stressed, generate advice on what relaxation methods to suggest. Data includes increased heart rate and negative emotion data."

[1651] Prompt 2:

[1652] "Generate specific activity suggestions to motivate users to maintain positive emotions and continue their activity habits. Recent activity data has been very positive."

[1653] (Application example 2)

[1654] 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."

[1655] In modern society, a variety of health issues affect individuals. In particular, it is difficult to provide appropriate health improvement guidance to individuals, and uniform advice that does not take into account each individual's emotional state makes effective health management difficult. In particular, there is a need for a method to provide real-time, personalized health improvement recommendations to users at physical gyms and other physical facilities.

[1656] 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.

[1657] In this invention, the server includes means for collecting biometric information and emotional information of an individual, means for transmitting the biometric information and emotional information to a cloud device, means for analyzing the biometric information and emotional information in the cloud device and identifying the user's behavioral patterns and emotional state, means for generating individual health improvement recommendations and instructions based on the behavioral patterns and emotional state, and means for transmitting and displaying the recommendations and instructions to the user's terminal, thereby enabling personalized health improvement recommendations that take into account the user's real-time emotional state.

[1658] "Personal biometric information" refers to data related to the user's health condition, such as the user's heart rate, number of steps, calories burned, and sleep data.

[1659] "Emotional information" refers to data about a user's emotional state, obtained from the user's facial expressions, voice tone, text input, etc.

[1660] "Cloud device" refers to a remote server connected via the Internet for storing, processing, and analyzing data.

[1661] "Behavioral patterns" refer to a series of actions and activity trends in a user's daily life that are identified through analysis and learning.

[1662] "Personalized health improvement recommendations and instructions" refers to specific suggestions and instructions for promoting and improving health that are generated specifically for a user based on the user's identified behavioral patterns and emotional state.

[1663] "User's device" refers to an electronic device used by a user, such as a smartphone, tablet, or personal computer.

[1664] "API integration" refers to the process of exchanging information with external cloud services using an application programming interface.

[1665] This invention is a system that collects and analyzes an individual's biometric and emotional information and provides personalized health recommendations in real time. Specifically, the invention is implemented using the following hardware and software:

[1666] Hardware and Software

[1667] 1. Wearable devices: Smartwatches and fitness trackers are used to collect real-time biometric information such as heart rate, steps taken, calories burned, and sleep.

[1668] 2. Smartphone or tablet: A device held by the user that acts as a central device for collecting, displaying, and transmitting health and emotional information.

[1669] 3. Cloud server: A remote server for storing and analyzing collected data and generating health improvement recommendations, such as AWS (Amazon Web Services) or Google Cloud Platform.

[1670] 4. Camera and microphone: The camera and microphone built into the smartphone are used to analyze emotional information from the user's facial expressions and tone of voice.

[1671] Data processing and calculation

[1672] 1. Data Collection:

[1673] Biometric information is collected from the user's wearable device.

[1674] Use a camera or microphone to capture emotional information, for example, by analyzing facial expressions using OpenCV and voice tone with a speech recognition library.

[1675] The collected data is sent from the smartphone to a cloud server using the HTTPS protocol.

[1676] 2. Data processing on cloud servers:

[1677] The collected biometric and emotional information is stored on a cloud server.

[1678] When data is saved to a database (e.g., MySQL or MongoDB), the integrity of the data is checked.

[1679] Cleanse the data by imputing missing values ​​and removing noise.

[1680] Machine learning algorithms (e.g., TensorFlow and PyTorch) are used to analyze the data and identify behavioral patterns and emotional states.

[1681] 3. Generate health improvement recommendations:

[1682] Generate personalized health improvement recommendations and instructions based on identified behavioral patterns and emotional states.

[1683] For example, if it determines that the user is feeling stressed, it will recommend that they "do some relaxation exercises."

[1684] 4. Recommendation Notification and Display:

[1685] The generated recommendations and instructions are then sent back to the user's device.

[1686] A notification will appear on your smartphone prompting you to take the next step.

[1687] Specific examples

[1688] Let us take the example of user A using this system while training at a sports gym.

[1689] 1. The user wears a smartwatch to collect biometric information such as heart rate and calories burned.

[1690] 2. Turn on the camera and microphone on your smartphone to capture facial expressions and audio during training.

[1691] 3. The collected data is sent to a cloud server.

[1692] 4. The cloud server analyzes the data and identifies the user's fatigue and stress levels.

[1693] 5. A notification appears on the user's smartphone with a recommendation such as, "Your fatigue is increasing. Try stretching for 5 minutes to warm down."

[1694] Prompt Sentence Examples

[1695] "Please compile today's training data. Create a report that includes exercise history, heart rate variability, and emotional changes (from facial expressions and voice tone) and come up with the next recommendation for the user."

[1696] The specific embodiments for carrying out the present invention have been described above.

[1697] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1698] Step 1:

[1699] The device collects biometric information (heart rate, steps, calories burned, sleep data, etc.) from wearable devices (smartwatches and fitness trackers).

[1700] Input: Biometric data from wearable devices

[1701] Output: Collected biometric data

[1702] Specific operation: The terminal connects to the wearable device via Bluetooth or Wi-Fi and periodically acquires biometric information.

[1703] Step 2:

[1704] The device uses a camera and microphone to capture emotional information from the user's facial expressions and voice tone.

[1705] Input: Camera video and audio data

[1706] Output: Analyzed emotion information data

[1707] Specific operation: The device recognizes the user's emotional state by analyzing facial expressions from camera footage using OpenCV and analyzing voice tones using a voice recognition library.

[1708] Step 3:

[1709] The terminal transmits the collected biometric information and emotional information to a cloud server.

[1710] Input: Biometric data and emotional data

[1711] Output: Comprehensive data sent to the cloud server

[1712] Specific operation: The device securely uploads biometric and emotional information to the cloud server using the HTTPS protocol.

[1713] Step 4:

[1714] The server stores the biometric information and emotion information stored in the cloud server in a database and checks the consistency of the data.

[1715] Input: Comprehensive data sent to the cloud server

[1716] Output: Consistent data stored in the database

[1717] Specific operation: The server stores the received data in MySQL or MongoDB, and cleanses the data by filling in missing data and removing noise.

[1718] Step 5:

[1719] The server uses machine learning algorithms to analyze the biometric and emotional information to identify the user's behavioral patterns and emotional state.

[1720] Input: Consistent data stored in a database

[1721] Output: Analyzed behavioral patterns and emotional states

[1722] Specific operation: The server uses TensorFlow and PyTorch to run data analysis models and identify the user's behavioral patterns and emotional state.

[1723] Step 6:

[1724] The server generates personalized health improvement recommendations and instructions based on the identified behavioral patterns and emotional state.

[1725] Input: Analyzed behavioral patterns and emotional states

[1726] Output: Generated health improvement recommendations and instructions

[1727] Specific Actions: The server uses the generative AI model to generate recommendations and specific instructions for health improvement that are optimized for each user.

[1728] Step 7:

[1729] The server transmits the generated health improvement recommendations and instructions to the terminal, which notifies and displays them to the user.

[1730] Input: Generated health improvement recommendations and instructions

[1731] Output: Notification displayed on the device

[1732] Specific operation: The server sends recommendations and instructions in JSON format to the device, which interprets them and displays them on the user interface, which the user can view on their smartphone screen.

[1733] 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.

[1734] 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.

[1735] 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 robot 414.

[1736] 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.

[1737] 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 arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1738] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1739] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1740] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1741] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1742] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1743] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1744] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1745] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1746] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1747] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1748] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1749] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1750] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1751] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1752] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1753] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1754] The following is further disclosed regarding the above embodiment.

[1755] (Claim 1)

[1756] a means of collecting personal health data;

[1757] means for transmitting the health data to a cloud server;

[1758] A means for analyzing the health data in the cloud server and identifying a user's behavioral pattern;

[1759] means for generating personalized health improvement advice and guidance based on said behavioral patterns;

[1760] means for transmitting the advice or guidance to a user's terminal and displaying the advice or guidance;

[1761] A system including:

[1762] (Claim 2)

[1763] means by which a user can manually input health data;

[1764] means for transmitting the manually input data to the cloud server;

[1765] The system of claim 1 further comprising:

[1766] (Claim 3)

[1767] A means for the cloud server to acquire data via API collaboration with an external cloud service;

[1768] A means for integrating and analyzing the acquired data with the health data;

[1769] 10. The system of claim 1, comprising:

[1770] "Example 1"

[1771] (Claim 1)

[1772] a means of collecting health data;

[1773] means for transmitting the health data to a cloud server;

[1774] A means for preprocessing the health data (missing value completion, noise removal) on the cloud server;

[1775] means for analyzing the health data in the cloud server and identifying individual behavioral patterns;

[1776] means for generating personalized health improvement advice and guidance based on said behavioral patterns;

[1777] means for transmitting the generated advice or guidance to a user's terminal and visually displaying the advice or guidance;

[1778] In a health data collection phase, a means for collecting data in real time from the wearable device;

[1779] a means for the user to manually input their diet and emotional state through the application;

[1780] means for encrypting and transmitting the collected data;

[1781] A system including:

[1782] (Claim 2)

[1783] means by which a user can manually input health data;

[1784] means for transmitting the manually input data to a cloud server;

[1785] A means for temporarily storing the data received by the cloud server;

[1786] A cloud server uses a preprocessing module to perform missing value imputation and noise removal on the data;

[1787] 10. The system of claim 1, comprising:

[1788] (Claim 3)

[1789] A means for the cloud server to acquire data via API integration with external cloud services;

[1790] A means for integrating and analyzing the acquired data with the health data;

[1791] a means for identifying user behavior patterns using a machine learning model;

[1792] A means for generating personalized health improvement advice and guidance using a natural language generation model; and

[1793] means for notifying a user terminal of the generated advice or guidance;

[1794] 10. The system of claim 1, comprising:

[1795] "Application Example 1"

[1796] (Claim 1)

[1797] a means of collecting personal health data;

[1798] means for transmitting the health data to a cloud server;

[1799] A means for analyzing the health data in the cloud server and identifying a user's behavioral pattern;

[1800] means for generating personalized health improvement advice and guidance based on said behavioral patterns;

[1801] means for transmitting the advice or guidance to a user's terminal and displaying the advice or guidance;

[1802] A method used in fitness gyms to provide training plans and nutritional advice,

[1803] A system including:

[1804] (Claim 2)

[1805] means by which a user can manually input health data;

[1806] means for transmitting the manually input data to the cloud server;

[1807] A means for sharing the data with a fitness gym instructor on the cloud server;

[1808] The system of claim 1 further comprising:

[1809] (Claim 3)

[1810] A means for the cloud server to acquire data via API collaboration with an external cloud service;

[1811] A means for integrating and analyzing the acquired data with the health data;

[1812] A means for allowing a fitness gym instructor to provide appropriate advice to the user in real time based on the analysis results;

[1813] 10. The system of claim 1, comprising:

[1814] "Example 2: Combining Emotion Engines"

[1815] (Claim 1)

[1816] means of collecting personal health information;

[1817] means for transmitting the health information to a remote server;

[1818] means for analyzing the health information at the remote server and identifying individual behavioral patterns;

[1819] means for generating personalized health improvement advice and guidance based on said behavioral patterns;

[1820] means for transmitting and displaying said advice or instruction on a personal device;

[1821] a means for detecting an emotional state of the individual, the emotional engine for analyzing the individual's emotional information;

[1822] means for generating personalized health improvement advice and guidance based on the emotional information;

[1823] A system including:

[1824] (Claim 2)

[1825] a means by which a user can manually input health information;

[1826] means for transmitting the manually entered data to the remote server;

[1827] The system of claim 1 further comprising:

[1828] (Claim 3)

[1829] A means for the remote server to acquire data through API collaboration with an external data service;

[1830] A means for integrating and analyzing the acquired data with the health information;

[1831] 10. The system of claim 1, comprising:

[1832] "Application example 2 when combining emotion engines"

[1833] (Claim 1)

[1834] a means of collecting biometric information of an individual;

[1835] means for transmitting the biometric information and emotional information to a cloud device;

[1836] means for analyzing the biometric information and emotional information in the cloud device and identifying the user's behavioral patterns and emotional state;

[1837] means for generating personalized health improvement recommendations and instructions based on said behavioral patterns and emotional states;

[1838] means for transmitting and displaying the recommendations and instructions on a user's terminal;

[1839] A system including:

[1840] (Claim 2)

[1841] a means for allowing a user to manually input biometric information;

[1842] means for transmitting the manually input information to the cloud device;

[1843] The system of claim 1 further comprising:

[1844] (Claim 3)

[1845] A means for the cloud device to acquire information through API collaboration with an external cloud service;

[1846] means for integrating and analyzing the acquired information with the biological information;

[1847] 10. The system of claim 1, comprising: [Explanation of symbols]

[1848] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means of collecting personal health data; means for transmitting the health data to a cloud server; A means for analyzing the health data in the cloud server and identifying a user's behavioral pattern; means for generating personalized health improvement advice and guidance based on said behavioral patterns; means for transmitting the advice or guidance to a user's terminal and displaying the advice or guidance; A system including:

2. means by which a user can manually input health data; means for transmitting the manually input data to the cloud server; The system of claim 1 further comprising:

3. A means for the cloud server to acquire data via API collaboration with an external cloud service; A means for integrating and analyzing the acquired data with the health data; The system of claim 1 , comprising:

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