system

Abstract

We provide a system that allows all students to enjoy the right to receive cutting-edge education equally. [Solution] The specific processing unit 290 of the data processing device 12 in the system performs the following processes: a process of receiving authentication information entered by the user; a manual processing stage of performing authentication processing based on the authentication information; a process of acquiring the learner's past learning data; a process of recommending optimal learning content based on the learning data; a process of analyzing the learner's answer data and generating feedback; a process of tracking learning progress and generating a performance report; and a process of supporting communication between educators and learners in real time.

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] Disparities in the quality and access to education are particularly pronounced between regions and economic situations. In modern society, all students are expected to enjoy the right to equal access to cutting-edge education, but a lack of resources and infrastructure hinders this. The lack of a personalized learning environment also limits the effectiveness of education. In addition, the lack of real-time progress management and feedback on learning hinders efficient learning. [Means for solving the problem]

[0005] In order to solve the above-mentioned problems, the present invention provides a system including the following means: means for receiving authentication information entered by a user and performing authentication processing based on the information; means for acquiring the learner's past learning data and recommending optimal learning content; means for analyzing the learner's answer data and generating feedback in real time, and means for tracking learning progress and generating performance reports; and finally means for supporting real-time communication between educators and learners, thereby solving the above-mentioned problems.

[0006] "Authentication Information" means information for verifying a user's identity, and typically includes a user ID and password.

[0007] "Authentication processing" is a process for verifying the identity of a user based on the entered authentication information.

[0008] "Learning data" refers to records of what a learner has learned and their learning outcomes.

[0009] "Learning content" refers to the learning materials and educational resources provided to learners, including texts, videos, quizzes, etc.

[0010] "Feedback" refers to evaluations and comments on the answers given by the learner, providing information such as areas for improvement and correct / incorrect answers.

[0011] "Progress" is a status that indicates how much progress a learner has made toward the set learning goal.

[0012] A "performance report" is a report summarizing a learner's grades and learning progress.

[0013] "Means of communication" are means by which learners and educators exchange information in real time, including chat and video calls.

[0014] A "system" is a complex of multiple elements working together to achieve a specific purpose. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

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

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] This invention is a system that utilizes AI to reduce educational disparities and enable people to receive cutting-edge education anywhere in the world. This system consists of the following elements: user authentication means, learning content recommendation means, real-time feedback means, grade management and progress tracking means, and communication means between teachers and students.

[0037] User Authentication and Login

[0038] First, a user logs in to the system using a terminal. The terminal displays a login screen to the user and prompts them to enter their user ID and password. Next, the terminal sends the entered authentication information to the server. The server compares the information with a database and returns the authentication result. If authentication is successful, the user is logged in to the system and the home screen is displayed.

[0039] Learning content recommendations

[0040] When a user logs in, the server retrieves the user's past learning history and grade data from a database. The server runs an AI algorithm based on the retrieved data and recommends optimal learning content to the user. The recommended content is sent to the device, which displays it to the user. For example, if the user has studied many math units in the past, the server will recommend useful math content to study next based on their progress and performance.

[0041] Real-time feedback

[0042] When a user answers a study assignment or quiz on their device, the answer data is sent to the server. The AI ​​in the server analyzes the user's answer data and generates correct or incorrect answers and detailed feedback. The generated feedback is sent from the server to the user's device, where it is displayed in real time. For example, when a user answers a math quiz, the system immediately analyzes the answer and provides detailed explanations and suggestions for improvement for any incorrect answers.

[0043] Grade management and progress tracking

[0044] The server continuously collects the user's learning activity data and stores it in a database. The server also periodically analyzes this data to evaluate the user's learning progress. Based on the analysis results, it generates a performance report and provides it to the user and the educational institution. The terminal displays the performance report to the user, allowing them to check their learning progress.

[0045] Teacher-student communication

[0046] The device displays an interface with chat and video call functions to the user, allowing educators and learners to communicate in real time. Users (students) send questions to teachers to resolve their doubts, and a real-time video call is initiated between the devices. The teacher then provides on-the-spot explanations to deepen the student's understanding. This process makes it possible to receive a high-quality education wherever they are.

[0047] As described above, the system of the present invention provides a comprehensive range of services, from user authentication to recommendation of learning content, real-time feedback, grade management, and communication with educators, thereby effectively reducing educational disparities.

[0048] The processing flow will be explained below.

[0049] User Authentication and Login

[0050] Step 1:

[0051] The terminal displays a login screen to the user.

[0052] Step 2:

[0053] The user enters their ID and password on the login screen.

[0054] Step 3:

[0055] The terminal sends the entered ID and password to the server.

[0056] Step 4:

[0057] The server checks the received ID and password against the database.

[0058] Step 5:

[0059] The server returns the authentication result to the device. If the authentication is successful, the device displays the home screen to the user.

[0060] Learning content recommendations

[0061] Step 1:

[0062] When a user logs in, the server retrieves the user's past learning history and grade data from the database.

[0063] Step 2:

[0064] The server inputs the acquired data into an AI algorithm to generate learning content that is optimal for the user.

[0065] Step 3:

[0066] The server transmits a list of recommended learning contents to the user's terminal.

[0067] Step 4:

[0068] The terminal displays a list of the received study content to the user.

[0069] Real-time feedback

[0070] Step 1:

[0071] Users answer study assignments and quizzes on their devices.

[0072] Step 2:

[0073] The terminal transmits the user's answer data to the server.

[0074] Step 3:

[0075] The AI ​​on the server analyzes the received answer data and generates correct and detailed feedback.

[0076] Step 4:

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

[0078] Step 5:

[0079] The terminal displays the received feedback to the user in real time.

[0080] Grade management and progress tracking

[0081] Step 1:

[0082] The server continuously collects the user's learning activity data and stores it in a database.

[0083] Step 2:

[0084] The server periodically analyzes the collected data and evaluates the user's learning progress.

[0085] Step 3:

[0086] The server generates a performance report based on the analysis results.

[0087] Step 4:

[0088] The server transmits the generated performance report to the user's terminal.

[0089] Step 5:

[0090] The terminal displays the received performance report to the user, allowing the user to check the progress of their studies.

[0091] Teacher-student communication

[0092] Step 1:

[0093] The terminal displays an interface for chat and video call functions to the user.

[0094] Step 2:

[0095] A user (student) sends a request on a terminal to start a chat or video call with a teacher.

[0096] Step 3:

[0097] The server receives the user's request and notifies the corresponding teacher's terminal.

[0098] Step 4:

[0099] The teacher's terminal receives the request and responds to it to start a chat or video call.

[0100] Step 5:

[0101] Real-time communication takes place between devices, and students and teachers interact through messaging and video calls.

[0102] Example 1

[0103] 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."

[0104] In the traditional education system, the quality of education varies greatly depending on the region and economic situation, creating educational disparities. Furthermore, real-time feedback based on individual learners' progress and level of understanding was rarely provided, resulting in insufficient learning outcomes. Furthermore, communication between teachers and students was limited, meaning that questions were often not resolved promptly.

[0105] 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.

[0106] In this invention, the server includes means for receiving authentication information entered by a user, means for performing authentication processing based on the authentication information, means for acquiring the learner's past learning data, means for recommending optimal learning content based on the learning data, means for analyzing the learner's answer data and generating feedback, means for tracking learning progress and generating a performance report, means for supporting communication between educators and learners in real time, means for displaying recommended learning content to the user, and means for sending questions to educators in real time and receiving answers. This enables access to high-quality education wherever one is, personalized learning, and rapid feedback, thereby reducing educational disparities and improving learning effectiveness.

[0107] The "means for receiving authentication information input by a user" refers to a means for receiving authentication information such as an ID and password input by a user from a terminal.

[0108] The "means for performing authentication processing based on authentication information" refers to a means for verifying received authentication information against a database and authenticating the user.

[0109] "Means for obtaining a learner's past learning data" refers to means for obtaining the learner's past learning content and grade data from a database.

[0110] "Means for recommending optimal learning content" refers to a means for using AI to recommend optimal learning content to learners based on acquired learning data.

[0111] The "means for analyzing the answer data of the learner and generating feedback" refers to a means for analyzing the data of the answer given by the learner and generating feedback based on the results.

[0112] "Means for tracking learning progress and generating performance reports" refers to means for continuously tracking a learner's learning activities, evaluating the learner's progress, and generating performance reports.

[0113] "Means to support real-time communication between educators and learners" refers to means that provide chat and video call functions to support real-time communication between educators and learners.

[0114] The "means for displaying recommended study content to the user" refers to a means for displaying the study content sent from the server on the user's terminal.

[0115] "Means for sending questions to educators in real time and receiving answers" refers to means for learners to send questions to educators in real time and receive answers.

[0116] The above definitions make it clear what role each element of the system plays.

[0117] This invention is a system that utilizes AI to reduce educational disparities and enable students to receive cutting-edge education wherever they are. The system is equipped with a means for user authentication, a means for recommending learning content, a means for real-time feedback, a means for managing grades and tracking progress, and a means for communication between teachers and students.

[0118] User Authentication and Login

[0119] First, a user logs in to the system using a terminal. The terminal displays a login screen to the user and prompts them to enter their user ID and password. Once the user has completed the input, the terminal sends the authentication information to the server. The server compares the information with its internal database and performs authentication. If authentication is successful, the home screen is displayed on the terminal and the user can access the system.

[0120] Learning content recommendations

[0121] When a user logs in, the server retrieves the user's past learning history and grade data from the database. Based on the retrieved data, the server uses an AI algorithm to recommend optimal learning content to the user. AI models used include TENSORFLOW (registered trademark) and PyTorch. The server sends the recommended learning content to the device, which then displays it to the user. For example, if the user has studied many math units in the past, useful math content to study next will be recommended.

[0122] Real-time feedback

[0123] When a user answers a study assignment or quiz on their device, the answer data is sent to the server. The AI ​​in the server analyzes the user's answer data and generates correct or incorrect answers and detailed feedback. This feedback is then sent back from the server to the device and displayed to the user in real time. For example, if a user takes a math quiz, the system will instantly analyze the answer and provide detailed explanations and suggestions for improvement for any incorrect answers.

[0124] Grade management and progress tracking

[0125] The server continuously collects data on the user's learning activities and stores it in a database. The server also periodically analyzes this data to evaluate the user's learning progress. This analysis is performed using data analysis software such as Python's Pandas and Scikit-learn. A performance report generated based on the evaluation is provided to the user and the educational institution. The terminal displays the performance report to the user, allowing them to check their learning progress.

[0126] Teacher-student communication

[0127] The device displays an interface with chat and video calling functions to users, allowing educators and learners to communicate in real time. For example, when a user (student) sends a question to a teacher to resolve a concern, a real-time video call is initiated between the devices. The teacher can then provide an explanation on the spot to deepen the student's understanding. This makes it possible to receive a high-quality education no matter where you are.

[0128] Prompt Sentence Examples

[0129] "Please recommend the next learning content based on the performance data of previously learned units. Please suggest the most suitable content to the user, focusing on progress and performance in the field of mathematics in particular."

[0130] As described above, the system of the present invention provides a comprehensive range of services, from user authentication to recommendation of learning content, real-time feedback, grade management, and communication with educators, with the aim of reducing educational disparities and improving learning outcomes.

[0131] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0132] Step 1: Enter and submit user credentials

[0133] Input: The user enters the user ID and password into the terminal.

[0134] What happens: The device displays a login screen and receives the authentication information entered by the user.

[0135] Output: The terminal sends the entered authentication information to the server.

[0136] Step 2: Verify Credentials

[0137] Input: User ID and password sent to the server

[0138] How it works: The server authenticates the received credentials against its internal database. It compares the hashed password with its records to determine if authentication is successful.

[0139] Output: The authentication result (success or failure) is returned from the server to the device.

[0140] Step 3: Display the Home Screen

[0141] Input: Authentication result from server (success)

[0142] Action: After successful authentication, the device displays the home screen to the user.

[0143] Output: The home screen is displayed on the user's device.

[0144] Step 4: Obtaining training data

[0145] Input: User login information

[0146] Operation: The server retrieves the user's past learning history and grade data from the database. It extracts the necessary data using SQL queries, etc.

[0147] Output: The acquired learning data is stored on the server.

[0148] Step 5: Content recommendation by AI algorithm

[0149] Input: Obtained training data

[0150] How it works: The server runs an AI algorithm (e.g., TensorFlow or PyTorch) to calculate the optimal learning content for the user. This algorithm selects appropriate content based on past learning data.

[0151] Output: Recommended learning content is generated and stored on the server.

[0152] Step 6: Submit and view your learning content

[0153] Input: Recommended learning content

[0154] Operation: The server sends the recommended learning content to the device, which then displays the content to the user.

[0155] Output: The learning content is displayed on the user's device.

[0156] Step 7: Submit your answers

[0157] Input: Data answered by the user

[0158] How it works: The user answers questions and assignments on their device, and the device sends the answer data to the server.

[0159] Output: The server receives the answer data.

[0160] Step 8: Analyze the answer data and generate feedback

[0161] Input: Submitted answer data

[0162] How it works: An AI (e.g., BERT or GPT, which implements NLP models) in the server analyzes the answer data and generates correct answers and detailed feedback. This analysis uses natural language processing and machine learning algorithms.

[0163] Output: The generated feedback is stored on the server.

[0164] Step 9: Submit and view feedback

[0165] Input: Generated feedback

[0166] Operation: The server sends the generated feedback to the device, which displays the sent feedback to the user in real time.

[0167] Output: Feedback is displayed on the user's device.

[0168] Step 10: Collect and analyze progress data

[0169] Input: User learning activity data

[0170] How it works: The server continuously collects user learning activity data, stores it in a database, and periodically analyzes this data using data analysis software (e.g., Python's Pandas or Scikit-learn).

[0171] Output: Analysis results and progress data are saved on the server.

[0172] Step 11: Generate and submit a grade report

[0173] Input: Analysis results and progress data

[0174] Operation: The server generates and periodically provides performance reports to users and educational institutions.

[0175] Output: The performance report is sent to the terminal and displayed to the user.

[0176] Step 12: Start real-time communication

[0177] Input: User's question and request to start a video call

[0178] How it works: The device displays the interface needed to start a chat or video call with the educator, sends questions to the educator in real time, and a video call is initiated between the devices.

[0179] Output: Answers and explanations from the educator are displayed in real time on the user's device.

[0180] As described above, through the specific actions taken at each step, the entire system functions smoothly, enabling the provision of high-quality education.

[0181] (Application example 1)

[0182] 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."

[0183] Educational inequality remains a problem in modern society, with access to educational resources and quality varying greatly depending on the region and environment. The coronavirus pandemic has made remote education even more important, but current remote education systems face challenges, such as difficulty in managing learner progress and implementing personalized learning. Furthermore, the lack of real-time feedback and smooth communication with educators is one of the factors contributing to a decline in the quality of education.

[0184] 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.

[0185] In this invention, the server includes means for receiving authentication information entered by a user, means for performing authentication processing based on the authentication information, means for acquiring the learner's past learning data, means for recommending optimal learning content based on the learning data, means for analyzing the learner's answer data and generating feedback, means for tracking learning progress and generating a performance report, means for supporting communication between educators and learners in real time, means for providing a virtual classroom interface in a virtual space, and means for delivering educational content in real time using smart devices. This reduces educational disparities and enables people to receive high-quality education wherever they are.

[0186] "Authentication Information" means information used to verify a user's identity, such as a user ID and password entered by a user when accessing a system.

[0187] "Authentication processing" is a process for verifying whether a user is a legitimate user based on the user's authentication information.

[0188] "Learning data" refers to information that records a learner's past learning history, grades, and progress.

[0189] "Learning content" refers to educational resources such as teaching materials, documents, quizzes, and videos that students can use to learn.

[0190] "Feedback" is a response that includes evaluation, advice, and specific areas for improvement provided to a learner in response to their learning activities.

[0191] "Progress" refers to the learner's progress and achievement in learning, including grades and course completion status.

[0192] "Real-time communication" is a function that allows educators and students to send and receive data simultaneously and interact instantly.

[0193] "Virtual space" refers to a virtual learning environment built on the Internet, an area where education is possible independent of physical location.

[0194] A "virtual classroom" is a virtual classroom for education that exists in a virtual space and is an educational environment that can be accessed via the Internet.

[0195] "Smart devices" refers to all electronic devices with internet connectivity, such as smartphones, tablets, smart glasses, and head-mounted displays.

[0196] "Delivery of educational content" refers to the provision of educational videos, texts, quizzes, etc. to learners in real time or on demand via the Internet.

[0197] The present invention is a system that utilizes AI to reduce educational disparities and enable students anywhere in the world to receive cutting-edge education. The system includes a user authentication means, a learning content recommendation means, a real-time feedback means, a grade management and progress tracking means, a teacher-student communication means, a virtual classroom interface means, and a means for delivering educational content via smart devices.

[0198] User Authentication and Login

[0199] First, the server receives the authentication information entered by the user. The user accesses the login screen using a smart device and enters their user ID and password. The authentication information is sent to the server, where it is verified against a database for authentication. If authentication is successful, the user is logged into the system and the home screen is displayed.

[0200] Learning content recommendations

[0201] When a user logs in, the server retrieves the user's past learning data from a database. Based on this data, a generative AI model is used to recommend the most suitable learning content to the user. For example, if a user has studied many math units in the past, useful math content to study next will be recommended based on their progress and performance.

[0202] Real-time feedback

[0203] Data on tasks and quizzes answered by users is sent to a server. The AI ​​in the server analyzes the user's answer data and generates correct or incorrect answers and detailed feedback. This feedback is sent to the user's smart device and displayed in real time. For example, when a user solves a math quiz, the system immediately analyzes the answer and provides detailed explanations and suggestions for improvement for any incorrect parts.

[0204] Grade management and progress tracking

[0205] The server continuously collects the user's learning activity data and stores it in a database. The server also periodically analyzes this data to evaluate the user's learning progress. Based on the analysis results, it generates a performance report and provides it to the user and the educational institution. The performance report is displayed in real time on the user's smart device.

[0206] Teacher-student communication

[0207] A chat and video call interface is displayed on the user's smart device, allowing real-time communication between educators and students. Students can send questions to the teacher to resolve their doubts, and a real-time video call is initiated between the devices. The teacher can then provide on-the-spot explanations to deepen students' understanding.

[0208] Virtual classroom interface and educational content delivery

[0209] A virtual classroom interface in a virtual space is also provided, allowing users to access the virtual classroom in real time using a smartphone or head-mounted display. In this environment, real-time lectures and discussions are held, allowing users to learn while interacting with educational content.

[0210] Hardware and software used

[0211] The implementation of this system uses the following hardware and software:

[0212] Hardware: Smart devices (smartphones, tablets, head-mounted displays)

[0213] Software: Flask framework, generative AI model (using TensorFlow)

[0214] Examples of concrete examples and prompts

[0215] For example, imagine a scenario where a student logs in to an application and is recommended next learning content based on their past learning history. The student then uses that content to answer quizzes and receives real-time feedback. After learning, the student can check their grade report and understand their progress. They can also ask a teacher any questions they have in real time and receive clarification in the virtual classroom.

[0216] An example of a specific prompt for a generative AI model is as follows:

[0217] Recommend the most relevant learning content based on the user's learning history when they log in.

[0218] Past learning history is as follows:

[0219] Mathematics: 85 points

[0220] Chemistry: 90 points

[0221] Recommend the next learning content that would be best for this student.

[0222] As a result, this system will reduce educational disparities and enable people to receive high-quality education wherever they are.

[0223] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0224] Step 1:

[0225] The user accesses the login screen using a smart device and enters their user ID and password. The entered authentication information is sent to the server. The server compares this information with the database and performs authentication processing. If authentication is successful, the home screen is displayed on the device. Input: User ID, password. Output: Authentication result, home screen.

[0226] Step 2:

[0227] When a user logs in to the system, the server retrieves the user's past learning data from the database. The retrieved learning data is input into the generative AI model. The AI ​​model analyzes this data and recommends the most suitable learning content for the user. The recommendation results are sent to the terminal and displayed. Input: Learning data. Output: Recommended learning content.

[0228] Step 3:

[0229] The user works on the recommended learning content and answers assignments and quizzes. The answer data is sent to the server. The AI ​​on the server analyzes this answer data, determines whether the answer is correct, and generates detailed feedback. The generated feedback is sent to the device in real time and displayed. Input: Answer data. Output: Feedback.

[0230] Step 4:

[0231] The server continuously collects the user's learning activity data and stores it in a database. The data is analyzed at specific intervals to evaluate the user's progress. Based on the evaluation results, a performance report is generated and provided to the user and the educational institution. The performance report is displayed on the terminal. Input: Learning activity data. Output: Performance report.

[0232] Step 5:

[0233] Chat and video call functions are displayed on the user's device. Users can use these to communicate with educators in real time. For example, a user can send a question to a teacher about a concern they have, which will initiate a video call. The teacher will then provide an explanation on the spot to deepen the user's understanding. Input: Question, chat message. Output: Explanation, response.

[0234] Step 6:

[0235] The virtual classroom interface in the virtual space is displayed on the terminal. Users can access the virtual classroom using their smart devices. Real-time lectures and discussions take place here, allowing users to progress with their learning while experiencing educational content. Input: Access request. Output: Virtual classroom interface, educational content.

[0236] Step 7:

[0237] Educational content is delivered to smart devices via the internet through a server. The software used here is the Flask framework and generative AI models such as TensorFlow. This provides a personalized learning experience. Input: Educational content. Output: Learning experience, learning resources.

[0238] 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.

[0239] This invention is a system that utilizes AI to reduce educational disparities and enable people to receive cutting-edge education anywhere in the world. This system consists of the following elements: a user authentication means, a learning content recommendation means, a real-time feedback means, a grade management and progress tracking means, a means of communication between educators and learners, and an emotion engine for recognizing user emotions.

[0240] User Authentication and Login

[0241] First, a user logs in to the system using a terminal. The terminal displays a login screen to the user and prompts them to enter their user ID and password. Next, the terminal sends the entered authentication information to the server. The server compares the information with a database and returns the authentication result. If authentication is successful, the user is logged in to the system and the home screen is displayed.

[0242] Learning content recommendations

[0243] When a user logs in, the server retrieves the user's past learning history and grade data from a database. The server inputs the retrieved data into an AI algorithm to generate learning content that is optimal for the user. The recommended content is sent to the device, which then displays it to the user. For example, if the user has studied many math units in the past, the server will recommend useful math content to study next based on their progress and performance.

[0244] Real-time feedback

[0245] When a user answers a study assignment or quiz on their device, the answer data is sent to the server. The AI ​​in the server analyzes the user's answer data and generates correct or incorrect answers and detailed feedback. The generated feedback is sent from the server to the user's device, where it is displayed in real time. For example, when a user answers a math quiz, the system immediately analyzes the answer and provides detailed explanations and suggestions for improvement for any incorrect answers.

[0246] Grade management and progress tracking

[0247] The server continuously collects the user's learning activity data and stores it in a database. The server also periodically analyzes this data to evaluate the user's learning progress. Based on the analysis results, it generates a performance report and provides it to the user and the educational institution. The terminal displays the performance report to the user, allowing them to check their learning progress.

[0248] Teacher-student communication

[0249] The device displays an interface with chat and video call functions to the user, allowing educators and learners to communicate in real time. Users (students) send questions to teachers to resolve their doubts, and a real-time video call is initiated between the devices. The teacher then provides on-the-spot explanations to deepen the student's understanding. This process makes it possible to receive a high-quality education wherever they are.

[0250] Utilizing the Emotion Engine

[0251] Furthermore, the present invention incorporates an emotion engine that can recognize the user's emotions in real time. Specifically, the emotion engine analyzes the user's facial expressions and tone of voice through the device's built-in camera and microphone. This information is sent to a server, which then identifies the user's current emotional state.

[0252] This emotional data can be used to:

[0253] Change of learning content: The server adaptively changes learning content based on the user's emotional state. For example, if the user is tired, it recommends light content that will help them relax.

[0254] Feedback adjustment: The server adjusts the feedback provided to the user based on the emotional data. For example, if the user is feeling stressed, it emphasizes encouraging messages and advice.

[0255] Real-time support: The emotion engine recognizes emotion data and provides it to educators in real time. Educators can use this information to take appropriate action. For example, if a user is depressed, the educator can provide additional support.

[0256] As described above, the system of the present invention provides a comprehensive range of services, from user authentication to learning content recommendations, real-time feedback, grade management, communication with educators, and emotion recognition and utilization using an emotion engine, thereby effectively reducing educational disparities.

[0257] The processing flow will be explained below.

[0258] User Authentication and Login

[0259] Step 1:

[0260] The terminal displays a login screen to the user.

[0261] Step 2:

[0262] The user enters their ID and password on the login screen.

[0263] Step 3:

[0264] The terminal sends the entered ID and password to the server.

[0265] Step 4:

[0266] The server checks the received ID and password against the database.

[0267] Step 5:

[0268] The server returns the authentication result to the device. If the authentication is successful, the device displays the home screen to the user.

[0269] Learning content recommendations

[0270] Step 1:

[0271] When a user logs in, the server retrieves the user's past learning history and grade data from the database.

[0272] Step 2:

[0273] The server inputs the acquired data into an AI algorithm to generate learning content that is optimal for the user.

[0274] Step 3:

[0275] The server transmits a list of recommended learning contents to the user's terminal.

[0276] Step 4:

[0277] The terminal displays a list of the received study content to the user.

[0278] Real-time feedback

[0279] Step 1:

[0280] Users answer study assignments and quizzes on their devices.

[0281] Step 2:

[0282] The terminal transmits the user's answer data to the server.

[0283] Step 3:

[0284] The AI ​​on the server analyzes the received answer data and generates correct and detailed feedback.

[0285] Step 4:

[0286] The server transmits the generated feedback to the user's terminal.

[0287] Step 5:

[0288] The terminal displays the received feedback to the user in real time.

[0289] Grade management and progress tracking

[0290] Step 1:

[0291] The server continuously collects the user's learning activity data and stores it in a database.

[0292] Step 2:

[0293] The server periodically analyzes the collected data and evaluates the user's learning progress.

[0294] Step 3:

[0295] The server generates a performance report based on the analysis results.

[0296] Step 4:

[0297] The server transmits the generated performance report to the user's terminal.

[0298] Step 5:

[0299] The terminal displays the received performance report to the user, allowing the user to check the progress of their studies.

[0300] Teacher-student communication

[0301] Step 1:

[0302] The terminal displays an interface for chat and video call functions to the user.

[0303] Step 2:

[0304] A user (student) sends a request on a terminal to start a chat or video call with a teacher.

[0305] Step 3:

[0306] The server receives the user's request and notifies the corresponding teacher's terminal.

[0307] Step 4:

[0308] The teacher's terminal receives the request and responds to it to start a chat or video call.

[0309] Step 5:

[0310] Real-time communication takes place between devices, and students and teachers interact through messaging and video calls.

[0311] Utilizing the Emotion Engine

[0312] Step 1:

[0313] The device collects the user's facial expressions and tone of voice in real time through an emotion engine.

[0314] Step 2:

[0315] The terminal transmits the collected emotion data to the server.

[0316] Step 3:

[0317] The server analyzes the received emotion data to understand the user's current emotional state.

[0318] Step 4:

[0319] The server adaptively changes learning content based on the user's emotional state, for example, recommending light, relaxing content if the user is tired.

[0320] Step 5:

[0321] The server adjusts the feedback based on the emotional data, for example, emphasizing encouraging messages and advice if the user is feeling stressed.

[0322] Step 6:

[0323] The server provides recognized emotion data to educators in real time, helping them take appropriate action.

[0324] As a result, the system of the present invention provides a consistent service ranging from user authentication to learning content recommendations, real-time feedback, grade management, communication with educators, and emotion recognition and utilization using an emotion engine, thereby effectively reducing educational disparities.

[0325] Example 2

[0326] 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."

[0327] Traditional educational systems have difficulty accurately understanding each learner's progress and emotional state and flexibly adjusting learning content and feedback based on that information. They also lack effective means to support real-time communication between educators and learners. This creates educational disparities and leads to uneven learning quality.

[0328] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0329] In this invention, the server includes means for receiving authentication information entered by a user, means for performing authentication processing based on the authentication information, means for acquiring the learner's past learning data, means for recommending optimal learning content based on the learning data, means for analyzing the learner's answer data and generating feedback, means for tracking learning progress and generating performance reports, means for supporting communication between educators and learners in real time, means for collecting and analyzing emotional data, means for adaptively changing learning content based on the emotional data, and means for adjusting feedback content based on the emotional data. This allows for real-time understanding of the individual progress and emotional state of learners, enabling flexible learning support based on this information.

[0330] "Authentication Information" means the identification information required for a User to access the System, including, specifically, a User ID and password.

[0331] "Authentication processing" is the process of checking the entered authentication information against a database to confirm the user's identity.

[0332] "Study Data" refers to information including a user's past learning history and performance data, including details of the user's learning activities.

[0333] "Learning content" refers to educational resources such as learning materials, assignments, and quizzes that users should study.

[0334] "Answer data" refers to data that a user provides in response to a study task or quiz.

[0335] "Feedback" refers to evaluation and advice provided to the user based on the results of analyzing the user's answer data.

[0336] A "performance report" is a report summarizing a user's learning progress and grades.

[0337] "Emotion data" is information that indicates a real-time emotional state of a user, obtained by analyzing the user's facial expressions and tone of voice.

[0338] "Adaptive change" refers to the process of adjusting learning content and feedback to suit the user's condition based on collected data.

[0339] This invention is a system that utilizes AI to reduce educational disparities and enable people to receive cutting-edge education anywhere in the world. This system consists of the following elements: a user authentication means, a learning content recommendation means, a real-time feedback means, a grade management and progress tracking means, a means of communication between educators and learners, and an emotion engine for recognizing user emotions.

[0340] First, the user logs in to the system using their own terminal. The terminal displays a login screen to the user and asks them to enter their user ID and password. Once the user has completed the input, the terminal sends the entered user ID and password to the server. When the server receives the authentication information, it compares it with a database and returns the authentication result to the terminal. If the authentication is successful, the terminal displays the home screen to the user.

[0341] When a user logs in, the server retrieves the user's past learning history and grade data from a database. This data is input into AI algorithms such as TensorFlow and Scikit-learn. The server uses the AI ​​algorithms to generate optimal learning content for the user. The generated learning content is sent to the device, which then displays it to the user.

[0342] When a user answers a learning task or quiz, the answer data is sent from the device to the server. The AI ​​feedback system in the server analyzes the answer data and generates correct or incorrect answers and detailed feedback. This feedback is sent from the server to the device, which displays it to the user in real time.

[0343] The server continuously collects the user's learning activity data and stores it in a database. It also periodically analyzes this data to evaluate the user's learning progress. It generates a performance report based on the analysis results and provides it to the user and the educational institution. The terminal displays the performance report to the user, allowing them to check their learning progress.

[0344] The device provides chat and video calling functions to support real-time communication between users and educators. Users (students) can send questions to teachers to resolve their doubts and exchange information in real time. The video calling function uses, for example, the Zoom API or WebRTC.

[0345] Using the emotion engine, the system recognizes the user's real-time emotional state. The device's built-in camera and microphone collect the user's facial expressions and tone of voice, which are then sent to the server. The emotion engine analyzes the data to understand the user's current emotional state. Based on this emotional data, the server adaptively changes the learning content and feedback. Educators can also obtain emotional data in real time and take appropriate action.

[0346] As a concrete example, let us consider a scenario in which a user is studying mathematics. For example, the system recommends the next mathematics topic to study based on the mathematics units the user has studied in the past. Furthermore, when the user answers a quiz and the results are sent to the server, they are immediately analyzed and detailed feedback is provided. If the emotion engine determines that the user is feeling stressed, the server will send an encouraging message to keep the user motivated.

[0347] Examples of input prompts for generative AI models include:

[0348] "If a user is studying a math unit, recommend what they should study next based on their past learning history. Also, provide detailed feedback if they get a quiz answer wrong, and suggest how to support them when they're feeling stressed."

[0349] The above is an embodiment of the present invention.

[0350] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0351] System program processing flow

[0352] Step 1: User authentication and login

[0353] The user displays the login screen on their device. They enter their user ID and password, which the device then sends to the server. The server compares the authentication information with the database and returns the authentication result to the device. If authentication is successful, the device displays the home screen to the user.

[0354] Input: User ID, Password

[0355] Data calculation: database search, authentication processing

[0356] Output: Authentication result, home screen display

[0357] Step 2: Obtaining training data

[0358] After a user logs in, the server retrieves the user's past learning history and performance data from the database, and prepares this data for input into the AI ​​algorithm.

[0359] Input: User ID

[0360] Data processing: Retrieving data from the database

[0361] Output: Past learning history, grade data

[0362] Step 3: Recommend learning content

[0363] The server inputs the acquired learning data into an AI algorithm (e.g., TensorFlow or Scikit-learn). The AI ​​algorithm generates optimal learning content for the user based on the data. The generated learning content is sent from the server to the device, which then displays it to the user.

[0364] Input: Past learning history, grade data

[0365] Data Computation: Learning Content Generation with AI Algorithms

[0366] Output: Learning content, displayed on the user screen

[0367] Step 4: Complete and submit your assignments

[0368] The user answers study assignments and quizzes on the device, and the answer data is sent from the device to the server.

[0369] Input: User's answer to the learning task

[0370] Data processing: Sending answer data

[0371] Output: Submitted answer data

[0372] Step 5: Generate and submit feedback

[0373] The AI ​​feedback system on the server analyzes the submitted answer data, generates correct and incorrect answers, and sends detailed feedback to the device, which then displays the feedback to the user in real time.

[0374] Input: Submitted answer data

[0375] Data calculation: Analysis and feedback generation by AI feedback system

[0376] Output: Generated feedback, displayed on the user screen

[0377] Step 6: Collect and analyze training data

[0378] The server continuously collects the user's learning activity data and stores it in a database. The server periodically analyzes this data and evaluates the user's learning progress. Based on the analysis results, it generates a performance report and provides it to the user and the educational institution. The terminal displays the performance report to the user, allowing them to check their learning progress.

[0379] Input: User learning activity data

[0380] Data calculation: storing data in database, periodic data analysis

[0381] Output: Grade report, display on user screen

[0382] Step 7: Communicate with Educators

[0383] Users can communicate with educators in real time using chat and video call functions on their devices. They can send questions and receive answers and explanations from educators in real time. The chat and video call functions use the Zoom API and WebRTC.

[0384] Input: User question, Educator answer

[0385] Data processing: Real-time communication

[0386] Output: Answers and explanations from the educator, displayed on the user's screen

[0387] Step 8: Emotion engine analysis and feedback adjustment

[0388] The device's built-in camera and microphone are used to collect the user's facial expressions and tone of voice. The server's emotion engine analyzes this data to understand the user's current emotional state. Based on this emotional data, the server adaptively changes the learning content and feedback.

[0389] Input: facial expression data, tone of voice data

[0390] Data Computing: Emotional data analysis, feedback and content adjustment

[0391] Output: Tailored learning content, feedback, and display on the user's screen

[0392] The above is a detailed description of the specific operations and inputs and outputs at each processing step of this system.

[0393] (Application example 2)

[0394] 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."

[0395] Conventional educational systems make it difficult for learners to receive high-quality education wherever they are. They also lack the means to provide real-time feedback on learning progress and assessment of answers. Furthermore, it is difficult to adjust learning content to take into account the learner's emotional state and to communicate with educators in real time. This has led to problems such as a decline in learner motivation and learning effectiveness.

[0396] 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.

[0397] In this invention, the server includes means for receiving authentication information entered by a user, means for performing authentication processing based on the authentication information, means for acquiring the learner's past learning data, means for recommending optimal learning content based on the learning data, means for analyzing the learner's answer data and generating feedback, means for tracking the learning progress and generating a performance report, means for analyzing the learner's emotional data and adjusting the learning content based on the learner's emotional state, means for supporting communication between educators and learners in real time, and means for providing feedback based on the learning progress and emotions to a smart device and enabling video calls with educators. This allows learners to receive high-quality education wherever they are, improving their learning effectiveness.

[0398] "Authentication information" refers to identification information such as a user ID and password required for a learner to log in to the system.

[0399] "Authentication processing" is a process for confirming the identity and access rights of a learner based on the input authentication information.

[0400] "Learning data" refers to information including a learner's past learning history and grade data.

[0401] "Learning content" refers to educational materials, videos, quizzes, and other content provided to learners.

[0402] "Feedback" is information that includes evaluation and advice on the learner's answers.

[0403] A "performance report" is a report summarizing a learner's learning progress and an evaluation of their performance.

[0404] "Emotional data" is information about the emotional state of a learner obtained by analyzing their facial expressions and tone of voice.

[0405] "Real-time communication" is a function that allows educators and learners to communicate directly without any time lag.

[0406] "Smart devices" are electronic devices with advanced functions such as smartphones, smart glasses, and tablets.

[0407] "Video calling" is a means of communicating with each other using video and audio over the Internet.

[0408] The system for implementing this invention is realized by combining various hardware and software. First, a user accesses the system using a smart device such as a smartphone or tablet. The main functions of the system are provided by three entities: a server, a terminal, and a user.

[0409] User Authentication and Login

[0410] The user accesses the login screen from their smart device and enters their user ID and password. The device receives the entered authentication information and sends it to the server. The server performs authentication based on the authentication information and returns the results of matching it with the database. If authentication is successful, the user can access the home screen.

[0411] Learning content recommendations

[0412] When a user logs in, the server retrieves the user's past learning data from a database. An AI algorithm then analyzes the data and recommends personalized learning content. The recommended content is displayed on the device for the user to view.

[0413] Real-time feedback

[0414] When a user answers a quiz or assignment, the answer data is sent from the device to the server. The AI ​​in the server analyzes the answer data and generates detailed feedback, which is displayed on the user's device in real time.

[0415] Grade management and progress tracking

[0416] The server collects the user's learning activity data and stores it in a database. It periodically analyzes the data and generates a learning progress and performance report. This report is provided to the user and the educational institution and is also displayed on the device.

[0417] Communication with Educators

[0418] Users can communicate with educators in real time using chat and video calling functions on their devices, and any questions or concerns can be resolved in real time, improving learning outcomes.

[0419] Utilizing the Emotion Engine

[0420] The emotion engine analyzes the user's facial expressions and tone of voice via the device's built-in camera and microphone. This emotion data is sent to a server and reflected in adjustments to the learning content and feedback. It is also provided to educators in real time, allowing them to take appropriate action.

[0421] Examples and prompts

[0422] Specific examples

[0423] User authentication: The learner enters their username and password into the login screen on their smartphone, and if authentication is successful, they are taken to the home screen.

[0424] Learning content recommendation: AI recommends the next math video to learn based on learning history and displays it on the device.

[0425] Real-time feedback: AI provides detailed explanations on the spot for the quiz you answer.

[0426] Learning support using an emotion engine: If a user is feeling stressed, the AI ​​will suggest light content to relieve them.

[0427] Prompt Sentence Examples

[0428] "Based on your learning history and progress, AI will recommend the most appropriate educational content for you to learn next. As you progress through the learning process, you will receive real-time feedback on your answers, and you can even have a video call with an educator if needed. In addition, an emotion engine will analyze your facial expressions and voice to adjust your learning method at the appropriate time."

[0429] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0430] Step 1:

[0431] A user uses a smart device to access the system's login screen and enter their user ID and password. The device receives the entered authentication information (user ID and password) and sends it to the server. The server compares the entered authentication information with the authentication database and returns the authentication result. If the user is successfully authenticated, they can access the home screen.

[0432] Input: User ID, Password

[0433] Data processing: Verification against authentication database

[0434] Output: Authentication result

[0435] Step 2:

[0436] When a user logs in, the server retrieves the user's past learning data (learning history and grade data) from the database. The retrieved data is input into an AI algorithm to generate individually optimized learning content. The generated learning content is sent to the device and displayed to the user.

[0437] Input: User's learning history, grade data

[0438] Data processing: Analysis using AI algorithms

[0439] Output: Recommended learning content

[0440] Step 3:

[0441] When users browse learning content and answer quizzes and assignments, the answer data is sent from the device to the server. The AI ​​on the server analyzes the answer data, determines whether the answer is correct or incorrect, and generates detailed feedback. The generated feedback is sent to the device and displayed to the user in real time.

[0442] Input: Answer data

[0443] Data processing: Analysis of answer data using AI

[0444] Output: Detailed feedback

[0445] Step 4:

[0446] The server continuously collects user learning activity data and stores it in a database. It periodically analyzes the data and generates learning progress and performance reports. The performance reports are provided to the user and the educational institution and are also displayed on the device.

[0447] Input: Learning activity data

[0448] Data processing: storing in database, analysis

[0449] Output: Learning progress, grade report

[0450] Step 5:

[0451] Users can communicate with educators in real time using chat and video calling functions from their devices. The device is responsible for initiating and terminating communications and sending and receiving messages, while the server manages the necessary interconnections.

[0452] Input: Chat message, video call start request

[0453] Data Processing: Managing Real-Time Communications

[0454] Output: Real-time communication with educators

[0455] Step 6:

[0456] The device's built-in camera and microphone collect the user's facial expressions and tone of voice. This data is sent to a server and analyzed by an emotion engine. The server uses the emotion analysis results to adjust learning content and change feedback, providing it to educators in real time as needed.

[0457] Input: facial expression data, tone of voice data

[0458] Data processing: Analysis using emotion engine

[0459] Output: Adjusted learning content, feedback

[0460] 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.

[0461] 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.

[0462] 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.

[0463] [Second embodiment]

[0464] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0465] 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.

[0466] 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).

[0467] 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.

[0468] 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.

[0469] 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).

[0470] 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.

[0471] 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.

[0472] 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.

[0473] 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.

[0474] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0475] 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."

[0476] This invention is a system that utilizes AI to reduce educational disparities and enable people to receive cutting-edge education anywhere in the world. This system consists of the following elements: user authentication means, learning content recommendation means, real-time feedback means, grade management and progress tracking means, and communication means between teachers and students.

[0477] User Authentication and Login

[0478] First, a user logs in to the system using a terminal. The terminal displays a login screen to the user and prompts them to enter their user ID and password. Next, the terminal sends the entered authentication information to the server. The server compares the information with a database and returns the authentication result. If authentication is successful, the user is logged in to the system and the home screen is displayed.

[0479] Learning content recommendations

[0480] When a user logs in, the server retrieves the user's past learning history and grade data from a database. The server runs an AI algorithm based on the retrieved data and recommends optimal learning content to the user. The recommended content is sent to the device, which displays it to the user. For example, if the user has studied many math units in the past, the server will recommend useful math content to study next based on their progress and performance.

[0481] Real-time feedback

[0482] When a user answers a study assignment or quiz on their device, the answer data is sent to the server. The AI ​​in the server analyzes the user's answer data and generates correct or incorrect answers and detailed feedback. The generated feedback is sent from the server to the user's device, where it is displayed in real time. For example, when a user answers a math quiz, the system immediately analyzes the answer and provides detailed explanations and suggestions for improvement for any incorrect answers.

[0483] Grade management and progress tracking

[0484] The server continuously collects the user's learning activity data and stores it in a database. The server also periodically analyzes this data to evaluate the user's learning progress. Based on the analysis results, it generates a performance report and provides it to the user and the educational institution. The terminal displays the performance report to the user, allowing them to check their learning progress.

[0485] Teacher-student communication

[0486] The device displays an interface with chat and video call functions to the user, allowing educators and learners to communicate in real time. Users (students) send questions to teachers to resolve their doubts, and a real-time video call is initiated between the devices. The teacher then provides on-the-spot explanations to deepen the student's understanding. This process makes it possible to receive a high-quality education wherever they are.

[0487] As described above, the system of the present invention provides a comprehensive range of services, from user authentication to recommendation of learning content, real-time feedback, grade management, and communication with educators, thereby effectively reducing educational disparities.

[0488] The processing flow will be explained below.

[0489] User Authentication and Login

[0490] Step 1:

[0491] The terminal displays a login screen to the user.

[0492] Step 2:

[0493] The user enters their ID and password on the login screen.

[0494] Step 3:

[0495] The terminal sends the entered ID and password to the server.

[0496] Step 4:

[0497] The server checks the received ID and password against the database.

[0498] Step 5:

[0499] The server returns the authentication result to the device. If the authentication is successful, the device displays the home screen to the user.

[0500] Learning content recommendations

[0501] Step 1:

[0502] When a user logs in, the server retrieves the user's past learning history and grade data from the database.

[0503] Step 2:

[0504] The server inputs the acquired data into an AI algorithm to generate learning content that is optimal for the user.

[0505] Step 3:

[0506] The server transmits a list of recommended learning contents to the user's terminal.

[0507] Step 4:

[0508] The terminal displays a list of the received study content to the user.

[0509] Real-time feedback

[0510] Step 1:

[0511] Users answer study assignments and quizzes on their devices.

[0512] Step 2:

[0513] The terminal transmits the user's answer data to the server.

[0514] Step 3:

[0515] The AI ​​on the server analyzes the received answer data and generates correct and detailed feedback.

[0516] Step 4:

[0517] The server transmits the generated feedback to the user's terminal.

[0518] Step 5:

[0519] The terminal displays the received feedback to the user in real time.

[0520] Grade management and progress tracking

[0521] Step 1:

[0522] The server continuously collects the user's learning activity data and stores it in a database.

[0523] Step 2:

[0524] The server periodically analyzes the collected data and evaluates the user's learning progress.

[0525] Step 3:

[0526] The server generates a performance report based on the analysis results.

[0527] Step 4:

[0528] The server transmits the generated performance report to the user's terminal.

[0529] Step 5:

[0530] The terminal displays the received performance report to the user, allowing the user to check the progress of their studies.

[0531] Teacher-student communication

[0532] Step 1:

[0533] The terminal displays an interface for chat and video call functions to the user.

[0534] Step 2:

[0535] A user (student) sends a request on a terminal to start a chat or video call with a teacher.

[0536] Step 3:

[0537] The server receives the user's request and notifies the corresponding teacher's terminal.

[0538] Step 4:

[0539] The teacher's terminal receives the request and responds to it to start a chat or video call.

[0540] Step 5:

[0541] Real-time communication takes place between devices, and students and teachers interact through messaging and video calls.

[0542] Example 1

[0543] 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."

[0544] In the traditional education system, the quality of education varies greatly depending on the region and economic situation, creating educational disparities. Furthermore, real-time feedback based on individual learners' progress and level of understanding was rarely provided, resulting in insufficient learning outcomes. Furthermore, communication between teachers and students was limited, meaning that questions were often not resolved promptly.

[0545] 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.

[0546] In this invention, the server includes means for receiving authentication information entered by a user, means for performing authentication processing based on the authentication information, means for acquiring the learner's past learning data, means for recommending optimal learning content based on the learning data, means for analyzing the learner's answer data and generating feedback, means for tracking learning progress and generating a performance report, means for supporting communication between educators and learners in real time, means for displaying recommended learning content to the user, and means for sending questions to educators in real time and receiving answers. This enables access to high-quality education wherever one is, personalized learning, and rapid feedback, thereby reducing educational disparities and improving learning effectiveness.

[0547] The "means for receiving authentication information input by a user" refers to a means for receiving authentication information such as an ID and password input by a user from a terminal.

[0548] The "means for performing authentication processing based on authentication information" refers to a means for verifying received authentication information against a database and authenticating the user.

[0549] "Means for obtaining a learner's past learning data" refers to means for obtaining the learner's past learning content and grade data from a database.

[0550] "Means for recommending optimal learning content" refers to a means for using AI to recommend optimal learning content to learners based on acquired learning data.

[0551] The "means for analyzing the answer data of the learner and generating feedback" refers to a means for analyzing the data of the answer given by the learner and generating feedback based on the results.

[0552] "Means for tracking learning progress and generating performance reports" refers to means for continuously tracking a learner's learning activities, evaluating the learner's progress, and generating performance reports.

[0553] "Means to support real-time communication between educators and learners" refers to means that provide chat and video call functions to support real-time communication between educators and learners.

[0554] The "means for displaying recommended study content to the user" refers to a means for displaying the study content sent from the server on the user's terminal.

[0555] "Means for sending questions to educators in real time and receiving answers" refers to means for learners to send questions to educators in real time and receive answers.

[0556] The above definitions make it clear what role each element of the system plays.

[0557] This invention is a system that utilizes AI to reduce educational disparities and enable students to receive cutting-edge education wherever they are. The system is equipped with a means for user authentication, a means for recommending learning content, a means for real-time feedback, a means for managing grades and tracking progress, and a means for communication between teachers and students.

[0558] User Authentication and Login

[0559] First, a user logs in to the system using a terminal. The terminal displays a login screen to the user and prompts them to enter their user ID and password. Once the user has completed the input, the terminal sends the authentication information to the server. The server compares the information with its internal database and performs authentication. If authentication is successful, the home screen is displayed on the terminal and the user can access the system.

[0560] Learning content recommendations

[0561] When a user logs in, the server retrieves the user's past learning history and grade data from the database. Based on the retrieved data, the server uses an AI algorithm to recommend optimal learning content to the user. The AI ​​models used include TensorFlow and PyTorch. The server sends the recommended learning content to the device, which then displays it to the user. For example, if the user has studied many math units in the past, useful math content to study next will be recommended.

[0562] Real-time feedback

[0563] When a user answers a study assignment or quiz on their device, the answer data is sent to the server. The AI ​​in the server analyzes the user's answer data and generates correct or incorrect answers and detailed feedback. This feedback is then sent back from the server to the device and displayed to the user in real time. For example, if a user takes a math quiz, the system will instantly analyze the answer and provide detailed explanations and suggestions for improvement for any incorrect answers.

[0564] Grade management and progress tracking

[0565] The server continuously collects data on the user's learning activities and stores it in a database. The server also periodically analyzes this data to evaluate the user's learning progress. This analysis is performed using data analysis software such as Python's Pandas and Scikit-learn. A performance report generated based on the evaluation is provided to the user and the educational institution. The terminal displays the performance report to the user, allowing them to check their learning progress.

[0566] Teacher-student communication

[0567] The device displays an interface with chat and video calling functions to users, allowing educators and learners to communicate in real time. For example, when a user (student) sends a question to a teacher to resolve a concern, a real-time video call is initiated between the devices. The teacher can then provide an explanation on the spot to deepen the student's understanding. This makes it possible to receive a high-quality education no matter where you are.

[0568] Prompt Sentence Examples

[0569] "Please recommend the next learning content based on the performance data of previously learned units. Please suggest the most suitable content to the user, focusing on progress and performance in the field of mathematics in particular."

[0570] As described above, the system of the present invention provides a comprehensive range of services, from user authentication to recommendation of learning content, real-time feedback, grade management, and communication with educators, with the aim of reducing educational disparities and improving learning outcomes.

[0571] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0572] Step 1: Enter and submit user credentials

[0573] Input: The user enters the user ID and password into the terminal.

[0574] What happens: The device displays a login screen and receives the authentication information entered by the user.

[0575] Output: The terminal sends the entered authentication information to the server.

[0576] Step 2: Verify Credentials

[0577] Input: User ID and password sent to the server

[0578] How it works: The server authenticates the received credentials against its internal database. It compares the hashed password with its records to determine if authentication is successful.

[0579] Output: The authentication result (success or failure) is returned from the server to the device.

[0580] Step 3: Display the Home Screen

[0581] Input: Authentication result from server (success)

[0582] Action: After successful authentication, the device displays the home screen to the user.

[0583] Output: The home screen is displayed on the user's device.

[0584] Step 4: Obtaining training data

[0585] Input: User login information

[0586] Operation: The server retrieves the user's past learning history and grade data from the database. It extracts the necessary data using SQL queries, etc.

[0587] Output: The acquired learning data is stored on the server.

[0588] Step 5: Content recommendation by AI algorithm

[0589] Input: Obtained training data

[0590] How it works: The server runs an AI algorithm (e.g., TensorFlow or PyTorch) to calculate the optimal learning content for the user. This algorithm selects appropriate content based on past learning data.

[0591] Output: Recommended learning content is generated and stored on the server.

[0592] Step 6: Submit and view your learning content

[0593] Input: Recommended learning content

[0594] Operation: The server sends the recommended learning content to the device, which then displays the content to the user.

[0595] Output: The learning content is displayed on the user's device.

[0596] Step 7: Submit your answers

[0597] Input: Data answered by the user

[0598] How it works: The user answers questions and assignments on their device, and the device sends the answer data to the server.

[0599] Output: The server receives the answer data.

[0600] Step 8: Analyze the answer data and generate feedback

[0601] Input: Submitted answer data

[0602] How it works: An AI (e.g., BERT or GPT, which implements NLP models) in the server analyzes the answer data and generates correct answers and detailed feedback. This analysis uses natural language processing and machine learning algorithms.

[0603] Output: The generated feedback is stored on the server.

[0604] Step 9: Submit and view feedback

[0605] Input: Generated feedback

[0606] Operation: The server sends the generated feedback to the device, which displays the sent feedback to the user in real time.

[0607] Output: Feedback is displayed on the user's device.

[0608] Step 10: Collect and analyze progress data

[0609] Input: User learning activity data

[0610] How it works: The server continuously collects user learning activity data, stores it in a database, and periodically analyzes this data using data analysis software (e.g., Python's Pandas or Scikit-learn).

[0611] Output: Analysis results and progress data are saved on the server.

[0612] Step 11: Generate and submit a grade report

[0613] Input: Analysis results and progress data

[0614] Operation: The server generates and periodically provides performance reports to users and educational institutions.

[0615] Output: The performance report is sent to the terminal and displayed to the user.

[0616] Step 12: Start real-time communication

[0617] Input: User's question and request to start a video call

[0618] How it works: The device displays the interface needed to start a chat or video call with the educator, sends questions to the educator in real time, and a video call is initiated between the devices.

[0619] Output: Answers and explanations from the educator are displayed in real time on the user's device.

[0620] As described above, through the specific actions taken at each step, the entire system functions smoothly, enabling the provision of high-quality education.

[0621] (Application example 1)

[0622] 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."

[0623] Educational inequality remains a problem in modern society, with access to educational resources and quality varying greatly depending on the region and environment. The coronavirus pandemic has made remote education even more important, but current remote education systems face challenges, such as difficulty in managing learner progress and implementing personalized learning. Furthermore, the lack of real-time feedback and smooth communication with educators is one of the factors contributing to a decline in the quality of education.

[0624] 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.

[0625] In this invention, the server includes means for receiving authentication information entered by a user, means for performing authentication processing based on the authentication information, means for acquiring the learner's past learning data, means for recommending optimal learning content based on the learning data, means for analyzing the learner's answer data and generating feedback, means for tracking learning progress and generating a performance report, means for supporting communication between educators and learners in real time, means for providing a virtual classroom interface in a virtual space, and means for delivering educational content in real time using smart devices. This reduces educational disparities and enables people to receive high-quality education wherever they are.

[0626] "Authentication Information" means information used to verify a user's identity, such as a user ID and password entered by a user when accessing a system.

[0627] "Authentication processing" is a process for verifying whether a user is a legitimate user based on the user's authentication information.

[0628] "Learning data" refers to information that records a learner's past learning history, grades, and progress.

[0629] "Learning content" refers to educational resources such as teaching materials, documents, quizzes, and videos that students can use to learn.

[0630] "Feedback" is a response that includes evaluation, advice, and specific areas for improvement provided to a learner in response to their learning activities.

[0631] "Progress" refers to the learner's progress and achievement in learning, including grades and course completion status.

[0632] "Real-time communication" is a function that allows educators and students to send and receive data simultaneously and interact instantly.

[0633] "Virtual space" refers to a virtual learning environment built on the Internet, an area where education is possible independent of physical location.

[0634] A "virtual classroom" is a virtual classroom for education that exists in a virtual space and is an educational environment that can be accessed via the Internet.

[0635] "Smart devices" refers to all electronic devices with internet connectivity, such as smartphones, tablets, smart glasses, and head-mounted displays.

[0636] "Delivery of educational content" refers to the provision of educational videos, texts, quizzes, etc. to learners in real time or on demand via the Internet.

[0637] The present invention is a system that utilizes AI to reduce educational disparities and enable students anywhere in the world to receive cutting-edge education. The system includes a user authentication means, a learning content recommendation means, a real-time feedback means, a grade management and progress tracking means, a teacher-student communication means, a virtual classroom interface means, and a means for delivering educational content via smart devices.

[0638] User Authentication and Login

[0639] First, the server receives the authentication information entered by the user. The user accesses the login screen using a smart device and enters their user ID and password. The authentication information is sent to the server, where it is verified against a database for authentication. If authentication is successful, the user is logged into the system and the home screen is displayed.

[0640] Learning content recommendations

[0641] When a user logs in, the server retrieves the user's past learning data from a database. Based on this data, a generative AI model is used to recommend the most suitable learning content to the user. For example, if a user has studied many math units in the past, useful math content to study next will be recommended based on their progress and performance.

[0642] Real-time feedback

[0643] Data on tasks and quizzes answered by users is sent to a server. The AI ​​in the server analyzes the user's answer data and generates correct or incorrect answers and detailed feedback. This feedback is sent to the user's smart device and displayed in real time. For example, when a user solves a math quiz, the system immediately analyzes the answer and provides detailed explanations and suggestions for improvement for any incorrect parts.

[0644] Grade management and progress tracking

[0645] The server continuously collects the user's learning activity data and stores it in a database. The server also periodically analyzes this data to evaluate the user's learning progress. Based on the analysis results, it generates a performance report and provides it to the user and the educational institution. The performance report is displayed in real time on the user's smart device.

[0646] Teacher-student communication

[0647] A chat and video call interface is displayed on the user's smart device, allowing real-time communication between educators and students. Students can send questions to the teacher to resolve their doubts, and a real-time video call is initiated between the devices. The teacher can then provide on-the-spot explanations to deepen students' understanding.

[0648] Virtual classroom interface and educational content delivery

[0649] A virtual classroom interface in a virtual space is also provided, allowing users to access the virtual classroom in real time using a smartphone or head-mounted display. In this environment, real-time lectures and discussions are held, allowing users to learn while interacting with educational content.

[0650] Hardware and software used

[0651] The implementation of this system uses the following hardware and software:

[0652] Hardware: Smart devices (smartphones, tablets, head-mounted displays)

[0653] Software: Flask framework, generative AI model (using TensorFlow)

[0654] Examples of concrete examples and prompts

[0655] For example, imagine a scenario where a student logs in to an application and is recommended next learning content based on their past learning history. The student then uses that content to answer quizzes and receives real-time feedback. After learning, the student can check their grade report and understand their progress. They can also ask a teacher any questions they have in real time and receive clarification in the virtual classroom.

[0656] An example of a specific prompt for a generative AI model is as follows:

[0657] Recommend the most relevant learning content based on the user's learning history when they log in.

[0658] Past learning history is as follows:

[0659] Mathematics: 85 points

[0660] Chemistry: 90 points

[0661] Recommend the next learning content that would be best for this student.

[0662] As a result, this system will reduce educational disparities and enable people to receive high-quality education wherever they are.

[0663] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0664] Step 1:

[0665] The user accesses the login screen using a smart device and enters their user ID and password. The entered authentication information is sent to the server. The server compares this information with the database and performs authentication processing. If authentication is successful, the home screen is displayed on the device. Input: User ID, password. Output: Authentication result, home screen.

[0666] Step 2:

[0667] When a user logs in to the system, the server retrieves the user's past learning data from the database. The retrieved learning data is input into the generative AI model. The AI ​​model analyzes this data and recommends the most suitable learning content for the user. The recommendation results are sent to the terminal and displayed. Input: Learning data. Output: Recommended learning content.

[0668] Step 3:

[0669] The user works on the recommended learning content and answers assignments and quizzes. The answer data is sent to the server. The AI ​​on the server analyzes this answer data, determines whether the answer is correct, and generates detailed feedback. The generated feedback is sent to the device in real time and displayed. Input: Answer data. Output: Feedback.

[0670] Step 4:

[0671] The server continuously collects the user's learning activity data and stores it in a database. The data is analyzed at specific intervals to evaluate the user's progress. Based on the evaluation results, a performance report is generated and provided to the user and the educational institution. The performance report is displayed on the terminal. Input: Learning activity data. Output: Performance report.

[0672] Step 5:

[0673] Chat and video call functions are displayed on the user's device. Users can use these to communicate with educators in real time. For example, a user can send a question to a teacher about a concern they have, which will initiate a video call. The teacher will then provide an explanation on the spot to deepen the user's understanding. Input: Question, chat message. Output: Explanation, response.

[0674] Step 6:

[0675] The virtual classroom interface in the virtual space is displayed on the terminal. Users can access the virtual classroom using their smart devices. Real-time lectures and discussions take place here, allowing users to progress with their learning while experiencing educational content. Input: Access request. Output: Virtual classroom interface, educational content.

[0676] Step 7:

[0677] Educational content is delivered to smart devices via the internet through a server. The software used here is the Flask framework and generative AI models such as TensorFlow. This provides a personalized learning experience. Input: Educational content. Output: Learning experience, learning resources.

[0678] 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.

[0679] This invention is a system that utilizes AI to reduce educational disparities and enable people to receive cutting-edge education anywhere in the world. This system consists of the following elements: a user authentication means, a learning content recommendation means, a real-time feedback means, a grade management and progress tracking means, a means of communication between educators and learners, and an emotion engine for recognizing user emotions.

[0680] User Authentication and Login

[0681] First, a user logs in to the system using a terminal. The terminal displays a login screen to the user and prompts them to enter their user ID and password. Next, the terminal sends the entered authentication information to the server. The server compares the information with a database and returns the authentication result. If authentication is successful, the user is logged in to the system and the home screen is displayed.

[0682] Learning content recommendations

[0683] When a user logs in, the server retrieves the user's past learning history and grade data from a database. The server inputs the retrieved data into an AI algorithm to generate learning content that is optimal for the user. The recommended content is sent to the device, which then displays it to the user. For example, if the user has studied many math units in the past, the server will recommend useful math content to study next based on their progress and performance.

[0684] Real-time feedback

[0685] When a user answers a study assignment or quiz on their device, the answer data is sent to the server. The AI ​​in the server analyzes the user's answer data and generates correct or incorrect answers and detailed feedback. The generated feedback is sent from the server to the user's device, where it is displayed in real time. For example, when a user answers a math quiz, the system immediately analyzes the answer and provides detailed explanations and suggestions for improvement for any incorrect answers.

[0686] Grade management and progress tracking

[0687] The server continuously collects the user's learning activity data and stores it in a database. The server also periodically analyzes this data to evaluate the user's learning progress. Based on the analysis results, it generates a performance report and provides it to the user and the educational institution. The terminal displays the performance report to the user, allowing them to check their learning progress.

[0688] Teacher-student communication

[0689] The device displays an interface with chat and video call functions to the user, allowing educators and learners to communicate in real time. Users (students) send questions to teachers to resolve their doubts, and a real-time video call is initiated between the devices. The teacher then provides on-the-spot explanations to deepen the student's understanding. This process makes it possible to receive a high-quality education wherever they are.

[0690] Utilizing the Emotion Engine

[0691] Furthermore, the present invention incorporates an emotion engine that can recognize the user's emotions in real time. Specifically, the emotion engine analyzes the user's facial expressions and tone of voice through the device's built-in camera and microphone. This information is sent to a server, which then identifies the user's current emotional state.

[0692] This emotional data can be used to:

[0693] Change of learning content: The server adaptively changes learning content based on the user's emotional state. For example, if the user is tired, it recommends light content that will help them relax.

[0694] Feedback adjustment: The server adjusts the feedback provided to the user based on the emotional data. For example, if the user is feeling stressed, it emphasizes encouraging messages and advice.

[0695] Real-time support: The emotion engine recognizes emotion data and provides it to educators in real time. Educators can use this information to take appropriate action. For example, if a user is depressed, the educator can provide additional support.

[0696] As described above, the system of the present invention provides a comprehensive range of services, from user authentication to learning content recommendations, real-time feedback, grade management, communication with educators, and emotion recognition and utilization using an emotion engine, thereby effectively reducing educational disparities.

[0697] The processing flow will be explained below.

[0698] User Authentication and Login

[0699] Step 1:

[0700] The terminal displays a login screen to the user.

[0701] Step 2:

[0702] The user enters their ID and password on the login screen.

[0703] Step 3:

[0704] The terminal sends the entered ID and password to the server.

[0705] Step 4:

[0706] The server checks the received ID and password against the database.

[0707] Step 5:

[0708] The server returns the authentication result to the device. If the authentication is successful, the device displays the home screen to the user.

[0709] Learning content recommendations

[0710] Step 1:

[0711] When a user logs in, the server retrieves the user's past learning history and grade data from the database.

[0712] Step 2:

[0713] The server inputs the acquired data into an AI algorithm to generate learning content that is optimal for the user.

[0714] Step 3:

[0715] The server transmits a list of recommended learning contents to the user's terminal.

[0716] Step 4:

[0717] The terminal displays a list of the received study content to the user.

[0718] Real-time feedback

[0719] Step 1:

[0720] Users answer study assignments and quizzes on their devices.

[0721] Step 2:

[0722] The terminal transmits the user's answer data to the server.

[0723] Step 3:

[0724] The AI ​​on the server analyzes the received answer data and generates correct and detailed feedback.

[0725] Step 4:

[0726] The server transmits the generated feedback to the user's terminal.

[0727] Step 5:

[0728] The terminal displays the received feedback to the user in real time.

[0729] Grade management and progress tracking

[0730] Step 1:

[0731] The server continuously collects the user's learning activity data and stores it in a database.

[0732] Step 2:

[0733] The server periodically analyzes the collected data and evaluates the user's learning progress.

[0734] Step 3:

[0735] The server generates a performance report based on the analysis results.

[0736] Step 4:

[0737] The server transmits the generated performance report to the user's terminal.

[0738] Step 5:

[0739] The terminal displays the received performance report to the user, allowing the user to check the progress of their studies.

[0740] Teacher-student communication

[0741] Step 1:

[0742] The terminal displays an interface for chat and video call functions to the user.

[0743] Step 2:

[0744] A user (student) sends a request on a terminal to start a chat or video call with a teacher.

[0745] Step 3:

[0746] The server receives the user's request and notifies the corresponding teacher's terminal.

[0747] Step 4:

[0748] The teacher's terminal receives the request and responds to it to start a chat or video call.

[0749] Step 5:

[0750] Real-time communication takes place between devices, and students and teachers interact through messaging and video calls.

[0751] Utilizing the Emotion Engine

[0752] Step 1:

[0753] The device collects the user's facial expressions and tone of voice in real time through an emotion engine.

[0754] Step 2:

[0755] The terminal transmits the collected emotion data to the server.

[0756] Step 3:

[0757] The server analyzes the received emotion data to understand the user's current emotional state.

[0758] Step 4:

[0759] The server adaptively changes learning content based on the user's emotional state, for example, recommending light, relaxing content if the user is tired.

[0760] Step 5:

[0761] The server adjusts the feedback based on the emotional data, for example, emphasizing encouraging messages and advice if the user is feeling stressed.

[0762] Step 6:

[0763] The server provides recognized emotion data to educators in real time, helping them take appropriate action.

[0764] As a result, the system of the present invention provides a consistent service ranging from user authentication to learning content recommendations, real-time feedback, grade management, communication with educators, and emotion recognition and utilization using an emotion engine, thereby effectively reducing educational disparities.

[0765] Example 2

[0766] 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."

[0767] Traditional educational systems have difficulty accurately understanding each learner's progress and emotional state and flexibly adjusting learning content and feedback based on that information. They also lack effective means to support real-time communication between educators and learners. This creates educational disparities and leads to uneven learning quality.

[0768] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0769] In this invention, the server includes means for receiving authentication information entered by a user, means for performing authentication processing based on the authentication information, means for acquiring the learner's past learning data, means for recommending optimal learning content based on the learning data, means for analyzing the learner's answer data and generating feedback, means for tracking learning progress and generating performance reports, means for supporting communication between educators and learners in real time, means for collecting and analyzing emotional data, means for adaptively changing learning content based on the emotional data, and means for adjusting feedback content based on the emotional data. This allows for real-time understanding of the individual progress and emotional state of learners, enabling flexible learning support based on this information.

[0770] "Authentication Information" means the identification information required for a User to access the System, including, specifically, a User ID and password.

[0771] "Authentication processing" is the process of checking the entered authentication information against a database to confirm the user's identity.

[0772] "Study Data" refers to information including a user's past learning history and performance data, including details of the user's learning activities.

[0773] "Learning content" refers to educational resources such as learning materials, assignments, and quizzes that users should study.

[0774] "Answer data" refers to data that a user provides in response to a study task or quiz.

[0775] "Feedback" refers to evaluation and advice provided to the user based on the results of analyzing the user's answer data.

[0776] A "performance report" is a report summarizing a user's learning progress and grades.

[0777] "Emotion data" is information that indicates a real-time emotional state of a user, obtained by analyzing the user's facial expressions and tone of voice.

[0778] "Adaptive change" refers to the process of adjusting learning content and feedback to suit the user's condition based on collected data.

[0779] This invention is a system that utilizes AI to reduce educational disparities and enable people to receive cutting-edge education anywhere in the world. This system consists of the following elements: a user authentication means, a learning content recommendation means, a real-time feedback means, a grade management and progress tracking means, a means of communication between educators and learners, and an emotion engine for recognizing user emotions.

[0780] First, the user logs in to the system using their own terminal. The terminal displays a login screen to the user and asks them to enter their user ID and password. Once the user has completed the input, the terminal sends the entered user ID and password to the server. When the server receives the authentication information, it compares it with a database and returns the authentication result to the terminal. If the authentication is successful, the terminal displays the home screen to the user.

[0781] When a user logs in, the server retrieves the user's past learning history and grade data from a database. This data is input into AI algorithms such as TensorFlow and Scikit-learn. The server uses the AI ​​algorithms to generate optimal learning content for the user. The generated learning content is sent to the device, which then displays it to the user.

[0782] When a user answers a learning task or quiz, the answer data is sent from the device to the server. The AI ​​feedback system in the server analyzes the answer data and generates correct or incorrect answers and detailed feedback. This feedback is sent from the server to the device, which displays it to the user in real time.

[0783] The server continuously collects the user's learning activity data and stores it in a database. It also periodically analyzes this data to evaluate the user's learning progress. It generates a performance report based on the analysis results and provides it to the user and the educational institution. The terminal displays the performance report to the user, allowing them to check their learning progress.

[0784] The device provides chat and video calling functions to support real-time communication between users and educators. Users (students) can send questions to teachers to resolve their doubts and exchange information in real time. The video calling function uses, for example, the Zoom API or WebRTC.

[0785] Using the emotion engine, the system recognizes the user's real-time emotional state. The device's built-in camera and microphone collect the user's facial expressions and tone of voice, which are then sent to the server. The emotion engine analyzes the data to understand the user's current emotional state. Based on this emotional data, the server adaptively changes the learning content and feedback. Educators can also obtain emotional data in real time and take appropriate action.

[0786] As a concrete example, let us consider a scenario in which a user is studying mathematics. For example, the system recommends the next mathematics topic to study based on the mathematics units the user has studied in the past. Furthermore, when the user answers a quiz and the results are sent to the server, they are immediately analyzed and detailed feedback is provided. If the emotion engine determines that the user is feeling stressed, the server will send an encouraging message to keep the user motivated.

[0787] Examples of input prompts for generative AI models include:

[0788] "If a user is studying a math unit, recommend what they should study next based on their past learning history. Also, provide detailed feedback if they get a quiz answer wrong, and suggest how to support them when they're feeling stressed."

[0789] The above is an embodiment of the present invention.

[0790] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0791] System program processing flow

[0792] Step 1: User authentication and login

[0793] The user displays the login screen on their device. They enter their user ID and password, which the device then sends to the server. The server compares the authentication information with the database and returns the authentication result to the device. If authentication is successful, the device displays the home screen to the user.

[0794] Input: User ID, Password

[0795] Data calculation: database search, authentication processing

[0796] Output: Authentication result, home screen display

[0797] Step 2: Obtaining training data

[0798] After a user logs in, the server retrieves the user's past learning history and performance data from the database, and prepares this data for input into the AI ​​algorithm.

[0799] Input: User ID

[0800] Data processing: Retrieving data from the database

[0801] Output: Past learning history, grade data

[0802] Step 3: Recommend learning content

[0803] The server inputs the acquired learning data into an AI algorithm (e.g., TensorFlow or Scikit-learn). The AI ​​algorithm generates optimal learning content for the user based on the data. The generated learning content is sent from the server to the device, which then displays it to the user.

[0804] Input: Past learning history, grade data

[0805] Data Computation: Learning Content Generation with AI Algorithms

[0806] Output: Learning content, displayed on the user screen

[0807] Step 4: Complete and submit your assignments

[0808] The user answers study assignments and quizzes on the device, and the answer data is sent from the device to the server.

[0809] Input: User's answer to the learning task

[0810] Data processing: Sending answer data

[0811] Output: Submitted answer data

[0812] Step 5: Generate and submit feedback

[0813] The AI ​​feedback system on the server analyzes the submitted answer data, generates correct and incorrect answers, and sends detailed feedback to the device, which then displays the feedback to the user in real time.

[0814] Input: Submitted answer data

[0815] Data calculation: Analysis and feedback generation by AI feedback system

[0816] Output: Generated feedback, displayed on the user screen

[0817] Step 6: Collect and analyze training data

[0818] The server continuously collects the user's learning activity data and stores it in a database. The server periodically analyzes this data and evaluates the user's learning progress. Based on the analysis results, it generates a performance report and provides it to the user and the educational institution. The terminal displays the performance report to the user, allowing them to check their learning progress.

[0819] Input: User learning activity data

[0820] Data calculation: storing data in database, periodic data analysis

[0821] Output: Grade report, display on user screen

[0822] Step 7: Communicate with Educators

[0823] Users can communicate with educators in real time using chat and video call functions on their devices. They can send questions and receive answers and explanations from educators in real time. The chat and video call functions use the Zoom API and WebRTC.

[0824] Input: User question, Educator answer

[0825] Data processing: Real-time communication

[0826] Output: Answers and explanations from the educator, displayed on the user's screen

[0827] Step 8: Emotion engine analysis and feedback adjustment

[0828] The device's built-in camera and microphone are used to collect the user's facial expressions and tone of voice. The server's emotion engine analyzes this data to understand the user's current emotional state. Based on this emotional data, the server adaptively changes the learning content and feedback.

[0829] Input: facial expression data, tone of voice data

[0830] Data Computing: Emotional data analysis, feedback and content adjustment

[0831] Output: Tailored learning content, feedback, and display on the user's screen

[0832] The above is a detailed description of the specific operations and inputs and outputs at each processing step of this system.

[0833] (Application example 2)

[0834] 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."

[0835] Conventional educational systems make it difficult for learners to receive high-quality education wherever they are. They also lack the means to provide real-time feedback on learning progress and assessment of answers. Furthermore, it is difficult to adjust learning content to take into account the learner's emotional state and to communicate with educators in real time. This has led to problems such as a decline in learner motivation and learning effectiveness.

[0836] 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.

[0837] In this invention, the server includes means for receiving authentication information entered by a user, means for performing authentication processing based on the authentication information, means for acquiring the learner's past learning data, means for recommending optimal learning content based on the learning data, means for analyzing the learner's answer data and generating feedback, means for tracking the learning progress and generating a performance report, means for analyzing the learner's emotional data and adjusting the learning content based on the learner's emotional state, means for supporting communication between educators and learners in real time, and means for providing feedback based on the learning progress and emotions to a smart device and enabling video calls with educators. This allows learners to receive high-quality education wherever they are, improving their learning effectiveness.

[0838] "Authentication information" refers to identification information such as a user ID and password required for a learner to log in to the system.

[0839] "Authentication processing" is a process for confirming the identity and access rights of a learner based on the input authentication information.

[0840] "Learning data" refers to information including a learner's past learning history and grade data.

[0841] "Learning content" refers to educational materials, videos, quizzes, and other content provided to learners.

[0842] "Feedback" is information that includes evaluation and advice on the learner's answers.

[0843] A "performance report" is a report summarizing a learner's learning progress and an evaluation of their performance.

[0844] "Emotional data" is information about the emotional state of a learner obtained by analyzing their facial expressions and tone of voice.

[0845] "Real-time communication" is a function that allows educators and learners to communicate directly without any time lag.

[0846] "Smart devices" are electronic devices with advanced functions such as smartphones, smart glasses, and tablets.

[0847] "Video calling" is a means of communicating with each other using video and audio over the Internet.

[0848] The system for implementing this invention is realized by combining various hardware and software. First, a user accesses the system using a smart device such as a smartphone or tablet. The main functions of the system are provided by three entities: a server, a terminal, and a user.

[0849] User Authentication and Login

[0850] The user accesses the login screen from their smart device and enters their user ID and password. The device receives the entered authentication information and sends it to the server. The server performs authentication based on the authentication information and returns the results of matching it with the database. If authentication is successful, the user can access the home screen.

[0851] Learning content recommendations

[0852] When a user logs in, the server retrieves the user's past learning data from a database. An AI algorithm then analyzes the data and recommends personalized learning content. The recommended content is displayed on the device for the user to view.

[0853] Real-time feedback

[0854] When a user answers a quiz or assignment, the answer data is sent from the device to the server. The AI ​​in the server analyzes the answer data and generates detailed feedback, which is displayed on the user's device in real time.

[0855] Grade management and progress tracking

[0856] The server collects the user's learning activity data and stores it in a database. It periodically analyzes the data and generates a learning progress and performance report. This report is provided to the user and the educational institution and is also displayed on the device.

[0857] Communication with Educators

[0858] Users can communicate with educators in real time using chat and video calling functions on their devices, and any questions or concerns can be resolved in real time, improving learning outcomes.

[0859] Utilizing the Emotion Engine

[0860] The emotion engine analyzes the user's facial expressions and tone of voice via the device's built-in camera and microphone. This emotion data is sent to a server and reflected in adjustments to the learning content and feedback. It is also provided to educators in real time, allowing them to take appropriate action.

[0861] Examples and prompts

[0862] Specific examples

[0863] User authentication: The learner enters their username and password into the login screen on their smartphone, and if authentication is successful, they are taken to the home screen.

[0864] Learning content recommendation: AI recommends the next math video to learn based on learning history and displays it on the device.

[0865] Real-time feedback: AI provides detailed explanations on the spot for the quiz you answer.

[0866] Learning support using an emotion engine: If a user is feeling stressed, the AI ​​will suggest light content to relieve them.

[0867] Prompt Sentence Examples

[0868] "Based on your learning history and progress, AI will recommend the most appropriate educational content for you to learn next. As you progress through the learning process, you will receive real-time feedback on your answers, and you can even have a video call with an educator if needed. In addition, an emotion engine will analyze your facial expressions and voice to adjust your learning method at the appropriate time."

[0869] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0870] Step 1:

[0871] A user uses a smart device to access the system's login screen and enter their user ID and password. The device receives the entered authentication information (user ID and password) and sends it to the server. The server compares the entered authentication information with the authentication database and returns the authentication result. If the user is successfully authenticated, they can access the home screen.

[0872] Input: User ID, Password

[0873] Data processing: Verification against authentication database

[0874] Output: Authentication result

[0875] Step 2:

[0876] When a user logs in, the server retrieves the user's past learning data (learning history and grade data) from the database. The retrieved data is input into an AI algorithm to generate individually optimized learning content. The generated learning content is sent to the device and displayed to the user.

[0877] Input: User's learning history, grade data

[0878] Data processing: Analysis using AI algorithms

[0879] Output: Recommended learning content

[0880] Step 3:

[0881] When users browse learning content and answer quizzes and assignments, the answer data is sent from the device to the server. The AI ​​on the server analyzes the answer data, determines whether the answer is correct or incorrect, and generates detailed feedback. The generated feedback is sent to the device and displayed to the user in real time.

[0882] Input: Answer data

[0883] Data processing: Analysis of answer data using AI

[0884] Output: Detailed feedback

[0885] Step 4:

[0886] The server continuously collects user learning activity data and stores it in a database. It periodically analyzes the data and generates learning progress and performance reports. The performance reports are provided to the user and the educational institution and are also displayed on the device.

[0887] Input: Learning activity data

[0888] Data processing: storing in database, analysis

[0889] Output: Learning progress, grade report

[0890] Step 5:

[0891] Users can communicate with educators in real time using chat and video calling functions from their devices. The device is responsible for initiating and terminating communications and sending and receiving messages, while the server manages the necessary interconnections.

[0892] Input: Chat message, video call start request

[0893] Data Processing: Managing Real-Time Communications

[0894] Output: Real-time communication with educators

[0895] Step 6:

[0896] The device's built-in camera and microphone collect the user's facial expressions and tone of voice. This data is sent to a server and analyzed by an emotion engine. The server uses the emotion analysis results to adjust learning content and change feedback, providing it to educators in real time as needed.

[0897] Input: facial expression data, tone of voice data

[0898] Data processing: Analysis using emotion engine

[0899] Output: Adjusted learning content, feedback

[0900] 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.

[0901] 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.

[0902] 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.

[0903] [Third embodiment]

[0904] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0905] 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.

[0906] 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).

[0907] 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.

[0908] 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.

[0909] 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).

[0910] 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.

[0911] 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.

[0912] 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.

[0913] 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.

[0914] 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.

[0915] 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."

[0916] This invention is a system that utilizes AI to reduce educational disparities and enable people to receive cutting-edge education anywhere in the world. This system consists of the following elements: user authentication means, learning content recommendation means, real-time feedback means, grade management and progress tracking means, and communication means between teachers and students.

[0917] User Authentication and Login

[0918] First, a user logs in to the system using a terminal. The terminal displays a login screen to the user and prompts them to enter their user ID and password. Next, the terminal sends the entered authentication information to the server. The server compares the information with a database and returns the authentication result. If authentication is successful, the user is logged in to the system and the home screen is displayed.

[0919] Learning content recommendations

[0920] When a user logs in, the server retrieves the user's past learning history and grade data from a database. The server runs an AI algorithm based on the retrieved data and recommends optimal learning content to the user. The recommended content is sent to the device, which displays it to the user. For example, if the user has studied many math units in the past, the server will recommend useful math content to study next based on their progress and performance.

[0921] Real-time feedback

[0922] When a user answers a study assignment or quiz on their device, the answer data is sent to the server. The AI ​​in the server analyzes the user's answer data and generates correct or incorrect answers and detailed feedback. The generated feedback is sent from the server to the user's device, where it is displayed in real time. For example, when a user answers a math quiz, the system immediately analyzes the answer and provides detailed explanations and suggestions for improvement for any incorrect answers.

[0923] Grade management and progress tracking

[0924] The server continuously collects the user's learning activity data and stores it in a database. The server also periodically analyzes this data to evaluate the user's learning progress. Based on the analysis results, it generates a performance report and provides it to the user and the educational institution. The terminal displays the performance report to the user, allowing them to check their learning progress.

[0925] Teacher-student communication

[0926] The device displays an interface with chat and video call functions to the user, allowing educators and learners to communicate in real time. Users (students) send questions to teachers to resolve their doubts, and a real-time video call is initiated between the devices. The teacher then provides on-the-spot explanations to deepen the student's understanding. This process makes it possible to receive a high-quality education wherever they are.

[0927] As described above, the system of the present invention provides a comprehensive range of services, from user authentication to recommendation of learning content, real-time feedback, grade management, and communication with educators, thereby effectively reducing educational disparities.

[0928] The processing flow will be explained below.

[0929] User Authentication and Login

[0930] Step 1:

[0931] The terminal displays a login screen to the user.

[0932] Step 2:

[0933] The user enters their ID and password on the login screen.

[0934] Step 3:

[0935] The terminal sends the entered ID and password to the server.

[0936] Step 4:

[0937] The server checks the received ID and password against the database.

[0938] Step 5:

[0939] The server returns the authentication result to the device. If the authentication is successful, the device displays the home screen to the user.

[0940] Learning content recommendations

[0941] Step 1:

[0942] When a user logs in, the server retrieves the user's past learning history and grade data from the database.

[0943] Step 2:

[0944] The server inputs the acquired data into an AI algorithm to generate learning content that is optimal for the user.

[0945] Step 3:

[0946] The server transmits a list of recommended learning contents to the user's terminal.

[0947] Step 4:

[0948] The terminal displays a list of the received study content to the user.

[0949] Real-time feedback

[0950] Step 1:

[0951] Users answer study assignments and quizzes on their devices.

[0952] Step 2:

[0953] The terminal transmits the user's answer data to the server.

[0954] Step 3:

[0955] The AI ​​on the server analyzes the received answer data and generates correct and detailed feedback.

[0956] Step 4:

[0957] The server transmits the generated feedback to the user's terminal.

[0958] Step 5:

[0959] The terminal displays the received feedback to the user in real time.

[0960] Grade management and progress tracking

[0961] Step 1:

[0962] The server continuously collects the user's learning activity data and stores it in a database.

[0963] Step 2:

[0964] The server periodically analyzes the collected data and evaluates the user's learning progress.

[0965] Step 3:

[0966] The server generates a performance report based on the analysis results.

[0967] Step 4:

[0968] The server transmits the generated performance report to the user's terminal.

[0969] Step 5:

[0970] The terminal displays the received performance report to the user, allowing the user to check the progress of their studies.

[0971] Teacher-student communication

[0972] Step 1:

[0973] The terminal displays an interface for chat and video call functions to the user.

[0974] Step 2:

[0975] A user (student) sends a request on a terminal to start a chat or video call with a teacher.

[0976] Step 3:

[0977] The server receives the user's request and notifies the corresponding teacher's terminal.

[0978] Step 4:

[0979] The teacher's terminal receives the request and responds to it to start a chat or video call.

[0980] Step 5:

[0981] Real-time communication takes place between devices, and students and teachers interact through messaging and video calls.

[0982] Example 1

[0983] 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."

[0984] In the traditional education system, the quality of education varies greatly depending on the region and economic situation, creating educational disparities. Furthermore, real-time feedback based on individual learners' progress and level of understanding was rarely provided, resulting in insufficient learning outcomes. Furthermore, communication between teachers and students was limited, meaning that questions were often not resolved promptly.

[0985] 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.

[0986] In this invention, the server includes means for receiving authentication information entered by a user, means for performing authentication processing based on the authentication information, means for acquiring the learner's past learning data, means for recommending optimal learning content based on the learning data, means for analyzing the learner's answer data and generating feedback, means for tracking learning progress and generating a performance report, means for supporting communication between educators and learners in real time, means for displaying recommended learning content to the user, and means for sending questions to educators in real time and receiving answers. This enables access to high-quality education wherever one is, personalized learning, and rapid feedback, thereby reducing educational disparities and improving learning effectiveness.

[0987] The "means for receiving authentication information input by a user" refers to a means for receiving authentication information such as an ID and password input by a user from a terminal.

[0988] The "means for performing authentication processing based on authentication information" refers to a means for verifying received authentication information against a database and authenticating the user.

[0989] "Means for obtaining a learner's past learning data" refers to means for obtaining the learner's past learning content and grade data from a database.

[0990] "Means for recommending optimal learning content" refers to a means for using AI to recommend optimal learning content to learners based on acquired learning data.

[0991] The "means for analyzing the answer data of the learner and generating feedback" refers to a means for analyzing the data of the answer given by the learner and generating feedback based on the results.

[0992] "Means for tracking learning progress and generating performance reports" refers to means for continuously tracking a learner's learning activities, evaluating the learner's progress, and generating performance reports.

[0993] "Means to support real-time communication between educators and learners" refers to means that provide chat and video call functions to support real-time communication between educators and learners.

[0994] The "means for displaying recommended study content to the user" refers to a means for displaying the study content sent from the server on the user's terminal.

[0995] "Means for sending questions to educators in real time and receiving answers" refers to means for learners to send questions to educators in real time and receive answers.

[0996] The above definitions make it clear what role each element of the system plays.

[0997] This invention is a system that utilizes AI to reduce educational disparities and enable students to receive cutting-edge education wherever they are. The system is equipped with a means for user authentication, a means for recommending learning content, a means for real-time feedback, a means for managing grades and tracking progress, and a means for communication between teachers and students.

[0998] User Authentication and Login

[0999] First, a user logs in to the system using a terminal. The terminal displays a login screen to the user and prompts them to enter their user ID and password. Once the user has completed the input, the terminal sends the authentication information to the server. The server compares the information with its internal database and performs authentication. If authentication is successful, the home screen is displayed on the terminal and the user can access the system.

[1000] Learning content recommendations

[1001] When a user logs in, the server retrieves the user's past learning history and grade data from the database. Based on the retrieved data, the server uses an AI algorithm to recommend optimal learning content to the user. The AI ​​models used include TensorFlow and PyTorch. The server sends the recommended learning content to the device, which then displays it to the user. For example, if the user has studied many math units in the past, useful math content to study next will be recommended.

[1002] Real-time feedback

[1003] When a user answers a study assignment or quiz on their device, the answer data is sent to the server. The AI ​​in the server analyzes the user's answer data and generates correct or incorrect answers and detailed feedback. This feedback is then sent back from the server to the device and displayed to the user in real time. For example, if a user takes a math quiz, the system will instantly analyze the answer and provide detailed explanations and suggestions for improvement for any incorrect answers.

[1004] Grade management and progress tracking

[1005] The server continuously collects data on the user's learning activities and stores it in a database. The server also periodically analyzes this data to evaluate the user's learning progress. This analysis is performed using data analysis software such as Python's Pandas and Scikit-learn. A performance report generated based on the evaluation is provided to the user and the educational institution. The terminal displays the performance report to the user, allowing them to check their learning progress.

[1006] Teacher-student communication

[1007] The device displays an interface with chat and video calling functions to users, allowing educators and learners to communicate in real time. For example, when a user (student) sends a question to a teacher to resolve a concern, a real-time video call is initiated between the devices. The teacher can then provide an explanation on the spot to deepen the student's understanding. This makes it possible to receive a high-quality education no matter where you are.

[1008] Prompt Sentence Examples

[1009] "Please recommend the next learning content based on the performance data of previously learned units. Please suggest the most suitable content to the user, focusing on progress and performance in the field of mathematics in particular."

[1010] As described above, the system of the present invention provides a comprehensive range of services, from user authentication to recommendation of learning content, real-time feedback, grade management, and communication with educators, with the aim of reducing educational disparities and improving learning outcomes.

[1011] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1012] Step 1: Enter and submit user credentials

[1013] Input: The user enters the user ID and password into the terminal.

[1014] What happens: The device displays a login screen and receives the authentication information entered by the user.

[1015] Output: The terminal sends the entered authentication information to the server.

[1016] Step 2: Verify Credentials

[1017] Input: User ID and password sent to the server

[1018] How it works: The server authenticates the received credentials against its internal database. It compares the hashed password with its records to determine if authentication is successful.

[1019] Output: The authentication result (success or failure) is returned from the server to the device.

[1020] Step 3: Display the Home Screen

[1021] Input: Authentication result from server (success)

[1022] Action: After successful authentication, the device displays the home screen to the user.

[1023] Output: The home screen is displayed on the user's device.

[1024] Step 4: Obtaining training data

[1025] Input: User login information

[1026] Operation: The server retrieves the user's past learning history and grade data from the database. It extracts the necessary data using SQL queries, etc.

[1027] Output: The acquired learning data is stored on the server.

[1028] Step 5: Content recommendation by AI algorithm

[1029] Input: Obtained training data

[1030] How it works: The server runs an AI algorithm (e.g., TensorFlow or PyTorch) to calculate the optimal learning content for the user. This algorithm selects appropriate content based on past learning data.

[1031] Output: Recommended learning content is generated and stored on the server.

[1032] Step 6: Submit and view your learning content

[1033] Input: Recommended learning content

[1034] Operation: The server sends the recommended learning content to the device, which then displays the content to the user.

[1035] Output: The learning content is displayed on the user's device.

[1036] Step 7: Submit your answers

[1037] Input: Data answered by the user

[1038] How it works: The user answers questions and assignments on their device, and the device sends the answer data to the server.

[1039] Output: The server receives the answer data.

[1040] Step 8: Analyze the answer data and generate feedback

[1041] Input: Submitted answer data

[1042] How it works: An AI (e.g., BERT or GPT, which implements NLP models) in the server analyzes the answer data and generates correct answers and detailed feedback. This analysis uses natural language processing and machine learning algorithms.

[1043] Output: The generated feedback is stored on the server.

[1044] Step 9: Submit and view feedback

[1045] Input: Generated feedback

[1046] Operation: The server sends the generated feedback to the device, which displays the sent feedback to the user in real time.

[1047] Output: Feedback is displayed on the user's device.

[1048] Step 10: Collect and analyze progress data

[1049] Input: User learning activity data

[1050] How it works: The server continuously collects user learning activity data, stores it in a database, and periodically analyzes this data using data analysis software (e.g., Python's Pandas or Scikit-learn).

[1051] Output: Analysis results and progress data are saved on the server.

[1052] Step 11: Generate and submit a grade report

[1053] Input: Analysis results and progress data

[1054] Operation: The server generates and periodically provides performance reports to users and educational institutions.

[1055] Output: The performance report is sent to the terminal and displayed to the user.

[1056] Step 12: Start real-time communication

[1057] Input: User's question and request to start a video call

[1058] How it works: The device displays the interface needed to start a chat or video call with the educator, sends questions to the educator in real time, and a video call is initiated between the devices.

[1059] Output: Answers and explanations from the educator are displayed in real time on the user's device.

[1060] As described above, through the specific actions taken at each step, the entire system functions smoothly, enabling the provision of high-quality education.

[1061] (Application example 1)

[1062] 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."

[1063] Educational inequality remains a problem in modern society, with access to educational resources and quality varying greatly depending on the region and environment. The coronavirus pandemic has made remote education even more important, but current remote education systems face challenges, such as difficulty in managing learner progress and implementing personalized learning. Furthermore, the lack of real-time feedback and smooth communication with educators is one of the factors contributing to a decline in the quality of education.

[1064] 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.

[1065] In this invention, the server includes means for receiving authentication information entered by a user, means for performing authentication processing based on the authentication information, means for acquiring the learner's past learning data, means for recommending optimal learning content based on the learning data, means for analyzing the learner's answer data and generating feedback, means for tracking learning progress and generating a performance report, means for supporting communication between educators and learners in real time, means for providing a virtual classroom interface in a virtual space, and means for delivering educational content in real time using smart devices. This reduces educational disparities and enables people to receive high-quality education wherever they are.

[1066] "Authentication Information" means information used to verify a user's identity, such as a user ID and password entered by a user when accessing a system.

[1067] "Authentication processing" is a process for verifying whether a user is a legitimate user based on the user's authentication information.

[1068] "Learning data" refers to information that records a learner's past learning history, grades, and progress.

[1069] "Learning content" refers to educational resources such as teaching materials, documents, quizzes, and videos that students can use to learn.

[1070] "Feedback" is a response that includes evaluation, advice, and specific areas for improvement provided to a learner in response to their learning activities.

[1071] "Progress" refers to the learner's progress and achievement in learning, including grades and course completion status.

[1072] "Real-time communication" is a function that allows educators and students to send and receive data simultaneously and interact instantly.

[1073] "Virtual space" refers to a virtual learning environment built on the Internet, an area where education is possible independent of physical location.

[1074] A "virtual classroom" is a virtual classroom for education that exists in a virtual space and is an educational environment that can be accessed via the Internet.

[1075] "Smart devices" refers to all electronic devices with internet connectivity, such as smartphones, tablets, smart glasses, and head-mounted displays.

[1076] "Delivery of educational content" refers to the provision of educational videos, texts, quizzes, etc. to learners in real time or on demand via the Internet.

[1077] The present invention is a system that utilizes AI to reduce educational disparities and enable students anywhere in the world to receive cutting-edge education. The system includes a user authentication means, a learning content recommendation means, a real-time feedback means, a grade management and progress tracking means, a teacher-student communication means, a virtual classroom interface means, and a means for delivering educational content via smart devices.

[1078] User Authentication and Login

[1079] First, the server receives the authentication information entered by the user. The user accesses the login screen using a smart device and enters their user ID and password. The authentication information is sent to the server, where it is verified against a database for authentication. If authentication is successful, the user is logged into the system and the home screen is displayed.

[1080] Learning content recommendations

[1081] When a user logs in, the server retrieves the user's past learning data from a database. Based on this data, a generative AI model is used to recommend the most suitable learning content to the user. For example, if a user has studied many math units in the past, useful math content to study next will be recommended based on their progress and performance.

[1082] Real-time feedback

[1083] Data on tasks and quizzes answered by users is sent to a server. The AI ​​in the server analyzes the user's answer data and generates correct or incorrect answers and detailed feedback. This feedback is sent to the user's smart device and displayed in real time. For example, when a user solves a math quiz, the system immediately analyzes the answer and provides detailed explanations and suggestions for improvement for any incorrect parts.

[1084] Grade management and progress tracking

[1085] The server continuously collects the user's learning activity data and stores it in a database. The server also periodically analyzes this data to evaluate the user's learning progress. Based on the analysis results, it generates a performance report and provides it to the user and the educational institution. The performance report is displayed in real time on the user's smart device.

[1086] Teacher-student communication

[1087] A chat and video call interface is displayed on the user's smart device, allowing real-time communication between educators and students. Students can send questions to the teacher to resolve their doubts, and a real-time video call is initiated between the devices. The teacher can then provide on-the-spot explanations to deepen students' understanding.

[1088] Virtual classroom interface and educational content delivery

[1089] A virtual classroom interface in a virtual space is also provided, allowing users to access the virtual classroom in real time using a smartphone or head-mounted display. In this environment, real-time lectures and discussions are held, allowing users to learn while interacting with educational content.

[1090] Hardware and software used

[1091] The implementation of this system uses the following hardware and software:

[1092] Hardware: Smart devices (smartphones, tablets, head-mounted displays)

[1093] Software: Flask framework, generative AI model (using TensorFlow)

[1094] Examples of concrete examples and prompts

[1095] For example, imagine a scenario where a student logs in to an application and is recommended next learning content based on their past learning history. The student then uses that content to answer quizzes and receives real-time feedback. After learning, the student can check their grade report and understand their progress. They can also ask a teacher any questions they have in real time and receive clarification in the virtual classroom.

[1096] An example of a specific prompt for a generative AI model is as follows:

[1097] Recommend the most relevant learning content based on the user's learning history when they log in.

[1098] Past learning history is as follows:

[1099] Mathematics: 85 points

[1100] Chemistry: 90 points

[1101] Recommend the next learning content that would be best for this student.

[1102] As a result, this system will reduce educational disparities and enable people to receive high-quality education wherever they are.

[1103] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1104] Step 1:

[1105] The user accesses the login screen using a smart device and enters their user ID and password. The entered authentication information is sent to the server. The server compares this information with the database and performs authentication processing. If authentication is successful, the home screen is displayed on the device. Input: User ID, password. Output: Authentication result, home screen.

[1106] Step 2:

[1107] When a user logs in to the system, the server retrieves the user's past learning data from the database. The retrieved learning data is input into the generative AI model. The AI ​​model analyzes this data and recommends the most suitable learning content for the user. The recommendation results are sent to the terminal and displayed. Input: Learning data. Output: Recommended learning content.

[1108] Step 3:

[1109] The user works on the recommended learning content and answers assignments and quizzes. The answer data is sent to the server. The AI ​​on the server analyzes this answer data, determines whether the answer is correct, and generates detailed feedback. The generated feedback is sent to the device in real time and displayed. Input: Answer data. Output: Feedback.

[1110] Step 4:

[1111] The server continuously collects the user's learning activity data and stores it in a database. The data is analyzed at specific intervals to evaluate the user's progress. Based on the evaluation results, a performance report is generated and provided to the user and the educational institution. The performance report is displayed on the terminal. Input: Learning activity data. Output: Performance report.

[1112] Step 5:

[1113] Chat and video call functions are displayed on the user's device. Users can use these to communicate with educators in real time. For example, a user can send a question to a teacher about a concern they have, which will initiate a video call. The teacher will then provide an explanation on the spot to deepen the user's understanding. Input: Question, chat message. Output: Explanation, response.

[1114] Step 6:

[1115] The virtual classroom interface in the virtual space is displayed on the terminal. Users can access the virtual classroom using their smart devices. Real-time lectures and discussions take place here, allowing users to progress with their learning while experiencing educational content. Input: Access request. Output: Virtual classroom interface, educational content.

[1116] Step 7:

[1117] Educational content is delivered to smart devices via the internet through a server. The software used here is the Flask framework and generative AI models such as TensorFlow. This provides a personalized learning experience. Input: Educational content. Output: Learning experience, learning resources.

[1118] 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.

[1119] This invention is a system that utilizes AI to reduce educational disparities and enable people to receive cutting-edge education anywhere in the world. This system consists of the following elements: a user authentication means, a learning content recommendation means, a real-time feedback means, a grade management and progress tracking means, a means of communication between educators and learners, and an emotion engine for recognizing user emotions.

[1120] User Authentication and Login

[1121] First, a user logs in to the system using a terminal. The terminal displays a login screen to the user and prompts them to enter their user ID and password. Next, the terminal sends the entered authentication information to the server. The server compares the information with a database and returns the authentication result. If authentication is successful, the user is logged in to the system and the home screen is displayed.

[1122] Learning content recommendations

[1123] When a user logs in, the server retrieves the user's past learning history and grade data from a database. The server inputs the retrieved data into an AI algorithm to generate learning content that is optimal for the user. The recommended content is sent to the device, which then displays it to the user. For example, if the user has studied many math units in the past, the server will recommend useful math content to study next based on their progress and performance.

[1124] Real-time feedback

[1125] When a user answers a study assignment or quiz on their device, the answer data is sent to the server. The AI ​​in the server analyzes the user's answer data and generates correct or incorrect answers and detailed feedback. The generated feedback is sent from the server to the user's device, where it is displayed in real time. For example, when a user answers a math quiz, the system immediately analyzes the answer and provides detailed explanations and suggestions for improvement for any incorrect answers.

[1126] Grade management and progress tracking

[1127] The server continuously collects the user's learning activity data and stores it in a database. The server also periodically analyzes this data to evaluate the user's learning progress. Based on the analysis results, it generates a performance report and provides it to the user and the educational institution. The terminal displays the performance report to the user, allowing them to check their learning progress.

[1128] Teacher-student communication

[1129] The device displays an interface with chat and video call functions to the user, allowing educators and learners to communicate in real time. Users (students) send questions to teachers to resolve their doubts, and a real-time video call is initiated between the devices. The teacher then provides on-the-spot explanations to deepen the student's understanding. This process makes it possible to receive a high-quality education wherever they are.

[1130] Utilizing the Emotion Engine

[1131] Furthermore, the present invention incorporates an emotion engine that can recognize the user's emotions in real time. Specifically, the emotion engine analyzes the user's facial expressions and tone of voice through the device's built-in camera and microphone. This information is sent to a server, which then identifies the user's current emotional state.

[1132] This emotional data can be used to:

[1133] Change of learning content: The server adaptively changes learning content based on the user's emotional state. For example, if the user is tired, it recommends light content that will help them relax.

[1134] Feedback adjustment: The server adjusts the feedback provided to the user based on the emotional data. For example, if the user is feeling stressed, it emphasizes encouraging messages and advice.

[1135] Real-time support: The emotion engine recognizes emotion data and provides it to educators in real time. Educators can use this information to take appropriate action. For example, if a user is depressed, the educator can provide additional support.

[1136] As described above, the system of the present invention provides a comprehensive range of services, from user authentication to learning content recommendations, real-time feedback, grade management, communication with educators, and emotion recognition and utilization using an emotion engine, thereby effectively reducing educational disparities.

[1137] The processing flow will be explained below.

[1138] User Authentication and Login

[1139] Step 1:

[1140] The terminal displays a login screen to the user.

[1141] Step 2:

[1142] The user enters their ID and password on the login screen.

[1143] Step 3:

[1144] The terminal sends the entered ID and password to the server.

[1145] Step 4:

[1146] The server checks the received ID and password against the database.

[1147] Step 5:

[1148] The server returns the authentication result to the device. If the authentication is successful, the device displays the home screen to the user.

[1149] Learning content recommendations

[1150] Step 1:

[1151] When a user logs in, the server retrieves the user's past learning history and grade data from the database.

[1152] Step 2:

[1153] The server inputs the acquired data into an AI algorithm to generate learning content that is optimal for the user.

[1154] Step 3:

[1155] The server transmits a list of recommended learning contents to the user's terminal.

[1156] Step 4:

[1157] The terminal displays a list of the received study content to the user.

[1158] Real-time feedback

[1159] Step 1:

[1160] Users answer study assignments and quizzes on their devices.

[1161] Step 2:

[1162] The terminal transmits the user's answer data to the server.

[1163] Step 3:

[1164] The AI ​​on the server analyzes the received answer data and generates correct and detailed feedback.

[1165] Step 4:

[1166] The server transmits the generated feedback to the user's terminal.

[1167] Step 5:

[1168] The terminal displays the received feedback to the user in real time.

[1169] Grade management and progress tracking

[1170] Step 1:

[1171] The server continuously collects the user's learning activity data and stores it in a database.

[1172] Step 2:

[1173] The server periodically analyzes the collected data and evaluates the user's learning progress.

[1174] Step 3:

[1175] The server generates a performance report based on the analysis results.

[1176] Step 4:

[1177] The server transmits the generated performance report to the user's terminal.

[1178] Step 5:

[1179] The terminal displays the received performance report to the user, allowing the user to check the progress of their studies.

[1180] Teacher-student communication

[1181] Step 1:

[1182] The terminal displays an interface for chat and video call functions to the user.

[1183] Step 2:

[1184] A user (student) sends a request on a terminal to start a chat or video call with a teacher.

[1185] Step 3:

[1186] The server receives the user's request and notifies the corresponding teacher's terminal.

[1187] Step 4:

[1188] The teacher's terminal receives the request and responds to it to start a chat or video call.

[1189] Step 5:

[1190] Real-time communication takes place between devices, and students and teachers interact through messaging and video calls.

[1191] Utilizing the Emotion Engine

[1192] Step 1:

[1193] The device collects the user's facial expressions and tone of voice in real time through an emotion engine.

[1194] Step 2:

[1195] The terminal transmits the collected emotion data to the server.

[1196] Step 3:

[1197] The server analyzes the received emotion data to understand the user's current emotional state.

[1198] Step 4:

[1199] The server adaptively changes learning content based on the user's emotional state, for example, recommending light, relaxing content if the user is tired.

[1200] Step 5:

[1201] The server adjusts the feedback based on the emotional data, for example, emphasizing encouraging messages and advice if the user is feeling stressed.

[1202] Step 6:

[1203] The server provides recognized emotion data to educators in real time, helping them take appropriate action.

[1204] As a result, the system of the present invention provides a consistent service ranging from user authentication to learning content recommendations, real-time feedback, grade management, communication with educators, and emotion recognition and utilization using an emotion engine, thereby effectively reducing educational disparities.

[1205] Example 2

[1206] 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."

[1207] Traditional educational systems have difficulty accurately understanding each learner's progress and emotional state and flexibly adjusting learning content and feedback based on that information. They also lack effective means to support real-time communication between educators and learners. This creates educational disparities and leads to uneven learning quality.

[1208] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1209] In this invention, the server includes means for receiving authentication information entered by a user, means for performing authentication processing based on the authentication information, means for acquiring the learner's past learning data, means for recommending optimal learning content based on the learning data, means for analyzing the learner's answer data and generating feedback, means for tracking learning progress and generating performance reports, means for supporting communication between educators and learners in real time, means for collecting and analyzing emotional data, means for adaptively changing learning content based on the emotional data, and means for adjusting feedback content based on the emotional data. This allows for real-time understanding of the individual progress and emotional state of learners, enabling flexible learning support based on this information.

[1210] "Authentication Information" means the identification information required for a User to access the System, including, specifically, a User ID and password.

[1211] "Authentication processing" is the process of checking the entered authentication information against a database to confirm the user's identity.

[1212] "Study Data" refers to information including a user's past learning history and performance data, including details of the user's learning activities.

[1213] "Learning content" refers to educational resources such as learning materials, assignments, and quizzes that users should study.

[1214] "Answer data" refers to data that a user provides in response to a study task or quiz.

[1215] "Feedback" refers to evaluation and advice provided to the user based on the results of analyzing the user's answer data.

[1216] A "performance report" is a report summarizing a user's learning progress and grades.

[1217] "Emotion data" is information that indicates a real-time emotional state of a user, obtained by analyzing the user's facial expressions and tone of voice.

[1218] "Adaptive change" refers to the process of adjusting learning content and feedback to suit the user's condition based on collected data.

[1219] This invention is a system that utilizes AI to reduce educational disparities and enable people to receive cutting-edge education anywhere in the world. This system consists of the following elements: a user authentication means, a learning content recommendation means, a real-time feedback means, a grade management and progress tracking means, a means of communication between educators and learners, and an emotion engine for recognizing user emotions.

[1220] First, the user logs in to the system using their own terminal. The terminal displays a login screen to the user and asks them to enter their user ID and password. Once the user has completed the input, the terminal sends the entered user ID and password to the server. When the server receives the authentication information, it compares it with a database and returns the authentication result to the terminal. If the authentication is successful, the terminal displays the home screen to the user.

[1221] When a user logs in, the server retrieves the user's past learning history and grade data from a database. This data is input into AI algorithms such as TensorFlow and Scikit-learn. The server uses the AI ​​algorithms to generate optimal learning content for the user. The generated learning content is sent to the device, which then displays it to the user.

[1222] When a user answers a learning task or quiz, the answer data is sent from the device to the server. The AI ​​feedback system in the server analyzes the answer data and generates correct or incorrect answers and detailed feedback. This feedback is sent from the server to the device, which displays it to the user in real time.

[1223] The server continuously collects the user's learning activity data and stores it in a database. It also periodically analyzes this data to evaluate the user's learning progress. It generates a performance report based on the analysis results and provides it to the user and the educational institution. The terminal displays the performance report to the user, allowing them to check their learning progress.

[1224] The device provides chat and video calling functions to support real-time communication between users and educators. Users (students) can send questions to teachers to resolve their doubts and exchange information in real time. The video calling function uses, for example, the Zoom API or WebRTC.

[1225] Using the emotion engine, the system recognizes the user's real-time emotional state. The device's built-in camera and microphone collect the user's facial expressions and tone of voice, which are then sent to the server. The emotion engine analyzes the data to understand the user's current emotional state. Based on this emotional data, the server adaptively changes the learning content and feedback. Educators can also obtain emotional data in real time and take appropriate action.

[1226] As a concrete example, let us consider a scenario in which a user is studying mathematics. For example, the system recommends the next mathematics topic to study based on the mathematics units the user has studied in the past. Furthermore, when the user answers a quiz and the results are sent to the server, they are immediately analyzed and detailed feedback is provided. If the emotion engine determines that the user is feeling stressed, the server will send an encouraging message to keep the user motivated.

[1227] Examples of input prompts for generative AI models include:

[1228] "If a user is studying a math unit, recommend what they should study next based on their past learning history. Also, provide detailed feedback if they get a quiz answer wrong, and suggest how to support them when they're feeling stressed."

[1229] The above is an embodiment of the present invention.

[1230] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1231] System program processing flow

[1232] Step 1: User authentication and login

[1233] The user displays the login screen on their device. They enter their user ID and password, which the device then sends to the server. The server compares the authentication information with the database and returns the authentication result to the device. If authentication is successful, the device displays the home screen to the user.

[1234] Input: User ID, Password

[1235] Data calculation: database search, authentication processing

[1236] Output: Authentication result, home screen display

[1237] Step 2: Obtaining training data

[1238] After a user logs in, the server retrieves the user's past learning history and performance data from the database, and prepares this data for input into the AI ​​algorithm.

[1239] Input: User ID

[1240] Data processing: Retrieving data from the database

[1241] Output: Past learning history, grade data

[1242] Step 3: Recommend learning content

[1243] The server inputs the acquired learning data into an AI algorithm (e.g., TensorFlow or Scikit-learn). The AI ​​algorithm generates optimal learning content for the user based on the data. The generated learning content is sent from the server to the device, which then displays it to the user.

[1244] Input: Past learning history, grade data

[1245] Data Computation: Learning Content Generation with AI Algorithms

[1246] Output: Learning content, displayed on the user screen

[1247] Step 4: Complete and submit your assignments

[1248] The user answers study assignments and quizzes on the device, and the answer data is sent from the device to the server.

[1249] Input: User's answer to the learning task

[1250] Data processing: Sending answer data

[1251] Output: Submitted answer data

[1252] Step 5: Generate and submit feedback

[1253] The AI ​​feedback system on the server analyzes the submitted answer data, generates correct and incorrect answers, and sends detailed feedback to the device, which then displays the feedback to the user in real time.

[1254] Input: Submitted answer data

[1255] Data calculation: Analysis and feedback generation by AI feedback system

[1256] Output: Generated feedback, displayed on the user screen

[1257] Step 6: Collect and analyze training data

[1258] The server continuously collects the user's learning activity data and stores it in a database. The server periodically analyzes this data and evaluates the user's learning progress. Based on the analysis results, it generates a performance report and provides it to the user and the educational institution. The terminal displays the performance report to the user, allowing them to check their learning progress.

[1259] Input: User learning activity data

[1260] Data calculation: storing data in database, periodic data analysis

[1261] Output: Grade report, display on user screen

[1262] Step 7: Communicate with Educators

[1263] Users can communicate with educators in real time using chat and video call functions on their devices. They can send questions and receive answers and explanations from educators in real time. The chat and video call functions use the Zoom API and WebRTC.

[1264] Input: User question, Educator answer

[1265] Data processing: Real-time communication

[1266] Output: Answers and explanations from the educator, displayed on the user's screen

[1267] Step 8: Emotion engine analysis and feedback adjustment

[1268] The device's built-in camera and microphone are used to collect the user's facial expressions and tone of voice. The server's emotion engine analyzes this data to understand the user's current emotional state. Based on this emotional data, the server adaptively changes the learning content and feedback.

[1269] Input: facial expression data, tone of voice data

[1270] Data Computing: Emotional data analysis, feedback and content adjustment

[1271] Output: Tailored learning content, feedback, and display on the user's screen

[1272] The above is a detailed description of the specific operations and inputs and outputs at each processing step of this system.

[1273] (Application example 2)

[1274] 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."

[1275] Conventional educational systems make it difficult for learners to receive high-quality education wherever they are. They also lack the means to provide real-time feedback on learning progress and assessment of answers. Furthermore, it is difficult to adjust learning content to take into account the learner's emotional state and to communicate with educators in real time. This has led to problems such as a decline in learner motivation and learning effectiveness.

[1276] 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.

[1277] In this invention, the server includes means for receiving authentication information entered by a user, means for performing authentication processing based on the authentication information, means for acquiring the learner's past learning data, means for recommending optimal learning content based on the learning data, means for analyzing the learner's answer data and generating feedback, means for tracking the learning progress and generating a performance report, means for analyzing the learner's emotional data and adjusting the learning content based on the learner's emotional state, means for supporting communication between educators and learners in real time, and means for providing feedback based on the learning progress and emotions to a smart device and enabling video calls with educators. This allows learners to receive high-quality education wherever they are, improving their learning effectiveness.

[1278] "Authentication information" refers to identification information such as a user ID and password required for a learner to log in to the system.

[1279] "Authentication processing" is a process for confirming the identity and access rights of a learner based on the input authentication information.

[1280] "Learning data" refers to information including a learner's past learning history and grade data.

[1281] "Learning content" refers to educational materials, videos, quizzes, and other content provided to learners.

[1282] "Feedback" is information that includes evaluation and advice on the learner's answers.

[1283] A "performance report" is a report summarizing a learner's learning progress and an evaluation of their performance.

[1284] "Emotional data" is information about the emotional state of a learner obtained by analyzing their facial expressions and tone of voice.

[1285] "Real-time communication" is a function that allows educators and learners to communicate directly without any time lag.

[1286] "Smart devices" are electronic devices with advanced functions such as smartphones, smart glasses, and tablets.

[1287] "Video calling" is a means of communicating with each other using video and audio over the Internet.

[1288] The system for implementing this invention is realized by combining various hardware and software. First, a user accesses the system using a smart device such as a smartphone or tablet. The main functions of the system are provided by three entities: a server, a terminal, and a user.

[1289] User Authentication and Login

[1290] The user accesses the login screen from their smart device and enters their user ID and password. The device receives the entered authentication information and sends it to the server. The server performs authentication based on the authentication information and returns the results of matching it with the database. If authentication is successful, the user can access the home screen.

[1291] Learning content recommendations

[1292] When a user logs in, the server retrieves the user's past learning data from a database. An AI algorithm then analyzes the data and recommends personalized learning content. The recommended content is displayed on the device for the user to view.

[1293] Real-time feedback

[1294] When a user answers a quiz or assignment, the answer data is sent from the device to the server. The AI ​​in the server analyzes the answer data and generates detailed feedback, which is displayed on the user's device in real time.

[1295] Grade management and progress tracking

[1296] The server collects the user's learning activity data and stores it in a database. It periodically analyzes the data and generates a learning progress and performance report. This report is provided to the user and the educational institution and is also displayed on the device.

[1297] Communication with Educators

[1298] Users can communicate with educators in real time using chat and video calling functions on their devices, and any questions or concerns can be resolved in real time, improving learning outcomes.

[1299] Utilizing the Emotion Engine

[1300] The emotion engine analyzes the user's facial expressions and tone of voice via the device's built-in camera and microphone. This emotion data is sent to a server and reflected in adjustments to the learning content and feedback. It is also provided to educators in real time, allowing them to take appropriate action.

[1301] Examples and prompts

[1302] Specific examples

[1303] User authentication: The learner enters their username and password into the login screen on their smartphone, and if authentication is successful, they are taken to the home screen.

[1304] Learning content recommendation: AI recommends the next math video to learn based on learning history and displays it on the device.

[1305] Real-time feedback: AI provides detailed explanations on the spot for the quiz you answer.

[1306] Learning support using an emotion engine: If a user is feeling stressed, the AI ​​will suggest light content to relieve them.

[1307] Prompt Sentence Examples

[1308] "Based on your learning history and progress, AI will recommend the most appropriate educational content for you to learn next. As you progress through the learning process, you will receive real-time feedback on your answers, and you can even have a video call with an educator if needed. In addition, an emotion engine will analyze your facial expressions and voice to adjust your learning method at the appropriate time."

[1309] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1310] Step 1:

[1311] A user uses a smart device to access the system's login screen and enter their user ID and password. The device receives the entered authentication information (user ID and password) and sends it to the server. The server compares the entered authentication information with the authentication database and returns the authentication result. If the user is successfully authenticated, they can access the home screen.

[1312] Input: User ID, Password

[1313] Data processing: Verification against authentication database

[1314] Output: Authentication result

[1315] Step 2:

[1316] When a user logs in, the server retrieves the user's past learning data (learning history and grade data) from the database. The retrieved data is input into an AI algorithm to generate individually optimized learning content. The generated learning content is sent to the device and displayed to the user.

[1317] Input: User's learning history, grade data

[1318] Data processing: Analysis using AI algorithms

[1319] Output: Recommended learning content

[1320] Step 3:

[1321] When users browse learning content and answer quizzes and assignments, the answer data is sent from the device to the server. The AI ​​on the server analyzes the answer data, determines whether the answer is correct or incorrect, and generates detailed feedback. The generated feedback is sent to the device and displayed to the user in real time.

[1322] Input: Answer data

[1323] Data processing: Analysis of answer data using AI

[1324] Output: Detailed feedback

[1325] Step 4:

[1326] The server continuously collects user learning activity data and stores it in a database. It periodically analyzes the data and generates learning progress and performance reports. The performance reports are provided to the user and the educational institution and are also displayed on the device.

[1327] Input: Learning activity data

[1328] Data processing: storing in database, analysis

[1329] Output: Learning progress, grade report

[1330] Step 5:

[1331] Users can communicate with educators in real time using chat and video calling functions from their devices. The device is responsible for initiating and terminating communications and sending and receiving messages, while the server manages the necessary interconnections.

[1332] Input: Chat message, video call start request

[1333] Data Processing: Managing Real-Time Communications

[1334] Output: Real-time communication with educators

[1335] Step 6:

[1336] The device's built-in camera and microphone collect the user's facial expressions and tone of voice. This data is sent to a server and analyzed by an emotion engine. The server uses the emotion analysis results to adjust learning content and change feedback, providing it to educators in real time as needed.

[1337] Input: facial expression data, tone of voice data

[1338] Data processing: Analysis using emotion engine

[1339] Output: Adjusted learning content, feedback

[1340] 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.

[1341] 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.

[1342] 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.

[1343] [Fourth embodiment]

[1344] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1345] 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.

[1346] 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).

[1347] 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.

[1348] 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.

[1349] 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).

[1350] 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.

[1351] 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.

[1352] 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.

[1353] 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.

[1354] 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.

[1355] 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.

[1356] 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."

[1357] This invention is a system that utilizes AI to reduce educational disparities and enable people to receive cutting-edge education anywhere in the world. This system consists of the following elements: user authentication means, learning content recommendation means, real-time feedback means, grade management and progress tracking means, and communication means between teachers and students.

[1358] User Authentication and Login

[1359] First, a user logs in to the system using a terminal. The terminal displays a login screen to the user and prompts them to enter their user ID and password. Next, the terminal sends the entered authentication information to the server. The server compares the information with a database and returns the authentication result. If authentication is successful, the user is logged in to the system and the home screen is displayed.

[1360] Learning content recommendations

[1361] When a user logs in, the server retrieves the user's past learning history and grade data from a database. The server runs an AI algorithm based on the retrieved data and recommends optimal learning content to the user. The recommended content is sent to the device, which displays it to the user. For example, if the user has studied many math units in the past, the server will recommend useful math content to study next based on their progress and performance.

[1362] Real-time feedback

[1363] When a user answers a study assignment or quiz on their device, the answer data is sent to the server. The AI ​​in the server analyzes the user's answer data and generates correct or incorrect answers and detailed feedback. The generated feedback is sent from the server to the user's device, where it is displayed in real time. For example, when a user answers a math quiz, the system immediately analyzes the answer and provides detailed explanations and suggestions for improvement for any incorrect answers.

[1364] Grade management and progress tracking

[1365] The server continuously collects the user's learning activity data and stores it in a database. The server also periodically analyzes this data to evaluate the user's learning progress. Based on the analysis results, it generates a performance report and provides it to the user and the educational institution. The terminal displays the performance report to the user, allowing them to check their learning progress.

[1366] Teacher-student communication

[1367] The device displays an interface with chat and video call functions to the user, allowing educators and learners to communicate in real time. Users (students) send questions to teachers to resolve their doubts, and a real-time video call is initiated between the devices. The teacher then provides on-the-spot explanations to deepen the student's understanding. This process makes it possible to receive a high-quality education wherever they are.

[1368] As described above, the system of the present invention provides a comprehensive range of services, from user authentication to recommendation of learning content, real-time feedback, grade management, and communication with educators, thereby effectively reducing educational disparities.

[1369] The processing flow will be explained below.

[1370] User Authentication and Login

[1371] Step 1:

[1372] The terminal displays a login screen to the user.

[1373] Step 2:

[1374] The user enters their ID and password on the login screen.

[1375] Step 3:

[1376] The terminal sends the entered ID and password to the server.

[1377] Step 4:

[1378] The server checks the received ID and password against the database.

[1379] Step 5:

[1380] The server returns the authentication result to the device. If the authentication is successful, the device displays the home screen to the user.

[1381] Learning content recommendations

[1382] Step 1:

[1383] When a user logs in, the server retrieves the user's past learning history and grade data from the database.

[1384] Step 2:

[1385] The server inputs the acquired data into an AI algorithm to generate learning content that is optimal for the user.

[1386] Step 3:

[1387] The server transmits a list of recommended learning contents to the user's terminal.

[1388] Step 4:

[1389] The terminal displays a list of the received study content to the user.

[1390] Real-time feedback

[1391] Step 1:

[1392] Users answer study assignments and quizzes on their devices.

[1393] Step 2:

[1394] The terminal transmits the user's answer data to the server.

[1395] Step 3:

[1396] The AI ​​on the server analyzes the received answer data and generates correct and detailed feedback.

[1397] Step 4:

[1398] The server transmits the generated feedback to the user's terminal.

[1399] Step 5:

[1400] The terminal displays the received feedback to the user in real time.

[1401] Grade management and progress tracking

[1402] Step 1:

[1403] The server continuously collects the user's learning activity data and stores it in a database.

[1404] Step 2:

[1405] The server periodically analyzes the collected data and evaluates the user's learning progress.

[1406] Step 3:

[1407] The server generates a performance report based on the analysis results.

[1408] Step 4:

[1409] The server transmits the generated performance report to the user's terminal.

[1410] Step 5:

[1411] The terminal displays the received performance report to the user, allowing the user to check the progress of their studies.

[1412] Teacher-student communication

[1413] Step 1:

[1414] The terminal displays an interface for chat and video call functions to the user.

[1415] Step 2:

[1416] A user (student) sends a request on a terminal to start a chat or video call with a teacher.

[1417] Step 3:

[1418] The server receives the user's request and notifies the corresponding teacher's terminal.

[1419] Step 4:

[1420] The teacher's terminal receives the request and responds to it to start a chat or video call.

[1421] Step 5:

[1422] Real-time communication takes place between devices, and students and teachers interact through messaging and video calls.

[1423] Example 1

[1424] 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."

[1425] In the traditional education system, the quality of education varies greatly depending on the region and economic situation, creating educational disparities. Furthermore, real-time feedback based on individual learners' progress and level of understanding was rarely provided, resulting in insufficient learning outcomes. Furthermore, communication between teachers and students was limited, meaning that questions were often not resolved promptly.

[1426] 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.

[1427] In this invention, the server includes means for receiving authentication information entered by a user, means for performing authentication processing based on the authentication information, means for acquiring the learner's past learning data, means for recommending optimal learning content based on the learning data, means for analyzing the learner's answer data and generating feedback, means for tracking learning progress and generating a performance report, means for supporting communication between educators and learners in real time, means for displaying recommended learning content to the user, and means for sending questions to educators in real time and receiving answers. This enables access to high-quality education wherever one is, personalized learning, and rapid feedback, thereby reducing educational disparities and improving learning effectiveness.

[1428] The "means for receiving authentication information input by a user" refers to a means for receiving authentication information such as an ID and password input by a user from a terminal.

[1429] The "means for performing authentication processing based on authentication information" refers to a means for verifying received authentication information against a database and authenticating the user.

[1430] "Means for obtaining a learner's past learning data" refers to means for obtaining the learner's past learning content and grade data from a database.

[1431] "Means for recommending optimal learning content" refers to a means for using AI to recommend optimal learning content to learners based on acquired learning data.

[1432] The "means for analyzing the answer data of the learner and generating feedback" refers to a means for analyzing the data of the answer given by the learner and generating feedback based on the results.

[1433] "Means for tracking learning progress and generating performance reports" refers to means for continuously tracking a learner's learning activities, evaluating the learner's progress, and generating performance reports.

[1434] "Means to support real-time communication between educators and learners" refers to means that provide chat and video call functions to support real-time communication between educators and learners.

[1435] The "means for displaying recommended study content to the user" refers to a means for displaying the study content sent from the server on the user's terminal.

[1436] "Means for sending questions to educators in real time and receiving answers" refers to means for learners to send questions to educators in real time and receive answers.

[1437] The above definitions make it clear what role each element of the system plays.

[1438] This invention is a system that utilizes AI to reduce educational disparities and enable students to receive cutting-edge education wherever they are. The system is equipped with a means for user authentication, a means for recommending learning content, a means for real-time feedback, a means for managing grades and tracking progress, and a means for communication between teachers and students.

[1439] User Authentication and Login

[1440] First, a user logs in to the system using a terminal. The terminal displays a login screen to the user and prompts them to enter their user ID and password. Once the user has completed the input, the terminal sends the authentication information to the server. The server compares the information with its internal database and performs authentication. If authentication is successful, the home screen is displayed on the terminal and the user can access the system.

[1441] Learning content recommendations

[1442] When a user logs in, the server retrieves the user's past learning history and grade data from the database. Based on the retrieved data, the server uses an AI algorithm to recommend optimal learning content to the user. The AI ​​models used include TensorFlow and PyTorch. The server sends the recommended learning content to the device, which then displays it to the user. For example, if the user has studied many math units in the past, useful math content to study next will be recommended.

[1443] Real-time feedback

[1444] When a user answers a study assignment or quiz on their device, the answer data is sent to the server. The AI ​​in the server analyzes the user's answer data and generates correct or incorrect answers and detailed feedback. This feedback is then sent back from the server to the device and displayed to the user in real time. For example, if a user takes a math quiz, the system will instantly analyze the answer and provide detailed explanations and suggestions for improvement for any incorrect answers.

[1445] Grade management and progress tracking

[1446] The server continuously collects data on the user's learning activities and stores it in a database. The server also periodically analyzes this data to evaluate the user's learning progress. This analysis is performed using data analysis software such as Python's Pandas and Scikit-learn. A performance report generated based on the evaluation is provided to the user and the educational institution. The terminal displays the performance report to the user, allowing them to check their learning progress.

[1447] Teacher-student communication

[1448] The device displays an interface with chat and video calling functions to users, allowing educators and learners to communicate in real time. For example, when a user (student) sends a question to a teacher to resolve a concern, a real-time video call is initiated between the devices. The teacher can then provide an explanation on the spot to deepen the student's understanding. This makes it possible to receive a high-quality education no matter where you are.

[1449] Prompt Sentence Examples

[1450] "Please recommend the next learning content based on the performance data of previously learned units. Please suggest the most suitable content to the user, focusing on progress and performance in the field of mathematics in particular."

[1451] As described above, the system of the present invention provides a comprehensive range of services, from user authentication to recommendation of learning content, real-time feedback, grade management, and communication with educators, with the aim of reducing educational disparities and improving learning outcomes.

[1452] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1453] Step 1: Enter and submit user credentials

[1454] Input: The user enters the user ID and password into the terminal.

[1455] What happens: The device displays a login screen and receives the authentication information entered by the user.

[1456] Output: The terminal sends the entered authentication information to the server.

[1457] Step 2: Verify Credentials

[1458] Input: User ID and password sent to the server

[1459] How it works: The server authenticates the received credentials against its internal database. It compares the hashed password with its records to determine if authentication is successful.

[1460] Output: The authentication result (success or failure) is returned from the server to the device.

[1461] Step 3: Display the Home Screen

[1462] Input: Authentication result from server (success)

[1463] Action: After successful authentication, the device displays the home screen to the user.

[1464] Output: The home screen is displayed on the user's device.

[1465] Step 4: Obtaining training data

[1466] Input: User login information

[1467] Operation: The server retrieves the user's past learning history and grade data from the database. It extracts the necessary data using SQL queries, etc.

[1468] Output: The acquired learning data is stored on the server.

[1469] Step 5: Content recommendation by AI algorithm

[1470] Input: Obtained training data

[1471] How it works: The server runs an AI algorithm (e.g., TensorFlow or PyTorch) to calculate the optimal learning content for the user. This algorithm selects appropriate content based on past learning data.

[1472] Output: Recommended learning content is generated and stored on the server.

[1473] Step 6: Submit and view your learning content

[1474] Input: Recommended learning content

[1475] Operation: The server sends the recommended learning content to the device, which then displays the content to the user.

[1476] Output: The learning content is displayed on the user's device.

[1477] Step 7: Submit your answers

[1478] Input: Data answered by the user

[1479] How it works: The user answers questions and assignments on their device, and the device sends the answer data to the server.

[1480] Output: The server receives the answer data.

[1481] Step 8: Analyze the answer data and generate feedback

[1482] Input: Submitted answer data

[1483] How it works: An AI (e.g., BERT or GPT, which implements NLP models) in the server analyzes the answer data and generates correct answers and detailed feedback. This analysis uses natural language processing and machine learning algorithms.

[1484] Output: The generated feedback is stored on the server.

[1485] Step 9: Submit and view feedback

[1486] Input: Generated feedback

[1487] Operation: The server sends the generated feedback to the device, which displays the sent feedback to the user in real time.

[1488] Output: Feedback is displayed on the user's device.

[1489] Step 10: Collect and analyze progress data

[1490] Input: User learning activity data

[1491] How it works: The server continuously collects user learning activity data, stores it in a database, and periodically analyzes this data using data analysis software (e.g., Python's Pandas or Scikit-learn).

[1492] Output: Analysis results and progress data are saved on the server.

[1493] Step 11: Generate and submit a grade report

[1494] Input: Analysis results and progress data

[1495] Operation: The server generates and periodically provides performance reports to users and educational institutions.

[1496] Output: The performance report is sent to the terminal and displayed to the user.

[1497] Step 12: Start real-time communication

[1498] Input: User's question and request to start a video call

[1499] How it works: The device displays the interface needed to start a chat or video call with the educator, sends questions to the educator in real time, and a video call is initiated between the devices.

[1500] Output: Answers and explanations from the educator are displayed in real time on the user's device.

[1501] As described above, through the specific actions taken at each step, the entire system functions smoothly, enabling the provision of high-quality education.

[1502] (Application example 1)

[1503] 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."

[1504] Educational inequality remains a problem in modern society, with access to educational resources and quality varying greatly depending on the region and environment. The coronavirus pandemic has made remote education even more important, but current remote education systems face challenges, such as difficulty in managing learner progress and implementing personalized learning. Furthermore, the lack of real-time feedback and smooth communication with educators is one of the factors contributing to a decline in the quality of education.

[1505] 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.

[1506] In this invention, the server includes means for receiving authentication information entered by a user, means for performing authentication processing based on the authentication information, means for acquiring the learner's past learning data, means for recommending optimal learning content based on the learning data, means for analyzing the learner's answer data and generating feedback, means for tracking learning progress and generating a performance report, means for supporting communication between educators and learners in real time, means for providing a virtual classroom interface in a virtual space, and means for delivering educational content in real time using smart devices. This reduces educational disparities and enables people to receive high-quality education wherever they are.

[1507] "Authentication Information" means information used to verify a user's identity, such as a user ID and password entered by a user when accessing a system.

[1508] "Authentication processing" is a process for verifying whether a user is a legitimate user based on the user's authentication information.

[1509] "Learning data" refers to information that records a learner's past learning history, grades, and progress.

[1510] "Learning content" refers to educational resources such as teaching materials, documents, quizzes, and videos that students can use to learn.

[1511] "Feedback" is a response that includes evaluation, advice, and specific areas for improvement provided to a learner in response to their learning activities.

[1512] "Progress" refers to the learner's progress and achievement in learning, including grades and course completion status.

[1513] "Real-time communication" is a function that allows educators and students to send and receive data simultaneously and interact instantly.

[1514] "Virtual space" refers to a virtual learning environment built on the Internet, an area where education is possible independent of physical location.

[1515] A "virtual classroom" is a virtual classroom for education that exists in a virtual space and is an educational environment that can be accessed via the Internet.

[1516] "Smart devices" refers to all electronic devices with internet connectivity, such as smartphones, tablets, smart glasses, and head-mounted displays.

[1517] "Delivery of educational content" refers to the provision of educational videos, texts, quizzes, etc. to learners in real time or on demand via the Internet.

[1518] The present invention is a system that utilizes AI to reduce educational disparities and enable students anywhere in the world to receive cutting-edge education. The system includes a user authentication means, a learning content recommendation means, a real-time feedback means, a grade management and progress tracking means, a teacher-student communication means, a virtual classroom interface means, and a means for delivering educational content via smart devices.

[1519] User Authentication and Login

[1520] First, the server receives the authentication information entered by the user. The user accesses the login screen using a smart device and enters their user ID and password. The authentication information is sent to the server, where it is verified against a database for authentication. If authentication is successful, the user is logged into the system and the home screen is displayed.

[1521] Learning content recommendations

[1522] When a user logs in, the server retrieves the user's past learning data from a database. Based on this data, a generative AI model is used to recommend the most suitable learning content to the user. For example, if a user has studied many math units in the past, useful math content to study next will be recommended based on their progress and performance.

[1523] Real-time feedback

[1524] Data on tasks and quizzes answered by users is sent to a server. The AI ​​in the server analyzes the user's answer data and generates correct or incorrect answers and detailed feedback. This feedback is sent to the user's smart device and displayed in real time. For example, when a user solves a math quiz, the system immediately analyzes the answer and provides detailed explanations and suggestions for improvement for any incorrect parts.

[1525] Grade management and progress tracking

[1526] The server continuously collects the user's learning activity data and stores it in a database. The server also periodically analyzes this data to evaluate the user's learning progress. Based on the analysis results, it generates a performance report and provides it to the user and the educational institution. The performance report is displayed in real time on the user's smart device.

[1527] Teacher-student communication

[1528] A chat and video call interface is displayed on the user's smart device, allowing real-time communication between educators and students. Students can send questions to the teacher to resolve their doubts, and a real-time video call is initiated between the devices. The teacher can then provide on-the-spot explanations to deepen students' understanding.

[1529] Virtual classroom interface and educational content delivery

[1530] A virtual classroom interface in a virtual space is also provided, allowing users to access the virtual classroom in real time using a smartphone or head-mounted display. In this environment, real-time lectures and discussions are held, allowing users to learn while interacting with educational content.

[1531] Hardware and software used

[1532] The implementation of this system uses the following hardware and software:

[1533] Hardware: Smart devices (smartphones, tablets, head-mounted displays)

[1534] Software: Flask framework, generative AI model (using TensorFlow)

[1535] Examples of concrete examples and prompts

[1536] For example, imagine a scenario where a student logs in to an application and is recommended next learning content based on their past learning history. The student then uses that content to answer quizzes and receives real-time feedback. After learning, the student can check their grade report and understand their progress. They can also ask a teacher any questions they have in real time and receive clarification in the virtual classroom.

[1537] An example of a specific prompt for a generative AI model is as follows:

[1538] Recommend the most relevant learning content based on the user's learning history when they log in.

[1539] Past learning history is as follows:

[1540] Mathematics: 85 points

[1541] Chemistry: 90 points

[1542] Recommend the next learning content that would be best for this student.

[1543] As a result, this system will reduce educational disparities and enable people to receive high-quality education wherever they are.

[1544] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1545] Step 1:

[1546] The user accesses the login screen using a smart device and enters their user ID and password. The entered authentication information is sent to the server. The server compares this information with the database and performs authentication processing. If authentication is successful, the home screen is displayed on the device. Input: User ID, password. Output: Authentication result, home screen.

[1547] Step 2:

[1548] When a user logs in to the system, the server retrieves the user's past learning data from the database. The retrieved learning data is input into the generative AI model. The AI ​​model analyzes this data and recommends the most suitable learning content for the user. The recommendation results are sent to the terminal and displayed. Input: Learning data. Output: Recommended learning content.

[1549] Step 3:

[1550] The user works on the recommended learning content and answers assignments and quizzes. The answer data is sent to the server. The AI ​​on the server analyzes this answer data, determines whether the answer is correct, and generates detailed feedback. The generated feedback is sent to the device in real time and displayed. Input: Answer data. Output: Feedback.

[1551] Step 4:

[1552] The server continuously collects the user's learning activity data and stores it in a database. The data is analyzed at specific intervals to evaluate the user's progress. Based on the evaluation results, a performance report is generated and provided to the user and the educational institution. The performance report is displayed on the terminal. Input: Learning activity data. Output: Performance report.

[1553] Step 5:

[1554] Chat and video call functions are displayed on the user's device. Users can use these to communicate with educators in real time. For example, a user can send a question to a teacher about a concern they have, which will initiate a video call. The teacher will then provide an explanation on the spot to deepen the user's understanding. Input: Question, chat message. Output: Explanation, response.

[1555] Step 6:

[1556] The virtual classroom interface in the virtual space is displayed on the terminal. Users can access the virtual classroom using their smart devices. Real-time lectures and discussions take place here, allowing users to progress with their learning while experiencing educational content. Input: Access request. Output: Virtual classroom interface, educational content.

[1557] Step 7:

[1558] Educational content is delivered to smart devices via the internet through a server. The software used here is the Flask framework and generative AI models such as TensorFlow. This provides a personalized learning experience. Input: Educational content. Output: Learning experience, learning resources.

[1559] 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.

[1560] This invention is a system that utilizes AI to reduce educational disparities and enable people to receive cutting-edge education anywhere in the world. This system consists of the following elements: a user authentication means, a learning content recommendation means, a real-time feedback means, a grade management and progress tracking means, a means of communication between educators and learners, and an emotion engine for recognizing user emotions.

[1561] User Authentication and Login

[1562] First, a user logs in to the system using a terminal. The terminal displays a login screen to the user and prompts them to enter their user ID and password. Next, the terminal sends the entered authentication information to the server. The server compares the information with a database and returns the authentication result. If authentication is successful, the user is logged in to the system and the home screen is displayed.

[1563] Learning content recommendations

[1564] When a user logs in, the server retrieves the user's past learning history and grade data from a database. The server inputs the retrieved data into an AI algorithm to generate learning content that is optimal for the user. The recommended content is sent to the device, which then displays it to the user. For example, if the user has studied many math units in the past, the server will recommend useful math content to study next based on their progress and performance.

[1565] Real-time feedback

[1566] When a user answers a study assignment or quiz on their device, the answer data is sent to the server. The AI ​​in the server analyzes the user's answer data and generates correct or incorrect answers and detailed feedback. The generated feedback is sent from the server to the user's device, where it is displayed in real time. For example, when a user answers a math quiz, the system immediately analyzes the answer and provides detailed explanations and suggestions for improvement for any incorrect answers.

[1567] Grade management and progress tracking

[1568] The server continuously collects the user's learning activity data and stores it in a database. The server also periodically analyzes this data to evaluate the user's learning progress. Based on the analysis results, it generates a performance report and provides it to the user and the educational institution. The terminal displays the performance report to the user, allowing them to check their learning progress.

[1569] Teacher-student communication

[1570] The device displays an interface with chat and video call functions to the user, allowing educators and learners to communicate in real time. Users (students) send questions to teachers to resolve their doubts, and a real-time video call is initiated between the devices. The teacher then provides on-the-spot explanations to deepen the student's understanding. This process makes it possible to receive a high-quality education wherever they are.

[1571] Utilizing the Emotion Engine

[1572] Furthermore, the present invention incorporates an emotion engine that can recognize the user's emotions in real time. Specifically, the emotion engine analyzes the user's facial expressions and tone of voice through the device's built-in camera and microphone. This information is sent to a server, which then identifies the user's current emotional state.

[1573] This emotional data can be used to:

[1574] Change of learning content: The server adaptively changes learning content based on the user's emotional state. For example, if the user is tired, it recommends light content that will help them relax.

[1575] Feedback adjustment: The server adjusts the feedback provided to the user based on the emotional data. For example, if the user is feeling stressed, it emphasizes encouraging messages and advice.

[1576] Real-time support: The emotion engine recognizes emotion data and provides it to educators in real time. Educators can use this information to take appropriate action. For example, if a user is depressed, the educator can provide additional support.

[1577] As described above, the system of the present invention provides a comprehensive range of services, from user authentication to learning content recommendations, real-time feedback, grade management, communication with educators, and emotion recognition and utilization using an emotion engine, thereby effectively reducing educational disparities.

[1578] The processing flow will be explained below.

[1579] User Authentication and Login

[1580] Step 1:

[1581] The terminal displays a login screen to the user.

[1582] Step 2:

[1583] The user enters their ID and password on the login screen.

[1584] Step 3:

[1585] The terminal sends the entered ID and password to the server.

[1586] Step 4:

[1587] The server checks the received ID and password against the database.

[1588] Step 5:

[1589] The server returns the authentication result to the device. If the authentication is successful, the device displays the home screen to the user.

[1590] Learning content recommendations

[1591] Step 1:

[1592] When a user logs in, the server retrieves the user's past learning history and grade data from the database.

[1593] Step 2:

[1594] The server inputs the acquired data into an AI algorithm to generate learning content that is optimal for the user.

[1595] Step 3:

[1596] The server transmits a list of recommended learning contents to the user's terminal.

[1597] Step 4:

[1598] The terminal displays a list of the received study content to the user.

[1599] Real-time feedback

[1600] Step 1:

[1601] Users answer study assignments and quizzes on their devices.

[1602] Step 2:

[1603] The terminal transmits the user's answer data to the server.

[1604] Step 3:

[1605] The AI ​​on the server analyzes the received answer data and generates correct and detailed feedback.

[1606] Step 4:

[1607] The server transmits the generated feedback to the user's terminal.

[1608] Step 5:

[1609] The terminal displays the received feedback to the user in real time.

[1610] Grade management and progress tracking

[1611] Step 1:

[1612] The server continuously collects the user's learning activity data and stores it in a database.

[1613] Step 2:

[1614] The server periodically analyzes the collected data and evaluates the user's learning progress.

[1615] Step 3:

[1616] The server generates a performance report based on the analysis results.

[1617] Step 4:

[1618] The server transmits the generated performance report to the user's terminal.

[1619] Step 5:

[1620] The terminal displays the received performance report to the user, allowing the user to check the progress of their studies.

[1621] Teacher-student communication

[1622] Step 1:

[1623] The terminal displays an interface for chat and video call functions to the user.

[1624] Step 2:

[1625] A user (student) sends a request on a terminal to start a chat or video call with a teacher.

[1626] Step 3:

[1627] The server receives the user's request and notifies the corresponding teacher's terminal.

[1628] Step 4:

[1629] The teacher's terminal receives the request and responds to it to start a chat or video call.

[1630] Step 5:

[1631] Real-time communication takes place between devices, and students and teachers interact through messaging and video calls.

[1632] Utilizing the Emotion Engine

[1633] Step 1:

[1634] The device collects the user's facial expressions and tone of voice in real time through an emotion engine.

[1635] Step 2:

[1636] The terminal transmits the collected emotion data to the server.

[1637] Step 3:

[1638] The server analyzes the received emotion data to understand the user's current emotional state.

[1639] Step 4:

[1640] The server adaptively changes learning content based on the user's emotional state, for example, recommending light, relaxing content if the user is tired.

[1641] Step 5:

[1642] The server adjusts the feedback based on the emotional data, for example, emphasizing encouraging messages and advice if the user is feeling stressed.

[1643] Step 6:

[1644] The server provides recognized emotion data to educators in real time, helping them take appropriate action.

[1645] As a result, the system of the present invention provides a consistent service ranging from user authentication to learning content recommendations, real-time feedback, grade management, communication with educators, and emotion recognition and utilization using an emotion engine, thereby effectively reducing educational disparities.

[1646] Example 2

[1647] 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."

[1648] Traditional educational systems have difficulty accurately understanding each learner's progress and emotional state and flexibly adjusting learning content and feedback based on that information. They also lack effective means to support real-time communication between educators and learners. This creates educational disparities and leads to uneven learning quality.

[1649] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1650] In this invention, the server includes means for receiving authentication information entered by a user, means for performing authentication processing based on the authentication information, means for acquiring the learner's past learning data, means for recommending optimal learning content based on the learning data, means for analyzing the learner's answer data and generating feedback, means for tracking learning progress and generating performance reports, means for supporting communication between educators and learners in real time, means for collecting and analyzing emotional data, means for adaptively changing learning content based on the emotional data, and means for adjusting feedback content based on the emotional data. This allows for real-time understanding of the individual progress and emotional state of learners, enabling flexible learning support based on this information.

[1651] "Authentication Information" means the identification information required for a User to access the System, including, specifically, a User ID and password.

[1652] "Authentication processing" is the process of checking the entered authentication information against a database to confirm the user's identity.

[1653] "Study Data" refers to information including a user's past learning history and performance data, including details of the user's learning activities.

[1654] "Learning content" refers to educational resources such as learning materials, assignments, and quizzes that users should study.

[1655] "Answer data" refers to data that a user provides in response to a study task or quiz.

[1656] "Feedback" refers to evaluation and advice provided to the user based on the results of analyzing the user's answer data.

[1657] A "performance report" is a report summarizing a user's learning progress and grades.

[1658] "Emotion data" is information that indicates a real-time emotional state of a user, obtained by analyzing the user's facial expressions and tone of voice.

[1659] "Adaptive change" refers to the process of adjusting learning content and feedback to suit the user's condition based on collected data.

[1660] This invention is a system that utilizes AI to reduce educational disparities and enable people to receive cutting-edge education anywhere in the world. This system consists of the following elements: a user authentication means, a learning content recommendation means, a real-time feedback means, a grade management and progress tracking means, a means of communication between educators and learners, and an emotion engine for recognizing user emotions.

[1661] First, the user logs in to the system using their own terminal. The terminal displays a login screen to the user and asks them to enter their user ID and password. Once the user has completed the input, the terminal sends the entered user ID and password to the server. When the server receives the authentication information, it compares it with a database and returns the authentication result to the terminal. If the authentication is successful, the terminal displays the home screen to the user.

[1662] When a user logs in, the server retrieves the user's past learning history and grade data from a database. This data is input into AI algorithms such as TensorFlow and Scikit-learn. The server uses the AI ​​algorithms to generate optimal learning content for the user. The generated learning content is sent to the device, which then displays it to the user.

[1663] When a user answers a learning task or quiz, the answer data is sent from the device to the server. The AI ​​feedback system in the server analyzes the answer data and generates correct or incorrect answers and detailed feedback. This feedback is sent from the server to the device, which displays it to the user in real time.

[1664] The server continuously collects the user's learning activity data and stores it in a database. It also periodically analyzes this data to evaluate the user's learning progress. It generates a performance report based on the analysis results and provides it to the user and the educational institution. The terminal displays the performance report to the user, allowing them to check their learning progress.

[1665] The device provides chat and video calling functions to support real-time communication between users and educators. Users (students) can send questions to teachers to resolve their doubts and exchange information in real time. The video calling function uses, for example, the Zoom API or WebRTC.

[1666] Using the emotion engine, the system recognizes the user's real-time emotional state. The device's built-in camera and microphone collect the user's facial expressions and tone of voice, which are then sent to the server. The emotion engine analyzes the data to understand the user's current emotional state. Based on this emotional data, the server adaptively changes the learning content and feedback. Educators can also obtain emotional data in real time and take appropriate action.

[1667] As a concrete example, let us consider a scenario in which a user is studying mathematics. For example, the system recommends the next mathematics topic to study based on the mathematics units the user has studied in the past. Furthermore, when the user answers a quiz and the results are sent to the server, they are immediately analyzed and detailed feedback is provided. If the emotion engine determines that the user is feeling stressed, the server will send an encouraging message to keep the user motivated.

[1668] Examples of input prompts for generative AI models include:

[1669] "If a user is studying a math unit, recommend what they should study next based on their past learning history. Also, provide detailed feedback if they get a quiz answer wrong, and suggest how to support them when they're feeling stressed."

[1670] The above is an embodiment of the present invention.

[1671] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1672] System program processing flow

[1673] Step 1: User authentication and login

[1674] The user displays the login screen on their device. They enter their user ID and password, which the device then sends to the server. The server compares the authentication information with the database and returns the authentication result to the device. If authentication is successful, the device displays the home screen to the user.

[1675] Input: User ID, Password

[1676] Data calculation: database search, authentication processing

[1677] Output: Authentication result, home screen display

[1678] Step 2: Obtaining training data

[1679] After a user logs in, the server retrieves the user's past learning history and performance data from the database, and prepares this data for input into the AI ​​algorithm.

[1680] Input: User ID

[1681] Data processing: Retrieving data from the database

[1682] Output: Past learning history, grade data

[1683] Step 3: Recommend learning content

[1684] The server inputs the acquired learning data into an AI algorithm (e.g., TensorFlow or Scikit-learn). The AI ​​algorithm generates optimal learning content for the user based on the data. The generated learning content is sent from the server to the device, which then displays it to the user.

[1685] Input: Past learning history, grade data

[1686] Data Computation: Learning Content Generation with AI Algorithms

[1687] Output: Learning content, displayed on the user screen

[1688] Step 4: Complete and submit your assignments

[1689] The user answers study assignments and quizzes on the device, and the answer data is sent from the device to the server.

[1690] Input: User's answer to the learning task

[1691] Data processing: Sending answer data

[1692] Output: Submitted answer data

[1693] Step 5: Generate and submit feedback

[1694] The AI ​​feedback system on the server analyzes the submitted answer data, generates correct and incorrect answers, and sends detailed feedback to the device, which then displays the feedback to the user in real time.

[1695] Input: Submitted answer data

[1696] Data calculation: Analysis and feedback generation by AI feedback system

[1697] Output: Generated feedback, displayed on the user screen

[1698] Step 6: Collect and analyze training data

[1699] The server continuously collects the user's learning activity data and stores it in a database. The server periodically analyzes this data and evaluates the user's learning progress. Based on the analysis results, it generates a performance report and provides it to the user and the educational institution. The terminal displays the performance report to the user, allowing them to check their learning progress.

[1700] Input: User learning activity data

[1701] Data calculation: storing data in database, periodic data analysis

[1702] Output: Grade report, display on user screen

[1703] Step 7: Communicate with Educators

[1704] Users can communicate with educators in real time using chat and video call functions on their devices. They can send questions and receive answers and explanations from educators in real time. The chat and video call functions use the Zoom API and WebRTC.

[1705] Input: User question, Educator answer

[1706] Data processing: Real-time communication

[1707] Output: Answers and explanations from the educator, displayed on the user's screen

[1708] Step 8: Emotion engine analysis and feedback adjustment

[1709] The device's built-in camera and microphone are used to collect the user's facial expressions and tone of voice. The server's emotion engine analyzes this data to understand the user's current emotional state. Based on this emotional data, the server adaptively changes the learning content and feedback.

[1710] Input: facial expression data, tone of voice data

[1711] Data Computing: Emotional data analysis, feedback and content adjustment

[1712] Output: Tailored learning content, feedback, and display on the user's screen

[1713] The above is a detailed description of the specific operations and inputs and outputs at each processing step of this system.

[1714] (Application example 2)

[1715] 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."

[1716] Conventional educational systems make it difficult for learners to receive high-quality education wherever they are. They also lack the means to provide real-time feedback on learning progress and assessment of answers. Furthermore, it is difficult to adjust learning content to take into account the learner's emotional state and to communicate with educators in real time. This has led to problems such as a decline in learner motivation and learning effectiveness.

[1717] 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.

[1718] In this invention, the server includes means for receiving authentication information entered by a user, means for performing authentication processing based on the authentication information, means for acquiring the learner's past learning data, means for recommending optimal learning content based on the learning data, means for analyzing the learner's answer data and generating feedback, means for tracking the learning progress and generating a performance report, means for analyzing the learner's emotional data and adjusting the learning content based on the learner's emotional state, means for supporting communication between educators and learners in real time, and means for providing feedback based on the learning progress and emotions to a smart device and enabling video calls with educators. This allows learners to receive high-quality education wherever they are, improving their learning effectiveness.

[1719] "Authentication information" refers to identification information such as a user ID and password required for a learner to log in to the system.

[1720] "Authentication processing" is a process for confirming the identity and access rights of a learner based on the input authentication information.

[1721] "Learning data" refers to information including a learner's past learning history and grade data.

[1722] "Learning content" refers to educational materials, videos, quizzes, and other content provided to learners.

[1723] "Feedback" is information that includes evaluation and advice on the learner's answers.

[1724] A "performance report" is a report summarizing a learner's learning progress and an evaluation of their performance.

[1725] "Emotional data" is information about the emotional state of a learner obtained by analyzing their facial expressions and tone of voice.

[1726] "Real-time communication" is a function that allows educators and learners to communicate directly without any time lag.

[1727] "Smart devices" are electronic devices with advanced functions such as smartphones, smart glasses, and tablets.

[1728] "Video calling" is a means of communicating with each other using video and audio over the Internet.

[1729] The system for implementing this invention is realized by combining various hardware and software. First, a user accesses the system using a smart device such as a smartphone or tablet. The main functions of the system are provided by three entities: a server, a terminal, and a user.

[1730] User Authentication and Login

[1731] The user accesses the login screen from their smart device and enters their user ID and password. The device receives the entered authentication information and sends it to the server. The server performs authentication based on the authentication information and returns the results of matching it with the database. If authentication is successful, the user can access the home screen.

[1732] Learning content recommendations

[1733] When a user logs in, the server retrieves the user's past learning data from a database. An AI algorithm then analyzes the data and recommends personalized learning content. The recommended content is displayed on the device for the user to view.

[1734] Real-time feedback

[1735] When a user answers a quiz or assignment, the answer data is sent from the device to the server. The AI ​​in the server analyzes the answer data and generates detailed feedback, which is displayed on the user's device in real time.

[1736] Grade management and progress tracking

[1737] The server collects the user's learning activity data and stores it in a database. It periodically analyzes the data and generates a learning progress and performance report. This report is provided to the user and the educational institution and is also displayed on the device.

[1738] Communication with Educators

[1739] Users can communicate with educators in real time using chat and video calling functions on their devices, and any questions or concerns can be resolved in real time, improving learning outcomes.

[1740] Utilizing the Emotion Engine

[1741] The emotion engine analyzes the user's facial expressions and tone of voice via the device's built-in camera and microphone. This emotion data is sent to a server and reflected in adjustments to the learning content and feedback. It is also provided to educators in real time, allowing them to take appropriate action.

[1742] Examples and prompts

[1743] Specific examples

[1744] User authentication: The learner enters their username and password into the login screen on their smartphone, and if authentication is successful, they are taken to the home screen.

[1745] Learning content recommendation: AI recommends the next math video to learn based on learning history and displays it on the device.

[1746] Real-time feedback: AI provides detailed explanations on the spot for the quiz you answer.

[1747] Learning support using an emotion engine: If a user is feeling stressed, the AI ​​will suggest light content to relieve them.

[1748] Prompt Sentence Examples

[1749] "Based on your learning history and progress, AI will recommend the most appropriate educational content for you to learn next. As you progress through the learning process, you will receive real-time feedback on your answers, and you can even have a video call with an educator if needed. In addition, an emotion engine will analyze your facial expressions and voice to adjust your learning method at the appropriate time."

[1750] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1751] Step 1:

[1752] A user uses a smart device to access the system's login screen and enter their user ID and password. The device receives the entered authentication information (user ID and password) and sends it to the server. The server compares the entered authentication information with the authentication database and returns the authentication result. If the user is successfully authenticated, they can access the home screen.

[1753] Input: User ID, Password

[1754] Data processing: Verification against authentication database

[1755] Output: Authentication result

[1756] Step 2:

[1757] When a user logs in, the server retrieves the user's past learning data (learning history and grade data) from the database. The retrieved data is input into an AI algorithm to generate individually optimized learning content. The generated learning content is sent to the device and displayed to the user.

[1758] Input: User's learning history, grade data

[1759] Data processing: Analysis using AI algorithms

[1760] Output: Recommended learning content

[1761] Step 3:

[1762] When users browse learning content and answer quizzes and assignments, the answer data is sent from the device to the server. The AI ​​on the server analyzes the answer data, determines whether the answer is correct or incorrect, and generates detailed feedback. The generated feedback is sent to the device and displayed to the user in real time.

[1763] Input: Answer data

[1764] Data processing: Analysis of answer data using AI

[1765] Output: Detailed feedback

[1766] Step 4:

[1767] The server continuously collects user learning activity data and stores it in a database. It periodically analyzes the data and generates learning progress and performance reports. The performance reports are provided to the user and the educational institution and are also displayed on the device.

[1768] Input: Learning activity data

[1769] Data processing: storing in database, analysis

[1770] Output: Learning progress, grade report

[1771] Step 5:

[1772] Users can communicate with educators in real time using chat and video calling functions from their devices. The device is responsible for initiating and terminating communications and sending and receiving messages, while the server manages the necessary interconnections.

[1773] Input: Chat message, video call start request

[1774] Data Processing: Managing Real-Time Communications

[1775] Output: Real-time communication with educators

[1776] Step 6:

[1777] The device's built-in camera and microphone collect the user's facial expressions and tone of voice. This data is sent to a server and analyzed by an emotion engine. The server uses the emotion analysis results to adjust learning content and change feedback, providing it to educators in real time as needed.

[1778] Input: facial expression data, tone of voice data

[1779] Data processing: Analysis using emotion engine

[1780] Output: Adjusted learning content, feedback

[1781] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1782] 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.

[1783] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1784] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1785] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1786] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1787] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1788] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1789] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1790] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1791] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1792] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1793] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1794] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1795] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1796] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1797] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1798] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1799] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1800] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1801] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1802] The following is further disclosed regarding the above embodiment.

[1803] (Claim 1)

[1804] means for receiving authentication information input by a user;

[1805] means for performing authentication processing based on the authentication information;

[1806] A means for acquiring past learning data of the learner;

[1807] a means for recommending optimal learning content based on the learning data;

[1808] A means for analyzing the learner's answer data and generating feedback;

[1809] a means of tracking learning progress and generating performance reports;

[1810] A means to support communication between educators and learners in real time;

[1811] A system including:

[1812] (Claim 2)

[1813] 10. The system of claim 1, further comprising means for setting user privileges and performing access control based on an authentication process.

[1814] (Claim 3)

[1815] 10. The system of claim 1, further comprising means for automatically generating a personalized learning plan based on the recommended learning content.

[1816] "Example 1"

[1817] (Claim 1)

[1818] means for receiving authentication information input by a user;

[1819] means for performing authentication processing based on the authentication information;

[1820] A means for acquiring past learning data of the learner;

[1821] a means for recommending optimal learning content based on the learning data;

[1822] A means for analyzing the learner's answer data and generating feedback;

[1823] a means of tracking learning progress and generating performance reports;

[1824] A means to support communication between educators and learners in real time;

[1825] means for displaying recommended learning content to the user;

[1826] A way to submit questions to educators in real time and receive answers;

[1827] A system including:

[1828] (Claim 2)

[1829] 10. The system of claim 1, further comprising means for setting user privileges and performing access control based on an authentication process.

[1830] (Claim 3)

[1831] 10. The system of claim 1, further comprising means for automatically generating a personalized learning plan based on the recommended learning content.

[1832] "Application Example 1"

[1833] (Claim 1)

[1834] means for receiving authentication information input by a user;

[1835] means for performing authentication processing based on the authentication information;

[1836] A means for acquiring past learning data of the learner;

[1837] a means for recommending optimal learning content based on the learning data;

[1838] A means for analyzing the learner's answer data and generating feedback;

[1839] a means of tracking learning progress and generating performance reports;

[1840] A means to support communication between educators and learners in real time;

[1841] a means for providing a virtual classroom interface in a virtual space;

[1842] A means for delivering educational content in real time using smart devices;

[1843] A system including:

[1844] (Claim 2)

[1845] 10. The system of claim 1, further comprising means for setting user privileges and performing access control based on an authentication process.

[1846] (Claim 3)

[1847] 10. The system of claim 1, further comprising means for automatically generating a personalized learning plan based on the recommended learning content.

[1848] "Example 2: Combining Emotion Engines"

[1849] (Claim 1)

[1850] means for receiving authentication information input by a user;

[1851] means for performing authentication processing based on the authentication information;

[1852] A means for acquiring past learning data of the learner;

[1853] a means for recommending optimal learning content based on the learning data;

[1854] A means for analyzing the learner's answer data and generating feedback;

[1855] a means of tracking learning progress and generating performance reports;

[1856] A means to support communication between educators and learners in real time;

[1857] a means for collecting and analyzing emotion data;

[1858] means for adaptively changing learning content based on the emotion data;

[1859] means for adjusting the feedback content based on the emotion data;

[1860] A system including:

[1861] (Claim 2)

[1862] 10. The system of claim 1, further comprising means for setting user privileges and performing access control based on an authentication process.

[1863] (Claim 3)

[1864] 10. The system of claim 1, further comprising means for automatically generating a personalized learning plan based on the recommended learning content.

[1865] "Application example 2 when combining emotion engines"

[1866] (Claim 1)

[1867] means for receiving authentication information input by a user;

[1868] means for performing authentication processing based on the authentication information;

[1869] A means for acquiring past learning data of the learner;

[1870] a means for recommending optimal learning content based on the learning data;

[1871] A means for analyzing the learner's answer data and generating feedback;

[1872] a means of tracking learning progress and generating performance reports;

[1873] means for analyzing the learner's emotional data and adjusting the learning content based on the learner's emotional state;

[1874] A means to support communication between educators and learners in real time;

[1875] a means for providing learning progress and emotion-based feedback to a smart device and enabling video calls with an educator;

[1876] A system including:

[1877] (Claim 2)

[1878] 10. The system of claim 1, further comprising means for setting user privileges and performing access control based on an authentication process.

[1879] (Claim 3)

[1880] 10. The system of claim 1, further comprising means for automatically generating a personalized learning plan based on the recommended learning content. [Explanation of symbols]

[1881] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving authentication information input by a user; means for performing authentication processing based on the authentication information; A means for acquiring past learning data of the learner; a means for recommending optimal learning content based on the learning data; A means for analyzing the learner's answer data and generating feedback; a means of tracking learning progress and generating performance reports; A means to support communication between educators and learners in real time; A system including:

2. 2. The system according to claim 1, further comprising means for setting user privileges and performing access control based on an authentication process.

3. The system of claim 1 , further comprising means for automatically generating a personalized learning plan based on the recommended learning content.

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