System

Abstract

A system is provided.SOLUTION: A system comprising: means for generating a custom-made learning plan in response to an individual's learning style and learning progress; means for providing educational content that can be accessed online; means for evaluating learning progress using an automated evaluation system; means for tracking learning data and generating reports; and means for coordinating learners and personnel providing educational assistance.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] Due to economic hardship and geographical constraints, children who have limited access to educational resources have limited opportunities to receive a high-quality education. This widens educational disparities, causing students to lose motivation and have difficulty concentrating, limiting their future options. Traditional education systems often do not provide sufficient personalized learning support and are unable to accommodate individual learning styles and progress. There is a need to resolve these issues and equalize educational quality and opportunities. [Means for solving the problem]

[0005] By providing a means to generate a custom-made learning plan based on an individual's learning style and progress, we can provide optimal educational content for each learner. Furthermore, by providing educational content that can be accessed online, we can enable students to continue learning without geographic or economic constraints. Furthermore, by incorporating a means to evaluate learning progress using an automated assessment system, we can provide real-time feedback and progress management. By tracking learning data and generating reports, we can visualize the effectiveness of learning and grasp individual progress in detail. Furthermore, by providing a means to connect learners with educational support providers, we can provide support that is closer to individual instruction. Furthermore, by providing a means to answer learners' questions in real time using natural language processing and a means to provide multilingual support, we can create a system that can accommodate learners in any environment.

[0006] "Learning styles" refer to the methods and techniques that learners use to most effectively acquire knowledge and skills.

[0007] "Learning progress" refers to an indicator that shows how far a learner has progressed in accordance with the educational curriculum or learning plan.

[0008] A "custom-made learning plan" refers to a learning schedule and content that is specially designed based on the characteristics and progress of each individual learner.

[0009] "Online educational content" refers to educational resources (e.g., videos, textbooks, exercises) that are accessible via the Internet.

[0010] An "automated assessment system" refers to a system that uses AI or algorithms to automatically evaluate learners' answers and performance and provide feedback.

[0011] "Learning data" refers to information related to a learner's learning activities, progress, assessment results, etc.

[0012] "Means for generating reports" refers to the processes and tools that analyze the collected learning data and create a visual learning progress report.

[0013] "Persons providing educational support" refers to teachers and tutors who provide guidance and support to learners.

[0014] "Natural language processing" refers to the technology that enables computers to understand, generate, and process human language.

[0015] "Multilingual support" refers to a system that provides educational content and support in multiple languages. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram 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

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

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

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

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

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

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

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

[0024] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0037] The educational system of the present invention aims to provide high-quality education regardless of factors such as financial difficulties or geographical limitations. The system generates a custom-made learning plan based on the user's learning style and progress, and provides educational content that can be accessed online. It also manages learning progress through an automated evaluation system using AI, tracks learning data, and creates reports. It also has a function for connecting learners with those providing educational support.

[0038] Specifically, the system operates as follows.

[0039] Learning plan generation

[0040] 1. A user logs in from a terminal

[0041] A user accesses the system and enters their login information. The server verifies the authentication information and displays the user's personal settings page.

[0042] 2. Determine your learning style and goals

[0043] Users answer questions about their learning styles and goals. The server receives this information and stores it in a database.

[0044] 3. Generate a custom learning plan

[0045] The server uses AI models based on the stored information to generate a custom learning plan for each learner, tailored to their learning style and goals.

[0046] Providing educational content

[0047] 1. Selection of online educational content

[0048] The server selects appropriate online educational content (videos, texts, exercises, etc.) based on the user's learning plan.

[0049] 2. Display of Content

[0050] When a user starts a learning session from a terminal, the server transmits selected content to the terminal for display.

[0051] Progress assessment and feedback

[0052] 1. Answers and evaluations for practice questions

[0053] The user answers the exercises provided and sends them to the server via their device, which then grades the answers using an automated evaluation system and provides feedback.

[0054] 2. Real-time feedback

[0055] The server provides instant feedback to the user on the results of the assessment, offering additional resources and advice to deepen their understanding.

[0056] Tracking and reporting on learning data

[0057] 1. Tracking training data

[0058] The server tracks users' learning activities in real time and stores them in a database, recording data such as study time, grades, and progress.

[0059] 2. Report Generation

[0060] When the user selects to check progress, the server generates a detailed learning report based on the accumulated data and displays it on the device.

[0061] Collaboration with educational support staff

[0062] 1. Choosing Tutor Support

[0063] If the user desires tutor support, he or she selects the function for linking with a tutor from the terminal.

[0064] 2. Tutor selection and booking

[0065] The server displays a list of tutors available online, and the user selects the tutor they want to book a session with. The server then notifies the tutor of the reservation information and adjusts the schedule.

[0066] Specific examples

[0067] For example, if a user wants to study mathematics, they log in and select "Mathematics" as their study subject. If the user prefers to study using video materials, the server uses an AI model to select and display the most suitable video materials for the user. After studying, the user answers and submits practice questions, and the server automatically grades them and provides real-time feedback. In addition, all learning progress data is tracked and presented to the user in a detailed report. If necessary, the user can also receive additional guidance from an online tutor.

[0068] In this way, the system of the present invention provides learners with a highly personalized educational experience tailored to their individual needs.

[0069] The processing flow will be explained below.

[0070] Step 1:

[0071] The user accesses the EduBridge URL from their device and logs in for the first time.

[0072] Step 2:

[0073] The server receives the user's login information and performs authentication. If successful, the user's dashboard is displayed on the device.

[0074] Step 3:

[0075] Users select the "Personal Settings" option from their dashboard and answer questions about their learning style and goals.

[0076] Step 4:

[0077] The server receives the user's answers and stores them in a database.

[0078] Step 5:

[0079] The server uses the stored information to create a custom learning plan based on the user's learning style and progress using an AI model, and stores the resulting plan in a database.

[0080] Step 6:

[0081] The server transmits the generated learning plan to the terminal and displays it to the user.

[0082] Step 7:

[0083] The user reviews the learning plan and makes any necessary adjustments, which are then sent from the device to the server.

[0084] Step 8:

[0085] The server stores the adjusted learning plan in a database.

[0086] Step 9:

[0087] The user selects a subject from the dashboard and clicks the "Start" button.

[0088] Step 10:

[0089] The server selects appropriate educational content (videos, text, practice questions, etc.) based on the user's learning plan and sends it to the device.

[0090] Step 11:

[0091] The user studies the displayed educational content and answers the exercises.

[0092] Step 12:

[0093] Once the user submits their answer, the server uses an automated rating system to grade the answer and generate instant feedback.

[0094] Step 13:

[0095] The server sends the evaluation results and feedback to the terminal and displays them to the user.

[0096] Step 14:

[0097] The server tracks users' learning data (study time, accuracy rate, etc.) in real time and stores it in a database.

[0098] Step 15:

[0099] When a user selects "Check Progress" from the dashboard, the server analyzes the accumulated learning data and generates a detailed learning report.

[0100] Step 16:

[0101] The server sends the generated report to the terminal for display to the user.

[0102] Step 17:

[0103] If the user desires tutor support, he / she selects the "Tutor Support" option from the dashboard.

[0104] Step 18:

[0105] The server displays a list of currently available tutors, and the user selects the tutor they desire.

[0106] Step 19:

[0107] The user selects the desired date and time, and transmits reservation information from the terminal to the server.

[0108] Step 20:

[0109] The server notifies the tutor of the reservation information and adjusts the schedule.

[0110] Step 21:

[0111] The user and tutor prepare to start the session at the specified date and time.

[0112] Example 1

[0113] 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."

[0114] In today's education system, financial hardship and geographical constraints make it difficult to obtain a high-quality education. Furthermore, traditional education systems often fail to adequately accommodate individual learners' learning styles and progress, hindering efficient learning. Furthermore, automated assessment and learning data tracking and feedback functions are often lacking, resulting in insufficient progress management for learners. These issues need to be resolved.

[0115] 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.

[0116] In this invention, the server includes a means for a user to log in from a terminal, a means for setting the user's learning style and goals, a means for using a generative AI model to generate a customized learning plan, a means for selecting educational content that can be accessed online, a means for displaying the selected educational content, a means for evaluating learning progress using an automated evaluation system, a means for providing feedback on the evaluation results to the user in real time, a means for tracking learning data and storing it in a database, a means for generating reports based on the tracked data, and a means for connecting learners with personnel providing educational support. This makes it possible to provide high-quality personalized education that meets the needs of individual learners.

[0117] "Means for users to log in from a terminal" refers to the method by which users access the system using a terminal such as a PC or smartphone, enter login information, and are authenticated.

[0118] The "means for setting user's learning style and goals" is a method by which a user inputs information about his or her learning style and goals, which is received by the system and stored in the database.

[0119] "Method using a generative AI model to generate a custom-made study plan" means a method using a generative AI model to create an individually optimized study plan based on a user's learning style and goals.

[0120] "Means for selecting online accessible educational content" refers to the method by which the system selects appropriate online educational content based on the user's learning plan.

[0121] The "means for displaying the selected educational content" refers to a method by which the server transmits the selected educational content to the user's terminal and displays it.

[0122] A "means for assessing learning progress using an automated assessment system" is a method for automatically scoring exercises answered by a user and generating an assessment result.

[0123] The "means for providing feedback of the evaluation results to the user in real time" is a method for instantly notifying the user of the graded evaluation results and providing that feedback.

[0124] The "means for tracking learning data and storing it in a database" is a method for monitoring data related to a user's learning activities in real time and recording the data in a database.

[0125] The "means for generating a report based on tracked data" is a method for analyzing the tracked learning data and creating a detailed learning report.

[0126] "Means for connecting learners with educational support providers" refers to a method by which learners can connect online with educational support providers and receive support.

[0127] The educational system of this invention aims to provide high-quality education by creating a custom-made learning plan based on the user's learning style and learning progress. The system is designed for users to access from a device such as a PC or smartphone. The specific hardware and software configuration and processing flow are shown below.

[0128] First, the user accesses the system from a terminal and enters their login information. The server compares this authentication information with the database, and if authentication is successful, displays the user's personal settings page. This login process may use two-factor authentication in addition to the user ID and password.

[0129] Next, the user answers a questionnaire on the system about their learning style and goals. The questionnaire includes items such as study time, target level, and preferred learning method (video, text, practice questions). The server receives this information and stores it in a database. At this point, the user's learning data is initialized.

[0130] Based on the stored data, the server uses a generative AI model to generate a custom learning plan. This model uses existing machine learning algorithms and natural language processing techniques. The generated learning plan is displayed on the user's personalized settings page. The learning plan includes specific learning content, a progress schedule, and recommended learning materials.

[0131] Based on the generated learning plan, the server selects appropriate online educational content from a database, including videos, texts, exercises, etc. This content is sent to and displayed on the device when the user starts a learning session.

[0132] The practice questions that users answer during their studies are sent to a server and instantly scored by an automated assessment system. The results are then fed back to the user in real time, and they may be given feedback on their understanding or additional resources. This feedback helps users to study more effectively.

[0133] All data related to learning activities is tracked in real time by the server and stored in a database. This includes data such as study time, grades, and progress. When a user selects to check their progress, the server generates a detailed learning report based on the accumulated data and displays it on the device. The report includes information such as total study time, progress on each learning item, and grades.

[0134] Furthermore, if a user needs educational support, they can use the tutor support function. With this function, the server displays a list of available tutors, and the user can select the tutor of their choice and reserve a session. The tutor receives the user's reservation information, adjusts their schedule, and provides instruction.

[0135] For example, if a user wants to learn intermediate level mathematics, they can input the following prompt into the generative AI model:

[0136] "Generate a custom learning plan for a user who prefers video learning, covering basic to advanced mathematics. The user's current level is intermediate."

[0137] The generated learning plan recommends intermediate-level math video materials and sets specific learning tasks based on the plan. Users receive real-time feedback as they watch the videos and answer practice questions. The learning data accumulated during this process is tracked by the server and presented to the user as a detailed report. Users can also book a session with a tutor to receive further in-depth learning support, if necessary.

[0138] In this way, the system allows users to enjoy a highly personalized learning experience, maximizing learning efficiency.

[0139] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0140] Step 1:

[0141] A user logs in from a terminal

[0142] The user accesses the system using a device such as a computer or smartphone and enters login information (user ID and password).

[0143] Input: User ID, Password

[0144] The server checks the authentication information against a database and performs authentication. If authentication is successful, the user's personal settings page is displayed on the terminal.

[0145] Example of operation: The user enters their ID and password and presses the "Login" button.

[0146] Step 2:

[0147] Define user learning styles and goals

[0148] Users answer a questionnaire on the system about their learning style and goals.

[0149] Input: Study time, desired level, preferred learning method (video, text, exercises), etc.

[0150] The server receives this information and stores it in a database.

[0151] Example of operation: A user answers a survey and presses the "Save" button.

[0152] Step 3:

[0153] Generate a custom learning plan

[0154] The server inputs prompt sentences into the generative AI model based on the stored data.

[0155] Input: User learning style, goal data

[0156] Example prompt: "Generate a custom learning plan for a user who prefers video instruction, covering basic and advanced mathematics. The user's current level is intermediate."

[0157] The server uses the generative AI model to generate an individually optimized learning plan, which is displayed on the user's personal settings page.

[0158] Output: Custom-made study plan

[0159] Example of operation: A learning plan is generated and the user reviews it.

[0160] Step 4:

[0161] Selection of online educational content

[0162] The server searches the database for appropriate online educational content (videos, texts, exercises, etc.) based on the generated learning plan.

[0163] Input: Study Plan

[0164] The server adds links to educational content to the learning plan.

[0165] Output: A list of selected educational content

[0166] Example of operation: Links to the selected content are displayed as a list.

[0167] Step 5:

[0168] View content

[0169] The user initiates a learning session and the server sends the content to the terminal for display.

[0170] Input: Start instruction for study session

[0171] The server transmits the selected educational content to the terminal and displays it.

[0172] Output: On-screen display of educational content

[0173] Example of how it works: A user starts a learning session and a video plays.

[0174] Step 6:

[0175] Exercises and answers

[0176] The user answers the exercises presented to them and sends the answers to the server.

[0177] Input: Answer to the exercise

[0178] The server grades the answers using an automated evaluation system and generates an evaluation result.

[0179] Output:Scoring results

[0180] Example of operation: When the user answers the questions and presses the "Submit" button, the scoring results are displayed immediately.

[0181] Step 7:

[0182] Real-time feedback

[0183] The server provides the user with real-time feedback on the results of the assessment to confirm their level of understanding.

[0184] Input:Scoring results

[0185] The server may also provide additional resources and exercises.

[0186] Output: Show feedback

[0187] Example of how it works: Additional questions are displayed as feedback regarding areas of insufficient understanding.

[0188] Step 8:

[0189] Tracking and storing learning data

[0190] The server tracks users' learning activities (study time, grades, progress) in real time and stores them in a database.

[0191] Input: Learning activity data

[0192] The server collects and stores this data for analysis.

[0193] Output: Training data stored in a database

[0194] Example of how it works: Data is automatically recorded during a learning session.

[0195] Step 9:

[0196] Report Generation

[0197] When the user selects to check progress, the server generates a detailed learning report based on the accumulated data.

[0198] Input: Progress check instructions

[0199] The server analyzes the data and generates a progress report that is displayed on the terminal.

[0200] Output: Learning report

[0201] Example of operation: A user opens the report and checks the total study time and progress of each learning content.

[0202] Step 10:

[0203] Selecting and booking tutor support

[0204] If the user desires tutor support, he or she selects the function for linking with a tutor from the terminal.

[0205] Input: Request for tutor support

[0206] The server displays a list of tutors available online, and the user selects the tutor of their choice and books a session.

[0207] Output: Notification of reservation information

[0208] The server notifies the tutor of the reservation information and adjusts the schedule.

[0209] Example of operation: The user selects the desired tutor and presses the "Reserve" button.

[0210] (Application example 1)

[0211] 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."

[0212] Today's learners demand high-quality education regardless of financial difficulties or geographical limitations. However, it is difficult to provide customized education that meets individual learning needs, and it is not easy to accurately evaluate learning progress and provide feedback. Furthermore, there are few ways to efficiently acquire product knowledge in physical or virtual stores while studying. A system that solves these challenges and provides a more effective and personalized learning experience is needed.

[0213] 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.

[0214] In this invention, the server includes: a means for generating a customized learning plan based on a user's learning style and learning progress; a means for providing educational content that can be accessed online; a means for evaluating learning progress using an automated evaluation system; a means for tracking learning data and generating reports; a means for connecting learners with educational support providers; a means for users to learn product information and answer practice questions in a virtual store; and a means for executing the above functions using a smartphone. This allows learners to overcome financial difficulties and geographical limitations and enjoy a highly personalized educational experience tailored to their individual learning needs. It also enables efficient product knowledge learning in a virtual store.

[0215] "User" refers to a person who uses the learning system.

[0216] "Learning style" refers to the method or format that a learner prefers to learn most effectively.

[0217] "Learning Progress" refers to the progress a learner has made in their learning plan.

[0218] "Custom-made learning plan" refers to a learning plan that is customized based on each learner's individual needs and characteristics.

[0219] "Online accessible educational content" refers to educational materials and teaching materials that are available to learners via the internet.

[0220] An "automated assessment system" refers to a system that uses AI or algorithms to assess learners' progress and achievements.

[0221] "Learning Data" refers to information about a learner's behavior, progress, grades, etc.

[0222] A "report" refers to a document that summarizes a learner's progress and achievements based on learning data.

[0223] "Persons providing educational support" refer to people who are responsible for providing education and guidance to learners.

[0224] A "virtual store" refers to a virtual store that sells products and services online.

[0225] A "smartphone" refers to a mobile phone terminal with internet connectivity and various functions.

[0226] A "server" refers to a computer that provides services and information to clients over a network.

[0227] To implement this invention, the following system configuration and processing are used: The main components of the system include a server, a terminal used by a user (e.g., a smartphone), and educational content that can be accessed online.

[0228] The server first provides a means for users to input their learning style and learning goals. Users log in to the system from their terminals and input information about their learning style and learning goals. The server then stores this information in a database.

[0229] The server then uses the stored information to generate a personalized learning plan using a generative AI model, which includes content tailored to the user's learning style and goals. For example, if a user prefers video learning materials, appropriate video content will be selected.

[0230] The server provides appropriate online educational content based on the user's learning plan. When the user starts a learning session from their device, the server sends the selected content to the device and displays it. The educational content includes videos, textbooks, exercises, etc.

[0231] The server also evaluates learning progress. Users answer the exercises provided and send them to the server via their device. The server then uses an automated evaluation system to grade the answers and provide feedback, allowing users to instantly check their level of understanding.

[0232] Learning progress data is tracked by the server and stored in a database. Data such as study time, grades, and progress are recorded, and when a user selects to check progress, a detailed learning report is generated and displayed on the device.

[0233] Furthermore, the system allows users to learn about products in the virtual store and answer practice questions, allowing them to test their understanding while learning how to select and use products in the virtual store.

[0234] The entire system is run using a smartphone, which is equipped with functions such as user login, learning style setting, educational content display, automatic assessment result display, and learning progress tracking.

[0235] The hardware and software used includes:

[0236] Hardware: Smartphone (iOS / Android)

[0237] Software: Python, Flask (web framework), Scikit-learn (machine learning library)

[0238] For example, when a user buys a new kitchen gadget, they can watch a video to learn how to use it and then test their understanding with practice questions. Here's an example of a prompt for the generative AI model:

[0239] "User just purchased a new kitchen gadget. Their learning style is videos, and their learning goal is to understand how to use the product. Generate an optimal learning plan."

[0240] This allows learners to overcome financial difficulties and geographical constraints, receive a highly personalized educational experience tailored to their individual learning needs, and efficiently learn about products in virtual stores.

[0241] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0242] Step 1:

[0243] The server receives authentication information for the user to log in from the terminal. The terminal sends the user ID and password to the server, which verifies the authentication information and displays the user's personal setting page. At this stage, the input is the user ID and password, and the output is the user's personal setting page.

[0244] Step 2:

[0245] The server provides a means for users to input information about their learning style and learning goals through their terminals. The learning style (e.g., video, text) and learning goals are sent as input data from the terminals to the server. The server receives this information and stores it in a database.

[0246] Step 3:

[0247] The server uses a generative AI model based on the stored information to generate a custom-made learning plan that is optimal for each learner. The input is data about the user's learning style and learning goals, and the AI ​​model calculates the data to obtain the optimal learning plan as output. This learning plan is then sent to the device.

[0248] Step 4:

[0249] The server selects appropriate online educational content based on the user's learning plan. The input is the generated learning plan, and the output is the selected educational content (videos, text, exercises, etc.). These contents are sent to the terminal and displayed.

[0250] Step 5:

[0251] A user answers exercises presented on a device. The input is the user's answer, which is sent from the device to a server. The server uses an automated evaluation system to grade the answer and generate feedback. Here, the input is the user's answer data and the output is the generated feedback.

[0252] Step 6:

[0253] The server tracks learning data in real time and stores it in a database, which records information such as learning time, grades, progress, etc. In this step, the input is the user's learning activity data, and the output is the saved learning data.

[0254] Step 7:

[0255] When the user selects to check progress, the server generates a detailed learning report based on the accumulated data and displays it on the terminal. The input is the accumulated learning data, and the output is a detailed learning report.

[0256] Step 8:

[0257] The server provides a function for users to learn about products in a virtual store and answer practice questions. Users use their devices to learn about products and then answer practice questions. The input is the user's learning content and answers, and the server automatically evaluates and outputs the results.

[0258] Step 9:

[0259] The server provides the user with additional educational resources and advice based on the learning results and feedback, thereby improving the user's understanding. The input is the evaluation result, and the output is additional educational resources and advice.

[0260] 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.

[0261] This invention relates to an educational system that provides learners with a more personalized learning experience. The system generates a custom-made learning plan based on the user's learning style and progress, and provides educational content that can be accessed online. It also manages learning progress through an automated assessment system using AI, tracks learning data, and creates reports. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it provides optimal support according to the learner's emotional state.

[0262] Learning plan generation

[0263] 1. A user logs in from a terminal

[0264] When a user accesses the system and enters their login information, the server verifies the authentication information and displays the dashboard on the terminal.

[0265] 2. Determine your learning style and goals

[0266] Users answer questions about their learning style and learning goals, and the server receives the information and stores it in a database.

[0267] 3. Create a custom learning plan

[0268] The server uses an AI model to analyze the user's information, generate a custom learning plan, and store it in a database.

[0269] Providing educational content

[0270] 1. Selection of online educational content

[0271] The server selects the most suitable educational content based on the user's learning plan.

[0272] 2. Display of Content

[0273] When a user starts a study session, the server sends selected content to the terminal for display.

[0274] Progress assessment and feedback

[0275] 1. Answers and evaluations for practice questions

[0276] Users answer exercises and send their answers from their device to a server, which uses an automated evaluation system to grade the answers and generate feedback.

[0277] 2. Real-time feedback

[0278] The server immediately sends and displays the evaluation results and feedback to the user.

[0279] Use of emotion engine

[0280] 1. User Emotion Recognition

[0281] The device's sensors, such as the camera and microphone, are used to collect the user's emotional state in real time.

[0282] 2. Emotion Data Analysis

[0283] The emotion data collected by the server is analyzed by an emotion engine to determine the learner's current emotional state.

[0284] 3. Adjust your study plan based on your emotions

[0285] Based on the emotion engine's judgment, the server dynamically adjusts the learning plan and presented content, for example, providing more relaxing content if the learner's concentration is declining.

[0286] Tracking and reporting on learning data

[0287] 1. Tracking training data

[0288] The server tracks users' learning activities and emotional data in real time and stores them in a database.

[0289] 2. Report Generation

[0290] When the user selects to check progress, the server generates a detailed report based on the accumulated learning data and emotional data and displays it on the device.

[0291] Collaboration with educational support staff

[0292] 1. Choosing Tutor Support

[0293] If the user desires tutor support, he selects the "tutor support" option.

[0294] 2. Tutor selection and booking

[0295] The server displays a list of available tutors, and the user selects and reserves the tutor of their choice. The server notifies the tutor of the reservation information and adjusts the schedule.

[0296] Specific examples

[0297] For example, if a user wants to study mathematics, they log in and select "Mathematics" as their study subject. If the user prefers video learning materials, the server uses AI to select the most suitable video learning materials and displays them on the device. After the user completes the practice problems and submits their answers, the server grades them using an automatic evaluation system and provides real-time feedback. Meanwhile, the device's camera and microphone recognize emotions from the user's facial expressions and tone of voice, which are analyzed by an emotion engine. If the user's concentration is low, the server suggests breaks or easy content to relax. All learning and emotional data is tracked and provided as detailed reports.

[0298] In this way, the system of the present invention can provide a more effective educational experience based on the user's emotional state, maximizing learning outcomes.

[0299] The processing flow will be explained below.

[0300] Step 1:

[0301] The user accesses the EduBridge URL from their device and logs in for the first time.

[0302] Step 2:

[0303] The server receives the user's login information, authenticates them, and if successful, displays the user's dashboard on the device.

[0304] Step 3:

[0305] Users select the "Personal Settings" option from their dashboard and answer questions about their learning style and goals.

[0306] Step 4:

[0307] The server receives the user's answers and stores them in a database.

[0308] Step 5:

[0309] The server uses the stored information to use AI models to generate a custom learning plan based on the user's learning style and progress, and stores the resulting plan in a database.

[0310] Step 6:

[0311] The server sends the generated learning plan to the device and displays it to the user.

[0312] Step 7:

[0313] The user can review the learning plan and make any necessary adjustments, which are then sent from the device to the server.

[0314] Step 8:

[0315] The server stores the adjusted learning plan in a database.

[0316] Step 9:

[0317] Users select a subject from the dashboard and click the "Start" button.

[0318] Step 10:

[0319] The server selects appropriate educational content (videos, text, practice questions, etc.) based on the user's learning plan and sends it to the device.

[0320] Step 11:

[0321] It uses the device's camera and microphone to collect the user's facial expressions and voice in real time and recognize the user's emotional state.

[0322] Step 12:

[0323] The emotion engine analyzes the collected data and determines the user's current emotional state.

[0324] Step 13:

[0325] The server dynamically adjusts the learning plan and presented content based on the results of the emotion engine, for example, providing more relaxing content when the user is not concentrating.

[0326] Step 14:

[0327] Users study the educational content displayed and answer practice questions.

[0328] Step 15:

[0329] Once users submit their answers, the server uses an automated rating system to grade the answers and generate instant feedback.

[0330] Step 16:

[0331] The server sends the evaluation results and feedback to the device and displays them to the user.

[0332] Step 17:

[0333] The server tracks users' learning data (study time, accuracy rate, emotional state, etc.) in real time and stores it in a database.

[0334] Step 18:

[0335] When a user selects "Check Progress" from the dashboard, the server generates a detailed report based on the accumulated learning data and emotional data and displays it on the device.

[0336] Step 19:

[0337] If a user wishes to receive tutor support, they can select the "Tutor Support" option from their dashboard.

[0338] Step 20:

[0339] The server displays a list of available tutors and the user selects the tutor of their choice.

[0340] Step 21:

[0341] The user selects the desired date and time and sends the reservation information from the terminal to the server.

[0342] Step 22:

[0343] The server notifies the tutor of the reservation information and adjusts the schedule.

[0344] Step 23:

[0345] The user and tutor prepare to start the session at the specified date and time.

[0346] Example 2

[0347] 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."

[0348] Traditional educational systems have difficulty providing individualized learning plans that match each learner's learning style and progress, and lack efficient means for assessing learning progress and providing feedback. Furthermore, there is no system in place to provide appropriate learning support based on the learner's emotional state, which can lead to reduced learning efficiency. This creates the problem of learners not receiving an optimal learning experience.

[0349] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0350] In this invention, the server includes means for generating a custom-made learning plan according to an individual's learning style and learning progress, means for providing educational content that can be accessed online, means for evaluating learning progress using an automated evaluation system, means for tracking learning data and generating reports, means for connecting learners with personnel providing educational support, means for collecting emotional data using sensors in the device, and means for analyzing the emotional data and dynamically adjusting the learning plan. This makes it possible to provide an optimized learning plan for each learner, evaluate and feedback learning progress in real time, and provide appropriate support according to the learner's emotional state.

[0351] "Learning style" refers to a learner's preferred learning method or format, such as whether they prefer video materials or textbooks, or whether they prefer self-study or tutor support.

[0352] "Learning progress" refers to the standard for measuring a learner's current learning progress and level of understanding.

[0353] A "custom-made learning plan" refers to a learning schedule and materials that are individually created based on each learner's learning style and progress.

[0354] "Online-accessible educational content" refers to learning materials such as videos, textbooks, and exercises that learners can access via the internet.

[0355] An "automated assessment system" refers to a system that automatically scores and evaluates learners' answers and progress and generates feedback.

[0356] "Learning data" refers to data related to a learner's learning activities, including, for example, study time, correct answer rate, and history of learning materials used.

[0357] A "report" is a document created based on accumulated learning data and evaluation results, and refers to a document that shows learning progress and achievement.

[0358] "Persons providing educational support" refers to those who have the role of providing direct learning support and advice to learners, including tutors and coaches.

[0359] "Device sensors" refer to devices such as cameras and microphones that are used to collect information about the user's emotional state.

[0360] "Emotional data" refers to information that indicates a learner's emotional state, such as information obtained from their facial expressions or tone of voice.

[0361] An "emotion engine" refers to a system that analyzes collected emotional data and determines the learner's emotional state.

[0362] "Dynamic adjustment" refers to changing the learning plan and content presented in response to changing conditions in real time.

[0363] Learning plan generation

[0364] To implement the invention, a user first launches a browser on their terminal and accesses the system's login page. They enter their user ID and password and click the "Login" button. This authentication information is sent to the server, which queries the database for authentication. If authentication is successful, the server sends the HTML data for the dashboard screen to the terminal, which then displays it on the terminal.

[0365] Next, the user selects the "Learning Settings" menu on the dashboard, enters their learning style and learning goals in the displayed question form, and clicks the "Submit" button. This input data is sent from the device to the server. The server stores the received data in a database, and then inputs the stored data into an AI model (e.g., TensorFlow or PyTorch) for analysis. The AI ​​model generates a custom learning plan, which is then stored in the database.

[0366] Providing educational content

[0367] The server selects appropriate educational content (videos, texts, exercises, etc.) based on the generated learning plan. When the user clicks the "Start Learning" button on the dashboard, the server sends the selected educational content to the device, which then displays it.

[0368] Progress assessment and feedback

[0369] The user answers the exercises on their device and sends the results to the server, which uses an automated evaluation system to grade the answers and generate an evaluation result, which is immediately fed back to the user and displayed on their device.

[0370] Use of emotion engine

[0371] While the user is studying, emotional data is collected through the device's camera and microphone. For example, the camera captures the user's facial expressions and the microphone records the tone of their voice. The collected emotional data is sent to a server, which analyzes it using an emotion engine (e.g., Python's OpenCV or Haar Cascade). Based on the analysis results, if the emotional state indicates a decrease in concentration or fatigue, the server dynamically adjusts the study plan and presented content. For example, if the user's concentration decreases, it will provide a light video or simple questions to help them relax.

[0372] Tracking and reporting on learning data

[0373] The server tracks the user's learning activities and emotional data in real time and stores them in a database. When the user clicks the "Check Progress" button, the server generates a detailed report based on the accumulated data and sends it to the device. The report visually displays the user's learning progress and emotional fluctuations.

[0374] Collaboration with educational support staff

[0375] When the user selects the "Tutor Support" option, the server displays a list of available tutors. Once the user selects the desired tutor and confirms the reservation, the server notifies the tutor of the reservation information and adjusts the schedule.

[0376] Specific examples

[0377] For example, if a user wants to study mathematics, they log in and select "Mathematics" as their study subject. If the user prefers video learning materials, the server uses AI to select the most suitable video learning materials and displays them on the device. After the user completes the practice problems, they submit their answers to the server, which then grades them with an automated evaluation system and provides instant feedback. At the same time, the device's camera and microphone recognize emotions from the user's facial expressions and tone of voice, which are then analyzed by the emotion engine. For example, if the server determines that the user's concentration is declining, it can provide content to help them relax. Learning data and emotional data are tracked and provided to the user in the form of a detailed report.

[0378] Example prompts for generative AI models

[0379] "Generate custom learning plans based on the user's learning style. For example, if the user prefers video materials, select the most suitable videos and incorporate them into the learning plan."

[0380] With the above-described configuration, the present invention can provide users with a personalized learning experience, maximizing learning efficiency and learning outcomes.

[0381] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0382] Step 1:

[0383] The user enters their login information and clicks the "Login" button.

[0384] Input: User ID, Password.

[0385] Processing: The terminal sends the input data to the server.

[0386] Output: The authentication information sent to the server.

[0387] Step 2:

[0388] The server checks the received authentication information and performs authentication.

[0389] Input: User ID, Password.

[0390] Processing: The server queries the database to perform authentication, and if successful, generates HTML data for the dashboard screen.

[0391] Output: Authentication result, HTML data of the dashboard screen (if authentication is successful).

[0392] Step 3:

[0393] The server sends the HTML data of the dashboard screen to the terminal, which displays it.

[0394] Input: HTML data of the dashboard screen.

[0395] Processing: The terminal analyzes the received HTML data and displays it in the browser.

[0396] Output: The dashboard that is displayed on the user's screen.

[0397] Step 4:

[0398] The user selects the "Learning Settings" menu on the dashboard and enters their learning style and learning goals.

[0399] Input: learning styles, learning goals.

[0400] Processing: The user enters data into the inquiry form and clicks the "Submit" button. The terminal sends the input data to the server.

[0401] Output: Learning style and learning goal data sent to the server.

[0402] Step 5:

[0403] The server stores the received learning data in a database and inputs it into the AI ​​model.

[0404] Input: Learning styles, learning goal data.

[0405] Processing: The server stores the data in a database and then inputs the stored data into an AI model (e.g., TensorFlow or PyTorch) for analysis.

[0406] Output: A generated custom lesson plan.

[0407] Step 6:

[0408] The educational content is selected based on the learning plan generated by the server.

[0409] Enter: Study Plan.

[0410] Processing: The server selects the most appropriate content (video, text, exercises, etc.) from the database.

[0411] Output: Selected educational content.

[0412] Step 7:

[0413] The user clicks the "Start Learning" button on the dashboard. The server sends the selected educational content to the device and displays it.

[0414] Input: Learning content.

[0415] Processing: The server sends the content to the device, and the device displays the content on the browser.

[0416] Output: The educational content displayed on the user's screen.

[0417] Step 8:

[0418] The user answers the exercises on the terminal and sends the answer data to the server.

[0419] Input: User's answer data.

[0420] Processing: The terminal sends the answer data to the server.

[0421] Output: The answer data sent to the server.

[0422] Step 9:

[0423] The server grades the received answer data using an automatic evaluation system to generate an evaluation result.

[0424] Input: Answer data.

[0425] Processing: The server uses an automated evaluation system to grade the answers and generate an evaluation result.

[0426] Output: The generated evaluation results.

[0427] Step 10:

[0428] The server feeds back the evaluation results to the user and displays them on the terminal.

[0429] Input: Evaluation result.

[0430] Processing: The server sends the evaluation results to the terminal, and the terminal displays the evaluation results in the browser.

[0431] Output: The evaluation results displayed on the user's screen.

[0432] Step 11:

[0433] The device collects the user's emotional data through the camera and microphone and sends it to the server.

[0434] Input: User's facial expression data, tone of voice data.

[0435] Processing: The device collects emotion data and sends it to the server.

[0436] Output: Emotion data sent to the server.

[0437] Step 12:

[0438] The server analyzes the emotion data using an emotion engine to determine the user's emotional state.

[0439] Input: Emotion data.

[0440] Processing: The server uses an emotion engine to analyze the emotion data and determine the user's emotional state.

[0441] Output: Parsed emotional state.

[0442] Step 13:

[0443] The server dynamically adjusts the learning plan and presented content based on the emotional state.

[0444] Input: Emotional state.

[0445] Processing: The server changes the learning plan and content based on the emotional state, and selects new content if necessary.

[0446] Output: Tailored learning plans and content.

[0447] Step 14:

[0448] The server tracks users' learning activities and emotional data in real time and stores them in a database.

[0449] Input: learning activity data, emotion data.

[0450] Processing: The server collects this data and stores it in a database.

[0451] Output: Real-time training data and sentiment data stored in a database.

[0452] Step 15:

[0453] When the user clicks the "Check Progress" button, the server generates a detailed report and sends it to the device.

[0454] Input: Training data, emotion data.

[0455] Processing: The server generates a report based on the data and sends it to the device.

[0456] Output: A detailed report that is displayed on the user's screen.

[0457] (Application example 2)

[0458] 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."

[0459] While traditional educational systems are customized to suit learners' learning styles and progress, they lack the ability to dynamically adjust learning experiences based on the learner's emotional state. This makes it difficult to maximize learning effectiveness by providing appropriate feedback and learning content based on the learner's concentration and emotional state. Furthermore, because emotion recognition and learning support utilizing the learner's device are not integrated, real-time adjustment of learning plans is also not possible.

[0460] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating a customized learning plan based on an individual's learning style and learning progress, means for providing educational content accessible online, means for evaluating learning progress using an automated evaluation system, means for tracking learning data and generating reports, means for connecting learners with educational support providers, and means for recognizing the learner's emotional state in real time and dynamically adjusting the learning plan based on the emotional state. This makes it possible to provide an optimal learning plan based on the learner's emotional state. Furthermore, by using wearable devices such as smart glasses or head-mounted displays, emotions can be recognized in real time and the learning experience can be instantly optimized.

[0461] "Learning style" refers to the method or approach that a learner uses to learn most effectively.

[0462] "Learning progress" is an indicator of how far a learner has progressed with a particular learning plan or curriculum.

[0463] A "custom-made learning plan" is an individualized learning schedule and materials created to suit the needs and characteristics of each individual learner.

[0464] "Online-accessible educational content" refers to educational materials, lessons, videos, e-books, etc. that are available via the internet.

[0465] An "automated assessment system" is a system that uses artificial intelligence and algorithms to automatically assess learners' assignments and tests and provide feedback.

[0466] "Learning data" refers to information related to a learner's learning activities, progress, grades, behavioral logs, etc.

[0467] A "report" is a report that summarizes learning progress, grades, and other related information generated based on learning data.

[0468] "Educational support personnel" are professionals such as teachers and tutors who provide educational advice and support to learners.

[0469] "To collaborate" refers to the act of coordinating two or more elements or systems to function together.

[0470] "Emotional state" refers to the learner's state of mind or psychological response, and includes emotions such as joy, sadness, surprise, and concentration.

[0471] "Dynamic adjustment" refers to automatically changing content and methods to adapt to changing conditions and situations in real time.

[0472] A "wearable device" is a computing device that is worn on the body and includes smart glasses and head-mounted displays.

[0473] This invention relates to an educational system that provides a personalized learning experience for learners, dynamically adjusts the learning plan based on the learner's emotional state, and utilizes wearable devices to provide real-time learning assistance.

[0474] The server includes means for generating a custom-made learning plan according to an individual's learning style and learning progress, means for providing educational content that can be accessed online, means for evaluating learning progress using an automated evaluation system, means for tracking learning data and generating reports, means for connecting learners with personnel providing educational support, and means for recognizing the learner's emotional state in real time and dynamically adjusting the learning plan based thereon.

[0475] The server receives login information from the user's device, authenticates them, and then displays a dashboard. The user answers questions about their learning style and goals, which are then stored in a database and analyzed by an AI model to generate a custom learning plan.

[0476] The server selects the most suitable online educational content based on the user's learning plan and displays it on the device. When the user starts a learning session, the device's camera and microphone collect the user's emotional state in real time and send the data to the server.

[0477] The emotion engine analyzes this emotional data to determine the learner's current emotional state. The server then dynamically adjusts the learning plan and content provided based on the learner's emotional state. For example, if the learner's concentration is declining, the server will provide the learner with simple video content to help them relax.

[0478] For example, when a user wears smart glasses, a camera analyzes their facial expressions in real time and transmits emotional data to a server, which then adjusts their learning plan and displays appropriate feedback and content on the smart glasses' display.

[0479] The hardware used is wearable devices such as smart glasses and head-mounted displays, and the software uses OpenCV, emotion_recognition, ai_learning_plan, and ai_evaluation_system.

[0480] Example prompt sentence:

[0481] "Design an app for smart glasses that analyzes the user's emotional state in real time and dynamically adjusts the study plan based on the results. Include a feature that provides relaxing video content when the user is not concentrating."

[0482] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0483] Step 1:

[0484] A user logs in to the system from a terminal. The input is the user's login information, and the output is authenticated by the server and a dashboard is displayed. Specifically, the server receives the login information sent from the terminal and compares it with the authentication information stored in the database.

[0485] Step 2:

[0486] The user answers questions about their learning style and learning goals. The input is the user's answer data, and the output is that information stored in a database. The server receives the information the user entered into the input form and stores it in the database as structured data.

[0487] Step 3:

[0488] The server uses an AI model to analyze the user's information and generate a custom learning plan. The input is the user's learning style and goal data, and the output is a custom learning plan. Specifically, the server inputs the user's data into the AI ​​model, generates an optimal learning plan, and stores it in a database.

[0489] Step 4:

[0490] The server selects online educational content based on the user's learning plan. The input is the generated learning plan, and the output is the selected educational content. Specifically, the server analyzes the content of the learning plan and extracts relevant educational materials from the content database.

[0491] Step 5:

[0492] The user starts a learning session and educational content is displayed on the device. The input is the selected educational content, and the output is the display on the device. Specifically, the server sends the educational content to the user's device and displays it in a browser or app.

[0493] Step 6:

[0494] The device's camera and microphone collect the user's emotional state in real time. The input is sensor data from the camera and microphone, and the output is emotional data. Specifically, the device collects video and audio data and sends it to a server for analysis.

[0495] Step 7:

[0496] The server analyzes the emotional data using an emotion engine to determine the user's current emotional state. The input is the collected emotional data, and the output is the emotional state resulting from the analysis. The server analyzes the data using the emotion engine to identify the type and intensity of the emotion.

[0497] Step 8:

[0498] The server dynamically adjusts the learning plan and presented content based on the emotional state. The input is the emotional state resulting from the analysis, and the output is the adjusted learning plan and content. Specifically, the server dynamically changes the learning plan and content based on the emotional state, and provides video content for relaxation as needed.

[0499] Step 9:

[0500] Learning data and emotional data are tracked, and the server generates a detailed report. The input is learning activity data and emotional data, and the output is the generated detailed report. Specifically, the server comprehensively analyzes the collected data, creates a report including progress and emotional state, and displays it on the device.

[0501] Step 10:

[0502] It provides learning support by linking multiple devices. The input is instruction data from the server, and the output is display and feedback to the user. Specifically, the server sends feedback and new content to wearable devices such as smart glasses and head-mounted displays, which then display it.

[0503] 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.

[0504] 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.

[0505] 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.

[0506] [Second embodiment]

[0507] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0508] 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.

[0509] 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).

[0510] 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.

[0511] 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.

[0512] 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).

[0513] 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.

[0514] 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.

[0515] 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.

[0516] 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.

[0517] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0518] 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."

[0519] The educational system of the present invention aims to provide high-quality education regardless of factors such as financial difficulties or geographical limitations. The system generates a custom-made learning plan based on the user's learning style and progress, and provides educational content that can be accessed online. It also manages learning progress through an automated evaluation system using AI, tracks learning data, and creates reports. It also has a function for connecting learners with those providing educational support.

[0520] Specifically, the system operates as follows.

[0521] Learning plan generation

[0522] 1. A user logs in from a terminal

[0523] A user accesses the system and enters their login information. The server verifies the authentication information and displays the user's personal settings page.

[0524] 2. Determine your learning style and goals

[0525] Users answer questions about their learning styles and goals. The server receives this information and stores it in a database.

[0526] 3. Generate a custom learning plan

[0527] The server uses AI models based on the stored information to generate a custom learning plan for each learner, tailored to their learning style and goals.

[0528] Providing educational content

[0529] 1. Selection of online educational content

[0530] The server selects appropriate online educational content (videos, texts, exercises, etc.) based on the user's learning plan.

[0531] 2. Display of Content

[0532] When a user starts a learning session from a terminal, the server transmits selected content to the terminal for display.

[0533] Progress assessment and feedback

[0534] 1. Answers and evaluations for practice questions

[0535] The user answers the exercises provided and sends them to the server via their device, which then grades the answers using an automated evaluation system and provides feedback.

[0536] 2. Real-time feedback

[0537] The server provides instant feedback to the user on the results of the assessment, offering additional resources and advice to deepen their understanding.

[0538] Tracking and reporting on learning data

[0539] 1. Tracking training data

[0540] The server tracks users' learning activities in real time and stores them in a database, recording data such as study time, grades, and progress.

[0541] 2. Report Generation

[0542] When the user selects to check progress, the server generates a detailed learning report based on the accumulated data and displays it on the device.

[0543] Collaboration with educational support staff

[0544] 1. Choosing Tutor Support

[0545] If the user desires tutor support, he or she selects the function for linking with a tutor from the terminal.

[0546] 2. Tutor selection and booking

[0547] The server displays a list of tutors available online, and the user selects the tutor they want to book a session with. The server then notifies the tutor of the reservation information and adjusts the schedule.

[0548] Specific examples

[0549] For example, if a user wants to study mathematics, they log in and select "Mathematics" as their study subject. If the user prefers to study using video materials, the server uses an AI model to select and display the most suitable video materials for the user. After studying, the user answers and submits practice questions, and the server automatically grades them and provides real-time feedback. In addition, all learning progress data is tracked and presented to the user in a detailed report. If necessary, the user can also receive additional guidance from an online tutor.

[0550] In this way, the system of the present invention provides learners with a highly personalized educational experience tailored to their individual needs.

[0551] The processing flow will be explained below.

[0552] Step 1:

[0553] The user accesses the EduBridge URL from their device and logs in for the first time.

[0554] Step 2:

[0555] The server receives the user's login information and performs authentication. If successful, the user's dashboard is displayed on the device.

[0556] Step 3:

[0557] Users select the "Personal Settings" option from their dashboard and answer questions about their learning style and goals.

[0558] Step 4:

[0559] The server receives the user's answers and stores them in a database.

[0560] Step 5:

[0561] The server uses the stored information to create a custom learning plan based on the user's learning style and progress using an AI model, and stores the resulting plan in a database.

[0562] Step 6:

[0563] The server transmits the generated learning plan to the terminal and displays it to the user.

[0564] Step 7:

[0565] The user reviews the learning plan and makes any necessary adjustments, which are then sent from the device to the server.

[0566] Step 8:

[0567] The server stores the adjusted learning plan in a database.

[0568] Step 9:

[0569] The user selects a subject from the dashboard and clicks the "Start" button.

[0570] Step 10:

[0571] The server selects appropriate educational content (videos, text, practice questions, etc.) based on the user's learning plan and sends it to the device.

[0572] Step 11:

[0573] The user studies the displayed educational content and answers the exercises.

[0574] Step 12:

[0575] Once the user submits their answer, the server uses an automated rating system to grade the answer and generate instant feedback.

[0576] Step 13:

[0577] The server sends the evaluation results and feedback to the terminal and displays them to the user.

[0578] Step 14:

[0579] The server tracks users' learning data (study time, accuracy rate, etc.) in real time and stores it in a database.

[0580] Step 15:

[0581] When a user selects "Check Progress" from the dashboard, the server analyzes the accumulated learning data and generates a detailed learning report.

[0582] Step 16:

[0583] The server sends the generated report to the terminal for display to the user.

[0584] Step 17:

[0585] If the user desires tutor support, he / she selects the "Tutor Support" option from the dashboard.

[0586] Step 18:

[0587] The server displays a list of currently available tutors, and the user selects the tutor they desire.

[0588] Step 19:

[0589] The user selects the desired date and time, and transmits reservation information from the terminal to the server.

[0590] Step 20:

[0591] The server notifies the tutor of the reservation information and adjusts the schedule.

[0592] Step 21:

[0593] The user and tutor prepare to start the session at the specified date and time.

[0594] Example 1

[0595] 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."

[0596] In today's education system, financial hardship and geographical constraints make it difficult to obtain a high-quality education. Furthermore, traditional education systems often fail to adequately accommodate individual learners' learning styles and progress, hindering efficient learning. Furthermore, automated assessment and learning data tracking and feedback functions are often lacking, resulting in insufficient progress management for learners. These issues need to be resolved.

[0597] 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.

[0598] In this invention, the server includes a means for a user to log in from a terminal, a means for setting the user's learning style and goals, a means for using a generative AI model to generate a customized learning plan, a means for selecting educational content that can be accessed online, a means for displaying the selected educational content, a means for evaluating learning progress using an automated evaluation system, a means for providing feedback on the evaluation results to the user in real time, a means for tracking learning data and storing it in a database, a means for generating reports based on the tracked data, and a means for connecting learners with personnel providing educational support. This makes it possible to provide high-quality personalized education that meets the needs of individual learners.

[0599] "Means for users to log in from a terminal" refers to the method by which users access the system using a terminal such as a PC or smartphone, enter login information, and are authenticated.

[0600] The "means for setting user's learning style and goals" is a method by which a user inputs information about his or her learning style and goals, which is received by the system and stored in the database.

[0601] "Method using a generative AI model to generate a custom-made study plan" means a method using a generative AI model to create an individually optimized study plan based on a user's learning style and goals.

[0602] "Means for selecting online accessible educational content" refers to the method by which the system selects appropriate online educational content based on the user's learning plan.

[0603] The "means for displaying the selected educational content" refers to a method by which the server transmits the selected educational content to the user's terminal and displays it.

[0604] A "means for assessing learning progress using an automated assessment system" is a method for automatically scoring exercises answered by a user and generating an assessment result.

[0605] The "means for providing feedback of the evaluation results to the user in real time" is a method for instantly notifying the user of the graded evaluation results and providing that feedback.

[0606] The "means for tracking learning data and storing it in a database" is a method for monitoring data related to a user's learning activities in real time and recording the data in a database.

[0607] The "means for generating a report based on tracked data" is a method for analyzing the tracked learning data and creating a detailed learning report.

[0608] "Means for connecting learners with educational support providers" refers to a method by which learners can connect online with educational support providers and receive support.

[0609] The educational system of this invention aims to provide high-quality education by creating a custom-made learning plan based on the user's learning style and learning progress. The system is designed for users to access from a device such as a PC or smartphone. The specific hardware and software configuration and processing flow are shown below.

[0610] First, the user accesses the system from a terminal and enters their login information. The server compares this authentication information with the database, and if authentication is successful, displays the user's personal settings page. This login process may use two-factor authentication in addition to the user ID and password.

[0611] Next, the user answers a questionnaire on the system about their learning style and goals. The questionnaire includes items such as study time, target level, and preferred learning method (video, text, practice questions). The server receives this information and stores it in a database. At this point, the user's learning data is initialized.

[0612] Based on the stored data, the server uses a generative AI model to generate a custom learning plan. This model uses existing machine learning algorithms and natural language processing techniques. The generated learning plan is displayed on the user's personalized settings page. The learning plan includes specific learning content, a progress schedule, and recommended learning materials.

[0613] Based on the generated learning plan, the server selects appropriate online educational content from a database, including videos, texts, exercises, etc. This content is sent to and displayed on the device when the user starts a learning session.

[0614] The practice questions that users answer during their studies are sent to a server and instantly scored by an automated assessment system. The results are then fed back to the user in real time, and they may be given feedback on their understanding or additional resources. This feedback helps users to study more effectively.

[0615] All data related to learning activities is tracked in real time by the server and stored in a database. This includes data such as study time, grades, and progress. When a user selects to check their progress, the server generates a detailed learning report based on the accumulated data and displays it on the device. The report includes information such as total study time, progress on each learning item, and grades.

[0616] Furthermore, if a user needs educational support, they can use the tutor support function. With this function, the server displays a list of available tutors, and the user can select the tutor of their choice and reserve a session. The tutor receives the user's reservation information, adjusts their schedule, and provides instruction.

[0617] For example, if a user wants to learn intermediate level mathematics, they can input the following prompt into the generative AI model:

[0618] "Generate a custom learning plan for a user who prefers video learning, covering basic to advanced mathematics. The user's current level is intermediate."

[0619] The generated learning plan recommends intermediate-level math video materials and sets specific learning tasks based on the plan. Users receive real-time feedback as they watch the videos and answer practice questions. The learning data accumulated during this process is tracked by the server and presented to the user as a detailed report. Users can also book a session with a tutor to receive further in-depth learning support, if necessary.

[0620] In this way, the system allows users to enjoy a highly personalized learning experience, maximizing learning efficiency.

[0621] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0622] Step 1:

[0623] A user logs in from a terminal

[0624] The user accesses the system using a device such as a computer or smartphone and enters login information (user ID and password).

[0625] Input: User ID, Password

[0626] The server checks the authentication information against a database and performs authentication. If authentication is successful, the user's personal settings page is displayed on the terminal.

[0627] Example of operation: The user enters their ID and password and presses the "Login" button.

[0628] Step 2:

[0629] Define user learning styles and goals

[0630] Users answer a questionnaire on the system about their learning style and goals.

[0631] Input: Study time, desired level, preferred learning method (video, text, exercises), etc.

[0632] The server receives this information and stores it in a database.

[0633] Example of operation: A user answers a survey and presses the "Save" button.

[0634] Step 3:

[0635] Generate a custom learning plan

[0636] The server inputs prompt sentences into the generative AI model based on the stored data.

[0637] Input: User learning style, goal data

[0638] Example prompt: "Generate a custom learning plan for a user who prefers video instruction, covering basic and advanced mathematics. The user's current level is intermediate."

[0639] The server uses the generative AI model to generate an individually optimized learning plan, which is displayed on the user's personal settings page.

[0640] Output: Custom-made study plan

[0641] Example of operation: A learning plan is generated and the user reviews it.

[0642] Step 4:

[0643] Selection of online educational content

[0644] The server searches the database for appropriate online educational content (videos, texts, exercises, etc.) based on the generated learning plan.

[0645] Input: Study Plan

[0646] The server adds links to educational content to the learning plan.

[0647] Output: A list of selected educational content

[0648] Example of operation: Links to the selected content are displayed as a list.

[0649] Step 5:

[0650] View content

[0651] The user initiates a learning session and the server sends the content to the terminal for display.

[0652] Input: Start instruction for study session

[0653] The server transmits the selected educational content to the terminal and displays it.

[0654] Output: On-screen display of educational content

[0655] Example of how it works: A user starts a learning session and a video plays.

[0656] Step 6:

[0657] Exercises and answers

[0658] The user answers the exercises presented to them and sends the answers to the server.

[0659] Input: Answer to the exercise

[0660] The server grades the answers using an automated evaluation system and generates an evaluation result.

[0661] Output:Scoring results

[0662] Example of operation: When the user answers the questions and presses the "Submit" button, the scoring results are displayed immediately.

[0663] Step 7:

[0664] Real-time feedback

[0665] The server provides the user with real-time feedback on the results of the assessment to confirm their level of understanding.

[0666] Input:Scoring results

[0667] The server may also provide additional resources and exercises.

[0668] Output: Show feedback

[0669] Example of how it works: Additional questions are displayed as feedback regarding areas of insufficient understanding.

[0670] Step 8:

[0671] Tracking and storing learning data

[0672] The server tracks users' learning activities (study time, grades, progress) in real time and stores them in a database.

[0673] Input: Learning activity data

[0674] The server collects and stores this data for analysis.

[0675] Output: Training data stored in a database

[0676] Example of how it works: Data is automatically recorded during a learning session.

[0677] Step 9:

[0678] Report Generation

[0679] When the user selects to check progress, the server generates a detailed learning report based on the accumulated data.

[0680] Input: Progress check instructions

[0681] The server analyzes the data and generates a progress report that is displayed on the terminal.

[0682] Output: Learning report

[0683] Example of operation: A user opens the report and checks the total study time and progress of each learning content.

[0684] Step 10:

[0685] Selecting and booking tutor support

[0686] If the user desires tutor support, he or she selects the function for linking with a tutor from the terminal.

[0687] Input: Request for tutor support

[0688] The server displays a list of tutors available online, and the user selects the tutor of their choice and books a session.

[0689] Output: Notification of reservation information

[0690] The server notifies the tutor of the reservation information and adjusts the schedule.

[0691] Example of operation: The user selects the desired tutor and presses the "Reserve" button.

[0692] (Application example 1)

[0693] 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."

[0694] Today's learners demand high-quality education regardless of financial difficulties or geographical limitations. However, it is difficult to provide customized education that meets individual learning needs, and it is not easy to accurately evaluate learning progress and provide feedback. Furthermore, there are few ways to efficiently acquire product knowledge in physical or virtual stores while studying. A system that solves these challenges and provides a more effective and personalized learning experience is needed.

[0695] 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.

[0696] In this invention, the server includes: a means for generating a customized learning plan based on a user's learning style and learning progress; a means for providing educational content that can be accessed online; a means for evaluating learning progress using an automated evaluation system; a means for tracking learning data and generating reports; a means for connecting learners with educational support providers; a means for users to learn product information and answer practice questions in a virtual store; and a means for executing the above functions using a smartphone. This allows learners to overcome financial difficulties and geographical limitations and enjoy a highly personalized educational experience tailored to their individual learning needs. It also enables efficient product knowledge learning in a virtual store.

[0697] "User" refers to a person who uses the learning system.

[0698] "Learning style" refers to the method or format that a learner prefers to learn most effectively.

[0699] "Learning Progress" refers to the progress a learner has made in their learning plan.

[0700] "Custom-made learning plan" refers to a learning plan that is customized based on each learner's individual needs and characteristics.

[0701] "Online accessible educational content" refers to educational materials and teaching materials that are available to learners via the internet.

[0702] An "automated assessment system" refers to a system that uses AI or algorithms to assess learners' progress and achievements.

[0703] "Learning Data" refers to information about a learner's behavior, progress, grades, etc.

[0704] A "report" refers to a document that summarizes a learner's progress and achievements based on learning data.

[0705] "Persons providing educational support" refer to people who are responsible for providing education and guidance to learners.

[0706] A "virtual store" refers to a virtual store that sells products and services online.

[0707] A "smartphone" refers to a mobile phone terminal with internet connectivity and various functions.

[0708] A "server" refers to a computer that provides services and information to clients over a network.

[0709] To implement this invention, the following system configuration and processing are used: The main components of the system include a server, a terminal used by a user (e.g., a smartphone), and educational content that can be accessed online.

[0710] The server first provides a means for users to input their learning style and learning goals. Users log in to the system from their terminals and input information about their learning style and learning goals. The server then stores this information in a database.

[0711] The server then uses the stored information to generate a personalized learning plan using a generative AI model, which includes content tailored to the user's learning style and goals. For example, if a user prefers video learning materials, appropriate video content will be selected.

[0712] The server provides appropriate online educational content based on the user's learning plan. When the user starts a learning session from their device, the server sends the selected content to the device and displays it. The educational content includes videos, textbooks, exercises, etc.

[0713] The server also evaluates learning progress. Users answer the exercises provided and send them to the server via their device. The server then uses an automated evaluation system to grade the answers and provide feedback, allowing users to instantly check their level of understanding.

[0714] Learning progress data is tracked by the server and stored in a database. Data such as study time, grades, and progress are recorded, and when a user selects to check progress, a detailed learning report is generated and displayed on the device.

[0715] Furthermore, the system allows users to learn about products in the virtual store and answer practice questions, allowing them to test their understanding while learning how to select and use products in the virtual store.

[0716] The entire system is run using a smartphone, which is equipped with functions such as user login, learning style setting, educational content display, automatic assessment result display, and learning progress tracking.

[0717] The hardware and software used includes:

[0718] Hardware: Smartphone (iOS / Android)

[0719] Software: Python, Flask (web framework), Scikit-learn (machine learning library)

[0720] For example, when a user buys a new kitchen gadget, they can watch a video to learn how to use it and then test their understanding with practice questions. Here's an example of a prompt for the generative AI model:

[0721] "User just purchased a new kitchen gadget. Their learning style is videos, and their learning goal is to understand how to use the product. Generate an optimal learning plan."

[0722] This allows learners to overcome financial difficulties and geographical constraints, receive a highly personalized educational experience tailored to their individual learning needs, and efficiently learn about products in virtual stores.

[0723] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0724] Step 1:

[0725] The server receives authentication information for the user to log in from the terminal. The terminal sends the user ID and password to the server, which verifies the authentication information and displays the user's personal setting page. At this stage, the input is the user ID and password, and the output is the user's personal setting page.

[0726] Step 2:

[0727] The server provides a means for users to input information about their learning style and learning goals through their terminals. The learning style (e.g., video, text) and learning goals are sent as input data from the terminals to the server. The server receives this information and stores it in a database.

[0728] Step 3:

[0729] The server uses a generative AI model based on the stored information to generate a custom-made learning plan that is optimal for each learner. The input is data about the user's learning style and learning goals, and the AI ​​model calculates the data to obtain the optimal learning plan as output. This learning plan is then sent to the device.

[0730] Step 4:

[0731] The server selects appropriate online educational content based on the user's learning plan. The input is the generated learning plan, and the output is the selected educational content (videos, text, exercises, etc.). These contents are sent to the terminal and displayed.

[0732] Step 5:

[0733] A user answers exercises presented on a device. The input is the user's answer, which is sent from the device to a server. The server uses an automated evaluation system to grade the answer and generate feedback. Here, the input is the user's answer data and the output is the generated feedback.

[0734] Step 6:

[0735] The server tracks learning data in real time and stores it in a database, which records information such as learning time, grades, progress, etc. In this step, the input is the user's learning activity data, and the output is the saved learning data.

[0736] Step 7:

[0737] When the user selects to check progress, the server generates a detailed learning report based on the accumulated data and displays it on the terminal. The input is the accumulated learning data, and the output is a detailed learning report.

[0738] Step 8:

[0739] The server provides a function for users to learn about products in a virtual store and answer practice questions. Users use their devices to learn about products and then answer practice questions. The input is the user's learning content and answers, and the server automatically evaluates and outputs the results.

[0740] Step 9:

[0741] The server provides the user with additional educational resources and advice based on the learning results and feedback, thereby improving the user's understanding. The input is the evaluation result, and the output is additional educational resources and advice.

[0742] 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.

[0743] This invention relates to an educational system that provides learners with a more personalized learning experience. The system generates a custom-made learning plan based on the user's learning style and progress, and provides educational content that can be accessed online. It also manages learning progress through an automated assessment system using AI, tracks learning data, and creates reports. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it provides optimal support according to the learner's emotional state.

[0744] Learning plan generation

[0745] 1. A user logs in from a terminal

[0746] When a user accesses the system and enters their login information, the server verifies the authentication information and displays the dashboard on the terminal.

[0747] 2. Determine your learning style and goals

[0748] Users answer questions about their learning style and learning goals, and the server receives the information and stores it in a database.

[0749] 3. Create a custom learning plan

[0750] The server uses an AI model to analyze the user's information, generate a custom learning plan, and store it in a database.

[0751] Providing educational content

[0752] 1. Selection of online educational content

[0753] The server selects the most suitable educational content based on the user's learning plan.

[0754] 2. Display of Content

[0755] When a user starts a study session, the server sends selected content to the terminal for display.

[0756] Progress assessment and feedback

[0757] 1. Answers and evaluations for practice questions

[0758] Users answer exercises and send their answers from their device to a server, which uses an automated evaluation system to grade the answers and generate feedback.

[0759] 2. Real-time feedback

[0760] The server immediately sends and displays the evaluation results and feedback to the user.

[0761] Use of emotion engine

[0762] 1. User Emotion Recognition

[0763] The device's sensors, such as the camera and microphone, are used to collect the user's emotional state in real time.

[0764] 2. Emotion Data Analysis

[0765] The emotion data collected by the server is analyzed by an emotion engine to determine the learner's current emotional state.

[0766] 3. Adjust your study plan based on your emotions

[0767] Based on the emotion engine's judgment, the server dynamically adjusts the learning plan and presented content, for example, providing more relaxing content if the learner's concentration is declining.

[0768] Tracking and reporting on learning data

[0769] 1. Tracking training data

[0770] The server tracks users' learning activities and emotional data in real time and stores them in a database.

[0771] 2. Report Generation

[0772] When the user selects to check progress, the server generates a detailed report based on the accumulated learning data and emotional data and displays it on the device.

[0773] Collaboration with educational support staff

[0774] 1. Choosing Tutor Support

[0775] If the user desires tutor support, he selects the "tutor support" option.

[0776] 2. Tutor selection and booking

[0777] The server displays a list of available tutors, and the user selects and reserves the tutor of their choice. The server notifies the tutor of the reservation information and adjusts the schedule.

[0778] Specific examples

[0779] For example, if a user wants to study mathematics, they log in and select "Mathematics" as their study subject. If the user prefers video learning materials, the server uses AI to select the most suitable video learning materials and displays them on the device. After the user completes the practice problems and submits their answers, the server grades them using an automatic evaluation system and provides real-time feedback. Meanwhile, the device's camera and microphone recognize emotions from the user's facial expressions and tone of voice, which are analyzed by an emotion engine. If the user's concentration is low, the server suggests breaks or easy content to relax. All learning and emotional data is tracked and provided as detailed reports.

[0780] In this way, the system of the present invention can provide a more effective educational experience based on the user's emotional state, maximizing learning outcomes.

[0781] The processing flow will be explained below.

[0782] Step 1:

[0783] The user accesses the EduBridge URL from their device and logs in for the first time.

[0784] Step 2:

[0785] The server receives the user's login information, authenticates them, and if successful, displays the user's dashboard on the device.

[0786] Step 3:

[0787] Users select the "Personal Settings" option from their dashboard and answer questions about their learning style and goals.

[0788] Step 4:

[0789] The server receives the user's answers and stores them in a database.

[0790] Step 5:

[0791] The server uses the stored information to use AI models to generate a custom learning plan based on the user's learning style and progress, and stores the resulting plan in a database.

[0792] Step 6:

[0793] The server sends the generated learning plan to the device and displays it to the user.

[0794] Step 7:

[0795] The user can review the learning plan and make any necessary adjustments, which are then sent from the device to the server.

[0796] Step 8:

[0797] The server stores the adjusted learning plan in a database.

[0798] Step 9:

[0799] Users select a subject from the dashboard and click the "Start" button.

[0800] Step 10:

[0801] The server selects appropriate educational content (videos, text, practice questions, etc.) based on the user's learning plan and sends it to the device.

[0802] Step 11:

[0803] It uses the device's camera and microphone to collect the user's facial expressions and voice in real time and recognize the user's emotional state.

[0804] Step 12:

[0805] The emotion engine analyzes the collected data and determines the user's current emotional state.

[0806] Step 13:

[0807] The server dynamically adjusts the learning plan and presented content based on the results of the emotion engine, for example, providing more relaxing content when the user is not concentrating.

[0808] Step 14:

[0809] Users study the educational content displayed and answer practice questions.

[0810] Step 15:

[0811] Once users submit their answers, the server uses an automated rating system to grade the answers and generate instant feedback.

[0812] Step 16:

[0813] The server sends the evaluation results and feedback to the device and displays them to the user.

[0814] Step 17:

[0815] The server tracks users' learning data (study time, accuracy rate, emotional state, etc.) in real time and stores it in a database.

[0816] Step 18:

[0817] When a user selects "Check Progress" from the dashboard, the server generates a detailed report based on the accumulated learning data and emotional data and displays it on the device.

[0818] Step 19:

[0819] If a user wishes to receive tutor support, they can select the "Tutor Support" option from their dashboard.

[0820] Step 20:

[0821] The server displays a list of available tutors and the user selects the tutor of their choice.

[0822] Step 21:

[0823] The user selects the desired date and time and sends the reservation information from the terminal to the server.

[0824] Step 22:

[0825] The server notifies the tutor of the reservation information and adjusts the schedule.

[0826] Step 23:

[0827] The user and tutor prepare to start the session at the specified date and time.

[0828] Example 2

[0829] 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."

[0830] Traditional educational systems have difficulty providing individualized learning plans that match each learner's learning style and progress, and lack efficient means for assessing learning progress and providing feedback. Furthermore, there is no system in place to provide appropriate learning support based on the learner's emotional state, which can lead to reduced learning efficiency. This creates the problem of learners not receiving an optimal learning experience.

[0831] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0832] In this invention, the server includes means for generating a custom-made learning plan according to an individual's learning style and learning progress, means for providing educational content that can be accessed online, means for evaluating learning progress using an automated evaluation system, means for tracking learning data and generating reports, means for connecting learners with personnel providing educational support, means for collecting emotional data using sensors in the device, and means for analyzing the emotional data and dynamically adjusting the learning plan. This makes it possible to provide an optimized learning plan for each learner, evaluate and feedback learning progress in real time, and provide appropriate support according to the learner's emotional state.

[0833] "Learning style" refers to a learner's preferred learning method or format, such as whether they prefer video materials or textbooks, or whether they prefer self-study or tutor support.

[0834] "Learning progress" refers to the standard for measuring a learner's current learning progress and level of understanding.

[0835] A "custom-made learning plan" refers to a learning schedule and materials that are individually created based on each learner's learning style and progress.

[0836] "Online-accessible educational content" refers to learning materials such as videos, textbooks, and exercises that learners can access via the internet.

[0837] An "automated assessment system" refers to a system that automatically scores and evaluates learners' answers and progress and generates feedback.

[0838] "Learning data" refers to data related to a learner's learning activities, including, for example, study time, correct answer rate, and history of learning materials used.

[0839] A "report" is a document created based on accumulated learning data and evaluation results, and refers to a document that shows learning progress and achievement.

[0840] "Persons providing educational support" refers to those who have the role of providing direct learning support and advice to learners, including tutors and coaches.

[0841] "Device sensors" refer to devices such as cameras and microphones that are used to collect information about the user's emotional state.

[0842] "Emotional data" refers to information that indicates a learner's emotional state, such as information obtained from their facial expressions or tone of voice.

[0843] An "emotion engine" refers to a system that analyzes collected emotional data and determines the learner's emotional state.

[0844] "Dynamic adjustment" refers to changing the learning plan and content presented in response to changing conditions in real time.

[0845] Learning plan generation

[0846] To implement the invention, a user first launches a browser on their terminal and accesses the system's login page. They enter their user ID and password and click the "Login" button. This authentication information is sent to the server, which queries the database for authentication. If authentication is successful, the server sends the HTML data for the dashboard screen to the terminal, which then displays it on the terminal.

[0847] Next, the user selects the "Learning Settings" menu on the dashboard, enters their learning style and learning goals in the displayed question form, and clicks the "Submit" button. This input data is sent from the device to the server. The server stores the received data in a database, and then inputs the stored data into an AI model (e.g., TensorFlow or PyTorch) for analysis. The AI ​​model generates a custom learning plan, which is then stored in the database.

[0848] Providing educational content

[0849] The server selects appropriate educational content (videos, texts, exercises, etc.) based on the generated learning plan. When the user clicks the "Start Learning" button on the dashboard, the server sends the selected educational content to the device, which then displays it.

[0850] Assessment and feedback of learning progress

[0851] The user answers the exercises on their device and sends the results to the server, which uses an automated evaluation system to grade the answers and generate an evaluation result, which is immediately fed back to the user and displayed on their device.

[0852] Use of emotion engine

[0853] While the user is studying, emotional data is collected through the device's camera and microphone. For example, the camera captures the user's facial expressions and the microphone records the tone of their voice. The collected emotional data is sent to a server, which analyzes it using an emotion engine (e.g., Python's OpenCV or Haar Cascade). Based on the analysis results, if the emotional state indicates a decrease in concentration or fatigue, the server dynamically adjusts the study plan and presented content. For example, if the user's concentration decreases, it will provide a light video or simple questions to help them relax.

[0854] Tracking and reporting on learning data

[0855] The server tracks the user's learning activities and emotional data in real time and stores them in a database. When the user clicks the "Check Progress" button, the server generates a detailed report based on the accumulated data and sends it to the device. The report visually displays the user's learning progress and emotional fluctuations.

[0856] Collaboration with educational support staff

[0857] When the user selects the "Tutor Support" option, the server displays a list of available tutors. Once the user selects the desired tutor and confirms the reservation, the server notifies the tutor of the reservation information and adjusts the schedule.

[0858] Specific examples

[0859] For example, if a user wants to study mathematics, they log in and select "Mathematics" as their study subject. If the user prefers video learning materials, the server uses AI to select the most suitable video learning materials and displays them on the device. After the user completes the practice problems, they submit their answers to the server, which then grades them with an automated evaluation system and provides instant feedback. At the same time, the device's camera and microphone recognize emotions from the user's facial expressions and tone of voice, which are then analyzed by the emotion engine. For example, if the server determines that the user's concentration is declining, it can provide content to help them relax. Learning data and emotional data are tracked and provided to the user in the form of a detailed report.

[0860] Example prompts for generative AI models

[0861] "Generate custom learning plans based on the user's learning style. For example, if the user prefers video materials, select the most suitable videos and incorporate them into the learning plan."

[0862] With the above-described configuration, the present invention can provide users with a personalized learning experience, maximizing learning efficiency and learning outcomes.

[0863] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0864] Step 1:

[0865] The user enters their login information and clicks the "Login" button.

[0866] Input: User ID, Password.

[0867] Processing: The terminal sends the input data to the server.

[0868] Output: The authentication information sent to the server.

[0869] Step 2:

[0870] The server checks the received authentication information and performs authentication.

[0871] Input: User ID, Password.

[0872] Processing: The server queries the database to perform authentication, and if successful, generates HTML data for the dashboard screen.

[0873] Output: Authentication result, HTML data of the dashboard screen (if authentication is successful).

[0874] Step 3:

[0875] The server sends the HTML data of the dashboard screen to the terminal, which displays it.

[0876] Input: HTML data of the dashboard screen.

[0877] Processing: The terminal analyzes the received HTML data and displays it in the browser.

[0878] Output: The dashboard that is displayed on the user's screen.

[0879] Step 4:

[0880] The user selects the "Learning Settings" menu on the dashboard and enters their learning style and learning goals.

[0881] Input: learning styles, learning goals.

[0882] Processing: The user enters data into the inquiry form and clicks the "Submit" button. The terminal sends the input data to the server.

[0883] Output: Learning style and learning goal data sent to the server.

[0884] Step 5:

[0885] The server stores the received learning data in a database and inputs it into the AI ​​model.

[0886] Input: Learning styles, learning goal data.

[0887] Processing: The server stores the data in a database and then inputs the stored data into an AI model (e.g., TensorFlow or PyTorch) for analysis.

[0888] Output: A generated custom lesson plan.

[0889] Step 6:

[0890] The educational content is selected based on the learning plan generated by the server.

[0891] Enter: Study Plan.

[0892] Processing: The server selects the most appropriate content (video, text, exercises, etc.) from the database.

[0893] Output: Selected educational content.

[0894] Step 7:

[0895] The user clicks the "Start Learning" button on the dashboard. The server sends the selected educational content to the device and displays it.

[0896] Input: Learning content.

[0897] Processing: The server sends the content to the device, and the device displays the content on the browser.

[0898] Output: The educational content displayed on the user's screen.

[0899] Step 8:

[0900] The user answers the exercises on the terminal and sends the answer data to the server.

[0901] Input: User's answer data.

[0902] Processing: The terminal sends the answer data to the server.

[0903] Output: The answer data sent to the server.

[0904] Step 9:

[0905] The server grades the received answer data using an automatic evaluation system to generate an evaluation result.

[0906] Input: Answer data.

[0907] Processing: The server uses an automated evaluation system to grade the answers and generate an evaluation result.

[0908] Output: The generated evaluation results.

[0909] Step 10:

[0910] The server feeds back the evaluation results to the user and displays them on the terminal.

[0911] Input: Evaluation result.

[0912] Processing: The server sends the evaluation results to the terminal, and the terminal displays the evaluation results in the browser.

[0913] Output: The evaluation results displayed on the user's screen.

[0914] Step 11:

[0915] The device collects the user's emotional data through the camera and microphone and sends it to the server.

[0916] Input: User's facial expression data, tone of voice data.

[0917] Processing: The device collects emotion data and sends it to the server.

[0918] Output: Emotion data sent to the server.

[0919] Step 12:

[0920] The server analyzes the emotion data using an emotion engine to determine the user's emotional state.

[0921] Input: Emotion data.

[0922] Processing: The server uses an emotion engine to analyze the emotion data and determine the user's emotional state.

[0923] Output: Parsed emotional state.

[0924] Step 13:

[0925] The server dynamically adjusts the learning plan and presented content based on the emotional state.

[0926] Input: Emotional state.

[0927] Processing: The server changes the learning plan and content based on the emotional state, and selects new content if necessary.

[0928] Output: Tailored learning plans and content.

[0929] Step 14:

[0930] The server tracks users' learning activities and emotional data in real time and stores them in a database.

[0931] Input: learning activity data, emotion data.

[0932] Processing: The server collects this data and stores it in a database.

[0933] Output: Real-time training data and sentiment data stored in a database.

[0934] Step 15:

[0935] When the user clicks the "Check Progress" button, the server generates a detailed report and sends it to the device.

[0936] Input: Training data, emotion data.

[0937] Processing: The server generates a report based on the data and sends it to the device.

[0938] Output: A detailed report that is displayed on the user's screen.

[0939] (Application example 2)

[0940] 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."

[0941] While traditional educational systems are customized to suit learners' learning styles and progress, they lack the ability to dynamically adjust learning experiences based on the learner's emotional state. This makes it difficult to maximize learning effectiveness by providing appropriate feedback and learning content based on the learner's concentration and emotional state. Furthermore, because emotion recognition and learning support utilizing the learner's device are not integrated, real-time adjustment of learning plans is also not possible.

[0942] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating a customized learning plan based on an individual's learning style and learning progress, means for providing educational content accessible online, means for evaluating learning progress using an automated evaluation system, means for tracking learning data and generating reports, means for connecting learners with educational support providers, and means for recognizing the learner's emotional state in real time and dynamically adjusting the learning plan based on the emotional state. This makes it possible to provide an optimal learning plan based on the learner's emotional state. Furthermore, by using wearable devices such as smart glasses or head-mounted displays, emotions can be recognized in real time and the learning experience can be instantly optimized.

[0943] "Learning style" refers to the method or approach that a learner uses to learn most effectively.

[0944] "Learning progress" is an indicator of how far a learner has progressed with a particular learning plan or curriculum.

[0945] A "custom-made learning plan" is an individualized learning schedule and materials created to suit the needs and characteristics of each individual learner.

[0946] "Online-accessible educational content" refers to educational materials, lessons, videos, e-books, etc. that are available via the internet.

[0947] An "automated assessment system" is a system that uses artificial intelligence and algorithms to automatically assess learners' assignments and tests and provide feedback.

[0948] "Learning data" refers to information related to a learner's learning activities, progress, grades, behavioral logs, etc.

[0949] A "report" is a report that summarizes learning progress, grades, and other related information generated based on learning data.

[0950] "Educational support personnel" are professionals such as teachers and tutors who provide educational advice and support to learners.

[0951] "To collaborate" refers to the act of coordinating two or more elements or systems to function together.

[0952] "Emotional state" refers to the learner's state of mind or psychological response, and includes emotions such as joy, sadness, surprise, and concentration.

[0953] "Dynamic adjustment" refers to automatically changing content and methods to adapt to changing conditions and situations in real time.

[0954] A "wearable device" is a computing device that is worn on the body and includes smart glasses and head-mounted displays.

[0955] This invention relates to an educational system that provides a personalized learning experience for learners, dynamically adjusts the learning plan based on the learner's emotional state, and utilizes wearable devices to provide real-time learning assistance.

[0956] The server includes means for generating a custom-made learning plan according to an individual's learning style and learning progress, means for providing educational content that can be accessed online, means for evaluating learning progress using an automated evaluation system, means for tracking learning data and generating reports, means for connecting learners with personnel providing educational support, and means for recognizing the learner's emotional state in real time and dynamically adjusting the learning plan based thereon.

[0957] The server receives login information from the user's device, authenticates them, and then displays a dashboard. The user answers questions about their learning style and goals, which are then stored in a database and analyzed by an AI model to generate a custom learning plan.

[0958] The server selects the most suitable online educational content based on the user's learning plan and displays it on the device. When the user starts a learning session, the device's camera and microphone collect the user's emotional state in real time and send the data to the server.

[0959] The emotion engine analyzes this emotional data to determine the learner's current emotional state. The server then dynamically adjusts the learning plan and content provided based on the learner's emotional state. For example, if the learner's concentration is declining, the server will provide the learner with simple video content to help them relax.

[0960] For example, when a user wears smart glasses, a camera analyzes their facial expressions in real time and transmits emotional data to a server, which then adjusts their learning plan and displays appropriate feedback and content on the smart glasses' display.

[0961] The hardware used is wearable devices such as smart glasses and head-mounted displays, and the software uses OpenCV, emotion_recognition, ai_learning_plan, and ai_evaluation_system.

[0962] Example prompt sentence:

[0963] "Design an app for smart glasses that analyzes the user's emotional state in real time and dynamically adjusts the study plan based on the results. Include a feature that provides relaxing video content when the user is not concentrating."

[0964] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0965] Step 1:

[0966] A user logs in to the system from a terminal. The input is the user's login information, and the output is authenticated by the server and a dashboard is displayed. Specifically, the server receives the login information sent from the terminal and compares it with the authentication information stored in the database.

[0967] Step 2:

[0968] The user answers questions about their learning style and learning goals. The input is the user's answer data, and the output is that information stored in a database. The server receives the information the user entered into the input form and stores it in the database as structured data.

[0969] Step 3:

[0970] The server uses an AI model to analyze the user's information and generate a custom learning plan. The input is the user's learning style and goal data, and the output is a custom learning plan. Specifically, the server inputs the user's data into the AI ​​model, generates an optimal learning plan, and stores it in a database.

[0971] Step 4:

[0972] The server selects online educational content based on the user's learning plan. The input is the generated learning plan, and the output is the selected educational content. Specifically, the server analyzes the content of the learning plan and extracts relevant educational materials from the content database.

[0973] Step 5:

[0974] The user starts a learning session and educational content is displayed on the device. The input is the selected educational content, and the output is the display on the device. Specifically, the server sends the educational content to the user's device and displays it in a browser or app.

[0975] Step 6:

[0976] The device's camera and microphone collect the user's emotional state in real time. The input is sensor data from the camera and microphone, and the output is emotional data. Specifically, the device collects video and audio data and sends it to a server for analysis.

[0977] Step 7:

[0978] The server analyzes the emotional data using an emotion engine to determine the user's current emotional state. The input is the collected emotional data, and the output is the emotional state resulting from the analysis. The server analyzes the data using the emotion engine to identify the type and intensity of the emotion.

[0979] Step 8:

[0980] The server dynamically adjusts the learning plan and presented content based on the emotional state. The input is the emotional state resulting from the analysis, and the output is the adjusted learning plan and content. Specifically, the server dynamically changes the learning plan and content based on the emotional state, and provides video content for relaxation as needed.

[0981] Step 9:

[0982] Learning data and emotional data are tracked, and the server generates detailed reports. The input is learning activity data and emotional data, and the output is the generated detailed report. Specifically, the server comprehensively analyzes the collected data, creates a report including progress and emotional state, and displays it on the device.

[0983] Step 10:

[0984] It provides learning support by linking multiple devices. The input is instruction data from the server, and the output is display and feedback to the user. Specifically, the server sends feedback and new content to wearable devices such as smart glasses and head-mounted displays, which then display it.

[0985] 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.

[0986] 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.

[0987] 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.

[0988] [Third embodiment]

[0989] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0990] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0991] 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).

[0992] 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.

[0993] 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.

[0994] 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).

[0995] 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.

[0996] 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.

[0997] 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.

[0998] 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.

[0999] 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.

[1000] 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."

[1001] The educational system of the present invention aims to provide high-quality education regardless of factors such as financial difficulties or geographical limitations. The system generates a custom-made learning plan based on the user's learning style and progress, and provides educational content that can be accessed online. It also manages learning progress through an automated evaluation system using AI, tracks learning data, and creates reports. It also has a function for connecting learners with those providing educational support.

[1002] Specifically, the system operates as follows.

[1003] Learning plan generation

[1004] 1. A user logs in from a terminal

[1005] A user accesses the system and enters their login information. The server verifies the authentication information and displays the user's personal settings page.

[1006] 2. Determine your learning style and goals

[1007] Users answer questions about their learning styles and goals. The server receives this information and stores it in a database.

[1008] 3. Generate a custom learning plan

[1009] The server uses AI models based on the stored information to generate a custom learning plan for each learner, tailored to their learning style and goals.

[1010] Providing educational content

[1011] 1. Selection of online educational content

[1012] The server selects appropriate online educational content (videos, texts, exercises, etc.) based on the user's learning plan.

[1013] 2. Display of Content

[1014] When a user starts a learning session from a terminal, the server transmits selected content to the terminal for display.

[1015] Progress assessment and feedback

[1016] 1. Answers and evaluations for practice questions

[1017] The user answers the exercises provided and sends them to the server via their device, which then grades the answers using an automated evaluation system and provides feedback.

[1018] 2. Real-time feedback

[1019] The server provides instant feedback to the user on the results of the assessment, offering additional resources and advice to deepen their understanding.

[1020] Tracking and reporting on learning data

[1021] 1. Tracking training data

[1022] The server tracks users' learning activities in real time and stores them in a database, recording data such as study time, grades, and progress.

[1023] 2. Report Generation

[1024] When the user selects to check progress, the server generates a detailed learning report based on the accumulated data and displays it on the device.

[1025] Collaboration with educational support staff

[1026] 1. Choosing Tutor Support

[1027] If the user desires tutor support, he or she selects the function for linking with a tutor from the terminal.

[1028] 2. Tutor selection and booking

[1029] The server displays a list of tutors available online, and the user selects the tutor they want to book a session with. The server then notifies the tutor of the reservation information and adjusts the schedule.

[1030] Specific examples

[1031] For example, if a user wants to study mathematics, they log in and select "Mathematics" as their study subject. If the user prefers to study using video materials, the server uses an AI model to select and display the most suitable video materials for the user. After studying, the user answers and submits practice questions, and the server automatically grades them and provides real-time feedback. In addition, all learning progress data is tracked and presented to the user in a detailed report. If necessary, the user can also receive additional guidance from an online tutor.

[1032] In this way, the system of the present invention provides learners with a highly personalized educational experience tailored to their individual needs.

[1033] The processing flow will be explained below.

[1034] Step 1:

[1035] The user accesses the EduBridge URL from their device and logs in for the first time.

[1036] Step 2:

[1037] The server receives the user's login information and performs authentication. If successful, the user's dashboard is displayed on the device.

[1038] Step 3:

[1039] Users select the "Personal Settings" option from their dashboard and answer questions about their learning style and goals.

[1040] Step 4:

[1041] The server receives the user's answers and stores them in a database.

[1042] Step 5:

[1043] The server uses the stored information to create a custom learning plan based on the user's learning style and progress using an AI model, and stores the resulting plan in a database.

[1044] Step 6:

[1045] The server transmits the generated learning plan to the terminal and displays it to the user.

[1046] Step 7:

[1047] The user reviews the learning plan and makes any necessary adjustments, which are then sent from the device to the server.

[1048] Step 8:

[1049] The server stores the adjusted learning plan in a database.

[1050] Step 9:

[1051] The user selects a subject from the dashboard and clicks the "Start" button.

[1052] Step 10:

[1053] The server selects appropriate educational content (videos, text, practice questions, etc.) based on the user's learning plan and sends it to the device.

[1054] Step 11:

[1055] The user studies the displayed educational content and answers the exercises.

[1056] Step 12:

[1057] Once the user submits their answer, the server uses an automated rating system to grade the answer and generate instant feedback.

[1058] Step 13:

[1059] The server sends the evaluation results and feedback to the terminal and displays them to the user.

[1060] Step 14:

[1061] The server tracks users' learning data (study time, accuracy rate, etc.) in real time and stores it in a database.

[1062] Step 15:

[1063] When a user selects "Check Progress" from the dashboard, the server analyzes the accumulated learning data and generates a detailed learning report.

[1064] Step 16:

[1065] The server sends the generated report to the terminal for display to the user.

[1066] Step 17:

[1067] If the user desires tutor support, he / she selects the "Tutor Support" option from the dashboard.

[1068] Step 18:

[1069] The server displays a list of currently available tutors, and the user selects the tutor they desire.

[1070] Step 19:

[1071] The user selects the desired date and time, and transmits reservation information from the terminal to the server.

[1072] Step 20:

[1073] The server notifies the tutor of the reservation information and adjusts the schedule.

[1074] Step 21:

[1075] The user and tutor prepare to start the session at the specified date and time.

[1076] Example 1

[1077] 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."

[1078] In today's education system, financial hardship and geographical constraints make it difficult to obtain a high-quality education. Furthermore, traditional education systems often fail to adequately accommodate individual learners' learning styles and progress, hindering efficient learning. Furthermore, automated assessment and learning data tracking and feedback functions are often lacking, resulting in insufficient progress management for learners. These issues need to be resolved.

[1079] 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.

[1080] In this invention, the server includes a means for a user to log in from a terminal, a means for setting the user's learning style and goals, a means for using a generative AI model to generate a customized learning plan, a means for selecting educational content that can be accessed online, a means for displaying the selected educational content, a means for evaluating learning progress using an automated evaluation system, a means for providing feedback on the evaluation results to the user in real time, a means for tracking learning data and storing it in a database, a means for generating reports based on the tracked data, and a means for connecting learners with personnel providing educational support. This makes it possible to provide high-quality personalized education that meets the needs of individual learners.

[1081] "Means for users to log in from a terminal" refers to the method by which users access the system using a terminal such as a PC or smartphone, enter login information, and are authenticated.

[1082] The "means for setting user's learning style and goals" is a method by which a user inputs information about his or her learning style and goals, which is received by the system and stored in the database.

[1083] "Method using a generative AI model to generate a custom-made study plan" means a method using a generative AI model to create an individually optimized study plan based on a user's learning style and goals.

[1084] "Means for selecting online accessible educational content" refers to the method by which the system selects appropriate online educational content based on the user's learning plan.

[1085] The "means for displaying the selected educational content" refers to a method by which the server transmits the selected educational content to the user's terminal and displays it.

[1086] A "means for assessing learning progress using an automated assessment system" is a method for automatically scoring exercises answered by a user and generating an assessment result.

[1087] The "means for providing feedback of the evaluation results to the user in real time" is a method for instantly notifying the user of the graded evaluation results and providing that feedback.

[1088] The "means for tracking learning data and storing it in a database" is a method for monitoring data related to a user's learning activities in real time and recording the data in a database.

[1089] The "means for generating a report based on tracked data" is a method for analyzing the tracked learning data and creating a detailed learning report.

[1090] "Means for connecting learners with educational support providers" refers to a method by which learners can connect online with educational support providers and receive support.

[1091] The educational system of this invention aims to provide high-quality education by creating a custom-made learning plan based on the user's learning style and learning progress. The system is designed for users to access from a device such as a PC or smartphone. The specific hardware and software configuration and processing flow are shown below.

[1092] First, the user accesses the system from a terminal and enters their login information. The server compares this authentication information with the database, and if authentication is successful, displays the user's personal settings page. This login process may use two-factor authentication in addition to the user ID and password.

[1093] Next, the user answers a questionnaire on the system about their learning style and goals. The questionnaire includes items such as study time, target level, and preferred learning method (video, text, practice questions). The server receives this information and stores it in a database. At this point, the user's learning data is initialized.

[1094] Based on the stored data, the server uses a generative AI model to generate a custom learning plan. This model uses existing machine learning algorithms and natural language processing techniques. The generated learning plan is displayed on the user's personalized settings page. The learning plan includes specific learning content, a progress schedule, and recommended learning materials.

[1095] Based on the generated learning plan, the server selects appropriate online educational content from a database, including videos, texts, exercises, etc. This content is sent to and displayed on the device when the user starts a learning session.

[1096] The practice questions that users answer during their studies are sent to a server and instantly scored by an automated assessment system. The results are then fed back to the user in real time, and they may be given feedback on their understanding or additional resources. This feedback helps users to study more effectively.

[1097] All data related to learning activities is tracked in real time by the server and stored in a database. This includes data such as study time, grades, and progress. When a user selects to check their progress, the server generates a detailed learning report based on the accumulated data and displays it on the device. The report includes information such as total study time, progress on each learning item, and grades.

[1098] Furthermore, if a user needs educational support, they can use the tutor support function. With this function, the server displays a list of available tutors, and the user can select the tutor of their choice and reserve a session. The tutor receives the user's reservation information, adjusts their schedule, and provides instruction.

[1099] For example, if a user wants to learn intermediate level mathematics, they can input the following prompt into the generative AI model:

[1100] "Generate a custom learning plan for a user who prefers video learning, covering basic to advanced mathematics. The user's current level is intermediate."

[1101] The generated learning plan recommends intermediate-level math video materials and sets specific learning tasks based on the plan. Users receive real-time feedback as they watch the videos and answer practice questions. The learning data accumulated during this process is tracked by the server and presented to the user as a detailed report. Users can also book a session with a tutor to receive further in-depth learning support, if necessary.

[1102] In this way, the system allows users to enjoy a highly personalized learning experience, maximizing learning efficiency.

[1103] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1104] Step 1:

[1105] A user logs in from a terminal

[1106] The user accesses the system using a device such as a computer or smartphone and enters login information (user ID and password).

[1107] Input: User ID, Password

[1108] The server checks the authentication information against a database and performs authentication. If authentication is successful, the user's personal settings page is displayed on the terminal.

[1109] Example of operation: The user enters their ID and password and presses the "Login" button.

[1110] Step 2:

[1111] Define user learning styles and goals

[1112] Users answer a questionnaire on the system about their learning style and goals.

[1113] Input: Study time, desired level, preferred learning method (video, text, exercises), etc.

[1114] The server receives this information and stores it in a database.

[1115] Example of operation: A user answers a survey and presses the "Save" button.

[1116] Step 3:

[1117] Generate a custom learning plan

[1118] The server inputs prompt sentences into the generative AI model based on the stored data.

[1119] Input: User learning style, goal data

[1120] Example prompt: "Generate a custom learning plan for a user who prefers video instruction, covering basic and advanced mathematics. The user's current level is intermediate."

[1121] The server uses the generative AI model to generate an individually optimized learning plan, which is displayed on the user's personal settings page.

[1122] Output: Custom-made study plan

[1123] Example of operation: A learning plan is generated and the user reviews it.

[1124] Step 4:

[1125] Selection of online educational content

[1126] The server searches the database for appropriate online educational content (videos, texts, exercises, etc.) based on the generated learning plan.

[1127] Input: Study Plan

[1128] The server adds links to educational content to the learning plan.

[1129] Output: A list of selected educational content

[1130] Example of operation: Links to the selected content are displayed as a list.

[1131] Step 5:

[1132] View content

[1133] The user initiates a learning session and the server sends the content to the terminal for display.

[1134] Input: Start instruction for study session

[1135] The server transmits the selected educational content to the terminal and displays it.

[1136] Output: On-screen display of educational content

[1137] Example of how it works: A user starts a learning session and a video plays.

[1138] Step 6:

[1139] Exercises and answers

[1140] The user answers the exercises presented to them and sends the answers to the server.

[1141] Input: Answer to the exercise

[1142] The server grades the answers using an automated evaluation system and generates an evaluation result.

[1143] Output:Scoring results

[1144] Example of operation: When the user answers the questions and presses the "Submit" button, the scoring results are displayed immediately.

[1145] Step 7:

[1146] Real-time feedback

[1147] The server provides the user with real-time feedback on the results of the assessment to confirm their level of understanding.

[1148] Input:Scoring results

[1149] The server may also provide additional resources and exercises.

[1150] Output: Show feedback

[1151] Example of how it works: Additional questions are displayed as feedback regarding areas of insufficient understanding.

[1152] Step 8:

[1153] Tracking and storing learning data

[1154] The server tracks users' learning activities (study time, grades, progress) in real time and stores them in a database.

[1155] Input: Learning activity data

[1156] The server collects and stores this data for analysis.

[1157] Output: Training data stored in a database

[1158] Example of how it works: Data is automatically recorded during a learning session.

[1159] Step 9:

[1160] Report Generation

[1161] When the user selects to check progress, the server generates a detailed learning report based on the accumulated data.

[1162] Input: Progress check instructions

[1163] The server analyzes the data and generates a progress report that is displayed on the terminal.

[1164] Output: Learning report

[1165] Example of operation: A user opens the report and checks the total study time and progress of each learning content.

[1166] Step 10:

[1167] Selecting and booking tutor support

[1168] If the user desires tutor support, he or she selects the function for linking with a tutor from the terminal.

[1169] Input: Request for tutor support

[1170] The server displays a list of tutors available online, and the user selects the tutor of their choice and books a session.

[1171] Output: Notification of reservation information

[1172] The server notifies the tutor of the reservation information and adjusts the schedule.

[1173] Example of operation: The user selects the desired tutor and presses the "Reserve" button.

[1174] (Application example 1)

[1175] 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."

[1176] Today's learners demand high-quality education regardless of financial difficulties or geographical limitations. However, it is difficult to provide customized education that meets individual learning needs, and it is not easy to accurately evaluate learning progress and provide feedback. Furthermore, there are few ways to efficiently acquire product knowledge in physical or virtual stores while studying. A system that solves these challenges and provides a more effective and personalized learning experience is needed.

[1177] 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.

[1178] In this invention, the server includes: a means for generating a customized learning plan based on a user's learning style and learning progress; a means for providing educational content that can be accessed online; a means for evaluating learning progress using an automated evaluation system; a means for tracking learning data and generating reports; a means for connecting learners with educational support providers; a means for users to learn product information and answer practice questions in a virtual store; and a means for executing the above functions using a smartphone. This allows learners to overcome financial difficulties and geographical limitations and enjoy a highly personalized educational experience tailored to their individual learning needs. It also enables efficient product knowledge learning in a virtual store.

[1179] "User" refers to a person who uses the learning system.

[1180] "Learning style" refers to the method or format that a learner prefers to learn most effectively.

[1181] "Learning Progress" refers to the progress a learner has made in their learning plan.

[1182] "Custom-made learning plan" refers to a learning plan that is customized based on each learner's individual needs and characteristics.

[1183] "Online accessible educational content" refers to educational materials and teaching materials that are available to learners via the internet.

[1184] An "automated assessment system" refers to a system that uses AI or algorithms to assess learners' progress and achievements.

[1185] "Learning Data" refers to information about a learner's behavior, progress, grades, etc.

[1186] A "report" refers to a document that summarizes a learner's progress and achievements based on learning data.

[1187] "Persons providing educational support" refer to people who are responsible for providing education and guidance to learners.

[1188] A "virtual store" refers to a virtual store that sells products and services online.

[1189] A "smartphone" refers to a mobile phone terminal with internet connectivity and various functions.

[1190] A "server" refers to a computer that provides services and information to clients over a network.

[1191] To implement this invention, the following system configuration and processing are used: The main components of the system include a server, a terminal used by a user (e.g., a smartphone), and educational content that can be accessed online.

[1192] The server first provides a means for users to input their learning style and learning goals. Users log in to the system from their terminals and input information about their learning style and learning goals. The server then stores this information in a database.

[1193] The server then uses the stored information to generate a personalized learning plan using a generative AI model, which includes content tailored to the user's learning style and goals. For example, if a user prefers video learning materials, appropriate video content will be selected.

[1194] The server provides appropriate online educational content based on the user's learning plan. When the user starts a learning session from their device, the server sends the selected content to the device and displays it. The educational content includes videos, textbooks, exercises, etc.

[1195] The server also evaluates learning progress. Users answer the exercises provided and send them to the server via their device. The server then uses an automated evaluation system to grade the answers and provide feedback, allowing users to instantly check their level of understanding.

[1196] Learning progress data is tracked by the server and stored in a database. Data such as study time, grades, and progress are recorded, and when a user selects to check progress, a detailed learning report is generated and displayed on the device.

[1197] Furthermore, the system allows users to learn about products in the virtual store and answer practice questions, allowing them to test their understanding while learning how to select and use products in the virtual store.

[1198] The entire system is run using a smartphone, which is equipped with functions such as user login, learning style setting, educational content display, automatic assessment result display, and learning progress tracking.

[1199] The hardware and software used includes:

[1200] Hardware: Smartphone (iOS / Android)

[1201] Software: Python, Flask (web framework), Scikit-learn (machine learning library)

[1202] For example, when a user buys a new kitchen gadget, they can watch a video to learn how to use it and then test their understanding with practice questions. Here's an example of a prompt for the generative AI model:

[1203] "User just purchased a new kitchen gadget. Their learning style is videos, and their learning goal is to understand how to use the product. Generate an optimal learning plan."

[1204] This allows learners to overcome financial difficulties and geographical constraints, receive a highly personalized educational experience tailored to their individual learning needs, and efficiently learn about products in virtual stores.

[1205] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1206] Step 1:

[1207] The server receives authentication information for the user to log in from the terminal. The terminal sends the user ID and password to the server, which verifies the authentication information and displays the user's personal setting page. At this stage, the input is the user ID and password, and the output is the user's personal setting page.

[1208] Step 2:

[1209] The server provides a means for users to input information about their learning style and learning goals through their terminals. The learning style (e.g., video, text) and learning goals are sent as input data from the terminals to the server. The server receives this information and stores it in a database.

[1210] Step 3:

[1211] The server uses a generative AI model based on the stored information to generate a custom-made learning plan that is optimal for each learner. The input is data about the user's learning style and learning goals, and the AI ​​model calculates the data to obtain the optimal learning plan as output. This learning plan is then sent to the device.

[1212] Step 4:

[1213] The server selects appropriate online educational content based on the user's learning plan. The input is the generated learning plan, and the output is the selected educational content (videos, text, exercises, etc.). These contents are sent to the terminal and displayed.

[1214] Step 5:

[1215] A user answers exercises presented on a device. The input is the user's answer, which is sent from the device to a server. The server uses an automated evaluation system to grade the answer and generate feedback. Here, the input is the user's answer data and the output is the generated feedback.

[1216] Step 6:

[1217] The server tracks learning data in real time and stores it in a database, which records information such as learning time, grades, progress, etc. In this step, the input is the user's learning activity data, and the output is the saved learning data.

[1218] Step 7:

[1219] When the user selects to check progress, the server generates a detailed learning report based on the accumulated data and displays it on the terminal. The input is the accumulated learning data, and the output is a detailed learning report.

[1220] Step 8:

[1221] The server provides a function for users to learn about products in a virtual store and answer practice questions. Users use their devices to learn about products and then answer practice questions. The input is the user's learning content and answers, and the server automatically evaluates and outputs the results.

[1222] Step 9:

[1223] The server provides the user with additional educational resources and advice based on the learning results and feedback, thereby improving the user's understanding. The input is the evaluation result, and the output is additional educational resources and advice.

[1224] 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.

[1225] This invention relates to an educational system that provides learners with a more personalized learning experience. The system generates a custom-made learning plan based on the user's learning style and progress, and provides educational content that can be accessed online. It also manages learning progress through an automated assessment system using AI, tracks learning data, and creates reports. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it provides optimal support according to the learner's emotional state.

[1226] Learning plan generation

[1227] 1. A user logs in from a terminal

[1228] When a user accesses the system and enters their login information, the server verifies the authentication information and displays the dashboard on the terminal.

[1229] 2. Determine your learning style and goals

[1230] Users answer questions about their learning style and learning goals, and the server receives the information and stores it in a database.

[1231] 3. Create a custom learning plan

[1232] The server uses an AI model to analyze the user's information, generate a custom learning plan, and store it in a database.

[1233] Providing educational content

[1234] 1. Selection of online educational content

[1235] The server selects the most suitable educational content based on the user's learning plan.

[1236] 2. Display of Content

[1237] When a user starts a study session, the server sends selected content to the terminal for display.

[1238] Assessment and feedback of learning progress

[1239] 1. Answers and evaluations for practice questions

[1240] Users answer exercises and send their answers from their device to a server, which uses an automated evaluation system to grade the answers and generate feedback.

[1241] 2. Real-time feedback

[1242] The server immediately sends and displays the evaluation results and feedback to the user.

[1243] Use of emotion engine

[1244] 1. User Emotion Recognition

[1245] The device's sensors, such as the camera and microphone, are used to collect the user's emotional state in real time.

[1246] 2. Emotion Data Analysis

[1247] The emotion data collected by the server is analyzed by an emotion engine to determine the learner's current emotional state.

[1248] 3. Adjust your study plan based on your emotions

[1249] Based on the emotion engine's judgment, the server dynamically adjusts the learning plan and presented content, for example, providing more relaxing content if the learner's concentration is declining.

[1250] Tracking and reporting on learning data

[1251] 1. Tracking training data

[1252] The server tracks users' learning activities and emotional data in real time and stores them in a database.

[1253] 2. Report Generation

[1254] When the user selects to check progress, the server generates a detailed report based on the accumulated learning data and emotional data and displays it on the device.

[1255] Collaboration with educational support staff

[1256] 1. Choosing Tutor Support

[1257] If the user desires tutor support, he selects the "tutor support" option.

[1258] 2. Tutor selection and booking

[1259] The server displays a list of available tutors, and the user selects and reserves the tutor of their choice. The server notifies the tutor of the reservation information and adjusts the schedule.

[1260] Specific examples

[1261] For example, if a user wants to study mathematics, they log in and select "Mathematics" as their study subject. If the user prefers video learning materials, the server uses AI to select the most suitable video learning materials and displays them on the device. After the user completes the practice problems and submits their answers, the server grades them using an automatic evaluation system and provides real-time feedback. Meanwhile, the device's camera and microphone recognize emotions from the user's facial expressions and tone of voice, which are analyzed by an emotion engine. If the user's concentration is low, the server suggests breaks or easy content to relax. All learning and emotional data is tracked and provided as detailed reports.

[1262] In this way, the system of the present invention can provide a more effective educational experience based on the user's emotional state, maximizing learning outcomes.

[1263] The processing flow will be explained below.

[1264] Step 1:

[1265] The user accesses the EduBridge URL from their device and logs in for the first time.

[1266] Step 2:

[1267] The server receives the user's login information, authenticates them, and if successful, displays the user's dashboard on the device.

[1268] Step 3:

[1269] Users select the "Personal Settings" option from their dashboard and answer questions about their learning style and goals.

[1270] Step 4:

[1271] The server receives the user's answers and stores them in a database.

[1272] Step 5:

[1273] The server uses the stored information to use AI models to generate a custom learning plan based on the user's learning style and progress, and stores the resulting plan in a database.

[1274] Step 6:

[1275] The server sends the generated learning plan to the device and displays it to the user.

[1276] Step 7:

[1277] The user can review the learning plan and make any necessary adjustments, which are then sent from the device to the server.

[1278] Step 8:

[1279] The server stores the adjusted learning plan in a database.

[1280] Step 9:

[1281] Users select a subject from the dashboard and click the "Start" button.

[1282] Step 10:

[1283] The server selects appropriate educational content (videos, text, practice questions, etc.) based on the user's learning plan and sends it to the device.

[1284] Step 11:

[1285] It uses the device's camera and microphone to collect the user's facial expressions and voice in real time and recognize the user's emotional state.

[1286] Step 12:

[1287] The emotion engine analyzes the collected data and determines the user's current emotional state.

[1288] Step 13:

[1289] The server dynamically adjusts the learning plan and presented content based on the results of the emotion engine, for example, providing more relaxing content when the user is not concentrating.

[1290] Step 14:

[1291] Users study the educational content displayed and answer practice questions.

[1292] Step 15:

[1293] Once users submit their answers, the server uses an automated rating system to grade the answers and generate instant feedback.

[1294] Step 16:

[1295] The server sends the evaluation results and feedback to the device and displays them to the user.

[1296] Step 17:

[1297] The server tracks users' learning data (study time, accuracy rate, emotional state, etc.) in real time and stores it in a database.

[1298] Step 18:

[1299] When a user selects "Check Progress" from the dashboard, the server generates a detailed report based on the accumulated learning data and emotional data and displays it on the device.

[1300] Step 19:

[1301] If a user wishes to receive tutor support, they can select the "Tutor Support" option from their dashboard.

[1302] Step 20:

[1303] The server displays a list of available tutors and the user selects the tutor of their choice.

[1304] Step 21:

[1305] The user selects the desired date and time and sends the reservation information from the terminal to the server.

[1306] Step 22:

[1307] The server notifies the tutor of the reservation information and adjusts the schedule.

[1308] Step 23:

[1309] The user and tutor prepare to start the session at the specified date and time.

[1310] Example 2

[1311] 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."

[1312] Traditional educational systems have difficulty providing individualized learning plans that match each learner's learning style and progress, and lack efficient means for assessing learning progress and providing feedback. Furthermore, there is no system in place to provide appropriate learning support based on the learner's emotional state, which can lead to reduced learning efficiency. This creates the problem of learners not receiving an optimal learning experience.

[1313] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1314] In this invention, the server includes means for generating a custom-made learning plan according to an individual's learning style and learning progress, means for providing educational content that can be accessed online, means for evaluating learning progress using an automated evaluation system, means for tracking learning data and generating reports, means for connecting learners with personnel providing educational support, means for collecting emotional data using sensors in the device, and means for analyzing the emotional data and dynamically adjusting the learning plan. This makes it possible to provide an optimized learning plan for each learner, evaluate and feedback learning progress in real time, and provide appropriate support according to the learner's emotional state.

[1315] "Learning style" refers to a learner's preferred learning method or format, such as whether they prefer video materials or textbooks, or whether they prefer self-study or tutor support.

[1316] "Learning progress" refers to the standard for measuring a learner's current learning progress and level of understanding.

[1317] A "custom-made learning plan" refers to a learning schedule and materials that are individually created based on each learner's learning style and progress.

[1318] "Online-accessible educational content" refers to learning materials such as videos, textbooks, and exercises that learners can access via the internet.

[1319] An "automated assessment system" refers to a system that automatically scores and evaluates learners' answers and progress and generates feedback.

[1320] "Learning data" refers to data related to a learner's learning activities, including, for example, study time, correct answer rate, and history of learning materials used.

[1321] A "report" is a document created based on accumulated learning data and evaluation results, and refers to a document that shows learning progress and achievement.

[1322] "Persons providing educational support" refers to those who have the role of providing direct learning support and advice to learners, including tutors and coaches.

[1323] "Device sensors" refer to devices such as cameras and microphones that are used to collect information about the user's emotional state.

[1324] "Emotional data" refers to information that indicates a learner's emotional state, such as information obtained from their facial expressions or tone of voice.

[1325] An "emotion engine" refers to a system that analyzes collected emotional data and determines the learner's emotional state.

[1326] "Dynamic adjustment" refers to changing the learning plan and content presented in response to changing conditions in real time.

[1327] Learning plan generation

[1328] To implement the invention, a user first launches a browser on their terminal and accesses the system's login page. They enter their user ID and password and click the "Login" button. This authentication information is sent to the server, which queries the database for authentication. If authentication is successful, the server sends the HTML data for the dashboard screen to the terminal, which then displays it on the terminal.

[1329] Next, the user selects the "Learning Settings" menu on the dashboard, enters their learning style and learning goals in the displayed question form, and clicks the "Submit" button. This input data is sent from the device to the server. The server stores the received data in a database, and then inputs the stored data into an AI model (e.g., TensorFlow or PyTorch) for analysis. The AI ​​model generates a custom learning plan, which is then stored in the database.

[1330] Providing educational content

[1331] The server selects appropriate educational content (videos, texts, exercises, etc.) based on the generated learning plan. When the user clicks the "Start Learning" button on the dashboard, the server sends the selected educational content to the device, which then displays it.

[1332] Progress assessment and feedback

[1333] The user answers the exercises on their device and sends the results to the server, which uses an automated evaluation system to grade the answers and generate an evaluation result, which is immediately fed back to the user and displayed on their device.

[1334] Use of emotion engine

[1335] While the user is studying, emotional data is collected through the device's camera and microphone. For example, the camera captures the user's facial expressions and the microphone records the tone of their voice. The collected emotional data is sent to a server, which analyzes it using an emotion engine (e.g., Python's OpenCV or Haar Cascade). Based on the analysis results, if the emotional state indicates a decrease in concentration or fatigue, the server dynamically adjusts the study plan and presented content. For example, if the user's concentration decreases, it will provide a light video or simple questions to help them relax.

[1336] Tracking and reporting on learning data

[1337] The server tracks the user's learning activities and emotional data in real time and stores them in a database. When the user clicks the "Check Progress" button, the server generates a detailed report based on the accumulated data and sends it to the device. The report visually displays the user's learning progress and emotional fluctuations.

[1338] Collaboration with educational support staff

[1339] When the user selects the "Tutor Support" option, the server displays a list of available tutors. Once the user selects the desired tutor and confirms the reservation, the server notifies the tutor of the reservation information and adjusts the schedule.

[1340] Specific examples

[1341] For example, if a user wants to study mathematics, they log in and select "Mathematics" as their study subject. If the user prefers video learning materials, the server uses AI to select the most suitable video learning materials and displays them on the device. After the user completes the practice problems, they submit their answers to the server, which then grades them with an automated evaluation system and provides instant feedback. At the same time, the device's camera and microphone recognize emotions from the user's facial expressions and tone of voice, which are then analyzed by the emotion engine. For example, if the server determines that the user's concentration is declining, it can provide content to help them relax. Learning data and emotional data are tracked and provided to the user in the form of a detailed report.

[1342] Example prompts for generative AI models

[1343] "Generate custom learning plans based on the user's learning style. For example, if the user prefers video materials, select the most suitable videos and incorporate them into the learning plan."

[1344] With the above-described configuration, the present invention can provide users with a personalized learning experience, maximizing learning efficiency and learning outcomes.

[1345] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1346] Step 1:

[1347] The user enters their login information and clicks the "Login" button.

[1348] Input: User ID, Password.

[1349] Processing: The terminal sends the input data to the server.

[1350] Output: The authentication information sent to the server.

[1351] Step 2:

[1352] The server checks the received authentication information and performs authentication.

[1353] Input: User ID, Password.

[1354] Processing: The server queries the database to perform authentication, and if successful, generates HTML data for the dashboard screen.

[1355] Output: Authentication result, HTML data of the dashboard screen (if authentication is successful).

[1356] Step 3:

[1357] The server sends the HTML data of the dashboard screen to the terminal, which displays it.

[1358] Input: HTML data of the dashboard screen.

[1359] Processing: The terminal analyzes the received HTML data and displays it in the browser.

[1360] Output: The dashboard that is displayed on the user's screen.

[1361] Step 4:

[1362] The user selects the "Learning Settings" menu on the dashboard and enters their learning style and learning goals.

[1363] Input: learning styles, learning goals.

[1364] Processing: The user enters data into the inquiry form and clicks the "Submit" button. The terminal sends the input data to the server.

[1365] Output: Learning style and learning goal data sent to the server.

[1366] Step 5:

[1367] The server stores the received learning data in a database and inputs it into the AI ​​model.

[1368] Input: Learning styles, learning goal data.

[1369] Processing: The server stores the data in a database and then inputs the stored data into an AI model (e.g., TensorFlow or PyTorch) for analysis.

[1370] Output: A generated custom lesson plan.

[1371] Step 6:

[1372] The educational content is selected based on the learning plan generated by the server.

[1373] Enter: Study Plan.

[1374] Processing: The server selects the most appropriate content (video, text, exercises, etc.) from the database.

[1375] Output: Selected educational content.

[1376] Step 7:

[1377] The user clicks the "Start Learning" button on the dashboard. The server sends the selected educational content to the device and displays it.

[1378] Input: Learning content.

[1379] Processing: The server sends the content to the device, and the device displays the content on the browser.

[1380] Output: The educational content displayed on the user's screen.

[1381] Step 8:

[1382] The user answers the exercises on the terminal and sends the answer data to the server.

[1383] Input: User's answer data.

[1384] Processing: The terminal sends the answer data to the server.

[1385] Output: The answer data sent to the server.

[1386] Step 9:

[1387] The server grades the received answer data using an automatic evaluation system to generate an evaluation result.

[1388] Input: Answer data.

[1389] Processing: The server uses an automated evaluation system to grade the answers and generate an evaluation result.

[1390] Output: The generated evaluation results.

[1391] Step 10:

[1392] The server feeds back the evaluation results to the user and displays them on the terminal.

[1393] Input: Evaluation result.

[1394] Processing: The server sends the evaluation results to the terminal, and the terminal displays the evaluation results in the browser.

[1395] Output: The evaluation results displayed on the user's screen.

[1396] Step 11:

[1397] The device collects the user's emotional data through the camera and microphone and sends it to the server.

[1398] Input: User's facial expression data, tone of voice data.

[1399] Processing: The device collects emotion data and sends it to the server.

[1400] Output: Emotion data sent to the server.

[1401] Step 12:

[1402] The server analyzes the emotion data using an emotion engine to determine the user's emotional state.

[1403] Input: Emotion data.

[1404] Processing: The server uses an emotion engine to analyze the emotion data and determine the user's emotional state.

[1405] Output: Parsed emotional state.

[1406] Step 13:

[1407] The server dynamically adjusts the learning plan and presented content based on the emotional state.

[1408] Input: Emotional state.

[1409] Processing: The server changes the learning plan and content based on the emotional state, and selects new content if necessary.

[1410] Output: Tailored learning plans and content.

[1411] Step 14:

[1412] The server tracks users' learning activities and emotional data in real time and stores them in a database.

[1413] Input: learning activity data, emotion data.

[1414] Processing: The server collects this data and stores it in a database.

[1415] Output: Real-time training data and sentiment data stored in a database.

[1416] Step 15:

[1417] When the user clicks the "Check Progress" button, the server generates a detailed report and sends it to the device.

[1418] Input: Training data, emotion data.

[1419] Processing: The server generates a report based on the data and sends it to the device.

[1420] Output: A detailed report that is displayed on the user's screen.

[1421] (Application example 2)

[1422] 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."

[1423] While traditional educational systems are customized to suit learners' learning styles and progress, they lack the ability to dynamically adjust learning experiences based on the learner's emotional state. This makes it difficult to maximize learning effectiveness by providing appropriate feedback and learning content based on the learner's concentration and emotional state. Furthermore, because emotion recognition and learning support utilizing the learner's device are not integrated, real-time adjustment of learning plans is also not possible.

[1424] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating a customized learning plan based on an individual's learning style and learning progress, means for providing educational content accessible online, means for evaluating learning progress using an automated evaluation system, means for tracking learning data and generating reports, means for connecting learners with educational support providers, and means for recognizing the learner's emotional state in real time and dynamically adjusting the learning plan based on the emotional state. This makes it possible to provide an optimal learning plan based on the learner's emotional state. Furthermore, by using wearable devices such as smart glasses or head-mounted displays, emotions can be recognized in real time and the learning experience can be instantly optimized.

[1425] "Learning style" refers to the method or approach that a learner uses to learn most effectively.

[1426] "Learning progress" is an indicator of how far a learner has progressed with a particular learning plan or curriculum.

[1427] A "custom-made learning plan" is an individualized learning schedule and materials created to suit the needs and characteristics of each individual learner.

[1428] "Online-accessible educational content" refers to educational materials, lessons, videos, e-books, etc. that are available via the internet.

[1429] An "automated assessment system" is a system that uses artificial intelligence and algorithms to automatically assess learners' assignments and tests and provide feedback.

[1430] "Learning data" refers to information related to a learner's learning activities, progress, grades, behavioral logs, etc.

[1431] A "report" is a report that summarizes learning progress, grades, and other related information generated based on learning data.

[1432] "Educational support personnel" are professionals such as teachers and tutors who provide educational advice and support to learners.

[1433] "To collaborate" refers to the act of coordinating two or more elements or systems to function together.

[1434] "Emotional state" refers to the learner's state of mind or psychological response, and includes emotions such as joy, sadness, surprise, and concentration.

[1435] "Dynamic adjustment" refers to automatically changing content and methods to adapt to changing conditions and situations in real time.

[1436] A "wearable device" is a computing device that is worn on the body and includes smart glasses and head-mounted displays.

[1437] This invention relates to an educational system that provides a personalized learning experience for learners, dynamically adjusts the learning plan based on the learner's emotional state, and utilizes wearable devices to provide real-time learning assistance.

[1438] The server includes means for generating a custom-made learning plan according to an individual's learning style and learning progress, means for providing educational content that can be accessed online, means for evaluating learning progress using an automated evaluation system, means for tracking learning data and generating reports, means for connecting learners with personnel providing educational support, and means for recognizing the learner's emotional state in real time and dynamically adjusting the learning plan based thereon.

[1439] The server receives login information from the user's device, authenticates them, and then displays a dashboard. The user answers questions about their learning style and goals, which are then stored in a database and analyzed by an AI model to generate a custom learning plan.

[1440] The server selects the most suitable online educational content based on the user's learning plan and displays it on the device. When the user starts a learning session, the device's camera and microphone collect the user's emotional state in real time and send the data to the server.

[1441] The emotion engine analyzes this emotional data to determine the learner's current emotional state. The server then dynamically adjusts the learning plan and content provided based on the learner's emotional state. For example, if the learner's concentration is declining, the server will provide the learner with simple video content to help them relax.

[1442] For example, when a user wears smart glasses, a camera analyzes their facial expressions in real time and transmits emotional data to a server, which then adjusts their learning plan and displays appropriate feedback and content on the smart glasses' display.

[1443] The hardware used is wearable devices such as smart glasses and head-mounted displays, and the software uses OpenCV, emotion_recognition, ai_learning_plan, and ai_evaluation_system.

[1444] Example prompt sentence:

[1445] "Design an app for smart glasses that analyzes the user's emotional state in real time and dynamically adjusts the study plan based on the results. Include a feature that provides relaxing video content when the user is not concentrating."

[1446] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1447] Step 1:

[1448] A user logs in to the system from a terminal. The input is the user's login information, and the output is authenticated by the server and a dashboard is displayed. Specifically, the server receives the login information sent from the terminal and compares it with the authentication information stored in the database.

[1449] Step 2:

[1450] The user answers questions about their learning style and learning goals. The input is the user's answer data, and the output is that information stored in a database. The server receives the information the user entered into the input form and stores it in the database as structured data.

[1451] Step 3:

[1452] The server uses an AI model to analyze the user's information and generate a custom learning plan. The input is the user's learning style and goal data, and the output is a custom learning plan. Specifically, the server inputs the user's data into the AI ​​model, generates an optimal learning plan, and stores it in a database.

[1453] Step 4:

[1454] The server selects online educational content based on the user's learning plan. The input is the generated learning plan, and the output is the selected educational content. Specifically, the server analyzes the content of the learning plan and extracts relevant educational materials from the content database.

[1455] Step 5:

[1456] The user starts a learning session and educational content is displayed on the device. The input is the selected educational content, and the output is the display on the device. Specifically, the server sends the educational content to the user's device and displays it in a browser or app.

[1457] Step 6:

[1458] The device's camera and microphone collect the user's emotional state in real time. The input is sensor data from the camera and microphone, and the output is emotional data. Specifically, the device collects video and audio data and sends it to a server for analysis.

[1459] Step 7:

[1460] The server analyzes the emotional data using an emotion engine to determine the user's current emotional state. The input is the collected emotional data, and the output is the emotional state resulting from the analysis. The server analyzes the data using the emotion engine to identify the type and intensity of the emotion.

[1461] Step 8:

[1462] The server dynamically adjusts the learning plan and presented content based on the emotional state. The input is the emotional state resulting from the analysis, and the output is the adjusted learning plan and content. Specifically, the server dynamically changes the learning plan and content based on the emotional state, and provides video content for relaxation as needed.

[1463] Step 9:

[1464] Learning data and emotional data are tracked, and the server generates a detailed report. The input is learning activity data and emotional data, and the output is the generated detailed report. Specifically, the server comprehensively analyzes the collected data, creates a report including progress and emotional state, and displays it on the device.

[1465] Step 10:

[1466] It provides learning support by linking multiple devices. The input is instruction data from the server, and the output is display and feedback to the user. Specifically, the server sends feedback and new content to wearable devices such as smart glasses and head-mounted displays, which then display it.

[1467] 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.

[1468] 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.

[1469] 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.

[1470] [Fourth embodiment]

[1471] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1472] 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.

[1473] 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).

[1474] 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.

[1475] 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.

[1476] 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).

[1477] 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.

[1478] 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.

[1479] 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.

[1480] 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.

[1481] 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.

[1482] 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.

[1483] 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."

[1484] The educational system of the present invention aims to provide high-quality education regardless of factors such as financial difficulties or geographical limitations. The system generates a custom-made learning plan based on the user's learning style and progress, and provides educational content that can be accessed online. It also manages learning progress through an automated evaluation system using AI, tracks learning data, and creates reports. It also has a function for connecting learners with those providing educational support.

[1485] Specifically, the system operates as follows.

[1486] Learning plan generation

[1487] 1. A user logs in from a terminal

[1488] A user accesses the system and enters their login information. The server verifies the authentication information and displays the user's personal settings page.

[1489] 2. Determine your learning style and goals

[1490] Users answer questions about their learning styles and goals. The server receives this information and stores it in a database.

[1491] 3. Generate a custom learning plan

[1492] The server uses AI models based on the stored information to generate a custom learning plan for each learner, tailored to their learning style and goals.

[1493] Providing educational content

[1494] 1. Selection of online educational content

[1495] The server selects appropriate online educational content (videos, texts, exercises, etc.) based on the user's learning plan.

[1496] 2. Display of Content

[1497] When a user starts a learning session from a terminal, the server transmits selected content to the terminal for display.

[1498] Progress assessment and feedback

[1499] 1. Answers and evaluations for practice questions

[1500] The user answers the exercises provided and sends them to the server via their device, which then grades the answers using an automated evaluation system and provides feedback.

[1501] 2. Real-time feedback

[1502] The server provides instant feedback to the user on the results of the assessment, offering additional resources and advice to deepen their understanding.

[1503] Tracking and reporting on learning data

[1504] 1. Tracking training data

[1505] The server tracks users' learning activities in real time and stores them in a database, recording data such as study time, grades, and progress.

[1506] 2. Report Generation

[1507] When the user selects to check progress, the server generates a detailed learning report based on the accumulated data and displays it on the device.

[1508] Collaboration with educational support staff

[1509] 1. Choosing Tutor Support

[1510] If the user desires tutor support, he or she selects the function for linking with a tutor from the terminal.

[1511] 2. Tutor selection and booking

[1512] The server displays a list of tutors available online, and the user selects the tutor they want to book a session with. The server then notifies the tutor of the reservation information and adjusts the schedule.

[1513] Specific examples

[1514] For example, if a user wants to study mathematics, they log in and select "Mathematics" as their study subject. If the user prefers to study using video materials, the server uses an AI model to select and display the most suitable video materials for the user. After studying, the user answers and submits practice questions, and the server automatically grades them and provides real-time feedback. In addition, all learning progress data is tracked and presented to the user in a detailed report. If necessary, the user can also receive additional guidance from an online tutor.

[1515] In this way, the system of the present invention provides learners with a highly personalized educational experience tailored to their individual needs.

[1516] The processing flow will be explained below.

[1517] Step 1:

[1518] The user accesses the EduBridge URL from their device and logs in for the first time.

[1519] Step 2:

[1520] The server receives the user's login information and performs authentication. If successful, the user's dashboard is displayed on the device.

[1521] Step 3:

[1522] Users select the "Personal Settings" option from their dashboard and answer questions about their learning style and goals.

[1523] Step 4:

[1524] The server receives the user's answers and stores them in a database.

[1525] Step 5:

[1526] The server uses the stored information to create a custom learning plan based on the user's learning style and progress using an AI model, and stores the resulting plan in a database.

[1527] Step 6:

[1528] The server transmits the generated learning plan to the terminal and displays it to the user.

[1529] Step 7:

[1530] The user reviews the learning plan and makes any necessary adjustments, which are then sent from the device to the server.

[1531] Step 8:

[1532] The server stores the adjusted learning plan in a database.

[1533] Step 9:

[1534] The user selects a subject from the dashboard and clicks the "Start" button.

[1535] Step 10:

[1536] The server selects appropriate educational content (videos, text, practice questions, etc.) based on the user's learning plan and sends it to the device.

[1537] Step 11:

[1538] The user studies the displayed educational content and answers the exercises.

[1539] Step 12:

[1540] Once the user submits their answer, the server uses an automated rating system to grade the answer and generate instant feedback.

[1541] Step 13:

[1542] The server sends the evaluation results and feedback to the terminal and displays them to the user.

[1543] Step 14:

[1544] The server tracks users' learning data (study time, accuracy rate, etc.) in real time and stores it in a database.

[1545] Step 15:

[1546] When a user selects "Check Progress" from the dashboard, the server analyzes the accumulated learning data and generates a detailed learning report.

[1547] Step 16:

[1548] The server sends the generated report to the terminal for display to the user.

[1549] Step 17:

[1550] If the user desires tutor support, he / she selects the "Tutor Support" option from the dashboard.

[1551] Step 18:

[1552] The server displays a list of currently available tutors, and the user selects the tutor they desire.

[1553] Step 19:

[1554] The user selects the desired date and time, and transmits reservation information from the terminal to the server.

[1555] Step 20:

[1556] The server notifies the tutor of the reservation information and adjusts the schedule.

[1557] Step 21:

[1558] The user and tutor prepare to start the session at the specified date and time.

[1559] Example 1

[1560] 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."

[1561] In today's education system, financial hardship and geographical constraints make it difficult to obtain a high-quality education. Furthermore, traditional education systems often fail to adequately accommodate individual learners' learning styles and progress, hindering efficient learning. Furthermore, automated assessment and learning data tracking and feedback functions are often lacking, resulting in insufficient progress management for learners. These issues need to be resolved.

[1562] 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.

[1563] In this invention, the server includes a means for a user to log in from a terminal, a means for setting the user's learning style and goals, a means for using a generative AI model to generate a customized learning plan, a means for selecting educational content that can be accessed online, a means for displaying the selected educational content, a means for evaluating learning progress using an automated evaluation system, a means for providing feedback on the evaluation results to the user in real time, a means for tracking learning data and storing it in a database, a means for generating reports based on the tracked data, and a means for connecting learners with personnel providing educational support. This makes it possible to provide high-quality personalized education that meets the needs of individual learners.

[1564] "Means for users to log in from a terminal" refers to the method by which users access the system using a terminal such as a PC or smartphone, enter login information, and are authenticated.

[1565] The "means for setting user's learning style and goals" is a method by which a user inputs information about his or her learning style and goals, which is received by the system and stored in the database.

[1566] "Method using a generative AI model to generate a custom-made study plan" means a method using a generative AI model to create an individually optimized study plan based on a user's learning style and goals.

[1567] "Means for selecting online accessible educational content" refers to the method by which the system selects appropriate online educational content based on the user's learning plan.

[1568] The "means for displaying the selected educational content" refers to a method by which the server transmits the selected educational content to the user's terminal and displays it.

[1569] A "means for assessing learning progress using an automated assessment system" is a method for automatically scoring exercises answered by a user and generating an assessment result.

[1570] The "means for providing feedback of the evaluation results to the user in real time" is a method for instantly notifying the user of the graded evaluation results and providing that feedback.

[1571] The "means for tracking learning data and storing it in a database" is a method for monitoring data related to a user's learning activities in real time and recording the data in a database.

[1572] The "means for generating a report based on tracked data" is a method for analyzing the tracked learning data and creating a detailed learning report.

[1573] "Means for connecting learners with educational support providers" refers to a method by which learners can connect online with educational support providers and receive support.

[1574] The educational system of this invention aims to provide high-quality education by creating a custom-made learning plan based on the user's learning style and learning progress. The system is designed for users to access from a device such as a PC or smartphone. The specific hardware and software configuration and processing flow are shown below.

[1575] First, the user accesses the system from a terminal and enters their login information. The server compares this authentication information with the database, and if authentication is successful, displays the user's personal settings page. This login process may use two-factor authentication in addition to the user ID and password.

[1576] Next, the user answers a questionnaire on the system about their learning style and goals. The questionnaire includes items such as study time, target level, and preferred learning method (video, text, practice questions). The server receives this information and stores it in a database. At this point, the user's learning data is initialized.

[1577] Based on the stored data, the server uses a generative AI model to generate a custom learning plan. This model uses existing machine learning algorithms and natural language processing techniques. The generated learning plan is displayed on the user's personalized settings page. The learning plan includes specific learning content, a progress schedule, and recommended learning materials.

[1578] Based on the generated learning plan, the server selects appropriate online educational content from a database, including videos, texts, exercises, etc. This content is sent to and displayed on the device when the user starts a learning session.

[1579] The practice questions that users answer during their studies are sent to a server and instantly scored by an automated assessment system. The results are then fed back to the user in real time, and they may be given feedback on their understanding or additional resources. This feedback helps users to study more effectively.

[1580] All data related to learning activities is tracked in real time by the server and stored in a database. This includes data such as study time, grades, and progress. When a user selects to check their progress, the server generates a detailed learning report based on the accumulated data and displays it on the device. The report includes information such as total study time, progress on each learning item, and grades.

[1581] Furthermore, if a user needs educational support, they can use the tutor support function. With this function, the server displays a list of available tutors, and the user can select the tutor of their choice and reserve a session. The tutor receives the user's reservation information, adjusts their schedule, and provides instruction.

[1582] For example, if a user wants to learn intermediate level mathematics, they can input the following prompt into the generative AI model:

[1583] "Generate a custom learning plan for a user who prefers video learning, covering basic to advanced mathematics. The user's current level is intermediate."

[1584] The generated learning plan recommends intermediate-level math video materials and sets specific learning tasks based on the plan. Users receive real-time feedback as they watch the videos and answer practice questions. The learning data accumulated during this process is tracked by the server and presented to the user as a detailed report. Users can also book a session with a tutor to receive further in-depth learning support, if necessary.

[1585] In this way, the system allows users to enjoy a highly personalized learning experience, maximizing learning efficiency.

[1586] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1587] Step 1:

[1588] A user logs in from a terminal

[1589] The user accesses the system using a device such as a computer or smartphone and enters login information (user ID and password).

[1590] Input: User ID, Password

[1591] The server checks the authentication information against a database and performs authentication. If authentication is successful, the user's personal settings page is displayed on the terminal.

[1592] Example of operation: The user enters their ID and password and presses the "Login" button.

[1593] Step 2:

[1594] Define user learning styles and goals

[1595] Users answer a questionnaire on the system about their learning style and goals.

[1596] Input: Study time, desired level, preferred learning method (video, text, exercises), etc.

[1597] The server receives this information and stores it in a database.

[1598] Example of operation: A user answers a survey and presses the "Save" button.

[1599] Step 3:

[1600] Generate a custom learning plan

[1601] The server inputs prompt sentences into the generative AI model based on the stored data.

[1602] Input: User learning style, goal data

[1603] Example prompt: "Generate a custom learning plan for a user who prefers video instruction, covering basic and advanced mathematics. The user's current level is intermediate."

[1604] The server uses the generative AI model to generate an individually optimized learning plan, which is displayed on the user's personal settings page.

[1605] Output: Custom-made study plan

[1606] Example of operation: A learning plan is generated and the user reviews it.

[1607] Step 4:

[1608] Selection of online educational content

[1609] The server searches the database for appropriate online educational content (videos, texts, exercises, etc.) based on the generated learning plan.

[1610] Input: Study Plan

[1611] The server adds links to educational content to the learning plan.

[1612] Output: A list of selected educational content

[1613] Example of operation: Links to the selected content are displayed as a list.

[1614] Step 5:

[1615] View content

[1616] The user initiates a learning session and the server sends the content to the terminal for display.

[1617] Input: Start instruction for study session

[1618] The server transmits the selected educational content to the terminal and displays it.

[1619] Output: On-screen display of educational content

[1620] Example of how it works: A user starts a learning session and a video plays.

[1621] Step 6:

[1622] Exercises and answers

[1623] The user answers the exercises presented to them and sends the answers to the server.

[1624] Input: Answer to the exercise

[1625] The server grades the answers using an automated evaluation system and generates an evaluation result.

[1626] Output:Scoring results

[1627] Example of operation: When the user answers the questions and presses the "Submit" button, the scoring results are displayed immediately.

[1628] Step 7:

[1629] Real-time feedback

[1630] The server provides the user with real-time feedback on the results of the assessment to confirm their level of understanding.

[1631] Input:Scoring results

[1632] The server may also provide additional resources and exercises.

[1633] Output: Show feedback

[1634] Example of how it works: Additional questions are displayed as feedback regarding areas of insufficient understanding.

[1635] Step 8:

[1636] Tracking and storing learning data

[1637] The server tracks users' learning activities (study time, grades, progress) in real time and stores them in a database.

[1638] Input: Learning activity data

[1639] The server collects and stores this data for analysis.

[1640] Output: Training data stored in a database

[1641] Example of how it works: Data is automatically recorded during a learning session.

[1642] Step 9:

[1643] Report Generation

[1644] When the user selects to check progress, the server generates a detailed learning report based on the accumulated data.

[1645] Input: Progress check instructions

[1646] The server analyzes the data and generates a progress report that is displayed on the terminal.

[1647] Output: Learning report

[1648] Example of operation: A user opens the report and checks the total study time and progress of each learning content.

[1649] Step 10:

[1650] Selecting and booking tutor support

[1651] If the user desires tutor support, he or she selects the function for linking with a tutor from the terminal.

[1652] Input: Request for tutor support

[1653] The server displays a list of tutors available online, and the user selects the tutor of their choice and books a session.

[1654] Output: Notification of reservation information

[1655] The server notifies the tutor of the reservation information and adjusts the schedule.

[1656] Example of operation: The user selects the desired tutor and presses the "Reserve" button.

[1657] (Application example 1)

[1658] 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."

[1659] Today's learners demand high-quality education regardless of financial difficulties or geographical limitations. However, it is difficult to provide customized education that meets individual learning needs, and it is not easy to accurately evaluate learning progress and provide feedback. Furthermore, there are few ways to efficiently acquire product knowledge in physical or virtual stores while studying. A system that solves these challenges and provides a more effective and personalized learning experience is needed.

[1660] 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.

[1661] In this invention, the server includes: a means for generating a customized learning plan based on a user's learning style and learning progress; a means for providing educational content that can be accessed online; a means for evaluating learning progress using an automated evaluation system; a means for tracking learning data and generating reports; a means for connecting learners with educational support providers; a means for users to learn product information and answer practice questions in a virtual store; and a means for executing the above functions using a smartphone. This allows learners to overcome financial difficulties and geographical limitations and enjoy a highly personalized educational experience tailored to their individual learning needs. It also enables efficient product knowledge learning in a virtual store.

[1662] "User" refers to a person who uses the learning system.

[1663] "Learning style" refers to the method or format that a learner prefers to learn most effectively.

[1664] "Learning Progress" refers to the progress a learner has made in their learning plan.

[1665] "Custom-made learning plan" refers to a learning plan that is customized based on each learner's individual needs and characteristics.

[1666] "Online accessible educational content" refers to educational materials and teaching materials that are available to learners via the internet.

[1667] An "automated assessment system" refers to a system that uses AI or algorithms to assess learners' progress and achievements.

[1668] "Learning Data" refers to information about a learner's behavior, progress, grades, etc.

[1669] A "report" refers to a document that summarizes a learner's progress and achievements based on learning data.

[1670] "Persons providing educational support" refer to people who are responsible for providing education and guidance to learners.

[1671] A "virtual store" refers to a virtual store that sells products and services online.

[1672] A "smartphone" refers to a mobile phone terminal with internet connectivity and various functions.

[1673] A "server" refers to a computer that provides services and information to clients over a network.

[1674] To implement this invention, the following system configuration and processing are used: The main components of the system include a server, a terminal used by a user (e.g., a smartphone), and educational content that can be accessed online.

[1675] The server first provides a means for users to input their learning style and learning goals. Users log in to the system from their terminals and input information about their learning style and learning goals. The server then stores this information in a database.

[1676] The server then uses the stored information to generate a personalized learning plan using a generative AI model, which includes content tailored to the user's learning style and goals. For example, if a user prefers video learning materials, appropriate video content will be selected.

[1677] The server provides appropriate online educational content based on the user's learning plan. When the user starts a learning session from their device, the server sends the selected content to the device and displays it. The educational content includes videos, textbooks, exercises, etc.

[1678] The server also evaluates learning progress. Users answer the exercises provided and send them to the server via their device. The server then uses an automated evaluation system to grade the answers and provide feedback, allowing users to instantly check their level of understanding.

[1679] Learning progress data is tracked by the server and stored in a database. Data such as study time, grades, and progress are recorded, and when a user selects to check progress, a detailed learning report is generated and displayed on the device.

[1680] Furthermore, the system allows users to learn about products in the virtual store and answer practice questions, allowing them to test their understanding while learning how to select and use products in the virtual store.

[1681] The entire system is run using a smartphone, which is equipped with functions such as user login, learning style setting, educational content display, automatic assessment result display, and learning progress tracking.

[1682] The hardware and software used includes:

[1683] Hardware: Smartphone (iOS / Android)

[1684] Software: Python, Flask (web framework), Scikit-learn (machine learning library)

[1685] For example, when a user buys a new kitchen gadget, they can watch a video to learn how to use it and then test their understanding with practice questions. Here's an example of a prompt for the generative AI model:

[1686] "User just purchased a new kitchen gadget. Their learning style is videos, and their learning goal is to understand how to use the product. Generate an optimal learning plan."

[1687] This allows learners to overcome financial difficulties and geographical constraints, receive a highly personalized educational experience tailored to their individual learning needs, and efficiently learn about products in virtual stores.

[1688] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1689] Step 1:

[1690] The server receives authentication information for the user to log in from the terminal. The terminal sends the user ID and password to the server, which verifies the authentication information and displays the user's personal setting page. At this stage, the input is the user ID and password, and the output is the user's personal setting page.

[1691] Step 2:

[1692] The server provides a means for users to input information about their learning style and learning goals through their terminals. The learning style (e.g., video, text) and learning goals are sent as input data from the terminals to the server. The server receives this information and stores it in a database.

[1693] Step 3:

[1694] The server uses a generative AI model based on the stored information to generate a custom-made learning plan that is optimal for each learner. The input is data about the user's learning style and learning goals, and the AI ​​model calculates the data to obtain the optimal learning plan as output. This learning plan is then sent to the device.

[1695] Step 4:

[1696] The server selects appropriate online educational content based on the user's learning plan. The input is the generated learning plan, and the output is the selected educational content (videos, text, exercises, etc.). These contents are sent to the terminal and displayed.

[1697] Step 5:

[1698] A user answers exercises presented on a device. The input is the user's answer, which is sent from the device to a server. The server uses an automated evaluation system to grade the answer and generate feedback. Here, the input is the user's answer data and the output is the generated feedback.

[1699] Step 6:

[1700] The server tracks learning data in real time and stores it in a database, which records information such as learning time, grades, progress, etc. In this step, the input is the user's learning activity data, and the output is the saved learning data.

[1701] Step 7:

[1702] When the user selects to check progress, the server generates a detailed learning report based on the accumulated data and displays it on the terminal. The input is the accumulated learning data, and the output is a detailed learning report.

[1703] Step 8:

[1704] The server provides a function for users to learn about products in a virtual store and answer practice questions. Users use their devices to learn about products and then answer practice questions. The input is the user's learning content and answers, and the server automatically evaluates and outputs the results.

[1705] Step 9:

[1706] The server provides the user with additional educational resources and advice based on the learning results and feedback, thereby improving the user's understanding. The input is the evaluation result, and the output is additional educational resources and advice.

[1707] 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.

[1708] This invention relates to an educational system that provides learners with a more personalized learning experience. The system generates a custom-made learning plan based on the user's learning style and progress, and provides educational content that can be accessed online. It also manages learning progress through an automated assessment system using AI, tracks learning data, and creates reports. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it provides optimal support according to the learner's emotional state.

[1709] Learning plan generation

[1710] 1. A user logs in from a terminal

[1711] When a user accesses the system and enters their login information, the server verifies the authentication information and displays the dashboard on the terminal.

[1712] 2. Determine your learning style and goals

[1713] Users answer questions about their learning style and learning goals, and the server receives the information and stores it in a database.

[1714] 3. Create a custom learning plan

[1715] The server uses an AI model to analyze the user's information, generate a custom learning plan, and store it in a database.

[1716] Providing educational content

[1717] 1. Selection of online educational content

[1718] The server selects the most suitable educational content based on the user's learning plan.

[1719] 2. Display of Content

[1720] When a user starts a study session, the server sends selected content to the terminal for display.

[1721] Progress assessment and feedback

[1722] 1. Answers and evaluations for practice questions

[1723] Users answer exercises and send their answers from their device to a server, which uses an automated evaluation system to grade the answers and generate feedback.

[1724] 2. Real-time feedback

[1725] The server immediately sends and displays the evaluation results and feedback to the user.

[1726] Use of emotion engine

[1727] 1. User Emotion Recognition

[1728] The device's sensors, such as the camera and microphone, are used to collect the user's emotional state in real time.

[1729] 2. Emotion Data Analysis

[1730] The emotion data collected by the server is analyzed by an emotion engine to determine the learner's current emotional state.

[1731] 3. Adjust your study plan based on your emotions

[1732] Based on the emotion engine's judgment, the server dynamically adjusts the learning plan and presented content, for example, providing more relaxing content if the learner's concentration is declining.

[1733] Tracking and reporting on learning data

[1734] 1. Tracking training data

[1735] The server tracks users' learning activities and emotional data in real time and stores them in a database.

[1736] 2. Report Generation

[1737] When the user selects to check progress, the server generates a detailed report based on the accumulated learning data and emotional data and displays it on the device.

[1738] Collaboration with educational support staff

[1739] 1. Choosing Tutor Support

[1740] If the user desires tutor support, he selects the "tutor support" option.

[1741] 2. Tutor selection and booking

[1742] The server displays a list of available tutors, and the user selects and reserves the tutor of their choice. The server notifies the tutor of the reservation information and adjusts the schedule.

[1743] Specific examples

[1744] For example, if a user wants to study mathematics, they log in and select "Mathematics" as their study subject. If the user prefers video learning materials, the server uses AI to select the most suitable video learning materials and displays them on the device. After the user completes the practice problems and submits their answers, the server grades them using an automatic evaluation system and provides real-time feedback. Meanwhile, the device's camera and microphone recognize emotions from the user's facial expressions and tone of voice, which are analyzed by an emotion engine. If the user's concentration is low, the server suggests breaks or easy content to relax. All learning and emotional data is tracked and provided as detailed reports.

[1745] In this way, the system of the present invention can provide a more effective educational experience based on the user's emotional state, maximizing learning outcomes.

[1746] The processing flow will be explained below.

[1747] Step 1:

[1748] The user accesses the EduBridge URL from their device and logs in for the first time.

[1749] Step 2:

[1750] The server receives the user's login information, authenticates them, and if successful, displays the user's dashboard on the device.

[1751] Step 3:

[1752] Users select the "Personal Settings" option from their dashboard and answer questions about their learning style and goals.

[1753] Step 4:

[1754] The server receives the user's answers and stores them in a database.

[1755] Step 5:

[1756] The server uses the stored information to use AI models to generate a custom learning plan based on the user's learning style and progress, and stores the resulting plan in a database.

[1757] Step 6:

[1758] The server sends the generated learning plan to the device and displays it to the user.

[1759] Step 7:

[1760] The user can review the learning plan and make any necessary adjustments, which are then sent from the device to the server.

[1761] Step 8:

[1762] The server stores the adjusted learning plan in a database.

[1763] Step 9:

[1764] Users select a subject from the dashboard and click the "Start" button.

[1765] Step 10:

[1766] The server selects appropriate educational content (videos, text, practice questions, etc.) based on the user's learning plan and sends it to the device.

[1767] Step 11:

[1768] It uses the device's camera and microphone to collect the user's facial expressions and voice in real time and recognize the user's emotional state.

[1769] Step 12:

[1770] The emotion engine analyzes the collected data and determines the user's current emotional state.

[1771] Step 13:

[1772] The server dynamically adjusts the learning plan and presented content based on the results of the emotion engine, for example, providing more relaxing content when the user is not concentrating.

[1773] Step 14:

[1774] Users study the educational content displayed and answer practice questions.

[1775] Step 15:

[1776] Once users submit their answers, the server uses an automated rating system to grade the answers and generate instant feedback.

[1777] Step 16:

[1778] The server sends the evaluation results and feedback to the device and displays them to the user.

[1779] Step 17:

[1780] The server tracks users' learning data (study time, accuracy rate, emotional state, etc.) in real time and stores it in a database.

[1781] Step 18:

[1782] When a user selects "Check Progress" from the dashboard, the server generates a detailed report based on the accumulated learning data and emotional data and displays it on the device.

[1783] Step 19:

[1784] If a user wishes to receive tutor support, they can select the "Tutor Support" option from their dashboard.

[1785] Step 20:

[1786] The server displays a list of available tutors and the user selects the tutor of their choice.

[1787] Step 21:

[1788] The user selects the desired date and time and sends the reservation information from the terminal to the server.

[1789] Step 22:

[1790] The server notifies the tutor of the reservation information and adjusts the schedule.

[1791] Step 23:

[1792] The user and tutor prepare to start the session at the specified date and time.

[1793] Example 2

[1794] 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."

[1795] Traditional educational systems have difficulty providing individualized learning plans that match each learner's learning style and progress, and lack efficient means for assessing learning progress and providing feedback. Furthermore, there is no system in place to provide appropriate learning support based on the learner's emotional state, which can lead to reduced learning efficiency. This creates the problem of learners not receiving an optimal learning experience.

[1796] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1797] In this invention, the server includes means for generating a custom-made learning plan according to an individual's learning style and learning progress, means for providing educational content that can be accessed online, means for evaluating learning progress using an automated evaluation system, means for tracking learning data and generating reports, means for connecting learners with personnel providing educational support, means for collecting emotional data using sensors in the device, and means for analyzing the emotional data and dynamically adjusting the learning plan. This makes it possible to provide an optimized learning plan for each learner, evaluate and feedback learning progress in real time, and provide appropriate support according to the learner's emotional state.

[1798] "Learning style" refers to a learner's preferred learning method or format, such as whether they prefer video materials or textbooks, or whether they prefer self-study or tutor support.

[1799] "Learning progress" refers to the standard for measuring a learner's current learning progress and level of understanding.

[1800] A "custom-made learning plan" refers to a learning schedule and materials that are individually created based on each learner's learning style and progress.

[1801] "Online-accessible educational content" refers to learning materials such as videos, textbooks, and exercises that learners can access via the internet.

[1802] An "automated assessment system" refers to a system that automatically scores and evaluates learners' answers and progress and generates feedback.

[1803] "Learning data" refers to data related to a learner's learning activities, including, for example, study time, correct answer rate, and history of learning materials used.

[1804] A "report" is a document created based on accumulated learning data and evaluation results, and refers to a document that shows learning progress and achievement.

[1805] "Persons providing educational support" refers to those who have the role of providing direct learning support and advice to learners, including tutors and coaches.

[1806] "Device sensors" refer to devices such as cameras and microphones that are used to collect information about the user's emotional state.

[1807] "Emotional data" refers to information that indicates a learner's emotional state, such as information obtained from their facial expressions or tone of voice.

[1808] An "emotion engine" refers to a system that analyzes collected emotional data and determines the learner's emotional state.

[1809] "Dynamic adjustment" refers to changing the learning plan and content presented in response to changing conditions in real time.

[1810] Learning plan generation

[1811] To implement the invention, a user first launches a browser on their terminal and accesses the system's login page. They enter their user ID and password and click the "Login" button. This authentication information is sent to the server, which queries the database for authentication. If authentication is successful, the server sends the HTML data for the dashboard screen to the terminal, which then displays it on the terminal.

[1812] Next, the user selects the "Learning Settings" menu on the dashboard, enters their learning style and learning goals in the displayed question form, and clicks the "Submit" button. This input data is sent from the device to the server. The server stores the received data in a database, and then inputs the stored data into an AI model (e.g., TensorFlow or PyTorch) for analysis. The AI ​​model generates a custom learning plan, which is then stored in the database.

[1813] Providing educational content

[1814] The server selects appropriate educational content (videos, texts, exercises, etc.) based on the generated learning plan. When the user clicks the "Start Learning" button on the dashboard, the server sends the selected educational content to the device, which then displays it.

[1815] Progress assessment and feedback

[1816] The user answers the exercises on their device and sends the results to the server, which uses an automated evaluation system to grade the answers and generate an evaluation result, which is immediately fed back to the user and displayed on their device.

[1817] Use of emotion engine

[1818] While the user is studying, emotional data is collected through the device's camera and microphone. For example, the camera captures the user's facial expressions and the microphone records the tone of their voice. The collected emotional data is sent to a server, which analyzes it using an emotion engine (e.g., Python's OpenCV or Haar Cascade). Based on the analysis results, if the emotional state indicates a decrease in concentration or fatigue, the server dynamically adjusts the study plan and presented content. For example, if the user's concentration decreases, it will provide a light video or simple questions to help them relax.

[1819] Tracking and reporting on learning data

[1820] The server tracks the user's learning activities and emotional data in real time and stores them in a database. When the user clicks the "Check Progress" button, the server generates a detailed report based on the accumulated data and sends it to the device. The report visually displays the user's learning progress and emotional fluctuations.

[1821] Collaboration with educational support staff

[1822] When the user selects the "Tutor Support" option, the server displays a list of available tutors. Once the user selects the desired tutor and confirms the reservation, the server notifies the tutor of the reservation information and adjusts the schedule.

[1823] Specific examples

[1824] For example, if a user wants to study mathematics, they log in and select "Mathematics" as their study subject. If the user prefers video learning materials, the server uses AI to select the most suitable video learning materials and displays them on the device. After the user completes the practice problems, they submit their answers to the server, which then grades them with an automated evaluation system and provides instant feedback. At the same time, the device's camera and microphone recognize emotions from the user's facial expressions and tone of voice, which are then analyzed by the emotion engine. For example, if the server determines that the user's concentration is declining, it can provide content to help them relax. Learning data and emotional data are tracked and provided to the user in the form of a detailed report.

[1825] Example prompts for generative AI models

[1826] "Generate custom learning plans based on the user's learning style. For example, if the user prefers video materials, select the most suitable videos and incorporate them into the learning plan."

[1827] With the above-described configuration, the present invention can provide users with a personalized learning experience, maximizing learning efficiency and learning outcomes.

[1828] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1829] Step 1:

[1830] The user enters their login information and clicks the "Login" button.

[1831] Input: User ID, Password.

[1832] Processing: The terminal sends the input data to the server.

[1833] Output: The authentication information sent to the server.

[1834] Step 2:

[1835] The server checks the received authentication information and performs authentication.

[1836] Input: User ID, Password.

[1837] Processing: The server queries the database to perform authentication, and if successful, generates HTML data for the dashboard screen.

[1838] Output: Authentication result, HTML data of the dashboard screen (if authentication is successful).

[1839] Step 3:

[1840] The server sends the HTML data of the dashboard screen to the terminal, which displays it.

[1841] Input: HTML data of the dashboard screen.

[1842] Processing: The terminal analyzes the received HTML data and displays it in the browser.

[1843] Output: The dashboard that is displayed on the user's screen.

[1844] Step 4:

[1845] The user selects the "Learning Settings" menu on the dashboard and enters their learning style and learning goals.

[1846] Input: learning styles, learning goals.

[1847] Processing: The user enters data into the inquiry form and clicks the "Submit" button. The terminal sends the input data to the server.

[1848] Output: Learning style and learning goal data sent to the server.

[1849] Step 5:

[1850] The server stores the received learning data in a database and inputs it into the AI ​​model.

[1851] Input: Learning styles, learning goal data.

[1852] Processing: The server stores the data in a database and then inputs the stored data into an AI model (e.g., TensorFlow or PyTorch) for analysis.

[1853] Output: A generated custom lesson plan.

[1854] Step 6:

[1855] The educational content is selected based on the learning plan generated by the server.

[1856] Enter: Study Plan.

[1857] Processing: The server selects the most appropriate content (video, text, exercises, etc.) from the database.

[1858] Output: Selected educational content.

[1859] Step 7:

[1860] The user clicks the "Start Learning" button on the dashboard. The server sends the selected educational content to the device and displays it.

[1861] Input: Learning content.

[1862] Processing: The server sends the content to the device, and the device displays the content on the browser.

[1863] Output: The educational content displayed on the user's screen.

[1864] Step 8:

[1865] The user answers the exercises on the terminal and sends the answer data to the server.

[1866] Input: User's answer data.

[1867] Processing: The terminal sends the answer data to the server.

[1868] Output: The answer data sent to the server.

[1869] Step 9:

[1870] The server grades the received answer data using an automatic evaluation system to generate an evaluation result.

[1871] Input: Answer data.

[1872] Processing: The server uses an automated evaluation system to grade the answers and generate an evaluation result.

[1873] Output: The generated evaluation results.

[1874] Step 10:

[1875] The server feeds back the evaluation results to the user and displays them on the terminal.

[1876] Input: Evaluation result.

[1877] Processing: The server sends the evaluation results to the terminal, and the terminal displays the evaluation results in the browser.

[1878] Output: The evaluation results displayed on the user's screen.

[1879] Step 11:

[1880] The device collects the user's emotional data through the camera and microphone and sends it to the server.

[1881] Input: User's facial expression data, tone of voice data.

[1882] Processing: The device collects emotion data and sends it to the server.

[1883] Output: Emotion data sent to the server.

[1884] Step 12:

[1885] The server analyzes the emotion data using an emotion engine to determine the user's emotional state.

[1886] Input: Emotion data.

[1887] Processing: The server uses an emotion engine to analyze the emotion data and determine the user's emotional state.

[1888] Output: Parsed emotional state.

[1889] Step 13:

[1890] The server dynamically adjusts the learning plan and presented content based on the emotional state.

[1891] Input: Emotional state.

[1892] Processing: The server changes the learning plan and content based on the emotional state, and selects new content if necessary.

[1893] Output: Tailored learning plans and content.

[1894] Step 14:

[1895] The server tracks users' learning activities and emotional data in real time and stores them in a database.

[1896] Input: learning activity data, emotion data.

[1897] Processing: The server collects this data and stores it in a database.

[1898] Output: Real-time training data and sentiment data stored in a database.

[1899] Step 15:

[1900] When the user clicks the "Check Progress" button, the server generates a detailed report and sends it to the device.

[1901] Input: Training data, emotion data.

[1902] Processing: The server generates a report based on the data and sends it to the device.

[1903] Output: A detailed report that is displayed on the user's screen.

[1904] (Application example 2)

[1905] 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."

[1906] While traditional educational systems are customized to suit learners' learning styles and progress, they lack the ability to dynamically adjust learning experiences based on the learner's emotional state. This makes it difficult to maximize learning effectiveness by providing appropriate feedback and learning content based on the learner's concentration and emotional state. Furthermore, because emotion recognition and learning support utilizing the learner's device are not integrated, real-time adjustment of learning plans is also not possible.

[1907] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating a customized learning plan based on an individual's learning style and learning progress, means for providing educational content accessible online, means for evaluating learning progress using an automated evaluation system, means for tracking learning data and generating reports, means for connecting learners with educational support providers, and means for recognizing the learner's emotional state in real time and dynamically adjusting the learning plan based on the emotional state. This makes it possible to provide an optimal learning plan based on the learner's emotional state. Furthermore, by using wearable devices such as smart glasses or head-mounted displays, emotions can be recognized in real time and the learning experience can be instantly optimized.

[1908] "Learning style" refers to the method or approach that a learner uses to learn most effectively.

[1909] "Learning progress" is an indicator of how far a learner has progressed with a particular learning plan or curriculum.

[1910] A "custom-made learning plan" is an individualized learning schedule and materials created to suit the needs and characteristics of each individual learner.

[1911] "Online-accessible educational content" refers to educational materials, lessons, videos, e-books, etc. that are available via the internet.

[1912] An "automated assessment system" is a system that uses artificial intelligence and algorithms to automatically assess learners' assignments and tests and provide feedback.

[1913] "Learning data" refers to information related to a learner's learning activities, progress, grades, behavioral logs, etc.

[1914] A "report" is a report that summarizes learning progress, grades, and other related information generated based on learning data.

[1915] "Educational support personnel" are professionals such as teachers and tutors who provide educational advice and support to learners.

[1916] "To collaborate" refers to the act of coordinating two or more elements or systems to function together.

[1917] "Emotional state" refers to the learner's state of mind or psychological response, and includes emotions such as joy, sadness, surprise, and concentration.

[1918] "Dynamic adjustment" refers to automatically changing content and methods to adapt to changing conditions and situations in real time.

[1919] A "wearable device" is a computing device that is worn on the body and includes smart glasses and head-mounted displays.

[1920] This invention relates to an educational system that provides a personalized learning experience for learners, dynamically adjusts the learning plan based on the learner's emotional state, and utilizes wearable devices to provide real-time learning assistance.

[1921] The server includes means for generating a custom-made learning plan according to an individual's learning style and learning progress, means for providing educational content that can be accessed online, means for evaluating learning progress using an automated evaluation system, means for tracking learning data and generating reports, means for connecting learners with personnel providing educational support, and means for recognizing the learner's emotional state in real time and dynamically adjusting the learning plan based thereon.

[1922] The server receives login information from the user's device, authenticates them, and then displays a dashboard. The user answers questions about their learning style and goals, which are then stored in a database and analyzed by an AI model to generate a custom learning plan.

[1923] The server selects the most suitable online educational content based on the user's learning plan and displays it on the device. When the user starts a learning session, the device's camera and microphone collect the user's emotional state in real time and send the data to the server.

[1924] The emotion engine analyzes this emotional data to determine the learner's current emotional state. The server then dynamically adjusts the learning plan and content provided based on the learner's emotional state. For example, if the learner's concentration is declining, the server will provide the learner with simple video content to help them relax.

[1925] For example, when a user wears smart glasses, a camera analyzes their facial expressions in real time and transmits emotional data to a server, which then adjusts their learning plan and displays appropriate feedback and content on the smart glasses' display.

[1926] The hardware used is wearable devices such as smart glasses and head-mounted displays, and the software uses OpenCV, emotion_recognition, ai_learning_plan, and ai_evaluation_system.

[1927] Example prompt sentence:

[1928] "Design an app for smart glasses that analyzes the user's emotional state in real time and dynamically adjusts the study plan based on the results. Include a feature that provides relaxing video content when the user is not concentrating."

[1929] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1930] Step 1:

[1931] A user logs in to the system from a terminal. The input is the user's login information, and the output is authenticated by the server and a dashboard is displayed. Specifically, the server receives the login information sent from the terminal and compares it with the authentication information stored in the database.

[1932] Step 2:

[1933] The user answers questions about their learning style and learning goals. The input is the user's answer data, and the output is that information stored in a database. The server receives the information the user entered into the input form and stores it in the database as structured data.

[1934] Step 3:

[1935] The server uses an AI model to analyze the user's information and generate a custom learning plan. The input is the user's learning style and goal data, and the output is a custom learning plan. Specifically, the server inputs the user's data into the AI ​​model, generates an optimal learning plan, and stores it in a database.

[1936] Step 4:

[1937] The server selects online educational content based on the user's learning plan. The input is the generated learning plan, and the output is the selected educational content. Specifically, the server analyzes the content of the learning plan and extracts relevant educational materials from the content database.

[1938] Step 5:

[1939] The user starts a learning session and educational content is displayed on the device. The input is the selected educational content, and the output is the display on the device. Specifically, the server sends the educational content to the user's device and displays it in a browser or app.

[1940] Step 6:

[1941] The device's camera and microphone collect the user's emotional state in real time. The input is sensor data from the camera and microphone, and the output is emotional data. Specifically, the device collects video and audio data and sends it to a server for analysis.

[1942] Step 7:

[1943] The server analyzes the emotional data using an emotion engine to determine the user's current emotional state. The input is the collected emotional data, and the output is the emotional state resulting from the analysis. The server analyzes the data using the emotion engine to identify the type and intensity of the emotion.

[1944] Step 8:

[1945] The server dynamically adjusts the learning plan and presented content based on the emotional state. The input is the emotional state resulting from the analysis, and the output is the adjusted learning plan and content. Specifically, the server dynamically changes the learning plan and content based on the emotional state, and provides video content for relaxation as needed.

[1946] Step 9:

[1947] Learning data and emotional data are tracked, and the server generates a detailed report. The input is learning activity data and emotional data, and the output is the generated detailed report. Specifically, the server comprehensively analyzes the collected data, creates a report including progress and emotional state, and displays it on the device.

[1948] Step 10:

[1949] It provides learning support by linking multiple devices. The input is instruction data from the server, and the output is display and feedback to the user. Specifically, the server sends feedback and new content to wearable devices such as smart glasses and head-mounted displays, which then display it.

[1950] 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.

[1951] 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.

[1952] 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.

[1953] 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.

[1954] 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.

[1955] 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.

[1956] 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).

[1957] 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.

[1958] 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."

[1959] 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.

[1960] 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).

[1961] 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.

[1962] 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.

[1963] 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.

[1964] 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.

[1965] 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.

[1966] 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.

[1967] 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.

[1968] 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.

[1969] 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.

[1970] 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.

[1971] The following is further disclosed regarding the above embodiment.

[1972] (Claim 1)

[1973] A means to generate a custom learning plan based on an individual's learning style and progress;

[1974] a means of providing educational content that can be accessed online;

[1975] a means for assessing learning progress using an automated assessment system;

[1976] a means of tracking learning data and generating reports;

[1977] A means of connecting learners with those providing educational support;

[1978] A system including:

[1979] (Claim 2)

[1980] 10. The system of claim 1, further comprising means for answering a learner's study questions in real time using natural language processing.

[1981] (Claim 3)

[1982] 10. The system of claim 1, further comprising means for providing multilingual support.

[1983] "Example 1"

[1984] (Claim 1)

[1985] a means for a user to log in from a terminal;

[1986] a means of determining the user's learning style and goals;

[1987] Using generative AI models to generate custom learning plans;

[1988] A means of selecting educational content that can be accessed online;

[1989] a means for displaying the selected educational content;

[1990] a means for assessing learning progress using an automated assessment system;

[1991] a means for providing feedback of the evaluation results to the user in real time;

[1992] a means of tracking and storing the learning data in a database;

[1993] a means for generating reports based on the tracked data;

[1994] A means of connecting learners with those providing educational support;

[1995] A system including:

[1996] (Claim 2)

[1997] 10. The system of claim 1, further comprising means for answering a learner's study questions in real time using natural language processing.

[1998] (Claim 3)

[1999] 10. The system of claim 1, further comprising means for providing multilingual support.

[2000] "Application Example 1"

[2001] (Claim 1)

[2002] A means for generating a custom learning plan based on the user's learning style and learning progress;

[2003] a means of providing educational content that can be accessed online;

[2004] a means for assessing learning progress using an automated assessment system;

[2005] a means of tracking learning data and generating reports;

[2006] A means of connecting learners with those providing educational support;

[2007] a means for users to learn product information and complete practice questions in a virtual store;

[2008] A means for executing the above functions using a smartphone;

[2009] A system including:

[2010] (Claim 2)

[2011] 10. The system of claim 1, further comprising means for answering a learner's study questions in real time using natural language processing.

[2012] (Claim 3)

[2013] 10. The system of claim 1, further comprising means for providing multilingual support.

[2014] "Example 2: Combining Emotion Engines"

[2015] (Claim 1)

[2016] A means to generate a custom learning plan based on an individual's learning style and progress;

[2017] a means of providing educational content that can be accessed online;

[2018] a means for assessing learning progress using an automated assessment system;

[2019] a means of tracking learning data and generating reports;

[2020] A means of connecting learners with those providing educational support;

[2021] A means for collecting emotion data using a sensor of the device;

[2022] A means of analyzing emotional data and dynamically adjusting learning plans;

[2023] A system including:

[2024] (Claim 2)

[2025] 10. The system of claim 1, further comprising means for answering a learner's study questions in real time using natural language processing.

[2026] (Claim 3)

[2027] 10. The system of claim 1, further comprising means for providing multilingual support.

[2028] "Application example 2 when combining emotion engines"

[2029] (Claim 1)

[2030] A means to generate a custom learning plan based on an individual's learning style and progress;

[2031] a means of providing educational content that can be accessed online;

[2032] a means for assessing learning progress using an automated assessment system;

[2033] a means of tracking learning data and generating reports;

[2034] A means of connecting learners with those providing educational support;

[2035] a means of recognizing the learner's emotional state in real time and dynamically adjusting the learning plan accordingly;

[2036] A system including:

[2037] (Claim 2)

[2038] 10. The system of claim 1, further comprising means for optimizing a user's learning experience based on the emotional data collected in real time.

[2039] (Claim 3)

[2040] 10. The system of claim 1, further comprising means for monitoring an emotional state using smart glasses, a head-mounted display, or other wearable device and providing educational content accordingly. [Explanation of symbols]

[2041] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means to generate a custom learning plan based on an individual's learning style and progress; a means of providing educational content that can be accessed online; a means for assessing learning progress using an automated assessment system; a means of tracking learning data and generating reports; A means of connecting learners with those providing educational support; A system including:

2. The system of claim 1 , further comprising means for answering learner's study questions in real time using natural language processing.

3. The system of claim 1 further comprising means for providing multilingual support.

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