System

Abstract

A system is provided.SOLUTION: A system, comprising: means for receiving user's basic information and storing the information in a database; means for analyzing the received information and identifying the user's life pattern; means for creating a personalized exercise plan based on the identified life pattern using a generative AI model; means for generating a personalized audio guide based on the created exercise plan and transmitting the personalized audio guide to the user's device; and means for recording the user's exercise history and generating and displaying a progress forecast.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, people in their 30s to 50s, especially, are busy with housework, childcare, work, and other activities, making it difficult to find time to exercise. Because lack of exercise can lead to serious health problems, a method is needed to efficiently incorporate exercise as part of daily life. However, it is difficult to create an exercise plan on one's own, and maintaining motivation to continue is also challenging. Therefore, there is a need for a system that provides personalized exercise programs tailored to each user's lifestyle, allowing them to easily incorporate exercise into their schedules between housework and childcare. [Means for solving the problem]

[0005] The present invention provides a means for receiving a user's basic information and storing that information in a database. It then provides a means for analyzing the received information and identifying the user's lifestyle patterns. It also provides a means for using a generative AI model to create a personalized exercise plan based on the identified lifestyle patterns. It also provides a means for generating personalized audio guidance based on this exercise plan and sending it to the user's device. It also includes a means for providing video materials corresponding to the exercise menu to support preparation and review. Finally, it provides a means for recording the user's exercise history, generating a progress forecast, and displaying the results. These means allow users to efficiently incorporate exercise into their daily lives, maintaining ongoing motivation and leading a healthy lifestyle.

[0006] "Basic user information" refers to comprehensive data about individual users, such as age, gender, lifestyle, and exercise experience.

[0007] A "database" is a system that organizes, stores, and manages data such as basic user information, exercise history, lifestyle pattern analysis results, and generated exercise plans.

[0008] "Lifestyle patterns" refer to the time periods and frequencies of activities such as housework, childcare, and work that a user performs on a daily basis.

[0009] A "generative AI model" is an artificial intelligence algorithm that generates an individual exercise plan based on a user's lifestyle pattern data.

[0010] A "personalized exercise plan" is an individually optimized exercise plan that combines an exercise menu suited to a specific user's lifestyle and needs.

[0011] "Audio guidance" is a system that provides audio instructions to users on proper movements, breathing techniques, etc. when exercising.

[0012] "Device" refers to an electronic device such as a smartphone, tablet, or PC that is used to play audio guides and video exercise menus.

[0013] "Exercise history" refers to a record of the type, duration, and frequency of exercise performed by the user.

[0014] "Progress forecast" refers to predictive data that shows the user's future health and fitness potential based on their past exercise history and personalized exercise plan.

[0015] "Video materials" are video content corresponding to the exercise menu, which users can refer to when preparing or reviewing.

[0016] "Preparation and review" refers to the act of the user studying the exercise menu in advance and reviewing it after the exercise. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] This is a system that allows users to incorporate personalized exercise into their daily lives. It analyzes their lifestyle patterns based on their basic information, generates an appropriate exercise plan, and supports them by providing audio guidance and video during exercise. It also provides a mechanism to maintain the user's motivation by recording their exercise history and predicting their progress based on that record.

[0039] Specifically, the user first accesses the system and enters basic information, including age, gender, lifestyle, exercise experience, etc. This information is received by the server and stored in a database.

[0040] The server then analyzes the received information to identify the user's lifestyle patterns, specifically the times and frequency of daily activities such as housework, childcare, and work, and uses a generative AI model to create a personalized exercise plan based on these lifestyle patterns.

[0041] Based on the created exercise plan, the server generates a personalized audio guide and sends it to the user's device. When the user starts exercising, the device plays the audio guide, instructing them on appropriate movements and breathing techniques.

[0042] In addition, video materials corresponding to the exercise menu are provided so that users can prepare and review. When the user selects an exercise menu, the device plays the video. This allows users to learn the exercise steps in advance and review them after exercising.

[0043] After completing an exercise session, the device records the exercise details and time and sends them to the server. The server then analyzes this exercise history and generates a progress forecast. The progress forecast is displayed in concrete graphs and numerical values, and is presented in a visually easy-to-understand format for users. This allows users to check their own progress and maintain motivation to continue exercising.

[0044] By using this system, users can easily incorporate exercise into their daily lives and maintain their health efficiently. As a specific example of its use, a 30-year-old female user is suggested an exercise plan that incorporates short stretches and yoga between 8:30 and 9:00 in the morning, even though she is busy with housework and childcare. Following this plan, the user exercises according to the audio guidance on the device, and watches the video to confirm correct movements. After the exercise, the record is saved and a progress forecast for one month later can be checked, maintaining motivation towards specific goals.

[0045] As described above, the system of the present invention enables people to efficiently incorporate exercise into their busy lives and supports healthy living.

[0046] The processing flow will be explained below.

[0047] Step 1:

[0048] Users access the system and enter basic information such as age, gender, lifestyle, exercise experience, etc. The server receives this information and stores it in a database.

[0049] Step 2:

[0050] The server analyzes the basic information stored in the database and identifies the user's daily life patterns (time periods and frequency of activities such as housework, childcare, and work).

[0051] Step 3:

[0052] The server uses the generative AI model to create a personalized exercise plan based on the identified lifestyle patterns, including exercises that can be easily incorporated into the user's daily life.

[0053] Step 4:

[0054] The server generates personalized audio guidance based on the created exercise plan and transmits it to the user's device. When the user starts exercising, the user can exercise according to the audio guidance received by the device.

[0055] Step 5:

[0056] When the user selects an exercise menu, the terminal receives the corresponding video material from the server and plays the video, allowing the user to visually learn the correct exercise steps.

[0057] Step 6:

[0058] After the user finishes exercising, the terminal records the exercise details (type, time, etc.) and sends the data to the server.

[0059] Step 7:

[0060] The server analyzes the received exercise history and generates a progress forecast that indicates the user's likely health and fitness improvement if they continue exercising.

[0061] Step 8:

[0062] The server sends the progress forecast generated to the user's device, which displays it as graphs and numerical values. Users can refer to this to check their own progress and maintain their motivation to continue.

[0063] Through the above steps, the system of the present invention helps users to effectively incorporate exercise as part of their daily lives and supports a healthy lifestyle.

[0064] Example 1

[0065] 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."

[0066] In recent years, it has become difficult to make exercise a habit in busy daily lives, and many people are not getting enough exercise to maintain a healthy lifestyle. Furthermore, the lack of personalized exercise plans and appropriate guidance impacts motivation to exercise. Furthermore, it is difficult to properly record exercise history and monitor progress, making it difficult to set specific goals.

[0067] 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.

[0068] In this invention, the server includes means for receiving a user's basic information and storing that information in a storage device, means for analyzing the received information and identifying the user's daily patterns, means for using a generative AI model to create an individual exercise plan based on the identified daily patterns, means for generating personalized audio guidance based on the created exercise plan and sending it to the user's device, means for providing video materials corresponding to the exercise menu and supporting pre-learning and review, and means for recording the user's exercise history and generating and displaying a progress forecast. This allows users to efficiently incorporate exercise into their busy daily lives and maintain a healthy lifestyle. Furthermore, the individually personalized guidance and progress management make it easier for them to continue exercising and achieve their goals.

[0069] "Basic information" refers to data such as the user's age, gender, lifestyle, and exercise experience.

[0070] "Storage device" refers to a device or system for storing data.

[0071] "Daily patterns" refer to characteristics such as the user's behavior and time allocation in daily life.

[0072] An "individual exercise plan" refers to an exercise plan personalized according to the user's lifestyle pattern.

[0073] "Audio guidance" refers to audio guidance that instructs proper movements and breathing techniques during exercise.

[0074] "Terminal" refers to a device used by a user, such as a computer, smartphone, or tablet.

[0075] "Visual materials" refers to visual content such as videos and animations that correspond to the exercise menu.

[0076] "Exercise history" refers to a record of the content and duration of exercise performed by the user.

[0077] "Progress prediction" refers to data that analyzes a user's exercise status based on their exercise history and predicts their future progress.

[0078] "Generative AI models" refer to algorithms or systems that use artificial intelligence technology to analyze user information and generate personalized exercise plans.

[0079] The present invention is a system for incorporating personalized exercise into a user's daily life. It analyzes the user's lifestyle patterns based on basic information, generates an appropriate exercise plan, and supports the user by providing audio guidance and video materials during exercise. It also provides a mechanism for maintaining the user's motivation by recording their exercise history and predicting their progress based on that record. Specific means for implementing this system are described below.

[0080] First, the user accesses the system and enters basic information. This basic information includes age, gender, lifestyle, and exercise experience. To enter this information, the system's web page or mobile application is used. Once the user enters this information, it is sent to the server. The server stores the received information in a storage device. A relational database (e.g., MySQL or PostgreSQL) is used for this storage.

[0081] The server then analyzes the user's daily patterns based on the stored basic information. This analysis uses data analysis tools (e.g., Python's pandas library and machine learning models). Specifically, the server analyzes data such as lifestyle and exercise experience to identify the optimal time and frequency for the user to exercise.

[0082] The server then uses a generative AI model to create an individual exercise plan based on the identified daily patterns. This generative AI model uses GPT-3 and other natural language processing models. The generated exercise plan includes specific exercise content and time, as well as appropriate stretching and yoga exercises. For example, an exercise plan can be created by inputting the following prompt into the generative AI model: "30-year-old female. Able to exercise between 8:30 and 9:00 AM every day. Housework and childcare are part of my daily life. I am a beginner at exercise. Please suggest an appropriate stretching and yoga exercise plan."

[0083] Based on the created exercise plan, the server generates individually tailored audio guidance and sends it to the user's device. A Text-to-Speech (TTS) engine (e.g., Google TTS or Amazon Polly) is used to generate the audio guidance. The server also provides video materials corresponding to the exercise menu. These video materials use existing video files or external video links (e.g., YouTube) so that users can watch them for advance learning or review.

[0084] When the user starts exercising, the device plays audio guidance, showing them the appropriate movements and breathing techniques. It also plays video material so the user can visually confirm the exercise. After the exercise is finished, the device records the exercise details and time and sends them to the server.

[0085] The server analyzes the exercise history and generates a progress forecast. This analysis is performed using data analysis tools (e.g., Python's pandas and NumPy). The generated progress forecast is displayed as specific graphs and numerical values, and is provided in a format that is easy for the user to understand visually. For example, the progress forecast is displayed as "Total exercise time this month: 12 hours" or "Next goal: 15 hours."

[0086] Through these means, this system enables users to incorporate exercise efficiently into their busy daily lives and provides support for maintaining a healthy lifestyle.

[0087] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0088] The flow of this system's program processing

[0089] Step 1:

[0090] The user enters basic information

[0091] Specific behavior:

[0092] Users access the system's web page or mobile app and enter basic information such as age, gender, lifestyle, and exercise experience. The input form provides text boxes and drop-down menus.

[0093] input:

[0094] User basic information (e.g., age, gender, lifestyle, exercise experience)

[0095] output:

[0096] Basic information is sent from the input form to the server.

[0097] Step 2:

[0098] The server receives the user's basic information and stores it in a database

[0099] Specific behavior:

[0100] The server receives the basic information submitted by the user and stores it in a relational database (e.g. MySQL, PostgreSQL), using SQL queries for this storage process.

[0101] input:

[0102] User basic information (e.g., age, gender, lifestyle, exercise experience)

[0103] output:

[0104] Basic information stored in the database

[0105] Step 3:

[0106] The server analyzes the user's daily patterns

[0107] Specific behavior:

[0108] The server reads the basic information stored in the database and uses data analysis tools (e.g., Python's pandas library and machine learning models) to analyze the user's daily patterns. Specifically, it identifies the optimal time and frequency for the user to exercise based on data such as lifestyle and exercise experience.

[0109] input:

[0110] Basic information read from the database

[0111] output:

[0112] Identified user's daily patterns (information on optimal exercise times and frequency)

[0113] Step 4:

[0114] The server creates an exercise plan using a generative AI model

[0115] Specific behavior:

[0116] The server creates an individualized exercise plan based on the identified daily patterns using a generative AI model (e.g., GPT-3). The generative AI model receives a prompt and generates an appropriate exercise plan as its output.

[0117] input:

[0118] Identified daily patterns (optimal time of day and frequency of exercise), prompt statement (e.g., "I am a 30-year-old woman. I am able to exercise between 8:30 and 9:00 AM every day. Housework and childcare are part of my daily life. I am a beginner at exercise. Please suggest an appropriate exercise plan including stretching and yoga.")

[0119] output:

[0120] Personalized exercise plans

[0121] Step 5:

[0122] The server generates audio guidance and video materials and sends them to the terminal.

[0123] Specific behavior:

[0124] The server generates audio guidance (e.g., a text-to-speech engine) and visual materials (e.g., video files and external links) based on the exercise plan and sends them to the user's device. The audio guidance instructs appropriate movements and breathing techniques, and the visual materials are used for advance learning and review of the exercise.

[0125] input:

[0126] Personalized exercise plans

[0127] output:

[0128] Audio guidance and video materials are sent to the user's device.

[0129] Step 6:

[0130] The user starts exercising, and the device plays audio guidance and video materials.

[0131] Specific behavior:

[0132] When the user starts exercising, the device plays audio guidance, showing them the appropriate movements and breathing techniques. It also plays video material on the device, allowing the user to visually check the exercise.

[0133] input:

[0134] Audio guide and video materials

[0135] output:

[0136] The user performs exercises based on visual and audio guidance

[0137] Step 7:

[0138] The device collects exercise records and sends them to the server.

[0139] Specific behavior:

[0140] When a user completes an exercise, the device records the exercise content and time, and this recorded data is sent to a server and saved as an exercise history.

[0141] input:

[0142] Exercise content and time

[0143] output:

[0144] Exercise history is sent to the server and stored in a database.

[0145] Step 8:

[0146] The server analyzes the exercise history and generates a progress forecast.

[0147] Specific behavior:

[0148] The server analyzes the received exercise history and generates a progress forecast using data analysis tools (e.g., Python's pandas or NumPy). The generated progress forecast is displayed on the user's device in the form of specific graphs and numerical values.

[0149] input:

[0150] Exercise history (exercise content and time)

[0151] output:

[0152] Progress forecast (graphs and specific figures)

[0153] The above is the specific processing procedure of the program for this system. This series of processing steps allows users to efficiently incorporate exercise into their daily lives and maintain a healthy lifestyle.

[0154] (Application example 1)

[0155] 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."

[0156] While conventional motion planning and maintenance systems can provide personalized motion plans based on human lifestyle patterns, they have not been able to adequately optimize the work efficiency and maintenance timing of robots in factories. Therefore, there is a need for a system that can provide personalized maintenance timing and procedures based on the robot's operation history.

[0157] 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.

[0158] In this invention, the server includes means for receiving basic information about the user and storing the information in a database, means for analyzing the received information and identifying the user's lifestyle patterns, means for using a generative AI model to create a personalized exercise plan based on the identified lifestyle patterns, means for generating a personalized audio guide based on the created exercise plan and sending it to the user's terminal, means for providing video materials corresponding to the exercise menu to support preparation and review, means for recording the user's exercise history and generating and displaying a progress forecast, means for receiving and storing the robot's movement history, means for analyzing the movement history and creating a personalized maintenance plan, means for providing audio guides to the robot and instructing appropriate maintenance procedures based on the created maintenance plan, and means for providing video materials and visual support for the maintenance procedures, thereby maximizing the robot's work efficiency and enabling appropriate maintenance timing to be predicted and performed.

[0159] "Basic information" refers to information that includes the individual characteristics and history of the user or robot, such as their age, gender, lifestyle, exercise experience, and movement history.

[0160] A "database" is an information system for storing received basic information and operation history, enabling efficient access and analysis.

[0161] "Lifestyle patterns" refer to the time periods and frequency of a user's daily activities, and are the subject of analysis to create personalized exercise plans based on that data.

[0162] A "generative AI model" is a machine learning model that creates personalized plans (exercise plans and maintenance plans) based on information about the user and the robot.

[0163] A "personalized exercise plan" is a plan that shows a customized exercise schedule and content based on the user's lifestyle patterns and basic information.

[0164] "Audio guide" is a system that provides appropriate instructions and procedures via voice to users and robots when performing exercise or maintenance.

[0165] "Visual materials" are materials such as videos and diagrams that visually show the procedures and details of exercise and maintenance.

[0166] "Exercise history" is a record of the exercises a user has performed, and that data is used to generate progress predictions.

[0167] "Progress prediction" is information that predicts future exercise progress and goals to be achieved based on the user's exercise history.

[0168] "Operation history" is a record of the robot's work content and operating time, and is data used to optimize maintenance timing and procedures.

[0169] A "maintenance plan" is a plan that indicates the optimal maintenance timing and procedures based on the robot's operating history.

[0170] The present invention provides a system for introducing a personalized exercise plan into a user's daily life and a system for optimizing the operation of a robot in a factory. The following configurations and procedures are included to implement the present invention.

[0171] First, basic information about the user or robot is received and stored in a database. This basic information includes age, gender, lifestyle, exercise experience, and movement history. This information is stored on a server and used for analysis.

[0172] The server then analyzes the received information to identify the user's lifestyle or the robot's behavioral patterns, and uses a generative AI model to create personalized exercise and maintenance plans based on the identified lifestyle and behavioral patterns.

[0173] Based on this, the server generates a personalized audio guide and sends it to the user's or robot's device. The device plays this audio guide and instructs the appropriate exercise or maintenance procedure. It also provides video materials corresponding to the exercise menu or maintenance procedure to help the user or operator prepare and review.

[0174] After completing exercise or maintenance, the device records the details and time of the exercise and sends it to the server. The server analyzes this history and generates a progress forecast, which is displayed in concrete graphs and numerical values ​​in a format that is easy for users and operators to understand visually.

[0175] The hardware required is a device (smartphone, smart glasses, head-mounted display, etc.) to receive information from the user or robot. The server also includes a system (cloud server or local server) for storing data and performing analysis using the generative AI model. The software required is a program that implements the analytical algorithm and generative AI model.

[0176] For example, a 30-year-old female user who is busy with housework and childcare will be suggested an exercise plan that incorporates a short stretch or yoga session between 8:30 and 9:00 a.m. For factory robots, the system will predict the date when the next maintenance is required based on the robot's operation history, and provide audio guidance and a video link with the appropriate maintenance procedures at that time.

[0177] Example prompt sentence:

[0178] Please predict the next maintenance date and generate a voice command and video link to robot ID "001".

[0179] Operation history: Operation1 on October 1, 2023 at 10:00, Operation2 on October 2, 2023 at 10:00

[0180] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0181] Step 1:

[0182] The system receives basic user information and stores it in a database. Specifically, the user enters information such as age, gender, lifestyle, and exercise experience into a form, and the data is sent to the server and stored in the database. The entered information is used in subsequent analysis steps.

[0183] Step 2:

[0184] Based on the received basic information, the server identifies the user's lifestyle patterns. The server then inputs the received data into an analysis algorithm, converting the data and recognizing patterns to identify the user's daily behavior patterns. The output is analyzed lifestyle pattern data.

[0185] Step 3:

[0186] A generative AI model is used to create a personalized exercise plan based on the identified lifestyle patterns. The server inputs lifestyle pattern data into the generative AI model and generates an exercise plan. The output is data for an exercise plan optimized for the user.

[0187] Step 4:

[0188] Based on the created exercise plan, the server generates a personalized audio guide and sends it to the user's device. Specifically, the server uses a speech synthesis engine to generate audio guide from text and sends it to the user's device. The input is the exercise plan data, and the output is an audio file.

[0189] Step 5:

[0190] It provides video materials corresponding to the exercise menu and supports preparation and review. The server searches the database for video materials based on the exercise plan and sends the links to the user's device. The input is the exercise plan, and the output is the link to the video materials.

[0191] Step 6:

[0192] It records the user's exercise history and generates and displays a progress forecast. After the user exercises, the device records the exercise content and time and sends it to the server. The server receives this data, analyzes it, and generates a progress forecast. The output is a visually displayed graph or number.

[0193] Step 7:

[0194] Receives and stores the robot's operation history. When a factory robot performs a task, its operation history is sent from the terminal to the server and stored in a database. The input is the robot's operation history data, and the output is the stored data.

[0195] Step 8:

[0196] Analyzes operation history and generates a personalized maintenance plan. The server analyzes the stored operation history data and determines the optimal maintenance timing and procedure. The input is operation history data, and the output is maintenance plan data.

[0197] Step 9:

[0198] Based on the generated maintenance plan, the server provides voice guidance to the robot, instructing it on the appropriate maintenance procedures. The server uses a speech synthesis engine to convert the maintenance procedures into voice and sends them to the robot's terminal. The input is the maintenance plan data, and the output is an audio file.

[0199] Step 10:

[0200] It provides visual support for maintenance procedures by providing video materials. The server searches for video materials based on the maintenance plan and sends the link to the robot's terminal. The input is the maintenance plan, and the output is the link to the video materials.

[0201] 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.

[0202] The present invention provides a system for incorporating personalized exercise into a user's daily life, and further incorporates a function for recognizing the user's emotions, thereby helping the user to continue exercising more effectively. The following describes an embodiment of the present invention.

[0203] First, a user accesses the system and enters basic information such as age, gender, lifestyle, and exercise experience. The server receives this basic information and stores it in a database. The server then analyzes the stored information to identify the user's daily life patterns (time periods and frequency of activities such as housework, childcare, and work). A generative AI model is then used to create a personalized exercise plan based on these lifestyle patterns.

[0204] Based on the created exercise plan, the server generates a personalized audio guide and sends it to the user's device. When the user starts exercising, the device plays the audio guide, instructing the user on the appropriate movements and breathing techniques. It also provides video materials corresponding to the exercise menu so that the user can prepare and review. When the user selects an exercise menu, the device plays the video, allowing the user to learn the exact steps of the exercise.

[0205] Furthermore, the present invention incorporates an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's facial expressions, voice, or other biometric information to identify the user's emotions. The emotion information identified by the emotion engine is transmitted from the device to a server, and the exercise plan and audio guidance are dynamically adjusted based on this emotion information. For example, if the user feels fatigued or stressed, the exercise plan may be reduced or an audio guidance promoting relaxation may be provided.

[0206] After the exercise is completed, the device records the exercise details (type, time, etc.) and any emotional data recognized during the exercise, and sends them to the server. The server analyzes this exercise history and emotional information to generate a progress forecast. The progress forecast is displayed in concrete graphs and numerical values, and is provided in a visually easy-to-understand format for users. This progress forecast allows users to continue checking their progress and adjust their future exercise plans.

[0207] Furthermore, by utilizing the emotion engine, the server provides personalized feedback to improve the user's motivation. For example, if the user shows positive emotions while exercising, the server will reinforce that feedback and provide a message to motivate them to exercise again next time.

[0208] As a specific example of use, a 30-year-old female user is offered an exercise plan that incorporates a short stretch or yoga session between 8:30 and 9:00 a.m. during a busy day of housework and childcare. Following this plan, the user exercises following the audio guidance on the device, and watches the video to confirm correct movements. During exercise, the emotion engine monitors the user's emotions, and if the user feels fatigued or stressed, appropriate adjustments are made. After the exercise, the record is saved and progress forecast for one month later can be checked to maintain motivation toward specific goals.

[0209] As described above, the system of the present invention provides personalized exercise plans and feedback based on lifestyle patterns and emotional information, enabling users to efficiently incorporate exercise into their daily lives and lead continuously healthy lives.

[0210] The processing flow will be explained below.

[0211] Step 1:

[0212] The user accesses the system and enters basic information (age, gender, lifestyle, exercise experience, etc.). The server receives this information and stores it in a database.

[0213] Step 2:

[0214] The server analyzes the basic information stored in the database and identifies the user's daily life patterns (time periods and frequency of activities such as housework, childcare, and work).

[0215] Step 3:

[0216] The server uses the generative AI model to create a personalized exercise plan based on the identified lifestyle patterns, including exercises that can be easily incorporated into the user's daily life.

[0217] Step 4:

[0218] The server generates personalized audio guidance based on the created exercise plan and transmits it to the user's device. When the user starts exercising, the user can exercise according to the audio guidance received by the device.

[0219] Step 5:

[0220] When the user selects an exercise menu, the terminal receives the corresponding video material from the server and plays the video, allowing the user to visually learn the correct exercise steps.

[0221] Step 6:

[0222] When the user starts exercising, the emotion engine monitors the user's facial expressions and voice to identify emotional information, which is then sent from the device to the server.

[0223] Step 7:

[0224] When a user exercises, the server dynamically adjusts the exercise plan and audio guidance based on data from the emotion engine. For example, if the user feels fatigued, the server will reduce the exercise menu and provide guidance to encourage relaxation.

[0225] Step 8:

[0226] After the user finishes exercising, the device records the exercise details (type, time, etc.) and the emotion data recognized during the exercise, and sends the data to the server.

[0227] Step 9:

[0228] The server analyzes the received exercise history and emotional information to generate a progress forecast, which is displayed in concrete graphs and numerical values ​​in a format that is easy for users to understand visually.

[0229] Step 10:

[0230] The server sends the progress forecast generated by the server to the user's device, which displays it. The user can refer to this to check their own progress and maintain motivation for future exercise.

[0231] Step 11:

[0232] Using the emotion engine, the server provides personalized feedback to improve the user's motivation. For example, if the user shows positive emotions while exercising, the server reinforces that feedback and provides a message to motivate the user to exercise next time.

[0233] Through the above steps, the system of the present invention enables users to efficiently incorporate exercise into their daily lives and provides personalized feedback based on emotional information, enabling continuous health management.

[0234] Example 2

[0235] 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."

[0236] In modern society, it is difficult for busy users to continue exercising to maintain their health. In particular, there are no appropriate exercise plans that take into account individual lifestyle patterns and emotional states, making it difficult to continue exercising effectively. Furthermore, if a user's emotions change during exercise, it is not possible to respond in real time. This often leads to a decrease in the user's motivation to exercise, making it difficult to continue exercising.

[0237] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0238] In this invention, the server includes means for receiving a user's basic information and storing that information in a database; means for analyzing the received information and identifying the user's lifestyle patterns; means for using a generative AI model to create a personalized exercise plan based on the identified lifestyle patterns; means for generating a personalized audio guide based on the created exercise plan and sending it to the user's device; means for providing video materials corresponding to the exercise menu and supporting preparation and review; means for recognizing the user's emotions and dynamically adjusting the exercise plan and audio guide based on those emotions; and means for recording the user's exercise history and emotional data and generating and displaying a progress forecast. This allows for an effective exercise plan tailored to the user's individual lifestyle patterns and emotional state, helping them to continue exercising. Furthermore, real-time emotion recognition helps maintain the user's motivation.

[0239] "Means for receiving basic information about the user" refers to means for receiving basic information such as age, gender, lifestyle, and exercise experience entered by the user.

[0240] "Means for storing in a database" refers to a means for storing and managing received basic user information for the long term.

[0241] The "means for identifying a user's lifestyle patterns" refers to a means for analyzing the stored basic information and identifying the time periods and frequencies of the user's daily activities.

[0242] "Means for creating personalized exercise plans using a generative AI model" means means for generating an exercise plan suitable for an individual user using a generative AI model based on identified lifestyle patterns.

[0243] The "means for generating personalized audio guidance" refers to a means for generating personalized exercise instructions using voice synthesis technology based on the created exercise plan.

[0244] "Means for sending to the user's device" refers to means for sending the generated audio guide and exercise plan to the user's device such as a smartphone or computer.

[0245] The "means for providing video materials corresponding to an exercise menu" refers to a means for providing video materials corresponding to an exercise plan so that the user can easily learn the correct movements.

[0246] "Means to support preparation and review" refers to means to support users in preparing for and reviewing exercises using the provided video materials.

[0247] "Means for recognizing the user's emotions" refers to a means for analyzing the user's facial expressions, voice, and biometric information to identify their emotional state at that time.

[0248] The "means for dynamically adjusting the exercise plan and audio guidance" refers to a means for adjusting the exercise plan and audio guidance content in real time based on the identified emotional state of the user.

[0249] The "means for recording the user's exercise history and emotional data" refers to a means for recording the exercise data performed by the user and the emotional data at that time.

[0250] The "means for generating and displaying a progress forecast" is a means for analyzing the recorded exercise history and emotional data, predicting the user's exercise progress, and visually displaying it.

[0251] This invention provides a system that allows users to incorporate personalized exercise into their daily lives and further incorporates a function that recognizes the user's emotions, allowing the user to continue exercising more effectively. A specific embodiment of this system will be described below.

[0252] First, the user accesses the system and enters basic information such as age, gender, lifestyle, and exercise experience. This basic information is sent from the user's smartphone, PC, or other device to the server. The server then uses a database management system (DBMS) to store this information in a database. Specifically, database technology such as PostgreSQL is used.

[0253] Based on the stored information, the server performs analysis using Python libraries (e.g., pandas and scikit-learn). This analysis identifies the user's daily life patterns (time periods and frequency of activities such as housework, childcare, and work). Based on the identified lifestyle patterns, a generative AI model (e.g., OpenAI GPT-4) is used to generate a personalized exercise plan tailored to the user.

[0254] Based on the created exercise plan, the server uses speech synthesis software (e.g., Google Text-to-Speech) to generate personalized audio guidance and sends it to the user's device. When the user begins exercising, the device plays the audio guidance and also provides video materials corresponding to the exercise menu. This allows the user to check the correct movements while watching the video.

[0255] The system also incorporates an emotion engine. While the user is exercising, the device transmits the user's facial expressions, voice, and other biometric information to the emotion engine for analysis. This emotion engine utilizes Microsoft Azure Cognitive Services, among other services. The emotion information identified by the emotion engine is sent from the device to a server. The server can dynamically adjust the exercise plan and audio guidance based on this emotion information. For example, if the user feels fatigued or stressed, it can reduce the intensity of the exercise or provide audio guidance encouraging relaxation.

[0256] After the exercise is completed, the device records the exercise details (type, time, etc.) and any emotional data recognized during the exercise, and sends them to the server. The server analyzes this exercise history and emotional information to generate a progress forecast. The progress forecast is displayed in concrete graphs and numerical values, in a visually easy-to-understand format for users. This allows users to continue checking their progress and adjust their future exercise plans.

[0257] Furthermore, by utilizing the emotion engine, the server provides personalized feedback to improve the user's motivation. For example, if the user shows positive emotions while exercising, the server will reinforce that feedback and provide a message to motivate them to exercise again next time.

[0258] As a specific example of usage, a 30-year-old female user is offered an exercise plan that incorporates a short stretch or yoga session between 8:30 and 9:00 a.m. during a busy day of housework and childcare. Following this plan, the user exercises following the audio guidance on the device, and watches the video to confirm correct movements. During exercise, the emotion engine monitors the user's emotions, and if fatigue or stress is felt, appropriate adjustments are made. After the exercise, the record is saved and progress forecast for one month later can be checked, allowing the user to maintain motivation toward specific goals.

[0259] An example of a prompt sentence would be, "Please suggest a morning exercise plan for a 30-year-old female user, including short stretches and yoga, that she can incorporate into her daily housework and childcare. Adjust the exercise plan based on the user's emotions and provide feedback to increase motivation." This would be input into the generative AI model.

[0260] As described above, this system provides personalized exercise plans and feedback based on lifestyle patterns and emotional information, helping users to incorporate exercise efficiently into their daily lives and maintain a healthy lifestyle.

[0261] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0262] Step 1:

[0263] Users access the system and enter basic information such as age, gender, lifestyle, and exercise experience.

[0264] Input: User's basic information (age, gender, lifestyle, exercise experience)

[0265] Output: Basic information is sent from the device to the server.

[0266] How it works: The user enters the necessary information into a dedicated application on their smartphone or computer and clicks the send button. This information is then sent to a server via the Internet.

[0267] Step 2:

[0268] The server receives the user's basic information and stores it in a database.

[0269] Input: User basic information

[0270] Output: Basic information stored in the database

[0271] Specific operation: The server stores the received basic information in a database using a database management system (e.g., PostgreSQL). Specifically, it inserts data using the "INSERT" query.

[0272] Step 3:

[0273] The server analyzes the stored basic information to identify the user's daily life patterns.

[0274] Input: Saved basic information

[0275] Output: User's daily life patterns

[0276] How it works: The server uses Python's pandas and scikit-learn libraries to process basic information and identify patterns of when users are doing housework, childcare, work, etc. Specifically, it performs feature extraction based on this.

[0277] Step 4:

[0278] The server inputs prompt sentences into a generative AI model (e.g., OpenAI GPT-4) to generate a personalized exercise plan.

[0279] Input: User's daily life patterns

[0280] Output: Generated personalized exercise plan

[0281] Specific operation: A prompt such as "Please suggest a morning exercise plan for a 30-year-old female user, including short stretches and yoga, that she can incorporate into her daily routine between housework and childcare," is input into the generative AI model, and the model generates an exercise plan and returns it to the server.

[0282] Step 5:

[0283] Based on the exercise plan, the server uses speech synthesis software (e.g., Google Text-to-Speech) to generate audio guidance and transmits it to the device.

[0284] Input: Generated exercise plan

[0285] Output: The generated audio guide is sent to the terminal.

[0286] How it works: The server passes the exercise plan to the Google Text-to-Speech API, generates an audio file, and sends the audio file to the user's device.

[0287] Step 6:

[0288] The terminal plays back the received audio guide and provides video materials corresponding to the exercise menu.

[0289] Input: Audio guide, video materials

[0290] Output: Audio guide played, video material displayed

[0291] How it works: When a user opens the app on their device and presses the play button, an audio guide will be played, and at the same time, a video showing the exercise will be displayed on the device screen.

[0292] Step 7:

[0293] The device sends the user's facial expressions, voice, and biometric information to the emotion engine for analysis.

[0294] Input: User's facial expressions, voice, biometric information

[0295] Output: Parsed emotion data

[0296] How it works: The device uses built-in devices (camera and microphone) to capture the user's facial expressions and voice, and then sends them in real time to an emotion engine such as Microsoft Azure Cognitive Services for analysis.

[0297] Step 8:

[0298] The device transmits the analyzed emotion data to the server.

[0299] Input: Parsed emotion data

[0300] Output: Emotion data sent to the server

[0301] Specific operation: The analysis results are returned to the terminal, and then sent to the server via the Internet.

[0302] Step 9:

[0303] Based on the emotion data received by the server, the generative AI model is reused to dynamically adjust the movement plan and audio guidance.

[0304] Input: Emotion data

[0305] Output: Dynamically adjusted exercise plan and audio guidance

[0306] Specific operation: Based on the new emotional data, the server inputs a prompt such as, "This user is feeling tired. Please provide audio guidance to reduce the exercise intensity and encourage relaxation," into the generative AI model, and regenerates the exercise plan.

[0307] Step 10:

[0308] The device records the exercise performed by the user (type, time, etc.) and emotional data during exercise, and sends this data to the server.

[0309] Input: Exercise content, emotion data

[0310] Output: Exercise history and emotion data sent to the server

[0311] Specific operation: The device collects exercise data from the user's activity tracker or manual input, and sends it along with emotional data to the server.

[0312] Step 11:

[0313] The server analyzes exercise history and emotional data, and generates and displays progress predictions in concrete graphs and figures.

[0314] Input: Exercise history, emotion data

[0315] Output: Progress forecast graph, numerical value

[0316] What it does: The server uses Python libraries such as matplotlib and seaborn to visualize the data and generate reports in HTML and PDF format to visually display the user's progress.

[0317] Through these steps, the system can provide personalized exercise plans and feedback for users' daily lives, and make appropriate adjustments based on their emotional state.

[0318] (Application example 2)

[0319] 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."

[0320] Conventional exercise plan providing systems only provide exercise plans based on the user's basic information and do not take into consideration the user's real-time emotional state or work patterns. As a result, appropriate adjustments are not made when the user feels stressed or fatigued, which can lead to a decrease in motivation to continue exercising. The present invention aims to solve these problems and provide a system that allows users to work while maintaining their health.

[0321] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0322] In this invention, the server includes means for receiving a user's basic information and storing the information in a database, means for analyzing the received information and identifying the user's lifestyle patterns, means for using a generative AI model to create a personalized exercise plan based on the identified lifestyle patterns, means for generating a personalized audio guide based on the created exercise plan and sending it to the user's terminal, means for providing video materials corresponding to the exercise menu and supporting preparation and review, means for recording the user's exercise history and generating and displaying a progress forecast, and means for analyzing the user's emotional information and dynamically adjusting the exercise plan and audio guide. This dynamically adjusts the exercise plan based on the user's emotional state and work patterns, enabling the user to exercise appropriately even when feeling stressed or tired, thereby maintaining continuous health.

[0323] "Basic user information" refers to personal data such as the user's age, gender, lifestyle, exercise experience, and work patterns.

[0324] A "database" is a collection of electronic data used to store information and manage a user's basic information and exercise history.

[0325] "Lifestyle patterns" are information indicating the time periods and frequencies of activities in the user's daily life.

[0326] A "generative AI model" is an artificial intelligence model used to generate personalized exercise plans based on a user's lifestyle patterns.

[0327] A "personalized exercise plan" is an exercise plan that is individually tailored to the user's basic information and lifestyle patterns.

[0328] "Audio guide" is a system that provides users with exercise instructions and advice via voice.

[0329] "Device" refers to an electronic device used by a user, such as a smartphone or tablet.

[0330] "Visual materials" refers to video content such as videos and animations that allow users to visually understand the exercise menu.

[0331] "Exercise history" refers to recorded data such as the type and duration of exercise performed by the user.

[0332] "Progress prediction" predicts future exercise results and progress based on the user's exercise history.

[0333] "Emotional information" is data on the user's emotional state analyzed from facial expressions, voice, etc.

[0334] "Dynamic adjustment" means instantly changing exercise plans and audio guidance according to the user's real-time condition and environment.

[0335] An embodiment of this invention is "Fresh Delivery Fit," which provides a health support system for food delivery companies. The system receives and analyzes basic information about the user, generates a personalized exercise plan, and further analyzes the user's emotional state in real time to dynamically adjust the exercise plan.

[0336] First, the user enters basic information (age, gender, working hours, workload, etc.) via a device such as a smartphone. The server receives this basic information and stores it in a database. The software used for this is database software for data management (e.g., MySQL).

[0337] The server then uses generative AI models (e.g., TensorFlow, PyTorch) to create a personalized exercise plan based on the user's basic information and work patterns, including, for example, light stretching, moderate aerobic exercise, and high-intensity interval training.

[0338] Once the exercise plan is created, the server uses a voice guidance system (e.g., Amazon Polly or Google Text-to-Speech) to generate and send audio guidance for the exercise to the user's device. The user then begins exercising according to the received audio guidance.

[0339] During exercise, the user's emotional state is monitored using the smartphone's camera and microphone. An emotion engine (e.g., OpenCV, Affectiva API) analyzes this data and identifies the user's emotional information in real time. The server dynamically adjusts the exercise plan and audio guidance based on this emotional information. For example, if the user feels fatigued, the exercise plan may be changed to a relaxation stretch.

[0340] After an exercise session, the device records the exercise details (type, time, etc.) and emotional data, and sends them to the server. The server stores this data in a database, analyzes it, and then generates a progress forecast. The progress forecast is displayed as specific graphs and numbers, allowing users to visually check it.

[0341] Additionally, the app uses a generative AI model to provide personalized feedback to motivate users. For example, if a user expresses positive emotions during exercise, the app will reinforce that feedback and display a message to motivate them to exercise again next time. This feedback feature is a key factor in encouraging users to exercise consistently.

[0342] As a concrete example, consider the case where a 30-year-old female delivery worker uses this system. Because she often sits for long periods of time during her daily work, the server generates an exercise plan that takes into account her working hours and activity level. In this case, an example of a prompt sentence input into the generative AI model is as follows:

[0343] Example prompt sentence:

[0344] "User basic information: 30-year-old female, work hours: 9-18, number of deliveries: 7 times / day"

[0345] With these features, the system provides personalized exercise plans based on each user's individual needs and dynamically adjusts based on their emotional state, helping them stay healthy while performing their delivery duties.

[0346] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0347] Step 1:

[0348] The user starts the smartphone application and enters basic information (age, gender, working hours, workload, etc.). The entered basic information is temporarily saved on the device.

[0349] Step 2:

[0350] The terminal sends the input basic information to the server. The server stores the received basic information in a database. At this stage, the input is basic information, and the output is stored in the database.

[0351] Step 3:

[0352] The server uses the generative AI model to analyze the user's daily life and work patterns and create a personalized exercise plan based on this. At this stage, the input is basic information and the output is a personalized exercise plan. The server processes the data as follows: First, it inputs a prompt sentence into the generative AI model and generates an appropriate exercise plan.

[0353] Example prompt sentence:

[0354] "User basic information: 30-year-old female, work hours: 9-18, number of deliveries: 7 times / day"

[0355] Step 4:

[0356] The server generates audio guidance based on the created exercise plan and sends it to the user's device. An audio guidance system (e.g., Amazon Polly or Google Text-to-Speech) is used to generate exercise instructions and advice. At this stage, the input is the exercise plan and the output is the audio guidance.

[0357] Step 5:

[0358] When the user starts exercising, the device plays audio guidance and provides appropriate exercise instructions. At the same time, the device monitors the user's emotional state by collecting facial and voice data using the smartphone's camera and microphone. The input is the audio guidance and real-time user data, and the output is the collected emotional data.

[0359] Step 6:

[0360] The device sends the collected emotion data to the server in real time. The server uses an emotion engine (e.g., OpenCV, Affectiva API) to analyze the emotion data and identify the user's emotion information. At this stage, the input is emotion data, and the output is analyzed emotion information.

[0361] Step 7:

[0362] The server dynamically adjusts the exercise plan and audio guidance based on the emotional information. For example, if the user feels tired, the server changes the exercise plan to encourage relaxation. The input is emotional information, and the output is the adjusted exercise plan and audio guidance.

[0363] Step 8:

[0364] After the exercise session ends, the device records the exercise details (type, time, etc.) and emotional data and sends them to the server. The server stores this data in a database and analyzes it. The input is exercise details and emotional data, and the output is stored in the database.

[0365] Step 9:

[0366] The server analyzes the exercise history and generates a progress prediction. The generated progress prediction is displayed as a concrete graph and numerical values ​​and sent to the device, where the user can visually confirm it. The input is the exercise history and emotion data, and the output is a progress prediction graph and numerical values.

[0367] Step 10:

[0368] The server uses a generative AI model to provide personalized feedback to improve the user's motivation. For example, if a user expresses positive emotions, a feedback message that reinforces this emotion is sent to the device. The input is emotion data and exercise history, and the output is the feedback message.

[0369] The specific operations and data processing / calculation procedures performed at each step are defined when the system is designed, along with detailed program code.

[0370] 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.

[0371] 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.

[0372] 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.

[0373] [Second embodiment]

[0374] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0375] 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.

[0376] 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).

[0377] 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.

[0378] 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.

[0379] 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).

[0380] 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.

[0381] 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.

[0382] 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.

[0383] 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.

[0384] 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.

[0385] 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."

[0386] This is a system that allows users to incorporate personalized exercise into their daily lives. It analyzes their lifestyle patterns based on their basic information, generates an appropriate exercise plan, and supports them by providing audio guidance and video during exercise. It also provides a mechanism to maintain the user's motivation by recording their exercise history and predicting their progress based on that record.

[0387] Specifically, the user first accesses the system and enters basic information, including age, gender, lifestyle, exercise experience, etc. This information is received by the server and stored in a database.

[0388] The server then analyzes the received information to identify the user's lifestyle patterns, specifically the times and frequency of daily activities such as housework, childcare, and work, and uses a generative AI model to create a personalized exercise plan based on these lifestyle patterns.

[0389] Based on the created exercise plan, the server generates a personalized audio guide and sends it to the user's device. When the user starts exercising, the device plays the audio guide, instructing them on appropriate movements and breathing techniques.

[0390] In addition, video materials corresponding to the exercise menu are provided so that users can prepare and review. When the user selects an exercise menu, the device plays the video. This allows users to learn the exercise steps in advance and review them after exercising.

[0391] After completing an exercise session, the device records the exercise details and time and sends them to the server. The server then analyzes this exercise history and generates a progress forecast. The progress forecast is displayed in concrete graphs and numerical values, and is presented in a visually easy-to-understand format for users. This allows users to check their own progress and maintain motivation to continue exercising.

[0392] By using this system, users can easily incorporate exercise into their daily lives and maintain their health efficiently. As a specific example of its use, a 30-year-old female user is suggested an exercise plan that incorporates short stretches and yoga between 8:30 and 9:00 in the morning, even though she is busy with housework and childcare. Following this plan, the user exercises according to the audio guidance on the device, and watches the video to confirm correct movements. After the exercise, the record is saved and a progress forecast for one month later can be checked, maintaining motivation towards specific goals.

[0393] As described above, the system of the present invention enables people to efficiently incorporate exercise into their busy lives and supports healthy living.

[0394] The processing flow will be explained below.

[0395] Step 1:

[0396] Users access the system and enter basic information such as age, gender, lifestyle, exercise experience, etc. The server receives this information and stores it in a database.

[0397] Step 2:

[0398] The server analyzes the basic information stored in the database and identifies the user's daily life patterns (time periods and frequency of activities such as housework, childcare, and work).

[0399] Step 3:

[0400] The server uses the generative AI model to create a personalized exercise plan based on the identified lifestyle patterns, including exercises that can be easily incorporated into the user's daily life.

[0401] Step 4:

[0402] The server generates personalized audio guidance based on the created exercise plan and transmits it to the user's device. When the user starts exercising, the user can exercise according to the audio guidance received by the device.

[0403] Step 5:

[0404] When the user selects an exercise menu, the terminal receives the corresponding video material from the server and plays the video, allowing the user to visually learn the correct exercise steps.

[0405] Step 6:

[0406] After the user finishes exercising, the terminal records the exercise details (type, time, etc.) and sends the data to the server.

[0407] Step 7:

[0408] The server analyzes the received exercise history and generates a progress forecast that indicates the user's likely health and fitness improvement if they continue exercising.

[0409] Step 8:

[0410] The server sends the progress forecast generated to the user's device, which displays it as graphs and numerical values. Users can refer to this to check their own progress and maintain their motivation to continue.

[0411] Through the above steps, the system of the present invention helps users to effectively incorporate exercise as part of their daily lives and supports a healthy lifestyle.

[0412] Example 1

[0413] 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."

[0414] In recent years, it has become difficult to make exercise a habit in busy daily lives, and many people are not getting enough exercise to maintain a healthy lifestyle. Furthermore, the lack of personalized exercise plans and appropriate guidance impacts motivation to exercise. Furthermore, it is difficult to properly record exercise history and monitor progress, making it difficult to set specific goals.

[0415] 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.

[0416] In this invention, the server includes means for receiving a user's basic information and storing that information in a storage device, means for analyzing the received information and identifying the user's daily patterns, means for using a generative AI model to create an individual exercise plan based on the identified daily patterns, means for generating personalized audio guidance based on the created exercise plan and sending it to the user's device, means for providing video materials corresponding to the exercise menu and supporting pre-learning and review, and means for recording the user's exercise history and generating and displaying a progress forecast. This allows users to efficiently incorporate exercise into their busy daily lives and maintain a healthy lifestyle. Furthermore, the individually personalized guidance and progress management make it easier for them to continue exercising and achieve their goals.

[0417] "Basic information" refers to data such as the user's age, gender, lifestyle, and exercise experience.

[0418] "Storage device" refers to a device or system for storing data.

[0419] "Daily patterns" refer to characteristics such as the user's behavior and time allocation in daily life.

[0420] An "individual exercise plan" refers to an exercise plan personalized according to the user's lifestyle pattern.

[0421] "Audio guidance" refers to audio guidance that instructs proper movements and breathing techniques during exercise.

[0422] "Terminal" refers to a device used by a user, such as a computer, smartphone, or tablet.

[0423] "Visual materials" refers to visual content such as videos and animations that correspond to the exercise menu.

[0424] "Exercise history" refers to a record of the content and duration of exercise performed by the user.

[0425] "Progress prediction" refers to data that analyzes a user's exercise status based on their exercise history and predicts their future progress.

[0426] "Generative AI models" refer to algorithms or systems that use artificial intelligence technology to analyze user information and generate personalized exercise plans.

[0427] The present invention is a system for incorporating personalized exercise into a user's daily life. It analyzes the user's lifestyle patterns based on basic information, generates an appropriate exercise plan, and supports the user by providing audio guidance and video materials during exercise. It also provides a mechanism for maintaining the user's motivation by recording their exercise history and predicting their progress based on that record. Specific means for implementing this system are described below.

[0428] First, the user accesses the system and enters basic information. This basic information includes age, gender, lifestyle, and exercise experience. To enter this information, the system's web page or mobile application is used. Once the user enters this information, it is sent to the server. The server stores the received information in a storage device. A relational database (e.g., MySQL or PostgreSQL) is used for this storage.

[0429] The server then analyzes the user's daily patterns based on the stored basic information. This analysis uses data analysis tools (e.g., Python's pandas library and machine learning models). Specifically, the server analyzes data such as lifestyle and exercise experience to identify the optimal time and frequency for the user to exercise.

[0430] The server then uses a generative AI model to create an individual exercise plan based on the identified daily patterns. This generative AI model uses GPT-3 and other natural language processing models. The generated exercise plan includes specific exercise content and time, as well as appropriate stretching and yoga exercises. For example, an exercise plan can be created by inputting the following prompt into the generative AI model: "30-year-old female. Able to exercise between 8:30 and 9:00 AM every day. Housework and childcare are part of my daily life. I am a beginner at exercise. Please suggest an appropriate stretching and yoga exercise plan."

[0431] Based on the created exercise plan, the server generates individually tailored audio guidance and sends it to the user's device. A Text-to-Speech (TTS) engine (e.g., Google TTS or Amazon Polly) is used to generate the audio guidance. The server also provides video materials corresponding to the exercise menu. These video materials use existing video files or external video links (e.g., YouTube) so that users can watch them for advance learning or review.

[0432] When the user starts exercising, the device plays audio guidance, showing them the appropriate movements and breathing techniques. It also plays video material so the user can visually confirm the exercise. After the exercise is finished, the device records the exercise details and time and sends them to the server.

[0433] The server analyzes the exercise history and generates a progress forecast. This analysis is performed using data analysis tools (e.g., Python's pandas and NumPy). The generated progress forecast is displayed as specific graphs and numerical values, and is provided in a format that is easy for the user to understand visually. For example, the progress forecast is displayed as "Total exercise time this month: 12 hours" or "Next goal: 15 hours."

[0434] Through these means, this system enables users to incorporate exercise efficiently into their busy daily lives and provides support for maintaining a healthy lifestyle.

[0435] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0436] The flow of this system's program processing

[0437] Step 1:

[0438] The user enters basic information

[0439] Specific behavior:

[0440] Users access the system's web page or mobile app and enter basic information such as age, gender, lifestyle, and exercise experience. The input form provides text boxes and drop-down menus.

[0441] input:

[0442] User basic information (e.g., age, gender, lifestyle, exercise experience)

[0443] output:

[0444] Basic information is sent from the input form to the server.

[0445] Step 2:

[0446] The server receives the user's basic information and stores it in a database

[0447] Specific behavior:

[0448] The server receives the basic information submitted by the user and stores it in a relational database (e.g. MySQL, PostgreSQL), using SQL queries for this storage process.

[0449] input:

[0450] User basic information (e.g., age, gender, lifestyle, exercise experience)

[0451] output:

[0452] Basic information stored in the database

[0453] Step 3:

[0454] The server analyzes the user's daily patterns

[0455] Specific behavior:

[0456] The server reads the basic information stored in the database and uses data analysis tools (e.g., Python's pandas library and machine learning models) to analyze the user's daily patterns. Specifically, it identifies the optimal time and frequency for the user to exercise based on data such as lifestyle and exercise experience.

[0457] input:

[0458] Basic information read from the database

[0459] output:

[0460] Identified user's daily patterns (information on optimal exercise times and frequency)

[0461] Step 4:

[0462] The server creates an exercise plan using a generative AI model

[0463] Specific behavior:

[0464] The server creates an individualized exercise plan based on the identified daily patterns using a generative AI model (e.g., GPT-3). The generative AI model receives a prompt and generates an appropriate exercise plan as its output.

[0465] input:

[0466] Identified daily patterns (optimal time of day and frequency of exercise), prompt statement (e.g., "I am a 30-year-old woman. I am able to exercise between 8:30 and 9:00 AM every day. Housework and childcare are part of my daily life. I am a beginner at exercise. Please suggest an appropriate exercise plan including stretching and yoga.")

[0467] output:

[0468] Personalized exercise plans

[0469] Step 5:

[0470] The server generates audio guidance and video materials and sends them to the terminal.

[0471] Specific behavior:

[0472] The server generates audio guidance (e.g., a text-to-speech engine) and visual materials (e.g., video files and external links) based on the exercise plan and sends them to the user's device. The audio guidance instructs appropriate movements and breathing techniques, and the visual materials are used for advance learning and review of the exercise.

[0473] input:

[0474] Personalized exercise plans

[0475] output:

[0476] Audio guidance and video materials are sent to the user's device.

[0477] Step 6:

[0478] The user starts exercising, and the device plays audio guidance and video materials.

[0479] Specific behavior:

[0480] When the user starts exercising, the device plays audio guidance, showing them the appropriate movements and breathing techniques. It also plays video material on the device, allowing the user to visually check the exercise.

[0481] input:

[0482] Audio guide and video materials

[0483] output:

[0484] The user performs exercises based on visual and audio guidance

[0485] Step 7:

[0486] The device collects exercise records and sends them to the server.

[0487] Specific behavior:

[0488] When a user completes an exercise, the device records the exercise content and time, and this recorded data is sent to a server and saved as an exercise history.

[0489] input:

[0490] Exercise content and time

[0491] output:

[0492] Exercise history is sent to the server and stored in a database.

[0493] Step 8:

[0494] The server analyzes the exercise history and generates a progress forecast.

[0495] Specific behavior:

[0496] The server analyzes the received exercise history and generates a progress forecast using data analysis tools (e.g., Python's pandas or NumPy). The generated progress forecast is displayed on the user's device in the form of specific graphs and numerical values.

[0497] input:

[0498] Exercise history (exercise content and time)

[0499] output:

[0500] Progress forecast (graphs and specific figures)

[0501] The above is the specific processing procedure of the program for this system. This series of processing steps allows users to efficiently incorporate exercise into their daily lives and maintain a healthy lifestyle.

[0502] (Application example 1)

[0503] 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."

[0504] While conventional motion planning and maintenance systems can provide personalized motion plans based on human lifestyle patterns, they have not been able to adequately optimize the work efficiency and maintenance timing of robots in factories. Therefore, there is a need for a system that can provide personalized maintenance timing and procedures based on the robot's operation history.

[0505] 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.

[0506] In this invention, the server includes means for receiving basic information about the user and storing the information in a database, means for analyzing the received information and identifying the user's lifestyle patterns, means for using a generative AI model to create a personalized exercise plan based on the identified lifestyle patterns, means for generating a personalized audio guide based on the created exercise plan and sending it to the user's terminal, means for providing video materials corresponding to the exercise menu to support preparation and review, means for recording the user's exercise history and generating and displaying a progress forecast, means for receiving and storing the robot's movement history, means for analyzing the movement history and creating a personalized maintenance plan, means for providing audio guides to the robot and instructing appropriate maintenance procedures based on the created maintenance plan, and means for providing video materials and visual support for the maintenance procedures, thereby maximizing the robot's work efficiency and enabling appropriate maintenance timing to be predicted and performed.

[0507] "Basic information" refers to information that includes the individual characteristics and history of the user or robot, such as their age, gender, lifestyle, exercise experience, and movement history.

[0508] A "database" is an information system for storing received basic information and operation history, enabling efficient access and analysis.

[0509] "Lifestyle patterns" refer to the time periods and frequency of a user's daily activities, and are the subject of analysis to create personalized exercise plans based on that data.

[0510] A "generative AI model" is a machine learning model that creates personalized plans (exercise plans and maintenance plans) based on information about the user and the robot.

[0511] A "personalized exercise plan" is a plan that shows a customized exercise schedule and content based on the user's lifestyle patterns and basic information.

[0512] "Audio guide" is a system that provides appropriate instructions and procedures via voice to users and robots when performing exercise or maintenance.

[0513] "Visual materials" are materials such as videos and diagrams that visually show the procedures and details of exercise and maintenance.

[0514] "Exercise history" is a record of the exercises a user has performed, and that data is used to generate progress predictions.

[0515] "Progress prediction" is information that predicts future exercise progress and goals to be achieved based on the user's exercise history.

[0516] "Operation history" is a record of the robot's work content and operating time, and is data used to optimize maintenance timing and procedures.

[0517] A "maintenance plan" is a plan that indicates the optimal maintenance timing and procedures based on the robot's operating history.

[0518] The present invention provides a system for introducing a personalized exercise plan into a user's daily life and a system for optimizing the operation of a robot in a factory. The following configurations and procedures are included to implement the present invention.

[0519] First, basic information about the user or robot is received and stored in a database. This basic information includes age, gender, lifestyle, exercise experience, and movement history. This information is stored on a server and used for analysis.

[0520] The server then analyzes the received information to identify the user's lifestyle or the robot's behavioral patterns, and uses a generative AI model to create personalized exercise and maintenance plans based on the identified lifestyle and behavioral patterns.

[0521] Based on this, the server generates a personalized audio guide and sends it to the user's or robot's device. The device plays this audio guide and instructs the appropriate exercise or maintenance procedure. It also provides video materials corresponding to the exercise menu or maintenance procedure to help the user or operator prepare and review.

[0522] After completing exercise or maintenance, the device records the details and time of the exercise and sends it to the server. The server analyzes this history and generates a progress forecast, which is displayed in concrete graphs and numerical values ​​in a format that is easy for users and operators to understand visually.

[0523] The hardware required is a device (smartphone, smart glasses, head-mounted display, etc.) to receive information from the user or robot. The server also includes a system (cloud server or local server) for storing data and performing analysis using the generative AI model. The software required is a program that implements the analytical algorithm and generative AI model.

[0524] For example, a 30-year-old female user who is busy with housework and childcare will be suggested an exercise plan that incorporates a short stretch or yoga session between 8:30 and 9:00 a.m. For factory robots, the system will predict the date when the next maintenance is required based on the robot's operation history, and provide audio guidance and a video link with the appropriate maintenance procedures at that time.

[0525] Example prompt sentence:

[0526] Please predict the next maintenance date and generate a voice command and video link to robot ID "001".

[0527] Operation history: Operation1 on October 1, 2023 at 10:00, Operation2 on October 2, 2023 at 10:00

[0528] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0529] Step 1:

[0530] The system receives basic user information and stores it in a database. Specifically, the user enters information such as age, gender, lifestyle, and exercise experience into a form, and the data is sent to the server and stored in the database. The entered information is used in subsequent analysis steps.

[0531] Step 2:

[0532] Based on the received basic information, the server identifies the user's lifestyle patterns. The server then inputs the received data into an analysis algorithm, converting the data and recognizing patterns to identify the user's daily behavior patterns. The output is analyzed lifestyle pattern data.

[0533] Step 3:

[0534] A generative AI model is used to create a personalized exercise plan based on the identified lifestyle patterns. The server inputs lifestyle pattern data into the generative AI model and generates an exercise plan. The output is data for an exercise plan optimized for the user.

[0535] Step 4:

[0536] Based on the created exercise plan, the server generates a personalized audio guide and sends it to the user's device. Specifically, the server uses a speech synthesis engine to generate audio guide from text and sends it to the user's device. The input is the exercise plan data, and the output is an audio file.

[0537] Step 5:

[0538] It provides video materials corresponding to the exercise menu and supports preparation and review. The server searches the database for video materials based on the exercise plan and sends the links to the user's device. The input is the exercise plan, and the output is the link to the video materials.

[0539] Step 6:

[0540] It records the user's exercise history and generates and displays a progress forecast. After the user exercises, the device records the exercise content and time and sends it to the server. The server receives this data, analyzes it, and generates a progress forecast. The output is a visually displayed graph or number.

[0541] Step 7:

[0542] Receives and stores the robot's operation history. When a factory robot performs a task, its operation history is sent from the terminal to the server and stored in a database. The input is the robot's operation history data, and the output is the stored data.

[0543] Step 8:

[0544] Analyzes operation history and generates a personalized maintenance plan. The server analyzes the stored operation history data and determines the optimal maintenance timing and procedure. The input is operation history data, and the output is maintenance plan data.

[0545] Step 9:

[0546] Based on the generated maintenance plan, the server provides voice guidance to the robot, instructing it on the appropriate maintenance procedures. The server uses a speech synthesis engine to convert the maintenance procedures into voice and sends them to the robot's terminal. The input is the maintenance plan data, and the output is an audio file.

[0547] Step 10:

[0548] It provides visual support for maintenance procedures by providing video materials. The server searches for video materials based on the maintenance plan and sends the link to the robot's terminal. The input is the maintenance plan, and the output is the link to the video materials.

[0549] 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.

[0550] The present invention provides a system for incorporating personalized exercise into a user's daily life, and further incorporates a function for recognizing the user's emotions, thereby helping the user to continue exercising more effectively. The following describes an embodiment of the present invention.

[0551] First, a user accesses the system and enters basic information such as age, gender, lifestyle, and exercise experience. The server receives this basic information and stores it in a database. The server then analyzes the stored information to identify the user's daily life patterns (time periods and frequency of activities such as housework, childcare, and work). A generative AI model is then used to create a personalized exercise plan based on these lifestyle patterns.

[0552] Based on the created exercise plan, the server generates a personalized audio guide and sends it to the user's device. When the user starts exercising, the device plays the audio guide, instructing the user on the appropriate movements and breathing techniques. It also provides video materials corresponding to the exercise menu so that the user can prepare and review. When the user selects an exercise menu, the device plays the video, allowing the user to learn the exact steps of the exercise.

[0553] Furthermore, the present invention incorporates an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's facial expressions, voice, or other biometric information to identify the user's emotions. The emotion information identified by the emotion engine is transmitted from the device to a server, and the exercise plan and audio guidance are dynamically adjusted based on this emotion information. For example, if the user feels fatigued or stressed, the exercise plan may be reduced or an audio guidance promoting relaxation may be provided.

[0554] After the exercise is completed, the device records the exercise details (type, time, etc.) and any emotional data recognized during the exercise, and sends them to the server. The server analyzes this exercise history and emotional information to generate a progress forecast. The progress forecast is displayed in concrete graphs and numerical values, and is provided in a visually easy-to-understand format for users. This progress forecast allows users to continue checking their progress and adjust their future exercise plans.

[0555] Furthermore, by utilizing the emotion engine, the server provides personalized feedback to improve the user's motivation. For example, if the user shows positive emotions while exercising, the server will reinforce that feedback and provide a message to motivate them to exercise again next time.

[0556] As a specific example of use, a 30-year-old female user is offered an exercise plan that incorporates a short stretch or yoga session between 8:30 and 9:00 a.m. during a busy day of housework and childcare. Following this plan, the user exercises following the audio guidance on the device, and watches the video to confirm correct movements. During exercise, the emotion engine monitors the user's emotions, and if the user feels fatigued or stressed, appropriate adjustments are made. After the exercise, the record is saved and progress forecast for one month later can be checked to maintain motivation toward specific goals.

[0557] As described above, the system of the present invention provides personalized exercise plans and feedback based on lifestyle patterns and emotional information, enabling users to efficiently incorporate exercise into their daily lives and lead continuously healthy lives.

[0558] The processing flow will be explained below.

[0559] Step 1:

[0560] The user accesses the system and enters basic information (age, gender, lifestyle, exercise experience, etc.). The server receives this information and stores it in a database.

[0561] Step 2:

[0562] The server analyzes the basic information stored in the database and identifies the user's daily life patterns (time periods and frequency of activities such as housework, childcare, and work).

[0563] Step 3:

[0564] The server uses the generative AI model to create a personalized exercise plan based on the identified lifestyle patterns, including exercises that can be easily incorporated into the user's daily life.

[0565] Step 4:

[0566] The server generates personalized audio guidance based on the created exercise plan and transmits it to the user's device. When the user starts exercising, the user can exercise according to the audio guidance received by the device.

[0567] Step 5:

[0568] When the user selects an exercise menu, the terminal receives the corresponding video material from the server and plays the video, allowing the user to visually learn the correct exercise steps.

[0569] Step 6:

[0570] When the user starts exercising, the emotion engine monitors the user's facial expressions and voice to identify emotional information, which is then sent from the device to the server.

[0571] Step 7:

[0572] When a user exercises, the server dynamically adjusts the exercise plan and audio guidance based on data from the emotion engine. For example, if the user feels fatigued, the server will reduce the exercise menu and provide guidance to encourage relaxation.

[0573] Step 8:

[0574] After the user finishes exercising, the device records the exercise details (type, time, etc.) and the emotion data recognized during the exercise, and sends the data to the server.

[0575] Step 9:

[0576] The server analyzes the received exercise history and emotional information to generate a progress forecast, which is displayed in concrete graphs and numerical values ​​in a format that is easy for users to understand visually.

[0577] Step 10:

[0578] The server sends the progress forecast generated by the server to the user's device, which displays it. The user can refer to this to check their own progress and maintain motivation for future exercise.

[0579] Step 11:

[0580] Using the emotion engine, the server provides personalized feedback to improve the user's motivation. For example, if the user shows positive emotions while exercising, the server reinforces that feedback and provides a message to motivate the user to exercise next time.

[0581] Through the above steps, the system of the present invention enables users to efficiently incorporate exercise into their daily lives and provides personalized feedback based on emotional information, enabling continuous health management.

[0582] Example 2

[0583] 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."

[0584] In modern society, it is difficult for busy users to continue exercising to maintain their health. In particular, there are no appropriate exercise plans that take into account individual lifestyle patterns and emotional states, making it difficult to continue exercising effectively. Furthermore, if a user's emotions change during exercise, it is not possible to respond in real time. This often leads to a decrease in the user's motivation to exercise, making it difficult to continue exercising.

[0585] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0586] In this invention, the server includes means for receiving a user's basic information and storing that information in a database; means for analyzing the received information and identifying the user's lifestyle patterns; means for using a generative AI model to create a personalized exercise plan based on the identified lifestyle patterns; means for generating a personalized audio guide based on the created exercise plan and sending it to the user's device; means for providing video materials corresponding to the exercise menu and supporting preparation and review; means for recognizing the user's emotions and dynamically adjusting the exercise plan and audio guide based on those emotions; and means for recording the user's exercise history and emotional data and generating and displaying a progress forecast. This allows for an effective exercise plan tailored to the user's individual lifestyle patterns and emotional state, helping them to continue exercising. Furthermore, real-time emotion recognition helps maintain the user's motivation.

[0587] "Means for receiving basic information about the user" refers to means for receiving basic information such as age, gender, lifestyle, and exercise experience entered by the user.

[0588] "Means for storing in a database" refers to a means for storing and managing received basic user information for the long term.

[0589] The "means for identifying a user's lifestyle patterns" refers to a means for analyzing the stored basic information and identifying the time periods and frequencies of the user's daily activities.

[0590] "Means for creating personalized exercise plans using a generative AI model" means means for generating an exercise plan suitable for an individual user using a generative AI model based on identified lifestyle patterns.

[0591] The "means for generating personalized audio guidance" refers to a means for generating personalized exercise instructions using voice synthesis technology based on the created exercise plan.

[0592] "Means for sending to the user's device" refers to means for sending the generated audio guide and exercise plan to the user's device such as a smartphone or computer.

[0593] The "means for providing video materials corresponding to an exercise menu" refers to a means for providing video materials corresponding to an exercise plan so that the user can easily learn the correct movements.

[0594] "Means to support preparation and review" refers to means to support users in preparing for and reviewing exercises using the provided video materials.

[0595] "Means for recognizing the user's emotions" refers to a means for analyzing the user's facial expressions, voice, and biometric information to identify their emotional state at that time.

[0596] The "means for dynamically adjusting the exercise plan and audio guidance" refers to a means for adjusting the exercise plan and audio guidance content in real time based on the identified emotional state of the user.

[0597] The "means for recording the user's exercise history and emotional data" refers to a means for recording the exercise data performed by the user and the emotional data at that time.

[0598] The "means for generating and displaying a progress forecast" is a means for analyzing the recorded exercise history and emotional data, predicting the user's exercise progress, and visually displaying it.

[0599] This invention provides a system that allows users to incorporate personalized exercise into their daily lives and further incorporates a function that recognizes the user's emotions, allowing the user to continue exercising more effectively. A specific embodiment of this system will be described below.

[0600] First, the user accesses the system and enters basic information such as age, gender, lifestyle, and exercise experience. This basic information is sent from the user's smartphone, PC, or other device to the server. The server then uses a database management system (DBMS) to store this information in a database. Specifically, database technology such as PostgreSQL is used.

[0601] Based on the stored information, the server performs analysis using Python libraries (e.g., pandas and scikit-learn). This analysis identifies the user's daily life patterns (time periods and frequency of activities such as housework, childcare, and work). Based on the identified lifestyle patterns, a generative AI model (e.g., OpenAI GPT-4) is used to generate a personalized exercise plan tailored to the user.

[0602] Based on the created exercise plan, the server uses speech synthesis software (e.g., Google Text-to-Speech) to generate personalized audio guidance and sends it to the user's device. When the user begins exercising, the device plays the audio guidance and also provides video materials corresponding to the exercise menu. This allows the user to check the correct movements while watching the video.

[0603] The system also incorporates an emotion engine. While the user is exercising, the device transmits the user's facial expressions, voice, and other biometric information to the emotion engine for analysis. This emotion engine utilizes Microsoft Azure Cognitive Services, among other services. The emotion information identified by the emotion engine is sent from the device to a server. The server can dynamically adjust the exercise plan and audio guidance based on this emotion information. For example, if the user feels fatigued or stressed, it can reduce the intensity of the exercise or provide audio guidance encouraging relaxation.

[0604] After the exercise is completed, the device records the exercise details (type, time, etc.) and any emotional data recognized during the exercise, and sends them to the server. The server analyzes this exercise history and emotional information to generate a progress forecast. The progress forecast is displayed in concrete graphs and numerical values, in a visually easy-to-understand format for users. This allows users to continue checking their progress and adjust their future exercise plans.

[0605] Furthermore, by utilizing the emotion engine, the server provides personalized feedback to improve the user's motivation. For example, if the user shows positive emotions while exercising, the server will reinforce that feedback and provide a message to motivate them to exercise again next time.

[0606] As a specific example of usage, a 30-year-old female user is offered an exercise plan that incorporates a short stretch or yoga session between 8:30 and 9:00 a.m. during a busy day of housework and childcare. Following this plan, the user exercises following the audio guidance on the device, and watches the video to confirm correct movements. During exercise, the emotion engine monitors the user's emotions, and if fatigue or stress is felt, appropriate adjustments are made. After the exercise, the record is saved and progress forecast for one month later can be checked, allowing the user to maintain motivation toward specific goals.

[0607] An example of a prompt sentence would be, "Please suggest a morning exercise plan for a 30-year-old female user, including short stretches and yoga, that she can incorporate into her daily housework and childcare. Adjust the exercise plan based on the user's emotions and provide feedback to increase motivation." This would be input into the generative AI model.

[0608] As described above, this system provides personalized exercise plans and feedback based on lifestyle patterns and emotional information, helping users to incorporate exercise efficiently into their daily lives and maintain a healthy lifestyle.

[0609] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0610] Step 1:

[0611] Users access the system and enter basic information such as age, gender, lifestyle, and exercise experience.

[0612] Input: User's basic information (age, gender, lifestyle, exercise experience)

[0613] Output: Basic information is sent from the device to the server.

[0614] How it works: The user enters the necessary information into a dedicated application on their smartphone or computer and clicks the send button. This information is then sent to a server via the Internet.

[0615] Step 2:

[0616] The server receives the user's basic information and stores it in a database.

[0617] Input: User basic information

[0618] Output: Basic information stored in the database

[0619] Specific operation: The server stores the received basic information in a database using a database management system (e.g., PostgreSQL). Specifically, it inserts data using the "INSERT" query.

[0620] Step 3:

[0621] The server analyzes the stored basic information to identify the user's daily life patterns.

[0622] Input: Saved basic information

[0623] Output: User's daily life patterns

[0624] How it works: The server uses Python's pandas and scikit-learn libraries to process basic information and identify patterns of when users are doing housework, childcare, work, etc. Specifically, it performs feature extraction based on this.

[0625] Step 4:

[0626] The server inputs prompt sentences into a generative AI model (e.g., OpenAI GPT-4) to generate a personalized exercise plan.

[0627] Input: User's daily life patterns

[0628] Output: Generated personalized exercise plan

[0629] Specific operation: A prompt such as "Please suggest a morning exercise plan for a 30-year-old female user, including short stretches and yoga, that she can incorporate into her daily routine between housework and childcare," is input into the generative AI model, and the model generates an exercise plan and returns it to the server.

[0630] Step 5:

[0631] Based on the exercise plan, the server uses speech synthesis software (e.g., Google Text-to-Speech) to generate audio guidance and transmits it to the device.

[0632] Input: Generated exercise plan

[0633] Output: The generated audio guide is sent to the terminal.

[0634] How it works: The server passes the exercise plan to the Google Text-to-Speech API, generates an audio file, and sends the audio file to the user's device.

[0635] Step 6:

[0636] The terminal plays back the received audio guide and provides video materials corresponding to the exercise menu.

[0637] Input: Audio guide, video materials

[0638] Output: Audio guide played, video material displayed

[0639] How it works: When a user opens the app on their device and presses the play button, an audio guide will be played, and at the same time, a video showing the exercise will be displayed on the device screen.

[0640] Step 7:

[0641] The device sends the user's facial expressions, voice, and biometric information to the emotion engine for analysis.

[0642] Input: User's facial expressions, voice, biometric information

[0643] Output: Parsed emotion data

[0644] How it works: The device uses built-in devices (camera and microphone) to capture the user's facial expressions and voice, and then sends them in real time to an emotion engine such as Microsoft Azure Cognitive Services for analysis.

[0645] Step 8:

[0646] The device transmits the analyzed emotion data to the server.

[0647] Input: Parsed emotion data

[0648] Output: Emotion data sent to the server

[0649] Specific operation: The analysis results are returned to the terminal, and then sent to the server via the Internet.

[0650] Step 9:

[0651] Based on the emotion data received by the server, the generative AI model is reused to dynamically adjust the movement plan and audio guidance.

[0652] Input: Emotion data

[0653] Output: Dynamically adjusted exercise plan and audio guidance

[0654] Specific operation: Based on the new emotional data, the server inputs a prompt such as, "This user is feeling tired. Please provide audio guidance to reduce the exercise intensity and encourage relaxation," into the generative AI model, and regenerates the exercise plan.

[0655] Step 10:

[0656] The device records the exercise performed by the user (type, time, etc.) and emotional data during exercise, and sends this data to the server.

[0657] Input: Exercise content, emotion data

[0658] Output: Exercise history and emotion data sent to the server

[0659] Specific operation: The device collects exercise data from the user's activity tracker or manual input, and sends it along with emotional data to the server.

[0660] Step 11:

[0661] The server analyzes exercise history and emotional data, and generates and displays progress predictions in concrete graphs and figures.

[0662] Input: Exercise history, emotion data

[0663] Output: Progress forecast graph, numerical value

[0664] What it does: The server uses Python libraries such as matplotlib and seaborn to visualize the data and generate reports in HTML and PDF format to visually display the user's progress.

[0665] Through these steps, the system can provide personalized exercise plans and feedback for users' daily lives, and make appropriate adjustments based on their emotional state.

[0666] (Application example 2)

[0667] 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."

[0668] Conventional exercise plan providing systems only provide exercise plans based on the user's basic information and do not take into consideration the user's real-time emotional state or work patterns. As a result, appropriate adjustments are not made when the user feels stressed or fatigued, which can lead to a decrease in motivation to continue exercising. The present invention aims to solve these problems and provide a system that allows users to work while maintaining their health.

[0669] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0670] In this invention, the server includes means for receiving a user's basic information and storing the information in a database, means for analyzing the received information and identifying the user's lifestyle patterns, means for using a generative AI model to create a personalized exercise plan based on the identified lifestyle patterns, means for generating a personalized audio guide based on the created exercise plan and sending it to the user's terminal, means for providing video materials corresponding to the exercise menu and supporting preparation and review, means for recording the user's exercise history and generating and displaying a progress forecast, and means for analyzing the user's emotional information and dynamically adjusting the exercise plan and audio guide. This dynamically adjusts the exercise plan based on the user's emotional state and work patterns, enabling the user to exercise appropriately even when feeling stressed or tired, thereby maintaining continuous health.

[0671] "Basic user information" refers to personal data such as the user's age, gender, lifestyle, exercise experience, and work patterns.

[0672] A "database" is a collection of electronic data used to store information and manage a user's basic information and exercise history.

[0673] "Lifestyle patterns" are information indicating the time periods and frequencies of activities in the user's daily life.

[0674] A "generative AI model" is an artificial intelligence model used to generate personalized exercise plans based on a user's lifestyle patterns.

[0675] A "personalized exercise plan" is an exercise plan that is individually tailored to the user's basic information and lifestyle patterns.

[0676] "Audio guide" is a system that provides users with exercise instructions and advice via voice.

[0677] "Device" refers to an electronic device used by a user, such as a smartphone or tablet.

[0678] "Visual materials" refers to video content such as videos and animations that allow users to visually understand the exercise menu.

[0679] "Exercise history" refers to recorded data such as the type and duration of exercise performed by the user.

[0680] "Progress prediction" predicts future exercise results and progress based on the user's exercise history.

[0681] "Emotional information" is data on the user's emotional state analyzed from facial expressions, voice, etc.

[0682] "Dynamic adjustment" means instantly changing exercise plans and audio guidance according to the user's real-time condition and environment.

[0683] An embodiment of this invention is "Fresh Delivery Fit," which provides a health support system for food delivery companies. The system receives and analyzes basic information about the user, generates a personalized exercise plan, and further analyzes the user's emotional state in real time to dynamically adjust the exercise plan.

[0684] First, the user enters basic information (age, gender, working hours, workload, etc.) via a device such as a smartphone. The server receives this basic information and stores it in a database. The software used for this is database software for data management (e.g., MySQL).

[0685] The server then uses generative AI models (e.g., TensorFlow, PyTorch) to create a personalized exercise plan based on the user's basic information and work patterns, including, for example, light stretching, moderate aerobic exercise, and high-intensity interval training.

[0686] Once the exercise plan is created, the server uses a voice guidance system (e.g., Amazon Polly or Google Text-to-Speech) to generate and send audio guidance for the exercise to the user's device. The user then begins exercising according to the received audio guidance.

[0687] During exercise, the user's emotional state is monitored using the smartphone's camera and microphone. An emotion engine (e.g., OpenCV, Affectiva API) analyzes this data and identifies the user's emotional information in real time. The server dynamically adjusts the exercise plan and audio guidance based on this emotional information. For example, if the user feels fatigued, the exercise plan may be changed to a relaxation stretch.

[0688] After an exercise session, the device records the exercise details (type, time, etc.) and emotional data, and sends them to the server. The server stores this data in a database, analyzes it, and then generates a progress forecast. The progress forecast is displayed as specific graphs and numbers, allowing users to visually check it.

[0689] Additionally, the app uses a generative AI model to provide personalized feedback to motivate users. For example, if a user expresses positive emotions during exercise, the app will reinforce that feedback and display a message to motivate them to exercise again next time. This feedback feature is a key factor in encouraging users to exercise consistently.

[0690] As a concrete example, consider the case where a 30-year-old female delivery worker uses this system. Because she often sits for long periods of time during her daily work, the server generates an exercise plan that takes into account her working hours and activity level. In this case, an example of a prompt sentence input into the generative AI model is as follows:

[0691] Example prompt sentence:

[0692] "User basic information: 30-year-old female, work hours: 9-18, number of deliveries: 7 times / day"

[0693] With these features, the system provides personalized exercise plans based on each user's individual needs and dynamically adjusts based on their emotional state, helping them stay healthy while performing their delivery duties.

[0694] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0695] Step 1:

[0696] The user starts the smartphone application and enters basic information (age, gender, working hours, workload, etc.). The entered basic information is temporarily saved on the device.

[0697] Step 2:

[0698] The terminal sends the input basic information to the server. The server stores the received basic information in a database. At this stage, the input is basic information, and the output is stored in the database.

[0699] Step 3:

[0700] The server uses the generative AI model to analyze the user's daily life and work patterns and create a personalized exercise plan based on this. At this stage, the input is basic information and the output is a personalized exercise plan. The server processes the data as follows: First, it inputs a prompt sentence into the generative AI model and generates an appropriate exercise plan.

[0701] Example prompt sentence:

[0702] "User basic information: 30-year-old female, work hours: 9-18, number of deliveries: 7 times / day"

[0703] Step 4:

[0704] The server generates audio guidance based on the created exercise plan and sends it to the user's device. An audio guidance system (e.g., Amazon Polly or Google Text-to-Speech) is used to generate exercise instructions and advice. At this stage, the input is the exercise plan and the output is the audio guidance.

[0705] Step 5:

[0706] When the user starts exercising, the device plays audio guidance and provides appropriate exercise instructions. At the same time, the device monitors the user's emotional state by collecting facial and voice data using the smartphone's camera and microphone. The input is the audio guidance and real-time user data, and the output is the collected emotional data.

[0707] Step 6:

[0708] The device sends the collected emotion data to the server in real time. The server uses an emotion engine (e.g., OpenCV, Affectiva API) to analyze the emotion data and identify the user's emotion information. At this stage, the input is emotion data, and the output is analyzed emotion information.

[0709] Step 7:

[0710] The server dynamically adjusts the exercise plan and audio guidance based on the emotional information. For example, if the user feels tired, the server changes the exercise plan to encourage relaxation. The input is emotional information, and the output is the adjusted exercise plan and audio guidance.

[0711] Step 8:

[0712] After the exercise session ends, the device records the exercise details (type, time, etc.) and emotional data and sends them to the server. The server stores this data in a database and analyzes it. The input is exercise details and emotional data, and the output is stored in the database.

[0713] Step 9:

[0714] The server analyzes the exercise history and generates a progress prediction. The generated progress prediction is displayed as a concrete graph and numerical values ​​and sent to the device, where the user can visually confirm it. The input is the exercise history and emotion data, and the output is a progress prediction graph and numerical values.

[0715] Step 10:

[0716] The server uses a generative AI model to provide personalized feedback to improve the user's motivation. For example, if a user expresses positive emotions, a feedback message that reinforces this emotion is sent to the device. The input is emotion data and exercise history, and the output is the feedback message.

[0717] The specific operations and data processing / calculation procedures performed at each step are defined when the system is designed, along with detailed program code.

[0718] 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.

[0719] 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.

[0720] 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.

[0721] [Third embodiment]

[0722] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0723] 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.

[0724] 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).

[0725] 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.

[0726] 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.

[0727] 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).

[0728] 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.

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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."

[0734] This is a system that allows users to incorporate personalized exercise into their daily lives. It analyzes their lifestyle patterns based on their basic information, generates an appropriate exercise plan, and supports them by providing audio guidance and video during exercise. It also provides a mechanism to maintain the user's motivation by recording their exercise history and predicting their progress based on that record.

[0735] Specifically, the user first accesses the system and enters basic information, including age, gender, lifestyle, exercise experience, etc. This information is received by the server and stored in a database.

[0736] The server then analyzes the received information to identify the user's lifestyle patterns, specifically the times and frequency of daily activities such as housework, childcare, and work, and uses a generative AI model to create a personalized exercise plan based on these lifestyle patterns.

[0737] Based on the created exercise plan, the server generates a personalized audio guide and sends it to the user's device. When the user starts exercising, the device plays the audio guide, instructing them on appropriate movements and breathing techniques.

[0738] In addition, video materials corresponding to the exercise menu are provided so that users can prepare and review. When the user selects an exercise menu, the device plays the video. This allows users to learn the exercise steps in advance and review them after exercising.

[0739] After completing an exercise session, the device records the exercise details and time and sends them to the server. The server then analyzes this exercise history and generates a progress forecast. The progress forecast is displayed in concrete graphs and numerical values, and is presented in a visually easy-to-understand format for users. This allows users to check their own progress and maintain motivation to continue exercising.

[0740] By using this system, users can easily incorporate exercise into their daily lives and maintain their health efficiently. As a specific example of its use, a 30-year-old female user is suggested an exercise plan that incorporates short stretches and yoga between 8:30 and 9:00 in the morning, even though she is busy with housework and childcare. Following this plan, the user exercises according to the audio guidance on the device, and watches the video to confirm correct movements. After the exercise, the record is saved and a progress forecast for one month later can be checked, maintaining motivation towards specific goals.

[0741] As described above, the system of the present invention enables people to efficiently incorporate exercise into their busy lives and supports healthy living.

[0742] The processing flow will be explained below.

[0743] Step 1:

[0744] Users access the system and enter basic information such as age, gender, lifestyle, exercise experience, etc. The server receives this information and stores it in a database.

[0745] Step 2:

[0746] The server analyzes the basic information stored in the database and identifies the user's daily life patterns (time periods and frequency of activities such as housework, childcare, and work).

[0747] Step 3:

[0748] The server uses the generative AI model to create a personalized exercise plan based on the identified lifestyle patterns, including exercises that can be easily incorporated into the user's daily life.

[0749] Step 4:

[0750] The server generates personalized audio guidance based on the created exercise plan and transmits it to the user's device. When the user starts exercising, the user can exercise according to the audio guidance received by the device.

[0751] Step 5:

[0752] When the user selects an exercise menu, the terminal receives the corresponding video material from the server and plays the video, allowing the user to visually learn the correct exercise steps.

[0753] Step 6:

[0754] After the user finishes exercising, the terminal records the exercise details (type, time, etc.) and sends the data to the server.

[0755] Step 7:

[0756] The server analyzes the received exercise history and generates a progress forecast that indicates the user's likely health and fitness improvement if they continue exercising.

[0757] Step 8:

[0758] The server sends the progress forecast generated to the user's device, which displays it as graphs and numerical values. Users can refer to this to check their own progress and maintain their motivation to continue.

[0759] Through the above steps, the system of the present invention helps users to effectively incorporate exercise as part of their daily lives and supports a healthy lifestyle.

[0760] Example 1

[0761] 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."

[0762] In recent years, it has become difficult to make exercise a habit in busy daily lives, and many people are not getting enough exercise to maintain a healthy lifestyle. Furthermore, the lack of personalized exercise plans and appropriate guidance impacts motivation to exercise. Furthermore, it is difficult to properly record exercise history and monitor progress, making it difficult to set specific goals.

[0763] 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.

[0764] In this invention, the server includes means for receiving a user's basic information and storing that information in a storage device, means for analyzing the received information and identifying the user's daily patterns, means for using a generative AI model to create an individual exercise plan based on the identified daily patterns, means for generating personalized audio guidance based on the created exercise plan and sending it to the user's device, means for providing video materials corresponding to the exercise menu and supporting pre-learning and review, and means for recording the user's exercise history and generating and displaying a progress forecast. This allows users to efficiently incorporate exercise into their busy daily lives and maintain a healthy lifestyle. Furthermore, the individually personalized guidance and progress management make it easier for them to continue exercising and achieve their goals.

[0765] "Basic information" refers to data such as the user's age, gender, lifestyle, and exercise experience.

[0766] "Storage device" refers to a device or system for storing data.

[0767] "Daily patterns" refer to characteristics such as the user's behavior and time allocation in daily life.

[0768] An "individual exercise plan" refers to an exercise plan personalized according to the user's lifestyle pattern.

[0769] "Audio guidance" refers to audio guidance that instructs proper movements and breathing techniques during exercise.

[0770] "Terminal" refers to a device used by a user, such as a computer, smartphone, or tablet.

[0771] "Visual materials" refers to visual content such as videos and animations that correspond to the exercise menu.

[0772] "Exercise history" refers to a record of the content and duration of exercise performed by the user.

[0773] "Progress prediction" refers to data that analyzes a user's exercise status based on their exercise history and predicts their future progress.

[0774] "Generative AI models" refer to algorithms or systems that use artificial intelligence technology to analyze user information and generate personalized exercise plans.

[0775] The present invention is a system for incorporating personalized exercise into a user's daily life. It analyzes the user's lifestyle patterns based on basic information, generates an appropriate exercise plan, and supports the user by providing audio guidance and video materials during exercise. It also provides a mechanism for maintaining the user's motivation by recording their exercise history and predicting their progress based on that record. Specific means for implementing this system are described below.

[0776] First, the user accesses the system and enters basic information. This basic information includes age, gender, lifestyle, and exercise experience. To enter this information, the system's web page or mobile application is used. Once the user enters this information, it is sent to the server. The server stores the received information in a storage device. A relational database (e.g., MySQL or PostgreSQL) is used for this storage.

[0777] The server then analyzes the user's daily patterns based on the stored basic information. This analysis uses data analysis tools (e.g., Python's pandas library and machine learning models). Specifically, the server analyzes data such as lifestyle and exercise experience to identify the optimal time and frequency for the user to exercise.

[0778] The server then uses a generative AI model to create an individual exercise plan based on the identified daily patterns. This generative AI model uses GPT-3 and other natural language processing models. The generated exercise plan includes specific exercise content and time, as well as appropriate stretching and yoga exercises. For example, an exercise plan can be created by inputting the following prompt into the generative AI model: "30-year-old female. Able to exercise between 8:30 and 9:00 AM every day. Housework and childcare are part of my daily life. I am a beginner at exercise. Please suggest an appropriate stretching and yoga exercise plan."

[0779] Based on the created exercise plan, the server generates individually tailored audio guidance and sends it to the user's device. A Text-to-Speech (TTS) engine (e.g., Google TTS or Amazon Polly) is used to generate the audio guidance. The server also provides video materials corresponding to the exercise menu. These video materials use existing video files or external video links (e.g., YouTube) so that users can watch them for advance learning or review.

[0780] When the user starts exercising, the device plays audio guidance, showing them the appropriate movements and breathing techniques. It also plays video material so the user can visually confirm the exercise. After the exercise is finished, the device records the exercise details and time and sends them to the server.

[0781] The server analyzes the exercise history and generates a progress forecast. This analysis is performed using data analysis tools (e.g., Python's pandas and NumPy). The generated progress forecast is displayed as specific graphs and numerical values, and is provided in a format that is easy for the user to understand visually. For example, the progress forecast is displayed as "Total exercise time this month: 12 hours" or "Next goal: 15 hours."

[0782] Through these means, this system enables users to incorporate exercise efficiently into their busy daily lives and provides support for maintaining a healthy lifestyle.

[0783] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0784] The flow of this system's program processing

[0785] Step 1:

[0786] The user enters basic information

[0787] Specific behavior:

[0788] Users access the system's web page or mobile app and enter basic information such as age, gender, lifestyle, and exercise experience. The input form provides text boxes and drop-down menus.

[0789] input:

[0790] User basic information (e.g., age, gender, lifestyle, exercise experience)

[0791] output:

[0792] Basic information is sent from the input form to the server.

[0793] Step 2:

[0794] The server receives the user's basic information and stores it in a database

[0795] Specific behavior:

[0796] The server receives the basic information submitted by the user and stores it in a relational database (e.g. MySQL, PostgreSQL), using SQL queries for this storage process.

[0797] input:

[0798] User basic information (e.g., age, gender, lifestyle, exercise experience)

[0799] output:

[0800] Basic information stored in the database

[0801] Step 3:

[0802] The server analyzes the user's daily patterns

[0803] Specific behavior:

[0804] The server reads the basic information stored in the database and uses data analysis tools (e.g., Python's pandas library and machine learning models) to analyze the user's daily patterns. Specifically, it identifies the optimal time and frequency for the user to exercise based on data such as lifestyle and exercise experience.

[0805] input:

[0806] Basic information read from the database

[0807] output:

[0808] Identified user's daily patterns (information on optimal exercise times and frequency)

[0809] Step 4:

[0810] The server creates an exercise plan using a generative AI model

[0811] Specific behavior:

[0812] The server creates an individualized exercise plan based on the identified daily patterns using a generative AI model (e.g., GPT-3). The generative AI model receives a prompt and generates an appropriate exercise plan as its output.

[0813] input:

[0814] Identified daily patterns (optimal time of day and frequency of exercise), prompt statement (e.g., "I am a 30-year-old woman. I am able to exercise between 8:30 and 9:00 AM every day. Housework and childcare are part of my daily life. I am a beginner at exercise. Please suggest an appropriate exercise plan including stretching and yoga.")

[0815] output:

[0816] Personalized exercise plans

[0817] Step 5:

[0818] The server generates audio guidance and video materials and sends them to the terminal.

[0819] Specific behavior:

[0820] The server generates audio guidance (e.g., a text-to-speech engine) and visual materials (e.g., video files and external links) based on the exercise plan and sends them to the user's device. The audio guidance instructs appropriate movements and breathing techniques, and the visual materials are used for advance learning and review of the exercise.

[0821] input:

[0822] Personalized exercise plans

[0823] output:

[0824] Audio guidance and video materials are sent to the user's device.

[0825] Step 6:

[0826] The user starts exercising, and the device plays audio guidance and video materials.

[0827] Specific behavior:

[0828] When the user starts exercising, the device plays audio guidance, showing them the appropriate movements and breathing techniques. It also plays video material on the device, allowing the user to visually check the exercise.

[0829] input:

[0830] Audio guide and video materials

[0831] output:

[0832] The user performs exercises based on visual and audio guidance

[0833] Step 7:

[0834] The device collects exercise records and sends them to the server.

[0835] Specific behavior:

[0836] When a user completes an exercise, the device records the exercise content and time, and this recorded data is sent to a server and saved as an exercise history.

[0837] input:

[0838] Exercise content and time

[0839] output:

[0840] Exercise history is sent to the server and stored in a database.

[0841] Step 8:

[0842] The server analyzes the exercise history and generates a progress forecast.

[0843] Specific behavior:

[0844] The server analyzes the received exercise history and generates a progress forecast using data analysis tools (e.g., Python's pandas or NumPy). The generated progress forecast is displayed on the user's device in the form of specific graphs and numerical values.

[0845] input:

[0846] Exercise history (exercise content and time)

[0847] output:

[0848] Progress forecast (graphs and specific figures)

[0849] The above is the specific processing procedure of the program for this system. This series of processing steps allows users to efficiently incorporate exercise into their daily lives and maintain a healthy lifestyle.

[0850] (Application example 1)

[0851] 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."

[0852] While conventional motion planning and maintenance systems can provide personalized motion plans based on human lifestyle patterns, they have not been able to adequately optimize the work efficiency and maintenance timing of robots in factories. Therefore, there is a need for a system that can provide personalized maintenance timing and procedures based on the robot's operation history.

[0853] 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.

[0854] In this invention, the server includes means for receiving basic information about the user and storing the information in a database, means for analyzing the received information and identifying the user's lifestyle patterns, means for using a generative AI model to create a personalized exercise plan based on the identified lifestyle patterns, means for generating a personalized audio guide based on the created exercise plan and sending it to the user's terminal, means for providing video materials corresponding to the exercise menu to support preparation and review, means for recording the user's exercise history and generating and displaying a progress forecast, means for receiving and storing the robot's movement history, means for analyzing the movement history and creating a personalized maintenance plan, means for providing audio guides to the robot and instructing appropriate maintenance procedures based on the created maintenance plan, and means for providing video materials and visual support for the maintenance procedures, thereby maximizing the robot's work efficiency and enabling appropriate maintenance timing to be predicted and performed.

[0855] "Basic information" refers to information that includes the individual characteristics and history of the user or robot, such as their age, gender, lifestyle, exercise experience, and movement history.

[0856] A "database" is an information system for storing received basic information and operation history, enabling efficient access and analysis.

[0857] "Lifestyle patterns" refer to the time periods and frequency of a user's daily activities, and are the subject of analysis to create personalized exercise plans based on that data.

[0858] A "generative AI model" is a machine learning model that creates personalized plans (exercise plans and maintenance plans) based on information about the user and the robot.

[0859] A "personalized exercise plan" is a plan that shows a customized exercise schedule and content based on the user's lifestyle patterns and basic information.

[0860] "Audio guide" is a system that provides appropriate instructions and procedures via voice to users and robots when performing exercise or maintenance.

[0861] "Visual materials" are materials such as videos and diagrams that visually show the procedures and details of exercise and maintenance.

[0862] "Exercise history" is a record of the exercises a user has performed, and that data is used to generate progress predictions.

[0863] "Progress prediction" is information that predicts future exercise progress and goals to be achieved based on the user's exercise history.

[0864] "Operation history" is a record of the robot's work content and operating time, and is data used to optimize maintenance timing and procedures.

[0865] A "maintenance plan" is a plan that indicates the optimal maintenance timing and procedures based on the robot's operating history.

[0866] The present invention provides a system for introducing a personalized exercise plan into a user's daily life and a system for optimizing the operation of a robot in a factory. The following configurations and procedures are included to implement the present invention.

[0867] First, basic information about the user or robot is received and stored in a database. This basic information includes age, gender, lifestyle, exercise experience, and movement history. This information is stored on a server and used for analysis.

[0868] The server then analyzes the received information to identify the user's lifestyle or the robot's behavioral patterns, and uses a generative AI model to create personalized exercise and maintenance plans based on the identified lifestyle and behavioral patterns.

[0869] Based on this, the server generates a personalized audio guide and sends it to the user's or robot's device. The device plays this audio guide and instructs the appropriate exercise or maintenance procedure. It also provides video materials corresponding to the exercise menu or maintenance procedure to help the user or operator prepare and review.

[0870] After completing exercise or maintenance, the device records the details and time of the exercise and sends it to the server. The server analyzes this history and generates a progress forecast, which is displayed in concrete graphs and numerical values ​​in a format that is easy for users and operators to understand visually.

[0871] The hardware required is a device (smartphone, smart glasses, head-mounted display, etc.) to receive information from the user or robot. The server also includes a system (cloud server or local server) for storing data and performing analysis using the generative AI model. The software required is a program that implements the analytical algorithm and generative AI model.

[0872] For example, a 30-year-old female user who is busy with housework and childcare will be suggested an exercise plan that incorporates a short stretch or yoga session between 8:30 and 9:00 a.m. For factory robots, the system will predict the date when the next maintenance is required based on the robot's operation history, and provide audio guidance and a video link with the appropriate maintenance procedures at that time.

[0873] Example prompt sentence:

[0874] Please predict the next maintenance date and generate a voice command and video link to robot ID "001".

[0875] Operation history: Operation1 on October 1, 2023 at 10:00, Operation2 on October 2, 2023 at 10:00

[0876] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0877] Step 1:

[0878] The system receives basic user information and stores it in a database. Specifically, the user enters information such as age, gender, lifestyle, and exercise experience into a form, and the data is sent to the server and stored in the database. The entered information is used in subsequent analysis steps.

[0879] Step 2:

[0880] Based on the received basic information, the server identifies the user's lifestyle patterns. The server then inputs the received data into an analysis algorithm, converting the data and recognizing patterns to identify the user's daily behavior patterns. The output is analyzed lifestyle pattern data.

[0881] Step 3:

[0882] A generative AI model is used to create a personalized exercise plan based on the identified lifestyle patterns. The server inputs lifestyle pattern data into the generative AI model and generates an exercise plan. The output is data for an exercise plan optimized for the user.

[0883] Step 4:

[0884] Based on the created exercise plan, the server generates a personalized audio guide and sends it to the user's device. Specifically, the server uses a speech synthesis engine to generate audio guide from text and sends it to the user's device. The input is the exercise plan data, and the output is an audio file.

[0885] Step 5:

[0886] It provides video materials corresponding to the exercise menu and supports preparation and review. The server searches the database for video materials based on the exercise plan and sends the links to the user's device. The input is the exercise plan, and the output is the link to the video materials.

[0887] Step 6:

[0888] It records the user's exercise history and generates and displays a progress forecast. After the user exercises, the device records the exercise content and time and sends it to the server. The server receives this data, analyzes it, and generates a progress forecast. The output is a visually displayed graph or number.

[0889] Step 7:

[0890] Receives and stores the robot's operation history. When a factory robot performs a task, its operation history is sent from the terminal to the server and stored in a database. The input is the robot's operation history data, and the output is the stored data.

[0891] Step 8:

[0892] Analyzes operation history and generates a personalized maintenance plan. The server analyzes the stored operation history data and determines the optimal maintenance timing and procedure. The input is operation history data, and the output is maintenance plan data.

[0893] Step 9:

[0894] Based on the generated maintenance plan, the server provides voice guidance to the robot, instructing it on the appropriate maintenance procedures. The server uses a speech synthesis engine to convert the maintenance procedures into voice and sends them to the robot's terminal. The input is the maintenance plan data, and the output is an audio file.

[0895] Step 10:

[0896] It provides visual support for maintenance procedures by providing video materials. The server searches for video materials based on the maintenance plan and sends the link to the robot's terminal. The input is the maintenance plan, and the output is the link to the video materials.

[0897] 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.

[0898] The present invention provides a system for incorporating personalized exercise into a user's daily life, and further incorporates a function for recognizing the user's emotions, thereby helping the user to continue exercising more effectively. The following describes an embodiment of the present invention.

[0899] First, a user accesses the system and enters basic information such as age, gender, lifestyle, and exercise experience. The server receives this basic information and stores it in a database. The server then analyzes the stored information to identify the user's daily life patterns (time periods and frequency of activities such as housework, childcare, and work). A generative AI model is then used to create a personalized exercise plan based on these lifestyle patterns.

[0900] Based on the created exercise plan, the server generates a personalized audio guide and sends it to the user's device. When the user starts exercising, the device plays the audio guide, instructing the user on the appropriate movements and breathing techniques. It also provides video materials corresponding to the exercise menu so that the user can prepare and review. When the user selects an exercise menu, the device plays the video, allowing the user to learn the exact steps of the exercise.

[0901] Furthermore, the present invention incorporates an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's facial expressions, voice, or other biometric information to identify the user's emotions. The emotion information identified by the emotion engine is transmitted from the device to a server, and the exercise plan and audio guidance are dynamically adjusted based on this emotion information. For example, if the user feels fatigued or stressed, the exercise plan may be reduced or an audio guidance promoting relaxation may be provided.

[0902] After the exercise is completed, the device records the exercise details (type, time, etc.) and any emotional data recognized during the exercise, and sends them to the server. The server analyzes this exercise history and emotional information to generate a progress forecast. The progress forecast is displayed in concrete graphs and numerical values, and is provided in a visually easy-to-understand format for users. This progress forecast allows users to continue checking their progress and adjust their future exercise plans.

[0903] Furthermore, by utilizing the emotion engine, the server provides personalized feedback to improve the user's motivation. For example, if the user shows positive emotions while exercising, the server will reinforce that feedback and provide a message to motivate them to exercise again next time.

[0904] As a specific example of use, a 30-year-old female user is offered an exercise plan that incorporates a short stretch or yoga session between 8:30 and 9:00 a.m. during a busy day of housework and childcare. Following this plan, the user exercises following the audio guidance on the device, and watches the video to confirm correct movements. During exercise, the emotion engine monitors the user's emotions, and if the user feels fatigued or stressed, appropriate adjustments are made. After the exercise, the record is saved and progress forecast for one month later can be checked to maintain motivation toward specific goals.

[0905] As described above, the system of the present invention provides personalized exercise plans and feedback based on lifestyle patterns and emotional information, enabling users to efficiently incorporate exercise into their daily lives and lead continuously healthy lives.

[0906] The processing flow will be explained below.

[0907] Step 1:

[0908] The user accesses the system and enters basic information (age, gender, lifestyle, exercise experience, etc.). The server receives this information and stores it in a database.

[0909] Step 2:

[0910] The server analyzes the basic information stored in the database and identifies the user's daily life patterns (time periods and frequency of activities such as housework, childcare, and work).

[0911] Step 3:

[0912] The server uses the generative AI model to create a personalized exercise plan based on the identified lifestyle patterns, including exercises that can be easily incorporated into the user's daily life.

[0913] Step 4:

[0914] The server generates personalized audio guidance based on the created exercise plan and transmits it to the user's device. When the user starts exercising, the user can exercise according to the audio guidance received by the device.

[0915] Step 5:

[0916] When the user selects an exercise menu, the terminal receives the corresponding video material from the server and plays the video, allowing the user to visually learn the correct exercise steps.

[0917] Step 6:

[0918] When the user starts exercising, the emotion engine monitors the user's facial expressions and voice to identify emotional information, which is then sent from the device to the server.

[0919] Step 7:

[0920] When a user exercises, the server dynamically adjusts the exercise plan and audio guidance based on data from the emotion engine. For example, if the user feels fatigued, the server will reduce the exercise menu and provide guidance to encourage relaxation.

[0921] Step 8:

[0922] After the user finishes exercising, the device records the exercise details (type, time, etc.) and the emotion data recognized during the exercise, and sends the data to the server.

[0923] Step 9:

[0924] The server analyzes the received exercise history and emotional information to generate a progress forecast, which is displayed in concrete graphs and numerical values ​​in a format that is easy for users to understand visually.

[0925] Step 10:

[0926] The server sends the progress forecast generated by the server to the user's device, which displays it. The user can refer to this to check their own progress and maintain motivation for future exercise.

[0927] Step 11:

[0928] Using the emotion engine, the server provides personalized feedback to improve the user's motivation. For example, if the user shows positive emotions while exercising, the server reinforces that feedback and provides a message to motivate the user to exercise next time.

[0929] Through the above steps, the system of the present invention enables users to efficiently incorporate exercise into their daily lives and provides personalized feedback based on emotional information, enabling continuous health management.

[0930] Example 2

[0931] 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."

[0932] In modern society, it is difficult for busy users to continue exercising to maintain their health. In particular, there are no appropriate exercise plans that take into account individual lifestyle patterns and emotional states, making it difficult to continue exercising effectively. Furthermore, if a user's emotions change during exercise, it is not possible to respond in real time. This often leads to a decrease in the user's motivation to exercise, making it difficult to continue exercising.

[0933] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0934] In this invention, the server includes means for receiving a user's basic information and storing that information in a database; means for analyzing the received information and identifying the user's lifestyle patterns; means for using a generative AI model to create a personalized exercise plan based on the identified lifestyle patterns; means for generating a personalized audio guide based on the created exercise plan and sending it to the user's device; means for providing video materials corresponding to the exercise menu and supporting preparation and review; means for recognizing the user's emotions and dynamically adjusting the exercise plan and audio guide based on those emotions; and means for recording the user's exercise history and emotional data and generating and displaying a progress forecast. This allows for an effective exercise plan tailored to the user's individual lifestyle patterns and emotional state, helping them to continue exercising. Furthermore, real-time emotion recognition helps maintain the user's motivation.

[0935] "Means for receiving basic information about the user" refers to means for receiving basic information such as age, gender, lifestyle, and exercise experience entered by the user.

[0936] "Means for storing in a database" refers to a means for storing and managing received basic user information for the long term.

[0937] The "means for identifying a user's lifestyle patterns" refers to a means for analyzing the stored basic information and identifying the time periods and frequencies of the user's daily activities.

[0938] "Means for creating personalized exercise plans using a generative AI model" means means for generating an exercise plan suitable for an individual user using a generative AI model based on identified lifestyle patterns.

[0939] The "means for generating personalized audio guidance" refers to a means for generating personalized exercise instructions using voice synthesis technology based on the created exercise plan.

[0940] "Means for sending to the user's device" refers to means for sending the generated audio guide and exercise plan to the user's device such as a smartphone or computer.

[0941] The "means for providing video materials corresponding to an exercise menu" refers to a means for providing video materials corresponding to an exercise plan so that the user can easily learn the correct movements.

[0942] "Means to support preparation and review" refers to means to support users in preparing for and reviewing exercises using the provided video materials.

[0943] "Means for recognizing the user's emotions" refers to a means for analyzing the user's facial expressions, voice, and biometric information to identify their emotional state at that time.

[0944] The "means for dynamically adjusting the exercise plan and audio guidance" refers to a means for adjusting the exercise plan and audio guidance content in real time based on the identified emotional state of the user.

[0945] The "means for recording the user's exercise history and emotional data" refers to a means for recording the exercise data performed by the user and the emotional data at that time.

[0946] The "means for generating and displaying a progress forecast" is a means for analyzing the recorded exercise history and emotional data, predicting the user's exercise progress, and visually displaying it.

[0947] This invention provides a system that allows users to incorporate personalized exercise into their daily lives and further incorporates a function that recognizes the user's emotions, allowing the user to continue exercising more effectively. A specific embodiment of this system will be described below.

[0948] First, the user accesses the system and enters basic information such as age, gender, lifestyle, and exercise experience. This basic information is sent from the user's smartphone, PC, or other device to the server. The server then uses a database management system (DBMS) to store this information in a database. Specifically, database technology such as PostgreSQL is used.

[0949] Based on the stored information, the server performs analysis using Python libraries (e.g., pandas and scikit-learn). This analysis identifies the user's daily life patterns (time periods and frequency of activities such as housework, childcare, and work). Based on the identified lifestyle patterns, a generative AI model (e.g., OpenAI GPT-4) is used to generate a personalized exercise plan tailored to the user.

[0950] Based on the created exercise plan, the server uses speech synthesis software (e.g., Google Text-to-Speech) to generate personalized audio guidance and sends it to the user's device. When the user begins exercising, the device plays the audio guidance and also provides video materials corresponding to the exercise menu. This allows the user to check the correct movements while watching the video.

[0951] The system also incorporates an emotion engine. While the user is exercising, the device transmits the user's facial expressions, voice, and other biometric information to the emotion engine for analysis. This emotion engine utilizes Microsoft Azure Cognitive Services, among other services. The emotion information identified by the emotion engine is sent from the device to a server. The server can dynamically adjust the exercise plan and audio guidance based on this emotion information. For example, if the user feels fatigued or stressed, it can reduce the intensity of the exercise or provide audio guidance encouraging relaxation.

[0952] After the exercise is completed, the device records the exercise details (type, time, etc.) and any emotional data recognized during the exercise, and sends them to the server. The server analyzes this exercise history and emotional information to generate a progress forecast. The progress forecast is displayed in concrete graphs and numerical values, in a visually easy-to-understand format for users. This allows users to continue checking their progress and adjust their future exercise plans.

[0953] Furthermore, by utilizing the emotion engine, the server provides personalized feedback to improve the user's motivation. For example, if the user shows positive emotions while exercising, the server will reinforce that feedback and provide a message to motivate them to exercise again next time.

[0954] As a specific example of usage, a 30-year-old female user is offered an exercise plan that incorporates a short stretch or yoga session between 8:30 and 9:00 a.m. during a busy day of housework and childcare. Following this plan, the user exercises following the audio guidance on the device, and watches the video to confirm correct movements. During exercise, the emotion engine monitors the user's emotions, and if fatigue or stress is felt, appropriate adjustments are made. After the exercise, the record is saved and progress forecast for one month later can be checked, allowing the user to maintain motivation toward specific goals.

[0955] An example of a prompt sentence would be, "Please suggest a morning exercise plan for a 30-year-old female user, including short stretches and yoga, that she can incorporate into her daily housework and childcare. Adjust the exercise plan based on the user's emotions and provide feedback to increase motivation." This would be input into the generative AI model.

[0956] As described above, this system provides personalized exercise plans and feedback based on lifestyle patterns and emotional information, helping users to incorporate exercise efficiently into their daily lives and maintain a healthy lifestyle.

[0957] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0958] Step 1:

[0959] Users access the system and enter basic information such as age, gender, lifestyle, and exercise experience.

[0960] Input: User's basic information (age, gender, lifestyle, exercise experience)

[0961] Output: Basic information is sent from the device to the server.

[0962] How it works: The user enters the necessary information into a dedicated application on their smartphone or computer and clicks the send button. This information is then sent to a server via the Internet.

[0963] Step 2:

[0964] The server receives the user's basic information and stores it in a database.

[0965] Input: User basic information

[0966] Output: Basic information stored in the database

[0967] Specific operation: The server stores the received basic information in a database using a database management system (e.g., PostgreSQL). Specifically, it inserts data using the "INSERT" query.

[0968] Step 3:

[0969] The server analyzes the stored basic information to identify the user's daily life patterns.

[0970] Input: Saved basic information

[0971] Output: User's daily life patterns

[0972] How it works: The server uses Python's pandas and scikit-learn libraries to process basic information and identify patterns of when users are doing housework, childcare, work, etc. Specifically, it performs feature extraction based on this.

[0973] Step 4:

[0974] The server inputs prompt sentences into a generative AI model (e.g., OpenAI GPT-4) to generate a personalized exercise plan.

[0975] Input: User's daily life patterns

[0976] Output: Generated personalized exercise plan

[0977] Specific operation: A prompt such as "Please suggest a morning exercise plan for a 30-year-old female user, including short stretches and yoga, that she can incorporate into her daily routine between housework and childcare," is input into the generative AI model, and the model generates an exercise plan and returns it to the server.

[0978] Step 5:

[0979] Based on the exercise plan, the server uses speech synthesis software (e.g., Google Text-to-Speech) to generate audio guidance and transmits it to the device.

[0980] Input: Generated exercise plan

[0981] Output: The generated audio guide is sent to the terminal.

[0982] How it works: The server passes the exercise plan to the Google Text-to-Speech API, generates an audio file, and sends the audio file to the user's device.

[0983] Step 6:

[0984] The terminal plays back the received audio guide and provides video materials corresponding to the exercise menu.

[0985] Input: Audio guide, video materials

[0986] Output: Audio guide played, video material displayed

[0987] How it works: When a user opens the app on their device and presses the play button, an audio guide will be played, and at the same time, a video showing the exercise will be displayed on the device screen.

[0988] Step 7:

[0989] The device sends the user's facial expressions, voice, and biometric information to the emotion engine for analysis.

[0990] Input: User's facial expressions, voice, biometric information

[0991] Output: Parsed emotion data

[0992] How it works: The device uses built-in devices (camera and microphone) to capture the user's facial expressions and voice, and then sends them in real time to an emotion engine such as Microsoft Azure Cognitive Services for analysis.

[0993] Step 8:

[0994] The device transmits the analyzed emotion data to the server.

[0995] Input: Parsed emotion data

[0996] Output: Emotion data sent to the server

[0997] Specific operation: The analysis results are returned to the terminal, and then sent to the server via the Internet.

[0998] Step 9:

[0999] Based on the emotion data received by the server, the generative AI model is reused to dynamically adjust the movement plan and audio guidance.

[1000] Input: Emotion data

[1001] Output: Dynamically adjusted exercise plan and audio guidance

[1002] Specific operation: Based on the new emotional data, the server inputs a prompt such as, "This user is feeling tired. Please provide audio guidance to reduce the exercise intensity and encourage relaxation," into the generative AI model, and regenerates the exercise plan.

[1003] Step 10:

[1004] The device records the exercise performed by the user (type, time, etc.) and emotional data during exercise, and sends this data to the server.

[1005] Input: Exercise content, emotion data

[1006] Output: Exercise history and emotion data sent to the server

[1007] Specific operation: The device collects exercise data from the user's activity tracker or manual input, and sends it along with emotional data to the server.

[1008] Step 11:

[1009] The server analyzes exercise history and emotional data, and generates and displays progress predictions in concrete graphs and figures.

[1010] Input: Exercise history, emotion data

[1011] Output: Progress forecast graph, numerical value

[1012] What it does: The server uses Python libraries such as matplotlib and seaborn to visualize the data and generate reports in HTML and PDF format to visually display the user's progress.

[1013] Through these steps, the system can provide personalized exercise plans and feedback for users' daily lives, and make appropriate adjustments based on their emotional state.

[1014] (Application example 2)

[1015] 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."

[1016] Conventional exercise plan providing systems only provide exercise plans based on the user's basic information and do not take into consideration the user's real-time emotional state or work patterns. As a result, appropriate adjustments are not made when the user feels stressed or fatigued, which can lead to a decrease in motivation to continue exercising. The present invention aims to solve these problems and provide a system that allows users to work while maintaining their health.

[1017] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1018] In this invention, the server includes means for receiving a user's basic information and storing the information in a database, means for analyzing the received information and identifying the user's lifestyle patterns, means for using a generative AI model to create a personalized exercise plan based on the identified lifestyle patterns, means for generating a personalized audio guide based on the created exercise plan and sending it to the user's terminal, means for providing video materials corresponding to the exercise menu and supporting preparation and review, means for recording the user's exercise history and generating and displaying a progress forecast, and means for analyzing the user's emotional information and dynamically adjusting the exercise plan and audio guide. This dynamically adjusts the exercise plan based on the user's emotional state and work patterns, enabling the user to exercise appropriately even when feeling stressed or tired, thereby maintaining continuous health.

[1019] "Basic user information" refers to personal data such as the user's age, gender, lifestyle, exercise experience, and work patterns.

[1020] A "database" is a collection of electronic data used to store information and manage a user's basic information and exercise history.

[1021] "Lifestyle patterns" are information indicating the time periods and frequencies of activities in the user's daily life.

[1022] A "generative AI model" is an artificial intelligence model used to generate personalized exercise plans based on a user's lifestyle patterns.

[1023] A "personalized exercise plan" is an exercise plan that is individually tailored to the user's basic information and lifestyle patterns.

[1024] "Audio guide" is a system that provides users with exercise instructions and advice via voice.

[1025] "Device" refers to an electronic device used by a user, such as a smartphone or tablet.

[1026] "Visual materials" refers to video content such as videos and animations that allow users to visually understand the exercise menu.

[1027] "Exercise history" refers to recorded data such as the type and duration of exercise performed by the user.

[1028] "Progress prediction" predicts future exercise results and progress based on the user's exercise history.

[1029] "Emotional information" is data on the user's emotional state analyzed from facial expressions, voice, etc.

[1030] "Dynamic adjustment" means instantly changing exercise plans and audio guidance according to the user's real-time condition and environment.

[1031] An embodiment of this invention is "Fresh Delivery Fit," which provides a health support system for food delivery companies. The system receives and analyzes basic information about the user, generates a personalized exercise plan, and further analyzes the user's emotional state in real time to dynamically adjust the exercise plan.

[1032] First, the user enters basic information (age, gender, working hours, workload, etc.) via a device such as a smartphone. The server receives this basic information and stores it in a database. The software used for this is database software for data management (e.g., MySQL).

[1033] The server then uses generative AI models (e.g., TensorFlow, PyTorch) to create a personalized exercise plan based on the user's basic information and work patterns, including, for example, light stretching, moderate aerobic exercise, and high-intensity interval training.

[1034] Once the exercise plan is created, the server uses a voice guidance system (e.g., Amazon Polly or Google Text-to-Speech) to generate and send audio guidance for the exercise to the user's device. The user then begins exercising according to the received audio guidance.

[1035] During exercise, the user's emotional state is monitored using the smartphone's camera and microphone. An emotion engine (e.g., OpenCV, Affectiva API) analyzes this data and identifies the user's emotional information in real time. The server dynamically adjusts the exercise plan and audio guidance based on this emotional information. For example, if the user feels fatigued, the exercise plan may be changed to a relaxation stretch.

[1036] After an exercise session, the device records the exercise details (type, time, etc.) and emotional data, and sends them to the server. The server stores this data in a database, analyzes it, and then generates a progress forecast. The progress forecast is displayed as specific graphs and numbers, allowing users to visually check it.

[1037] Additionally, the app uses a generative AI model to provide personalized feedback to motivate users. For example, if a user expresses positive emotions during exercise, the app will reinforce that feedback and display a message to motivate them to exercise again next time. This feedback feature is a key factor in encouraging users to exercise consistently.

[1038] As a concrete example, consider the case where a 30-year-old female delivery worker uses this system. Because she often sits for long periods of time during her daily work, the server generates an exercise plan that takes into account her working hours and activity level. In this case, an example of a prompt sentence input into the generative AI model is as follows:

[1039] Example prompt sentence:

[1040] "User basic information: 30-year-old female, work hours: 9-18, number of deliveries: 7 times / day"

[1041] With these features, the system provides personalized exercise plans based on each user's individual needs and dynamically adjusts based on their emotional state, helping them stay healthy while performing their delivery duties.

[1042] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1043] Step 1:

[1044] The user starts the smartphone application and enters basic information (age, gender, working hours, workload, etc.). The entered basic information is temporarily saved on the device.

[1045] Step 2:

[1046] The terminal sends the input basic information to the server. The server stores the received basic information in a database. At this stage, the input is basic information, and the output is stored in the database.

[1047] Step 3:

[1048] The server uses the generative AI model to analyze the user's daily life and work patterns and create a personalized exercise plan based on this. At this stage, the input is basic information and the output is a personalized exercise plan. The server processes the data as follows: First, it inputs a prompt sentence into the generative AI model and generates an appropriate exercise plan.

[1049] Example prompt sentence:

[1050] "User basic information: 30-year-old female, work hours: 9-18, number of deliveries: 7 times / day"

[1051] Step 4:

[1052] The server generates audio guidance based on the created exercise plan and sends it to the user's device. An audio guidance system (e.g., Amazon Polly or Google Text-to-Speech) is used to generate exercise instructions and advice. At this stage, the input is the exercise plan and the output is the audio guidance.

[1053] Step 5:

[1054] When the user starts exercising, the device plays audio guidance and provides appropriate exercise instructions. At the same time, the device monitors the user's emotional state by collecting facial and voice data using the smartphone's camera and microphone. The input is the audio guidance and real-time user data, and the output is the collected emotional data.

[1055] Step 6:

[1056] The device sends the collected emotion data to the server in real time. The server uses an emotion engine (e.g., OpenCV, Affectiva API) to analyze the emotion data and identify the user's emotion information. At this stage, the input is emotion data, and the output is analyzed emotion information.

[1057] Step 7:

[1058] The server dynamically adjusts the exercise plan and audio guidance based on the emotional information. For example, if the user feels tired, the server changes the exercise plan to encourage relaxation. The input is emotional information, and the output is the adjusted exercise plan and audio guidance.

[1059] Step 8:

[1060] After the exercise session ends, the device records the exercise details (type, time, etc.) and emotional data and sends them to the server. The server stores this data in a database and analyzes it. The input is exercise details and emotional data, and the output is stored in the database.

[1061] Step 9:

[1062] The server analyzes the exercise history and generates a progress prediction. The generated progress prediction is displayed as a concrete graph and numerical values ​​and sent to the device, where the user can visually confirm it. The input is the exercise history and emotion data, and the output is a progress prediction graph and numerical values.

[1063] Step 10:

[1064] The server uses a generative AI model to provide personalized feedback to improve the user's motivation. For example, if a user expresses positive emotions, a feedback message that reinforces this emotion is sent to the device. The input is emotion data and exercise history, and the output is the feedback message.

[1065] The specific operations and data processing / calculation procedures performed at each step are defined when the system is designed, along with detailed program code.

[1066] 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.

[1067] 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.

[1068] 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.

[1069] [Fourth embodiment]

[1070] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1071] 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.

[1072] 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).

[1073] 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.

[1074] 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.

[1075] 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).

[1076] 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.

[1077] 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.

[1078] 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.

[1079] 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.

[1080] 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.

[1081] 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.

[1082] 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."

[1083] This is a system that allows users to incorporate personalized exercise into their daily lives. It analyzes their lifestyle patterns based on their basic information, generates an appropriate exercise plan, and supports them by providing audio guidance and video during exercise. It also provides a mechanism to maintain the user's motivation by recording their exercise history and predicting their progress based on that record.

[1084] Specifically, the user first accesses the system and enters basic information, including age, gender, lifestyle, exercise experience, etc. This information is received by the server and stored in a database.

[1085] The server then analyzes the received information to identify the user's lifestyle patterns, specifically the times and frequency of daily activities such as housework, childcare, and work, and uses a generative AI model to create a personalized exercise plan based on these lifestyle patterns.

[1086] Based on the created exercise plan, the server generates a personalized audio guide and sends it to the user's device. When the user starts exercising, the device plays the audio guide, instructing them on appropriate movements and breathing techniques.

[1087] In addition, video materials corresponding to the exercise menu are provided so that users can prepare and review. When the user selects an exercise menu, the device plays the video. This allows users to learn the exercise steps in advance and review them after exercising.

[1088] After completing an exercise session, the device records the exercise details and time and sends them to the server. The server then analyzes this exercise history and generates a progress forecast. The progress forecast is displayed in concrete graphs and numerical values, and is presented in a visually easy-to-understand format for users. This allows users to check their own progress and maintain motivation to continue exercising.

[1089] By using this system, users can easily incorporate exercise into their daily lives and maintain their health efficiently. As a specific example of its use, a 30-year-old female user is suggested an exercise plan that incorporates short stretches and yoga between 8:30 and 9:00 in the morning, even though she is busy with housework and childcare. Following this plan, the user exercises according to the audio guidance on the device, and watches the video to confirm correct movements. After the exercise, the record is saved and a progress forecast for one month later can be checked, maintaining motivation towards specific goals.

[1090] As described above, the system of the present invention enables people to efficiently incorporate exercise into their busy lives and supports healthy living.

[1091] The processing flow will be explained below.

[1092] Step 1:

[1093] Users access the system and enter basic information such as age, gender, lifestyle, exercise experience, etc. The server receives this information and stores it in a database.

[1094] Step 2:

[1095] The server analyzes the basic information stored in the database and identifies the user's daily life patterns (time periods and frequency of activities such as housework, childcare, and work).

[1096] Step 3:

[1097] The server uses the generative AI model to create a personalized exercise plan based on the identified lifestyle patterns, including exercises that can be easily incorporated into the user's daily life.

[1098] Step 4:

[1099] The server generates personalized audio guidance based on the created exercise plan and transmits it to the user's device. When the user starts exercising, the user can exercise according to the audio guidance received by the device.

[1100] Step 5:

[1101] When the user selects an exercise menu, the terminal receives the corresponding video material from the server and plays the video, allowing the user to visually learn the correct exercise steps.

[1102] Step 6:

[1103] After the user finishes exercising, the terminal records the exercise details (type, time, etc.) and sends the data to the server.

[1104] Step 7:

[1105] The server analyzes the received exercise history and generates a progress forecast that indicates the user's likely health and fitness improvement if they continue exercising.

[1106] Step 8:

[1107] The server sends the progress forecast generated to the user's device, which displays it as graphs and numerical values. Users can refer to this to check their own progress and maintain their motivation to continue.

[1108] Through the above steps, the system of the present invention helps users to effectively incorporate exercise as part of their daily lives and supports a healthy lifestyle.

[1109] Example 1

[1110] 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."

[1111] In recent years, it has become difficult to make exercise a habit in busy daily lives, and many people are not getting enough exercise to maintain a healthy lifestyle. Furthermore, the lack of personalized exercise plans and appropriate guidance impacts motivation to exercise. Furthermore, it is difficult to properly record exercise history and monitor progress, making it difficult to set specific goals.

[1112] 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.

[1113] In this invention, the server includes means for receiving a user's basic information and storing that information in a storage device, means for analyzing the received information and identifying the user's daily patterns, means for using a generative AI model to create an individual exercise plan based on the identified daily patterns, means for generating personalized audio guidance based on the created exercise plan and sending it to the user's device, means for providing video materials corresponding to the exercise menu and supporting pre-learning and review, and means for recording the user's exercise history and generating and displaying a progress forecast. This allows users to efficiently incorporate exercise into their busy daily lives and maintain a healthy lifestyle. Furthermore, the individually personalized guidance and progress management make it easier for them to continue exercising and achieve their goals.

[1114] "Basic information" refers to data such as the user's age, gender, lifestyle, and exercise experience.

[1115] "Storage device" refers to a device or system for storing data.

[1116] "Daily patterns" refer to characteristics such as the user's behavior and time allocation in daily life.

[1117] An "individual exercise plan" refers to an exercise plan personalized according to the user's lifestyle pattern.

[1118] "Audio guidance" refers to audio guidance that instructs proper movements and breathing techniques during exercise.

[1119] "Terminal" refers to a device used by a user, such as a computer, smartphone, or tablet.

[1120] "Visual materials" refers to visual content such as videos and animations that correspond to the exercise menu.

[1121] "Exercise history" refers to a record of the content and duration of exercise performed by the user.

[1122] "Progress prediction" refers to data that analyzes a user's exercise status based on their exercise history and predicts their future progress.

[1123] "Generative AI models" refer to algorithms or systems that use artificial intelligence technology to analyze user information and generate personalized exercise plans.

[1124] The present invention is a system for incorporating personalized exercise into a user's daily life. It analyzes the user's lifestyle patterns based on basic information, generates an appropriate exercise plan, and supports the user by providing audio guidance and video materials during exercise. It also provides a mechanism for maintaining the user's motivation by recording their exercise history and predicting their progress based on that record. Specific means for implementing this system are described below.

[1125] First, the user accesses the system and enters basic information. This basic information includes age, gender, lifestyle, and exercise experience. To enter this information, the system's web page or mobile application is used. Once the user enters this information, it is sent to the server. The server stores the received information in a storage device. A relational database (e.g., MySQL or PostgreSQL) is used for this storage.

[1126] The server then analyzes the user's daily patterns based on the stored basic information. This analysis uses data analysis tools (e.g., Python's pandas library and machine learning models). Specifically, the server analyzes data such as lifestyle and exercise experience to identify the optimal time and frequency for the user to exercise.

[1127] The server then uses a generative AI model to create an individual exercise plan based on the identified daily patterns. This generative AI model uses GPT-3 and other natural language processing models. The generated exercise plan includes specific exercise content and time, as well as appropriate stretching and yoga exercises. For example, an exercise plan can be created by inputting the following prompt into the generative AI model: "30-year-old female. Able to exercise between 8:30 and 9:00 AM every day. Housework and childcare are part of my daily life. I am a beginner at exercise. Please suggest an appropriate stretching and yoga exercise plan."

[1128] Based on the created exercise plan, the server generates individually tailored audio guidance and sends it to the user's device. A Text-to-Speech (TTS) engine (e.g., Google TTS or Amazon Polly) is used to generate the audio guidance. The server also provides video materials corresponding to the exercise menu. These video materials use existing video files or external video links (e.g., YouTube) so that users can watch them for advance learning or review.

[1129] When the user starts exercising, the device plays audio guidance, showing them the appropriate movements and breathing techniques. It also plays video material so the user can visually confirm the exercise. After the exercise is finished, the device records the exercise details and time and sends them to the server.

[1130] The server analyzes the exercise history and generates a progress forecast. This analysis is performed using data analysis tools (e.g., Python's pandas and NumPy). The generated progress forecast is displayed as specific graphs and numerical values, and is provided in a format that is easy for the user to understand visually. For example, the progress forecast is displayed as "Total exercise time this month: 12 hours" or "Next goal: 15 hours."

[1131] Through these means, this system enables users to incorporate exercise efficiently into their busy daily lives and provides support for maintaining a healthy lifestyle.

[1132] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1133] The flow of this system's program processing

[1134] Step 1:

[1135] The user enters basic information

[1136] Specific behavior:

[1137] Users access the system's web page or mobile app and enter basic information such as age, gender, lifestyle, and exercise experience. The input form provides text boxes and drop-down menus.

[1138] input:

[1139] User basic information (e.g., age, gender, lifestyle, exercise experience)

[1140] output:

[1141] Basic information is sent from the input form to the server.

[1142] Step 2:

[1143] The server receives the user's basic information and stores it in a database

[1144] Specific behavior:

[1145] The server receives the basic information submitted by the user and stores it in a relational database (e.g. MySQL, PostgreSQL), using SQL queries for this storage process.

[1146] input:

[1147] User basic information (e.g., age, gender, lifestyle, exercise experience)

[1148] output:

[1149] Basic information stored in the database

[1150] Step 3:

[1151] The server analyzes the user's daily patterns

[1152] Specific behavior:

[1153] The server reads the basic information stored in the database and uses data analysis tools (e.g., Python's pandas library and machine learning models) to analyze the user's daily patterns. Specifically, it identifies the optimal time and frequency for the user to exercise based on data such as lifestyle and exercise experience.

[1154] input:

[1155] Basic information read from the database

[1156] output:

[1157] Identified user's daily patterns (information on optimal exercise times and frequency)

[1158] Step 4:

[1159] The server creates an exercise plan using a generative AI model

[1160] Specific behavior:

[1161] The server creates an individualized exercise plan based on the identified daily patterns using a generative AI model (e.g., GPT-3). The generative AI model receives a prompt and generates an appropriate exercise plan as its output.

[1162] input:

[1163] Identified daily patterns (optimal time of day and frequency of exercise), prompt statement (e.g., "I am a 30-year-old woman. I am able to exercise between 8:30 and 9:00 AM every day. Housework and childcare are part of my daily life. I am a beginner at exercise. Please suggest an appropriate exercise plan including stretching and yoga.")

[1164] output:

[1165] Personalized exercise plans

[1166] Step 5:

[1167] The server generates audio guidance and video materials and sends them to the terminal.

[1168] Specific behavior:

[1169] The server generates audio guidance (e.g., a text-to-speech engine) and visual materials (e.g., video files and external links) based on the exercise plan and sends them to the user's device. The audio guidance instructs appropriate movements and breathing techniques, and the visual materials are used for advance learning and review of the exercise.

[1170] input:

[1171] Personalized exercise plans

[1172] output:

[1173] Audio guidance and video materials are sent to the user's device.

[1174] Step 6:

[1175] The user starts exercising, and the device plays audio guidance and video materials.

[1176] Specific behavior:

[1177] When the user starts exercising, the device plays audio guidance, showing them the appropriate movements and breathing techniques. It also plays video material on the device, allowing the user to visually check the exercise.

[1178] input:

[1179] Audio guide and video materials

[1180] output:

[1181] The user performs exercises based on visual and audio guidance

[1182] Step 7:

[1183] The device collects exercise records and sends them to the server.

[1184] Specific behavior:

[1185] When a user completes an exercise, the device records the exercise content and time, and this recorded data is sent to a server and saved as an exercise history.

[1186] input:

[1187] Exercise content and time

[1188] output:

[1189] Exercise history is sent to the server and stored in a database.

[1190] Step 8:

[1191] The server analyzes the exercise history and generates a progress forecast.

[1192] Specific behavior:

[1193] The server analyzes the received exercise history and generates a progress forecast using data analysis tools (e.g., Python's pandas or NumPy). The generated progress forecast is displayed on the user's device in the form of specific graphs and numerical values.

[1194] input:

[1195] Exercise history (exercise content and time)

[1196] output:

[1197] Progress forecast (graphs and specific figures)

[1198] The above is the specific processing procedure of the program for this system. This series of processing steps allows users to efficiently incorporate exercise into their daily lives and maintain a healthy lifestyle.

[1199] (Application example 1)

[1200] 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."

[1201] While conventional motion planning and maintenance systems can provide personalized motion plans based on human lifestyle patterns, they have not been able to adequately optimize the work efficiency and maintenance timing of robots in factories. Therefore, there is a need for a system that can provide personalized maintenance timing and procedures based on the robot's operation history.

[1202] 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.

[1203] In this invention, the server includes means for receiving basic information about the user and storing the information in a database, means for analyzing the received information and identifying the user's lifestyle patterns, means for using a generative AI model to create a personalized exercise plan based on the identified lifestyle patterns, means for generating a personalized audio guide based on the created exercise plan and sending it to the user's terminal, means for providing video materials corresponding to the exercise menu to support preparation and review, means for recording the user's exercise history and generating and displaying a progress forecast, means for receiving and storing the robot's movement history, means for analyzing the movement history and creating a personalized maintenance plan, means for providing audio guides to the robot and instructing appropriate maintenance procedures based on the created maintenance plan, and means for providing video materials and visual support for the maintenance procedures, thereby maximizing the robot's work efficiency and enabling appropriate maintenance timing to be predicted and performed.

[1204] "Basic information" refers to information that includes the individual characteristics and history of the user or robot, such as their age, gender, lifestyle, exercise experience, and movement history.

[1205] A "database" is an information system for storing received basic information and operation history, enabling efficient access and analysis.

[1206] "Lifestyle patterns" refer to the time periods and frequency of a user's daily activities, and are the subject of analysis to create personalized exercise plans based on that data.

[1207] A "generative AI model" is a machine learning model that creates personalized plans (exercise plans and maintenance plans) based on information about the user and the robot.

[1208] A "personalized exercise plan" is a plan that shows a customized exercise schedule and content based on the user's lifestyle patterns and basic information.

[1209] "Audio guide" is a system that provides appropriate instructions and procedures via voice to users and robots when performing exercise or maintenance.

[1210] "Visual materials" are materials such as videos and diagrams that visually show the procedures and details of exercise and maintenance.

[1211] "Exercise history" is a record of the exercises a user has performed, and that data is used to generate progress predictions.

[1212] "Progress prediction" is information that predicts future exercise progress and goals to be achieved based on the user's exercise history.

[1213] "Operation history" is a record of the robot's work content and operating time, and is data used to optimize maintenance timing and procedures.

[1214] A "maintenance plan" is a plan that indicates the optimal maintenance timing and procedures based on the robot's operating history.

[1215] The present invention provides a system for introducing a personalized exercise plan into a user's daily life and a system for optimizing the operation of a robot in a factory. The following configurations and procedures are included to implement the present invention.

[1216] First, basic information about the user or robot is received and stored in a database. This basic information includes age, gender, lifestyle, exercise experience, and movement history. This information is stored on a server and used for analysis.

[1217] The server then analyzes the received information to identify the user's lifestyle or the robot's behavioral patterns, and uses a generative AI model to create personalized exercise and maintenance plans based on the identified lifestyle and behavioral patterns.

[1218] Based on this, the server generates a personalized audio guide and sends it to the user's or robot's device. The device plays this audio guide and instructs the appropriate exercise or maintenance procedure. It also provides video materials corresponding to the exercise menu or maintenance procedure to help the user or operator prepare and review.

[1219] After completing exercise or maintenance, the device records the details and time of the exercise and sends it to the server. The server analyzes this history and generates a progress forecast, which is displayed in concrete graphs and numerical values ​​in a format that is easy for users and operators to understand visually.

[1220] The hardware required is a device (smartphone, smart glasses, head-mounted display, etc.) to receive information from the user or robot. The server also includes a system (cloud server or local server) for storing data and performing analysis using the generative AI model. The software required is a program that implements the analytical algorithm and generative AI model.

[1221] For example, a 30-year-old female user who is busy with housework and childcare will be suggested an exercise plan that incorporates a short stretch or yoga session between 8:30 and 9:00 a.m. For factory robots, the system will predict the date when the next maintenance is required based on the robot's operation history, and provide audio guidance and a video link with the appropriate maintenance procedures at that time.

[1222] Example prompt sentence:

[1223] Please predict the next maintenance date and generate a voice command and video link to robot ID "001".

[1224] Operation history: Operation1 on October 1, 2023 at 10:00, Operation2 on October 2, 2023 at 10:00

[1225] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1226] Step 1:

[1227] The system receives basic user information and stores it in a database. Specifically, the user enters information such as age, gender, lifestyle, and exercise experience into a form, and the data is sent to the server and stored in the database. The entered information is used in subsequent analysis steps.

[1228] Step 2:

[1229] Based on the received basic information, the server identifies the user's lifestyle patterns. The server then inputs the received data into an analysis algorithm, converting the data and recognizing patterns to identify the user's daily behavior patterns. The output is analyzed lifestyle pattern data.

[1230] Step 3:

[1231] A generative AI model is used to create a personalized exercise plan based on the identified lifestyle patterns. The server inputs lifestyle pattern data into the generative AI model and generates an exercise plan. The output is data for an exercise plan optimized for the user.

[1232] Step 4:

[1233] Based on the created exercise plan, the server generates a personalized audio guide and sends it to the user's device. Specifically, the server uses a speech synthesis engine to generate audio guide from text and sends it to the user's device. The input is the exercise plan data, and the output is an audio file.

[1234] Step 5:

[1235] It provides video materials corresponding to the exercise menu and supports preparation and review. The server searches the database for video materials based on the exercise plan and sends the links to the user's device. The input is the exercise plan, and the output is the link to the video materials.

[1236] Step 6:

[1237] It records the user's exercise history and generates and displays a progress forecast. After the user exercises, the device records the exercise content and time and sends it to the server. The server receives this data, analyzes it, and generates a progress forecast. The output is a visually displayed graph or number.

[1238] Step 7:

[1239] Receives and stores the robot's operation history. When a factory robot performs a task, its operation history is sent from the terminal to the server and stored in a database. The input is the robot's operation history data, and the output is the stored data.

[1240] Step 8:

[1241] Analyzes operation history and generates a personalized maintenance plan. The server analyzes the stored operation history data and determines the optimal maintenance timing and procedure. The input is operation history data, and the output is maintenance plan data.

[1242] Step 9:

[1243] Based on the generated maintenance plan, the server provides voice guidance to the robot, instructing it on the appropriate maintenance procedures. The server uses a speech synthesis engine to convert the maintenance procedures into voice and sends them to the robot's terminal. The input is the maintenance plan data, and the output is an audio file.

[1244] Step 10:

[1245] It provides visual support for maintenance procedures by providing video materials. The server searches for video materials based on the maintenance plan and sends the link to the robot's terminal. The input is the maintenance plan, and the output is the link to the video materials.

[1246] 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.

[1247] The present invention provides a system for incorporating personalized exercise into a user's daily life, and further incorporates a function for recognizing the user's emotions, thereby helping the user to continue exercising more effectively. The following describes an embodiment of the present invention.

[1248] First, a user accesses the system and enters basic information such as age, gender, lifestyle, and exercise experience. The server receives this basic information and stores it in a database. The server then analyzes the stored information to identify the user's daily life patterns (time periods and frequency of activities such as housework, childcare, and work). A generative AI model is then used to create a personalized exercise plan based on these lifestyle patterns.

[1249] Based on the created exercise plan, the server generates a personalized audio guide and sends it to the user's device. When the user starts exercising, the device plays the audio guide, instructing the user on the appropriate movements and breathing techniques. It also provides video materials corresponding to the exercise menu so that the user can prepare and review. When the user selects an exercise menu, the device plays the video, allowing the user to learn the exact steps of the exercise.

[1250] Furthermore, the present invention incorporates an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's facial expressions, voice, or other biometric information to identify the user's emotions. The emotion information identified by the emotion engine is transmitted from the device to a server, and the exercise plan and audio guidance are dynamically adjusted based on this emotion information. For example, if the user feels fatigued or stressed, the exercise plan may be reduced or an audio guidance promoting relaxation may be provided.

[1251] After the exercise is completed, the device records the exercise details (type, time, etc.) and any emotional data recognized during the exercise, and sends them to the server. The server analyzes this exercise history and emotional information to generate a progress forecast. The progress forecast is displayed in concrete graphs and numerical values, and is provided in a visually easy-to-understand format for users. This progress forecast allows users to continue checking their progress and adjust their future exercise plans.

[1252] Furthermore, by utilizing the emotion engine, the server provides personalized feedback to improve the user's motivation. For example, if the user shows positive emotions while exercising, the server will reinforce that feedback and provide a message to motivate them to exercise again next time.

[1253] As a specific example of use, a 30-year-old female user is offered an exercise plan that incorporates a short stretch or yoga session between 8:30 and 9:00 a.m. during a busy day of housework and childcare. Following this plan, the user exercises following the audio guidance on the device, and watches the video to confirm correct movements. During exercise, the emotion engine monitors the user's emotions, and if the user feels fatigued or stressed, appropriate adjustments are made. After the exercise, the record is saved and progress forecast for one month later can be checked to maintain motivation toward specific goals.

[1254] As described above, the system of the present invention provides personalized exercise plans and feedback based on lifestyle patterns and emotional information, enabling users to efficiently incorporate exercise into their daily lives and lead continuously healthy lives.

[1255] The processing flow will be explained below.

[1256] Step 1:

[1257] The user accesses the system and enters basic information (age, gender, lifestyle, exercise experience, etc.). The server receives this information and stores it in a database.

[1258] Step 2:

[1259] The server analyzes the basic information stored in the database and identifies the user's daily life patterns (time periods and frequency of activities such as housework, childcare, and work).

[1260] Step 3:

[1261] The server uses the generative AI model to create a personalized exercise plan based on the identified lifestyle patterns, including exercises that can be easily incorporated into the user's daily life.

[1262] Step 4:

[1263] The server generates personalized audio guidance based on the created exercise plan and transmits it to the user's device. When the user starts exercising, the user can exercise according to the audio guidance received by the device.

[1264] Step 5:

[1265] When the user selects an exercise menu, the terminal receives the corresponding video material from the server and plays the video, allowing the user to visually learn the correct exercise steps.

[1266] Step 6:

[1267] When the user starts exercising, the emotion engine monitors the user's facial expressions and voice to identify emotional information, which is then sent from the device to the server.

[1268] Step 7:

[1269] When a user exercises, the server dynamically adjusts the exercise plan and audio guidance based on data from the emotion engine. For example, if the user feels fatigued, the server will reduce the exercise menu and provide guidance to encourage relaxation.

[1270] Step 8:

[1271] After the user finishes exercising, the device records the exercise details (type, time, etc.) and the emotion data recognized during the exercise, and sends the data to the server.

[1272] Step 9:

[1273] The server analyzes the received exercise history and emotional information to generate a progress forecast, which is displayed in concrete graphs and numerical values ​​in a format that is easy for users to understand visually.

[1274] Step 10:

[1275] The server sends the progress forecast generated by the server to the user's device, which displays it. The user can refer to this to check their own progress and maintain motivation for future exercise.

[1276] Step 11:

[1277] Using the emotion engine, the server provides personalized feedback to improve the user's motivation. For example, if the user shows positive emotions while exercising, the server reinforces that feedback and provides a message to motivate the user to exercise next time.

[1278] Through the above steps, the system of the present invention enables users to efficiently incorporate exercise into their daily lives and provides personalized feedback based on emotional information, enabling continuous health management.

[1279] Example 2

[1280] 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."

[1281] In modern society, it is difficult for busy users to continue exercising to maintain their health. In particular, there are no appropriate exercise plans that take into account individual lifestyle patterns and emotional states, making it difficult to continue exercising effectively. Furthermore, if a user's emotions change during exercise, it is not possible to respond in real time. This often leads to a decrease in the user's motivation to exercise, making it difficult to continue exercising.

[1282] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1283] In this invention, the server includes means for receiving a user's basic information and storing that information in a database; means for analyzing the received information and identifying the user's lifestyle patterns; means for using a generative AI model to create a personalized exercise plan based on the identified lifestyle patterns; means for generating a personalized audio guide based on the created exercise plan and sending it to the user's device; means for providing video materials corresponding to the exercise menu and supporting preparation and review; means for recognizing the user's emotions and dynamically adjusting the exercise plan and audio guide based on those emotions; and means for recording the user's exercise history and emotional data and generating and displaying a progress forecast. This allows for an effective exercise plan tailored to the user's individual lifestyle patterns and emotional state, helping them to continue exercising. Furthermore, real-time emotion recognition helps maintain the user's motivation.

[1284] "Means for receiving basic information about the user" refers to means for receiving basic information such as age, gender, lifestyle, and exercise experience entered by the user.

[1285] "Means for storing in a database" refers to a means for storing and managing received basic user information for the long term.

[1286] The "means for identifying a user's lifestyle patterns" refers to a means for analyzing the stored basic information and identifying the time periods and frequencies of the user's daily activities.

[1287] "Means for creating personalized exercise plans using a generative AI model" means means for generating an exercise plan suitable for an individual user using a generative AI model based on identified lifestyle patterns.

[1288] The "means for generating personalized audio guidance" refers to a means for generating personalized exercise instructions using voice synthesis technology based on the created exercise plan.

[1289] "Means for sending to the user's device" refers to means for sending the generated audio guide and exercise plan to the user's device such as a smartphone or computer.

[1290] The "means for providing video materials corresponding to an exercise menu" refers to a means for providing video materials corresponding to an exercise plan so that the user can easily learn the correct movements.

[1291] "Means to support preparation and review" refers to means to support users in preparing for and reviewing exercises using the provided video materials.

[1292] "Means for recognizing the user's emotions" refers to a means for analyzing the user's facial expressions, voice, and biometric information to identify their emotional state at that time.

[1293] The "means for dynamically adjusting the exercise plan and audio guidance" refers to a means for adjusting the exercise plan and audio guidance content in real time based on the identified emotional state of the user.

[1294] The "means for recording the user's exercise history and emotional data" refers to a means for recording the exercise data performed by the user and the emotional data at that time.

[1295] The "means for generating and displaying a progress forecast" is a means for analyzing the recorded exercise history and emotional data, predicting the user's exercise progress, and visually displaying it.

[1296] This invention provides a system that allows users to incorporate personalized exercise into their daily lives and further incorporates a function that recognizes the user's emotions, allowing the user to continue exercising more effectively. A specific embodiment of this system will be described below.

[1297] First, the user accesses the system and enters basic information such as age, gender, lifestyle, and exercise experience. This basic information is sent from the user's smartphone, PC, or other device to the server. The server then uses a database management system (DBMS) to store this information in a database. Specifically, database technology such as PostgreSQL is used.

[1298] Based on the stored information, the server performs analysis using Python libraries (e.g., pandas and scikit-learn). This analysis identifies the user's daily life patterns (time periods and frequency of activities such as housework, childcare, and work). Based on the identified lifestyle patterns, a generative AI model (e.g., OpenAI GPT-4) is used to generate a personalized exercise plan tailored to the user.

[1299] Based on the created exercise plan, the server uses speech synthesis software (e.g., Google Text-to-Speech) to generate personalized audio guidance and sends it to the user's device. When the user begins exercising, the device plays the audio guidance and also provides video materials corresponding to the exercise menu. This allows the user to check the correct movements while watching the video.

[1300] The system also incorporates an emotion engine. While the user is exercising, the device transmits the user's facial expressions, voice, and other biometric information to the emotion engine for analysis. This emotion engine utilizes Microsoft Azure Cognitive Services, among other services. The emotion information identified by the emotion engine is sent from the device to a server. The server can dynamically adjust the exercise plan and audio guidance based on this emotion information. For example, if the user feels fatigued or stressed, it can reduce the intensity of the exercise or provide audio guidance encouraging relaxation.

[1301] After the exercise is completed, the device records the exercise details (type, time, etc.) and any emotional data recognized during the exercise, and sends them to the server. The server analyzes this exercise history and emotional information to generate a progress forecast. The progress forecast is displayed in concrete graphs and numerical values, in a visually easy-to-understand format for users. This allows users to continue checking their progress and adjust their future exercise plans.

[1302] Furthermore, by utilizing the emotion engine, the server provides personalized feedback to improve the user's motivation. For example, if the user shows positive emotions while exercising, the server will reinforce that feedback and provide a message to motivate them to exercise again next time.

[1303] As a specific example of usage, a 30-year-old female user is offered an exercise plan that incorporates a short stretch or yoga session between 8:30 and 9:00 a.m. during a busy day of housework and childcare. Following this plan, the user exercises following the audio guidance on the device, and watches the video to confirm correct movements. During exercise, the emotion engine monitors the user's emotions, and if fatigue or stress is felt, appropriate adjustments are made. After the exercise, the record is saved and progress forecast for one month later can be checked, allowing the user to maintain motivation toward specific goals.

[1304] An example of a prompt sentence would be, "Please suggest a morning exercise plan for a 30-year-old female user, including short stretches and yoga, that she can incorporate into her daily housework and childcare. Adjust the exercise plan based on the user's emotions and provide feedback to increase motivation." This would be input into the generative AI model.

[1305] As described above, this system provides personalized exercise plans and feedback based on lifestyle patterns and emotional information, helping users to incorporate exercise efficiently into their daily lives and maintain a healthy lifestyle.

[1306] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1307] Step 1:

[1308] Users access the system and enter basic information such as age, gender, lifestyle, and exercise experience.

[1309] Input: User's basic information (age, gender, lifestyle, exercise experience)

[1310] Output: Basic information is sent from the device to the server.

[1311] How it works: The user enters the necessary information into a dedicated application on their smartphone or computer and clicks the send button. This information is then sent to a server via the Internet.

[1312] Step 2:

[1313] The server receives the user's basic information and stores it in a database.

[1314] Input: User basic information

[1315] Output: Basic information stored in the database

[1316] Specific operation: The server stores the received basic information in a database using a database management system (e.g., PostgreSQL). Specifically, it inserts data using the "INSERT" query.

[1317] Step 3:

[1318] The server analyzes the stored basic information to identify the user's daily life patterns.

[1319] Input: Saved basic information

[1320] Output: User's daily life patterns

[1321] How it works: The server uses Python's pandas and scikit-learn libraries to process basic information and identify patterns of when users are doing housework, childcare, work, etc. Specifically, it performs feature extraction based on this.

[1322] Step 4:

[1323] The server inputs prompt sentences into a generative AI model (e.g., OpenAI GPT-4) to generate a personalized exercise plan.

[1324] Input: User's daily life patterns

[1325] Output: Generated personalized exercise plan

[1326] Specific operation: A prompt such as "Please suggest a morning exercise plan for a 30-year-old female user, including short stretches and yoga, that she can incorporate into her daily routine between housework and childcare," is input into the generative AI model, and the model generates an exercise plan and returns it to the server.

[1327] Step 5:

[1328] Based on the exercise plan, the server uses speech synthesis software (e.g., Google Text-to-Speech) to generate audio guidance and transmits it to the device.

[1329] Input: Generated exercise plan

[1330] Output: The generated audio guide is sent to the terminal.

[1331] How it works: The server passes the exercise plan to the Google Text-to-Speech API, generates an audio file, and sends the audio file to the user's device.

[1332] Step 6:

[1333] The terminal plays back the received audio guide and provides video materials corresponding to the exercise menu.

[1334] Input: Audio guide, video materials

[1335] Output: Audio guide played, video material displayed

[1336] How it works: When a user opens the app on their device and presses the play button, an audio guide will be played, and at the same time, a video showing the exercise will be displayed on the device screen.

[1337] Step 7:

[1338] The device sends the user's facial expressions, voice, and biometric information to the emotion engine for analysis.

[1339] Input: User's facial expressions, voice, biometric information

[1340] Output: Parsed emotion data

[1341] How it works: The device uses built-in devices (camera and microphone) to capture the user's facial expressions and voice, and then sends them in real time to an emotion engine such as Microsoft Azure Cognitive Services for analysis.

[1342] Step 8:

[1343] The device transmits the analyzed emotion data to the server.

[1344] Input: Parsed emotion data

[1345] Output: Emotion data sent to the server

[1346] Specific operation: The analysis results are returned to the terminal, and then sent to the server via the Internet.

[1347] Step 9:

[1348] Based on the emotion data received by the server, the generative AI model is reused to dynamically adjust the movement plan and audio guidance.

[1349] Input: Emotion data

[1350] Output: Dynamically adjusted exercise plan and audio guidance

[1351] Specific operation: Based on the new emotional data, the server inputs a prompt such as, "This user is feeling tired. Please provide audio guidance to reduce the exercise intensity and encourage relaxation," into the generative AI model, and regenerates the exercise plan.

[1352] Step 10:

[1353] The device records the exercise performed by the user (type, time, etc.) and emotional data during exercise, and sends this data to the server.

[1354] Input: Exercise content, emotion data

[1355] Output: Exercise history and emotion data sent to the server

[1356] Specific operation: The device collects exercise data from the user's activity tracker or manual input, and sends it along with emotional data to the server.

[1357] Step 11:

[1358] The server analyzes exercise history and emotional data, and generates and displays progress predictions in concrete graphs and figures.

[1359] Input: Exercise history, emotion data

[1360] Output: Progress forecast graph, numerical value

[1361] What it does: The server uses Python libraries such as matplotlib and seaborn to visualize the data and generate reports in HTML and PDF format to visually display the user's progress.

[1362] Through these steps, the system can provide personalized exercise plans and feedback for users' daily lives, and make appropriate adjustments based on their emotional state.

[1363] (Application example 2)

[1364] 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."

[1365] Conventional exercise plan providing systems only provide exercise plans based on the user's basic information and do not take into consideration the user's real-time emotional state or work patterns. As a result, appropriate adjustments are not made when the user feels stressed or fatigued, which can lead to a decrease in motivation to continue exercising. The present invention aims to solve these problems and provide a system that allows users to work while maintaining their health.

[1366] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1367] In this invention, the server includes means for receiving a user's basic information and storing the information in a database, means for analyzing the received information and identifying the user's lifestyle patterns, means for using a generative AI model to create a personalized exercise plan based on the identified lifestyle patterns, means for generating a personalized audio guide based on the created exercise plan and sending it to the user's terminal, means for providing video materials corresponding to the exercise menu and supporting preparation and review, means for recording the user's exercise history and generating and displaying a progress forecast, and means for analyzing the user's emotional information and dynamically adjusting the exercise plan and audio guide. This dynamically adjusts the exercise plan based on the user's emotional state and work patterns, enabling the user to exercise appropriately even when feeling stressed or tired, thereby maintaining continuous health.

[1368] "Basic user information" refers to personal data such as the user's age, gender, lifestyle, exercise experience, and work patterns.

[1369] A "database" is a collection of electronic data used to store information and manage a user's basic information and exercise history.

[1370] "Lifestyle patterns" are information indicating the time periods and frequencies of activities in the user's daily life.

[1371] A "generative AI model" is an artificial intelligence model used to generate personalized exercise plans based on a user's lifestyle patterns.

[1372] A "personalized exercise plan" is an exercise plan that is individually tailored to the user's basic information and lifestyle patterns.

[1373] "Audio guide" is a system that provides users with exercise instructions and advice via voice.

[1374] "Device" refers to an electronic device used by a user, such as a smartphone or tablet.

[1375] "Visual materials" refers to video content such as videos and animations that allow users to visually understand the exercise menu.

[1376] "Exercise history" refers to recorded data such as the type and duration of exercise performed by the user.

[1377] "Progress prediction" predicts future exercise results and progress based on the user's exercise history.

[1378] "Emotional information" is data on the user's emotional state analyzed from facial expressions, voice, etc.

[1379] "Dynamic adjustment" means instantly changing exercise plans and audio guidance according to the user's real-time condition and environment.

[1380] An embodiment of this invention is "Fresh Delivery Fit," which provides a health support system for food delivery companies. The system receives and analyzes basic information about the user, generates a personalized exercise plan, and further analyzes the user's emotional state in real time to dynamically adjust the exercise plan.

[1381] First, the user enters basic information (age, gender, working hours, workload, etc.) via a device such as a smartphone. The server receives this basic information and stores it in a database. The software used for this is database software for data management (e.g., MySQL).

[1382] The server then uses generative AI models (e.g., TensorFlow, PyTorch) to create a personalized exercise plan based on the user's basic information and work patterns, including, for example, light stretching, moderate aerobic exercise, and high-intensity interval training.

[1383] Once the exercise plan is created, the server uses a voice guidance system (e.g., Amazon Polly or Google Text-to-Speech) to generate and send audio guidance for the exercise to the user's device. The user then begins exercising according to the received audio guidance.

[1384] During exercise, the user's emotional state is monitored using the smartphone's camera and microphone. An emotion engine (e.g., OpenCV, Affectiva API) analyzes this data and identifies the user's emotional information in real time. The server dynamically adjusts the exercise plan and audio guidance based on this emotional information. For example, if the user feels fatigued, the exercise plan may be changed to a relaxation stretch.

[1385] After an exercise session, the device records the exercise details (type, time, etc.) and emotional data, and sends them to the server. The server stores this data in a database, analyzes it, and then generates a progress forecast. The progress forecast is displayed as specific graphs and numbers, allowing users to visually check it.

[1386] Additionally, the app uses a generative AI model to provide personalized feedback to motivate users. For example, if a user expresses positive emotions during exercise, the app will reinforce that feedback and display a message to motivate them to exercise again next time. This feedback feature is a key factor in encouraging users to exercise consistently.

[1387] As a concrete example, consider the case where a 30-year-old female delivery worker uses this system. Because she often sits for long periods of time during her daily work, the server generates an exercise plan that takes into account her working hours and activity level. In this case, an example of a prompt sentence input into the generative AI model is as follows:

[1388] Example prompt sentence:

[1389] "User basic information: 30-year-old female, work hours: 9-18, number of deliveries: 7 times / day"

[1390] With these features, the system provides personalized exercise plans based on each user's individual needs and dynamically adjusts based on their emotional state, helping them stay healthy while performing their delivery duties.

[1391] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1392] Step 1:

[1393] The user starts the smartphone application and enters basic information (age, gender, working hours, workload, etc.). The entered basic information is temporarily saved on the device.

[1394] Step 2:

[1395] The terminal sends the input basic information to the server. The server stores the received basic information in a database. At this stage, the input is basic information, and the output is stored in the database.

[1396] Step 3:

[1397] The server uses the generative AI model to analyze the user's daily life and work patterns and create a personalized exercise plan based on this. At this stage, the input is basic information and the output is a personalized exercise plan. The server processes the data as follows: First, it inputs a prompt sentence into the generative AI model and generates an appropriate exercise plan.

[1398] Example prompt sentence:

[1399] "User basic information: 30-year-old female, work hours: 9-18, number of deliveries: 7 times / day"

[1400] Step 4:

[1401] The server generates audio guidance based on the created exercise plan and sends it to the user's device. An audio guidance system (e.g., Amazon Polly or Google Text-to-Speech) is used to generate exercise instructions and advice. At this stage, the input is the exercise plan and the output is the audio guidance.

[1402] Step 5:

[1403] When the user starts exercising, the device plays audio guidance and provides appropriate exercise instructions. At the same time, the device monitors the user's emotional state by collecting facial and voice data using the smartphone's camera and microphone. The input is the audio guidance and real-time user data, and the output is the collected emotional data.

[1404] Step 6:

[1405] The device sends the collected emotion data to the server in real time. The server uses an emotion engine (e.g., OpenCV, Affectiva API) to analyze the emotion data and identify the user's emotion information. At this stage, the input is emotion data, and the output is analyzed emotion information.

[1406] Step 7:

[1407] The server dynamically adjusts the exercise plan and audio guidance based on the emotional information. For example, if the user feels tired, the server changes the exercise plan to encourage relaxation. The input is emotional information, and the output is the adjusted exercise plan and audio guidance.

[1408] Step 8:

[1409] After the exercise session ends, the device records the exercise details (type, time, etc.) and emotional data and sends them to the server. The server stores this data in a database and analyzes it. The input is exercise details and emotional data, and the output is stored in the database.

[1410] Step 9:

[1411] The server analyzes the exercise history and generates a progress prediction. The generated progress prediction is displayed as a concrete graph and numerical values ​​and sent to the device, where the user can visually confirm it. The input is the exercise history and emotion data, and the output is a progress prediction graph and numerical values.

[1412] Step 10:

[1413] The server uses a generative AI model to provide personalized feedback to improve the user's motivation. For example, if a user expresses positive emotions, a feedback message that reinforces this emotion is sent to the device. The input is emotion data and exercise history, and the output is the feedback message.

[1414] The specific operations and data processing / calculation procedures performed at each step are defined when the system is designed, along with detailed program code.

[1415] 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.

[1416] 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.

[1417] 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.

[1418] 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.

[1419] 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.

[1420] 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.

[1421] 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).

[1422] 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.

[1423] 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."

[1424] 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.

[1425] 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).

[1426] 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.

[1427] 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.

[1428] 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.

[1429] 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.

[1430] 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.

[1431] 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.

[1432] 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.

[1433] 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.

[1434] 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.

[1435] 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.

[1436] The following is further disclosed regarding the above embodiment.

[1437] (Claim 1)

[1438] a means for receiving basic information about the user and storing that information in a database;

[1439] A means for analyzing the received information and identifying the user's lifestyle patterns;

[1440] a means for generating a personalized exercise plan based on the identified lifestyle patterns using a generative AI model;

[1441] A means for generating a personalized audio guide based on the created exercise plan and transmitting the audio guide to the user's device;

[1442] Provide video materials corresponding to the exercise menu, and support preparation and review.

[1443] means for recording a user's exercise history and generating and displaying a progress forecast;

[1444] A system including:

[1445] (Claim 2)

[1446] 2. The system according to claim 1, further comprising means for receiving basic information of the user, including age, sex, lifestyle, and exercise experience.

[1447] (Claim 3)

[1448] 2. The system according to claim 1, further comprising means for analyzing exercise history and displaying progress predictions in concrete graphs and numerical values.

[1449] "Example 1"

[1450] (Claim 1)

[1451] means for receiving basic information about a user and storing the information in a storage device;

[1452] means for analyzing the received information and identifying the user's daily patterns;

[1453] a means for generating an individualized exercise plan based on the identified daily patterns using a generative AI model;

[1454] A means for generating individually set voice guidance based on the created exercise plan and transmitting the voice guidance to the user's terminal;

[1455] Provide video materials corresponding to the exercise menu to support pre-learning and review;

[1456] means for recording a user's exercise history and generating and displaying a progress forecast;

[1457] A system including:

[1458] (Claim 2)

[1459] 2. The system according to claim 1, further comprising means for receiving basic information of the user, including age, sex, lifestyle, and exercise experience.

[1460] (Claim 3)

[1461] 2. The system according to claim 1, further comprising means for analyzing exercise history and displaying progress predictions in concrete graphs and numerical values.

[1462] "Application Example 1"

[1463] (Claim 1)

[1464] a means for receiving basic information about the user and storing that information in a database;

[1465] A means for analyzing the received information and identifying the user's lifestyle patterns;

[1466] a means for generating a personalized exercise plan based on the identified lifestyle patterns using a generative AI model;

[1467] A means for generating a personalized audio guide based on the created exercise plan and transmitting the audio guide to the user's device;

[1468] Provide video materials corresponding to the exercise menu, and support preparation and review.

[1469] means for recording a user's exercise history and generating and displaying a progress forecast;

[1470] means for receiving and storing the robot's operation history;

[1471] A means for analyzing operation history and generating a personalized maintenance plan;

[1472] a means for providing voice guidance to the robot based on the generated maintenance plan and instructing the robot on appropriate maintenance procedures;

[1473] A means of providing visual support for maintenance procedures through video materials;

[1474] A system including:

[1475] (Claim 2)

[1476] 2. The system according to claim 1, further comprising means for receiving basic information of the user, including age, sex, lifestyle, and exercise experience.

[1477] (Claim 3)

[1478] 2. The system according to claim 1, further comprising means for analyzing exercise history and displaying progress predictions in concrete graphs and numerical values.

[1479] "Example 2: Combining Emotion Engines"

[1480] (Claim 1)

[1481] a means for receiving basic information about the user and storing that information in a database;

[1482] A means for analyzing the received information and identifying the user's lifestyle patterns;

[1483] a means for generating a personalized exercise plan based on the identified lifestyle patterns using a generative AI model;

[1484] A means for generating a personalized audio guide based on the created exercise plan and transmitting the audio guide to the user's device;

[1485] Provide video materials corresponding to the exercise menu, and support preparation and review.

[1486] means for recognizing a user's emotions and dynamically adjusting the exercise plan and audio prompts based on the emotions;

[1487] means for recording a user's exercise history and emotional data, and generating and displaying a progress forecast;

[1488] A system including:

[1489] (Claim 2)

[1490] 2. The system according to claim 1, further comprising means for receiving basic information of the user, including age, sex, lifestyle, and exercise experience.

[1491] (Claim 3)

[1492] 2. The system according to claim 1, further comprising means for analyzing exercise history and emotional data and displaying progress predictions in concrete graphs and numerical values.

[1493] "Application example 2 when combining emotion engines"

[1494] (Claim 1)

[1495] a means for receiving basic information about the user and storing that information in a database;

[1496] A means for analyzing the received information and identifying the user's lifestyle patterns;

[1497] a means for generating a personalized exercise plan based on the identified lifestyle patterns using a generative AI model;

[1498] A means for generating a personalized audio guide based on the created exercise plan and transmitting the audio guide to the user's device;

[1499] Provide video materials corresponding to the exercise menu, and support preparation and review.

[1500] means for recording a user's exercise history and generating and displaying a progress forecast;

[1501] A means to analyze the user's emotional information and dynamically adjust the exercise plan and audio guidance.

[1502] A system including:

[1503] (Claim 2)

[1504] 2. The system according to claim 1, further comprising means for receiving basic information of the user, such as age, gender, lifestyle, and exercise experience, and analyzing the user's work patterns.

[1505] (Claim 3)

[1506] 10. The system of claim 1, further comprising means for analyzing the user's emotional information in real time using an emotion engine and adjusting the exercise plan based thereon. [Explanation of symbols]

[1507] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for receiving basic information about the user and storing that information in a database; A means for analyzing the received information and identifying the user's lifestyle patterns; a means for generating a personalized exercise plan based on the identified lifestyle patterns using a generative AI model; A means for generating a personalized audio guide based on the created exercise plan and transmitting the audio guide to the user's device; Provide video materials corresponding to the exercise menu to support preparation and review, means for recording a user's exercise history and generating and displaying a progress forecast; A system including:

2. 2. The system according to claim 1, further comprising means for receiving basic information of the user, including age, sex, lifestyle, and exercise experience.

3. 2. The system according to claim 1, further comprising means for analyzing an exercise history and displaying a progress forecast in the form of specific graphs and numerical values.

Images