System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving individual information of a user as an input and analyzing a learning style and needs based thereon; means for automatically generating a learning material optimal for the user based on the learning style and needs; means for providing the generated learning material to a terminal of the user; means for tracking a learning progress of the user and updating contents of the learning material according to the progress; and means for recording learning data offline and synchronizing the learning data with a server online.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] Today's learners have diverse learning styles and needs, and learning opportunities are often limited due to time and location constraints. Difficulty in accessing effective skill development and vocational training is also an issue. Traditional learning platforms have difficulty providing personalized learning materials based on individual learning progress and needs, limiting the effectiveness of learning. There is a need to improve this situation, maximize learners' potential, and provide an efficient and effective learning experience. [Means for solving the problem]

[0005] The present invention provides a learning system that includes: a means for receiving a user's individual information as input and analyzing the user's learning style and needs based on the information; a means for automatically generating learning materials optimal for the user based on the learning style and needs; a means for providing the generated learning materials to the user's device; a means for tracking the user's learning progress and updating the content of the learning materials according to the progress; and a means for recording offline learning data and synchronizing it with a server when the user is online. This system can provide personalized educational programs to individual learners, improving learning efficiency and effectiveness. Furthermore, by using general artificial intelligence technology to analyze learning styles and needs and integrating online and offline learning, a flexible learning environment that is not bound by time or place is realized.

[0006] "User Personal Information" includes the user's name, email address, password, learning style, learning goals, and other personal learning-related information.

[0007] "Learning style" refers to the way a user finds most effective when learning, and includes categories such as visual learning, auditory learning, and experiential learning.

[0008] "Needs" refers to requirements such as the learning goals that users want to achieve, the skills they want to acquire, and areas of interest.

[0009] An "analysis tool" is a system that includes algorithms and models to understand users' learning styles and needs based on collected data.

[0010] "Optimal learning materials" are materials selected based on the user's learning style and needs, to maximize the user's learning efficiency.

[0011] "Automatic generation means" refers to a system that includes algorithms and software that utilizes AI models to analyze user data and create personalized learning materials.

[0012] A "terminal" is an electronic device used by a user to study, including a PC, tablet, smartphone, etc.

[0013] The "means for providing" is a system including a network communication function for transmitting data and educational materials from a server to a terminal.

[0014] "Study progress" is data that indicates the progress of the user, such as how much content the user has mastered during the learning activity and the speed at which the user is progressing with the learning.

[0015] The "means for updating content" is a system for proposing new learning materials and modifying existing learning materials based on the user's learning progress data.

[0016] "Offline learning data" is a record of learning activities performed by a user without an internet connection.

[0017] "Means for synchronizing with the server when online" refers to a system that includes a function for transmitting and matching learning data collected while offline to the server when the internet connection is restored.

[0018] "General artificial intelligence technology" refers to AI technology that can handle a wide range of tasks, not just specific ones, and is used to analyze learning styles and needs. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0024] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0025] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0027] [First embodiment]

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

[0029] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0036] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] The present invention relates to a learning system that analyzes a user's learning style based on their individual information and provides an optimal learning plan. This system is implemented by linking a server and terminals (PCs, tablets, smartphones, etc.).

[0041] 1. Initial registration process

[0042] The user enters the required information (such as name, email address, and password) on the account registration screen. The device sends this information to the server, which stores it in a database. The server then presents the user with a questionnaire about their learning style and goals, and collects the results.

[0043] 2. Learning Style and Needs Analysis

[0044] Based on the survey results, the server uses AI models to analyze the user's learning style and needs. For example, if the user is determined to be a visual learner, visual learning materials will be selected.

[0045] 3. Provision of teaching materials

[0046] The server automatically generates an optimized learning plan based on the analysis results. The generated learning material list is sent to the user's device, which then displays the learning material. For example, if the user selects the "Introduction to Programming" course, the user will be provided with videos and quizzes aimed at beginners.

[0047] 4. Track your learning progress

[0048] As users use the learning materials, their devices record their learning activities and progress. Progress data is sent to a server, which stores it in a database and adaptively updates the learning plan. For example, if a user achieves a high score in a lesson, more challenging learning materials will be recommended as the next step.

[0049] 5. Integrating online and offline learning

[0050] The device caches the next study material and some of the progress data so that users can study offline. Users can study even when offline, and the progress data is synchronized with the server the next time they go online. This allows users to continue studying even in offline environments such as on the train.

[0051] Specific examples

[0052] If a user selects the "Introduction to Data Science" course and prefers a visual learning style, the system operates as follows:

[0053] 1. When users first log in, they fill out a profile information and learning style questionnaire.

[0054] 2. The server selects visual teaching materials (e.g., video lectures and infographics) based on the survey information.

[0055] 3. The terminal provides the selected learning materials to the user, who then uses these materials to advance their studies.

[0056] 4. As users watch video lectures and answer quizzes, the device records this information and sends progress data to the server.

[0057] 5. Based on the received data, the server updates the next study plan and provides more appropriate learning materials.

[0058] This provides a flexible learning environment that suits the user's learning style and progress, maximizing learning efficiency and effectiveness.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] The user enters the required information (name, email address, password, etc.) on the account registration screen.

[0062] Step 2:

[0063] The terminal transmits the input information to the server.

[0064] Step 3:

[0065] The server stores the received registration information in a database.

[0066] Step 4:

[0067] The server presents the user with a questionnaire about their learning style and goals, including the skills they want to learn, their areas of interest, and their learning preferences (visual, auditory, etc.).

[0068] Step 5:

[0069] The user responds to the survey.

[0070] Step 6:

[0071] The terminal sends the survey results to the server.

[0072] Step 7:

[0073] The server uses an AI model based on the survey results to analyze the user's learning style and needs. For example, it may determine that the user is a visual learner.

[0074] Step 8:

[0075] Based on the analysis, the server automatically generates an optimal initial learning plan for the user, which includes materials (videos, infographics, etc.) that suit visual learning styles.

[0076] Step 9:

[0077] The server transmits the generated learning plan to the user's terminal.

[0078] Step 10:

[0079] The device displays learning materials to the user based on the learning plan received. For example, if the "Introduction to Programming" course is selected, beginner-friendly videos and quizzes are displayed.

[0080] Step 11:

[0081] Users use the provided learning materials to advance their studies, watching videos and answering quizzes, among other learning activities.

[0082] Step 12:

[0083] The device records the user's learning activity (video viewing time, quiz answer results, etc.).

[0084] Step 13:

[0085] The terminal transmits the recorded learning progress data to the server.

[0086] Step 14:

[0087] The server stores the received progress data in a database and adaptively updates the next step of the learning material. For example, if the user answers a quiz correctly, the next learning material with a higher level of difficulty is selected.

[0088] Step 15:

[0089] The device caches the learning materials and some of the progress data needed for the next study session, preparing for offline study.

[0090] Step 16:

[0091] Users can continue learning even when they are offline, for example, while on the train using pre-downloaded learning materials.

[0092] Step 17:

[0093] The device stores offline learning data locally.

[0094] Step 18:

[0095] When the device regains online connectivity, the learning data from the offline period is synchronized with the server.

[0096] Step 19:

[0097] The server receives the synchronized data and updates the database. The new progress data is reflected in the learning plan.

[0098] This will result in a system that provides a flexible and effective learning environment that meets the diverse learning styles and needs of users.

[0099] Example 1

[0100] 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."

[0101] Conventional learning systems have struggled to provide flexible educational materials that adapt to users' learning styles and needs. They also struggled to effectively integrate online and offline learning data, resulting in reduced learning efficiency. Furthermore, they were unable to track users' learning progress in real time and provide optimal learning materials accordingly.

[0102] 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.

[0103] In this invention, the server includes: means for receiving a user's individual information as input and analyzing the user's learning style and needs based on the information; means for automatically generating educational materials optimal for the user based on the user's learning style and needs; means for providing the generated educational materials to the user's information processing device; means for tracking the user's learning progress and updating the content of the educational materials according to the progress; means for recording offline learning data and synchronizing it with the central processing device when online; means for analyzing the user's learning style and needs using a generative AI model based on questionnaire results; and means for caching the generated educational materials and progress data in local storage. This enables the provision of educational materials adapted to the user's learning style and the integration of online and offline learning data. Furthermore, the system can track the user's learning progress in real time and continuously provide appropriate learning materials.

[0104] definition statement

[0105] "User" refers to an individual who uses the system to learn.

[0106] "Individual information" refers to information about a user, such as their name, email address, password, learning style, and learning objectives.

[0107] "Learning style" refers to a particular learning method or teaching technique that a user prefers, such as visual, auditory, or tactile learning.

[0108] "Needs" refers to the user's learning goals and requirements, and the range of skills and knowledge required.

[0109] "Educational Materials" means educational materials and resources provided for User learning purposes, including videos, texts, quizzes, infographics, etc.

[0110] "Information processing device" refers to a device that allows a user to use the system. Specifically, it includes PCs, tablets, smartphones, etc.

[0111] "Progress Data" refers to the learning progress recorded as the user progresses through the course of their studies. Specifically, this includes the status of viewing of learning materials and quiz results.

[0112] "Central Processing Unit" refers to the computer server that forms the core of the system. It processes, stores, and manages user data.

[0113] A "generative AI model" refers to a mathematical model that uses artificial intelligence technology to analyze user data and make predictions and optimizations.

[0114] "Local storage" refers to memory or storage areas used to temporarily store data within an information processing device. Specifically, this includes hard disk drives and solid-state drives.

[0115] "Caching" refers to temporarily storing data to improve system efficiency, which increases the speed at which data can be loaded.

[0116] "Survey Results" refers to survey data regarding learning styles and needs provided by users.

[0117] "Synchronizing" refers to matching data across different devices or systems, specifically including sending data recorded offline to a central processing unit and matching it when the device is back online.

[0118] MODE FOR CARRYING OUT THE INVENTION

[0119] The present invention relates to a learning system that analyzes a user's learning style based on their individual information and provides an optimal learning plan. This system is implemented by linking a server and terminals (PCs, tablets, smartphones, etc.).

[0120] First, the user enters the required information, such as name, email address, and password, on the account registration screen. The device sends this information to the server, which then stores it in a database. This registers the user's basic information in the system. The server then presents the user with a questionnaire about their learning style and goals, and by collecting the results, the server can understand the user's learning needs.

[0121] The server then uses a generative AI model based on the survey results to analyze the user's learning style and needs. For example, if the server determines that the user is a visual learner, it will select visual learning materials. This process uses general artificial intelligence techniques (such as Python's scikit-learn and TensorFlow).

[0122] The server then automatically generates an optimized learning plan based on the analysis results and sends the generated learning material list to the user's device. The device then provides the received learning materials to the user, allowing the user to proceed with their learning. For example, if the "Introduction to Programming" course is selected, the user will be provided with videos and quizzes aimed at beginners.

[0123] Furthermore, as the user progresses through the learning materials, progress data is recorded on the device. The device then sends the progress data to the server, which stores it in a database. The server then adaptively updates the learning plan based on the progress data and may recommend more challenging learning materials as the next step. For example, if the user achieves a high score in a lesson, the next most challenging learning material will be recommended.

[0124] The device also caches the next learning material and some of the progress data so that users can continue learning even when they are offline, such as on a train. The learning data obtained while offline is synchronized with the server the next time the device is online. This allows for the integration of online and offline learning data.

[0125] As a concrete example, if a user selects the "Introduction to Data Science" course and prefers a visual learning style, the system operates as follows: When the user logs in for the first time, they fill out a questionnaire about their profile information and learning style, and the server selects visual learning materials (e.g., video lectures and infographics) based on the questionnaire information. The device provides the selected learning materials, and the user uses them to progress with their studies. As the user watches video lectures and answers quizzes, the device records the information and sends progress data to the server. The server updates the next learning plan based on the received data and provides more appropriate learning materials.

[0126] Examples of prompts include:

[0127] "How can I deliver video lectures and infographics for my introductory data science course? Users indicated in our survey that they prefer a visual learning style."

[0128] This system provides a flexible learning environment that adapts to the user's learning style and progress, maximizing learning efficiency and effectiveness.

[0129] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0130] Specific steps of the program's processing

[0131] Step 1:

[0132] A user enters their name, email address, and password on the account registration screen.

[0133] Input: Name, Email Address, Password

[0134] Specific operations: Enter information into the form using a browser or dedicated app and press the confirmation button.

[0135] Step 2:

[0136] The terminal transmits the input information to the server.

[0137] Input: Name, Email Address, Password

[0138] Output: User information sent to the server

[0139] Specific operation: When the submit button of the form is pressed, the device sends the data to the server using an HTTP POST request.

[0140] Step 3:

[0141] The server stores the received information in a database.

[0142] Input: Submitted user information

[0143] Output: User information stored in the database

[0144] Specific operation: The server analyzes the received data, generates an SQL query, and saves the information in the database using an INSERT statement.

[0145] Step 4:

[0146] The server presents users with a questionnaire about their learning style and goals and collects the results.

[0147] Input: User information (name, email address, password)

[0148] Output: Collected survey results

[0149] Specific operation: The server generates a questionnaire form in HTML format and sends it to the user's device. The user answers the questionnaire, and the answers are sent back to the server.

[0150] Step 5:

[0151] The server uses a generative AI model based on the survey results to analyze the user's learning style and needs.

[0152] Input: Survey results

[0153] Output: Classification of user learning styles and needs

[0154] How it works: The server inputs the collected survey data into an AI model (using, for example, Python's scikit-learn or TensorFlow) to classify and predict the user's learning style and needs.

[0155] Step 6:

[0156] The server automatically generates an optimized learning plan based on the analysis results.

[0157] Input: Learning style and needs classification results

[0158] Output: Optimized study plan

[0159] Specific operation: The server generates a list of teaching materials based on the results of the AI ​​model and the user's needs.

[0160] Step 7:

[0161] The server transmits the generated teaching material list to the user's terminal.

[0162] Input: Optimized Study Plan

[0163] Output: List of teaching materials sent to the device

[0164] Specific operation: Generate a list of teaching materials in JSON format and send it to the terminal as an HTTP response.

[0165] Step 8:

[0166] The terminal provides the received educational material to the user.

[0167] Input: List of submitted teaching materials

[0168] Output: The teaching material displayed on the user interface

[0169] Specific operation: Analyzes the received JSON data and displays a list of learning materials in the user interface. When the user clicks, learning materials (such as video playback or quiz screens) are displayed.

[0170] Step 9:

[0171] As the user progresses with their learning using the learning materials, the terminal records their progress data.

[0172] Input: User learning activity

[0173] Output: Recorded progress data

[0174] Specific operation: Record the completion of video viewing and quiz answer results in local storage or temporary memory.

[0175] Step 10:

[0176] The terminal transmits the progress data to the server.

[0177] Input: Recorded progress data

[0178] Output: Progress data sent to the server

[0179] Specific behavior: Uploads progress data to the server via HTTP POST requests periodically or for each event.

[0180] Step 11:

[0181] The server adaptively updates the next learning plan based on the received data.

[0182] Input: Received progress data

[0183] Output: Updated learning plan

[0184] What happens: The server saves the new progress data to a database and re-runs the AI ​​model to adjust the next learning plan.

[0185] Step 12:

[0186] The device caches the next study material and some of the progress data so you can study offline.

[0187] Input: Next study material and progress data

[0188] Output: Cached data

[0189] What it does: Saves your next learning material and progress data to local storage or device cache.

[0190] Step 13:

[0191] Users can study offline and their progress will be synced to the server the next time they are online.

[0192] Input: Offline training data

[0193] Output: Progress data synced to the server

[0194] Specific operation: Operation records and progress data are saved locally when offline, and then sent to the server in bulk when the device is online again.

[0195] (Application example 1)

[0196] 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."

[0197] There is a need for a method that allows robot operators to receive training efficiently and be provided with learning materials that best suit their learning style. However, conventional training systems provide uniform learning materials to all users, which makes it difficult to adapt to individual learning styles. Furthermore, they lack the ability to flexibly update learning materials according to progress. This often results in the robot operators' learning efficiency and effectiveness not being maximized, resulting in wasted time and effort. Therefore, a system is needed that analyzes the learning style of each user based on their individual information and provides optimal learning materials.

[0198] 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.

[0199] In this invention, the server includes means for receiving individual user information as input and analyzing the user's learning style and needs based on the information, means for automatically generating learning materials optimal for the user based on the learning style and needs, and means for providing the generated learning materials to the user's terminal, thereby enabling the robot operator to receive learning materials optimal for his or her own learning style and maximizing learning efficiency and effectiveness.

[0200] "User's personal information" refers to information that can be used to identify a specific individual, including the learner's name, email address, password, and survey results regarding learning style and purpose.

[0201] A "learning style" refers to the method or tendency in which a particular learner learns most effectively, such as visual learning or hands-on learning.

[0202] "Demand" refers to the goals that learners want to achieve through their studies and the knowledge and skills they need.

[0203] A "generative AI model" is an artificial intelligence model used to analyze a user's learning style and needs based on learning data and determine the most appropriate learning materials.

[0204] "Teaching materials" refer to materials and content provided to learners for learning activities, including, for example, video lectures and simulators.

[0205] A "terminal" is a device such as a smartphone, tablet, or PC that displays the learning materials provided by the system and allows users to progress through their studies.

[0206] "Offline" refers to a state where there is no internet connection, such as when a learner is studying on a train.

[0207] "Synchronization" refers to the process of sending learning data created offline to the server when the device is back online and matching it.

[0208] A "robot operator" is a technician who receives training to operate and manage robots in factories and facilities and perform their work efficiently.

[0209] "Visual aids" are materials that convey information visually, such as videos, infographics, and illustrations.

[0210] "Practical teaching materials" are teaching materials that primarily involve hands-on operation and work, and include simulators and on-the-job training.

[0211] This invention shows a specific method for realizing a system that analyzes a user's learning style based on their individual information and provides an optimal learning plan. The overall configuration of the system and the operation of each component are explained in detail below.

[0212] The server receives individual user information as input and has a means to analyze the user's learning style and needs based on that information. To achieve this, it uses a generative AI model. The server uses a machine learning framework such as TensorFlow to analyze survey results and past learning data to detect the user's learning style. For example, it determines whether the user is a visual learner or a hands-on learner.

[0213] The server also has a means for automatically generating optimal learning materials for the user based on the learning style and demands. This means selects visual learning materials (video lectures, infographics, etc.) or practical learning materials (simulators, on-the-job training, etc.) to provide learning materials that best suit the user's learning style. The learning materials are stored in a database as preprocessed content.

[0214] Next, the server has a means for providing the generated learning materials to the user's terminal. The learning materials are displayed on the user's terminal (such as a smartphone or tablet). The user begins learning using the selected learning materials.

[0215] The user's learning progress is recorded by the device, and the progress data is sent to the server as appropriate. The server adaptively updates the learning plan based on the received progress data and provides the most appropriate learning materials for the next step. In this way, a flexible learning environment tailored to each individual user is realized.

[0216] Furthermore, the device has a means for recording offline learning data and synchronizing it with the server when the device is online, allowing users to continue learning even in an offline environment, and progress data is kept on the server without being lost.

[0217] As a concrete example, consider the case where robot operator A is using the app for the first time. First, A registers an account by entering their name, email address, and password. After that, A answers a questionnaire about their learning style and needs. The server analyzes this using a generative AI model and determines that A is a visual learner. The server selects the most suitable learning materials for A, a "robot operation video" and an "illustrated manual," and provides them to the device. As A uses these materials to progress with their learning, the device records their progress and syncs it with the server.

[0218] An example of a prompt sentence would be, "I am robot operator A. My learning style is visual. Please provide me with the most effective training materials." This would allow the system to select and provide the most appropriate materials.

[0219] This invention makes it possible to provide optimal learning materials based on individual user information, thereby maximizing learning efficiency and effectiveness.

[0220] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0221] Step 1:

[0222] The server receives the user's individual information as input. The user enters their name, email address, password, etc. into the account registration screen and sends this information from their device to the server. The server stores the received individual information in a database.

[0223] Input: User's personal information (name, email address, password, etc.)

[0224] Output: User information stored in the database

[0225] Step 2:

[0226] The server presents the user with a questionnaire about their learning style and needs. The user answers the questionnaire and sends the results from their device to the server. The server receives the questionnaire results and analyzes the user's learning style and needs using a generative AI model.

[0227] Input: User survey results

[0228] Output: Learning styles and demands as analyzed results

[0229] Step 3:

[0230] The server automatically generates the most suitable learning materials for the user based on the analysis results. The learning materials can be selected from videos and infographics for visual learning, or simulators and on-the-job training materials for practical learning. The selected learning materials are retrieved from the database.

[0231] Input: Learning Style and Demand Analysis Results

[0232] Output: The best learning material for the user

[0233] Step 4:

[0234] The server provides the selected learning materials to the user's device, and the user uses the materials provided through the device for learning. The content of the learning materials is displayed on the device in the form of videos, infographics, practical simulators, etc.

[0235] Input: Selected teaching materials

[0236] Output: Teaching materials displayed on the device

[0237] Step 5:

[0238] The device records the user's learning progress. As the user progresses through the learning activity, progress data is generated. The device transmits this progress data to the server.

[0239] Input: User learning activity

[0240] Output: Recorded learning progress data

[0241] Step 6:

[0242] The server adaptively updates the learning plan based on the received learning progress data. Based on the progress data, the next most suitable learning material to be provided is reselected. For example, if the user achieves a high score, the next most difficult learning material will be selected.

[0243] Input: Learning progress data

[0244] Output: Updated learning plan

[0245] Step 7:

[0246] The device records learning data while offline and synchronizes it with the server the next time it goes online, allowing users to continue learning even in offline environments.

[0247] Input: Offline training data

[0248] Output: Training data synchronized while online

[0249] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0250] This invention relates to a learning system that analyzes a user's individual information and learning style to provide an optimal learning plan, and further enhances the learning experience by combining an emotion engine. This system is implemented by linking a server and terminals (PCs, tablets, smartphones, etc.).

[0251] 1. Initial registration process

[0252] The user enters the required information (such as name, email address, and password) on the account registration screen. The device sends this information to the server, which stores it in a database. The server then presents the user with a questionnaire about their learning style and goals, and collects the results.

[0253] 2. Learning Style and Needs Analysis

[0254] Based on the survey results, the server uses AI models to analyze the user's learning style and needs, for example determining that the user is a visual learner.

[0255] 3. Provision of teaching materials

[0256] The server automatically generates an optimized learning plan based on the analysis results. The generated learning material list is sent to the user's device, which then displays the learning material. For example, if the user selects the "Introduction to Programming" course, the user will be provided with videos and quizzes aimed at beginners.

[0257] 4. Track your learning progress

[0258] As users use the learning materials, their devices record their learning activities and progress. Progress data is sent to a server, which stores it in a database and adaptively updates the learning plan. For example, if a user achieves a high score in a lesson, more challenging learning materials will be recommended as the next step.

[0259] 5. Integrating online and offline learning

[0260] The device caches the next study material and some of the progress data so that users can study offline. Users can study even when offline, and the progress data is synchronized with the server the next time they go online. This allows users to continue studying even in offline environments such as on the train.

[0261] 6. Incorporating an Emotional Engine

[0262] The device uses an emotion engine to recognize emotions in real time from the user's facial expressions, voice, and input data. For example, it can analyze facial expressions and tone of voice using a webcam or microphone.

[0263] 7. Adjusting teaching materials based on emotions

[0264] The server dynamically adjusts the content of the learning materials based on the emotional data recognized by the emotion engine. For example, if the server detects that the user is tired, it will provide less stressful learning materials or interactive content to refresh the user.

[0265] 8. Integrating emotion and learning progress data

[0266] The server integrates the emotion data with the learning progress data to update a comprehensive learning plan, optimizing the user's learning experience and providing emotional feedback.

[0267] Specific examples

[0268] If a user selects the "Introduction to Data Science" course and prefers a visual learning style, the system operates as follows:

[0269] 1. When users first log in, they fill out a profile information and learning style questionnaire.

[0270] 2. The server selects visual teaching materials (e.g., video lectures and infographics) based on the survey information.

[0271] 3. The terminal provides the selected learning materials to the user, who then uses these materials to advance their studies.

[0272] 4. As users watch video lectures and answer quizzes, the device records this information and sends progress data to the server.

[0273] 5. Based on the received data, the server updates the next study plan and provides more appropriate learning materials.

[0274] 6. The device uses an emotion engine to recognize the user's emotions, and if it detects fatigue or a loss of concentration, it will provide light quizzes or mindfulness videos to refresh the user.

[0275] 7. Accordingly, the server integrates the emotional data and learning progress data to adjust the overall learning plan.

[0276] In this way, a system is realized that provides an optimized learning experience by comprehensively taking into account the user's learning style, progress, and emotions.

[0277] The processing flow will be explained below.

[0278] This invention relates to a learning system that analyzes a user's individual information and learning style to provide an optimal learning plan, and further enhances the learning experience by combining an emotion engine. This system is implemented by linking a server and terminals (PCs, tablets, smartphones, etc.).

[0279] Processing Steps

[0280] Step 1:

[0281] The user enters the required information (name, email address, password, etc.) on the account registration screen.

[0282] Step 2:

[0283] The terminal transmits the input information to the server.

[0284] Step 3:

[0285] The server stores the received registration information in a database.

[0286] Step 4:

[0287] The server presents the user with a questionnaire about their learning style and goals, including the skills they want to learn, their areas of interest, and their learning preferences (visual, auditory, etc.).

[0288] Step 5:

[0289] The user responds to the survey.

[0290] Step 6:

[0291] The terminal sends the survey results to the server.

[0292] Step 7:

[0293] The server uses an AI model based on the survey results to analyze the user's learning style and needs. For example, it may determine that the user is a visual learner.

[0294] Step 8:

[0295] Based on the analysis, the server automatically generates an optimal initial learning plan for the user, which includes materials (videos, infographics, etc.) that suit visual learning styles.

[0296] Step 9:

[0297] The server transmits the generated learning plan to the user's terminal.

[0298] Step 10:

[0299] The device displays learning materials to the user based on the learning plan received. For example, if the "Introduction to Programming" course is selected, beginner-friendly videos and quizzes are displayed.

[0300] Step 11:

[0301] Users use the provided learning materials to advance their studies, watching videos and answering quizzes, among other learning activities.

[0302] Step 12:

[0303] The device records the user's learning activity (video viewing time, quiz answer results, etc.).

[0304] Step 13:

[0305] The terminal transmits the recorded learning progress data to the server.

[0306] Step 14:

[0307] The server stores the received progress data in a database and adaptively updates the learning materials for the next step.

[0308] Step 15:

[0309] The device caches the learning materials and some of the progress data needed for the next study session, preparing for offline study.

[0310] Step 16:

[0311] Users can continue learning even when they are offline, for example, while on the train using pre-downloaded learning materials.

[0312] Step 17:

[0313] The learning data acquired while the device was offline is stored locally.

[0314] Step 18:

[0315] When the device regains online connectivity, the learning data recorded while offline is synchronized with the server.

[0316] Step 19:

[0317] The server receives the synchronized data and updates the database. The new progress data is reflected in the learning plan.

[0318] Incorporating an emotion engine

[0319] Step 20:

[0320] The device uses a webcam and microphone to recognize emotions in real time using an emotion engine based on the user's facial expressions, voice, and input data. For example, it can detect happiness, sadness, fatigue, etc. from the user's facial expressions.

[0321] Step 21:

[0322] The server receives the emotion data recognized by the emotion engine and dynamically adjusts the content of the learning materials based on that data. For example, if it recognizes that the user is tired, it will provide less stressful learning materials or interactive content to refresh the user.

[0323] Step 22:

[0324] The emotion data recognized by the emotion engine is integrated with the learning progress data, and the server updates the overall learning plan.

[0325] Specific examples

[0326] If a user selects the "Introduction to Data Science" course and prefers a visual learning style, the system operates as follows:

[0327] 1. When users first log in, they fill out a profile information and learning style questionnaire.

[0328] 2. The server selects visual teaching materials (e.g., video lectures and infographics) based on the survey information.

[0329] 3. The terminal provides the selected learning materials to the user, who then uses these materials to advance their studies.

[0330] 4. As users watch video lectures and answer quizzes, the device records this information and sends progress data to the server.

[0331] 5. Based on the received data, the server updates the next study plan and provides more appropriate learning materials.

[0332] 6. The device uses an emotion engine to recognize the user's emotions, and if it detects fatigue or a loss of concentration, it will provide light quizzes or mindfulness videos to refresh the user.

[0333] 7. Accordingly, the server integrates the emotional data and learning progress data to adjust the overall learning plan.

[0334] In this way, a system is realized that provides an optimized learning experience by comprehensively taking into account the user's learning style, progress, and emotions.

[0335] Example 2

[0336] 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."

[0337] Conventional learning systems have difficulty providing optimal learning materials based on the user's learning style and needs, and are unable to flexibly update learning plans based on learning progress and emotional data. As a result, it is not possible to provide an optimal learning experience for each learner, resulting in problems such as reduced learning efficiency and effectiveness.

[0338] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving individual information of a user as input and analyzing the user's learning style and needs based on the information; means for automatically generating learning materials optimal for the user based on the learning style and needs; means for providing the generated learning materials to the user's terminal; means for tracking the user's learning progress and updating the content of the learning materials according to the progress; means for recording offline learning data and synchronizing it with the server when online; means for recognizing emotions from the user's facial expressions, voice, and input data and dynamically adjusting the content of the learning materials based on the emotions; and means for integrating the emotion data and learning progress data and updating the comprehensive learning plan. This makes it possible to provide a learning experience optimized for each user's individual learning style, progress, and emotional state.

[0339] "User" means a person or organization who uses the learning system and is provided with an optimal learning plan based on their learning style and needs.

[0340] "Personal Information" refers to data unique to a user, including name, email address, password, learning style survey results, etc.

[0341] "Learning style" refers to the method or technique in which a user learns most effectively, and can be visual, auditory, tactile, or other types.

[0342] "Needs" refer to the goals or needs that a user wants to achieve through learning, and specific examples include improving specialized knowledge or acquiring specific skills.

[0343] "Learning materials" refers to content provided to assist users in their learning, and includes video lectures, texts, quizzes, infographics, and the like.

[0344] "Terminal" means a device used by a user to access the system, including a PC, tablet, smartphone, etc.

[0345] A "server" refers to an information processing device that processes and stores various types of data and communicates with user terminals.

[0346] "Study progress" is data that indicates how much progress a user has made in the process of studying, and includes the amount of time spent watching videos and the percentage of correct answers to quizzes.

[0347] "Offline learning data" is data recorded when a user studies without a network connection, and is synchronized with the server the next time the user goes online.

[0348] "Emotion data" is data that indicates the user's emotional state, and is extracted from facial expressions, tone of voice, input data, and the like.

[0349] "Comprehensive Learning Plan" refers to a series of learning activities that are optimized based on the user's learning style, progress data, and emotional data.

[0350] An "emotion engine" refers to a collection of algorithms and software that recognizes a user's emotions and takes necessary action based on them.

[0351] The present invention is a learning system that analyzes a user's individual information and learning style to provide an optimal learning plan, and further enhances the learning experience by combining an emotion engine. This system is implemented by linking a server and a terminal. Below, we will explain embodiments of the present invention, using specific examples to explain what hardware or software is used to perform data processing and data calculations.

[0352] This system consists of multiple steps. First, the user accesses the system using a device (PC, tablet, smartphone). When registering for the first time, the user enters the required information (name, email address, password, etc.), which the device sends to the server. The server validates the data and stores it in a database (e.g., MySQL). The server then provides a questionnaire regarding learning style and goals, which the user fills out. The survey results are then sent to the server via the device.

[0353] The server inputs the collected survey results into an AI model (e.g., a model built with TensorFlow or PyTorch) to analyze the user's learning style and needs. Based on the analysis results, the server automatically generates an optimized learning plan and sends this plan in JSON format to the device. The device then analyzes the received data and displays it in a user interface.

[0354] As a user progresses through their studies using learning materials (e.g., video lectures, textbooks, quizzes), the device records their activity log and periodically sends it to the server. The server analyzes the received data and dynamically updates the next learning plan based on the user's progress. Furthermore, the device has the ability to record learning data even when offline and synchronize it with the server the next time it is online.

[0355] Additionally, the system incorporates an emotion engine. The device uses a webcam and microphone to capture the user's facial and voice data, which is then analyzed in real time using emotion recognition algorithms (e.g., OpenCV and DeepEmotion). The emotion data is sent to a server, which analyzes it using an AI model to determine the user's current emotional state. Depending on the emotion data, the server provides low-impact educational materials and refresher content.

[0356] The server integrates emotional data and learning progress data and updates a comprehensive learning plan to provide a learning experience optimized for each user's individual learning style, progress, and emotional state.

[0357] Specific examples

[0358] If a user selects the "Introduction to Data Science" course and prefers a visual learning style, the system operates as follows:

[0359] 1. When users log in for the first time, they fill out their profile information and a learning style questionnaire.

[0360] 2. The server selects visual teaching materials (e.g., video lectures and infographics) based on the survey information.

[0361] 3. The terminal provides the selected learning materials to the user, who then uses these materials to advance their studies.

[0362] 4. As users watch video lectures and answer quizzes, the device records this information and sends progress data to the server.

[0363] 5. The server updates the next study plan based on the received data and provides the corresponding study materials.

[0364] 6. The device uses an emotion engine to recognize the user's emotions, and if it detects fatigue or a loss of concentration, it will provide light quizzes or mindfulness videos to refresh the user.

[0365] 7. The server integrates the emotional data and learning progress data to tailor a comprehensive learning plan.

[0366] Prompt Sentence Examples

[0367] "How can I create an AI model that generates the best learning material list for a user who is a visual learner?"

[0368] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0369] Step 1: First-time registration process

[0370] 1.1 The user enters the required information such as name, email address, and password on the account registration screen.

[0371] Input: Name, Email Address, Password

[0372] Output: User input data

[0373] 1.2 The device converts this information into JSON format and sends it to the server using the HTTPS protocol.

[0374] Input: User-entered data

[0375] Output: User data in JSON format

[0376] 1.3 The server validates the received information (e.g., checks the email address format, password strength) and stores it in a database (e.g., MySQL).

[0377] Input: User data in JSON format

[0378] Output: Save results to database

[0379] 1.4 The server generates questionnaire data regarding learning styles and learning objectives and sends it to the terminal.

[0380] Input: None

[0381] Output: Questionnaire form data

[0382] Step 2: Analyze your learning styles and needs

[0383] 2.1 The user fills out the questionnaire form regarding their learning style and purpose and clicks the submit button.

[0384] Input: User learning style and goals

[0385] Output: Survey results

[0386] 2.2 The terminal converts the survey results into JSON format and sends them to the server.

[0387] Input: Survey results

[0388] Output: Survey data in JSON format

[0389] 2.3 The server inputs the received survey results into an AI model (e.g., built with TensorFlow and PyTorch) to analyze learning styles and needs.

[0390] Input: Survey data in JSON format

[0391] Output: Analysis results (user learning styles and needs)

[0392] 2.4 The server stores the analysis results in a database for further processing.

[0393] Input: Analysis results

[0394] Output: Save results to database

[0395] Step 3: Providing educational materials

[0396] 3.1 The server automatically generates an optimized learning plan based on the analysis results, such as a list of appropriate video lectures and quizzes.

[0397] Input: Analysis results

[0398] Output: Generated teaching material list

[0399] 3.2 The server sends the generated teaching material list to the terminal in JSON format.

[0400] Input: Generated teaching material list

[0401] Output: JSON formatted learning material list

[0402] 3.3 The device analyzes the received data and displays it on the user interface. Specifically, it dynamically generates thumbnail images of the video lectures and quiz links.

[0403] Input: JSON format teaching material list

[0404] Output: A list of teaching materials displayed on the user interface

[0405] Step 4: Track your progress

[0406] 4.1 The user uses the provided learning materials (e.g., video lectures, quizzes) to advance their learning.

[0407] Input: Usage of teaching materials

[0408] Output: Learning progress information

[0409] 4.2 The device records the user's learning activities and progress data, e.g., video viewing time, quiz correct answer rate.

[0410] Input: Learning progress information

[0411] Output: Recorded progress data

[0412] 4.3 The device periodically converts the progress data into JSON format and sends it to the server.

[0413] Input: Recorded progress data

[0414] Output: Progress data in JSON format

[0415] 4.4 The server analyzes the received data and dynamically updates the next study plan. For example, if a high score is obtained, more difficult study materials will be recommended.

[0416] Input: Progress data in JSON format

[0417] Output: Updated learning plan

[0418] Step 5: Integrating online and offline learning

[0419] 5.1 The device will cache some of the learning materials and progress data for the next time you use it in local storage.

[0420] Input: Updated learning plan and progress data

[0421] Output: Data saved in local storage

[0422] 5.2 Users can continue learning using cached data even when they do not have a network connection.

[0423] Input: Data stored in local storage

[0424] Output: Offline learning progress

[0425] 5.3 The next time your device goes online, it will sync its locally stored progress data with the server.

[0426] Input: Offline learning progress

[0427] Output: Data synchronized with the server

[0428] Step 6: Incorporating the Emotion Engine

[0429] 6.1 The device uses a webcam and microphone to capture the user's facial expressions and voice data.

[0430] Input: facial expression data and voice data

[0431] Output: Retrieved data

[0432] 6.2 The device uses emotion recognition algorithms (e.g., OpenCV, DeepEmotion) to analyze facial expressions and tone of voice in real time.

[0433] Input: Retrieved data

[0434] Output: Emotion data

[0435] 6.3 The terminal converts the analysis results into JSON format and sends them to the server.

[0436] Input: Emotion data

[0437] Output: Emotion data in JSON format

[0438] Step 7: Emotionally adjust your materials

[0439] 7.1 The server inputs the received emotion data into the AI ​​model to determine the user's current emotional state.

[0440] Input: Emotion data in JSON format

[0441] Output: Emotion determination result

[0442] 7.2 The server dynamically adjusts the content of educational materials provided based on emotional data. For example, if fatigue is detected, it will provide refreshing light quizzes or mindfulness videos.

[0443] Input: Emotion determination result

[0444] Output: Dynamically adjusted teaching content

[0445] Step 8: Integrating sentiment and learning progress data

[0446] 8.1 The server integrates the emotion data and learning progress data to update the overall learning plan.

[0447] Input: Emotion judgment results and learning progress data

[0448] Output: Consolidated learning plan

[0449] 8.2 The server sends the updated learning plan to the device and provides it to the user.

[0450] Input: Integrated Learning Plan

[0451] Output: Provided to the user

[0452] (Application example 2)

[0453] 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."

[0454] Previous learning and training systems lacked the ability to provide optimal learning plans based on individual user and robot information. They also lacked the ability to monitor learning progress and performance decline in real time and dynamically adjust learning plans accordingly. Furthermore, there were issues with recording data offline and synchronizing it with the online environment.

[0455] 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.

[0456] In this invention, the server includes means for receiving individual user information as input and analyzing the user's learning style and needs based thereon, means for automatically generating learning materials optimal for the user, means for providing the generated learning materials to the user's terminal, means for tracking the user's learning progress and updating the content of the learning materials according to the progress, means for recording offline learning data and synchronizing it with the server when online, means for receiving robot identification information as input and generating an appropriate training plan based thereon, means for analyzing the robot's motion data and monitoring performance in real time using an emotion engine, and means for instructing a refresher task when performance degradation is detected. This makes it possible to provide an optimal learning plan based on the individual user and robot information, monitor performance in real time, and effectively manage data even in an offline environment.

[0457] "Personal information" is information that includes a user's or robot's identity, personal characteristics, learning style, and needs.

[0458] "Learning style" is information that indicates a user's learning method and preferences, and includes visual, auditory, tactile, and other methods.

[0459] "Needs" refers to information that indicates the requirements or objectives that users have in learning or training.

[0460] "Instructional Materials" includes educational resources, such as videos, quizzes, infographics, etc., tailored to a user's learning style and needs.

[0461] A "terminal" is a device used by a user or robot to receive learning materials, and includes a PC, tablet, smartphone, etc.

[0462] The "server" is a central computer that manages the entire learning system and processes, records, and analyzes individual information, progress data, emotional data, etc. of users or robots.

[0463] "Study progress" is data indicating the progress of the user's learning activities using learning materials.

[0464] An "emotion engine" is software that analyzes the facial expressions, voice, and movement data of a user or robot to recognize their emotional state in real time.

[0465] A "training plan" is a plan of optimal work procedures and training content provided to a robot, and it changes adaptively.

[0466] "Performance" is an evaluation index that indicates the efficiency and accuracy with which a robot performs a specific task.

[0467] "Real-time monitoring" means reading learning progress and performance on the spot and analyzing them immediately.

[0468] "Offline training data" means training data collected and recorded when a user or robot does not have an internet connection.

[0469] "Synchronizing to server when online" is the process by which data recorded offline is sent to the server and stored in the database when you are connected to the Internet again.

[0470] A "refreshment task" is a task that instructs the robot to do light work or take a break to reduce stress when a decline in its performance is detected.

[0471] The present invention relates to a system that analyzes individual user information and robot identification information, and provides optimal learning and training plans based on the analysis. Specific embodiments are described below.

[0472] 1. Initial Setup and Registration

[0473] The server first receives input from the user or robot's personal information. For users, this includes name, email address, password, and information about learning style. For robots, this includes identification information and training objectives. This information is sent to the server and stored in a database.

[0474] 2. Learning Style and Needs Analysis

[0475] The server uses a generative AI model to analyze a user's learning style and needs based on the survey results and input data. For example, if the user is a visual learner, visual learning materials will be selected. Similarly, for robots, training content tailored to their characteristics will be generated.

[0476] 3. Providing educational materials or training plans

[0477] The server automatically generates optimal learning materials or training plans based on the analysis results, which are then sent to the user's or robot's device and displayed in the form of videos, quizzes, infographics, and more.

[0478] 4. Track your progress

[0479] As the user or robot progresses in their learning or training, the device records their progress data. This progress data is sent to the server and stored in a database. The server then adaptively updates the next learning or training plan based on the progress data.

[0480] 5. Integrating offline learning

[0481] The device has the ability to cache data so that learning and training can be done offline. A user or robot can record their progress in an offline environment and sync it with the server when they come online.

[0482] 6. Leveraging Emotional Engines

[0483] The device uses an emotion engine to recognize the emotional state of the user or robot in real time, using a webcam and microphone to analyze facial expressions and voice to detect signs of stress or fatigue.

[0484] 7. Adjusting the movement

[0485] If the emotion engine detects a decline in performance, the server will instruct the user or robot to perform a refreshing task or light work, allowing them to continue learning or training without unnecessary stress.

[0486] Specific examples

[0487] Example of a training system for factory robots:

[0488] Imagine a factory robot learning a new assembly task. The robot's identity and task characteristics are sent to a server, which generates an appropriate training plan. The robot works on the task, collecting behavioral data along the way. If the emotion engine detects signs of stress, it will recommend lighter work or a break.

[0489] Example prompt sentence:

[0490] "If the robot's performance drops on the current task, what's the next appropriate training task to offer it?"

[0491] "If the robot assembles the parts with 75% accuracy, what is the next task it should move on to?"

[0492] As described above, this invention provides optimal learning and training plans based on individual information about the user and the robot, and monitors and adjusts progress and emotional state in real time, thereby achieving an effective learning experience.

[0493] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0494] Step 1:

[0495] The server receives the user's or robot's personal information as input. In the case of a user, this includes name, email address, password, and information about learning style. In the case of a robot, identification information and training goals are entered. The data is sent to the server and stored in a database. The input is the user's or robot's information, and the output is the personal information stored in the database.

[0496] Step 2:

[0497] The server uses a generative AI model to analyze the user's learning style and needs based on the received personalized information. This process determines whether the user is a visual learner and what the robot's working characteristics are. The input is personalized information, and the output is the analysis results.

[0498] Step 3:

[0499] The server automatically generates optimal teaching materials or training plans based on the analysis results. Appropriate teaching materials are selected, and the generated teaching materials or plans are sent to the user's or robot's terminal. The input is the analysis results, and the output is the generated teaching materials or training plans.

[0500] Step 4:

[0501] The user or robot learns or works according to the learning materials or training plan. The terminal records the progress data and sends it to the server. The input is the user's or robot's learning and work data, and the output is the recorded and sent progress data.

[0502] Step 5:

[0503] The server stores the received progress data in a database and adaptively updates the next learning or training plan. The input is the progress data, and the output is the updated learning or training plan.

[0504] Step 6:

[0505] The device caches the necessary data so that you can continue studying or working even when offline. It records your progress data even when offline and synchronizes it with the server when you come online. The input is your study plan and progress data, and the output is the cached data and synchronized data.

[0506] Step 7:

[0507] The device uses an emotion engine to recognize the emotional state of the user or robot in real time. It uses a webcam and microphone to analyze facial expressions and voice to detect signs of stress or fatigue. The input is the user or robot's behavior data, and the output is analyzed emotional data.

[0508] Step 8:

[0509] Based on the data analyzed by the emotion engine, the server instructs users to perform refreshing tasks or light work when a decline in performance is detected. The input is emotional data, and the output is the instructed refreshing task.

[0510] 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.

[0511] 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.

[0512] 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.

[0513] [Second embodiment]

[0514] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0515] 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.

[0516] 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).

[0517] 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.

[0518] 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.

[0519] 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).

[0520] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0521] 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.

[0522] 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.

[0523] 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.

[0524] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0525] 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."

[0526] The present invention relates to a learning system that analyzes a user's learning style based on their individual information and provides an optimal learning plan. This system is implemented by linking a server and terminals (PCs, tablets, smartphones, etc.).

[0527] 1. Initial registration process

[0528] The user enters the required information (such as name, email address, and password) on the account registration screen. The device sends this information to the server, which stores it in a database. The server then presents the user with a questionnaire about their learning style and goals, and collects the results.

[0529] 2. Learning Style and Needs Analysis

[0530] Based on the survey results, the server uses AI models to analyze the user's learning style and needs. For example, if the user is determined to be a visual learner, visual learning materials will be selected.

[0531] 3. Provision of teaching materials

[0532] The server automatically generates an optimized learning plan based on the analysis results. The generated learning material list is sent to the user's device, which then displays the learning material. For example, if the user selects the "Introduction to Programming" course, the user will be provided with videos and quizzes aimed at beginners.

[0533] 4. Track your learning progress

[0534] As users use the learning materials, their devices record their learning activities and progress. Progress data is sent to a server, which stores it in a database and adaptively updates the learning plan. For example, if a user achieves a high score in a lesson, more challenging learning materials will be recommended as the next step.

[0535] 5. Integrating online and offline learning

[0536] The device caches the next study material and some of the progress data so that users can study offline. Users can study even when offline, and the progress data is synchronized with the server the next time they go online. This allows users to continue studying even in offline environments such as on the train.

[0537] Specific examples

[0538] If a user selects the "Introduction to Data Science" course and prefers a visual learning style, the system operates as follows:

[0539] 1. When users first log in, they fill out a profile information and learning style questionnaire.

[0540] 2. The server selects visual teaching materials (e.g., video lectures and infographics) based on the survey information.

[0541] 3. The terminal provides the selected learning materials to the user, who then uses these materials to advance their studies.

[0542] 4. As users watch video lectures and answer quizzes, the device records this information and sends progress data to the server.

[0543] 5. Based on the received data, the server updates the next study plan and provides more appropriate learning materials.

[0544] This provides a flexible learning environment that suits the user's learning style and progress, maximizing learning efficiency and effectiveness.

[0545] The processing flow will be explained below.

[0546] Step 1:

[0547] The user enters the required information (name, email address, password, etc.) on the account registration screen.

[0548] Step 2:

[0549] The terminal transmits the input information to the server.

[0550] Step 3:

[0551] The server stores the received registration information in a database.

[0552] Step 4:

[0553] The server presents the user with a questionnaire about their learning style and goals, including the skills they want to learn, their areas of interest, and their learning preferences (visual, auditory, etc.).

[0554] Step 5:

[0555] The user responds to the survey.

[0556] Step 6:

[0557] The terminal sends the survey results to the server.

[0558] Step 7:

[0559] The server uses an AI model based on the survey results to analyze the user's learning style and needs. For example, it may determine that the user is a visual learner.

[0560] Step 8:

[0561] Based on the analysis, the server automatically generates an optimal initial learning plan for the user, which includes materials (videos, infographics, etc.) that suit visual learning styles.

[0562] Step 9:

[0563] The server transmits the generated learning plan to the user's terminal.

[0564] Step 10:

[0565] The device displays learning materials to the user based on the learning plan received. For example, if the "Introduction to Programming" course is selected, beginner-friendly videos and quizzes are displayed.

[0566] Step 11:

[0567] Users use the provided learning materials to advance their studies, watching videos and answering quizzes, among other learning activities.

[0568] Step 12:

[0569] The device records the user's learning activity (video viewing time, quiz answer results, etc.).

[0570] Step 13:

[0571] The terminal transmits the recorded learning progress data to the server.

[0572] Step 14:

[0573] The server stores the received progress data in a database and adaptively updates the next step of the learning material. For example, if the user answers a quiz correctly, the next learning material with a higher level of difficulty is selected.

[0574] Step 15:

[0575] The device caches the learning materials and some of the progress data needed for the next study session, preparing for offline study.

[0576] Step 16:

[0577] Users can continue learning even when they are offline, for example, while on the train using pre-downloaded learning materials.

[0578] Step 17:

[0579] The device stores offline learning data locally.

[0580] Step 18:

[0581] When the device regains online connectivity, the learning data from the offline period is synchronized with the server.

[0582] Step 19:

[0583] The server receives the synchronized data and updates the database. The new progress data is reflected in the learning plan.

[0584] This will result in a system that provides a flexible and effective learning environment that meets the diverse learning styles and needs of users.

[0585] Example 1

[0586] 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."

[0587] Conventional learning systems have struggled to provide flexible educational materials that adapt to users' learning styles and needs. They also struggled to effectively integrate online and offline learning data, resulting in reduced learning efficiency. Furthermore, they were unable to track users' learning progress in real time and provide optimal learning materials accordingly.

[0588] 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.

[0589] In this invention, the server includes: means for receiving a user's individual information as input and analyzing the user's learning style and needs based on the information; means for automatically generating educational materials optimal for the user based on the user's learning style and needs; means for providing the generated educational materials to the user's information processing device; means for tracking the user's learning progress and updating the content of the educational materials according to the progress; means for recording offline learning data and synchronizing it with the central processing device when online; means for analyzing the user's learning style and needs using a generative AI model based on questionnaire results; and means for caching the generated educational materials and progress data in local storage. This enables the provision of educational materials adapted to the user's learning style and the integration of online and offline learning data. Furthermore, the system can track the user's learning progress in real time and continuously provide appropriate learning materials.

[0590] definition statement

[0591] "User" refers to an individual who uses the system to learn.

[0592] "Individual information" refers to information about a user, such as their name, email address, password, learning style, and learning objectives.

[0593] "Learning style" refers to a particular learning method or teaching technique that a user prefers, such as visual, auditory, or tactile learning.

[0594] "Needs" refers to the user's learning goals and requirements, and the range of skills and knowledge required.

[0595] "Educational Materials" means educational materials and resources provided for User learning purposes, including videos, texts, quizzes, infographics, etc.

[0596] "Information processing device" refers to a device that allows a user to use the system. Specifically, it includes PCs, tablets, smartphones, etc.

[0597] "Progress Data" refers to the learning progress recorded as the user progresses through the course of their studies. Specifically, this includes the status of viewing of learning materials and quiz results.

[0598] "Central Processing Unit" refers to the computer server that forms the core of the system. It processes, stores, and manages user data.

[0599] A "generative AI model" refers to a mathematical model that uses artificial intelligence technology to analyze user data and make predictions and optimizations.

[0600] "Local storage" refers to memory or storage areas used to temporarily store data within an information processing device. Specifically, this includes hard disk drives and solid-state drives.

[0601] "Caching" refers to temporarily storing data to improve system efficiency, which increases the speed at which data can be loaded.

[0602] "Survey Results" refers to survey data regarding learning styles and needs provided by users.

[0603] "Synchronizing" refers to matching data across different devices or systems, specifically including sending data recorded offline to a central processing unit and matching it when the device is back online.

[0604] MODE FOR CARRYING OUT THE INVENTION

[0605] The present invention relates to a learning system that analyzes a user's learning style based on their individual information and provides an optimal learning plan. This system is implemented by linking a server and terminals (PCs, tablets, smartphones, etc.).

[0606] First, the user enters the required information, such as name, email address, and password, on the account registration screen. The device sends this information to the server, which then stores it in a database. This registers the user's basic information in the system. The server then presents the user with a questionnaire about their learning style and goals, and by collecting the results, the server can understand the user's learning needs.

[0607] The server then uses a generative AI model based on the survey results to analyze the user's learning style and needs. For example, if the server determines that the user is a visual learner, it will select visual learning materials. This process uses general artificial intelligence techniques (such as Python's scikit-learn and TensorFlow).

[0608] The server then automatically generates an optimized learning plan based on the analysis results and sends the generated learning material list to the user's device. The device then provides the received learning materials to the user, allowing the user to proceed with their learning. For example, if the "Introduction to Programming" course is selected, the user will be provided with videos and quizzes aimed at beginners.

[0609] Furthermore, as the user progresses through the learning materials, progress data is recorded on the device. The device then sends the progress data to the server, which stores it in a database. The server then adaptively updates the learning plan based on the progress data and may recommend more challenging learning materials as the next step. For example, if the user achieves a high score in a lesson, the next most challenging learning material will be recommended.

[0610] The device also caches the next learning material and some of the progress data so that users can continue learning even when they are offline, such as on a train. The learning data obtained while offline is synchronized with the server the next time the device is online. This allows for the integration of online and offline learning data.

[0611] As a concrete example, if a user selects the "Introduction to Data Science" course and prefers a visual learning style, the system operates as follows: When the user logs in for the first time, they fill out a questionnaire about their profile information and learning style, and the server selects visual learning materials (e.g., video lectures and infographics) based on the questionnaire information. The device provides the selected learning materials, and the user uses them to progress with their studies. As the user watches video lectures and answers quizzes, the device records the information and sends progress data to the server. The server updates the next learning plan based on the received data and provides more appropriate learning materials.

[0612] Examples of prompts include:

[0613] "How can I deliver video lectures and infographics for my introductory data science course? Users indicated in our survey that they prefer a visual learning style."

[0614] This system provides a flexible learning environment that adapts to the user's learning style and progress, maximizing learning efficiency and effectiveness.

[0615] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0616] Specific steps of the program's processing

[0617] Step 1:

[0618] A user enters their name, email address, and password on the account registration screen.

[0619] Input: Name, Email Address, Password

[0620] Specific operations: Enter information into the form using a browser or dedicated app and press the confirmation button.

[0621] Step 2:

[0622] The terminal transmits the input information to the server.

[0623] Input: Name, Email Address, Password

[0624] Output: User information sent to the server

[0625] Specific operation: When the submit button of the form is pressed, the device sends the data to the server using an HTTP POST request.

[0626] Step 3:

[0627] The server stores the received information in a database.

[0628] Input: Submitted user information

[0629] Output: User information stored in the database

[0630] Specific operation: The server analyzes the received data, generates an SQL query, and saves the information in the database using an INSERT statement.

[0631] Step 4:

[0632] The server presents users with a questionnaire about their learning style and goals and collects the results.

[0633] Input: User information (name, email address, password)

[0634] Output: Collected survey results

[0635] Specific operation: The server generates a questionnaire form in HTML format and sends it to the user's device. The user answers the questionnaire, and the answers are sent back to the server.

[0636] Step 5:

[0637] The server uses a generative AI model based on the survey results to analyze the user's learning style and needs.

[0638] Input: Survey results

[0639] Output: Classification of user learning styles and needs

[0640] How it works: The server inputs the collected survey data into an AI model (using, for example, Python's scikit-learn or TensorFlow) to classify and predict the user's learning style and needs.

[0641] Step 6:

[0642] The server automatically generates an optimized learning plan based on the analysis results.

[0643] Input: Learning style and needs classification results

[0644] Output: Optimized study plan

[0645] Specific operation: The server generates a list of teaching materials based on the results of the AI ​​model and the user's needs.

[0646] Step 7:

[0647] The server transmits the generated teaching material list to the user's terminal.

[0648] Input: Optimized Study Plan

[0649] Output: List of teaching materials sent to the device

[0650] Specific operation: Generate a list of teaching materials in JSON format and send it to the terminal as an HTTP response.

[0651] Step 8:

[0652] The terminal provides the received educational material to the user.

[0653] Input: List of submitted teaching materials

[0654] Output: The teaching material displayed on the user interface

[0655] Specific operation: Analyzes the received JSON data and displays a list of learning materials in the user interface. When the user clicks, learning materials (such as video playback or quiz screens) are displayed.

[0656] Step 9:

[0657] As the user progresses with their learning using the learning materials, the terminal records their progress data.

[0658] Input: User learning activity

[0659] Output: Recorded progress data

[0660] Specific operation: Record the completion of video viewing and quiz answer results in local storage or temporary memory.

[0661] Step 10:

[0662] The terminal transmits the progress data to the server.

[0663] Input: Recorded progress data

[0664] Output: Progress data sent to the server

[0665] Specific behavior: Uploads progress data to the server via HTTP POST requests periodically or for each event.

[0666] Step 11:

[0667] The server adaptively updates the next learning plan based on the received data.

[0668] Input: Received progress data

[0669] Output: Updated learning plan

[0670] What happens: The server saves the new progress data to a database and re-runs the AI ​​model to adjust the next learning plan.

[0671] Step 12:

[0672] The device caches the next study material and some of the progress data so you can study offline.

[0673] Input: Next study material and progress data

[0674] Output: Cached data

[0675] What it does: Saves your next learning material and progress data to local storage or device cache.

[0676] Step 13:

[0677] Users can study offline and their progress will be synced to the server the next time they are online.

[0678] Input: Offline training data

[0679] Output: Progress data synced to the server

[0680] Specific operation: Operation records and progress data are saved locally when offline, and then sent to the server in bulk when the device is online again.

[0681] (Application example 1)

[0682] 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."

[0683] There is a need for a method that allows robot operators to receive training efficiently and be provided with learning materials that best suit their learning style. However, conventional training systems provide uniform learning materials to all users, which makes it difficult to adapt to individual learning styles. Furthermore, they lack the ability to flexibly update learning materials according to progress. This often results in the robot operators' learning efficiency and effectiveness not being maximized, resulting in wasted time and effort. Therefore, a system is needed that analyzes the learning style of each user based on their individual information and provides optimal learning materials.

[0684] 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.

[0685] In this invention, the server includes means for receiving individual user information as input and analyzing the user's learning style and needs based on the information, means for automatically generating learning materials optimal for the user based on the learning style and needs, and means for providing the generated learning materials to the user's terminal, thereby enabling the robot operator to receive learning materials optimal for his or her own learning style and maximizing learning efficiency and effectiveness.

[0686] "User's personal information" refers to information that can be used to identify a specific individual, including the learner's name, email address, password, and survey results regarding learning style and purpose.

[0687] A "learning style" refers to the method or tendency in which a particular learner learns most effectively, such as visual learning or hands-on learning.

[0688] "Demand" refers to the goals that learners want to achieve through their studies and the knowledge and skills they need.

[0689] A "generative AI model" is an artificial intelligence model used to analyze a user's learning style and needs based on learning data and determine the most appropriate learning materials.

[0690] "Teaching materials" refer to materials and content provided to learners for learning activities, including, for example, video lectures and simulators.

[0691] A "terminal" is a device such as a smartphone, tablet, or PC that displays the learning materials provided by the system and allows users to progress through their studies.

[0692] "Offline" refers to a state where there is no internet connection, such as when a learner is studying on a train.

[0693] "Synchronization" refers to the process of sending learning data created offline to the server when the device is back online and matching it.

[0694] A "robot operator" is a technician who receives training to operate and manage robots in factories and facilities and perform their work efficiently.

[0695] "Visual aids" are materials that convey information visually, such as videos, infographics, and illustrations.

[0696] "Practical teaching materials" are teaching materials that primarily involve hands-on operation and work, and include simulators and on-the-job training.

[0697] This invention shows a specific method for realizing a system that analyzes a user's learning style based on their individual information and provides an optimal learning plan. The overall configuration of the system and the operation of each component are explained in detail below.

[0698] The server receives individual user information as input and has a means to analyze the user's learning style and needs based on that information. To achieve this, it uses a generative AI model. The server uses a machine learning framework such as TensorFlow to analyze survey results and past learning data to detect the user's learning style. For example, it determines whether the user is a visual learner or a hands-on learner.

[0699] The server also has a means for automatically generating optimal learning materials for the user based on the learning style and demands. This means selects visual learning materials (video lectures, infographics, etc.) or practical learning materials (simulators, on-the-job training, etc.) to provide learning materials that best suit the user's learning style. The learning materials are stored in a database as preprocessed content.

[0700] Next, the server has a means for providing the generated learning materials to the user's terminal. The learning materials are displayed on the user's terminal (such as a smartphone or tablet). The user begins learning using the selected learning materials.

[0701] The user's learning progress is recorded by the device, and the progress data is sent to the server as appropriate. The server adaptively updates the learning plan based on the received progress data and provides the most appropriate learning materials for the next step. In this way, a flexible learning environment tailored to each individual user is realized.

[0702] Furthermore, the device has a means for recording offline learning data and synchronizing it with the server when the device is online, allowing users to continue learning even in an offline environment, and progress data is kept on the server without being lost.

[0703] As a concrete example, consider the case where robot operator A is using the app for the first time. First, A registers an account by entering their name, email address, and password. After that, A answers a questionnaire about their learning style and needs. The server analyzes this using a generative AI model and determines that A is a visual learner. The server selects the most suitable learning materials for A, a "robot operation video" and an "illustrated manual," and provides them to the device. As A uses these materials to progress with their learning, the device records their progress and syncs it with the server.

[0704] An example of a prompt sentence would be, "I am robot operator A. My learning style is visual. Please provide me with the most effective training materials." This would allow the system to select and provide the most appropriate materials.

[0705] This invention makes it possible to provide optimal learning materials based on individual user information, thereby maximizing learning efficiency and effectiveness.

[0706] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0707] Step 1:

[0708] The server receives the user's individual information as input. The user enters their name, email address, password, etc. into the account registration screen and sends this information from their device to the server. The server stores the received individual information in a database.

[0709] Input: User's personal information (name, email address, password, etc.)

[0710] Output: User information stored in the database

[0711] Step 2:

[0712] The server presents the user with a questionnaire about their learning style and needs. The user answers the questionnaire and sends the results from their device to the server. The server receives the questionnaire results and analyzes the user's learning style and needs using a generative AI model.

[0713] Input: User survey results

[0714] Output: Learning styles and demands as analyzed results

[0715] Step 3:

[0716] The server automatically generates the most suitable learning materials for the user based on the analysis results. The learning materials can be selected from videos and infographics for visual learning, or simulators and on-the-job training materials for practical learning. The selected learning materials are retrieved from the database.

[0717] Input: Learning Style and Demand Analysis Results

[0718] Output: The best learning material for the user

[0719] Step 4:

[0720] The server provides the selected learning materials to the user's device, and the user uses the materials provided through the device for learning. The content of the learning materials is displayed on the device in the form of videos, infographics, practical simulators, etc.

[0721] Input: Selected teaching materials

[0722] Output: Teaching materials displayed on the device

[0723] Step 5:

[0724] The device records the user's learning progress. As the user progresses through the learning activity, progress data is generated. The device transmits this progress data to the server.

[0725] Input: User learning activity

[0726] Output: Recorded learning progress data

[0727] Step 6:

[0728] The server adaptively updates the learning plan based on the received learning progress data. Based on the progress data, the next most suitable learning material to be provided is reselected. For example, if the user achieves a high score, the next most difficult learning material will be selected.

[0729] Input: Learning progress data

[0730] Output: Updated learning plan

[0731] Step 7:

[0732] The device records learning data while offline and synchronizes it with the server the next time it goes online, allowing users to continue learning even in offline environments.

[0733] Input: Offline training data

[0734] Output: Training data synchronized while online

[0735] 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.

[0736] This invention relates to a learning system that analyzes a user's individual information and learning style to provide an optimal learning plan, and further enhances the learning experience by combining an emotion engine. This system is implemented by linking a server and terminals (PCs, tablets, smartphones, etc.).

[0737] 1. Initial registration process

[0738] The user enters the required information (such as name, email address, and password) on the account registration screen. The device sends this information to the server, which stores it in a database. The server then presents the user with a questionnaire about their learning style and goals, and collects the results.

[0739] 2. Learning Style and Needs Analysis

[0740] Based on the survey results, the server uses AI models to analyze the user's learning style and needs, for example determining that the user is a visual learner.

[0741] 3. Provision of teaching materials

[0742] The server automatically generates an optimized learning plan based on the analysis results. The generated learning material list is sent to the user's device, which then displays the learning material. For example, if the user selects the "Introduction to Programming" course, the user will be provided with videos and quizzes aimed at beginners.

[0743] 4. Track your learning progress

[0744] As users use the learning materials, their devices record their learning activities and progress. Progress data is sent to a server, which stores it in a database and adaptively updates the learning plan. For example, if a user achieves a high score in a lesson, more challenging learning materials will be recommended as the next step.

[0745] 5. Integrating online and offline learning

[0746] The device caches the next study material and some of the progress data so that users can study offline. Users can study even when offline, and the progress data is synchronized with the server the next time they go online. This allows users to continue studying even in offline environments such as on the train.

[0747] 6. Incorporating an Emotional Engine

[0748] The device uses an emotion engine to recognize emotions in real time from the user's facial expressions, voice, and input data. For example, it can analyze facial expressions and tone of voice using a webcam or microphone.

[0749] 7. Adjusting teaching materials based on emotions

[0750] The server dynamically adjusts the content of the learning materials based on the emotional data recognized by the emotion engine. For example, if the server detects that the user is tired, it will provide less stressful learning materials or interactive content to refresh the user.

[0751] 8. Integrating emotion and learning progress data

[0752] The server integrates the emotion data with the learning progress data to update a comprehensive learning plan, optimizing the user's learning experience and providing emotional feedback.

[0753] Specific examples

[0754] If a user selects the "Introduction to Data Science" course and prefers a visual learning style, the system operates as follows:

[0755] 1. When users first log in, they fill out a profile information and learning style questionnaire.

[0756] 2. The server selects visual teaching materials (e.g., video lectures and infographics) based on the survey information.

[0757] 3. The terminal provides the selected learning materials to the user, who then uses these materials to advance their studies.

[0758] 4. As users watch video lectures and answer quizzes, the device records this information and sends progress data to the server.

[0759] 5. Based on the received data, the server updates the next study plan and provides more appropriate learning materials.

[0760] 6. The device uses an emotion engine to recognize the user's emotions, and if it detects fatigue or a loss of concentration, it will provide light quizzes or mindfulness videos to refresh the user.

[0761] 7. Accordingly, the server integrates the emotional data and learning progress data to adjust the overall learning plan.

[0762] In this way, a system is realized that provides an optimized learning experience by comprehensively taking into account the user's learning style, progress, and emotions.

[0763] The processing flow will be explained below.

[0764] This invention relates to a learning system that analyzes a user's individual information and learning style to provide an optimal learning plan, and further enhances the learning experience by combining an emotion engine. This system is implemented by linking a server and terminals (PCs, tablets, smartphones, etc.).

[0765] Processing Steps

[0766] Step 1:

[0767] The user enters the required information (name, email address, password, etc.) on the account registration screen.

[0768] Step 2:

[0769] The terminal transmits the input information to the server.

[0770] Step 3:

[0771] The server stores the received registration information in a database.

[0772] Step 4:

[0773] The server presents the user with a questionnaire about their learning style and goals, including the skills they want to learn, their areas of interest, and their learning preferences (visual, auditory, etc.).

[0774] Step 5:

[0775] The user responds to the survey.

[0776] Step 6:

[0777] The terminal sends the survey results to the server.

[0778] Step 7:

[0779] The server uses an AI model based on the survey results to analyze the user's learning style and needs. For example, it may determine that the user is a visual learner.

[0780] Step 8:

[0781] Based on the analysis, the server automatically generates an optimal initial learning plan for the user, which includes materials (videos, infographics, etc.) that suit visual learning styles.

[0782] Step 9:

[0783] The server transmits the generated learning plan to the user's terminal.

[0784] Step 10:

[0785] The device displays learning materials to the user based on the learning plan received. For example, if the "Introduction to Programming" course is selected, beginner-friendly videos and quizzes are displayed.

[0786] Step 11:

[0787] Users use the provided learning materials to advance their studies, watching videos and answering quizzes, among other learning activities.

[0788] Step 12:

[0789] The device records the user's learning activity (video viewing time, quiz answer results, etc.).

[0790] Step 13:

[0791] The terminal transmits the recorded learning progress data to the server.

[0792] Step 14:

[0793] The server stores the received progress data in a database and adaptively updates the learning materials for the next step.

[0794] Step 15:

[0795] The device caches the learning materials and some of the progress data needed for the next study session, preparing for offline study.

[0796] Step 16:

[0797] Users can continue learning even when they are offline, for example, while on the train using pre-downloaded learning materials.

[0798] Step 17:

[0799] The learning data acquired while the device was offline is stored locally.

[0800] Step 18:

[0801] When the device regains online connectivity, the learning data recorded while offline is synchronized with the server.

[0802] Step 19:

[0803] The server receives the synchronized data and updates the database. The new progress data is reflected in the learning plan.

[0804] Incorporating an emotion engine

[0805] Step 20:

[0806] The device uses a webcam and microphone to recognize emotions in real time using an emotion engine based on the user's facial expressions, voice, and input data. For example, it can detect happiness, sadness, fatigue, etc. from the user's facial expressions.

[0807] Step 21:

[0808] The server receives the emotion data recognized by the emotion engine and dynamically adjusts the content of the learning materials based on that data. For example, if it recognizes that the user is tired, it will provide less stressful learning materials or interactive content to refresh the user.

[0809] Step 22:

[0810] The emotion data recognized by the emotion engine is integrated with the learning progress data, and the server updates the overall learning plan.

[0811] Specific examples

[0812] If a user selects the "Introduction to Data Science" course and prefers a visual learning style, the system operates as follows:

[0813] 1. When users first log in, they fill out a profile information and learning style questionnaire.

[0814] 2. The server selects visual teaching materials (e.g., video lectures and infographics) based on the survey information.

[0815] 3. The terminal provides the selected learning materials to the user, who then uses these materials to advance their studies.

[0816] 4. As users watch video lectures and answer quizzes, the device records this information and sends progress data to the server.

[0817] 5. Based on the received data, the server updates the next study plan and provides more appropriate learning materials.

[0818] 6. The device uses an emotion engine to recognize the user's emotions, and if it detects fatigue or a loss of concentration, it will provide light quizzes or mindfulness videos to refresh the user.

[0819] 7. Accordingly, the server integrates the emotional data and learning progress data to adjust the overall learning plan.

[0820] In this way, a system is realized that provides an optimized learning experience by comprehensively taking into account the user's learning style, progress, and emotions.

[0821] Example 2

[0822] 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."

[0823] Conventional learning systems have difficulty providing optimal learning materials based on the user's learning style and needs, and are unable to flexibly update learning plans based on learning progress and emotional data. As a result, it is not possible to provide an optimal learning experience for each learner, resulting in problems such as reduced learning efficiency and effectiveness.

[0824] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving individual information of a user as input and analyzing the user's learning style and needs based on the information; means for automatically generating learning materials optimal for the user based on the learning style and needs; means for providing the generated learning materials to the user's terminal; means for tracking the user's learning progress and updating the content of the learning materials according to the progress; means for recording offline learning data and synchronizing it with the server when online; means for recognizing emotions from the user's facial expressions, voice, and input data and dynamically adjusting the content of the learning materials based on the emotions; and means for integrating the emotion data and learning progress data and updating the comprehensive learning plan. This makes it possible to provide a learning experience optimized for each user's individual learning style, progress, and emotional state.

[0825] "User" means a person or organization who uses the learning system and is provided with an optimal learning plan based on their learning style and needs.

[0826] "Personal Information" refers to data unique to a user, including name, email address, password, learning style survey results, etc.

[0827] "Learning style" refers to the method or technique in which a user learns most effectively, and can be visual, auditory, tactile, or other types.

[0828] "Needs" refer to the goals or needs that a user wants to achieve through learning, and specific examples include improving specialized knowledge or acquiring specific skills.

[0829] "Learning materials" refers to content provided to assist users in their learning, and includes video lectures, texts, quizzes, infographics, and the like.

[0830] "Terminal" means a device used by a user to access the system, including a PC, tablet, smartphone, etc.

[0831] A "server" refers to an information processing device that processes and stores various types of data and communicates with user terminals.

[0832] "Study progress" is data that indicates how much progress a user has made in the process of studying, and includes the amount of time spent watching videos and the percentage of correct answers to quizzes.

[0833] "Offline learning data" is data recorded when a user studies without a network connection, and is synchronized with the server the next time the user goes online.

[0834] "Emotion data" is data that indicates the user's emotional state, and is extracted from facial expressions, tone of voice, input data, and the like.

[0835] "Comprehensive Learning Plan" refers to a series of learning activities that are optimized based on the user's learning style, progress data, and emotional data.

[0836] An "emotion engine" refers to a collection of algorithms and software that recognizes a user's emotions and takes necessary action based on them.

[0837] The present invention is a learning system that analyzes a user's individual information and learning style to provide an optimal learning plan, and further enhances the learning experience by combining an emotion engine. This system is implemented by linking a server and a terminal. Below, we will explain embodiments of the present invention, using specific examples to explain what hardware or software is used to perform data processing and data calculations.

[0838] This system consists of multiple steps. First, the user accesses the system using a device (PC, tablet, smartphone). When registering for the first time, the user enters the required information (name, email address, password, etc.), which the device sends to the server. The server validates the data and stores it in a database (e.g., MySQL). The server then provides a questionnaire regarding learning style and goals, which the user fills out. The survey results are then sent to the server via the device.

[0839] The server inputs the collected survey results into an AI model (e.g., a model built with TensorFlow or PyTorch) to analyze the user's learning style and needs. Based on the analysis results, the server automatically generates an optimized learning plan and sends this plan in JSON format to the device. The device then analyzes the received data and displays it in a user interface.

[0840] As a user progresses through their studies using learning materials (e.g., video lectures, textbooks, quizzes), the device records their activity log and periodically sends it to the server. The server analyzes the received data and dynamically updates the next learning plan based on the user's progress. Furthermore, the device has the ability to record learning data even when offline and synchronize it with the server the next time it is online.

[0841] Additionally, the system incorporates an emotion engine. The device uses a webcam and microphone to capture the user's facial and voice data, which is then analyzed in real time using emotion recognition algorithms (e.g., OpenCV and DeepEmotion). The emotion data is sent to a server, which analyzes it using an AI model to determine the user's current emotional state. Depending on the emotion data, the server provides low-impact educational materials and refresher content.

[0842] The server integrates emotional data and learning progress data and updates a comprehensive learning plan to provide a learning experience optimized for each user's individual learning style, progress, and emotional state.

[0843] Specific examples

[0844] If a user selects the "Introduction to Data Science" course and prefers a visual learning style, the system operates as follows:

[0845] 1. When users log in for the first time, they fill out their profile information and a learning style questionnaire.

[0846] 2. The server selects visual teaching materials (e.g., video lectures and infographics) based on the survey information.

[0847] 3. The terminal provides the selected learning materials to the user, who then uses these materials to advance their studies.

[0848] 4. As users watch video lectures and answer quizzes, the device records this information and sends progress data to the server.

[0849] 5. The server updates the next study plan based on the received data and provides the corresponding study materials.

[0850] 6. The device uses an emotion engine to recognize the user's emotions, and if it detects fatigue or a loss of concentration, it will provide light quizzes or mindfulness videos to refresh the user.

[0851] 7. The server integrates the emotional data and learning progress data to tailor a comprehensive learning plan.

[0852] Prompt Sentence Examples

[0853] "How can I create an AI model that generates the best learning material list for a user who is a visual learner?"

[0854] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0855] Step 1: First-time registration process

[0856] 1.1 The user enters the required information such as name, email address, and password on the account registration screen.

[0857] Input: Name, Email Address, Password

[0858] Output: User input data

[0859] 1.2 The device converts this information into JSON format and sends it to the server using the HTTPS protocol.

[0860] Input: User-entered data

[0861] Output: User data in JSON format

[0862] 1.3 The server validates the received information (e.g., checks the email address format, password strength) and stores it in a database (e.g., MySQL).

[0863] Input: User data in JSON format

[0864] Output: Save results to database

[0865] 1.4 The server generates questionnaire data regarding learning styles and learning objectives and sends it to the terminal.

[0866] Input: None

[0867] Output: Questionnaire form data

[0868] Step 2: Analyze your learning styles and needs

[0869] 2.1 The user fills out the questionnaire form regarding their learning style and purpose and clicks the submit button.

[0870] Input: User learning style and goals

[0871] Output: Survey results

[0872] 2.2 The terminal converts the survey results into JSON format and sends them to the server.

[0873] Input: Survey results

[0874] Output: Survey data in JSON format

[0875] 2.3 The server inputs the received survey results into an AI model (e.g., built with TensorFlow and PyTorch) to analyze learning styles and needs.

[0876] Input: Survey data in JSON format

[0877] Output: Analysis results (user learning styles and needs)

[0878] 2.4 The server stores the analysis results in a database for further processing.

[0879] Input: Analysis results

[0880] Output: Save results to database

[0881] Step 3: Providing educational materials

[0882] 3.1 The server automatically generates an optimized learning plan based on the analysis results, such as a list of appropriate video lectures and quizzes.

[0883] Input: Analysis results

[0884] Output: Generated teaching material list

[0885] 3.2 The server sends the generated teaching material list to the terminal in JSON format.

[0886] Input: Generated teaching material list

[0887] Output: JSON formatted learning material list

[0888] 3.3 The device analyzes the received data and displays it on the user interface. Specifically, it dynamically generates thumbnail images of the video lectures and quiz links.

[0889] Input: JSON format teaching material list

[0890] Output: A list of teaching materials displayed on the user interface

[0891] Step 4: Track your progress

[0892] 4.1 The user uses the provided learning materials (e.g., video lectures, quizzes) to advance their learning.

[0893] Input: Usage of teaching materials

[0894] Output: Learning progress information

[0895] 4.2 The device records the user's learning activities and progress data, e.g., video viewing time, quiz correct answer rate.

[0896] Input: Learning progress information

[0897] Output: Recorded progress data

[0898] 4.3 The device periodically converts the progress data into JSON format and sends it to the server.

[0899] Input: Recorded progress data

[0900] Output: Progress data in JSON format

[0901] 4.4 The server analyzes the received data and dynamically updates the next study plan. For example, if a high score is obtained, more difficult study materials will be recommended.

[0902] Input: Progress data in JSON format

[0903] Output: Updated learning plan

[0904] Step 5: Integrating online and offline learning

[0905] 5.1 The device will cache some of the learning materials and progress data for the next time you use it in local storage.

[0906] Input: Updated learning plan and progress data

[0907] Output: Data saved in local storage

[0908] 5.2 Users can continue learning using cached data even when they do not have a network connection.

[0909] Input: Data stored in local storage

[0910] Output: Offline learning progress

[0911] 5.3 The next time your device goes online, it will sync its locally stored progress data with the server.

[0912] Input: Offline learning progress

[0913] Output: Data synchronized with the server

[0914] Step 6: Incorporating the Emotion Engine

[0915] 6.1 The device uses a webcam and microphone to capture the user's facial expressions and voice data.

[0916] Input: facial expression data and voice data

[0917] Output: Retrieved data

[0918] 6.2 The device uses emotion recognition algorithms (e.g., OpenCV, DeepEmotion) to analyze facial expressions and tone of voice in real time.

[0919] Input: Retrieved data

[0920] Output: Emotion data

[0921] 6.3 The terminal converts the analysis results into JSON format and sends them to the server.

[0922] Input: Emotion data

[0923] Output: Emotion data in JSON format

[0924] Step 7: Emotionally adjust your materials

[0925] 7.1 The server inputs the received emotion data into the AI ​​model to determine the user's current emotional state.

[0926] Input: Emotion data in JSON format

[0927] Output: Emotion determination result

[0928] 7.2 The server dynamically adjusts the content of educational materials provided based on emotional data. For example, if fatigue is detected, it will provide refreshing light quizzes or mindfulness videos.

[0929] Input: Emotion determination result

[0930] Output: Dynamically adjusted teaching content

[0931] Step 8: Integrating sentiment and learning progress data

[0932] 8.1 The server integrates the emotion data and learning progress data to update the overall learning plan.

[0933] Input: Emotion judgment results and learning progress data

[0934] Output: Consolidated learning plan

[0935] 8.2 The server sends the updated learning plan to the device and provides it to the user.

[0936] Input: Integrated Learning Plan

[0937] Output: Provided to the user

[0938] (Application example 2)

[0939] 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."

[0940] Previous learning and training systems lacked the ability to provide optimal learning plans based on individual user and robot information. They also lacked the ability to monitor learning progress and performance decline in real time and dynamically adjust learning plans accordingly. Furthermore, there were issues with recording data offline and synchronizing it with the online environment.

[0941] 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.

[0942] In this invention, the server includes means for receiving individual user information as input and analyzing the user's learning style and needs based thereon, means for automatically generating learning materials optimal for the user, means for providing the generated learning materials to the user's terminal, means for tracking the user's learning progress and updating the content of the learning materials according to the progress, means for recording offline learning data and synchronizing it with the server when online, means for receiving robot identification information as input and generating an appropriate training plan based thereon, means for analyzing the robot's motion data and monitoring performance in real time using an emotion engine, and means for instructing a refresher task when performance degradation is detected. This makes it possible to provide an optimal learning plan based on the individual user and robot information, monitor performance in real time, and effectively manage data even in an offline environment.

[0943] "Personal information" is information that includes a user's or robot's identity, personal characteristics, learning style, and needs.

[0944] "Learning style" is information that indicates a user's learning method and preferences, and includes visual, auditory, tactile, and other methods.

[0945] "Needs" refers to information that indicates the requirements or objectives that users have in learning or training.

[0946] "Instructional Materials" includes educational resources, such as videos, quizzes, infographics, etc., tailored to a user's learning style and needs.

[0947] A "terminal" is a device used by a user or robot to receive learning materials, and includes a PC, tablet, smartphone, etc.

[0948] The "server" is a central computer that manages the entire learning system and processes, records, and analyzes individual information, progress data, emotional data, etc. of users or robots.

[0949] "Study progress" is data indicating the progress of the user's learning activities using learning materials.

[0950] An "emotion engine" is software that analyzes the facial expressions, voice, and movement data of a user or robot to recognize their emotional state in real time.

[0951] A "training plan" is a plan of optimal work procedures and training content provided to a robot, and it changes adaptively.

[0952] "Performance" is an evaluation index that indicates the efficiency and accuracy with which a robot performs a specific task.

[0953] "Real-time monitoring" means reading learning progress and performance on the spot and analyzing them immediately.

[0954] "Offline training data" means training data collected and recorded when a user or robot does not have an internet connection.

[0955] "Synchronizing to server when online" is the process by which data recorded offline is sent to the server and stored in the database when you are connected to the Internet again.

[0956] A "refreshment task" is a task that instructs the robot to do light work or take a break to reduce stress when a decline in its performance is detected.

[0957] The present invention relates to a system that analyzes individual user information and robot identification information, and provides optimal learning and training plans based on the analysis. Specific embodiments are described below.

[0958] 1. Initial Setup and Registration

[0959] The server first receives input from the user or robot's personal information. For users, this includes name, email address, password, and information about learning style. For robots, this includes identification information and training objectives. This information is sent to the server and stored in a database.

[0960] 2. Learning Style and Needs Analysis

[0961] The server uses a generative AI model to analyze a user's learning style and needs based on the survey results and input data. For example, if the user is a visual learner, visual learning materials will be selected. Similarly, for robots, training content tailored to their characteristics will be generated.

[0962] 3. Providing educational materials or training plans

[0963] The server automatically generates optimal learning materials or training plans based on the analysis results, which are then sent to the user's or robot's device and displayed in the form of videos, quizzes, infographics, and more.

[0964] 4. Track your progress

[0965] As the user or robot progresses in their learning or training, the device records their progress data. This progress data is sent to the server and stored in a database. The server then adaptively updates the next learning or training plan based on the progress data.

[0966] 5. Integrating offline learning

[0967] The device has the ability to cache data so that learning and training can be done offline. A user or robot can record their progress in an offline environment and sync it with the server when they come online.

[0968] 6. Leveraging Emotional Engines

[0969] The device uses an emotion engine to recognize the emotional state of the user or robot in real time, using a webcam and microphone to analyze facial expressions and voice to detect signs of stress or fatigue.

[0970] 7. Adjusting the movement

[0971] If the emotion engine detects a decline in performance, the server will instruct the user or robot to perform a refreshing task or light work, allowing them to continue learning or training without unnecessary stress.

[0972] Specific examples

[0973] Example of a training system for factory robots:

[0974] Imagine a factory robot learning a new assembly task. The robot's identity and task characteristics are sent to a server, which generates an appropriate training plan. The robot works on the task, collecting behavioral data along the way. If the emotion engine detects signs of stress, it will recommend lighter work or a break.

[0975] Example prompt sentence:

[0976] "If the robot's performance drops on the current task, what's the next appropriate training task to offer it?"

[0977] "If the robot assembles the parts with 75% accuracy, what is the next task it should move on to?"

[0978] As described above, this invention provides optimal learning and training plans based on individual information about the user and the robot, and monitors and adjusts progress and emotional state in real time, thereby achieving an effective learning experience.

[0979] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0980] Step 1:

[0981] The server receives the user's or robot's personal information as input. In the case of a user, this includes name, email address, password, and information about learning style. In the case of a robot, identification information and training goals are entered. The data is sent to the server and stored in a database. The input is the user's or robot's information, and the output is the personal information stored in the database.

[0982] Step 2:

[0983] The server uses a generative AI model to analyze the user's learning style and needs based on the received personalized information. This process determines whether the user is a visual learner and what the robot's working characteristics are. The input is personalized information, and the output is the analysis results.

[0984] Step 3:

[0985] The server automatically generates optimal teaching materials or training plans based on the analysis results. Appropriate teaching materials are selected, and the generated teaching materials or plans are sent to the user's or robot's terminal. The input is the analysis results, and the output is the generated teaching materials or training plans.

[0986] Step 4:

[0987] The user or robot learns or works according to the learning materials or training plan. The terminal records the progress data and sends it to the server. The input is the user's or robot's learning and work data, and the output is the recorded and sent progress data.

[0988] Step 5:

[0989] The server stores the received progress data in a database and adaptively updates the next learning or training plan. The input is the progress data, and the output is the updated learning or training plan.

[0990] Step 6:

[0991] The device caches the necessary data so that you can continue studying or working even when offline. It records your progress data even when offline and synchronizes it with the server when you come online. The input is your study plan and progress data, and the output is the cached data and synchronized data.

[0992] Step 7:

[0993] The device uses an emotion engine to recognize the emotional state of the user or robot in real time. It uses a webcam and microphone to analyze facial expressions and voice to detect signs of stress or fatigue. The input is the user or robot's behavior data, and the output is analyzed emotional data.

[0994] Step 8:

[0995] Based on the data analyzed by the emotion engine, the server instructs users to perform refreshing tasks or light work when a decline in performance is detected. The input is emotional data, and the output is the instructed refreshing task.

[0996] 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.

[0997] 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.

[0998] 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.

[0999] [Third embodiment]

[1000] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1001] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1002] 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).

[1003] 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.

[1004] 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.

[1005] 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).

[1006] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1007] 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.

[1008] 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.

[1009] 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.

[1010] 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.

[1011] 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."

[1012] The present invention relates to a learning system that analyzes a user's learning style based on their individual information and provides an optimal learning plan. This system is implemented by linking a server and terminals (PCs, tablets, smartphones, etc.).

[1013] 1. Initial registration process

[1014] The user enters the required information (such as name, email address, and password) on the account registration screen. The device sends this information to the server, which stores it in a database. The server then presents the user with a questionnaire about their learning style and goals, and collects the results.

[1015] 2. Learning Style and Needs Analysis

[1016] Based on the survey results, the server uses AI models to analyze the user's learning style and needs. For example, if the user is determined to be a visual learner, visual learning materials will be selected.

[1017] 3. Provision of teaching materials

[1018] The server automatically generates an optimized learning plan based on the analysis results. The generated learning material list is sent to the user's device, which then displays the learning material. For example, if the user selects the "Introduction to Programming" course, the user will be provided with videos and quizzes aimed at beginners.

[1019] 4. Track your learning progress

[1020] As users use the learning materials, their devices record their learning activities and progress. Progress data is sent to a server, which stores it in a database and adaptively updates the learning plan. For example, if a user achieves a high score in a lesson, more challenging learning materials will be recommended as the next step.

[1021] 5. Integrating online and offline learning

[1022] The device caches the next study material and some of the progress data so that users can study offline. Users can study even when offline, and the progress data is synchronized with the server the next time they go online. This allows users to continue studying even in offline environments such as on the train.

[1023] Specific examples

[1024] If a user selects the "Introduction to Data Science" course and prefers a visual learning style, the system operates as follows:

[1025] 1. When users first log in, they fill out a profile information and learning style questionnaire.

[1026] 2. The server selects visual teaching materials (e.g., video lectures and infographics) based on the survey information.

[1027] 3. The terminal provides the selected learning materials to the user, who then uses these materials to advance their studies.

[1028] 4. As users watch video lectures and answer quizzes, the device records this information and sends progress data to the server.

[1029] 5. Based on the received data, the server updates the next study plan and provides more appropriate learning materials.

[1030] This provides a flexible learning environment that suits the user's learning style and progress, maximizing learning efficiency and effectiveness.

[1031] The processing flow will be explained below.

[1032] Step 1:

[1033] The user enters the required information (name, email address, password, etc.) on the account registration screen.

[1034] Step 2:

[1035] The terminal transmits the input information to the server.

[1036] Step 3:

[1037] The server stores the received registration information in a database.

[1038] Step 4:

[1039] The server presents the user with a questionnaire about their learning style and goals, including the skills they want to learn, their areas of interest, and their learning preferences (visual, auditory, etc.).

[1040] Step 5:

[1041] The user responds to the survey.

[1042] Step 6:

[1043] The terminal sends the survey results to the server.

[1044] Step 7:

[1045] The server uses an AI model based on the survey results to analyze the user's learning style and needs. For example, it may determine that the user is a visual learner.

[1046] Step 8:

[1047] Based on the analysis, the server automatically generates an optimal initial learning plan for the user, which includes materials (videos, infographics, etc.) that suit visual learning styles.

[1048] Step 9:

[1049] The server transmits the generated learning plan to the user's terminal.

[1050] Step 10:

[1051] The device displays learning materials to the user based on the learning plan received. For example, if the "Introduction to Programming" course is selected, beginner-friendly videos and quizzes are displayed.

[1052] Step 11:

[1053] Users use the provided learning materials to advance their studies, watching videos and answering quizzes, among other learning activities.

[1054] Step 12:

[1055] The device records the user's learning activity (video viewing time, quiz answer results, etc.).

[1056] Step 13:

[1057] The terminal transmits the recorded learning progress data to the server.

[1058] Step 14:

[1059] The server stores the received progress data in a database and adaptively updates the next step of the learning material. For example, if the user answers a quiz correctly, the next learning material with a higher level of difficulty is selected.

[1060] Step 15:

[1061] The device caches the learning materials and some of the progress data needed for the next study session, preparing for offline study.

[1062] Step 16:

[1063] Users can continue learning even when they are offline, for example, while on the train using pre-downloaded learning materials.

[1064] Step 17:

[1065] The device stores offline learning data locally.

[1066] Step 18:

[1067] When the device regains online connectivity, the learning data from the offline period is synchronized with the server.

[1068] Step 19:

[1069] The server receives the synchronized data and updates the database. The new progress data is reflected in the learning plan.

[1070] This will result in a system that provides a flexible and effective learning environment that meets the diverse learning styles and needs of users.

[1071] Example 1

[1072] 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."

[1073] Conventional learning systems have struggled to provide flexible educational materials that adapt to users' learning styles and needs. They also struggled to effectively integrate online and offline learning data, resulting in reduced learning efficiency. Furthermore, they were unable to track users' learning progress in real time and provide optimal learning materials accordingly.

[1074] 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.

[1075] In this invention, the server includes: means for receiving a user's individual information as input and analyzing the user's learning style and needs based on the information; means for automatically generating educational materials optimal for the user based on the user's learning style and needs; means for providing the generated educational materials to the user's information processing device; means for tracking the user's learning progress and updating the content of the educational materials according to the progress; means for recording offline learning data and synchronizing it with the central processing device when online; means for analyzing the user's learning style and needs using a generative AI model based on questionnaire results; and means for caching the generated educational materials and progress data in local storage. This enables the provision of educational materials adapted to the user's learning style and the integration of online and offline learning data. Furthermore, the system can track the user's learning progress in real time and continuously provide appropriate learning materials.

[1076] definition statement

[1077] "User" refers to an individual who uses the system to learn.

[1078] "Individual information" refers to information about a user, such as their name, email address, password, learning style, and learning objectives.

[1079] "Learning style" refers to a particular learning method or teaching technique that a user prefers, such as visual, auditory, or tactile learning.

[1080] "Needs" refers to the user's learning goals and requirements, and the range of skills and knowledge required.

[1081] "Educational Materials" means educational materials and resources provided for User learning purposes, including videos, texts, quizzes, infographics, etc.

[1082] "Information processing device" refers to a device that allows a user to use the system. Specifically, it includes PCs, tablets, smartphones, etc.

[1083] "Progress Data" refers to the learning progress recorded as the user progresses through the course of their studies. Specifically, this includes the status of viewing of learning materials and quiz results.

[1084] "Central Processing Unit" refers to the computer server that forms the core of the system. It processes, stores, and manages user data.

[1085] A "generative AI model" refers to a mathematical model that uses artificial intelligence technology to analyze user data and make predictions and optimizations.

[1086] "Local storage" refers to memory or storage areas used to temporarily store data within an information processing device. Specifically, this includes hard disk drives and solid-state drives.

[1087] "Caching" refers to temporarily storing data to improve system efficiency, which increases the speed at which data can be loaded.

[1088] "Survey Results" refers to survey data regarding learning styles and needs provided by users.

[1089] "Synchronizing" refers to matching data across different devices or systems, specifically including sending data recorded offline to a central processing unit and matching it when the device is back online.

[1090] MODE FOR CARRYING OUT THE INVENTION

[1091] The present invention relates to a learning system that analyzes a user's learning style based on their individual information and provides an optimal learning plan. This system is implemented by linking a server and terminals (PCs, tablets, smartphones, etc.).

[1092] First, the user enters the required information, such as name, email address, and password, on the account registration screen. The device sends this information to the server, which then stores it in a database. This registers the user's basic information in the system. The server then presents the user with a questionnaire about their learning style and goals, and by collecting the results, the server can understand the user's learning needs.

[1093] The server then uses a generative AI model based on the survey results to analyze the user's learning style and needs. For example, if the server determines that the user is a visual learner, it will select visual learning materials. This process uses general artificial intelligence techniques (such as Python's scikit-learn and TensorFlow).

[1094] The server then automatically generates an optimized learning plan based on the analysis results and sends the generated learning material list to the user's device. The device then provides the received learning materials to the user, allowing the user to proceed with their learning. For example, if the "Introduction to Programming" course is selected, the user will be provided with videos and quizzes aimed at beginners.

[1095] Furthermore, as the user progresses through the learning materials, progress data is recorded on the device. The device then sends the progress data to the server, which stores it in a database. The server then adaptively updates the learning plan based on the progress data and may recommend more challenging learning materials as the next step. For example, if the user achieves a high score in a lesson, the next most challenging learning material will be recommended.

[1096] The device also caches the next learning material and some of the progress data so that users can continue learning even when they are offline, such as on a train. The learning data obtained while offline is synchronized with the server the next time the device is online. This allows for the integration of online and offline learning data.

[1097] As a concrete example, if a user selects the "Introduction to Data Science" course and prefers a visual learning style, the system operates as follows: When the user logs in for the first time, they fill out a questionnaire about their profile information and learning style, and the server selects visual learning materials (e.g., video lectures and infographics) based on the questionnaire information. The device provides the selected learning materials, and the user uses them to progress with their studies. As the user watches video lectures and answers quizzes, the device records the information and sends progress data to the server. The server updates the next learning plan based on the received data and provides more appropriate learning materials.

[1098] Examples of prompts include:

[1099] "How can I deliver video lectures and infographics for my introductory data science course? Users indicated in our survey that they prefer a visual learning style."

[1100] This system provides a flexible learning environment that adapts to the user's learning style and progress, maximizing learning efficiency and effectiveness.

[1101] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1102] Specific steps of the program's processing

[1103] Step 1:

[1104] A user enters their name, email address, and password on the account registration screen.

[1105] Input: Name, Email Address, Password

[1106] Specific operations: Enter information into the form using a browser or dedicated app and press the confirmation button.

[1107] Step 2:

[1108] The terminal transmits the input information to the server.

[1109] Input: Name, Email Address, Password

[1110] Output: User information sent to the server

[1111] Specific operation: When the submit button of the form is pressed, the device sends the data to the server using an HTTP POST request.

[1112] Step 3:

[1113] The server stores the received information in a database.

[1114] Input: Submitted user information

[1115] Output: User information stored in the database

[1116] Specific operation: The server analyzes the received data, generates an SQL query, and saves the information in the database using an INSERT statement.

[1117] Step 4:

[1118] The server presents users with a questionnaire about their learning style and goals and collects the results.

[1119] Input: User information (name, email address, password)

[1120] Output: Collected survey results

[1121] Specific operation: The server generates a questionnaire form in HTML format and sends it to the user's device. The user answers the questionnaire, and the answers are sent back to the server.

[1122] Step 5:

[1123] The server uses a generative AI model based on the survey results to analyze the user's learning style and needs.

[1124] Input: Survey results

[1125] Output: Classification of user learning styles and needs

[1126] How it works: The server inputs the collected survey data into an AI model (using, for example, Python's scikit-learn or TensorFlow) to classify and predict the user's learning style and needs.

[1127] Step 6:

[1128] The server automatically generates an optimized learning plan based on the analysis results.

[1129] Input: Learning style and needs classification results

[1130] Output: Optimized study plan

[1131] Specific operation: The server generates a list of teaching materials based on the results of the AI ​​model and the user's needs.

[1132] Step 7:

[1133] The server transmits the generated teaching material list to the user's terminal.

[1134] Input: Optimized Study Plan

[1135] Output: List of teaching materials sent to the device

[1136] Specific operation: Generate a list of teaching materials in JSON format and send it to the terminal as an HTTP response.

[1137] Step 8:

[1138] The terminal provides the received educational material to the user.

[1139] Input: List of submitted teaching materials

[1140] Output: The teaching material displayed on the user interface

[1141] Specific operation: Analyzes the received JSON data and displays a list of learning materials in the user interface. When the user clicks, learning materials (such as video playback or quiz screens) are displayed.

[1142] Step 9:

[1143] As the user progresses with their learning using the learning materials, the terminal records their progress data.

[1144] Input: User learning activity

[1145] Output: Recorded progress data

[1146] Specific operation: Record the completion of video viewing and quiz answer results in local storage or temporary memory.

[1147] Step 10:

[1148] The terminal transmits the progress data to the server.

[1149] Input: Recorded progress data

[1150] Output: Progress data sent to the server

[1151] Specific behavior: Uploads progress data to the server via HTTP POST requests periodically or for each event.

[1152] Step 11:

[1153] The server adaptively updates the next learning plan based on the received data.

[1154] Input: Received progress data

[1155] Output: Updated learning plan

[1156] What happens: The server saves the new progress data to a database and re-runs the AI ​​model to adjust the next learning plan.

[1157] Step 12:

[1158] The device caches the next study material and some of the progress data so you can study offline.

[1159] Input: Next study material and progress data

[1160] Output: Cached data

[1161] What it does: Saves your next learning material and progress data to local storage or device cache.

[1162] Step 13:

[1163] Users can study offline and their progress will be synced to the server the next time they are online.

[1164] Input: Offline training data

[1165] Output: Progress data synced to the server

[1166] Specific operation: Operation records and progress data are saved locally when offline, and then sent to the server in bulk when the device is online again.

[1167] (Application example 1)

[1168] 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."

[1169] There is a need for a method that allows robot operators to receive training efficiently and be provided with learning materials that best suit their learning style. However, conventional training systems provide uniform learning materials to all users, which makes it difficult to adapt to individual learning styles. Furthermore, they lack the ability to flexibly update learning materials according to progress. This often results in the robot operators' learning efficiency and effectiveness not being maximized, resulting in wasted time and effort. Therefore, a system is needed that analyzes the learning style of each user based on their individual information and provides optimal learning materials.

[1170] 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.

[1171] In this invention, the server includes means for receiving individual user information as input and analyzing the user's learning style and needs based on the information, means for automatically generating learning materials optimal for the user based on the learning style and needs, and means for providing the generated learning materials to the user's terminal, thereby enabling the robot operator to receive learning materials optimal for his or her own learning style and maximizing learning efficiency and effectiveness.

[1172] "User's personal information" refers to information that can be used to identify a specific individual, including the learner's name, email address, password, and survey results regarding learning style and purpose.

[1173] A "learning style" refers to the method or tendency in which a particular learner learns most effectively, such as visual learning or hands-on learning.

[1174] "Demand" refers to the goals that learners want to achieve through their studies and the knowledge and skills they need.

[1175] A "generative AI model" is an artificial intelligence model used to analyze a user's learning style and needs based on learning data and determine the most appropriate learning materials.

[1176] "Teaching materials" refer to materials and content provided to learners for learning activities, including, for example, video lectures and simulators.

[1177] A "terminal" is a device such as a smartphone, tablet, or PC that displays the learning materials provided by the system and allows users to progress through their studies.

[1178] "Offline" refers to a state where there is no internet connection, such as when a learner is studying on a train.

[1179] "Synchronization" refers to the process of sending learning data created offline to the server when the device is back online and matching it.

[1180] A "robot operator" is a technician who receives training to operate and manage robots in factories and facilities and perform their work efficiently.

[1181] "Visual aids" are materials that convey information visually, such as videos, infographics, and illustrations.

[1182] "Practical teaching materials" are teaching materials that primarily involve hands-on operation and work, and include simulators and on-the-job training.

[1183] This invention shows a specific method for realizing a system that analyzes a user's learning style based on their individual information and provides an optimal learning plan. The overall configuration of the system and the operation of each component are explained in detail below.

[1184] The server receives individual user information as input and has a means to analyze the user's learning style and needs based on that information. To achieve this, it uses a generative AI model. The server uses a machine learning framework such as TensorFlow to analyze survey results and past learning data to detect the user's learning style. For example, it determines whether the user is a visual learner or a hands-on learner.

[1185] The server also has a means for automatically generating optimal learning materials for the user based on the learning style and demands. This means selects visual learning materials (video lectures, infographics, etc.) or practical learning materials (simulators, on-the-job training, etc.) to provide learning materials that best suit the user's learning style. The learning materials are stored in a database as preprocessed content.

[1186] Next, the server has a means for providing the generated learning materials to the user's terminal. The learning materials are displayed on the user's terminal (such as a smartphone or tablet). The user begins learning using the selected learning materials.

[1187] The user's learning progress is recorded by the device, and the progress data is sent to the server as appropriate. The server adaptively updates the learning plan based on the received progress data and provides the most appropriate learning materials for the next step. In this way, a flexible learning environment tailored to each individual user is realized.

[1188] Furthermore, the device has a means for recording offline learning data and synchronizing it with the server when the device is online, allowing users to continue learning even in an offline environment, and progress data is kept on the server without being lost.

[1189] As a concrete example, consider the case where robot operator A is using the app for the first time. First, A registers an account by entering their name, email address, and password. After that, A answers a questionnaire about their learning style and needs. The server analyzes this using a generative AI model and determines that A is a visual learner. The server selects the most suitable learning materials for A, a "robot operation video" and an "illustrated manual," and provides them to the device. As A uses these materials to progress with their learning, the device records their progress and syncs it with the server.

[1190] An example of a prompt sentence would be, "I am robot operator A. My learning style is visual. Please provide me with the most effective training materials." This would allow the system to select and provide the most appropriate materials.

[1191] This invention makes it possible to provide optimal learning materials based on individual user information, thereby maximizing learning efficiency and effectiveness.

[1192] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1193] Step 1:

[1194] The server receives the user's individual information as input. The user enters their name, email address, password, etc. into the account registration screen and sends this information from their device to the server. The server stores the received individual information in a database.

[1195] Input: User's personal information (name, email address, password, etc.)

[1196] Output: User information stored in the database

[1197] Step 2:

[1198] The server presents the user with a questionnaire about their learning style and needs. The user answers the questionnaire and sends the results from their device to the server. The server receives the questionnaire results and analyzes the user's learning style and needs using a generative AI model.

[1199] Input: User survey results

[1200] Output: Learning styles and demands as analyzed results

[1201] Step 3:

[1202] The server automatically generates the most suitable learning materials for the user based on the analysis results. The learning materials can be selected from videos and infographics for visual learning, or simulators and on-the-job training materials for practical learning. The selected learning materials are retrieved from the database.

[1203] Input: Learning Style and Demand Analysis Results

[1204] Output: The best learning material for the user

[1205] Step 4:

[1206] The server provides the selected learning materials to the user's device, and the user uses the materials provided through the device for learning. The content of the learning materials is displayed on the device in the form of videos, infographics, practical simulators, etc.

[1207] Input: Selected teaching materials

[1208] Output: Teaching materials displayed on the device

[1209] Step 5:

[1210] The device records the user's learning progress. As the user progresses through the learning activity, progress data is generated. The device transmits this progress data to the server.

[1211] Input: User learning activity

[1212] Output: Recorded learning progress data

[1213] Step 6:

[1214] The server adaptively updates the learning plan based on the received learning progress data. Based on the progress data, the next most suitable learning material to be provided is reselected. For example, if the user achieves a high score, the next most difficult learning material will be selected.

[1215] Input: Learning progress data

[1216] Output: Updated learning plan

[1217] Step 7:

[1218] The device records learning data while offline and synchronizes it with the server the next time it goes online, allowing users to continue learning even in offline environments.

[1219] Input: Offline training data

[1220] Output: Training data synchronized while online

[1221] 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.

[1222] This invention relates to a learning system that analyzes a user's individual information and learning style to provide an optimal learning plan, and further enhances the learning experience by combining an emotion engine. This system is implemented by linking a server and terminals (PCs, tablets, smartphones, etc.).

[1223] 1. Initial registration process

[1224] The user enters the required information (such as name, email address, and password) on the account registration screen. The device sends this information to the server, which stores it in a database. The server then presents the user with a questionnaire about their learning style and goals, and collects the results.

[1225] 2. Learning Style and Needs Analysis

[1226] Based on the survey results, the server uses AI models to analyze the user's learning style and needs, for example determining that the user is a visual learner.

[1227] 3. Provision of teaching materials

[1228] The server automatically generates an optimized learning plan based on the analysis results. The generated learning material list is sent to the user's device, which then displays the learning material. For example, if the user selects the "Introduction to Programming" course, the user will be provided with videos and quizzes aimed at beginners.

[1229] 4. Track your learning progress

[1230] As users use the learning materials, their devices record their learning activities and progress. Progress data is sent to a server, which stores it in a database and adaptively updates the learning plan. For example, if a user achieves a high score in a lesson, more challenging learning materials will be recommended as the next step.

[1231] 5. Integrating online and offline learning

[1232] The device caches the next study material and some of the progress data so that users can study offline. Users can study even when offline, and the progress data is synchronized with the server the next time they go online. This allows users to continue studying even in offline environments such as on the train.

[1233] 6. Incorporating an Emotional Engine

[1234] The device uses an emotion engine to recognize emotions in real time from the user's facial expressions, voice, and input data. For example, it can analyze facial expressions and tone of voice using a webcam or microphone.

[1235] 7. Adjusting teaching materials based on emotions

[1236] The server dynamically adjusts the content of the learning materials based on the emotional data recognized by the emotion engine. For example, if the server detects that the user is tired, it will provide less stressful learning materials or interactive content to refresh the user.

[1237] 8. Integrating emotion and learning progress data

[1238] The server integrates the emotion data with the learning progress data to update a comprehensive learning plan, optimizing the user's learning experience and providing emotional feedback.

[1239] Specific examples

[1240] If a user selects the "Introduction to Data Science" course and prefers a visual learning style, the system operates as follows:

[1241] 1. When users first log in, they fill out a profile information and learning style questionnaire.

[1242] 2. The server selects visual teaching materials (e.g., video lectures and infographics) based on the survey information.

[1243] 3. The terminal provides the selected learning materials to the user, who then uses these materials to advance their studies.

[1244] 4. As users watch video lectures and answer quizzes, the device records this information and sends progress data to the server.

[1245] 5. Based on the received data, the server updates the next study plan and provides more appropriate learning materials.

[1246] 6. The device uses an emotion engine to recognize the user's emotions, and if it detects fatigue or a loss of concentration, it will provide light quizzes or mindfulness videos to refresh the user.

[1247] 7. Accordingly, the server integrates the emotional data and learning progress data to adjust the overall learning plan.

[1248] In this way, a system is realized that provides an optimized learning experience by comprehensively taking into account the user's learning style, progress, and emotions.

[1249] The processing flow will be explained below.

[1250] This invention relates to a learning system that analyzes a user's individual information and learning style to provide an optimal learning plan, and further enhances the learning experience by combining an emotion engine. This system is implemented by linking a server and terminals (PCs, tablets, smartphones, etc.).

[1251] Processing Steps

[1252] Step 1:

[1253] The user enters the required information (name, email address, password, etc.) on the account registration screen.

[1254] Step 2:

[1255] The terminal transmits the input information to the server.

[1256] Step 3:

[1257] The server stores the received registration information in a database.

[1258] Step 4:

[1259] The server presents the user with a questionnaire about their learning style and goals, including the skills they want to learn, their areas of interest, and their learning preferences (visual, auditory, etc.).

[1260] Step 5:

[1261] The user responds to the survey.

[1262] Step 6:

[1263] The terminal sends the survey results to the server.

[1264] Step 7:

[1265] The server uses an AI model based on the survey results to analyze the user's learning style and needs. For example, it may determine that the user is a visual learner.

[1266] Step 8:

[1267] Based on the analysis, the server automatically generates an optimal initial learning plan for the user, which includes materials (videos, infographics, etc.) that suit visual learning styles.

[1268] Step 9:

[1269] The server transmits the generated learning plan to the user's terminal.

[1270] Step 10:

[1271] The device displays learning materials to the user based on the learning plan received. For example, if the "Introduction to Programming" course is selected, beginner-friendly videos and quizzes are displayed.

[1272] Step 11:

[1273] Users use the provided learning materials to advance their studies, watching videos and answering quizzes, among other learning activities.

[1274] Step 12:

[1275] The device records the user's learning activity (video viewing time, quiz answer results, etc.).

[1276] Step 13:

[1277] The terminal transmits the recorded learning progress data to the server.

[1278] Step 14:

[1279] The server stores the received progress data in a database and adaptively updates the learning materials for the next step.

[1280] Step 15:

[1281] The device caches the learning materials and some of the progress data needed for the next study session, preparing for offline study.

[1282] Step 16:

[1283] Users can continue learning even when they are offline, for example, while on the train using pre-downloaded learning materials.

[1284] Step 17:

[1285] The learning data acquired while the device was offline is stored locally.

[1286] Step 18:

[1287] When the device regains online connectivity, the learning data recorded while offline is synchronized with the server.

[1288] Step 19:

[1289] The server receives the synchronized data and updates the database. The new progress data is reflected in the learning plan.

[1290] Incorporating an emotion engine

[1291] Step 20:

[1292] The device uses a webcam and microphone to recognize emotions in real time using an emotion engine based on the user's facial expressions, voice, and input data. For example, it can detect happiness, sadness, fatigue, etc. from the user's facial expressions.

[1293] Step 21:

[1294] The server receives the emotion data recognized by the emotion engine and dynamically adjusts the content of the learning materials based on that data. For example, if it recognizes that the user is tired, it will provide less stressful learning materials or interactive content to refresh the user.

[1295] Step 22:

[1296] The emotion data recognized by the emotion engine is integrated with the learning progress data, and the server updates the overall learning plan.

[1297] Specific examples

[1298] If a user selects the "Introduction to Data Science" course and prefers a visual learning style, the system operates as follows:

[1299] 1. When users first log in, they fill out a profile information and learning style questionnaire.

[1300] 2. The server selects visual teaching materials (e.g., video lectures and infographics) based on the survey information.

[1301] 3. The terminal provides the selected learning materials to the user, who then uses these materials to advance their studies.

[1302] 4. As users watch video lectures and answer quizzes, the device records this information and sends progress data to the server.

[1303] 5. Based on the received data, the server updates the next study plan and provides more appropriate learning materials.

[1304] 6. The device uses an emotion engine to recognize the user's emotions, and if it detects fatigue or a loss of concentration, it will provide light quizzes or mindfulness videos to refresh the user.

[1305] 7. Accordingly, the server integrates the emotional data and learning progress data to adjust the overall learning plan.

[1306] In this way, a system is realized that provides an optimized learning experience by comprehensively taking into account the user's learning style, progress, and emotions.

[1307] Example 2

[1308] 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."

[1309] Conventional learning systems have difficulty providing optimal learning materials based on the user's learning style and needs, and are unable to flexibly update learning plans based on learning progress and emotional data. As a result, it is not possible to provide an optimal learning experience for each learner, resulting in problems such as reduced learning efficiency and effectiveness.

[1310] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving individual information of a user as input and analyzing the user's learning style and needs based on the information; means for automatically generating learning materials optimal for the user based on the learning style and needs; means for providing the generated learning materials to the user's terminal; means for tracking the user's learning progress and updating the content of the learning materials according to the progress; means for recording offline learning data and synchronizing it with the server when online; means for recognizing emotions from the user's facial expressions, voice, and input data and dynamically adjusting the content of the learning materials based on the emotions; and means for integrating the emotion data and learning progress data and updating the comprehensive learning plan. This makes it possible to provide a learning experience optimized for each user's individual learning style, progress, and emotional state.

[1311] "User" means a person or organization who uses the learning system and is provided with an optimal learning plan based on their learning style and needs.

[1312] "Personal Information" refers to data unique to a user, including name, email address, password, learning style survey results, etc.

[1313] "Learning style" refers to the method or technique in which a user learns most effectively, and can be visual, auditory, tactile, or other types.

[1314] "Needs" refer to the goals or needs that a user wants to achieve through learning, and specific examples include improving specialized knowledge or acquiring specific skills.

[1315] "Learning materials" refers to content provided to assist users in their learning, and includes video lectures, texts, quizzes, infographics, and the like.

[1316] "Terminal" means a device used by a user to access the system, including a PC, tablet, smartphone, etc.

[1317] A "server" refers to an information processing device that processes and stores various types of data and communicates with user terminals.

[1318] "Study progress" is data that indicates how much progress a user has made in the process of studying, and includes the amount of time spent watching videos and the percentage of correct answers to quizzes.

[1319] "Offline learning data" is data recorded when a user studies without a network connection, and is synchronized with the server the next time the user goes online.

[1320] "Emotion data" is data that indicates the user's emotional state, and is extracted from facial expressions, tone of voice, input data, and the like.

[1321] "Comprehensive Learning Plan" refers to a series of learning activities that are optimized based on the user's learning style, progress data, and emotional data.

[1322] An "emotion engine" refers to a collection of algorithms and software that recognizes a user's emotions and takes necessary action based on them.

[1323] The present invention is a learning system that analyzes a user's individual information and learning style to provide an optimal learning plan, and further enhances the learning experience by combining an emotion engine. This system is implemented by linking a server and a terminal. Below, we will explain embodiments of the present invention, using specific examples to explain what hardware or software is used to perform data processing and data calculations.

[1324] This system consists of multiple steps. First, the user accesses the system using a device (PC, tablet, smartphone). When registering for the first time, the user enters the required information (name, email address, password, etc.), which the device sends to the server. The server validates the data and stores it in a database (e.g., MySQL). The server then provides a questionnaire regarding learning style and goals, which the user fills out. The survey results are then sent to the server via the device.

[1325] The server inputs the collected survey results into an AI model (e.g., a model built with TensorFlow or PyTorch) to analyze the user's learning style and needs. Based on the analysis results, the server automatically generates an optimized learning plan and sends this plan in JSON format to the device. The device then analyzes the received data and displays it in a user interface.

[1326] As a user progresses through their studies using learning materials (e.g., video lectures, textbooks, quizzes), the device records their activity log and periodically sends it to the server. The server analyzes the received data and dynamically updates the next learning plan based on the user's progress. Furthermore, the device has the ability to record learning data even when offline and synchronize it with the server the next time it is online.

[1327] Additionally, the system incorporates an emotion engine. The device uses a webcam and microphone to capture the user's facial and voice data, which is then analyzed in real time using emotion recognition algorithms (e.g., OpenCV and DeepEmotion). The emotion data is sent to a server, which analyzes it using an AI model to determine the user's current emotional state. Depending on the emotion data, the server provides low-impact educational materials and refresher content.

[1328] The server integrates emotional data and learning progress data and updates a comprehensive learning plan to provide a learning experience optimized for each user's individual learning style, progress, and emotional state.

[1329] Specific examples

[1330] If a user selects the "Introduction to Data Science" course and prefers a visual learning style, the system operates as follows:

[1331] 1. When users log in for the first time, they fill out their profile information and a learning style questionnaire.

[1332] 2. The server selects visual teaching materials (e.g., video lectures and infographics) based on the survey information.

[1333] 3. The terminal provides the selected learning materials to the user, who then uses these materials to advance their studies.

[1334] 4. As users watch video lectures and answer quizzes, the device records this information and sends progress data to the server.

[1335] 5. The server updates the next study plan based on the received data and provides the corresponding study materials.

[1336] 6. The device uses an emotion engine to recognize the user's emotions, and if it detects fatigue or a loss of concentration, it will provide light quizzes or mindfulness videos to refresh the user.

[1337] 7. The server integrates the emotional data and learning progress data to tailor a comprehensive learning plan.

[1338] Prompt Sentence Examples

[1339] "How can I create an AI model that generates the best learning material list for a user who is a visual learner?"

[1340] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1341] Step 1: First-time registration process

[1342] 1.1 The user enters the required information such as name, email address, and password on the account registration screen.

[1343] Input: Name, Email Address, Password

[1344] Output: User input data

[1345] 1.2 The device converts this information into JSON format and sends it to the server using the HTTPS protocol.

[1346] Input: User-entered data

[1347] Output: User data in JSON format

[1348] 1.3 The server validates the received information (e.g., checks the email address format, password strength) and stores it in a database (e.g., MySQL).

[1349] Input: User data in JSON format

[1350] Output: Save results to database

[1351] 1.4 The server generates questionnaire data regarding learning styles and learning objectives and sends it to the terminal.

[1352] Input: None

[1353] Output: Questionnaire form data

[1354] Step 2: Analyze your learning styles and needs

[1355] 2.1 The user fills out the questionnaire form regarding their learning style and purpose and clicks the submit button.

[1356] Input: User learning style and goals

[1357] Output: Survey results

[1358] 2.2 The terminal converts the survey results into JSON format and sends them to the server.

[1359] Input: Survey results

[1360] Output: Survey data in JSON format

[1361] 2.3 The server inputs the received survey results into an AI model (e.g., built with TensorFlow and PyTorch) to analyze learning styles and needs.

[1362] Input: Survey data in JSON format

[1363] Output: Analysis results (user learning styles and needs)

[1364] 2.4 The server stores the analysis results in a database for further processing.

[1365] Input: Analysis results

[1366] Output: Save results to database

[1367] Step 3: Providing educational materials

[1368] 3.1 The server automatically generates an optimized learning plan based on the analysis results, such as a list of appropriate video lectures and quizzes.

[1369] Input: Analysis results

[1370] Output: Generated teaching material list

[1371] 3.2 The server sends the generated teaching material list to the terminal in JSON format.

[1372] Input: Generated teaching material list

[1373] Output: JSON formatted learning material list

[1374] 3.3 The device analyzes the received data and displays it on the user interface. Specifically, it dynamically generates thumbnail images of the video lectures and quiz links.

[1375] Input: JSON format teaching material list

[1376] Output: A list of teaching materials displayed on the user interface

[1377] Step 4: Track your progress

[1378] 4.1 The user uses the provided learning materials (e.g., video lectures, quizzes) to advance their learning.

[1379] Input: Usage of teaching materials

[1380] Output: Learning progress information

[1381] 4.2 The device records the user's learning activities and progress data, e.g., video viewing time, quiz correct answer rate.

[1382] Input: Learning progress information

[1383] Output: Recorded progress data

[1384] 4.3 The device periodically converts the progress data into JSON format and sends it to the server.

[1385] Input: Recorded progress data

[1386] Output: Progress data in JSON format

[1387] 4.4 The server analyzes the received data and dynamically updates the next study plan. For example, if a high score is obtained, more difficult study materials will be recommended.

[1388] Input: Progress data in JSON format

[1389] Output: Updated learning plan

[1390] Step 5: Integrating online and offline learning

[1391] 5.1 The device will cache some of the learning materials and progress data for the next time you use it in local storage.

[1392] Input: Updated learning plan and progress data

[1393] Output: Data saved in local storage

[1394] 5.2 Users can continue learning using cached data even when they do not have a network connection.

[1395] Input: Data stored in local storage

[1396] Output: Offline learning progress

[1397] 5.3 The next time your device goes online, it will sync its locally stored progress data with the server.

[1398] Input: Offline learning progress

[1399] Output: Data synchronized with the server

[1400] Step 6: Incorporating the Emotion Engine

[1401] 6.1 The device uses a webcam and microphone to capture the user's facial expressions and voice data.

[1402] Input: facial expression data and voice data

[1403] Output: Retrieved data

[1404] 6.2 The device uses emotion recognition algorithms (e.g., OpenCV, DeepEmotion) to analyze facial expressions and tone of voice in real time.

[1405] Input: Retrieved data

[1406] Output: Emotion data

[1407] 6.3 The terminal converts the analysis results into JSON format and sends them to the server.

[1408] Input: Emotion data

[1409] Output: Emotion data in JSON format

[1410] Step 7: Emotionally adjust your materials

[1411] 7.1 The server inputs the received emotion data into the AI ​​model to determine the user's current emotional state.

[1412] Input: Emotion data in JSON format

[1413] Output: Emotion determination result

[1414] 7.2 The server dynamically adjusts the content of educational materials provided based on emotional data. For example, if fatigue is detected, it will provide refreshing light quizzes or mindfulness videos.

[1415] Input: Emotion determination result

[1416] Output: Dynamically adjusted teaching content

[1417] Step 8: Integrating sentiment and learning progress data

[1418] 8.1 The server integrates the emotion data and learning progress data to update the overall learning plan.

[1419] Input: Emotion judgment results and learning progress data

[1420] Output: Consolidated learning plan

[1421] 8.2 The server sends the updated learning plan to the device and provides it to the user.

[1422] Input: Integrated Learning Plan

[1423] Output: Provided to the user

[1424] (Application example 2)

[1425] 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."

[1426] Previous learning and training systems lacked the ability to provide optimal learning plans based on individual user and robot information. They also lacked the ability to monitor learning progress and performance decline in real time and dynamically adjust learning plans accordingly. Furthermore, there were issues with recording data offline and synchronizing it with the online environment.

[1427] 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.

[1428] In this invention, the server includes means for receiving individual user information as input and analyzing the user's learning style and needs based thereon, means for automatically generating learning materials optimal for the user, means for providing the generated learning materials to the user's terminal, means for tracking the user's learning progress and updating the content of the learning materials according to the progress, means for recording offline learning data and synchronizing it with the server when online, means for receiving robot identification information as input and generating an appropriate training plan based thereon, means for analyzing the robot's motion data and monitoring performance in real time using an emotion engine, and means for instructing a refresher task when performance degradation is detected. This makes it possible to provide an optimal learning plan based on the individual user and robot information, monitor performance in real time, and effectively manage data even in an offline environment.

[1429] "Personal information" is information that includes a user's or robot's identity, personal characteristics, learning style, and needs.

[1430] "Learning style" is information that indicates a user's learning method and preferences, and includes visual, auditory, tactile, and other methods.

[1431] "Needs" refers to information that indicates the requirements or objectives that users have in learning or training.

[1432] "Instructional Materials" includes educational resources, such as videos, quizzes, infographics, etc., tailored to a user's learning style and needs.

[1433] A "terminal" is a device used by a user or robot to receive learning materials, and includes a PC, tablet, smartphone, etc.

[1434] The "server" is a central computer that manages the entire learning system and processes, records, and analyzes individual information, progress data, emotional data, etc. of users or robots.

[1435] "Study progress" is data indicating the progress of the user's learning activities using learning materials.

[1436] An "emotion engine" is software that analyzes the facial expressions, voice, and movement data of a user or robot to recognize their emotional state in real time.

[1437] A "training plan" is a plan of optimal work procedures and training content provided to a robot, and it changes adaptively.

[1438] "Performance" is an evaluation index that indicates the efficiency and accuracy with which a robot performs a specific task.

[1439] "Real-time monitoring" means reading learning progress and performance on the spot and analyzing them immediately.

[1440] "Offline training data" means training data collected and recorded when a user or robot does not have an internet connection.

[1441] "Synchronizing to server when online" is the process by which data recorded offline is sent to the server and stored in the database when you are connected to the Internet again.

[1442] A "refreshment task" is a task that instructs the robot to do light work or take a break to reduce stress when a decline in its performance is detected.

[1443] The present invention relates to a system that analyzes individual user information and robot identification information, and provides optimal learning and training plans based on the analysis. Specific embodiments are described below.

[1444] 1. Initial Setup and Registration

[1445] The server first receives input from the user or robot's personal information. For users, this includes name, email address, password, and information about learning style. For robots, this includes identification information and training objectives. This information is sent to the server and stored in a database.

[1446] 2. Learning Style and Needs Analysis

[1447] The server uses a generative AI model to analyze a user's learning style and needs based on the survey results and input data. For example, if the user is a visual learner, visual learning materials will be selected. Similarly, for robots, training content tailored to their characteristics will be generated.

[1448] 3. Providing educational materials or training plans

[1449] The server automatically generates optimal learning materials or training plans based on the analysis results, which are then sent to the user's or robot's device and displayed in the form of videos, quizzes, infographics, and more.

[1450] 4. Track your progress

[1451] As the user or robot progresses in their learning or training, the device records their progress data. This progress data is sent to the server and stored in a database. The server then adaptively updates the next learning or training plan based on the progress data.

[1452] 5. Integrating offline learning

[1453] The device has the ability to cache data so that learning and training can be done offline. A user or robot can record their progress in an offline environment and sync it with the server when they come online.

[1454] 6. Leveraging Emotional Engines

[1455] The device uses an emotion engine to recognize the emotional state of the user or robot in real time, using a webcam and microphone to analyze facial expressions and voice to detect signs of stress or fatigue.

[1456] 7. Adjusting the movement

[1457] If the emotion engine detects a decline in performance, the server will instruct the user or robot to perform a refreshing task or light work, allowing them to continue learning or training without unnecessary stress.

[1458] Specific examples

[1459] Example of a training system for factory robots:

[1460] Imagine a factory robot learning a new assembly task. The robot's identity and task characteristics are sent to a server, which generates an appropriate training plan. The robot works on the task, collecting behavioral data along the way. If the emotion engine detects signs of stress, it will recommend lighter work or a break.

[1461] Example prompt sentence:

[1462] "If the robot's performance drops on the current task, what's the next appropriate training task to offer it?"

[1463] "If the robot assembles the parts with 75% accuracy, what is the next task it should move on to?"

[1464] As described above, this invention provides optimal learning and training plans based on individual information about the user and the robot, and monitors and adjusts progress and emotional state in real time, thereby achieving an effective learning experience.

[1465] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1466] Step 1:

[1467] The server receives the user's or robot's personal information as input. In the case of a user, this includes name, email address, password, and information about learning style. In the case of a robot, identification information and training goals are entered. The data is sent to the server and stored in a database. The input is the user's or robot's information, and the output is the personal information stored in the database.

[1468] Step 2:

[1469] The server uses a generative AI model to analyze the user's learning style and needs based on the received personalized information. This process determines whether the user is a visual learner and what the robot's working characteristics are. The input is personalized information, and the output is the analysis results.

[1470] Step 3:

[1471] The server automatically generates optimal teaching materials or training plans based on the analysis results. Appropriate teaching materials are selected, and the generated teaching materials or plans are sent to the user's or robot's terminal. The input is the analysis results, and the output is the generated teaching materials or training plans.

[1472] Step 4:

[1473] The user or robot learns or works according to the learning materials or training plan. The terminal records the progress data and sends it to the server. The input is the user's or robot's learning and work data, and the output is the recorded and sent progress data.

[1474] Step 5:

[1475] The server stores the received progress data in a database and adaptively updates the next learning or training plan. The input is the progress data, and the output is the updated learning or training plan.

[1476] Step 6:

[1477] The device caches the necessary data so that you can continue studying or working offline. It records your progress data even when offline and synchronizes it with the server when you come online. The input is your study plan and progress data, and the output is the cached and synchronized data.

[1478] Step 7:

[1479] The device uses an emotion engine to recognize the emotional state of the user or robot in real time. It uses a webcam and microphone to analyze facial expressions and voice to detect signs of stress or fatigue. The input is the user or robot's behavior data, and the output is analyzed emotional data.

[1480] Step 8:

[1481] Based on the data analyzed by the emotion engine, the server instructs users to perform refreshing tasks or light work when a decline in performance is detected. The input is emotional data, and the output is the instructed refreshing task.

[1482] 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.

[1483] 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.

[1484] 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.

[1485] [Fourth embodiment]

[1486] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1487] 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.

[1488] 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).

[1489] 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.

[1490] 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.

[1491] 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).

[1492] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1493] 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.

[1494] 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.

[1495] 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.

[1496] 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.

[1497] 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.

[1498] 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."

[1499] The present invention relates to a learning system that analyzes a user's learning style based on their individual information and provides an optimal learning plan. This system is implemented by linking a server and terminals (PCs, tablets, smartphones, etc.).

[1500] 1. Initial registration process

[1501] The user enters the required information (such as name, email address, and password) on the account registration screen. The device sends this information to the server, which stores it in a database. The server then presents the user with a questionnaire about their learning style and goals, and collects the results.

[1502] 2. Learning Style and Needs Analysis

[1503] Based on the survey results, the server uses AI models to analyze the user's learning style and needs. For example, if the user is determined to be a visual learner, visual learning materials will be selected.

[1504] 3. Provision of teaching materials

[1505] The server automatically generates an optimized learning plan based on the analysis results. The generated learning material list is sent to the user's device, which then displays the learning material. For example, if the user selects the "Introduction to Programming" course, the user will be provided with videos and quizzes aimed at beginners.

[1506] 4. Track your learning progress

[1507] As users use the learning materials, their devices record their learning activities and progress. Progress data is sent to a server, which stores it in a database and adaptively updates the learning plan. For example, if a user achieves a high score in a lesson, more challenging learning materials will be recommended as the next step.

[1508] 5. Integrating online and offline learning

[1509] The device caches the next study material and some of the progress data so that users can study offline. Users can study even when offline, and the progress data is synchronized with the server the next time they go online. This allows users to continue studying even in offline environments such as on the train.

[1510] Specific examples

[1511] If a user selects the "Introduction to Data Science" course and prefers a visual learning style, the system operates as follows:

[1512] 1. When users first log in, they fill out a profile information and learning style questionnaire.

[1513] 2. The server selects visual teaching materials (e.g., video lectures and infographics) based on the survey information.

[1514] 3. The terminal provides the selected learning materials to the user, who then uses these materials to advance their studies.

[1515] 4. As users watch video lectures and answer quizzes, the device records this information and sends progress data to the server.

[1516] 5. Based on the received data, the server updates the next study plan and provides more appropriate learning materials.

[1517] This provides a flexible learning environment that suits the user's learning style and progress, maximizing learning efficiency and effectiveness.

[1518] The processing flow will be explained below.

[1519] Step 1:

[1520] The user enters the required information (name, email address, password, etc.) on the account registration screen.

[1521] Step 2:

[1522] The terminal transmits the input information to the server.

[1523] Step 3:

[1524] The server stores the received registration information in a database.

[1525] Step 4:

[1526] The server presents the user with a questionnaire about their learning style and goals, including the skills they want to learn, their areas of interest, and their learning preferences (visual, auditory, etc.).

[1527] Step 5:

[1528] The user responds to the survey.

[1529] Step 6:

[1530] The terminal sends the survey results to the server.

[1531] Step 7:

[1532] The server uses an AI model based on the survey results to analyze the user's learning style and needs. For example, it may determine that the user is a visual learner.

[1533] Step 8:

[1534] Based on the analysis, the server automatically generates an optimal initial learning plan for the user, which includes materials (videos, infographics, etc.) that suit visual learning styles.

[1535] Step 9:

[1536] The server transmits the generated learning plan to the user's terminal.

[1537] Step 10:

[1538] The device displays learning materials to the user based on the learning plan received. For example, if the "Introduction to Programming" course is selected, beginner-friendly videos and quizzes are displayed.

[1539] Step 11:

[1540] Users use the provided learning materials to advance their studies, watching videos and answering quizzes, among other learning activities.

[1541] Step 12:

[1542] The device records the user's learning activity (video viewing time, quiz answer results, etc.).

[1543] Step 13:

[1544] The terminal transmits the recorded learning progress data to the server.

[1545] Step 14:

[1546] The server stores the received progress data in a database and adaptively updates the next step of the learning material. For example, if the user answers a quiz correctly, the next learning material with a higher level of difficulty is selected.

[1547] Step 15:

[1548] The device caches the learning materials and some of the progress data needed for the next study session, preparing for offline study.

[1549] Step 16:

[1550] Users can continue learning even when they are offline, for example, while on the train using pre-downloaded learning materials.

[1551] Step 17:

[1552] The device stores offline learning data locally.

[1553] Step 18:

[1554] When the device regains online connectivity, the learning data from the offline period is synchronized with the server.

[1555] Step 19:

[1556] The server receives the synchronized data and updates the database. The new progress data is reflected in the learning plan.

[1557] This will result in a system that provides a flexible and effective learning environment that meets the diverse learning styles and needs of users.

[1558] Example 1

[1559] 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."

[1560] Conventional learning systems have struggled to provide flexible educational materials that adapt to users' learning styles and needs. They also struggled to effectively integrate online and offline learning data, resulting in reduced learning efficiency. Furthermore, they were unable to track users' learning progress in real time and provide optimal learning materials accordingly.

[1561] 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.

[1562] In this invention, the server includes: means for receiving a user's individual information as input and analyzing the user's learning style and needs based on the information; means for automatically generating educational materials optimal for the user based on the user's learning style and needs; means for providing the generated educational materials to the user's information processing device; means for tracking the user's learning progress and updating the content of the educational materials according to the progress; means for recording offline learning data and synchronizing it with the central processing device when online; means for analyzing the user's learning style and needs using a generative AI model based on questionnaire results; and means for caching the generated educational materials and progress data in local storage. This enables the provision of educational materials adapted to the user's learning style and the integration of online and offline learning data. Furthermore, the system can track the user's learning progress in real time and continuously provide appropriate learning materials.

[1563] definition statement

[1564] "User" refers to an individual who uses the system to learn.

[1565] "Individual information" refers to information about a user, such as their name, email address, password, learning style, and learning objectives.

[1566] "Learning style" refers to a particular learning method or teaching technique that a user prefers, such as visual, auditory, or tactile learning.

[1567] "Needs" refers to the user's learning goals and requirements, and the range of skills and knowledge required.

[1568] "Educational Materials" means educational materials and resources provided for User learning purposes, including videos, texts, quizzes, infographics, etc.

[1569] "Information processing device" refers to a device that allows a user to use the system. Specifically, it includes PCs, tablets, smartphones, etc.

[1570] "Progress Data" refers to the learning progress recorded as the user progresses through the course of their studies. Specifically, this includes the status of viewing of learning materials and quiz results.

[1571] "Central Processing Unit" refers to the computer server that forms the core of the system. It processes, stores, and manages user data.

[1572] A "generative AI model" refers to a mathematical model that uses artificial intelligence technology to analyze user data and make predictions and optimizations.

[1573] "Local storage" refers to memory or storage areas used to temporarily store data within an information processing device. Specifically, this includes hard disk drives and solid-state drives.

[1574] "Caching" refers to temporarily storing data to improve system efficiency, which increases the speed at which data can be loaded.

[1575] "Survey Results" refers to survey data regarding learning styles and needs provided by users.

[1576] "Synchronizing" refers to matching data across different devices or systems, specifically including sending data recorded offline to a central processing unit and matching it when the device is back online.

[1577] MODE FOR CARRYING OUT THE INVENTION

[1578] The present invention relates to a learning system that analyzes a user's learning style based on their individual information and provides an optimal learning plan. This system is implemented by linking a server and terminals (PCs, tablets, smartphones, etc.).

[1579] First, the user enters the required information, such as name, email address, and password, on the account registration screen. The device sends this information to the server, which then stores it in a database. This registers the user's basic information in the system. The server then presents the user with a questionnaire about their learning style and goals, and by collecting the results, the server can understand the user's learning needs.

[1580] The server then uses a generative AI model based on the survey results to analyze the user's learning style and needs. For example, if the server determines that the user is a visual learner, it will select visual learning materials. This process uses general artificial intelligence techniques (such as Python's scikit-learn and TensorFlow).

[1581] The server then automatically generates an optimized learning plan based on the analysis results and sends the generated learning material list to the user's device. The device then provides the received learning materials to the user, allowing the user to proceed with their learning. For example, if the "Introduction to Programming" course is selected, the user will be provided with videos and quizzes aimed at beginners.

[1582] Furthermore, as the user progresses through the learning materials, progress data is recorded on the device. The device then sends the progress data to the server, which stores it in a database. The server then adaptively updates the learning plan based on the progress data and may recommend more challenging learning materials as the next step. For example, if the user achieves a high score in a lesson, the next most challenging learning material will be recommended.

[1583] The device also caches the next learning material and some of the progress data so that users can continue learning even when they are offline, such as on a train. The learning data obtained while offline is synchronized with the server the next time the device is online. This allows for the integration of online and offline learning data.

[1584] As a concrete example, if a user selects the "Introduction to Data Science" course and prefers a visual learning style, the system operates as follows: When the user logs in for the first time, they fill out a questionnaire about their profile information and learning style, and the server selects visual learning materials (e.g., video lectures and infographics) based on the questionnaire information. The device provides the selected learning materials, and the user uses them to progress with their studies. As the user watches video lectures and answers quizzes, the device records the information and sends progress data to the server. The server updates the next learning plan based on the received data and provides more appropriate learning materials.

[1585] Examples of prompts include:

[1586] "How can I deliver video lectures and infographics for my introductory data science course? Users indicated in our survey that they prefer a visual learning style."

[1587] This system provides a flexible learning environment that adapts to the user's learning style and progress, maximizing learning efficiency and effectiveness.

[1588] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1589] Specific steps of the program's processing

[1590] Step 1:

[1591] A user enters their name, email address, and password on the account registration screen.

[1592] Input: Name, Email Address, Password

[1593] Specific operations: Enter information into the form using a browser or dedicated app and press the confirmation button.

[1594] Step 2:

[1595] The terminal transmits the input information to the server.

[1596] Input: Name, Email Address, Password

[1597] Output: User information sent to the server

[1598] Specific operation: When the submit button of the form is pressed, the device sends the data to the server using an HTTP POST request.

[1599] Step 3:

[1600] The server stores the received information in a database.

[1601] Input: Submitted user information

[1602] Output: User information stored in the database

[1603] Specific operation: The server analyzes the received data, generates an SQL query, and saves the information in the database using an INSERT statement.

[1604] Step 4:

[1605] The server presents users with a questionnaire about their learning style and goals and collects the results.

[1606] Input: User information (name, email address, password)

[1607] Output: Collected survey results

[1608] Specific operation: The server generates a questionnaire form in HTML format and sends it to the user's device. The user answers the questionnaire, and the answers are sent back to the server.

[1609] Step 5:

[1610] The server uses a generative AI model based on the survey results to analyze the user's learning style and needs.

[1611] Input: Survey results

[1612] Output: Classification of user learning styles and needs

[1613] How it works: The server inputs the collected survey data into an AI model (using, for example, Python's scikit-learn or TensorFlow) to classify and predict the user's learning style and needs.

[1614] Step 6:

[1615] The server automatically generates an optimized learning plan based on the analysis results.

[1616] Input: Learning style and needs classification results

[1617] Output: Optimized study plan

[1618] Specific operation: The server generates a list of teaching materials based on the results of the AI ​​model and the user's needs.

[1619] Step 7:

[1620] The server transmits the generated teaching material list to the user's terminal.

[1621] Input: Optimized Study Plan

[1622] Output: List of teaching materials sent to the device

[1623] Specific operation: Generate a list of teaching materials in JSON format and send it to the terminal as an HTTP response.

[1624] Step 8:

[1625] The terminal provides the received educational material to the user.

[1626] Input: List of submitted teaching materials

[1627] Output: The teaching material displayed on the user interface

[1628] Specific operation: Analyzes the received JSON data and displays a list of learning materials in the user interface. When the user clicks, learning materials (such as video playback or quiz screens) are displayed.

[1629] Step 9:

[1630] As the user progresses with their learning using the learning materials, the terminal records their progress data.

[1631] Input: User learning activity

[1632] Output: Recorded progress data

[1633] Specific operation: Record the completion of video viewing and quiz answer results in local storage or temporary memory.

[1634] Step 10:

[1635] The terminal transmits the progress data to the server.

[1636] Input: Recorded progress data

[1637] Output: Progress data sent to the server

[1638] Specific behavior: Uploads progress data to the server via HTTP POST requests periodically or for each event.

[1639] Step 11:

[1640] The server adaptively updates the next learning plan based on the received data.

[1641] Input: Received progress data

[1642] Output: Updated learning plan

[1643] What happens: The server saves the new progress data to a database and re-runs the AI ​​model to adjust the next learning plan.

[1644] Step 12:

[1645] The device caches the next study material and some of the progress data so you can study offline.

[1646] Input: Next study material and progress data

[1647] Output: Cached data

[1648] What it does: Saves your next learning material and progress data to local storage or device cache.

[1649] Step 13:

[1650] Users can study offline and their progress will be synced to the server the next time they are online.

[1651] Input: Offline training data

[1652] Output: Progress data synced to the server

[1653] Specific operation: Operation records and progress data are saved locally when offline, and then sent to the server in bulk when the device is online again.

[1654] (Application example 1)

[1655] 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."

[1656] There is a need for a method that allows robot operators to receive training efficiently and be provided with learning materials that best suit their learning style. However, conventional training systems provide uniform learning materials to all users, which makes it difficult to adapt to individual learning styles. Furthermore, they lack the ability to flexibly update learning materials according to progress. This often results in the robot operators' learning efficiency and effectiveness not being maximized, resulting in wasted time and effort. Therefore, a system is needed that analyzes the learning style of each user based on their individual information and provides optimal learning materials.

[1657] 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.

[1658] In this invention, the server includes means for receiving individual user information as input and analyzing the user's learning style and needs based on the information, means for automatically generating learning materials optimal for the user based on the learning style and needs, and means for providing the generated learning materials to the user's terminal, thereby enabling the robot operator to receive learning materials optimal for his or her own learning style and maximizing learning efficiency and effectiveness.

[1659] "User's personal information" refers to information that can be used to identify a specific individual, including the learner's name, email address, password, and survey results regarding learning style and purpose.

[1660] A "learning style" refers to the method or tendency in which a particular learner learns most effectively, such as visual learning or hands-on learning.

[1661] "Demand" refers to the goals that learners want to achieve through their studies and the knowledge and skills they need.

[1662] A "generative AI model" is an artificial intelligence model used to analyze a user's learning style and needs based on learning data and determine the most appropriate learning materials.

[1663] "Teaching materials" refer to materials and content provided to learners for learning activities, including, for example, video lectures and simulators.

[1664] A "terminal" is a device such as a smartphone, tablet, or PC that displays the learning materials provided by the system and allows users to progress through their studies.

[1665] "Offline" refers to a state where there is no internet connection, such as when a learner is studying on a train.

[1666] "Synchronization" refers to the process of sending learning data created offline to the server when the device is back online and matching it.

[1667] A "robot operator" is a technician who receives training to operate and manage robots in factories and facilities and perform their work efficiently.

[1668] "Visual aids" are materials that convey information visually, such as videos, infographics, and illustrations.

[1669] "Practical teaching materials" are teaching materials that primarily involve hands-on operation and work, and include simulators and on-the-job training.

[1670] This invention shows a specific method for realizing a system that analyzes a user's learning style based on their individual information and provides an optimal learning plan. The overall configuration of the system and the operation of each component are explained in detail below.

[1671] The server receives individual user information as input and has a means to analyze the user's learning style and needs based on that information. To achieve this, it uses a generative AI model. The server uses a machine learning framework such as TensorFlow to analyze survey results and past learning data to detect the user's learning style. For example, it determines whether the user is a visual learner or a hands-on learner.

[1672] The server also has a means for automatically generating optimal learning materials for the user based on the learning style and demands. This means selects visual learning materials (video lectures, infographics, etc.) or practical learning materials (simulators, on-the-job training, etc.) to provide learning materials that best suit the user's learning style. The learning materials are stored in a database as preprocessed content.

[1673] Next, the server has a means for providing the generated learning materials to the user's terminal. The learning materials are displayed on the user's terminal (such as a smartphone or tablet). The user begins learning using the selected learning materials.

[1674] The user's learning progress is recorded by the device, and the progress data is sent to the server as appropriate. The server adaptively updates the learning plan based on the received progress data and provides the most appropriate learning materials for the next step. In this way, a flexible learning environment tailored to each individual user is realized.

[1675] Furthermore, the device has a means for recording offline learning data and synchronizing it with the server when the device is online, allowing users to continue learning even in an offline environment, and progress data is kept on the server without being lost.

[1676] As a concrete example, consider the case where robot operator A is using the app for the first time. First, A registers an account by entering their name, email address, and password. After that, A answers a questionnaire about their learning style and needs. The server analyzes this using a generative AI model and determines that A is a visual learner. The server selects the most suitable learning materials for A, a "robot operation video" and an "illustrated manual," and provides them to the device. As A uses these materials to progress with their learning, the device records their progress and syncs it with the server.

[1677] An example of a prompt sentence would be, "I am robot operator A. My learning style is visual. Please provide me with the most effective training materials." This would allow the system to select and provide the most appropriate materials.

[1678] This invention makes it possible to provide optimal learning materials based on individual user information, thereby maximizing learning efficiency and effectiveness.

[1679] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1680] Step 1:

[1681] The server receives the user's individual information as input. The user enters their name, email address, password, etc. into the account registration screen and sends this information from their device to the server. The server stores the received individual information in a database.

[1682] Input: User's personal information (name, email address, password, etc.)

[1683] Output: User information stored in the database

[1684] Step 2:

[1685] The server presents the user with a questionnaire about their learning style and needs. The user answers the questionnaire and sends the results from their device to the server. The server receives the questionnaire results and analyzes the user's learning style and needs using a generative AI model.

[1686] Input: User survey results

[1687] Output: Learning styles and demands as analyzed results

[1688] Step 3:

[1689] The server automatically generates the most suitable learning materials for the user based on the analysis results. The learning materials can be selected from videos and infographics for visual learning, or simulators and on-the-job training materials for practical learning. The selected learning materials are retrieved from the database.

[1690] Input: Learning Style and Demand Analysis Results

[1691] Output: The best learning material for the user

[1692] Step 4:

[1693] The server provides the selected learning materials to the user's device, and the user uses the materials provided through the device for learning. The content of the learning materials is displayed on the device in the form of videos, infographics, practical simulators, etc.

[1694] Input: Selected teaching materials

[1695] Output: Teaching materials displayed on the device

[1696] Step 5:

[1697] The device records the user's learning progress. As the user progresses through the learning activity, progress data is generated. The device transmits this progress data to the server.

[1698] Input: User learning activity

[1699] Output: Recorded learning progress data

[1700] Step 6:

[1701] The server adaptively updates the learning plan based on the received learning progress data. Based on the progress data, the next most suitable learning material to be provided is reselected. For example, if the user achieves a high score, the next most difficult learning material will be selected.

[1702] Input: Learning progress data

[1703] Output: Updated learning plan

[1704] Step 7:

[1705] The device records learning data while offline and synchronizes it with the server the next time it goes online, allowing users to continue learning even in offline environments.

[1706] Input: Offline training data

[1707] Output: Training data synchronized while online

[1708] 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.

[1709] This invention relates to a learning system that analyzes a user's individual information and learning style to provide an optimal learning plan, and further enhances the learning experience by combining an emotion engine. This system is implemented by linking a server and terminals (PCs, tablets, smartphones, etc.).

[1710] 1. Initial registration process

[1711] The user enters the required information (such as name, email address, and password) on the account registration screen. The device sends this information to the server, which stores it in a database. The server then presents the user with a questionnaire about their learning style and goals, and collects the results.

[1712] 2. Learning Style and Needs Analysis

[1713] Based on the survey results, the server uses AI models to analyze the user's learning style and needs, for example determining that the user is a visual learner.

[1714] 3. Provision of teaching materials

[1715] The server automatically generates an optimized learning plan based on the analysis results. The generated learning material list is sent to the user's device, which then displays the learning material. For example, if the user selects the "Introduction to Programming" course, the user will be provided with videos and quizzes aimed at beginners.

[1716] 4. Track your learning progress

[1717] As users use the learning materials, their devices record their learning activities and progress. Progress data is sent to a server, which stores it in a database and adaptively updates the learning plan. For example, if a user achieves a high score in a lesson, more challenging learning materials will be recommended as the next step.

[1718] 5. Integrating online and offline learning

[1719] The device caches the next study material and some of the progress data so that users can study offline. Users can study even when offline, and the progress data is synchronized with the server the next time they go online. This allows users to continue studying even in offline environments such as on the train.

[1720] 6. Incorporating an Emotional Engine

[1721] The device uses an emotion engine to recognize emotions in real time from the user's facial expressions, voice, and input data. For example, it can analyze facial expressions and tone of voice using a webcam or microphone.

[1722] 7. Adjusting teaching materials based on emotions

[1723] The server dynamically adjusts the content of the learning materials based on the emotional data recognized by the emotion engine. For example, if the server detects that the user is tired, it will provide less stressful learning materials or interactive content to refresh the user.

[1724] 8. Integrating emotion and learning progress data

[1725] The server integrates the emotion data with the learning progress data to update a comprehensive learning plan, optimizing the user's learning experience and providing emotional feedback.

[1726] Specific examples

[1727] If a user selects the "Introduction to Data Science" course and prefers a visual learning style, the system operates as follows:

[1728] 1. When users first log in, they fill out a profile information and learning style questionnaire.

[1729] 2. The server selects visual teaching materials (e.g., video lectures and infographics) based on the survey information.

[1730] 3. The terminal provides the selected learning materials to the user, who then uses these materials to advance their studies.

[1731] 4. As users watch video lectures and answer quizzes, the device records this information and sends progress data to the server.

[1732] 5. Based on the received data, the server updates the next study plan and provides more appropriate learning materials.

[1733] 6. The device uses an emotion engine to recognize the user's emotions, and if it detects fatigue or a loss of concentration, it will provide light quizzes or mindfulness videos to refresh the user.

[1734] 7. Accordingly, the server integrates the emotional data and learning progress data to adjust the overall learning plan.

[1735] In this way, a system is realized that provides an optimized learning experience by comprehensively taking into account the user's learning style, progress, and emotions.

[1736] The processing flow will be explained below.

[1737] This invention relates to a learning system that analyzes a user's individual information and learning style to provide an optimal learning plan, and further enhances the learning experience by combining an emotion engine. This system is implemented by linking a server and terminals (PCs, tablets, smartphones, etc.).

[1738] Processing Steps

[1739] Step 1:

[1740] The user enters the required information (name, email address, password, etc.) on the account registration screen.

[1741] Step 2:

[1742] The terminal transmits the input information to the server.

[1743] Step 3:

[1744] The server stores the received registration information in a database.

[1745] Step 4:

[1746] The server presents the user with a questionnaire about their learning style and goals, including the skills they want to learn, their areas of interest, and their learning preferences (visual, auditory, etc.).

[1747] Step 5:

[1748] The user responds to the survey.

[1749] Step 6:

[1750] The terminal sends the survey results to the server.

[1751] Step 7:

[1752] The server uses an AI model based on the survey results to analyze the user's learning style and needs. For example, it may determine that the user is a visual learner.

[1753] Step 8:

[1754] Based on the analysis, the server automatically generates an optimal initial learning plan for the user, which includes materials (videos, infographics, etc.) that suit visual learning styles.

[1755] Step 9:

[1756] The server transmits the generated learning plan to the user's terminal.

[1757] Step 10:

[1758] The device displays learning materials to the user based on the learning plan received. For example, if the "Introduction to Programming" course is selected, beginner-friendly videos and quizzes are displayed.

[1759] Step 11:

[1760] Users use the provided learning materials to advance their studies, watching videos and answering quizzes, among other learning activities.

[1761] Step 12:

[1762] The device records the user's learning activity (video viewing time, quiz answer results, etc.).

[1763] Step 13:

[1764] The terminal transmits the recorded learning progress data to the server.

[1765] Step 14:

[1766] The server stores the received progress data in a database and adaptively updates the learning materials for the next step.

[1767] Step 15:

[1768] The device caches the learning materials and some of the progress data needed for the next study session, preparing for offline study.

[1769] Step 16:

[1770] Users can continue learning even when they are offline, for example, while on the train using pre-downloaded learning materials.

[1771] Step 17:

[1772] The learning data acquired while the device was offline is stored locally.

[1773] Step 18:

[1774] When the device regains online connectivity, the learning data recorded while offline is synchronized with the server.

[1775] Step 19:

[1776] The server receives the synchronized data and updates the database. The new progress data is reflected in the learning plan.

[1777] Incorporating an emotion engine

[1778] Step 20:

[1779] The device uses a webcam and microphone to recognize emotions in real time using an emotion engine based on the user's facial expressions, voice, and input data. For example, it can detect happiness, sadness, fatigue, etc. from the user's facial expressions.

[1780] Step 21:

[1781] The server receives the emotion data recognized by the emotion engine and dynamically adjusts the content of the learning materials based on that data. For example, if it recognizes that the user is tired, it will provide less stressful learning materials or interactive content to refresh the user.

[1782] Step 22:

[1783] The emotion data recognized by the emotion engine is integrated with the learning progress data, and the server updates the overall learning plan.

[1784] Specific examples

[1785] If a user selects the "Introduction to Data Science" course and prefers a visual learning style, the system operates as follows:

[1786] 1. When users first log in, they fill out a profile information and learning style questionnaire.

[1787] 2. The server selects visual teaching materials (e.g., video lectures and infographics) based on the survey information.

[1788] 3. The terminal provides the selected learning materials to the user, who then uses these materials to advance their studies.

[1789] 4. As users watch video lectures and answer quizzes, the device records this information and sends progress data to the server.

[1790] 5. Based on the received data, the server updates the next study plan and provides more appropriate learning materials.

[1791] 6. The device uses an emotion engine to recognize the user's emotions, and if it detects fatigue or a loss of concentration, it will provide light quizzes or mindfulness videos to refresh the user.

[1792] 7. Accordingly, the server integrates the emotional data and learning progress data to adjust the overall learning plan.

[1793] In this way, a system is realized that provides an optimized learning experience by comprehensively taking into account the user's learning style, progress, and emotions.

[1794] Example 2

[1795] 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."

[1796] Conventional learning systems have difficulty providing optimal learning materials based on the user's learning style and needs, and are unable to flexibly update learning plans based on learning progress and emotional data. As a result, it is not possible to provide an optimal learning experience for each learner, resulting in problems such as reduced learning efficiency and effectiveness.

[1797] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving individual information of a user as input and analyzing the user's learning style and needs based on the information; means for automatically generating learning materials optimal for the user based on the learning style and needs; means for providing the generated learning materials to the user's terminal; means for tracking the user's learning progress and updating the content of the learning materials according to the progress; means for recording offline learning data and synchronizing it with the server when online; means for recognizing emotions from the user's facial expressions, voice, and input data and dynamically adjusting the content of the learning materials based on the emotions; and means for integrating the emotion data and learning progress data and updating the comprehensive learning plan. This makes it possible to provide a learning experience optimized for each user's individual learning style, progress, and emotional state.

[1798] "User" means a person or organization who uses the learning system and is provided with an optimal learning plan based on their learning style and needs.

[1799] "Personal Information" refers to data unique to a user, including name, email address, password, learning style survey results, etc.

[1800] "Learning style" refers to the method or technique in which a user learns most effectively, and can be visual, auditory, tactile, or other types.

[1801] "Needs" refer to the goals or needs that a user wants to achieve through learning, and specific examples include improving specialized knowledge or acquiring specific skills.

[1802] "Learning materials" refers to content provided to assist users in their learning, and includes video lectures, texts, quizzes, infographics, and the like.

[1803] "Terminal" means a device used by a user to access the system, including a PC, tablet, smartphone, etc.

[1804] A "server" refers to an information processing device that processes and stores various types of data and communicates with user terminals.

[1805] "Study progress" is data that indicates how much progress a user has made in the process of studying, and includes the amount of time spent watching videos and the percentage of correct answers to quizzes.

[1806] "Offline learning data" is data recorded when a user studies without a network connection, and is synchronized with the server the next time the user goes online.

[1807] "Emotion data" is data that indicates the user's emotional state, and is extracted from facial expressions, tone of voice, input data, and the like.

[1808] "Comprehensive Learning Plan" refers to a series of learning activities that are optimized based on the user's learning style, progress data, and emotional data.

[1809] An "emotion engine" refers to a collection of algorithms and software that recognizes a user's emotions and takes necessary action based on them.

[1810] The present invention is a learning system that analyzes a user's individual information and learning style to provide an optimal learning plan, and further enhances the learning experience by combining an emotion engine. This system is implemented by linking a server and a terminal. Below, we will explain embodiments of the present invention, using specific examples to explain what hardware or software is used to perform data processing and data calculations.

[1811] This system consists of multiple steps. First, the user accesses the system using a device (PC, tablet, smartphone). When registering for the first time, the user enters the required information (name, email address, password, etc.), which the device sends to the server. The server validates the data and stores it in a database (e.g., MySQL). The server then provides a questionnaire regarding learning style and goals, which the user fills out. The survey results are then sent to the server via the device.

[1812] The server inputs the collected survey results into an AI model (e.g., a model built with TensorFlow or PyTorch) to analyze the user's learning style and needs. Based on the analysis results, the server automatically generates an optimized learning plan and sends this plan in JSON format to the device. The device then analyzes the received data and displays it in a user interface.

[1813] As a user progresses through their studies using learning materials (e.g., video lectures, textbooks, quizzes), the device records their activity log and periodically sends it to the server. The server analyzes the received data and dynamically updates the next learning plan based on the user's progress. Furthermore, the device has the ability to record learning data even when offline and synchronize it with the server the next time it is online.

[1814] Additionally, the system incorporates an emotion engine. The device uses a webcam and microphone to capture the user's facial and voice data, which is then analyzed in real time using emotion recognition algorithms (e.g., OpenCV and DeepEmotion). The emotion data is sent to a server, which analyzes it using an AI model to determine the user's current emotional state. Depending on the emotion data, the server provides low-impact educational materials and refresher content.

[1815] The server integrates emotional data and learning progress data and updates a comprehensive learning plan to provide a learning experience optimized for each user's individual learning style, progress, and emotional state.

[1816] Specific examples

[1817] If a user selects the "Introduction to Data Science" course and prefers a visual learning style, the system operates as follows:

[1818] 1. When users log in for the first time, they fill out their profile information and a learning style questionnaire.

[1819] 2. The server selects visual teaching materials (e.g., video lectures and infographics) based on the survey information.

[1820] 3. The terminal provides the selected learning materials to the user, who then uses these materials to advance their studies.

[1821] 4. As users watch video lectures and answer quizzes, the device records this information and sends progress data to the server.

[1822] 5. The server updates the next study plan based on the received data and provides the corresponding study materials.

[1823] 6. The device uses an emotion engine to recognize the user's emotions, and if it detects fatigue or a loss of concentration, it will provide light quizzes or mindfulness videos to refresh the user.

[1824] 7. The server integrates the emotional data and learning progress data to tailor a comprehensive learning plan.

[1825] Prompt Sentence Examples

[1826] "How can I create an AI model that generates the best learning material list for a user who is a visual learner?"

[1827] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1828] Step 1: First-time registration process

[1829] 1.1 The user enters the required information such as name, email address, and password on the account registration screen.

[1830] Input: Name, Email Address, Password

[1831] Output: User input data

[1832] 1.2 The device converts this information into JSON format and sends it to the server using the HTTPS protocol.

[1833] Input: User-entered data

[1834] Output: User data in JSON format

[1835] 1.3 The server validates the received information (e.g., checks the email address format, password strength) and stores it in a database (e.g., MySQL).

[1836] Input: User data in JSON format

[1837] Output: Save results to database

[1838] 1.4 The server generates questionnaire data regarding learning styles and learning objectives and sends it to the terminal.

[1839] Input: None

[1840] Output: Questionnaire form data

[1841] Step 2: Analyze your learning styles and needs

[1842] 2.1 The user fills out the questionnaire form regarding their learning style and purpose and clicks the submit button.

[1843] Input: User learning style and goals

[1844] Output: Survey results

[1845] 2.2 The terminal converts the survey results into JSON format and sends them to the server.

[1846] Input: Survey results

[1847] Output: Survey data in JSON format

[1848] 2.3 The server inputs the received survey results into an AI model (e.g., built with TensorFlow and PyTorch) to analyze learning styles and needs.

[1849] Input: Survey data in JSON format

[1850] Output: Analysis results (user learning styles and needs)

[1851] 2.4 The server stores the analysis results in a database for further processing.

[1852] Input: Analysis results

[1853] Output: Save results to database

[1854] Step 3: Providing educational materials

[1855] 3.1 The server automatically generates an optimized learning plan based on the analysis results, such as a list of appropriate video lectures and quizzes.

[1856] Input: Analysis results

[1857] Output: Generated teaching material list

[1858] 3.2 The server sends the generated teaching material list to the terminal in JSON format.

[1859] Input: Generated teaching material list

[1860] Output: JSON formatted learning material list

[1861] 3.3 The device analyzes the received data and displays it on the user interface. Specifically, it dynamically generates thumbnail images of the video lectures and quiz links.

[1862] Input: JSON format teaching material list

[1863] Output: A list of teaching materials displayed on the user interface

[1864] Step 4: Track your progress

[1865] 4.1 The user uses the provided learning materials (e.g., video lectures, quizzes) to advance their learning.

[1866] Input: Usage of teaching materials

[1867] Output: Learning progress information

[1868] 4.2 The device records the user's learning activities and progress data, e.g., video viewing time, quiz correct answer rate.

[1869] Input: Learning progress information

[1870] Output: Recorded progress data

[1871] 4.3 The device periodically converts the progress data into JSON format and sends it to the server.

[1872] Input: Recorded progress data

[1873] Output: Progress data in JSON format

[1874] 4.4 The server analyzes the received data and dynamically updates the next study plan. For example, if a high score is obtained, more difficult study materials will be recommended.

[1875] Input: Progress data in JSON format

[1876] Output: Updated learning plan

[1877] Step 5: Integrating online and offline learning

[1878] 5.1 The device will cache some of the learning materials and progress data for the next time you use it in local storage.

[1879] Input: Updated learning plan and progress data

[1880] Output: Data saved in local storage

[1881] 5.2 Users can continue learning using cached data even when they do not have a network connection.

[1882] Input: Data stored in local storage

[1883] Output: Offline learning progress

[1884] 5.3 The next time your device goes online, it will sync its locally stored progress data with the server.

[1885] Input: Offline learning progress

[1886] Output: Data synchronized with the server

[1887] Step 6: Incorporating the Emotion Engine

[1888] 6.1 The device uses a webcam and microphone to capture the user's facial expressions and voice data.

[1889] Input: facial expression data and voice data

[1890] Output: Retrieved data

[1891] 6.2 The device uses emotion recognition algorithms (e.g., OpenCV, DeepEmotion) to analyze facial expressions and tone of voice in real time.

[1892] Input: Retrieved data

[1893] Output: Emotion data

[1894] 6.3 The terminal converts the analysis results into JSON format and sends them to the server.

[1895] Input: Emotion data

[1896] Output: Emotion data in JSON format

[1897] Step 7: Emotionally adjust your materials

[1898] 7.1 The server inputs the received emotion data into the AI ​​model to determine the user's current emotional state.

[1899] Input: Emotion data in JSON format

[1900] Output: Emotion determination result

[1901] 7.2 The server dynamically adjusts the content of educational materials provided based on emotional data. For example, if fatigue is detected, it will provide refreshing light quizzes or mindfulness videos.

[1902] Input: Emotion determination result

[1903] Output: Dynamically adjusted teaching content

[1904] Step 8: Integrating sentiment and learning progress data

[1905] 8.1 The server integrates the emotion data and learning progress data to update the overall learning plan.

[1906] Input: Emotion judgment results and learning progress data

[1907] Output: Consolidated learning plan

[1908] 8.2 The server sends the updated learning plan to the device and provides it to the user.

[1909] Input: Integrated Learning Plan

[1910] Output: Provided to the user

[1911] (Application example 2)

[1912] 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."

[1913] Previous learning and training systems lacked the ability to provide optimal learning plans based on individual user and robot information. They also lacked the ability to monitor learning progress and performance decline in real time and dynamically adjust learning plans accordingly. Furthermore, there were issues with recording data offline and synchronizing it with the online environment.

[1914] 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.

[1915] In this invention, the server includes means for receiving individual user information as input and analyzing the user's learning style and needs based thereon, means for automatically generating learning materials optimal for the user, means for providing the generated learning materials to the user's terminal, means for tracking the user's learning progress and updating the content of the learning materials according to the progress, means for recording offline learning data and synchronizing it with the server when online, means for receiving robot identification information as input and generating an appropriate training plan based thereon, means for analyzing the robot's motion data and monitoring performance in real time using an emotion engine, and means for instructing a refresher task when performance degradation is detected. This makes it possible to provide an optimal learning plan based on the individual user and robot information, monitor performance in real time, and effectively manage data even in an offline environment.

[1916] "Personal information" is information that includes a user's or robot's identity, personal characteristics, learning style, and needs.

[1917] "Learning style" is information that indicates a user's learning method and preferences, and includes visual, auditory, tactile, and other methods.

[1918] "Needs" refers to information that indicates the requirements or objectives that users have in learning or training.

[1919] "Instructional Materials" includes educational resources, such as videos, quizzes, infographics, etc., tailored to a user's learning style and needs.

[1920] A "terminal" is a device used by a user or robot to receive learning materials, and includes a PC, tablet, smartphone, etc.

[1921] The "server" is a central computer that manages the entire learning system and processes, records, and analyzes individual information, progress data, emotional data, etc. of users or robots.

[1922] "Study progress" is data indicating the progress of the user's learning activities using learning materials.

[1923] An "emotion engine" is software that analyzes the facial expressions, voice, and movement data of a user or robot to recognize their emotional state in real time.

[1924] A "training plan" is a plan of optimal work procedures and training content provided to a robot, and it changes adaptively.

[1925] "Performance" is an evaluation index that indicates the efficiency and accuracy with which a robot performs a specific task.

[1926] "Real-time monitoring" means reading learning progress and performance on the spot and analyzing them immediately.

[1927] "Offline training data" means training data collected and recorded when a user or robot does not have an internet connection.

[1928] "Synchronizing to server when online" is the process by which data recorded offline is sent to the server and stored in the database when you are connected to the Internet again.

[1929] A "refreshment task" is a task that instructs the robot to do light work or take a break to reduce stress when a decline in its performance is detected.

[1930] The present invention relates to a system that analyzes individual user information and robot identification information, and provides optimal learning and training plans based on the analysis. Specific embodiments are described below.

[1931] 1. Initial Setup and Registration

[1932] The server first receives input from the user or robot's personal information. For users, this includes name, email address, password, and information about learning style. For robots, this includes identification information and training objectives. This information is sent to the server and stored in a database.

[1933] 2. Learning Style and Needs Analysis

[1934] The server uses a generative AI model to analyze a user's learning style and needs based on the survey results and input data. For example, if the user is a visual learner, visual learning materials will be selected. Similarly, for robots, training content tailored to their characteristics will be generated.

[1935] 3. Providing educational materials or training plans

[1936] The server automatically generates optimal learning materials or training plans based on the analysis results, which are then sent to the user's or robot's device and displayed in the form of videos, quizzes, infographics, and more.

[1937] 4. Track your progress

[1938] As the user or robot progresses in their learning or training, the device records their progress data. This progress data is sent to the server and stored in a database. The server then adaptively updates the next learning or training plan based on the progress data.

[1939] 5. Integrating offline learning

[1940] The device has the ability to cache data so that learning and training can be done offline. A user or robot can record their progress in an offline environment and sync it with the server when they come online.

[1941] 6. Leveraging Emotional Engines

[1942] The device uses an emotion engine to recognize the emotional state of the user or robot in real time, using a webcam and microphone to analyze facial expressions and voice to detect signs of stress or fatigue.

[1943] 7. Adjusting the movement

[1944] If the emotion engine detects a decline in performance, the server will instruct the user or robot to perform a refreshing task or light work, allowing them to continue learning or training without unnecessary stress.

[1945] Specific examples

[1946] Example of a training system for factory robots:

[1947] Imagine a factory robot learning a new assembly task. The robot's identity and task characteristics are sent to a server, which generates an appropriate training plan. The robot works on the task, collecting behavioral data along the way. If the emotion engine detects signs of stress, it will recommend lighter work or a break.

[1948] Example prompt sentence:

[1949] "If the robot's performance drops on the current task, what's the next appropriate training task to offer it?"

[1950] "If the robot assembles the parts with 75% accuracy, what is the next task it should move on to?"

[1951] As described above, this invention provides optimal learning and training plans based on individual information about the user and the robot, and monitors and adjusts progress and emotional state in real time, thereby achieving an effective learning experience.

[1952] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1953] Step 1:

[1954] The server receives the user's or robot's personal information as input. In the case of a user, this includes name, email address, password, and information about learning style. In the case of a robot, identification information and training goals are entered. The data is sent to the server and stored in a database. The input is the user's or robot's information, and the output is the personal information stored in the database.

[1955] Step 2:

[1956] The server uses a generative AI model to analyze the user's learning style and needs based on the received personalized information. This process determines whether the user is a visual learner and what the robot's working characteristics are. The input is personalized information, and the output is the analysis results.

[1957] Step 3:

[1958] The server automatically generates optimal teaching materials or training plans based on the analysis results. Appropriate teaching materials are selected, and the generated teaching materials or plans are sent to the user's or robot's terminal. The input is the analysis results, and the output is the generated teaching materials or training plans.

[1959] Step 4:

[1960] The user or robot learns or works according to the learning materials or training plan. The terminal records the progress data and sends it to the server. The input is the user's or robot's learning and work data, and the output is the recorded and sent progress data.

[1961] Step 5:

[1962] The server stores the received progress data in a database and adaptively updates the next learning or training plan. The input is the progress data, and the output is the updated learning or training plan.

[1963] Step 6:

[1964] The device caches the necessary data so that you can continue studying or working offline. It records your progress data even when offline and synchronizes it with the server when you come online. The input is your study plan and progress data, and the output is the cached and synchronized data.

[1965] Step 7:

[1966] The device uses an emotion engine to recognize the emotional state of the user or robot in real time. It uses a webcam and microphone to analyze facial expressions and voice to detect signs of stress or fatigue. The input is the user or robot's behavior data, and the output is analyzed emotional data.

[1967] Step 8:

[1968] Based on the data analyzed by the emotion engine, the server instructs users to perform refreshing tasks or light work when a decline in performance is detected. The input is emotional data, and the output is the instructed refreshing task.

[1969] 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.

[1970] 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.

[1971] 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.

[1972] 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.

[1973] 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.

[1974] 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.

[1975] 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).

[1976] 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.

[1977] 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."

[1978] 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.

[1979] 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).

[1980] 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.

[1981] 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.

[1982] 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.

[1983] 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.

[1984] 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.

[1985] 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.

[1986] 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.

[1987] 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.

[1988] 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.

[1989] 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.

[1990] The following is further disclosed regarding the above embodiment.

[1991] (Claim 1)

[1992] means for receiving as input individual information of a user and analyzing the user's learning style and needs based thereon;

[1993] means for automatically generating learning materials that are optimal for a user...

Claims

1. means for receiving as input individual information of a user and analyzing the user's learning style and needs based thereon; means for automatically generating learning materials that are optimal for a user based on said learning style and needs; means for providing the generated teaching material to a user's terminal; means for tracking the user's learning progress and updating the content of the learning materials accordingly; A means to record offline learning data and synchronize it with the server when online; A system including:

2. 10. The system of claim 1, wherein the analysis of learning styles and needs uses artificial general intelligence techniques.

3. 10. The system of claim 1, further comprising means for caching learning data on the terminal to integrate online and offline learning.

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