System

Abstract

A system is provided.SOLUTION: This system includes a means for collecting necessary information from the Internet, a means for generating a training plan on the basis of the collected information, a means for providing the generated training plan to a user terminal, a means for recording a progress situation input by a user and reporting it to a server, and a means for storing progress data received by the server.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] Setting individual goals, developing training plans, and tracking progress are important for self-realization, but these tasks are difficult for individuals to perform manually. Elderly people and professional athletes, in particular, require specific methods for maintaining their health and performance, but managing these independently is complex. Furthermore, the vast amount of information available on the Internet presents challenges, making it difficult to effectively collect and analyze it. Furthermore, it is difficult for users to record their daily training progress and effectively utilize that data. To address these challenges, a system that can provide efficient and continuous support is needed. [Means for solving the problem]

[0005] This invention is a system that includes a means for collecting necessary information from the Internet, a means for generating a training plan based on the collected information, a means for providing the generated training plan to a user terminal, a means for recording the progress entered by the user and reporting it to a server, and a means for saving the progress data received by the server. The system also includes a means for creating a training plan using a generative AI model based on the user's profile and goals, thereby providing training tailored to individual needs. Furthermore, the system includes a means for graph-structuring the collected information and systematizing it according to content level, thereby increasing the accuracy and usefulness of the information. This allows for efficient and effective support for individual self-realization.

[0006] "Means of collecting necessary information from the Internet" refers to methods and tools for obtaining data from sources connected to the Internet, such as websites and online databases.

[0007] A "means for generating a training plan" is a method or algorithm that automatically creates training content tailored to individual goals and needs based on collected data.

[0008] The "means for providing to a user terminal" refers to a method for distributing the generated training plan to a device that the user can access.

[0009] The "means for recording progress and reporting to the server" refers to a method or interface for a user to input the results of the training and progress data they have performed and to transmit that data to a remote server.

[0010] The "means for saving received progress data" refers to a method for saving the progress data sent from the user in a storage device such as a database.

[0011] "User profile and goals" refers to basic information about the individual receiving the training and the goals they wish to achieve.

[0012] A "generative AI model" is an artificial intelligence model that takes collected data and the user's profile and goals as input and outputs a training plan.

[0013] "Graph structuring" is a method of organizing collected information into a graph with nodes and edges to make it easier to visualize and analyze.

[0014] "Means for organizing according to content level" refers to a method for classifying collected information according to its importance and relevance and for systematically organizing it. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] This invention is a system that collects necessary information from the Internet, creates a training plan based on that information, and provides it to the user.The user then reports their recorded progress to a server, which stores the data, thereby supporting personal self-realization.

[0037] The server first automatically collects the necessary information from the Internet. For example, it uses scraping techniques to obtain data from online databases containing health websites or exercise instruction guides. This data is then organized by extracting relevant information from specific HTML elements. The scraped data is then converted into the required format and stored in a database.

[0038] The server then uses a generative AI model based on the collected data to generate a training plan tailored to the user's profile and goals. For example, for a professional athlete, a plan would be generated that includes specific training menus and nutritional management for a match. On the other hand, for the elderly, a light exercise plan aimed at maintaining health would be generated. The collected information is then graph-structured and organized according to the importance and relevance of the content, improving the accuracy and effectiveness of the plan.

[0039] The generated training plan is sent from the server to the user's device. The user can check the training plan that suits them best via their smartphone, tablet, or other device. The device displays the details of each day's training, and the user carries out the training based on the plan. For example, specific details such as "On Mondays, I will jog for 30 minutes and stretch for 15 minutes" are displayed.

[0040] After completing a training session, the user records their progress using their device. This progress data is saved on the device as input by the user and then sent to the server. The server saves the received progress data in a database and manages the user's training history. This ensures that the user's progress is always recorded up to date and is reflected in the next training plan.

[0041] As a concrete example, in the case of supporting self-realization for the elderly, the server collects health information from sites such as "http: / / example.com / health-tips," and then uses the collected information to create a training plan consisting of "daily light exercise" and "balanced diet" using a generative AI model. When the user enters "I walked for 15 minutes" into the device, the progress information is sent to the server and reflected in the next plan.

[0042] This system will efficiently support the self-realization of individuals and effectively maintain the health and improve the abilities of elderly people and professional athletes.

[0043] The processing flow will be explained below.

[0044] Step 1:

[0045] The server collects the necessary information from the Internet. Here, the server sends HTTP requests to the URLs of multiple configured websites and scrapes the resulting HTML documents. In doing so, it extracts the necessary data from specific HTML elements and temporarily stores this data in memory.

[0046] Step 2:

[0047] The server analyzes and organizes the extracted data. The server filters out only the necessary parts of the scraped information and converts it into a format that can be stored in a database. For example, it extracts the article title, body text, and important keywords and stores this in the database as structured data.

[0048] Step 3:

[0049] The server retrieves the user's profile information and goals from the database, including the user's age, gender, physical fitness level, and specific self-realization goals.

[0050] Step 4:

[0051] Based on the information collected by the server and the user's profile information, a generative AI model is used to generate a training plan, such as a gentle exercise menu for the elderly or a detailed plan for professional athletes that will improve their athletic performance.

[0052] Step 5:

[0053] The server provides the generated training plan to the user's device, and the server synchronizes the training plan with the user's device so that the user can check the plan at any time.

[0054] Step 6:

[0055] The user performs the training using the device. The user checks the daily training content on the device screen and performs the exercise according to the instructions. For example, specific exercises such as "30 minutes of jogging" or "15 minutes of stretching" are instructed.

[0056] Step 7:

[0057] After completing the training, the user enters their progress into the terminal, and an interface is provided to record the content of the training, the time required, and subjective impressions.

[0058] Step 8:

[0059] The terminal reports the user's input data to the server. The input progress data is immediately sent to the server.

[0060] Step 9:

[0061] The server stores the received progress data in a database and reflects it in the next training plan. The server evaluates the user's training progress based on the new data and adjusts the training plan as necessary.

[0062] This continuous processing effectively supports the self-actualization of individuals and helps users maintain their health and improve their abilities.

[0063] Example 1

[0064] 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."

[0065] Conventional training plans have the problem of not being able to fully reflect the needs and goals of individual users and can only provide generic exercise programs. Furthermore, progress management is often done manually, which often results in delays in updating and improving plans. Therefore, there is a need for a system that can automatically generate optimal training plans for individual users and efficiently manage progress.

[0066] 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.

[0067] In this invention, the server includes means for collecting necessary information from the Internet, means for converting the collected information into a specific format and saving it, means for generating prompts using a generative AI model based on the collected information and the user's profile to create a training plan, means for providing the generated training plan to the user's terminal, means for recording the progress entered by the user and reporting it to the server, and means for saving the progress data received by the server. This makes it possible to automatically generate a training plan according to the user's individual needs and goals and efficiently manage the user's progress.

[0068] "Means for collecting necessary information from the Internet" refers to means for automatically collecting data such as health information and exercise methods from websites and online databases on the Internet.

[0069] "Means of converting and saving data in a specific format based on collected information" refers to means of converting collected data into a specific format (e.g., JSON, CSV) and saving that data in a database, etc.

[0070] "Means for generating prompt sentences using a generative AI model and creating a training plan" means means for generating prompt sentences based on collected information and a user profile, and for automatically creating a training plan using a generative AI model (e.g., a natural language generation model) with the generated prompt sentences.

[0071] The "means for providing the generated training plan to the user terminal" refers to a means for delivering the generated training plan to the terminal used by the user so that the training plan can be confirmed on that terminal.

[0072] The "means for recording the progress status input by the user and reporting it to the server" is a means for the user to input the progress status of his / her training, record the information, and send it to the server.

[0073] The "means for storing the progress data received by the server" refers to a means for the server to store the progress data received from the user in a database or the like, and use the data to generate future training plans.

[0074] This invention is a system that collects necessary information from the Internet, generates a training plan based on that information, and provides it to the user. This system is operated using a server, a user terminal, and a generative AI model. The server collects health information and exercise methods from the Internet, and the generated training plan is distributed to the user terminal. Furthermore, the user's progress is recorded on the server and reflected in the next training plan.

[0075] Information gathering

[0076] The server accesses specific health information websites and databases and automatically extracts relevant information using a scraping tool (e.g., BeautifulSoup or Scrapy). For example, the server collects information such as "jogging three times a week is good for your health" from "http: / / example.com / health-tips." This information is saved as a temporary file (e.g., a CSV file).

[0077] Data conversion and storage

[0078] The server converts the collected information into JSON format and stores it in a relational database (e.g., MySQL). For example, the text information "jogging three times a week is good for your health" is converted into JSON format and stored in the database.

[0079] Generate a training plan

[0080] The server uses a generative AI model (e.g., GPT-4) to create a training plan based on the collected information and the user's profile. For example, if the user's information is "60-year-old male, high blood pressure, medium physical fitness level, goal: maintain health," the server inputs the following prompt sentence into the generative AI model:

[0081] User: Elderly, Goal: Maintaining health, Current physical condition: Able to walk for 15 minutes

[0082] Prompt: "Generate a moderate exercise plan for maintaining good health. Include a one-week plan with daily exercise and balanced meal suggestions."

[0083] This allows the generative AI model to generate specific training plans such as "jogging three times a week" and "daily vegetable intake plan."

[0084] Training plan distribution

[0085] The server delivers the generated training plan to the user's device. The user's device displays the delivered information, and the user carries out the training based on it. For example, a detailed plan such as "30 minutes of jogging and 15 minutes of stretching on Mondays" is displayed on the smartphone.

[0086] Record and manage progress

[0087] After completing a workout, the user uses the device to input progress information (e.g., "I walked for 15 minutes"). The device temporarily saves the user's input and sends it to the server. The server saves the received progress data in a database and manages the user's training history. This information is reflected in the next workout plan.

[0088] This system allows users to easily obtain training plans that suit their needs and goals, and efficiently maintain or improve their health. In addition, because progress is properly recorded and managed, the accuracy and effectiveness of training plans are improved.

[0089] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0090] Step 1:

[0091] Information gathering

[0092] The server accesses a specific health information website on the Internet (e.g., http: / / example.com / health-tips). As input, it uses a list of URLs and uses a scraping tool (e.g., BeautifulSoup or Scrapy) to extract relevant information (e.g., "jogging three times a week is good for your health"). Specifically, it extracts HTML elements (e.g., The necessary information is extracted from the tags (text within tags) and saved to a temporary file (e.g., a CSV file). The output is a temporary file containing the collected health information.

[0093] Step 2:

[0094] Data conversion and storage

[0095] The server converts data stored in a temporary file into JSON format. As input, there is data stored in the form of a temporary file. This data is converted into a specific format (e.g. JSON) and stored in a relational database (e.g. MySQL). Specifically, the server converts the text "Jogging three times a week is good for your health" into JSON format and stores it in a MySQL database. As output, it gets the information stored in the database.

[0096] Step 3:

[0097] Generate a training plan

[0098] The server creates a training plan using a generative AI model (e.g., GPT-4) based on the collected information and the user's profile. The inputs are the user's profile information (e.g., age, gender, and fitness level) and the collected data. The server generates a prompt sentence and inputs it into the generative AI model. Specifically, the server generates the following prompt sentence:

[0099] User: Elderly, Goal: Maintaining health, Current physical condition: Able to walk for 15 minutes

[0100] Prompt: "Generate a moderate exercise plan for maintaining good health. Include a one-week plan with daily exercise and balanced meal suggestions."

[0101] Based on this prompt, the generative AI model outputs a specific training plan such as "jogging three times a week" and "daily vegetable intake plan."

[0102] Step 4:

[0103] Training plan distribution

[0104] The server distributes the generated training plan to the user's device. The input is the generated training plan. The server sends the plan to the user's device (e.g., smartphone or tablet), which receives it. Specifically, the server distributes information such as "On Mondays, do 30 minutes of jogging and 15 minutes of stretching" to the user's smartphone, which then displays the information on the device. The output is the training plan displayed on the user's device.

[0105] Step 5:

[0106] Record and manage progress

[0107] After completing a workout, the user uses the device to input progress information (e.g., "Walked for 15 minutes"). The input is the user's progress information. The device temporarily stores this information and sends it to the server. Specifically, the user enters the progress information into their smartphone, and the device sends the information to the server. The server saves the received progress data in a database and manages the user's training history. The output is the updated progress data saved in the database.

[0108] This allows users to keep track of their progress and reflect it in their next training plan.

[0109] (Application example 1)

[0110] 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."

[0111] It is difficult for physical fitness facilities such as fitness clubs and gyms to provide personalized training plans in real time that meet the diverse needs of users and to properly manage their progress. A system that can effectively and efficiently solve this problem is needed. There is also a need for technology that can generate optimal training plans for each user based on collected health information and training data.

[0112] 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.

[0113] In this invention, the server includes means for collecting necessary information from the Internet, means for generating a training plan based on the collected information, means for providing the generated training plan to a user terminal, means for recording progress input by the user and reporting it to the server, means for saving progress data received by the server, and means for providing a training plan to the user at a fitness club or gym in real time and reflecting the user's progress. This makes it possible to provide a personalized training plan for each user in real time and appropriately manage the user's progress.

[0114] "Means of collecting necessary information from the Internet" refers to a method of automatically obtaining specific information from websites, online databases, etc.

[0115] The "means for generating a training plan based on collected information" refers to a method or device for analyzing the acquired data and creating an individual training menu based on the data.

[0116] The "means for providing the generated training plan to a user terminal" refers to a method or device for displaying or transmitting the generated training plan to a user terminal such as a smartphone or tablet.

[0117] The "means for recording the progress status input by the user and reporting it to the server" refers to a method or device in which the user inputs his or her own progress, records the data, and transmits it to the server.

[0118] The "means for storing the progress data received by the server" refers to a method or device by which the server stores the progress data sent from the user in a database.

[0119] "Means for providing users with training plans in real time at fitness clubs or gyms and reflecting progress" refers to a method or device that presents a training plan to users on the spot at a physical store and immediately reflects the progress and results in the plan.

[0120] The system for realizing the present invention mainly uses a server, a user terminal, and various software.

[0121] The server first collects the necessary information from the Internet. Specifically, it uses web scraping technology to obtain data from health-related websites and online databases, organizes this data, and stores it in a database. For scraping, it uses the Python libraries Requests and BeautifulSoup. For example, health information can be collected from "http: / / example.com / health-tips."

[0122] Based on the collected data, the server generates a training plan using a generative AI model based on the user's profile and goals. This is done using OpenAI's API. Here is an example of a prompt:

[0123] User profile: Elderly, 65 years old, looking for light exercise without strain

[0124] User goals: Light exercise every day to maintain health

[0125] Health tips: ['15 minutes of walking every day is recommended', 'Eat a balanced diet']

[0126] Generate a personalized training plan.

[0127] The generated training plan is provided from the server to the user's device, which could be a smartphone, tablet, smart glasses, or head-mounted display. The user can view their personalized training plan through these devices. For example, detailed content such as "On Mondays, I plan to jog for 30 minutes and stretch for 15 minutes" is displayed.

[0128] After completing a workout, the user enters their progress into the device. The entered progress data is sent to the server, which stores it in a database. The server then adjusts the next training plan as needed based on the received progress data. This ensures that the user's progress is always recorded up to date, maximizing the effectiveness of their individual training.

[0129] For example, if a user at a fitness club or gym inputs "I walked for 15 minutes" into a device, that information is sent to the server in real time and immediately reflected in the next training plan, effectively providing training tailored to the user's individual needs.

[0130] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0131] Step 1:

[0132] The server collects the necessary information from the Internet. Specifically, it uses Python's Requests library to access health-related websites and retrieve their content. Next, it uses the BeautifulSoup library to parse HTML elements and extract health information in text format. The input for this process is the website URL, and the output is the extracted health information in text format.

[0133] Step 2:

[0134] The server stores the collected health information in a database. For example, it uses a database service such as Amazon RDS to store the acquired data. The input of this process is the text data extracted in step 1, and the output is the information stored in the database.

[0135] Step 3:

[0136] The server receives the user's profile and goals. It collects the profile information (age, exercise experience, health status, etc.) and goals (maintaining health, losing weight, etc.) entered by the user through the terminal. The input of this process is the information entered by the user into the terminal, and the output is the user's profile and goal data stored on the server.

[0137] Step 4:

[0138] The server generates a training plan using a generative AI model based on the collected health information and the user's profile and goals. The generative AI model uses OpenAI's API. For example, the following prompt sentence can be input into the generative AI model to generate a training plan:

[0139] User profile: Elderly, 65 years old, looking for light exercise without strain

[0140] User goals: Light exercise every day to maintain health

[0141] Health tips: ['15 minutes of walking every day is recommended', 'Eat a balanced diet']

[0142] Generate a personalized training plan.

[0143] The input to this process is a prompt sentence, and the output is the generated training plan text data.

[0144] Step 5:

[0145] The server provides the generated training plan to the user's device, where the user can view the training plan via a smartphone, tablet, smart glasses, etc. The input of this process is the generated training plan, and the output is the training plan displayed on the user's device.

[0146] Step 6:

[0147] The user performs training and inputs their progress into the device. For example, they enter information such as "I walked for 15 minutes" into their smartphone. The input of this process is the progress information that the user enters into the device, and the output is the progress data stored on the device.

[0148] Step 7:

[0149] The device sends the user's progress data to the server, which stores the received progress data in a database and reflects it in the next training plan. The input of this process is the user's progress data, and the output is the updated progress data stored in the database.

[0150] Step 8:

[0151] The server regenerates the next training plan based on the progress data. The new training plan is adjusted based on the current plan and progress. The input to this process is the updated progress data, and the output is the updated training plan.

[0152] 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.

[0153] This invention is a system that collects necessary information from the Internet, creates a training plan based on that information, and provides it to the user. The system also supports the user's self-realization by reporting the user's recorded progress to a server, which then stores the data. Furthermore, it incorporates an emotion engine that recognizes the user's emotions and adjusts the training plan based on those emotions.

[0154] The server first automatically collects the necessary information from the Internet, for example, by scraping data from online databases containing health websites or exercise instruction guides. This data is then organized by extracting relevant information from specific HTML elements and stored in a database.

[0155] The server then uses the collected data to generate a training plan based on the user's profile and goals using a generative AI model. For example, a professional athlete might receive a plan with specific training and nutritional information for a match, while an elderly person might receive a low-intensity exercise plan aimed at maintaining health.

[0156] Furthermore, the emotion engine recognizes the user's emotions. The emotion engine analyzes the user's voice, facial expressions, text input, etc. to identify their emotional state. This emotion data is provided to a generative AI model and used to adjust the training plan. For example, if the user is feeling stressed, a training plan will be generated that includes relaxation exercises and stretches. In this way, a more appropriate training plan is provided based on the user's emotional state.

[0157] The generated training plan is sent from the server to the user's device. The user can check the training plan that suits them best via their smartphone, tablet, or other device. The device displays the details of each day's training, and the user carries out the training based on the plan. For example, specific details such as "On Mondays, I will jog for 30 minutes and stretch for 15 minutes" are displayed.

[0158] After completing the training, the user uses the device to record their progress. An interface is provided to record the training content, duration, subjective impressions, etc. An emotion engine is also used to record the user's emotional state during training and evaluate the effectiveness of the training.

[0159] The device reports the progress and emotional data entered by the user to the server, which stores the received data in a database and reflects it in the next training plan. This ensures that the user's progress and emotional state are always recorded up to date, improving the quality of the next plan.

[0160] As a concrete example, in the case of supporting self-realization for the elderly, the server collects health information from sites such as "http: / / example.com / health-tips," and then uses the collected information to create a training plan consisting of "daily light exercise" and "balanced meals" using a generative AI model. The emotion engine analyzes the user's voice and facial expressions, and if it recognizes, for example, that the user is "very tired," relaxation exercises are added to the plan. When the user enters "I walked for 15 minutes" into the device, the progress information and emotional state are sent to the server and reflected in the next plan.

[0161] This system will efficiently support the self-realization of individuals, as well as effectively maintain the health and improve the performance of elderly people and professional athletes. By incorporating an emotion engine, more personalized training plans will be provided, further improving user satisfaction and the effectiveness of training.

[0162] The processing flow will be explained below.

[0163] Step 1:

[0164] The server gathers the necessary information from the Internet, specifically by sending an HTTP request to retrieve HTML documents from online databases containing health websites and exercise instruction.

[0165] Step 2:

[0166] The server extracts specific elements from the HTML documents obtained using scraping techniques, such as article titles, body text, and related keywords, and stores them temporarily in memory.

[0167] Step 3:

[0168] The server analyzes and organizes the extracted data, filtering out the necessary information, structuring it into an easy-to-read format, and storing it in a database.

[0169] Step 4:

[0170] The server retrieves the user's profile and goal data from a database, including the user's age, gender, fitness level, and specific self-actualization goals.

[0171] Step 5:

[0172] Based on the collected data and the user's profile data, the server uses a generative AI model to generate a training plan, for example, creating a gentle exercise menu for the elderly and a vigorous exercise menu for users who want high-intensity training.

[0173] Step 6:

[0174] The emotion engine recognizes the user's emotional state. Before training, the user inputs voice and facial expressions, and the emotion engine analyzes the emotions.

[0175] Step 7:

[0176] The server adjusts the training plan based on the emotional data obtained from the emotion engine, for example adding relaxation exercises if the user is feeling stressed.

[0177] Step 8:

[0178] The server provides the generated and adjusted training plan to the user's device, where the user can check the training plan on their smartphone or tablet.

[0179] Step 9:

[0180] The user begins training according to the training plan presented to them through the device. For example, specific training content such as "30 minutes of jogging" or "15 minutes of stretching" is displayed on the device screen.

[0181] Step 10:

[0182] After completing the training, the user inputs their progress into the device, for example, recording data such as "completed 30 minutes of jogging" or "performed 15 minutes of stretching."

[0183] Step 11:

[0184] The emotion engine will again recognize and collect data on the user's emotional state during and after training. For example, if the user feels fatigued, it will record that emotional data.

[0185] Step 12:

[0186] The device reports the user's progress and emotional data to the server, and the collected data is immediately sent to the server.

[0187] Step 13:

[0188] The server stores the received progress and emotion data in a database and incorporates it into a new training plan, which is then further adjusted based on this data for the next training session.

[0189] Through these steps, the system efficiently supports individual self-realization and helps elderly people and professional athletes maintain their health and improve their abilities. By incorporating an emotion engine, the system provides personalized training plans according to the user's emotional state, resulting in higher satisfaction.

[0190] Example 2

[0191] 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."

[0192] In today's world, it is important for individuals to create training plans to maintain their health and achieve self-realization. However, current systems have difficulty effectively utilizing information collected from the internet to automatically generate and adjust personalized training plans based on the user's profile and emotions. Furthermore, there are few ways for users to easily record their daily training progress and emotional state and reflect that data in their next plan. This poses a challenge, limiting user satisfaction and training effectiveness.

[0193] 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.

[0194] In this invention, the server includes means for collecting necessary information from the Internet, means for organizing the collected information and storing it in a database, means for acquiring a user's profile and goals, means for generating a training plan using a generative AI model, means for recognizing the user's emotions, means for adjusting the training plan based on the emotion data, means for providing the generated training plan to a user terminal, means for recording progress input by the user and reporting it to the server, and means for storing the progress data and emotion data received by the server.

[0195] This makes it possible to automatically generate and adjust personalized training plans based on the user's profile and emotional state by utilizing information from the internet. It also allows users to easily record their daily training progress and emotional state and reflect that data in their next plan, improving user satisfaction and training effectiveness.

[0196] "Means for collecting necessary information from the Internet" refers to devices or methods that automatically obtain specific information, such as health information or exercise methods, from websites or online databases on the Internet.

[0197] "Means for organizing collected information and storing it in a database" refers to a device or method that analyzes the acquired data, extracts and systematically organizes the necessary information, and stores that information in a database in an appropriate format.

[0198] A "means for obtaining a user's profile and goals" is a device or method that collects personal data provided by a user, such as age, gender, weight, goals, etc., and stores it in a database.

[0199] A "means for generating a training plan using a generative AI model" is a device or method that inputs a user's profile and collected health information into a pre-trained generative AI model, and automatically generates a training plan optimized for the user based on that information.

[0200] A "means for recognizing user emotions" is a device or method that detects and classifies a user's emotional state by analyzing the user's voice input and facial expressions.

[0201] "Means for adjusting a training plan based on emotional data" refers to a device or method that provides a generative AI model with user emotional data detected by an emotion engine and dynamically adjusts a training plan to match the user's emotional state.

[0202] The "means for providing the generated training plan to the user terminal" refers to a device or method for transmitting the training plan generated by the server to the user's terminal such as a smartphone or tablet via a network and displaying it.

[0203] "Means for recording progress input by the user and reporting it to the server" refers to a device or method that allows the user to input progress information, such as the training content, time required, and subjective impressions, into a terminal and automatically transmits that data to the server.

[0204] "Means for storing progress data and emotion data received by the server" refers to a device or method by which the server stores the progress data and emotion data sent from the terminal in a database and uses it to generate the next training plan.

[0205] MODE FOR CARRYING OUT THE INVENTION

[0206] This invention is a system that collects necessary information from the Internet, creates a training plan based on that information, and provides it to the user. Additionally, the system supports the user's self-realization by reporting the user's recorded progress to a server, which then stores the data. Furthermore, it incorporates an emotion engine that recognizes the user's emotions and adjusts the training plan based on those emotions.

[0207] Server Processing

[0208] The server first automatically collects the necessary information from the Internet. For example, it uses scraping techniques to obtain data from online databases containing health websites or exercise instruction guides. This work is performed using Python's BeautifulSoup and Scrapy libraries. This data is then organized by extracting relevant information from specific HTML elements and stored in a database such as MySQL or PostgreSQL.

[0209] Next, the server uses a generative AI model (e.g., GPT-4) based on the collected data to generate a training plan tailored to the user's profile and goals. An example of a prompt is, "Please generate a training plan suitable for a 65-year-old elderly male. Health information is ____." This generative AI model provides the optimal training menu tailored to the user's needs.

[0210] Additionally, an emotion engine recognizes the user's emotions. Using voice and facial expression analysis tools such as the Google Speech-to-Text API and OpenCV, the emotion engine analyzes the user's voice, facial expressions, and text input to identify their emotional state. This emotion data is also provided to the generative AI model, which uses it to adjust the training plan. For example, if the user is feeling stressed, a training plan will be generated that includes relaxation exercises and stretches.

[0211] Terminal handling

[0212] The generated training plan is sent from the server to the user's device. The user can then use their smartphone, tablet, or other device to check the training plan that suits them best. Specific training content is displayed in detail. For example, it might say, "On Mondays, do 30 minutes of jogging and 15 minutes of stretching."

[0213] After completing the training, the user records their progress using a device that has an interface for recording the training content, duration, subjective impressions, etc. An emotion engine also records the user's emotional state during training and is used to evaluate the effectiveness of the training.

[0214] User operations

[0215] The user checks their training plan through the device and carries out the training according to the plan. For example, "On Mondays, do 30 minutes of jogging and 15 minutes of stretching." Then, they use the device interface to input their progress and emotional state, which is then sent to the server.

[0216] Specific examples

[0217] As a concrete example, in the case of supporting self-realization for the elderly, the server collects health information from sites such as "http: / / example.com / health-tips," and based on that information, the generative AI model creates a training plan consisting of "daily light exercise" and "balanced meals." If the emotion engine analyzes the user's voice and facial expressions and recognizes that they are "very tired," relaxation exercises are added to the plan. When the user enters "I walked for 15 minutes" into their device, their progress information and emotional state are sent to the server and reflected in the next plan.

[0218] This system will efficiently support the self-realization of individuals, as well as effectively maintain the health and improve the abilities of the elderly and athletes. By incorporating an emotion engine, more personalized training plans will be provided, further improving user satisfaction and the effectiveness of training.

[0219] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0220] Step 1:

[0221] The server collects the necessary information from designated websites and online databases on the Internet. Specifically, it uses Python's BeautifulSoup and Scrapy libraries to obtain data on health-related information and exercise methods. The input here is a list of URLs to be collected, and the output is the raw HTML data collected by scraping.

[0222] Step 2:

[0223] The server analyzes the collected HTML data and extracts the necessary information. It uses regular expressions and XPath to extract health information and exercise descriptions from specific HTML elements. In this step, the input is raw HTML data, and the output is organized health information data. Specific operations include HTML parsing, text extraction, and data cleaning.

[0224] Step 3:

[0225] The server stores the organized information in a database. It uses a relational database such as MySQL or PostgreSQL to store the extracted health information and exercise methods as structured data. The input is the organized health information data, and the output is the result of the database save operation. Specific operations include executing SQL queries.

[0226] Step 4:

[0227] The server retrieves the user's profile and goals. It retrieves personal information provided by the user, such as age, gender, weight, and goals, from a database and generates a prompt based on that information. The input is the user ID and authentication information, and the output is the user's profile data. Specific operations include user authentication and SQL query execution.

[0228] Step 5:

[0229] The server generates a training plan by prompting the generative AI model based on the collected health information and the user's profile data. For example, using GPT-4, a prompt sentence such as "Please generate a training plan suitable for a 65-year-old elderly male. The health information is ____." The input is the prompt sentence, and the output is the generated training plan. Specific operations include an API request to the generative AI model.

[0230] Step 6:

[0231] The server recognizes the user's emotions. It analyzes the audio data and camera footage sent from the device and classifies the user's emotional state. The emotion engine uses Google Speech-to-Text API and OpenCV. The input is audio and video data, and the output is emotional data. Specific operations include voice recognition and facial expression analysis.

[0232] Step 7:

[0233] The server adjusts the training plan based on the emotion data. It supplies the emotion data to the generative AI model and dynamically adjusts the training plan. For example, if the user feels fatigued, it adds relaxation exercises. The input is emotion data, and the output is an adjusted training plan. Specific actions include re-prompting the generative AI model.

[0234] Step 8:

[0235] The server provides the generated training plan to the user's device. The training plan is sent to the user's smartphone or tablet via an HTTP request and displayed in the app. The input is the adjusted training plan, and the output is the data delivery results to the user's device. Specific operations include sending an HTTP request.

[0236] Step 9:

[0237] Users perform workouts and record their progress. They use checklists and input fields within the app to record the workouts they performed and the time it took. The input is a description of the workout and their emotional state, and the output is progress data. Specific actions include filling out forms within the app.

[0238] Step 10:

[0239] The device reports the recorded progress data and emotion data to the server. It calls an API to send the data to the server. The input is the progress data and emotion data, and the output is the result of sending the data to the server. Specific operations include executing an API request.

[0240] Step 11:

[0241] The server stores the received progress and emotion data in a database and reflects it in the next training plan. This data is used to update the input prompts of the generative AI model and optimize the next plan. The input is the progress and emotion data, and the output is the next training plan. Specific operations include updating the database and updating the prompts of the generative AI model.

[0242] (Application example 2)

[0243] 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."

[0244] Conventional training planning systems often lack personalization based on the user's health status and emotions, and are unable to properly reflect the user's progress. Furthermore, food delivery services, in particular, face the challenge of providing appropriate meal plans based on the user's emotions and progress. This leads to reduced user satisfaction and training effectiveness.

[0245] 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.

[0246] In this invention, the server includes means for collecting necessary information from the Internet, means for generating a training plan based on the collected information, means for providing the generated training plan to a user terminal, means for recording progress input by the user and reporting it to the server, means for saving the progress data received by the server, and means for analyzing the user's emotions and adjusting the training plan based on the emotions. This makes it possible to provide a personalized training plan and meal plan based on the user's health condition and progress, thereby improving user satisfaction and the effectiveness of training.

[0247] The "Internet" is a global information and communications network that interconnects multiple computer networks.

[0248] "Information" refers to knowledge or data about a particular phenomenon or subject.

[0249] A "training plan" is a structured training schedule designed to improve strength or skill toward a specific goal.

[0250] "User terminal" means an electronic device primarily used by a user to access the Internet or a system.

[0251] "Progress" is the progress achieved to date on a planned activity or project.

[0252] A "server" is a computer system that provides services to other computers on a network.

[0253] A "generative AI model" is a machine learning model that is trained using artificial intelligence techniques to automatically perform specific tasks.

[0254] "Emotions" are psychological and cognitive responses that indicate a person's state of mind.

[0255] "Meal Plan" means a plan that includes the food and schedule for a specific period of time.

[0256] "Personalization" refers to customizing something to suit an individual's specific needs and preferences.

[0257] "Progress Data" means information or records about an activity in progress.

[0258] "Voice analysis" refers to the technology of analyzing voice data to recognize meaning, emotions, etc.

[0259] "Facial expression analysis" refers to the technology of analyzing emotions and intentions from facial expressions.

[0260] "Text input" refers to the act of a user providing textual information to a system using a keyboard or other input device.

[0261] "Stress reduction" means the reduction of psychological or physical tension.

[0262] "Analysis" refers to the act of examining and evaluating data or information in detail.

[0263] This invention is a system that provides personalized training and meal plans for users. The system collects necessary information from the Internet, generates training plans and meal plans using a generative AI model, and provides them to the user's device. It also records the progress entered by the user and reports it to a server, so that the progress can be reflected in the next plan.

[0264] Required Hardware and Software

[0265] Hardware

[0266] Server: A high-performance server computer

[0267] User devices: smartphones, smart glasses, head-mounted displays

[0268] software

[0269] Server frameworks: Django and Flask

[0270] Data collection libraries: BeautifulSoup, Requests

[0271] Sentiment analysis libraries: TensorFlow, PyTorch (Transformers library)

[0272] Database: PostgreSQL or MySQL

[0273] Front-end technologies: HTML, CSS, JavaScript

[0274] System Operation

[0275] Information gathering

[0276] The server collects the necessary information from the Internet, for example by scraping data from websites that provide health and nutrition information, using Python's BeautifulSoup and Requests libraries.

[0277] Generate training and meal plans

[0278] The server uses the collected information to generate training and meal plans using a generative AI model that uses TensorFlow and PyTorch Transformers, and adjusts the plan based on the user's profile (age, weight, goals, etc.).

[0279] For example, for elderly users, a plan of gentle exercise including relaxation exercises and a nutritionally balanced meal plan is generated.

[0280] Recording and reporting user progress

[0281] Users can record their progress using their smartphone or other device, for example by entering "I walked for 15 minutes," and this data is sent to the server and reflected in the next plan.

[0282] Sentiment analysis and plan adjustment

[0283] The server analyzes the user's emotional state using an emotion analysis engine. The user's voice, facial expressions, and text input are used as input data. A Transformers model of sentiment analysis is used for emotion analysis.

[0284] For example, if a user enters a prompt such as "I've been feeling stressed lately and would like a comforting meal," the sentiment analysis engine will identify the emotion of "stress relief" and adjust the meal plan accordingly, recommending foods that are suitable for stress relief, such as comfort food (chicken soup).

[0285] Follow-up and user feedback

[0286] Progress and emotional data are stored on the server and reflected in the generation of the next training plan or meal plan, so that the user's progress and emotional state are always kept up to date, improving the quality of the next plan.

[0287] In this way, personalized training and meal plans can be adjusted in real time to effectively support users in maintaining their health and managing stress.

[0288] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0289] Step 1:

[0290] Gather the necessary information from the Internet.

[0291] What it does: The server uses Python's BeautifulSoup and Requests libraries to scrape data from websites, including health and nutrition information.

[0292] Input: The server receives the URL of the specified website as input.

[0293] Data processing: Extract relevant information from specific HTML elements of a specified website and format the data.

[0294] Output: The extracted data is saved as health and nutrition information.

[0295] Step 2:

[0296] Based on the collected information, a generative AI model is used to generate training and meal plans.

[0297] How it works: The server uses Transformers models using TensorFlow and PyTorch to generate workout and meal plans using the collected information and the user's profile data as input.

[0298] Input: User profile information (age, weight, goals, etc.) and collected health data.

[0299] Data processing: A generative AI model analyzes user profile and health data to generate appropriate training and meal plans.

[0300] Output: Personalized training and meal plans.

[0301] Step 3:

[0302] The generated training plan and meal plan are provided to the user terminal.

[0303] Specific operation: The server notifies the user of the generated training plan and meal plan on their smartphone or other device.

[0304] Input: Generated training plan and meal plan.

[0305] Data processing: Converting information into a user-friendly format and generating notification messages.

[0306] Output: Training plan and meal plan displayed on user device.

[0307] Step 4:

[0308] Records the progress entered by the user and reports it to the server.

[0309] What it does: The user uses the device to input their progress, and that data is sent to the server.

[0310] Input: User-entered progress (e.g., "I walked for 15 minutes").

[0311] Data processing: The server analyzes the entered data and saves it as progress data.

[0312] Output: Progress data stored on the server.

[0313] Step 5:

[0314] Analyze user emotions and adjust plans based on emotions.

[0315] Specific operation: The server analyzes the user's voice, facial expression, and text input using an emotion analysis engine to obtain emotion data.

[0316] Input: User voice, facial expressions, and text input data.

[0317] Data processing: A sentiment analysis engine analyzes input data to identify emotions (e.g., "I feel stressed").

[0318] Output: Emotional data (e.g., "I need stress relief").

[0319] Step 6:

[0320] Adjust your training and meal plans based on emotional data.

[0321] How it works: The server provides the acquired emotional data to a generative AI model to adjust training and meal plans.

[0322] Input: Emotional data and your current training and meal plans.

[0323] Data processing: Generative AI models analyze input data and update it into more appropriate plans and plans.

[0324] Output: Tailored training and meal plans.

[0325] Step 7:

[0326] The adjusted plan is provided to the user terminal.

[0327] Specific operation: The server re-notifies the user terminal of the adjusted training plan and meal plan.

[0328] Input: tailored training and meal plans.

[0329] Data processing: Converting information into a user-friendly format and generating a re-notification message.

[0330] Output: Training plan and meal plan re-notified to user device.

[0331] These steps allow for personalized training and meal plans to be provided to users in real time, and adapted to their progress and emotional state.

[0332] 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.

[0333] 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.

[0334] 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.

[0335] [Second embodiment]

[0336] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0337] 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.

[0338] 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).

[0339] 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.

[0340] 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.

[0341] 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).

[0342] 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. 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.

[0343] 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.

[0344] 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.

[0345] 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.

[0346] 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.

[0347] 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."

[0348] This invention is a system that collects necessary information from the Internet, creates a training plan based on that information, and provides it to the user.The user then reports their recorded progress to a server, which stores the data, thereby supporting personal self-realization.

[0349] The server first automatically collects the necessary information from the Internet. For example, it uses scraping techniques to obtain data from online databases containing health websites or exercise instruction guides. This data is then organized by extracting relevant information from specific HTML elements. The scraped data is then converted into the required format and stored in a database.

[0350] The server then uses a generative AI model based on the collected data to generate a training plan tailored to the user's profile and goals. For example, for a professional athlete, a plan would be generated that includes specific training menus and nutritional management for a match. On the other hand, for the elderly, a light exercise plan aimed at maintaining health would be generated. The collected information is then graph-structured and organized according to the importance and relevance of the content, improving the accuracy and effectiveness of the plan.

[0351] The generated training plan is sent from the server to the user's device. The user can check the training plan that suits them best via their smartphone, tablet, or other device. The device displays the details of each day's training, and the user carries out the training based on the plan. For example, specific details such as "On Mondays, I will jog for 30 minutes and stretch for 15 minutes" are displayed.

[0352] After completing a training session, the user records their progress using their device. This progress data is saved on the device as input by the user and then sent to the server. The server saves the received progress data in a database and manages the user's training history. This ensures that the user's progress is always recorded up to date and is reflected in the next training plan.

[0353] As a concrete example, in the case of supporting self-realization for the elderly, the server collects health information from sites such as "http: / / example.com / health-tips," and then uses the collected information to create a training plan consisting of "daily light exercise" and "balanced diet" using a generative AI model. When the user enters "I walked for 15 minutes" into the device, the progress information is sent to the server and reflected in the next plan.

[0354] This system will efficiently support the self-realization of individuals and effectively maintain the health and improve the abilities of elderly people and professional athletes.

[0355] The processing flow will be explained below.

[0356] Step 1:

[0357] The server collects the necessary information from the Internet. Here, the server sends HTTP requests to the URLs of multiple configured websites and scrapes the resulting HTML documents. In doing so, it extracts the necessary data from specific HTML elements and temporarily stores this data in memory.

[0358] Step 2:

[0359] The server analyzes and organizes the extracted data. The server filters out only the necessary parts of the scraped information and converts it into a format that can be stored in a database. For example, it extracts the article title, body text, and important keywords and stores this in the database as structured data.

[0360] Step 3:

[0361] The server retrieves the user's profile information and goals from the database, including the user's age, gender, physical fitness level, and specific self-realization goals.

[0362] Step 4:

[0363] Based on the information collected by the server and the user's profile information, a generative AI model is used to generate a training plan, such as a gentle exercise menu for the elderly or a detailed plan for professional athletes that will improve their athletic performance.

[0364] Step 5:

[0365] The server provides the generated training plan to the user's device, and the server synchronizes the training plan with the user's device so that the user can check the plan at any time.

[0366] Step 6:

[0367] The user performs the training using the device. The user checks the daily training content on the device screen and performs the exercise according to the instructions. For example, specific exercises such as "30 minutes of jogging" or "15 minutes of stretching" are instructed.

[0368] Step 7:

[0369] After completing the training, the user enters their progress into the terminal, and an interface is provided to record the content of the training, the time required, and subjective impressions.

[0370] Step 8:

[0371] The terminal reports the user's input data to the server. The input progress data is immediately sent to the server.

[0372] Step 9:

[0373] The server stores the received progress data in a database and reflects it in the next training plan. The server evaluates the user's training progress based on the new data and adjusts the training plan as necessary.

[0374] This continuous processing effectively supports the self-actualization of individuals and helps users maintain their health and improve their abilities.

[0375] Example 1

[0376] 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."

[0377] Conventional training plans have the problem of not being able to fully reflect the needs and goals of individual users and can only provide generic exercise programs. Furthermore, progress management is often done manually, which often results in delays in updating and improving plans. Therefore, there is a need for a system that can automatically generate optimal training plans for individual users and efficiently manage progress.

[0378] 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.

[0379] In this invention, the server includes means for collecting necessary information from the Internet, means for converting the collected information into a specific format and saving it, means for generating prompts using a generative AI model based on the collected information and the user's profile to create a training plan, means for providing the generated training plan to the user's terminal, means for recording the progress entered by the user and reporting it to the server, and means for saving the progress data received by the server. This makes it possible to automatically generate a training plan according to the user's individual needs and goals and efficiently manage the user's progress.

[0380] "Means for collecting necessary information from the Internet" refers to means for automatically collecting data such as health information and exercise methods from websites and online databases on the Internet.

[0381] "Means of converting and saving data in a specific format based on collected information" refers to means of converting collected data into a specific format (e.g., JSON, CSV) and saving that data in a database, etc.

[0382] "Means for generating prompt sentences using a generative AI model and creating a training plan" means means for generating prompt sentences based on collected information and a user profile, and for automatically creating a training plan using a generative AI model (e.g., a natural language generation model) with the generated prompt sentences.

[0383] The "means for providing the generated training plan to the user terminal" refers to a means for delivering the generated training plan to the terminal used by the user so that the training plan can be confirmed on that terminal.

[0384] The "means for recording the progress status input by the user and reporting it to the server" is a means for the user to input the progress status of his / her training, record the information, and send it to the server.

[0385] The "means for storing the progress data received by the server" refers to a means for the server to store the progress data received from the user in a database or the like, and use the data to generate future training plans.

[0386] This invention is a system that collects necessary information from the Internet, generates a training plan based on that information, and provides it to the user. This system is operated using a server, a user terminal, and a generative AI model. The server collects health information and exercise methods from the Internet, and the generated training plan is distributed to the user terminal. Furthermore, the user's progress is recorded on the server and reflected in the next training plan.

[0387] Information gathering

[0388] The server accesses specific health information websites and databases and automatically extracts relevant information using a scraping tool (e.g., BeautifulSoup or Scrapy). For example, the server collects information such as "jogging three times a week is good for your health" from "http: / / example.com / health-tips." This information is saved as a temporary file (e.g., a CSV file).

[0389] Data conversion and storage

[0390] The server converts the collected information into JSON format and stores it in a relational database (e.g., MySQL). For example, the text information "jogging three times a week is good for your health" is converted into JSON format and stored in the database.

[0391] Generate a training plan

[0392] The server uses a generative AI model (e.g., GPT-4) to create a training plan based on the collected information and the user's profile. For example, if the user's information is "60-year-old male, high blood pressure, medium physical fitness level, goal: maintain health," the server inputs the following prompt sentence into the generative AI model:

[0393] User: Elderly, Goal: Maintaining health, Current physical condition: Able to walk for 15 minutes

[0394] Prompt: "Generate a moderate exercise plan for maintaining good health. Include a one-week plan with daily exercise and balanced meal suggestions."

[0395] This allows the generative AI model to generate specific training plans such as "jogging three times a week" and "daily vegetable intake plan."

[0396] Training plan distribution

[0397] The server delivers the generated training plan to the user's device. The user's device displays the delivered information, and the user carries out the training based on it. For example, a detailed plan such as "30 minutes of jogging and 15 minutes of stretching on Mondays" is displayed on the smartphone.

[0398] Record and manage progress

[0399] After completing a workout, the user uses the device to input progress information (e.g., "I walked for 15 minutes"). The device temporarily saves the user's input and sends it to the server. The server saves the received progress data in a database and manages the user's training history. This information is reflected in the next workout plan.

[0400] This system allows users to easily obtain training plans that suit their needs and goals, and efficiently maintain or improve their health. In addition, because progress is properly recorded and managed, the accuracy and effectiveness of training plans are improved.

[0401] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0402] Step 1:

[0403] Information gathering

[0404] The server accesses a specific health information website on the Internet (e.g., http: / / example.com / health-tips). As input, it uses a list of URLs and uses a scraping tool (e.g., BeautifulSoup or Scrapy) to extract relevant information (e.g., "jogging three times a week is good for your health"). Specifically, it extracts HTML elements (e.g., The necessary information is extracted from the tags (text within tags) and saved to a temporary file (e.g., a CSV file). The output is a temporary file containing the collected health information.

[0405] Step 2:

[0406] Data conversion and storage

[0407] The server converts data stored in a temporary file into JSON format. As input, there is data stored in the form of a temporary file. This data is converted into a specific format (e.g. JSON) and stored in a relational database (e.g. MySQL). Specifically, the server converts the text "Jogging three times a week is good for your health" into JSON format and stores it in a MySQL database. As output, it gets the information stored in the database.

[0408] Step 3:

[0409] Generate a training plan

[0410] The server creates a training plan using a generative AI model (e.g., GPT-4) based on the collected information and the user's profile. The inputs are the user's profile information (e.g., age, gender, and fitness level) and the collected data. The server generates a prompt sentence and inputs it into the generative AI model. Specifically, the server generates the following prompt sentence:

[0411] User: Elderly, Goal: Maintaining health, Current physical condition: Able to walk for 15 minutes

[0412] Prompt: "Generate a moderate exercise plan for maintaining good health. Include a one-week plan with daily exercise and balanced meal suggestions."

[0413] Based on this prompt, the generative AI model outputs a specific training plan such as "jogging three times a week" and "daily vegetable intake plan."

[0414] Step 4:

[0415] Training plan distribution

[0416] The server distributes the generated training plan to the user's device. The input is the generated training plan. The server sends the plan to the user's device (e.g., smartphone or tablet), which receives it. Specifically, the server distributes information such as "On Mondays, do 30 minutes of jogging and 15 minutes of stretching" to the user's smartphone, which then displays the information on the device. The output is the training plan displayed on the user's device.

[0417] Step 5:

[0418] Record and manage progress

[0419] After completing a workout, the user uses the device to input progress information (e.g., "Walked for 15 minutes"). The input is the user's progress information. The device temporarily stores this information and sends it to the server. Specifically, the user enters the progress information into their smartphone, and the device sends the information to the server. The server saves the received progress data in a database and manages the user's training history. The output is the updated progress data saved in the database.

[0420] This allows users to keep track of their progress and reflect it in their next training plan.

[0421] (Application example 1)

[0422] 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."

[0423] It is difficult for physical fitness facilities such as fitness clubs and gyms to provide personalized training plans in real time that meet the diverse needs of users and to properly manage their progress. A system that can effectively and efficiently solve this problem is needed. There is also a need for technology that can generate optimal training plans for each user based on collected health information and training data.

[0424] 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.

[0425] In this invention, the server includes means for collecting necessary information from the Internet, means for generating a training plan based on the collected information, means for providing the generated training plan to a user terminal, means for recording progress input by the user and reporting it to the server, means for saving progress data received by the server, and means for providing a training plan to the user at a fitness club or gym in real time and reflecting the user's progress. This makes it possible to provide a personalized training plan for each user in real time and appropriately manage the user's progress.

[0426] "Means of collecting necessary information from the Internet" refers to a method of automatically obtaining specific information from websites, online databases, etc.

[0427] The "means for generating a training plan based on collected information" refers to a method or device for analyzing the acquired data and creating an individual training menu based on the data.

[0428] The "means for providing the generated training plan to a user terminal" refers to a method or device for displaying or transmitting the generated training plan to a user terminal such as a smartphone or tablet.

[0429] The "means for recording the progress status input by the user and reporting it to the server" refers to a method or device in which the user inputs his or her own progress, records the data, and transmits it to the server.

[0430] The "means for storing the progress data received by the server" refers to a method or device by which the server stores the progress data sent from the user in a database.

[0431] "Means for providing users with training plans in real time at fitness clubs or gyms and reflecting progress" refers to a method or device that presents a training plan to users on the spot at a physical store and immediately reflects the progress and results in the plan.

[0432] The system for realizing the present invention mainly uses a server, a user terminal, and various software.

[0433] The server first collects the necessary information from the Internet. Specifically, it uses web scraping technology to obtain data from health-related websites and online databases, organizes this data, and stores it in a database. For scraping, it uses the Python libraries Requests and BeautifulSoup. For example, health information can be collected from "http: / / example.com / health-tips."

[0434] Based on the collected data, the server generates a training plan using a generative AI model based on the user's profile and goals. This is done using OpenAI's API. Here is an example of a prompt:

[0435] User profile: Elderly, 65 years old, looking for light exercise without strain

[0436] User goals: Light exercise every day to maintain health

[0437] Health tips: ['15 minutes of walking every day is recommended', 'Eat a balanced diet']

[0438] Generate a personalized training plan.

[0439] The generated training plan is provided from the server to the user's device, which could be a smartphone, tablet, smart glasses, or head-mounted display. The user can view their personalized training plan through these devices. For example, detailed content such as "On Mondays, I plan to jog for 30 minutes and stretch for 15 minutes" is displayed.

[0440] After completing a workout, the user enters their progress into the device. The entered progress data is sent to the server, which stores it in a database. The server then adjusts the next training plan as needed based on the received progress data. This ensures that the user's progress is always recorded up to date, maximizing the effectiveness of their individual training.

[0441] For example, if a user at a fitness club or gym inputs "I walked for 15 minutes" into a device, that information is sent to the server in real time and immediately reflected in the next training plan, effectively providing training tailored to the user's individual needs.

[0442] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0443] Step 1:

[0444] The server collects the necessary information from the Internet. Specifically, it uses Python's Requests library to access health-related websites and retrieve their content. Next, it uses the BeautifulSoup library to parse HTML elements and extract health information in text format. The input for this process is the website URL, and the output is the extracted health information in text format.

[0445] Step 2:

[0446] The server stores the collected health information in a database. For example, it uses a database service such as Amazon RDS to store the acquired data. The input of this process is the text data extracted in step 1, and the output is the information stored in the database.

[0447] Step 3:

[0448] The server receives the user's profile and goals. It collects the profile information (age, exercise experience, health status, etc.) and goals (maintaining health, losing weight, etc.) entered by the user through the terminal. The input of this process is the information entered by the user into the terminal, and the output is the user's profile and goal data stored on the server.

[0449] Step 4:

[0450] The server generates a training plan using a generative AI model based on the collected health information and the user's profile and goals. The generative AI model uses OpenAI's API. For example, the following prompt sentence can be input into the generative AI model to generate a training plan:

[0451] User profile: Elderly, 65 years old, looking for light exercise without strain

[0452] User goals: Light exercise every day to maintain health

[0453] Health tips: ['15 minutes of walking every day is recommended', 'Eat a balanced diet']

[0454] Generate a personalized training plan.

[0455] The input to this process is a prompt sentence, and the output is the generated training plan text data.

[0456] Step 5:

[0457] The server provides the generated training plan to the user's device, where the user can view the training plan via a smartphone, tablet, smart glasses, etc. The input of this process is the generated training plan, and the output is the training plan displayed on the user's device.

[0458] Step 6:

[0459] The user performs training and inputs their progress into the device. For example, they enter information such as "I walked for 15 minutes" into their smartphone. The input of this process is the progress information that the user enters into the device, and the output is the progress data stored on the device.

[0460] Step 7:

[0461] The device sends the user's progress data to the server, which stores the received progress data in a database and reflects it in the next training plan. The input of this process is the user's progress data, and the output is the updated progress data stored in the database.

[0462] Step 8:

[0463] The server regenerates the next training plan based on the progress data. The new training plan is adjusted based on the current plan and progress. The input to this process is the updated progress data, and the output is the updated training plan.

[0464] 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.

[0465] This invention is a system that collects necessary information from the Internet, creates a training plan based on that information, and provides it to the user. The system also supports the user's self-realization by reporting the user's recorded progress to a server, which then stores the data. Furthermore, it incorporates an emotion engine that recognizes the user's emotions and adjusts the training plan based on those emotions.

[0466] The server first automatically collects the necessary information from the Internet, for example, by scraping data from online databases containing health websites or exercise instruction guides. This data is then organized by extracting relevant information from specific HTML elements and stored in a database.

[0467] The server then uses the collected data to generate a training plan based on the user's profile and goals using a generative AI model. For example, a professional athlete might receive a plan with specific training and nutritional information for a match, while an elderly person might receive a low-intensity exercise plan aimed at maintaining health.

[0468] Furthermore, the emotion engine recognizes the user's emotions. The emotion engine analyzes the user's voice, facial expressions, text input, etc. to identify their emotional state. This emotion data is provided to a generative AI model and used to adjust the training plan. For example, if the user is feeling stressed, a training plan will be generated that includes relaxation exercises and stretches. In this way, a more appropriate training plan is provided based on the user's emotional state.

[0469] The generated training plan is sent from the server to the user's device. The user can check the training plan that suits them best via their smartphone, tablet, or other device. The device displays the details of each day's training, and the user carries out the training based on the plan. For example, specific details such as "On Mondays, I will jog for 30 minutes and stretch for 15 minutes" are displayed.

[0470] After completing the training, the user uses the device to record their progress. An interface is provided to record the training content, duration, subjective impressions, etc. An emotion engine is also used to record the user's emotional state during training and evaluate the effectiveness of the training.

[0471] The device reports the progress and emotional data entered by the user to the server, which stores the received data in a database and reflects it in the next training plan. This ensures that the user's progress and emotional state are always recorded up to date, improving the quality of the next plan.

[0472] As a concrete example, in the case of supporting self-realization for the elderly, the server collects health information from sites such as "http: / / example.com / health-tips," and then uses the collected information to create a training plan consisting of "daily light exercise" and "balanced meals" using a generative AI model. The emotion engine analyzes the user's voice and facial expressions, and if it recognizes, for example, that the user is "very tired," relaxation exercises are added to the plan. When the user enters "I walked for 15 minutes" into the device, the progress information and emotional state are sent to the server and reflected in the next plan.

[0473] This system will efficiently support the self-realization of individuals, as well as effectively maintain the health and improve the performance of elderly people and professional athletes. By incorporating an emotion engine, more personalized training plans will be provided, further improving user satisfaction and the effectiveness of training.

[0474] The processing flow will be explained below.

[0475] Step 1:

[0476] The server gathers the necessary information from the Internet, specifically by sending an HTTP request to retrieve HTML documents from online databases containing health websites and exercise instruction.

[0477] Step 2:

[0478] The server extracts specific elements from the HTML documents obtained using scraping techniques, such as article titles, body text, and related keywords, and stores them temporarily in memory.

[0479] Step 3:

[0480] The server analyzes and organizes the extracted data, filtering out the necessary information, structuring it into an easy-to-read format, and storing it in a database.

[0481] Step 4:

[0482] The server retrieves the user's profile and goal data from a database, including the user's age, gender, fitness level, and specific self-actualization goals.

[0483] Step 5:

[0484] Based on the collected data and the user's profile data, the server uses a generative AI model to generate a training plan, for example, creating a gentle exercise menu for the elderly and a vigorous exercise menu for users who want high-intensity training.

[0485] Step 6:

[0486] The emotion engine recognizes the user's emotional state. Before training, the user inputs voice and facial expressions, and the emotion engine analyzes the emotions.

[0487] Step 7:

[0488] The server adjusts the training plan based on the emotional data obtained from the emotion engine, for example adding relaxation exercises if the user is feeling stressed.

[0489] Step 8:

[0490] The server provides the generated and adjusted training plan to the user's device, where the user can check the training plan on their smartphone or tablet.

[0491] Step 9:

[0492] The user begins training according to the training plan presented to them through the device. For example, specific training content such as "30 minutes of jogging" or "15 minutes of stretching" is displayed on the device screen.

[0493] Step 10:

[0494] After completing the training, the user inputs their progress into the device, for example, recording data such as "completed 30 minutes of jogging" or "performed 15 minutes of stretching."

[0495] Step 11:

[0496] The emotion engine will again recognize and collect data on the user's emotional state during and after training. For example, if the user feels fatigued, it will record that emotional data.

[0497] Step 12:

[0498] The device reports the user's progress and emotional data to the server, and the collected data is immediately sent to the server.

[0499] Step 13:

[0500] The server stores the received progress and emotion data in a database and incorporates it into a new training plan, which is then further adjusted based on this data for the next training session.

[0501] Through these steps, the system efficiently supports individual self-realization and helps elderly people and professional athletes maintain their health and improve their abilities. By incorporating an emotion engine, the system provides personalized training plans according to the user's emotional state, resulting in higher satisfaction.

[0502] Example 2

[0503] 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."

[0504] In today's world, it is important for individuals to create training plans to maintain their health and achieve self-realization. However, current systems have difficulty effectively utilizing information collected from the internet to automatically generate and adjust personalized training plans based on the user's profile and emotions. Furthermore, there are few ways for users to easily record their daily training progress and emotional state and reflect that data in their next plan. This poses a challenge, limiting user satisfaction and training effectiveness.

[0505] 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.

[0506] In this invention, the server includes means for collecting necessary information from the Internet, means for organizing the collected information and storing it in a database, means for acquiring a user's profile and goals, means for generating a training plan using a generative AI model, means for recognizing the user's emotions, means for adjusting the training plan based on the emotion data, means for providing the generated training plan to a user terminal, means for recording progress input by the user and reporting it to the server, and means for storing the progress data and emotion data received by the server.

[0507] This makes it possible to automatically generate and adjust personalized training plans based on the user's profile and emotional state by utilizing information from the internet. It also allows users to easily record their daily training progress and emotional state and reflect that data in their next plan, improving user satisfaction and training effectiveness.

[0508] "Means for collecting necessary information from the Internet" refers to devices or methods that automatically obtain specific information, such as health information or exercise methods, from websites or online databases on the Internet.

[0509] "Means for organizing collected information and storing it in a database" refers to a device or method that analyzes the acquired data, extracts and systematically organizes the necessary information, and stores that information in a database in an appropriate format.

[0510] A "means for obtaining a user's profile and goals" is a device or method that collects personal data provided by a user, such as age, gender, weight, goals, etc., and stores it in a database.

[0511] A "means for generating a training plan using a generative AI model" is a device or method that inputs a user's profile and collected health information into a pre-trained generative AI model, and automatically generates a training plan optimized for the user based on that information.

[0512] A "means for recognizing user emotions" is a device or method that detects and classifies a user's emotional state by analyzing the user's voice input and facial expressions.

[0513] "Means for adjusting a training plan based on emotional data" refers to a device or method that provides a generative AI model with user emotional data detected by an emotion engine and dynamically adjusts a training plan to match the user's emotional state.

[0514] The "means for providing the generated training plan to the user terminal" refers to a device or method for transmitting the training plan generated by the server to the user's terminal such as a smartphone or tablet via a network and displaying it.

[0515] "Means for recording progress input by the user and reporting it to the server" refers to a device or method that allows the user to input progress information, such as the training content, time required, and subjective impressions, into a terminal and automatically transmits that data to the server.

[0516] "Means for storing progress data and emotion data received by the server" refers to a device or method by which the server stores the progress data and emotion data sent from the terminal in a database and uses it to generate the next training plan.

[0517] MODE FOR CARRYING OUT THE INVENTION

[0518] This invention is a system that collects necessary information from the Internet, creates a training plan based on that information, and provides it to the user. Additionally, the system supports the user's self-realization by reporting the user's recorded progress to a server, which then stores the data. Furthermore, it incorporates an emotion engine that recognizes the user's emotions and adjusts the training plan based on those emotions.

[0519] Server Processing

[0520] The server first automatically collects the necessary information from the Internet. For example, it uses scraping techniques to obtain data from online databases containing health websites or exercise instruction guides. This work is performed using Python's BeautifulSoup and Scrapy libraries. This data is then organized by extracting relevant information from specific HTML elements and stored in a database such as MySQL or PostgreSQL.

[0521] Next, the server uses a generative AI model (e.g., GPT-4) based on the collected data to generate a training plan tailored to the user's profile and goals. An example of a prompt is, "Please generate a training plan suitable for a 65-year-old elderly male. Health information is ____." This generative AI model provides the optimal training menu tailored to the user's needs.

[0522] Additionally, an emotion engine recognizes the user's emotions. Using voice and facial expression analysis tools such as the Google Speech-to-Text API and OpenCV, the emotion engine analyzes the user's voice, facial expressions, and text input to identify their emotional state. This emotion data is also provided to the generative AI model, which uses it to adjust the training plan. For example, if the user is feeling stressed, a training plan will be generated that includes relaxation exercises and stretches.

[0523] Terminal handling

[0524] The generated training plan is sent from the server to the user's device. The user can then use their smartphone, tablet, or other device to check the training plan that suits them best. Specific training content is displayed in detail. For example, it might say, "On Mondays, do 30 minutes of jogging and 15 minutes of stretching."

[0525] After completing the training, the user records their progress using a device that has an interface for recording the training content, duration, subjective impressions, etc. An emotion engine also records the user's emotional state during training and is used to evaluate the effectiveness of the training.

[0526] User operations

[0527] The user checks their training plan through the device and carries out the training according to the plan. For example, "On Mondays, do 30 minutes of jogging and 15 minutes of stretching." Then, they use the device interface to input their progress and emotional state, which is then sent to the server.

[0528] Specific examples

[0529] As a concrete example, in the case of supporting self-realization for the elderly, the server collects health information from sites such as "http: / / example.com / health-tips," and based on that information, the generative AI model creates a training plan consisting of "daily light exercise" and "balanced meals." If the emotion engine analyzes the user's voice and facial expressions and recognizes that they are "very tired," relaxation exercises are added to the plan. When the user enters "I walked for 15 minutes" into their device, their progress information and emotional state are sent to the server and reflected in the next plan.

[0530] This system will efficiently support the self-realization of individuals, as well as effectively maintain the health and improve the abilities of the elderly and athletes. By incorporating an emotion engine, more personalized training plans will be provided, further improving user satisfaction and the effectiveness of training.

[0531] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0532] Step 1:

[0533] The server collects the necessary information from designated websites and online databases on the Internet. Specifically, it uses Python's BeautifulSoup and Scrapy libraries to obtain data on health-related information and exercise methods. The input here is a list of URLs to be collected, and the output is the raw HTML data collected by scraping.

[0534] Step 2:

[0535] The server analyzes the collected HTML data and extracts the necessary information. It uses regular expressions and XPath to extract health information and exercise descriptions from specific HTML elements. In this step, the input is raw HTML data, and the output is organized health information data. Specific operations include HTML parsing, text extraction, and data cleaning.

[0536] Step 3:

[0537] The server stores the organized information in a database. It uses a relational database such as MySQL or PostgreSQL to store the extracted health information and exercise methods as structured data. The input is the organized health information data, and the output is the result of the database save operation. Specific operations include executing SQL queries.

[0538] Step 4:

[0539] The server retrieves the user's profile and goals. It retrieves personal information provided by the user, such as age, gender, weight, and goals, from a database and generates a prompt based on that information. The input is the user ID and authentication information, and the output is the user's profile data. Specific operations include user authentication and SQL query execution.

[0540] Step 5:

[0541] The server generates a training plan by prompting the generative AI model based on the collected health information and the user's profile data. For example, using GPT-4, a prompt sentence such as "Please generate a training plan suitable for a 65-year-old elderly male. The health information is ____." The input is the prompt sentence, and the output is the generated training plan. Specific operations include an API request to the generative AI model.

[0542] Step 6:

[0543] The server recognizes the user's emotions. It analyzes the audio data and camera footage sent from the device and classifies the user's emotional state. The emotion engine uses Google Speech-to-Text API and OpenCV. The input is audio and video data, and the output is emotional data. Specific operations include voice recognition and facial expression analysis.

[0544] Step 7:

[0545] The server adjusts the training plan based on the emotion data. It supplies the emotion data to the generative AI model and dynamically adjusts the training plan. For example, if the user feels fatigued, it adds relaxation exercises. The input is emotion data, and the output is an adjusted training plan. Specific actions include re-prompting the generative AI model.

[0546] Step 8:

[0547] The server provides the generated training plan to the user's device. The training plan is sent to the user's smartphone or tablet via an HTTP request and displayed in the app. The input is the adjusted training plan, and the output is the data delivery results to the user's device. Specific operations include sending an HTTP request.

[0548] Step 9:

[0549] Users perform workouts and record their progress. They use checklists and input fields within the app to record the workouts they performed and the time it took. The input is a description of the workout and their emotional state, and the output is progress data. Specific actions include filling out forms within the app.

[0550] Step 10:

[0551] The device reports the recorded progress data and emotion data to the server. It calls an API to send the data to the server. The input is the progress data and emotion data, and the output is the result of sending the data to the server. Specific operations include executing an API request.

[0552] Step 11:

[0553] The server stores the received progress and emotion data in a database and reflects it in the next training plan. This data is used to update the input prompts of the generative AI model and optimize the next plan. The input is the progress and emotion data, and the output is the next training plan. Specific operations include updating the database and updating the prompts of the generative AI model.

[0554] (Application example 2)

[0555] 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."

[0556] Conventional training planning systems often lack personalization based on the user's health status and emotions, and are unable to properly reflect the user's progress. Furthermore, food delivery services, in particular, face the challenge of providing appropriate meal plans based on the user's emotions and progress. This leads to reduced user satisfaction and training effectiveness.

[0557] 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.

[0558] In this invention, the server includes means for collecting necessary information from the Internet, means for generating a training plan based on the collected information, means for providing the generated training plan to a user terminal, means for recording progress input by the user and reporting it to the server, means for saving the progress data received by the server, and means for analyzing the user's emotions and adjusting the training plan based on the emotions. This makes it possible to provide a personalized training plan and meal plan based on the user's health condition and progress, thereby improving user satisfaction and the effectiveness of training.

[0559] The "Internet" is a global information and communications network that interconnects multiple computer networks.

[0560] "Information" refers to knowledge or data about a particular phenomenon or subject.

[0561] A "training plan" is a structured training schedule designed to improve strength or skill toward a specific goal.

[0562] "User terminal" means an electronic device primarily used by a user to access the Internet or a system.

[0563] "Progress" is the progress achieved to date on a planned activity or project.

[0564] A "server" is a computer system that provides services to other computers on a network.

[0565] A "generative AI model" is a machine learning model that is trained using artificial intelligence techniques to automatically perform specific tasks.

[0566] "Emotions" are psychological and cognitive responses that indicate a person's state of mind.

[0567] "Meal Plan" means a plan that includes the food and schedule for a specific period of time.

[0568] "Personalization" refers to customizing something to suit an individual's specific needs and preferences.

[0569] "Progress Data" means information or records about an activity in progress.

[0570] "Voice analysis" refers to the technology of analyzing voice data to recognize meaning, emotions, etc.

[0571] "Facial expression analysis" refers to the technology of analyzing emotions and intentions from facial expressions.

[0572] "Text input" refers to the act of a user providing textual information to a system using a keyboard or other input device.

[0573] "Stress reduction" means the reduction of psychological or physical tension.

[0574] "Analysis" refers to the act of examining and evaluating data or information in detail.

[0575] This invention is a system that provides personalized training and meal plans for users. The system collects necessary information from the Internet, generates training plans and meal plans using a generative AI model, and provides them to the user's device. It also records the progress entered by the user and reports it to a server, so that the progress can be reflected in the next plan.

[0576] Required Hardware and Software

[0577] Hardware

[0578] Server: A high-performance server computer

[0579] User devices: smartphones, smart glasses, head-mounted displays

[0580] software

[0581] Server frameworks: Django and Flask

[0582] Data collection libraries: BeautifulSoup, Requests

[0583] Sentiment analysis libraries: TensorFlow, PyTorch (Transformers library)

[0584] Database: PostgreSQL or MySQL

[0585] Front-end technologies: HTML, CSS, JavaScript

[0586] System Operation

[0587] Information gathering

[0588] The server collects the necessary information from the Internet, for example by scraping data from websites that provide health and nutrition information, using Python's BeautifulSoup and Requests libraries.

[0589] Generate training and meal plans

[0590] The server uses the collected information to generate training and meal plans using a generative AI model that uses TensorFlow and PyTorch Transformers, and adjusts the plan based on the user's profile (age, weight, goals, etc.).

[0591] For example, for elderly users, a plan of gentle exercise including relaxation exercises and a nutritionally balanced meal plan is generated.

[0592] Recording and reporting user progress

[0593] Users can record their progress using their smartphone or other device, for example by entering "I walked for 15 minutes," and this data is sent to the server and reflected in the next plan.

[0594] Sentiment analysis and plan adjustment

[0595] The server analyzes the user's emotional state using an emotion analysis engine. The user's voice, facial expressions, and text input are used as input data. A Transformers model of sentiment analysis is used for emotion analysis.

[0596] For example, if a user enters a prompt such as "I've been feeling stressed lately and would like a comforting meal," the sentiment analysis engine will identify the emotion of "stress relief" and adjust the meal plan accordingly, recommending foods that are suitable for stress relief, such as comfort food (chicken soup).

[0597] Follow-up and user feedback

[0598] Progress and emotional data are stored on the server and reflected in the generation of the next training plan or meal plan, so that the user's progress and emotional state are always kept up to date, improving the quality of the next plan.

[0599] In this way, personalized training and meal plans can be adjusted in real time to effectively support users in maintaining their health and managing stress.

[0600] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0601] Step 1:

[0602] Gather the necessary information from the Internet.

[0603] What it does: The server uses Python's BeautifulSoup and Requests libraries to scrape data from websites, including health and nutrition information.

[0604] Input: The server receives the URL of the specified website as input.

[0605] Data processing: Extract relevant information from specific HTML elements of a specified website and format the data.

[0606] Output: The extracted data is saved as health and nutrition information.

[0607] Step 2:

[0608] Based on the collected information, a generative AI model is used to generate training and meal plans.

[0609] How it works: The server uses Transformers models using TensorFlow and PyTorch to generate workout and meal plans using the collected information and the user's profile data as input.

[0610] Input: User profile information (age, weight, goals, etc.) and collected health data.

[0611] Data processing: A generative AI model analyzes user profile and health data to generate appropriate training and meal plans.

[0612] Output: Personalized training and meal plans.

[0613] Step 3:

[0614] The generated training plan and meal plan are provided to the user terminal.

[0615] Specific operation: The server notifies the user of the generated training plan and meal plan on their smartphone or other device.

[0616] Input: Generated training plan and meal plan.

[0617] Data processing: Converting information into a user-friendly format and generating notification messages.

[0618] Output: Training plan and meal plan displayed on user device.

[0619] Step 4:

[0620] Records the progress entered by the user and reports it to the server.

[0621] What it does: The user uses the device to input their progress, and that data is sent to the server.

[0622] Input: User-entered progress (e.g., "I walked for 15 minutes").

[0623] Data processing: The server analyzes the entered data and saves it as progress data.

[0624] Output: Progress data stored on the server.

[0625] Step 5:

[0626] Analyze user emotions and adjust plans based on emotions.

[0627] Specific operation: The server analyzes the user's voice, facial expression, and text input using an emotion analysis engine to obtain emotion data.

[0628] Input: User voice, facial expressions, and text input data.

[0629] Data processing: A sentiment analysis engine analyzes input data to identify emotions (e.g., "I feel stressed").

[0630] Output: Emotional data (e.g., "I need stress relief").

[0631] Step 6:

[0632] Adjust your training and meal plans based on emotional data.

[0633] How it works: The server provides the acquired emotional data to a generative AI model to adjust training and meal plans.

[0634] Input: Emotional data and your current training and meal plans.

[0635] Data processing: Generative AI models analyze input data and update it into more appropriate plans and plans.

[0636] Output: Tailored training and meal plans.

[0637] Step 7:

[0638] The adjusted plan is provided to the user terminal.

[0639] Specific operation: The server re-notifies the user terminal of the adjusted training plan and meal plan.

[0640] Input: tailored training and meal plans.

[0641] Data processing: Converting information into a user-friendly format and generating a re-notification message.

[0642] Output: Training plan and meal plan re-notified to user device.

[0643] These steps allow for personalized training and meal plans to be provided to users in real time, and adapted to their progress and emotional state.

[0644] 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.

[0645] 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.

[0646] 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.

[0647] [Third embodiment]

[0648] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0649] 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.

[0650] 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).

[0651] 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.

[0652] 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.

[0653] 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).

[0654] 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. 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.

[0655] 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.

[0656] 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.

[0657] 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.

[0658] 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.

[0659] 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."

[0660] This invention is a system that collects necessary information from the Internet, creates a training plan based on that information, and provides it to the user.The user then reports their recorded progress to a server, which stores the data, thereby supporting personal self-realization.

[0661] The server first automatically collects the necessary information from the Internet. For example, it uses scraping techniques to obtain data from online databases containing health websites or exercise instruction guides. This data is then organized by extracting relevant information from specific HTML elements. The scraped data is then converted into the required format and stored in a database.

[0662] The server then uses a generative AI model based on the collected data to generate a training plan tailored to the user's profile and goals. For example, for a professional athlete, a plan would be generated that includes specific training menus and nutritional management for a match. On the other hand, for the elderly, a light exercise plan aimed at maintaining health would be generated. The collected information is then graph-structured and organized according to the importance and relevance of the content, improving the accuracy and effectiveness of the plan.

[0663] The generated training plan is sent from the server to the user's device. The user can check the training plan that suits them best via their smartphone, tablet, or other device. The device displays the details of each day's training, and the user carries out the training based on the plan. For example, specific details such as "On Mondays, I will jog for 30 minutes and stretch for 15 minutes" are displayed.

[0664] After completing a training session, the user records their progress using their device. This progress data is saved on the device as input by the user and then sent to the server. The server saves the received progress data in a database and manages the user's training history. This ensures that the user's progress is always recorded up to date and is reflected in the next training plan.

[0665] As a concrete example, in the case of supporting self-realization for the elderly, the server collects health information from sites such as "http: / / example.com / health-tips," and then uses the collected information to create a training plan consisting of "daily light exercise" and "balanced diet" using a generative AI model. When the user enters "I walked for 15 minutes" into the device, the progress information is sent to the server and reflected in the next plan.

[0666] This system will efficiently support the self-realization of individuals and effectively maintain the health and improve the abilities of elderly people and professional athletes.

[0667] The processing flow will be explained below.

[0668] Step 1:

[0669] The server collects the necessary information from the Internet. Here, the server sends HTTP requests to the URLs of multiple configured websites and scrapes the resulting HTML documents. In doing so, it extracts the necessary data from specific HTML elements and temporarily stores this data in memory.

[0670] Step 2:

[0671] The server analyzes and organizes the extracted data. The server filters out only the necessary parts of the scraped information and converts it into a format that can be stored in a database. For example, it extracts the article title, body text, and important keywords and stores this in the database as structured data.

[0672] Step 3:

[0673] The server retrieves the user's profile information and goals from the database, including the user's age, gender, physical fitness level, and specific self-realization goals.

[0674] Step 4:

[0675] Based on the information collected by the server and the user's profile information, a generative AI model is used to generate a training plan, such as a gentle exercise menu for the elderly or a detailed plan for professional athletes that will improve their athletic performance.

[0676] Step 5:

[0677] The server provides the generated training plan to the user's device, and the server synchronizes the training plan with the user's device so that the user can check the plan at any time.

[0678] Step 6:

[0679] The user performs the training using the device. The user checks the daily training content on the device screen and performs the exercise according to the instructions. For example, specific exercises such as "30 minutes of jogging" or "15 minutes of stretching" are instructed.

[0680] Step 7:

[0681] After completing the training, the user enters their progress into the terminal, and an interface is provided to record the content of the training, the time required, and subjective impressions.

[0682] Step 8:

[0683] The terminal reports the user's input data to the server. The input progress data is immediately sent to the server.

[0684] Step 9:

[0685] The server stores the received progress data in a database and reflects it in the next training plan. The server evaluates the user's training progress based on the new data and adjusts the training plan as necessary.

[0686] This continuous processing effectively supports the self-actualization of individuals and helps users maintain their health and improve their abilities.

[0687] Example 1

[0688] 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."

[0689] Conventional training plans have the problem of not being able to fully reflect the needs and goals of individual users and can only provide generic exercise programs. Furthermore, progress management is often done manually, which often results in delays in updating and improving plans. Therefore, there is a need for a system that can automatically generate optimal training plans for individual users and efficiently manage progress.

[0690] 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.

[0691] In this invention, the server includes means for collecting necessary information from the Internet, means for converting the collected information into a specific format and saving it, means for generating prompts using a generative AI model based on the collected information and the user's profile to create a training plan, means for providing the generated training plan to the user's terminal, means for recording the progress entered by the user and reporting it to the server, and means for saving the progress data received by the server. This makes it possible to automatically generate a training plan according to the user's individual needs and goals and efficiently manage the user's progress.

[0692] "Means for collecting necessary information from the Internet" refers to means for automatically collecting data such as health information and exercise methods from websites and online databases on the Internet.

[0693] "Means of converting and saving data in a specific format based on collected information" refers to means of converting collected data into a specific format (e.g., JSON, CSV) and saving that data in a database, etc.

[0694] "Means for generating prompt sentences using a generative AI model and creating a training plan" means means for generating prompt sentences based on collected information and a user profile, and for automatically creating a training plan using a generative AI model (e.g., a natural language generation model) with the generated prompt sentences.

[0695] The "means for providing the generated training plan to the user terminal" refers to a means for delivering the generated training plan to the terminal used by the user so that the training plan can be confirmed on that terminal.

[0696] The "means for recording the progress status input by the user and reporting it to the server" is a means for the user to input the progress status of his / her training, record the information, and send it to the server.

[0697] The "means for storing the progress data received by the server" refers to a means for the server to store the progress data received from the user in a database or the like, and use the data to generate future training plans.

[0698] This invention is a system that collects necessary information from the Internet, generates a training plan based on that information, and provides it to the user. This system is operated using a server, a user terminal, and a generative AI model. The server collects health information and exercise methods from the Internet, and the generated training plan is distributed to the user terminal. Furthermore, the user's progress is recorded on the server and reflected in the next training plan.

[0699] Information gathering

[0700] The server accesses specific health information websites and databases and automatically extracts relevant information using a scraping tool (e.g., BeautifulSoup or Scrapy). For example, the server collects information such as "jogging three times a week is good for your health" from "http: / / example.com / health-tips." This information is saved as a temporary file (e.g., a CSV file).

[0701] Data conversion and storage

[0702] The server converts the collected information into JSON format and stores it in a relational database (e.g., MySQL). For example, the text information "jogging three times a week is good for your health" is converted into JSON format and stored in the database.

[0703] Generate a training plan

[0704] The server uses a generative AI model (e.g., GPT-4) to create a training plan based on the collected information and the user's profile. For example, if the user's information is "60-year-old male, high blood pressure, medium physical fitness level, goal: maintain health," the server inputs the following prompt sentence into the generative AI model:

[0705] User: Elderly, Goal: Maintaining health, Current physical condition: Able to walk for 15 minutes

[0706] Prompt: "Generate a moderate exercise plan for maintaining good health. Include a one-week plan with daily exercise and balanced meal suggestions."

[0707] This allows the generative AI model to generate specific training plans such as "jogging three times a week" and "daily vegetable intake plan."

[0708] Training plan distribution

[0709] The server delivers the generated training plan to the user's device. The user's device displays the delivered information, and the user carries out the training based on it. For example, a detailed plan such as "30 minutes of jogging and 15 minutes of stretching on Mondays" is displayed on the smartphone.

[0710] Record and manage progress

[0711] After completing a workout, the user uses the device to input progress information (e.g., "I walked for 15 minutes"). The device temporarily saves the user's input and sends it to the server. The server saves the received progress data in a database and manages the user's training history. This information is reflected in the next workout plan.

[0712] This system allows users to easily obtain training plans that suit their needs and goals, and efficiently maintain or improve their health. In addition, because progress is properly recorded and managed, the accuracy and effectiveness of training plans are improved.

[0713] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0714] Step 1:

[0715] Information gathering

[0716] The server accesses a specific health information website on the Internet (e.g., http: / / example.com / health-tips). As input, it uses a list of URLs and uses a scraping tool (e.g., BeautifulSoup or Scrapy) to extract relevant information (e.g., "jogging three times a week is good for your health"). Specifically, it extracts HTML elements (e.g., The necessary information is extracted from the tags (text within tags) and saved to a temporary file (e.g., a CSV file). The output is a temporary file containing the collected health information.

[0717] Step 2:

[0718] Data conversion and storage

[0719] The server converts data stored in a temporary file into JSON format. As input, there is data stored in the form of a temporary file. This data is converted into a specific format (e.g. JSON) and stored in a relational database (e.g. MySQL). Specifically, the server converts the text "Jogging three times a week is good for your health" into JSON format and stores it in a MySQL database. As output, it gets the information stored in the database.

[0720] Step 3:

[0721] Generate a training plan

[0722] The server creates a training plan using a generative AI model (e.g., GPT-4) based on the collected information and the user's profile. The inputs are the user's profile information (e.g., age, gender, and fitness level) and the collected data. The server generates a prompt sentence and inputs it into the generative AI model. Specifically, the server generates the following prompt sentence:

[0723] User: Elderly, Goal: Maintaining health, Current physical condition: Able to walk for 15 minutes

[0724] Prompt: "Generate a moderate exercise plan for maintaining good health. Include a one-week plan with daily exercise and balanced meal suggestions."

[0725] Based on this prompt, the generative AI model outputs a specific training plan such as "jogging three times a week" and "daily vegetable intake plan."

[0726] Step 4:

[0727] Training plan distribution

[0728] The server distributes the generated training plan to the user's device. The input is the generated training plan. The server sends the plan to the user's device (e.g., smartphone or tablet), which receives it. Specifically, the server distributes information such as "On Mondays, do 30 minutes of jogging and 15 minutes of stretching" to the user's smartphone, which then displays the information on the device. The output is the training plan displayed on the user's device.

[0729] Step 5:

[0730] Record and manage progress

[0731] After completing a workout, the user uses the device to input progress information (e.g., "Walked for 15 minutes"). The input is the user's progress information. The device temporarily stores this information and sends it to the server. Specifically, the user enters the progress information into their smartphone, and the device sends the information to the server. The server saves the received progress data in a database and manages the user's training history. The output is the updated progress data saved in the database.

[0732] This allows users to keep track of their progress and reflect it in their next training plan.

[0733] (Application example 1)

[0734] 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."

[0735] It is difficult for physical fitness facilities such as fitness clubs and gyms to provide personalized training plans in real time that meet the diverse needs of users and to properly manage their progress. A system that can effectively and efficiently solve this problem is needed. There is also a need for technology that can generate optimal training plans for each user based on collected health information and training data.

[0736] 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.

[0737] In this invention, the server includes means for collecting necessary information from the Internet, means for generating a training plan based on the collected information, means for providing the generated training plan to a user terminal, means for recording progress input by the user and reporting it to the server, means for saving progress data received by the server, and means for providing a training plan to the user at a fitness club or gym in real time and reflecting the user's progress. This makes it possible to provide a personalized training plan for each user in real time and appropriately manage the user's progress.

[0738] "Means of collecting necessary information from the Internet" refers to a method of automatically obtaining specific information from websites, online databases, etc.

[0739] The "means for generating a training plan based on collected information" refers to a method or device for analyzing the acquired data and creating an individual training menu based on the data.

[0740] The "means for providing the generated training plan to a user terminal" refers to a method or device for displaying or transmitting the generated training plan to a user terminal such as a smartphone or tablet.

[0741] The "means for recording the progress status input by the user and reporting it to the server" refers to a method or device in which the user inputs his or her own progress, records the data, and transmits it to the server.

[0742] The "means for storing the progress data received by the server" refers to a method or device by which the server stores the progress data sent from the user in a database.

[0743] "Means for providing users with training plans in real time at fitness clubs or gyms and reflecting progress" refers to a method or device that presents a training plan to users on the spot at a physical store and immediately reflects the progress and results in the plan.

[0744] The system for realizing the present invention mainly uses a server, a user terminal, and various software.

[0745] The server first collects the necessary information from the Internet. Specifically, it uses web scraping technology to obtain data from health-related websites and online databases, organizes this data, and stores it in a database. For scraping, it uses the Python libraries Requests and BeautifulSoup. For example, health information can be collected from "http: / / example.com / health-tips."

[0746] Based on the collected data, the server generates a training plan using a generative AI model based on the user's profile and goals. This is done using OpenAI's API. Here is an example of a prompt:

[0747] User profile: Elderly, 65 years old, looking for light exercise without strain

[0748] User goals: Light exercise every day to maintain health

[0749] Health tips: ['15 minutes of walking every day is recommended', 'Eat a balanced diet']

[0750] Generate a personalized training plan.

[0751] The generated training plan is provided from the server to the user's device, which could be a smartphone, tablet, smart glasses, or head-mounted display. The user can view their personalized training plan through these devices. For example, detailed content such as "On Mondays, I plan to jog for 30 minutes and stretch for 15 minutes" is displayed.

[0752] After completing a workout, the user enters their progress into the device. The entered progress data is sent to the server, which stores it in a database. The server then adjusts the next training plan as needed based on the received progress data. This ensures that the user's progress is always recorded up to date, maximizing the effectiveness of their individual training.

[0753] For example, if a user at a fitness club or gym inputs "I walked for 15 minutes" into a device, that information is sent to the server in real time and immediately reflected in the next training plan, effectively providing training tailored to the user's individual needs.

[0754] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0755] Step 1:

[0756] The server collects the necessary information from the Internet. Specifically, it uses Python's Requests library to access health-related websites and retrieve their content. Next, it uses the BeautifulSoup library to parse HTML elements and extract health information in text format. The input for this process is the website URL, and the output is the extracted health information in text format.

[0757] Step 2:

[0758] The server stores the collected health information in a database. For example, it uses a database service such as Amazon RDS to store the acquired data. The input of this process is the text data extracted in step 1, and the output is the information stored in the database.

[0759] Step 3:

[0760] The server receives the user's profile and goals. It collects the profile information (age, exercise experience, health status, etc.) and goals (maintaining health, losing weight, etc.) entered by the user through the terminal. The input of this process is the information entered by the user into the terminal, and the output is the user's profile and goal data stored on the server.

[0761] Step 4:

[0762] The server generates a training plan using a generative AI model based on the collected health information and the user's profile and goals. The generative AI model uses OpenAI's API. For example, the following prompt sentence can be input into the generative AI model to generate a training plan:

[0763] User profile: Elderly, 65 years old, looking for light exercise without strain

[0764] User goals: Light exercise every day to maintain health

[0765] Health tips: ['15 minutes of walking every day is recommended', 'Eat a balanced diet']

[0766] Generate a personalized training plan.

[0767] The input to this process is a prompt sentence, and the output is the generated training plan text data.

[0768] Step 5:

[0769] The server provides the generated training plan to the user's device, where the user can view the training plan via a smartphone, tablet, smart glasses, etc. The input of this process is the generated training plan, and the output is the training plan displayed on the user's device.

[0770] Step 6:

[0771] The user performs training and inputs their progress into the device. For example, they enter information such as "I walked for 15 minutes" into their smartphone. The input of this process is the progress information that the user enters into the device, and the output is the progress data stored on the device.

[0772] Step 7:

[0773] The device sends the user's progress data to the server, which stores the received progress data in a database and reflects it in the next training plan. The input of this process is the user's progress data, and the output is the updated progress data stored in the database.

[0774] Step 8:

[0775] The server regenerates the next training plan based on the progress data. The new training plan is adjusted based on the current plan and progress. The input to this process is the updated progress data, and the output is the updated training plan.

[0776] 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.

[0777] This invention is a system that collects necessary information from the Internet, creates a training plan based on that information, and provides it to the user. The system also supports the user's self-realization by reporting the user's recorded progress to a server, which then stores the data. Furthermore, it incorporates an emotion engine that recognizes the user's emotions and adjusts the training plan based on those emotions.

[0778] The server first automatically collects the necessary information from the Internet, for example, by scraping data from online databases containing health websites or exercise instruction guides. This data is then organized by extracting relevant information from specific HTML elements and stored in a database.

[0779] The server then uses the collected data to generate a training plan based on the user's profile and goals using a generative AI model. For example, a professional athlete might receive a plan with specific training and nutritional information for a match, while an elderly person might receive a low-intensity exercise plan aimed at maintaining health.

[0780] Furthermore, the emotion engine recognizes the user's emotions. The emotion engine analyzes the user's voice, facial expressions, text input, etc. to identify their emotional state. This emotion data is provided to a generative AI model and used to adjust the training plan. For example, if the user is feeling stressed, a training plan will be generated that includes relaxation exercises and stretches. In this way, a more appropriate training plan is provided based on the user's emotional state.

[0781] The generated training plan is sent from the server to the user's device. The user can check the training plan that suits them best via their smartphone, tablet, or other device. The device displays the details of each day's training, and the user carries out the training based on the plan. For example, specific details such as "On Mondays, I will jog for 30 minutes and stretch for 15 minutes" are displayed.

[0782] After completing the training, the user uses the device to record their progress. An interface is provided to record the training content, duration, subjective impressions, etc. An emotion engine is also used to record the user's emotional state during training and evaluate the effectiveness of the training.

[0783] The device reports the progress and emotional data entered by the user to the server, which stores the received data in a database and reflects it in the next training plan. This ensures that the user's progress and emotional state are always recorded up to date, improving the quality of the next plan.

[0784] As a concrete example, in the case of supporting self-realization for the elderly, the server collects health information from sites such as "http: / / example.com / health-tips," and then uses the collected information to create a training plan consisting of "daily light exercise" and "balanced meals" using a generative AI model. The emotion engine analyzes the user's voice and facial expressions, and if it recognizes, for example, that the user is "very tired," relaxation exercises are added to the plan. When the user enters "I walked for 15 minutes" into the device, the progress information and emotional state are sent to the server and reflected in the next plan.

[0785] This system will efficiently support the self-realization of individuals, as well as effectively maintain the health and improve the performance of elderly people and professional athletes. By incorporating an emotion engine, more personalized training plans will be provided, further improving user satisfaction and the effectiveness of training.

[0786] The processing flow will be explained below.

[0787] Step 1:

[0788] The server gathers the necessary information from the Internet, specifically by sending an HTTP request to retrieve HTML documents from online databases containing health websites and exercise instruction.

[0789] Step 2:

[0790] The server extracts specific elements from the HTML documents obtained using scraping techniques, such as article titles, body text, and related keywords, and stores them temporarily in memory.

[0791] Step 3:

[0792] The server analyzes and organizes the extracted data, filtering out the necessary information, structuring it into an easy-to-read format, and storing it in a database.

[0793] Step 4:

[0794] The server retrieves the user's profile and goal data from a database, including the user's age, gender, fitness level, and specific self-actualization goals.

[0795] Step 5:

[0796] Based on the collected data and the user's profile data, the server uses a generative AI model to generate a training plan, for example, creating a gentle exercise menu for the elderly and a vigorous exercise menu for users who want high-intensity training.

[0797] Step 6:

[0798] The emotion engine recognizes the user's emotional state. Before training, the user inputs voice and facial expressions, and the emotion engine analyzes the emotions.

[0799] Step 7:

[0800] The server adjusts the training plan based on the emotional data obtained from the emotion engine, for example adding relaxation exercises if the user is feeling stressed.

[0801] Step 8:

[0802] The server provides the generated and adjusted training plan to the user's device, where the user can check the training plan on their smartphone or tablet.

[0803] Step 9:

[0804] The user begins training according to the training plan presented to them through the device. For example, specific training content such as "30 minutes of jogging" or "15 minutes of stretching" is displayed on the device screen.

[0805] Step 10:

[0806] After completing the training, the user inputs their progress into the device, for example, recording data such as "completed 30 minutes of jogging" or "performed 15 minutes of stretching."

[0807] Step 11:

[0808] The emotion engine will again recognize and collect data on the user's emotional state during and after training. For example, if the user feels fatigued, it will record that emotional data.

[0809] Step 12:

[0810] The device reports the user's progress and emotional data to the server, and the collected data is immediately sent to the server.

[0811] Step 13:

[0812] The server stores the received progress and emotion data in a database and incorporates it into a new training plan, which is then further adjusted based on this data for the next training session.

[0813] Through these steps, the system efficiently supports individual self-realization and helps elderly people and professional athletes maintain their health and improve their abilities. By incorporating an emotion engine, the system provides personalized training plans according to the user's emotional state, resulting in higher satisfaction.

[0814] Example 2

[0815] 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."

[0816] In today's world, it is important for individuals to create training plans to maintain their health and achieve self-realization. However, current systems have difficulty effectively utilizing information collected from the internet to automatically generate and adjust personalized training plans based on the user's profile and emotions. Furthermore, there are few ways for users to easily record their daily training progress and emotional state and reflect that data in their next plan. This poses a challenge, limiting user satisfaction and training effectiveness.

[0817] 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.

[0818] In this invention, the server includes means for collecting necessary information from the Internet, means for organizing the collected information and storing it in a database, means for acquiring a user's profile and goals, means for generating a training plan using a generative AI model, means for recognizing the user's emotions, means for adjusting the training plan based on the emotion data, means for providing the generated training plan to a user terminal, means for recording progress input by the user and reporting it to the server, and means for storing the progress data and emotion data received by the server.

[0819] This makes it possible to automatically generate and adjust personalized training plans based on the user's profile and emotional state by utilizing information from the internet. It also allows users to easily record their daily training progress and emotional state and reflect that data in their next plan, improving user satisfaction and training effectiveness.

[0820] "Means for collecting necessary information from the Internet" refers to devices or methods that automatically obtain specific information, such as health information or exercise methods, from websites or online databases on the Internet.

[0821] "Means for organizing collected information and storing it in a database" refers to a device or method that analyzes the acquired data, extracts and systematically organizes the necessary information, and stores that information in a database in an appropriate format.

[0822] A "means for obtaining a user's profile and goals" is a device or method that collects personal data provided by a user, such as age, gender, weight, goals, etc., and stores it in a database.

[0823] A "means for generating a training plan using a generative AI model" is a device or method that inputs a user's profile and collected health information into a pre-trained generative AI model, and automatically generates a training plan optimized for the user based on that information.

[0824] A "means for recognizing user emotions" is a device or method that detects and classifies a user's emotional state by analyzing the user's voice input and facial expressions.

[0825] "Means for adjusting a training plan based on emotional data" refers to a device or method that provides a generative AI model with user emotional data detected by an emotion engine and dynamically adjusts a training plan to match the user's emotional state.

[0826] The "means for providing the generated training plan to the user terminal" refers to a device or method for transmitting the training plan generated by the server to the user's terminal such as a smartphone or tablet via a network and displaying it.

[0827] "Means for recording progress input by the user and reporting it to the server" refers to a device or method that allows the user to input progress information, such as the training content, time required, and subjective impressions, into a terminal and automatically transmits that data to the server.

[0828] "Means for storing progress data and emotion data received by the server" refers to a device or method by which the server stores the progress data and emotion data sent from the terminal in a database and uses it to generate the next training plan.

[0829] MODE FOR CARRYING OUT THE INVENTION

[0830] This invention is a system that collects necessary information from the Internet, creates a training plan based on that information, and provides it to the user. Additionally, the system supports the user's self-realization by reporting the user's recorded progress to a server, which then stores the data. Furthermore, it incorporates an emotion engine that recognizes the user's emotions and adjusts the training plan based on those emotions.

[0831] Server Processing

[0832] The server first automatically collects the necessary information from the Internet. For example, it uses scraping techniques to obtain data from online databases containing health websites or exercise instruction guides. This work is performed using Python's BeautifulSoup and Scrapy libraries. This data is then organized by extracting relevant information from specific HTML elements and stored in a database such as MySQL or PostgreSQL.

[0833] Next, the server uses a generative AI model (e.g., GPT-4) based on the collected data to generate a training plan tailored to the user's profile and goals. An example of a prompt is, "Please generate a training plan suitable for a 65-year-old elderly male. Health information is ____." This generative AI model provides the optimal training menu tailored to the user's needs.

[0834] Additionally, an emotion engine recognizes the user's emotions. Using voice and facial expression analysis tools such as the Google Speech-to-Text API and OpenCV, the emotion engine analyzes the user's voice, facial expressions, and text input to identify their emotional state. This emotion data is also provided to the generative AI model, which uses it to adjust the training plan. For example, if the user is feeling stressed, a training plan will be generated that includes relaxation exercises and stretches.

[0835] Terminal handling

[0836] The generated training plan is sent from the server to the user's device. The user can then use their smartphone, tablet, or other device to check the training plan that suits them best. Specific training content is displayed in detail. For example, it might say, "On Mondays, do 30 minutes of jogging and 15 minutes of stretching."

[0837] After completing the training, the user records their progress using a device that has an interface for recording the training content, duration, subjective impressions, etc. An emotion engine also records the user's emotional state during training and is used to evaluate the effectiveness of the training.

[0838] User operations

[0839] The user checks their training plan through the device and carries out the training according to the plan. For example, "On Mondays, do 30 minutes of jogging and 15 minutes of stretching." Then, they use the device interface to input their progress and emotional state, which is then sent to the server.

[0840] Specific examples

[0841] As a concrete example, in the case of supporting self-realization for the elderly, the server collects health information from sites such as "http: / / example.com / health-tips," and based on that information, the generative AI model creates a training plan consisting of "daily light exercise" and "balanced meals." If the emotion engine analyzes the user's voice and facial expressions and recognizes that they are "very tired," relaxation exercises are added to the plan. When the user enters "I walked for 15 minutes" into their device, their progress information and emotional state are sent to the server and reflected in the next plan.

[0842] This system will efficiently support the self-realization of individuals, as well as effectively maintain the health and improve the abilities of the elderly and athletes. By incorporating an emotion engine, more personalized training plans will be provided, further improving user satisfaction and the effectiveness of training.

[0843] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0844] Step 1:

[0845] The server collects the necessary information from designated websites and online databases on the Internet. Specifically, it uses Python's BeautifulSoup and Scrapy libraries to obtain data on health-related information and exercise methods. The input here is a list of URLs to be collected, and the output is the raw HTML data collected by scraping.

[0846] Step 2:

[0847] The server analyzes the collected HTML data and extracts the necessary information. It uses regular expressions and XPath to extract health information and exercise descriptions from specific HTML elements. In this step, the input is raw HTML data, and the output is organized health information data. Specific operations include HTML parsing, text extraction, and data cleaning.

[0848] Step 3:

[0849] The server stores the organized information in a database. It uses a relational database such as MySQL or PostgreSQL to store the extracted health information and exercise methods as structured data. The input is the organized health information data, and the output is the result of the database save operation. Specific operations include executing SQL queries.

[0850] Step 4:

[0851] The server retrieves the user's profile and goals. It retrieves personal information provided by the user, such as age, gender, weight, and goals, from a database and generates a prompt based on that information. The input is the user ID and authentication information, and the output is the user's profile data. Specific operations include user authentication and SQL query execution.

[0852] Step 5:

[0853] The server generates a training plan by prompting the generative AI model based on the collected health information and the user's profile data. For example, using GPT-4, a prompt sentence such as "Please generate a training plan suitable for a 65-year-old elderly male. The health information is ____." The input is the prompt sentence, and the output is the generated training plan. Specific operations include an API request to the generative AI model.

[0854] Step 6:

[0855] The server recognizes the user's emotions. It analyzes the audio data and camera footage sent from the device and classifies the user's emotional state. The emotion engine uses Google Speech-to-Text API and OpenCV. The input is audio and video data, and the output is emotional data. Specific operations include voice recognition and facial expression analysis.

[0856] Step 7:

[0857] The server adjusts the training plan based on the emotion data. It supplies the emotion data to the generative AI model and dynamically adjusts the training plan. For example, if the user feels fatigued, it adds relaxation exercises. The input is emotion data, and the output is an adjusted training plan. Specific actions include re-prompting the generative AI model.

[0858] Step 8:

[0859] The server provides the generated training plan to the user's device. The training plan is sent to the user's smartphone or tablet via an HTTP request and displayed in the app. The input is the adjusted training plan, and the output is the data delivery results to the user's device. Specific operations include sending an HTTP request.

[0860] Step 9:

[0861] Users perform workouts and record their progress. They use checklists and input fields within the app to record the workouts they performed and the time it took. The input is a description of the workout and their emotional state, and the output is progress data. Specific actions include filling out forms within the app.

[0862] Step 10:

[0863] The device reports the recorded progress data and emotion data to the server. It calls an API to send the data to the server. The input is the progress data and emotion data, and the output is the result of sending the data to the server. Specific operations include executing an API request.

[0864] Step 11:

[0865] The server stores the received progress and emotion data in a database and reflects it in the next training plan. This data is used to update the input prompts of the generative AI model and optimize the next plan. The input is the progress and emotion data, and the output is the next training plan. Specific operations include updating the database and updating the prompts of the generative AI model.

[0866] (Application example 2)

[0867] 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."

[0868] Conventional training planning systems often lack personalization based on the user's health status and emotions, and are unable to properly reflect the user's progress. Furthermore, food delivery services, in particular, face the challenge of providing appropriate meal plans based on the user's emotions and progress. This leads to reduced user satisfaction and training effectiveness.

[0869] 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.

[0870] In this invention, the server includes means for collecting necessary information from the Internet, means for generating a training plan based on the collected information, means for providing the generated training plan to a user terminal, means for recording progress input by the user and reporting it to the server, means for saving the progress data received by the server, and means for analyzing the user's emotions and adjusting the training plan based on the emotions. This makes it possible to provide a personalized training plan and meal plan based on the user's health condition and progress, thereby improving user satisfaction and the effectiveness of training.

[0871] The "Internet" is a global information and communications network that interconnects multiple computer networks.

[0872] "Information" refers to knowledge or data about a particular phenomenon or subject.

[0873] A "training plan" is a structured training schedule designed to improve strength or skill toward a specific goal.

[0874] "User terminal" means an electronic device primarily used by a user to access the Internet or a system.

[0875] "Progress" is the progress achieved to date on a planned activity or project.

[0876] A "server" is a computer system that provides services to other computers on a network.

[0877] A "generative AI model" is a machine learning model that is trained using artificial intelligence techniques to automatically perform specific tasks.

[0878] "Emotions" are psychological and cognitive responses that indicate a person's state of mind.

[0879] "Meal Plan" means a plan that includes the food and schedule for a specific period of time.

[0880] "Personalization" refers to customizing something to suit an individual's specific needs and preferences.

[0881] "Progress Data" means information or records about an activity in progress.

[0882] "Voice analysis" refers to the technology of analyzing voice data to recognize meaning, emotions, etc.

[0883] "Facial expression analysis" refers to the technology of analyzing emotions and intentions from facial expressions.

[0884] "Text input" refers to the act of a user providing textual information to a system using a keyboard or other input device.

[0885] "Stress reduction" means the reduction of psychological or physical tension.

[0886] "Analysis" refers to the act of examining and evaluating data or information in detail.

[0887] This invention is a system that provides personalized training and meal plans for users. The system collects necessary information from the Internet, generates training plans and meal plans using a generative AI model, and provides them to the user's device. It also records the progress entered by the user and reports it to a server, so that the progress can be reflected in the next plan.

[0888] Required Hardware and Software

[0889] Hardware

[0890] Server: A high-performance server computer

[0891] User devices: smartphones, smart glasses, head-mounted displays

[0892] software

[0893] Server frameworks: Django and Flask

[0894] Data collection libraries: BeautifulSoup, Requests

[0895] Sentiment analysis libraries: TensorFlow, PyTorch (Transformers library)

[0896] Database: PostgreSQL or MySQL

[0897] Front-end technologies: HTML, CSS, JavaScript

[0898] System Operation

[0899] Information gathering

[0900] The server collects the necessary information from the Internet, for example by scraping data from websites that provide health and nutrition information, using Python's BeautifulSoup and Requests libraries.

[0901] Generate training and meal plans

[0902] The server uses the collected information to generate training and meal plans using a generative AI model that uses TensorFlow and PyTorch Transformers, and adjusts the plan based on the user's profile (age, weight, goals, etc.).

[0903] For example, for elderly users, a plan of gentle exercise including relaxation exercises and a nutritionally balanced meal plan is generated.

[0904] Recording and reporting user progress

[0905] Users can record their progress using their smartphone or other device, for example by entering "I walked for 15 minutes," and this data is sent to the server and reflected in the next plan.

[0906] Sentiment analysis and plan adjustment

[0907] The server analyzes the user's emotional state using an emotion analysis engine. The user's voice, facial expressions, and text input are used as input data. A Transformers model of sentiment analysis is used for emotion analysis.

[0908] For example, if a user enters a prompt such as "I've been feeling stressed lately and would like a comforting meal," the sentiment analysis engine will identify the emotion of "stress relief" and adjust the meal plan accordingly, recommending foods that are suitable for stress relief, such as comfort food (chicken soup).

[0909] Follow-up and user feedback

[0910] Progress and emotional data are stored on the server and reflected in the generation of the next training plan or meal plan, so that the user's progress and emotional state are always kept up to date, improving the quality of the next plan.

[0911] In this way, personalized training and meal plans can be adjusted in real time to effectively support users in maintaining their health and managing stress.

[0912] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0913] Step 1:

[0914] Gather the necessary information from the Internet.

[0915] What it does: The server uses Python's BeautifulSoup and Requests libraries to scrape data from websites, including health and nutrition information.

[0916] Input: The server receives the URL of the specified website as input.

[0917] Data processing: Extract relevant information from specific HTML elements of a specified website and format the data.

[0918] Output: The extracted data is saved as health and nutrition information.

[0919] Step 2:

[0920] Based on the collected information, a generative AI model is used to generate training and meal plans.

[0921] How it works: The server uses Transformers models using TensorFlow and PyTorch to generate workout and meal plans using the collected information and the user's profile data as input.

[0922] Input: User profile information (age, weight, goals, etc.) and collected health data.

[0923] Data processing: A generative AI model analyzes user profile and health data to generate appropriate training and meal plans.

[0924] Output: Personalized training and meal plans.

[0925] Step 3:

[0926] The generated training plan and meal plan are provided to the user terminal.

[0927] Specific operation: The server notifies the user of the generated training plan and meal plan on their smartphone or other device.

[0928] Input: Generated training plan and meal plan.

[0929] Data processing: Converting information into a user-friendly format and generating notification messages.

[0930] Output: Training plan and meal plan displayed on user device.

[0931] Step 4:

[0932] Records the progress entered by the user and reports it to the server.

[0933] What it does: The user uses the device to input their progress, and that data is sent to the server.

[0934] Input: User-entered progress (e.g., "I walked for 15 minutes").

[0935] Data processing: The server analyzes the entered data and saves it as progress data.

[0936] Output: Progress data stored on the server.

[0937] Step 5:

[0938] Analyze user emotions and adjust plans based on emotions.

[0939] Specific operation: The server analyzes the user's voice, facial expression, and text input using an emotion analysis engine to obtain emotion data.

[0940] Input: User voice, facial expressions, and text input data.

[0941] Data processing: A sentiment analysis engine analyzes input data to identify emotions (e.g., "I feel stressed").

[0942] Output: Emotional data (e.g., "I need stress relief").

[0943] Step 6:

[0944] Adjust your training and meal plans based on emotional data.

[0945] How it works: The server provides the acquired emotional data to a generative AI model to adjust training and meal plans.

[0946] Input: Emotional data and your current training and meal plans.

[0947] Data processing: Generative AI models analyze input data and update it into more appropriate plans and plans.

[0948] Output: Tailored training and meal plans.

[0949] Step 7:

[0950] The adjusted plan is provided to the user terminal.

[0951] Specific operation: The server re-notifies the user terminal of the adjusted training plan and meal plan.

[0952] Input: tailored training and meal plans.

[0953] Data processing: Converting information into a user-friendly format and generating a re-notification message.

[0954] Output: Training plan and meal plan re-notified to user device.

[0955] These steps allow for personalized training and meal plans to be provided to users in real time, and adapted to their progress and emotional state.

[0956] 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.

[0957] 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.

[0958] 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.

[0959] [Fourth embodiment]

[0960] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0961] 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.

[0962] 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).

[0963] 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.

[0964] 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.

[0965] 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).

[0966] 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. 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.

[0967] 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.

[0968] 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.

[0969] 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.

[0970] 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.

[0971] 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.

[0972] 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."

[0973] This invention is a system that collects necessary information from the Internet, creates a training plan based on that information, and provides it to the user.The user then reports their recorded progress to a server, which stores the data, thereby supporting personal self-realization.

[0974] The server first automatically collects the necessary information from the Internet. For example, it uses scraping techniques to obtain data from online databases containing health websites or exercise instruction guides. This data is then organized by extracting relevant information from specific HTML elements. The scraped data is then converted into the required format and stored in a database.

[0975] The server then uses a generative AI model based on the collected data to generate a training plan tailored to the user's profile and goals. For example, for a professional athlete, a plan would be generated that includes specific training menus and nutritional management for a match. On the other hand, for the elderly, a light exercise plan aimed at maintaining health would be generated. The collected information is then graph-structured and organized according to the importance and relevance of the content, improving the accuracy and effectiveness of the plan.

[0976] The generated training plan is sent from the server to the user's device. The user can check the training plan that suits them best via their smartphone, tablet, or other device. The device displays the details of each day's training, and the user carries out the training based on the plan. For example, specific details such as "On Mondays, I will jog for 30 minutes and stretch for 15 minutes" are displayed.

[0977] After completing a training session, the user records their progress using their device. This progress data is saved on the device as input by the user and then sent to the server. The server saves the received progress data in a database and manages the user's training history. This ensures that the user's progress is always recorded up to date and is reflected in the next training plan.

[0978] As a concrete example, in the case of supporting self-realization for the elderly, the server collects health information from sites such as "http: / / example.com / health-tips," and then uses the collected information to create a training plan consisting of "daily light exercise" and "balanced diet" using a generative AI model. When the user enters "I walked for 15 minutes" into the device, the progress information is sent to the server and reflected in the next plan.

[0979] This system will efficiently support the self-realization of individuals and effectively maintain the health and improve the abilities of elderly people and professional athletes.

[0980] The processing flow will be explained below.

[0981] Step 1:

[0982] The server collects the necessary information from the Internet. Here, the server sends HTTP requests to the URLs of multiple configured websites and scrapes the resulting HTML documents. In doing so, it extracts the necessary data from specific HTML elements and temporarily stores this data in memory.

[0983] Step 2:

[0984] The server analyzes and organizes the extracted data. The server filters out only the necessary parts of the scraped information and converts it into a format that can be stored in a database. For example, it extracts the article title, body text, and important keywords and stores this in the database as structured data.

[0985] Step 3:

[0986] The server retrieves the user's profile information and goals from the database, including the user's age, gender, physical fitness level, and specific self-realization goals.

[0987] Step 4:

[0988] Based on the information collected by the server and the user's profile information, a generative AI model is used to generate a training plan, such as a gentle exercise menu for the elderly or a detailed plan for professional athletes that will improve their athletic performance.

[0989] Step 5:

[0990] The server provides the generated training plan to the user's device, and the server synchronizes the training plan with the user's device so that the user can check the plan at any time.

[0991] Step 6:

[0992] The user performs the training using the device. The user checks the daily training content on the device screen and performs the exercise according to the instructions. For example, specific exercises such as "30 minutes of jogging" or "15 minutes of stretching" are instructed.

[0993] Step 7:

[0994] After completing the training, the user enters their progress into the terminal, and an interface is provided to record the content of the training, the time required, and subjective impressions.

[0995] Step 8:

[0996] The terminal reports the user's input data to the server. The input progress data is immediately sent to the server.

[0997] Step 9:

[0998] The server stores the received progress data in a database and reflects it in the next training plan. The server evaluates the user's training progress based on the new data and adjusts the training plan as necessary.

[0999] This continuous processing effectively supports the self-actualization of individuals and helps users maintain their health and improve their abilities.

[1000] Example 1

[1001] 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."

[1002] Conventional training plans have the problem of not being able to fully reflect the needs and goals of individual users and can only provide generic exercise programs. Furthermore, progress management is often done manually, which often results in delays in updating and improving plans. Therefore, there is a need for a system that can automatically generate optimal training plans for individual users and efficiently manage progress.

[1003] 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.

[1004] In this invention, the server includes means for collecting necessary information from the Internet, means for converting the collected information into a specific format and saving it, means for generating prompts using a generative AI model based on the collected information and the user's profile to create a training plan, means for providing the generated training plan to the user's terminal, means for recording the progress entered by the user and reporting it to the server, and means for saving the progress data received by the server. This makes it possible to automatically generate a training plan according to the user's individual needs and goals and efficiently manage the user's progress.

[1005] "Means for collecting necessary information from the Internet" refers to means for automatically collecting data such as health information and exercise methods from websites and online databases on the Internet.

[1006] "Means of converting and saving data in a specific format based on collected information" refers to means of converting collected data into a specific format (e.g., JSON, CSV) and saving that data in a database, etc.

[1007] "Means for generating prompt sentences using a generative AI model and creating a training plan" means means for generating prompt sentences based on collected information and a user profile, and for automatically creating a training plan using a generative AI model (e.g., a natural language generation model) with the generated prompt sentences.

[1008] The "means for providing the generated training plan to the user terminal" refers to a means for delivering the generated training plan to the terminal used by the user so that the training plan can be confirmed on that terminal.

[1009] The "means for recording the progress status input by the user and reporting it to the server" is a means for the user to input the progress status of his / her training, record the information, and send it to the server.

[1010] The "means for storing the progress data received by the server" refers to a means for the server to store the progress data received from the user in a database or the like, and use the data to generate future training plans.

[1011] This invention is a system that collects necessary information from the Internet, generates a training plan based on that information, and provides it to the user. This system is operated using a server, a user terminal, and a generative AI model. The server collects health information and exercise methods from the Internet, and the generated training plan is distributed to the user terminal. Furthermore, the user's progress is recorded on the server and reflected in the next training plan.

[1012] Information gathering

[1013] The server accesses specific health information websites and databases and automatically extracts relevant information using a scraping tool (e.g., BeautifulSoup or Scrapy). For example, the server collects information such as "jogging three times a week is good for your health" from "http: / / example.com / health-tips." This information is saved as a temporary file (e.g., a CSV file).

[1014] Data conversion and storage

[1015] The server converts the collected information into JSON format and stores it in a relational database (e.g., MySQL). For example, the text information "jogging three times a week is good for your health" is converted into JSON format and stored in the database.

[1016] Generate a training plan

[1017] The server uses a generative AI model (e.g., GPT-4) to create a training plan based on the collected information and the user's profile. For example, if the user's information is "60-year-old male, high blood pressure, medium physical fitness level, goal: maintain health," the server inputs the following prompt sentence into the generative AI model:

[1018] User: Elderly, Goal: Maintaining health, Current physical condition: Able to walk for 15 minutes

[1019] Prompt: "Generate a moderate exercise plan for maintaining good health. Include a one-week plan with daily exercise and balanced meal suggestions."

[1020] This allows the generative AI model to generate specific training plans such as "jogging three times a week" and "daily vegetable intake plan."

[1021] Training plan distribution

[1022] The server delivers the generated training plan to the user's device. The user's device displays the delivered information, and the user carries out the training based on it. For example, a detailed plan such as "30 minutes of jogging and 15 minutes of stretching on Mondays" is displayed on the smartphone.

[1023] Record and manage progress

[1024] After completing a workout, the user uses the device to input progress information (e.g., "I walked for 15 minutes"). The device temporarily saves the user's input and sends it to the server. The server saves the received progress data in a database and manages the user's training history. This information is reflected in the next workout plan.

[1025] This system allows users to easily obtain training plans that suit their needs and goals, and efficiently maintain or improve their health. In addition, because progress is properly recorded and managed, the accuracy and effectiveness of training plans are improved.

[1026] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1027] Step 1:

[1028] Information gathering

[1029] The server accesses a specific health information website on the Internet (e.g., http: / / example.com / health-tips). As input, it uses a list of URLs and uses a scraping tool (e.g., BeautifulSoup or Scrapy) to extract relevant information (e.g., "jogging three times a week is good for your health"). Specifically, it extracts HTML elements (e.g., The necessary information is extracted from the tags (text within tags) and saved to a temporary file (e.g., a CSV file). The output is a temporary file containing the collected health information.

[1030] Step 2:

[1031] Data conversion and storage

[1032] The server converts data stored in a temporary file into JSON format. As input, there is data stored in the form of a temporary file. This data is converted into a specific format (e.g. JSON) and stored in a relational database (e.g. MySQL). Specifically, the server converts the text "Jogging three times a week is good for your health" into JSON format and stores it in a MySQL database. As output, it gets the information stored in the database.

[1033] Step 3:

[1034] Generate a training plan

[1035] The server creates a training plan using a generative AI model (e.g., GPT-4) based on the collected information and the user's profile. The inputs are the user's profile information (e.g., age, gender, and fitness level) and the collected data. The server generates a prompt sentence and inputs it into the generative AI model. Specifically, the server generates the following prompt sentence:

[1036] User: Elderly, Goal: Maintaining health, Current physical condition: Able to walk for 15 minutes

[1037] Prompt: "Generate a moderate exercise plan for maintaining good health. Include a one-week plan with daily exercise and balanced meal suggestions."

[1038] Based on this prompt, the generative AI model outputs a specific training plan such as "jogging three times a week" and "daily vegetable intake plan."

[1039] Step 4:

[1040] Training plan distribution

[1041] The server distributes the generated training plan to the user's device. The input is the generated training plan. The server sends the plan to the user's device (e.g., smartphone or tablet), which receives it. Specifically, the server distributes information such as "On Mondays, do 30 minutes of jogging and 15 minutes of stretching" to the user's smartphone, which then displays the information on the device. The output is the training plan displayed on the user's device.

[1042] Step 5:

[1043] Record and manage progress

[1044] After completing a workout, the user uses the device to input progress information (e.g., "Walked for 15 minutes"). The input is the user's progress information. The device temporarily stores this information and sends it to the server. Specifically, the user enters the progress information into their smartphone, and the device sends the information to the server. The server saves the received progress data in a database and manages the user's training history. The output is the updated progress data saved in the database.

[1045] This allows users to keep track of their progress and reflect it in their next training plan.

[1046] (Application example 1)

[1047] 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."

[1048] It is difficult for physical fitness facilities such as fitness clubs and gyms to provide personalized training plans in real time that meet the diverse needs of users and to properly manage their progress. A system that can effectively and efficiently solve this problem is needed. There is also a need for technology that can generate optimal training plans for each user based on collected health information and training data.

[1049] 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.

[1050] In this invention, the server includes means for collecting necessary information from the Internet, means for generating a training plan based on the collected information, means for providing the generated training plan to a user terminal, means for recording progress input by the user and reporting it to the server, means for saving progress data received by the server, and means for providing a training plan to the user at a fitness club or gym in real time and reflecting the user's progress. This makes it possible to provide a personalized training plan for each user in real time and appropriately manage the user's progress.

[1051] "Means of collecting necessary information from the Internet" refers to a method of automatically obtaining specific information from websites, online databases, etc.

[1052] The "means for generating a training plan based on collected information" refers to a method or device for analyzing the acquired data and creating an individual training menu based on the data.

[1053] The "means for providing the generated training plan to a user terminal" refers to a method or device for displaying or transmitting the generated training plan to a user terminal such as a smartphone or tablet.

[1054] The "means for recording the progress status input by the user and reporting it to the server" refers to a method or device in which the user inputs his or her own progress, records the data, and transmits it to the server.

[1055] The "means for storing the progress data received by the server" refers to a method or device by which the server stores the progress data sent from the user in a database.

[1056] "Means for providing users with training plans in real time at fitness clubs or gyms and reflecting progress" refers to a method or device that presents a training plan to users on the spot at a physical store and immediately reflects the progress and results in the plan.

[1057] The system for realizing the present invention mainly uses a server, a user terminal, and various software.

[1058] The server first collects the necessary information from the Internet. Specifically, it uses web scraping technology to obtain data from health-related websites and online databases, organizes this data, and stores it in a database. For scraping, it uses the Python libraries Requests and BeautifulSoup. For example, health information can be collected from "http: / / example.com / health-tips."

[1059] Based on the collected data, the server generates a training plan using a generative AI model based on the user's profile and goals. This is done using OpenAI's API. Here is an example of a prompt:

[1060] User profile: Elderly, 65 years old, looking for light exercise without strain

[1061] User goals: Light exercise every day to maintain health

[1062] Health tips: ['15 minutes of walking every day is recommended', 'Eat a balanced diet']

[1063] Generate a personalized training plan.

[1064] The generated training plan is provided from the server to the user's device, which could be a smartphone, tablet, smart glasses, or head-mounted display. The user can view their personalized training plan through these devices. For example, detailed content such as "On Mondays, I plan to jog for 30 minutes and stretch for 15 minutes" is displayed.

[1065] After completing a workout, the user enters their progress into the device. The entered progress data is sent to the server, which stores it in a database. The server then adjusts the next training plan as needed based on the received progress data. This ensures that the user's progress is always recorded up to date, maximizing the effectiveness of their individual training.

[1066] For example, if a user at a fitness club or gym inputs "I walked for 15 minutes" into a device, that information is sent to the server in real time and immediately reflected in the next training plan, effectively providing training tailored to the user's individual needs.

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

[1068] Step 1:

[1069] The server collects the necessary information from the Internet. Specifically, it uses Python's Requests library to access health-related websites and retrieve their content. Next, it uses the BeautifulSoup library to parse HTML elements and extract health information in text format. The input for this process is the website URL, and the output is the extracted health information in text format.

[1070] Step 2:

[1071] The server stores the collected health information in a database. For example, it uses a database service such as Amazon RDS to store the acquired data. The input of this process is the text data extracted in step 1, and the output is the information stored in the database.

[1072] Step 3:

[1073] The server receives the user's profile and goals. It collects the profile information (age, exercise experience, health status, etc.) and goals (maintaining health, losing weight, etc.) entered by the user through the terminal. The input of this process is the information entered by the user into the terminal, and the output is the user's profile and goal data stored on the server.

[1074] Step 4:

[1075] The server generates a training plan using a generative AI model based on the collected health information and the user's profile and goals. The generative AI model uses OpenAI's API. For example, the following prompt sentence can be input into the generative AI model to generate a training plan:

[1076] User profile: Elderly, 65 years old, looking for light exercise without strain

[1077] User goals: Light exercise every day to maintain health

[1078] Health tips: ['15 minutes of walking every day is recommended', 'Eat a balanced diet']

[1079] Generate a personalized training plan.

[1080] The input to this process is a prompt sentence, and the output is the generated training plan text data.

[1081] Step 5:

[1082] The server provides the generated training plan to the user's device, where the user can view the training plan via a smartphone, tablet, smart glasses, etc. The input of this process is the generated training plan, and the output is the training plan displayed on the user's device.

[1083] Step 6:

[1084] The user performs training and inputs their progress into the device. For example, they enter information such as "I walked for 15 minutes" into their smartphone. The input of this process is the progress information that the user enters into the device, and the output is the progress data stored on the device.

[1085] Step 7:

[1086] The device sends the user's progress data to the server, which stores the received progress data in a database and reflects it in the next training plan. The input of this process is the user's progress data, and the output is the updated progress data stored in the database.

[1087] Step 8:

[1088] The server regenerates the next training plan based on the progress data. The new training plan is adjusted based on the current plan and progress. The input to this process is the updated progress data, and the output is the updated training plan.

[1089] 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.

[1090] This invention is a system that collects necessary information from the Internet, creates a training plan based on that information, and provides it to the user. The system also supports the user's self-realization by reporting the user's recorded progress to a server, which then stores the data. Furthermore, it incorporates an emotion engine that recognizes the user's emotions and adjusts the training plan based on those emotions.

[1091] The server first automatically collects the necessary information from the Internet, for example, by scraping data from online databases containing health websites or exercise instruction guides. This data is then organized by extracting relevant information from specific HTML elements and stored in a database.

[1092] The server then uses the collected data to generate a training plan based on the user's profile and goals using a generative AI model. For example, a professional athlete might receive a plan with specific training and nutritional information for a match, while an elderly person might receive a low-intensity exercise plan aimed at maintaining health.

[1093] Furthermore, the emotion engine recognizes the user's emotions. The emotion engine analyzes the user's voice, facial expressions, text input, etc. to identify their emotional state. This emotion data is provided to a generative AI model and used to adjust the training plan. For example, if the user is feeling stressed, a training plan will be generated that includes relaxation exercises and stretches. In this way, a more appropriate training plan is provided based on the user's emotional state.

[1094] The generated training plan is sent from the server to the user's device. The user can check the training plan that suits them best via their smartphone, tablet, or other device. The device displays the details of each day's training, and the user carries out the training based on the plan. For example, specific details such as "On Mondays, I will jog for 30 minutes and stretch for 15 minutes" are displayed.

[1095] After completing the training, the user uses the device to record their progress. An interface is provided to record the training content, duration, subjective impressions, etc. An emotion engine is also used to record the user's emotional state during training and evaluate the effectiveness of the training.

[1096] The device reports the progress and emotional data entered by the user to the server, which stores the received data in a database and reflects it in the next training plan. This ensures that the user's progress and emotional state are always recorded up to date, improving the quality of the next plan.

[1097] As a concrete example, in the case of supporting self-realization for the elderly, the server collects health information from sites such as "http: / / example.com / health-tips," and then uses the collected information to create a training plan consisting of "daily light exercise" and "balanced meals" using a generative AI model. The emotion engine analyzes the user's voice and facial expressions, and if it recognizes, for example, that the user is "very tired," relaxation exercises are added to the plan. When the user enters "I walked for 15 minutes" into the device, the progress information and emotional state are sent to the server and reflected in the next plan.

[1098] This system will efficiently support the self-realization of individuals, as well as effectively maintain the health and improve the performance of elderly people and professional athletes. By incorporating an emotion engine, more personalized training plans will be provided, further improving user satisfaction and the effectiveness of training.

[1099] The processing flow will be explained below.

[1100] Step 1:

[1101] The server gathers the necessary information from the Internet, specifically by sending an HTTP request to retrieve HTML documents from online databases containing health websites and exercise instruction.

[1102] Step 2:

[1103] The server extracts specific elements from the HTML documents obtained using scraping techniques, such as article titles, body text, and related keywords, and stores them temporarily in memory.

[1104] Step 3:

[1105] The server analyzes and organizes the extracted data, filtering out the necessary information, structuring it into an easy-to-read format, and storing it in a database.

[1106] Step 4:

[1107] The server retrieves the user's profile and goal data from a database, including the user's age, gender, fitness level, and specific self-actualization goals.

[1108] Step 5:

[1109] Based on the collected data and the user's profile data, the server uses a generative AI model to generate a training plan, for example, creating a gentle exercise menu for the elderly and a vigorous exercise menu for users who want high-intensity training.

[1110] Step 6:

[1111] The emotion engine recognizes the user's emotional state. Before training, the user inputs voice and facial expressions, and the emotion engine analyzes the emotions.

[1112] Step 7:

[1113] The server adjusts the training plan based on the emotional data obtained from the emotion engine, for example adding relaxation exercises if the user is feeling stressed.

[1114] Step 8:

[1115] The server provides the generated and adjusted training plan to the user's device, where the user can check the training plan on their smartphone or tablet.

[1116] Step 9:

[1117] The user begins training according to the training plan presented to them through the device. For example, specific training content such as "30 minutes of jogging" or "15 minutes of stretching" is displayed on the device screen.

[1118] Step 10:

[1119] After completing the training, the user inputs their progress into the device, for example, recording data such as "completed 30 minutes of jogging" or "performed 15 minutes of stretching."

[1120] Step 11:

[1121] The emotion engine will again recognize and collect data on the user's emotional state during and after training. For example, if the user feels fatigued, it will record that emotional data.

[1122] Step 12:

[1123] The device reports the user's progress and emotional data to the server, and the collected data is immediately sent to the server.

[1124] Step 13:

[1125] The server stores the received progress and emotion data in a database and incorporates it into a new training plan, which is then further adjusted based on this data for the next training session.

[1126] Through these steps, the system efficiently supports individual self-realization and helps elderly people and professional athletes maintain their health and improve their abilities. By incorporating an emotion engine, the system provides personalized training plans according to the user's emotional state, resulting in higher satisfaction.

[1127] Example 2

[1128] 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."

[1129] In today's world, it is important for individuals to create training plans to maintain their health and achieve self-realization. However, current systems have difficulty effectively utilizing information collected from the internet to automatically generate and adjust personalized training plans based on the user's profile and emotions. Furthermore, there are few ways for users to easily record their daily training progress and emotional state and reflect that data in their next plan. This poses a challenge, limiting user satisfaction and training effectiveness.

[1130] 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.

[1131] In this invention, the server includes means for collecting necessary information from the Internet, means for organizing the collected information and storing it in a database, means for acquiring a user's profile and goals, means for generating a training plan using a generative AI model, means for recognizing the user's emotions, means for adjusting the training plan based on the emotion data, means for providing the generated training plan to a user terminal, means for recording progress input by the user and reporting it to the server, and means for storing the progress data and emotion data received by the server.

[1132] This makes it possible to automatically generate and adjust personalized training plans based on the user's profile and emotional state by utilizing information from the internet. It also allows users to easily record their daily training progress and emotional state and reflect that data in their next plan, improving user satisfaction and training effectiveness.

[1133] "Means for collecting necessary information from the Internet" refers to devices or methods that automatically obtain specific information, such as health information or exercise methods, from websites or online databases on the Internet.

[1134] "Means for organizing collected information and storing it in a database" refers to a device or method that analyzes the acquired data, extracts and systematically organizes the necessary information, and stores that information in a database in an appropriate format.

[1135] A "means for obtaining a user's profile and goals" is a device or method that collects personal data provided by a user, such as age, gender, weight, goals, etc., and stores it in a database.

[1136] A "means for generating a training plan using a generative AI model" is a device or method that inputs a user's profile and collected health information into a pre-trained generative AI model, and automatically generates a training plan optimized for the user based on that information.

[1137] A "means for recognizing user emotions" is a device or method that detects and classifies a user's emotional state by analyzing the user's voice input and facial expressions.

[1138] "Means for adjusting a training plan based on emotional data" refers to a device or method that provides a generative AI model with user emotional data detected by an emotion engine and dynamically adjusts a training plan to match the user's emotional state.

[1139] The "means for providing the generated training plan to the user terminal" refers to a device or method for transmitting the training plan generated by the server to the user's terminal such as a smartphone or tablet via a network and displaying it.

[1140] "Means for recording progress input by the user and reporting it to the server" refers to a device or method that allows the user to input progress information, such as the training content, time required, and subjective impressions, into a terminal and automatically transmits that data to the server.

[1141] "Means for storing progress data and emotion data received by the server" refers to a device or method by which the server stores the progress data and emotion data sent from the terminal in a database and uses it to generate the next training plan.

[1142] MODE FOR CARRYING OUT THE INVENTION

[1143] This invention is a system that collects necessary information from the Internet, creates a training plan based on that information, and provides it to the user. Additionally, the system supports the user's self-realization by reporting the user's recorded progress to a server, which then stores the data. Furthermore, it incorporates an emotion engine that recognizes the user's emotions and adjusts the training plan based on those emotions.

[1144] Server Processing

[1145] The server first automatically collects the necessary information from the Internet. For example, it uses scraping techniques to obtain data from online databases containing health websites or exercise instruction guides. This work is performed using Python's BeautifulSoup and Scrapy libraries. This data is then organized by extracting relevant information from specific HTML elements and stored in a database such as MySQL or PostgreSQL.

[1146] Next, the server uses a generative AI model (e.g., GPT-4) based on the collected data to generate a training plan tailored to the user's profile and goals. An example of a prompt is, "Please generate a training plan suitable for a 65-year-old elderly male. Health information is ____." This generative AI model provides the optimal training menu tailored to the user's needs.

[1147] Additionally, an emotion engine recognizes the user's emotions. Using voice and facial expression analysis tools such as the Google Speech-to-Text API and OpenCV, the emotion engine analyzes the user's voice, facial expressions, and text input to identify their emotional state. This emotion data is also provided to the generative AI model, which uses it to adjust the training plan. For example, if the user is feeling stressed, a training plan will be generated that includes relaxation exercises and stretches.

[1148] Terminal handling

[1149] The generated training plan is sent from the server to the user's device. The user can then use their smartphone, tablet, or other device to check the training plan that suits them best. Specific training content is displayed in detail. For example, it might say, "On Mondays, do 30 minutes of jogging and 15 minutes of stretching."

[1150] After completing the training, the user records their progress using a device that has an interface for recording the training content, duration, subjective impressions, etc. An emotion engine also records the user's emotional state during training and is used to evaluate the effectiveness of the training.

[1151] User operations

[1152] The user checks their training plan through the device and carries out the training according to the plan. For example, "On Mondays, do 30 minutes of jogging and 15 minutes of stretching." Then, they use the device interface to input their progress and emotional state, which is then sent to the server.

[1153] Specific examples

[1154] As a concrete example, in the case of supporting self-realization for the elderly, the server collects health information from sites such as "http: / / example.com / health-tips," and based on that information, the generative AI model creates a training plan consisting of "daily light exercise" and "balanced meals." If the emotion engine analyzes the user's voice and facial expressions and recognizes that they are "very tired," relaxation exercises are added to the plan. When the user enters "I walked for 15 minutes" into their device, their progress information and emotional state are sent to the server and reflected in the next plan.

[1155] This system will efficiently support the self-realization of individuals, as well as effectively maintain the health and improve the abilities of the elderly and athletes. By incorporating an emotion engine, more personalized training plans will be provided, further improving user satisfaction and the effectiveness of training.

[1156] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1157] Step 1:

[1158] The server collects the necessary information from designated websites and online databases on the Internet. Specifically, it uses Python's BeautifulSoup and Scrapy libraries to obtain data on health-related information and exercise methods. The input here is a list of URLs to be collected, and the output is the raw HTML data collected by scraping.

[1159] Step 2:

[1160] The server analyzes the collected HTML data and extracts the necessary information. It uses regular expressions and XPath to extract health information and exercise descriptions from specific HTML elements. In this step, the input is raw HTML data, and the output is organized health information data. Specific operations include HTML parsing, text extraction, and data cleaning.

[1161] Step 3:

[1162] The server stores the organized information in a database. It uses a relational database such as MySQL or PostgreSQL to store the extracted health information and exercise methods as structured data. The input is the organized health information data, and the output is the result of the database save operation. Specific operations include executing SQL queries.

[1163] Step 4:

[1164] The server retrieves the user's profile and goals. It retrieves personal information provided by the user, such as age, gender, weight, and goals, from a database and generates a prompt based on that information. The input is the user ID and authentication information, and the output is the user's profile data. Specific operations include user authentication and SQL query execution.

[1165] Step 5:

[1166] The server generates a training plan by prompting the generative AI model based on the collected health information and the user's profile data. For example, using GPT-4, a prompt sentence such as "Please generate a training plan suitable for a 65-year-old elderly male. The health information is ____." The input is the prompt sentence, and the output is the generated training plan. Specific operations include an API request to the generative AI model.

[1167] Step 6:

[1168] The server recognizes the user's emotions. It analyzes the audio data and camera footage sent from the device and classifies the user's emotional state. The emotion engine uses Google Speech-to-Text API and OpenCV. The input is audio and video data, and the output is emotional data. Specific operations include voice recognition and facial expression analysis.

[1169] Step 7:

[1170] The server adjusts the training plan based on the emotion data. It supplies the emotion data to the generative AI model and dynamically adjusts the training plan. For example, if the user feels fatigued, it adds relaxation exercises. The input is emotion data, and the output is an adjusted training plan. Specific actions include re-prompting the generative AI model.

[1171] Step 8:

[1172] The server provides the generated training plan to the user's device. The training plan is sent to the user's smartphone or tablet via an HTTP request and displayed in the app. The input is the adjusted training plan, and the output is the data delivery results to the user's device. Specific operations include sending an HTTP request.

[1173] Step 9:

[1174] Users perform workouts and record their progress. They use checklists and input fields within the app to record the workouts they performed and the time it took. The input is a description of the workout and their emotional state, and the output is progress data. Specific actions include filling out forms within the app.

[1175] Step 10:

[1176] The device reports the recorded progress data and emotion data to the server. It calls an API to send the data to the server. The input is the progress data and emotion data, and the output is the result of sending the data to the server. Specific operations include executing an API request.

[1177] Step 11:

[1178] The server stores the received progress and emotion data in a database and reflects it in the next training plan. This data is used to update the input prompts of the generative AI model and optimize the next plan. The input is the progress and emotion data, and the output is the next training plan. Specific operations include updating the database and updating the prompts of the generative AI model.

[1179] (Application example 2)

[1180] 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."

[1181] Conventional training planning systems often lack personalization based on the user's health status and emotions, and are unable to properly reflect the user's progress. Furthermore, food delivery services, in particular, face the challenge of providing appropriate meal plans based on the user's emotions and progress. This leads to reduced user satisfaction and training effectiveness.

[1182] 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.

[1183] In this invention, the server includes means for collecting necessary information from the Internet, means for generating a training plan based on the collected information, means for providing the generated training plan to a user terminal, means for recording progress input by the user and reporting it to the server, means for saving the progress data received by the server, and means for analyzing the user's emotions and adjusting the training plan based on the emotions. This makes it possible to provide a personalized training plan and meal plan based on the user's health condition and progress, thereby improving user satisfaction and the effectiveness of training.

[1184] The "Internet" is a global information and communications network that interconnects multiple computer networks.

[1185] "Information" refers to knowledge or data about a particular phenomenon or subject.

[1186] A "training plan" is a structured training schedule designed to improve strength or skill toward a specific goal.

[1187] "User terminal" means an electronic device primarily used by a user to access the Internet or a system.

[1188] "Progress" is the progress achieved to date on a planned activity or project.

[1189] A "server" is a computer system that provides services to other computers on a network.

[1190] A "generative AI model" is a machine learning model that is trained using artificial intelligence techniques to automatically perform specific tasks.

[1191] "Emotions" are psychological and cognitive responses that indicate a person's state of mind.

[1192] "Meal Plan" means a plan that includes the food and schedule for a specific period of time.

[1193] "Personalization" refers to customizing something to suit an individual's specific needs and preferences.

[1194] "Progress Data" means information or records about an activity in progress.

[1195] "Voice analysis" refers to the technology of analyzing voice data to recognize meaning, emotions, etc.

[1196] "Facial expression analysis" refers to the technology of analyzing emotions and intentions from facial expressions.

[1197] "Text input" refers to the act of a user providing textual information to a system using a keyboard or other input device.

[1198] "Stress reduction" means the reduction of psychological or physical tension.

[1199] "Analysis" refers to the act of examining and evaluating data or information in detail.

[1200] This invention is a system that provides personalized training and meal plans for users. The system collects necessary information from the Internet, generates training plans and meal plans using a generative AI model, and provides them to the user's device. It also records the progress entered by the user and reports it to a server, so that the progress can be reflected in the next plan.

[1201] Required Hardware and Software

[1202] Hardware

[1203] Server: A high-performance server computer

[1204] User devices: smartphones, smart glasses, head-mounted displays

[1205] software

[1206] Server frameworks: Django and Flask

[1207] Data collection libraries: BeautifulSoup, Requests

[1208] Sentiment analysis libraries: TensorFlow, PyTorch (Transformers library)

[1209] Database: PostgreSQL or MySQL

[1210] Front-end technologies: HTML, CSS, JavaScript

[1211] System Operation

[1212] Information gathering

[1213] The server collects the necessary information from the Internet, for example by scraping data from websites that provide health and nutrition information, using Python's BeautifulSoup and Requests libraries.

[1214] Generate training and meal plans

[1215] The server uses the collected information to generate training and meal plans using a generative AI model that uses TensorFlow and PyTorch Transformers, and adjusts the plan based on the user's profile (age, weight, goals, etc.).

[1216] For example, for elderly users, a plan of gentle exercise including relaxation exercises and a nutritionally balanced meal plan is generated.

[1217] Recording and reporting user progress

[1218] Users can record their progress using their smartphone or other device, for example by entering "I walked for 15 minutes," and this data is sent to the server and reflected in the next plan.

[1219] Sentiment analysis and plan adjustment

[1220] The server analyzes the user's emotional state using an emotion analysis engine. The user's voice, facial expressions, and text input are used as input data. A Transformers model of sentiment analysis is used for emotion analysis.

[1221] For example, if a user enters a prompt such as "I've been feeling stressed lately and would like a comforting meal," the sentiment analysis engine will identify the emotion of "stress relief" and adjust the meal plan accordingly, recommending foods that are suitable for stress relief, such as comfort food (chicken soup).

[1222] Follow-up and user feedback

[1223] Progress and emotional data are stored on the server and reflected in the generation of the next training plan or meal plan, so that the user's progress and emotional state are always kept up to date, improving the quality of the next plan.

[1224] In this way, personalized training and meal plans can be adjusted in real time to effectively support users in maintaining their health and managing stress.

[1225] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1226] Step 1:

[1227] Gather the necessary information from the Internet.

[1228] What it does: The server uses Python's BeautifulSoup and Requests libraries to scrape data from websites, including health and nutrition information.

[1229] Input: The server receives the URL of the specified website as input.

[1230] Data processing: Extract relevant information from specific HTML elements of a specified website and format the data.

[1231] Output: The extracted data is saved as health and nutrition information.

[1232] Step 2:

[1233] Based on the collected information, a generative AI model is used to generate training and meal plans.

[1234] How it works: The server uses Transformers models using TensorFlow and PyTorch to generate workout and meal plans using the collected information and the user's profile data as input.

[1235] Input: User profile information (age, weight, goals, etc.) and collected health data.

[1236] Data processing: A generative AI model analyzes user profile and health data to generate appropriate training and meal plans.

[1237] Output: Personalized training and meal plans.

[1238] Step 3:

[1239] The generated training plan and meal plan are provided to the user terminal.

[1240] Specific operation: The server notifies the user of the generated training plan and meal plan on their smartphone or other device.

[1241] Input: Generated training plan and meal plan.

[1242] Data processing: Converting information into a user-friendly format and generating notification messages.

[1243] Output: Training plan and meal plan displayed on user device.

[1244] Step 4:

[1245] Records the progress entered by the user and reports it to the server.

[1246] What it does: The user uses the device to input their progress, and that data is sent to the server.

[1247] Input: User-entered progress (e.g., "I walked for 15 minutes").

[1248] Data processing: The server analyzes the entered data and saves it as progress data.

[1249] Output: Progress data stored on the server.

[1250] Step 5:

[1251] Analyze user emotions and adjust plans based on emotions.

[1252] Specific operation: The server analyzes the user's voice, facial expression, and text input using an emotion analysis engine to obtain emotion data.

[1253] Input: User voice, facial expressions, and text input data.

[1254] Data processing: A sentiment analysis engine analyzes input data to identify emotions (e.g., "I feel stressed").

[1255] Output: Emotional data (e.g., "I need stress relief").

[1256] Step 6:

[1257] Adjust your training and meal plans based on emotional data.

[1258] How it works: The server provides the acquired emotional data to a generative AI model to adjust training and meal plans.

[1259] Input: Emotional data and your current training and meal plans.

[1260] Data processing: Generative AI models analyze input data and update it into more appropriate plans and plans.

[1261] Output: Tailored training and meal plans.

[1262] Step 7:

[1263] The adjusted plan is provided to the user terminal.

[1264] Specific operation: The server re-notifies the user terminal of the adjusted training plan and meal plan.

[1265] Input: tailored training and meal plans.

[1266] Data processing: Converting information into a user-friendly format and generating a re-notification message.

[1267] Output: Training plan and meal plan re-notified to user device.

[1268] These steps allow for personalized training and meal plans to be provided to users in real time, and adapted to their progress and emotional state.

[1269] 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.

[1270] 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.

[1271] 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.

[1272] 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.

[1273] 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.

[1274] 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.

[1275] 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).

[1276] 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.

[1277] 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."

[1278] 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.

[1279] 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).

[1280] 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.

[1281] 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.

[1282] 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.

[1283] 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.

[1284] 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.

[1285] 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.

[1286] 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.

[1287] 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.

[1288] 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.

[1289] 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.

[1290] The following is further disclosed regarding the above embodiment.

[1291] (Claim 1)

[1292] How to gather necessary information from the internet,

[1293] a means for generating a training plan based on the collected information;

[1294] means for providing the generated training plan to a user terminal;

[1295] means for recording the progress entered by the user and reporting it to the server;

[1296] a means for the server to store the received progress data;

[1297] A system including:

[1298] (Claim 2)

[1299] 10. The system of claim 1, further comprising means for generating a training plan using the generative AI model based on a user's profile and goals.

[1300] (Claim 3)

[1301] 10. The system of claim 1, further comprising means for graph-structuring the collected information and organizing it according to content level.

[1302] "Example 1"

[1303] (Claim 1)

[1304] How to gather necessary information from the internet,

[1305] A means of converting and storing data in a specific format based on the collected information;

[1306] a means for generating prompts using a generative AI model based on the collected information and the user's profile to create a training plan;

[1307] means for providing the generated training plan to a user terminal;

[1308] means for recording the progress entered by the user and reporting it to the server;

[1309] a means for the server to store the received progress data;

[1310] A system including:

[1311] (Claim 2)

[1312] 10. The system of claim 1, further comprising means for generating a training plan using the generative AI model based on a user's profile and goals.

[1313] (Claim 3)

[1314] 10. The system of claim 1, further comprising means for graph-structuring the collected information and organizing it according to content level.

[1315] "Application Example 1"

[1316] (Claim 1)

[1317] How to gather necessary information from the internet,

[1318] a means for generating a training plan based on the collected information;

[1319] means for providing the generated training plan to a user terminal;

[1320] means for recording the progress entered by the user and reporting it to the server;

[1321] a means for the server to store the received progress data;

[1322] a means for providing a user with a real-time training plan and progress feedback at a fitness club or gym;

[1323] A system including:

[1324] (Claim 2)

[1325] 10. The system of claim 1, further comprising means for generating a training plan using the generative AI model based on a user's profile and goals.

[1326] (Claim 3)

[1327] 10. The system of claim 1, further comprising means for graph-structuring the collected information and organizing it according to content level.

[1328] "Example 2: Combining Emotion Engines"

[1329] (Claim 1)

[1330] How to gather necessary information from the internet,

[1331] A means of organizing the collected information and storing it in a database;

[1332] a means for obtaining a user's profile and goals;

[1333] a means for generating a training plan using the generative AI model;

[1334] means for recognizing a user's emotion;

[1335] a means for adjusting a training plan based on the emotional data;

[1336] means for providing the generated training plan to a user terminal;

[1337] means for recording the progress entered by the user and reporting it to the server;

[1338] a means for storing the progress data and emotion data received by the server;

[1339] A system including:

[1340] (Claim 2)

[1341] 10. The system of claim 1, further comprising means for generating a training plan using the generative AI model based on a user's profile and goals.

[1342] (Claim 3)

[1343] 10. The system of claim 1, further comprising means for graph-structuring the collected information and organizing it according to content level.

[1344] "Application example 2 when combining emotion engines"

[1345] (Claim 1)

[1346] How to gather necessary information from the internet,

[1347] a means for generating a training plan based on the collected information;

[1348] means for providing the generated training plan to a user terminal;

[1349] means for recording the progress entered by the user and reporting it to the server;

[1350] a means for the server to store the received progress data;

[1351] means for analyzing the user's emotions and adjusting a training plan based on the emotions;

[1352] A system including:

[1353] (Claim 2)

[1354] 10. The system of claim 1, further comprising means for generating a training plan using the generative AI model based on a user's profile and goals.

[1355] (Claim 3)

[1356] 10. The system of claim 1, further comprising means for graph-structuring the collected information and organizing it according to content level.

[1357] (Claim 4)

[1358] 10. The system of claim 1, further comprising analysis means for processing voice, facial expressions, and text input to analyze user emotions.

[1359] (Claim 5)

[1360] 10. The system of claim 1, further comprising means for generating a meal plan including meals suitable for stress reduction according to the user's emotions. [Explanation of symbols]

[1361] 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. How to gather necessary information from the Internet, a means for generating a training plan based on the collected information; means for providing the generated training plan to a user terminal; means for recording the progress entered by the user and reporting it to the server; a means for the server to store the received progress data; A system including:

2. 10. The system of claim 1, further comprising means for generating a training plan using the generative AI model based on a user's profile and goals.

3. 2. The system of claim 1, further comprising means for graph-structuring the collected information and organizing it according to content level.

Images