system

Abstract

To provide a system that can provide an effective individual learning environment and support for home learning, thereby reducing the burden on parents and teachers. A means for a user to input and transmit information; The system includes: a means for a terminal to send transmission data to a server; a means for the server to store the received data in a database; a means for the terminal to collect learning data and send it to the server; a means for the server to run an AI algorithm based on the stored data and analyze learning patterns; a means for the server to identify individual learning needs from the analysis results; a means for the server to generate custom learning materials based on the needs identified by the server; a means for the terminal to send a signal to VR goggles corresponding to the student and execute a specific learning scenario; and a means for the terminal to launch a home learning app and allow the student to study at home.

Description

[Technical Field]

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

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

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

[0004] In today's educational environment, it is difficult to provide personalized education tailored to the learning needs of each student. In addition, ineffective learning support at home places an increased burden on parents and teachers. Traditional classrooms and teaching materials also make it difficult to maintain students' interest and concentration. To solve these issues, it is necessary to effectively utilize AI and VR technologies to create a next-generation learning environment. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means: a means for a user to input and transmit information, a means for the terminal to transmit the transmitted data to a server, and a means for the server to store the received data in a database. The device also includes a means for the terminal to collect learning data and transmit it to the server, and a means for the server to run an AI algorithm based on the stored data and analyze learning patterns. The device also provides a means for identifying individual learning needs from the analysis results and generating customized learning materials based on the identified needs. The device also includes a means for the terminal to send a signal to the student's corresponding VR goggles to execute a specific learning scenario, and a means for the terminal to launch a home learning app so that the student can study at home. The device also includes a means for the server to monitor the progress of home learning and provide feedback to parents, and a means for the user (teacher) to set the content and scenario of the VR classroom and for the server to distribute instructions to the device. This provides an effective individual learning environment and home learning support, reducing the burden on parents and teachers.

[0006] "User" refers to the person responsible for operating the system and inputting and submitting information.

[0007] A "terminal" is a device operated by a user that has the function of collecting learning data and transmitting data to a server.

[0008] "Server" refers to the central processing unit that receives and stores data sent from the device and executes AI algorithms.

[0009] "Database" refers to a storage device or system that the server uses to store user and learning data.

[0010] "AI Algorithm" refers to the artificial intelligence computational methods and processes used within the server to analyze learning patterns and generate custom learning materials.

[0011] "Learning patterns" refer to a set of characteristics obtained by analyzing a student's learning methods, progress, answer tendencies, etc.

[0012] "Customized learning materials" refers to personalized learning materials generated by AI algorithms based on each student's specific learning needs.

[0013] "VR goggles" refers to a device for displaying and executing learning scenarios using virtual reality technology.

[0014] "Home learning apps" refer to specialized software applications used by students to study at home.

[0015] "Feedback" refers to the information and instructions provided to parents by the server as a result of assessing home learning progress.

[0016] A "learning scenario" refers to a plan or content for setting up a series of learning activities or exercises based on a specific educational objective. [Brief explanation of the drawings]

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

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

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

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The present invention is a next-generation education system that combines AI and VR technologies to provide an effective learning environment, reducing the burden on parents and teachers while meeting the learning needs of individual students. Specific embodiments of the system are described below.

[0039] User registration and information management

[0040] 1. User Registration

[0041] The user (parent) accesses the system using a web browser or mobile application, enters the required information (parent information, child's basic information, contact information, etc.) into the designated registration form, and submits it.

[0042] The device sends the input data to the server, which includes basic information about the parent and child.

[0043] The server receives the submitted data, validates it, and then saves it to the database.

[0044] Analyzing learning patterns and generating custom learning materials

[0045] 2. Collecting training data

[0046] The device collects students' daily learning progress data, test results, assignment submission status, etc. This data is periodically sent to the server.

[0047] The server receives the data and stores it in a database, where it performs the necessary integrity checks before storing it.

[0048] 3. Analysis of learning patterns

[0049] The server runs AI algorithms on the stored learning data to analyze each student's learning patterns, such as identifying repeated mistakes on specific questions, learning speed, and level of understanding.

[0050] The results of the analysis are used to identify individual student learning needs and are stored in a database.

[0051] 4. Creating custom learning materials

[0052] Based on the analysis, the server generates custom learning materials tailored to each student's learning needs, including exercises, video lectures, and interactive quizzes.

[0053] The custom learning materials are then delivered to teachers in a format that they can review and modify as needed.

[0054] Setting up and operating a VR classroom

[0055] 5. VR classroom setup

[0056] The user (teacher) opens the VR classroom settings page using a web browser or application and selects the VR scenario to be used in the next lesson, such as a historical simulation or a 3D virtual science experiment.

[0057] The server receives the settings and sends the corresponding VR signal to each student's device.

[0058] 6. VR classroom operation

[0059] The device sends signals to each student's VR goggles to execute specific learning scenarios, allowing students to wear the goggles and deepen their understanding through an immersive learning experience.

[0060] Support for home learning

[0061] 7. Use home learning apps

[0062] The device runs a dedicated home learning application, providing students with a learning environment at home, including custom learning materials, homework assignments, and progress tracking.

[0063] The device sends data on the progress of home learning to a server, which stores the received data in a database and monitors it in real time.

[0064] 8. Providing Feedback

[0065] The server analyzes the progress of home learning and provides parents with feedback on their child's learning, including progress, level of understanding, and suggestions for necessary supplementary learning.

[0066] Specific examples

[0067] For example, if Student A is struggling with adding and subtracting fractions in math class, the system will identify this information through learning pattern analysis. The server will generate custom learning materials with appropriate practice problems, which the teacher will review and then distribute to Student A's device. Furthermore, a home learning app will suggest related practice problems and video courses so that the student can continue their learning at home.

[0068] In chemistry classes, VR classrooms are used to conduct 3D simulations to deepen understanding of molecular structures. The teacher sets up the VR scenario, and the server distributes corresponding instructions to the devices. Students put on VR goggles and observe and manipulate molecular structures in the virtual environment, thereby gaining a deeper understanding.

[0069] In this way, the present invention provides a system that responds to the diverse needs of users (parents, teachers, students) and provides effective learning support by utilizing advanced technology.

[0070] The processing flow will be explained below.

[0071] Step 1:

[0072] The user (parent) accesses the system and opens the registration form using a web browser or mobile app. The user enters the required information (parent information, child basic information, contact information, etc.) into the form and presses the submit button.

[0073] Step 2:

[0074] The terminal generates and transmits an HTTP request for transmitting the data input by the user to the server.

[0075] Step 3:

[0076] The server validates the received data and, if there are no problems, saves it in the database. After saving, the server generates a notification to the user that registration is complete and sends it to the terminal.

[0077] Step 4:

[0078] The device collects students' daily learning progress data, test results, assignment submission status, etc. This data is sent to the server at specified intervals.

[0079] Step 5:

[0080] The server receives the collected learning data, checks its integrity, and stores it in a database. The server then runs AI algorithms to analyze learning patterns and identify each student's learning needs.

[0081] Step 6:

[0082] The server uses the analysis to generate custom learning materials tailored to each student's learning needs, including practice questions, video lectures, and interactive quizzes.

[0083] Step 7:

[0084] The server generates custom teaching materials and sends them to the teacher's account, where the teacher can review and make corrections as needed.

[0085] Step 8:

[0086] The device downloads the final version of the custom learning material to the student's device and sends a reminder to start learning.

[0087] Step 9:

[0088] The user (teacher) uses a web browser or application to set up the VR classroom scenario for the next lesson, including selecting the learning subject and customizing the scenario.

[0089] Step 10:

[0090] The server receives the configured VR scenario and sends corresponding signals to each student's device, which then executes the specific learning scenario through the VR goggles.

[0091] Step 11:

[0092] The user (student) uses the device to input and send a question to the AI ​​teacher assistant, which then sends the question data to the server.

[0093] Step 12:

[0094] The server analyzes the received question and generates an appropriate answer using an AI model. The generated answer is sent back to the device and displayed to the user (student).

[0095] Step 13:

[0096] The device launches a dedicated home learning application and notifies the student, and home learning progress data is periodically sent to the server.

[0097] Step 14:

[0098] The server monitors the progress of home learning and provides parents with feedback based on the analysis results, including progress, level of understanding, and suggestions for necessary supplementary learning.

[0099] Step 15:

[0100] Users (parents) receive feedback provided by the server and improve the learning environment or provide additional support. This process is repeated to support continuous learning and maximize educational effectiveness.

[0101] Example 1

[0102] 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."

[0103] In today's educational environment, there is a need to address the learning needs of individual students while reducing the burden on parents and teachers. In particular, it is necessary to provide learning materials tailored to each student's unique learning pace and level of understanding, to monitor learning progress in real time, and to support home learning. However, systems that achieve these goals are still insufficient. Providing an effective learning environment using virtual reality technology is also an important challenge.

[0104] 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.

[0105] In this invention, the server includes: a means for a user to input and transmit information; a means for a terminal to transmit transmitted data to the server; a means for the server to store the received data in a database; a means for the terminal to collect learning data and transmit it to the server; a means for the server to execute an AI algorithm based on the stored data and analyze learning patterns; a means for the server to identify individual learning needs from the analysis results; a means for the server to generate customized learning materials based on the needs identified by the server; a means for a teacher to confirm and modify the customized learning materials generated by the server; a means for the terminal to transmit a signal to a virtual reality device corresponding to the student and execute a specific learning scenario; and a means for the terminal to launch a home learning program and allow the student to study at home. This makes it possible to provide customized learning materials according to the learning needs of each student, manage learning progress in real time, support home learning, and provide an effective learning environment using virtual reality technology.

[0106] "User" is any entity that inputs and submits information to the system, and includes students, parents, and teachers.

[0107] A "terminal" is a computer device used by a user that has the function of inputting, sending, and receiving information.

[0108] The "server" is a central computing device that receives data sent from terminals via a network, stores it in a database, and runs AI algorithms to analyze learning patterns.

[0109] A "database" is a system for systematically storing and managing data received by a server.

[0110] An "AI algorithm" is a mathematical model that uses artificial intelligence to analyze learning data and identify individual learning patterns and needs.

[0111] "Learning patterns" refer to the learning tendencies and characteristics that are generated based on a student's learning behavior and level of understanding.

[0112] "Customized learning materials" are learning materials that are optimized for individual students based on an analysis of their learning patterns, and may include exercises, video lectures, interactive quizzes, etc.

[0113] A "virtual reality device" is a device that uses virtual reality technology to provide a learning environment that is as immersive as reality, and includes VR goggles and the like.

[0114] A "home learning program" is a software application that allows students to continue their learning at home, providing access to custom learning materials, progress tracking, and learning support.

[0115] A "learning scenario" is a sequence of specific educational materials or content that is executed by a virtual reality device, such as a historical simulation or a 3D model of a scientific experiment.

[0116] "Progress" is data that shows the results and progress a student has made in the learning process.

[0117] The present invention is an educational system that combines AI technology and virtual reality technology to provide an effective learning environment. This system aims to provide next-generation educational support that reduces the burden on parents and teachers and meets the learning needs of individual students. Specific embodiments of the system are described below.

[0118] User registration and information management

[0119] 1. User Registration

[0120] The user (parent) accesses the system using a web browser or mobile application, enters the required information in the designated registration form, and submits it. Web browsers such as GOOGLE CHROME (registered trademark) and Firefox are applicable, and mobile applications compatible with ANDROID (registered trademark) or iOS are applicable.

[0121] The terminal sends the input data to the server, using the SSL / TLS protocol to ensure the security of the data transmission.

[0122] The server receives the transmitted data, validates it, and then stores the data in a relational database such as MySQL (registered trademark) or PostgreSQL.

[0123] Analyzing learning patterns and generating custom learning materials

[0124] 2. Collecting training data

[0125] The device collects students' daily learning progress data, test results, assignment submission status, etc., and periodically sends them to a server. Sensors and input devices are used to collect the data.

[0126] The server receives the data and stores it in the database. SQL transaction processing is used to check the integrity of the data.

[0127] 3. Analysis of learning patterns

[0128] The server runs AI algorithms based on the stored learning data to analyze each student's learning patterns, using machine learning libraries such as Python's scikit-learn and TENSORFLOW (registered trademark).

[0129] The analysis results are stored in a database to identify each student's individual learning needs.

[0130] 4. Creating custom learning materials

[0131] Based on the analysis results, the server uses a generative AI model to generate custom learning materials optimized for each student, including practice questions, video lectures, and interactive quizzes.

[0132] The generated custom teaching materials are delivered to the teacher's device, where they can be checked and corrected.

[0133] Setting up and operating a virtual reality classroom

[0134] 5. VR classroom setup

[0135] The user (teacher) opens the VR classroom settings page using a web browser or application and selects the VR scenario to be used in the next lesson, such as a historical simulation or a 3D virtual science experiment.

[0136] The server receives the settings and sends a signal to the virtual reality device corresponding to each student's device.

[0137] 6. VR classroom operation

[0138] The device sends signals to each student's virtual reality device, such as an Oculus Rift or HTC Vive, to execute a specific learning scenario.

[0139] Students can wear virtual reality devices and deepen their understanding through an immersive learning experience.

[0140] Support for home learning

[0141] 7. Use of home learning programs

[0142] The device runs a dedicated home learning program, providing students with a learning environment at home, including custom learning materials, homework assignments, and progress tracking.

[0143] The device sends home learning progress data to a server, which monitors the data in real time.

[0144] 8. Providing Feedback

[0145] The server analyzes the progress of home learning and provides parents with feedback on their child's learning, including progress, level of understanding, and suggestions for necessary supplementary learning.

[0146] Specific examples

[0147] For example, if Student A struggles with adding and subtracting fractions in math class, the system will identify this information through learning pattern analysis. The server will generate custom learning materials with appropriate practice problems, which the teacher will review and then distribute to Student A's device. Additionally, the home learning program will suggest related practice problems and video courses.

[0148] In chemistry classes, virtual reality technology is used to conduct 3D simulations to deepen understanding of molecular structures. The teacher sets up a virtual reality scenario, and a server distributes corresponding instructions to the devices. Students wear the virtual reality device and can observe and manipulate molecular structures in the virtual environment to gain a deeper understanding.

[0149] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0150] Step 1:

[0151] The user (parent) enters the required information into the registration form using a web browser or mobile application and submits it. The input includes basic information about the parent, basic information about the child, contact information, etc. The input data includes basic information about the parent and child, and is submitted by the device.

[0152] Step 2:

[0153] The terminal sends the data entered by the user to the server. The HTTP POST request is used for transmission, and the SSL / TLS protocol is used to ensure communication security. The input of the transmitted data includes the information entered by the user, and the output is the data transferred to the server.

[0154] Step 3:

[0155] The server analyzes the data it receives and performs validation. Validation includes checking for unfilled fields, type checks, and data consistency checks. The input to the validation process is the data sent from the terminal, and the output is the result of checking the validity of the data. If the data is normal, it is saved in the database.

[0156] Step 4:

[0157] The server stores the data that is confirmed to be valid in a database. A relational database such as MySQL or PostgreSQL is used here. The input is the data that has passed validation, and the output is the result of saving it to the database.

[0158] Step 5:

[0159] The device collects learning data such as students' daily learning progress, test results, and assignment submission status. Sensors and input devices are used to collect the data, which is then stored on the device. The input includes information about students' learning activities, and the output includes the collected learning data.

[0160] Step 6:

[0161] The device sends the collected learning data to the server. The transmission is also performed using an HTTP POST request, and communication security is ensured by the SSL / TLS protocol. The input is the learning data stored on the device, and the output is the learning data sent to the server.

[0162] Step 7:

[0163] The server stores the received training data in a database. At this time, SQL transaction processing is used to check consistency. The input is the training data sent from the device, and the output includes the results saved in the database.

[0164] Step 8:

[0165] The server runs an AI algorithm based on the learning data stored in the database to analyze each student's learning patterns. The analysis uses machine learning libraries such as scikit-learn and TensorFlow. The input is the stored learning data, and the output contains the analysis results of the learning patterns.

[0166] Step 9:

[0167] The server identifies individual student learning needs based on the analysis of learning patterns, using the results of an AI algorithm, with the analysis results as input and individual learning needs as output.

[0168] Step 10:

[0169] The server generates custom learning materials based on identified learning needs using a generative AI model, including exercises, video courses, and interactive quizzes. The input is the learning needs data, and the output is the generated custom learning materials.

[0170] Step 11:

[0171] The server delivers the generated custom teaching materials to the teacher's device, where the teacher reviews the teaching materials and makes corrections as necessary. The input is the generated custom teaching materials, and the output is the teacher-reviewed teaching materials.

[0172] Step 12:

[0173] The user (teacher) opens the virtual reality classroom setting page using a web browser or application and selects a scenario for the VR classroom. The setting includes the scenario to be used in the next lesson. The input is the teacher's setting information, and the output is the selected VR scenario.

[0174] Step 13:

[0175] The server receives the settings and sends signals to the virtual reality devices corresponding to each student's device. The input contains the selected VR scenario, and the output contains the signals to each device.

[0176] Step 14:

[0177] The device sends signals to the student's corresponding virtual reality device to execute a specific learning scenario. The specified scenario file is played on a device such as an Oculus Rift or HTC Vive. The input is instructions from the server, and the output is the VR scenario to be executed.

[0178] Step 15:

[0179] The device runs a home learning program, providing an environment where students can continue their studies at home, including custom learning materials, homework assignments, and progress management functions. The input is student learning information, and the output is accumulated home learning progress data.

[0180] Step 16:

[0181] The device sends home learning progress data to the server, which monitors the data in real time and receives it. The input includes home learning progress data, and the output includes data stored on the server.

[0182] Step 17:

[0183] The server analyzes the progress of home learning and provides feedback to parents, including the progress and understanding of the child and suggestions for supplementary learning. The input is home learning data, and the output is the generated feedback.

[0184] Specific examples of implementation

[0185] For example, if Student A struggles with adding and subtracting fractions in math, the system can identify their learning patterns and generate custom learning materials, including appropriate practice problems and video tutorials, on the server, which can then be sent to Student A's device after being reviewed by the teacher. A home learning program can be similarly configured to provide relevant learning materials for Student A to practice at home. In a chemistry class, the teacher can set up a virtual reality scenario, and the server can send instructions to each student's virtual reality device. Students can then use the virtual reality device to deepen their understanding of molecular structures.

[0186] (Application example 1)

[0187] 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."

[0188] Traditional educational systems place a heavy burden on parents and teachers, and are difficult to respond to the individual learning needs of each student. Even when virtual reality technology is utilized, educational content is often uniform and learning data analysis is insufficient. This makes it difficult to provide an effective learning environment.

[0189] 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.

[0190] In this invention, the server includes: means for a user to input and transmit information; means for a terminal to transmit transmitted data to the server; means for the server to store the received data in a database; means for the terminal to collect learning data and transmit it to the server; means for the server to run an AI algorithm based on the stored data and analyze learning patterns; means for the server to identify individual learning needs from the analysis results; means for the server to generate customized learning materials based on the needs identified by the server; means for the terminal to send a signal to a virtual reality device corresponding to the student and execute a specific learning scenario; means for the terminal to launch a home learning app and the student to study at home; means for an instructor to select and set lesson content and select a virtual reality scenario; means for collecting learning progress data, comprehension tests, and interaction data in the virtual reality environment; means for analyzing the collected data and running an AI model to generate appropriate customized learning materials; and means for providing the generated customized learning materials and learning progress information to instructors and parents. This reduces the burden on parents and teachers and makes it possible to provide customized learning materials that appropriately meet the learning needs of each student.

[0191] "User" refers to anyone who utilizes the system to input information and submit education-related data.

[0192] "Terminal" refers to a hardware or software device for transmitting data entered by a user to a server and for collecting learning data.

[0193] "Server" refers to a computer system that stores received data in a database and runs AI algorithms to analyze learning patterns.

[0194] A "database" is an information management system that organizes and stores data received by the server.

[0195] An "AI algorithm" is an artificial intelligence computational method that analyzes students' learning patterns based on collected learning data and generates appropriate custom teaching materials.

[0196] "Learning patterns" are the results of data analysis that include individual students' learning progress and problem-solving tendencies.

[0197] "Customized Learning Materials" refers to specialized educational content created to meet the learning needs of individual students.

[0198] A "virtual reality device" is a device that allows students to have an interactive learning experience in a VR environment.

[0199] "Virtual reality scenario" refers to a specific learning content or environment that is executed using a virtual reality device.

[0200] A "home learning app" is a software application that provides students with the ability to study at home.

[0201] "Instructor" refers to an educator who is responsible for managing educational content and running classes.

[0202] "Interaction data" refers to data about the operations and behavior of students in a virtual reality environment.

[0203] "Learning data" refers to information such as a student's daily learning progress, test results, and assignment submission status.

[0204] "Study progress information" is data that indicates how far a student has progressed in their studies.

[0205] "Progress Data" refers to information that records how learning is progressing.

[0206] The system of the present invention combines AI technology and virtual reality (VR) technology to provide an effective learning environment. The system aims to address the learning needs of individual students while reducing the burden on parents and teachers. Specific embodiments of the system are described below.

[0207] User registration and information management

[0208] First, a user (parent or teacher) accesses the system using a web browser or mobile application, enters the required information in a designated registration form, and submits it. The device then sends the input data to the server, which then receives and stores it in a database. This data includes basic information about the parent and child, as well as contact details.

[0209] Analyzing learning patterns and generating custom learning materials

[0210] The device then collects the student's daily learning progress data, test results, assignment submission status, etc., and periodically sends them to the server. The server stores the received data in a database, performs necessary consistency checks, and then runs AI algorithms. Based on the learning data, the server analyzes each student's learning patterns, analysing repeated mistakes on specific questions, learning speed, and level of understanding. Based on this, the server identifies each student's learning needs and generates optimal custom learning materials. These custom materials include practice questions, video lectures, and interactive quizzes.

[0211] Setting up and operating a VR classroom

[0212] Teachers can open the VR classroom settings page using a web browser or application and select the VR scenario to be used in the next lesson. The selected information is sent to the server, which then sends a corresponding VR signal to each student's device. When students put on the VR goggles, the device receives the signal and executes the specific learning scenario, allowing students to have an immersive learning experience in the VR environment.

[0213] Support for home learning

[0214] The device runs a dedicated home learning application, providing a learning environment for students at home. This application includes custom learning materials, homework assignments, and progress management functions. The device sends home learning progress data to a server, which monitors the received data in real time. The server then analyzes the home learning progress and provides parents with feedback on their child's learning status, including their learning progress and level of understanding, as well as suggestions for necessary supplementary learning.

[0215] Specific Applications

[0216] For example, if Student A is struggling with adding and subtracting fractions in math class, the server identifies this information through learning pattern analysis and generates custom learning materials including appropriate practice problems. After the teacher verifies this, the server distributes it to the student's device. Furthermore, a home learning app containing related practice problems and video courses is provided so that the student can continue their learning at home. In chemistry class, a VR classroom is used to conduct a 3D simulation to deepen understanding of molecular structures. The teacher sets up the VR scenario, and the server distributes corresponding instructions to the device. Students put on VR goggles and observe and manipulate molecular structures in the virtual environment, thereby gaining a deeper understanding.

[0217] The system's program uses Python and its libraries (Pandas, Scikit-Learn) to collect and analyze learning data. The server uses sensor-equipped VR goggles and a data server to collect data on students' learning progress, comprehension, and interaction, and generates appropriate custom learning materials based on that data. As a concrete example, in a history class, progress data is collected by exploring a virtual battlefield and answering quizzes about the battlefield, and the following example prompts are analyzed by the AI ​​model:

[0218] We provide learning data on the Warring States period in history. Based on student progress data and quiz results, we analyze and generate the next custom learning materials to provide.

[0219] Example data:

[0220] Student ID: 12345

[0221] Progress stats: 75%

[0222] Quiz success rate: 60%

[0223] Interaction Level: High

[0224] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0225] Step 1:

[0226] The user enters information and submits it.

[0227] Input: Basic information entered by a parent or teacher using a web browser or mobile application (parent information, child basic information, contact details, etc.).

[0228] Data processing and data calculation: The terminal formats this information and configures it as transmission data.

[0229] Output: The configured data to send.

[0230] Specific action: The user completes the form and clicks the submit button.

[0231] Step 2:

[0232] The terminal transmits the transmission data to the server.

[0233] Input: The transmission data obtained in step 1.

[0234] Data processing and data calculation: The terminal sends the data to the server using the appropriate protocol.

[0235] Output: The data sent to the server.

[0236] Specific operation: The device sends data to the server's receiving API.

[0237] Step 3:

[0238] The server stores the received data in a database.

[0239] Input: The transmission data received in step 2.

[0240] Data processing and data calculation: The server validates the data received and converts it into a format suitable for writing to the database.

[0241] Output: Basic user information stored in the database.

[0242] Specific behavior: The server performs data validation and writes the properly formatted data to the database.

[0243] Step 4:

[0244] The device collects learning data and sends it to the server.

[0245] Input: Learning data such as daily learning progress data, test results, assignment submission status, etc.

[0246] Data processing and data calculation: The data collected by the terminal is formatted and used as transmission data.

[0247] Output: The training data submitted.

[0248] Specific operation: The device continues to record learning data through the application and automatically transmits it to the server.

[0249] Step 5:

[0250] The server runs AI algorithms based on the stored data and analyzes learning patterns.

[0251] Input: Training data stored in a database.

[0252] Data processing and calculation: Analyzing learning patterns using AI algorithms (e.g., machine learning models).

[0253] Output: Analysis of each student's learning patterns.

[0254] Specific operation: The server periodically runs the AI ​​algorithm and performs analysis based on the latest learning data.

[0255] Step 6:

[0256] The server uses the analysis results to identify individual learning needs.

[0257] Input: Analysis results from step 5.

[0258] Data processing and calculation: By looking at the analysis results, we identify each student's weaknesses and areas that need improvement.

[0259] Output: Data about each student's learning needs.

[0260] Specific actions: The server evaluates the analysis results in detail and lists necessary reinforcement points and learning needs.

[0261] Step 7:

[0262] The server generates custom learning materials based on identified needs.

[0263] Input: Learning needs obtained in step 6.

[0264] Data processing and calculation: Using AI algorithms, we generate the most suitable educational content for each student. We extract relevant content from the database and create custom learning materials.

[0265] Output: The generated custom teaching materials.

[0266] Specific behavior: The server generates custom teaching materials and makes them available for teachers to review.

[0267] Step 8:

[0268] The device sends a signal to the student's corresponding virtual reality device to execute a specific learning scenario.

[0269] Input: Custom learning materials generated in step 7.

[0270] Data processing and calculation: The terminal generates a signal and sends it to the virtual reality device, which then loads a specific learning scenario.

[0271] Output: The learning scenario loaded into the VR device.

[0272] Specific actions: Students put on VR goggles and begin learning in the specified scenario.

[0273] Step 9:

[0274] The device launches the home study app, and the student begins their home study.

[0275] Input: Custom learning materials and learning activities for home learning.

[0276] Data processing and calculation: The home learning app displays learning materials and records students' progress in real time.

[0277] Output: Home learning progress data.

[0278] What it does: Students use the app at home to study and automatically send progress and understanding data.

[0279] Step 10:

[0280] The server monitors the progress of home learning and provides feedback to parents.

[0281] Input: Step 9 home learning progress data.

[0282] Data processing and calculation: The server analyzes the progress data and generates reports in real time.

[0283] Output: Feedback report provided to parents.

[0284] What it does: Parents view feedback reports on their child's learning progress and understanding through a web portal or app.

[0285] 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.

[0286] The present invention integrates an emotion engine into an educational system that combines AI and VR technologies, optimizing the learning experience by taking into account the emotional state of the user (student). Specific embodiments of the present invention are described below.

[0287] User registration and information management

[0288] 1. User Registration

[0289] The user (parent) accesses the system and opens the registration form using a web browser or mobile app. The user enters the required information (parent information, child basic information, contact information, etc.) and presses the submit button.

[0290] The device sends the input data to the server, which includes basic information about the parent and child.

[0291] The server receives the submitted data, validates it, and then saves it to the database.

[0292] Analyzing learning patterns and using sentiment engines

[0293] 2. Collecting training data and emotion data

[0294] The device collects students' daily learning progress data, test results, assignment submission status, etc. At the same time, the device's built-in emotion engine uses cameras and sensors to analyze the students' facial expressions and voices and recognize their emotional state.

[0295] The training data and emotion data are periodically sent to the server.

[0296] The server receives the data, checks its integrity, and then stores it in a database.

[0297] 3. Learning pattern analysis and emotion integration

[0298] The server runs an AI algorithm based on learning data and emotional data to analyze each student's learning patterns and emotional fluctuations, identifying, for example, repeated mistakes on a particular question or extreme emotional fluctuations.

[0299] The analysis results are used to generate custom learning materials tailored to students' learning needs and emotional state.

[0300] 4. Creating custom learning materials

[0301] Based on the analysis, the server generates custom learning materials tailored to each student's learning needs and emotional state, such as relaxing videos to reduce stress or interactive quizzes to deepen understanding.

[0302] The custom learning materials are then delivered to teachers in a format that they can review and modify as needed.

[0303] VR classroom settings and emotional responses

[0304] 5. VR classroom setup

[0305] The user (teacher) opens the VR classroom settings page using a web browser or application and selects the VR scenario to be used in the next lesson, including selecting the learning subject and customizing the scenario.

[0306] The server receives the settings and sends the corresponding VR signal to each student's device.

[0307] 6. VR classroom operation

[0308] The device sends signals to each student's VR goggles to execute a specific learning scenario. User emotional data is analyzed in real time, and the learning scenario is dynamically changed as needed. For example, a student who is losing interest can be presented with new, more interesting content.

[0309] Support for home learning

[0310] 7. Use home learning apps

[0311] The device runs a dedicated home learning application, providing students with a learning environment at home, including custom learning materials, homework assignments, and progress tracking.

[0312] The device sends data on home learning progress and emotion to a server, which stores the received data in a database and monitors it in real time.

[0313] 8. Providing Feedback

[0314] The server analyzes the home learning progress and emotional data and provides parents with feedback on their child's learning and emotional state, including progress, comprehension, and suggestions for necessary supplementary learning.

[0315] Specific examples

[0316] For example, when Student B is doing homework in English class, the system simultaneously collects his / her emotional state and learning data. If Student B is feeling stressed, the emotion engine will recognize this and the server will incorporate relaxing music and simple exercises into the custom learning materials, allowing Student B to calm down and focus on their studies.

[0317] In addition, virtual experiments are conducted in science classes using the VR classroom. The user (teacher) sets up the VR scenario, and the server monitors emotional data in real time. When students start to lose interest during the lesson, the system automatically presents a new interactive experiment scenario to recapture their interest.

[0318] Thus, the present invention provides a system that utilizes real-time emotional data to optimize the learning experience and maximize student learning effectiveness.

[0319] The processing flow will be explained below.

[0320] Step 1:

[0321] The user (parent) accesses the system and opens the registration form using a web browser or mobile application. The user enters the required information (parent information, child basic information, contact information, etc.) and presses the submit button.

[0322] Step 2:

[0323] The terminal generates and transmits an HTTP request for transmitting the data input by the user to the server.

[0324] Step 3:

[0325] The server validates the received data and, if there are no problems, saves it in the database. After saving, the server generates a notification to the user that registration is complete and sends it to the terminal.

[0326] Step 4:

[0327] The device collects students' daily learning progress data, test results, assignment submission status, etc. At the same time, the device's built-in emotion engine uses cameras and sensors to analyze the student's facial expressions and voice to recognize their emotional state.

[0328] Step 5:

[0329] The terminal generates and transmits a request for periodically transmitting the learning data and emotion data to the server.

[0330] Step 6:

[0331] The server checks the consistency of the received learning data and emotion data, and if there are no problems, stores it in the database.

[0332] Step 7:

[0333] The server runs AI algorithms on the stored data to analyze learning patterns and emotional states, identifying, for example, repeated mistakes on a particular question or periods of intense emotional activity.

[0334] Step 8:

[0335] The server uses the analysis to identify individual learning needs and emotional states, and then generates custom learning materials based on those needs, such as relaxing videos to reduce stress or interactive quizzes to deepen understanding.

[0336] Step 9:

[0337] The server sends the generated custom teaching materials to the teacher's account, where the teacher can review and correct them if necessary.

[0338] Step 10:

[0339] The device downloads the final version of the custom learning material to the student's device and sends a reminder to start learning.

[0340] Step 11:

[0341] The user (teacher) uses a web browser or application to set up the VR classroom scenario for the next lesson, including selecting the learning subject and customizing the scenario.

[0342] Step 12:

[0343] The server receives the configured VR scenario and sends corresponding signals to each student's device, which then executes the specific learning scenario through the VR goggles.

[0344] Step 13:

[0345] The device sends signals to each student's VR goggles to execute a specific learning scenario, during which an emotion engine monitors the student's emotions in real time and dynamically changes the scenario as needed.

[0346] Step 14:

[0347] If a user (student) feels stressed while studying, the emotion engine will recognize it and the server will automatically present new interactive content or relaxing videos.

[0348] Step 15:

[0349] The device runs a dedicated home learning application, providing students with a learning environment at home, including custom learning materials, homework assignments, and progress tracking.

[0350] Step 16:

[0351] The terminal generates and transmits a request for periodically transmitting home learning progress data and emotion data to the server.

[0352] Step 17:

[0353] The server monitors and analyzes home learning progress and emotional data, providing parents with feedback on their child's learning progress and emotional state.

[0354] Step 18:

[0355] The feedback generated by the server includes information on the child's progress and understanding, as well as suggestions for supplementary learning. The user (parent) receives this feedback and can improve the learning environment or provide additional support.

[0356] Example 2

[0357] 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."

[0358] Conventional educational systems are unable to simultaneously consider students' learning needs and emotional state, making it difficult to provide an individually optimized learning experience. Furthermore, because feedback based on learning progress and emotional state is not provided in real time, parents and educators are unable to accurately grasp students' situations. To address these issues, a new educational system was needed that maximizes students' learning outcomes and optimizes their learning experience.

[0359] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input and transmit information; a means for the terminal to transmit transmission data to the processing unit; a means for the processing unit to store the received data in a storage device; a means for the terminal to collect learning data and emotional data and transmit it to the processing unit; a means for the processing unit to execute a generative AI model based on the stored data and analyze learning patterns and emotional fluctuations; a means for the processing unit to identify individual learning needs and emotional states from the analysis results; a means for the terminal to generate customized learning materials based on the needs and emotional states identified by the processing unit; a means for the terminal to send a signal to a visual device corresponding to the learner and execute a specific learning scenario; and a means for the terminal to launch a home learning application and allow the learner to study at home. This makes it possible to collect students' learning data and emotional data in real time and provide optimal customized learning materials based on the analysis results. Furthermore, feedback on home learning progress and emotional data can be provided to parents and educators, maximizing students' learning effectiveness.

[0360] "User" means any person or entity that uses the System and inputs and submits information.

[0361] A "terminal" is an electronic device used to accept user operations and communicate with a server.

[0362] A "processing unit" is a computer system that receives, stores, and analyzes the transmitted data.

[0363] A "storage device" is hardware for permanently or temporarily storing data received by a processing unit.

[0364] "Learning data" refers to information such as a student's daily learning progress, test results, and assignment submission status.

[0365] "Emotional data" refers to information about a student's emotional state obtained from their facial expressions and voice.

[0366] A "generative AI model" is an artificial intelligence algorithm that analyzes learning patterns and emotional fluctuations from collected data and generates custom teaching materials.

[0367] "Learning patterns" are information that indicates the trends in students' learning behavior and achievements.

[0368] "Emotional fluctuation" is information that indicates changes in a student's emotional state.

[0369] "Customized learning materials" are educational resources optimized for the learning needs and emotional state of individual students.

[0370] "Visual devices" are head-mounted displays and related devices used by students to engage in learning experiences in a virtual reality (VR) environment.

[0371] A "home learning application" is software that allows students to study at home.

[0372] "Parents" are those responsible for receiving feedback on students' academic progress and emotional state.

[0373] An "educator" is a user who sets the content and scenario of the VR classroom and supports students' learning.

[0374] The present invention relates to a system that takes into account the emotional state of the learner and optimizes the learning experience. The system provides a series of functions, including information input by the user (parent or educator), data transmission from the device, data processing by the server, and custom learning material generation using a generative AI model.

[0375] User registration and information management

[0376] The user (parent) accesses the system using a web browser or mobile app and enters the required information into the registration form. The entered information is sent from the device to the server, which validates the received data before saving it in a storage device. This is how user information is managed.

[0377] Collection of training data and emotion data

[0378] The device collects learners' daily learning progress data, test results, assignment submission status, etc. The device's built-in emotion engine also uses cameras and sensors to analyze the learner's facial expressions and voice to recognize their emotional state. The collected learning data and emotion data are periodically sent to a server, where they are checked for consistency and then stored in a storage device.

[0379] Analyzing learning patterns and generating custom learning materials

[0380] The server uses a generative AI model to analyze the stored learning data and emotional data, analyzing learning patterns and emotional fluctuations. This identifies individual learning needs and emotional states. Based on the analysis results, the server generates custom learning materials optimized for each learner. Examples of generative AI models used include TensorFlow and PyTorch.

[0381] Setting up and operating a VR classroom

[0382] The user (educator) opens the VR classroom settings page using a web browser or application and selects the VR scenario to be used in the next lesson. The settings are sent to the server, which then transmits the corresponding VR signal to each learner's device. The device then transmits the signal to each learner's VR goggles, which executes the specific learning scenario. Learners' emotional data is analyzed in real time, and the learning scenario is dynamically changed as needed.

[0383] Support for home learning

[0384] The device runs a dedicated home learning application, providing a learning environment for learners at home. This includes custom learning materials, homework, and progress management functions. The collected home learning progress and emotional data is sent to a server and stored in a storage device. The server monitors the home learning progress and emotional data and provides feedback to parents.

[0385] Specific examples

[0386] For example, when Student B is doing his / her homework in English class, the device simultaneously collects his / her emotional state and learning data. If Student B is feeling stressed, the emotion engine will recognize the stress and the server will provide customized learning materials such as relaxing music and simple exercises. This will allow Student B to concentrate on his / her studies in a calm manner.

[0387] In addition, virtual experiments are conducted in science classes using the VR classroom. The user (educator) sets up the VR scenario, and the server monitors emotional data in real time. If students start to lose interest during the lesson, the system automatically presents a new interactive experiment scenario to recapture their interest.

[0388] Prompt Sentence Examples

[0389] Please explain in detail the specifications of the web form that parents use to register their child's information and the process by which that information is transmitted to the system, taking into account the child's age.

[0390] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0391] Step 1:

[0392] The user (parent) opens a web browser or mobile app, accesses the registration form, and enters the required information (parent's name, basic information about the child, contact information, etc.). The entered information is sent from the device to the server by pressing the send button. The server receives a request for the registration form HTML page or app screen and sends it to the device. The input here is the web form data, and the output is the user information sent to the server.

[0393] Step 2:

[0394] The device encrypts the input data (basic information of the parent and child) and sends it to the server via HTTPS. The server validates the received data and confirms its accuracy before storing it in a storage device in the processing unit. The input of this step is the encrypted user information, and the output is the user information stored in the storage device.

[0395] Step 3:

[0396] The device collects learners' daily learning progress data, test results, assignment submission status, etc. The emotion engine built into the device also uses cameras and sensors to analyze the learners' facial expressions and voices to recognize their emotional state. The collected learning data and emotion data are periodically sent to the server. The input is the learning data and emotion data, and the output is this data sent to the server.

[0397] Step 4:

[0398] The server receives the transmitted training data and emotion data and checks their consistency. These data are stored in a storage device by a processing unit. The input is the training data and emotion data transmitted from the terminal, and the output is the data stored in the storage device.

[0399] Step 5:

[0400] The server uses a generative AI model to analyze the stored training data and emotional data. Specifically, it uses Python machine learning libraries (TensorFlow and PyTorch) to analyze the learner's learning patterns and emotional fluctuations. The input is the training data and emotional data stored in the storage device, and the output is the analysis results.

[0401] Step 6:

[0402] The server uses the analysis results to identify each learner's learning needs and emotional state. Optimal custom learning materials are generated based on the identified needs. These custom learning materials are generated using a generative AI model. The input is the analysis results, and the output is the generated custom learning materials.

[0403] Step 7:

[0404] The terminal receives the custom learning materials generated by the server and provides them to the learner. A specific learning scenario is sent to the visual device (VR goggles), and the learner learns in the VR environment. The input is the generated custom learning materials and VR signals, and the output is the learning content presented to the learner.

[0405] Step 8:

[0406] The device runs a dedicated home learning application, providing a learning environment for the learner at home. This includes custom learning materials, homework, and progress management functions. The device transmits home learning progress data and emotion data to the server. The input is the home learning progress data and emotion data, and the output is the data transmitted to the server.

[0407] Step 9:

[0408] The server monitors the progress and emotional data of home learning and provides feedback to parents. The feedback includes learning progress, level of understanding, and suggestions for necessary supplementary learning. The input is home learning progress data and emotional data, and the output is feedback provided to parents.

[0409] Prompt Sentence Examples

[0410] Please explain in detail the specifications of the web form that parents use to register their child's information and the process by which that information is transmitted to the system, taking into account the child's age.

[0411] (Application example 2)

[0412] 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."

[0413] Current factory robot management and training systems lack the means to closely monitor the robot's operating status in real time and automatically prompt appropriate actions. In particular, they lack a mechanism to provide custom scenarios that enable rapid response when a robot experiences an operational error or overheats. Furthermore, when training and managing robot operators, there is a need to provide optimal learning scenarios that take into account the user's emotional state. This can lead to problems such as reduced production efficiency and an increased risk of serious breakdowns.

[0414] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0415] In this invention, the server includes a means for executing an AI algorithm based on the operation data to analyze the operation patterns, a means for identifying individual operation needs from the analysis results, and a means for generating custom teaching materials based on the identified needs. This makes it possible to monitor the operation status of factory robots in real time and provide prompt problem solving and education using appropriate VR scenarios.

[0416] "User" refers to the person who operates the system to input and manage information.

[0417] "Terminal" refers to a device used to collect or transmit data, receive signals, etc.

[0418] "Server" refers to a computer system that stores collected data, analyzes it, and distributes various instructions and signals based on the results.

[0419] A "database" refers to a large collection of data stored on a server, which allows for efficient management and retrieval of the data.

[0420] "Operational Data" refers to information relating to the operational status and performance of a factory robot.

[0421] "AI algorithm" refers to the computational procedures of artificial intelligence that analyze movement patterns based on collected movement data and infer specific behaviors and needs.

[0422] "Behavioral patterns" refer to information that indicates a tendency toward a particular behavior or action based on collected data.

[0423] "Custom Instructional Materials" means educational content created to provide individualized learning or response scenarios based on specific operational needs.

[0424] "VR goggles" refers to a headset that allows users to visually experience a virtual reality environment.

[0425] "Signal" refers to a signal sent from a terminal to convey specific instructions or information.

[0426] "Home learning app" refers to software designed to enable workers to continue learning at home.

[0427] This invention provides a system for effectively monitoring and managing the operation of factory robots. This system combines AI and VR technologies, and in particular integrates an emotion engine to optimally operate and train robots according to their operating status.

[0428] User registration and information management

[0429] 1. User Registration

[0430] A user accesses the system and enters information into a registration form using a web browser or mobile app, then submits it. The device receives the information and sends it to the server, where it is validated and stored in a database.

[0431] Using behavioral data and emotion engines

[0432] 2. Collecting motion and emotion data

[0433] The terminal collects operational data from the factory robot, while the built-in emotion engine uses cameras and sensors to monitor the robot's operating status, for example, recognizing errors and overheating. The collected data is periodically sent to a server, which stores it in a database.

[0434] 3. Analysis of movement patterns and integration of emotions

[0435] The server runs AI algorithms on the behavioral and emotional data to analyze behavioral patterns and emotional fluctuations. For example, it can identify repeated errors in a particular task or frequent overheating. The analysis results are used to generate custom teaching materials tailored to the robot's behavioral needs and state.

[0436] 4. Creating custom learning materials

[0437] Based on the analysis results, the server generates custom teaching materials tailored to the operating needs and status of each robot, including guide videos to avoid errors and cool-down scenarios to prevent overheating. The generated custom teaching materials are distributed in a format that allows administrators to review and modify them as necessary.

[0438] VR scenario settings and emotional responses

[0439] 5. Setting up the VR scenario

[0440] The user (administrator) opens the VR scenario setting page using a web browser or application and selects the VR scenario to be used for the next task. The server receives the setting and sends a VR signal to each robot's terminal.

[0441] 6. VR Scenario Operation

[0442] The terminal sends signals to each robot's VR goggles to execute specific learning scenarios. Emotional data is analyzed in real time, and the scenarios are dynamically changed as needed. For example, if an error occurs, new guidelines for error avoidance are presented.

[0443] Support for home learning

[0444] 7. Use home learning apps

[0445] The device launches a dedicated home learning application, providing a learning environment for workers at home. This includes custom learning materials and progress management functions. The device sends home learning progress data and behavior data to a server. The server stores the received data in a database and monitors it in real time.

[0446] 8. Providing Feedback

[0447] The server analyzes the home learning progress and operation data and provides the administrator with feedback on the robot's operation status and progress, including the progress of the operation and suggestions for necessary supplementary learning.

[0448] Specific examples

[0449] For example, if a factory robot A frequently makes errors during a specific task, the system will simultaneously collect its behavioral data and emotional state. When an error is detected, the emotion engine will recognize it, and the server will generate custom educational materials (guide videos) to help robot A avoid the error and present them to the robot through VR goggles.

[0450] Prompt Sentence Examples

[0451] "I would like to build an education and management system that utilizes the emotion engine of factory robots. This system will monitor the robot's operating status in real time and execute VR scenarios as needed. The hardware to be used will include cameras and sensors, and the software will use OpenCV and TensorFlow. Please tell me the specific configuration and implementation steps."

[0452] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0453] Step 1:

[0454] A user accesses the system and uses a web browser or mobile app to enter information into a registration form and submit it. The entered data (user information, robot information, etc.) is sent from the device to the server. The server validates the received data and stores the data that is confirmed to be valid in the database. Input: Information entered into the registration form. Output: User information stored in the database.

[0455] Step 2:

[0456] The terminal collects the operation data and emotion data of the factory robot. Specifically, the camera captures images of the robot and the sensor monitors its operation status. The collected data is sent from the terminal to the server. Input: Camera images, sensor data. Output: Operation data and emotion data sent to the server.

[0457] Step 3:

[0458] The server stores the received movement and emotion data in a database. An AI algorithm is run based on the stored data to analyze movement patterns and emotional fluctuations. OpenCV and TensorFlow are used for this analysis. Input: Movement and emotion data in the database. Output: Analysis results (movement patterns and emotional fluctuations).

[0459] Step 4:

[0460] The server uses the analysis results to identify the operational needs of each robot. Custom teaching materials are generated based on the identified operational needs. For example, guide videos to avoid errors and cool-down scenarios to prevent overheating are prepared. Input: Analysis results. Output: Custom teaching materials (guide videos and cool-down scenarios).

[0461] Step 5:

[0462] The user sets the VR scenario to be used in the next task using a web browser or application. The server receives the settings and sends the corresponding VR signal to each robot's terminal. Input: Setting the VR scenario. Output: Sending the VR signal to the terminal.

[0463] Step 6:

[0464] The terminal sends signals to the VR goggles of each robot to execute a specific learning scenario. It analyzes the behavioral data in real time and dynamically changes the scenario as needed. For example, if an error occurs, it will present new guidelines to avoid the error. Input: VR signal. Output: The learning scenario to be executed.

[0465] Step 7:

[0466] The terminal launches a dedicated home learning application, providing an environment in which the worker can study at home. Home learning progress data and operation data are sent from the terminal to the server. Input: Home learning progress data. Output: Progress data sent to the server.

[0467] Step 8:

[0468] The server analyzes the home learning progress data and operation data in real time and provides feedback to the administrator. The feedback includes comments on the robot's operation status and progress, as well as suggestions for necessary supplementary learning. Input: Home learning progress data and operation data. Output: Feedback to the administrator.

[0469] 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.

[0470] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search<url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0471] 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.

[0472] [Second embodiment]

[0473] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0474] 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.

[0475] 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).

[0476] 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.

[0477] 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.

[0478] 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).

[0479] 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.

[0480] 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.

[0481] 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.

[0482] 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.

[0483] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0484] 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."

[0485] The present invention is a next-generation education system that combines AI and VR technologies to provide an effective learning environment, reducing the burden on parents and teachers while meeting the learning needs of individual students. Specific embodiments of the system are described below.

[0486] User registration and information management

[0487] 1. User Registration

[0488] The user (parent) accesses the system using a web browser or mobile application, enters the required information (parent information, child's basic information, contact information, etc.) into the designated registration form, and submits it.

[0489] The device sends the input data to the server, which includes basic information about the parent and child.

[0490] The server receives the submitted data, validates it, and then saves it to the database.

[0491] Analyzing learning patterns and generating custom learning materials

[0492] 2. Collecting training data

[0493] The device collects students' daily learning progress data, test results, assignment submission status, etc. This data is periodically sent to the server.

[0494] The server receives the data and stores it in a database, where it performs the necessary integrity checks before storing it.

[0495] 3. Analysis of learning patterns

[0496] The server runs AI algorithms on the stored learning data to analyze each student's learning patterns, such as identifying repeated mistakes on specific questions, learning speed, and level of understanding.

[0497] The results of the analysis are used to identify individual student learning needs and are stored in a database.

[0498] 4. Creating custom learning materials

[0499] Based on the analysis, the server generates custom learning materials tailored to each student's learning needs, including exercises, video lectures, and interactive quizzes.

[0500] The custom learning materials are then delivered to teachers in a format that they can review and modify as needed.

[0501] Setting up and operating a VR classroom

[0502] 5. VR classroom setup

[0503] The user (teacher) opens the VR classroom settings page using a web browser or application and selects the VR scenario to be used in the next lesson, such as a historical simulation or a 3D virtual science experiment.

[0504] The server receives the settings and sends the corresponding VR signal to each student's device.

[0505] 6. VR classroom operation

[0506] The device sends signals to each student's VR goggles to execute specific learning scenarios, allowing students to wear the goggles and deepen their understanding through an immersive learning experience.

[0507] Support for home learning

[0508] 7. Use home learning apps

[0509] The device runs a dedicated home learning application, providing students with a learning environment at home, including custom learning materials, homework assignments, and progress tracking.

[0510] The device sends data on the progress of home learning to a server, which stores the received data in a database and monitors it in real time.

[0511] 8. Providing Feedback

[0512] The server analyzes the progress of home learning and provides parents with feedback on their child's learning, including progress, level of understanding, and suggestions for necessary supplementary learning.

[0513] Specific examples

[0514] For example, if Student A is struggling with adding and subtracting fractions in math class, the system will identify this information through learning pattern analysis. The server will generate custom learning materials with appropriate practice problems, which the teacher will review and then distribute to Student A's device. Furthermore, a home learning app will suggest related practice problems and video courses so that the student can continue their learning at home.

[0515] In chemistry classes, VR classrooms are used to conduct 3D simulations to deepen understanding of molecular structures. The teacher sets up the VR scenario, and the server distributes corresponding instructions to the devices. Students put on VR goggles and observe and manipulate molecular structures in the virtual environment, thereby gaining a deeper understanding.

[0516] In this way, the present invention provides a system that responds to the diverse needs of users (parents, teachers, students) and provides effective learning support by utilizing advanced technology.

[0517] The processing flow will be explained below.

[0518] Step 1:

[0519] The user (parent) accesses the system and opens the registration form using a web browser or mobile app. The user enters the required information (parent information, child basic information, contact information, etc.) into the form and presses the submit button.

[0520] Step 2:

[0521] The terminal generates and transmits an HTTP request for transmitting the data input by the user to the server.

[0522] Step 3:

[0523] The server validates the received data and, if there are no problems, saves it in the database. After saving, the server generates a notification to the user that registration is complete and sends it to the terminal.

[0524] Step 4:

[0525] The device collects students' daily learning progress data, test results, assignment submission status, etc. This data is sent to the server at specified intervals.

[0526] Step 5:

[0527] The server receives the collected learning data, checks its integrity, and stores it in a database. The server then runs AI algorithms to analyze learning patterns and identify each student's learning needs.

[0528] Step 6:

[0529] The server uses the analysis to generate custom learning materials tailored to each student's learning needs, including practice questions, video lectures, and interactive quizzes.

[0530] Step 7:

[0531] The server generates custom teaching materials and sends them to the teacher's account, where the teacher can review and make corrections as needed.

[0532] Step 8:

[0533] The device downloads the final version of the custom learning material to the student's device and sends a reminder to start learning.

[0534] Step 9:

[0535] The user (teacher) uses a web browser or application to set up the VR classroom scenario for the next lesson, including selecting the learning subject and customizing the scenario.

[0536] Step 10:

[0537] The server receives the configured VR scenario and sends corresponding signals to each student's device, which then executes the specific learning scenario through the VR goggles.

[0538] Step 11:

[0539] The user (student) uses the device to input and send a question to the AI ​​teacher assistant, which then sends the question data to the server.

[0540] Step 12:

[0541] The server analyzes the received question and generates an appropriate answer using an AI model. The generated answer is sent back to the device and displayed to the user (student).

[0542] Step 13:

[0543] The device launches a dedicated home learning application and notifies the student, and home learning progress data is periodically sent to the server.

[0544] Step 14:

[0545] The server monitors the progress of home learning and provides parents with feedback based on the analysis results, including progress, level of understanding, and suggestions for necessary supplementary learning.

[0546] Step 15:

[0547] Users (parents) receive feedback provided by the server and improve the learning environment or provide additional support. This process is repeated to support continuous learning and maximize educational effectiveness.

[0548] Example 1

[0549] 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."

[0550] In today's educational environment, there is a need to address the learning needs of individual students while reducing the burden on parents and teachers. In particular, it is necessary to provide learning materials tailored to each student's unique learning pace and level of understanding, to monitor learning progress in real time, and to support home learning. However, systems that achieve these goals are still insufficient. Providing an effective learning environment using virtual reality technology is also an important challenge.

[0551] 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.

[0552] In this invention, the server includes: a means for a user to input and transmit information; a means for a terminal to transmit transmitted data to the server; a means for the server to store the received data in a database; a means for the terminal to collect learning data and transmit it to the server; a means for the server to execute an AI algorithm based on the stored data and analyze learning patterns; a means for the server to identify individual learning needs from the analysis results; a means for the server to generate customized learning materials based on the needs identified by the server; a means for a teacher to confirm and modify the customized learning materials generated by the server; a means for the terminal to transmit a signal to a virtual reality device corresponding to the student and execute a specific learning scenario; and a means for the terminal to launch a home learning program and allow the student to study at home. This makes it possible to provide customized learning materials according to the learning needs of each student, manage learning progress in real time, support home learning, and provide an effective learning environment using virtual reality technology.

[0553] "User" is any entity that inputs and submits information to the system, and includes students, parents, and teachers.

[0554] A "terminal" is a computer device used by a user that has the function of inputting, sending, and receiving information.

[0555] The "server" is a central computing device that receives data sent from terminals via a network, stores it in a database, and runs AI algorithms to analyze learning patterns.

[0556] A "database" is a system for systematically storing and managing data received by a server.

[0557] An "AI algorithm" is a mathematical model that uses artificial intelligence to analyze learning data and identify individual learning patterns and needs.

[0558] "Learning patterns" refer to the learning tendencies and characteristics that are generated based on a student's learning behavior and level of understanding.

[0559] "Customized learning materials" are learning materials that are optimized for individual students based on an analysis of their learning patterns, and may include exercises, video lectures, interactive quizzes, etc.

[0560] A "virtual reality device" is a device that uses virtual reality technology to provide a learning environment that is as immersive as reality, and includes VR goggles and the like.

[0561] A "home learning program" is a software application that allows students to continue their learning at home, providing access to custom learning materials, progress tracking, and learning support.

[0562] A "learning scenario" is a sequence of specific educational materials or content that is executed by a virtual reality device, such as a historical simulation or a 3D model of a scientific experiment.

[0563] "Progress" is data that shows the results and progress a student has made in the learning process.

[0564] The present invention is an educational system that combines AI technology and virtual reality technology to provide an effective learning environment. This system aims to provide next-generation educational support that reduces the burden on parents and teachers and meets the learning needs of individual students. Specific embodiments of the system are described below.

[0565] User registration and information management

[0566] 1. User Registration

[0567] The user (parent) accesses the system using a web browser or mobile application, enters the required information in the designated registration form, and submits it. Web browsers used can be Google (registered trademark) Chrome or Firefox, and mobile applications can be Android or iOS compatible.

[0568] The terminal sends the input data to the server, using the SSL / TLS protocol to ensure the security of the data transmission.

[0569] The server receives the submitted data, validates it, and then stores it in a relational database, such as MySQL or PostgreSQL.

[0570] Analyzing learning patterns and generating custom learning materials

[0571] 2. Collecting training data

[0572] The device collects students' daily learning progress data, test results, assignment submission status, etc., and periodically sends them to a server. Sensors and input devices are used to collect the data.

[0573] The server receives the data and stores it in the database. SQL transaction processing is used to check the integrity of the data.

[0574] 3. Analysis of learning patterns

[0575] The server runs AI algorithms based on the stored learning data to analyze each student's learning patterns, using machine learning libraries such as Python's scikit-learn and TensorFlow.

[0576] The analysis results are stored in a database to identify each student's individual learning needs.

[0577] 4. Creating custom learning materials

[0578] Based on the analysis results, the server uses a generative AI model to generate custom learning materials optimized for each student, including practice questions, video lectures, and interactive quizzes.

[0579] The generated custom teaching materials are delivered to the teacher's device, where they can be checked and corrected.

[0580] Setting up and operating a virtual reality classroom

[0581] 5. VR classroom setup

[0582] The user (teacher) opens the VR classroom settings page using a web browser or application and selects the VR scenario to be used in the next lesson, such as a historical simulation or a 3D virtual science experiment.

[0583] The server receives the settings and sends a signal to the virtual reality device corresponding to each student's device.

[0584] 6. VR classroom operation

[0585] The device sends signals to each student's virtual reality device, such as an Oculus Rift or HTC Vive, to execute a specific learning scenario.

[0586] Students can wear virtual reality devices and deepen their understanding through an immersive learning experience.

[0587] Support for home learning

[0588] 7. Use of home learning programs

[0589] The device runs a dedicated home learning program, providing students with a learning environment at home, including custom learning materials, homework assignments, and progress tracking.

[0590] The device sends home learning progress data to a server, which monitors the data in real time.

[0591] 8. Providing Feedback

[0592] The server analyzes the progress of home learning and provides parents with feedback on their child's learning, including progress, level of understanding, and suggestions for necessary supplementary learning.

[0593] Specific examples

[0594] For example, if Student A struggles with adding and subtracting fractions in math class, the system will identify this information through learning pattern analysis. The server will generate custom learning materials with appropriate practice problems, which the teacher will review and then distribute to Student A's device. Additionally, the home learning program will suggest related practice problems and video courses.

[0595] In chemistry classes, virtual reality technology is used to conduct 3D simulations to deepen understanding of molecular structures. The teacher sets up a virtual reality scenario, and a server distributes corresponding instructions to the devices. Students wear the virtual reality device and can observe and manipulate molecular structures in the virtual environment to gain a deeper understanding.

[0596] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0597] Step 1:

[0598] The user (parent) enters the required information into the registration form using a web browser or mobile application and submits it. The input includes basic information about the parent, basic information about the child, contact information, etc. The input data includes basic information about the parent and child, and is submitted by the device.

[0599] Step 2:

[0600] The terminal sends the data entered by the user to the server. The HTTP POST request is used for transmission, and the SSL / TLS protocol is used to ensure communication security. The input of the transmitted data includes the information entered by the user, and the output is the data transferred to the server.

[0601] Step 3:

[0602] The server analyzes the data it receives and performs validation. Validation includes checking for unfilled fields, type checks, and data consistency checks. The input to the validation process is the data sent from the terminal, and the output is the result of checking the validity of the data. If the data is normal, it is saved in the database.

[0603] Step 4:

[0604] The server stores the data that is confirmed to be valid in a database. A relational database such as MySQL or PostgreSQL is used here. The input is the data that has passed validation, and the output is the result of saving it to the database.

[0605] Step 5:

[0606] The device collects learning data such as students' daily learning progress, test results, and assignment submission status. Sensors and input devices are used to collect the data, which is then stored on the device. The input includes information about students' learning activities, and the output includes the collected learning data.

[0607] Step 6:

[0608] The device sends the collected learning data to the server. The transmission is also performed using an HTTP POST request, and communication security is ensured by the SSL / TLS protocol. The input is the learning data stored on the device, and the output is the learning data sent to the server.

[0609] Step 7:

[0610] The server stores the received training data in a database. At this time, SQL transaction processing is used to check consistency. The input is the training data sent from the device, and the output includes the results saved in the database.

[0611] Step 8:

[0612] The server runs an AI algorithm based on the learning data stored in the database to analyze each student's learning patterns. The analysis uses machine learning libraries such as scikit-learn and TensorFlow. The input is the stored learning data, and the output contains the analysis results of the learning patterns.

[0613] Step 9:

[0614] The server identifies individual student learning needs based on the analysis of learning patterns, using the results of an AI algorithm, with the analysis results as input and individual learning needs as output.

[0615] Step 10:

[0616] The server generates custom learning materials based on identified learning needs using a generative AI model, including exercises, video courses, and interactive quizzes. The input is the learning needs data, and the output is the generated custom learning materials.

[0617] Step 11:

[0618] The server delivers the generated custom teaching materials to the teacher's device, where the teacher reviews the teaching materials and makes corrections as necessary. The input is the generated custom teaching materials, and the output is the teacher-reviewed teaching materials.

[0619] Step 12:

[0620] The user (teacher) opens the virtual reality classroom setting page using a web browser or application and selects a scenario for the VR classroom. The setting includes the scenario to be used in the next lesson. The input is the teacher's setting information, and the output is the selected VR scenario.

[0621] Step 13:

[0622] The server receives the settings and sends signals to the virtual reality devices corresponding to each student's device. The input contains the selected VR scenario, and the output contains the signals to each device.

[0623] Step 14:

[0624] The device sends signals to the student's corresponding virtual reality device to execute a specific learning scenario. The specified scenario file is played on a device such as an Oculus Rift or HTC Vive. The input is instructions from the server, and the output is the VR scenario to be executed.

[0625] Step 15:

[0626] The device runs a home learning program, providing an environment where students can continue their studies at home, including custom learning materials, homework assignments, and progress management functions. The input is student learning information, and the output is accumulated home learning progress data.

[0627] Step 16:

[0628] The device sends home learning progress data to the server, which monitors the data in real time and receives it. The input includes home learning progress data, and the output includes data stored on the server.

[0629] Step 17:

[0630] The server analyzes the progress of home learning and provides feedback to parents, including the progress and understanding of the child and suggestions for supplementary learning. The input is home learning data, and the output is the generated feedback.

[0631] Specific examples of implementation

[0632] For example, if Student A struggles with adding and subtracting fractions in math, the system can identify their learning patterns and generate custom learning materials, including appropriate practice problems and video tutorials, on the server, which can then be sent to Student A's device after being reviewed by the teacher. A home learning program can be similarly configured to provide relevant learning materials for Student A to practice at home. In a chemistry class, the teacher can set up a virtual reality scenario, and the server can send instructions to each student's virtual reality device. Students can then use the virtual reality device to deepen their understanding of molecular structures.

[0633] (Application example 1)

[0634] 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."

[0635] Traditional educational systems place a heavy burden on parents and teachers, and are difficult to respond to the individual learning needs of each student. Even when virtual reality technology is utilized, educational content is often uniform and learning data analysis is insufficient. This makes it difficult to provide an effective learning environment.

[0636] 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.

[0637] In this invention, the server includes: means for a user to input and transmit information; means for a terminal to transmit transmitted data to the server; means for the server to store the received data in a database; means for the terminal to collect learning data and transmit it to the server; means for the server to run an AI algorithm based on the stored data and analyze learning patterns; means for the server to identify individual learning needs from the analysis results; means for the server to generate customized learning materials based on the needs identified by the server; means for the terminal to send a signal to a virtual reality device corresponding to the student and execute a specific learning scenario; means for the terminal to launch a home learning app and the student to study at home; means for an instructor to select and set lesson content and select a virtual reality scenario; means for collecting learning progress data, comprehension tests, and interaction data in the virtual reality environment; means for analyzing the collected data and running an AI model to generate appropriate customized learning materials; and means for providing the generated customized learning materials and learning progress information to instructors and parents. This reduces the burden on parents and teachers and makes it possible to provide customized learning materials that appropriately meet the learning needs of each student.

[0638] "User" refers to anyone who utilizes the system to input information and submit education-related data.

[0639] "Terminal" refers to a hardware or software device for transmitting data entered by a user to a server and for collecting learning data.

[0640] "Server" refers to a computer system that stores received data in a database and runs AI algorithms to analyze learning patterns.

[0641] A "database" is an information management system that organizes and stores data received by the server.

[0642] An "AI algorithm" is an artificial intelligence computational method that analyzes students' learning patterns based on collected learning data and generates appropriate custom teaching materials.

[0643] "Learning patterns" are the results of data analysis that include individual students' learning progress and problem-solving tendencies.

[0644] "Customized Learning Materials" refers to specialized educational content created to meet the learning needs of individual students.

[0645] A "virtual reality device" is a device that allows students to have an interactive learning experience in a VR environment.

[0646] "Virtual reality scenario" refers to a specific learning content or environment that is executed using a virtual reality device.

[0647] A "home learning app" is a software application that provides students with the ability to study at home.

[0648] "Instructor" refers to an educator who is responsible for managing educational content and running classes.

[0649] "Interaction data" refers to data about the operations and behavior of students in a virtual reality environment.

[0650] "Learning data" refers to information such as a student's daily learning progress, test results, and assignment submission status.

[0651] "Study progress information" is data that indicates how far a student has progressed in their studies.

[0652] "Progress Data" refers to information that records how learning is progressing.

[0653] The system of the present invention combines AI technology and virtual reality (VR) technology to provide an effective learning environment. The system aims to address the learning needs of individual students while reducing the burden on parents and teachers. Specific embodiments of the system are described below.

[0654] User registration and information management

[0655] First, a user (parent or teacher) accesses the system using a web browser or mobile application, enters the required information in a designated registration form, and submits it. The device then sends the input data to the server, which then receives and stores it in a database. This data includes basic information about the parent and child, as well as contact details.

[0656] Analyzing learning patterns and generating custom learning materials

[0657] The device then collects the student's daily learning progress data, test results, assignment submission status, etc., and periodically sends them to the server. The server stores the received data in a database, performs necessary consistency checks, and then runs AI algorithms. Based on the learning data, the server analyzes each student's learning patterns, analysing repeated mistakes on specific questions, learning speed, and level of understanding. Based on this, the server identifies each student's learning needs and generates optimal custom learning materials. These custom materials include practice questions, video lectures, and interactive quizzes.

[0658] Setting up and operating a VR classroom

[0659] Teachers can open the VR classroom settings page using a web browser or application and select the VR scenario to be used in the next lesson. The selected information is sent to the server, which then sends a corresponding VR signal to each student's device. When students put on the VR goggles, the device receives the signal and executes the specific learning scenario, allowing students to have an immersive learning experience in the VR environment.

[0660] Support for home learning

[0661] The device runs a dedicated home learning application, providing a learning environment for students at home. This application includes custom learning materials, homework assignments, and progress management functions. The device sends home learning progress data to a server, which monitors the received data in real time. The server then analyzes the home learning progress and provides parents with feedback on their child's learning status, including their learning progress and level of understanding, as well as suggestions for necessary supplementary learning.

[0662] Specific Applications

[0663] For example, if Student A is struggling with adding and subtracting fractions in math class, the server identifies this information through learning pattern analysis and generates custom learning materials including appropriate practice problems. After the teacher verifies this, the server distributes it to the student's device. Furthermore, a home learning app containing related practice problems and video courses is provided so that the student can continue their learning at home. In chemistry class, a VR classroom is used to conduct a 3D simulation to deepen understanding of molecular structures. The teacher sets up the VR scenario, and the server distributes corresponding instructions to the device. Students put on VR goggles and observe and manipulate molecular structures in the virtual environment, thereby gaining a deeper understanding.

[0664] The system's program uses Python and its libraries (Pandas, Scikit-Learn) to collect and analyze learning data. The server uses sensor-equipped VR goggles and a data server to collect data on students' learning progress, comprehension, and interaction, and generates appropriate custom learning materials based on that data. As a concrete example, in a history class, progress data is collected by exploring a virtual battlefield and answering quizzes about the battlefield, and the following example prompts are analyzed by the AI ​​model:

[0665] We provide learning data on the Warring States period in history. Based on student progress data and quiz results, we analyze and generate the next custom learning materials to provide.

[0666] Example data:

[0667] Student ID: 12345

[0668] Progress stats: 75%

[0669] Quiz success rate: 60%

[0670] Interaction Level: High

[0671] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0672] Step 1:

[0673] The user enters information and submits it.

[0674] Input: Basic information entered by a parent or teacher using a web browser or mobile application (parent information, child basic information, contact details, etc.).

[0675] Data processing and data calculation: The terminal formats this information and configures it as transmission data.

[0676] Output: The configured data to send.

[0677] Specific action: The user completes the form and clicks the submit button.

[0678] Step 2:

[0679] The terminal transmits the transmission data to the server.

[0680] Input: The transmission data obtained in step 1.

[0681] Data processing and data calculation: The terminal sends the data to the server using the appropriate protocol.

[0682] Output: The data sent to the server.

[0683] Specific operation: The device sends data to the server's receiving API.

[0684] Step 3:

[0685] The server stores the received data in a database.

[0686] Input: The transmission data received in step 2.

[0687] Data processing and data calculation: The server validates the data received and converts it into a format suitable for writing to the database.

[0688] Output: Basic user information stored in the database.

[0689] Specific behavior: The server performs data validation and writes the properly formatted data to the database.

[0690] Step 4:

[0691] The device collects learning data and sends it to the server.

[0692] Input: Learning data such as daily learning progress data, test results, assignment submission status, etc.

[0693] Data processing and data calculation: The data collected by the terminal is formatted and used as transmission data.

[0694] Output: The training data submitted.

[0695] Specific operation: The device continues to record learning data through the application and automatically transmits it to the server.

[0696] Step 5:

[0697] The server runs AI algorithms based on the stored data and analyzes learning patterns.

[0698] Input: Training data stored in a database.

[0699] Data processing and calculation: Analyzing learning patterns using AI algorithms (e.g., machine learning models).

[0700] Output: Analysis of each student's learning patterns.

[0701] Specific operation: The server periodically runs the AI ​​algorithm and performs analysis based on the latest learning data.

[0702] Step 6:

[0703] The server uses the analysis results to identify individual learning needs.

[0704] Input: Analysis results from step 5.

[0705] Data processing and calculation: By looking at the analysis results, we identify each student's weaknesses and areas that need improvement.

[0706] Output: Data about each student's learning needs.

[0707] Specific actions: The server evaluates the analysis results in detail and lists necessary reinforcement points and learning needs.

[0708] Step 7:

[0709] The server generates custom learning materials based on identified needs.

[0710] Input: Learning needs obtained in step 6.

[0711] Data processing and calculation: Using AI algorithms, we generate the most suitable educational content for each student. We extract relevant content from the database and create custom learning materials.

[0712] Output: The generated custom teaching materials.

[0713] Specific behavior: The server generates custom teaching materials and makes them available for teachers to review.

[0714] Step 8:

[0715] The device sends a signal to the student's corresponding virtual reality device to execute a specific learning scenario.

[0716] Input: Custom learning materials generated in step 7.

[0717] Data processing and calculation: The terminal generates a signal and sends it to the virtual reality device, which then loads a specific learning scenario.

[0718] Output: The learning scenario loaded into the VR device.

[0719] Specific actions: Students put on VR goggles and begin learning in the specified scenario.

[0720] Step 9:

[0721] The device launches the home study app, and the student begins their home study.

[0722] Input: Custom learning materials and learning activities for home learning.

[0723] Data processing and calculation: The home learning app displays learning materials and records students' progress in real time.

[0724] Output: Home learning progress data.

[0725] What it does: Students use the app at home to study and automatically send progress and understanding data.

[0726] Step 10:

[0727] The server monitors the progress of home learning and provides feedback to parents.

[0728] Input: Step 9 home learning progress data.

[0729] Data processing and calculation: The server analyzes the progress data and generates reports in real time.

[0730] Output: Feedback report provided to parents.

[0731] What it does: Parents view feedback reports on their child's learning progress and understanding through a web portal or app.

[0732] 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.

[0733] The present invention integrates an emotion engine into an educational system that combines AI and VR technologies, optimizing the learning experience by taking into account the emotional state of the user (student). Specific embodiments of the present invention are described below.

[0734] User registration and information management

[0735] 1. User Registration

[0736] The user (parent) accesses the system and opens the registration form using a web browser or mobile app. The user enters the required information (parent information, child basic information, contact information, etc.) and presses the submit button.

[0737] The device sends the input data to the server, which includes basic information about the parent and child.

[0738] The server receives the submitted data, validates it, and then saves it to the database.

[0739] Analyzing learning patterns and using sentiment engines

[0740] 2. Collecting training data and emotion data

[0741] The device collects students' daily learning progress data, test results, assignment submission status, etc. At the same time, the device's built-in emotion engine uses cameras and sensors to analyze the students' facial expressions and voices and recognize their emotional state.

[0742] The training data and emotion data are periodically sent to the server.

[0743] The server receives the data, checks its integrity, and then stores it in a database.

[0744] 3. Learning pattern analysis and emotion integration

[0745] The server runs an AI algorithm based on learning data and emotional data to analyze each student's learning patterns and emotional fluctuations, identifying, for example, repeated mistakes on a particular question or extreme emotional fluctuations.

[0746] The analysis results are used to generate custom learning materials tailored to students' learning needs and emotional state.

[0747] 4. Creating custom learning materials

[0748] Based on the analysis, the server generates custom learning materials tailored to each student's learning needs and emotional state, such as relaxing videos to reduce stress or interactive quizzes to deepen understanding.

[0749] The custom learning materials are then delivered to teachers in a format that they can review and modify as needed.

[0750] VR classroom settings and emotional responses

[0751] 5. VR classroom setup

[0752] The user (teacher) opens the VR classroom settings page using a web browser or application and selects the VR scenario to be used in the next lesson, including selecting the learning subject and customizing the scenario.

[0753] The server receives the settings and sends the corresponding VR signal to each student's device.

[0754] 6. VR classroom operation

[0755] The device sends signals to each student's VR goggles to execute a specific learning scenario. User emotional data is analyzed in real time, and the learning scenario is dynamically changed as needed. For example, a student who is losing interest can be presented with new, more interesting content.

[0756] Support for home learning

[0757] 7. Use home learning apps

[0758] The device runs a dedicated home learning application, providing students with a learning environment at home, including custom learning materials, homework assignments, and progress tracking.

[0759] The device sends data on home learning progress and emotion to a server, which stores the received data in a database and monitors it in real time.

[0760] 8. Providing Feedback

[0761] The server analyzes the home learning progress and emotional data and provides parents with feedback on their child's learning and emotional state, including progress, comprehension, and suggestions for necessary supplementary learning.

[0762] Specific examples

[0763] For example, when Student B is doing homework in English class, the system simultaneously collects his / her emotional state and learning data. If Student B is feeling stressed, the emotion engine will recognize this and the server will incorporate relaxing music and simple exercises into the custom learning materials, allowing Student B to calm down and focus on their studies.

[0764] In addition, virtual experiments are conducted in science classes using the VR classroom. The user (teacher) sets up the VR scenario, and the server monitors emotional data in real time. When students start to lose interest during the lesson, the system automatically presents a new interactive experiment scenario to recapture their interest.

[0765] Thus, the present invention provides a system that utilizes real-time emotional data to optimize the learning experience and maximize student learning effectiveness.

[0766] The processing flow will be explained below.

[0767] Step 1:

[0768] The user (parent) accesses the system and opens the registration form using a web browser or mobile application. The user enters the required information (parent information, child basic information, contact information, etc.) and presses the submit button.

[0769] Step 2:

[0770] The terminal generates and transmits an HTTP request for transmitting the data input by the user to the server.

[0771] Step 3:

[0772] The server validates the received data and, if there are no problems, saves it in the database. After saving, the server generates a notification to the user that registration is complete and sends it to the terminal.

[0773] Step 4:

[0774] The device collects students' daily learning progress data, test results, assignment submission status, etc. At the same time, the device's built-in emotion engine uses cameras and sensors to analyze the student's facial expressions and voice to recognize their emotional state.

[0775] Step 5:

[0776] The terminal generates and transmits a request for periodically transmitting the learning data and emotion data to the server.

[0777] Step 6:

[0778] The server checks the consistency of the received learning data and emotion data, and if there are no problems, stores it in the database.

[0779] Step 7:

[0780] The server runs AI algorithms on the stored data to analyze learning patterns and emotional states, identifying, for example, repeated mistakes on a particular question or periods of intense emotional activity.

[0781] Step 8:

[0782] The server uses the analysis to identify individual learning needs and emotional states, and then generates custom learning materials based on those needs, such as relaxing videos to reduce stress or interactive quizzes to deepen understanding.

[0783] Step 9:

[0784] The server sends the generated custom teaching materials to the teacher's account, where the teacher can review and correct them if necessary.

[0785] Step 10:

[0786] The device downloads the final version of the custom learning material to the student's device and sends a reminder to start learning.

[0787] Step 11:

[0788] The user (teacher) uses a web browser or application to set up the VR classroom scenario for the next lesson, including selecting the learning subject and customizing the scenario.

[0789] Step 12:

[0790] The server receives the configured VR scenario and sends corresponding signals to each student's device, which then executes the specific learning scenario through the VR goggles.

[0791] Step 13:

[0792] The device sends signals to each student's VR goggles to execute a specific learning scenario, during which an emotion engine monitors the student's emotions in real time and dynamically changes the scenario as needed.

[0793] Step 14:

[0794] If a user (student) feels stressed while studying, the emotion engine will recognize it and the server will automatically present new interactive content or relaxing videos.

[0795] Step 15:

[0796] The device runs a dedicated home learning application, providing students with a learning environment at home, including custom learning materials, homework assignments, and progress tracking.

[0797] Step 16:

[0798] The terminal generates and transmits a request for periodically transmitting home learning progress data and emotion data to the server.

[0799] Step 17:

[0800] The server monitors and analyzes home learning progress and emotional data, providing parents with feedback on their child's learning progress and emotional state.

[0801] Step 18:

[0802] The feedback generated by the server includes information on the child's progress and understanding, as well as suggestions for supplementary learning. The user (parent) receives this feedback and can improve the learning environment or provide additional support.

[0803] Example 2

[0804] 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."

[0805] Conventional educational systems are unable to simultaneously consider students' learning needs and emotional state, making it difficult to provide an individually optimized learning experience. Furthermore, because feedback based on learning progress and emotional state is not provided in real time, parents and educators are unable to accurately grasp students' situations. To address these issues, a new educational system was needed that maximizes students' learning outcomes and optimizes their learning experience.

[0806] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input and transmit information; a means for the terminal to transmit transmission data to the processing unit; a means for the processing unit to store the received data in a storage device; a means for the terminal to collect learning data and emotional data and transmit it to the processing unit; a means for the processing unit to execute a generative AI model based on the stored data and analyze learning patterns and emotional fluctuations; a means for the processing unit to identify individual learning needs and emotional states from the analysis results; a means for the terminal to generate customized learning materials based on the needs and emotional states identified by the processing unit; a means for the terminal to send a signal to a visual device corresponding to the learner and execute a specific learning scenario; and a means for the terminal to launch a home learning application and allow the learner to study at home. This makes it possible to collect students' learning data and emotional data in real time and provide optimal customized learning materials based on the analysis results. Furthermore, feedback on home learning progress and emotional data can be provided to parents and educators, maximizing students' learning effectiveness.

[0807] "User" means any person or entity that uses the System and inputs and submits information.

[0808] A "terminal" is an electronic device used to accept user operations and communicate with a server.

[0809] A "processing unit" is a computer system that receives, stores, and analyzes the transmitted data.

[0810] A "storage device" is hardware for permanently or temporarily storing data received by a processing unit.

[0811] "Learning data" refers to information such as a student's daily learning progress, test results, and assignment submission status.

[0812] "Emotional data" refers to information about a student's emotional state obtained from their facial expressions and voice.

[0813] A "generative AI model" is an artificial intelligence algorithm that analyzes learning patterns and emotional fluctuations from collected data and generates custom teaching materials.

[0814] "Learning patterns" are information that indicates the trends in students' learning behavior and achievements.

[0815] "Emotional fluctuation" is information that indicates changes in a student's emotional state.

[0816] "Customized learning materials" are educational resources optimized for the learning needs and emotional state of individual students.

[0817] "Visual devices" are head-mounted displays and related devices used by students to engage in learning experiences in a virtual reality (VR) environment.

[0818] A "home learning application" is software that allows students to study at home.

[0819] "Parents" are those responsible for receiving feedback on students' academic progress and emotional state.

[0820] An "educator" is a user who sets the content and scenario of the VR classroom and supports students' learning.

[0821] The present invention relates to a system that takes into account the emotional state of the learner and optimizes the learning experience. The system provides a series of functions, including information input by the user (parent or educator), data transmission from the device, data processing by the server, and custom learning material generation using a generative AI model.

[0822] User registration and information management

[0823] The user (parent) accesses the system using a web browser or mobile app and enters the required information into the registration form. The entered information is sent from the device to the server, which validates the received data before saving it in a storage device. This is how user information is managed.

[0824] Collection of training data and emotion data

[0825] The device collects learners' daily learning progress data, test results, assignment submission status, etc. The device's built-in emotion engine also uses cameras and sensors to analyze the learner's facial expressions and voice to recognize their emotional state. The collected learning data and emotion data are periodically sent to a server, where they are checked for consistency and then stored in a storage device.

[0826] Analyzing learning patterns and generating custom learning materials

[0827] The server uses a generative AI model to analyze the stored learning data and emotional data, analyzing learning patterns and emotional fluctuations. This identifies individual learning needs and emotional states. Based on the analysis results, the server generates custom learning materials optimized for each learner. Examples of generative AI models used include TensorFlow and PyTorch.

[0828] Setting up and operating a VR classroom

[0829] The user (educator) opens the VR classroom settings page using a web browser or application and selects the VR scenario to be used in the next lesson. The settings are sent to the server, which then transmits the corresponding VR signal to each learner's device. The device then transmits the signal to each learner's VR goggles, which executes the specific learning scenario. Learners' emotional data is analyzed in real time, and the learning scenario is dynamically changed as needed.

[0830] Support for home learning

[0831] The device runs a dedicated home learning application, providing a learning environment for learners at home. This includes custom learning materials, homework, and progress management functions. The collected home learning progress and emotional data is sent to a server and stored in a storage device. The server monitors the home learning progress and emotional data and provides feedback to parents.

[0832] Specific examples

[0833] For example, when Student B is doing his / her homework in English class, the device simultaneously collects his / her emotional state and learning data. If Student B is feeling stressed, the emotion engine will recognize the stress and the server will provide customized learning materials such as relaxing music and simple exercises. This will allow Student B to concentrate on his / her studies in a calm manner.

[0834] In addition, virtual experiments are conducted in science classes using the VR classroom. The user (educator) sets up the VR scenario, and the server monitors emotional data in real time. If students start to lose interest during the lesson, the system automatically presents a new interactive experiment scenario to recapture their interest.

[0835] Prompt Sentence Examples

[0836] Please explain in detail the specifications of the web form that parents use to register their child's information and the process by which that information is transmitted to the system, taking into account the child's age.

[0837] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0838] Step 1:

[0839] The user (parent) opens a web browser or mobile app, accesses the registration form, and enters the required information (parent's name, basic information about the child, contact information, etc.). The entered information is sent from the device to the server by pressing the send button. The server receives a request for the registration form HTML page or app screen and sends it to the device. The input here is the web form data, and the output is the user information sent to the server.

[0840] Step 2:

[0841] The device encrypts the input data (basic information of the parent and child) and sends it to the server via HTTPS. The server validates the received data and confirms its accuracy before storing it in a storage device in the processing unit. The input of this step is the encrypted user information, and the output is the user information stored in the storage device.

[0842] Step 3:

[0843] The device collects learners' daily learning progress data, test results, assignment submission status, etc. The emotion engine built into the device also uses cameras and sensors to analyze the learners' facial expressions and voices to recognize their emotional state. The collected learning data and emotion data are periodically sent to the server. The input is the learning data and emotion data, and the output is this data sent to the server.

[0844] Step 4:

[0845] The server receives the transmitted training data and emotion data and checks their consistency. These data are stored in a storage device by a processing unit. The input is the training data and emotion data transmitted from the terminal, and the output is the data stored in the storage device.

[0846] Step 5:

[0847] The server uses a generative AI model to analyze the stored training data and emotional data. Specifically, it uses Python machine learning libraries (TensorFlow and PyTorch) to analyze the learner's learning patterns and emotional fluctuations. The input is the training data and emotional data stored in the storage device, and the output is the analysis results.

[0848] Step 6:

[0849] The server uses the analysis results to identify each learner's learning needs and emotional state. Optimal custom learning materials are generated based on the identified needs. These custom learning materials are generated using a generative AI model. The input is the analysis results, and the output is the generated custom learning materials.

[0850] Step 7:

[0851] The terminal receives the custom learning materials generated by the server and provides them to the learner. A specific learning scenario is sent to the visual device (VR goggles), and the learner learns in the VR environment. The input is the generated custom learning materials and VR signals, and the output is the learning content presented to the learner.

[0852] Step 8:

[0853] The device runs a dedicated home learning application, providing a learning environment for the learner at home. This includes custom learning materials, homework, and progress management functions. The device transmits home learning progress data and emotion data to the server. The input is the home learning progress data and emotion data, and the output is the data transmitted to the server.

[0854] Step 9:

[0855] The server monitors the progress and emotional data of home learning and provides feedback to parents. The feedback includes learning progress, level of understanding, and suggestions for necessary supplementary learning. The input is home learning progress data and emotional data, and the output is feedback provided to parents.

[0856] Prompt Sentence Examples

[0857] Please explain in detail the specifications of the web form that parents use to register their child's information and the process by which that information is transmitted to the system, taking into account the child's age.

[0858] (Application example 2)

[0859] 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."

[0860] Current factory robot management and training systems lack the means to closely monitor the robot's operating status in real time and automatically prompt appropriate actions. In particular, they lack a mechanism to provide custom scenarios that enable rapid response when a robot experiences an operational error or overheats. Furthermore, when training and managing robot operators, there is a need to provide optimal learning scenarios that take into account the user's emotional state. This can lead to problems such as reduced production efficiency and an increased risk of serious breakdowns.

[0861] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0862] In this invention, the server includes a means for executing an AI algorithm based on the operation data to analyze the operation patterns, a means for identifying individual operation needs from the analysis results, and a means for generating custom teaching materials based on the identified needs. This makes it possible to monitor the operation status of factory robots in real time and provide prompt problem solving and education using appropriate VR scenarios.

[0863] "User" refers to the person who operates the system to input and manage information.

[0864] "Terminal" refers to a device used to collect or transmit data, receive signals, etc.

[0865] "Server" refers to a computer system that stores collected data, analyzes it, and distributes various instructions and signals based on the results.

[0866] A "database" refers to a large collection of data stored on a server, which allows for efficient management and retrieval of the data.

[0867] "Operational Data" refers to information relating to the operational status and performance of a factory robot.

[0868] "AI algorithm" refers to the computational procedures of artificial intelligence that analyze movement patterns based on collected movement data and infer specific behaviors and needs.

[0869] "Behavioral patterns" refer to information that indicates a tendency toward a particular behavior or action based on collected data.

[0870] "Custom Instructional Materials" means educational content created to provide individualized learning or response scenarios based on specific operational needs.

[0871] "VR goggles" refers to a headset that allows users to visually experience a virtual reality environment.

[0872] "Signal" refers to a signal sent from a terminal to convey specific instructions or information.

[0873] "Home learning app" refers to software designed to enable workers to continue learning at home.

[0874] This invention provides a system for effectively monitoring and managing the operation of factory robots. This system combines AI and VR technologies, and in particular integrates an emotion engine to optimally operate and train robots according to their operating status.

[0875] User registration and information management

[0876] 1. User Registration

[0877] A user accesses the system and enters information into a registration form using a web browser or mobile app, then submits it. The device receives the information and sends it to the server, where it is validated and stored in a database.

[0878] Using behavioral data and emotion engines

[0879] 2. Collecting motion and emotion data

[0880] The terminal collects operational data from the factory robot, while the built-in emotion engine uses cameras and sensors to monitor the robot's operating status, for example, recognizing errors and overheating. The collected data is periodically sent to a server, which stores it in a database.

[0881] 3. Analysis of movement patterns and integration of emotions

[0882] The server runs AI algorithms on the behavioral and emotional data to analyze behavioral patterns and emotional fluctuations. For example, it can identify repeated errors in a particular task or frequent overheating. The analysis results are used to generate custom teaching materials tailored to the robot's behavioral needs and state.

[0883] 4. Creating custom learning materials

[0884] Based on the analysis results, the server generates custom teaching materials tailored to the operating needs and status of each robot, including guide videos to avoid errors and cool-down scenarios to prevent overheating. The generated custom teaching materials are distributed in a format that allows administrators to review and modify them as necessary.

[0885] VR scenario settings and emotional responses

[0886] 5. Setting up the VR scenario

[0887] The user (administrator) opens the VR scenario setting page using a web browser or application and selects the VR scenario to be used for the next task. The server receives the setting and sends a VR signal to each robot's terminal.

[0888] 6. VR Scenario Operation

[0889] The terminal sends signals to each robot's VR goggles to execute specific learning scenarios. Emotional data is analyzed in real time, and the scenarios are dynamically changed as needed. For example, if an error occurs, new guidelines for error avoidance are presented.

[0890] Support for home learning

[0891] 7. Use home learning apps

[0892] The device launches a dedicated home learning application, providing a learning environment for workers at home. This includes custom learning materials and progress management functions. The device sends home learning progress data and behavior data to a server. The server stores the received data in a database and monitors it in real time.

[0893] 8. Providing Feedback

[0894] The server analyzes the home learning progress and operation data and provides the administrator with feedback on the robot's operation status and progress, including the progress of the operation and suggestions for necessary supplementary learning.

[0895] Specific examples

[0896] For example, if a factory robot A frequently makes errors during a specific task, the system will simultaneously collect its behavioral data and emotional state. When an error is detected, the emotion engine will recognize it, and the server will generate custom educational materials (guide videos) to help robot A avoid the error and present them to the robot through VR goggles.

[0897] Prompt Sentence Examples

[0898] "I would like to build an education and management system that utilizes the emotion engine of factory robots. This system will monitor the robot's operating status in real time and execute VR scenarios as needed. The hardware to be used will include cameras and sensors, and the software will use OpenCV and TensorFlow. Please tell me the specific configuration and implementation steps."

[0899] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0900] Step 1:

[0901] A user accesses the system and uses a web browser or mobile app to enter information into a registration form and submit it. The entered data (user information, robot information, etc.) is sent from the device to the server. The server validates the received data and stores the data that is confirmed to be valid in the database. Input: Information entered into the registration form. Output: User information stored in the database.

[0902] Step 2:

[0903] The terminal collects the operation data and emotion data of the factory robot. Specifically, the camera captures images of the robot and the sensor monitors its operation status. The collected data is sent from the terminal to the server. Input: Camera images, sensor data. Output: Operation data and emotion data sent to the server.

[0904] Step 3:

[0905] The server stores the received movement and emotion data in a database. An AI algorithm is run based on the stored data to analyze movement patterns and emotional fluctuations. OpenCV and TensorFlow are used for this analysis. Input: Movement and emotion data in the database. Output: Analysis results (movement patterns and emotional fluctuations).

[0906] Step 4:

[0907] The server uses the analysis results to identify the operational needs of each robot. Custom teaching materials are generated based on the identified operational needs. For example, guide videos to avoid errors and cool-down scenarios to prevent overheating are prepared. Input: Analysis results. Output: Custom teaching materials (guide videos and cool-down scenarios).

[0908] Step 5:

[0909] The user sets the VR scenario to be used in the next task using a web browser or application. The server receives the settings and sends the corresponding VR signal to each robot's terminal. Input: Setting the VR scenario. Output: Sending the VR signal to the terminal.

[0910] Step 6:

[0911] The terminal sends signals to the VR goggles of each robot to execute a specific learning scenario. It analyzes the behavioral data in real time and dynamically changes the scenario as needed. For example, if an error occurs, it will present new guidelines to avoid the error. Input: VR signal. Output: The learning scenario to be executed.

[0912] Step 7:

[0913] The terminal launches a dedicated home learning application, providing an environment in which the worker can study at home. Home learning progress data and operation data are sent from the terminal to the server. Input: Home learning progress data. Output: Progress data sent to the server.

[0914] Step 8:

[0915] The server analyzes the home learning progress data and operation data in real time and provides feedback to the administrator. The feedback includes comments on the robot's operation status and progress, as well as suggestions for necessary supplementary learning. Input: Home learning progress data and operation data. Output: Feedback to the administrator.

[0916] 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.

[0917] 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.

[0918] 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.

[0919] [Third embodiment]

[0920] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0921] 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.

[0922] 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).

[0923] 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.

[0924] 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.

[0925] 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).

[0926] 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.

[0927] 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.

[0928] 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.

[0929] 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.

[0930] 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.

[0931] 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."

[0932] The present invention is a next-generation education system that combines AI and VR technologies to provide an effective learning environment, reducing the burden on parents and teachers while meeting the learning needs of individual students. Specific embodiments of the system are described below.

[0933] User registration and information management

[0934] 1. User Registration

[0935] The user (parent) accesses the system using a web browser or mobile application, enters the required information (parent information, child's basic information, contact information, etc.) into the designated registration form, and submits it.

[0936] The device sends the input data to the server, which includes basic information about the parent and child.

[0937] The server receives the submitted data, validates it, and then saves it to the database.

[0938] Analyzing learning patterns and generating custom learning materials

[0939] 2. Collecting training data

[0940] The device collects students' daily learning progress data, test results, assignment submission status, etc. This data is periodically sent to the server.

[0941] The server receives the data and stores it in a database, where it performs the necessary integrity checks before storing it.

[0942] 3. Analysis of learning patterns

[0943] The server runs AI algorithms on the stored learning data to analyze each student's learning patterns, such as identifying repeated mistakes on specific questions, learning speed, and level of understanding.

[0944] The results of the analysis are used to identify individual student learning needs and are stored in a database.

[0945] 4. Creating custom learning materials

[0946] Based on the analysis, the server generates custom learning materials tailored to each student's learning needs, including exercises, video lectures, and interactive quizzes.

[0947] The custom learning materials are then delivered to teachers in a format that they can review and modify as needed.

[0948] Setting up and operating a VR classroom

[0949] 5. VR classroom setup

[0950] The user (teacher) opens the VR classroom settings page using a web browser or application and selects the VR scenario to be used in the next lesson, such as a historical simulation or a 3D virtual science experiment.

[0951] The server receives the settings and sends the corresponding VR signal to each student's device.

[0952] 6. VR classroom operation

[0953] The device sends signals to each student's VR goggles to execute specific learning scenarios, allowing students to wear the goggles and deepen their understanding through an immersive learning experience.

[0954] Support for home learning

[0955] 7. Use home learning apps

[0956] The device runs a dedicated home learning application, providing students with a learning environment at home, including custom learning materials, homework assignments, and progress tracking.

[0957] The device sends data on the progress of home learning to a server, which stores the received data in a database and monitors it in real time.

[0958] 8. Providing Feedback

[0959] The server analyzes the progress of home learning and provides parents with feedback on their child's learning, including progress, level of understanding, and suggestions for necessary supplementary learning.

[0960] Specific examples

[0961] For example, if Student A is struggling with adding and subtracting fractions in math class, the system will identify this information through learning pattern analysis. The server will generate custom learning materials with appropriate practice problems, which the teacher will review and then distribute to Student A's device. Furthermore, a home learning app will suggest related practice problems and video courses so that the student can continue their learning at home.

[0962] In chemistry classes, VR classrooms are used to conduct 3D simulations to deepen understanding of molecular structures. The teacher sets up the VR scenario, and the server distributes corresponding instructions to the devices. Students put on VR goggles and observe and manipulate molecular structures in the virtual environment, thereby gaining a deeper understanding.

[0963] In this way, the present invention provides a system that responds to the diverse needs of users (parents, teachers, students) and provides effective learning support by utilizing advanced technology.

[0964] The processing flow will be explained below.

[0965] Step 1:

[0966] The user (parent) accesses the system and opens the registration form using a web browser or mobile app. The user enters the required information (parent information, child basic information, contact information, etc.) into the form and presses the submit button.

[0967] Step 2:

[0968] The terminal generates and transmits an HTTP request for transmitting the data input by the user to the server.

[0969] Step 3:

[0970] The server validates the received data and, if there are no problems, saves it in the database. After saving, the server generates a notification to the user that registration is complete and sends it to the terminal.

[0971] Step 4:

[0972] The device collects students' daily learning progress data, test results, assignment submission status, etc. This data is sent to the server at specified intervals.

[0973] Step 5:

[0974] The server receives the collected learning data, checks its integrity, and stores it in a database. The server then runs AI algorithms to analyze learning patterns and identify each student's learning needs.

[0975] Step 6:

[0976] The server uses the analysis to generate custom learning materials tailored to each student's learning needs, including practice questions, video lectures, and interactive quizzes.

[0977] Step 7:

[0978] The server generates custom teaching materials and sends them to the teacher's account, where the teacher can review and make corrections as needed.

[0979] Step 8:

[0980] The device downloads the final version of the custom learning material to the student's device and sends a reminder to start learning.

[0981] Step 9:

[0982] The user (teacher) uses a web browser or application to set up the VR classroom scenario for the next lesson, including selecting the learning subject and customizing the scenario.

[0983] Step 10:

[0984] The server receives the configured VR scenario and sends corresponding signals to each student's device, which then executes the specific learning scenario through the VR goggles.

[0985] Step 11:

[0986] The user (student) uses the device to input and send a question to the AI ​​teacher assistant, which then sends the question data to the server.

[0987] Step 12:

[0988] The server analyzes the received question and generates an appropriate answer using an AI model. The generated answer is sent back to the device and displayed to the user (student).

[0989] Step 13:

[0990] The device launches a dedicated home learning application and notifies the student, and home learning progress data is periodically sent to the server.

[0991] Step 14:

[0992] The server monitors the progress of home learning and provides parents with feedback based on the analysis results, including progress, level of understanding, and suggestions for necessary supplementary learning.

[0993] Step 15:

[0994] Users (parents) receive feedback provided by the server and improve the learning environment or provide additional support. This process is repeated to support continuous learning and maximize educational effectiveness.

[0995] Example 1

[0996] 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."

[0997] In today's educational environment, there is a need to address the learning needs of individual students while reducing the burden on parents and teachers. In particular, it is necessary to provide learning materials tailored to each student's unique learning pace and level of understanding, to monitor learning progress in real time, and to support home learning. However, systems that achieve these goals are still insufficient. Providing an effective learning environment using virtual reality technology is also an important challenge.

[0998] 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.

[0999] In this invention, the server includes: a means for a user to input and transmit information; a means for a terminal to transmit transmitted data to the server; a means for the server to store the received data in a database; a means for the terminal to collect learning data and transmit it to the server; a means for the server to execute an AI algorithm based on the stored data and analyze learning patterns; a means for the server to identify individual learning needs from the analysis results; a means for the server to generate customized learning materials based on the needs identified by the server; a means for a teacher to confirm and modify the customized learning materials generated by the server; a means for the terminal to transmit a signal to a virtual reality device corresponding to the student and execute a specific learning scenario; and a means for the terminal to launch a home learning program and allow the student to study at home. This makes it possible to provide customized learning materials according to the learning needs of each student, manage learning progress in real time, support home learning, and provide an effective learning environment using virtual reality technology.

[1000] "User" is any entity that inputs and submits information to the system, and includes students, parents, and teachers.

[1001] A "terminal" is a computer device used by a user that has the function of inputting, sending, and receiving information.

[1002] The "server" is a central computing device that receives data sent from terminals via a network, stores it in a database, and runs AI algorithms to analyze learning patterns.

[1003] A "database" is a system for systematically storing and managing data received by a server.

[1004] An "AI algorithm" is a mathematical model that uses artificial intelligence to analyze learning data and identify individual learning patterns and needs.

[1005] "Learning patterns" refer to the learning tendencies and characteristics that are generated based on a student's learning behavior and level of understanding.

[1006] "Customized learning materials" are learning materials that are optimized for individual students based on an analysis of their learning patterns, and may include exercises, video lectures, interactive quizzes, etc.

[1007] A "virtual reality device" is a device that uses virtual reality technology to provide a learning environment that is as immersive as reality, and includes VR goggles and the like.

[1008] A "home learning program" is a software application that allows students to continue their learning at home, providing access to custom learning materials, progress tracking, and learning support.

[1009] A "learning scenario" is a sequence of specific educational materials or content that is executed by a virtual reality device, such as a historical simulation or a 3D model of a scientific experiment.

[1010] "Progress" is data that shows the results and progress a student has made in the learning process.

[1011] The present invention is an educational system that combines AI technology and virtual reality technology to provide an effective learning environment. This system aims to provide next-generation educational support that reduces the burden on parents and teachers and meets the learning needs of individual students. Specific embodiments of the system are described below.

[1012] User registration and information management

[1013] 1. User Registration

[1014] The user (parent) accesses the system using a web browser or mobile application, enters the required information in the designated registration form, and submits it. Web browsers such as Google Chrome or Firefox can be used, and mobile applications compatible with Android or iOS can be used.

[1015] The terminal sends the input data to the server, using the SSL / TLS protocol to ensure the security of the data transmission.

[1016] The server receives the submitted data, validates it, and then stores it in a relational database, such as MySQL or PostgreSQL.

[1017] Analyzing learning patterns and generating custom learning materials

[1018] 2. Collecting training data

[1019] The device collects students' daily learning progress data, test results, assignment submission status, etc., and periodically sends them to a server. Sensors and input devices are used to collect the data.

[1020] The server receives the data and stores it in the database. SQL transaction processing is used to check the integrity of the data.

[1021] 3. Analysis of learning patterns

[1022] The server runs AI algorithms based on the stored learning data to analyze each student's learning patterns, using machine learning libraries such as Python's scikit-learn and TensorFlow.

[1023] The analysis results are stored in a database to identify each student's individual learning needs.

[1024] 4. Creating custom learning materials

[1025] Based on the analysis results, the server uses a generative AI model to generate custom learning materials optimized for each student, including practice questions, video lectures, and interactive quizzes.

[1026] The generated custom teaching materials are delivered to the teacher's device, where they can be checked and corrected.

[1027] Setting up and operating a virtual reality classroom

[1028] 5. VR classroom setup

[1029] The user (teacher) opens the VR classroom settings page using a web browser or application and selects the VR scenario to be used in the next lesson, such as a historical simulation or a 3D virtual science experiment.

[1030] The server receives the settings and sends a signal to the virtual reality device corresponding to each student's device.

[1031] 6. VR classroom operation

[1032] The device sends signals to each student's virtual reality device, such as an Oculus Rift or HTC Vive, to execute a specific learning scenario.

[1033] Students can wear virtual reality devices and deepen their understanding through an immersive learning experience.

[1034] Support for home learning

[1035] 7. Use of home learning programs

[1036] The device runs a dedicated home learning program, providing students with a learning environment at home, including custom learning materials, homework assignments, and progress tracking.

[1037] The device sends home learning progress data to a server, which monitors the data in real time.

[1038] 8. Providing Feedback

[1039] The server analyzes the progress of home learning and provides parents with feedback on their child's learning, including progress, level of understanding, and suggestions for necessary supplementary learning.

[1040] Specific examples

[1041] For example, if Student A struggles with adding and subtracting fractions in math class, the system will identify this information through learning pattern analysis. The server will generate custom learning materials with appropriate practice problems, which the teacher will review and then distribute to Student A's device. Additionally, the home learning program will suggest related practice problems and video courses.

[1042] In chemistry classes, virtual reality technology is used to conduct 3D simulations to deepen understanding of molecular structures. The teacher sets up a virtual reality scenario, and a server distributes corresponding instructions to the devices. Students wear the virtual reality device and can observe and manipulate molecular structures in the virtual environment to gain a deeper understanding.

[1043] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1044] Step 1:

[1045] The user (parent) enters the required information into the registration form using a web browser or mobile application and submits it. The input includes basic information about the parent, basic information about the child, contact information, etc. The input data includes basic information about the parent and child, and is submitted by the device.

[1046] Step 2:

[1047] The terminal sends the data entered by the user to the server. The HTTP POST request is used for transmission, and the SSL / TLS protocol is used to ensure communication security. The input of the transmitted data includes the information entered by the user, and the output is the data transferred to the server.

[1048] Step 3:

[1049] The server analyzes the data it receives and performs validation. Validation includes checking for unfilled fields, type checks, and data consistency checks. The input to the validation process is the data sent from the terminal, and the output is the result of checking the validity of the data. If the data is normal, it is saved in the database.

[1050] Step 4:

[1051] The server stores the data that is confirmed to be valid in a database. A relational database such as MySQL or PostgreSQL is used here. The input is the data that has passed validation, and the output is the result of saving it to the database.

[1052] Step 5:

[1053] The device collects learning data such as students' daily learning progress, test results, and assignment submission status. Sensors and input devices are used to collect the data, which is then stored on the device. The input includes information about students' learning activities, and the output includes the collected learning data.

[1054] Step 6:

[1055] The device sends the collected learning data to the server. The transmission is also performed using an HTTP POST request, and communication security is ensured by the SSL / TLS protocol. The input is the learning data stored on the device, and the output is the learning data sent to the server.

[1056] Step 7:

[1057] The server stores the received training data in a database. At this time, SQL transaction processing is used to check consistency. The input is the training data sent from the device, and the output includes the results saved in the database.

[1058] Step 8:

[1059] The server runs an AI algorithm based on the learning data stored in the database to analyze each student's learning patterns. The analysis uses machine learning libraries such as scikit-learn and TensorFlow. The input is the stored learning data, and the output contains the analysis results of the learning patterns.

[1060] Step 9:

[1061] The server identifies individual student learning needs based on the analysis of learning patterns, using the results of an AI algorithm, with the analysis results as input and individual learning needs as output.

[1062] Step 10:

[1063] The server generates custom learning materials based on identified learning needs using a generative AI model, including exercises, video courses, and interactive quizzes. The input is the learning needs data, and the output is the generated custom learning materials.

[1064] Step 11:

[1065] The server delivers the generated custom teaching materials to the teacher's device, where the teacher reviews the teaching materials and makes corrections as necessary. The input is the generated custom teaching materials, and the output is the teacher-reviewed teaching materials.

[1066] Step 12:

[1067] The user (teacher) opens the virtual reality classroom setting page using a web browser or application and selects a scenario for the VR classroom. The setting includes the scenario to be used in the next lesson. The input is the teacher's setting information, and the output is the selected VR scenario.

[1068] Step 13:

[1069] The server receives the settings and sends signals to the virtual reality devices corresponding to each student's device. The input contains the selected VR scenario, and the output contains the signals to each device.

[1070] Step 14:

[1071] The device sends signals to the student's corresponding virtual reality device to execute a specific learning scenario. The specified scenario file is played on a device such as an Oculus Rift or HTC Vive. The input is instructions from the server, and the output is the VR scenario to be executed.

[1072] Step 15:

[1073] The device runs a home learning program, providing an environment where students can continue their studies at home, including custom learning materials, homework assignments, and progress management functions. The input is student learning information, and the output is accumulated home learning progress data.

[1074] Step 16:

[1075] The device sends home learning progress data to the server, which monitors the data in real time and receives it. The input includes home learning progress data, and the output includes data stored on the server.

[1076] Step 17:

[1077] The server analyzes the progress of home learning and provides feedback to parents, including the progress and understanding of the child and suggestions for supplementary learning. The input is home learning data, and the output is the generated feedback.

[1078] Specific examples of implementation

[1079] For example, if Student A struggles with adding and subtracting fractions in math, the system can identify their learning patterns and generate custom learning materials, including appropriate practice problems and video tutorials, on the server, which can then be sent to Student A's device after being reviewed by the teacher. A home learning program can be similarly configured to provide relevant learning materials for Student A to practice at home. In a chemistry class, the teacher can set up a virtual reality scenario, and the server can send instructions to each student's virtual reality device. Students can then use the virtual reality device to deepen their understanding of molecular structures.

[1080] (Application example 1)

[1081] 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."

[1082] Traditional educational systems place a heavy burden on parents and teachers, and are difficult to respond to the individual learning needs of each student. Even when virtual reality technology is utilized, educational content is often uniform and learning data analysis is insufficient. This makes it difficult to provide an effective learning environment.

[1083] 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.

[1084] In this invention, the server includes: means for a user to input and transmit information; means for a terminal to transmit transmitted data to the server; means for the server to store the received data in a database; means for the terminal to collect learning data and transmit it to the server; means for the server to run an AI algorithm based on the stored data and analyze learning patterns; means for the server to identify individual learning needs from the analysis results; means for the server to generate customized learning materials based on the needs identified by the server; means for the terminal to send a signal to a virtual reality device corresponding to the student and execute a specific learning scenario; means for the terminal to launch a home learning app and the student to study at home; means for an instructor to select and set lesson content and select a virtual reality scenario; means for collecting learning progress data, comprehension tests, and interaction data in the virtual reality environment; means for analyzing the collected data and running an AI model to generate appropriate customized learning materials; and means for providing the generated customized learning materials and learning progress information to instructors and parents. This reduces the burden on parents and teachers and makes it possible to provide customized learning materials that appropriately meet the learning needs of each student.

[1085] "User" refers to anyone who utilizes the system to input information and submit education-related data.

[1086] "Terminal" refers to a hardware or software device for transmitting data entered by a user to a server and for collecting learning data.

[1087] "Server" refers to a computer system that stores received data in a database and runs AI algorithms to analyze learning patterns.

[1088] A "database" is an information management system that organizes and stores data received by the server.

[1089] An "AI algorithm" is an artificial intelligence computational method that analyzes students' learning patterns based on collected learning data and generates appropriate custom teaching materials.

[1090] "Learning patterns" are the results of data analysis that include individual students' learning progress and problem-solving tendencies.

[1091] "Customized Learning Materials" refers to specialized educational content created to meet the learning needs of individual students.

[1092] A "virtual reality device" is a device that allows students to have an interactive learning experience in a VR environment.

[1093] "Virtual reality scenario" refers to a specific learning content or environment that is executed using a virtual reality device.

[1094] A "home learning app" is a software application that provides students with the ability to study at home.

[1095] "Instructor" refers to an educator who is responsible for managing educational content and running classes.

[1096] "Interaction data" refers to data about the operations and behavior of students in a virtual reality environment.

[1097] "Learning data" refers to information such as a student's daily learning progress, test results, and assignment submission status.

[1098] "Study progress information" is data that indicates how far a student has progressed in their studies.

[1099] "Progress Data" refers to information that records how learning is progressing.

[1100] The system of the present invention combines AI technology and virtual reality (VR) technology to provide an effective learning environment. The system aims to address the learning needs of individual students while reducing the burden on parents and teachers. Specific embodiments of the system are described below.

[1101] User registration and information management

[1102] First, a user (parent or teacher) accesses the system using a web browser or mobile application, enters the required information in a designated registration form, and submits it. The device then sends the input data to the server, which then receives and stores it in a database. This data includes basic information about the parent and child, as well as contact details.

[1103] Analyzing learning patterns and generating custom learning materials

[1104] The device then collects the student's daily learning progress data, test results, assignment submission status, etc., and periodically sends them to the server. The server stores the received data in a database, performs necessary consistency checks, and then runs AI algorithms. Based on the learning data, the server analyzes each student's learning patterns, analysing repeated mistakes on specific questions, learning speed, and level of understanding. Based on this, the server identifies each student's learning needs and generates optimal custom learning materials. These custom materials include practice questions, video lectures, and interactive quizzes.

[1105] Setting up and operating a VR classroom

[1106] Teachers can open the VR classroom settings page using a web browser or application and select the VR scenario to be used in the next lesson. The selected information is sent to the server, which then sends a corresponding VR signal to each student's device. When students put on the VR goggles, the device receives the signal and executes the specific learning scenario, allowing students to have an immersive learning experience in the VR environment.

[1107] Support for home learning

[1108] The device runs a dedicated home learning application, providing a learning environment for students at home. This application includes custom learning materials, homework assignments, and progress management functions. The device sends home learning progress data to a server, which monitors the received data in real time. The server then analyzes the home learning progress and provides parents with feedback on their child's learning status, including their learning progress and level of understanding, as well as suggestions for necessary supplementary learning.

[1109] Specific Applications

[1110] For example, if Student A is struggling with adding and subtracting fractions in math class, the server identifies this information through learning pattern analysis and generates custom learning materials including appropriate practice problems. After the teacher verifies this, the server distributes it to the student's device. Furthermore, a home learning app containing related practice problems and video courses is provided so that the student can continue their learning at home. In chemistry class, a VR classroom is used to conduct a 3D simulation to deepen understanding of molecular structures. The teacher sets up the VR scenario, and the server distributes corresponding instructions to the device. Students put on VR goggles and observe and manipulate molecular structures in the virtual environment, thereby gaining a deeper understanding.

[1111] The system's program uses Python and its libraries (Pandas, Scikit-Learn) to collect and analyze learning data. The server uses sensor-equipped VR goggles and a data server to collect data on students' learning progress, comprehension, and interaction, and generates appropriate custom learning materials based on that data. As a concrete example, in a history class, progress data is collected by exploring a virtual battlefield and answering quizzes about the battlefield, and the following example prompts are analyzed by the AI ​​model:

[1112] We provide learning data on the Warring States period in history. Based on student progress data and quiz results, we analyze and generate the next custom learning materials to provide.

[1113] Example data:

[1114] Student ID: 12345

[1115] Progress stats: 75%

[1116] Quiz success rate: 60%

[1117] Interaction Level: High

[1118] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1119] Step 1:

[1120] The user enters information and submits it.

[1121] Input: Basic information entered by a parent or teacher using a web browser or mobile application (parent information, child basic information, contact details, etc.).

[1122] Data processing and data calculation: The terminal formats this information and configures it as transmission data.

[1123] Output: The configured data to send.

[1124] Specific action: The user completes the form and clicks the submit button.

[1125] Step 2:

[1126] The terminal transmits the transmission data to the server.

[1127] Input: The transmission data obtained in step 1.

[1128] Data processing and data calculation: The terminal sends the data to the server using the appropriate protocol.

[1129] Output: The data sent to the server.

[1130] Specific operation: The device sends data to the server's receiving API.

[1131] Step 3:

[1132] The server stores the received data in a database.

[1133] Input: The transmission data received in step 2.

[1134] Data processing and data calculation: The server validates the data received and converts it into a format suitable for writing to the database.

[1135] Output: Basic user information stored in the database.

[1136] Specific behavior: The server performs data validation and writes the properly formatted data to the database.

[1137] Step 4:

[1138] The device collects learning data and sends it to the server.

[1139] Input: Learning data such as daily learning progress data, test results, assignment submission status, etc.

[1140] Data processing and data calculation: The data collected by the terminal is formatted and used as transmission data.

[1141] Output: The training data submitted.

[1142] Specific operation: The device continues to record learning data through the application and automatically transmits it to the server.

[1143] Step 5:

[1144] The server runs AI algorithms based on the stored data and analyzes learning patterns.

[1145] Input: Training data stored in a database.

[1146] Data processing and calculation: Analyzing learning patterns using AI algorithms (e.g., machine learning models).

[1147] Output: Analysis of each student's learning patterns.

[1148] Specific operation: The server periodically runs the AI ​​algorithm and performs analysis based on the latest learning data.

[1149] Step 6:

[1150] The server uses the analysis results to identify individual learning needs.

[1151] Input: Analysis results from step 5.

[1152] Data processing and calculation: By looking at the analysis results, we identify each student's weaknesses and areas that need improvement.

[1153] Output: Data about each student's learning needs.

[1154] Specific actions: The server evaluates the analysis results in detail and lists necessary reinforcement points and learning needs.

[1155] Step 7:

[1156] The server generates custom learning materials based on identified needs.

[1157] Input: Learning needs obtained in step 6.

[1158] Data processing and calculation: Using AI algorithms, we generate the most suitable educational content for each student. We extract relevant content from the database and create custom learning materials.

[1159] Output: The generated custom teaching materials.

[1160] Specific behavior: The server generates custom teaching materials and makes them available for teachers to review.

[1161] Step 8:

[1162] The device sends a signal to the student's corresponding virtual reality device to execute a specific learning scenario.

[1163] Input: Custom learning materials generated in step 7.

[1164] Data processing and calculation: The terminal generates a signal and sends it to the virtual reality device, which then loads a specific learning scenario.

[1165] Output: The learning scenario loaded into the VR device.

[1166] Specific actions: Students put on VR goggles and begin learning in the specified scenario.

[1167] Step 9:

[1168] The device launches the home study app, and the student begins their home study.

[1169] Input: Custom learning materials and learning activities for home learning.

[1170] Data processing and calculation: The home learning app displays learning materials and records students' progress in real time.

[1171] Output: Home learning progress data.

[1172] What it does: Students use the app at home to study and automatically send progress and understanding data.

[1173] Step 10:

[1174] The server monitors the progress of home learning and provides feedback to parents.

[1175] Input: Step 9 home learning progress data.

[1176] Data processing and calculation: The server analyzes the progress data and generates reports in real time.

[1177] Output: Feedback report provided to parents.

[1178] What it does: Parents view feedback reports on their child's learning progress and understanding through a web portal or app.

[1179] 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.

[1180] The present invention integrates an emotion engine into an educational system that combines AI and VR technologies, optimizing the learning experience by taking into account the emotional state of the user (student). Specific embodiments of the present invention are described below.

[1181] User registration and information management

[1182] 1. User Registration

[1183] The user (parent) accesses the system and opens the registration form using a web browser or mobile app. The user enters the required information (parent information, child basic information, contact information, etc.) and presses the submit button.

[1184] The device sends the input data to the server, which includes basic information about the parent and child.

[1185] The server receives the submitted data, validates it, and then saves it to the database.

[1186] Analyzing learning patterns and using sentiment engines

[1187] 2. Collecting training data and emotion data

[1188] The device collects students' daily learning progress data, test results, assignment submission status, etc. At the same time, the device's built-in emotion engine uses cameras and sensors to analyze the students' facial expressions and voices and recognize their emotional state.

[1189] The training data and emotion data are periodically sent to the server.

[1190] The server receives the data, checks its integrity, and then stores it in a database.

[1191] 3. Learning pattern analysis and emotion integration

[1192] The server runs an AI algorithm based on learning data and emotional data to analyze each student's learning patterns and emotional fluctuations, identifying, for example, repeated mistakes on a particular question or extreme emotional fluctuations.

[1193] The analysis results are used to generate custom learning materials tailored to students' learning needs and emotional state.

[1194] 4. Creating custom learning materials

[1195] Based on the analysis, the server generates custom learning materials tailored to each student's learning needs and emotional state, such as relaxing videos to reduce stress or interactive quizzes to deepen understanding.

[1196] The custom learning materials are then delivered to teachers in a format that they can review and modify as needed.

[1197] VR classroom settings and emotional responses

[1198] 5. VR classroom setup

[1199] The user (teacher) opens the VR classroom settings page using a web browser or application and selects the VR scenario to be used in the next lesson, including selecting the learning subject and customizing the scenario.

[1200] The server receives the settings and sends the corresponding VR signal to each student's device.

[1201] 6. VR classroom operation

[1202] The device sends signals to each student's VR goggles to execute a specific learning scenario. User emotional data is analyzed in real time, and the learning scenario is dynamically changed as needed. For example, a student who is losing interest can be presented with new, more interesting content.

[1203] Support for home learning

[1204] 7. Use home learning apps

[1205] The device runs a dedicated home learning application, providing students with a learning environment at home, including custom learning materials, homework assignments, and progress tracking.

[1206] The device sends data on home learning progress and emotion to a server, which stores the received data in a database and monitors it in real time.

[1207] 8. Providing Feedback

[1208] The server analyzes the home learning progress and emotional data and provides parents with feedback on their child's learning and emotional state, including progress, comprehension, and suggestions for necessary supplementary learning.

[1209] Specific examples

[1210] For example, when Student B is doing homework in English class, the system simultaneously collects his / her emotional state and learning data. If Student B is feeling stressed, the emotion engine will recognize this and the server will incorporate relaxing music and simple exercises into the custom learning materials, allowing Student B to calm down and focus on their studies.

[1211] In addition, virtual experiments are conducted in science classes using the VR classroom. The user (teacher) sets up the VR scenario, and the server monitors emotional data in real time. When students start to lose interest during the lesson, the system automatically presents a new interactive experiment scenario to recapture their interest.

[1212] Thus, the present invention provides a system that utilizes real-time emotional data to optimize the learning experience and maximize student learning effectiveness.

[1213] The processing flow will be explained below.

[1214] Step 1:

[1215] The user (parent) accesses the system and opens the registration form using a web browser or mobile application. The user enters the required information (parent information, child basic information, contact information, etc.) and presses the submit button.

[1216] Step 2:

[1217] The terminal generates and transmits an HTTP request for transmitting the data input by the user to the server.

[1218] Step 3:

[1219] The server validates the received data and, if there are no problems, saves it in the database. After saving, the server generates a notification to the user that registration is complete and sends it to the terminal.

[1220] Step 4:

[1221] The device collects students' daily learning progress data, test results, assignment submission status, etc. At the same time, the device's built-in emotion engine uses cameras and sensors to analyze the student's facial expressions and voice to recognize their emotional state.

[1222] Step 5:

[1223] The terminal generates and transmits a request for periodically transmitting the learning data and emotion data to the server.

[1224] Step 6:

[1225] The server checks the consistency of the received learning data and emotion data, and if there are no problems, stores it in the database.

[1226] Step 7:

[1227] The server runs AI algorithms on the stored data to analyze learning patterns and emotional states, identifying, for example, repeated mistakes on a particular question or periods of intense emotional activity.

[1228] Step 8:

[1229] The server uses the analysis to identify individual learning needs and emotional states, and then generates custom learning materials based on those needs, such as relaxing videos to reduce stress or interactive quizzes to deepen understanding.

[1230] Step 9:

[1231] The server sends the generated custom teaching materials to the teacher's account, where the teacher can review and correct them if necessary.

[1232] Step 10:

[1233] The device downloads the final version of the custom learning material to the student's device and sends a reminder to start learning.

[1234] Step 11:

[1235] The user (teacher) uses a web browser or application to set up the VR classroom scenario for the next lesson, including selecting the learning subject and customizing the scenario.

[1236] Step 12:

[1237] The server receives the configured VR scenario and sends corresponding signals to each student's device, which then executes the specific learning scenario through the VR goggles.

[1238] Step 13:

[1239] The device sends signals to each student's VR goggles to execute a specific learning scenario, during which an emotion engine monitors the student's emotions in real time and dynamically changes the scenario as needed.

[1240] Step 14:

[1241] If a user (student) feels stressed while studying, the emotion engine will recognize it and the server will automatically present new interactive content or relaxing videos.

[1242] Step 15:

[1243] The device runs a dedicated home learning application, providing students with a learning environment at home, including custom learning materials, homework assignments, and progress tracking.

[1244] Step 16:

[1245] The terminal generates and transmits a request for periodically transmitting home learning progress data and emotion data to the server.

[1246] Step 17:

[1247] The server monitors and analyzes home learning progress and emotional data, providing parents with feedback on their child's learning progress and emotional state.

[1248] Step 18:

[1249] The feedback generated by the server includes information on the child's progress and understanding, as well as suggestions for supplementary learning. The user (parent) receives this feedback and can improve the learning environment or provide additional support.

[1250] Example 2

[1251] 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."

[1252] Conventional educational systems are unable to simultaneously consider students' learning needs and emotional state, making it difficult to provide an individually optimized learning experience. Furthermore, because feedback based on learning progress and emotional state is not provided in real time, parents and educators are unable to accurately grasp students' situations. To address these issues, a new educational system was needed that maximizes students' learning outcomes and optimizes their learning experience.

[1253] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input and transmit information; a means for the terminal to transmit transmission data to the processing unit; a means for the processing unit to store the received data in a storage device; a means for the terminal to collect learning data and emotional data and transmit it to the processing unit; a means for the processing unit to execute a generative AI model based on the stored data and analyze learning patterns and emotional fluctuations; a means for the processing unit to identify individual learning needs and emotional states from the analysis results; a means for the terminal to generate customized learning materials based on the needs and emotional states identified by the processing unit; a means for the terminal to send a signal to a visual device corresponding to the learner and execute a specific learning scenario; and a means for the terminal to launch a home learning application and allow the learner to study at home. This makes it possible to collect students' learning data and emotional data in real time and provide optimal customized learning materials based on the analysis results. Furthermore, feedback on home learning progress and emotional data can be provided to parents and educators, maximizing students' learning effectiveness.

[1254] "User" means any person or entity that uses the System and inputs and submits information.

[1255] A "terminal" is an electronic device used to accept user operations and communicate with a server.

[1256] A "processing unit" is a computer system that receives, stores, and analyzes the transmitted data.

[1257] A "storage device" is hardware for permanently or temporarily storing data received by a processing unit.

[1258] "Learning data" refers to information such as a student's daily learning progress, test results, and assignment submission status.

[1259] "Emotional data" refers to information about a student's emotional state obtained from their facial expressions and voice.

[1260] A "generative AI model" is an artificial intelligence algorithm that analyzes learning patterns and emotional fluctuations from collected data and generates custom teaching materials.

[1261] "Learning patterns" are information that indicates the trends in students' learning behavior and achievements.

[1262] "Emotional fluctuation" is information that indicates changes in a student's emotional state.

[1263] "Customized learning materials" are educational resources optimized for the learning needs and emotional state of individual students.

[1264] "Visual devices" are head-mounted displays and related devices used by students to engage in learning experiences in a virtual reality (VR) environment.

[1265] A "home learning application" is software that allows students to study at home.

[1266] "Parents" are those responsible for receiving feedback on students' academic progress and emotional state.

[1267] An "educator" is a user who sets the content and scenario of the VR classroom and supports students' learning.

[1268] The present invention relates to a system that takes into account the emotional state of the learner and optimizes the learning experience. The system provides a series of functions, including information input by the user (parent or educator), data transmission from the device, data processing by the server, and custom learning material generation using a generative AI model.

[1269] User registration and information management

[1270] The user (parent) accesses the system using a web browser or mobile app and enters the required information into the registration form. The entered information is sent from the device to the server, which validates the received data before saving it in a storage device. This is how user information is managed.

[1271] Collection of training data and emotion data

[1272] The device collects learners' daily learning progress data, test results, assignment submission status, etc. The device's built-in emotion engine also uses cameras and sensors to analyze the learner's facial expressions and voice to recognize their emotional state. The collected learning data and emotion data are periodically sent to a server, where they are checked for consistency and then stored in a storage device.

[1273] Analyzing learning patterns and generating custom learning materials

[1274] The server uses a generative AI model to analyze the stored learning data and emotional data, analyzing learning patterns and emotional fluctuations. This identifies individual learning needs and emotional states. Based on the analysis results, the server generates custom learning materials optimized for each learner. Examples of generative AI models used include TensorFlow and PyTorch.

[1275] Setting up and operating a VR classroom

[1276] The user (educator) opens the VR classroom settings page using a web browser or application and selects the VR scenario to be used in the next lesson. The settings are sent to the server, which then transmits the corresponding VR signal to each learner's device. The device then transmits the signal to each learner's VR goggles, which executes the specific learning scenario. Learners' emotional data is analyzed in real time, and the learning scenario is dynamically changed as needed.

[1277] Support for home learning

[1278] The device runs a dedicated home learning application, providing a learning environment for learners at home. This includes custom learning materials, homework, and progress management functions. The collected home learning progress and emotional data is sent to a server and stored in a storage device. The server monitors the home learning progress and emotional data and provides feedback to parents.

[1279] Specific examples

[1280] For example, when Student B is doing his / her homework in English class, the device simultaneously collects his / her emotional state and learning data. If Student B is feeling stressed, the emotion engine will recognize the stress and the server will provide customized learning materials such as relaxing music and simple exercises. This will allow Student B to concentrate on his / her studies in a calm manner.

[1281] In addition, virtual experiments are conducted in science classes using the VR classroom. The user (educator) sets up the VR scenario, and the server monitors emotional data in real time. If students start to lose interest during the lesson, the system automatically presents a new interactive experiment scenario to recapture their interest.

[1282] Prompt Sentence Examples

[1283] Please explain in detail the specifications of the web form that parents use to register their child's information and the process by which that information is transmitted to the system, taking into account the child's age.

[1284] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1285] Step 1:

[1286] The user (parent) opens a web browser or mobile app, accesses the registration form, and enters the required information (parent's name, basic information about the child, contact information, etc.). The entered information is sent from the device to the server by pressing the send button. The server receives a request for the registration form HTML page or app screen and sends it to the device. The input here is the web form data, and the output is the user information sent to the server.

[1287] Step 2:

[1288] The device encrypts the input data (basic information of the parent and child) and sends it to the server via HTTPS. The server validates the received data and confirms its accuracy before storing it in a storage device in the processing unit. The input of this step is the encrypted user information, and the output is the user information stored in the storage device.

[1289] Step 3:

[1290] The device collects learners' daily learning progress data, test results, assignment submission status, etc. The emotion engine built into the device also uses cameras and sensors to analyze the learners' facial expressions and voices to recognize their emotional state. The collected learning data and emotion data are periodically sent to the server. The input is the learning data and emotion data, and the output is this data sent to the server.

[1291] Step 4:

[1292] The server receives the transmitted training data and emotion data and checks their consistency. These data are stored in a storage device by a processing unit. The input is the training data and emotion data transmitted from the terminal, and the output is the data stored in the storage device.

[1293] Step 5:

[1294] The server uses a generative AI model to analyze the stored training data and emotional data. Specifically, it uses Python machine learning libraries (TensorFlow and PyTorch) to analyze the learner's learning patterns and emotional fluctuations. The input is the training data and emotional data stored in the storage device, and the output is the analysis results.

[1295] Step 6:

[1296] The server uses the analysis results to identify each learner's learning needs and emotional state. Optimal custom learning materials are generated based on the identified needs. These custom learning materials are generated using a generative AI model. The input is the analysis results, and the output is the generated custom learning materials.

[1297] Step 7:

[1298] The terminal receives the custom learning materials generated by the server and provides them to the learner. A specific learning scenario is sent to the visual device (VR goggles), and the learner learns in the VR environment. The input is the generated custom learning materials and VR signals, and the output is the learning content presented to the learner.

[1299] Step 8:

[1300] The device runs a dedicated home learning application, providing a learning environment for the learner at home. This includes custom learning materials, homework, and progress management functions. The device transmits home learning progress data and emotion data to the server. The input is the home learning progress data and emotion data, and the output is the data transmitted to the server.

[1301] Step 9:

[1302] The server monitors the progress and emotional data of home learning and provides feedback to parents. The feedback includes learning progress, level of understanding, and suggestions for necessary supplementary learning. The input is home learning progress data and emotional data, and the output is feedback provided to parents.

[1303] Prompt Sentence Examples

[1304] Please explain in detail the specifications of the web form that parents use to register their child's information and the process by which that information is transmitted to the system, taking into account the child's age.

[1305] (Application example 2)

[1306] 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."

[1307] Current factory robot management and training systems lack the means to closely monitor the robot's operating status in real time and automatically prompt appropriate actions. In particular, they lack a mechanism to provide custom scenarios that enable rapid response when a robot experiences an operational error or overheats. Furthermore, when training and managing robot operators, there is a need to provide optimal learning scenarios that take into account the user's emotional state. This can lead to problems such as reduced production efficiency and an increased risk of serious breakdowns.

[1308] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1309] In this invention, the server includes a means for executing an AI algorithm based on the operation data to analyze the operation patterns, a means for identifying individual operation needs from the analysis results, and a means for generating custom teaching materials based on the identified needs. This makes it possible to monitor the operation status of factory robots in real time and provide prompt problem solving and education using appropriate VR scenarios.

[1310] "User" refers to the person who operates the system to input and manage information.

[1311] "Terminal" refers to a device used to collect or transmit data, receive signals, etc.

[1312] "Server" refers to a computer system that stores collected data, analyzes it, and distributes various instructions and signals based on the results.

[1313] A "database" refers to a large collection of data stored on a server, which allows for efficient management and retrieval of the data.

[1314] "Operational Data" refers to information relating to the operational status and performance of a factory robot.

[1315] "AI algorithm" refers to the computational procedures of artificial intelligence that analyze movement patterns based on collected movement data and infer specific behaviors and needs.

[1316] "Behavioral patterns" refer to information that indicates a tendency toward a particular behavior or action based on collected data.

[1317] "Custom Instructional Materials" means educational content created to provide individualized learning or response scenarios based on specific operational needs.

[1318] "VR goggles" refers to a headset that allows users to visually experience a virtual reality environment.

[1319] "Signal" refers to a signal sent from a terminal to convey specific instructions or information.

[1320] "Home learning app" refers to software designed to enable workers to continue learning at home.

[1321] This invention provides a system for effectively monitoring and managing the operation of factory robots. This system combines AI and VR technologies, and in particular integrates an emotion engine to optimally operate and train robots according to their operating status.

[1322] User registration and information management

[1323] 1. User Registration

[1324] A user accesses the system and enters information into a registration form using a web browser or mobile app, then submits it. The device receives the information and sends it to the server, where it is validated and stored in a database.

[1325] Using behavioral data and emotion engines

[1326] 2. Collecting motion and emotion data

[1327] The terminal collects operational data from the factory robot, while the built-in emotion engine uses cameras and sensors to monitor the robot's operating status, for example, recognizing errors and overheating. The collected data is periodically sent to a server, which stores it in a database.

[1328] 3. Analysis of movement patterns and integration of emotions

[1329] The server runs AI algorithms on the behavioral and emotional data to analyze behavioral patterns and emotional fluctuations. For example, it can identify repeated errors in a particular task or frequent overheating. The analysis results are used to generate custom teaching materials tailored to the robot's behavioral needs and state.

[1330] 4. Creating custom learning materials

[1331] Based on the analysis results, the server generates custom teaching materials tailored to the operating needs and status of each robot, including guide videos to avoid errors and cool-down scenarios to prevent overheating. The generated custom teaching materials are distributed in a format that allows administrators to review and modify them as necessary.

[1332] VR scenario settings and emotional responses

[1333] 5. Setting up the VR scenario

[1334] The user (administrator) opens the VR scenario setting page using a web browser or application and selects the VR scenario to be used for the next task. The server receives the setting and sends a VR signal to each robot's terminal.

[1335] 6. VR Scenario Operation

[1336] The terminal sends signals to each robot's VR goggles to execute specific learning scenarios. Emotional data is analyzed in real time, and the scenarios are dynamically changed as needed. For example, if an error occurs, new guidelines for error avoidance are presented.

[1337] Support for home learning

[1338] 7. Use home learning apps

[1339] The device launches a dedicated home learning application, providing a learning environment for workers at home. This includes custom learning materials and progress management functions. The device sends home learning progress data and behavior data to a server. The server stores the received data in a database and monitors it in real time.

[1340] 8. Providing Feedback

[1341] The server analyzes the home learning progress and operation data and provides the administrator with feedback on the robot's operation status and progress, including the progress of the operation and suggestions for necessary supplementary learning.

[1342] Specific examples

[1343] For example, if a factory robot A frequently makes errors during a specific task, the system will simultaneously collect its behavioral data and emotional state. When an error is detected, the emotion engine will recognize it, and the server will generate custom educational materials (guide videos) to help robot A avoid the error and present them to the robot through VR goggles.

[1344] Prompt Sentence Examples

[1345] "I would like to build an education and management system that utilizes the emotion engine of factory robots. This system will monitor the robot's operating status in real time and execute VR scenarios as needed. The hardware to be used will include cameras and sensors, and the software will use OpenCV and TensorFlow. Please tell me the specific configuration and implementation steps."

[1346] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1347] Step 1:

[1348] A user accesses the system and uses a web browser or mobile app to enter information into a registration form and submit it. The entered data (user information, robot information, etc.) is sent from the device to the server. The server validates the received data and stores the data that is confirmed to be valid in the database. Input: Information entered into the registration form. Output: User information stored in the database.

[1349] Step 2:

[1350] The terminal collects the operation data and emotion data of the factory robot. Specifically, the camera captures images of the robot and the sensor monitors its operation status. The collected data is sent from the terminal to the server. Input: Camera images, sensor data. Output: Operation data and emotion data sent to the server.

[1351] Step 3:

[1352] The server stores the received movement and emotion data in a database. An AI algorithm is run based on the stored data to analyze movement patterns and emotional fluctuations. OpenCV and TensorFlow are used for this analysis. Input: Movement and emotion data in the database. Output: Analysis results (movement patterns and emotional fluctuations).

[1353] Step 4:

[1354] The server uses the analysis results to identify the operational needs of each robot. Custom teaching materials are generated based on the identified operational needs. For example, guide videos to avoid errors and cool-down scenarios to prevent overheating are prepared. Input: Analysis results. Output: Custom teaching materials (guide videos and cool-down scenarios).

[1355] Step 5:

[1356] The user sets the VR scenario to be used in the next task using a web browser or application. The server receives the settings and sends the corresponding VR signal to each robot's terminal. Input: Setting the VR scenario. Output: Sending the VR signal to the terminal.

[1357] Step 6:

[1358] The terminal sends signals to the VR goggles of each robot to execute a specific learning scenario. It analyzes the behavioral data in real time and dynamically changes the scenario as needed. For example, if an error occurs, it will present new guidelines to avoid the error. Input: VR signal. Output: The learning scenario to be executed.

[1359] Step 7:

[1360] The terminal launches a dedicated home learning application, providing an environment in which the worker can study at home. Home learning progress data and operation data are sent from the terminal to the server. Input: Home learning progress data. Output: Progress data sent to the server.

[1361] Step 8:

[1362] The server analyzes the home learning progress data and operation data in real time and provides feedback to the administrator. The feedback includes comments on the robot's operation status and progress, as well as suggestions for necessary supplementary learning. Input: Home learning progress data and operation data. Output: Feedback to the administrator.

[1363] 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.

[1364] 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.

[1365] 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.

[1366] [Fourth embodiment]

[1367] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1368] 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.

[1369] 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).

[1370] 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.

[1371] 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.

[1372] 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).

[1373] 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.

[1374] 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.

[1375] 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.

[1376] 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.

[1377] 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.

[1378] 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.

[1379] 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."

[1380] The present invention is a next-generation education system that combines AI and VR technologies to provide an effective learning environment, reducing the burden on parents and teachers while meeting the learning needs of individual students. Specific embodiments of the system are described below.

[1381] User registration and information management

[1382] 1. User Registration

[1383] The user (parent) accesses the system using a web browser or mobile application, enters the required information (parent information, child's basic information, contact information, etc.) into the designated registration form, and submits it.

[1384] The device sends the input data to the server, which includes basic information about the parent and child.

[1385] The server receives the submitted data, validates it, and then saves it to the database.

[1386] Analyzing learning patterns and generating custom learning materials

[1387] 2. Collecting training data

[1388] The device collects students' daily learning progress data, test results, assignment submission status, etc. This data is periodically sent to the server.

[1389] The server receives the data and stores it in a database, where it performs the necessary integrity checks before storing it.

[1390] 3. Analysis of learning patterns

[1391] The server runs AI algorithms on the stored learning data to analyze each student's learning patterns, such as identifying repeated mistakes on specific questions, learning speed, and level of understanding.

[1392] The results of the analysis are used to identify individual student learning needs and are stored in a database.

[1393] 4. Creating custom learning materials

[1394] Based on the analysis, the server generates custom learning materials tailored to each student's learning needs, including exercises, video lectures, and interactive quizzes.

[1395] The custom learning materials are then delivered to teachers in a format that they can review and modify as needed.

[1396] Setting up and operating a VR classroom

[1397] 5. VR classroom setup

[1398] The user (teacher) opens the VR classroom settings page using a web browser or application and selects the VR scenario to be used in the next lesson, such as a historical simulation or a 3D virtual science experiment.

[1399] The server receives the settings and sends the corresponding VR signal to each student's device.

[1400] 6. VR classroom operation

[1401] The device sends signals to each student's VR goggles to execute specific learning scenarios, allowing students to wear the goggles and deepen their understanding through an immersive learning experience.

[1402] Support for home learning

[1403] 7. Use home learning apps

[1404] The device runs a dedicated home learning application, providing students with a learning environment at home, including custom learning materials, homework assignments, and progress tracking.

[1405] The device sends data on the progress of home learning to a server, which stores the received data in a database and monitors it in real time.

[1406] 8. Providing Feedback

[1407] The server analyzes the progress of home learning and provides parents with feedback on their child's learning, including progress, level of understanding, and suggestions for necessary supplementary learning.

[1408] Specific examples

[1409] For example, if Student A is struggling with adding and subtracting fractions in math class, the system will identify this information through learning pattern analysis. The server will generate custom learning materials with appropriate practice problems, which the teacher will review and then distribute to Student A's device. Furthermore, a home learning app will suggest related practice problems and video courses so that the student can continue their learning at home.

[1410] In chemistry classes, VR classrooms are used to conduct 3D simulations to deepen understanding of molecular structures. The teacher sets up the VR scenario, and the server distributes corresponding instructions to the devices. Students put on VR goggles and observe and manipulate molecular structures in the virtual environment, thereby gaining a deeper understanding.

[1411] In this way, the present invention provides a system that responds to the diverse needs of users (parents, teachers, students) and provides effective learning support by utilizing advanced technology.

[1412] The processing flow will be explained below.

[1413] Step 1:

[1414] The user (parent) accesses the system and opens the registration form using a web browser or mobile app. The user enters the required information (parent information, child basic information, contact information, etc.) into the form and presses the submit button.

[1415] Step 2:

[1416] The terminal generates and transmits an HTTP request for transmitting the data input by the user to the server.

[1417] Step 3:

[1418] The server validates the received data and, if there are no problems, saves it in the database. After saving, the server generates a notification to the user that registration is complete and sends it to the terminal.

[1419] Step 4:

[1420] The device collects students' daily learning progress data, test results, assignment submission status, etc. This data is sent to the server at specified intervals.

[1421] Step 5:

[1422] The server receives the collected learning data, checks its integrity, and stores it in a database. The server then runs AI algorithms to analyze learning patterns and identify each student's learning needs.

[1423] Step 6:

[1424] The server uses the analysis to generate custom learning materials tailored to each student's learning needs, including practice questions, video lectures, and interactive quizzes.

[1425] Step 7:

[1426] The server generates custom teaching materials and sends them to the teacher's account, where the teacher can review and make corrections as needed.

[1427] Step 8:

[1428] The device downloads the final version of the custom learning material to the student's device and sends a reminder to start learning.

[1429] Step 9:

[1430] The user (teacher) uses a web browser or application to set up the VR classroom scenario for the next lesson, including selecting the learning subject and customizing the scenario.

[1431] Step 10:

[1432] The server receives the configured VR scenario and sends corresponding signals to each student's device, which then executes the specific learning scenario through the VR goggles.

[1433] Step 11:

[1434] The user (student) uses the device to input and send a question to the AI ​​teacher assistant, which then sends the question data to the server.

[1435] Step 12:

[1436] The server analyzes the received question and generates an appropriate answer using an AI model. The generated answer is sent back to the device and displayed to the user (student).

[1437] Step 13:

[1438] The device launches a dedicated home learning application and notifies the student, and home learning progress data is periodically sent to the server.

[1439] Step 14:

[1440] The server monitors the progress of home learning and provides parents with feedback based on the analysis results, including progress, level of understanding, and suggestions for necessary supplementary learning.

[1441] Step 15:

[1442] Users (parents) receive feedback provided by the server and improve the learning environment or provide additional support. This process is repeated to support continuous learning and maximize educational effectiveness.

[1443] Example 1

[1444] 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."

[1445] In today's educational environment, there is a need to address the learning needs of individual students while reducing the burden on parents and teachers. In particular, it is necessary to provide learning materials tailored to each student's unique learning pace and level of understanding, to monitor learning progress in real time, and to support home learning. However, systems that achieve these goals are still insufficient. Providing an effective learning environment using virtual reality technology is also an important challenge.

[1446] 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.

[1447] In this invention, the server includes: a means for a user to input and transmit information; a means for a terminal to transmit transmitted data to the server; a means for the server to store the received data in a database; a means for the terminal to collect learning data and transmit it to the server; a means for the server to execute an AI algorithm based on the stored data and analyze learning patterns; a means for the server to identify individual learning needs from the analysis results; a means for the server to generate customized learning materials based on the needs identified by the server; a means for a teacher to confirm and modify the customized learning materials generated by the server; a means for the terminal to transmit a signal to a virtual reality device corresponding to the student and execute a specific learning scenario; and a means for the terminal to launch a home learning program and allow the student to study at home. This makes it possible to provide customized learning materials according to the learning needs of each student, manage learning progress in real time, support home learning, and provide an effective learning environment using virtual reality technology.

[1448] "User" is any entity that inputs and submits information to the system, and includes students, parents, and teachers.

[1449] A "terminal" is a computer device used by a user that has the function of inputting, sending, and receiving information.

[1450] The "server" is a central computing device that receives data sent from terminals via a network, stores it in a database, and runs AI algorithms to analyze learning patterns.

[1451] A "database" is a system for systematically storing and managing data received by a server.

[1452] An "AI algorithm" is a mathematical model that uses artificial intelligence to analyze learning data and identify individual learning patterns and needs.

[1453] "Learning patterns" refer to the learning tendencies and characteristics that are generated based on a student's learning behavior and level of understanding.

[1454] "Customized learning materials" are learning materials that are optimized for individual students based on an analysis of their learning patterns, and may include exercises, video lectures, interactive quizzes, etc.

[1455] A "virtual reality device" is a device that uses virtual reality technology to provide a learning environment that is as immersive as reality, and includes VR goggles and the like.

[1456] A "home learning program" is a software application that allows students to continue their learning at home, providing access to custom learning materials, progress tracking, and learning support.

[1457] A "learning scenario" is a sequence of specific educational materials or content that is executed by a virtual reality device, such as a historical simulation or a 3D model of a scientific experiment.

[1458] "Progress" is data that shows the results and progress a student has made in the learning process.

[1459] The present invention is an educational system that combines AI technology and virtual reality technology to provide an effective learning environment. This system aims to provide next-generation educational support that reduces the burden on parents and teachers and meets the learning needs of individual students. Specific embodiments of the system are described below.

[1460] User registration and information management

[1461] 1. User Registration

[1462] The user (parent) accesses the system using a web browser or mobile application, enters the required information in the designated registration form, and submits it. Web browsers such as Google Chrome or Firefox can be used, and mobile applications compatible with Android or iOS can be used.

[1463] The terminal sends the input data to the server, using the SSL / TLS protocol to ensure the security of the data transmission.

[1464] The server receives the submitted data, validates it, and then stores it in a relational database, such as MySQL or PostgreSQL.

[1465] Analyzing learning patterns and generating custom learning materials

[1466] 2. Collecting training data

[1467] The device collects students' daily learning progress data, test results, assignment submission status, etc., and periodically sends them to a server. Sensors and input devices are used to collect the data.

[1468] The server receives the data and stores it in the database. SQL transaction processing is used to check the integrity of the data.

[1469] 3. Analysis of learning patterns

[1470] The server runs AI algorithms based on the stored learning data to analyze each student's learning patterns, using machine learning libraries such as Python's scikit-learn and TensorFlow.

[1471] The analysis results are stored in a database to identify each student's individual learning needs.

[1472] 4. Creating custom learning materials

[1473] Based on the analysis results, the server uses a generative AI model to generate custom learning materials optimized for each student, including practice questions, video lectures, and interactive quizzes.

[1474] The generated custom teaching materials are delivered to the teacher's device, where they can be checked and corrected.

[1475] Setting up and operating a virtual reality classroom

[1476] 5. VR classroom setup

[1477] The user (teacher) opens the VR classroom settings page using a web browser or application and selects the VR scenario to be used in the next lesson, such as a historical simulation or a 3D virtual science experiment.

[1478] The server receives the settings and sends a signal to the virtual reality device corresponding to each student's device.

[1479] 6. VR classroom operation

[1480] The device sends signals to each student's virtual reality device, such as an Oculus Rift or HTC Vive, to execute a specific learning scenario.

[1481] Students can wear virtual reality devices and deepen their understanding through an immersive learning experience.

[1482] Support for home learning

[1483] 7. Use of home learning programs

[1484] The device runs a dedicated home learning program, providing students with a learning environment at home, including custom learning materials, homework assignments, and progress tracking.

[1485] The device sends home learning progress data to a server, which monitors the data in real time.

[1486] 8. Providing Feedback

[1487] The server analyzes the progress of home learning and provides parents with feedback on their child's learning, including progress, level of understanding, and suggestions for necessary supplementary learning.

[1488] Specific examples

[1489] For example, if Student A struggles with adding and subtracting fractions in math class, the system will identify this information through learning pattern analysis. The server will generate custom learning materials with appropriate practice problems, which the teacher will review and then distribute to Student A's device. Additionally, the home learning program will suggest related practice problems and video courses.

[1490] In chemistry classes, virtual reality technology is used to conduct 3D simulations to deepen understanding of molecular structures. The teacher sets up a virtual reality scenario, and a server distributes corresponding instructions to the devices. Students wear the virtual reality device and can observe and manipulate molecular structures in the virtual environment to gain a deeper understanding.

[1491] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1492] Step 1:

[1493] The user (parent) enters the required information into the registration form using a web browser or mobile application and submits it. The input includes basic information about the parent, basic information about the child, contact information, etc. The input data includes basic information about the parent and child, and is submitted by the device.

[1494] Step 2:

[1495] The terminal sends the data entered by the user to the server. The HTTP POST request is used for transmission, and the SSL / TLS protocol is used to ensure communication security. The input of the transmitted data includes the information entered by the user, and the output is the data transferred to the server.

[1496] Step 3:

[1497] The server analyzes the data it receives and performs validation. Validation includes checking for unfilled fields, type checks, and data consistency checks. The input to the validation process is the data sent from the terminal, and the output is the result of checking the validity of the data. If the data is normal, it is saved in the database.

[1498] Step 4:

[1499] The server stores the data that is confirmed to be valid in a database. A relational database such as MySQL or PostgreSQL is used here. The input is the data that has passed validation, and the output is the result of saving it to the database.

[1500] Step 5:

[1501] The device collects learning data such as students' daily learning progress, test results, and assignment submission status. Sensors and input devices are used to collect the data, which is then stored on the device. The input includes information about students' learning activities, and the output includes the collected learning data.

[1502] Step 6:

[1503] The device sends the collected learning data to the server. The transmission is also performed using an HTTP POST request, and communication security is ensured by the SSL / TLS protocol. The input is the learning data stored on the device, and the output is the learning data sent to the server.

[1504] Step 7:

[1505] The server stores the received training data in a database. At this time, SQL transaction processing is used to check consistency. The input is the training data sent from the device, and the output includes the results saved in the database.

[1506] Step 8:

[1507] The server runs an AI algorithm based on the learning data stored in the database to analyze each student's learning patterns. The analysis uses machine learning libraries such as scikit-learn and TensorFlow. The input is the stored learning data, and the output contains the analysis results of the learning patterns.

[1508] Step 9:

[1509] The server identifies individual student learning needs based on the analysis of learning patterns, using the results of an AI algorithm, with the analysis results as input and individual learning needs as output.

[1510] Step 10:

[1511] The server generates custom learning materials based on identified learning needs using a generative AI model, including exercises, video courses, and interactive quizzes. The input is the learning needs data, and the output is the generated custom learning materials.

[1512] Step 11:

[1513] The server delivers the generated custom teaching materials to the teacher's device, where the teacher reviews the teaching materials and makes corrections as necessary. The input is the generated custom teaching materials, and the output is the teacher-reviewed teaching materials.

[1514] Step 12:

[1515] The user (teacher) opens the virtual reality classroom setting page using a web browser or application and selects a scenario for the VR classroom. The setting includes the scenario to be used in the next lesson. The input is the teacher's setting information, and the output is the selected VR scenario.

[1516] Step 13:

[1517] The server receives the settings and sends signals to the virtual reality devices corresponding to each student's device. The input contains the selected VR scenario, and the output contains the signals to each device.

[1518] Step 14:

[1519] The device sends signals to the student's corresponding virtual reality device to execute a specific learning scenario. The specified scenario file is played on a device such as an Oculus Rift or HTC Vive. The input is instructions from the server, and the output is the VR scenario to be executed.

[1520] Step 15:

[1521] The device runs a home learning program, providing an environment where students can continue their studies at home, including custom learning materials, homework assignments, and progress management functions. The input is student learning information, and the output is accumulated home learning progress data.

[1522] Step 16:

[1523] The device sends home learning progress data to the server, which monitors the data in real time and receives it. The input includes home learning progress data, and the output includes data stored on the server.

[1524] Step 17:

[1525] The server analyzes the progress of home learning and provides feedback to parents, including the progress and understanding of the child and suggestions for supplementary learning. The input is home learning data, and the output is the generated feedback.

[1526] Specific examples of implementation

[1527] For example, if Student A struggles with adding and subtracting fractions in math, the system can identify their learning patterns and generate custom learning materials, including appropriate practice problems and video tutorials, on the server, which can then be sent to Student A's device after being reviewed by the teacher. A home learning program can be similarly configured to provide relevant learning materials for Student A to practice at home. In a chemistry class, the teacher can set up a virtual reality scenario, and the server can send instructions to each student's virtual reality device. Students can then use the virtual reality device to deepen their understanding of molecular structures.

[1528] (Application example 1)

[1529] 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."

[1530] Traditional educational systems place a heavy burden on parents and teachers, and are difficult to respond to the individual learning needs of each student. Even when virtual reality technology is utilized, educational content is often uniform and learning data analysis is insufficient. This makes it difficult to provide an effective learning environment.

[1531] 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.

[1532] In this invention, the server includes: means for a user to input and transmit information; means for a terminal to transmit transmitted data to the server; means for the server to store the received data in a database; means for the terminal to collect learning data and transmit it to the server; means for the server to run an AI algorithm based on the stored data and analyze learning patterns; means for the server to identify individual learning needs from the analysis results; means for the server to generate customized learning materials based on the needs identified by the server; means for the terminal to send a signal to a virtual reality device corresponding to the student and execute a specific learning scenario; means for the terminal to launch a home learning app and the student to study at home; means for an instructor to select and set lesson content and select a virtual reality scenario; means for collecting learning progress data, comprehension tests, and interaction data in the virtual reality environment; means for analyzing the collected data and running an AI model to generate appropriate customized learning materials; and means for providing the generated customized learning materials and learning progress information to instructors and parents. This reduces the burden on parents and teachers and makes it possible to provide customized learning materials that appropriately meet the learning needs of each student.

[1533] "User" refers to anyone who utilizes the system to input information and submit education-related data.

[1534] "Terminal" refers to a hardware or software device for transmitting data entered by a user to a server and for collecting learning data.

[1535] "Server" refers to a computer system that stores received data in a database and runs AI algorithms to analyze learning patterns.

[1536] A "database" is an information management system that organizes and stores data received by the server.

[1537] An "AI algorithm" is an artificial intelligence computational method that analyzes students' learning patterns based on collected learning data and generates appropriate custom teaching materials.

[1538] "Learning patterns" are the results of data analysis that include individual students' learning progress and problem-solving tendencies.

[1539] "Customized Learning Materials" refers to specialized educational content created to meet the learning needs of individual students.

[1540] A "virtual reality device" is a device that allows students to have an interactive learning experience in a VR environment.

[1541] "Virtual reality scenario" refers to a specific learning content or environment that is executed using a virtual reality device.

[1542] A "home learning app" is a software application that provides students with the ability to study at home.

[1543] "Instructor" refers to an educator who is responsible for managing educational content and running classes.

[1544] "Interaction data" refers to data about the operations and behavior of students in a virtual reality environment.

[1545] "Learning data" refers to information such as a student's daily learning progress, test results, and assignment submission status.

[1546] "Study progress information" is data that indicates how far a student has progressed in their studies.

[1547] "Progress Data" refers to information that records how learning is progressing.

[1548] The system of the present invention combines AI technology and virtual reality (VR) technology to provide an effective learning environment. The system aims to address the learning needs of individual students while reducing the burden on parents and teachers. Specific embodiments of the system are described below.

[1549] User registration and information management

[1550] First, a user (parent or teacher) accesses the system using a web browser or mobile application, enters the required information in a designated registration form, and submits it. The device then sends the input data to the server, which then receives and stores it in a database. This data includes basic information about the parent and child, as well as contact details.

[1551] Analyzing learning patterns and generating custom learning materials

[1552] The device then collects the student's daily learning progress data, test results, assignment submission status, etc., and periodically sends them to the server. The server stores the received data in a database, performs necessary consistency checks, and then runs AI algorithms. Based on the learning data, the server analyzes each student's learning patterns, analysing repeated mistakes on specific questions, learning speed, and level of understanding. Based on this, the server identifies each student's learning needs and generates optimal custom learning materials. These custom materials include practice questions, video lectures, and interactive quizzes.

[1553] Setting up and operating a VR classroom

[1554] Teachers can open the VR classroom settings page using a web browser or application and select the VR scenario to be used in the next lesson. The selected information is sent to the server, which then sends a corresponding VR signal to each student's device. When students put on the VR goggles, the device receives the signal and executes the specific learning scenario, allowing students to have an immersive learning experience in the VR environment.

[1555] Support for home learning

[1556] The device runs a dedicated home learning application, providing a learning environment for students at home. This application includes custom learning materials, homework assignments, and progress management functions. The device sends home learning progress data to a server, which monitors the received data in real time. The server then analyzes the home learning progress and provides parents with feedback on their child's learning status, including their learning progress and level of understanding, as well as suggestions for necessary supplementary learning.

[1557] Specific Applications

[1558] For example, if Student A is struggling with adding and subtracting fractions in math class, the server identifies this information through learning pattern analysis and generates custom learning materials including appropriate practice problems. After the teacher verifies this, the server distributes it to the student's device. Furthermore, a home learning app containing related practice problems and video courses is provided so that the student can continue their learning at home. In chemistry class, a VR classroom is used to conduct a 3D simulation to deepen understanding of molecular structures. The teacher sets up the VR scenario, and the server distributes corresponding instructions to the device. Students put on VR goggles and observe and manipulate molecular structures in the virtual environment, thereby gaining a deeper understanding.

[1559] The system's program uses Python and its libraries (Pandas, Scikit-Learn) to collect and analyze learning data. The server uses sensor-equipped VR goggles and a data server to collect data on students' learning progress, comprehension, and interaction, and generates appropriate custom learning materials based on that data. As a concrete example, in a history class, progress data is collected by exploring a virtual battlefield and answering quizzes about the battlefield, and the following example prompts are analyzed by the AI ​​model:

[1560] We provide learning data on the Warring States period in history. Based on student progress data and quiz results, we analyze and generate the next custom learning materials to provide.

[1561] Example data:

[1562] Student ID: 12345

[1563] Progress stats: 75%

[1564] Quiz success rate: 60%

[1565] Interaction Level: High

[1566] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1567] Step 1:

[1568] The user enters information and submits it.

[1569] Input: Basic information entered by a parent or teacher using a web browser or mobile application (parent information, child basic information, contact details, etc.).

[1570] Data processing and data calculation: The terminal formats this information and configures it as transmission data.

[1571] Output: The configured data to send.

[1572] Specific action: The user completes the form and clicks the submit button.

[1573] Step 2:

[1574] The terminal transmits the transmission data to the server.

[1575] Input: The transmission data obtained in step 1.

[1576] Data processing and data calculation: The terminal sends the data to the server using the appropriate protocol.

[1577] Output: The data sent to the server.

[1578] Specific operation: The device sends data to the server's receiving API.

[1579] Step 3:

[1580] The server stores the received data in a database.

[1581] Input: The transmission data received in step 2.

[1582] Data processing and data calculation: The server validates the data received and converts it into a format suitable for writing to the database.

[1583] Output: Basic user information stored in the database.

[1584] Specific behavior: The server performs data validation and writes the properly formatted data to the database.

[1585] Step 4:

[1586] The device collects learning data and sends it to the server.

[1587] Input: Learning data such as daily learning progress data, test results, assignment submission status, etc.

[1588] Data processing and data calculation: The data collected by the terminal is formatted and used as transmission data.

[1589] Output: The training data submitted.

[1590] Specific operation: The device continues to record learning data through the application and automatically transmits it to the server.

[1591] Step 5:

[1592] The server runs AI algorithms based on the stored data and analyzes learning patterns.

[1593] Input: Training data stored in a database.

[1594] Data processing and calculation: Analyzing learning patterns using AI algorithms (e.g., machine learning models).

[1595] Output: Analysis of each student's learning patterns.

[1596] Specific operation: The server periodically runs the AI ​​algorithm and performs analysis based on the latest learning data.

[1597] Step 6:

[1598] The server uses the analysis results to identify individual learning needs.

[1599] Input: Analysis results from step 5.

[1600] Data processing and calculation: By looking at the analysis results, we identify each student's weaknesses and areas that need improvement.

[1601] Output: Data about each student's learning needs.

[1602] Specific actions: The server evaluates the analysis results in detail and lists necessary reinforcement points and learning needs.

[1603] Step 7:

[1604] The server generates custom learning materials based on identified needs.

[1605] Input: Learning needs obtained in step 6.

[1606] Data processing and calculation: Using AI algorithms, we generate the most suitable educational content for each student. We extract relevant content from the database and create custom learning materials.

[1607] Output: The generated custom teaching materials.

[1608] Specific behavior: The server generates custom teaching materials and makes them available for teachers to review.

[1609] Step 8:

[1610] The device sends a signal to the student's corresponding virtual reality device to execute a specific learning scenario.

[1611] Input: Custom learning materials generated in step 7.

[1612] Data processing and calculation: The terminal generates a signal and sends it to the virtual reality device, which then loads a specific learning scenario.

[1613] Output: The learning scenario loaded into the VR device.

[1614] Specific actions: Students put on VR goggles and begin learning in the specified scenario.

[1615] Step 9:

[1616] The device launches the home study app, and the student begins their home study.

[1617] Input: Custom learning materials and learning activities for home learning.

[1618] Data processing and calculation: The home learning app displays learning materials and records students' progress in real time.

[1619] Output: Home learning progress data.

[1620] What it does: Students use the app at home to study and automatically send progress and understanding data.

[1621] Step 10:

[1622] The server monitors the progress of home learning and provides feedback to parents.

[1623] Input: Step 9 home learning progress data.

[1624] Data processing and calculation: The server analyzes the progress data and generates reports in real time.

[1625] Output: Feedback report provided to parents.

[1626] What it does: Parents view feedback reports on their child's learning progress and understanding through a web portal or app.

[1627] 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.

[1628] The present invention integrates an emotion engine into an educational system that combines AI and VR technologies, optimizing the learning experience by taking into account the emotional state of the user (student). Specific embodiments of the present invention are described below.

[1629] User registration and information management

[1630] 1. User Registration

[1631] The user (parent) accesses the system and opens the registration form using a web browser or mobile app. The user enters the required information (parent information, child basic information, contact information, etc.) and presses the submit button.

[1632] The device sends the input data to the server, which includes basic information about the parent and child.

[1633] The server receives the submitted data, validates it, and then saves it to the database.

[1634] Analyzing learning patterns and using sentiment engines

[1635] 2. Collecting training data and emotion data

[1636] The device collects students' daily learning progress data, test results, assignment submission status, etc. At the same time, the device's built-in emotion engine uses cameras and sensors to analyze the students' facial expressions and voices and recognize their emotional state.

[1637] The training data and emotion data are periodically sent to the server.

[1638] The server receives the data, checks its integrity, and then stores it in a database.

[1639] 3. Learning pattern analysis and emotion integration

[1640] The server runs an AI algorithm based on learning data and emotional data to analyze each student's learning patterns and emotional fluctuations, identifying, for example, repeated mistakes on a particular question or extreme emotional fluctuations.

[1641] The analysis results are used to generate custom learning materials tailored to students' learning needs and emotional state.

[1642] 4. Creating custom learning materials

[1643] Based on the analysis, the server generates custom learning materials tailored to each student's learning needs and emotional state, such as relaxing videos to reduce stress or interactive quizzes to deepen understanding.

[1644] The custom learning materials are then delivered to teachers in a format that they can review and modify as needed.

[1645] VR classroom settings and emotional responses

[1646] 5. VR classroom setup

[1647] The user (teacher) opens the VR classroom settings page using a web browser or application and selects the VR scenario to be used in the next lesson, including selecting the learning subject and customizing the scenario.

[1648] The server receives the settings and sends the corresponding VR signal to each student's device.

[1649] 6. VR classroom operation

[1650] The device sends signals to each student's VR goggles to execute a specific learning scenario. User emotional data is analyzed in real time, and the learning scenario is dynamically changed as needed. For example, a student who is losing interest can be presented with new, more interesting content.

[1651] Support for home learning

[1652] 7. Use home learning apps

[1653] The device runs a dedicated home learning application, providing students with a learning environment at home, including custom learning materials, homework assignments, and progress tracking.

[1654] The device sends data on home learning progress and emotion to a server, which stores the received data in a database and monitors it in real time.

[1655] 8. Providing Feedback

[1656] The server analyzes the home learning progress and emotional data and provides parents with feedback on their child's learning and emotional state, including progress, comprehension, and suggestions for necessary supplementary learning.

[1657] Specific examples

[1658] For example, when Student B is doing homework in English class, the system simultaneously collects his / her emotional state and learning data. If Student B is feeling stressed, the emotion engine will recognize this and the server will incorporate relaxing music and simple exercises into the custom learning materials, allowing Student B to calm down and focus on their studies.

[1659] In addition, virtual experiments are conducted in science classes using the VR classroom. The user (teacher) sets up the VR scenario, and the server monitors emotional data in real time. When students start to lose interest during the lesson, the system automatically presents a new interactive experiment scenario to recapture their interest.

[1660] Thus, the present invention provides a system that utilizes real-time emotional data to optimize the learning experience and maximize student learning effectiveness.

[1661] The processing flow will be explained below.

[1662] Step 1:

[1663] The user (parent) accesses the system and opens the registration form using a web browser or mobile application. The user enters the required information (parent information, child basic information, contact information, etc.) and presses the submit button.

[1664] Step 2:

[1665] The terminal generates and transmits an HTTP request for transmitting the data input by the user to the server.

[1666] Step 3:

[1667] The server validates the received data and, if there are no problems, saves it in the database. After saving, the server generates a notification to the user that registration is complete and sends it to the terminal.

[1668] Step 4:

[1669] The device collects students' daily learning progress data, test results, assignment submission status, etc. At the same time, the device's built-in emotion engine uses cameras and sensors to analyze the student's facial expressions and voice to recognize their emotional state.

[1670] Step 5:

[1671] The terminal generates and transmits a request for periodically transmitting the learning data and emotion data to the server.

[1672] Step 6:

[1673] The server checks the consistency of the received learning data and emotion data, and if there are no problems, stores it in the database.

[1674] Step 7:

[1675] The server runs AI algorithms on the stored data to analyze learning patterns and emotional states, identifying, for example, repeated mistakes on a particular question or periods of intense emotional activity.

[1676] Step 8:

[1677] The server uses the analysis to identify individual learning needs and emotional states, and then generates custom learning materials based on those needs, such as relaxing videos to reduce stress or interactive quizzes to deepen understanding.

[1678] Step 9:

[1679] The server sends the generated custom teaching materials to the teacher's account, where the teacher can review and correct them if necessary.

[1680] Step 10:

[1681] The device downloads the final version of the custom learning material to the student's device and sends a reminder to start learning.

[1682] Step 11:

[1683] The user (teacher) uses a web browser or application to set up the VR classroom scenario for the next lesson, including selecting the learning subject and customizing the scenario.

[1684] Step 12:

[1685] The server receives the configured VR scenario and sends corresponding signals to each student's device, which then executes the specific learning scenario through the VR goggles.

[1686] Step 13:

[1687] The device sends signals to each student's VR goggles to execute a specific learning scenario, during which an emotion engine monitors the student's emotions in real time and dynamically changes the scenario as needed.

[1688] Step 14:

[1689] If a user (student) feels stressed while studying, the emotion engine will recognize it and the server will automatically present new interactive content or relaxing videos.

[1690] Step 15:

[1691] The device runs a dedicated home learning application, providing students with a learning environment at home, including custom learning materials, homework assignments, and progress tracking.

[1692] Step 16:

[1693] The terminal generates and transmits a request for periodically transmitting home learning progress data and emotion data to the server.

[1694] Step 17:

[1695] The server monitors and analyzes home learning progress and emotional data, providing parents with feedback on their child's learning progress and emotional state.

[1696] Step 18:

[1697] The feedback generated by the server includes information on the child's progress and understanding, as well as suggestions for supplementary learning. The user (parent) receives this feedback and can improve the learning environment or provide additional support.

[1698] Example 2

[1699] 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."

[1700] Conventional educational systems are unable to simultaneously consider students' learning needs and emotional state, making it difficult to provide an individually optimized learning experience. Furthermore, because feedback based on learning progress and emotional state is not provided in real time, parents and educators are unable to accurately grasp students' situations. To address these issues, a new educational system was needed that maximizes students' learning outcomes and optimizes their learning experience.

[1701] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input and transmit information; a means for the terminal to transmit transmission data to the processing unit; a means for the processing unit to store the received data in a storage device; a means for the terminal to collect learning data and emotional data and transmit it to the processing unit; a means for the processing unit to execute a generative AI model based on the stored data and analyze learning patterns and emotional fluctuations; a means for the processing unit to identify individual learning needs and emotional states from the analysis results; a means for the terminal to generate customized learning materials based on the needs and emotional states identified by the processing unit; a means for the terminal to send a signal to a visual device corresponding to the learner and execute a specific learning scenario; and a means for the terminal to launch a home learning application and allow the learner to study at home. This makes it possible to collect students' learning data and emotional data in real time and provide optimal customized learning materials based on the analysis results. Furthermore, feedback on home learning progress and emotional data can be provided to parents and educators, maximizing students' learning effectiveness.

[1702] "User" means any person or entity that uses the System and inputs and submits information.

[1703] A "terminal" is an electronic device used to accept user operations and communicate with a server.

[1704] A "processing unit" is a computer system that receives, stores, and analyzes the transmitted data.

[1705] A "storage device" is hardware for permanently or temporarily storing data received by a processing unit.

[1706] "Learning data" refers to information such as a student's daily learning progress, test results, and assignment submission status.

[1707] "Emotional data" refers to information about a student's emotional state obtained from their facial expressions and voice.

[1708] A "generative AI model" is an artificial intelligence algorithm that analyzes learning patterns and emotional fluctuations from collected data and generates custom teaching materials.

[1709] "Learning patterns" are information that indicates the trends in students' learning behavior and achievements.

[1710] "Emotional fluctuation" is information that indicates changes in a student's emotional state.

[1711] "Customized learning materials" are educational resources optimized for the learning needs and emotional state of individual students.

[1712] "Visual devices" are head-mounted displays and related devices used by students to engage in learning experiences in a virtual reality (VR) environment.

[1713] A "home learning application" is software that allows students to study at home.

[1714] "Parents" are those responsible for receiving feedback on students' academic progress and emotional state.

[1715] An "educator" is a user who sets the content and scenario of the VR classroom and supports students' learning.

[1716] The present invention relates to a system that takes into account the emotional state of the learner and optimizes the learning experience. The system provides a series of functions, including information input by the user (parent or educator), data transmission from the device, data processing by the server, and custom learning material generation using a generative AI model.

[1717] User registration and information management

[1718] The user (parent) accesses the system using a web browser or mobile app and enters the required information into the registration form. The entered information is sent from the device to the server, which validates the received data before saving it in a storage device. This is how user information is managed.

[1719] Collection of training data and emotion data

[1720] The device collects learners' daily learning progress data, test results, assignment submission status, etc. The device's built-in emotion engine also uses cameras and sensors to analyze the learner's facial expressions and voice to recognize their emotional state. The collected learning data and emotion data are periodically sent to a server, where they are checked for consistency and then stored in a storage device.

[1721] Analyzing learning patterns and generating custom learning materials

[1722] The server uses a generative AI model to analyze the stored learning data and emotional data, analyzing learning patterns and emotional fluctuations. This identifies individual learning needs and emotional states. Based on the analysis results, the server generates custom learning materials optimized for each learner. Examples of generative AI models used include TensorFlow and PyTorch.

[1723] Setting up and operating a VR classroom

[1724] The user (educator) opens the VR classroom settings page using a web browser or application and selects the VR scenario to be used in the next lesson. The settings are sent to the server, which then transmits the corresponding VR signal to each learner's device. The device then transmits the signal to each learner's VR goggles, which executes the specific learning scenario. Learners' emotional data is analyzed in real time, and the learning scenario is dynamically changed as needed.

[1725] Support for home learning

[1726] The device runs a dedicated home learning application, providing a learning environment for learners at home. This includes custom learning materials, homework, and progress management functions. The collected home learning progress and emotional data is sent to a server and stored in a storage device. The server monitors the home learning progress and emotional data and provides feedback to parents.

[1727] Specific examples

[1728] For example, when Student B is doing his / her homework in English class, the device simultaneously collects his / her emotional state and learning data. If Student B is feeling stressed, the emotion engine will recognize the stress and the server will provide customized learning materials such as relaxing music and simple exercises. This will allow Student B to concentrate on his / her studies in a calm manner.

[1729] In addition, virtual experiments are conducted in science classes using the VR classroom. The user (educator) sets up the VR scenario, and the server monitors emotional data in real time. If students start to lose interest during the lesson, the system automatically presents a new interactive experiment scenario to recapture their interest.

[1730] Prompt Sentence Examples

[1731] Please explain in detail the specifications of the web form that parents use to register their child's information and the process by which that information is transmitted to the system, taking into account the child's age.

[1732] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1733] Step 1:

[1734] The user (parent) opens a web browser or mobile app, accesses the registration form, and enters the required information (parent's name, basic information about the child, contact information, etc.). The entered information is sent from the device to the server by pressing the send button. The server receives a request for the registration form HTML page or app screen and sends it to the device. The input here is the web form data, and the output is the user information sent to the server.

[1735] Step 2:

[1736] The device encrypts the input data (basic information of the parent and child) and sends it to the server via HTTPS. The server validates the received data and confirms its accuracy before storing it in a storage device in the processing unit. The input of this step is the encrypted user information, and the output is the user information stored in the storage device.

[1737] Step 3:

[1738] The device collects learners' daily learning progress data, test results, assignment submission status, etc. The emotion engine built into the device also uses cameras and sensors to analyze the learners' facial expressions and voices to recognize their emotional state. The collected learning data and emotion data are periodically sent to the server. The input is the learning data and emotion data, and the output is this data sent to the server.

[1739] Step 4:

[1740] The server receives the transmitted training data and emotion data and checks their consistency. These data are stored in a storage device by a processing unit. The input is the training data and emotion data transmitted from the terminal, and the output is the data stored in the storage device.

[1741] Step 5:

[1742] The server uses a generative AI model to analyze the stored training data and emotional data. Specifically, it uses Python machine learning libraries (TensorFlow and PyTorch) to analyze the learner's learning patterns and emotional fluctuations. The input is the training data and emotional data stored in the storage device, and the output is the analysis results.

[1743] Step 6:

[1744] The server uses the analysis results to identify each learner's learning needs and emotional state. Optimal custom learning materials are generated based on the identified needs. These custom learning materials are generated using a generative AI model. The input is the analysis results, and the output is the generated custom learning materials.

[1745] Step 7:

[1746] The terminal receives the custom learning materials generated by the server and provides them to the learner. A specific learning scenario is sent to the visual device (VR goggles), and the learner learns in the VR environment. The input is the generated custom learning materials and VR signals, and the output is the learning content presented to the learner.

[1747] Step 8:

[1748] The device runs a dedicated home learning application, providing a learning environment for the learner at home. This includes custom learning materials, homework, and progress management functions. The device transmits home learning progress data and emotion data to the server. The input is the home learning progress data and emotion data, and the output is the data transmitted to the server.

[1749] Step 9:

[1750] The server monitors the progress and emotional data of home learning and provides feedback to parents. The feedback includes learning progress, level of understanding, and suggestions for necessary supplementary learning. The input is home learning progress data and emotional data, and the output is feedback provided to parents.

[1751] Prompt Sentence Examples

[1752] Please explain in detail the specifications of the web form that parents use to register their child's information and the process by which that information is transmitted to the system, taking into account the child's age.

[1753] (Application example 2)

[1754] 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."

[1755] Current factory robot management and training systems lack the means to closely monitor the robot's operating status in real time and automatically prompt appropriate actions. In particular, they lack a mechanism to provide custom scenarios that enable rapid response when a robot experiences an operational error or overheats. Furthermore, when training and managing robot operators, there is a need to provide optimal learning scenarios that take into account the user's emotional state. This can lead to problems such as reduced production efficiency and an increased risk of serious breakdowns.

[1756] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1757] In this invention, the server includes a means for executing an AI algorithm based on the operation data to analyze the operation patterns, a means for identifying individual operation needs from the analysis results, and a means for generating custom teaching materials based on the identified needs. This makes it possible to monitor the operation status of factory robots in real time and provide prompt problem solving and education using appropriate VR scenarios.

[1758] "User" refers to the person who operates the system to input and manage information.

[1759] "Terminal" refers to a device used to collect or transmit data, receive signals, etc.

[1760] "Server" refers to a computer system that stores collected data, analyzes it, and distributes various instructions and signals based on the results.

[1761] A "database" refers to a large collection of data stored on a server, which allows for efficient management and retrieval of the data.

[1762] "Operational Data" refers to information relating to the operational status and performance of a factory robot.

[1763] "AI algorithm" refers to the computational procedures of artificial intelligence that analyze movement patterns based on collected movement data and infer specific behaviors and needs.

[1764] "Behavioral patterns" refer to information that indicates a tendency toward a particular behavior or action based on collected data.

[1765] "Custom Instructional Materials" means educational content created to provide individualized learning or response scenarios based on specific operational needs.

[1766] "VR goggles" refers to a headset that allow...

Claims

1. a means for a user to input and submit information; A means for the terminal to transmit transmission data to the server; A means for the server to store the received data in a database; A means for the terminal to collect learning data and transmit it to a server; The server runs AI algorithms based on the stored data and analyzes learning patterns; A means for the server to identify individual learning needs from the analysis results; means for generating custom educational materials based on the server-identified needs; A means for the device to send a signal to the student's corresponding VR goggles to execute a specific learning scenario; The device launches the home learning app, allowing students to study at home. A system including:

2. 10. The system of claim 1, wherein the server further comprises means for monitoring home learning progress and providing feedback to parents.

3. The system according to claim 1, further comprising means for a user (teacher) to set the contents and scenario of the VR classroom and for the server to deliver instructions to the terminal.

Images