system

Abstract

Provide a system. [Solution] means for collecting health data of a user; a means for storing the collected health data in a database; means for analyzing the stored health data to assess the user's health status; means for generating a personalized exercise plan based on the user's health status; and means for providing the generated exercise plan to a user.

Description

[Technical Field]

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

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

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

[0004] In modern society, many people face the problems of maintaining health and lack of exercise. Finding an appropriate exercise plan tailored to an individual's health condition and lifestyle is particularly difficult, and conventional health management methods and exercise instruction have not always been effective. While there are systems that collect health data and provide individualized exercise plans, these rely only on specific data and do not provide comprehensive health management. The present invention aims to solve these problems and provide more effective and personalized health exercise instruction. [Means for solving the problem]

[0005] The present invention provides a system including a means for collecting a user's health data, a means for storing the collected health data in a database, a means for analyzing the stored health data to evaluate the user's health condition, a means for generating a personalized exercise plan based on the user's health condition, and a means for providing the generated exercise plan to the user. The system further includes a means for collecting data from a wearable device, a means for detecting abnormal values ​​in the collected data, a means for sending an alert to the user when an abnormal value is detected, and a means for customizing the exercise plan taking into account the user's age, gender, and past exercise history, thereby achieving comprehensive and personalized health exercise guidance. This allows the user to accurately understand their health condition and maintain and improve their health by following an individually optimized exercise plan.

[0006] "Health data" refers to data related to a user's biometric information and daily activities, and specifically includes heart rate, number of steps, blood pressure, weight, sleep patterns, etc.

[0007] "Means of collection" refers to the methods, devices, and software used to obtain a user's health data.

[0008] "Means for storing data in a database" refers to the systems and technologies used to digitally record and manage collected health data.

[0009] "Means for analyzing and assessing the user's health status" refers to methods or techniques for analyzing stored health data and statistically or algorithmically assessing the user's health status.

[0010] "Means for generating an individual exercise plan" refers to a method or technology for creating an exercise plan based on the analysis results, according to the health condition and needs of each individual user.

[0011] The term "means for providing an exercise plan to a user" refers to a method or technology for effectively communicating the generated individual exercise plan to a user.

[0012] A "wearable device" refers to an electronic device that is worn by the user on a daily basis to collect health data.

[0013] "Anomaly detection methods" refers to methods or techniques for identifying values ​​or data patterns in health data that deviate from normal ranges.

[0014] "Means for sending an alert" refers to the method or technology for sending a notification or alert to a user when an abnormal value is detected.

[0015] "Means for taking age, gender, and past exercise history into consideration" refers to methods and technologies for customizing an exercise plan using a user's basic information and past history data. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0024] [First embodiment]

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

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

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

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

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

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

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

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

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

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

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

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

[0037] This system collects and analyzes a user's health data and provides an individually customized exercise plan. This system consists of a server, a terminal, and a user module.

[0038] System Configuration

[0039] 1. Data Collection Module

[0040] User: Wears the wearable device and performs daily activities.

[0041] Terminal: The data collected from the wearable device is transferred to the smartphone, which then transmits the data to the server.

[0042] Server: Receives data sent from the smartphone and stores it in a database.

[0043] Data analysis

[0044] 2. Data Analysis Module

[0045] Server: Reads the user's health data from the database, organizes it by time series and category, and applies analysis algorithms to it.

[0046] Server: Evaluates the user's health status. For example, it analyzes a user's heart rate and step count data to assess the health risks associated with daily activities.

[0047] Exercise plan generation

[0048] 3. Movement plan generation module

[0049] Server: Based on the user's health assessment results, the server generates an individually customized exercise plan, taking into account parameters such as age, gender, past exercise history, and current physical condition.

[0050] Server: Determines the optimal exercise plan for the user, incorporating exercise guidelines provided by experts as needed.

[0051] Providing Feedback

[0052] 4. Feedback provision module

[0053] Server: Formats the generated exercise plan and evaluation results and sends them to a smartphone.

[0054] Device: The smartphone displays the exercise plan and evaluation results received from the server. The user can view this information through the smartphone app.

[0055] User: Follows the provided exercise plan and actually exercises. Records exercise progress and results on a smartphone.

[0056] Specific examples

[0057] For example, suppose a user wears a wearable watch and walks daily. The watch records the number of steps and heart rate taken each day and transfers the data to a smartphone. The smartphone then sends this data to a server, where it is stored in a database. The server analyzes the data from the past month and evaluates the user's health status, such as trends in average steps and heart rate.

[0058] Based on the analysis results, the server generates an exercise plan that includes "walking for 30 minutes three times a week." This exercise plan is customized taking into account the user's age and past exercise history. The generated exercise plan is sent to the user's smartphone and displayed as a notification.

[0059] The user then follows this plan and records their progress on their smartphone. The recorded data is then sent back to the server, where it is stored in a database. The server then analyzes this new data and adjusts the plan as needed.

[0060] As described above, by using this system, users can constantly monitor their own health status and implement individually optimized exercise plans, thereby effectively maintaining and improving their health.

[0061] The processing flow will be explained below.

[0062] Step 1: Data collection

[0063] User: Wears the wearable device and goes about their daily life.

[0064] Device: Transfers heart rate, step count, and other biometric data collected from the wearable device to a smartphone.

[0065] Terminal: A smartphone receives data from the wearable device via a dedicated app.

[0066] Step 2: Send data

[0067] Terminal: The smartphone formats the data it receives and sends it to a server via the Internet.

[0068] Server: Receives data sent from the smartphone.

[0069] Step 3: Save data

[0070] Server: Check the format of the received data and check for any errors.

[0071] Server: Stores data in a database, including heart rate, steps, sleep data, etc.

[0072] Server: Log the successful data save.

[0073] Step 4: Data analysis

[0074] Server: Reads the target user's past health data from the database.

[0075] Server: Cleanses the data and handles missing and outlier values.

[0076] Server: Performs time series analysis and other statistical processing on the data to assess the user's current health status.

[0077] Step 5: Generate exercise plan

[0078] Server: Based on the health assessment results, an exercise plan is generated that takes into account the user's age, gender, past exercise history, and current physical condition.

[0079] Server: Incorporates expert guidelines into the generated exercise plan and optimizes it.

[0080] Step 6: Provide feedback

[0081] Server: Formats the generated exercise plan and evaluation results for the user interface.

[0082] Server: Sends the formatted data to the smartphone.

[0083] Device: Smartphone displays exercise plan and evaluation results within the application.

[0084] User: Uses the application to view exercise plans.

[0085] Step 7: Exercise

[0086] User: Actually exercises based on the exercise plan provided.

[0087] User: Records exercise progress using a smartphone app.

[0088] Step 8: Sending progress data

[0089] Device: The smartphone collects exercise progress data and sends it to the server.

[0090] Server: Receives progress data and stores it in a database.

[0091] Step 9: Ongoing data analysis and feedback

[0092] Server: Reassess your health and exercise plan based on your latest progress data.

[0093] Server: Adjusts exercise plan as needed and provides feedback.

[0094] Through this process, users can continuously collect health data and receive personalized exercise plans, which the system automatically supports, effectively managing their health.

[0095] Example 1

[0096] 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."

[0097] Conventional health management systems only collect users' health data, but do not adequately analyze that data in detail or provide individual exercise plans. This makes it difficult to provide users with an optimized exercise plan and effectively improve their health. Furthermore, they lack the ability to continuously adjust exercise plans based on new data, making them unable to flexibly respond to changing health conditions.

[0098] 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.

[0099] In this invention, the server

[0100] A means to organize health data stored in a database by time series and category and apply analytical algorithms;

[0101] means for assessing the health status of the user based on the analyzed data;

[0102] means for generating a personalized exercise plan based on the user's health status assessment results;

[0103] a means of tailoring an exercise plan based on expert exercise guidelines;

[0104] means for providing the generated exercise plan to a user's terminal;

[0105] It also includes a means for continuously adjusting the exercise plan based on new data, which allows the user to receive the optimal exercise plan tailored to their health condition and effectively manage their health.

[0106] "User" refers to an individual who utilizes the System to manage their own health data and receive a personalized exercise plan.

[0107] "Health data" refers to various data that indicate the user's health status, such as heart rate, number of steps, and calories burned.

[0108] "Database" refers to a data storage system that manages user health data stored on a server.

[0109] "Analysis Algorithm" refers to a mathematical or statistical method for analyzing health data stored in the database and assessing the user's health status.

[0110] "Health status assessment" refers to the process of using analytical algorithms to review a user's health data and determine the user's health level and risk.

[0111] The term "exercise plan" refers to an exercise schedule and exercise content for an individual user that is generated based on the results of the user's health status evaluation.

[0112] "Exercise guidelines" refer to guiding principles and standards for exercise established by experts.

[0113] "Terminal" refers to a device used by the user, such as a smartphone or tablet, through which the system provides an exercise plan.

[0114] "New data" refers to newly collected health data by the user.

[0115] This system collects and analyzes a user's health data and provides an individually customized exercise plan. This system consists of a server, a terminal, and a user module.

[0116] Data collection

[0117] 1. The user wears the wearable device and performs daily activities, for example, jogging or walking while wearing the wearable watch.

[0118] 2. The terminal (smartphone) receives data such as steps and heart rate from the wearable device and sends it to the server via Bluetooth and HTTPS protocol.

[0119] 3. The server stores the received data in a database, typically a relational database management system (RDBMS) such as MySQL (registered trademark).

[0120] Data analysis

[0121] 1. The server reads the user's health data from the database and organizes it by time series and category using SQL queries.

[0122] 2. The server processes the data using analytical algorithms, such as machine learning models and statistical functions, to analyze the data and assess the user's health status. The algorithms are written in programming languages ​​such as Python and R.

[0123] 3. The server evaluates the user's health status based on the analysis results, for example, assessing health risks based on trends in heart rate fluctuations and step counts.

[0124] Exercise plan generation

[0125] 1. The server generates a personalized exercise plan based on the health assessment results, taking into account the user's age, gender, past exercise history, current physical condition, etc.

[0126] 2. The server adjusts the generated exercise plan based on expert exercise guidelines, for example, incorporating exercise recommendations provided by a sports physician.

[0127] Providing Feedback

[0128] 1. The server formats the generated exercise plan and evaluation results and sends them to the smartphone in JSON format.

[0129] 2. The device (smartphone) analyzes the data received from the server and displays it to the user using a dedicated application.

[0130] 3. The user exercises based on the displayed exercise plan and records their progress and results on their smartphone.

[0131] 4. The device again sends the newly recorded data to the server, where it is analyzed again and the exercise plan is continuously adjusted.

[0132] Specific examples

[0133] For example, a user wears a wearable watch and jogs daily. The watch records heart rate, distance traveled, and calorie consumption. The recorded data is transferred to a smartphone via Bluetooth, and the smartphone sends the data to a server. The server stores the data in a database and evaluates the user's health status using a Python script.

[0134] Based on the evaluation results, the server generates an exercise plan that includes jogging for 30 minutes three times a week. This plan is customized taking into account expert exercise guidelines and is sent to the smartphone. The user jogs according to this exercise plan and records their progress on the smartphone. New data is sent back to the server, and the exercise plan is continuously adjusted.

[0135] Prompt Sentence Examples

[0136] "Design a system that generates and provides a personalized exercise plan based on data collected from a user's wearable device. Include details of each step: data collection, analysis, exercise plan generation, and feedback provision."

[0137] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0138] Step 1:

[0139] A user wears a wearable device and performs daily activities. The input includes the user's exercise data (e.g., number of steps, heart rate). Specifically, the wearable device periodically records health data such as heart rate and number of steps. The output is the collected raw data.

[0140] Step 2:

[0141] The terminal (smartphone) receives exercise data from the wearable device using Bluetooth. The input is the raw data sent from the wearable device. Specifically, the smartphone app establishes a Bluetooth connection and acquires data such as heart rate and step count. The output is the exercise data stored on the smartphone.

[0142] Step 3:

[0143] The device sends exercise data to the server using the HTTPS protocol. The input is the exercise data stored on the smartphone. Specifically, the smartphone app generates an HTTPS request, encrypts the data, and sends it to the server. The output is the exercise data sent to the server.

[0144] Step 4:

[0145] The server stores the received exercise data in a database. The input is the exercise data sent from the smartphone. Specifically, an INSERT query is executed on the database (e.g., MySQL). The output is the exercise data stored in the database.

[0146] Step 5:

[0147] The server reads the motion data from the database and applies an analysis algorithm. The input is the motion data stored in the database. Specifically, a Python script reads the data from the database and performs the analysis. The analysis algorithm uses time series analysis and machine learning models. The output is the analysis results.

[0148] Step 6:

[0149] The server evaluates the user's health status based on the analysis results. The input is the analysis results. Specifically, health risk assessment is performed using statistical models or machine learning models. For example, heart rate trends are evaluated to determine the risk of arrhythmia. The output is the health status assessment result.

[0150] Step 7:

[0151] The server generates a personalized exercise plan based on the health assessment results. The input is the health assessment results. Specifically, an algorithm is used that takes into account age, gender, and past exercise history. The output is a customized exercise plan.

[0152] Step 8:

[0153] The server adjusts the exercise plan based on the expert exercise guidelines. The inputs are the generated exercise plan and the exercise guidelines. Specifically, the server applies the rules of the exercise guidelines to optimize the plan. The output is the optimized exercise plan.

[0154] Step 9:

[0155] The server sends the optimized exercise plan and the evaluation results to the device. The inputs are the optimized exercise plan and the evaluation results. Specifically, this data is formatted in JSON format and sent via the HTTPS protocol. The output is the exercise plan sent to the device and the evaluation results.

[0156] Step 10:

[0157] The device (smartphone) analyzes the data received from the server and displays it on the user interface. The inputs are the exercise plan and evaluation results received from the server. Specifically, the application analyzes the JSON data and displays it in an easy-to-read format. The output is the exercise plan and evaluation results provided to the user.

[0158] Step 11:

[0159] The user exercises based on the displayed exercise plan and records their progress and results on their smartphone. The input is the exercise plan, and the user performs the exercise as a specific action and enters the exercise results (e.g., exercise time, distance traveled) into the application. The output is data on progress and results.

[0160] Step 12:

[0161] The device sends the newly recorded data to the server. The input is the progress and achievement data recorded by the user. Specifically, the application sends this data to the server using the HTTPS protocol. The output is the new data sent to the server.

[0162] Step 13:

[0163] The server stores newly received data in a database and continuously adjusts the exercise plan. The input is newly received progress and performance data. Specifically, INSERT queries are performed on the database, and then the analysis algorithm is reapplied to update the exercise plan. The output is the adjusted exercise plan.

[0164] (Application example 1)

[0165] 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."

[0166] Conventional user health management systems lack individuality in the collection and analysis of health data and the provision of exercise plans, and are not available for direct use by users in physical stores. This makes it difficult for users to receive practical advice tailored to their own health status. Furthermore, there is a demand for real-time advice and an interactive experience in stores.

[0167] 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.

[0168] In this invention, the server includes: means for collecting user health data; means for storing the collected health data in a database; means for analyzing the stored health data to evaluate the user's health condition; means for generating a personalized exercise plan based on the user's health condition; means for providing the generated exercise plan to the user; means for a robot in a physical store to collect health data from the user's wearable device; means for providing health advice and an exercise plan in real time based on the collected health data; and means for displaying the exercise plan generated by the robot. This allows users to receive individually customized exercise plans and health advice in real time in the physical store based on the health data collected from their wearable device. By having the robot present this information, interaction with the user is improved, and specific action guidelines for improving their health can be obtained immediately in the store.

[0169] "Health data" is numerical information that indicates the user's physical condition, such as the user's heart rate, number of steps, and calorie consumption.

[0170] A "database" is a collection of information that stores collected health data in an organized manner and can be accessed and analyzed as needed.

[0171] "Analysis" is the process of statistically processing collected health data to evaluate the user's health status and trends.

[0172] "Exercise Plan" means a personalized exercise or workout plan to improve or maintain a User's health and wellness.

[0173] The "robot" is an automated device installed in a brick-and-mortar store that collects health data from users and provides advice and exercise plans based on that data.

[0174] A "wearable device" is a device that can be worn and used by users on a daily basis to collect health data.

[0175] "Real-time" refers to the fact that the process from data collection to analysis and feedback provision is immediate.

[0176] "Customization" refers to optimizing an exercise plan based on a user's individual health and lifestyle.

[0177] An "abnormal value" is a health data value that is outside the normal range and may indicate a health risk to the user.

[0178] A "warning" is a notification that warns the user or provides guidelines for action when an abnormal value or risk is detected.

[0179] The present invention is a system that collects and analyzes a user's health data and provides an individually customized exercise plan, and includes the following specific configuration and operation method.

[0180] System Configuration

[0181] 1. Data Collection Module

[0182] User: Wears a wearable device while performing daily activities. The wearable device collects health data such as heart rate and number of steps taken.

[0183] Terminal: Health data collected from the wearable device is transferred to a smartphone, which then transmits the data to a server.

[0184] Server: Receives data sent from the smartphone and stores it in a database.

[0185] Data Analysis Module

[0186] 2. Data Analysis Module

[0187] Server: Reads the user's health data from the database. The read data is organized by time series and category, and statistical analysis algorithms are applied to it. The results of the analysis are used to evaluate the user's health status. For example, the average heart rate or number of steps over a certain period of time is calculated.

[0188] Server: If abnormal values ​​are detected in the health data, it also has the function of sending a warning to the user based on that data.

[0189] Movement plan generation module

[0190] 3. Movement plan generation module

[0191] Server: Based on the user's health assessment results, the server generates an individually customized exercise plan taking into account parameters such as age, gender, past exercise history, and current physical condition. If necessary, it incorporates exercise guidelines provided by experts to determine the optimal exercise plan for the user.

[0192] Feedback provision module

[0193] 4. Feedback provision module

[0194] Server: Formats the generated exercise plan and evaluation results and sends them to a smartphone.

[0195] Device: The smartphone displays the exercise plan and evaluation results received from the server. The user can view this information through the smartphone app.

[0196] User: Follows the provided exercise plan and actually exercises. Records exercise progress and results on their smartphone. The recorded data is again sent to the server and stored in a database. The server then analyzes this new data and adjusts the exercise plan as necessary.

[0197] Examples from physical stores

[0198] A robot installed in a physical store connects with the wearable devices of customers who visit the store. When a customer enters the store, the robot collects health data from the wearable device and sends it to a server in real time. The server analyzes this data and generates an individually customized exercise plan. The robot displays the generated exercise plan and presents it to the customer.

[0199] Prompt Sentence Examples

[0200] For example, if a 40-year-old woman wears a wearable watch and comes into the store, the system will collect her heart rate and step count for that day. After analyzing the data, the robot will present her with an exercise plan, such as "We recommend 30 minutes of aerobic exercise three times a week."

[0201] Example prompts

[0202] I want to collect health data and generate a customized exercise plan.

[0203] Age: 40

[0204] Gender: Female

[0205] Normal steps: 6,000 steps / day

[0206] Recent heart rate: Average 105 bpm

[0207] Based on this data, what exercise plan would you suggest?

[0208] In this way, users can receive specific advice based on their own health data in real time within the physical store. This system allows users to obtain practical guidelines for maintaining and improving their health and put them into practice on the spot.

[0209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0210] Step 1:

[0211] A user enters a store wearing a wearable device. The device collects health data such as heart rate and number of steps. The input is the user's real-time health data, and the output is the data recorded on the wearable device.

[0212] Step 2:

[0213] The terminal (smartphone) receives health data collected from the wearable device. Specifically, the data is transferred via wireless communication such as Bluetooth. The input is data from the wearable device, and the output is health data stored in the terminal's memory.

[0214] Step 3:

[0215] The health data received by the device is sent to a server. Specifically, the data is sent via the Internet and stored in the server's database. The input is the health data stored on the device, and the output is the data stored on the server.

[0216] Step 4:

[0217] The server analyzes the collected health data. Specifically, it organizes the data chronologically and applies statistical analysis algorithms to evaluate the user's health status. For example, it calculates trends in average daily heart rate and number of steps. The input is the health data stored on the server, and the output is the health status evaluation results.

[0218] Step 5:

[0219] The server generates an individually customized exercise plan based on the analysis results, taking into account parameters such as the user's age, gender, and past exercise history. The input is the health assessment results and the user's individual information, and the output is a customized exercise plan.

[0220] Step 6:

[0221] The server sends the generated exercise plan to the device. Specifically, the exercise plan is sent to the smartphone via a communication line. The input is the exercise plan generated by the server, and the output is the exercise plan received by the device.

[0222] Step 7:

[0223] The terminal transfers the received exercise plan to the robot in the physical store. Specifically, data is sent to the robot via a local network. The input is the exercise plan stored on the terminal, and the output is the exercise plan received by the robot.

[0224] Step 8:

[0225] The robot displays the received exercise plan to the user in the physical store. Specifically, the robot presents the exercise plan on a screen or using voice synthesis. The input is the exercise plan transferred to the robot, and the output is the exercise plan presented to the user.

[0226] Step 9:

[0227] The user exercises according to the robot's instructions. Specifically, the user follows the exercise plan presented to them and records their exercise progress and results on their smartphone. The input is the user's exercise performance data, and the output is the exercise data recorded on the smartphone.

[0228] 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.

[0229] The present invention combines an emotion engine with a system that collects and analyzes a user's health data and provides an individually customized exercise plan, thereby adjusting the exercise plan based on the user's emotions and improving motivation. The main components of this system include a server, a terminal, a user, and an emotion engine.

[0230] System Configuration

[0231] 1. Data Collection Module

[0232] User: Wears the wearable device and goes about daily life.

[0233] Terminal: The wearable device transmits heart rate, step count, and other biometric data to a smartphone, which then transmits the data to a server.

[0234] Server: Receives data sent from the smartphone and stores it in a database.

[0235] 2. Data Analysis Module

[0236] Server: Reads user health data from the database, cleans the data, and applies analytical algorithms to evaluate the user's health status.

[0237] 3. Movement plan generation module

[0238] Server: Generates an individually customized exercise plan based on the user's health assessment results, age, gender, past exercise history, and current physical condition.

[0239] 4. Emotion Engine

[0240] Device: Collects user voice and facial expression data from smartphones and other devices and sends it to the emotion engine.

[0241] Server: Analyzes the user's emotions using an emotion engine and generates messages to adjust exercise plans and increase motivation based on the user's emotional state.

[0242] 5. Feedback provision module

[0243] Server: Formats the generated exercise plan, evaluation results, and support messages and sends them to the smartphone.

[0244] Device: The smartphone displays the information received from the server within the application. The user can then view this information through the application.

[0245] Specific examples

[0246] For example, suppose a user wears a wearable device and walks daily. The wearable device records the number of steps and heart rate each day and transfers the data to a smartphone. The smartphone then sends this data to a server, where it is stored in a database. The server analyzes the data from the past month and evaluates the user's health status (e.g., average number of steps, heart rate trends).

[0247] Furthermore, when a user checks their latest exercise plan on their smartphone, the emotion engine analyzes their facial expressions and tone of voice to identify their current emotional state (e.g., energetic, tired, stressed). Based on the analysis results, the server adjusts the exercise plan, including "walking for 30 minutes three times a week," and generates a supportive message tailored to the user's emotions (e.g., "You may not feel like it today, but even a little exercise can have a big effect.").

[0248] The user then follows this exercise plan and records their progress using a smartphone app. The recorded data is then sent back to the server, where it is stored in a database. The server then analyzes this new data, adjusts the exercise plan as needed, and uses an emotion engine to provide feedback tailored to the user's emotional state.

[0249] This system allows users to comprehensively manage their health and emotional state, leading to more effective and sustainable health and exercise plans.

[0250] The processing flow will be explained below.

[0251] Step 1: Health data collection

[0252] User: Wears the wearable device and engages in daily activities, such as walking or jogging.

[0253] Device: A wearable device records the user's heart rate, steps, and other biometric data.

[0254] Terminal: Data collected from the wearable device is transferred to a smartphone via Bluetooth, etc. A dedicated smartphone app receives this data.

[0255] Step 2: Send data

[0256] Terminal: The smartphone formats the data it receives and sends it to a server via the Internet.

[0257] Server: Receives the data sent from the smartphone and starts the storage process.

[0258] Step 3: Save data

[0259] Server: Checks the format of the received data and checks for inconsistencies.

[0260] Server: Stores the verified data in a database, including heart rate, steps, sleep data, etc.

[0261] Server: Log that the data has been saved.

[0262] Step 4: Data analysis

[0263] Server: Reads the target user's past health data from the database.

[0264] Server: Cleanses the loaded data and handles missing and outlier values.

[0265] Server: Performs time series analysis and other statistical processing on the data to assess the user's health status.

[0266] Step 5: Generate exercise plan

[0267] Server: Based on the health assessment results, an exercise plan is generated that takes into account the user's age, gender, past exercise history, and current physical condition.

[0268] Server: Incorporates expert guidelines into the generated exercise plan and optimizes it.

[0269] Step 6: Collect emotional data

[0270] User: Sits in front of a smartphone or PC and opens an application.

[0271] Device: A smartphone or PC captures the user's face with a camera and records their voice with a microphone.

[0272] Device: The emotion engine performs image and audio analysis to recognize the user's emotions.

[0273] Step 7: Processing the emotion data

[0274] Server: Receives the user's emotion data (e.g., joy, stress, fatigue) analyzed by the emotion engine.

[0275] Server: Further adjust the exercise plan based on emotional data, for example adding relaxation exercises if the user is feeling stressed.

[0276] Step 8: Feedback Generation

[0277] Server: Formats the generated exercise plan, evaluation results, and cheering messages.

[0278] Server: Sends the formatted data to the smartphone.

[0279] Step 9: Feedback display

[0280] Device: The smartphone displays the information received from the server within the application.

[0281] Users: View exercise plans, health assessments, and encouraging messages through the app.

[0282] Step 10: Exercise

[0283] User: The user actually exercises based on the provided exercise plan. The user performs the recommended exercises, such as walking or yoga.

[0284] User: Records exercise progress using a smartphone app.

[0285] Step 11: Sending progress data

[0286] Device: The smartphone collects exercise progress data and sends it to the server.

[0287] Server: Receives progress data and stores it in a database.

[0288] Step 12: Ongoing data analysis and feedback

[0289] Server: Reassess your health and exercise plan based on your latest progress data.

[0290] Server: Adjusts the exercise plan as needed and uses the emotion engine to provide feedback that matches the user's emotional state.

[0291] Through this process, the system automatically collects health and emotional data from users and provides optimal exercise plans based on that information, effectively managing users' health and maintaining their motivation.

[0292] Example 2

[0293] 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."

[0294] While conventional health management systems can collect and analyze users' health data, they have difficulty adjusting exercise plans that take into account the user's emotional state or providing feedback to improve motivation. This makes it difficult for users to continue with an exercise plan over the long term, and has led to the problem of ineffective health management.

[0295] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting user health data, means for storing the collected health data in a database, means for analyzing the stored health data to evaluate the user's health condition, means for generating a personalized exercise plan based on the user's health condition, means for providing the generated exercise plan to the user, means for collecting user emotion data, means for analyzing the collected emotion data to evaluate the user's emotional condition, and means for adjusting the exercise plan based on the user's health condition and emotional condition and generating a message to increase motivation. This makes it possible to comprehensively manage the user's health condition and emotional state and provide a more effective and sustainable exercise plan.

[0296] "User" refers to an individual who uses the system, primarily a provider of health data and emotional data.

[0297] "Health Data" refers to information about a user's physical condition, including heart rate, number of steps taken, and other biometric data.

[0298] "Database" refers to a structured data repository for storing, managing, and searching collected health data and analysis results.

[0299] "Analysis" refers to the general process of processing collected data and extracting useful information for a specific purpose.

[0300] "Health status" refers to the results of an assessment of the user's physical condition and tendencies based on analyzed data.

[0301] "Exercise plan" refers to a customized exercise plan based on the user's health status and individual conditions.

[0302] "Emotional data" refers to information that indicates a user's psychological state, obtained from the user's voice, facial expressions, various actions, or other means.

[0303] "Emotional state" refers to the result of evaluating the user's psychological state based on the analyzed emotional data.

[0304] "Emotion engine" refers to the general term for algorithms, models, and software that process collected emotional data and analyze the user's emotional state.

[0305] A "motivational message" refers to a message that is created taking into consideration the user's emotional state and includes words of encouragement to increase the user's motivation to exercise.

[0306] The present invention provides a system that adjusts exercise plans and improves motivation based on the user's emotions by combining an emotion engine with a system that collects and analyzes a user's health data and provides individually customized exercise plans. The main components of this system include a server, a terminal, a user, and an emotion engine.

[0307] Data Collection Module

[0308] Users wear a wearable device while going about their daily lives, which records biometric data such as heart rate and number of steps taken.

[0309] The terminal (smartphone) receives heart rate and step count data from the wearable device and sends it to the server. Specifically, the data is transferred using Bluetooth or Wi-Fi.

[0310] The server receives the data sent from the smartphone and stores it in a database.

[0311] Data Analysis Module

[0312] The server retrieves user data from a database, cleans it using Python libraries such as Pandas and NumPy, and applies analytical algorithms (e.g., machine learning models) to assess health status.

[0313] Movement plan generation module

[0314] The server generates a customized exercise plan based on the user's health assessment results, taking into account the user's age, gender, past exercise history, and current physical condition. Here, libraries such as scikit-learn are used to create an appropriate exercise plan.

[0315] Emotion Engine

[0316] The device uses a camera and microphone to collect voice and facial expression data while the user is using the smartphone app, and this data is sent to the emotion engine in real time.

[0317] The server analyzes the user's emotional data using an emotion engine. Specifically, it uses a deep learning model using TENSORFLOW® to identify the user's emotional state (e.g., stress, fatigue, energy).

[0318] Exercise plan adjustment and message generation module

[0319] The server adjusts the exercise plan based on the results of the emotion analysis and generates motivational messages, such as "Try exercising at a slower pace today" if the user feels fatigued.

[0320] Feedback provision module

[0321] The server formats the generated exercise plan and cheering message and sends them to the smartphone in JSON format, so that they can be displayed in the application.

[0322] The terminal displays the received information in the application, and the user confirms it.

[0323] Specific examples

[0324] Let us take the example of a user who walks every day and wears a wearable device to measure his / her heart rate and number of steps.

[0325] 1. Data collection: The wearable device records data while you go for a morning walk.

[0326] 2. Data transmission: After the walk is completed, the smartphone receives the data from the device and transmits it to the server.

[0327] 3. Database storage and analysis: The server stores the received data in a database and analyzes it. It uses Python's Pandas and NumPy to evaluate trends in the data.

[0328] 4. Exercise plan generation: The server generates an optimal exercise plan for the user based on the evaluation results.

[0329] 5. Emotion data collection and analysis: When a user uses a smartphone app, emotional data is collected using the camera and microphone, and the emotion engine analyzes it.

[0330] 6. Providing feedback: Feedback from the server (e.g., an adjusted exercise plan and encouraging messages) is displayed on the smartphone for the user to review.

[0331] Prompt Sentence Examples

[0332] "Generate personalized exercise plans and motivational messages based on the user's health and emotional data. The user's current emotional state is fatigued."

[0333] In this way, the system can comprehensively manage the user's health and emotional state and provide an individually customized exercise plan, enabling effective and sustainable health management.

[0334] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0335] Step 1:

[0336] Data collection

[0337] Users wear the wearable device while going about their daily activities, and the device records biometric data such as heart rate and number of steps taken.

[0338] Input: User biometric data (heart rate, steps)

[0339] Output: Recorded biometric data

[0340] What it does: The wearable device collects data in real time and stores it in its internal memory.

[0341] Step 2:

[0342] Data transmission

[0343] The terminal (smartphone) receives data from the wearable device via Bluetooth or Wi-Fi and sends the data to a server.

[0344] Input: Biometric data stored on a wearable device

[0345] Output: Biometric data sent to the server

[0346] How it works: The smartphone periodically syncs with the wearable device and sends the received data to a server via the Internet.

[0347] Step 3:

[0348] Database storage

[0349] The server stores the received biometric data in a database, standardizes the data format, and performs data cleansing to remove unnecessary information.

[0350] Input: Biometric data sent from smartphone

[0351] Output: Cleansed data stored in a database

[0352] What it does: Uses database management software to store incoming data in the appropriate tables.

[0353] Step 4:

[0354] Data analysis

[0355] The server retrieves the user's health data from the database, analyzes it using Python's Pandas and NumPy, and applies machine learning algorithms to assess their health status.

[0356] Input: Health data stored in a database

[0357] Output: Health status assessment results (e.g. average steps, heart rate trend)

[0358] What it does: A Python script reads the data, preprocesses it (by filling in missing values ​​and removing outliers), and then applies an analysis algorithm to assess health status.

[0359] Step 5:

[0360] Exercise plan generation

[0361] The server generates an individually customized exercise plan based on the health assessment results, the user's age, gender, past exercise history, and current physical condition. Libraries such as scikit-learn are used here.

[0362] Input: Health assessment results, user's age, gender, past exercise history, current physical condition

[0363] Output: Personalized exercise plan

[0364] Specific operation: Analyze the data and apply an algorithm to generate an exercise plan that is optimal for the user's attributes.

[0365] Step 6:

[0366] Emotional Data Collection

[0367] The device uses the smartphone's camera and microphone to collect the user's voice and facial expression data, which is then sent to a server in real time.

[0368] Input: User voice and facial expression data

[0369] Output: Emotion data sent to the server

[0370] How it works: Your smartphone uses its camera and microphone to perform facial and voice recognition and collect emotional data.

[0371] Step 7:

[0372] Emotion analysis

[0373] The server analyzes the emotional data using a deep learning model with TensorFlow to identify the user's emotional state.

[0374] Input: Collected emotion data

[0375] Output: Emotional state assessment result

[0376] What it does: Uses a deep learning model to classify the user's emotions (e.g., stress, fatigue, energy) from collected data.

[0377] Step 8:

[0378] Exercise plan adjustment and message generation

[0379] The server adjusts the exercise plan based on the results of the emotion analysis and generates motivational messages, such as "Try exercising at a slower pace today" if the user feels fatigued.

[0380] Input: Health status assessment results, emotional status assessment results

[0381] Output: A tailored exercise plan and encouraging messages

[0382] Specific operations: Comprehensively assess the user's health and emotional state, tailor an optimal exercise plan, and create encouraging messages using a text generation algorithm.

[0383] Step 9:

[0384] Providing Feedback

[0385] The server formats the generated exercise plan and support message in JSON format and sends it to the smartphone.

[0386] Input: Tailored exercise plans and encouraging messages

[0387] Output: Information sent to your smartphone

[0388] Specific operation: The generated data is packaged in an appropriate format and sent to the smartphone over the network.

[0389] Step 10:

[0390] User Verification

[0391] The device displays the exercise plan and encouraging messages received from the server within the app.

[0392] Input: Information sent from the server

[0393] Output: Exercise plan and encouragement message displayed to the user

[0394] What it does: The smartphone app analyzes the data and displays it on the screen for the user to easily view.

[0395] In this way, by specifically explaining the processing flow of the entire system, it is possible to comprehensively manage the user's health and emotional state, and to achieve effective health management.

[0396] (Application example 2)

[0397] 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."

[0398] Conventional health management systems simply collect users' biometric data and provide exercise plans based on that data. This means that a uniform exercise plan is provided without taking into account the user's emotional state, making it difficult to maintain motivation and failing to adequately address individual needs. Furthermore, there is a lack of real-time feedback when it comes to detecting abnormal values.

[0399] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting biometric data of the user, means for storing the collected biometric data in a storage device, means for analyzing the stored biometric data to evaluate the user's health condition, means for analyzing the user's voice and facial expressions to evaluate the user's emotional state, means for adjusting an exercise plan based on the emotional state, means for providing the generated exercise plan to the user, means for collecting data from the wearable device, means for detecting abnormal values ​​in the collected data, means for sending an alert to the user when an abnormal value is detected, and means for providing the user with an exercise plan and feedback in real time via the smart glasses. This makes it possible to provide an individual exercise plan that takes into account both the user's health condition and emotional state, thereby maintaining motivation and providing appropriate feedback.

[0400] "User's biological data" refers to measurement data related to the user's physical condition, such as heart rate, number of steps, amount of exercise, and body temperature.

[0401] A "storage device" is a hardware or software component for storing data.

[0402] "Analysis" is the process of evaluating and calculating collected data to derive specific conclusions or insights.

[0403] "User's voice and facial expression" is data for determining the user's emotional state based on voice and facial expression.

[0404] "Emotional state" is information that indicates the user's current psychological state (e.g., happy, sad, stressed, etc.).

[0405] An "exercise plan" is a recommended exercise plan based on the user's health and emotional state.

[0406] A "wearable device" is an electronic device that can be worn on the user's body and has the ability to collect and communicate biometric data.

[0407] An "abnormal value" is data that indicates a value or condition that is outside the normal range and suggests a possible health problem.

[0408] "Warning" is the process of notifying the user of important or urgent information.

[0409] "Smart glasses" are devices that, in addition to the functions of regular glasses, can also function as electronic devices, displaying information and collecting data.

[0410] "Feedback" refers to information or responses provided by a system to a user, which provide guidance for improving the user's behavior or state.

[0411] This invention relates to a system that collects and analyzes a user's biometric and emotional data to provide a customized exercise plan. By combining it with an emotion engine, it is possible to adjust the exercise plan based on the user's emotions and improve motivation.

[0412] System Configuration

[0413] The system of the present invention comprises the following components:

[0414] 1. Data Collection Module

[0415] User: The user wears a wearable device equipped with a heart rate sensor and a pedometer while going about their daily life.

[0416] Device: This includes smart glasses and smartphones. It has the function of receiving biometric data collected from wearable devices and sending it to a server.

[0417] Server: Receives data sent from smart glasses or smartphones and stores it in a storage device.

[0418] 2. Data Analysis Module

[0419] Server: Cleanses stored biometric data and applies analytical algorithms to assess health status.

[0420] 3. Movement plan generation module

[0421] Server: Generates an individual exercise plan based on the health assessment results, the user's age, gender, and past exercise history.

[0422] 4. Emotion Engine

[0423] Device: Analyzes the user's voice and facial expressions and sends them to the emotion engine, which evaluates the user's emotional state and sends the results to the server.

[0424] Server: Analyzes data from the emotion engine and adjusts motor plans based on emotional state.

[0425] 5. Feedback provision module

[0426] Server: Formats the generated exercise plan, evaluation results, and motivational messages and sends them to the smart glasses or smartphone.

[0427] Terminal: Smart glasses or smartphone displays the information received from the server, and the user can view it.

[0428] Specific examples

[0429] For example, when a user goes for a morning run, the smart glasses record their heart rate and number of steps in real time. Furthermore, they analyze the user's fatigue and stress level based on their facial expression and complexion. If the user's heart rate is high and their facial expression shows signs of fatigue, the server will generate a message such as "Try to slow down a bit today," and display it on the smart glasses. In this way, users can simultaneously manage their health and improve their motivation.

[0430] Example prompt sentence:

[0431] "Generate a new customized exercise plan based on the latest health data (heart rate: 75, steps: 12000) of user ID 12345, taking into account emotion data (emotion detected from voice: stress, emotion detected from facial expression: fatigue)."

[0432] The present invention allows users to comprehensively manage their health and emotional state, enabling them to implement more effective and sustainable health and exercise plans.

[0433] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0434] Step 1:

[0435] Users wear wearable devices (e.g., smartwatches and fitness trackers) while going about their daily lives. The wearable devices collect biometric data, such as heart rate and step count, in real time. The collected biometric data is then transmitted to smart glasses or a smartphone.

[0436] Input: User's biometric data

[0437] Output: Data transfer to smart glasses or smartphone

[0438] Step 2:

[0439] The terminal (smart glasses or smartphone) transmits the biometric data received from the wearable terminal to the server, which then stores the received biometric data in a storage device.

[0440] Input: Biometric data received from a wearable device

[0441] Output: Data stored in the server's storage device

[0442] Step 3:

[0443] The server cleanses the biometric data stored in the storage device and applies an analysis algorithm to evaluate the user's health status, generating a health status assessment report.

[0444] Input: Biometric data stored in a storage device

[0445] Output: Health status assessment report

[0446] Step 4:

[0447] Users collect voice and facial expression data through smart glasses or smartphones, which are then sent to the emotion engine to analyze their emotional state.

[0448] Input: User's voice and facial expression data

[0449] Output: Evaluation result of emotional state by emotion engine

[0450] Step 5:

[0451] The server receives the emotional state assessment results from the emotion engine and generates a personalized exercise plan based on the health assessment results, as well as the user's age, gender, and past exercise history. The generated exercise plan also includes a message to increase motivation.

[0452] Input: Health status assessment results, emotional status assessment results, user information

[0453] Output: personalized exercise plan and encouraging messages

[0454] Step 6:

[0455] The server then sends the generated exercise plan and encouraging messages to the smart glasses or smartphone, where the user can check the information and follow the exercise plan.

[0456] Input: personalized exercise plan and encouraging message

[0457] Output: Exercise plan and messages displayed on smart glasses or smartphone

[0458] Step 7:

[0459] The user actually follows the exercise plan and engages in activities such as running or walking. During the activity, the wearable device collects and transmits biometric data to the device.

[0460] Input: User activity based on exercise plan

[0461] Output: New biometric data

[0462] Step 8:

[0463] The device sends newly collected biometric data to a server, which analyzes the effectiveness of the exercise plan based on the data and adjusts the plan as needed.

[0464] Input: Newly collected biometric data

[0465] Output: Updated exercise plan and encouragement message

[0466] The above is a specific processing flow of the system according to the present invention.

[0467] 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.

[0468] 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.

[0469] 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.

[0470] [Second embodiment]

[0471] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0472] 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.

[0473] 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).

[0474] 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.

[0475] 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.

[0476] 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).

[0477] 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.

[0478] 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.

[0479] 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.

[0480] 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.

[0481] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0482] 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."

[0483] This system collects and analyzes a user's health data and provides an individually customized exercise plan. This system consists of a server, a terminal, and a user module.

[0484] System Configuration

[0485] 1. Data Collection Module

[0486] User: Wears the wearable device and performs daily activities.

[0487] Terminal: The data collected from the wearable device is transferred to the smartphone, which then transmits the data to the server.

[0488] Server: Receives data sent from the smartphone and stores it in a database.

[0489] Data analysis

[0490] 2. Data Analysis Module

[0491] Server: Reads the user's health data from the database, organizes it by time series and category, and applies analysis algorithms to it.

[0492] Server: Evaluates the user's health status. For example, it analyzes a user's heart rate and step count data to assess the health risks associated with daily activities.

[0493] Exercise plan generation

[0494] 3. Movement plan generation module

[0495] Server: Based on the user's health assessment results, the server generates an individually customized exercise plan, taking into account parameters such as age, gender, past exercise history, and current physical condition.

[0496] Server: Determines the optimal exercise plan for the user, incorporating exercise guidelines provided by experts as needed.

[0497] Providing Feedback

[0498] 4. Feedback provision module

[0499] Server: Formats the generated exercise plan and evaluation results and sends them to a smartphone.

[0500] Device: The smartphone displays the exercise plan and evaluation results received from the server. The user can view this information through the smartphone app.

[0501] User: Follows the provided exercise plan and actually exercises. Records exercise progress and results on a smartphone.

[0502] Specific examples

[0503] For example, suppose a user wears a wearable watch and walks daily. The watch records the number of steps and heart rate taken each day and transfers the data to a smartphone. The smartphone then sends this data to a server, where it is stored in a database. The server analyzes the data from the past month and evaluates the user's health status, such as trends in average steps and heart rate.

[0504] Based on the analysis results, the server generates an exercise plan that includes "walking for 30 minutes three times a week." This exercise plan is customized taking into account the user's age and past exercise history. The generated exercise plan is sent to the user's smartphone and displayed as a notification.

[0505] The user then follows this plan and records their progress on their smartphone. The recorded data is then sent back to the server, where it is stored in a database. The server then analyzes this new data and adjusts the plan as needed.

[0506] As described above, by using this system, users can constantly monitor their own health status and implement individually optimized exercise plans, thereby effectively maintaining and improving their health.

[0507] The processing flow will be explained below.

[0508] Step 1: Data collection

[0509] User: Wears the wearable device and goes about their daily life.

[0510] Device: Transfers heart rate, step count, and other biometric data collected from the wearable device to a smartphone.

[0511] Terminal: A smartphone receives data from the wearable device via a dedicated app.

[0512] Step 2: Send data

[0513] Terminal: The smartphone formats the data it receives and sends it to a server via the Internet.

[0514] Server: Receives data sent from the smartphone.

[0515] Step 3: Save data

[0516] Server: Check the format of the received data and check for any errors.

[0517] Server: Stores data in a database, including heart rate, steps, sleep data, etc.

[0518] Server: Log the successful data save.

[0519] Step 4: Data analysis

[0520] Server: Reads the target user's past health data from the database.

[0521] Server: Cleanses the data and handles missing and outlier values.

[0522] Server: Performs time series analysis and other statistical processing on the data to assess the user's current health status.

[0523] Step 5: Generate exercise plan

[0524] Server: Based on the health assessment results, an exercise plan is generated that takes into account the user's age, gender, past exercise history, and current physical condition.

[0525] Server: Incorporates expert guidelines into the generated exercise plan and optimizes it.

[0526] Step 6: Provide feedback

[0527] Server: Formats the generated exercise plan and evaluation results for the user interface.

[0528] Server: Sends the formatted data to the smartphone.

[0529] Device: Smartphone displays exercise plan and evaluation results within the application.

[0530] User: Uses the application to view exercise plans.

[0531] Step 7: Exercise

[0532] User: Actually exercises based on the exercise plan provided.

[0533] User: Records exercise progress using a smartphone app.

[0534] Step 8: Sending progress data

[0535] Device: The smartphone collects exercise progress data and sends it to the server.

[0536] Server: Receives progress data and stores it in a database.

[0537] Step 9: Ongoing data analysis and feedback

[0538] Server: Reassess your health and exercise plan based on your latest progress data.

[0539] Server: Adjusts exercise plan as needed and provides feedback.

[0540] Through this process, users can continuously collect health data and receive personalized exercise plans, which the system automatically supports, effectively managing their health.

[0541] Example 1

[0542] 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."

[0543] Conventional health management systems only collect users' health data, but do not adequately analyze that data in detail or provide individual exercise plans. This makes it difficult to provide users with an optimized exercise plan and effectively improve their health. Furthermore, they lack the ability to continuously adjust exercise plans based on new data, making them unable to flexibly respond to changing health conditions.

[0544] 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.

[0545] In this invention, the server

[0546] A means to organize health data stored in a database by time series and category and apply analytical algorithms;

[0547] means for assessing the health status of the user based on the analyzed data;

[0548] means for generating a personalized exercise plan based on the user's health status assessment results;

[0549] a means of tailoring an exercise plan based on expert exercise guidelines;

[0550] means for providing the generated exercise plan to a user's terminal;

[0551] It also includes a means for continuously adjusting the exercise plan based on new data, which allows the user to receive the optimal exercise plan tailored to their health condition and effectively manage their health.

[0552] "User" refers to an individual who utilizes the System to manage their own health data and receive a personalized exercise plan.

[0553] "Health data" refers to various data that indicate the user's health status, such as heart rate, number of steps, and calories burned.

[0554] "Database" refers to a data storage system that manages user health data stored on a server.

[0555] "Analysis Algorithm" refers to a mathematical or statistical method for analyzing health data stored in the database and assessing the user's health status.

[0556] "Health status assessment" refers to the process of using analytical algorithms to review a user's health data and determine the user's health level and risk.

[0557] The term "exercise plan" refers to an exercise schedule and exercise content for an individual user that is generated based on the results of the user's health status evaluation.

[0558] "Exercise guidelines" refer to guiding principles and standards for exercise established by experts.

[0559] "Terminal" refers to a device used by the user, such as a smartphone or tablet, through which the system provides an exercise plan.

[0560] "New data" refers to newly collected health data by the user.

[0561] This system collects and analyzes a user's health data and provides an individually customized exercise plan. This system consists of a server, a terminal, and a user module.

[0562] Data collection

[0563] 1. The user wears the wearable device and performs daily activities, for example, jogging or walking while wearing the wearable watch.

[0564] 2. The terminal (smartphone) receives data such as steps and heart rate from the wearable device and sends it to the server via Bluetooth and HTTPS protocol.

[0565] 3. The server stores the received data in a database, typically a relational database management system (RDBMS) such as MySQL.

[0566] Data analysis

[0567] 1. The server reads the user's health data from the database and organizes it by time series and category using SQL queries.

[0568] 2. The server processes the data using analytical algorithms, such as machine learning models and statistical functions, to analyze the data and assess the user's health status. The algorithms are written in programming languages ​​such as Python and R.

[0569] 3. The server evaluates the user's health status based on the analysis results, for example, assessing health risks based on trends in heart rate fluctuations and step counts.

[0570] Exercise plan generation

[0571] 1. The server generates a personalized exercise plan based on the health assessment results, taking into account the user's age, gender, past exercise history, current physical condition, etc.

[0572] 2. The server adjusts the generated exercise plan based on expert exercise guidelines, for example, incorporating exercise recommendations provided by a sports physician.

[0573] Providing Feedback

[0574] 1. The server formats the generated exercise plan and evaluation results and sends them to the smartphone in JSON format.

[0575] 2. The device (smartphone) analyzes the data received from the server and displays it to the user using a dedicated application.

[0576] 3. The user exercises based on the displayed exercise plan and records their progress and results on their smartphone.

[0577] 4. The device again sends the newly recorded data to the server, where it is analyzed again and the exercise plan is continuously adjusted.

[0578] Specific examples

[0579] For example, a user wears a wearable watch and jogs daily. The watch records heart rate, distance traveled, and calorie consumption. The recorded data is transferred to a smartphone via Bluetooth, and the smartphone sends the data to a server. The server stores the data in a database and evaluates the user's health status using a Python script.

[0580] Based on the evaluation results, the server generates an exercise plan that includes jogging for 30 minutes three times a week. This plan is customized taking into account expert exercise guidelines and is sent to the smartphone. The user jogs according to this exercise plan and records their progress on the smartphone. New data is sent back to the server, and the exercise plan is continuously adjusted.

[0581] Prompt Sentence Examples

[0582] "Design a system that generates and provides a personalized exercise plan based on data collected from a user's wearable device. Include details of each step: data collection, analysis, exercise plan generation, and feedback provision."

[0583] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0584] Step 1:

[0585] A user wears a wearable device and performs daily activities. The input includes the user's exercise data (e.g., number of steps, heart rate). Specifically, the wearable device periodically records health data such as heart rate and number of steps. The output is the collected raw data.

[0586] Step 2:

[0587] The terminal (smartphone) receives exercise data from the wearable device using Bluetooth. The input is the raw data sent from the wearable device. Specifically, the smartphone app establishes a Bluetooth connection and acquires data such as heart rate and step count. The output is the exercise data stored on the smartphone.

[0588] Step 3:

[0589] The device sends exercise data to the server using the HTTPS protocol. The input is the exercise data stored on the smartphone. Specifically, the smartphone app generates an HTTPS request, encrypts the data, and sends it to the server. The output is the exercise data sent to the server.

[0590] Step 4:

[0591] The server stores the received exercise data in a database. The input is the exercise data sent from the smartphone. Specifically, an INSERT query is executed on the database (e.g., MySQL). The output is the exercise data stored in the database.

[0592] Step 5:

[0593] The server reads the motion data from the database and applies an analysis algorithm. The input is the motion data stored in the database. Specifically, a Python script reads the data from the database and performs the analysis. The analysis algorithm uses time series analysis and machine learning models. The output is the analysis results.

[0594] Step 6:

[0595] The server evaluates the user's health status based on the analysis results. The input is the analysis results. Specifically, health risk assessment is performed using statistical models or machine learning models. For example, heart rate trends are evaluated to determine the risk of arrhythmia. The output is the health status assessment result.

[0596] Step 7:

[0597] The server generates a personalized exercise plan based on the health assessment results. The input is the health assessment results. Specifically, an algorithm is used that takes into account age, gender, and past exercise history. The output is a customized exercise plan.

[0598] Step 8:

[0599] The server adjusts the exercise plan based on the expert exercise guidelines. The inputs are the generated exercise plan and the exercise guidelines. Specifically, the server applies the rules of the exercise guidelines to optimize the plan. The output is the optimized exercise plan.

[0600] Step 9:

[0601] The server sends the optimized exercise plan and the evaluation results to the device. The inputs are the optimized exercise plan and the evaluation results. Specifically, this data is formatted in JSON format and sent via the HTTPS protocol. The output is the exercise plan sent to the device and the evaluation results.

[0602] Step 10:

[0603] The device (smartphone) analyzes the data received from the server and displays it on the user interface. The inputs are the exercise plan and evaluation results received from the server. Specifically, the application analyzes the JSON data and displays it in an easy-to-read format. The output is the exercise plan and evaluation results provided to the user.

[0604] Step 11:

[0605] The user exercises based on the displayed exercise plan and records their progress and results on their smartphone. The input is the exercise plan, and the user performs the exercise as a specific action and enters the exercise results (e.g., exercise time, distance traveled) into the application. The output is data on progress and results.

[0606] Step 12:

[0607] The device sends the newly recorded data to the server. The input is the progress and achievement data recorded by the user. Specifically, the application sends this data to the server using the HTTPS protocol. The output is the new data sent to the server.

[0608] Step 13:

[0609] The server stores newly received data in a database and continuously adjusts the exercise plan. The input is newly received progress and performance data. Specifically, INSERT queries are performed on the database, and then the analysis algorithm is reapplied to update the exercise plan. The output is the adjusted exercise plan.

[0610] (Application example 1)

[0611] 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."

[0612] Conventional user health management systems lack individuality in the collection and analysis of health data and the provision of exercise plans, and are not available for direct use by users in physical stores. This makes it difficult for users to receive practical advice tailored to their own health status. Furthermore, there is a demand for real-time advice and an interactive experience in stores.

[0613] 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.

[0614] In this invention, the server includes: means for collecting user health data; means for storing the collected health data in a database; means for analyzing the stored health data to evaluate the user's health condition; means for generating a personalized exercise plan based on the user's health condition; means for providing the generated exercise plan to the user; means for a robot in a physical store to collect health data from the user's wearable device; means for providing health advice and an exercise plan in real time based on the collected health data; and means for displaying the exercise plan generated by the robot. This allows users to receive individually customized exercise plans and health advice in real time in the physical store based on the health data collected from their wearable device. By having the robot present this information, interaction with the user is improved, and specific action guidelines for improving their health can be obtained immediately in the store.

[0615] "Health data" is numerical information that indicates the user's physical condition, such as the user's heart rate, number of steps, and calorie consumption.

[0616] A "database" is a collection of information that stores collected health data in an organized manner and can be accessed and analyzed as needed.

[0617] "Analysis" is the process of statistically processing collected health data to evaluate the user's health status and trends.

[0618] "Exercise Plan" means a personalized exercise or workout plan to improve or maintain a User's health and wellness.

[0619] The "robot" is an automated device installed in a brick-and-mortar store that collects health data from users and provides advice and exercise plans based on that data.

[0620] A "wearable device" is a device that can be worn and used by users on a daily basis to collect health data.

[0621] "Real-time" refers to the fact that the process from data collection to analysis and feedback provision is immediate.

[0622] "Customization" refers to optimizing an exercise plan based on a user's individual health and lifestyle.

[0623] An "abnormal value" is a health data value that is outside the normal range and may indicate a health risk to the user.

[0624] A "warning" is a notification that warns the user or provides guidelines for action when an abnormal value or risk is detected.

[0625] The present invention is a system that collects and analyzes a user's health data and provides an individually customized exercise plan, and includes the following specific configuration and operation method.

[0626] System Configuration

[0627] 1. Data Collection Module

[0628] User: Wears a wearable device while performing daily activities. The wearable device collects health data such as heart rate and number of steps taken.

[0629] Terminal: Health data collected from the wearable device is transferred to a smartphone, which then transmits the data to a server.

[0630] Server: Receives data sent from the smartphone and stores it in a database.

[0631] Data Analysis Module

[0632] 2. Data Analysis Module

[0633] Server: Reads the user's health data from the database. The read data is organized by time series and category, and statistical analysis algorithms are applied to it. The results of the analysis are used to evaluate the user's health status. For example, the average heart rate or number of steps over a certain period of time is calculated.

[0634] Server: If abnormal values ​​are detected in the health data, it also has the function of sending a warning to the user based on that data.

[0635] Movement plan generation module

[0636] 3. Movement plan generation module

[0637] Server: Based on the user's health assessment results, the server generates an individually customized exercise plan taking into account parameters such as age, gender, past exercise history, and current physical condition. If necessary, it incorporates exercise guidelines provided by experts to determine the optimal exercise plan for the user.

[0638] Feedback provision module

[0639] 4. Feedback provision module

[0640] Server: Formats the generated exercise plan and evaluation results and sends them to a smartphone.

[0641] Device: The smartphone displays the exercise plan and evaluation results received from the server. The user can view this information through the smartphone app.

[0642] User: Follows the provided exercise plan and actually exercises. Records exercise progress and results on their smartphone. The recorded data is again sent to the server and stored in a database. The server then analyzes this new data and adjusts the exercise plan as necessary.

[0643] Examples from physical stores

[0644] A robot installed in a physical store connects with the wearable devices of customers who visit the store. When a customer enters the store, the robot collects health data from the wearable device and sends it to a server in real time. The server analyzes this data and generates an individually customized exercise plan. The robot displays the generated exercise plan and presents it to the customer.

[0645] Prompt Sentence Examples

[0646] For example, if a 40-year-old woman wears a wearable watch and comes into the store, the system will collect her heart rate and step count for that day. After analyzing the data, the robot will present her with an exercise plan, such as "We recommend 30 minutes of aerobic exercise three times a week."

[0647] Example prompts

[0648] I want to collect health data and generate a customized exercise plan.

[0649] Age: 40

[0650] Gender: Female

[0651] Normal steps: 6,000 steps / day

[0652] Recent heart rate: Average 105 bpm

[0653] Based on this data, what exercise plan would you suggest?

[0654] In this way, users can receive specific advice based on their own health data in real time within the physical store. This system allows users to obtain practical guidelines for maintaining and improving their health and put them into practice on the spot.

[0655] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0656] Step 1:

[0657] A user enters a store wearing a wearable device. The device collects health data such as heart rate and number of steps. The input is the user's real-time health data, and the output is the data recorded on the wearable device.

[0658] Step 2:

[0659] The terminal (smartphone) receives health data collected from the wearable device. Specifically, the data is transferred via wireless communication such as Bluetooth. The input is data from the wearable device, and the output is health data stored in the terminal's memory.

[0660] Step 3:

[0661] The health data received by the device is sent to a server. Specifically, the data is sent via the Internet and stored in the server's database. The input is the health data stored on the device, and the output is the data stored on the server.

[0662] Step 4:

[0663] The server analyzes the collected health data. Specifically, it organizes the data chronologically and applies statistical analysis algorithms to evaluate the user's health status. For example, it calculates trends in average daily heart rate and number of steps. The input is the health data stored on the server, and the output is the health status evaluation results.

[0664] Step 5:

[0665] The server generates an individually customized exercise plan based on the analysis results, taking into account parameters such as the user's age, gender, and past exercise history. The input is the health assessment results and the user's individual information, and the output is a customized exercise plan.

[0666] Step 6:

[0667] The server sends the generated exercise plan to the device. Specifically, the exercise plan is sent to the smartphone via a communication line. The input is the exercise plan generated by the server, and the output is the exercise plan received by the device.

[0668] Step 7:

[0669] The terminal transfers the received exercise plan to the robot in the physical store. Specifically, data is sent to the robot via a local network. The input is the exercise plan stored on the terminal, and the output is the exercise plan received by the robot.

[0670] Step 8:

[0671] The robot displays the received exercise plan to the user in the physical store. Specifically, the robot presents the exercise plan on a screen or using voice synthesis. The input is the exercise plan transferred to the robot, and the output is the exercise plan presented to the user.

[0672] Step 9:

[0673] The user exercises according to the robot's instructions. Specifically, the user follows the exercise plan presented to them and records their exercise progress and results on their smartphone. The input is the user's exercise performance data, and the output is the exercise data recorded on the smartphone.

[0674] 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.

[0675] The present invention combines an emotion engine with a system that collects and analyzes a user's health data and provides an individually customized exercise plan, thereby adjusting the exercise plan based on the user's emotions and improving motivation. The main components of this system include a server, a terminal, a user, and an emotion engine.

[0676] System Configuration

[0677] 1. Data Collection Module

[0678] User: Wears the wearable device and goes about daily life.

[0679] Terminal: The wearable device transmits heart rate, step count, and other biometric data to a smartphone, which then transmits the data to a server.

[0680] Server: Receives data sent from the smartphone and stores it in a database.

[0681] 2. Data Analysis Module

[0682] Server: Reads user health data from the database, cleans the data, and applies analytical algorithms to evaluate the user's health status.

[0683] 3. Movement plan generation module

[0684] Server: Generates an individually customized exercise plan based on the user's health assessment results, age, gender, past exercise history, and current physical condition.

[0685] 4. Emotion Engine

[0686] Device: Collects user voice and facial expression data from smartphones and other devices and sends it to the emotion engine.

[0687] Server: Analyzes the user's emotions using an emotion engine and generates messages to adjust exercise plans and increase motivation based on the user's emotional state.

[0688] 5. Feedback provision module

[0689] Server: Formats the generated exercise plan, evaluation results, and support messages and sends them to the smartphone.

[0690] Device: The smartphone displays the information received from the server within the application. The user can then view this information through the application.

[0691] Specific examples

[0692] For example, suppose a user wears a wearable device and walks daily. The wearable device records the number of steps and heart rate each day and transfers the data to a smartphone. The smartphone then sends this data to a server, where it is stored in a database. The server analyzes the data from the past month and evaluates the user's health status (e.g., average number of steps, heart rate trends).

[0693] Furthermore, when a user checks their latest exercise plan on their smartphone, the emotion engine analyzes their facial expressions and tone of voice to identify their current emotional state (e.g., energetic, tired, stressed). Based on the analysis results, the server adjusts the exercise plan, including "walking for 30 minutes three times a week," and generates a supportive message tailored to the user's emotions (e.g., "You may not feel like it today, but even a little exercise can have a big effect.").

[0694] The user then follows this exercise plan and records their progress using a smartphone app. The recorded data is then sent back to the server, where it is stored in a database. The server then analyzes this new data, adjusts the exercise plan as needed, and uses an emotion engine to provide feedback tailored to the user's emotional state.

[0695] This system allows users to comprehensively manage their health and emotional state, leading to more effective and sustainable health and exercise plans.

[0696] The processing flow will be explained below.

[0697] Step 1: Health data collection

[0698] User: Wears the wearable device and engages in daily activities, such as walking or jogging.

[0699] Device: A wearable device records the user's heart rate, steps, and other biometric data.

[0700] Terminal: Data collected from the wearable device is transferred to a smartphone via Bluetooth, etc. A dedicated smartphone app receives this data.

[0701] Step 2: Send data

[0702] Terminal: The smartphone formats the data it receives and sends it to a server via the Internet.

[0703] Server: Receives the data sent from the smartphone and starts the storage process.

[0704] Step 3: Save data

[0705] Server: Checks the format of the received data and checks for inconsistencies.

[0706] Server: Stores the verified data in a database, including heart rate, steps, sleep data, etc.

[0707] Server: Log that the data has been saved.

[0708] Step 4: Data analysis

[0709] Server: Reads the target user's past health data from the database.

[0710] Server: Cleanses the loaded data and handles missing and outlier values.

[0711] Server: Performs time series analysis and other statistical processing on the data to assess the user's health status.

[0712] Step 5: Generate exercise plan

[0713] Server: Based on the health assessment results, an exercise plan is generated that takes into account the user's age, gender, past exercise history, and current physical condition.

[0714] Server: Incorporates expert guidelines into the generated exercise plan and optimizes it.

[0715] Step 6: Collect emotional data

[0716] User: Sits in front of a smartphone or PC and opens an application.

[0717] Device: A smartphone or PC captures the user's face with a camera and records their voice with a microphone.

[0718] Device: The emotion engine performs image and audio analysis to recognize the user's emotions.

[0719] Step 7: Processing the emotion data

[0720] Server: Receives the user's emotion data (e.g., joy, stress, fatigue) analyzed by the emotion engine.

[0721] Server: Further adjust the exercise plan based on emotional data, for example adding relaxation exercises if the user is feeling stressed.

[0722] Step 8: Feedback Generation

[0723] Server: Formats the generated exercise plan, evaluation results, and cheering messages.

[0724] Server: Sends the formatted data to the smartphone.

[0725] Step 9: Feedback display

[0726] Device: The smartphone displays the information received from the server within the application.

[0727] Users: View exercise plans, health assessments, and encouraging messages through the app.

[0728] Step 10: Exercise

[0729] User: The user actually exercises based on the provided exercise plan. The user performs the recommended exercises, such as walking or yoga.

[0730] User: Records exercise progress using a smartphone app.

[0731] Step 11: Sending progress data

[0732] Device: The smartphone collects exercise progress data and sends it to the server.

[0733] Server: Receives progress data and stores it in a database.

[0734] Step 12: Ongoing data analysis and feedback

[0735] Server: Reassess your health and exercise plan based on your latest progress data.

[0736] Server: Adjusts the exercise plan as needed and uses the emotion engine to provide feedback that matches the user's emotional state.

[0737] Through this process, the system automatically collects health and emotional data from users and provides optimal exercise plans based on that information, effectively managing users' health and maintaining their motivation.

[0738] Example 2

[0739] 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."

[0740] While conventional health management systems can collect and analyze users' health data, they have difficulty adjusting exercise plans that take into account the user's emotional state or providing feedback to improve motivation. This makes it difficult for users to continue with an exercise plan over the long term, and has led to the problem of ineffective health management.

[0741] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting user health data, means for storing the collected health data in a database, means for analyzing the stored health data to evaluate the user's health condition, means for generating a personalized exercise plan based on the user's health condition, means for providing the generated exercise plan to the user, means for collecting user emotion data, means for analyzing the collected emotion data to evaluate the user's emotional condition, and means for adjusting the exercise plan based on the user's health condition and emotional condition and generating a message to increase motivation. This makes it possible to comprehensively manage the user's health condition and emotional state and provide a more effective and sustainable exercise plan.

[0742] "User" refers to an individual who uses the system, primarily a provider of health data and emotional data.

[0743] "Health Data" refers to information about a user's physical condition, including heart rate, number of steps taken, and other biometric data.

[0744] "Database" refers to a structured data repository for storing, managing, and searching collected health data and analysis results.

[0745] "Analysis" refers to the general process of processing collected data and extracting useful information for a specific purpose.

[0746] "Health status" refers to the results of an assessment of the user's physical condition and tendencies based on analyzed data.

[0747] "Exercise plan" refers to a customized exercise plan based on the user's health status and individual conditions.

[0748] "Emotional data" refers to information that indicates a user's psychological state, obtained from the user's voice, facial expressions, various actions, or other means.

[0749] "Emotional state" refers to the result of evaluating the user's psychological state based on the analyzed emotional data.

[0750] "Emotion engine" refers to the general term for algorithms, models, and software that process collected emotional data and analyze the user's emotional state.

[0751] A "motivational message" refers to a message that is created taking into consideration the user's emotional state and includes words of encouragement to increase the user's motivation to exercise.

[0752] The present invention provides a system that adjusts exercise plans and improves motivation based on the user's emotions by combining an emotion engine with a system that collects and analyzes a user's health data and provides individually customized exercise plans. The main components of this system include a server, a terminal, a user, and an emotion engine.

[0753] Data Collection Module

[0754] Users wear a wearable device while going about their daily lives, which records biometric data such as heart rate and number of steps taken.

[0755] The terminal (smartphone) receives heart rate and step count data from the wearable device and sends it to the server. Specifically, the data is transferred using Bluetooth or Wi-Fi.

[0756] The server receives the data sent from the smartphone and stores it in a database.

[0757] Data Analysis Module

[0758] The server retrieves user data from a database, cleans it using Python libraries such as Pandas and NumPy, and applies analytical algorithms (e.g., machine learning models) to assess health status.

[0759] Movement plan generation module

[0760] The server generates a customized exercise plan based on the user's health assessment results, taking into account the user's age, gender, past exercise history, and current physical condition. Here, libraries such as scikit-learn are used to create an appropriate exercise plan.

[0761] Emotion Engine

[0762] The device uses a camera and microphone to collect voice and facial expression data while the user is using the smartphone app, and this data is sent to the emotion engine in real time.

[0763] The server analyzes the user's emotional data using an emotion engine. Specifically, it uses a deep learning model with TensorFlow to identify the user's emotional state (e.g., stress, fatigue, energy).

[0764] Exercise plan adjustment and message generation module

[0765] The server adjusts the exercise plan based on the results of the emotion analysis and generates motivational messages, such as "Try exercising at a slower pace today" if the user feels fatigued.

[0766] Feedback provision module

[0767] The server formats the generated exercise plan and cheering message and sends them to the smartphone in JSON format, so that they can be displayed in the application.

[0768] The terminal displays the received information in the application, and the user confirms it.

[0769] Specific examples

[0770] Let us take the example of a user who walks every day and wears a wearable device to measure his / her heart rate and number of steps.

[0771] 1. Data collection: The wearable device records data while you go for a morning walk.

[0772] 2. Data transmission: After the walk is completed, the smartphone receives the data from the device and transmits it to the server.

[0773] 3. Database storage and analysis: The server stores the received data in a database and analyzes it. It uses Python's Pandas and NumPy to evaluate trends in the data.

[0774] 4. Exercise plan generation: The server generates an optimal exercise plan for the user based on the evaluation results.

[0775] 5. Emotion data collection and analysis: When a user uses a smartphone app, emotional data is collected using the camera and microphone, and the emotion engine analyzes it.

[0776] 6. Providing feedback: Feedback from the server (e.g., an adjusted exercise plan and encouraging messages) is displayed on the smartphone for the user to review.

[0777] Prompt Sentence Examples

[0778] "Generate personalized exercise plans and motivational messages based on the user's health and emotional data. The user's current emotional state is fatigued."

[0779] In this way, the system can comprehensively manage the user's health and emotional state and provide an individually customized exercise plan, enabling effective and sustainable health management.

[0780] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0781] Step 1:

[0782] Data collection

[0783] Users wear the wearable device while going about their daily activities, and the device records biometric data such as heart rate and number of steps taken.

[0784] Input: User biometric data (heart rate, steps)

[0785] Output: Recorded biometric data

[0786] What it does: The wearable device collects data in real time and stores it in its internal memory.

[0787] Step 2:

[0788] Data transmission

[0789] The terminal (smartphone) receives data from the wearable device via Bluetooth or Wi-Fi and sends the data to a server.

[0790] Input: Biometric data stored on a wearable device

[0791] Output: Biometric data sent to the server

[0792] How it works: The smartphone periodically syncs with the wearable device and sends the received data to a server via the Internet.

[0793] Step 3:

[0794] Database storage

[0795] The server stores the received biometric data in a database, standardizes the data format, and performs data cleansing to remove unnecessary information.

[0796] Input: Biometric data sent from smartphone

[0797] Output: Cleansed data stored in a database

[0798] What it does: Uses database management software to store incoming data in the appropriate tables.

[0799] Step 4:

[0800] Data analysis

[0801] The server retrieves the user's health data from the database, analyzes it using Python's Pandas and NumPy, and applies machine learning algorithms to assess their health status.

[0802] Input: Health data stored in a database

[0803] Output: Health status assessment results (e.g. average steps, heart rate trend)

[0804] What it does: A Python script reads the data, preprocesses it (by filling in missing values ​​and removing outliers), and then applies an analysis algorithm to assess health status.

[0805] Step 5:

[0806] Exercise plan generation

[0807] The server generates an individually customized exercise plan based on the health assessment results, the user's age, gender, past exercise history, and current physical condition. Libraries such as scikit-learn are used here.

[0808] Input: Health assessment results, user's age, gender, past exercise history, current physical condition

[0809] Output: Personalized exercise plan

[0810] Specific operation: Analyze the data and apply an algorithm to generate an exercise plan that is optimal for the user's attributes.

[0811] Step 6:

[0812] Emotional Data Collection

[0813] The device uses the smartphone's camera and microphone to collect the user's voice and facial expression data, which is then sent to a server in real time.

[0814] Input: User voice and facial expression data

[0815] Output: Emotion data sent to the server

[0816] How it works: Your smartphone uses its camera and microphone to perform facial and voice recognition and collect emotional data.

[0817] Step 7:

[0818] Emotion analysis

[0819] The server analyzes the emotional data using a deep learning model with TensorFlow to identify the user's emotional state.

[0820] Input: Collected emotion data

[0821] Output: Emotional state assessment result

[0822] What it does: Uses a deep learning model to classify the user's emotions (e.g., stress, fatigue, energy) from collected data.

[0823] Step 8:

[0824] Exercise plan adjustment and message generation

[0825] The server adjusts the exercise plan based on the results of the emotion analysis and generates motivational messages, such as "Try exercising at a slower pace today" if the user feels fatigued.

[0826] Input: Health status assessment results, emotional status assessment results

[0827] Output: A tailored exercise plan and encouraging messages

[0828] Specific operations: Comprehensively assess the user's health and emotional state, tailor an optimal exercise plan, and create encouraging messages using a text generation algorithm.

[0829] Step 9:

[0830] Providing Feedback

[0831] The server formats the generated exercise plan and support message in JSON format and sends it to the smartphone.

[0832] Input: Tailored exercise plans and encouraging messages

[0833] Output: Information sent to your smartphone

[0834] Specific operation: The generated data is packaged in an appropriate format and sent to the smartphone over the network.

[0835] Step 10:

[0836] User Verification

[0837] The device displays the exercise plan and encouraging messages received from the server within the app.

[0838] Input: Information sent from the server

[0839] Output: Exercise plan and encouragement message displayed to the user

[0840] What it does: The smartphone app analyzes the data and displays it on the screen for the user to easily view.

[0841] In this way, by specifically explaining the processing flow of the entire system, it is possible to comprehensively manage the user's health and emotional state, and to achieve effective health management.

[0842] (Application example 2)

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

[0844] Conventional health management systems simply collect users' biometric data and provide exercise plans based on that data. This means that a uniform exercise plan is provided without taking into account the user's emotional state, making it difficult to maintain motivation and failing to adequately address individual needs. Furthermore, there is a lack of real-time feedback when it comes to detecting abnormal values.

[0845] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting biometric data of the user, means for storing the collected biometric data in a storage device, means for analyzing the stored biometric data to evaluate the user's health condition, means for analyzing the user's voice and facial expressions to evaluate the user's emotional state, means for adjusting an exercise plan based on the emotional state, means for providing the generated exercise plan to the user, means for collecting data from the wearable device, means for detecting abnormal values ​​in the collected data, means for sending an alert to the user when an abnormal value is detected, and means for providing the user with an exercise plan and feedback in real time via the smart glasses. This makes it possible to provide an individual exercise plan that takes into account both the user's health condition and emotional state, thereby maintaining motivation and providing appropriate feedback.

[0846] "User's biological data" refers to measurement data related to the user's physical condition, such as heart rate, number of steps, amount of exercise, and body temperature.

[0847] A "storage device" is a hardware or software component for storing data.

[0848] "Analysis" is the process of evaluating and calculating collected data to derive specific conclusions or insights.

[0849] "User's voice and facial expression" is data for determining the user's emotional state based on voice and facial expression.

[0850] "Emotional state" is information that indicates the user's current psychological state (e.g., happy, sad, stressed, etc.).

[0851] An "exercise plan" is a recommended exercise plan based on the user's health and emotional state.

[0852] A "wearable device" is an electronic device that can be worn on the user's body and has the ability to collect and communicate biometric data.

[0853] An "abnormal value" is data that indicates a value or condition that is outside the normal range and suggests a possible health problem.

[0854] "Warning" is the process of notifying the user of important or urgent information.

[0855] "Smart glasses" are devices that, in addition to the functions of regular glasses, can also function as electronic devices, displaying information and collecting data.

[0856] "Feedback" refers to information or responses provided by a system to a user, which provide guidance for improving the user's behavior or state.

[0857] This invention relates to a system that collects and analyzes a user's biometric and emotional data to provide a customized exercise plan. By combining it with an emotion engine, it is possible to adjust the exercise plan based on the user's emotions and improve motivation.

[0858] System Configuration

[0859] The system of the present invention comprises the following components:

[0860] 1. Data Collection Module

[0861] User: The user wears a wearable device equipped with a heart rate sensor and a pedometer while going about their daily life.

[0862] Device: This includes smart glasses and smartphones. It has the function of receiving biometric data collected from wearable devices and sending it to a server.

[0863] Server: Receives data sent from smart glasses or smartphones and stores it in a storage device.

[0864] 2. Data Analysis Module

[0865] Server: Cleanses stored biometric data and applies analytical algorithms to assess health status.

[0866] 3. Movement plan generation module

[0867] Server: Generates an individual exercise plan based on the health assessment results, the user's age, gender, and past exercise history.

[0868] 4. Emotion Engine

[0869] Device: Analyzes the user's voice and facial expressions and sends them to the emotion engine, which evaluates the user's emotional state and sends the results to the server.

[0870] Server: Analyzes data from the emotion engine and adjusts motor plans based on emotional state.

[0871] 5. Feedback provision module

[0872] Server: Formats the generated exercise plan, evaluation results, and motivational messages and sends them to the smart glasses or smartphone.

[0873] Terminal: Smart glasses or smartphone displays the information received from the server, and the user can view it.

[0874] Specific examples

[0875] For example, when a user goes for a morning run, the smart glasses record their heart rate and number of steps in real time. Furthermore, they analyze the user's fatigue and stress level based on their facial expression and complexion. If the user's heart rate is high and their facial expression shows signs of fatigue, the server will generate a message such as "Try to slow down a bit today," and display it on the smart glasses. In this way, users can simultaneously manage their health and improve their motivation.

[0876] Example prompt sentence:

[0877] "Generate a new customized exercise plan based on the latest health data (heart rate: 75, steps: 12000) of user ID 12345, taking into account emotion data (emotion detected from voice: stress, emotion detected from facial expression: fatigue)."

[0878] The present invention allows users to comprehensively manage their health and emotional state, enabling them to implement more effective and sustainable health and exercise plans.

[0879] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0880] Step 1:

[0881] Users wear wearable devices (e.g., smartwatches and fitness trackers) while going about their daily lives. The wearable devices collect biometric data, such as heart rate and step count, in real time. The collected biometric data is then transmitted to smart glasses or a smartphone.

[0882] Input: User's biometric data

[0883] Output: Data transfer to smart glasses or smartphone

[0884] Step 2:

[0885] The terminal (smart glasses or smartphone) transmits the biometric data received from the wearable terminal to the server, which then stores the received biometric data in a storage device.

[0886] Input: Biometric data received from a wearable device

[0887] Output: Data stored in the server's storage device

[0888] Step 3:

[0889] The server cleanses the biometric data stored in the storage device and applies an analysis algorithm to evaluate the user's health status, generating a health status assessment report.

[0890] Input: Biometric data stored in a storage device

[0891] Output: Health status assessment report

[0892] Step 4:

[0893] Users collect voice and facial expression data through smart glasses or smartphones, which are then sent to the emotion engine to analyze their emotional state.

[0894] Input: User's voice and facial expression data

[0895] Output: Evaluation result of emotional state by emotion engine

[0896] Step 5:

[0897] The server receives the emotional state assessment results from the emotion engine and generates a personalized exercise plan based on the health assessment results, as well as the user's age, gender, and past exercise history. The generated exercise plan also includes a message to increase motivation.

[0898] Input: Health status assessment results, emotional status assessment results, user information

[0899] Output: personalized exercise plan and encouraging messages

[0900] Step 6:

[0901] The server then sends the generated exercise plan and encouraging messages to the smart glasses or smartphone, where the user can check the information and follow the exercise plan.

[0902] Input: personalized exercise plan and encouraging message

[0903] Output: Exercise plan and messages displayed on smart glasses or smartphone

[0904] Step 7:

[0905] The user actually follows the exercise plan and engages in activities such as running or walking. During the activity, the wearable device collects and transmits biometric data to the device.

[0906] Input: User activity based on exercise plan

[0907] Output: New biometric data

[0908] Step 8:

[0909] The device sends newly collected biometric data to a server, which analyzes the effectiveness of the exercise plan based on the data and adjusts the plan as needed.

[0910] Input: Newly collected biometric data

[0911] Output: Updated exercise plan and encouragement message

[0912] The above is a specific processing flow of the system according to the present invention.

[0913] 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.

[0914] 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.

[0915] 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.

[0916] [Third embodiment]

[0917] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0918] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0919] 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).

[0920] 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.

[0921] 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.

[0922] 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).

[0923] 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.

[0924] 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.

[0925] 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.

[0926] 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.

[0927] 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.

[0928] 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."

[0929] This system collects and analyzes a user's health data and provides an individually customized exercise plan. This system consists of a server, a terminal, and a user module.

[0930] System Configuration

[0931] 1. Data Collection Module

[0932] User: Wears the wearable device and performs daily activities.

[0933] Terminal: The data collected from the wearable device is transferred to the smartphone, which then transmits the data to the server.

[0934] Server: Receives data sent from the smartphone and stores it in a database.

[0935] Data analysis

[0936] 2. Data Analysis Module

[0937] Server: Reads the user's health data from the database, organizes it by time series and category, and applies analysis algorithms to it.

[0938] Server: Evaluates the user's health status. For example, it analyzes a user's heart rate and step count data to assess the health risks associated with daily activities.

[0939] Exercise plan generation

[0940] 3. Movement plan generation module

[0941] Server: Based on the user's health assessment results, the server generates an individually customized exercise plan, taking into account parameters such as age, gender, past exercise history, and current physical condition.

[0942] Server: Determines the optimal exercise plan for the user, incorporating exercise guidelines provided by experts as needed.

[0943] Providing Feedback

[0944] 4. Feedback provision module

[0945] Server: Formats the generated exercise plan and evaluation results and sends them to a smartphone.

[0946] Device: The smartphone displays the exercise plan and evaluation results received from the server. The user can view this information through the smartphone app.

[0947] User: Follows the provided exercise plan and actually exercises. Records exercise progress and results on a smartphone.

[0948] Specific examples

[0949] For example, suppose a user wears a wearable watch and walks daily. The watch records the number of steps and heart rate taken each day and transfers the data to a smartphone. The smartphone then sends this data to a server, where it is stored in a database. The server analyzes the data from the past month and evaluates the user's health status, such as trends in average steps and heart rate.

[0950] Based on the analysis results, the server generates an exercise plan that includes "walking for 30 minutes three times a week." This exercise plan is customized taking into account the user's age and past exercise history. The generated exercise plan is sent to the user's smartphone and displayed as a notification.

[0951] The user then follows this plan and records their progress on their smartphone. The recorded data is then sent back to the server, where it is stored in a database. The server then analyzes this new data and adjusts the plan as needed.

[0952] As described above, by using this system, users can constantly monitor their own health status and implement individually optimized exercise plans, thereby effectively maintaining and improving their health.

[0953] The processing flow will be explained below.

[0954] Step 1: Data collection

[0955] User: Wears the wearable device and goes about their daily life.

[0956] Device: Transfers heart rate, step count, and other biometric data collected from the wearable device to a smartphone.

[0957] Terminal: A smartphone receives data from the wearable device via a dedicated app.

[0958] Step 2: Send data

[0959] Terminal: The smartphone formats the data it receives and sends it to a server via the Internet.

[0960] Server: Receives data sent from the smartphone.

[0961] Step 3: Save data

[0962] Server: Check the format of the received data and check for any errors.

[0963] Server: Stores data in a database, including heart rate, steps, sleep data, etc.

[0964] Server: Log the successful data save.

[0965] Step 4: Data analysis

[0966] Server: Reads the target user's past health data from the database.

[0967] Server: Cleanses the data and handles missing and outlier values.

[0968] Server: Performs time series analysis and other statistical processing on the data to assess the user's current health status.

[0969] Step 5: Generate exercise plan

[0970] Server: Based on the health assessment results, an exercise plan is generated that takes into account the user's age, gender, past exercise history, and current physical condition.

[0971] Server: Incorporates expert guidelines into the generated exercise plan and optimizes it.

[0972] Step 6: Provide feedback

[0973] Server: Formats the generated exercise plan and evaluation results for the user interface.

[0974] Server: Sends the formatted data to the smartphone.

[0975] Device: Smartphone displays exercise plan and evaluation results within the application.

[0976] User: Uses the application to view exercise plans.

[0977] Step 7: Exercise

[0978] User: Actually exercises based on the exercise plan provided.

[0979] User: Records exercise progress using a smartphone app.

[0980] Step 8: Sending progress data

[0981] Device: The smartphone collects exercise progress data and sends it to the server.

[0982] Server: Receives progress data and stores it in a database.

[0983] Step 9: Ongoing data analysis and feedback

[0984] Server: Reassess your health and exercise plan based on your latest progress data.

[0985] Server: Adjusts exercise plan as needed and provides feedback.

[0986] Through this process, users can continuously collect health data and receive personalized exercise plans, which the system automatically supports, effectively managing their health.

[0987] Example 1

[0988] 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."

[0989] Conventional health management systems only collect users' health data, but do not adequately analyze that data in detail or provide individual exercise plans. This makes it difficult to provide users with an optimized exercise plan and effectively improve their health. Furthermore, they lack the ability to continuously adjust exercise plans based on new data, making them unable to flexibly respond to changing health conditions.

[0990] 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.

[0991] In this invention, the server

[0992] A means to organize health data stored in a database by time series and category and apply analytical algorithms;

[0993] means for assessing the health status of the user based on the analyzed data;

[0994] means for generating a personalized exercise plan based on the user's health status assessment results;

[0995] a means of tailoring an exercise plan based on expert exercise guidelines;

[0996] means for providing the generated exercise plan to a user's terminal;

[0997] It also includes a means for continuously adjusting the exercise plan based on new data, which allows the user to receive the optimal exercise plan tailored to their health condition and effectively manage their health.

[0998] "User" refers to an individual who utilizes the System to manage their own health data and receive a personalized exercise plan.

[0999] "Health data" refers to various data that indicate the user's health status, such as heart rate, number of steps, and calories burned.

[1000] "Database" refers to a data storage system that manages user health data stored on a server.

[1001] "Analysis Algorithm" refers to a mathematical or statistical method for analyzing health data stored in the database and assessing the user's health status.

[1002] "Health status assessment" refers to the process of using analytical algorithms to review a user's health data and determine the user's health level and risk.

[1003] The term "exercise plan" refers to an exercise schedule and exercise content for an individual user that is generated based on the results of the user's health status evaluation.

[1004] "Exercise guidelines" refer to guiding principles and standards for exercise established by experts.

[1005] "Terminal" refers to a device used by the user, such as a smartphone or tablet, through which the system provides an exercise plan.

[1006] "New data" refers to newly collected health data by the user.

[1007] This system collects and analyzes a user's health data and provides an individually customized exercise plan. This system consists of a server, a terminal, and a user module.

[1008] Data collection

[1009] 1. The user wears the wearable device and performs daily activities, for example, jogging or walking while wearing the wearable watch.

[1010] 2. The terminal (smartphone) receives data such as steps and heart rate from the wearable device and sends it to the server via Bluetooth and HTTPS protocol.

[1011] 3. The server stores the received data in a database, typically a relational database management system (RDBMS) such as MySQL.

[1012] Data analysis

[1013] 1. The server reads the user's health data from the database and organizes it by time series and category using SQL queries.

[1014] 2. The server processes the data using analytical algorithms, such as machine learning models and statistical functions, to analyze the data and assess the user's health status. The algorithms are written in programming languages ​​such as Python and R.

[1015] 3. The server evaluates the user's health status based on the analysis results, for example, assessing health risks based on trends in heart rate fluctuations and step counts.

[1016] Exercise plan generation

[1017] 1. The server generates a personalized exercise plan based on the health assessment results, taking into account the user's age, gender, past exercise history, current physical condition, etc.

[1018] 2. The server adjusts the generated exercise plan based on expert exercise guidelines, for example, incorporating exercise recommendations provided by a sports physician.

[1019] Providing Feedback

[1020] 1. The server formats the generated exercise plan and evaluation results and sends them to the smartphone in JSON format.

[1021] 2. The device (smartphone) analyzes the data received from the server and displays it to the user using a dedicated application.

[1022] 3. The user exercises based on the displayed exercise plan and records their progress and results on their smartphone.

[1023] 4. The device again sends the newly recorded data to the server, where it is analyzed again and the exercise plan is continuously adjusted.

[1024] Specific examples

[1025] For example, a user wears a wearable watch and jogs daily. The watch records heart rate, distance traveled, and calorie consumption. The recorded data is transferred to a smartphone via Bluetooth, and the smartphone sends the data to a server. The server stores the data in a database and evaluates the user's health status using a Python script.

[1026] Based on the evaluation results, the server generates an exercise plan that includes jogging for 30 minutes three times a week. This plan is customized taking into account expert exercise guidelines and is sent to the smartphone. The user jogs according to this exercise plan and records their progress on the smartphone. New data is sent back to the server, and the exercise plan is continuously adjusted.

[1027] Prompt Sentence Examples

[1028] "Design a system that generates and provides a personalized exercise plan based on data collected from a user's wearable device. Include details of each step: data collection, analysis, exercise plan generation, and feedback provision."

[1029] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1030] Step 1:

[1031] A user wears a wearable device and performs daily activities. The input includes the user's exercise data (e.g., number of steps, heart rate). Specifically, the wearable device periodically records health data such as heart rate and number of steps. The output is the collected raw data.

[1032] Step 2:

[1033] The terminal (smartphone) receives exercise data from the wearable device using Bluetooth. The input is the raw data sent from the wearable device. Specifically, the smartphone app establishes a Bluetooth connection and acquires data such as heart rate and step count. The output is the exercise data stored on the smartphone.

[1034] Step 3:

[1035] The device sends exercise data to the server using the HTTPS protocol. The input is the exercise data stored on the smartphone. Specifically, the smartphone app generates an HTTPS request, encrypts the data, and sends it to the server. The output is the exercise data sent to the server.

[1036] Step 4:

[1037] The server stores the received exercise data in a database. The input is the exercise data sent from the smartphone. Specifically, an INSERT query is executed on the database (e.g., MySQL). The output is the exercise data stored in the database.

[1038] Step 5:

[1039] The server reads the motion data from the database and applies an analysis algorithm. The input is the motion data stored in the database. Specifically, a Python script reads the data from the database and performs the analysis. The analysis algorithm uses time series analysis and machine learning models. The output is the analysis results.

[1040] Step 6:

[1041] The server evaluates the user's health status based on the analysis results. The input is the analysis results. Specifically, health risk assessment is performed using statistical models or machine learning models. For example, heart rate trends are evaluated to determine the risk of arrhythmia. The output is the health status assessment result.

[1042] Step 7:

[1043] The server generates a personalized exercise plan based on the health assessment results. The input is the health assessment results. Specifically, an algorithm is used that takes into account age, gender, and past exercise history. The output is a customized exercise plan.

[1044] Step 8:

[1045] The server adjusts the exercise plan based on the expert exercise guidelines. The inputs are the generated exercise plan and the exercise guidelines. Specifically, the server applies the rules of the exercise guidelines to optimize the plan. The output is the optimized exercise plan.

[1046] Step 9:

[1047] The server sends the optimized exercise plan and the evaluation results to the device. The inputs are the optimized exercise plan and the evaluation results. Specifically, this data is formatted in JSON format and sent via the HTTPS protocol. The output is the exercise plan sent to the device and the evaluation results.

[1048] Step 10:

[1049] The device (smartphone) analyzes the data received from the server and displays it on the user interface. The inputs are the exercise plan and evaluation results received from the server. Specifically, the application analyzes the JSON data and displays it in an easy-to-read format. The output is the exercise plan and evaluation results provided to the user.

[1050] Step 11:

[1051] The user exercises based on the displayed exercise plan and records their progress and results on their smartphone. The input is the exercise plan, and the user performs the exercise as a specific action and enters the exercise results (e.g., exercise time, distance traveled) into the application. The output is data on progress and results.

[1052] Step 12:

[1053] The device sends the newly recorded data to the server. The input is the progress and achievement data recorded by the user. Specifically, the application sends this data to the server using the HTTPS protocol. The output is the new data sent to the server.

[1054] Step 13:

[1055] The server stores newly received data in a database and continuously adjusts the exercise plan. The input is newly received progress and performance data. Specifically, INSERT queries are performed on the database, and then the analysis algorithm is reapplied to update the exercise plan. The output is the adjusted exercise plan.

[1056] (Application example 1)

[1057] 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."

[1058] Conventional user health management systems lack individuality in the collection and analysis of health data and the provision of exercise plans, and are not available for direct use by users in physical stores. This makes it difficult for users to receive practical advice tailored to their own health status. Furthermore, there is a demand for real-time advice and an interactive experience in stores.

[1059] 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.

[1060] In this invention, the server includes: means for collecting user health data; means for storing the collected health data in a database; means for analyzing the stored health data to evaluate the user's health condition; means for generating a personalized exercise plan based on the user's health condition; means for providing the generated exercise plan to the user; means for a robot in a physical store to collect health data from the user's wearable device; means for providing health advice and an exercise plan in real time based on the collected health data; and means for displaying the exercise plan generated by the robot. This allows users to receive individually customized exercise plans and health advice in real time in the physical store based on the health data collected from their wearable device. By having the robot present this information, interaction with the user is improved, and specific action guidelines for improving their health can be obtained immediately in the store.

[1061] "Health data" is numerical information that indicates the user's physical condition, such as the user's heart rate, number of steps, and calorie consumption.

[1062] A "database" is a collection of information that stores collected health data in an organized manner and can be accessed and analyzed as needed.

[1063] "Analysis" is the process of statistically processing collected health data to evaluate the user's health status and trends.

[1064] "Exercise Plan" means a personalized exercise or workout plan to improve or maintain a User's health and wellness.

[1065] The "robot" is an automated device installed in a brick-and-mortar store that collects health data from users and provides advice and exercise plans based on that data.

[1066] A "wearable device" is a device that can be worn and used by users on a daily basis to collect health data.

[1067] "Real-time" refers to the fact that the process from data collection to analysis and feedback provision is immediate.

[1068] "Customization" refers to optimizing an exercise plan based on a user's individual health and lifestyle.

[1069] An "abnormal value" is a health data value that is outside the normal range and may indicate a health risk to the user.

[1070] A "warning" is a notification that warns the user or provides guidelines for action when an abnormal value or risk is detected.

[1071] The present invention is a system that collects and analyzes a user's health data and provides an individually customized exercise plan, and includes the following specific configuration and operation method.

[1072] System Configuration

[1073] 1. Data Collection Module

[1074] User: Wears a wearable device while performing daily activities. The wearable device collects health data such as heart rate and number of steps taken.

[1075] Terminal: Health data collected from the wearable device is transferred to a smartphone, which then transmits the data to a server.

[1076] Server: Receives data sent from the smartphone and stores it in a database.

[1077] Data Analysis Module

[1078] 2. Data Analysis Module

[1079] Server: Reads the user's health data from the database. The read data is organized by time series and category, and statistical analysis algorithms are applied to it. The results of the analysis are used to evaluate the user's health status. For example, the average heart rate or number of steps over a certain period of time is calculated.

[1080] Server: If abnormal values ​​are detected in the health data, it also has the function of sending a warning to the user based on that data.

[1081] Movement plan generation module

[1082] 3. Movement plan generation module

[1083] Server: Based on the user's health assessment results, the server generates an individually customized exercise plan taking into account parameters such as age, gender, past exercise history, and current physical condition. If necessary, it incorporates exercise guidelines provided by experts to determine the optimal exercise plan for the user.

[1084] Feedback provision module

[1085] 4. Feedback provision module

[1086] Server: Formats the generated exercise plan and evaluation results and sends them to a smartphone.

[1087] Device: The smartphone displays the exercise plan and evaluation results received from the server. The user can view this information through the smartphone app.

[1088] User: Follows the provided exercise plan and actually exercises. Records exercise progress and results on their smartphone. The recorded data is again sent to the server and stored in a database. The server then analyzes this new data and adjusts the exercise plan as necessary.

[1089] Examples from physical stores

[1090] A robot installed in a physical store connects with the wearable devices of customers who visit the store. When a customer enters the store, the robot collects health data from the wearable device and sends it to a server in real time. The server analyzes this data and generates an individually customized exercise plan. The robot displays the generated exercise plan and presents it to the customer.

[1091] Prompt Sentence Examples

[1092] For example, if a 40-year-old woman wears a wearable watch and comes into the store, the system will collect her heart rate and step count for that day. After analyzing the data, the robot will present her with an exercise plan, such as "We recommend 30 minutes of aerobic exercise three times a week."

[1093] Example prompts

[1094] I want to collect health data and generate a customized exercise plan.

[1095] Age: 40

[1096] Gender: Female

[1097] Normal steps: 6,000 steps / day

[1098] Recent heart rate: Average 105 bpm

[1099] Based on this data, what exercise plan would you suggest?

[1100] In this way, users can receive specific advice based on their own health data in real time within the physical store. This system allows users to obtain practical guidelines for maintaining and improving their health and put them into practice on the spot.

[1101] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1102] Step 1:

[1103] A user enters a store wearing a wearable device. The device collects health data such as heart rate and number of steps. The input is the user's real-time health data, and the output is the data recorded on the wearable device.

[1104] Step 2:

[1105] The terminal (smartphone) receives health data collected from the wearable device. Specifically, the data is transferred via wireless communication such as Bluetooth. The input is data from the wearable device, and the output is health data stored in the terminal's memory.

[1106] Step 3:

[1107] The health data received by the device is sent to a server. Specifically, the data is sent via the Internet and stored in the server's database. The input is the health data stored on the device, and the output is the data stored on the server.

[1108] Step 4:

[1109] The server analyzes the collected health data. Specifically, it organizes the data chronologically and applies statistical analysis algorithms to evaluate the user's health status. For example, it calculates trends in average daily heart rate and number of steps. The input is the health data stored on the server, and the output is the health status evaluation results.

[1110] Step 5:

[1111] The server generates an individually customized exercise plan based on the analysis results, taking into account parameters such as the user's age, gender, and past exercise history. The input is the health assessment results and the user's individual information, and the output is a customized exercise plan.

[1112] Step 6:

[1113] The server sends the generated exercise plan to the device. Specifically, the exercise plan is sent to the smartphone via a communication line. The input is the exercise plan generated by the server, and the output is the exercise plan received by the device.

[1114] Step 7:

[1115] The terminal transfers the received exercise plan to the robot in the physical store. Specifically, data is sent to the robot via a local network. The input is the exercise plan stored on the terminal, and the output is the exercise plan received by the robot.

[1116] Step 8:

[1117] The robot displays the received exercise plan to the user in the physical store. Specifically, the robot presents the exercise plan on a screen or using voice synthesis. The input is the exercise plan transferred to the robot, and the output is the exercise plan presented to the user.

[1118] Step 9:

[1119] The user exercises according to the robot's instructions. Specifically, the user follows the exercise plan presented to them and records their exercise progress and results on their smartphone. The input is the user's exercise performance data, and the output is the exercise data recorded on the smartphone.

[1120] 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.

[1121] The present invention combines an emotion engine with a system that collects and analyzes a user's health data and provides an individually customized exercise plan, thereby adjusting the exercise plan based on the user's emotions and improving motivation. The main components of this system include a server, a terminal, a user, and an emotion engine.

[1122] System Configuration

[1123] 1. Data Collection Module

[1124] User: Wears the wearable device and goes about daily life.

[1125] Terminal: The wearable device transmits heart rate, step count, and other biometric data to a smartphone, which then transmits the data to a server.

[1126] Server: Receives data sent from the smartphone and stores it in a database.

[1127] 2. Data Analysis Module

[1128] Server: Reads user health data from the database, cleans the data, and applies analytical algorithms to evaluate the user's health status.

[1129] 3. Movement plan generation module

[1130] Server: Generates an individually customized exercise plan based on the user's health assessment results, age, gender, past exercise history, and current physical condition.

[1131] 4. Emotion Engine

[1132] Device: Collects user voice and facial expression data from smartphones and other devices and sends it to the emotion engine.

[1133] Server: Analyzes the user's emotions using an emotion engine and generates messages to adjust exercise plans and increase motivation based on the user's emotional state.

[1134] 5. Feedback provision module

[1135] Server: Formats the generated exercise plan, evaluation results, and support messages and sends them to the smartphone.

[1136] Device: The smartphone displays the information received from the server within the application. The user can then view this information through the application.

[1137] Specific examples

[1138] For example, suppose a user wears a wearable device and walks daily. The wearable device records the number of steps and heart rate each day and transfers the data to a smartphone. The smartphone then sends this data to a server, where it is stored in a database. The server analyzes the data from the past month and evaluates the user's health status (e.g., average number of steps, heart rate trends).

[1139] Furthermore, when a user checks their latest exercise plan on their smartphone, the emotion engine analyzes their facial expressions and tone of voice to identify their current emotional state (e.g., energetic, tired, stressed). Based on the analysis results, the server adjusts the exercise plan, including "walking for 30 minutes three times a week," and generates a supportive message tailored to the user's emotions (e.g., "You may not feel like it today, but even a little exercise can have a big effect.").

[1140] The user then follows this exercise plan and records their progress using a smartphone app. The recorded data is then sent back to the server, where it is stored in a database. The server then analyzes this new data, adjusts the exercise plan as needed, and uses an emotion engine to provide feedback tailored to the user's emotional state.

[1141] This system allows users to comprehensively manage their health and emotional state, leading to more effective and sustainable health and exercise plans.

[1142] The processing flow will be explained below.

[1143] Step 1: Health data collection

[1144] User: Wears the wearable device and engages in daily activities, such as walking or jogging.

[1145] Device: A wearable device records the user's heart rate, steps, and other biometric data.

[1146] Terminal: Data collected from the wearable device is transferred to a smartphone via Bluetooth, etc. A dedicated smartphone app receives this data.

[1147] Step 2: Send data

[1148] Terminal: The smartphone formats the data it receives and sends it to a server via the Internet.

[1149] Server: Receives the data sent from the smartphone and starts the storage process.

[1150] Step 3: Save data

[1151] Server: Checks the format of the received data and checks for inconsistencies.

[1152] Server: Stores the verified data in a database, including heart rate, steps, sleep data, etc.

[1153] Server: Log that the data has been saved.

[1154] Step 4: Data analysis

[1155] Server: Reads the target user's past health data from the database.

[1156] Server: Cleanses the loaded data and handles missing and outlier values.

[1157] Server: Performs time series analysis and other statistical processing on the data to assess the user's health status.

[1158] Step 5: Generate exercise plan

[1159] Server: Based on the health assessment results, an exercise plan is generated that takes into account the user's age, gender, past exercise history, and current physical condition.

[1160] Server: Incorporates expert guidelines into the generated exercise plan and optimizes it.

[1161] Step 6: Collect emotional data

[1162] User: Sits in front of a smartphone or PC and opens an application.

[1163] Device: A smartphone or PC captures the user's face with a camera and records their voice with a microphone.

[1164] Device: The emotion engine performs image and audio analysis to recognize the user's emotions.

[1165] Step 7: Processing the emotion data

[1166] Server: Receives the user's emotion data (e.g., joy, stress, fatigue) analyzed by the emotion engine.

[1167] Server: Further adjust the exercise plan based on emotional data, for example adding relaxation exercises if the user is feeling stressed.

[1168] Step 8: Feedback Generation

[1169] Server: Formats the generated exercise plan, evaluation results, and cheering messages.

[1170] Server: Sends the formatted data to the smartphone.

[1171] Step 9: Feedback display

[1172] Device: The smartphone displays the information received from the server within the application.

[1173] Users: View exercise plans, health assessments, and encouraging messages through the app.

[1174] Step 10: Exercise

[1175] User: The user actually exercises based on the provided exercise plan. The user performs the recommended exercises, such as walking or yoga.

[1176] User: Records exercise progress using a smartphone app.

[1177] Step 11: Sending progress data

[1178] Device: The smartphone collects exercise progress data and sends it to the server.

[1179] Server: Receives progress data and stores it in a database.

[1180] Step 12: Ongoing data analysis and feedback

[1181] Server: Reassess your health and exercise plan based on your latest progress data.

[1182] Server: Adjusts the exercise plan as needed and uses the emotion engine to provide feedback that matches the user's emotional state.

[1183] Through this process, the system automatically collects health and emotional data from users and provides optimal exercise plans based on that information, effectively managing users' health and maintaining their motivation.

[1184] Example 2

[1185] 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."

[1186] While conventional health management systems can collect and analyze users' health data, they have difficulty adjusting exercise plans that take into account the user's emotional state or providing feedback to improve motivation. This makes it difficult for users to continue with an exercise plan over the long term, and has led to the problem of ineffective health management.

[1187] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting user health data, means for storing the collected health data in a database, means for analyzing the stored health data to evaluate the user's health condition, means for generating a personalized exercise plan based on the user's health condition, means for providing the generated exercise plan to the user, means for collecting user emotion data, means for analyzing the collected emotion data to evaluate the user's emotional condition, and means for adjusting the exercise plan based on the user's health condition and emotional condition and generating a message to increase motivation. This makes it possible to comprehensively manage the user's health condition and emotional state and provide a more effective and sustainable exercise plan.

[1188] "User" refers to an individual who uses the system, primarily a provider of health data and emotional data.

[1189] "Health Data" refers to information about a user's physical condition, including heart rate, number of steps taken, and other biometric data.

[1190] "Database" refers to a structured data repository for storing, managing, and searching collected health data and analysis results.

[1191] "Analysis" refers to the general process of processing collected data and extracting useful information for a specific purpose.

[1192] "Health status" refers to the results of an assessment of the user's physical condition and tendencies based on analyzed data.

[1193] "Exercise plan" refers to a customized exercise plan based on the user's health status and individual conditions.

[1194] "Emotional data" refers to information that indicates a user's psychological state, obtained from the user's voice, facial expressions, various actions, or other means.

[1195] "Emotional state" refers to the result of evaluating the user's psychological state based on the analyzed emotional data.

[1196] "Emotion engine" refers to the general term for algorithms, models, and software that process collected emotional data and analyze the user's emotional state.

[1197] A "motivational message" refers to a message that is created taking into consideration the user's emotional state and includes words of encouragement to increase the user's motivation to exercise.

[1198] The present invention provides a system that adjusts exercise plans and improves motivation based on the user's emotions by combining an emotion engine with a system that collects and analyzes a user's health data and provides individually customized exercise plans. The main components of this system include a server, a terminal, a user, and an emotion engine.

[1199] Data Collection Module

[1200] Users wear a wearable device while going about their daily lives, which records biometric data such as heart rate and number of steps taken.

[1201] The terminal (smartphone) receives heart rate and step count data from the wearable device and sends it to the server. Specifically, the data is transferred using Bluetooth or Wi-Fi.

[1202] The server receives the data sent from the smartphone and stores it in a database.

[1203] Data Analysis Module

[1204] The server retrieves user data from a database, cleans it using Python libraries such as Pandas and NumPy, and applies analytical algorithms (e.g., machine learning models) to assess health status.

[1205] Movement plan generation module

[1206] The server generates a customized exercise plan based on the user's health assessment results, taking into account the user's age, gender, past exercise history, and current physical condition. Here, libraries such as scikit-learn are used to create an appropriate exercise plan.

[1207] Emotion Engine

[1208] The device uses a camera and microphone to collect voice and facial expression data while the user is using the smartphone app, and this data is sent to the emotion engine in real time.

[1209] The server analyzes the user's emotional data using an emotion engine. Specifically, it uses a deep learning model with TensorFlow to identify the user's emotional state (e.g., stress, fatigue, energy).

[1210] Exercise plan adjustment and message generation module

[1211] The server adjusts the exercise plan based on the results of the emotion analysis and generates motivational messages, such as "Try exercising at a slower pace today" if the user feels fatigued.

[1212] Feedback provision module

[1213] The server formats the generated exercise plan and cheering message and sends them to the smartphone in JSON format, so that they can be displayed in the application.

[1214] The terminal displays the received information in the application, and the user confirms it.

[1215] Specific examples

[1216] Let us take the example of a user who walks every day and wears a wearable device to measure his / her heart rate and number of steps.

[1217] 1. Data collection: The wearable device records data while you go for a morning walk.

[1218] 2. Data transmission: After the walk is completed, the smartphone receives the data from the device and transmits it to the server.

[1219] 3. Database storage and analysis: The server stores the received data in a database and analyzes it. It uses Python's Pandas and NumPy to evaluate trends in the data.

[1220] 4. Exercise plan generation: The server generates an optimal exercise plan for the user based on the evaluation results.

[1221] 5. Emotion data collection and analysis: When a user uses a smartphone app, emotional data is collected using the camera and microphone, and the emotion engine analyzes it.

[1222] 6. Providing feedback: Feedback from the server (e.g., an adjusted exercise plan and encouraging messages) is displayed on the smartphone for the user to review.

[1223] Prompt Sentence Examples

[1224] "Generate personalized exercise plans and motivational messages based on the user's health and emotional data. The user's current emotional state is fatigued."

[1225] In this way, the system can comprehensively manage the user's health and emotional state and provide an individually customized exercise plan, enabling effective and sustainable health management.

[1226] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1227] Step 1:

[1228] Data collection

[1229] Users wear the wearable device while going about their daily activities, and the device records biometric data such as heart rate and number of steps taken.

[1230] Input: User biometric data (heart rate, steps)

[1231] Output: Recorded biometric data

[1232] What it does: The wearable device collects data in real time and stores it in its internal memory.

[1233] Step 2:

[1234] Data transmission

[1235] The terminal (smartphone) receives data from the wearable device via Bluetooth or Wi-Fi and sends the data to a server.

[1236] Input: Biometric data stored on a wearable device

[1237] Output: Biometric data sent to the server

[1238] How it works: The smartphone periodically syncs with the wearable device and sends the received data to a server via the Internet.

[1239] Step 3:

[1240] Database storage

[1241] The server stores the received biometric data in a database, standardizes the data format, and performs data cleansing to remove unnecessary information.

[1242] Input: Biometric data sent from smartphone

[1243] Output: Cleansed data stored in a database

[1244] What it does: Uses database management software to store incoming data in the appropriate tables.

[1245] Step 4:

[1246] Data analysis

[1247] The server retrieves the user's health data from the database, analyzes it using Python's Pandas and NumPy, and applies machine learning algorithms to assess their health status.

[1248] Input: Health data stored in a database

[1249] Output: Health status assessment results (e.g. average steps, heart rate trend)

[1250] What it does: A Python script reads the data, preprocesses it (by filling in missing values ​​and removing outliers), and then applies an analysis algorithm to assess health status.

[1251] Step 5:

[1252] Exercise plan generation

[1253] The server generates an individually customized exercise plan based on the health assessment results, the user's age, gender, past exercise history, and current physical condition. Libraries such as scikit-learn are used here.

[1254] Input: Health assessment results, user's age, gender, past exercise history, current physical condition

[1255] Output: Personalized exercise plan

[1256] Specific operation: Analyze the data and apply an algorithm to generate an exercise plan that is optimal for the user's attributes.

[1257] Step 6:

[1258] Emotional Data Collection

[1259] The device uses the smartphone's camera and microphone to collect the user's voice and facial expression data, which is then sent to a server in real time.

[1260] Input: User voice and facial expression data

[1261] Output: Emotion data sent to the server

[1262] How it works: Your smartphone uses its camera and microphone to perform facial and voice recognition and collect emotional data.

[1263] Step 7:

[1264] Emotion analysis

[1265] The server analyzes the emotional data using a deep learning model with TensorFlow to identify the user's emotional state.

[1266] Input: Collected emotion data

[1267] Output: Emotional state assessment result

[1268] What it does: Uses a deep learning model to classify the user's emotions (e.g., stress, fatigue, energy) from collected data.

[1269] Step 8:

[1270] Exercise plan adjustment and message generation

[1271] The server adjusts the exercise plan based on the results of the emotion analysis and generates motivational messages, such as "Try exercising at a slower pace today" if the user feels fatigued.

[1272] Input: Health status assessment results, emotional status assessment results

[1273] Output: A tailored exercise plan and encouraging messages

[1274] Specific operations: Comprehensively assess the user's health and emotional state, tailor an optimal exercise plan, and create encouraging messages using a text generation algorithm.

[1275] Step 9:

[1276] Providing Feedback

[1277] The server formats the generated exercise plan and support message in JSON format and sends it to the smartphone.

[1278] Input: Tailored exercise plans and encouraging messages

[1279] Output: Information sent to your smartphone

[1280] Specific operation: The generated data is packaged in an appropriate format and sent to the smartphone over the network.

[1281] Step 10:

[1282] User Verification

[1283] The device displays the exercise plan and encouraging messages received from the server within the app.

[1284] Input: Information sent from the server

[1285] Output: Exercise plan and encouragement message displayed to the user

[1286] What it does: The smartphone app analyzes the data and displays it on the screen for the user to easily view.

[1287] In this way, by specifically explaining the processing flow of the entire system, it is possible to comprehensively manage the user's health and emotional state, and to achieve effective health management.

[1288] (Application example 2)

[1289] 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."

[1290] Conventional health management systems simply collect users' biometric data and provide exercise plans based on that data. This means that a uniform exercise plan is provided without taking into account the user's emotional state, making it difficult to maintain motivation and failing to adequately address individual needs. Furthermore, there is a lack of real-time feedback when it comes to detecting abnormal values.

[1291] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting biometric data of the user, means for storing the collected biometric data in a storage device, means for analyzing the stored biometric data to evaluate the user's health condition, means for analyzing the user's voice and facial expressions to evaluate the user's emotional state, means for adjusting an exercise plan based on the emotional state, means for providing the generated exercise plan to the user, means for collecting data from the wearable device, means for detecting abnormal values ​​in the collected data, means for sending an alert to the user when an abnormal value is detected, and means for providing the user with an exercise plan and feedback in real time via the smart glasses. This makes it possible to provide an individual exercise plan that takes into account both the user's health condition and emotional state, thereby maintaining motivation and providing appropriate feedback.

[1292] "User's biological data" refers to measurement data related to the user's physical condition, such as heart rate, number of steps, amount of exercise, and body temperature.

[1293] A "storage device" is a hardware or software component for storing data.

[1294] "Analysis" is the process of evaluating and calculating collected data to derive specific conclusions or insights.

[1295] "User's voice and facial expression" is data for determining the user's emotional state based on voice and facial expression.

[1296] "Emotional state" is information that indicates the user's current psychological state (e.g., happy, sad, stressed, etc.).

[1297] An "exercise plan" is a recommended exercise plan based on the user's health and emotional state.

[1298] A "wearable device" is an electronic device that can be worn on the user's body and has the ability to collect and communicate biometric data.

[1299] An "abnormal value" is data that indicates a value or condition that is outside the normal range and suggests a possible health problem.

[1300] "Warning" is the process of notifying the user of important or urgent information.

[1301] "Smart glasses" are devices that, in addition to the functions of regular glasses, can also function as electronic devices, displaying information and collecting data.

[1302] "Feedback" refers to information or responses provided by a system to a user, which provide guidance for improving the user's behavior or state.

[1303] This invention relates to a system that collects and analyzes a user's biometric and emotional data to provide a customized exercise plan. By combining it with an emotion engine, it is possible to adjust the exercise plan based on the user's emotions and improve motivation.

[1304] System Configuration

[1305] The system of the present invention comprises the following components:

[1306] 1. Data Collection Module

[1307] User: The user wears a wearable device equipped with a heart rate sensor and a pedometer while going about their daily life.

[1308] Device: This includes smart glasses and smartphones. It has the function of receiving biometric data collected from wearable devices and sending it to a server.

[1309] Server: Receives data sent from smart glasses or smartphones and stores it in a storage device.

[1310] 2. Data Analysis Module

[1311] Server: Cleanses stored biometric data and applies analytical algorithms to assess health status.

[1312] 3. Movement plan generation module

[1313] Server: Generates an individual exercise plan based on the health assessment results, the user's age, gender, and past exercise history.

[1314] 4. Emotion Engine

[1315] Device: Analyzes the user's voice and facial expressions and sends them to the emotion engine, which evaluates the user's emotional state and sends the results to the server.

[1316] Server: Analyzes data from the emotion engine and adjusts motor plans based on emotional state.

[1317] 5. Feedback provision module

[1318] Server: Formats the generated exercise plan, evaluation results, and motivational messages and sends them to the smart glasses or smartphone.

[1319] Terminal: Smart glasses or smartphone displays the information received from the server, and the user can view it.

[1320] Specific examples

[1321] For example, when a user goes for a morning run, the smart glasses record their heart rate and number of steps in real time. Furthermore, they analyze the user's fatigue and stress level based on their facial expression and complexion. If the user's heart rate is high and their facial expression shows signs of fatigue, the server will generate a message such as "Try to slow down a bit today," and display it on the smart glasses. In this way, users can simultaneously manage their health and improve their motivation.

[1322] Example prompt sentence:

[1323] "Generate a new customized exercise plan based on the latest health data (heart rate: 75, steps: 12000) of user ID 12345, taking into account emotion data (emotion detected from voice: stress, emotion detected from facial expression: fatigue)."

[1324] The present invention allows users to comprehensively manage their health and emotional state, enabling them to implement more effective and sustainable health and exercise plans.

[1325] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1326] Step 1:

[1327] Users wear wearable devices (e.g., smartwatches and fitness trackers) while going about their daily lives. The wearable devices collect biometric data, such as heart rate and step count, in real time. The collected biometric data is then transmitted to smart glasses or a smartphone.

[1328] Input: User's biometric data

[1329] Output: Data transfer to smart glasses or smartphone

[1330] Step 2:

[1331] The terminal (smart glasses or smartphone) transmits the biometric data received from the wearable terminal to the server, which then stores the received biometric data in a storage device.

[1332] Input: Biometric data received from a wearable device

[1333] Output: Data stored in the server's storage device

[1334] Step 3:

[1335] The server cleanses the biometric data stored in the storage device and applies an analysis algorithm to evaluate the user's health status, generating a health status assessment report.

[1336] Input: Biometric data stored in a storage device

[1337] Output: Health status assessment report

[1338] Step 4:

[1339] Users collect voice and facial expression data through smart glasses or smartphones, which are then sent to the emotion engine to analyze their emotional state.

[1340] Input: User's voice and facial expression data

[1341] Output: Evaluation result of emotional state by emotion engine

[1342] Step 5:

[1343] The server receives the emotional state assessment results from the emotion engine and generates a personalized exercise plan based on the health assessment results, as well as the user's age, gender, and past exercise history. The generated exercise plan also includes a message to increase motivation.

[1344] Input: Health status assessment results, emotional status assessment results, user information

[1345] Output: personalized exercise plan and encouraging messages

[1346] Step 6:

[1347] The server then sends the generated exercise plan and encouraging messages to the smart glasses or smartphone, where the user can check the information and follow the exercise plan.

[1348] Input: personalized exercise plan and encouraging message

[1349] Output: Exercise plan and messages displayed on smart glasses or smartphone

[1350] Step 7:

[1351] The user actually follows the exercise plan and engages in activities such as running or walking. During the activity, the wearable device collects and transmits biometric data to the device.

[1352] Input: User activity based on exercise plan

[1353] Output: New biometric data

[1354] Step 8:

[1355] The device sends newly collected biometric data to a server, which analyzes the effectiveness of the exercise plan based on the data and adjusts the plan as needed.

[1356] Input: Newly collected biometric data

[1357] Output: Updated exercise plan and encouragement message

[1358] The above is a specific processing flow of the system according to the present invention.

[1359] 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.

[1360] 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.

[1361] 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.

[1362] [Fourth embodiment]

[1363] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1364] 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.

[1365] 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).

[1366] 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.

[1367] 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.

[1368] 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).

[1369] 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.

[1370] 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.

[1371] 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.

[1372] 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.

[1373] 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.

[1374] 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.

[1375] 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."

[1376] This system collects and analyzes a user's health data and provides an individually customized exercise plan. This system consists of a server, a terminal, and a user module.

[1377] System Configuration

[1378] 1. Data Collection Module

[1379] User: Wears the wearable device and performs daily activities.

[1380] Terminal: The data collected from the wearable device is transferred to the smartphone, which then transmits the data to the server.

[1381] Server: Receives data sent from the smartphone and stores it in a database.

[1382] Data analysis

[1383] 2. Data Analysis Module

[1384] Server: Reads the user's health data from the database, organizes it by time series and category, and applies analysis algorithms to it.

[1385] Server: Evaluates the user's health status. For example, it analyzes a user's heart rate and step count data to assess the health risks associated with daily activities.

[1386] Exercise plan generation

[1387] 3. Movement plan generation module

[1388] Server: Based on the user's health assessment results, the server generates an individually customized exercise plan, taking into account parameters such as age, gender, past exercise history, and current physical condition.

[1389] Server: Determines the optimal exercise plan for the user, incorporating exercise guidelines provided by experts as needed.

[1390] Providing Feedback

[1391] 4. Feedback provision module

[1392] Server: Formats the generated exercise plan and evaluation results and sends them to a smartphone.

[1393] Device: The smartphone displays the exercise plan and evaluation results received from the server. The user can view this information through the smartphone app.

[1394] User: Follows the provided exercise plan and actually exercises. Records exercise progress and results on a smartphone.

[1395] Specific examples

[1396] For example, suppose a user wears a wearable watch and walks daily. The watch records the number of steps and heart rate taken each day and transfers the data to a smartphone. The smartphone then sends this data to a server, where it is stored in a database. The server analyzes the data from the past month and evaluates the user's health status, such as trends in average steps and heart rate.

[1397] Based on the analysis results, the server generates an exercise plan that includes "walking for 30 minutes three times a week." This exercise plan is customized taking into account the user's age and past exercise history. The generated exercise plan is sent to the user's smartphone and displayed as a notification.

[1398] The user then follows this plan and records their progress on their smartphone. The recorded data is then sent back to the server, where it is stored in a database. The server then analyzes this new data and adjusts the plan as needed.

[1399] As described above, by using this system, users can constantly monitor their own health status and implement individually optimized exercise plans, thereby effectively maintaining and improving their health.

[1400] The processing flow will be explained below.

[1401] Step 1: Data collection

[1402] User: Wears the wearable device and goes about their daily life.

[1403] Device: Transfers heart rate, step count, and other biometric data collected from the wearable device to a smartphone.

[1404] Terminal: A smartphone receives data from the wearable device via a dedicated app.

[1405] Step 2: Send data

[1406] Terminal: The smartphone formats the data it receives and sends it to a server via the Internet.

[1407] Server: Receives data sent from the smartphone.

[1408] Step 3: Save data

[1409] Server: Check the format of the received data and check for any errors.

[1410] Server: Stores data in a database, including heart rate, steps, sleep data, etc.

[1411] Server: Log the successful data save.

[1412] Step 4: Data analysis

[1413] Server: Reads the target user's past health data from the database.

[1414] Server: Cleanses the data and handles missing and outlier values.

[1415] Server: Performs time series analysis and other statistical processing on the data to assess the user's current health status.

[1416] Step 5: Generate exercise plan

[1417] Server: Based on the health assessment results, an exercise plan is generated that takes into account the user's age, gender, past exercise history, and current physical condition.

[1418] Server: Incorporates expert guidelines into the generated exercise plan and optimizes it.

[1419] Step 6: Provide feedback

[1420] Server: Formats the generated exercise plan and evaluation results for the user interface.

[1421] Server: Sends the formatted data to the smartphone.

[1422] Device: Smartphone displays exercise plan and evaluation results within the application.

[1423] User: Uses the application to view exercise plans.

[1424] Step 7: Exercise

[1425] User: Actually exercises based on the exercise plan provided.

[1426] User: Records exercise progress using a smartphone app.

[1427] Step 8: Sending progress data

[1428] Device: The smartphone collects exercise progress data and sends it to the server.

[1429] Server: Receives progress data and stores it in a database.

[1430] Step 9: Ongoing data analysis and feedback

[1431] Server: Reassess your health and exercise plan based on your latest progress data.

[1432] Server: Adjusts exercise plan as needed and provides feedback.

[1433] Through this process, users can continuously collect health data and receive personalized exercise plans, which the system automatically supports, effectively managing their health.

[1434] Example 1

[1435] 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."

[1436] Conventional health management systems only collect users' health data, but do not adequately analyze that data in detail or provide individual exercise plans. This makes it difficult to provide users with an optimized exercise plan and effectively improve their health. Furthermore, they lack the ability to continuously adjust exercise plans based on new data, making them unable to flexibly respond to changing health conditions.

[1437] 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.

[1438] In this invention, the server

[1439] A means to organize health data stored in a database by time series and category and apply analytical algorithms;

[1440] means for assessing the health status of the user based on the analyzed data;

[1441] means for generating a personalized exercise plan based on the user's health status assessment results;

[1442] a means of tailoring an exercise plan based on expert exercise guidelines;

[1443] means for providing the generated exercise plan to a user's terminal;

[1444] It also includes a means for continuously adjusting the exercise plan based on new data, which allows the user to receive the optimal exercise plan tailored to their health condition and effectively manage their health.

[1445] "User" refers to an individual who utilizes the System to manage their own health data and receive a personalized exercise plan.

[1446] "Health data" refers to various data that indicate the user's health status, such as heart rate, number of steps, and calories burned.

[1447] "Database" refers to a data storage system that manages user health data stored on a server.

[1448] "Analysis Algorithm" refers to a mathematical or statistical method for analyzing health data stored in the database and assessing the user's health status.

[1449] "Health status assessment" refers to the process of using analytical algorithms to review a user's health data and determine the user's health level and risk.

[1450] The term "exercise plan" refers to an exercise schedule and exercise content for an individual user that is generated based on the results of the user's health status evaluation.

[1451] "Exercise guidelines" refer to guiding principles and standards for exercise established by experts.

[1452] "Terminal" refers to a device used by the user, such as a smartphone or tablet, through which the system provides an exercise plan.

[1453] "New data" refers to newly collected health data by the user.

[1454] This system collects and analyzes a user's health data and provides an individually customized exercise plan. This system consists of a server, a terminal, and a user module.

[1455] Data collection

[1456] 1. The user wears the wearable device and performs daily activities, for example, jogging or walking while wearing the wearable watch.

[1457] 2. The terminal (smartphone) receives data such as steps and heart rate from the wearable device and sends it to the server via Bluetooth and HTTPS protocol.

[1458] 3. The server stores the received data in a database, typically a relational database management system (RDBMS) such as MySQL.

[1459] Data analysis

[1460] 1. The server reads the user's health data from the database and organizes it by time series and category using SQL queries.

[1461] 2. The server processes the data using analytical algorithms, such as machine learning models and statistical functions, to analyze the data and assess the user's health status. The algorithms are written in programming languages ​​such as Python and R.

[1462] 3. The server evaluates the user's health status based on the analysis results, for example, assessing health risks based on trends in heart rate fluctuations and step counts.

[1463] Exercise plan generation

[1464] 1. The server generates a personalized exercise plan based on the health assessment results, taking into account the user's age, gender, past exercise history, current physical condition, etc.

[1465] 2. The server adjusts the generated exercise plan based on expert exercise guidelines, for example, incorporating exercise recommendations provided by a sports physician.

[1466] Providing Feedback

[1467] 1. The server formats the generated exercise plan and evaluation results and sends them to the smartphone in JSON format.

[1468] 2. The device (smartphone) analyzes the data received from the server and displays it to the user using a dedicated application.

[1469] 3. The user exercises based on the displayed exercise plan and records their progress and results on their smartphone.

[1470] 4. The device again sends the newly recorded data to the server, where it is analyzed again and the exercise plan is continuously adjusted.

[1471] Specific examples

[1472] For example, a user wears a wearable watch and jogs daily. The watch records heart rate, distance traveled, and calorie consumption. The recorded data is transferred to a smartphone via Bluetooth, and the smartphone sends the data to a server. The server stores the data in a database and evaluates the user's health status using a Python script.

[1473] Based on the evaluation results, the server generates an exercise plan that includes jogging for 30 minutes three times a week. This plan is customized taking into account expert exercise guidelines and is sent to the smartphone. The user jogs according to this exercise plan and records their progress on the smartphone. New data is sent back to the server, and the exercise plan is continuously adjusted.

[1474] Prompt Sentence Examples

[1475] "Design a system that generates and provides a personalized exercise plan based on data collected from a user's wearable device. Include details of each step: data collection, analysis, exercise plan generation, and feedback provision."

[1476] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1477] Step 1:

[1478] A user wears a wearable device and performs daily activities. The input includes the user's exercise data (e.g., number of steps, heart rate). Specifically, the wearable device periodically records health data such as heart rate and number of steps. The output is the collected raw data.

[1479] Step 2:

[1480] The terminal (smartphone) receives exercise data from the wearable device using Bluetooth. The input is the raw data sent from the wearable device. Specifically, the smartphone app establishes a Bluetooth connection and acquires data such as heart rate and step count. The output is the exercise data stored on the smartphone.

[1481] Step 3:

[1482] The device sends exercise data to the server using the HTTPS protocol. The input is the exercise data stored on the smartphone. Specifically, the smartphone app generates an HTTPS request, encrypts the data, and sends it to the server. The output is the exercise data sent to the server.

[1483] Step 4:

[1484] The server stores the received exercise data in a database. The input is the exercise data sent from the smartphone. Specifically, an INSERT query is executed on the database (e.g., MySQL). The output is the exercise data stored in the database.

[1485] Step 5:

[1486] The server reads the motion data from the database and applies an analysis algorithm. The input is the motion data stored in the database. Specifically, a Python script reads the data from the database and performs the analysis. The analysis algorithm uses time series analysis and machine learning models. The output is the analysis results.

[1487] Step 6:

[1488] The server evaluates the user's health status based on the analysis results. The input is the analysis results. Specifically, health risk assessment is performed using statistical models or machine learning models. For example, heart rate trends are evaluated to determine the risk of arrhythmia. The output is the health status assessment result.

[1489] Step 7:

[1490] The server generates a personalized exercise plan based on the health assessment results. The input is the health assessment results. Specifically, an algorithm is used that takes into account age, gender, and past exercise history. The output is a customized exercise plan.

[1491] Step 8:

[1492] The server adjusts the exercise plan based on the expert exercise guidelines. The inputs are the generated exercise plan and the exercise guidelines. Specifically, the server applies the rules of the exercise guidelines to optimize the plan. The output is the optimized exercise plan.

[1493] Step 9:

[1494] The server sends the optimized exercise plan and the evaluation results to the device. The inputs are the optimized exercise plan and the evaluation results. Specifically, this data is formatted in JSON format and sent via the HTTPS protocol. The output is the exercise plan sent to the device and the evaluation results.

[1495] Step 10:

[1496] The device (smartphone) analyzes the data received from the server and displays it on the user interface. The inputs are the exercise plan and evaluation results received from the server. Specifically, the application analyzes the JSON data and displays it in an easy-to-read format. The output is the exercise plan and evaluation results provided to the user.

[1497] Step 11:

[1498] The user exercises based on the displayed exercise plan and records their progress and results on their smartphone. The input is the exercise plan, and the user performs the exercise as a specific action and enters the exercise results (e.g., exercise time, distance traveled) into the application. The output is data on progress and results.

[1499] Step 12:

[1500] The device sends the newly recorded data to the server. The input is the progress and achievement data recorded by the user. Specifically, the application sends this data to the server using the HTTPS protocol. The output is the new data sent to the server.

[1501] Step 13:

[1502] The server stores newly received data in a database and continuously adjusts the exercise plan. The input is newly received progress and performance data. Specifically, INSERT queries are performed on the database, and then the analysis algorithm is reapplied to update the exercise plan. The output is the adjusted exercise plan.

[1503] (Application example 1)

[1504] 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."

[1505] Conventional user health management systems lack individuality in the collection and analysis of health data and the provision of exercise plans, and are not available for direct use by users in physical stores. This makes it difficult for users to receive practical advice tailored to their own health status. Furthermore, there is a demand for real-time advice and an interactive experience in stores.

[1506] 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.

[1507] In this invention, the server includes: means for collecting user health data; means for storing the collected health data in a database; means for analyzing the stored health data to evaluate the user's health condition; means for generating a personalized exercise plan based on the user's health condition; means for providing the generated exercise plan to the user; means for a robot in a physical store to collect health data from the user's wearable device; means for providing health advice and an exercise plan in real time based on the collected health data; and means for displaying the exercise plan generated by the robot. This allows users to receive individually customized exercise plans and health advice in real time in the physical store based on the health data collected from their wearable device. By having the robot present this information, interaction with the user is improved, and specific action guidelines for improving their health can be obtained immediately in the store.

[1508] "Health data" is numerical information that indicates the user's physical condition, such as the user's heart rate, number of steps, and calorie consumption.

[1509] A "database" is a collection of information that stores collected health data in an organized manner and can be accessed and analyzed as needed.

[1510] "Analysis" is the process of statistically processing collected health data to evaluate the user's health status and trends.

[1511] "Exercise Plan" means a personalized exercise or workout plan to improve or maintain a User's health and wellness.

[1512] The "robot" is an automated device installed in a brick-and-mortar store that collects health data from users and provides advice and exercise plans based on that data.

[1513] A "wearable device" is a device that can be worn and used by users on a daily basis to collect health data.

[1514] "Real-time" refers to the fact that the process from data collection to analysis and feedback provision is immediate.

[1515] "Customization" refers to optimizing an exercise plan based on a user's individual health and lifestyle.

[1516] An "abnormal value" is a health data value that is outside the normal range and may indicate a health risk to the user.

[1517] A "warning" is a notification that warns the user or provides guidelines for action when an abnormal value or risk is detected.

[1518] The present invention is a system that collects and analyzes a user's health data and provides an individually customized exercise plan, and includes the following specific configuration and operation method.

[1519] System Configuration

[1520] 1. Data Collection Module

[1521] User: Wears a wearable device while performing daily activities. The wearable device collects health data such as heart rate and number of steps taken.

[1522] Terminal: Health data collected from the wearable device is transferred to a smartphone, which then transmits the data to a server.

[1523] Server: Receives data sent from the smartphone and stores it in a database.

[1524] Data Analysis Module

[1525] 2. Data Analysis Module

[1526] Server: Reads the user's health data from the database. The read data is organized by time series and category, and statistical analysis algorithms are applied to it. The results of the analysis are used to evaluate the user's health status. For example, the average heart rate or number of steps over a certain period of time is calculated.

[1527] Server: If abnormal values ​​are detected in the health data, it also has the function of sending a warning to the user based on that data.

[1528] Movement plan generation module

[1529] 3. Movement plan generation module

[1530] Server: Based on the user's health assessment results, the server generates an individually customized exercise plan taking into account parameters such as age, gender, past exercise history, and current physical condition. If necessary, it incorporates exercise guidelines provided by experts to determine the optimal exercise plan for the user.

[1531] Feedback provision module

[1532] 4. Feedback provision module

[1533] Server: Formats the generated exercise plan and evaluation results and sends them to a smartphone.

[1534] Device: The smartphone displays the exercise plan and evaluation results received from the server. The user can view this information through the smartphone app.

[1535] User: Follows the provided exercise plan and actually exercises. Records exercise progress and results on their smartphone. The recorded data is again sent to the server and stored in a database. The server then analyzes this new data and adjusts the exercise plan as necessary.

[1536] Examples from physical stores

[1537] A robot installed in a physical store connects with the wearable devices of customers who visit the store. When a customer enters the store, the robot collects health data from the wearable device and sends it to a server in real time. The server analyzes this data and generates an individually customized exercise plan. The robot displays the generated exercise plan and presents it to the customer.

[1538] Prompt Sentence Examples

[1539] For example, if a 40-year-old woman wears a wearable watch and comes into the store, the system will collect her heart rate and step count for that day. After analyzing the data, the robot will present her with an exercise plan, such as "We recommend 30 minutes of aerobic exercise three times a week."

[1540] Example prompts

[1541] I want to collect health data and generate a customized exercise plan.

[1542] Age: 40

[1543] Gender: Female

[1544] Normal steps: 6,000 steps / day

[1545] Recent heart rate: Average 105 bpm

[1546] Based on this data, what exercise plan would you suggest?

[1547] In this way, users can receive specific advice based on their own health data in real time within the physical store. This system allows users to obtain practical guidelines for maintaining and improving their health and put them into practice on the spot.

[1548] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1549] Step 1:

[1550] A user enters a store wearing a wearable device. The device collects health data such as heart rate and number of steps. The input is the user's real-time health data, and the output is the data recorded on the wearable device.

[1551] Step 2:

[1552] The terminal (smartphone) receives health data collected from the wearable device. Specifically, the data is transferred via wireless communication such as Bluetooth. The input is data from the wearable device, and the output is health data stored in the terminal's memory.

[1553] Step 3:

[1554] The health data received by the device is sent to a server. Specifically, the data is sent via the Internet and stored in the server's database. The input is the health data stored on the device, and the output is the data stored on the server.

[1555] Step 4:

[1556] The server analyzes the collected health data. Specifically, it organizes the data chronologically and applies statistical analysis algorithms to evaluate the user's health status. For example, it calculates trends in average daily heart rate and number of steps. The input is the health data stored on the server, and the output is the health status evaluation results.

[1557] Step 5:

[1558] The server generates an individually customized exercise plan based on the analysis results, taking into account parameters such as the user's age, gender, and past exercise history. The input is the health assessment results and the user's individual information, and the output is a customized exercise plan.

[1559] Step 6:

[1560] The server sends the generated exercise plan to the device. Specifically, the exercise plan is sent to the smartphone via a communication line. The input is the exercise plan generated by the server, and the output is the exercise plan received by the device.

[1561] Step 7:

[1562] The terminal transfers the received exercise plan to the robot in the physical store. Specifically, data is sent to the robot via a local network. The input is the exercise plan stored on the terminal, and the output is the exercise plan received by the robot.

[1563] Step 8:

[1564] The robot displays the received exercise plan to the user in the physical store. Specifically, the robot presents the exercise plan on a screen or using voice synthesis. The input is the exercise plan transferred to the robot, and the output is the exercise plan presented to the user.

[1565] Step 9:

[1566] The user exercises according to the robot's instructions. Specifically, the user follows the exercise plan presented to them and records their exercise progress and results on their smartphone. The input is the user's exercise performance data, and the output is the exercise data recorded on the smartphone.

[1567] 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.

[1568] The present invention combines an emotion engine with a system that collects and analyzes a user's health data and provides an individually customized exercise plan, thereby adjusting the exercise plan based on the user's emotions and improving motivation. The main components of this system include a server, a terminal, a user, and an emotion engine.

[1569] System Configuration

[1570] 1. Data Collection Module

[1571] User: Wears the wearable device and goes about daily life.

[1572] Terminal: The wearable device transmits heart rate, step count, and other biometric data to a smartphone, which then transmits the data to a server.

[1573] Server: Receives data sent from the smartphone and stores it in a database.

[1574] 2. Data Analysis Module

[1575] Server: Reads user health data from the database, cleans the data, and applies analytical algorithms to evaluate the user's health status.

[1576] 3. Movement plan generation module

[1577] Server: Generates an individually customized exercise plan based on the user's health assessment results, age, gender, past exercise history, and current physical condition.

[1578] 4. Emotion Engine

[1579] Device: Collects user voice and facial expression data from smartphones and other devices and sends it to the emotion engine.

[1580] Server: Analyzes the user's emotions using an emotion engine and generates messages to adjust exercise plans and increase motivation based on the user's emotional state.

[1581] 5. Feedback provision module

[1582] Server: Formats the generated exercise plan, evaluation results, and support messages and sends them to the smartphone.

[1583] Device: The smartphone displays the information received from the server within the application. The user can then view this information through the application.

[1584] Specific examples

[1585] For example, suppose a user wears a wearable device and walks daily. The wearable device records the number of steps and heart rate each day and transfers the data to a smartphone. The smartphone then sends this data to a server, where it is stored in a database. The server analyzes the data from the past month and evaluates the user's health status (e.g., average number of steps, heart rate trends).

[1586] Furthermore, when a user checks their latest exercise plan on their smartphone, the emotion engine analyzes their facial expressions and tone of voice to identify their current emotional state (e.g., energetic, tired, stressed). Based on the analysis results, the server adjusts the exercise plan, including "walking for 30 minutes three times a week," and generates a supportive message tailored to the user's emotions (e.g., "You may not feel like it today, but even a little exercise can have a big effect.").

[1587] The user then follows this exercise plan and records their progress using a smartphone app. The recorded data is then sent back to the server, where it is stored in a database. The server then analyzes this new data, adjusts the exercise plan as needed, and uses an emotion engine to provide feedback tailored to the user's emotional state.

[1588] This system allows users to comprehensively manage their health and emotional state, leading to more effective and sustainable health and exercise plans.

[1589] The processing flow will be explained below.

[1590] Step 1: Health data collection

[1591] User: Wears the wearable device and engages in daily activities, such as walking or jogging.

[1592] Device: A wearable device records the user's heart rate, steps, and other biometric data.

[1593] Terminal: Data collected from the wearable device is transferred to a smartphone via Bluetooth, etc. A dedicated smartphone app receives this data.

[1594] Step 2: Send data

[1595] Terminal: The smartphone formats the data it receives and sends it to a server via the Internet.

[1596] Server: Receives the data sent from the smartphone and starts the storage process.

[1597] Step 3: Save data

[1598] Server: Checks the format of the received data and checks for inconsistencies.

[1599] Server: Stores the verified data in a database, including heart rate, steps, sleep data, etc.

[1600] Server: Log that the data has been saved.

[1601] Step 4: Data analysis

[1602] Server: Reads the target user's past health data from the database.

[1603] Server: Cleanses the loaded data and handles missing and outlier values.

[1604] Server: Performs time series analysis and other statistical processing on the data to assess the user's health status.

[1605] Step 5: Generate exercise plan

[1606] Server: Based on the health assessment results, an exercise plan is generated that takes into account the user's age, gender, past exercise history, and current physical condition.

[1607] Server: Incorporates expert guidelines into the generated exercise plan and optimizes it.

[1608] Step 6: Collect emotional data

[1609] User: Sits in front of a smartphone or PC and opens an application.

[1610] Device: A smartphone or PC captures the user's face with a camera and records their voice with a microphone.

[1611] Device: The emotion engine performs image and audio analysis to recognize the user's emotions.

[1612] Step 7: Processing the emotion data

[1613] Server: Receives the user's emotion data (e.g., joy, stress, fatigue) analyzed by the emotion engine.

[1614] Server: Further adjust the exercise plan based on emotional data, for example adding relaxation exercises if the user is feeling stressed.

[1615] Step 8: Feedback Generation

[1616] Server: Formats the generated exercise plan, evaluation results, and cheering messages.

[1617] Server: Sends the formatted data to the smartphone.

[1618] Step 9: Feedback display

[1619] Device: The smartphone displays the information received from the server within the application.

[1620] Users: View exercise plans, health assessments, and encouraging messages through the app.

[1621] Step 10: Exercise

[1622] User: The user actually exercises based on the provided exercise plan. The user performs the recommended exercises, such as walking or yoga.

[1623] User: Records exercise progress using a smartphone app.

[1624] Step 11: Sending progress data

[1625] Device: The smartphone collects exercise progress data and sends it to the server.

[1626] Server: Receives progress data and stores it in a database.

[1627] Step 12: Ongoing data analysis and feedback

[1628] Server: Reassess your health and exercise plan based on your latest progress data.

[1629] Server: Adjusts the exercise plan as needed and uses the emotion engine to provide feedback that matches the user's emotional state.

[1630] Through this process, the system automatically collects health and emotional data from users and provides optimal exercise plans based on that information, effectively managing users' health and maintaining their motivation.

[1631] Example 2

[1632] 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."

[1633] While conventional health management systems can collect and analyze users' health data, they have difficulty adjusting exercise plans that take into account the user's emotional state or providing feedback to improve motivation. This makes it difficult for users to continue with an exercise plan over the long term, and has led to the problem of ineffective health management.

[1634] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting user health data, means for storing the collected health data in a database, means for analyzing the stored health data to evaluate the user's health condition, means for generating a personalized exercise plan based on the user's health condition, means for providing the generated exercise plan to the user, means for collecting user emotion data, means for analyzing the collected emotion data to evaluate the user's emotional condition, and means for adjusting the exercise plan based on the user's health condition and emotional condition and generating a message to increase motivation. This makes it possible to comprehensively manage the user's health condition and emotional state and provide a more effective and sustainable exercise plan.

[1635] "User" refers to an individual who uses the system, primarily a provider of health data and emotional data.

[1636] "Health Data" refers to information about a user's physical condition, including heart rate, number of steps taken, and other biometric data.

[1637] "Database" refers to a structured data repository for storing, managing, and searching collected health data and analysis results.

[1638] "Analysis" refers to the general process of processing collected data and extracting useful information for a specific purpose.

[1639] "Health status" refers to the results of an assessment of the user's physical condition and tendencies based on analyzed data.

[1640] "Exercise plan" refers to a customized exercise plan based on the user's health status and individual conditions.

[1641] "Emotional data" refers to information that indicates a user's psychological state, obtained from the user's voice, facial expressions, various actions, or other means.

[1642] "Emotional state" refers to the result of evaluating the user's psychological state based on the analyzed emotional data.

[1643] "Emotion engine" refers to the general term for algorithms, models, and software that process collected emotional data and analyze the user's emotional state.

[1644] A "motivational message" refers to a message that is created taking into consideration the user's emotional state and includes words of encouragement to increase the user's motivation to exercise.

[1645] The present invention provides a system that adjusts exercise plans and improves motivation based on the user's emotions by combining an emotion engine with a system that collects and analyzes a user's health data and provides individually customized exercise plans. The main components of this system include a server, a terminal, a user, and an emotion engine.

[1646] Data Collection Module

[1647] Users wear a wearable device while going about their daily lives, which records biometric data such as heart rate and number of steps taken.

[1648] The terminal (smartphone) receives heart rate and step count data from the wearable device and sends it to the server. Specifically, the data is transferred using Bluetooth or Wi-Fi.

[1649] The server receives the data sent from the smartphone and stores it in a database.

[1650] Data Analysis Module

[1651] The server retrieves user data from a database, cleans it using Python libraries such as Pandas and NumPy, and applies analytical algorithms (e.g., machine learning models) to assess health status.

[1652] Movement plan generation module

[1653] The server generates a customized exercise plan based on the user's health assessment results, taking into account the user's age, gender, past exercise history, and current physical condition. Here, libraries such as scikit-learn are used to create an appropriate exercise plan.

[1654] Emotion Engine

[1655] The device uses a camera and microphone to collect voice and facial expression data while the user is using the smartphone app, and this data is sent to the emotion engine in real time.

[1656] The server analyzes the user's emotional data using an emotion engine. Specifically, it uses a deep learning model with TensorFlow to identify the user's emotional state (e.g., stress, fatigue, energy).

[1657] Exercise plan adjustment and message generation module

[1658] The server adjusts the exercise plan based on the results of the emotion analysis and generates motivational messages, such as "Try exercising at a slower pace today" if the user feels fatigued.

[1659] Feedback provision module

[1660] The server formats the generated exercise plan and cheering message and sends them to the smartphone in JSON format, so that they can be displayed in the application.

[1661] The terminal displays the received information in the application, and the user confirms it.

[1662] Specific examples

[1663] Let us take the example of a user who walks every day and wears a wearable device to measure his / her heart rate and number of steps.

[1664] 1. Data collection: The wearable device records data while you go for a morning walk.

[1665] 2. Data transmission: After the walk is completed, the smartphone receives the data from the device and transmits it to the server.

[1666] 3. Database storage and analysis: The server stores the received data in a database and analyzes it. It uses Python's Pandas and NumPy to evaluate trends in the data.

[1667] 4. Exercise plan generation: The server generates an optimal exercise plan for the user based on the evaluation results.

[1668] 5. Emotion data collection and analysis: When a user uses a smartphone app, emotional data is collected using the camera and microphone, and the emotion engine analyzes it.

[1669] 6. Providing feedback: Feedback from the server (e.g., an adjusted exercise plan and encouraging messages) is displayed on the smartphone for the user to review.

[1670] Prompt Sentence Examples

[1671] "Generate personalized exercise plans and motivational messages based on the user's health and emotional data. The user's current emotional state is fatigued."

[1672] In this way, the system can comprehensively manage the user's health and emotional state and provide an individually customized exercise plan, enabling effective and sustainable health management.

[1673] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1674] Step 1:

[1675] Data collection

[1676] Users wear the wearable device while going about their daily activities, and the device records biometric data such as heart rate and number of steps taken.

[1677] Input: User biometric data (heart rate, steps)

[1678] Output: Recorded biometric data

[1679] What it does: The wearable device collects data in real time and stores it in its internal memory.

[1680] Step 2:

[1681] Data transmission

[1682] The terminal (smartphone) receives data from the wearable device via Bluetooth or Wi-Fi and sends the data to a server.

[1683] Input: Biometric data stored on a wearable device

[1684] Output: Biometric data sent to the server

[1685] How it works: The smartphone periodically syncs with the wearable device and sends the received data to a server via the Internet.

[1686] Step 3:

[1687] Database storage

[1688] The server stores the received biometric data in a database, standardizes the data format, and performs data cleansing to remove unnecessary information.

[1689] Input: Biometric data sent from smartphone

[1690] Output: Cleansed data stored in a database

[1691] What it does: Uses database management software to store incoming data in the appropriate tables.

[1692] Step 4:

[1693] Data analysis

[1694] The server retrieves the user's health data from the database, analyzes it using Python's Pandas and NumPy, and applies machine learning algorithms to assess their health status.

[1695] Input: Health data stored in a database

[1696] Output: Health status assessment results (e.g. average steps, heart rate trend)

[1697] What it does: A Python script reads the data, preprocesses it (by filling in missing values ​​and removing outliers), and then applies an analysis algorithm to assess health status.

[1698] Step 5:

[1699] Exercise plan generation

[1700] The server generates an individually customized exercise plan based on the health assessment results, the user's age, gender, past exercise history, and current physical condition. Libraries such as scikit-learn are used here.

[1701] Input: Health assessment results, user's age, gender, past exercise history, current physical condition

[1702] Output: Personalized exercise plan

[1703] Specific operation: Analyze the data and apply an algorithm to generate an exercise plan that is optimal for the user's attributes.

[1704] Step 6:

[1705] Emotional Data Collection

[1706] The device uses the smartphone's camera and microphone to collect the user's voice and facial expression data, which is then sent to a server in real time.

[1707] Input: User voice and facial expression data

[1708] Output: Emotion data sent to the server

[1709] How it works: Your smartphone uses its camera and microphone to perform facial and voice recognition and collect emotional data.

[1710] Step 7:

[1711] Emotion analysis

[1712] The server analyzes the emotional data using a deep learning model with TensorFlow to identify the user's emotional state.

[1713] Input: Collected emotion data

[1714] Output: Emotional state assessment result

[1715] What it does: Uses a deep learning model to classify the user's emotions (e.g., stress, fatigue, energy) from collected data.

[1716] Step 8:

[1717] Exercise plan adjustment and message generation

[1718] The server adjusts the exercise plan based on the results of the emotion analysis and generates motivational messages, such as "Try exercising at a slower pace today" if the user feels fatigued.

[1719] Input: Health status assessment results, emotional status assessment results

[1720] Output: A tailored exercise plan and encouraging messages

[1721] Specific operations: Comprehensively assess the user's health and emotional state, tailor an optimal exercise plan, and create encouraging messages using a text generation algorithm.

[1722] Step 9:

[1723] Providing Feedback

[1724] The server formats the generated exercise plan and support message in JSON format and sends it to the smartphone.

[1725] Input: Tailored exercise plans and encouraging messages

[1726] Output: Information sent to your smartphone

[1727] Specific operation: The generated data is packaged in an appropriate format and sent to the smartphone over the network.

[1728] Step 10:

[1729] User Verification

[1730] The device displays the exercise plan and encouraging messages received from the server within the app.

[1731] Input: Information sent from the server

[1732] Output: Exercise plan and encouragement message displayed to the user

[1733] What it does: The smartphone app analyzes the data and displays it on the screen for the user to easily view.

[1734] In this way, by specifically explaining the processing flow of the entire system, it is possible to comprehensively manage the user's health and emotional state, and to achieve effective health management.

[1735] (Application example 2)

[1736] 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."

[1737] Conventional health management systems simply collect users' biometric data and provide exercise plans based on that data. This means that a uniform exercise plan is provided without taking into account the user's emotional state, making it difficult to maintain motivation and failing to adequately address individual needs. Furthermore, there is a lack of real-time feedback when it comes to detecting abnormal values.

[1738] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting biometric data of the user, means for storing the collected biometric data in a storage device, means for analyzing the stored biometric data to evaluate the user's health condition, means for analyzing the user's voice and facial expressions to evaluate the user's emotional state, means for adjusting an exercise plan based on the emotional state, means for providing the generated exercise plan to the user, means for collecting data from the wearable device, means for detecting abnormal values ​​in the collected data, means for sending an alert to the user when an abnormal value is detected, and means for providing the user with an exercise plan and feedback in real time via the smart glasses. This makes it possible to provide an individual exercise plan that takes into account both the user's health condition and emotional state, thereby maintaining motivation and providing appropriate feedback.

[1739] "User's biological data" refers to measurement data related to the user's physical condition, such as heart rate, number of steps, amount of exercise, and body temperature.

[1740] A "storage device" is a hardware or software component for storing data.

[1741] "Analysis" is the process of evaluating and calculating collected data to derive specific conclusions or insights.

[1742] "User's voice and facial expression" is data for determining the user's emotional state based on voice and facial expression.

[1743] "Emotional state" is information that indicates the user's current psychological state (e.g., happy, sad, stressed, etc.).

[1744] An "exercise plan" is a recommended exercise plan based on the user's health and emotional state.

[1745] A "wearable device" is an electronic device that can be worn on the user's body and has the ability to collect and communicate biometric data.

[1746] An "abnormal value" is data that indicates a value or condition that is outside the normal range and suggests a possible health problem.

[1747] "Warning" is the process of notifying the user of important or urgent information.

[1748] "Smart glasses" are devices that, in addition to the functions of regular glasses, can also function as electronic devices, displaying information and collecting data.

[1749] "Feedback" refers to information or responses provided by a system to a user, which provide guidance for improving the user's behavior or state.

[1750] This invention relates to a system that collects and analyzes a user's biometric and emotional data to provide a customized exercise plan. By combining it with an emotion engine, it is possible to adjust the exercise plan based on the user's emotions and improve motivation.

[1751] System Configuration

[1752] The system of the present invention comprises the following components:

[1753] 1. Data Collection Module

[1754] User: The user wears a wearable device equipped with a heart rate sensor and a pedometer while going about their daily life.

[1755] Device: This includes smart glasses and smartphones. It has the function of receiving biometric data collected from wearable devices and sending it to a server.

[1756] Server: Receives data sent from smart glasses or smartphones and stores it in a storage device.

[1757] 2. Data Analysis Module

[1758] Server: Cleanses stored biometric data and applies analytical algorithms to assess health status.

[1759] 3. Movement plan generation module

[1760] Server: Generates an individual exercise plan based on the health assessment results, the user's age, gender, and past exercise history.

[1761] 4. Emotion Engine

[1762] Device: Analyzes the user's voice and facial expressions and sends them to the emotion engine, which evaluates the user's emotional state and sends the results to the server.

[1763] Server: Analyzes data from the emotion engine and adjusts motor plans based on emotional state.

[1764] 5. Feedback provision module

[1765] Server: Formats the generated exercise plan, evaluation results, and motivational messages and sends them to the smart glasses or smartphone.

[1766] Terminal: Smart glasses or smartphone displays the information received from the server, and the user can view it.

[1767] Specific examples

[1768] For example, when a user goes for a morning run, the smart glasses record their heart rate and number of steps in real time. Furthermore, they analyze the user's fatigue and stress level based on their facial expression and complexion. If the user's heart rate is high and their facial expression shows signs of fatigue, the server will generate a message such as "Try to slow down a bit today," and display it on the smart glasses. In this way, users can simultaneously manage their health and improve their motivation.

[1769] Example prompt sentence:

[1770] "Generate a new customized exercise plan based on the latest health data (heart rate: 75, steps: 12000) of user ID 12345, taking into account emotion data (emotion detected from voice: stress, emotion detected from facial expression: fatigue)."

[1771] The present invention allows users to comprehensively manage their health and emotional state, enabling them to implement more effective and sustainable health and exercise plans.

[1772] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1773] Step 1:

[1774] Users wear wearable devices (e.g., smartwatches and fitness trackers) while going about their daily lives. The wearable devices collect biometric data, such as heart rate and step count, in real time. The collected biometric data is then transmitted to smart glasses or a smartphone.

[1775] Input: User's biometric data

[1776] Output: Data transfer to smart glasses or smartphone

[1777] Step 2:

[1778] The terminal (smart glasses or smartphone) transmits the biometric data received from the wearable terminal to the server, which then stores the received biometric data in a storage device.

[1779] Input: Biometric data received from a wearable device

[1780] Output: Data stored in the server's storage device

[1781] Step 3:

[1782] The server cleanses the biometric data stored in the storage device and applies an analysis algorithm to evaluate the user's health status, generating a health status assessment report.

[1783] Input: Biometric data stored in a storage device

[1784] Output: Health status assessment report

[1785] Step 4:

[1786] Users collect voice and facial expression data through smart glasses or smartphones, which are then sent to the emotion engine to analyze their emotional state.

[1787] Input: User's voice and facial expression data

[1788] Output: Evaluation result of emotional state by emotion engine

[1789] Step 5:

[1790] The server receives the emotional state assessment results from the emotion engine and generates a personalized exercise plan based on the health assessment results, as well as the user's age, gender, and past exercise history. The generated exercise plan also includes a message to increase motivation.

[1791] Input: Health status assessment results, emotional status assessment results, user information

[1792] Output: personalized exercise plan and encouraging messages

[1793] Step 6:

[1794] The server then sends the generated exercise plan and encouraging messages to the smart glasses or smartphone, where the user can check the information and follow the exercise plan.

[1795] Input: personalized exercise plan and encouraging message

[1796] Output: Exercise plan and messages displayed on smart glasses or smartphone

[1797] Step 7:

[1798] The user actually follows the exercise plan and engages in activities such as running or walking. During the activity, the wearable device collects and transmits biometric data to the device.

[1799] Input: User activity based on exercise plan

[1800] Output: New biometric data

[1801] Step 8:

[1802] The device sends newly collected biometric data to a server, which analyzes the effectiveness of the exercise plan based on the data and adjusts the plan as needed.

[1803] Input: Newly collected biometric data

[1804] Output: Updated exercise plan and encouragement message

[1805] The above is a specific processing flow of the system according to the present invention.

[1806] 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.

[1807] 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.

[1808] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1809] 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.

[1810] 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.

[1811] 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.

[1812] 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).

[1813] 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.

[1814] 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."

[1815] 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.

[1816] 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).

[1817] 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.

[1818] 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.

[1819] 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.

[1820] 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.

[1821] 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.

[1822] 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.

[1823] 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.

[1824] 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.

[1825] 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.

[1826] 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.

[1827] The following is further disclosed regarding the above embodiment.

[1828] (Claim 1)

[1829] means for collecting health data of a user;

[1830] a means for storing the collected health data in a database;

[1831] means for analyzing the stored health data to assess the user's health status;

[1832] means for generating a personalized exercise plan based on the user's health status;

[1833] The system includes a means for providing the generated exercise plan to a user.

[1834] (Claim 2)

[1835] a means for collecting data from the wearable device;

[1836] means for detecting outliers in the collected data;

[1837] 10. The system of claim 1, further comprising means for sending an alert to a user if an abnormal value is detected.

[1838] (Claim 3)

[1839] 10. The system of claim 1, further comprising means for customizing an exercise plan taking into account a user's age, gender, and past exercise history.

[1840] "Example 1"

[1841] (Claim 1)

[1842] means for collecting health data of a user;

[1843] a means for storing the collected health data in a database;

[1844] A means to organize stored health data by time series and category and apply analytical algorithms;

[1845] means for assessing the health status of the user based on the analyzed data;

[1846] means for generating a personalized exercise plan based on the user's health status assessment results;

[1847] a means of tailoring an exercise plan based on expert exercise guidelines;

[1848] means for providing the generated exercise plan to a user's terminal;

[1849] A system that includes a means to continually adjust exercise plans based on new data.

[1850] (Claim 2)

[1851] a means for collecting data from the wearable device;

[1852] means for detecting outliers in the collected data;

[1853] 10. The system of claim 1, further comprising means for sending an alert to a user if an abnormal value is detected.

[1854] (Claim 3)

[1855] 10. The system of claim 1, further comprising means for customizing an exercise plan taking into account a user's age, gender, and past exercise history.

[1856] "Application Example 1"

[1857] (Claim 1)

[1858] means for collecting health data of a user;

[1859] a means for storing the collected health data in a database;

[1860] means for analyzing the stored health data to assess the user's health status;

[1861] means for generating a personalized exercise plan based on the user's health status;

[1862] A means for providing the generated exercise plan to a user;

[1863] A means for a robot in a physical store to collect health data from a user's wearable device;

[1864] A means to provide real-time health advice and exercise plans based on collected health data;

[1865] The system includes a means for the robot to display the generated motion plan.

[1866] (Claim 2)

[1867] a means for collecting data from the wearable device;

[1868] means for detecting outliers in the collected data;

[1869] means for sending an alert to a user when an abnormal value is detected;

[1870] 10. The system of claim 1, further comprising means for adjusting the exercise plan based on new data collected.

[1871] (Claim 3)

[1872] a means for customizing an exercise plan taking into account the user's age, gender, and past exercise history;

[1873] 10. The system of claim 1, further comprising means for the robot to provide a customized motion plan in real time.

[1874] "Example 2: Combining Emotion Engines"

[1875] (Claim 1)

[1876] means for collecting health data of a user;

[1877] a means for storing the collected health data in a database;

[1878] means for analyzing the stored health data to assess the user's health status;

[1879] means for generating a personalized exercise plan based on the user's health status;

[1880] A means for providing the generated exercise plan to a user;

[1881] means for collecting user emotion data;

[1882] means for analyzing the collected emotion data to assess the user's emotional state;

[1883] The system includes a means for adjusting an exercise plan based on the user's health and emotional state and generating motivational messages.

[1884] (Claim 2)

[1885] a means for collecting data from the wearable device;

[1886] means for detecting outliers in the collected data;

[1887] 10. The system of claim 1, further comprising means for sending an alert to a user if an abnormal value is detected.

[1888] (Claim 3)

[1889] 10. The system of claim 1, further comprising means for customizing an exercise plan taking into account a user's age, gender, and past exercise history.

[1890] "Application example 2 when combining emotion engines"

[1891] (Claim 1)

[1892] means for collecting biometric data of a user;

[1893] means for storing the collected biometric data in a storage device;

[1894] means for analyzing the stored biometric data to assess the user's health status;

[1895] means for generating a personalized exercise plan based on the user's health status;

[1896] a means for analyzing the user's voice and facial expressions to assess their emotional state;

[1897] a means for adjusting an exercise plan based on emotional state;

[1898] The system includes a means for providing the generated exercise plan to a user.

[1899] (Claim 2)

[1900] a means for collecting data from the wearable device;

[1901] means for detecting outliers in the collected data;

[1902] means for sending an alert to a user when an abnormal value is detected;

[1903] 10. The system of claim 1, further comprising means for providing real-time exercise planning and feedback to the user via the smart glasses.

[1904] (Claim 3)

[1905] 10. The system of claim 1, further comprising means for customizing an exercise plan taking into account the user's age, gender, past exercise history, and emotional state. [Explanation of symbols]

[1906] 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. means for collecting health data of a user; a means for storing the collected health data in a database; means for analyzing the stored health data to assess the user's health status; means for generating a personalized exercise plan based on the user's health status; and means for providing the generated exercise plan to a user.

2. a means for collecting data from the wearable device; means for detecting outliers in the collected data; 10. The system of claim 1, further comprising: means for sending an alert to a user if an abnormal value is detected.

3. 10. The system of claim 1, further comprising means for customizing an exercise plan taking into account the user's age, gender, and past exercise history.

Images