System

Abstract

A system is provided.SOLUTION: A system comprising: means for acquiring learning history and progress data of a student; means for generating an optimal learning plan and teaching materials based on the acquired data; means for providing the generated learning plan and teaching materials to the student; means for analyzing learning activity data of the student in real time and providing feedback; means for updating a progress chart based on the analysis result; and means for automatically generating a custom report.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's educational environment, personalized learning experiences tailored to each student are required, but providing them at a consistently high level requires a great deal of effort and cost. Specifically, there is a lack of systems that efficiently and effectively support each aspect of education, such as creating high-quality teaching materials, individualized instruction, progress management, question answering, feedback, and generating custom reports. To solve these issues, there is a need to provide an efficient and high-quality educational system that utilizes generative AI models. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including: means for acquiring a student's learning history and progress data; means for generating an optimal learning plan and learning materials based on the acquired data; means for providing the generated learning plan and learning materials to the student; means for analyzing the student's learning activity data in real time and providing feedback; means for updating a progress chart based on the analysis results; and means for automatically generating a custom report. Furthermore, the present invention enables the continuous provision of high-quality learning materials by including means for storing the automatically generated learning materials in a database and means for providing the stored learning materials according to the student's learning progress. The system also includes means for generating and providing appropriate answers to questions entered by the student using a generative AI model, thereby supporting students' independent learning. The system also includes means for analyzing the acquired learning data and automatically generating and providing a custom report using a generative AI model. Furthermore, the system includes means for adding new educational content through integration with other companies' APIs, translating the generated content using a multilingual model, and providing it in the corresponding language, enabling multilingual learning. In this way, the system of the present invention utilizes a generative AI model to efficiently and effectively provide various educational support functions.

[0006] "Student" means a person engaged in learning activities at an educational institution.

[0007] A "learning history" is a record of a student's past learning activities.

[0008] "Progress data" refers to data on the learning progress achieved by a student through learning activities.

[0009] A "generative AI model" is a model that uses artificial intelligence to analyze data and generate new content and information.

[0010] A "learning plan" is a planned sequence of learning activities or materials designed to achieve a specific learning objective.

[0011] "Instructional materials" are educational resources such as textbooks, videos, and workbooks used to support learning.

[0012] "Feedback" is information that includes evaluation and advice regarding students' learning activities.

[0013] A "progress chart" is a diagram that visually displays a student's learning progress.

[0014] A "Custom Report" is a personalized report summarizing a student's learning activities and progress.

[0015] "Automatic generation" is the process by which a system independently analyzes data and creates new content or information.

[0016] A "database" is a system that systematically stores large amounts of data and enables it to be searched and retrieved.

[0017] A "question" is a question that a student asks the system to deepen their understanding.

[0018] An "answer" is the answer information provided by the system to a student's question.

[0019] "API" stands for Application Program Interface, an interface for sharing functions and data between different software programs.

[0020] "Translation" is the process of converting text written in one language into another.

[0021] "Multilingual support" refers to the ability to provide content and functions in multiple languages.

[0022] A "problem" is a specific problem or goal that needs to be solved.

[0023] A "system" is a comprehensive structure in which multiple elements interact with each other to achieve a specific function. [Brief explanation of the drawings]

[0024] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0025] 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.

[0026] First, the terms used in the following description will be explained.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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."

[0032] [First embodiment]

[0033] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0034] 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.

[0035] 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).

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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."

[0045] This invention relates to an educational system that utilizes generative AI models and proposes specific means for providing each student with a personalized learning experience. The system acquires and analyzes students' learning history and progress data to generate and provide optimal learning plans and teaching materials. It also analyzes students' learning activities in real time and provides feedback, improving learning effectiveness and allowing students to visually check their progress.

[0046] System Configuration

[0047] The system of the present invention has the following main functions:

[0048] 1. How to obtain learning history and progress data

[0049] 2. A means of generating learning plans and materials

[0050] 3. Means of providing generated learning plans and teaching materials

[0051] 4. A way to provide real-time feedback

[0052] 5. How to update the progress chart

[0053] 6. Automated generation of custom reports

[0054] 7. Storage method for the teaching material database

[0055] 8. Question-Answering Systems

[0056] 9. How to connect with other companies' APIs

[0057] 10. Multilingual Support

[0058] Program processing and behavior

[0059] The program processing for each function is explained below in natural language.

[0060] 1. How to obtain learning history and progress data

[0061] When a user logs in to their learning account, the device sends the login information to the server, which then authenticates the user and, if authentication is successful, retrieves the user's learning history and progress data from the database.

[0062] 2. A means of generating learning plans and materials

[0063] Based on the acquired data, the server uses a generative AI model to analyze the user's current learning situation and generate an optimal learning plan and learning materials, which are then sent from the server to the device and provided to the user.

[0064] 3. Means of providing generated learning plans and teaching materials

[0065] The device displays the study plan and learning materials sent from the server on the user interface, allowing the user to study using the most appropriate learning materials for their own learning situation.

[0066] 4. A way to provide real-time feedback

[0067] When a user performs a learning activity (e.g., answering a question), the device sends the data to the server, which analyzes the received data in real time and generates appropriate feedback. The generated feedback is sent back from the server to the device and displayed to the user.

[0068] 5. How to update the progress chart

[0069] As feedback is provided, the server updates a progress chart based on the progress, which is then sent to the device and displayed visually to the user.

[0070] 6. Automated generation of custom reports

[0071] Periodically, the server analyzes student learning data and automatically generates custom reports using AI models. The reports are stored in a database and provided to users, parents, and teachers via their devices.

[0072] 7. Storage method for the teaching material database

[0073] The generated learning materials are stored in a database by the server. The terminal requests appropriate learning materials from this database according to the user's learning progress and provides them to the user.

[0074] 8. Question-Answering Systems

[0075] When a user types in a question or concern, the device sends the question to the server, which uses a generative AI model to analyze the question and generate an appropriate answer, which is then sent back to the device and provided to the user.

[0076] 9. How to connect with other companies' APIs

[0077] The server connects with other companies' APIs to add new educational content to the database, which is provided as the user progresses through their studies.

[0078] 10. Multilingual Support

[0079] The generated teaching materials and content are translated as needed using a multilingual model, and the translated content is sent from the server to the terminal and provided to the user.

[0080] Specific examples

[0081] If a user is studying junior high school mathematics, they first log in to the system. The server acquires and analyzes the user's learning history and progress data. As a result, the next unit to be studied (e.g., solving equations) is determined, and appropriate learning materials (e.g., explanatory videos and practice problems) are generated. The generated learning plan and materials are provided to the user via their device.

[0082] When a user solves a problem and inputs the results into the system, the server instantly analyzes the data and generates feedback based on the accuracy rate and level of understanding. A progress chart is also updated, allowing users to visually check their current learning status. In this way, the system of the present invention provides an optimized learning experience for each student, supporting effective learning.

[0083] The above is a specific embodiment for carrying out the present invention.

[0084] The processing flow will be explained below.

[0085] 1. How to obtain learning history and progress data

[0086] Processing Steps

[0087] Step 1:

[0088] The user accesses the login screen and enters their user ID and password.

[0089] Step 2:

[0090] The device encrypts the entered login information using SSL / TLS and sends a request to the authentication API.

[0091] Step 3:

[0092] The server checks the authentication information against a database and returns the authentication result to the terminal.

[0093] Step 4:

[0094] If the server is successful in authenticating the user, it queries the database to retrieve the learning history and progress data associated with the user ID.

[0095] 2. A means of generating learning plans and materials

[0096] Processing Steps

[0097] Step 1:

[0098] The learning history and progress data acquired by the server are input into the generative AI model.

[0099] Step 2:

[0100] The server uses the generated AI model to analyze learning history and progress, and generates optimal learning plans and materials.

[0101] Step 3:

[0102] The server sends the generated learning plan and learning material metadata to the terminal in JSON format.

[0103] 3. Means of providing generated learning plans and teaching materials

[0104] Processing Steps

[0105] Step 1:

[0106] The device analyzes the learning plan and teaching material data received from the server.

[0107] Step 2:

[0108] The device displays a personalized learning plan and learning materials in a user interface.

[0109] 4. A way to provide real-time feedback

[0110] Processing Steps

[0111] Step 1:

[0112] The user performs a learning activity (e.g., answers a question).

[0113] Step 2:

[0114] The device sends the user's learning activity data to the server as an API request.

[0115] Step 3:

[0116] The server passes the received data to a real-time analysis system, which calculates the accuracy rate and learning progress.

[0117] Step 4:

[0118] The server generates feedback messages and progress chart information.

[0119] Step 5:

[0120] The server sends feedback and progress chart data in JSON format to the device.

[0121] Step 6:

[0122] The device provides the user with real-time feedback and a visual progress chart.

[0123] 5. How to update the progress chart

[0124] Processing Steps

[0125] Step 1:

[0126] The server analyzes the user's learning data and generates a progress chart.

[0127] Step 2:

[0128] The server transmits the progress chart data to the terminal.

[0129] Step 3:

[0130] The terminal displays the progress chart on the user interface.

[0131] 6. Automated generation of custom reports

[0132] Processing Steps

[0133] Step 1:

[0134] The server periodically retrieves student learning and progress data from the database.

[0135] Step 2:

[0136] The server analyzes the acquired data and automatically generates custom reports using AI models.

[0137] Step 3:

[0138] The server stores the generated reports in a database and sends them to the terminal.

[0139] Step 4:

[0140] The terminal displays the custom report to the user.

[0141] 7. Storage method for the teaching material database

[0142] Processing Steps

[0143] Step 1:

[0144] The server stores the generated teaching material data in a database.

[0145] Step 2:

[0146] The terminal requests the learning material data according to the user's learning progress.

[0147] Step 3:

[0148] The server receives the request and transmits the corresponding educational material data to the terminal.

[0149] Step 4:

[0150] The terminal displays appropriate educational materials to the user.

[0151] 8. Question-Answering Systems

[0152] Processing Steps

[0153] Step 1:

[0154] The user enters a question or concern.

[0155] Step 2:

[0156] The device sends the question to the server as an API request.

[0157] Step 3:

[0158] The server uses a generative AI model to analyze the question and generate an appropriate answer.

[0159] Step 4:

[0160] The server sends the generated answer to the terminal.

[0161] Step 5:

[0162] The terminal displays the answer to the user.

[0163] 9. How to connect with other companies' APIs

[0164] Processing Steps

[0165] Step 1:

[0166] The server connects with other companies' APIs to obtain new educational content.

[0167] Step 2:

[0168] The server stores the acquired educational content in a database.

[0169] Step 3:

[0170] The terminal requests new educational content according to the user's learning progress.

[0171] Step 4:

[0172] The server receives the request and transmits the corresponding educational content to the terminal.

[0173] Step 5:

[0174] The terminal displays new educational content to the user.

[0175] 10. Multilingual Support

[0176] Processing Steps

[0177] Step 1:

[0178] The server inputs the generated teaching materials and content into a multilingual model for translation.

[0179] Step 2:

[0180] The server stores the translated content in a database.

[0181] Step 3:

[0182] The device requests translated content according to the language set by the user.

[0183] Step 4:

[0184] The server receives the request and sends the content in the corresponding language to the terminal.

[0185] Step 5:

[0186] The terminal displays the content to the user in the selected language.

[0187] Example 1

[0188] 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."

[0189] Current education systems struggle to provide personalized learning experiences based on each student's progress and level of understanding. They also need to address a wide range of needs, including real-time feedback, progress visualization, automatic custom report generation, and multilingual support. However, there are no systems that integrate all of these functions, making it difficult to maximize learning efficiency.

[0190] 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.

[0191] In this invention, the server includes means for acquiring student learning history and progress data, means for generating optimal learning plans and teaching materials using a generative AI model based on the acquired data, means for providing the generated learning plans and teaching materials to students, means for analyzing student learning activity data in real time and providing feedback, means for updating a progress chart based on the analysis results, means for automatically generating custom reports, means for providing generated answers to students, means for translating the generated teaching materials and content using a multilingual model, means for saving the automatically generated teaching materials in a database, means for providing the saved teaching materials according to the student's learning progress, means for adding new educational content to the database using a third-party API, and means for generating appropriate answers to questions entered by students using a generative AI model. This enables the provision of an individually optimized learning experience, real-time feedback, progress visualization, multilingual support, and the addition of new content.

[0192] A "learning history" is a record of a student's past learning activities and progress.

[0193] "Progress data" is information that indicates the learning content and progress that a student is currently making.

[0194] A "generative AI model" is an artificial intelligence model that analyzes students' learning data and generates optimal learning plans, teaching materials, and feedback.

[0195] A "study plan" is a study schedule and content that is optimized to help students progress through their studies efficiently.

[0196] "Teaching Materials" means the educational materials and exercises used in accordance with the Study Plan.

[0197] "Feedback" refers to evaluation and advice provided in real time to students' learning activities.

[0198] A "progress chart" is a graph or chart that visually shows a student's learning progress.

[0199] A "custom report" is a learning report that is automatically generated individually by analyzing a student's learning data.

[0200] A "multilingual model" is an artificial intelligence model for translating teaching materials and content into multiple languages.

[0201] A "database" is a system that systematically stores acquired data and generated teaching materials.

[0202] "Third-party APIs" refer to application program interfaces published by other companies or service providers.

[0203] "Questions" are questions that students have while studying or things they want to clarify.

[0204] An "answer" is an appropriate answer or explanation provided by a generative AI model in response to a question.

[0205] This invention is an educational system that utilizes a generative AI model and proposes specific means for providing a personalized learning experience to each student. The system acquires and analyzes students' learning history and progress data to generate and provide optimal learning plans and materials. It also analyzes students' learning activities in real time and provides feedback to improve learning effectiveness and allow students to visually check their progress.

[0206] System Configuration

[0207] The system of the present invention comprises the following main components:

[0208] How to obtain learning history and progress data

[0209] A means of generating lesson plans and materials

[0210] Means of providing generated learning plans and teaching materials

[0211] A way to provide real-time feedback

[0212] Progress chart update method

[0213] Automated generation of custom reports

[0214] A means of providing generated answers to students

[0215] A means of translating generated learning materials and content using a multilingual model

[0216] A means of storing automatically generated teaching materials in a database

[0217] A means of providing saved learning materials as students progress

[0218] A way to add new educational content to the database using third-party APIs

[0219] A means of generating appropriate answers using generative AI models for questions entered by students

[0220] Hardware and software used

[0221] The system uses the following hardware and software:

[0222] Hardware: Servers, devices (PCs, tablets, smartphones, etc.)

[0223] Software: Generative AI model, database management system, user interface, real-time analysis engine, multilingual model, third-party API

[0224] System Operation

[0225] When a user logs in to their learning account, the device sends the login information to the server. The server authenticates the user, and if authentication is successful, retrieves the user's learning history and progress data from the database. Based on the retrieved data, the server uses a generative AI model to analyze the user's current learning situation and generate an optimal learning plan and learning materials. The generated learning plan and learning materials are sent from the server to the device and provided to the user.

[0226] The device displays the learning plan and learning materials sent from the server on a user interface, allowing the user to proceed with their learning using the materials. When the user performs a learning activity (e.g., answering a question), the device sends the data to the server. The server analyzes the received data in real time and generates appropriate feedback. The generated feedback is sent back from the server to the device and displayed to the user.

[0227] As feedback is provided, the server updates a progress chart based on the progress, and the updated progress chart is sent to the device and displayed visually to the user. Periodically, the server analyzes the student's learning data and automatically generates custom reports using an AI model, which are stored in a database. The generated reports are then provided to the user, parents, and teachers via the device.

[0228] The generated learning materials are stored in a database by the server, and the device requests appropriate learning materials from this database and provides them to the user as the user progresses. When the user enters a question or concern, the device sends the question to the server, which uses a generative AI model to analyze the question and generate an appropriate answer. The answer is then sent back from the server to the device and provided to the user.

[0229] The server also connects with other companies' APIs to add new educational content to the database. This content is provided appropriately according to the user's learning progress. The generated teaching materials and content are translated using a multilingual model as needed, and the translated content is sent from the server to the terminal and provided to the user.

[0230] Examples and prompts

[0231] If a user is studying junior high school mathematics, they first log in to the system, and the server obtains the user's learning history and progress data. Based on that data, the generative AI model analyzes it to determine the next unit to study (e.g., equation solving) and generates appropriate learning materials (e.g., explanatory videos and practice problems). The generated learning plan and materials are provided to the user via their device. When the user solves problems and enters the results into the system, the server immediately analyzes the data and generates feedback based on the accuracy rate and level of understanding. At the same time, a progress chart is updated, allowing the user to visually check their current learning status.

[0232] Here is an example of a prompt to input to a generative AI model:

[0233] "Please generate teaching materials for solving equations in junior high school mathematics. The user's learning data includes the following progress information. In particular, practice problems and explanatory videos are needed to improve understanding of equation solving."

[0234] The above is a specific embodiment for carrying out the present invention.

[0235] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0236] Step 1:

[0237] The user logs into their learning account.

[0238] Input: Username and Password

[0239] How it works: The device sends the username and password to the server.

[0240] Output: Login information sent to the server

[0241] Step 2:

[0242] The server authenticates the user.

[0243] Input: Login information

[0244] How it works: The server retrieves and verifies user authentication information from a database based on the login information.

[0245] Output: Authentication success or failure result

[0246] Step 3:

[0247] The server retrieves the user's learning history and progress data from the database.

[0248] Input: Authentication success result

[0249] Operation: If authentication is successful, the server retrieves the user's learning history and progress data from the database.

[0250] Output: Learning history and progress data

[0251] Step 4:

[0252] The server generates a prompt based on the data it retrieves.

[0253] Input: Learning history and progress data

[0254] How it works: The server creates a prompt sentence appropriate for the generative AI model based on the acquired data.

[0255] Output: prompt statement

[0256] Step 5:

[0257] The server uses the generated AI model to analyze the user's learning status.

[0258] Input: prompt statement

[0259] How it works: The server inputs prompt sentences into the generative AI model and analyzes the user's learning status.

[0260] Output: Analysis results (optimal learning plan and materials)

[0261] Step 6:

[0262] The server sends the generated learning plan and learning materials to the terminal.

[0263] Input: Analysis results

[0264] Operation: The server sends the generated learning plan and learning materials to the terminal.

[0265] Output: Study plans and materials

[0266] Step 7:

[0267] The terminal receives the learning plan and learning materials sent from the server.

[0268] Input: Study plan and materials

[0269] Operation: The device displays the received learning plan and learning materials on the user interface.

[0270] Output: The lesson plan and materials displayed in a user interface

[0271] Step 8:

[0272] The user progresses through the learning process based on the displayed learning plan and materials.

[0273] Input: Study plan and materials

[0274] Operation: The user studies using the presented learning materials.

[0275] Output: Learning activity data (question answers, etc.)

[0276] Step 9:

[0277] The terminal transmits the user's learning activity data to the server.

[0278] Input: Learning activity data

[0279] Operation: The terminal transmits learning activity data to the server.

[0280] Output: Learning activity data is sent to the server

[0281] Step 10:

[0282] The server analyzes learning activity data in real time.

[0283] Input: Learning activity data

[0284] Operation: The server analyzes learning activity data using a real-time analysis engine.

[0285] Output: Analysis results (feedback)

[0286] Step 11:

[0287] The server generates appropriate feedback.

[0288] Input: Analysis results

[0289] How it works: The server uses a generative AI model to generate appropriate feedback.

[0290] Output: Feedback

[0291] Step 12:

[0292] The server transmits the generated feedback to the terminal.

[0293] Input: Feedback

[0294] Operation: The server sends the generated feedback to the device.

[0295] Output: Feedback is sent to the device

[0296] Step 13:

[0297] The device displays the feedback to the user.

[0298] Input: Feedback

[0299] Operation: The device displays the received feedback in the user interface.

[0300] Output: Feedback is displayed in the user interface

[0301] Step 14:

[0302] The server updates the learning progress chart.

[0303] Input: Learning activity data

[0304] Operation: The server updates the progress chart based on the learning activity data.

[0305] Output: Updated progress chart

[0306] Step 15:

[0307] The server sends the updated progress chart to the terminal.

[0308] Input: Updated progress chart

[0309] Operation: The server sends a progress chart to the terminal.

[0310] Output: An updated progress chart is sent to the terminal

[0311] Step 16:

[0312] The terminal displays a progress chart to the user.

[0313] Input: Progress Chart

[0314] Action: The terminal displays an updated progress chart in the user interface.

[0315] Output: A progress chart is displayed in the user interface.

[0316] Step 17:

[0317] The server periodically analyzes the user's learning data and automatically generates custom reports.

[0318] Input: Training data

[0319] How it works: The server uses the generative AI model based on the training data to generate a custom report.

[0320] Output: Custom Report

[0321] Step 18:

[0322] The server stores the custom report in a database and notifies you.

[0323] Input: Custom Report

[0324] How it works: The server saves the custom report to a database and sends notifications to users and interested parties.

[0325] Output: Saved custom reports and notifications

[0326] Step 19:

[0327] The terminal displays the report.

[0328] Input: Custom Report

[0329] Action: The terminal displays the custom report in the user interface.

[0330] Output: The custom report displayed in the user interface

[0331] Step 20:

[0332] The server stores the generated teaching materials in a database.

[0333] Input: Generated teaching materials

[0334] Operation: The server stores the generated teaching materials in a database.

[0335] Output: Saved materials

[0336] Step 21:

[0337] The terminal requests appropriate learning materials from the learning material database according to the student's learning progress.

[0338] Input: Learning progress data

[0339] Operation: The terminal requests the appropriate learning materials from the server based on the learning progress data.

[0340] Output: Requested materials

[0341] Step 22:

[0342] The server provides the requested educational material to the terminal.

[0343] Input: Requested materials

[0344] Operation: The server sends the appropriate educational material to the device.

[0345] Output: Provided teaching materials

[0346] Step 23:

[0347] The terminal displays the provided educational material to the user.

[0348] Input: Provided materials

[0349] Operation: The terminal displays the provided teaching materials on the user interface.

[0350] Output: Displayed teaching materials

[0351] Step 24:

[0352] The user enters a question or concern.

[0353] Input: Questions and concerns

[0354] How it works: The device sends a question or inquiry to the server.

[0355] Output: Question submitted

[0356] Step 25:

[0357] The server analyzes the question using a generative AI model.

[0358] Input: Submitted question

[0359] How it works: The server inputs the question into the generative AI model and analyzes it.

[0360] Output: Analysis result (appropriate answer)

[0361] Step 26:

[0362] The server generates an appropriate response.

[0363] Input: Analysis results

[0364] How it works: The server uses a generative AI model to generate an appropriate answer.

[0365] Output: The generated answer

[0366] Step 27:

[0367] The server sends the generated response to the terminal.

[0368] Input: Generated Answer

[0369] Operation: The server generates a response and sends it to the device.

[0370] Output: Submitted response

[0371] Step 28:

[0372] The terminal displays the answer on the user interface.

[0373] Input: Submitted answer

[0374] Action: The terminal displays the answer in the user interface.

[0375] Output: Displayed answer

[0376] (Application example 1)

[0377] 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."

[0378] It is necessary to build an optimal system that can respond to learners' diverse learning needs and progress and provide an effective and personalized learning experience, and also to enable learners to receive appropriate feedback and learning materials in real time and visually grasp their own learning progress.In addition, a means is needed to efficiently accumulate and analyze learning data and provide an optimized learning experience for each learner.

[0379] 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.

[0380] In this invention, the server includes a means for acquiring student learning history and progress data, a means for generating optimal learning plans and learning materials based on the acquired data, and a means for providing the generated learning plans and learning materials to students, thereby providing an optimized learning experience for each student and improving learning effectiveness.

[0381] "Means for obtaining student learning history and progress data" refers to a method for recording the learning content that learners have engaged in in the past and their progress, and incorporating this into the system.

[0382] The "means for generating optimal learning plans and learning materials" is a method for analyzing collected learning history and progress data and automatically creating optimal learning plans and learning materials for individual learners.

[0383] "Means for providing students with generated learning plans and teaching materials" refers to a method for effectively delivering the generated learning plans and teaching materials to learners.

[0384] "Means for analyzing student learning activity data in real time and providing feedback" refers to a method for monitoring the learning activities of learners in real time and providing immediate feedback based on the analysis results.

[0385] The "means for updating the progress chart" is a method for updating the progress chart, which visually displays the progress of the learner, based on the latest data.

[0386] The "means for automatically generating custom reports" is a method for automatically creating reports tailored to individual learners based on collected learning data.

[0387] "Means for visually providing learning materials and feedback using smart devices" refers to a method for visually displaying learning materials and feedback to learners using devices such as smart glasses or tablets.

[0388] "Means for obtaining user interactions in real time and generating feedback based on that" refers to a method for collecting interaction data such as learners' learning status and responses in real time, analyzing that data, and providing appropriate feedback.

[0389] The system that realizes this application example collects students' learning history and progress data, and based on that data, generates and provides optimal learning plans and teaching materials. It also aims to improve learning effectiveness by analyzing learners' progress in real time and providing feedback.

[0390] Program implementation form

[0391] Hardware and Software Configuration

[0392] The hardware used includes a server, smart devices (smart glasses, smartphones, tablets, etc.), and learner terminals.

[0393] The software used includes a generative AI model, a database management system, a user interface, and a real-time feedback analysis module.

[0394] Specific operation of the system

[0395] Acquire learning history and progress data

[0396] When a learner logs in to their learning account, the device sends the login information to the server, which then authenticates the learner and, if authentication is successful, retrieves their learning history and progress data from the database.

[0397] Learning plan and material generation

[0398] Based on the acquired historical data, the server uses a generative AI model to analyze the learner's current learning situation and generate the optimal learning plan and materials, thereby providing the learner with an optimized learning plan.

[0399] Provision of materials

[0400] The generated learning plan and learning materials are sent from the server to the device, where the provided data is displayed on a user interface to help the learner progress through their studies in an easy-to-understand manner.

[0401] Providing real-time feedback

[0402] Every time a learner performs a learning activity, the data is sent to the server, which analyzes the data in real time and uses a generative AI model to generate appropriate feedback, which is then instantly sent to the learner's device and provided to them.

[0403] Progress Chart Update

[0404] As feedback is provided, the server updates a progress chart, which is then sent to the device, allowing the learner to visually monitor their progress.

[0405] Generate custom reports

[0406] The server periodically analyzes learner data and automatically generates custom reports, which are stored in a database and provided to learners via their devices.

[0407] Using a Smart Device

[0408] The generated learning plans, materials, and feedback are presented visually using smart devices such as smart glasses and tablets, which help learners receive real-time feedback and track their learning progress.

[0409] Specific examples

[0410] Real-world usage scenarios

[0411] For example, when a student studying middle school mathematics logs into the system, the server retrieves their learning history and progress data. As a result, the next unit to be studied (e.g., solving equations) is determined, and specific teaching materials (videos and exercises) are generated. These teaching materials are visually presented to the student through smart glasses.

[0412] When students solve problems and input their results into the system, the server analyzes the data and generates feedback based on the accuracy rate and level of understanding. This feedback is instantly displayed on the smart glasses, allowing students to see the direction of their learning in real time.

[0413] Prompt Sentence Examples

[0414] When students use a generative AI model to solve a math problem, they are prompted with the following prompt:

[0415] "Based on Student A's learning history, please suggest the next math unit they should study."

[0416] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0417] Step 1: Login and Data Retrieval

[0418] When a user logs in to their learning account, the device sends the login information to the server. The server authenticates the user and retrieves the learning history and progress data from the database after successful authentication. The input includes the user's login information, and the output includes the learning history and progress data.

[0419] Step 2: Generate your lesson plan and materials

[0420] The server uses a generative AI model to analyze the acquired learning history and progress data, and generates the optimal learning plan and learning materials for the user. The inputs are learning history and progress data, and the output is the optimal learning plan and learning materials.

[0421] Step 3: Provide materials and plans

[0422] The generated learning plan and learning materials are sent from the server to the device, which displays them and provides a guide for the user to proceed with their learning. The inputs include the optimal learning plan and learning materials, and the output is the visual content provided to the user.

[0423] Step 4: Submit learning activity data

[0424] The user performs a learning activity and inputs the results (e.g., answers to questions) into the terminal. The terminal sends the data to the server. The input includes the user's learning activity data, and the output includes the data sent to the server.

[0425] Step 5: Generate and provide real-time feedback

[0426] The server analyzes the received learning activity data in real time and generates appropriate feedback using a generative AI model, which is then immediately sent back to the device and displayed to the user.The input includes the learning activity data, and the output includes the feedback provided to the user.

[0427] Step 6: Update the progress chart

[0428] As feedback is provided, the server updates a progress chart based on the progress. The updated chart is sent to the device, allowing the user to visually check their progress. The inputs include the latest feedback and learning activity data, and the output is the updated progress chart.

[0429] Step 7: Generate and deliver custom reports

[0430] The server periodically analyzes the learning data and automatically generates a custom report using a generative AI model. The generated report is stored in a database and provided to the user via their device. The input includes learning history and accumulated progress data, and the output includes an automatically generated custom report.

[0431] Step 8: Using a Smart Device

[0432] The present invention provides users with visually generated learning plans and learning materials, as well as real-time feedback and progress charts using smart devices such as smart glasses and tablets.The present invention includes inputs such as learning plans, learning materials, feedback, and progress charts, and outputs such as visual information displayed on the smart devices.

[0433] 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.

[0434] This invention relates to an educational system that utilizes a generative AI model and an emotion engine, proposing specific means for providing each student with a personalized learning experience. The system acquires and analyzes students' learning history, progress data, and emotion data to generate and provide optimal learning plans and teaching materials. It also analyzes students' learning activities in real time, provides feedback, and allows students to visually check their progress, thereby improving learning effectiveness.

[0435] System Configuration

[0436] The system of the present invention has the following main functions:

[0437] 1. How to obtain learning history and progress data

[0438] 2. How to obtain emotion data

[0439] 3. A means of generating learning plans and materials

[0440] 4. Means of providing generated learning plans and teaching materials

[0441] 5. Real-time feedback methods

[0442] 6. How to update the progress chart

[0443] 7. Automated generation of custom reports

[0444] 8. Storage method for the teaching material database

[0445] 9. Question-Answering Systems

[0446] 10. How to connect with other companies' APIs

[0447] 11. Multilingual Support

[0448] Program processing and behavior

[0449] The program processing for each function is explained below in natural language.

[0450] 1. How to obtain learning history and progress data

[0451] The user accesses the login screen and enters their user ID and password. The device encrypts the entered login information using SSL / TLS and sends a request to the authentication API. The server checks the authentication information against the database and returns the authentication result to the device. If the authentication is successful, the server queries the database to obtain the learning history and progress data associated with the user ID.

[0452] 2. How to obtain emotion data

[0453] When a user engages in learning activities, the device uses its built-in camera and microphone to capture the user's facial expressions and tone of voice. The device then sends the captured data to an emotion engine, which analyzes it as emotional data. The analysis results are then sent from the device to a server and stored along with the user's learning history and progress data.

[0454] 3. A means of generating learning plans and materials

[0455] The server inputs the acquired learning history, progress data, and emotional data into the generative AI model. The server then analyzes the data using the generative AI model to generate an optimal learning plan and learning materials. The generated learning plan and learning material metadata are sent to the device in JSON format.

[0456] 4. Means of providing generated learning plans and teaching materials

[0457] The device analyzes the learning plan and learning material data received from the server. The device displays the personalized learning plan and learning materials on the user interface. This allows the user to proceed with learning using learning materials based on the optimized learning plan.

[0458] 5. Real-time feedback methods

[0459] When a user performs a learning activity (e.g., answering a question), the device sends the data to the server. The server analyzes the received data in real time and generates appropriate feedback along with the user's emotional data. The generated feedback is sent back from the server to the device and displayed to the user.

[0460] 6. How to update the progress chart

[0461] As feedback is provided, the server updates the progress chart based on the progress and emotion data, and the updated progress chart is sent to the device and displayed visually to the user.

[0462] 7. Automated generation of custom reports

[0463] The server periodically analyzes students' learning data, progress data, and emotional data, and automatically generates custom reports using AI models. The generated reports are stored in a database and provided to users, parents, and teachers via their devices.

[0464] 8. Storage method for the teaching material database

[0465] The server stores the generated learning material data in a database. The terminal requests appropriate learning materials from this database according to the user's learning progress and provides them to the user.

[0466] 9. Question-Answering Systems

[0467] When a user types in a question or concern, the device sends the question to the server, which uses a generative AI model to analyze the question and generate an appropriate answer, which is then sent back to the device and provided to the user.

[0468] 10. How to connect with other companies' APIs

[0469] The server connects with other companies' APIs to add new educational content to the database, which is provided as the user progresses through their studies.

[0470] 11. Multilingual Support

[0471] The generated teaching materials and content are translated as needed using a multilingual model, and the translated content is sent from the server to the terminal and provided to the user.

[0472] Specific examples

[0473] If a user is studying junior high school mathematics, they first log in to the system. The server obtains the user's learning history and progress data, and analyzes it along with facial expression and tone of voice data collected by the emotion engine. As a result, the next unit to be studied (e.g., solving equations) is determined, and appropriate learning materials (e.g., explanatory videos and practice problems) are generated. The generated learning plan and materials are provided to the user via their device.

[0474] When a user solves a problem and inputs the results into the system, the server instantly analyzes the data and generates feedback based on the accuracy rate, level of understanding, and emotional data. A progress chart is also updated, providing a visual indication of the student's current learning status. In this way, the system provides an optimized learning experience that takes into account each student's emotional state, supporting effective learning.

[0475] The above is a specific embodiment for carrying out the present invention.

[0476] The processing flow will be explained below.

[0477] Processing steps of a system that combines emotion engines

[0478] 1. How to obtain learning history and progress data

[0479] Step 1:

[0480] The user accesses the login screen and enters their user ID and password.

[0481] Step 2:

[0482] The device encrypts the entered login information using SSL / TLS and sends a request to the authentication API.

[0483] Step 3:

[0484] The server checks the authentication information against a database and returns the authentication result to the terminal.

[0485] Step 4:

[0486] If the server is successful in authenticating the user, it queries the database to retrieve the learning history and progress data associated with the user ID.

[0487] 2. How to obtain emotion data

[0488] Step 1:

[0489] When a user is engaged in learning activities, the device uses its built-in camera and microphone to capture the user's facial expressions and tone of voice.

[0490] Step 2:

[0491] The device sends the captured data to the emotion engine, where it is analyzed as emotion data.

[0492] Step 3:

[0493] The device sends the analysis results to a server, where they are stored along with the user's learning history and progress data.

[0494] 3. A means of generating learning plans and materials

[0495] Step 1:

[0496] The learning history, progress data, and emotional data acquired by the server are input into the generative AI model.

[0497] Step 2:

[0498] The server uses a generative AI model to analyze the data and generate optimal learning plans and materials.

[0499] Step 3:

[0500] The server sends the generated learning plan and learning material metadata to the terminal in JSON format.

[0501] 4. Means of providing generated learning plans and teaching materials

[0502] Step 1:

[0503] The device analyzes the learning plan and teaching material data received from the server.

[0504] Step 2:

[0505] The device displays a personalized learning plan and learning materials in a user interface.

[0506] 5. Real-time feedback methods

[0507] Step 1:

[0508] The user performs a learning activity (e.g., answers a question).

[0509] Step 2:

[0510] The device sends the user's learning activity data to the server as an API request.

[0511] Step 3:

[0512] The server passes the received data to a real-time analysis system, which calculates the accuracy rate and learning progress.

[0513] Step 4:

[0514] The server generates appropriate feedback along with the user's emotional data.

[0515] Step 5:

[0516] The server sends feedback and progress chart data in JSON format to the device.

[0517] Step 6:

[0518] The device provides the user with real-time feedback and a visual progress chart.

[0519] 6. How to update the progress chart

[0520] Step 1:

[0521] The server analyzes the user's learning data and emotional data and generates a progress chart.

[0522] Step 2:

[0523] The server transmits the progress chart data to the terminal.

[0524] Step 3:

[0525] The terminal displays the progress chart on the user interface.

[0526] 7. Automated generation of custom reports

[0527] Step 1:

[0528] The server periodically retrieves the student's learning data, progress data and emotion data from the database.

[0529] Step 2:

[0530] The server analyzes the acquired data and automatically generates custom reports using AI models.

[0531] Step 3:

[0532] The server stores the generated reports in a database and sends them to the terminal.

[0533] Step 4:

[0534] The terminal displays the custom report to the user.

[0535] 8. Storage method for the teaching material database

[0536] Step 1:

[0537] The server stores the generated teaching material data in a database.

[0538] Step 2:

[0539] The terminal requests the learning material data according to the user's learning progress.

[0540] Step 3:

[0541] The server receives the request and transmits the corresponding educational material data to the terminal.

[0542] Step 4:

[0543] The terminal displays appropriate educational materials to the user.

[0544] 9. Question-Answering Systems

[0545] Step 1:

[0546] The user enters a question or concern.

[0547] Step 2:

[0548] The device sends the question to the server as an API request.

[0549] Step 3:

[0550] The server uses a generative AI model to analyze the question and generate an appropriate answer.

[0551] Step 4:

[0552] The server sends the answer to the device.

[0553] Step 5:

[0554] The terminal displays the answer to the user.

[0555] 10. How to connect with other companies' APIs

[0556] Step 1:

[0557] The server connects with other companies' APIs to obtain new educational content.

[0558] Step 2:

[0559] The server stores the acquired educational content in a database.

[0560] Step 3:

[0561] The terminal requests new educational content according to the user's learning progress.

[0562] Step 4:

[0563] The server receives the request and transmits the corresponding educational content to the terminal.

[0564] Step 5:

[0565] The terminal displays new educational content to the user.

[0566] 11. Multilingual Support

[0567] Step 1:

[0568] The server inputs the generated teaching materials and content into a multilingual model for translation.

[0569] Step 2:

[0570] The server stores the translated content in a database.

[0571] Step 3:

[0572] The device requests translated content according to the language set by the user.

[0573] Step 4:

[0574] The server receives the request and sends the content in the corresponding language to the terminal.

[0575] Step 5:

[0576] The terminal displays the content to the user in the selected language.

[0577] Example 2

[0578] 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."

[0579] Traditional education systems struggle to effectively track each student's learning progress and level of understanding, making it difficult to provide individually optimized learning plans and materials. Furthermore, they fail to provide feedback or adjust learning plans that take into account the student's emotional state during learning, preventing the most effective learning outcomes. Furthermore, while students need timely and appropriate answers to their questions, traditional systems fail to adequately address them.

[0580] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring student learning history and progress data, means for generating an optimal learning plan and learning materials based on the acquired data, means for providing the generated learning plan and learning materials to the student, means for analyzing learning activity data in real time and providing feedback, means for updating a progress chart based on the analysis results, means for automatically generating a custom report, means for acquiring and analyzing student emotional data, and means for adjusting the learning plan and feedback based on the emotional data. This makes it possible to provide an individually optimized learning plan and learning materials for each student and to provide effective feedback that takes into account the student's emotional state.

[0581] "Student Learning History and Progress Data" refers to information about a student's learning history, learning progress, and related assessments and grades.

[0582] "Emotional data" refers to data that indicates a student's emotions or mental state, such as facial expressions or tone of voice.

[0583] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to analyze data and generate optimal learning plans, materials, and feedback.

[0584] A "learning plan" is a plan that outlines the content and order in which a student should study in order to achieve a specific goal.

[0585] "Instructional Materials" refers to learning resources such as textbooks, videos, and exercises that students use when studying.

[0586] "Feedback" refers to comments, instructions, and evaluations provided based on students' learning activities.

[0587] A "progress chart" is a graph or chart that visually shows a student's learning progress.

[0588] "Custom Report" refers to an individual report generated based on each student's learning history, progress, and emotional data.

[0589] "Questions" refer to any doubts or questions that students have while studying.

[0590] A "prompt" refers to the wording of instructions or questions given to a generative AI model.

[0591] MODE FOR CARRYING OUT THE INVENTION

[0592] This invention relates to an educational system that utilizes generative AI models and emotion engines to provide personalized learning experiences for individual students. This system acquires data from the user's learning process and generates and provides optimal learning plans and learning materials based on that data.

[0593] Overall system configuration

[0594] The system mainly consists of the following components:

[0595] 1. A means of capturing student learning history and progress data

[0596] 2. Means of acquiring and analyzing emotion data

[0597] 3. A means to generate optimal learning plans and materials based on the acquired data

[0598] 4. A means of providing generated lesson plans and materials to students

[0599] 5. A means of analyzing student learning activity data in real time and providing feedback

[0600] 6. A way to update the progress chart based on the analysis results

[0601] 7. Automated generation of custom reports

[0602] Specific embodiments of each means

[0603] 1. A means of capturing student learning history and progress data

[0604] The user accesses the login screen and enters their user ID and password. The device encrypts the entered login information using SSL / TLS and sends a request to the authentication API. The server checks the authentication information against a database and returns the authentication result. If authentication is successful, the server retrieves the user's learning history and progress data from the database. MySQL or PostgreSQL are generally used as the database.

[0605] 2. Means of acquiring and analyzing emotion data

[0606] As the user engages in learning activities, the device uses its built-in camera and microphone to capture the user's facial expressions and tone of voice. The device then sends the captured data to an emotion engine for analysis. The emotion engine uses the Microsoft Azure Emotion API and Google Cloud Speech-to-Text.

[0607] 3. A means to generate optimal learning plans and materials based on the acquired data

[0608] The server inputs the acquired learning history, progress data, and emotional data into a generative AI model. GPT-3, BERT, T5, and other models are used. The data is analyzed to generate an optimal learning plan and learning materials. The generated learning plan and learning materials are converted into JSON format and sent to the device.

[0609] 4. A means of providing generated lesson plans and materials to students

[0610] The device analyzes the learning plan and learning material data received from the server and displays them on a user interface, which is built using frameworks such as React and Angular.

[0611] 5. A means of analyzing learning activity data in real time and providing feedback

[0612] When a user performs a learning activity, the device sends the data to the server, which analyzes the received data in real time and generates appropriate feedback. The feedback is sent to the device in text format and displayed to the user.

[0613] 6. A way to update the progress chart based on the analysis results

[0614] After providing the feedback, the server updates the progress chart based on the progress and emotion data. The progress chart is generated using D3.js and Chart.js and sent to the device.

[0615] 7. Automated generation of custom reports

[0616] Periodically, the server analyzes students' learning data, progress data, and emotional data, and automatically generates custom reports, which are converted into PDF or HTML format, stored in a database, and later provided to users via their devices.

[0617] Specific examples

[0618] If a user is studying junior high school mathematics, they first log in to the system. The server obtains the user's learning history and progress data, and analyzes the facial expression and tone of voice data collected by the emotion engine. As a result, the next unit to be studied (e.g., solving equations) is determined, and appropriate learning materials (e.g., explanatory videos and practice problems) are generated. The generated learning plan and materials are provided to the user via their device.

[0619] When a user solves a problem and inputs the results into the system, the server instantly analyzes the data and generates feedback based on the accuracy rate, level of understanding, and emotional data. A progress chart is also updated, allowing users to visually check their current learning status. In this way, the system of the present invention provides an optimized learning experience that takes into account each student's emotional state, supporting effective learning.

[0620] Prompt Sentence Examples

[0621] "I would like to learn how to solve equations in junior high school mathematics. Please suggest the best study plan and materials."

[0622] The above is a specific embodiment for carrying out the present invention.

[0623] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0624] Step 1:

[0625] The user accesses the login screen and enters their user ID and password. The user ID and password are input. The device encrypts the entered login information using SSL / TLS and sends a request to the authentication API. The server compares the received authentication information with a database, generates an authentication result, and returns the authentication success or failure result to the device as output.

[0626] Step 2:

[0627] When the server receives a successful authentication result, it retrieves the learning history and progress data associated with the user ID from the database. It takes the user ID as input and accesses the database using an SQL query. Example:

[0628] sql

[0629] SELECT FROM user_data WHERE user_id = 'User ID';

[0630] The output is the acquired learning history and progress data.

[0631] Step 3:

[0632] The device receives learning history and progress data, and the user begins learning. As the user engages in learning activities, the device uses the built-in camera and microphone to capture the user's facial expressions and tone of voice. The inputs include camera video data and audio data. The device sends this data to an emotion engine, where it is analyzed as emotion data. The output is the analyzed emotion data.

[0633] Step 4:

[0634] The device sends emotion data to the server, which receives it and stores it in a database along with the user's learning history and progress data. The inputs are emotion data, learning history, and progress data. By storing it in the database, a complete user dataset is generated as the output.

[0635] Step 5:

[0636] The server inputs the acquired learning history, progress data, and emotional data into the generative AI model. The input is all of the user's data. The server uses the generative AI model to analyze the data and generate the optimal learning plan and learning materials. Specifically, it uses the following prompt:

[0637] "A user wants to learn how to solve equations in junior high school mathematics. Please suggest the best study plan and study materials."

[0638] The output is a JSON-formatted learning plan and learning material data.

[0639] Step 6:

[0640] The device parses the learning plan and learning material data received from the server. The input is JSON format data. The device displays the personalized learning plan and learning material on the user interface. The output is a user-friendly display of the learning plan and learning material.

[0641] Step 7:

[0642] The user solves a problem and inputs the results into the terminal. The input is the user's answer data. The terminal sends the data to the server. The server analyzes the received data in real time and generates feedback based on the accuracy rate, comprehension level, and emotion data. The output is a feedback message.

[0643] Step 8:

[0644] The server sends feedback back to the terminal, which displays it to the user. The input is the feedback message from the server, and the output is the feedback displayed to the user.

[0645] Step 9:

[0646] After providing feedback, the server updates the progress chart based on the progress and emotion data. The input is the latest progress data, and the output is the updated progress chart. The progress chart is generated using D3.js and Chart.js and sent to the device.

[0647] Step 10:

[0648] The server periodically analyzes students' learning data, progress data, and emotion data, and automatically generates custom reports. The input is all user data, and the output is a custom report (PDF or HTML format). This report is stored in a database.

[0649] Step 11:

[0650] The device provides saved custom reports to users, parents, and teachers. The input is the generated report and the output is the delivered report.

[0651] (Application example 2)

[0652] 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."

[0653] In conventional educational systems, uniform learning plans and teaching materials are provided, making it difficult to accommodate the different learning paces and levels of understanding of each student. This can lead to a decrease in students' learning effectiveness. Similarly, virtual stores have the problem of being unable to recommend products that match individual customer preferences and needs, making it difficult to stimulate purchasing motivation.

[0654] 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.

[0655] In this invention, the server includes a means for acquiring a student's learning history and progress data, a means for generating an optimal learning plan and educational materials based on the acquired data, and a means for providing the generated learning plan and educational materials to the student. This makes it possible to provide educational materials tailored to each student's learning pace and level of understanding. Furthermore, the server includes a means for acquiring a customer's purchase history, browsing history, and emotional data, and for generating appropriate product recommendations and promotions, and a means for providing the generated product recommendations and promotions. This makes it possible to recommend products tailored to the individual preferences and needs of each customer.

[0656] "Learning history" is data that records what a student has learned in the past and their progress.

[0657] "Progress data" is data that records a student's progress and achievements in learning activities.

[0658] A "study plan" is an individual study schedule formulated based on each student's learning history and progress data.

[0659] "Educational materials" are teaching materials and reference materials provided to support students' learning activities.

[0660] "Purchase history" refers to data such as the products a customer has purchased in the past, the quantity, and the date of purchase.

[0661] "Browsing history" is data about the pages and products a customer views on a website or within an app.

[0662] "Emotion data" refers to data that indicates the emotional state of a customer, analyzed from facial expressions, tone of voice, etc.

[0663] "Product recommendations" are suitable product suggestions provided based on a customer's purchasing history, browsing history, and emotional data.

[0664] "Promotion" refers to information that recommends and advertises specific products or services to customers.

[0665] "Feedback" is a response, such as evaluation or advice, provided in response to the behavior of a student or customer.

[0666] A "progress chart" is a diagram that visually displays the progress of a student or client's activities.

[0667] A "Custom Report" is a personalized analytical report generated based on student or client data.

[0668] This invention relates to an educational system and a virtual store system that use a generative AI model and an emotion engine. To implement this invention, three main components work together: a server, a terminal, and a user. The specific system configuration and its operation are described below in detail.

[0669] System Configuration

[0670] 1. Server

[0671] The server contains a database, a generative AI model, and an emotion engine.

[0672] The database stores learning history, progress data, educational materials, purchase history, browsing history, emotional data, and more.

[0673] Generative AI models are used to generate lesson plans and educational materials, product recommendations and promotions.

[0674] The emotion engine analyzes the user's facial expressions and tone of voice to generate emotion data.

[0675] 2. Terminal

[0676] A terminal is a device operated by a user, and includes a smartphone, tablet, PC, etc.

[0677] The device has a built-in camera and microphone, which are used to capture the user's facial expressions and voice.

[0678] 3. Users

[0679] The users are the students or customers who use the system.

[0680] Users access the system through terminals and use various functions.

[0681] Program processing and behavior

[0682] 1. Data Acquisition and Storage

[0683] The user logs in and the server obtains learning history, progress data, purchase history, and browsing history.

[0684] While studying or shopping, the device's camera and microphone capture the user's facial expressions and voice, which are then analyzed by the emotion engine and sent to the server as emotional data.

[0685] All data is stored in a database on the server.

[0686] 2. Data analysis and plan generation

[0687] The server inputs the acquired data into a generative AI model to generate learning plans, educational materials, product recommendations and promotions.

[0688] Learning plans and educational materials, product recommendations and promotions are sent to the device in JSON format.

[0689] 3. Data provision and feedback

[0690] The terminal displays the provided study plans and educational materials, as well as product recommendations and promotions, to the user.

[0691] Every time a user studies or shops, the data is sent from the device to the server, which immediately analyzes it and generates feedback.

[0692] The feedback is sent to the terminal and provided to the user.

[0693] Furthermore, the server updates the progress chart and displays it on the terminal in a form that the user can visually understand.

[0694] Specific examples

[0695] Consider a case where a junior high school student logs into the system and begins studying junior high school mathematics. First, the server retrieves learning history and progress data, and the emotion engine analyzes the student's facial expressions and tone of voice. Based on the resulting data, an optimal learning plan and educational materials (e.g., videos and practice problems on how to solve equations) are generated. These educational materials are displayed on the student's device, and immediate feedback is provided as the student progresses.

[0696] In the virtual store, customers log in and their emotional data is captured using a camera and microphone. This data, along with their past purchase and browsing history, is analyzed to provide personalized product recommendations (e.g., scented candles if they're feeling relaxed) and promotions.

[0697] Prompt Sentence Examples

[0698] "Based on the user's purchase history, suggest products they are likely to purchase next."

[0699] "Recommend appropriate promotional items based on user emotional data (e.g., relaxation, stress, etc.)."

[0700] "Analyze data to provide users with a personalized shopping experience based on their purchase history, browsing history, and emotional state."

[0701] The above is an embodiment of the present invention. Other specific processing steps will be described later.

[0702] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0703] Step 1:

[0704] The user accesses the login screen and enters their user ID and password. The device encrypts the entered login information using SSL / TLS and sends a request to the authentication API.

[0705] Input: User ID, Password

[0706] Output: Authentication token

[0707] Specific operation: Enter the user ID and password on the terminal's login screen and send a request to the server.

[0708] Step 2:

[0709] The server checks the authentication information against the database and returns the authentication result to the device. If authentication is successful, it queries the database to obtain the learning history and progress data associated with the user ID.

[0710] Input: Authentication Token

[0711] Output: Learning history, progress data

[0712] Specific operation: The server executes a database query for authentication and returns the authentication result to the device. After successful authentication, the learning history and progress data are retrieved.

[0713] Step 3:

[0714] The device uses its built-in camera and microphone to capture the user's facial expressions and tone of voice, which are then analyzed as emotional data.

[0715] Input: User's facial expression, tone of voice

[0716] Output: Emotion data

[0717] What it does: The device activates the camera and microphone, processes the captured data in real time, and sends it to the sentiment analysis engine.

[0718] Step 4:

[0719] The server inputs the acquired learning history, progress data, and emotional data into a generative AI model to generate optimal learning plans and educational materials, and inputs purchase history, browsing history, and emotional data to generate optimal product recommendations and promotions.

[0720] Input: learning history, progress data, emotional data, purchase history, browsing history

[0721] Output: Study plans, educational materials, product recommendations, promotions

[0722] Specific operation: The server inputs various data into the generated AI model and generates the optimal plan and materials from the analysis results.

[0723] Step 5:

[0724] The server generates learning plans, educational materials, product recommendations, and promotions and sends them to the device, which analyzes the data and displays it on the user interface.

[0725] Input: Study plans, educational materials, product recommendations, promotions

[0726] Output: Data displayed in the user interface

[0727] Specific operation: Data is sent from the server to the device, which analyzes it and displays it on the screen. The user confirms the displayed content.

[0728] Step 6:

[0729] Users conduct learning activities or purchases and enter the results into the terminal, which then sends the data to the server, which analyzes it immediately and generates feedback.

[0730] Input: learning activity data, purchasing activity data

[0731] Output: Feedback

[0732] Specific operation: The user inputs the results of their learning or purchasing activities, and the data is sent to the server, which analyzes them immediately and sends feedback to the device.

[0733] Step 7:

[0734] The server updates the progress chart based on the progress status and emotion data, and the updated progress chart is sent to the device and displayed in a visually recognizable format for the user.

[0735] Input: Progress data, emotion data

[0736] Output: Updated progress chart

[0737] Specific behavior: The server receives new data, updates the progress chart, sends it to the device, and displays it in the user interface.

[0738] Step 8:

[0739] The server periodically analyzes student and customer data and automatically generates custom reports using AI models. The generated reports are stored in a database and provided to users via their devices.

[0740] Input: learning history, progress data, purchase history, emotional data

[0741] Output: Custom Report

[0742] What it does: The server periodically analyzes the data, generates custom reports, stores them in a database, and makes them available for viewing on the device.

[0743] The above is a description of the specific processing steps of the system that realizes the application example.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] [Second embodiment]

[0748] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0749] 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.

[0750] 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).

[0751] 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.

[0752] 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.

[0753] 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).

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0759] 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."

[0760] This invention relates to an educational system that utilizes generative AI models and proposes specific means for providing each student with a personalized learning experience. The system acquires and analyzes students' learning history and progress data to generate and provide optimal learning plans and teaching materials. It also analyzes students' learning activities in real time and provides feedback, improving learning effectiveness and allowing students to visually check their progress.

[0761] System Configuration

[0762] The system of the present invention has the following main functions:

[0763] 1. How to obtain learning history and progress data

[0764] 2. A means of generating learning plans and materials

[0765] 3. Means of providing generated learning plans and teaching materials

[0766] 4. A way to provide real-time feedback

[0767] 5. How to update the progress chart

[0768] 6. Automated generation of custom reports

[0769] 7. Storage method for the teaching material database

[0770] 8. Question-Answering Systems

[0771] 9. How to connect with other companies' APIs

[0772] 10. Multilingual Support

[0773] Program processing and behavior

[0774] The program processing for each function is explained below in natural language.

[0775] 1. How to obtain learning history and progress data

[0776] When a user logs in to their learning account, the device sends the login information to the server, which then authenticates the user and, if authentication is successful, retrieves the user's learning history and progress data from the database.

[0777] 2. A means of generating learning plans and materials

[0778] Based on the acquired data, the server uses a generative AI model to analyze the user's current learning situation and generate an optimal learning plan and learning materials, which are then sent from the server to the device and provided to the user.

[0779] 3. Means of providing generated learning plans and teaching materials

[0780] The device displays the study plan and learning materials sent from the server on the user interface, allowing the user to study using the most appropriate learning materials for their own learning situation.

[0781] 4. A way to provide real-time feedback

[0782] When a user performs a learning activity (e.g., answering a question), the device sends the data to the server, which analyzes the received data in real time and generates appropriate feedback. The generated feedback is sent back from the server to the device and displayed to the user.

[0783] 5. How to update the progress chart

[0784] As feedback is provided, the server updates a progress chart based on the progress, which is then sent to the device and displayed visually to the user.

[0785] 6. Automated generation of custom reports

[0786] Periodically, the server analyzes student learning data and automatically generates custom reports using AI models. The reports are stored in a database and provided to users, parents, and teachers via their devices.

[0787] 7. Storage method for the teaching material database

[0788] The generated learning materials are stored in a database by the server. The terminal requests appropriate learning materials from this database according to the user's learning progress and provides them to the user.

[0789] 8. Question-Answering Systems

[0790] When a user types in a question or concern, the device sends the question to the server, which uses a generative AI model to analyze the question and generate an appropriate answer, which is then sent back to the device and provided to the user.

[0791] 9. How to connect with other companies' APIs

[0792] The server connects with other companies' APIs to add new educational content to the database, which is provided as the user progresses through their studies.

[0793] 10. Multilingual Support

[0794] The generated teaching materials and content are translated as needed using a multilingual model, and the translated content is sent from the server to the terminal and provided to the user.

[0795] Specific examples

[0796] If a user is studying junior high school mathematics, they first log in to the system. The server acquires and analyzes the user's learning history and progress data. As a result, the next unit to be studied (e.g., solving equations) is determined, and appropriate learning materials (e.g., explanatory videos and practice problems) are generated. The generated learning plan and materials are provided to the user via their device.

[0797] When a user solves a problem and inputs the results into the system, the server instantly analyzes the data and generates feedback based on the accuracy rate and level of understanding. A progress chart is also updated, allowing users to visually check their current learning status. In this way, the system of the present invention provides an optimized learning experience for each student, supporting effective learning.

[0798] The above is a specific embodiment for carrying out the present invention.

[0799] The processing flow will be explained below.

[0800] 1. How to obtain learning history and progress data

[0801] Processing Steps

[0802] Step 1:

[0803] The user accesses the login screen and enters their user ID and password.

[0804] Step 2:

[0805] The device encrypts the entered login information using SSL / TLS and sends a request to the authentication API.

[0806] Step 3:

[0807] The server checks the authentication information against a database and returns the authentication result to the terminal.

[0808] Step 4:

[0809] If the server is successful in authenticating the user, it queries the database to retrieve the learning history and progress data associated with the user ID.

[0810] 2. A means of generating learning plans and materials

[0811] Processing Steps

[0812] Step 1:

[0813] The learning history and progress data acquired by the server are input into the generative AI model.

[0814] Step 2:

[0815] The server uses the generated AI model to analyze learning history and progress, and generates optimal learning plans and materials.

[0816] Step 3:

[0817] The server sends the generated learning plan and learning material metadata to the terminal in JSON format.

[0818] 3. Means of providing generated learning plans and teaching materials

[0819] Processing Steps

[0820] Step 1:

[0821] The device analyzes the learning plan and teaching material data received from the server.

[0822] Step 2:

[0823] The device displays a personalized learning plan and learning materials in a user interface.

[0824] 4. A way to provide real-time feedback

[0825] Processing Steps

[0826] Step 1:

[0827] The user performs a learning activity (e.g., answers a question).

[0828] Step 2:

[0829] The device sends the user's learning activity data to the server as an API request.

[0830] Step 3:

[0831] The server passes the received data to a real-time analysis system, which calculates the accuracy rate and learning progress.

[0832] Step 4:

[0833] The server generates feedback messages and progress chart information.

[0834] Step 5:

[0835] The server sends feedback and progress chart data in JSON format to the device.

[0836] Step 6:

[0837] The device provides the user with real-time feedback and a visual progress chart.

[0838] 5. How to update the progress chart

[0839] Processing Steps

[0840] Step 1:

[0841] The server analyzes the user's learning data and generates a progress chart.

[0842] Step 2:

[0843] The server transmits the progress chart data to the terminal.

[0844] Step 3:

[0845] The terminal displays the progress chart on the user interface.

[0846] 6. Automated generation of custom reports

[0847] Processing Steps

[0848] Step 1:

[0849] The server periodically retrieves student learning and progress data from the database.

[0850] Step 2:

[0851] The server analyzes the acquired data and automatically generates custom reports using AI models.

[0852] Step 3:

[0853] The server stores the generated reports in a database and sends them to the terminal.

[0854] Step 4:

[0855] The terminal displays the custom report to the user.

[0856] 7. Storage method for the teaching material database

[0857] Processing Steps

[0858] Step 1:

[0859] The server stores the generated teaching material data in a database.

[0860] Step 2:

[0861] The terminal requests the learning material data according to the user's learning progress.

[0862] Step 3:

[0863] The server receives the request and transmits the corresponding educational material data to the terminal.

[0864] Step 4:

[0865] The terminal displays appropriate educational materials to the user.

[0866] 8. Question-Answering Systems

[0867] Processing Steps

[0868] Step 1:

[0869] The user enters a question or concern.

[0870] Step 2:

[0871] The device sends the question to the server as an API request.

[0872] Step 3:

[0873] The server uses a generative AI model to analyze the question and generate an appropriate answer.

[0874] Step 4:

[0875] The server sends the generated answer to the terminal.

[0876] Step 5:

[0877] The terminal displays the answer to the user.

[0878] 9. How to connect with other companies' APIs

[0879] Processing Steps

[0880] Step 1:

[0881] The server connects with other companies' APIs to obtain new educational content.

[0882] Step 2:

[0883] The server stores the acquired educational content in a database.

[0884] Step 3:

[0885] The terminal requests new educational content according to the user's learning progress.

[0886] Step 4:

[0887] The server receives the request and transmits the corresponding educational content to the terminal.

[0888] Step 5:

[0889] The terminal displays new educational content to the user.

[0890] 10. Multilingual Support

[0891] Processing Steps

[0892] Step 1:

[0893] The server inputs the generated teaching materials and content into a multilingual model for translation.

[0894] Step 2:

[0895] The server stores the translated content in a database.

[0896] Step 3:

[0897] The device requests translated content according to the language set by the user.

[0898] Step 4:

[0899] The server receives the request and sends the content in the corresponding language to the terminal.

[0900] Step 5:

[0901] The terminal displays the content to the user in the selected language.

[0902] Example 1

[0903] 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."

[0904] Current education systems struggle to provide personalized learning experiences based on each student's progress and level of understanding. They also need to address a wide range of needs, including real-time feedback, progress visualization, automatic custom report generation, and multilingual support. However, there are no systems that integrate all of these functions, making it difficult to maximize learning efficiency.

[0905] 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.

[0906] In this invention, the server includes means for acquiring student learning history and progress data, means for generating optimal learning plans and teaching materials using a generative AI model based on the acquired data, means for providing the generated learning plans and teaching materials to students, means for analyzing student learning activity data in real time and providing feedback, means for updating a progress chart based on the analysis results, means for automatically generating custom reports, means for providing generated answers to students, means for translating the generated teaching materials and content using a multilingual model, means for saving the automatically generated teaching materials in a database, means for providing the saved teaching materials according to the student's learning progress, means for adding new educational content to the database using a third-party API, and means for generating appropriate answers to questions entered by students using a generative AI model. This enables the provision of an individually optimized learning experience, real-time feedback, progress visualization, multilingual support, and the addition of new content.

[0907] A "learning history" is a record of a student's past learning activities and progress.

[0908] "Progress data" is information that indicates the learning content and progress that a student is currently making.

[0909] A "generative AI model" is an artificial intelligence model that analyzes students' learning data and generates optimal learning plans, teaching materials, and feedback.

[0910] A "study plan" is a study schedule and content that is optimized to help students progress through their studies efficiently.

[0911] "Teaching Materials" means the educational materials and exercises used in accordance with the Study Plan.

[0912] "Feedback" refers to evaluation and advice provided in real time to students' learning activities.

[0913] A "progress chart" is a graph or chart that visually shows a student's learning progress.

[0914] A "custom report" is a learning report that is automatically generated individually by analyzing a student's learning data.

[0915] A "multilingual model" is an artificial intelligence model for translating teaching materials and content into multiple languages.

[0916] A "database" is a system that systematically stores acquired data and generated teaching materials.

[0917] "Third-party APIs" refer to application program interfaces published by other companies or service providers.

[0918] "Questions" are questions that students have while studying or things they want to clarify.

[0919] An "answer" is an appropriate answer or explanation provided by a generative AI model in response to a question.

[0920] This invention is an educational system that utilizes a generative AI model and proposes specific means for providing a personalized learning experience to each student. The system acquires and analyzes students' learning history and progress data to generate and provide optimal learning plans and materials. It also analyzes students' learning activities in real time and provides feedback to improve learning effectiveness and allow students to visually check their progress.

[0921] System Configuration

[0922] The system of the present invention comprises the following main components:

[0923] How to obtain learning history and progress data

[0924] A means of generating lesson plans and materials

[0925] Means of providing generated learning plans and teaching materials

[0926] A way to provide real-time feedback

[0927] Progress chart update method

[0928] Automated generation of custom reports

[0929] A means of providing generated answers to students

[0930] A means of translating generated learning materials and content using a multilingual model

[0931] A means of storing automatically generated teaching materials in a database

[0932] A means of providing saved learning materials as students progress

[0933] A way to add new educational content to the database using third-party APIs

[0934] A means of generating appropriate answers using generative AI models for questions entered by students

[0935] Hardware and software used

[0936] The system uses the following hardware and software:

[0937] Hardware: Servers, devices (PCs, tablets, smartphones, etc.)

[0938] Software: Generative AI model, database management system, user interface, real-time analysis engine, multilingual model, third-party API

[0939] System Operation

[0940] When a user logs in to their learning account, the device sends the login information to the server. The server authenticates the user, and if authentication is successful, retrieves the user's learning history and progress data from the database. Based on the retrieved data, the server uses a generative AI model to analyze the user's current learning situation and generate an optimal learning plan and learning materials. The generated learning plan and learning materials are sent from the server to the device and provided to the user.

[0941] The device displays the learning plan and learning materials sent from the server on a user interface, allowing the user to proceed with their learning using the materials. When the user performs a learning activity (e.g., answering a question), the device sends the data to the server. The server analyzes the received data in real time and generates appropriate feedback. The generated feedback is sent back from the server to the device and displayed to the user.

[0942] As feedback is provided, the server updates a progress chart based on the progress, and the updated progress chart is sent to the device and displayed visually to the user. Periodically, the server analyzes the student's learning data and automatically generates custom reports using an AI model, which are stored in a database. The generated reports are then provided to the user, parents, and teachers via the device.

[0943] The generated learning materials are stored in a database by the server, and the device requests appropriate learning materials from this database and provides them to the user as the user progresses. When the user enters a question or concern, the device sends the question to the server, which uses a generative AI model to analyze the question and generate an appropriate answer. The answer is then sent back from the server to the device and provided to the user.

[0944] The server also connects with other companies' APIs to add new educational content to the database. This content is provided appropriately according to the user's learning progress. The generated teaching materials and content are translated using a multilingual model as needed, and the translated content is sent from the server to the terminal and provided to the user.

[0945] Examples and prompts

[0946] If a user is studying junior high school mathematics, they first log in to the system, and the server obtains the user's learning history and progress data. Based on that data, the generative AI model analyzes it to determine the next unit to study (e.g., equation solving) and generates appropriate learning materials (e.g., explanatory videos and practice problems). The generated learning plan and materials are provided to the user via their device. When the user solves problems and enters the results into the system, the server immediately analyzes the data and generates feedback based on the accuracy rate and level of understanding. At the same time, a progress chart is updated, allowing the user to visually check their current learning status.

[0947] Here is an example of a prompt to input to a generative AI model:

[0948] "Please generate teaching materials for solving equations in junior high school mathematics. The user's learning data includes the following progress information. In particular, practice problems and explanatory videos are needed to improve understanding of equation solving."

[0949] The above is a specific embodiment for carrying out the present invention.

[0950] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0951] Step 1:

[0952] The user logs into their learning account.

[0953] Input: Username and Password

[0954] How it works: The device sends the username and password to the server.

[0955] Output: Login information sent to the server

[0956] Step 2:

[0957] The server authenticates the user.

[0958] Input: Login information

[0959] How it works: The server retrieves and verifies user authentication information from a database based on the login information.

[0960] Output: Authentication success or failure result

[0961] Step 3:

[0962] The server retrieves the user's learning history and progress data from the database.

[0963] Input: Authentication success result

[0964] Operation: If authentication is successful, the server retrieves the user's learning history and progress data from the database.

[0965] Output: Learning history and progress data

[0966] Step 4:

[0967] The server generates a prompt based on the data it retrieves.

[0968] Input: Learning history and progress data

[0969] How it works: The server creates a prompt sentence appropriate for the generative AI model based on the acquired data.

[0970] Output: prompt statement

[0971] Step 5:

[0972] The server uses the generated AI model to analyze the user's learning status.

[0973] Input: prompt statement

[0974] How it works: The server inputs prompt sentences into the generative AI model and analyzes the user's learning status.

[0975] Output: Analysis results (optimal learning plan and materials)

[0976] Step 6:

[0977] The server sends the generated learning plan and learning materials to the terminal.

[0978] Input: Analysis results

[0979] Operation: The server sends the generated learning plan and learning materials to the terminal.

[0980] Output: Study plans and materials

[0981] Step 7:

[0982] The terminal receives the learning plan and learning materials sent from the server.

[0983] Input: Study plan and materials

[0984] Operation: The device displays the received learning plan and learning materials on the user interface.

[0985] Output: The lesson plan and materials displayed in a user interface

[0986] Step 8:

[0987] The user progresses through the learning process based on the displayed learning plan and materials.

[0988] Input: Study plan and materials

[0989] Operation: The user studies using the presented learning materials.

[0990] Output: Learning activity data (question answers, etc.)

[0991] Step 9:

[0992] The terminal transmits the user's learning activity data to the server.

[0993] Input: Learning activity data

[0994] Operation: The terminal transmits learning activity data to the server.

[0995] Output: Learning activity data is sent to the server

[0996] Step 10:

[0997] The server analyzes learning activity data in real time.

[0998] Input: Learning activity data

[0999] Operation: The server analyzes learning activity data using a real-time analysis engine.

[1000] Output: Analysis results (feedback)

[1001] Step 11:

[1002] The server generates appropriate feedback.

[1003] Input: Analysis results

[1004] How it works: The server uses a generative AI model to generate appropriate feedback.

[1005] Output: Feedback

[1006] Step 12:

[1007] The server transmits the generated feedback to the terminal.

[1008] Input: Feedback

[1009] Operation: The server sends the generated feedback to the device.

[1010] Output: Feedback is sent to the device

[1011] Step 13:

[1012] The device displays the feedback to the user.

[1013] Input: Feedback

[1014] Operation: The device displays the received feedback in the user interface.

[1015] Output: Feedback is displayed in the user interface

[1016] Step 14:

[1017] The server updates the learning progress chart.

[1018] Input: Learning activity data

[1019] Operation: The server updates the progress chart based on the learning activity data.

[1020] Output: Updated progress chart

[1021] Step 15:

[1022] The server sends the updated progress chart to the terminal.

[1023] Input: Updated progress chart

[1024] Operation: The server sends a progress chart to the terminal.

[1025] Output: An updated progress chart is sent to the terminal

[1026] Step 16:

[1027] The terminal displays a progress chart to the user.

[1028] Input: Progress Chart

[1029] Action: The terminal displays an updated progress chart in the user interface.

[1030] Output: A progress chart is displayed in the user interface.

[1031] Step 17:

[1032] The server periodically analyzes the user's learning data and automatically generates custom reports.

[1033] Input: Training data

[1034] How it works: The server uses the generative AI model based on the training data to generate a custom report.

[1035] Output: Custom Report

[1036] Step 18:

[1037] The server stores the custom report in a database and notifies you.

[1038] Input: Custom Report

[1039] How it works: The server saves the custom report to a database and sends notifications to users and interested parties.

[1040] Output: Saved custom reports and notifications

[1041] Step 19:

[1042] The terminal displays the report.

[1043] Input: Custom Report

[1044] Action: The terminal displays the custom report in the user interface.

[1045] Output: The custom report displayed in the user interface

[1046] Step 20:

[1047] The server stores the generated teaching materials in a database.

[1048] Input: Generated teaching materials

[1049] Operation: The server stores the generated teaching materials in a database.

[1050] Output: Saved materials

[1051] Step 21:

[1052] The terminal requests appropriate learning materials from the learning material database according to the student's learning progress.

[1053] Input: Learning progress data

[1054] Operation: The terminal requests the appropriate learning materials from the server based on the learning progress data.

[1055] Output: Requested materials

[1056] Step 22:

[1057] The server provides the requested educational material to the terminal.

[1058] Input: Requested materials

[1059] Operation: The server sends the appropriate educational material to the device.

[1060] Output: Provided teaching materials

[1061] Step 23:

[1062] The terminal displays the provided educational material to the user.

[1063] Input: Provided materials

[1064] Operation: The terminal displays the provided teaching materials on the user interface.

[1065] Output: Displayed teaching materials

[1066] Step 24:

[1067] The user enters a question or concern.

[1068] Input: Questions and concerns

[1069] How it works: The device sends a question or inquiry to the server.

[1070] Output: Question submitted

[1071] Step 25:

[1072] The server analyzes the question using a generative AI model.

[1073] Input: Submitted question

[1074] How it works: The server inputs the question into the generative AI model and analyzes it.

[1075] Output: Analysis result (appropriate answer)

[1076] Step 26:

[1077] The server generates an appropriate response.

[1078] Input: Analysis results

[1079] How it works: The server uses a generative AI model to generate an appropriate answer.

[1080] Output: The generated answer

[1081] Step 27:

[1082] The server sends the generated response to the terminal.

[1083] Input: Generated Answer

[1084] Operation: The server generates a response and sends it to the device.

[1085] Output: Submitted response

[1086] Step 28:

[1087] The terminal displays the answer on the user interface.

[1088] Input: Submitted answer

[1089] Action: The terminal displays the answer in the user interface.

[1090] Output: Displayed answer

[1091] (Application example 1)

[1092] 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."

[1093] It is necessary to build an optimal system that can respond to learners' diverse learning needs and progress and provide an effective and personalized learning experience, and also to enable learners to receive appropriate feedback and learning materials in real time and visually grasp their own learning progress.In addition, a means is needed to efficiently accumulate and analyze learning data and provide an optimized learning experience for each learner.

[1094] 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.

[1095] In this invention, the server includes a means for acquiring student learning history and progress data, a means for generating optimal learning plans and learning materials based on the acquired data, and a means for providing the generated learning plans and learning materials to students, thereby providing an optimized learning experience for each student and improving learning effectiveness.

[1096] "Means for obtaining student learning history and progress data" refers to a method for recording the learning content that learners have engaged in in the past and their progress, and incorporating this into the system.

[1097] The "means for generating optimal learning plans and learning materials" is a method for analyzing collected learning history and progress data and automatically creating optimal learning plans and learning materials for individual learners.

[1098] "Means for providing students with generated learning plans and teaching materials" refers to a method for effectively delivering the generated learning plans and teaching materials to learners.

[1099] "Means for analyzing student learning activity data in real time and providing feedback" refers to a method for monitoring the learning activities of learners in real time and providing immediate feedback based on the analysis results.

[1100] The "means for updating the progress chart" is a method for updating the progress chart, which visually displays the progress of the learner, based on the latest data.

[1101] The "means for automatically generating custom reports" is a method for automatically creating reports tailored to individual learners based on collected learning data.

[1102] "Means for visually providing learning materials and feedback using smart devices" refers to a method for visually displaying learning materials and feedback to learners using devices such as smart glasses or tablets.

[1103] "Means for obtaining user interactions in real time and generating feedback based on that" refers to a method for collecting interaction data such as learners' learning status and responses in real time, analyzing that data, and providing appropriate feedback.

[1104] The system that realizes this application example collects students' learning history and progress data, and based on that data, generates and provides optimal learning plans and teaching materials. It also aims to improve learning effectiveness by analyzing learners' progress in real time and providing feedback.

[1105] Program implementation form

[1106] Hardware and Software Configuration

[1107] The hardware used includes a server, smart devices (smart glasses, smartphones, tablets, etc.), and learner terminals.

[1108] The software used includes a generative AI model, a database management system, a user interface, and a real-time feedback analysis module.

[1109] Specific operation of the system

[1110] Acquire learning history and progress data

[1111] When a learner logs in to their learning account, the device sends the login information to the server, which then authenticates the learner and, if authentication is successful, retrieves their learning history and progress data from the database.

[1112] Learning plan and material generation

[1113] Based on the acquired historical data, the server uses a generative AI model to analyze the learner's current learning situation and generate the optimal learning plan and materials, thereby providing the learner with an optimized learning plan.

[1114] Provision of materials

[1115] The generated learning plan and learning materials are sent from the server to the device, where the provided data is displayed on a user interface to help the learner progress through their studies in an easy-to-understand manner.

[1116] Providing real-time feedback

[1117] Every time a learner performs a learning activity, the data is sent to the server, which analyzes the data in real time and uses a generative AI model to generate appropriate feedback, which is then instantly sent to the learner's device and provided to them.

[1118] Progress Chart Update

[1119] As feedback is provided, the server updates a progress chart, which is then sent to the device, allowing the learner to visually monitor their progress.

[1120] Generate custom reports

[1121] The server periodically analyzes learner data and automatically generates custom reports, which are stored in a database and provided to learners via their devices.

[1122] Using a Smart Device

[1123] The generated learning plans, materials, and feedback are presented visually using smart devices such as smart glasses and tablets, which help learners receive real-time feedback and track their learning progress.

[1124] Specific examples

[1125] Real-world usage scenarios

[1126] For example, when a student studying middle school mathematics logs into the system, the server retrieves their learning history and progress data. As a result, the next unit to be studied (e.g., solving equations) is determined, and specific teaching materials (videos and exercises) are generated. These teaching materials are visually presented to the student through smart glasses.

[1127] When students solve problems and input their results into the system, the server analyzes the data and generates feedback based on the accuracy rate and level of understanding. This feedback is instantly displayed on the smart glasses, allowing students to see the direction of their learning in real time.

[1128] Prompt Sentence Examples

[1129] When students use a generative AI model to solve a math problem, they are prompted with the following prompt:

[1130] "Based on Student A's learning history, please suggest the next math unit they should study."

[1131] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1132] Step 1: Login and Data Retrieval

[1133] When a user logs in to their learning account, the device sends the login information to the server. The server authenticates the user and retrieves the learning history and progress data from the database after successful authentication. The input includes the user's login information, and the output includes the learning history and progress data.

[1134] Step 2: Generate your lesson plan and materials

[1135] The server uses a generative AI model to analyze the acquired learning history and progress data, and generates the optimal learning plan and learning materials for the user. The inputs are learning history and progress data, and the output is the optimal learning plan and learning materials.

[1136] Step 3: Provide materials and plans

[1137] The generated learning plan and learning materials are sent from the server to the device, which displays them and provides a guide for the user to proceed with their learning. The inputs include the optimal learning plan and learning materials, and the output is the visual content provided to the user.

[1138] Step 4: Submit learning activity data

[1139] The user performs a learning activity and inputs the results (e.g., answers to questions) into the terminal. The terminal sends the data to the server. The input includes the user's learning activity data, and the output includes the data sent to the server.

[1140] Step 5: Generate and provide real-time feedback

[1141] The server analyzes the received learning activity data in real time and generates appropriate feedback using a generative AI model, which is then immediately sent back to the device and displayed to the user.The input includes the learning activity data, and the output includes the feedback provided to the user.

[1142] Step 6: Update the progress chart

[1143] As feedback is provided, the server updates a progress chart based on the progress. The updated chart is sent to the device, allowing the user to visually check their progress. The inputs include the latest feedback and learning activity data, and the output is the updated progress chart.

[1144] Step 7: Generate and deliver custom reports

[1145] The server periodically analyzes the learning data and automatically generates a custom report using a generative AI model. The generated report is stored in a database and provided to the user via their device. The input includes learning history and accumulated progress data, and the output includes an automatically generated custom report.

[1146] Step 8: Using a Smart Device

[1147] The present invention provides users with visually generated learning plans and learning materials, as well as real-time feedback and progress charts using smart devices such as smart glasses and tablets.The present invention includes inputs such as learning plans, learning materials, feedback, and progress charts, and outputs such as visual information displayed on the smart devices.

[1148] 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.

[1149] This invention relates to an educational system that utilizes a generative AI model and an emotion engine, proposing specific means for providing each student with a personalized learning experience. The system acquires and analyzes students' learning history, progress data, and emotion data to generate and provide optimal learning plans and teaching materials. It also analyzes students' learning activities in real time, provides feedback, and allows students to visually check their progress, thereby improving learning effectiveness.

[1150] System Configuration

[1151] The system of the present invention has the following main functions:

[1152] 1. How to obtain learning history and progress data

[1153] 2. How to obtain emotion data

[1154] 3. A means of generating learning plans and materials

[1155] 4. Means of providing generated learning plans and teaching materials

[1156] 5. Real-time feedback methods

[1157] 6. How to update the progress chart

[1158] 7. Automated generation of custom reports

[1159] 8. Storage method for the teaching material database

[1160] 9. Question-Answering Systems

[1161] 10. How to connect with other companies' APIs

[1162] 11. Multilingual Support

[1163] Program processing and behavior

[1164] The program processing for each function is explained below in natural language.

[1165] 1. How to obtain learning history and progress data

[1166] The user accesses the login screen and enters their user ID and password. The device encrypts the entered login information using SSL / TLS and sends a request to the authentication API. The server checks the authentication information against the database and returns the authentication result to the device. If the authentication is successful, the server queries the database to obtain the learning history and progress data associated with the user ID.

[1167] 2. How to obtain emotion data

[1168] When a user engages in learning activities, the device uses its built-in camera and microphone to capture the user's facial expressions and tone of voice. The device then sends the captured data to an emotion engine, which analyzes it as emotional data. The analysis results are then sent from the device to a server and stored along with the user's learning history and progress data.

[1169] 3. A means of generating learning plans and materials

[1170] The server inputs the acquired learning history, progress data, and emotional data into the generative AI model. The server then analyzes the data using the generative AI model to generate an optimal learning plan and learning materials. The generated learning plan and learning material metadata are sent to the device in JSON format.

[1171] 4. Means of providing generated learning plans and teaching materials

[1172] The device analyzes the learning plan and learning material data received from the server. The device displays the personalized learning plan and learning materials on the user interface. This allows the user to proceed with learning using learning materials based on the optimized learning plan.

[1173] 5. Real-time feedback methods

[1174] When a user performs a learning activity (e.g., answering a question), the device sends the data to the server. The server analyzes the received data in real time and generates appropriate feedback along with the user's emotional data. The generated feedback is sent back from the server to the device and displayed to the user.

[1175] 6. How to update the progress chart

[1176] As feedback is provided, the server updates the progress chart based on the progress and emotion data, and the updated progress chart is sent to the device and displayed visually to the user.

[1177] 7. Automated generation of custom reports

[1178] The server periodically analyzes students' learning data, progress data, and emotional data, and automatically generates custom reports using AI models. The generated reports are stored in a database and provided to users, parents, and teachers via their devices.

[1179] 8. Storage method for the teaching material database

[1180] The server stores the generated learning material data in a database. The terminal requests appropriate learning materials from this database according to the user's learning progress and provides them to the user.

[1181] 9. Question-Answering Systems

[1182] When a user types in a question or concern, the device sends the question to the server, which uses a generative AI model to analyze the question and generate an appropriate answer, which is then sent back to the device and provided to the user.

[1183] 10. How to connect with other companies' APIs

[1184] The server connects with other companies' APIs to add new educational content to the database, which is provided as the user progresses through their studies.

[1185] 11. Multilingual Support

[1186] The generated teaching materials and content are translated as needed using a multilingual model, and the translated content is sent from the server to the terminal and provided to the user.

[1187] Specific examples

[1188] If a user is studying junior high school mathematics, they first log in to the system. The server obtains the user's learning history and progress data, and analyzes it along with facial expression and tone of voice data collected by the emotion engine. As a result, the next unit to be studied (e.g., solving equations) is determined, and appropriate learning materials (e.g., explanatory videos and practice problems) are generated. The generated learning plan and materials are provided to the user via their device.

[1189] When a user solves a problem and inputs the results into the system, the server instantly analyzes the data and generates feedback based on the accuracy rate, level of understanding, and emotional data. A progress chart is also updated, providing a visual indication of the student's current learning status. In this way, the system provides an optimized learning experience that takes into account each student's emotional state, supporting effective learning.

[1190] The above is a specific embodiment for carrying out the present invention.

[1191] The processing flow will be explained below.

[1192] Processing steps of a system that combines emotion engines

[1193] 1. How to obtain learning history and progress data

[1194] Step 1:

[1195] The user accesses the login screen and enters their user ID and password.

[1196] Step 2:

[1197] The device encrypts the entered login information using SSL / TLS and sends a request to the authentication API.

[1198] Step 3:

[1199] The server checks the authentication information against a database and returns the authentication result to the terminal.

[1200] Step 4:

[1201] If the server is successful in authenticating the user, it queries the database to retrieve the learning history and progress data associated with the user ID.

[1202] 2. How to obtain emotion data

[1203] Step 1:

[1204] When a user is engaged in learning activities, the device uses its built-in camera and microphone to capture the user's facial expressions and tone of voice.

[1205] Step 2:

[1206] The device sends the captured data to the emotion engine, where it is analyzed as emotion data.

[1207] Step 3:

[1208] The device sends the analysis results to a server, where they are stored along with the user's learning history and progress data.

[1209] 3. A means of generating learning plans and materials

[1210] Step 1:

[1211] The learning history, progress data, and emotional data acquired by the server are input into the generative AI model.

[1212] Step 2:

[1213] The server uses a generative AI model to analyze the data and generate optimal learning plans and materials.

[1214] Step 3:

[1215] The server sends the generated learning plan and learning material metadata to the terminal in JSON format.

[1216] 4. Means of providing generated learning plans and teaching materials

[1217] Step 1:

[1218] The device analyzes the learning plan and teaching material data received from the server.

[1219] Step 2:

[1220] The device displays a personalized learning plan and learning materials in a user interface.

[1221] 5. Real-time feedback methods

[1222] Step 1:

[1223] The user performs a learning activity (e.g., answers a question).

[1224] Step 2:

[1225] The device sends the user's learning activity data to the server as an API request.

[1226] Step 3:

[1227] The server passes the received data to a real-time analysis system, which calculates the accuracy rate and learning progress.

[1228] Step 4:

[1229] The server generates appropriate feedback along with the user's emotional data.

[1230] Step 5:

[1231] The server sends feedback and progress chart data in JSON format to the device.

[1232] Step 6:

[1233] The device provides the user with real-time feedback and a visual progress chart.

[1234] 6. How to update the progress chart

[1235] Step 1:

[1236] The server analyzes the user's learning data and emotional data and generates a progress chart.

[1237] Step 2:

[1238] The server transmits the progress chart data to the terminal.

[1239] Step 3:

[1240] The terminal displays the progress chart on the user interface.

[1241] 7. Automated generation of custom reports

[1242] Step 1:

[1243] The server periodically retrieves the student's learning data, progress data and emotion data from the database.

[1244] Step 2:

[1245] The server analyzes the acquired data and automatically generates custom reports using AI models.

[1246] Step 3:

[1247] The server stores the generated reports in a database and sends them to the terminal.

[1248] Step 4:

[1249] The terminal displays the custom report to the user.

[1250] 8. Storage method for the teaching material database

[1251] Step 1:

[1252] The server stores the generated teaching material data in a database.

[1253] Step 2:

[1254] The terminal requests the learning material data according to the user's learning progress.

[1255] Step 3:

[1256] The server receives the request and transmits the corresponding educational material data to the terminal.

[1257] Step 4:

[1258] The terminal displays appropriate educational materials to the user.

[1259] 9. Question-Answering Systems

[1260] Step 1:

[1261] The user enters a question or concern.

[1262] Step 2:

[1263] The device sends the question to the server as an API request.

[1264] Step 3:

[1265] The server uses a generative AI model to analyze the question and generate an appropriate answer.

[1266] Step 4:

[1267] The server sends the answer to the device.

[1268] Step 5:

[1269] The terminal displays the answer to the user.

[1270] 10. How to connect with other companies' APIs

[1271] Step 1:

[1272] The server connects with other companies' APIs to obtain new educational content.

[1273] Step 2:

[1274] The server stores the acquired educational content in a database.

[1275] Step 3:

[1276] The terminal requests new educational content according to the user's learning progress.

[1277] Step 4:

[1278] The server receives the request and transmits the corresponding educational content to the terminal.

[1279] Step 5:

[1280] The terminal displays new educational content to the user.

[1281] 11. Multilingual Support

[1282] Step 1:

[1283] The server inputs the generated teaching materials and content into a multilingual model for translation.

[1284] Step 2:

[1285] The server stores the translated content in a database.

[1286] Step 3:

[1287] The device requests translated content according to the language set by the user.

[1288] Step 4:

[1289] The server receives the request and sends the content in the corresponding language to the terminal.

[1290] Step 5:

[1291] The terminal displays the content to the user in the selected language.

[1292] Example 2

[1293] 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."

[1294] Traditional education systems struggle to effectively track each student's learning progress and level of understanding, making it difficult to provide individually optimized learning plans and materials. Furthermore, they fail to provide feedback or adjust learning plans that take into account the student's emotional state during learning, preventing the most effective learning outcomes. Furthermore, while students need timely and appropriate answers to their questions, traditional systems fail to adequately address them.

[1295] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring student learning history and progress data, means for generating an optimal learning plan and learning materials based on the acquired data, means for providing the generated learning plan and learning materials to the student, means for analyzing learning activity data in real time and providing feedback, means for updating a progress chart based on the analysis results, means for automatically generating a custom report, means for acquiring and analyzing student emotional data, and means for adjusting the learning plan and feedback based on the emotional data. This makes it possible to provide an individually optimized learning plan and learning materials for each student and to provide effective feedback that takes into account the student's emotional state.

[1296] "Student Learning History and Progress Data" refers to information about a student's learning history, learning progress, and related assessments and grades.

[1297] "Emotional data" refers to data that indicates a student's emotions or mental state, such as facial expressions or tone of voice.

[1298] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to analyze data and generate optimal learning plans, materials, and feedback.

[1299] A "learning plan" is a plan that outlines the content and order in which a student should study in order to achieve a specific goal.

[1300] "Instructional Materials" refers to learning resources such as textbooks, videos, and exercises that students use when studying.

[1301] "Feedback" refers to comments, instructions, and evaluations provided based on students' learning activities.

[1302] A "progress chart" is a graph or chart that visually shows a student's learning progress.

[1303] "Custom Report" refers to an individual report generated based on each student's learning history, progress, and emotional data.

[1304] "Questions" refer to any doubts or questions that students have while studying.

[1305] A "prompt" refers to the wording of instructions or questions given to a generative AI model.

[1306] MODE FOR CARRYING OUT THE INVENTION

[1307] This invention relates to an educational system that utilizes generative AI models and emotion engines to provide personalized learning experiences for individual students. This system acquires data from the user's learning process and generates and provides optimal learning plans and learning materials based on that data.

[1308] Overall system configuration

[1309] The system mainly consists of the following components:

[1310] 1. A means of capturing student learning history and progress data

[1311] 2. Means of acquiring and analyzing emotion data

[1312] 3. A means to generate optimal learning plans and materials based on the acquired data

[1313] 4. A means of providing generated lesson plans and materials to students

[1314] 5. A means of analyzing student learning activity data in real time and providing feedback

[1315] 6. A way to update the progress chart based on the analysis results

[1316] 7. Automated generation of custom reports

[1317] Specific embodiments of each means

[1318] 1. A means of capturing student learning history and progress data

[1319] The user accesses the login screen and enters their user ID and password. The device encrypts the entered login information using SSL / TLS and sends a request to the authentication API. The server checks the authentication information against a database and returns the authentication result. If authentication is successful, the server retrieves the user's learning history and progress data from the database. MySQL or PostgreSQL are generally used as the database.

[1320] 2. Means of acquiring and analyzing emotion data

[1321] As the user engages in learning activities, the device uses its built-in camera and microphone to capture the user's facial expressions and tone of voice. The device then sends the captured data to an emotion engine for analysis. The emotion engine uses the Microsoft Azure Emotion API and Google Cloud Speech-to-Text.

[1322] 3. A means to generate optimal learning plans and materials based on the acquired data

[1323] The server inputs the acquired learning history, progress data, and emotional data into a generative AI model. GPT-3, BERT, T5, and other models are used. The data is analyzed to generate an optimal learning plan and learning materials. The generated learning plan and learning materials are converted into JSON format and sent to the device.

[1324] 4. A means of providing generated lesson plans and materials to students

[1325] The device analyzes the learning plan and learning material data received from the server and displays them on a user interface, which is built using frameworks such as React and Angular.

[1326] 5. A means of analyzing learning activity data in real time and providing feedback

[1327] When a user performs a learning activity, the device sends the data to the server, which analyzes the received data in real time and generates appropriate feedback. The feedback is sent to the device in text format and displayed to the user.

[1328] 6. A way to update the progress chart based on the analysis results

[1329] After providing the feedback, the server updates the progress chart based on the progress and emotion data. The progress chart is generated using D3.js and Chart.js and sent to the device.

[1330] 7. Automated generation of custom reports

[1331] Periodically, the server analyzes students' learning data, progress data, and emotional data, and automatically generates custom reports, which are converted into PDF or HTML format, stored in a database, and later provided to users via their devices.

[1332] Specific examples

[1333] If a user is studying junior high school mathematics, they first log in to the system. The server obtains the user's learning history and progress data, and analyzes the facial expression and tone of voice data collected by the emotion engine. As a result, the next unit to be studied (e.g., solving equations) is determined, and appropriate learning materials (e.g., explanatory videos and practice problems) are generated. The generated learning plan and materials are provided to the user via their device.

[1334] When a user solves a problem and inputs the results into the system, the server instantly analyzes the data and generates feedback based on the accuracy rate, level of understanding, and emotional data. A progress chart is also updated, allowing users to visually check their current learning status. In this way, the system of the present invention provides an optimized learning experience that takes into account each student's emotional state, supporting effective learning.

[1335] Prompt Sentence Examples

[1336] "I would like to learn how to solve equations in junior high school mathematics. Please suggest the best study plan and materials."

[1337] The above is a specific embodiment for carrying out the present invention.

[1338] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1339] Step 1:

[1340] The user accesses the login screen and enters their user ID and password. The user ID and password are input. The device encrypts the entered login information using SSL / TLS and sends a request to the authentication API. The server compares the received authentication information with a database, generates an authentication result, and returns the authentication success or failure result to the device as output.

[1341] Step 2:

[1342] When the server receives a successful authentication result, it retrieves the learning history and progress data associated with the user ID from the database. It takes the user ID as input and accesses the database using an SQL query. Example:

[1343] sql

[1344] SELECT FROM user_data WHERE user_id = 'User ID';

[1345] The output is the acquired learning history and progress data.

[1346] Step 3:

[1347] The device receives learning history and progress data, and the user begins learning. As the user engages in learning activities, the device uses the built-in camera and microphone to capture the user's facial expressions and tone of voice. The inputs include camera video data and audio data. The device sends this data to an emotion engine, where it is analyzed as emotion data. The output is the analyzed emotion data.

[1348] Step 4:

[1349] The device sends emotion data to the server, which receives it and stores it in a database along with the user's learning history and progress data. The inputs are emotion data, learning history, and progress data. By storing it in the database, a complete user dataset is generated as the output.

[1350] Step 5:

[1351] The server inputs the acquired learning history, progress data, and emotional data into the generative AI model. The input is all of the user's data. The server uses the generative AI model to analyze the data and generate the optimal learning plan and learning materials. Specifically, it uses the following prompt:

[1352] "A user wants to learn how to solve equations in junior high school mathematics. Please suggest the best study plan and study materials."

[1353] The output is a JSON-formatted learning plan and learning material data.

[1354] Step 6:

[1355] The device parses the learning plan and learning material data received from the server. The input is JSON format data. The device displays the personalized learning plan and learning material on the user interface. The output is a user-friendly display of the learning plan and learning material.

[1356] Step 7:

[1357] The user solves a problem and inputs the results into the terminal. The input is the user's answer data. The terminal sends the data to the server. The server analyzes the received data in real time and generates feedback based on the accuracy rate, comprehension level, and emotion data. The output is a feedback message.

[1358] Step 8:

[1359] The server sends feedback back to the terminal, which displays it to the user. The input is the feedback message from the server, and the output is the feedback displayed to the user.

[1360] Step 9:

[1361] After providing feedback, the server updates the progress chart based on the progress and emotion data. The input is the latest progress data, and the output is the updated progress chart. The progress chart is generated using D3.js and Chart.js and sent to the device.

[1362] Step 10:

[1363] The server periodically analyzes students' learning data, progress data, and emotion data, and automatically generates custom reports. The input is all user data, and the output is a custom report (PDF or HTML format). This report is stored in a database.

[1364] Step 11:

[1365] The device provides saved custom reports to users, parents, and teachers. The input is the generated report and the output is the delivered report.

[1366] (Application example 2)

[1367] 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."

[1368] In conventional educational systems, uniform learning plans and teaching materials are provided, making it difficult to accommodate the different learning paces and levels of understanding of each student. This can lead to a decrease in students' learning effectiveness. Similarly, virtual stores have the problem of being unable to recommend products that match individual customer preferences and needs, making it difficult to stimulate purchasing motivation.

[1369] 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.

[1370] In this invention, the server includes a means for acquiring a student's learning history and progress data, a means for generating an optimal learning plan and educational materials based on the acquired data, and a means for providing the generated learning plan and educational materials to the student. This makes it possible to provide educational materials tailored to each student's learning pace and level of understanding. Furthermore, the server includes a means for acquiring a customer's purchase history, browsing history, and emotional data, and for generating appropriate product recommendations and promotions, and a means for providing the generated product recommendations and promotions. This makes it possible to recommend products tailored to the individual preferences and needs of each customer.

[1371] "Learning history" is data that records what a student has learned in the past and their progress.

[1372] "Progress data" is data that records a student's progress and achievements in learning activities.

[1373] A "study plan" is an individual study schedule formulated based on each student's learning history and progress data.

[1374] "Educational materials" are teaching materials and reference materials provided to support students' learning activities.

[1375] "Purchase history" refers to data such as the products a customer has purchased in the past, the quantity, and the date of purchase.

[1376] "Browsing history" is data about the pages and products a customer views on a website or within an app.

[1377] "Emotion data" refers to data that indicates the emotional state of a customer, analyzed from facial expressions, tone of voice, etc.

[1378] "Product recommendations" are suitable product suggestions provided based on a customer's purchasing history, browsing history, and emotional data.

[1379] "Promotion" refers to information that recommends and advertises specific products or services to customers.

[1380] "Feedback" is a response, such as evaluation or advice, provided in response to the behavior of a student or customer.

[1381] A "progress chart" is a diagram that visually displays the progress of a student or client's activities.

[1382] A "Custom Report" is a personalized analytical report generated based on student or client data.

[1383] This invention relates to an educational system and a virtual store system that use a generative AI model and an emotion engine. To implement this invention, three main components work together: a server, a terminal, and a user. The specific system configuration and its operation are described below in detail.

[1384] System Configuration

[1385] 1. Server

[1386] The server contains a database, a generative AI model, and an emotion engine.

[1387] The database stores learning history, progress data, educational materials, purchase history, browsing history, emotional data, and more.

[1388] Generative AI models are used to generate lesson plans and educational materials, product recommendations and promotions.

[1389] The emotion engine analyzes the user's facial expressions and tone of voice to generate emotion data.

[1390] 2. Terminal

[1391] A terminal is a device operated by a user, and includes a smartphone, tablet, PC, etc.

[1392] The device has a built-in camera and microphone, which are used to capture the user's facial expressions and voice.

[1393] 3. Users

[1394] The users are the students or customers who use the system.

[1395] Users access the system through terminals and use various functions.

[1396] Program processing and behavior

[1397] 1. Data Acquisition and Storage

[1398] The user logs in and the server obtains learning history, progress data, purchase history, and browsing history.

[1399] While studying or shopping, the device's camera and microphone capture the user's facial expressions and voice, which are then analyzed by the emotion engine and sent to the server as emotional data.

[1400] All data is stored in a database on the server.

[1401] 2. Data analysis and plan generation

[1402] The server inputs the acquired data into a generative AI model to generate learning plans, educational materials, product recommendations and promotions.

[1403] Learning plans and educational materials, product recommendations and promotions are sent to the device in JSON format.

[1404] 3. Data provision and feedback

[1405] The terminal displays the provided study plans and educational materials, as well as product recommendations and promotions, to the user.

[1406] Every time a user studies or shops, the data is sent from the device to the server, which immediately analyzes it and generates feedback.

[1407] The feedback is sent to the terminal and provided to the user.

[1408] Furthermore, the server updates the progress chart and displays it on the terminal in a form that the user can visually understand.

[1409] Specific examples

[1410] Consider a case where a junior high school student logs into the system and begins studying junior high school mathematics. First, the server retrieves learning history and progress data, and the emotion engine analyzes the student's facial expressions and tone of voice. Based on the resulting data, an optimal learning plan and educational materials (e.g., videos and practice problems on how to solve equations) are generated. These educational materials are displayed on the student's device, and immediate feedback is provided as the student progresses.

[1411] In the virtual store, customers log in and their emotional data is captured using a camera and microphone. This data, along with their past purchase and browsing history, is analyzed to provide personalized product recommendations (e.g., scented candles if they're feeling relaxed) and promotions.

[1412] Prompt Sentence Examples

[1413] "Based on the user's purchase history, suggest products they are likely to purchase next."

[1414] "Recommend appropriate promotional items based on user emotional data (e.g., relaxation, stress, etc.)."

[1415] "Analyze data to provide users with a personalized shopping experience based on their purchase history, browsing history, and emotional state."

[1416] The above is an embodiment of the present invention. Other specific processing steps will be described later.

[1417] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1418] Step 1:

[1419] The user accesses the login screen and enters their user ID and password. The device encrypts the entered login information using SSL / TLS and sends a request to the authentication API.

[1420] Input: User ID, Password

[1421] Output: Authentication token

[1422] Specific operation: Enter the user ID and password on the terminal's login screen and send a request to the server.

[1423] Step 2:

[1424] The server checks the authentication information against the database and returns the authentication result to the device. If authentication is successful, it queries the database to obtain the learning history and progress data associated with the user ID.

[1425] Input: Authentication Token

[1426] Output: Learning history, progress data

[1427] Specific operation: The server executes a database query for authentication and returns the authentication result to the device. After successful authentication, the learning history and progress data are retrieved.

[1428] Step 3:

[1429] The device uses its built-in camera and microphone to capture the user's facial expressions and tone of voice, which are then analyzed as emotional data.

[1430] Input: User's facial expression, tone of voice

[1431] Output: Emotion data

[1432] What it does: The device activates the camera and microphone, processes the captured data in real time, and sends it to the sentiment analysis engine.

[1433] Step 4:

[1434] The server inputs the acquired learning history, progress data, and emotional data into a generative AI model to generate optimal learning plans and educational materials, and inputs purchase history, browsing history, and emotional data to generate optimal product recommendations and promotions.

[1435] Input: learning history, progress data, emotional data, purchase history, browsing history

[1436] Output: Study plans, educational materials, product recommendations, promotions

[1437] Specific operation: The server inputs various data into the generated AI model and generates the optimal plan and materials from the analysis results.

[1438] Step 5:

[1439] The server generates learning plans, educational materials, product recommendations, and promotions and sends them to the device, which analyzes the data and displays it on the user interface.

[1440] Input: Study plans, educational materials, product recommendations, promotions

[1441] Output: Data displayed in the user interface

[1442] Specific operation: Data is sent from the server to the device, which analyzes it and displays it on the screen. The user confirms the displayed content.

[1443] Step 6:

[1444] Users conduct learning activities or purchases and enter the results into the terminal, which then sends the data to the server, which analyzes it immediately and generates feedback.

[1445] Input: learning activity data, purchasing activity data

[1446] Output: Feedback

[1447] Specific operation: The user inputs the results of their learning or purchasing activities, and the data is sent to the server, which analyzes them immediately and sends feedback to the device.

[1448] Step 7:

[1449] The server updates the progress chart based on the progress status and emotion data, and the updated progress chart is sent to the device and displayed in a visually recognizable format for the user.

[1450] Input: Progress data, emotion data

[1451] Output: Updated progress chart

[1452] Specific behavior: The server receives new data, updates the progress chart, sends it to the device, and displays it in the user interface.

[1453] Step 8:

[1454] The server periodically analyzes student and customer data and automatically generates custom reports using AI models. The generated reports are stored in a database and provided to users via their devices.

[1455] Input: learning history, progress data, purchase history, emotional data

[1456] Output: Custom Report

[1457] What it does: The server periodically analyzes the data, generates custom reports, stores them in a database, and makes them available for viewing on the device.

[1458] The above is a description of the specific processing steps of the system that realizes the application example.

[1459] 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.

[1460] 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.

[1461] 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.

[1462] [Third embodiment]

[1463] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1464] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1465] 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).

[1466] 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.

[1467] 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.

[1468] 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).

[1469] 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.

[1470] 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.

[1471] 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.

[1472] 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.

[1473] 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.

[1474] 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."

[1475] This invention relates to an educational system that utilizes generative AI models and proposes specific means for providing each student with a personalized learning experience. The system acquires and analyzes students' learning history and progress data to generate and provide optimal learning plans and teaching materials. It also analyzes students' learning activities in real time and provides feedback, improving learning effectiveness and allowing students to visually check their progress.

[1476] System Configuration

[1477] The system of the present invention has the following main functions:

[1478] 1. How to obtain learning history and progress data

[1479] 2. A means of generating learning plans and materials

[1480] 3. Means of providing generated learning plans and teaching materials

[1481] 4. A way to provide real-time feedback

[1482] 5. How to update the progress chart

[1483] 6. Automated generation of custom reports

[1484] 7. Storage method for the teaching material database

[1485] 8. Question-Answering Systems

[1486] 9. How to connect with other companies' APIs

[1487] 10. Multilingual Support

[1488] Program processing and behavior

[1489] The program processing for each function is explained below in natural language.

[1490] 1. How to obtain learning history and progress data

[1491] When a user logs in to their learning account, the device sends the login information to the server, which then authenticates the user and, if authentication is successful, retrieves the user's learning history and progress data from the database.

[1492] 2. A means of generating learning plans and materials

[1493] Based on the acquired data, the server uses a generative AI model to analyze the user's current learning situation and generate an optimal learning plan and learning materials, which are then sent from the server to the device and provided to the user.

[1494] 3. Means of providing generated learning plans and teaching materials

[1495] The device displays the study plan and learning materials sent from the server on the user interface, allowing the user to study using the most appropriate learning materials for their own learning situation.

[1496] 4. A way to provide real-time feedback

[1497] When a user performs a learning activity (e.g., answering a question), the device sends the data to the server, which analyzes the received data in real time and generates appropriate feedback. The generated feedback is sent back from the server to the device and displayed to the user.

[1498] 5. How to update the progress chart

[1499] As feedback is provided, the server updates a progress chart based on the progress, which is then sent to the device and displayed visually to the user.

[1500] 6. Automated generation of custom reports

[1501] Periodically, the server analyzes student learning data and automatically generates custom reports using AI models. The reports are stored in a database and provided to users, parents, and teachers via their devices.

[1502] 7. Storage method for the teaching material database

[1503] The generated learning materials are stored in a database by the server. The terminal requests appropriate learning materials from this database according to the user's learning progress and provides them to the user.

[1504] 8. Question-Answering Systems

[1505] When a user types in a question or concern, the device sends the question to the server, which uses a generative AI model to analyze the question and generate an appropriate answer, which is then sent back to the device and provided to the user.

[1506] 9. How to connect with other companies' APIs

[1507] The server connects with other companies' APIs to add new educational content to the database, which is provided as the user progresses through their studies.

[1508] 10. Multilingual Support

[1509] The generated teaching materials and content are translated as needed using a multilingual model, and the translated content is sent from the server to the terminal and provided to the user.

[1510] Specific examples

[1511] If a user is studying junior high school mathematics, they first log in to the system. The server acquires and analyzes the user's learning history and progress data. As a result, the next unit to be studied (e.g., solving equations) is determined, and appropriate learning materials (e.g., explanatory videos and practice problems) are generated. The generated learning plan and materials are provided to the user via their device.

[1512] When a user solves a problem and inputs the results into the system, the server instantly analyzes the data and generates feedback based on the accuracy rate and level of understanding. A progress chart is also updated, allowing users to visually check their current learning status. In this way, the system of the present invention provides an optimized learning experience for each student, supporting effective learning.

[1513] The above is a specific embodiment for carrying out the present invention.

[1514] The processing flow will be explained below.

[1515] 1. How to obtain learning history and progress data

[1516] Processing Steps

[1517] Step 1:

[1518] The user accesses the login screen and enters their user ID and password.

[1519] Step 2:

[1520] The device encrypts the entered login information using SSL / TLS and sends a request to the authentication API.

[1521] Step 3:

[1522] The server checks the authentication information against a database and returns the authentication result to the terminal.

[1523] Step 4:

[1524] If the server is successful in authenticating the user, it queries the database to retrieve the learning history and progress data associated with the user ID.

[1525] 2. A means of generating learning plans and materials

[1526] Processing Steps

[1527] Step 1:

[1528] The learning history and progress data acquired by the server are input into the generative AI model.

[1529] Step 2:

[1530] The server uses the generated AI model to analyze learning history and progress, and generates optimal learning plans and materials.

[1531] Step 3:

[1532] The server sends the generated learning plan and learning material metadata to the terminal in JSON format.

[1533] 3. Means of providing generated learning plans and teaching materials

[1534] Processing Steps

[1535] Step 1:

[1536] The device analyzes the learning plan and teaching material data received from the server.

[1537] Step 2:

[1538] The device displays a personalized learning plan and learning materials in a user interface.

[1539] 4. A way to provide real-time feedback

[1540] Processing Steps

[1541] Step 1:

[1542] The user performs a learning activity (e.g., answers a question).

[1543] Step 2:

[1544] The device sends the user's learning activity data to the server as an API request.

[1545] Step 3:

[1546] The server passes the received data to a real-time analysis system, which calculates the accuracy rate and learning progress.

[1547] Step 4:

[1548] The server generates feedback messages and progress chart information.

[1549] Step 5:

[1550] The server sends feedback and progress chart data in JSON format to the device.

[1551] Step 6:

[1552] The device provides the user with real-time feedback and a visual progress chart.

[1553] 5. How to update the progress chart

[1554] Processing Steps

[1555] Step 1:

[1556] The server analyzes the user's learning data and generates a progress chart.

[1557] Step 2:

[1558] The server transmits the progress chart data to the terminal.

[1559] Step 3:

[1560] The terminal displays the progress chart on the user interface.

[1561] 6. Automated generation of custom reports

[1562] Processing Steps

[1563] Step 1:

[1564] The server periodically retrieves student learning and progress data from the database.

[1565] Step 2:

[1566] The server analyzes the acquired data and automatically generates custom reports using AI models.

[1567] Step 3:

[1568] The server stores the generated reports in a database and sends them to the terminal.

[1569] Step 4:

[1570] The terminal displays the custom report to the user.

[1571] 7. Storage method for the teaching material database

[1572] Processing Steps

[1573] Step 1:

[1574] The server stores the generated teaching material data in a database.

[1575] Step 2:

[1576] The terminal requests the learning material data according to the user's learning progress.

[1577] Step 3:

[1578] The server receives the request and transmits the corresponding educational material data to the terminal.

[1579] Step 4:

[1580] The terminal displays appropriate educational materials to the user.

[1581] 8. Question-Answering Systems

[1582] Processing Steps

[1583] Step 1:

[1584] The user enters a question or concern.

[1585] Step 2:

[1586] The device sends the question to the server as an API request.

[1587] Step 3:

[1588] The server uses a generative AI model to analyze the question and generate an appropriate answer.

[1589] Step 4:

[1590] The server sends the generated answer to the terminal.

[1591] Step 5:

[1592] The terminal displays the answer to the user.

[1593] 9. How to connect with other companies' APIs

[1594] Processing Steps

[1595] Step 1:

[1596] The server connects with other companies' APIs to obtain new educational content.

[1597] Step 2:

[1598] The server stores the acquired educational content in a database.

[1599] Step 3:

[1600] The terminal requests new educational content according to the user's learning progress.

[1601] Step 4:

[1602] The server receives the request and transmits the corresponding educational content to the terminal.

[1603] Step 5:

[1604] The terminal displays new educational content to the user.

[1605] 10. Multilingual Support

[1606] Processing Steps

[1607] Step 1:

[1608] The server inputs the generated teaching materials and content into a multilingual model for translation.

[1609] Step 2:

[1610] The server stores the translated content in a database.

[1611] Step 3:

[1612] The device requests translated content according to the language set by the user.

[1613] Step 4:

[1614] The server receives the request and sends the content in the corresponding language to the terminal.

[1615] Step 5:

[1616] The terminal displays the content to the user in the selected language.

[1617] Example 1

[1618] 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."

[1619] Current education systems struggle to provide personalized learning experiences based on each student's progress and level of understanding. They also need to address a wide range of needs, including real-time feedback, progress visualization, automatic custom report generation, and multilingual support. However, there are no systems that integrate all of these functions, making it difficult to maximize learning efficiency.

[1620] 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.

[1621] In this invention, the server includes means for acquiring student learning history and progress data, means for generating optimal learning plans and teaching materials using a generative AI model based on the acquired data, means for providing the generated learning plans and teaching materials to students, means for analyzing student learning activity data in real time and providing feedback, means for updating a progress chart based on the analysis results, means for automatically generating custom reports, means for providing generated answers to students, means for translating the generated teaching materials and content using a multilingual model, means for saving the automatically generated teaching materials in a database, means for providing the saved teaching materials according to the student's learning progress, means for adding new educational content to the database using a third-party API, and means for generating appropriate answers to questions entered by students using a generative AI model. This enables the provision of an individually optimized learning experience, real-time feedback, progress visualization, multilingual support, and the addition of new content.

[1622] A "learning history" is a record of a student's past learning activities and progress.

[1623] "Progress data" is information that indicates the learning content and progress that a student is currently making.

[1624] A "generative AI model" is an artificial intelligence model that analyzes students' learning data and generates optimal learning plans, teaching materials, and feedback.

[1625] A "study plan" is a study schedule and content that is optimized to help students progress through their studies efficiently.

[1626] "Teaching Materials" means the educational materials and exercises used in accordance with the Study Plan.

[1627] "Feedback" refers to evaluation and advice provided in real time to students' learning activities.

[1628] A "progress chart" is a graph or chart that visually shows a student's learning progress.

[1629] A "custom report" is a learning report that is automatically generated individually by analyzing a student's learning data.

[1630] A "multilingual model" is an artificial intelligence model for translating teaching materials and content into multiple languages.

[1631] A "database" is a system that systematically stores acquired data and generated teaching materials.

[1632] "Third-party APIs" refer to application program interfaces published by other companies or service providers.

[1633] "Questions" are questions that students have while studying or things they want to clarify.

[1634] An "answer" is an appropriate answer or explanation provided by a generative AI model in response to a question.

[1635] This invention is an educational system that utilizes a generative AI model and proposes specific means for providing a personalized learning experience to each student. The system acquires and analyzes students' learning history and progress data to generate and provide optimal learning plans and materials. It also analyzes students' learning activities in real time and provides feedback to improve learning effectiveness and allow students to visually check their progress.

[1636] System Configuration

[1637] The system of the present invention comprises the following main components:

[1638] How to obtain learning history and progress data

[1639] A means of generating lesson plans and materials

[1640] Means of providing generated learning plans and teaching materials

[1641] A way to provide real-time feedback

[1642] Progress chart update method

[1643] Automated generation of custom reports

[1644] A means of providing generated answers to students

[1645] A means of translating generated learning materials and content using a multilingual model

[1646] A means of storing automatically generated teaching materials in a database

[1647] A means of providing saved learning materials as students progress

[1648] A way to add new educational content to the database using third-party APIs

[1649] A means of generating appropriate answers using generative AI models for questions entered by students

[1650] Hardware and software used

[1651] The system uses the following hardware and software:

[1652] Hardware: Servers, devices (PCs, tablets, smartphones, etc.)

[1653] Software: Generative AI model, database management system, user interface, real-time analysis engine, multilingual model, third-party API

[1654] System Operation

[1655] When a user logs in to their learning account, the device sends the login information to the server. The server authenticates the user, and if authentication is successful, retrieves the user's learning history and progress data from the database. Based on the retrieved data, the server uses a generative AI model to analyze the user's current learning situation and generate an optimal learning plan and learning materials. The generated learning plan and learning materials are sent from the server to the device and provided to the user.

[1656] The device displays the learning plan and learning materials sent from the server on a user interface, allowing the user to proceed with their learning using the materials. When the user performs a learning activity (e.g., answering a question), the device sends the data to the server. The server analyzes the received data in real time and generates appropriate feedback. The generated feedback is sent back from the server to the device and displayed to the user.

[1657] As feedback is provided, the server updates a progress chart based on the progress, and the updated progress chart is sent to the device and displayed visually to the user. Periodically, the server analyzes the student's learning data and automatically generates custom reports using an AI model, which are stored in a database. The generated reports are then provided to the user, parents, and teachers via the device.

[1658] The generated learning materials are stored in a database by the server, and the device requests appropriate learning materials from this database and provides them to the user as the user progresses. When the user enters a question or concern, the device sends the question to the server, which uses a generative AI model to analyze the question and generate an appropriate answer. The answer is then sent back from the server to the device and provided to the user.

[1659] The server also connects with other companies' APIs to add new educational content to the database. This content is provided appropriately according to the user's learning progress. The generated teaching materials and content are translated using a multilingual model as needed, and the translated content is sent from the server to the terminal and provided to the user.

[1660] Examples and prompts

[1661] If a user is studying junior high school mathematics, they first log in to the system, and the server obtains the user's learning history and progress data. Based on that data, the generative AI model analyzes it to determine the next unit to study (e.g., equation solving) and generates appropriate learning materials (e.g., explanatory videos and practice problems). The generated learning plan and materials are provided to the user via their device. When the user solves problems and enters the results into the system, the server immediately analyzes the data and generates feedback based on the accuracy rate and level of understanding. At the same time, a progress chart is updated, allowing the user to visually check their current learning status.

[1662] Here is an example of a prompt to input to a generative AI model:

[1663] "Please generate teaching materials for solving equations in junior high school mathematics. The user's learning data includes the following progress information. In particular, practice problems and explanatory videos are needed to improve understanding of equation solving."

[1664] The above is a specific embodiment for carrying out the present invention.

[1665] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1666] Step 1:

[1667] The user logs into their learning account.

[1668] Input: Username and Password

[1669] How it works: The device sends the username and password to the server.

[1670] Output: Login information sent to the server

[1671] Step 2:

[1672] The server authenticates the user.

[1673] Input: Login information

[1674] How it works: The server retrieves and verifies user authentication information from a database based on the login information.

[1675] Output: Authentication success or failure result

[1676] Step 3:

[1677] The server retrieves the user's learning history and progress data from the database.

[1678] Input: Authentication success result

[1679] Operation: If authentication is successful, the server retrieves the user's learning history and progress data from the database.

[1680] Output: Learning history and progress data

[1681] Step 4:

[1682] The server generates a prompt based on the data it retrieves.

[1683] Input: Learning history and progress data

[1684] How it works: The server creates a prompt sentence appropriate for the generative AI model based on the acquired data.

[1685] Output: prompt statement

[1686] Step 5:

[1687] The server uses the generated AI model to analyze the user's learning status.

[1688] Input: prompt statement

[1689] How it works: The server inputs prompt sentences into the generative AI model and analyzes the user's learning status.

[1690] Output: Analysis results (optimal learning plan and materials)

[1691] Step 6:

[1692] The server sends the generated learning plan and learning materials to the terminal.

[1693] Input: Analysis results

[1694] Operation: The server sends the generated learning plan and learning materials to the terminal.

[1695] Output: Study plans and materials

[1696] Step 7:

[1697] The terminal receives the learning plan and learning materials sent from the server.

[1698] Input: Study plan and materials

[1699] Operation: The device displays the received learning plan and learning materials on the user interface.

[1700] Output: The lesson plan and materials displayed in a user interface

[1701] Step 8:

[1702] The user progresses through the learning process based on the displayed learning plan and materials.

[1703] Input: Study plan and materials

[1704] Operation: The user studies using the presented learning materials.

[1705] Output: Learning activity data (question answers, etc.)

[1706] Step 9:

[1707] The terminal transmits the user's learning activity data to the server.

[1708] Input: Learning activity data

[1709] Operation: The terminal transmits learning activity data to the server.

[1710] Output: Learning activity data is sent to the server

[1711] Step 10:

[1712] The server analyzes learning activity data in real time.

[1713] Input: Learning activity data

[1714] Operation: The server analyzes learning activity data using a real-time analysis engine.

[1715] Output: Analysis results (feedback)

[1716] Step 11:

[1717] The server generates appropriate feedback.

[1718] Input: Analysis results

[1719] How it works: The server uses a generative AI model to generate appropriate feedback.

[1720] Output: Feedback

[1721] Step 12:

[1722] The server transmits the generated feedback to the terminal.

[1723] Input: Feedback

[1724] Operation: The server sends the generated feedback to the device.

[1725] Output: Feedback is sent to the device

[1726] Step 13:

[1727] The device displays the feedback to the user.

[1728] Input: Feedback

[1729] Operation: The device displays the received feedback in the user interface.

[1730] Output: Feedback is displayed in the user interface

[1731] Step 14:

[1732] The server updates the learning progress chart.

[1733] Input: Learning activity data

[1734] Operation: The server updates the progress chart based on the learning activity data.

[1735] Output: Updated progress chart

[1736] Step 15:

[1737] The server sends the updated progress chart to the terminal.

[1738] Input: Updated progress chart

[1739] Operation: The server sends a progress chart to the terminal.

[1740] Output: An updated progress chart is sent to the terminal

[1741] Step 16:

[1742] The terminal displays a progress chart to the user.

[1743] Input: Progress Chart

[1744] Action: The terminal displays an updated progress chart in the user interface.

[1745] Output: A progress chart is displayed in the user interface.

[1746] Step 17:

[1747] The server periodically analyzes the user's learning data and automatically generates custom reports.

[1748] Input: Training data

[1749] How it works: The server uses the generative AI model based on the training data to generate a custom report.

[1750] Output: Custom Report

[1751] Step 18:

[1752] The server stores the custom report in a database and notifies you.

[1753] Input: Custom Report

[1754] How it works: The server saves the custom report to a database and sends notifications to users and interested parties.

[1755] Output: Saved custom reports and notifications

[1756] Step 19:

[1757] The terminal displays the report.

[1758] Input: Custom Report

[1759] Action: The terminal displays the custom report in the user interface.

[1760] Output: The custom report displayed in the user interface

[1761] Step 20:

[1762] The server stores the generated teaching materials in a database.

[1763] Input: Generated teaching materials

[1764] Operation: The server stores the generated teaching materials in a database.

[1765] Output: Saved materials

[1766] Step 21:

[1767] The terminal requests appropriate learning materials from the learning material database according to the student's learning progress.

[1768] Input: Learning progress data

[1769] Operation: The terminal requests the appropriate learning materials from the server based on the learning progress data.

[1770] Output: Requested materials

[1771] Step 22:

[1772] The server provides the requested educational material to the terminal.

[1773] Input: Requested materials

[1774] Operation: The server sends the appropriate educational material to the device.

[1775] Output: Provided teaching materials

[1776] Step 23:

[1777] The terminal displays the provided educational material to the user.

[1778] Input: Provided materials

[1779] Operation: The terminal displays the provided teaching materials on the user interface.

[1780] Output: Displayed teaching materials

[1781] Step 24:

[1782] The user enters a question or concern.

[1783] Input: Questions and concerns

[1784] How it works: The device sends a question or inquiry to the server.

[1785] Output: Question submitted

[1786] Step 25:

[1787] The server analyzes the question using a generative AI model.

[1788] Input: Submitted question

[1789] How it works: The server inputs the question into the generative AI model and analyzes it.

[1790] Output: Analysis result (appropriate answer)

[1791] Step 26:

[1792] The server generates an appropriate response.

[1793] Input: Analysis results

[1794] How it works: The server uses a generative AI model to generate an appropriate answer.

[1795] Output: The generated answer

[1796] Step 27:

[1797] The server sends the generated response to the terminal.

[1798] Input: Generated Answer

[1799] Operation: The server generates a response and sends it to the device.

[1800] Output: Submitted response

[1801] Step 28:

[1802] The terminal displays the answer on the user interface.

[1803] Input: Submitted answer

[1804] Action: The terminal displays the answer in the user interface.

[1805] Output: Displayed answer

[1806] (Application example 1)

[1807] 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."

[1808] It is necessary to build an optimal system that can respond to learners' diverse learning needs and progress and provide an effective and personalized learning experience, and also to enable learners to receive appropriate feedback and learning materials in real time and visually grasp their own learning progress.In addition, a means is needed to efficiently accumulate and analyze learning data and provide an optimized learning experience for each learner.

[1809] 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.

[1810] In this invention, the server includes a means for acquiring student learning history and progress data, a means for generating optimal learning plans and learning materials based on the acquired data, and a means for providing the generated learning plans and learning materials to students, thereby providing an optimized learning experience for each student and improving learning effectiveness.

[1811] "Means for obtaining student learning history and progress data" refers to a method for recording the learning content that learners have engaged in in the past and their progress, and incorporating this into the system.

[1812] The "means for generating optimal learning plans and learning materials" is a method for analyzing collected learning history and progress data and automatically creating optimal learning plans and learning materials for individual learners.

[1813] "Means for providing students with generated learning plans and teaching materials" refers to a method for effectively delivering the generated learning plans and teaching materials to learners.

[1814] "Means for analyzing student learning activity data in real time and providing feedback" refers to a method for monitoring the learning activities of learners in real time and providing immediate feedback based on the analysis results.

[1815] The "means for updating the progress chart" is a method for updating the progress chart, which visually displays the progress of the learner, based on the latest data.

[1816] The "means for automatically generating custom reports" is a method for automatically creating reports tailored to individual learners based on collected learning data.

[1817] "Means for visually providing learning materials and feedback using smart devices" refers to a method for visually displaying learning materials and feedback to learners using devices such as smart glasses or tablets.

[1818] "Means for obtaining user interactions in real time and generating feedback based on that" refers to a method for collecting interaction data such as learners' learning status and responses in real time, analyzing that data, and providing appropriate feedback.

[1819] The system that realizes this application example collects students' learning history and progress data, and based on that data, generates and provides optimal learning plans and teaching materials. It also aims to improve learning effectiveness by analyzing learners' progress in real time and providing feedback.

[1820] Program implementation form

[1821] Hardware and Software Configuration

[1822] The hardware used includes a server, smart devices (smart glasses, smartphones, tablets, etc.), and learner terminals.

[1823] The software used includes a generative AI model, a database management system, a user interface, and a real-time feedback analysis module.

[1824] Specific operation of the system

[1825] Acquire learning history and progress data

[1826] When a learner logs in to their learning account, the device sends the login information to the server, which then authenticates the learner and, if authentication is successful, retrieves their learning history and progress data from the database.

[1827] Learning plan and material generation

[1828] Based on the acquired historical data, the server uses a generative AI model to analyze the learner's current learning situation and generate the optimal learning plan and materials, thereby providing the learner with an optimized learning plan.

[1829] Provision of materials

[1830] The generated learning plan and learning materials are sent from the server to the device, where the provided data is displayed on a user interface to help the learner progress through their studies in an easy-to-understand manner.

[1831] Providing real-time feedback

[1832] Every time a learner performs a learning activity, the data is sent to the server, which analyzes the data in real time and uses a generative AI model to generate appropriate feedback, which is then instantly sent to the learner's device and provided to them.

[1833] Progress Chart Update

[1834] As feedback is provided, the server updates a progress chart, which is then sent to the device, allowing the learner to visually monitor their progress.

[1835] Generate custom reports

[1836] The server periodically analyzes learner data and automatically generates custom reports, which are stored in a database and provided to learners via their devices.

[1837] Using a Smart Device

[1838] The generated learning plans, materials, and feedback are presented visually using smart devices such as smart glasses and tablets, which help learners receive real-time feedback and track their learning progress.

[1839] Specific examples

[1840] Real-world usage scenarios

[1841] For example, when a student studying middle school mathematics logs into the system, the server retrieves their learning history and progress data. As a result, the next unit to be studied (e.g., solving equations) is determined, and specific teaching materials (videos and exercises) are generated. These teaching materials are visually presented to the student through smart glasses.

[1842] When students solve problems and input their results into the system, the server analyzes the data and generates feedback based on the accuracy rate and level of understanding. This feedback is instantly displayed on the smart glasses, allowing students to see the direction of their learning in real time.

[1843] Prompt Sentence Examples

[1844] When students use a generative AI model to solve a math problem, they are prompted with the following prompt:

[1845] "Based on Student A's learning history, please suggest the next math unit they should study."

[1846] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1847] Step 1: Login and Data Retrieval

[1848] When a user logs in to their learning account, the device sends the login information to the server. The server authenticates the user and retrieves the learning history and progress data from the database after successful authentication. The input includes the user's login information, and the output includes the learning history and progress data.

[1849] Step 2: Generate your lesson plan and materials

[1850] The server uses a generative AI model to analyze the acquired learning history and progress data, and generates the optimal learning plan and learning materials for the user. The inputs are learning history and progress data, and the output is the optimal learning plan and learning materials.

[1851] Step 3: Provide materials and plans

[1852] The generated learning plan and learning materials are sent from the server to the device, which displays them and provides a guide for the user to proceed with their learning. The inputs include the optimal learning plan and learning materials, and the output is the visual content provided to the user.

[1853] Step 4: Submit learning activity data

[1854] The user performs a learning activity and inputs the results (e.g., answers to questions) into the terminal. The terminal sends the data to the server. The input includes the user's learning activity data, and the output includes the data sent to the server.

[1855] Step 5: Generate and provide real-time feedback

[1856] The server analyzes the received learning activity data in real time and generates appropriate feedback using a generative AI model, which is then immediately sent back to the device and displayed to the user.The input includes the learning activity data, and the output includes the feedback provided to the user.

[1857] Step 6: Update the progress chart

[1858] As feedback is provided, the server updates a progress chart based on the progress. The updated chart is sent to the device, allowing the user to visually check their progress. The inputs include the latest feedback and learning activity data, and the output is the updated progress chart.

[1859] Step 7: Generate and deliver custom reports

[1860] The server periodically analyzes the learning data and automatically generates a custom report using a generative AI model. The generated report is stored in a database and provided to the user via their device. The input includes learning history and accumulated progress data, and the output includes an automatically generated custom report.

[1861] Step 8: Using a Smart Device

[1862] The present invention provides users with visually generated learning plans and learning materials, as well as real-time feedback and progress charts using smart devices such as smart glasses and tablets.The present invention includes inputs such as learning plans, learning materials, feedback, and progress charts, and outputs such as visual information displayed on the smart devices.

[1863] 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.

[1864] This invention relates to an educational system that utilizes a generative AI model and an emotion engine, proposing specific means for providing each student with a personalized learning experience. The system acquires and analyzes students' learning history, progress data, and emotion data to generate and provide optimal learning plans and teaching materials. It also analyzes students' learning activities in real time, provides feedback, and allows students to visually check their progress, thereby improving learning effectiveness.

[1865] System Configuration

[1866] The system of the present invention has the following main functions:

[1867] 1. How to obtain learning history and progress data

[1868] 2. How to obtain emotion data

[1869] 3. A means of generating learning plans and materials

[1870] 4. Means of providing generated learning plans and teaching materials

[1871] 5. Real-time feedback methods

[1872] 6. How to update the progress chart

[1873] 7. Automated generation of custom reports

[1874] 8. Storage method for the teaching material database

[1875] 9. Question-Answering Systems

[1876] 10. How to connect with other companies' APIs

[1877] 11. Multilingual Support

[1878] Program processing and behavior

[1879] The program processing for each function is explained below in natural language.

[1880] 1. How to obtain learning history and progress data

[1881] The user accesses the login screen and enters their user ID and password. The device encrypts the entered login information using SSL / TLS and sends a request to the authentication API. The server checks the authentication information against the database and returns the authentication result to the device. If the authentication is successful, the server queries the database to obtain the learning history and progress data associated with the user ID.

[1882] 2. How to obtain emotion data

[1883] When a user engages in learning activities, the device uses its built-in camera and microphone to capture the user's facial expressions and tone of voice. The device then sends the captured data to an emotion engine, which analyzes it as emotional data. The analysis results are then sent from the device to a server and stored along with the user's learning history and progress data.

[1884] 3. A means of generating learning plans and materials

[1885] The server inputs the acquired learning history, progress data, and emotional data into the generative AI model. The server then analyzes the data using the generative AI model to generate an optimal learning plan and learning materials. The generated learning plan and learning material metadata are sent to the device in JSON format.

[1886] 4. Means of providing generated learning plans and teaching materials

[1887] The device analyzes the learning plan and learning material data received from the server. The device displays the personalized learning plan and learning materials on the user interface. This allows the user to proceed with learning using learning materials based on the optimized learning plan.

[1888] 5. Real-time feedback methods

[1889] When a user performs a learning activity (e.g., answering a question), the device sends the data to the server. The server analyzes the received data in real time and generates appropriate feedback along with the user's emotional data. The generated feedback is sent back from the server to the device and displayed to the user.

[1890] 6. How to update the progress chart

[1891] As feedback is provided, the server updates the progress chart based on the progress and emotion data, and the updated progress chart is sent to the device and displayed visually to the user.

[1892] 7. Automated generation of custom reports

[1893] The server periodically analyzes students' learning data, progress data, and emotional data, and automatically generates custom reports using AI models. The generated reports are stored in a database and provided to users, parents, and teachers via their devices.

[1894] 8. Storage method for the teaching material database

[1895] The server stores the generated learning material data in a database. The terminal requests appropriate learning materials from this database according to the user's learning progress and provides them to the user.

[1896] 9. Question-Answering Systems

[1897] When a user types in a question or concern, the device sends the question to the server, which uses a generative AI model to analyze the question and generate an appropriate answer, which is then sent back to the device and provided to the user.

[1898] 10. How to connect with other companies' APIs

[1899] The server connects with other companies' APIs to add new educational content to the database, which is provided as the user progresses through their studies.

[1900] 11. Multilingual Support

[1901] The generated teaching materials and content are translated as needed using a multilingual model, and the translated content is sent from the server to the terminal and provided to the user.

[1902] Specific examples

[1903] If a user is studying junior high school mathematics, they first log in to the system. The server obtains the user's learning history and progress data, and analyzes it along with facial expression and tone of voice data collected by the emotion engine. As a result, the next unit to be studied (e.g., solving equations) is determined, and appropriate learning materials (e.g., explanatory videos and practice problems) are generated. The generated learning plan and materials are provided to the user via their device.

[1904] When a user solves a problem and inputs the results into the system, the server instantly analyzes the data and generates feedback based on the accuracy rate, level of understanding, and emotional data. A progress chart is also updated, providing a visual indication of the student's current learning status. In this way, the system provides an optimized learning experience that takes into account each student's emotional state, supporting effective learning.

[1905] The above is a specific embodiment for carrying out the present invention.

[1906] The processing flow will be explained below.

[1907] Processing steps of a system that combines emotion engines

[1908] 1. How to obtain learning history and progress data

[1909] Step 1:

[1910] The user accesses the login screen and enters their user ID and password.

[1911] Step 2:

[1912] The device encrypts the entered login information using SSL / TLS and sends a request to the authentication API.

[1913] Step 3:

[1914] The server checks the authentication information against a database and returns the authentication result to the terminal.

[1915] Step 4:

[1916] If the server is successful in authenticating the user, it queries the database to retrieve the learning history and progress data associated with the user ID.

[1917] 2. How to obtain emotion data

[1918] Step 1:

[1919] When a user is engaged in learning activities, the device uses its built-in camera and microphone to capture the user's facial expressions and tone of voice.

[1920] Step 2:

[1921] The device sends the captured data to the emotion engine, where it is analyzed as emotion data.

[1922] Step 3:

[1923] The device sends the analysis results to a server, where they are stored along with the user's learning history and progress data.

[1924] 3. A means of generating learning plans and materials

[1925] Step 1:

[1926] The learning history, progress data, and emotional data acquired by the server are input into the generative AI model.

[1927] Step 2:

[1928] The server uses a generative AI model to analyze the data and generate optimal learning plans and materials.

[1929] Step 3:

[1930] The server sends the generated learning plan and learning material metadata to the terminal in JSON format.

[1931] 4. Means of providing generated learning plans and teaching materials

[1932] Step 1:

[1933] The device analyzes the learning plan and teaching material data received from the server.

[1934] Step 2:

[1935] The device displays a personalized learning plan and learning materials in a user interface.

[1936] 5. Real-time feedback methods

[1937] Step 1:

[1938] The user performs a learning activity (e.g., answers a question).

[1939] Step 2:

[1940] The device sends the user's learning activity data to the server as an API request.

[1941] Step 3:

[1942] The server passes the received data to a real-time analysis system, which calculates the accuracy rate and learning progress.

[1943] Step 4:

[1944] The server generates appropriate feedback along with the user's emotional data.

[1945] Step 5:

[1946] The server sends feedback and progress chart data in JSON format to the device.

[1947] Step 6:

[1948] The device provides the user with real-time feedback and a visual progress chart.

[1949] 6. How to update the progress chart

[1950] Step 1:

[1951] The server analyzes the user's learning data and emotional data and generates a progress chart.

[1952] Step 2:

[1953] The server transmits the progress chart data to the terminal.

[1954] Step 3:

[1955] The terminal displays the progress chart on the user interface.

[1956] 7. Automated generation of custom reports

[1957] Step 1:

[1958] The server periodically retrieves the student's learning data, progress data and emotion data from the database.

[1959] Step 2:

[1960] The server analyzes the acquired data and automatically generates custom reports using AI models.

[1961] Step 3:

[1962] The server stores the generated reports in a database and sends them to the terminal.

[1963] Step 4:

[1964] The terminal displays the custom report to the user.

[1965] 8. Storage method for the teaching material database

[1966] Step 1:

[1967] The server stores the generated teaching material data in a database.

[1968] Step 2:

[1969] The terminal requests the learning material data according to the user's learning progress.

[1970] Step 3:

[1971] The server receives the request and transmits the corresponding educational material data to the terminal.

[1972] Step 4:

[1973] The terminal displays appropriate educational materials to the user.

[1974] 9. Question-Answering Systems

[1975] Step 1:

[1976] The user enters a question or concern.

[1977] Step 2:

[1978] The device sends the question to the server as an API request.

[1979] Step 3:

[1980] The server uses a generative AI model to analyze the question and generate an appropriate answer.

[1981] Step 4:

[1982] The server sends the answer to the device.

[1983] Step 5:

[1984] The terminal displays the answer to the user.

[1985] 10. How to connect with other companies' APIs

[1986] Step 1:

[1987] The server connects with other companies' APIs to obtain new educational content.

[1988] Step 2:

[1989] The server stores the acquired educational content in a database.

[1990] Step 3:

[1991] The terminal requests new educational content according to the user's learning progress.

[1992] Step 4:

[1993] The server receives the request and transmits the corresponding educational content to the terminal.

[1994] Step 5:

[1995] The terminal displays new educational content to the user.

[1996] 11. Multilingual Support

[1997] Step 1:

[1998] The server inputs the generated teaching materials and content into a multilingual model for translation.

[1999] Step 2:

[2000] The server stores the translated content in a database.

[2001] Step 3:

[2002] The device requests translated content according to the language set by the user.

[2003] Step 4:

[2004] The server receives the request and sends the content in the corresponding language to the terminal.

[2005] Step 5:

[2006] The terminal displays the content to the user in the selected language.

[2007] Example 2

[2008] 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."

[2009] Traditional education systems struggle to effectively track each student's learning progress and level of understanding, making it difficult to provide individually optimized learning plans and materials. Furthermore, they fail to provide feedback or adjust learning plans that take into account the student's emotional state during learning, preventing the most effective learning outcomes. Furthermore, while students need timely and appropriate answers to their questions, traditional systems fail to adequately address them.

[2010] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring student learning history and progress data, means for generating an optimal learning plan and learning materials based on the acquired data, means for providing the generated learning plan and learning materials to the student, means for analyzing learning activity data in real time and providing feedback, means for updating a progress chart based on the analysis results, means for automatically generating a custom report, means for acquiring and analyzing student emotional data, and means for adjusting the learning plan and feedback based on the emotional data. This makes it possible to provide an individually optimized learning plan and learning materials for each student and to provide effective feedback that takes into account the student's emotional state.

[2011] "Student Learning History and Progress Data" refers to information about a student's learning history, learning progress, and related assessments and grades.

[2012] "Emotional data" refers to data that indicates a student's emotions or mental state, such as facial expressions or tone of voice.

[2013] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to analyze data and generate optimal learning plans, materials, and feedback.

[2014] A "learning plan" is a plan that outlines the content and order in which a student should study in order to achieve a specific goal.

[2015] "Instructional Materials" refers to learning resources such as textbooks, videos, and exercises that students use when studying.

[2016] "Feedback" refers to comments, instructions, and evaluations provided based on students' learning activities.

[2017] A "progress chart" is a graph or chart that visually shows a student's learning progress.

[2018] "Custom Report" refers to an individual report generated based on each student's learning history, progress, and emotional data.

[2019] "Questions" refer to any doubts or questions that students have while studying.

[2020] A "prompt" refers to the wording of instructions or questions given to a generative AI model.

[2021] MODE FOR CARRYING OUT THE INVENTION

[2022] This invention relates to an educational system that utilizes generative AI models and emotion engines to provide personalized learning experiences for individual students. This system acquires data from the user's learning process and generates and provides optimal learning plans and learning materials based on that data.

[2023] Overall system configuration

[2024] The system mainly consists of the following components:

[2025] 1. A means of capturing student learning history and progress data

[2026] 2. Means of acquiring and analyzing emotion data

[2027] 3. A means to generate optimal learning plans and materials based on the acquired data

[2028] 4. A means of providing generated lesson plans and materials to students

[2029] 5. A means of analyzing student learning activity data in real time and providing feedback

[2030] 6. A way to update the progress chart based on the analysis results

[2031] 7. Automated generation of custom reports

[2032] Specific embodiments of each means

[2033] 1. A means of capturing student learning history and progress data

[2034] The user accesses the login screen and enters their user ID and password. The device encrypts the entered login information using SSL / TLS and sends a request to the authentication API. The server checks the authentication information against a database and returns the authentication result. If authentication is successful, the server retrieves the user's learning history and progress data from the database. MySQL or PostgreSQL are generally used as the database.

[2035] 2. Means of acquiring and analyzing emotion data

[2036] As the user engages in learning activities, the device uses its built-in camera and microphone to capture the user's facial expressions and tone of voice. The device then sends the captured data to an emotion engine for analysis. The emotion engine uses the Microsoft Azure Emotion API and Google Cloud Speech-to-Text.

[2037] 3. A means to generate optimal learning plans and materials based on the acquired data

[2038] The server inputs the acquired learning history, progress data, and emotional data into a generative AI model. GPT-3, BERT, T5, and other models are used. The data is analyzed to generate an optimal learning plan and learning materials. The generated learning plan and learning materials are converted into JSON format and sent to the device.

[2039] 4. A means of providing generated lesson plans and materials to students

[2040] The device analyzes the learning plan and learning material data received from the server and displays them on a user interface, which is built using frameworks such as React and Angular.

[2041] 5. A means of analyzing learning activity data in real time and providing feedback

[2042] When a user performs a learning activity, the device sends the data to the server, which analyzes the received data in real time and generates appropriate feedback. The feedback is sent to the device in text format and displayed to the user.

[2043] 6. A way to update the progress chart based on the analysis results

[2044] After providing the feedback, the server updates the progress chart based on the progress and emotion data. The progress chart is generated using D3.js and Chart.js and sent to the device.

[2045] 7. Automated generation of custom reports

[2046] Periodically, the server analyzes students' learning data, progress data, and emotional data, and automatically generates custom reports, which are converted into PDF or HTML format, stored in a database, and later provided to users via their devices.

[2047] Specific examples

[2048] If a user is studying junior high school mathematics, they first log in to the system. The server obtains the user's learning history and progress data, and analyzes the facial expression and tone of voice data collected by the emotion engine. As a result, the next unit to be studied (e.g., solving equations) is determined, and appropriate learning materials (e.g., explanatory videos and practice problems) are generated. The generated learning plan and materials are provided to the user via their device.

[2049] When a user solves a problem and inputs the results into the system, the server instantly analyzes the data and generates feedback based on the accuracy rate, level of understanding, and emotional data. A progress chart is also updated, allowing users to visually check their current learning status. In this way, the system of the present invention provides an optimized learning experience that takes into account each student's emotional state, supporting effective learning.

[2050] Prompt Sentence Examples

[2051] "I would like to learn how to solve equations in junior high school mathematics. Please suggest the best study plan and materials."

[2052] The above is a specific embodiment for carrying out the present invention.

[2053] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2054] Step 1:

[2055] The user accesses the login screen and enters their user ID and password. The user ID and password are input. The device encrypts the entered login information using SSL / TLS and sends a request to the authentication API. The server compares the received authentication information with a database, generates an authentication result, and returns the authentication success or failure result to the device as output.

[2056] Step 2:

[2057] When the server receives a successful authentication result, it retrieves the learning history and progress data associated with the user ID from the database. It takes the user ID as input and accesses the database using an SQL query. Example:

[2058] sql

[2059] SELECT FROM user_data WHERE user_id = 'User ID';

[2060] The output is the acquired learning history and progress data.

[2061] Step 3:

[2062] The device receives learning history and progress data, and the user begins learning. As the user engages in learning activities, the device uses the built-in camera and microphone to capture the user's facial expressions and tone of voice. The inputs include camera video data and audio data. The device sends this data to an emotion engine, where it is analyzed as emotion data. The output is the analyzed emotion data.

[2063] Step 4:

[2064] The device sends emotion data to the server, which receives it and stores it in a database along with the user's learning history and progress data. The inputs are emotion data, learning history, and progress data. By storing it in the database, a complete user dataset is generated as the output.

[2065] Step 5:

[2066] The server inputs the acquired learning history, progress data, and emotional data into the generative AI model. The input is all of the user's data. The server uses the generative AI model to analyze the data and generate the optimal learning plan and learning materials. Specifically, it uses the following prompt:

[2067] "A user wants to learn how to solve equations in junior high school mathematics. Please suggest the best study plan and study materials."

[2068] The output is a JSON-formatted learning plan and learning material data.

[2069] Step 6:

[2070] The device parses the learning plan and learning material data received from the server. The input is JSON format data. The device displays the personalized learning plan and learning material on the user interface. The output is a user-friendly display of the learning plan and learning material.

[2071] Step 7:

[2072] The user solves a problem and inputs the results into the terminal. The input is the user's answer data. The terminal sends the data to the server. The server analyzes the received data in real time and generates feedback based on the accuracy rate, comprehension level, and emotion data. The output is a feedback message.

[2073] Step 8:

[2074] The server sends feedback back to the terminal, which displays it to the user. The input is the feedback message from the server, and the output is the feedback displayed to the user.

[2075] Step 9:

[2076] After providing feedback, the server updates the progress chart based on the progress and emotion data. The input is the latest progress data, and the output is the updated progress chart. The progress chart is generated using D3.js and Chart.js and sent to the device.

[2077] Step 10:

[2078] The server periodically analyzes students' learning data, progress data, and emotion data, and automatically generates custom reports. The input is all user data, and the output is a custom report (PDF or HTML format). This report is stored in a database.

[2079] Step 11:

[2080] The device provides saved custom reports to users, parents, and teachers. The input is the generated report and the output is the delivered report.

[2081] (Application example 2)

[2082] 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."

[2083] In conventional educational systems, uniform learning plans and teaching materials are provided, making it difficult to accommodate the different learning paces and levels of understanding of each student. This can lead to a decrease in students' learning effectiveness. Similarly, virtual stores have the problem of being unable to recommend products that match individual customer preferences and needs, making it difficult to stimulate purchasing motivation.

[2084] 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.

[2085] In this invention, the server includes a means for acquiring a student's learning history and progress data, a means for generating an optimal learning plan and educational materials based on the acquired data, and a means for providing the generated learning plan and educational materials to the student. This makes it possible to provide educational materials tailored to each student's learning pace and level of understanding. Furthermore, the server includes a means for acquiring a customer's purchase history, browsing history, and emotional data, and for generating appropriate product recommendations and promotions, and a means for providing the generated product recommendations and promotions. This makes it possible to recommend products tailored to the individual preferences and needs of each customer.

[2086] "Learning history" is data that records what a student has learned in the past and their progress.

[2087] "Progress data" is data that records a student's progress and achievements in learning activities.

[2088] A "study plan" is an individual study schedule formulated based on each student's learning history and progress data.

[2089] "Educational materials" are teaching materials and reference materials provided to support students' learning activities.

[2090] "Purchase history" refers to data such as the products a customer has purchased in the past, the quantity, and the date of purchase.

[2091] "Browsing history" is data about the pages and products a customer views on a website or within an app.

[2092] "Emotion data" refers to data that indicates the emotional state of a customer, analyzed from facial expressions, tone of voice, etc.

[2093] "Product recommendations" are suitable product suggestions provided based on a customer's purchasing history, browsing history, and emotional data.

[2094] "Promotion" refers to information that recommends and advertises specific products or services to customers.

[2095] "Feedback" is a response, such as evaluation or advice, provided in response to the behavior of a student or customer.

[2096] A "progress chart" is a diagram that visually displays the progress of a student or client's activities.

[2097] A "Custom Report" is a personalized analytical report generated based on student or client data.

[2098] This invention relates to an educational system and a virtual store system that use a generative AI model and an emotion engine. To implement this invention, three main components work together: a server, a terminal, and a user. The specific system configuration and its operation are described below in detail.

[2099] System Configuration

[2100] 1. Server

[2101] The server contains a database, a generative AI model, and an emotion engine.

[2102] The database stores learning history, progress data, educational materials, purchase history, browsing history, emotional data, and more.

[2103] Generative AI models are used to generate lesson plans and educational materials, product recommendations and promotions.

[2104] The emotion engine analyzes the user's facial expressions and tone of voice to generate emotion data.

[2105] 2. Terminal

[2106] A terminal is a device operated by a user, and includes a smartphone, tablet, PC, etc.

[2107] The device has a built-in camera and microphone, which are used to capture the user's facial expressions and voice.

[2108] 3. Users

[2109] The users are the students or customers who use the system.

[2110] Users access the system through terminals and use various functions.

[2111] Program processing and behavior

[2112] 1. Data Acquisition and Storage

[2113] The user logs in and the server obtains learning history, progress data, purchase history, and browsing history.

[2114] While studying or shopping, the device's camera and microphone capture the user's facial expressions and voice, which are then analyzed by the emotion engine and sent to the server as emotional data.

[2115] All data is stored in a database on the server.

[2116] 2. Data analysis and plan generation

[2117] The server inputs the acquired data into a generative AI model to generate learning plans, educational materials, product recommendations and promotions.

[2118] Learning plans and educational materials, product recommendations and promotions are sent to the device in JSON format.

[2119] 3. Data provision and feedback

[2120] The terminal displays the provided study plans and educational materials, as well as product recommendations and promotions, to the user.

[2121] Every time a user studies or shops, the data is sent from the device to the server, which immediately analyzes it and generates feedback.

[2122] The feedback is sent to the terminal and provided to the user.

[2123] Furthermore, the server updates the progress chart and displays it on the terminal in a form that the user can visually understand.

[2124] Specific examples

[2125] Consider a case where a junior high school student logs into the system and begins studying junior high school mathematics. First, the server retrieves learning history and progress data, and the emotion engine analyzes the student's facial expressions and tone of voice. Based on the resulting data, an optimal learning plan and educational materials (e.g., videos and practice problems on how to solve equations) are generated. These educational materials are displayed on the student's device, and immediate feedback is provided as the student progresses.

[2126] In the virtual store, customers log in and their emotional data is captured using a camera and microphone. This data, along with their past purchase and browsing history, is analyzed to provide personalized product recommendations (e.g., scented candles if they're feeling relaxed) and promotions.

[2127] Prompt Sentence Examples

[2128] "Based on the user's purchase history, suggest products they are likely to purchase next."

[2129] "Recommend appropriate promotional items based on user emotional data (e.g., relaxation, stress, etc.)."

[2130] "Analyze data to provide users with a personalized shopping experience based on their purchase history, browsing history, and emotional state."

[2131] The above is an embodiment of the present invention. Other specific processing steps will be described later.

[2132] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2133] Step 1:

[2134] The user accesses the login screen and enters their user ID and password. The device encrypts the entered login information using SSL / TLS and sends a request to the authentication API.

[2135] Input: User ID, Password

[2136] Output: Authentication token

[2137] Specific operation: Enter the user ID and password on the terminal's login screen and send a request to the server.

[2138] Step 2:

[2139] The server checks the authentication information against the database and returns the authentication result to the device. If authentication is successful, it queries the database to obtain the learning history and progress data associated with the user ID.

[2140] Input: Authentication Token

[2141] Output: Learning history, progress data

[2142] Specific operation: The server executes a database query for authentication and returns the authentication result to the device. After successful authentication, the learning history and progress data are retrieved.

[2143] Step 3:

[2144] The device uses its built-in camera and microphone to capture the user's facial expressions and tone of voice, which are then analyzed as emotional data.

[2145] Input: User's facial expression, tone of voice

[2146] Output: Emotion data

[2147] What it does: The device activates the camera and microphone, processes the captured data in real time, and sends it to the sentiment analysis engine.

[2148] Step 4:

[2149] The server inputs the acquired learning history, progress data, and emotional data into a generative AI model to generate optimal learning plans and educational materials, and inputs purchase history, browsing history, and emotional data to generate optimal product recommendations and promotions.

[2150] Input: learning history, progress data, emotional data, purchase history, browsing history

[2151] Output: Study plans, educational materials, product recommendations, promotions

[2152] Specific operation: The server inputs various data into the generated AI model and generates the optimal plan and materials from the analysis results.

[2153] Step 5:

[2154] The server generates learning plans, educational materials, product recommendations, and promotions and sends them to the device, which analyzes the data and displays it on the user interface.

[2155] Input: Study plans, educational materials, product recommendations, promotions

[2156] Output: Data displayed in the user interface

[2157] Specific operation: Data is sent from the server to the device, which analyzes it and displays it on the screen. The user confirms the displayed content.

[2158] Step 6:

[2159] Users conduct learning activities or purchases and enter the results into the terminal, which then sends the data to the server, which analyzes it immediately and generates feedback.

[2160] Input: learning activity data, purchasing activity data

[2161] Output: Feedback

[2162] Specific operation: The user inputs the results of their learning or purchasing activities, and the data is sent to the server, which analyzes them immediately and sends feedback to the device.

[2163] Step 7:

[2164] The server updates the progress chart based on the progress status and emotion data, and the updated progress chart is sent to the device and displayed in a visually recognizable format for the user.

[2165] Input: Progress data, emotion data

[2166] Output: Updated progress chart

[2167] Specific behavior: The server receives new data, updates the progress chart, sends it to the device, and displays it in the user interface.

[2168] Step 8:

[2169] The server periodically analyzes student and customer data and automatically generates custom reports using AI models. The generated reports are stored in a database and provided to users via their devices.

[2170] Input: learning history, progress data, purchase history, emotional data

[2171] Output: Custom Report

[2172] What it does: The server periodically analyzes the data, generates custom reports, stores them in a database, and makes them available for viewing on the device.

[2173] The above is a description of the specific processing steps of the system that realizes the application example.

[2174] 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.

[2175] 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.

[2176] 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.

[2177] [Fourth embodiment]

[2178] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2179] 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.

[2180] 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).

[2181] 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.

[2182] 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.

[2183] 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).

[2184] 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.

[2185] 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.

[2186] 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.

[2187] 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.

[2188] 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.

[2189] 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.

[2190] 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."

[2191] This invention relates to an educational system that utilizes generative AI models and proposes specific means for providing each student with a personalized learning experience. The system acquires and analyzes students' learning history and progress data to generate and provide optimal learning plans and teaching materials. It also analyzes students' learning activities in real time and provides feedback, improving learning effectiveness and allowing students to visually check their progress.

[2192] System Configuration

[2193] The system of the present invention has the following main functions:

[2194] 1. How to obtain learning history and progress data

[2195] 2. A means of generating learning plans and materials

[2196] 3. Means of providing generated learning plans and teaching materials

[2197] 4. A way to provide real-time feedback

[2198] 5. How to update the progress chart

[2199] 6. Automated generation of custom reports

[2200] 7. Storage method for the teaching material database

[2201] 8. Question-Answering Systems

[2202] 9. How to connect with other companies' APIs

[2203] 10. Multilingual Support

[2204] Program processing and behavior

[2205] The program processing for each function is explained below in natural language.

[2206] 1. How to obtain learning history and progress data

[2207] When a user logs in to their learning account, the device sends the login information to the server, which then authenticates the user and, if authentication is successful, retrieves the user's learning history and progress data from the database.

[2208] 2. A means of generating learning plans and materials

[2209] Based on the acquired data, the server uses a generative AI model to analyze the user's current learning situation and generate an optimal learning plan and learning materials, which are then sent from the server to the device and provided to the user.

[2210] 3. Means of providing generated learning plans and teaching materials

[2211] The device displays the study plan and learning materials sent from the server on the user interface, allowing the user to study using the most appropriate learning materials for their own learning situation.

[2212] 4. A way to provide real-time feedback

[2213] When a user performs a learning activity (e.g., answering a question), the device sends the data to the server, which analyzes the received data in real time and generates appropriate feedback. The generated feedback is sent back from the server to the device and displayed to the user.

[2214] 5. How to update the progress chart

[2215] As feedback is provided, the server updates a progress chart based on the progress, which is then sent to the device and displayed visually to the user.

[2216] 6. Automated generation of custom reports

[2217] Periodically, the server analyzes student learning data and automatically generates custom reports using AI models. The reports are stored in a database and provided to users, parents, and teachers via their devices.

[2218] 7. Storage method for the teaching material database

[2219] The generated learning materials are stored in a database by the server. The terminal requests appropriate learning materials from this database according to the user's learning progress and provides them to the user.

[2220] 8. Question-Answering Systems

[2221] When a user types in a question or concern, the device sends the question to the server, which uses a generative AI model to analyze the question and generate an appropriate answer, which is then sent back to the device and provided to the user.

[2222] 9. How to connect with other companies' APIs

[2223] The server connects with other companies' APIs to add new educational content to the database, which is provided as the user progresses through their studies.

[2224] 10. Multilingual Support

[2225] The generated teaching materials and content are translated as needed using a multilingual model, and the translated content is sent from the server to the terminal and provided to the user.

[2226] Specific examples

[2227] If a user is studying junior high school mathematics, they first log in to the system. The server acquires and analyzes the user's learning history and progress data. As a result, the next unit to be studied (e.g., solving equations) is determined, and appropriate learning materials (e.g., explanatory videos and practice problems) are generated. The generated learning plan and materials are provided to the user via their device.

[2228] When a user solves a problem and inputs the results into the system, the server instantly analyzes the data and generates feedback based on the accuracy rate and level of understanding. A progress chart is also updated, allowing users to visually check their current learning status. In this way, the system of the present invention provides an optimized learning experience for each student, supporting effective learning.

[2229] The above is a specific embodiment for carrying out the present invention.

[2230] The processing flow will be explained below.

[2231] 1. How to obtain learning history and progress data

[2232] Processing Steps

[2233] Step 1:

[2234] The user accesses the login screen and enters their user ID and password.

[2235] Step 2:

[2236] The device encrypts the entered login information using SSL / TLS and sends a request to the authentication API.

[2237] Step 3:

[2238] The server checks the authentication information against a database and returns the authentication result to the terminal.

[2239] Step 4:

[2240] If the server is successful in authenticating the user, it queries the database to retrieve the learning history and progress data associated with the user ID.

[2241] 2. A means of generating learning plans and materials

[2242] Processing Steps

[2243] Step 1:

[2244] The learning history and progress data acquired by the server are input into the generative AI model.

[2245] Step 2:

[2246] The server uses the generated AI model to analyze learning history and progress, and generates optimal learning plans and materials.

[2247] Step 3:

[2248] The server sends the generated learning plan and learning material metadata to the terminal in JSON format.

[2249] 3. Means of providing generated learning plans and teaching materials

[2250] Processing Steps

[2251] Step 1:

[2252] The device analyzes the learning plan and teaching material data received from the server.

[2253] Step 2:

[2254] The device displays a personalized learning plan and learning materials in a user interface.

[2255] 4. A way to provide real-time feedback

[2256] Processing Steps

[2257] Step 1:

[2258] The user performs a learning activity (e.g., answers a question).

[2259] Step 2:

[2260] The device sends the user's learning activity data to the server as an API request.

[2261] Step 3:

[2262] The server passes the received data to a real-time analysis system, which calculates the accuracy rate and learning progress.

[2263] Step 4:

[2264] The server generates feedback messages and progress chart information.

[2265] Step 5:

[2266] The server sends feedback and progress chart data in JSON format to the device.

[2267] Step 6:

[2268] The device provides the user with real-time feedback and a visual progress chart.

[2269] 5. How to update the progress chart

[2270] Processing Steps

[2271] Step 1:

[2272] The server analyzes the user's learning data and generates a progress chart.

[2273] Step 2:

[2274] The server transmits the progress chart data to the terminal.

[2275] Step 3:

[2276] The terminal displays the progress chart on the user interface.

[2277] 6. Automated generation of custom reports

[2278] Processing Steps

[2279] Step 1:

[2280] The server periodically retrieves student learning and progress data from the database.

[2281] Step 2:

[2282] The server analyzes the acquired data and automatically generates custom reports using AI models.

[2283] Step 3:

[2284] The server stores the generated reports in a database and sends them to the terminal.

[2285] Step 4:

[2286] The terminal displays the custom report to the user.

[2287] 7. Storage method for the teaching material database

[2288] Processing Steps

[2289] Step 1:

[2290] The server stores the generated teaching material data in a database.

[2291] Step 2:

[2292] The terminal requests the learning material data according to the user's learning progress.

[2293] Step 3:

[2294] The server receives the request and transmits the corresponding educational material data to the terminal.

[2295] Step 4:

[2296] The terminal displays appropriate educational materials to the user.

[2297] 8. Question-Answering Systems

[2298] Processing Steps

[2299] Step 1:

[2300] The user enters a question or concern.

[2301] Step 2:

[2302] The device sends the question to the server as an API request.

[2303] Step 3:

[2304] The server uses a generative AI model to analyze the question and generate an appropriate answer.

[2305] Step 4:

[2306] The server sends the generated answer to the terminal.

[2307] Step 5:

[2308] The terminal displays the answer to the user.

[2309] 9. How to connect with other companies' APIs

[2310] Processing Steps

[2311] Step 1:

[2312] The server connects with other companies' APIs to obtain new educational content.

[2313] Step 2:

[2314] The server stores the acquired educational content in a database.

[2315] Step 3:

[2316] The terminal requests new educational content according to the user's learning progress.

[2317] Step 4:

[2318] The server receives the request and transmits the corresponding educational content to the terminal.

[2319] Step 5:

[2320] The terminal displays new educational content to the user.

[2321] 10. Multilingual Support

[2322] Processing Steps

[2323] Step 1:

[2324] The server inputs the generated teaching materials and content into a multilingual model for translation.

[2325] Step 2:

[2326] The server stores the translated content in a database.

[2327] Step 3:

[2328] The device requests translated content according to the language set by the user.

[2329] Step 4:

[2330] The server receives the request and sends the content in the corresponding language to the terminal.

[2331] Step 5:

[2332] The terminal displays the content to the user in the selected language.

[2333] Example 1

[2334] 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."

[2335] Current education systems struggle to provide personalized learning experiences based on each student's progress and level of understanding. They also need to address a wide range of needs, including real-time feedback, progress visualization, automatic custom report generation, and multilingual support. However, there are no systems that integrate all of these functions, making it difficult to maximize learning efficiency.

[2336] 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.

[2337] In this invention, the server includes means for acquiring student learning history and progress data, means for generating optimal learning plans and teaching materials using a generative AI model based on the acquired data, means for providing the generated learning plans and teaching materials to students, means for analyzing student learning activity data in real time and providing feedback, means for updating a progress chart based on the analysis results, means for automatically generating custom reports, means for providing generated answers to students, means for translating the generated teaching materials and content using a multilingual model, means for saving the automatically generated teaching materials in a database, means for providing the saved teaching materials according to the student's learning progress, means for adding new educational content to the database using a third-party API, and means for generating appropriate answers to questions entered by students using a generative AI model. This enables the provision of an individually optimized learning experience, real-time feedback, progress visualization, multilingual support, and the addition of new content.

[2338] A "learning history" is a record of a student's past learning activities and progress.

[2339] "Progress data" is information that indicates the learning content and progress that a student is currently making.

[2340] A "generative AI model" is an artificial intelligence model that analyzes students' learning data and generates optimal learning plans, teaching materials, and feedback.

[2341] A "study plan" is a study schedule and content that is optimized to help students progress through their studies efficiently.

[2342] "Teaching Materials" means the educational materials and exercises used in accordance with the Study Plan.

[2343] "Feedback" refers to evaluation and advice provided in real time to students' learning activities.

[2344] A "progress chart" is a graph or chart that visually shows a student's learning progress.

[2345] A "custom report" is a learning report that is automatically generated individually by analyzing a student's learning data.

[2346] A "multilingual model" is an artificial intelligence model for translating teaching materials and content into multiple languages.

[2347] A "database" is a system that systematically stores acquired data and generated teaching materials.

[2348] "Third-party APIs" refer to application program interfaces published by other companies or service providers.

[2349] "Questions" are questions that students have while studying or things they want to clarify.

[2350] An "answer" is an appropriate answer or explanation provided by a generative AI model in response to a question.

[2351] This invention is an educational system that utilizes a generative AI model and proposes specific means for providing a personalized learning experience to each student. The system acquires and analyzes students' learning history and progress data to generate and provide optimal learning plans and materials. It also analyzes students' learning activities in real time and provides feedback to improve learning effectiveness and allow students to visually check their progress.

[2352] System Configuration

[2353] The system of the present invention comprises the following main components:

[2354] How to obtain learning history and progress data

[2355] A means of generating lesson plans and materials

[2356] Means of providing generated learning plans and teaching materials

[2357] A way to provide real-time feedback

[2358] Progress chart update method

[2359] Automated generation of custom reports

[2360] A means of providing generated answers to students

[2361] A means of translating generated learning materials and content using a multilingual model

[2362] A means of storing automatically generated teaching materials in a database

[2363] A means of providing saved learning materials as students progress

[2364] A way to add new educational content to the database using third-party APIs

[2365] A means of generating appropriate answers using generative AI models for questions entered by students

[2366] Hardware and software used

[2367] The system uses the following hardware and software:

[2368] Hardware: Servers, devices (PCs, tablets, smartphones, etc.)

[2369] Software: Generative AI model, database management system, user interface, real-time analysis engine, multilingual model, third-party API

[2370] System Operation

[2371] When a user logs in to their learning account, the device sends the login information to the server. The server authenticates the user, and if authentication is successful, retrieves the user's learning history and progress data from the database. Based on the retrieved data, the server uses a generative AI model to analyze the user's current learning situation and generate an optimal learning plan and learning materials. The generated learning plan and learning materials are sent from the server to the device and provided to the user.

[2372] The device displays the learning plan and learning materials sent from the server on a user interface, allowing the user to proceed with their learning using the materials. When the user performs a learning activity (e.g., answering a question), the device sends the data to the server. The server analyzes the received data in real time and generates appropriate feedback. The generated feedback is sent back from the server to the device and displayed to the user.

[2373] As feedback is provided, the server updates a progress chart based on the progress, and the updated progress chart is sent to the device and displayed visually to the user. Periodically, the server analyzes the student's learning data and automatically generates custom reports using an AI model, which are stored in a database. The generated reports are then provided to the user, parents, and teachers via the device.

[2374] The generated learning materials are stored in a database by the server, and the device requests appropriate learning materials from this database and provides them to the user as the user progresses. When the user enters a question or concern, the device sends the question to the server, which uses a generative AI model to analyze the question and generate an appropriate answer. The answer is then sent back from the server to the device and provided to the user.

[2375] The server also connects with other companies' APIs to add new educational content to the database. This content is provided appropriately according to the user's learning progress. The generated teaching materials and content are translated using a multilingual model as needed, and the translated content is sent from the server to the terminal and provided to the user.

[2376] Examples and prompts

[2377] If a user is studying junior high school mathematics, they first log in to the system, and the server obtains the user's learning history and progress data. Based on that data, the generative AI model analyzes it to determine the next unit to study (e.g., equation solving) and generates appropriate learning materials (e.g., explanatory videos and practice problems). The generated learning plan and materials are provided to the user via their device. When the user solves problems and enters the results into the system, the server immediately analyzes the data and generates feedback based on the accuracy rate and level of understanding. At the same time, a progress chart is updated, allowing the user to visually check their current learning status.

[2378] Here is an example of a prompt to input to a generative AI model:

[2379] "Please generate teaching materials for solving equations in junior high school mathematics. The user's learning data includes the following progress information. In particular, practice problems and explanatory videos are needed to improve understanding of equation solving."

[2380] The above is a specific embodiment for carrying out the present invention.

[2381] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2382] Step 1:

[2383] The user logs into their learning account.

[2384] Input: Username and Password

[2385] How it works: The device sends the username and password to the server.

[2386] Output: Login information sent to the server

[2387] Step 2:

[2388] The server authenticates the user.

[2389] Input: Login information

[2390] How it works: The server retrieves and verifies user authentication information from a database based on the login information.

[2391] Output: Authentication success or failure result

[2392] Step 3:

[2393] The server retrieves the user's learning history and progress data from the database.

[2394] Input: Authentication success result

[2395] Operation: If authentication is successful, the server retrieves the user's learning history and progress data from the database.

[2396] Output: Learning history and progress data

[2397] Step 4:

[2398] The server generates a prompt based on the data it retrieves.

[2399] Input: Learning history and progress data

[2400] How it works: The server creates a prompt sentence appropriate for the generative AI model based on the acquired data.

[2401] Output: prompt statement

[2402] Step 5:

[2403] The server uses the generated AI model to analyze the user's learning status.

[2404] Input: prompt statement

[2405] How it works: The server inputs prompt sentences into the generative AI model and analyzes the user's learning status.

[2406] Output: Analysis results (optimal learning plan and materials)

[2407] Step 6:

[2408] The server sends the generated learning plan and learning materials to the terminal.

[2409] Input: Analysis results

[2410] Operation: The server sends the generated learning plan and learning materials to the terminal.

[2411] Output: Study plans and materials

[2412] Step 7:

[2413] The terminal receives the learning plan and learning materials sent from the server.

[2414] Input: Study plan and materials

[2415] Operation: The device displays the received learning plan and learning materials on the user interface.

[2416] Output: The lesson plan and materials displayed in a user interface

[2417] Step 8:

[2418] The user progresses through the learning process based on the displayed learning plan and materials.

[2419] Input: Study plan and materials

[2420] Operation: The user studies using the presented learning materials.

[2421] Output: Learning activity data (question answers, etc.)

[2422] Step 9:

[2423] The terminal transmits the user's learning activity data to the server.

[2424] Input: Learning activity data

[2425] Operation: The terminal transmits learning activity data to the server.

[2426] Output: Learning activity data is sent to the server

[2427] Step 10:

[2428] The server analyzes learning activity data in real time.

[2429] Input: Learning activity data

[2430] Operation: The server analyzes learning activity data using a real-time analysis engine.

[2431] Output: Analysis results (feedback)

[2432] Step 11:

[2433] The server generates appropriate feedback.

[2434] Input: Analysis results

[2435] How it works: The server uses a generative AI model to generate appropriate feedback.

[2436] Output: Feedback

[2437] Step 12:

[2438] The server transmits the generated feedback to the terminal.

[2439] Input: Feedback

[2440] Operation: The server sends the generated feedback to the device.

[2441] Output: Feedback is sent to the device

[2442] Step 13:

[2443] The device displays the feedback to the user.

[2444] Input: Feedback

[2445] Operation: The device displays the received feedback in the user interface.

[2446] Output: Feedback is displayed in the user interface

[2447] Step 14:

[2448] The server updates the learning progress chart.

[2449] Input: Learning activity data

[2450] Operation: The server updates the progress chart based on the learning activity data.

[2451] Output: Updated progress chart

[2452] Step 15:

[2453] The server sends the updated progress chart to the terminal.

[2454] Input: Updated progress chart

[2455] Operation: The server sends a progress chart to the terminal.

[2456] Output: An updated progress chart is sent to the terminal

[2457] Step 16:

[2458] The terminal displays a progress chart to the user.

[2459] Input: Progress Chart

[2460] Action: The terminal displays an updated progress chart in the user interface.

[2461] Output: A progress chart is displayed in the user interface.

[2462] Step 17:

[2463] The server periodically analyzes the user's learning data and automatically generates custom reports.

[2464] Input: Training data

[2465] How it works: The server uses the generative AI model based on the training data to generate a custom report.

[2466] Output: Custom Report

[2467] Step 18:

[2468] The server stores the custom report in a database and notifies you.

[2469] Input: Custom Report

[2470] How it works: The server saves the custom report to a database and sends notifications to users and interested parties.

[2471] Output: Saved custom reports and notifications

[2472] Step 19:

[2473] The terminal displays the report.

[2474] Input: Custom Report

[2475] Action: The terminal displays the custom report in the user interface.

[2476] Output: The custom report displayed in the user interface

[2477] Step 20:

[2478] The server stores the generated teaching materials in a database.

[2479] Input: Generated teaching materials

[2480] Operation: The server stores the generated teaching materials in a database.

[2481] Output: Saved materials

[2482] Step 21:

[2483] The terminal requests appropriate learning materials from the learning material database according to the student's learning progress.

[2484] Input: Learning progress data

[2485] Operation: The terminal requests the appropriate learning materials from the server based on the learning progress data.

[2486] Output: Requested materials

[2487] Step 22:

[2488] The server provides the requested educational material to the terminal.

[2489] Input: Requested materials

[2490] Operation: The server sends the appropriate educational material to the device.

[2491] Output: Provided teaching materials

[2492] Step 23:

[2493] The terminal displays the provided educational material to the user.

[2494] Input: Provided materials

[2495] Operation: The terminal displays the provided teaching materials on the user interface.

[2496] Output: Displayed teaching materials

[2497] Step 24:

[2498] The user enters a question or concern.

[2499] Input: Questions and concerns

[2500] How it works: The device sends a question or inquiry to the server.

[2501] Output: Question submitted

[2502] Step 25:

[2503] The server analyzes the question using a generative AI model.

[2504] Input: Submitted question

[2505] How it works: The server inputs the question into the generative AI model and analyzes it.

[2506] Output: Analysis result (appropriate answer)

[2507] Step 26:

[2508] The server generates an appropriate response.

[2509] Input: Analysis results

[2510] How it works: The server uses a generative AI model to generate an appropriate answer.

[2511] Output: The generated answer

[2512] Step 27:

[2513] The server sends the generated response to the terminal.

[2514] Input: Generated Answer

[2515] Operation: The server generates a response and sends it to the device.

[2516] Output: Submitted response

[2517] Step 28:

[2518] The terminal displays the answer on the user interface.

[2519] Input: Submitted answer

[2520] Action: The terminal displays the answer in the user interface.

[2521] Output: Displayed answer

[2522] (Application example 1)

[2523] 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."

[2524] It is necessary to build an optimal system that can respond to learners' diverse learning needs and progress and provide an effective and personalized learning experience, and also to enable learners to receive appropriate feedback and learning materials in real time and visually grasp their own learning progress.In addition, a means is needed to efficiently accumulate and analyze learning data and provide an optimized learning experience for each learner.

[2525] 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.

[2526] In this invention, the server includes a means for acquiring student learning history and progress data, a means for generating optimal learning plans and learning materials based on the acquired data, and a means for providing the generated learning plans and learning materials to students, thereby providing an optimized learning experience for each student and improving learning effectiveness.

[2527] "Means for obtaining student learning history and progress data" refers to a method for recording the learning content that learners have engaged in in the past and their progress, and incorporating this into the system.

[2528] The "means for generating optimal learning plans and learning materials" is a method for analyzing collected learning history and progress data and automatically creating optimal learning plans and learning materials for individual learners.

[2529] "Means for providing students with generated learning plans and teaching materials" refers to a method for effectively delivering the generated learning plans and teaching materials to learners.

[2530] "Means for analyzing student learning activity data in real time and providing feedback" refers to a method for monitoring the learning activities of learners in real time and providing immediate feedback based on the analysis results.

[2531] The "means for updating the progress chart" is a method for updating the progress chart, which visually displays the progress of the learner, based on the latest data.

[2532] The "means for automatically generating custom reports" is a method for automatically creating reports tailored to individual learners based on collected learning data.

[2533] "Means for visually providing learning materials and feedback using smart devices" refers to a method for visually displaying learning materials and feedback to learners using devices such as smart glasses or tablets.

[2534] "Means for obtaining user interactions in real time and generating feedback based on that" refers to a method for collecting interaction data such as learners' learning status and responses in real time, analyzing that data, and providing appropriate feedback.

[2535] The system that realizes this application example collects students' learning history and progress data, and based on that data, generates and provides optimal learning plans and teaching materials. It also aims to improve learning effectiveness by analyzing learners' progress in real time and providing feedback.

[2536] Program implementation form

[2537] Hardware and Software Configuration

[2538] The hardware used includes a server, smart devices (smart glasses, smartphones, tablets, etc.), and learner terminals.

[2539] The software used includes a generative AI model, a database management system, a user interface, and a real-time feedback analysis module.

[2540] Specific operation of the system

[2541] Acquire learning history and progress data

[2542] When a learner logs in to their learning account, the device sends the login information to the server, which then authenticates the learner and, if authentication is successful, retrieves their learning history and progress data from the database.

[2543] Learning plan and material generation

[2544] Based on the acquired historical data, the server uses a generative AI model to analyze the learner's current learning situation and generate the optimal learning plan and materials, thereby providing the learner with an optimized learning plan.

[2545] Provision of materials

[2546] The generated learning plan and learning materials are sent from the server to the device, where the provided data is displayed on a user interface to help the learner progress through their studies in an easy-to-understand manner.

[2547] Providing real-time feedback

[2548] Every time a learner performs a learning activity, the data is sent to the server, which analyzes the data in real time and uses a generative AI model to generate appropriate feedback, which is then instantly sent to the learner's device and provided to them.

[2549] Progress Chart Update

[2550] As feedback is provided, the server updates a progress chart, which is then sent to the device, allowing the learner to visually monitor their progress.

[2551] Generate custom reports

[2552] The server periodically analyzes learner data and automatically generates custom reports, which are stored in a database and provided to learners via their devices.

[2553] Using a Smart Device

[2554] The generated learning plans, materials, and feedback are presented visually using smart devices such as smart glasses and tablets, which help learners receive real-time feedback and track their learning progress.

[2555] Specific examples

[2556] Real-world usage scenarios

[2557] For example, when a student studying middle school mathematics logs into the system, the server retrieves their learning history and progress data. As a result, the next unit to be studied (e.g., solving equations) is determined, and specific teaching materials (videos and exercises) are generated. These teaching materials are visually presented to the student through smart glasses.

[2558] When students solve problems and input their results into the system, the server analyzes the data and generates feedback based on the accuracy rate and level of understanding. This feedback is instantly displayed on the smart glasses, allowing students to see the direction of their learning in real time.

[2559] Prompt Sentence Examples

[2560] When students use a generative AI model to solve a math problem, they are prompted with the following prompt:

[2561] "Based on Student A's learning history, please suggest the next math unit they should study."

[2562] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2563] Step 1: Login and Data Retrieval

[2564] When a user logs in to their learning account, the device sends the login information to the server. The server authenticates the user and retrieves the learning history and progress data from the database after successful authentication. The input includes the user's login information, and the output includes the learning history and progress data.

[2565] Step 2: Generate your lesson plan and materials

[2566] The server uses a generative AI model to analyze the acquired learning history and progress data, and generates the optimal learning plan and learning materials for the user. The inputs are learning history and progress data, and the output is the optimal learning plan and learning materials.

[2567] Step 3: Provide materials and plans

[2568] The generated learning plan and learning materials are sent from the server to the device, which displays them and provides a guide for the user to proceed with their learning. The inputs include the optimal learning plan and learning materials, and the output is the visual content provided to the user.

[2569] Step 4: Submit learning activity data

[2570] The user performs a learning activity and inputs the results (e.g., answers to questions) into the terminal. The terminal sends the data to the server. The input includes the user's learning activity data, and the output includes the data sent to the server.

[2571] Step 5: Generate and provide real-time feedback

[2572] The server analyzes the received learning activity data in real time and generates appropriate feedback using a generative AI model, which is then immediately sent back to the device and displayed to the user.The input includes the learning activity data, and the output includes the feedback provided to the user.

[2573] Step 6: Update the progress chart

[2574] As feedback is provided, the server updates a progress chart based on the progress. The updated chart is sent to the device, allowing the user to visually check their progress. The inputs include the latest feedback and learning activity data, and the output is the updated progress chart.

[2575] Step 7: Generate and deliver custom reports 【...

Claims

1. a means of capturing student learning history and progress data; A means to generate optimal learning plans and materials based on the acquired data; a means of providing the generated lesson plans and materials to students; A means of analyzing students' learning activity data in real time and providing feedback; means for updating the progress chart based on the analysis results; A means to automatically generate custom reports; A system including:

2. A means for storing the automatically generated teaching materials in a database; A means for providing the stored learning materials according to the student's learning progress; The system of claim 1 further comprising:

3. A means for generating appropriate answers to questions entered by students using a generative AI model; and a means of providing the generated answers to the student; The system of claim 1 further comprising:

4. A means to analyze the acquired learning data and automatically generate custom reports using an AI model; A means of storing and providing the generated reports to students; The system of claim 1 further comprising:

5. A means to add new educational content by linking with other companies' APIs, A means for translating the generated content using a multilingual model; A means of providing translated content to students in the corresponding language; and The system of claim 1 further comprising:

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